From d2af4c9f815361b78ebf9c4a891ff4fa02b28cb9 Mon Sep 17 00:00:00 2001 From: weiglszonja Date: Wed, 28 Aug 2024 13:41:47 +0200 Subject: [PATCH] add example notebook --- .../tutorials/mah_2024_example_notebook.ipynb | 2009 ++++------------- 1 file changed, 477 insertions(+), 1532 deletions(-) diff --git a/src/constantinople_lab_to_nwb/mah_2024/tutorials/mah_2024_example_notebook.ipynb b/src/constantinople_lab_to_nwb/mah_2024/tutorials/mah_2024_example_notebook.ipynb index 937b4c2..679799b 100644 --- a/src/constantinople_lab_to_nwb/mah_2024/tutorials/mah_2024_example_notebook.ipynb +++ b/src/constantinople_lab_to_nwb/mah_2024/tutorials/mah_2024_example_notebook.ipynb @@ -25,14 +25,6 @@ "![NWB mapping](../mah_2024_uml.png)\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a7155cb-4876-4ff4-9e87-18be6ee3707c", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "173c17e4-7ee4-4e58-884e-2353a4ee46d5", @@ -45,999 +37,258 @@ }, { "cell_type": "code", - "execution_count": 5, "id": "52d493c5-1c7a-487f-971c-4e3ef6442abe", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-28T11:41:17.442258Z", + "start_time": "2024-08-28T11:41:15.123900Z" + } + }, + "source": [ + "from pynwb import NWBHDF5IO\n", + "import ndx_structured_behavior\n", + "\n", + "nwbfile_path = \"/Volumes/T9/Constantinople/nwbfiles/C005_RWTautowait_20190909_1456292.nwb\"\n", + "\n", + "io = NWBHDF5IO(nwbfile_path, \"r\")\n", + "nwbfile = io.read()" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "id": "750d82d9-13df-404e-8512-960613255b88", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-28T08:55:08.865637Z", + "start_time": "2024-08-28T08:55:07.711552Z" + } + }, + "source": [ + "## Accessing the task metadata\n", + "\n", + "The task-related general metadata is stored in a `Task` object which can be accessed as `nwbfile.lab_meta_data[\"task\"]`.\n", + "\n", + "The `EventTypesTable` is a column-based table to store the type of events that occur during the task (e.g. port poke from the animal), one type per row.\n", + "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].event_types`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "662468a8-23c6-4d90-8070-0575579b7e44", "metadata": {}, "outputs": [ { "data": { "text/html": [ + "
\n", + "\n", - " \n", - " \n", - "

root (NWBFile)

session_description: We developed a temporal wagering task for rats, in which they were offered one of several water rewards on each trial, the volume of which (5, 10, 20, 40, 80μL) was indicated by a tone. The reward was assigned randomly to one of two ports, indicated by an LED. The rat could wait for an unpredictable delay to obtain the reward, or at any time could terminate the trial by poking in the other port (opt-out). Wait times were defined as how long rats waited before opting out. Trial initiation times were defined as the time from opting out or consuming reward to initiating a new trial. Reward delays were drawn from an exponential distribution, and on 15–25 percent of trials, rewards were withheld to force rats to opt-out, providing a continuous behavioral readout of subjective value. We used a high-throughput facility to train 291 rats using computerized, semi-automated procedures. The task contained latent structure; rats experienced blocks of 40 completed trials (hidden states) in which they were presented with low (5, 10, or 20μL) or high (20, 40, or 80μL) rewards. These were interleaved with “mixed\" blocks which offered all rewards. 20μL was present in all blocks, so comparing behavior on trials offering this reward revealed contextual effects (i.e., effects of hidden states). The hidden states differed in their average reward and therefore in their opportunity costs, or what the rat might miss out on by continuing to wait.
identifier: 85da9943-6b94-4995-b357-1dfa562fed25
session_start_time2019-09-09 15:03:58-04:00
timestamps_reference_time2019-09-09 15:03:58-04:00
file_create_date
02024-08-26 16:38:50.004510+02:00
experimenter('Mah, Andrew',)
related_publications('https://doi.org/10.1038/s41467-023-43250-x', 'https://doi.org/10.5281/zenodo.10031483')
acquisition
task_recording
events
description: Contains the onset times of events in the task.
table\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "\n", + "
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timestampevent_typevalueevent_name
id
018321.21483Instate_timer
118321.27333Inleft_port_poke
218388.10323Incenter_port_poke
318392.69483Inright_port_poke

... and 9941 more rows.

states
description: Contains the start and end times of each state in the task.
table\n", + "
\n", + "
" + ], + "text/plain": [ + " event_name\n", + "id \n", + "0 state_timer\n", + "1 left_port_poke\n", + "2 center_port_poke\n", + "3 right_port_poke" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.lab_meta_data[\"task\"].event_types[:]" + ] + }, + { + "cell_type": "markdown", + "id": "d8035e39-8405-42c2-92cf-758b51845c77", + "metadata": {}, + "source": [ + "The `ActionTypesTable` is a column-based table to store the type of actions that occur during the task (e.g. sound output from the acquisition system), one type per row.\n", + "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].action_types`." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "2c1a5c0e-e4b6-4c16-af8a-fdaef0ffa353", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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start_timestop_timestate_typeaction_name
id
017950.090718390.37210
118390.372118391.24131
218391.241318391.24332
318391.243318395.37893sound_output

... and 1786 more rows.

actions
description: Contains the onset times of the task output actions (e.g. LED turned on/off).
table\n", + "
\n", + "
" + ], + "text/plain": [ + " action_name\n", + "id \n", + "0 sound_output" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.lab_meta_data[\"task\"].action_types[:]" + ] + }, + { + "cell_type": "markdown", + "id": "99b05bc7-d23e-4a19-8e5f-2a228702dc59", + "metadata": {}, + "source": [ + "The `StateTypesTable` is a column-based table to store the type of states that occur during the task (e.g. while the animal is waiting for reward), one type per row.\n", + "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].state_types`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "3ecfb7c9-e58f-4856-8916-e73d16bb326e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timestampaction_typevaluestate_name
id
017950.09080Onwait_for_poke
117950.09090Onnose_in_center
218391.24340Ongo_cue
318391.24350On

... and 2476 more rows.

epoch_tagsset()
devices
bpod
description: State Machine Version: Bpod 2.0
manufacturer: Sanworks
intervals
trials
description: LED illumination from the center port indicated that the animal could initiate a trial by poking its nose in that \n", - "port - upon trial initiation the center LED turned off. While in the center port, rats needed to maintain center\n", - "fixation for a duration drawn uniformly from [0.8, 1.2] seconds. During the fixation period, a tone played from \n", - "both speakers, the frequency of which indicated the volume of the offered water reward for that trial \n", - "[1, 2, 4, 8, 16kHz, indicating 5, 10, 20, 40, 80μL rewards]. Following the fixation period, one of the two side \n", - "LEDs was illuminated, indicating that the reward might be delivered at that port; the side was randomly chosen on \n", - "each trial.This event (side LED ON) also initiated a variable and unpredictable delay period, which was randomly \n", - "drawn from an exponential distribution with mean=2.5s. The reward port LED remained illuminated for the duration \n", - "of the delay period, and rats were not required to maintain fixation during this period, although they tended to \n", - "fixate in the reward port. When reward was available, the reward port LED turned off, and rats could collect the \n", - "offered reward by nose poking in that port. The rat could also choose to terminate the trial (opt-out) at any time\n", - "by nose poking in the opposite, un-illuminated side port, after which a new trial would immediately begin. On a \n", - "proportion of trials (15–25%), the delay period would only end if the rat opted out (catch trials). If rats did \n", - "not opt-out within 100s on catch trials, the trial would terminate. The trials were self-paced: after receiving \n", - "their reward or opting out, rats were free to initiate another trial immediately. However, if rats terminated \n", - "center fixation prematurely, they were penalized with a white noise sound and a time out penalty (typically 2s, \n", - "although adjusted to individual animals). Following premature fixation breaks, the rats received the same offered \n", - "reward, in order to disincentivize premature terminations for small volume offers. We introduced semi-observable, \n", - "hidden states in the task by including uncued blocks of trials with varying reward statistics: high and low blocks\n", - ", which offered the highest three or lowest three rewards, respectively, and were interspersed with mixed blocks, \n", - "which offered all volumes. There was a hierarchical structure to the blocks, such that high and low blocks \n", - "alternated after mixed blocks (e.g., mixed-high-mixed-low, or mixed-low-mixed-high). The first block of each \n", - "session was a mixed block. Blocks transitioned after 40 successfully completed trials. Because rats prematurely \n", - "broke fixation on a subset of trials, in practice, block durations were variable.\n", - "
table\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
start_timestop_timestateseventsactionsreward_volume_ulprevious_was_violationis_warm_upcatch_percentagechangedtime_increment_for_delay_to_rewardtraining_stagecumulative_reward_volume_ulpunish_sound_enabledauto_change_catch_probabilitynose_in_centerblock_typetarget_delay_to_rewardtrials_in_stageis_catchdelay_to_rewardtarget_duration_for_nose_in_centerviolation_time_outtime_increment_for_nose_in_center
id
017950.090718395.7043[0, 1, 2, 3, 4, 5][0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30][0, 1, 2, 3, 4, 5, 6]20FalseFalse0.15False0.02590TrueFalse0.869210High1.523023False4.135600120
118395.704318402.2559[6, 7, 8, 9, 10, 11][31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51][7, 8, 9, 10, 11, 12, 13, 14]80FalseFalse0.15False0.025920TrueFalse0.979292High1.523025False1.264520120
218402.255918410.3677[12, 13, 14, 15, 16, 17][52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72][15, 16, 17, 18, 19, 20, 21]40FalseFalse0.15False0.0259100TrueFalse0.835958High1.523026False0.619385120
318410.367718421.6165[18, 19, 20, 21, 22, 23][73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131][22, 23, 24, 25, 26, 27, 28]20FalseFalse0.15False0.0259140TrueFalse0.846073High1.523027False5.369254120

... and 362 more rows.

subject
age: P6M/P24M
age__reference: birth
sex: U
species: Rattus norvegicus
subject_id: C005
lab_meta_data
task
event_types
description: Contains the name of the events in the task.
table\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
event_name
id
0state_timer
1left_port_poke
2center_port_poke
3right_port_poke
state_types
description: Contains the name of the states in the task.
table\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
state_name
id
0wait_for_poke
1nose_in_center
2go_cue
3wait_for_side_poke

... and 4 more rows.

action_types
description: Contains the name of the task output actions.
table\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
action_name
id
0sound_output
task_arguments
description: Task arguments for the task.
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argument_nameargument_descriptionexpressionexpression_typeoutput_type
id
0reward_volume_ulThe volume of reward in microliters.20integernumeric
1nose_in_centerThe time in seconds when the animal is required to maintain center port to initiate the trial (uniformly drawn from 0.8 - 1.2 seconds).0.8692142692974026doublenumeric
2time_increment_for_nose_in_centerThe time increment for nose in center in seconds.0doublenumeric
3target_duration_for_nose_in_centerThe goal for how long the animal must poke center in seconds.1doublenumeric

... and 24 more rows.

trials
description: LED illumination from the center port indicated that the animal could initiate a trial by poking its nose in that \n", - "port - upon trial initiation the center LED turned off. While in the center port, rats needed to maintain center\n", - "fixation for a duration drawn uniformly from [0.8, 1.2] seconds. During the fixation period, a tone played from \n", - "both speakers, the frequency of which indicated the volume of the offered water reward for that trial \n", - "[1, 2, 4, 8, 16kHz, indicating 5, 10, 20, 40, 80μL rewards]. Following the fixation period, one of the two side \n", - "LEDs was illuminated, indicating that the reward might be delivered at that port; the side was randomly chosen on \n", - "each trial.This event (side LED ON) also initiated a variable and unpredictable delay period, which was randomly \n", - "drawn from an exponential distribution with mean=2.5s. The reward port LED remained illuminated for the duration \n", - "of the delay period, and rats were not required to maintain fixation during this period, although they tended to \n", - "fixate in the reward port. When reward was available, the reward port LED turned off, and rats could collect the \n", - "offered reward by nose poking in that port. The rat could also choose to terminate the trial (opt-out) at any time\n", - "by nose poking in the opposite, un-illuminated side port, after which a new trial would immediately begin. On a \n", - "proportion of trials (15–25%), the delay period would only end if the rat opted out (catch trials). If rats did \n", - "not opt-out within 100s on catch trials, the trial would terminate. The trials were self-paced: after receiving \n", - "their reward or opting out, rats were free to initiate another trial immediately. However, if rats terminated \n", - "center fixation prematurely, they were penalized with a white noise sound and a time out penalty (typically 2s, \n", - "although adjusted to individual animals). Following premature fixation breaks, the rats received the same offered \n", - "reward, in order to disincentivize premature terminations for small volume offers. We introduced semi-observable, \n", - "hidden states in the task by including uncued blocks of trials with varying reward statistics: high and low blocks\n", - ", which offered the highest three or lowest three rewards, respectively, and were interspersed with mixed blocks, \n", - "which offered all volumes. There was a hierarchical structure to the blocks, such that high and low blocks \n", - "alternated after mixed blocks (e.g., mixed-high-mixed-low, or mixed-low-mixed-high). The first block of each \n", - "session was a mixed block. Blocks transitioned after 40 successfully completed trials. Because rats prematurely \n", - "broke fixation on a subset of trials, in practice, block durations were variable.\n", - "
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... and 362 more rows.

experiment_description: The value of the environment determines animals’ motivational states and sets expectations for error-based learning.\n", - "How are values computed? Reinforcement learning systems can store or cache values of states or actions that are \n", - "learned from experience, or they can compute values using a model of the environment to simulate possible futures.\n", - "These value computations have distinct trade-offs, and a central question is how neural systems decide which \n", - "computations to use or whether/how to combine them. Here we show that rats use distinct value computations for \n", - "sequential decisions within single trials. We used high-throughput training to collect statistically powerful \n", - "datasets from 291 rats performing a temporal wagering task with hidden reward states. Rats adjusted how quickly they\n", - "initiated trials and how long they waited for rewards across states, balancing effort and time costs against \n", - "expected rewards. Statistical modeling revealed that animals computed the value of the environment differently when\n", - "initiating trials versus when deciding how long to wait for rewards, even though these decisions were only seconds\n", - "apart. Moreover, value estimates interacted via a dynamic learning rate. Our results reveal how distinct value \n", - "computations interact on rapid timescales, and demonstrate the power of using high-throughput training to \n", - "understand rich, cognitive behaviors.\n", - "
session_id: RWTautowait-20190909-145629
lab: Constantinople
institution: NYU Center for Neural Science
source_script: Created using NeuroConv v0.5.1
source_script_file_name: /Users/weian/catalystneuro/neuroconv/src/neuroconv/basedatainterface.py
" - ], - "text/plain": [ - "root pynwb.file.NWBFile at 0x5092437648\n", - "Fields:\n", - " acquisition: {\n", - " task_recording \n", - " }\n", - " devices: {\n", - " bpod \n", - " }\n", - " experiment_description: The value of the environment determines animals’ motivational states and sets expectations for error-based learning.\n", - "How are values computed? Reinforcement learning systems can store or cache values of states or actions that are \n", - "learned from experience, or they can compute values using a model of the environment to simulate possible futures.\n", - "These value computations have distinct trade-offs, and a central question is how neural systems decide which \n", - "computations to use or whether/how to combine them. Here we show that rats use distinct value computations for \n", - "sequential decisions within single trials. We used high-throughput training to collect statistically powerful \n", - "datasets from 291 rats performing a temporal wagering task with hidden reward states. Rats adjusted how quickly they\n", - "initiated trials and how long they waited for rewards across states, balancing effort and time costs against \n", - "expected rewards. Statistical modeling revealed that animals computed the value of the environment differently when\n", - "initiating trials versus when deciding how long to wait for rewards, even though these decisions were only seconds\n", - "apart. Moreover, value estimates interacted via a dynamic learning rate. Our results reveal how distinct value \n", - "computations interact on rapid timescales, and demonstrate the power of using high-throughput training to \n", - "understand rich, cognitive behaviors.\n", - "\n", - " experimenter: ['Mah, Andrew']\n", - " file_create_date: [datetime.datetime(2024, 8, 26, 16, 38, 50, 4510, tzinfo=tzoffset(None, 7200))]\n", - " identifier: 85da9943-6b94-4995-b357-1dfa562fed25\n", - " institution: NYU Center for Neural Science\n", - " intervals: {\n", - " trials \n", - " }\n", - " lab: Constantinople\n", - " lab_meta_data: {\n", - " task \n", - " }\n", - " related_publications: ['https://doi.org/10.1038/s41467-023-43250-x'\n", - " 'https://doi.org/10.5281/zenodo.10031483']\n", - " session_description: We developed a temporal wagering task for rats, in which they were offered one of several water rewards on each trial, the volume of which (5, 10, 20, 40, 80μL) was indicated by a tone. The reward was assigned randomly to one of two ports, indicated by an LED. The rat could wait for an unpredictable delay to obtain the reward, or at any time could terminate the trial by poking in the other port (opt-out). Wait times were defined as how long rats waited before opting out. Trial initiation times were defined as the time from opting out or consuming reward to initiating a new trial. Reward delays were drawn from an exponential distribution, and on 15–25 percent of trials, rewards were withheld to force rats to opt-out, providing a continuous behavioral readout of subjective value. We used a high-throughput facility to train 291 rats using computerized, semi-automated procedures. The task contained latent structure; rats experienced blocks of 40 completed trials (hidden states) in which they were presented with low (5, 10, or 20μL) or high (20, 40, or 80μL) rewards. These were interleaved with “mixed\" blocks which offered all rewards. 20μL was present in all blocks, so comparing behavior on trials offering this reward revealed contextual effects (i.e., effects of hidden states). The hidden states differed in their average reward and therefore in their opportunity costs, or what the rat might miss out on by continuing to wait.\n", - " session_id: RWTautowait-20190909-145629\n", - " session_start_time: 2019-09-09 15:03:58-04:00\n", - " source_script: Created using NeuroConv v0.5.1\n", - " source_script_file_name: /Users/weian/catalystneuro/neuroconv/src/neuroconv/basedatainterface.py\n", - " subject: subject pynwb.file.Subject at 0x5090802448\n", - "Fields:\n", - " age: P6M/P24M\n", - " age__reference: birth\n", - " sex: U\n", - " species: Rattus norvegicus\n", - " subject_id: C005\n", - "\n", - " timestamps_reference_time: 2019-09-09 15:03:58-04:00\n", - " trials: trials " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pynwb import NWBHDF5IO\n", - "import ndx_structured_behavior\n", - "\n", - "nwbfile_path = \"/Volumes/T9/Constantinople/nwbfiles/C005_RWTautowait_20190909_1456292.nwb\"\n", - "\n", - "io = NWBHDF5IO(nwbfile_path, \"r\")\n", - "nwbfile = io.read()\n", - "nwbfile" - ] - }, - { - "cell_type": "markdown", - "id": "750d82d9-13df-404e-8512-960613255b88", - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-28T08:55:08.865637Z", - "start_time": "2024-08-28T08:55:07.711552Z" - } - }, - "source": [ - "## Accessing the task metadata\n", - "\n", - "The task-related general metadata is stored in a `Task` object which can be accessed as `nwbfile.lab_meta_data[\"task\"]`.\n", - "\n", - "The `EventTypesTable` is a column-based table to store the type of events that occur during the task (e.g. port poke from the animal), one type per row.\n", - "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].event_types`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "662468a8-23c6-4d90-8070-0575579b7e44", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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event_name
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" - ], - "text/plain": [ - " timestamp action_type value action_name\n", - "0 17950.0908 0 On sound_output\n", - "1 17950.0909 0 On sound_output\n", - "2 18391.2434 0 On sound_output\n", - "3 18391.2435 0 On sound_output\n", - "4 18390.3722 0 On sound_output" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.merge(\n", - " nwbfile.acquisition[\"task_recording\"].actions[:],\n", - " nwbfile.lab_meta_data[\"task\"].action_types[:],\n", - " left_on=\"action_type\",\n", - " right_on=\"id\",\n", - ").head()" - ] - }, - { - "cell_type": "markdown", - "id": "4d47f67a-a13c-4f46-a703-6ab6753fe62b", - "metadata": {}, - "source": [ - "The `StatesTable` is a column-based table to store the information about the states (e.g. the duration while nose is in center port). This table can be accessed as `nwbfile.acquisition[\"task_recording\"].states`." - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "a4fd969f-8b11-4bbd-986a-b275413c8079", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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start_timestop_timestate_typestate_name
017950.090718390.37210wait_for_poke
118390.372118391.24131nose_in_center
218391.241318391.24332go_cue
318391.243318395.37893wait_for_side_poke
418395.378918395.47434announce_reward
\n", - "
" - ], - "text/plain": [ - " start_time stop_time state_type state_name\n", - "0 17950.0907 18390.3721 0 wait_for_poke\n", - "1 18390.3721 18391.2413 1 nose_in_center\n", - "2 18391.2413 18391.2433 2 go_cue\n", - "3 18391.2433 18395.3789 3 wait_for_side_poke\n", - "4 18395.3789 18395.4743 4 announce_reward" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.merge(\n", - " nwbfile.acquisition[\"task_recording\"].states[:],\n", - " nwbfile.lab_meta_data[\"task\"].state_types[:],\n", - " left_on=\"state_type\",\n", - " right_on=\"id\",\n", - ").head()" - ] - }, - { - "cell_type": "markdown", - "id": "d983e620-b3d4-424b-bb53-cd64c5ec6cd8", - "metadata": {}, - "source": [ - "### Plot the events, actions, and states\n", - "\n", - "The ``plot_events``, ``plot_actions``, and ``plot_states`` functions can consume both the raw table as well as a subset of the table as a pandas DataFrame created through slicing, e.g., via ``events[:100]`` will plot only the first 100 rows from the events table.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "9e145e47-ebd3-4eb3-93c5-6e9d036c111b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "from ndx_structured_behavior.plot import plot_events, plot_actions, plot_states, plot_trials\n", - "\n", - "# Get the events from file\n", - "events = nwbfile.get_acquisition(\"task_recording\").events\n", - "event_types = nwbfile.get_lab_meta_data(\"task\").event_types\n", - "\n", - "# Plot the data\n", - "fig = plot_events(\n", - " events=events[20:100],\n", - " event_types=event_types,\n", - " show_event_values=True,\n", - " figsize=(18,4),\n", - " marker_size=500,\n", - ")\n", - "plt.title(\"Events\", fontsize=18)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "b14f720f-2e2e-423a-ac16-35940f92e775", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Get the actions from file\n", - "actions = nwbfile.get_acquisition(\"task_recording\").actions\n", - "action_types = nwbfile.get_lab_meta_data(\"task\").action_types\n", - "\n", - "# Plot the data\n", - "fig = plot_actions(\n", - " actions=actions[20:100],\n", - " action_types=action_types,\n", - " figsize=(18,4),\n", - " marker_size=500,\n", - ")\n", - "plt.title(\"Actions\", fontsize=18)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "68ecfe11-c8f4-4449-a1f9-23a331258fea", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", + " \n", + " 4\n", + " 18390.3722\n", + " 0\n", + " On\n", + " sound_output\n", + " \n", + " \n", + "\n", + "
" + ], "text/plain": [ - "
" + " timestamp action_type value action_name\n", + "0 17950.0908 0 On sound_output\n", + "1 17950.0909 0 On sound_output\n", + "2 18391.2434 0 On sound_output\n", + "3 18391.2435 0 On sound_output\n", + "4 18390.3722 0 On sound_output" ] }, + "execution_count": 71, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "# Get the states from file\n", - "states = nwbfile.get_acquisition(\"task_recording\").states\n", - "state_types = nwbfile.get_lab_meta_data(\"task\").state_types\n", - "\n", - "# Plot the data\n", - "plot_states(states=states[20:100],\n", - " state_types=state_types,\n", - " marker_size=500)\n", - "plt.title(\"States\", fontsize=18)\n", - "plt.show()" + "pd.merge(\n", + " nwbfile.acquisition[\"task_recording\"].actions[:],\n", + " nwbfile.lab_meta_data[\"task\"].action_types[:],\n", + " left_on=\"action_type\",\n", + " right_on=\"id\",\n", + ").head()" ] }, { "cell_type": "markdown", - "id": "d811ac1c-771a-4fc0-a995-613065ae60fd", + "id": "4d47f67a-a13c-4f46-a703-6ab6753fe62b", "metadata": {}, "source": [ - "## Accessing the trials\n", - "\n", - "The `TrialsTable` is a column-based table to store information about trials, one trial per row.\n", - "The table can be accessed from the file as `nwbfile.trials`.\n" + "The `StatesTable` is a column-based table to store the information about the states (e.g. the duration while nose is in center port). This table can be accessed as `nwbfile.acquisition[\"task_recording\"].states`." ] }, { "cell_type": "code", - "execution_count": null, - "id": "c23cd956-7ccc-4104-8349-0275dd1c3e7e", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "ca66b7b5-c6ac-405f-8297-8aeb6cc4d92e", + "execution_count": 74, + "id": "a4fd969f-8b11-4bbd-986a-b275413c8079", "metadata": {}, "outputs": [ { "data": { "text/html": [ + "
\n", + "\n", - " \n", - " \n", - "

trials (TrialsTable)

description: LED illumination from the center port indicated that the animal could initiate a trial by poking its nose in that \n", - "port - upon trial initiation the center LED turned off. While in the center port, rats needed to maintain center\n", - "fixation for a duration drawn uniformly from [0.8, 1.2] seconds. During the fixation period, a tone played from \n", - "both speakers, the frequency of which indicated the volume of the offered water reward for that trial \n", - "[1, 2, 4, 8, 16kHz, indicating 5, 10, 20, 40, 80μL rewards]. Following the fixation period, one of the two side \n", - "LEDs was illuminated, indicating that the reward might be delivered at that port; the side was randomly chosen on \n", - "each trial.This event (side LED ON) also initiated a variable and unpredictable delay period, which was randomly \n", - "drawn from an exponential distribution with mean=2.5s. The reward port LED remained illuminated for the duration \n", - "of the delay period, and rats were not required to maintain fixation during this period, although they tended to \n", - "fixate in the reward port. When reward was available, the reward port LED turned off, and rats could collect the \n", - "offered reward by nose poking in that port. The rat could also choose to terminate the trial (opt-out) at any time\n", - "by nose poking in the opposite, un-illuminated side port, after which a new trial would immediately begin. On a \n", - "proportion of trials (15–25%), the delay period would only end if the rat opted out (catch trials). If rats did \n", - "not opt-out within 100s on catch trials, the trial would terminate. The trials were self-paced: after receiving \n", - "their reward or opting out, rats were free to initiate another trial immediately. However, if rats terminated \n", - "center fixation prematurely, they were penalized with a white noise sound and a time out penalty (typically 2s, \n", - "although adjusted to individual animals). Following premature fixation breaks, the rats received the same offered \n", - "reward, in order to disincentivize premature terminations for small volume offers. We introduced semi-observable, \n", - "hidden states in the task by including uncued blocks of trials with varying reward statistics: high and low blocks\n", - ", which offered the highest three or lowest three rewards, respectively, and were interspersed with mixed blocks, \n", - "which offered all volumes. There was a hierarchical structure to the blocks, such that high and low blocks \n", - "alternated after mixed blocks (e.g., mixed-high-mixed-low, or mixed-low-mixed-high). The first block of each \n", - "session was a mixed block. Blocks transitioned after 40 successfully completed trials. Because rats prematurely \n", - "broke fixation on a subset of trials, in practice, block durations were variable.\n", - "
table\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "\n", + "
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start_timestop_timestateseventsactionsreward_volume_ulprevious_was_violationis_warm_upcatch_percentagechangedtime_increment_for_delay_to_rewardtraining_stagecumulative_reward_volume_ulpunish_sound_enabledauto_change_catch_probabilitynose_in_centerblock_typetarget_delay_to_rewardtrials_in_stageis_catchdelay_to_rewardtarget_duration_for_nose_in_centerviolation_time_outtime_increment_for_nose_in_center
idstate_typestate_name
017950.090718395.7043[0, 1, 2, 3, 4, 5][0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30][0, 1, 2, 3, 4, 5, 6]20FalseFalse0.15False0.02590TrueFalse0.869210High1.523023False4.1356001218390.37210wait_for_poke
118395.704318402.2559[6, 7, 8, 9, 10, 11][31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51][7, 8, 9, 10, 11, 12, 13, 14]80FalseFalse0.15False0.025920TrueFalse0.979292High1.523025False1.26452018390.372118391.2413120nose_in_center
218402.255918410.3677[12, 13, 14, 15, 16, 17][52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72][15, 16, 17, 18, 19, 20, 21]40FalseFalse0.15False0.0259100TrueFalse0.835958High1.523026False0.619385118391.241318391.243320go_cue
318410.367718421.6165[18, 19, 20, 21, 22, 23][73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131][22, 23, 24, 25, 26, 27, 28]20FalseFalse0.15False0.0259140TrueFalse0.846073High1.523027False5.36925412018391.243318395.37893wait_for_side_poke
418395.378918395.47434announce_reward

... and 362 more rows.

" + "\n", + "
" ], "text/plain": [ - "trials ndx_structured_behavior.trials_table.TrialsTable at 0x5091712208\n", - "Fields:\n", - " colnames: ['start_time' 'stop_time' 'states' 'events' 'actions' 'reward_volume_ul'\n", - " 'previous_was_violation' 'is_warm_up' 'catch_percentage' 'changed'\n", - " 'time_increment_for_delay_to_reward' 'training_stage'\n", - " 'cumulative_reward_volume_ul' 'punish_sound_enabled'\n", - " 'auto_change_catch_probability' 'nose_in_center' 'block_type'\n", - " 'target_delay_to_reward' 'trials_in_stage' 'is_catch' 'delay_to_reward'\n", - " 'target_duration_for_nose_in_center' 'violation_time_out'\n", - " 'time_increment_for_nose_in_center']\n", - " columns: (\n", - " start_time ,\n", - " stop_time ,\n", - " states_index ,\n", - " states ,\n", - " events_index ,\n", - " events ,\n", - " actions_index ,\n", - " actions ,\n", - " reward_volume_ul ,\n", - " previous_was_violation ,\n", - " is_warm_up ,\n", - " catch_percentage ,\n", - " changed ,\n", - " time_increment_for_delay_to_reward ,\n", - " training_stage ,\n", - " cumulative_reward_volume_ul ,\n", - " punish_sound_enabled ,\n", - " auto_change_catch_probability ,\n", - " nose_in_center ,\n", - " block_type ,\n", - " target_delay_to_reward ,\n", - " trials_in_stage ,\n", - " is_catch ,\n", - " delay_to_reward ,\n", - " target_duration_for_nose_in_center ,\n", - " violation_time_out ,\n", - " time_increment_for_nose_in_center \n", - " )\n", - " description: LED illumination from the center port indicated that the animal could initiate a trial by poking its nose in that \n", - "port - upon trial initiation the center LED turned off. While in the center port, rats needed to maintain center\n", - "fixation for a duration drawn uniformly from [0.8, 1.2] seconds. During the fixation period, a tone played from \n", - "both speakers, the frequency of which indicated the volume of the offered water reward for that trial \n", - "[1, 2, 4, 8, 16kHz, indicating 5, 10, 20, 40, 80μL rewards]. Following the fixation period, one of the two side \n", - "LEDs was illuminated, indicating that the reward might be delivered at that port; the side was randomly chosen on \n", - "each trial.This event (side LED ON) also initiated a variable and unpredictable delay period, which was randomly \n", - "drawn from an exponential distribution with mean=2.5s. The reward port LED remained illuminated for the duration \n", - "of the delay period, and rats were not required to maintain fixation during this period, although they tended to \n", - "fixate in the reward port. When reward was available, the reward port LED turned off, and rats could collect the \n", - "offered reward by nose poking in that port. The rat could also choose to terminate the trial (opt-out) at any time\n", - "by nose poking in the opposite, un-illuminated side port, after which a new trial would immediately begin. On a \n", - "proportion of trials (15–25%), the delay period would only end if the rat opted out (catch trials). If rats did \n", - "not opt-out within 100s on catch trials, the trial would terminate. The trials were self-paced: after receiving \n", - "their reward or opting out, rats were free to initiate another trial immediately. However, if rats terminated \n", - "center fixation prematurely, they were penalized with a white noise sound and a time out penalty (typically 2s, \n", - "although adjusted to individual animals). Following premature fixation breaks, the rats received the same offered \n", - "reward, in order to disincentivize premature terminations for small volume offers. We introduced semi-observable, \n", - "hidden states in the task by including uncued blocks of trials with varying reward statistics: high and low blocks\n", - ", which offered the highest three or lowest three rewards, respectively, and were interspersed with mixed blocks, \n", - "which offered all volumes. There was a hierarchical structure to the blocks, such that high and low blocks \n", - "alternated after mixed blocks (e.g., mixed-high-mixed-low, or mixed-low-mixed-high). The first block of each \n", - "session was a mixed block. Blocks transitioned after 40 successfully completed trials. Because rats prematurely \n", - "broke fixation on a subset of trials, in practice, block durations were variable.\n", - "\n", - " id: id " + " start_time stop_time state_type state_name\n", + "0 17950.0907 18390.3721 0 wait_for_poke\n", + "1 18390.3721 18391.2413 1 nose_in_center\n", + "2 18391.2413 18391.2433 2 go_cue\n", + "3 18391.2433 18395.3789 3 wait_for_side_poke\n", + "4 18395.3789 18395.4743 4 announce_reward" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.merge(\n", + " nwbfile.acquisition[\"task_recording\"].states[:],\n", + " nwbfile.lab_meta_data[\"task\"].state_types[:],\n", + " left_on=\"state_type\",\n", + " right_on=\"id\",\n", + ").head()" + ] + }, + { + "cell_type": "markdown", + "id": "d983e620-b3d4-424b-bb53-cd64c5ec6cd8", + "metadata": {}, + "source": [ + "### Plot the events, actions, and states\n", + "\n", + "The ``plot_events``, ``plot_actions``, and ``plot_states`` functions can consume both the raw table as well as a subset of the table as a pandas DataFrame created through slicing, e.g., via ``events[:100]`` will plot only the first 100 rows from the events table.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "9e145e47-ebd3-4eb3-93c5-6e9d036c111b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" ] }, - "execution_count": 99, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "trials = nwbfile.trials\n", - "trials" + "from matplotlib import pyplot as plt\n", + "from ndx_structured_behavior.plot import plot_events, plot_actions, plot_states, plot_trials\n", + "\n", + "# Get the events from file\n", + "events = nwbfile.get_acquisition(\"task_recording\").events\n", + "event_types = nwbfile.get_lab_meta_data(\"task\").event_types\n", + "\n", + "# Plot the data\n", + "fig = plot_events(\n", + " events=events[20:100],\n", + " event_types=event_types,\n", + " show_event_values=True,\n", + " figsize=(18,4),\n", + " marker_size=500,\n", + ")\n", + "plt.title(\"Events\", fontsize=18)\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 100, - "id": "cc9adeaf-ae23-403f-ad66-5a6ed695760f", + "execution_count": 89, + "id": "b14f720f-2e2e-423a-ac16-35940f92e775", + "metadata": {}, + "outputs": [ + { + "data": { + 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the actions from file\n", + "actions = nwbfile.get_acquisition(\"task_recording\").actions\n", + "action_types = nwbfile.get_lab_meta_data(\"task\").action_types\n", + "\n", + "# Plot the data\n", + "fig = plot_actions(\n", + " actions=actions[20:100],\n", + " action_types=action_types,\n", + " figsize=(18,4),\n", + " marker_size=500,\n", + ")\n", + "plt.title(\"Actions\", fontsize=18)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "68ecfe11-c8f4-4449-a1f9-23a331258fea", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the states from file\n", + "states = nwbfile.get_acquisition(\"task_recording\").states\n", + "state_types = nwbfile.get_lab_meta_data(\"task\").state_types\n", + "\n", + "# Plot the data\n", + "plot_states(states=states[20:100],\n", + " state_types=state_types,\n", + " marker_size=500)\n", + "plt.title(\"States\", fontsize=18)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d811ac1c-771a-4fc0-a995-613065ae60fd", + "metadata": {}, + "source": [ + "## Accessing the trials\n", + "\n", + "The `TrialsTable` is a column-based table to store information about trials, one trial per row.\n", + "The table can be accessed from the file as `nwbfile.trials`.\n" + ] + }, + { + "cell_type": "code", + "id": "ca66b7b5-c6ac-405f-8297-8aeb6cc4d92e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-28T11:41:33.949842Z", + "start_time": "2024-08-28T11:41:33.947843Z" + } + }, + "source": "trials = nwbfile.trials", + "outputs": [], + "execution_count": 2 + }, + { + "cell_type": "code", + "id": "cc9adeaf-ae23-403f-ad66-5a6ed695760f", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-28T11:41:35.210373Z", + "start_time": "2024-08-28T11:41:35.192278Z" + } + }, + "source": [ + "trials[:].head()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " start_time stop_time states \\\n", + "id \n", + "0 17950.0907 18395.7043 [0, 1, 2, 3, 4, 5] \n", + "1 18395.7043 18402.2559 [6, 7, 8, 9, 10, 11] \n", + "2 18402.2559 18410.3677 [12, 13, 14, 15, 16, 17] \n", + "3 18410.3677 18421.6165 [18, 19, 20, 21, 22, 23] \n", + "4 18421.6165 18429.0515 [24, 25, 26, 27, 28, 29] \n", + "\n", + " events \\\n", + "id \n", + "0 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n", + "1 [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 4... \n", + "2 [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 6... \n", + "3 [73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 8... \n", + "4 [132, 133, 134, 135, 136, 137, 138, 139, 140, ... \n", + "\n", + " actions reward_volume_ul \\\n", + "id \n", + "0 [0, 1, 2, 3, 4, 5, 6] 20 \n", + "1 [7, 8, 9, 10, 11, 12, 13, 14] 80 \n", + "2 [15, 16, 17, 18, 19, 20, 21] 40 \n", + "3 [22, 23, 24, 25, 26, 27, 28] 20 \n", + "4 [29, 30, 31, 32, 33, 34, 35, 36] 80 \n", + "\n", + " previous_was_violation is_warm_up catch_percentage changed ... \\\n", + "id ... \n", + "0 False False 0.15 False ... \n", + "1 False False 0.15 False ... \n", + "2 False False 0.15 False ... \n", + "3 False False 0.15 False ... \n", + "4 False False 0.15 False ... \n", + "\n", + " auto_change_catch_probability nose_in_center block_type \\\n", + "id \n", + "0 False 0.869210 High \n", + "1 False 0.979292 High \n", + "2 False 0.835958 High \n", + "3 False 0.846073 High \n", + "4 False 0.838370 High \n", + "\n", + " target_delay_to_reward trials_in_stage is_catch delay_to_reward \\\n", + "id \n", + "0 1.5 23023 False 4.135600 \n", + "1 1.5 23025 False 1.264520 \n", + "2 1.5 23026 False 0.619385 \n", + "3 1.5 23027 False 5.369254 \n", + "4 1.5 23028 False 1.220980 \n", + "\n", + " target_duration_for_nose_in_center violation_time_out \\\n", + "id \n", + "0 1 2 \n", + "1 1 2 \n", + "2 1 2 \n", + "3 1 2 \n", + "4 1 2 \n", + "\n", + " time_increment_for_nose_in_center \n", + "id \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "\n", + "[5 rows x 24 columns]" + ], "text/html": [ "
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