From 5d656ed338e9632524a8f4a03203d1c836cdf418 Mon Sep 17 00:00:00 2001
From: Kay Robbins <1189050+VisLab@users.noreply.github.com>
Date: Tue, 31 Jan 2023 16:16:31 -0600
Subject: [PATCH 01/13] Updated the description of remap to include integer
source columns
---
.../{models => remodeling_files}/remove_extra_rmdl.json | 0
.../summarize_columns_rmdl.json | 0
.../summarize_hed_types_rmdl.json | 0
docs/source/FileRemodelingTools.md | 6 ++++++
hedcode/matlab_scripts/web_services/getTestData.m | 6 +++---
5 files changed, 9 insertions(+), 3 deletions(-)
rename datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/{models => remodeling_files}/remove_extra_rmdl.json (100%)
rename datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/{models => remodeling_files}/summarize_columns_rmdl.json (100%)
rename datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/{models => remodeling_files}/summarize_hed_types_rmdl.json (100%)
diff --git a/datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/models/remove_extra_rmdl.json b/datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/remodeling_files/remove_extra_rmdl.json
similarity index 100%
rename from datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/models/remove_extra_rmdl.json
rename to datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/remodeling_files/remove_extra_rmdl.json
diff --git a/datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/models/summarize_columns_rmdl.json b/datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/remodeling_files/summarize_columns_rmdl.json
similarity index 100%
rename from datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/models/summarize_columns_rmdl.json
rename to datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/remodeling_files/summarize_columns_rmdl.json
diff --git a/datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/models/summarize_hed_types_rmdl.json b/datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/remodeling_files/summarize_hed_types_rmdl.json
similarity index 100%
rename from datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/models/summarize_hed_types_rmdl.json
rename to datasets/eeg_ds003654s_hed_remodel/derivatives/remodel/remodeling_files/summarize_hed_types_rmdl.json
diff --git a/docs/source/FileRemodelingTools.md b/docs/source/FileRemodelingTools.md
index da49ce9..8c7b0f2 100644
--- a/docs/source/FileRemodelingTools.md
+++ b/docs/source/FileRemodelingTools.md
@@ -1131,11 +1131,17 @@ Remapping can be used to convert the column containing these codes into one or m
| *destination_columns* | list | A list of *n* names of the destination columns for the map. |
| *map_list* | list | A list of mappings. Each element is a list of *m* source
column values followed by *n* destination values.
Mapping source values are treated as strings. |
| *ignore_missing* | bool | If false, source column values not in the map generate "n/a"
destination values instead of errors. |
+| *integer_sources* | list | [**Optional**] A list of source columns that are integers.
The *integer_sources* must be a subset of *source_columns*. |
```
A column cannot be both a source and a destination,
and all source columns must be present in the data files.
New columns are created for destination columns that are missing from a data file.
+The *remap_columns* operation only works for columns containing strings or integers,
+as it is meant for remapping categorical codes.
+You must specify the which source columns contain integers so that `n/a` values
+can be handled appropriately.
+
The *map_list* parameter specifies how each unique combination of values from the source
columns will be mapped into the destination columns.
If there are *m* source columns and *n* destination columns,
diff --git a/hedcode/matlab_scripts/web_services/getTestData.m b/hedcode/matlab_scripts/web_services/getTestData.m
index 6c3b583..461b13a 100644
--- a/hedcode/matlab_scripts/web_services/getTestData.m
+++ b/hedcode/matlab_scripts/web_services/getTestData.m
@@ -16,12 +16,12 @@
data.eventsText = fileread([remodelPath ...
'sub-002/eeg/sub-002_task-FacePerception_run-1_events.tsv']);
data.remodel1Text = fileread(...
- [remodelPath 'derivatives/remodel/models/remove_extra_rmdl.json']);
+ [remodelPath 'derivatives/remodel/remodeling_files/remove_extra_rmdl.json']);
data.remodel2Text = fileread(...
- [remodelPath 'derivatives/remodel/models/' ...
+ [remodelPath 'derivatives/remodel/remodeling_files/' ...
'summarize_columns_rmdl.json']);
data.remodel3Text = fileread(...
- [remodelPath 'derivatives/remodel/models/' ...
+ [remodelPath 'derivatives/remodel/remodeling_files/' ...
'summarize_hed_types_rmdl.json']);
data.jsonBadText = ...
fileread('../../data/bids_data/both_types_events_errors.json');
From c222e46a69360ba351794b899f5903067eae4994 Mon Sep 17 00:00:00 2001
From: Kay Robbins <1189050+VisLab@users.noreply.github.com>
Date: Wed, 1 Feb 2023 13:45:31 -0600
Subject: [PATCH 02/13] Started work on the search document
---
docs/source/HedSearchGuide.md | 286 ++++++++++++++++++++--------------
1 file changed, 165 insertions(+), 121 deletions(-)
diff --git a/docs/source/HedSearchGuide.md b/docs/source/HedSearchGuide.md
index 782ffb8..d4dca59 100644
--- a/docs/source/HedSearchGuide.md
+++ b/docs/source/HedSearchGuide.md
@@ -7,90 +7,185 @@
Many analysis methods locate event markers with specified properties and
extract sections of the data surrounding these markers for analysis.
+This extraction process is often referred to as **epoching** the data
+or sometimes **trial selection**.
-Other approaches include finding sections of the data with particular
-signal characteristics in order to identify whether particular types of event markers
+Analysis may also exclude data surrounding particular event markers.
+
+Other approaches find sections of the data with particular
+signal characteristics and then determine which types of event markers
are more likely to be associated with data sections having these characteristics.
At a more global level, analysts may want to locate datasets
whose event markers have certain properties in choosing data for
initial analysis or for comparisons with their own data.
-## Where can HED search be used?
-The HED search facility allows users to form sophisticated queries
-based on HED annotations in a dataset-independent manner.
-These queries can be used to locate data sets satisfying the specified criteria
-and to find the relevant event markers in that data.
+## HED search basics
-For example, the [**factor_hed_tags**](https://www.hed-resources.org/en/latest/FileRemodelingTools.html#factor-hed-tags)
-operation of the HED [**file remodeling tools**](https://www.hed-resources.org/en/latest/FileRemodelingTools.html)
-creates factor vectors for selecting events satisfying general HED queries.
+Datasets whose event markers are annotated with HED (Hierarchical Event Descriptors)
+can be searched in a dataset independent manner.
+The HED search facility has been implemented in the
+Python [**HEDTools**](https://pypi.org/project/hedtools/) library,
+an open source Python library.
+The latest versions are available in the
+[**hed-python**](https://github.com/hed-standard/hed-python) GitHub repository.
-The [**HED-based epoching**](https://www.hed-resources.org/en/latest/HedMatlabTools.html#hed-based-epoching) tools in [**EEGLAB**](https://sccn.ucsd.edu/eeglab/index.php)
-can use HED-based search to epoch data based on HED tags.
+To perform a query using HEDTools,
+users create a query object containing the parsed query.
+Once created, this query object can then be applied to any number of HED annotations
+-- say to the annotations for each event-marker associated with a data recording.
-Work is underway to integrate HED-based search into other tools including
-[**Fieldtrip**](https://www.fieldtriptoolbox.org/) and
-[**MNE-python**](https://mne.tools/stable/index.html)
-as well into the analysis platforms [**NEMAR**](https://nemar.org/) and
-[**EEGNET**](http://eegnet.org/)
+The query object returns a list of matches within the annotation.
+Usually, users just test whether this list is empty or not to determine whether the query
+was satisfied.
-## Search overview
+### Calling syntax
-## Calling syntax
+To perform a search, create a `TagExpressionParser` object, which parses the query.
+Then use this query object to search a HED annotation as demonstrated in the following
+example:
-Searching is handled similar to regular expressions. You create a `TagExpressionParser`,
-then use it to search individual `HedString` objects one by one.
````{admonition} Format of calling syntax for HED search.
```python
hed_string = HedString(..., schema=schema)
-query_string = "Experiment-structure"
+query_string = "Sensory-event"
query = TagExpressionParser(query_string)
result = query.search_hed_string(hed_string)
+if result:
+ print(f"{query_string} found in {str(hed_string)})
```
````
-The `search_hed_string` method returns a list of groups in the HED string which match the query.
-Treat the `result` as a bool unless you're interested in the exact groups.
-Notes: If you're searching many strings for the same expression, make sure you create the TagExpressionParser only once.
+The `HedString` object is a parsed representation of a HED annotation.
+The query facility assumes that the annotations have been validated.
+
+The `search_hed_string` method returns a list of groups in the HED string that match the query.
+As the example demonstrates, we are usually not interested in the exact groups, but
+just whether there was a match.
+Thus, `result` is treated as a boolean value.
+
+**Note:** If you are searching many strings for the same expression,
+be sure to create the `TagExpressionParser` only once.
-## Query types
+### Single tag queries
-### Single term queries
+The most basic query form is just a single tag.
+The query returns any child of that tag.
+| Query type | Example query | Matches | Does not match |
+|------------ |----------------| --------------- | ---------------- |
+| **Single term**
Match the term or any child.
Don't consider values or
extensions when matching.| *Agent-trait* | *Agent-trait*
*Age*
*Age/35*
*Right-handed*
*Agent-trait/Glasses*
*Agent-property/Agent-trait*
*(Age, Blue)* |*Agent-property* |
+| **Tag path with slash**
Match the exact tag with
extension or value | *Age/* | *Age* | *Age/35* |
+| | *Age/35* | *Age/35* | *Age/36*
*Agent-trait/Age/35* |
+| **Tag with wildcard**
Match tags that
start with wildcard | | |
+Query tags with no slashes are **term searches**.
+The tag must correspond to a node in a HED schema.
+If the annotation to be searched has the tag or any of its child tags,
+the annotation matches. **What is returned?**
-| Query form | Meaning | Example query | Matches | Does not match |
-|------------|-------------------------------|----------------| --------------- | ---------------- |
-| Single term | Match the term or any of its children.
Don't consider values or extensions.| *Event* | *Event*
*Sensory-event*
*Event/New-event*
*Event/Sensory-event*|
-| Tag path
with slash | Match the exact tag with extension or value | Def/Def1 | Find strings that contain the exact tag Def/Def1. |
-| A, B | Match both A and B. | *Event*, *Sensory-event* | | |
-| and | and | A and B | Find strings or groups with both A and B |
-| or | or | A or B | Find strings or groups with either A or B |
-| [[ | Exact group start | [[A, B]] | Find a group that contains both A and B. |
-| ]] | Exact group end | | eg "(A, B)" or "(A, B, C)" |
-| [ | Containing group start | [A, B] | Find a group that contains A and B, but both A and B could be any levels of subgroups down. |
-| ] | Containing group end | | eg "(A, B)" or "(A, (B, C))" |
-| ( | Logical group Start | A or (B and C) | Logically group operations to override precedence |
-| ) | Logical group End | | Find string with A or both B and C. |
-| ~ | Negate | ~A | Find lines that do not contain A |
+### Logical queries
-These operations(including exact, containing, and logical groups) can be arbitrarily nested and combined. e.g. "[A or ~[[B or C]] ]"
+In the following `A` and `B` represent arbitrary HED strings.
+| Query form | Example query | Matches | Does not match |
+|------------|----------------------|----------------| --------------- |
+| **`A`, `B`**
Match if both `A` and `B`
are matched. | *Event*, *Sensory-event* | *Event*, *Sensory-event*
*Sensory-event*, *Event*
(*Event*, *Sensory-event*) | *Event* |
+| **`A` and `B`**
Match strings or groups
with both `A` and `B`. | | |
+| **`A` or `B`**
Match strings or groups
with either `A` or `B`. | | |
+| **~`A`**
Match strings that do
not contain `A` | | | |
+
+### Group matches
+
+| Query form | Example query | Matches | Does not match |
+|------------|----------------------|----------------| --------------- |
+| **[[`A`, `B`]]**
Match a group that
contains both `A` and `B`
at the same level
in the same group. | *[[Red, Blue]]* | *(Red, Blue)*
*(Red, Blue, Green)* | *(Red, (Blue, Green))* |
+| **[`A`, `B`]**
Match a group that
contains `A` and `B`.
Both `A` and `B` could
be any subgroup level. | *[Red, Blue]* |*(Red, (Blue, Green))*
*((Red, Yellow), (Blue, Green))*| *Red, (Blue, Green)* |
+
+
+These operations (including exact, containing, and logical groups) can be
+arbitrarily nested and combined,
+as for example in the query:
+
+> *[`A` or ~[[`B` or `C`]] ]*
Ordering on either the search terms or strings to be searched doesn't matter unless it will impact precedence on the expression. Use logical grouping if you aren't sure.
Precedence is purely left to right outside of grouping operations. This may change in the future.
-## Example Basic Search Strings
+### Notations under discussion
+
+| Query form | Example query | Matches | Does not match |
+|----------- |--------------- |-------- | ---------|
+| **[[`A`, ?]]**
Match a group with `A`
and any tag or subgroup. | | |
+| **[[`A`, ??]]**
Match a group with `A`
and any tag. | | |
+| **[[`A`, ???]]**
Match a group with `A`
and any subgroup. | | |
+| **{`A`}**
Match a group or string
with `A` and nothing else. | | |
+
+
+ Note: {} notation does NOT require a group.
+
+ {A} would match "A" or "(A)" or "B, (A)"
+ [{A}] would match "(A)" or "B, (A)"
+
+---
+
+ Note: Adding a wildcard to a search will automatically make whatever group it's found in an "exact group"
+ e.g.: Query: A ?
+ This would match:
+ 1. A, B
+ 2. (A, B)
+ 3. A, (B)
+ 4. (A, (B))
+ But NOT:
+ 1. (A), B
+ This is because once A is found, there needs to be wildcard group or tag at the same level.
+
+ Note: Wildcards should be as late in a search as possible for efficency. eg "A and ?" rather than "? and A"
+
+### Wildcards
+ Wildcards are supported under the new system. They mostly make sense in containing groups. They can be mixed and matched,
+ e.g: [[A or B and ??? and ??]]
+ Meaning: Find a group containing tag A or tag B, while also containing another unrelated tag and another unrelated group.
+
+### Search Term Wildcards
+Term search - Checks all parent terms in the tags searched as well, does not interact with extensions of values in any way.
+Tag search - Finds tags where the short form IS the indicated search string, unless it has a wildcard.(any search term with a "/" or a "*" or wrapped in double quotes turns it into a tag search)
+
+Examples:
+1. Orange : would be a TERM search, finding the children as well since Orange is a term in any children of orange.
+2. Orange/* : Would be a TAG search, finding any Orange tags that have an extension or value
+3. Orange/2*: Would be a TAG search, finding any Orange tags that have an extension or value beginning with 2.
+4. Orange* : would be a TAG search, but the wildcard would have it find the children too.
+ This would additionally find any potentially completely unrelated tags that start with Orange in short form. Say "OrangeTheFruit"
+5. "Orange": would be a TAG search, with no extensions allowed due to the quotes.
+ The entire short form of the tag must be "Orange" and nothing else.
+
+### Updated search:
+Query: Event and Sensory-event
+String being searched: Sensory-event
+#### Old System
+ In the old system this would match. It would first find an Event tag, matching Sensory-event. It would then find a Sensory-event tag, also matching Sensory-event.
+ It did not care it was matching the same tag twice.
+
+#### New System
+ Once it matches a tag, it "consumes" the tag and marks it as used.
+ So it would find Event, then be unable to match Sensory-event as there were no tags left.
+
+
+
+
+
+## Example queries
+
https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003654s_hed
These strings are used in the basic examples.
-| # | Line |
-|-----|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| 1 | Experiment-structure,(Def/Right-sym-cond,Onset),(Def/Initialize-recording,Onset) |
+| # | Line |
+|-----|-----------|
+| 1 | *Experiment-structure,(Def/Right-sym-cond,Onset),
(Def/Initialize-recording,Onset) |
| 2 | Sensory-event,Experimental-stimulus,(Def/Face-image,Onset),(Def/Blink-inhibition-task,Onset),(Def/Fixation-task,Onset),Def/Scrambled-face-cond,Def/First-show-cond,Experimental-trial/1,(Image,Pathname/s096.bmp) |
| 3 | Sensory-event,(Intended-effect,Cue),(Def/Circle-only,Onset),(Def/Face-image,Offset),(Def/Blink-inhibition-task,Offset),(Def/Fixation-task,Offset),Experimental-trial/1,(Image,Pathname/circle.bmp) |
| 4 | Sensory-event,(Intended-effect,Cue),(Def/Cross-only,Onset),(Def/Fixation-task,Onset),(Def/Circle-only,Offset),Experimental-trial/2,(Image,Pathname/cross.bmp) |
@@ -101,7 +196,7 @@ These strings are used in the basic examples.
| 9 | Sensory-event,Experimental-stimulus,(Def/Face-image,Onset),(Def/Blink-inhibition-task,Onset),(Def/Cross-only,Offset),Def/Famous-face-cond,Def/Immediate-repeat-cond,Experimental-trial/3,(Face,Item-interval/1),(Image,Pathname/f148.bmp) |
| 10 | Agent-action,Participant-response,Def/Press-right-finger,Experimental-trial/3 |
-## Basic queries
+### Basic query examples
| Query | Meaning | Lines matching | Explanation/Notes |
|------- |----------|-----------|------------|
@@ -112,7 +207,7 @@ These strings are used in the basic examples.
| Pathname/circle.bmp or Pathname/cross.bmp | Match either exact tag *Pathname/circle.bmp* or *Pathname/cross.bmp* | [3, 4, 7, 8] | |
| Def/Cross-only, Onset |Match the exact tag *Def/Cross-only*
and the term *Onset* | [4, 5, 8, 9] | Matches every line with *Def/Cross-only*
because it isn't checking if the
*Onset* tag is in the same group. |
-## Queries based on groups
+### Group query examples
| Query | Meaning | Lines matching |
|----------|-------------|----------------|
@@ -122,15 +217,15 @@ These strings are used in the basic examples.
| [[Def/Cross-only, Onset]] and Experimental-trial/3 | Same as previous, but now the line must also
contain *Experimental-trial/3* | [8] |
-## Complex queries
+### Complex query examples
Here are some slightly more complex queries and example results from searching simple strings. These examples are more contrived than those above to show group searching abilities.
Expression: **"(Item or Agent) and [[Action or Event]]"**
Meaning: Find a line that contains Item or Agent anywhere, and also contains a subgroup somewhere containing an Action or Event.
-| Query | Query | Result | Notes |
-|-------------------------|-------------------------------------------|--------|---------------------------------------------------------------------------------------------|
+| Query | String | Result | Notes |
+| ----- | -------- | ------ |--------|
| "(Clear-throat),Item" | "(Item or Agent) and [[Action or Event]]" | True | |
| "((Clear-throat),Item)" | "(Item or Agent) and [[Action or Event]]" | True | |
| "Clear-throat,Item" | "(Item or Agent) and [[Action or Event]]" | False | This is not found as the [[Action or Event]] part means the event term must be in a group. |
@@ -152,8 +247,8 @@ Meaning: Find a group that has a direct child subgroup containing Event, and a d
Expression: **"[[ [Event], [Action] ]]"**
Meaning: Find a group that has a descendant subgroup containing Event, and a descendant subgroup containing Action
-| Search String | Expression | Result | Notes |
-|------------------------------|----------------------------|--------|---------------------------------------------------------------------------------------------------------------------------------------|
+| Search String | Expression | Result | Notes |
+|----------------|------------|--------|--------|
| '((Event),(Clear-throat))' | "[[ [Event]], [Action] ]]" | True | This is the most basic example perfectly mirroring the search string. |
| '(Clear-throat,Event)' | "[[ [Event]], [Action] ]]" | False | This is not found because the two pairs of double brackets require at least one nested subgroup. |
| '((Event),((Clear-throat)))' | "[[ [Event]], [Action] ]]" | True | This is now found as the single brackets don't require it to be a direct child. |
@@ -162,82 +257,31 @@ Meaning: Find a group that has a descendant subgroup containing Event, and a des
Expression: **"[[ [[~Event]] ]]"**
Meaning: Find a group containing a direct child group that does not contain Event.
-| Search String | Expression | Result | Notes |
-|------------------------------|--------------------|--------|------------------------------------------------------------------------------|
+| Search String | Expression | Result | Notes |
+|-----------------|--------------------|--------|-----------|
| '(Clear-throat,Event)' | "[[ [[~Event]] ]]" | False | This is not found as the group has no subgroups. |
| '((Clear-throat),(Event))' | "[[ [[~Event]] ]]" | True | This is found because one of the subgroups does not contain Event |
| '((Event))' | "[[ [[~Event]] ]]" | False | This is not found because the only subgroup contains Event |
| '(((Clear-throat)),(Event))' | "[[ [[~Event]] ]]" | True | This is found because the ((Clear-throat)) subgroup does not contain Event |
| '((Clear-throat,Event))' | "[[ [[~Event]] ]]" | False | This is not found because the child subgroup contains Clear-throat and Event |
----
-
-| Token | Meaning (* Denotes recently renamed) | Example | Example Meaning |
-|---------------------|--------------------------------------------------------------------------------------------------|----------------|----------------------------------------------------------------------|
-| Single term | Searches matches the term
or any of its children.
No values or extensions are considered. | *Event* | Matches string containing *Event*
tag, or any child tag of Event. |
-| Tag with slash | A tag with extension or value | Def/Def1 | Find strings that contain the exact tag Def/Def1. |
-| * | Wildcard on tag searches. Turns term searches into tag searches. | Def/Def1/* | Find tags that start with Def/Def1/ |
-| , | and | A, B | Find strings or groups with both A and B |
-| and | and | A and B | Find strings or groups with both A and B |
-| or | or | A or B | Find strings or groups with either A or B |
-| [[ | Containing* group start | [[A, B]] | Find a group that contains both A and B. |
-| ]] | Containing* group end | | eg "(A, B)" or "(A, B, C)" |
-| [ | Descendant* group start | [A, B] | Find a group that contains A and B as descendant members |
-| ] | Descendant* group end | | eg "(A, B)" or "(A, (B, C))" |
-| ( | Logical group Start | A or (B and C) | Logically group operations to override precedence |
-| ) | Logical group End | | Find string with A or both B and C. |
-| ~ | Logical Negation | ~A | Find lines that do not contain A |
-| ? - Temp notation | Any Tag or Group | [[A, ?]] | Find a group with A and any tag or subgroup. |
-| ?? - Temp notation | Any Tag | [[A, ??]] | Find a group with A and any other tag. |
-| ??? - Temp notation | Any Group | [[A, ???]] | Find a group with A and any subgroup. |
-| {} - Temp notation | No additional tags or groups | {A} | Find a group or string with A nothing else. |
- Note: {} notation does NOT require a group.
-
- {A} would match "A" or "(A)" or "B, (A)"
- [{A}] would match "(A)" or "B, (A)"
-
----
-
- Note: Adding a wildcard to a search will automatically make whatever group it's found in an "exact group"
- e.g.: Query: A ?
- This would match:
- 1. A, B
- 2. (A, B)
- 3. A, (B)
- 4. (A, (B))
- But NOT:
- 1. (A), B
- This is because once A is found, there needs to be wildcard group or tag at the same level.
-
- Note: Wildcards should be as late in a search as possible for efficency. eg "A and ?" rather than "? and A"
+## Where can HED search be used?
-### Wildcards
- Wildcards are supported under the new system. They mostly make sense in containing groups. They can be mixed and matched,
- e.g: [[A or B and ??? and ??]]
- Meaning: Find a group containing tag A or tag B, while also containing another unrelated tag and another unrelated group.
-
-### Search Term Wildcards
-Term search - Checks all parent terms in the tags searched as well, does not interact with extensions of values in any way.
-Tag search - Finds tags where the short form IS the indicated search string, unless it has a wildcard.(any search term with a "/" or a "*" or wrapped in double quotes turns it into a tag search)
+The HED search facility allows users to form sophisticated queries
+based on HED annotations in a dataset-independent manner.
+These queries can be used to locate data sets satisfying the specified criteria
+and to find the relevant event markers in that data.
-Examples:
-1. Orange : would be a TERM search, finding the children as well since Orange is a term in any children of orange.
-2. Orange/* : Would be a TAG search, finding any Orange tags that have an extension or value
-3. Orange/2*: Would be a TAG search, finding any Orange tags that have an extension or value beginning with 2.
-4. Orange* : would be a TAG search, but the wildcard would have it find the children too.
- This would additionally find any potentially completely unrelated tags that start with Orange in short form. Say "OrangeTheFruit"
-5. "Orange": would be a TAG search, with no extensions allowed due to the quotes.
- The entire short form of the tag must be "Orange" and nothing else.
+For example, the [**factor_hed_tags**](https://www.hed-resources.org/en/latest/FileRemodelingTools.html#factor-hed-tags)
+operation of the HED [**file remodeling tools**](https://www.hed-resources.org/en/latest/FileRemodelingTools.html)
+creates factor vectors for selecting events satisfying general HED queries.
-### Updated search:
-Query: Event and Sensory-event
-String being searched: Sensory-event
-#### Old System
- In the old system this would match. It would first find an Event tag, matching Sensory-event. It would then find a Sensory-event tag, also matching Sensory-event.
- It did not care it was matching the same tag twice.
+The [**HED-based epoching**](https://www.hed-resources.org/en/latest/HedMatlabTools.html#hed-based-epoching) tools in [**EEGLAB**](https://sccn.ucsd.edu/eeglab/index.php)
+can use HED-based search to epoch data based on HED tags.
-#### New System
- Once it matches a tag, it "consumes" the tag and marks it as used.
- So it would find Event, then be unable to match Sensory-event as there were no tags left.
-
+Work is underway to integrate HED-based search into other tools including
+[**Fieldtrip**](https://www.fieldtriptoolbox.org/) and
+[**MNE-python**](https://mne.tools/stable/index.html)
+as well into the analysis platforms [**NEMAR**](https://nemar.org/) and
+[**EEGNET**](http://eegnet.org/)
From a0d7ad2d326dc45cd09a3827cb8084bb46443a78 Mon Sep 17 00:00:00 2001
From: Kay Robbins <1189050+VisLab@users.noreply.github.com>
Date: Wed, 1 Feb 2023 18:36:43 -0600
Subject: [PATCH 03/13] Updated the search documentation
---
docs/source/HedSearchGuide.md | 120 ++++++++++++++++++++++++++--------
1 file changed, 92 insertions(+), 28 deletions(-)
diff --git a/docs/source/HedSearchGuide.md b/docs/source/HedSearchGuide.md
index d4dca59..4d45c3e 100644
--- a/docs/source/HedSearchGuide.md
+++ b/docs/source/HedSearchGuide.md
@@ -42,14 +42,14 @@ was satisfied.
### Calling syntax
To perform a search, create a `TagExpressionParser` object, which parses the query.
-Then use this query object to search a HED annotation as demonstrated in the following
-example:
+Once created, this query object can be applied to search multiple HED annotations.
+The syntax is demonstrated in the following example:
-````{admonition} Format of calling syntax for HED search.
+````{admonition} Example calling syntax for HED search.
```python
-hed_string = HedString(..., schema=schema)
+hed_string = HedString("Sensory-event, Sensory-presentation", schema=schema)
query_string = "Sensory-event"
query = TagExpressionParser(query_string)
result = query.search_hed_string(hed_string)
@@ -58,46 +58,103 @@ if result:
```
````
-
-The `HedString` object is a parsed representation of a HED annotation.
+In the example the strings containing HED annotations are converted to a `HedString` object,
+which is a parsed representation of the HED annotation.
The query facility assumes that the annotations have been validated.
+The query is represented by a `TagExpressionParser` object.
The `search_hed_string` method returns a list of groups in the HED string that match the query.
-As the example demonstrates, we are usually not interested in the exact groups, but
-just whether there was a match.
-Thus, `result` is treated as a boolean value.
+This return list can be quite complex and usually must be filtered before being used directly.
+In many applications, we are not interested in the exact groups,
+but just whether the query was satisfied.
+In the above example, the `result` is treated as a boolean value.
-**Note:** If you are searching many strings for the same expression,
+````{warning}
+- If you are searching many strings for the same expression,
be sure to create the `TagExpressionParser` only once.
+- The current search facility is case-insensitive.
+````
### Single tag queries
-The most basic query form is just a single tag.
-The query returns any child of that tag.
+The simplest type of query is to search for the presence of absence of a single tag.
+HED offers four variations on the single tag query as summarized in the following table.
| Query type | Example query | Matches | Does not match |
|------------ |----------------| --------------- | ---------------- |
-| **Single term**
Match the term or any child.
Don't consider values or
extensions when matching.| *Agent-trait* | *Agent-trait*
*Age*
*Age/35*
*Right-handed*
*Agent-trait/Glasses*
*Agent-property/Agent-trait*
*(Age, Blue)* |*Agent-property* |
-| **Tag path with slash**
Match the exact tag with
extension or value | *Age/* | *Age* | *Age/35* |
-| | *Age/35* | *Age/35* | *Age/36*
*Agent-trait/Age/35* |
-| **Tag with wildcard**
Match tags that
start with wildcard | | |
+| **Single-term**
Match the term or any child.
Don't consider values or
extensions when matching.| *Agent-trait* | *Agent-trait*
*Age*
*Age/35*
*Right-handed*
*Agent-trait/Glasses*
*Agent-property/Agent-trait*
*(Age, Blue)* |*Agent-property* |
+| **Quoted-tag**
Match the exact tag with
extension or value | "*Age*" | *Age*
*Agent-trait/Age* | *Age/35* |
+| | "*Age/34*" | *Age/34*
*Agent-trait/Age/34* | *Age/35* |
+| **Tag-path with slash**
Match the exact tag with
extension or value | *Age/34* | *Age/34* | *Age*
*Age/35*
*Agent-trait/Age/34* |
+| **Tag-prefix with wildcard**
Match the starting portion
of a tag and possibly its
value or extension. | Age/3* | *Age/34*
*Age/3*
*Agent-trait/Age/34* | *Age*
*Age/40*
+
+The meanings of the different queries are explained in the following subsections.
+
+#### Single-term search
+
+In a single-term search, the query is a single term or node in the
+[**HED schema**](https://www.hedtags.org/display_hed.html).
+The query may not contain any slashes or wildcards.
+
+Single-term queries leverage the HED hierarchical structure,
+recognizing that schema children of the query term should also satisfy the query.
+
+The example the query in the above table is *Agent-trait*.
+The full path of *Agent-trait* in the HED schema is *Property/Agent-property/Agent-trait*.
+Further, the *Agent-trait* has several child nodes including: *Age*, *Agent-experience-level*,
+*Gender*, *Sex*, and *Handedness*
+
+The single-term query matches child tags without regard to tag extension or value.
+Hence, *Agent-trait* matches *Age* which is a child and *Age/35* which is child with a value.
+*Agent-trait*, itself, may be extended, so *Agent-trait* also matches *Agent-trait/Glasses*.
+Here *Glasses* is a user-extension.
+
+#### Quoted-tag search
+
+If you enclose a tag-term in quotes, the search matches that tag exactly.
+If you want to match a value as well, you must include that value in the quoted tag-term.
+**IAN** check this --- how is this different from tag-path with slash
+
+#### Tag-path with slash
-Query tags with no slashes are **term searches**.
-The tag must correspond to a node in a HED schema.
-If the annotation to be searched has the tag or any of its child tags,
-the annotation matches. **What is returned?**
+If the query includes a slash in the tag path, then the query must match the exact value with
+the slash. Thus, *Age/34* does not match *Age* or *Age/35*.
+The query does match *Agent-trait/Age/34* because the short-form of this tag-path is *Age/34*.
+The tag short forms are used for the matching to assure consistency.
+
+#### Tag-prefix with wildcard
+
+Matching using a tag prefix with the `*` wildcard, matches the starting portion of the tag.
+Thus, Age/3* matches *Age/3* as well as *Age/34.
+
+Note that the query Age* matches a myraid of tags including *Agent*, *Agent-state*,
+and *Agent-property*.
### Logical queries
-In the following `A` and `B` represent arbitrary HED strings.
+In the following `A` and `B` represent arbitrary HED strings, which may contain multiple
+comma separated tags and parenthesized groups.
+`A` and `B` may also contain group queries as described in the next section.
+The expressions for `A` and `B` are each evaluated and then combined using standard logic.
+
| Query form | Example query | Matches | Does not match |
|------------|----------------------|----------------| --------------- |
| **`A`, `B`**
Match if both `A` and `B`
are matched. | *Event*, *Sensory-event* | *Event*, *Sensory-event*
*Sensory-event*, *Event*
(*Event*, *Sensory-event*) | *Event* |
| **`A` and `B`**
Match strings or groups
with both `A` and `B`. | | |
| **`A` or `B`**
Match strings or groups
with either `A` or `B`. | | |
-| **~`A`**
Match strings that do
not contain `A` | | | |
+| **~`A`**
Match strings that do
not contain `A`
Note: not what you think. | | | |
+| **@`A`**
Match a line that does not contain `A`. | | | |
+
+**Ian:** please fill in the examples.
+Also, what does `A` and `B` return?
-### Group matches
+### Group queries
+
+Tag grouping with parentheses is an essential part of HED annotation since HED strings are
+independent of ordering of tags or tag groups at the same level.
+
+Thus, tools have no way of distinguishing which color goes with which shape in the annotation *Red*, *Square*, *Blue, *Triangle*. Annotators must group tags using parentheses to make the meaning clear:
+(*Red*, *Square*), (*Blue, *Triangle*). Group queries allow analysts to detect these groupings.
| Query form | Example query | Matches | Does not match |
|------------|----------------------|----------------| --------------- |
@@ -106,17 +163,21 @@ In the following `A` and `B` represent arbitrary HED strings.
These operations (including exact, containing, and logical groups) can be
-arbitrarily nested and combined,
-as for example in the query:
+arbitrarily nested and combined, as for example in the query:
-> *[`A` or ~[[`B` or `C`]] ]*
+> *[`A` or [[`B` and `C`]] ]*
-Ordering on either the search terms or strings to be searched doesn't matter unless it will impact precedence on the expression. Use logical grouping if you aren't sure.
+Ordering on either the search terms or strings to be searched doesn't matter unless it will impact
+precedence on the expression. Use logical grouping with parentheses if you aren't sure.
-Precedence is purely left to right outside of grouping operations. This may change in the future.
+Precedence is purely left to right outside of grouping operations.
+Thus, unlike many traditional programming languages, **and** does not take precedence over **or**.
+This may change in the future.
### Notations under discussion
+Some more advanced wild card searches are also under discussion.
+
| Query form | Example query | Matches | Does not match |
|----------- |--------------- |-------- | ---------|
| **[[`A`, ?]]**
Match a group with `A`
and any tag or subgroup. | | |
@@ -177,6 +238,9 @@ String being searched: Sensory-event
+
+Examples not for discussion---under revision.
+
## Example queries
From 23e51fdd69bc027c998bf326eadbcff4f7ba1cee Mon Sep 17 00:00:00 2001
From: IanCa
Date: Thu, 2 Feb 2023 12:42:45 -0600
Subject: [PATCH 04/13] Update search guide with tested examples. Minor other
fixes
---
docs/source/HedSearchGuide.md | 78 +++++++++++++++--------------------
1 file changed, 33 insertions(+), 45 deletions(-)
diff --git a/docs/source/HedSearchGuide.md b/docs/source/HedSearchGuide.md
index 4d45c3e..d108231 100644
--- a/docs/source/HedSearchGuide.md
+++ b/docs/source/HedSearchGuide.md
@@ -80,13 +80,13 @@ be sure to create the `TagExpressionParser` only once.
The simplest type of query is to search for the presence of absence of a single tag.
HED offers four variations on the single tag query as summarized in the following table.
-| Query type | Example query | Matches | Does not match |
-|------------ |----------------| --------------- | ---------------- |
-| **Single-term**
Match the term or any child.
Don't consider values or
extensions when matching.| *Agent-trait* | *Agent-trait*
*Age*
*Age/35*
*Right-handed*
*Agent-trait/Glasses*
*Agent-property/Agent-trait*
*(Age, Blue)* |*Agent-property* |
-| **Quoted-tag**
Match the exact tag with
extension or value | "*Age*" | *Age*
*Agent-trait/Age* | *Age/35* |
-| | "*Age/34*" | *Age/34*
*Agent-trait/Age/34* | *Age/35* |
-| **Tag-path with slash**
Match the exact tag with
extension or value | *Age/34* | *Age/34* | *Age*
*Age/35*
*Agent-trait/Age/34* |
-| **Tag-prefix with wildcard**
Match the starting portion
of a tag and possibly its
value or extension. | Age/3* | *Age/34*
*Age/3*
*Agent-trait/Age/34* | *Age*
*Age/40*
+| Query type | Example query | Matches | Does not match |
+|-------------------------------------------------------------------------------------------------------------------|-----------------|--------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------|
+| **Single-term**
Match the term or any child.
Don't consider values or
extensions when matching. | *Agent-trait* | *Agent-trait*
*Age*
*Age/35*
*Right-handed*
*Agent-trait/Glasses*
*Agent-property/Agent-trait*
*(Age, Blue)* | *Agent-property* |
+| **Quoted-tag**
Match the exact tag with
extension or value | "*Age*" | *Age*
*Agent-trait/Age* | *Age/35* |
+| | "*Age/34*" | *Age/34*
*Agent-trait/Age/34* | *Age/35* |
+| **Tag-path with slash**
Match the exact tag with
extension or value | *Age/34* | *Age/34* | *Age*
*Age/35*
*Agent-trait/Age/34* |
+| **Tag-prefix with wildcard**
Match the starting portion
of a tag and possibly its
value or extension. | Age/3* | *Age/34*
*Age/3*
*Agent-trait/Age/34* | *Age*
*Age/40* |
The meanings of the different queries are explained in the following subsections.
@@ -109,12 +109,6 @@ Hence, *Agent-trait* matches *Age* which is a child and *Age/35* which is child
*Agent-trait*, itself, may be extended, so *Agent-trait* also matches *Agent-trait/Glasses*.
Here *Glasses* is a user-extension.
-#### Quoted-tag search
-
-If you enclose a tag-term in quotes, the search matches that tag exactly.
-If you want to match a value as well, you must include that value in the quoted tag-term.
-**IAN** check this --- how is this different from tag-path with slash
-
#### Tag-path with slash
If the query includes a slash in the tag path, then the query must match the exact value with
@@ -122,6 +116,11 @@ the slash. Thus, *Age/34* does not match *Age* or *Age/35*.
The query does match *Agent-trait/Age/34* because the short-form of this tag-path is *Age/34*.
The tag short forms are used for the matching to assure consistency.
+#### Quoted-tag search
+
+If you enclose a tag-term in quotes, the search matches that tag exactly.
+If you want to match a value as well, you must include that value in the quoted tag-term. This is exactly the same as Tag-path with slash, except you can search a single term without a slash.
+
#### Tag-prefix with wildcard
Matching using a tag prefix with the `*` wildcard, matches the starting portion of the tag.
@@ -137,16 +136,14 @@ comma separated tags and parenthesized groups.
`A` and `B` may also contain group queries as described in the next section.
The expressions for `A` and `B` are each evaluated and then combined using standard logic.
-| Query form | Example query | Matches | Does not match |
-|------------|----------------------|----------------| --------------- |
-| **`A`, `B`**
Match if both `A` and `B`
are matched. | *Event*, *Sensory-event* | *Event*, *Sensory-event*
*Sensory-event*, *Event*
(*Event*, *Sensory-event*) | *Event* |
-| **`A` and `B`**
Match strings or groups
with both `A` and `B`. | | |
-| **`A` or `B`**
Match strings or groups
with either `A` or `B`. | | |
-| **~`A`**
Match strings that do
not contain `A`
Note: not what you think. | | | |
-| **@`A`**
Match a line that does not contain `A`. | | | |
-**Ian:** please fill in the examples.
-Also, what does `A` and `B` return?
+| Query form | Example query | Matches | Does not match |
+|-----------------------------------------------------------------------------------------------------------------------|-----------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|
+| **`A`, `B`**
Match if both `A` and `B`
are matched. | *Event*, *Sensory-event* | *Event*, *Sensory-event*
*Sensory-event*, *Event*
(*Event*, *Sensory-event*) | *Event* |
+| **`A` and `B`**
Match strings or groups
with both `A` and `B`.
This is identical to comma notation above. | *Event* and *Sensory-event* | *Event*, *Sensory-event*
*Sensory-event*, *Event*
(*Event*, *Sensory-event*) | | *Event* |
+| **`A` or `B`**
Match strings or groups
with either `A` or `B`. | *Event* or *Sensory-event* | *Event*, *Sensory-event*
*Sensory-event*, *Event*
(*Event*, *Sensory-event*)
*Event*
*Sensory-event* | *Agent-trait* |
+| **~`A`**
Match groups that do
not contain `A`
`A` can be an arbitrary expression. | [[ *Event*, ~*Action* ]] | (*Event*)
(*Event*, *Animal-agent*)
(*Sensory-event*, (*Action*)) | *Event*
*Event*, *Action*
(*Event*, *Action*)
|
+| **@`A`**
Match a line that does not contain `A`. | @*Event* | *Action*
*Agent-trait*
*Action, Agent-Trait*
(*Action*, *Agent*) | *Event*
(*Action*, *Event*)
(*Action*, *Sensory-event*)
(*Agent*, (*Sensory-event*, *Blue*)) |
### Group queries
@@ -156,11 +153,11 @@ independent of ordering of tags or tag groups at the same level.
Thus, tools have no way of distinguishing which color goes with which shape in the annotation *Red*, *Square*, *Blue, *Triangle*. Annotators must group tags using parentheses to make the meaning clear:
(*Red*, *Square*), (*Blue, *Triangle*). Group queries allow analysts to detect these groupings.
-| Query form | Example query | Matches | Does not match |
-|------------|----------------------|----------------| --------------- |
-| **[[`A`, `B`]]**
Match a group that
contains both `A` and `B`
at the same level
in the same group. | *[[Red, Blue]]* | *(Red, Blue)*
*(Red, Blue, Green)* | *(Red, (Blue, Green))* |
-| **[`A`, `B`]**
Match a group that
contains `A` and `B`.
Both `A` and `B` could
be any subgroup level. | *[Red, Blue]* |*(Red, (Blue, Green))*
*((Red, Yellow), (Blue, Green))*| *Red, (Blue, Green)* |
-
+| Query form | Example query | Matches | Does not match |
+|--------------------------------------------------------------------------------------------------------------------------|-----------------|-------------------------------------------------------------|------------------------|
+| **[[`A`, `B`]]**
Match a group that
contains both `A` and `B`
at the same level
in the same group. | *[[Red, Blue]]* | *(Red, Blue)*
*(Red, Blue, Green)* | *(Red, (Blue, Green))* |
+| **[`A`, `B`]**
Match a group that
contains `A` and `B`.
Both `A` and `B` could
be any subgroup level. | *[Red, Blue]* | *(Red, (Blue, Green))*
*((Red, Yellow), (Blue, Green))* | *Red, (Blue, Green)* |
+
These operations (including exact, containing, and logical groups) can be
arbitrarily nested and combined, as for example in the query:
@@ -178,15 +175,16 @@ This may change in the future.
Some more advanced wild card searches are also under discussion.
-| Query form | Example query | Matches | Does not match |
-|----------- |--------------- |-------- | ---------|
-| **[[`A`, ?]]**
Match a group with `A`
and any tag or subgroup. | | |
-| **[[`A`, ??]]**
Match a group with `A`
and any tag. | | |
-| **[[`A`, ???]]**
Match a group with `A`
and any subgroup. | | |
-| **{`A`}**
Match a group or string
with `A` and nothing else. | | |
+| Query form | Example query | Matches | Does not match |
+|---------------------------------------------------------------------------------------------------------------------------------------|------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| **?**
Match any tag or subgroup. | [[ *Event*, ? ]] | (*Event*, *Action*)
(*Event*, *Agent*)
(*Event*, *Agent*, Action)
(*Event*, *Agent*, (*Action*))
(*Event*, (*Action*))
(*Event*, (*Action*), *Agent*)
(*Event*, (*Action*), (*Agent*))
(*Event*, (*Action*, *Agent*)) | *Event*, *Agent*
*Event*
(*Event*), *Action*
(*Event*) |
+| **??**
Match any tag. | [[ *Event*, ??]] | (*Event*, *Action*)
(*Event*, *Agent*)
(*Event*, *Agent*, *Action*)
(*Event*, *Agent*, (*Action*)) | *Event*, *Agent*
*Event*
(*Event*), *Action*
(*Event*)
(*Event*, (*Action*)) |
+| **???**
Match any subgroup | [[ *Event*, ???]] | (*Event*, (*Action*))
(*Event*, (*Action*), *Agent*)
(*Event*, (*Action*), (*Agent*))
(*Event*, (*Action*, *Agent*)) | (*Event*, *Action*)
(*Event*, *Agent*)
(*Event*, *Agent*, *Action*)
*Event*, *Agent*
*Event*
(*Event*), *Action*
(*Event*) |
+| **{`A`}**
Match a group or string
with `A` and nothing else. | { *Event* } | *Event*
(*Event*)
*Agent*, (*Event*)
(*Action*, (*Event*)) | *Event*, *Action*
(*Event*, *Action*)
(*Sensory-event*, (*Action*)) |
+| **[[ {A, ???} ]]**
More complex example. This query means A must be in a group with a single sibling subgroup and nothing else. | [[ { *Event*, ??? } ]] | (*Event*, (*Action*))
(*Event*, (*Action*, *Agent*))
(*Agent*, (*Event*, (*Action*))) | (*Event*, *Action*)
(*Event*, *Agent*)
(*Event*, *Agent*, *Action*)
*Event*, *Agent*
*Event*
(*Event*), *Action*
(*Event*)
(*Event*, (*Action*), *Agent*)
(*Event*, (*Action*), (*Agent*)) |
-
- Note: {} notation does NOT require a group.
+Note: None of the wildcard terms require groups, but they are unlikely to make sense without them and implicitly convert the surrounding section to an exact group.
+Note: {} notation does NOT require a group.
{A} would match "A" or "(A)" or "B, (A)"
[{A}] would match "(A)" or "B, (A)"
@@ -224,17 +222,7 @@ Examples:
5. "Orange": would be a TAG search, with no extensions allowed due to the quotes.
The entire short form of the tag must be "Orange" and nothing else.
-### Updated search:
-Query: Event and Sensory-event
-String being searched: Sensory-event
-#### Old System
- In the old system this would match. It would first find an Event tag, matching Sensory-event. It would then find a Sensory-event tag, also matching Sensory-event.
- It did not care it was matching the same tag twice.
-#### New System
- Once it matches a tag, it "consumes" the tag and marks it as used.
- So it would find Event, then be unable to match Sensory-event as there were no tags left.
-
From 5fa6d0f35b6d1e5b683001a93aa3b211d3163790 Mon Sep 17 00:00:00 2001
From: Kay Robbins <1189050+VisLab@users.noreply.github.com>
Date: Fri, 3 Feb 2023 14:18:32 -0600
Subject: [PATCH 05/13] Minor updates to the documentation
---
hedcode/matlab_scripts/web_services/runAllTests.m | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/hedcode/matlab_scripts/web_services/runAllTests.m b/hedcode/matlab_scripts/web_services/runAllTests.m
index a9fb636..c50499a 100644
--- a/hedcode/matlab_scripts/web_services/runAllTests.m
+++ b/hedcode/matlab_scripts/web_services/runAllTests.m
@@ -1,6 +1,6 @@
host = 'https://hedtools.ucsd.edu/hed_dev';
%host = 'https://hedtools.ucsd.edu/hed';
-%host = 'http://127.0.0.1:5000/';
+host = 'http://127.0.0.1:5000/';
errorMap = containers.Map('KeyType', 'char', 'ValueType', 'any');
errorMap('testGetServices') = testGetServices(host);
errorMap('testEventServices') = testEventServices(host);
From fd4174584118c0d55a1890d5b3c9db40539f4f97 Mon Sep 17 00:00:00 2001
From: Kay Robbins <1189050+VisLab@users.noreply.github.com>
Date: Sat, 4 Feb 2023 09:21:15 -0600
Subject: [PATCH 06/13] Renamed the split_events remodeling op
---
docs/source/FileRemodelingQuickstart.md | 28 ++--
docs/source/FileRemodelingTools.md | 62 +++----
docs/source/HedSearchGuide.md | 158 +-----------------
docs/source/HowCanYouUseHed.md | 22 +--
.../_static/data/AOMIC_splitevents_rmdl.json | 4 +-
5 files changed, 62 insertions(+), 212 deletions(-)
diff --git a/docs/source/FileRemodelingQuickstart.md b/docs/source/FileRemodelingQuickstart.md
index 1ebdac9..0e01b36 100644
--- a/docs/source/FileRemodelingQuickstart.md
+++ b/docs/source/FileRemodelingQuickstart.md
@@ -76,11 +76,11 @@ The following table gives a summary of the tools available in the HED remodeling
| **restructure** | | |
| | [*merge_consecutive*](merge-consecutive-anchor) | Replace multiple consecutive events of the same type
with one event of longer duration. |
| | [*remap_columns*](remap-columns-anchor) | Create *m* columns from values in *n* columns (for recoding). |
-| | [*split_event*](split-event-anchor) | Split trial-encoded rows into multiple events. |
+| | [*split_rows*](split-rows-anchor) | Split trial-encoded rows into multiple events. |
| **summarization** | | |
| | [*summarize_column_names*](summarize-column-names-anchor) | Summarize column names and order in the files. |
| | [*summarize_column_values*](summarize-column-values-anchor) |Count the occurrences of the unique column values. |
-| | [*summarize_events_to_sidecar*](summarize-events-to-sidecar-anchor) | Generate a sidecar template from an event file. |
+| | [*summarize_sidecar_from_events*](summarize-sidecar-from-events-anchor) | Generate a sidecar template from an event file. |
| | [*summarize_hed_tags*](summarize-hed-tags-anchor) | Summarize the HED tags present in the
HED annotations for the dataset. |
| | [*summarize_hed_type*](summarize-hed-type-anchor) | Summarize the detailed usage of a particular type tag
such as *Condition-variable* or *Task*
(used to automatically extract experimental designs). |
| | [*summarize_hed_validation*](summarize-hed-validation-anchor) | Validate the data files and report any errors. |
@@ -255,16 +255,16 @@ at a single temporal marker.
This strategy is known as *trial-level* encoding.
Our goal is to represent all the trial events (e.g., go signal, stop signal, and response)
-in separate rows of the event file using the *split_event* restructuring operation.
+in separate rows of the event file using the *split_rows* restructuring operation.
The following example shows the remodeling operations to perform the splitting.
-````{admonition} Example of split_events operation for the AOMIC stop signal task.
+````{admonition} Example of split_rows operation for the AOMIC stop signal task.
:class: tip
```json
[
{
- "operation": "split_event",
+ "operation": "split_rows",
"description": "Split response event from trial event based on response_time column.",
"parameters": {
"anchor_column": "trial_type",
@@ -280,14 +280,14 @@ The following example shows the remodeling operations to perform the splitting.
"copy_columns": []
}
},
- "remove_parent_event": false
+ "remove_parent_row": false
}
}
]
```
````
-The example uses the *split_event* operation to convert this
+The example uses the *split_rows* operation to convert this
file from trial encoding to event encoding.
In trial encoding each event marker (row in the event file) represents
all the information in a single trial.
@@ -295,10 +295,10 @@ Event markers such as the participant's response key-press are encoded implicitl
as an offset from the stimulus presentation.
while event encoding includes event markers for each individual event within the trial.
-The [**Split event**](./FileRemodelingTools.md#split-event)
+The [**Split rows**](./FileRemodelingTools.md#split-rows)
explanation under [**File remodeling tools**](./FileRemodelingTools.md)
-shows the required parameters for the *split_event* operation.
-The required parameters are *anchor_column*, *new_events*, and *remove_parent_event*.
+shows the required parameters for the *split_rows* operation.
+The required parameters are *anchor_column*, *new_events*, and *remove_parent_row*.
The *anchor_column* is the column we want to add new events corresponding to the stop signal and the response.
In this case we are going to add events to an existing column: *trial_type*.
@@ -378,7 +378,7 @@ but will soon move to the regular [**HED tools online tools server**](https://he
To use the online remodeling tools, navigate to the events page and select the *Remodel file* action.
Browse to select the data file to be remodeled and the JSON remodel file
containing the remodeling operations.
-The following screenshot shows these selections for the split event example of the previous section.
+The following screenshot shows these selections for the split rows example of the previous section.
@@ -432,13 +432,13 @@ A number of optional key-value arguments are also available.
After the `run_remodel` finishes, it overwrites the data files (not the backups)
and writes any requested summaries in `derivatives/remodel/summaries`.
-If we want to run the split events operation we demonstrated earlier on the full AOMIC dataset,
+If we want to run the split rows operation we demonstrated earlier on the full AOMIC dataset,
we might first want to check whether the response_time exists for all subjects.
We can do this by running the `summarize_column_headers` operation.
First we prepare the remodeler json file again.
-````{admonition} Split events remodeler json file for the AOMIC stop signal task.
+````{admonition} Split rows remodeler json file for the AOMIC stop signal task.
:class: tip
```json
@@ -486,7 +486,7 @@ The [**summary file**](./_static/data/AOMIC_column_names_2022_12_21_T_13_12_35_6
Looking at the different column combinations you can see there are three, one for each task that was performed for this dataset.
All event files for the stop signal task contain the `stop_signal_delay` column and the `response_time` column.
-Now you can try out the *split_events* on the full dataset!
+Now you can try out the *split_rows* on the full dataset!
(jupyter-notebooks-for-remodeling-anchor)=
diff --git a/docs/source/FileRemodelingTools.md b/docs/source/FileRemodelingTools.md
index 8c7b0f2..3a3668a 100644
--- a/docs/source/FileRemodelingTools.md
+++ b/docs/source/FileRemodelingTools.md
@@ -50,11 +50,11 @@ This user's guide contains the following topics:
* [**Remove rows**](remove-rows-anchor)
* [**Rename columns**](rename-columns-anchor)
* [**Reorder columns**](reorder-columns-anchor)
- * [**Split event**](split-event-anchor)
+ * [**Split rows**](split-rows-anchor)
* [**Remodel summarizations**](remodel-summarizations-anchor)
* [**Summarize column names**](summarize-column-names-anchor)
* [**Summarize column values**](summarize-column-values-anchor)
- * [**Summarize events to sidecar**](summarize-events-to-sidecar-anchor)
+ * [**Summarize sidecar from events**](summarize-sidecar-from-events-anchor)
* [**Summarize hed tags**](summarize-hed-tags-anchor)
* [**Summarize hed type**](summarize-hed-type-anchor)
* [**Summarize hed validation**](summarize-hed-validation-anchor)
@@ -140,11 +140,11 @@ and links to further documentation. Operations not listed in the summarize secti
| **restructure** | | |
| | [*merge_consecutive*](merge-consecutive-anchor) | Replace multiple consecutive events of the same type
with one event of longer duration. |
| | [*remap_columns*](remap-columns-anchor) | Create m columns from values in n columns (for recoding). |
-| | [*split_event*](split-event-anchor) | Split trial-encoded rows into multiple events. |
+| | [*split_rows*](split-rows-anchor) | Split trial-encoded rows into multiple events. |
| **summarize** | | |
| | [*summarize_column_names*](summarize-column-names-anchor) | Summarize column names and order in the files. |
| | [*summarize_column_values*](summarize-column-values-anchor) | Count the occurrences of the unique column values. |
-| | [*summarize_events_to_sidecar*](summarize-events-to-sidecar-anchor) | Generate a sidecar template from an event file. |
+| | [*summarize_sidecar_from_events*](summarize-sidecar-from-events-anchor) | Generate a sidecar template from an event file. |
| | [*summarize_hed_tags*](summarize-hed-tags-anchor) | Summarize the HED tags present in the
HED annotations for the dataset. |
| | [*summarize_hed_type*](summarize-hed-type-anchor) | Summarize the detailed usage of a particular type tag
such as *Condition-variable* or *Task*
(used to automatically extract experimental designs). |
| | [*summarize_hed_validation*](summarize-hed-validation-anchor) | Validate the data files and report any errors. |
@@ -1450,10 +1450,10 @@ The results of executing the previous *reorder_columns* transformation on the
| 21.6103 | 0.5083 | 0.443 | go |
````
-(split-event-anchor)=
-### Split event
+(split-rows-anchor)=
+### Split rows
-The *split_event* operation
+The *split_rows* operation
is often used to convert event files from trial-level encoding to event-level encoding.
In **trial-level** encoding, all the events in a single trial
@@ -1466,22 +1466,22 @@ In **event-level** encoding, each row represents the temporal marker for a singl
In this case a trial consists of a sequence of multiple events.
-(split-event-parameters-anchor)=
-#### Split event parameters
+(split-rows-parameters-anchor)=
+#### Split rows parameters
-```{admonition} Parameters for the *split_event* operation.
+```{admonition} Parameters for the *split_rows* operation.
:class: tip
| Parameter | Type | Description |
| ------------ | ---- | ----------- |
-| *anchor_column* | str | The name of the column that will be used for split-event codes.|
+| *anchor_column* | str | The name of the column that will be used for split_rows codes.|
| *new_events* | dict | Dictionary whose keys are the codes to be inserted as new events
in the *anchor_column* and whose values are dictionaries with
keys *onset_source*, *duration*, and *copy_columns*. |
| *remove_parent_event* | bool | If true, remove parent event. |
```
-The *split_event* operation requires an *anchor_column*, which could be an existing
+The *split_rows* operation requires an *anchor_column*, which could be an existing
column or a new column to be appended to the data.
The purpose of the *anchor_column* is to hold the codes for the new events.
@@ -1498,23 +1498,23 @@ to compute the `duration` column value for the new event.
Unlisted columns are filled with `n/a`.
-The *split_event* operation sorts the split events by the `onset` column and raises a `TypeError`
+The *split_rows* operation sorts the split rows by the `onset` column and raises a `TypeError`
if the `onset` and `duration` are improperly defined.
The `onset` column is converted to numeric values as part splitting process.
-(split-events-example-anchor)=
-#### Split events example
+(split-rows-example-anchor)=
+#### Split rows example
-The *split_event* operation in the following example specifies that new rows should be added
+The *split_rows* operation in the following example specifies that new rows should be added
to encode the response and stop signal. The anchor column is `trial_type`.
-````{admonition} A JSON file with a single *split_event* transformation operation.
+````{admonition} A JSON file with a single *split_rows* transformation operation.
:class: tip
```json
[{
- "operation": "split_events",
+ "operation": "split_rows",
"description": "add response events to the trials.",
"parameters": {
"anchor_column": "trial_type",
@@ -1536,10 +1536,10 @@ to encode the response and stop signal. The anchor column is `trial_type`.
```
````
-The results of executing this *split_event* operation on the
+The results of executing this *split_rows* operation on the
[**sample remodel event file**](sample-remodel-event-file-anchor) are:
-````{admonition} Results of the previous *split_event* operation.
+````{admonition} Results of the previous *split_rows* operation.
| onset | duration | trial_type | stop_signal_delay | response_time | response_accuracy | response_hand | sex |
| ----- | -------- | ---------- | ----------------- | ------------- | ----------------- | ------------- | --- |
@@ -1785,19 +1785,19 @@ results from the individual data files are shown after the overall summary.
The individual results are similar to the overall summary because only one data file
was processed.
-(summarize-events-to-sidecar-anchor)=
-### Summarize events to sidecar
+(summarize-sidecar-from-events-anchor)=
+### Summarize sidecar from events
-The summarize events to sidecar operation generates a sidecar template from the event
+The summarize sidecar from events operation generates a sidecar template from the event
files in the dataset.
-(summarize-events-to-sidecar-parameters-anchor)=
-#### Summarize events to sidecar parameters
+(summarize-sidecar-from-events-parameters-anchor)=
+#### Summarize sidecar from events parameters
The following table lists the parameters required for using the summary.
-```{admonition} Parameters for the *summarize_events_to_sidecar* operation.
+```{admonition} Parameters for the *summarize_sidcar_from_eventsr* operation.
:class: tip
| Parameter | Type | Description |
@@ -1826,16 +1826,16 @@ generating the sidecar template.
The *value_columns* list specifies the names of columns to treat as value columns
when generating the sidecar template.
-(summarize-events-to-sidecar-example-anchor)=
-#### Summarize events to sidecar example
+(summarize-sidecar-from-events-example-anchor)=
+#### Summarize sidecar from events example
The following example shows the JSON for including this operation in a remodeling file.
-````{admonition} A JSON file with a single *summarize_events_to_sidecar* summarization operation.
+````{admonition} A JSON file with a single *summarize_sidecar_from_events* summarization operation.
:class: tip
```json
[{
- "operation": "summarize_events_to_sidecar",
+ "operation": "summarize_sidecar_from_events",
"description": "Generate a sidecar from the excerpted events file.",
"parameters": {
"summary_name": "AOMIC_generate_sidecar",
@@ -1852,7 +1852,7 @@ The results of executing this operation on the
[**sample remodel event file**](sample-remodel-event-file-anchor)
are shown in the following example using the text format.
-````{admonition} Sample *summarize_events_to_sidecar* operation results in text format.
+````{admonition} Sample *summarize_sidecar_from_events* operation results in text format.
:class: tip
```text
Context name: AOMIC_generate_sidecar
diff --git a/docs/source/HedSearchGuide.md b/docs/source/HedSearchGuide.md
index d108231..41aa1ef 100644
--- a/docs/source/HedSearchGuide.md
+++ b/docs/source/HedSearchGuide.md
@@ -1,14 +1,8 @@
# HED search guide
-
-UNDER CONSTRUCTION
-
-
-
Many analysis methods locate event markers with specified properties and
extract sections of the data surrounding these markers for analysis.
-This extraction process is often referred to as **epoching** the data
-or sometimes **trial selection**.
+This extraction process is called **epoching** or **trial selection**.
Analysis may also exclude data surrounding particular event markers.
@@ -36,8 +30,7 @@ Once created, this query object can then be applied to any number of HED annotat
-- say to the annotations for each event-marker associated with a data recording.
The query object returns a list of matches within the annotation.
-Usually, users just test whether this list is empty or not to determine whether the query
-was satisfied.
+Usually, users just test whether this list is empty to determine if the query was satisfied.
### Calling syntax
@@ -49,6 +42,7 @@ The syntax is demonstrated in the following example:
````{admonition} Example calling syntax for HED search.
```python
+schema = load_schema_version(
hed_string = HedString("Sensory-event, Sensory-presentation", schema=schema)
query_string = "Sensory-event"
query = TagExpressionParser(query_string)
@@ -61,6 +55,7 @@ if result:
In the example the strings containing HED annotations are converted to a `HedString` object,
which is a parsed representation of the HED annotation.
The query facility assumes that the annotations have been validated.
+A `HedSchema` is required
The query is represented by a `TagExpressionParser` object.
The `search_hed_string` method returns a list of groups in the HED string that match the query.
@@ -171,151 +166,6 @@ Precedence is purely left to right outside of grouping operations.
Thus, unlike many traditional programming languages, **and** does not take precedence over **or**.
This may change in the future.
-### Notations under discussion
-
-Some more advanced wild card searches are also under discussion.
-
-| Query form | Example query | Matches | Does not match |
-|---------------------------------------------------------------------------------------------------------------------------------------|------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| **?**
Match any tag or subgroup. | [[ *Event*, ? ]] | (*Event*, *Action*)
(*Event*, *Agent*)
(*Event*, *Agent*, Action)
(*Event*, *Agent*, (*Action*))
(*Event*, (*Action*))
(*Event*, (*Action*), *Agent*)
(*Event*, (*Action*), (*Agent*))
(*Event*, (*Action*, *Agent*)) | *Event*, *Agent*
*Event*
(*Event*), *Action*
(*Event*) |
-| **??**
Match any tag. | [[ *Event*, ??]] | (*Event*, *Action*)
(*Event*, *Agent*)
(*Event*, *Agent*, *Action*)
(*Event*, *Agent*, (*Action*)) | *Event*, *Agent*
*Event*
(*Event*), *Action*
(*Event*)
(*Event*, (*Action*)) |
-| **???**
Match any subgroup | [[ *Event*, ???]] | (*Event*, (*Action*))
(*Event*, (*Action*), *Agent*)
(*Event*, (*Action*), (*Agent*))
(*Event*, (*Action*, *Agent*)) | (*Event*, *Action*)
(*Event*, *Agent*)
(*Event*, *Agent*, *Action*)
*Event*, *Agent*
*Event*
(*Event*), *Action*
(*Event*) |
-| **{`A`}**
Match a group or string
with `A` and nothing else. | { *Event* } | *Event*
(*Event*)
*Agent*, (*Event*)
(*Action*, (*Event*)) | *Event*, *Action*
(*Event*, *Action*)
(*Sensory-event*, (*Action*)) |
-| **[[ {A, ???} ]]**
More complex example. This query means A must be in a group with a single sibling subgroup and nothing else. | [[ { *Event*, ??? } ]] | (*Event*, (*Action*))
(*Event*, (*Action*, *Agent*))
(*Agent*, (*Event*, (*Action*))) | (*Event*, *Action*)
(*Event*, *Agent*)
(*Event*, *Agent*, *Action*)
*Event*, *Agent*
*Event*
(*Event*), *Action*
(*Event*)
(*Event*, (*Action*), *Agent*)
(*Event*, (*Action*), (*Agent*)) |
-
-Note: None of the wildcard terms require groups, but they are unlikely to make sense without them and implicitly convert the surrounding section to an exact group.
-Note: {} notation does NOT require a group.
-
- {A} would match "A" or "(A)" or "B, (A)"
- [{A}] would match "(A)" or "B, (A)"
-
----
-
- Note: Adding a wildcard to a search will automatically make whatever group it's found in an "exact group"
- e.g.: Query: A ?
- This would match:
- 1. A, B
- 2. (A, B)
- 3. A, (B)
- 4. (A, (B))
- But NOT:
- 1. (A), B
- This is because once A is found, there needs to be wildcard group or tag at the same level.
-
- Note: Wildcards should be as late in a search as possible for efficency. eg "A and ?" rather than "? and A"
-
-### Wildcards
- Wildcards are supported under the new system. They mostly make sense in containing groups. They can be mixed and matched,
- e.g: [[A or B and ??? and ??]]
- Meaning: Find a group containing tag A or tag B, while also containing another unrelated tag and another unrelated group.
-
-### Search Term Wildcards
-Term search - Checks all parent terms in the tags searched as well, does not interact with extensions of values in any way.
-Tag search - Finds tags where the short form IS the indicated search string, unless it has a wildcard.(any search term with a "/" or a "*" or wrapped in double quotes turns it into a tag search)
-
-Examples:
-1. Orange : would be a TERM search, finding the children as well since Orange is a term in any children of orange.
-2. Orange/* : Would be a TAG search, finding any Orange tags that have an extension or value
-3. Orange/2*: Would be a TAG search, finding any Orange tags that have an extension or value beginning with 2.
-4. Orange* : would be a TAG search, but the wildcard would have it find the children too.
- This would additionally find any potentially completely unrelated tags that start with Orange in short form. Say "OrangeTheFruit"
-5. "Orange": would be a TAG search, with no extensions allowed due to the quotes.
- The entire short form of the tag must be "Orange" and nothing else.
-
-
-
-
-
-
-Examples not for discussion---under revision.
-
-
-## Example queries
-
-https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003654s_hed
-These strings are used in the basic examples.
-
-| # | Line |
-|-----|-----------|
-| 1 | *Experiment-structure,(Def/Right-sym-cond,Onset),
(Def/Initialize-recording,Onset) |
-| 2 | Sensory-event,Experimental-stimulus,(Def/Face-image,Onset),(Def/Blink-inhibition-task,Onset),(Def/Fixation-task,Onset),Def/Scrambled-face-cond,Def/First-show-cond,Experimental-trial/1,(Image,Pathname/s096.bmp) |
-| 3 | Sensory-event,(Intended-effect,Cue),(Def/Circle-only,Onset),(Def/Face-image,Offset),(Def/Blink-inhibition-task,Offset),(Def/Fixation-task,Offset),Experimental-trial/1,(Image,Pathname/circle.bmp) |
-| 4 | Sensory-event,(Intended-effect,Cue),(Def/Cross-only,Onset),(Def/Fixation-task,Onset),(Def/Circle-only,Offset),Experimental-trial/2,(Image,Pathname/cross.bmp) |
-| 5 | Sensory-event,Experimental-stimulus,(Def/Face-image,Onset),(Def/Blink-inhibition-task,Onset),(Def/Cross-only,Offset),Def/Famous-face-cond,Def/First-show-cond,Experimental-trial/2,(Image,Pathname/f148.bmp) |
-| 6 | Agent-action,Participant-response,Def/Press-right-finger,Experimental-trial/2 |
-| 7 | Sensory-event,(Intended-effect,Cue),(Def/Circle-only,Onset),(Def/Face-image,Offset),(Def/Blink-inhibition-task,Offset),(Def/Fixation-task,Offset),Experimental-trial/2,(Image,Pathname/circle.bmp) |
-| 8 | Sensory-event,(Intended-effect,Cue),(Def/Cross-only,Onset),(Def/Fixation-task,Onset),(Def/Circle-only,Offset),Experimental-trial/3,(Image,Pathname/cross.bmp) |
-| 9 | Sensory-event,Experimental-stimulus,(Def/Face-image,Onset),(Def/Blink-inhibition-task,Onset),(Def/Cross-only,Offset),Def/Famous-face-cond,Def/Immediate-repeat-cond,Experimental-trial/3,(Face,Item-interval/1),(Image,Pathname/f148.bmp) |
-| 10 | Agent-action,Participant-response,Def/Press-right-finger,Experimental-trial/3 |
-
-### Basic query examples
-
-| Query | Meaning | Lines matching | Explanation/Notes |
-|------- |----------|-----------|------------|
-| Experiment-structure | Match *Experiment-structure* the term | [1] | The tag is only on line 1 |
-| Event | Find lines with Event term | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | Finds every line as the first
tag in each line is
inherited from Event |
-| Image, Pathname/f148.bmp | Find lines with term *Image*
or any of its child terms
and the exact tag *Pathname/f148.bmp* | [5, 9] | Match *Pathname/f148.bmp* and *Image*. |
-| Image and ~Pathname/f148.bmp | Match *Image* but without the exact
tag *Pathname/f148.bmp* | [2, 3, 4, 7, 8] | Lines 5 and 9 contain *Pathname/f148.bmp* |
-| Pathname/circle.bmp or Pathname/cross.bmp | Match either exact tag *Pathname/circle.bmp* or *Pathname/cross.bmp* | [3, 4, 7, 8] | |
-| Def/Cross-only, Onset |Match the exact tag *Def/Cross-only*
and the term *Onset* | [4, 5, 8, 9] | Matches every line with *Def/Cross-only*
because it isn't checking if the
*Onset* tag is in the same group. |
-
-### Group query examples
-
-| Query | Meaning | Lines matching |
-|----------|-------------|----------------|
-| [Def/Cross-only, Onset] | Match exact tag *Def/Cross-only* and
the term *Onset* in the same group or subgroup. | [4, 8] |
- | [Def/Cross-only, Onset] and Experimental-trial/3 | Same as previous, but now the line must also
contain *Experimental-trial/3* | [8] |
-| [[Def/Cross-only, Onset]] | Match the exact tag *Def/Cross-only* and
the term *Onset* in the exact same group. | [4, 8] |
- | [[Def/Cross-only, Onset]] and Experimental-trial/3 | Same as previous, but now the line must also
contain *Experimental-trial/3* | [8] |
-
-
-### Complex query examples
-
-Here are some slightly more complex queries and example results from searching simple strings. These examples are more contrived than those above to show group searching abilities.
-
-Expression: **"(Item or Agent) and [[Action or Event]]"**
-Meaning: Find a line that contains Item or Agent anywhere, and also contains a subgroup somewhere containing an Action or Event.
-
-| Query | String | Result | Notes |
-| ----- | -------- | ------ |--------|
-| "(Clear-throat),Item" | "(Item or Agent) and [[Action or Event]]" | True | |
-| "((Clear-throat),Item)" | "(Item or Agent) and [[Action or Event]]" | True | |
-| "Clear-throat,Item" | "(Item or Agent) and [[Action or Event]]" | False | This is not found as the [[Action or Event]] part means the event term must be in a group. |
-| "Clear-throat,Agent" | "(Item or Agent) and [[Action or Event]]" | False | This is not found as the [[Action or Event]] part means the event term must be in a group. |
-| 'Agent,Event' | "(Item or Agent) and [[Action or Event]]" | False | This is not found as the [[Action or Event]] part means the event term must be in a group. |
-| 'Agent,(Event)' | "(Item or Agent) and [[Action or Event]]" | True | |
-| (Event),(Item)' | "(Item or Agent) and [[Action or Event]]" | True | This is found as the (Item or Agent) part can be anywhere in the string, including a group. |
-
-Expression: **"[[ [[Event]], [[Action]] ]]"**
-Meaning: Find a group that has a direct child subgroup containing Event, and a direct child subgroup containing Action.
-
-| Search String | Expression | Result | Notes |
-|------------------------------|-------------------------------|--------|---------------------------------------------------------------------------------------------------------------------------------------|
-| '((Event),(Clear-throat))' | "[[ [[Event]], [[Action]] ]]" | True | This is the most basic example perfectly mirroring the search string. |
-| '(Clear-throat,Event)' | "[[ [[Event]], [[Action]] ]]" | False | This is not found because the two pairs of double brackets require at least one nested subgroup. |
-| '((Event),((Clear-throat)))' | "[[ [[Event]], [[Action]] ]]" | False | This is not found because the Clear-throat subgroup is not a direct child of the group containing (Event) |
-| '((Clear-throat,Event))' | "[[ [[Event]], [[Action]] ]]" | True | This is found due to a quirk in the searching. It does not strictly require [[Event]] and [[Action]] to be in separate child groups. |
-
-Expression: **"[[ [Event], [Action] ]]"**
-Meaning: Find a group that has a descendant subgroup containing Event, and a descendant subgroup containing Action
-
-| Search String | Expression | Result | Notes |
-|----------------|------------|--------|--------|
-| '((Event),(Clear-throat))' | "[[ [Event]], [Action] ]]" | True | This is the most basic example perfectly mirroring the search string. |
-| '(Clear-throat,Event)' | "[[ [Event]], [Action] ]]" | False | This is not found because the two pairs of double brackets require at least one nested subgroup. |
-| '((Event),((Clear-throat)))' | "[[ [Event]], [Action] ]]" | True | This is now found as the single brackets don't require it to be a direct child. |
-| '((Clear-throat,Event))' | "[[ [Event]], [Action] ]]" | True | This is found due to a quirk in the searching. It does not strictly require [[Event]] and [[Action]] to be in separate child groups. |
-
-Expression: **"[[ [[~Event]] ]]"**
-Meaning: Find a group containing a direct child group that does not contain Event.
-
-| Search String | Expression | Result | Notes |
-|-----------------|--------------------|--------|-----------|
-| '(Clear-throat,Event)' | "[[ [[~Event]] ]]" | False | This is not found as the group has no subgroups. |
-| '((Clear-throat),(Event))' | "[[ [[~Event]] ]]" | True | This is found because one of the subgroups does not contain Event |
-| '((Event))' | "[[ [[~Event]] ]]" | False | This is not found because the only subgroup contains Event |
-| '(((Clear-throat)),(Event))' | "[[ [[~Event]] ]]" | True | This is found because the ((Clear-throat)) subgroup does not contain Event |
-| '((Clear-throat,Event))' | "[[ [[~Event]] ]]" | False | This is not found because the child subgroup contains Clear-throat and Event |
## Where can HED search be used?
diff --git a/docs/source/HowCanYouUseHed.md b/docs/source/HowCanYouUseHed.md
index c0ce7bb..d0b668d 100644
--- a/docs/source/HowCanYouUseHed.md
+++ b/docs/source/HowCanYouUseHed.md
@@ -10,10 +10,10 @@ but these annotations can also be applied to other types of tabular data.
**This guide organizes HED resources based on how you might use HED:**
* [**As an experimenter**](as-an-experimenter-anchor)
-* [**As a data curator**](as-a-data-curator-anchor)
+* [**As a data annotator**](as-a-data-annotator-anchor)
* [**As a data analyst**](as-a-data-analyst-anchor)
* [**As a tool developer**](as-a-tool-developer-anchor)
-* [**As a schema designer**](as-a-schema-designer-anchor)
+* [**As a schema builder**](as-a-schema-builder-anchor)
@@ -27,7 +27,7 @@ The focus of the discussion here is not explicitly on how an experiment
should be designed,
but rather on how data should be recorded and transformed to maximize its downstream usability.
-**Here are some topics of interest to experiment designers:**
+**Here are some topics of interest to experimenters:**
* [**Planning and running an experiment**](planning-and-running-an-experiment-anchor)
* [**Post processing the data**](post-processing-the-data-anchor)
@@ -181,9 +181,9 @@ respectively. The remodeling JSON file can be expanded to include these transfor
-(as-a-data-curator-anchor)=
-## As a data curator
-> ... organizing data for sharing and analysis:
+(as-a-data-annotator-anchor)=
+## As a data annotator
+> ... organizing data and tagging events:
The move towards open, reproducible science, both in the scientific community and
by funding agencies, makes data sharing a requirement.
@@ -191,7 +191,7 @@ An added benefit, is that data used by others is likely to garner increased reco
and additional citations.
This section emphasizes the importance of complete and accurate metadata to enable analysis.
-**Here are some topics of interest to data curators:**
+**Here are some topics of interest to data annotators:**
* [**Standardizing the format**](standardizing-the-format-anchor)
* [**Learning about BIDS**](learning-about-bids-anchor)
@@ -392,7 +392,7 @@ forget to press the key, press the wrong key, press the key multiple times,
or press the key both before and after the feedback.
````
-Ideally, a curator would provide information in the event file marking
+Ideally, a data annotator would provide information in the event file marking
unusual things such as these bad trials, since it is easy for downstream users
to improperly handle these situations, reducing the accuracy of analysis.
@@ -662,9 +662,9 @@ more complex and sophisticated automated analysis.
-(as-a-schema-designer-anchor)=
-## As a schema designer
-> ... extending HED in new directions:
+(as-a-schema-builder-anchor)=
+## As a schema builder
+> ... extending HED vocabulary in new directions:
HED annotations consist of comma-separated terms drawn from a hierarchically
structured vocabulary called a HED schema.
diff --git a/docs/source/_static/data/AOMIC_splitevents_rmdl.json b/docs/source/_static/data/AOMIC_splitevents_rmdl.json
index df0bb58..e806778 100644
--- a/docs/source/_static/data/AOMIC_splitevents_rmdl.json
+++ b/docs/source/_static/data/AOMIC_splitevents_rmdl.json
@@ -1,6 +1,6 @@
[
{
- "operation": "split_event",
+ "operation": "split_rows",
"description": "Split response event from trial event based on response_time column.",
"parameters": {
"anchor_column": "trial_type",
@@ -16,7 +16,7 @@
"copy_columns": []
}
},
- "remove_parent_event": false
+ "remove_parent_row": false
}
}
]
\ No newline at end of file
From 3057d8964f9eaa00740976f5d2ea7db504795955 Mon Sep 17 00:00:00 2001
From: Kay Robbins <1189050+VisLab@users.noreply.github.com>
Date: Sat, 4 Feb 2023 09:25:41 -0600
Subject: [PATCH 07/13] Corrected typos and changed split_row to split_rows
---
docs/source/FileRemodelingTools.md | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/docs/source/FileRemodelingTools.md b/docs/source/FileRemodelingTools.md
index 3a3668a..31fa685 100644
--- a/docs/source/FileRemodelingTools.md
+++ b/docs/source/FileRemodelingTools.md
@@ -114,7 +114,7 @@ The dispatcher stores information from each uniquely named summarization operati
as a separate context.
Within its context information, most summarization operations keep a separate
summary for each individual file and have methods to create an overall summary
-of the information for all the files that have been processed by the summarization..
+of the information for all the files that have been processed by the summarization.
Summarization results are available in JSON (`.json`) and text (`.txt`) formats.
@@ -434,7 +434,7 @@ before running the remodeling operations.
Going without backup is not recommended unless you are only doing summarization operations.
The operations are specified as a list of dictionaries in a JSON file in the
-[**remodel sampe files**](remodel-sample-files-anchor) as discussed below.
+[**remodel sample files**](remodel-sample-files-anchor) as discussed below.
Before running remodeling transformations on an entire dataset,
consider using the [**HED online tools**](https://hedtools.ucsd.edu/hed)
@@ -2384,5 +2384,5 @@ The `BaseContext` provides universal methods for converting this summary to JSON
get_summary_details(self, verbose=True)
```
````
-The operation associated with this instance of it associated with a given informat
+The operation associated with this instance of it associated with a given format
implementation
\ No newline at end of file
From 8f17a86039ccec7a9ec26314636f5e23ab2505ed Mon Sep 17 00:00:00 2001
From: Kay Robbins <1189050+VisLab@users.noreply.github.com>
Date: Sun, 5 Feb 2023 15:41:53 -0600
Subject: [PATCH 08/13] Edited the search document
---
docs/source/HedSearchGuide.md | 82 ++++++++++++-------
.../matlab_scripts/web_services/getTestData.m | 2 +-
hedcode/matlab_scripts/web_services/runTest.m | 6 +-
3 files changed, 56 insertions(+), 34 deletions(-)
diff --git a/docs/source/HedSearchGuide.md b/docs/source/HedSearchGuide.md
index 41aa1ef..553242c 100644
--- a/docs/source/HedSearchGuide.md
+++ b/docs/source/HedSearchGuide.md
@@ -42,11 +42,11 @@ The syntax is demonstrated in the following example:
````{admonition} Example calling syntax for HED search.
```python
-schema = load_schema_version(
+schema = load_schema_version("8.1.0")
hed_string = HedString("Sensory-event, Sensory-presentation", schema=schema)
query_string = "Sensory-event"
-query = TagExpressionParser(query_string)
-result = query.search_hed_string(hed_string)
+query = QueryParser(query_string)
+result = query.search(hed_string)
if result:
print(f"{query_string} found in {str(hed_string)})
```
@@ -55,10 +55,12 @@ if result:
In the example the strings containing HED annotations are converted to a `HedString` object,
which is a parsed representation of the HED annotation.
The query facility assumes that the annotations have been validated.
-A `HedSchema` is required
+A `HedSchema` is required.
+In the example standard schema version 8.1.0 is loaded.
+The schemas are available on GitHub.
-The query is represented by a `TagExpressionParser` object.
-The `search_hed_string` method returns a list of groups in the HED string that match the query.
+The query is represented by a `QueryParser` object.
+The `search` method returns a list of groups in the HED string that match the query.
This return list can be quite complex and usually must be filtered before being used directly.
In many applications, we are not interested in the exact groups,
but just whether the query was satisfied.
@@ -66,7 +68,7 @@ In the above example, the `result` is treated as a boolean value.
````{warning}
- If you are searching many strings for the same expression,
-be sure to create the `TagExpressionParser` only once.
+be sure to create the `QueryParser` only once.
- The current search facility is case-insensitive.
````
@@ -93,41 +95,45 @@ The query may not contain any slashes or wildcards.
Single-term queries leverage the HED hierarchical structure,
recognizing that schema children of the query term should also satisfy the query.
+This is HED's **is-a** principle.
-The example the query in the above table is *Agent-trait*.
+The example query in the above table is *Agent-trait*.
The full path of *Agent-trait* in the HED schema is *Property/Agent-property/Agent-trait*.
Further, the *Agent-trait* has several child nodes including: *Age*, *Agent-experience-level*,
-*Gender*, *Sex*, and *Handedness*
+*Gender*, *Sex*, and *Handedness*.
The single-term query matches child tags without regard to tag extension or value.
Hence, *Agent-trait* matches *Age* which is a child and *Age/35* which is child with a value.
*Agent-trait*, itself, may be extended, so *Agent-trait* also matches *Agent-trait/Glasses*.
Here *Glasses* is a user-extension.
+#### Quoted-tag search
+
+If the tag-term is enclosed in quotes, the search matches that tag exactly.
+If you want to match a value as well, you must include that specific value in the quoted tag-term. This is exactly the same as Tag-path with slash, except you can search a single term without a slash.
+
+
#### Tag-path with slash
-If the query includes a slash in the tag path, then the query must match the exact value with
-the slash. Thus, *Age/34* does not match *Age* or *Age/35*.
-The query does match *Agent-trait/Age/34* because the short-form of this tag-path is *Age/34*.
+If the query includes a slash in the tag path, then the query must match the
+exact value with the slash.
+Thus, *Age/34* does not match *Age* or *Age/35*.
+The query matches *Agent-trait/Age/34* because the short-form of this tag-path is *Age/34*.
The tag short forms are used for the matching to assure consistency.
-#### Quoted-tag search
-
-If you enclose a tag-term in quotes, the search matches that tag exactly.
-If you want to match a value as well, you must include that value in the quoted tag-term. This is exactly the same as Tag-path with slash, except you can search a single term without a slash.
#### Tag-prefix with wildcard
Matching using a tag prefix with the `*` wildcard, matches the starting portion of the tag.
-Thus, Age/3* matches *Age/3* as well as *Age/34.
+Thus, Age/3* matches *Age/3* as well as *Age/34*.
-Note that the query Age* matches a myraid of tags including *Agent*, *Agent-state*,
+Notice that the query Age* matches a myraid of tags including *Agent*, *Agent-state*,
and *Agent-property*.
### Logical queries
-In the following `A` and `B` represent arbitrary HED strings, which may contain multiple
-comma separated tags and parenthesized groups.
+In the following `A` and `B` represent HED expressions that may contain multiple
+comma-separated tags and parenthesized groups.
`A` and `B` may also contain group queries as described in the next section.
The expressions for `A` and `B` are each evaluated and then combined using standard logic.
@@ -135,18 +141,32 @@ The expressions for `A` and `B` are each evaluated and then combined using stand
| Query form | Example query | Matches | Does not match |
|-----------------------------------------------------------------------------------------------------------------------|-----------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|
| **`A`, `B`**
Match if both `A` and `B`
are matched. | *Event*, *Sensory-event* | *Event*, *Sensory-event*
*Sensory-event*, *Event*
(*Event*, *Sensory-event*) | *Event* |
-| **`A` and `B`**
Match strings or groups
with both `A` and `B`.
This is identical to comma notation above. | *Event* and *Sensory-event* | *Event*, *Sensory-event*
*Sensory-event*, *Event*
(*Event*, *Sensory-event*) | | *Event* |
-| **`A` or `B`**
Match strings or groups
with either `A` or `B`. | *Event* or *Sensory-event* | *Event*, *Sensory-event*
*Sensory-event*, *Event*
(*Event*, *Sensory-event*)
*Event*
*Sensory-event* | *Agent-trait* |
+| **`A` and `B`**
Match if both `A` and `B`
are matched.
Same as the comma notation. | *Event* and *Sensory-event* | *Event*, *Sensory-event*
*Sensory-event*, *Event*
(*Event*, *Sensory-event*) | | *Event* |
+| **`A` or `B`**
Match if either `A` or `B`. | *Event* or *Sensory-event* | *Event*, *Sensory-event*
*Sensory-event*, *Event*
(*Event*, *Sensory-event*)
*Event*
*Sensory-event* | *Agent-trait* |
| **~`A`**
Match groups that do
not contain `A`
`A` can be an arbitrary expression. | [[ *Event*, ~*Action* ]] | (*Event*)
(*Event*, *Animal-agent*)
(*Sensory-event*, (*Action*)) | *Event*
*Event*, *Action*
(*Event*, *Action*)
|
-| **@`A`**
Match a line that does not contain `A`. | @*Event* | *Action*
*Agent-trait*
*Action, Agent-Trait*
(*Action*, *Agent*) | *Event*
(*Action*, *Event*)
(*Action*, *Sensory-event*)
(*Agent*, (*Sensory-event*, *Blue*)) |
+| **@`A`**
Match a line that
does not contain `A`. | @*Event* | *Action*
*Agent-trait*
*Action, Agent-Trait*
(*Action*, *Agent*) | *Event*
(*Action*, *Event*)
(*Action*, *Sensory-event*)
(*Agent*, (*Sensory-event*, *Blue*)) |
### Group queries
-Tag grouping with parentheses is an essential part of HED annotation since HED strings are
+Tag grouping with parentheses is an essential part of HED annotation,
+since HED strings are
independent of ordering of tags or tag groups at the same level.
-Thus, tools have no way of distinguishing which color goes with which shape in the annotation *Red*, *Square*, *Blue, *Triangle*. Annotators must group tags using parentheses to make the meaning clear:
-(*Red*, *Square*), (*Blue, *Triangle*). Group queries allow analysts to detect these groupings.
+Consider the annotation:
+
+> *Red*, *Square*, *Blue*, *Triangle*
+
+In this form, tools cannot distinguish which color goes with which shape.
+Annotators must group tags using parentheses to make the meaning clear:
+
+> (*Red*, *Square*), (*Blue, *Triangle*)
+
+Indicates a red square and a blue triangle.
+Group queries allow analysts to detect these groupings.
+
+As with logical queries,
+`A` and `B` represent HED expressions that may contain multiple
+comma-separated tags and parenthesized groups.
| Query form | Example query | Matches | Does not match |
|--------------------------------------------------------------------------------------------------------------------------|-----------------|-------------------------------------------------------------|------------------------|
@@ -154,20 +174,22 @@ Thus, tools have no way of distinguishing which color goes with which shape in t
| **[`A`, `B`]**
Match a group that
contains `A` and `B`.
Both `A` and `B` could
be any subgroup level. | *[Red, Blue]* | *(Red, (Blue, Green))*
*((Red, Yellow), (Blue, Green))* | *Red, (Blue, Green)* |
-These operations (including exact, containing, and logical groups) can be
+These operations can be
arbitrarily nested and combined, as for example in the query:
> *[`A` or [[`B` and `C`]] ]*
+In this query
Ordering on either the search terms or strings to be searched doesn't matter unless it will impact
-precedence on the expression. Use logical grouping with parentheses if you aren't sure.
+precedence on the expression. Use logical grouping with parentheses to assure
+the expected order.
Precedence is purely left to right outside of grouping operations.
-Thus, unlike many traditional programming languages, **and** does not take precedence over **or**.
+Thus, unlike many traditional programming languages,
+**and** does not take precedence over **or**.
This may change in the future.
-
## Where can HED search be used?
The HED search facility allows users to form sophisticated queries
diff --git a/hedcode/matlab_scripts/web_services/getTestData.m b/hedcode/matlab_scripts/web_services/getTestData.m
index 461b13a..6a4c409 100644
--- a/hedcode/matlab_scripts/web_services/getTestData.m
+++ b/hedcode/matlab_scripts/web_services/getTestData.m
@@ -29,7 +29,7 @@
data.descPrefix = 'Property/Informational-property/Description/';
data.schemaText = fileread('../../data/schema_data/HED8.0.0.xml');
data.schemaUrl = ['https://raw.githubusercontent.com/hed-standard/' ...
- 'hed-specification/master/hedxml/HED8.0.0.xml'];
+ 'hed-schemas/master/standard_schema/hedxml/HED8.0.0.xml'];
data.spreadsheetText = ...
fileread('../../data/spreadsheet_data/LKTEventCodesHED3.tsv');
data.spreadsheetTextExtracted = ...
diff --git a/hedcode/matlab_scripts/web_services/runTest.m b/hedcode/matlab_scripts/web_services/runTest.m
index 4534f2e..adf4279 100644
--- a/hedcode/matlab_scripts/web_services/runTest.m
+++ b/hedcode/matlab_scripts/web_services/runTest.m
@@ -1,5 +1,5 @@
%% Use this script to run an individual type of service.
% host = 'https://hedtools.ucsd.edu/hed';
-%host = 'http://127.0.0.1:5000/';
-host = 'https://hedtools.ucsd.edu/hed_dev';
-errors = testEventRemodelingServices(host);
\ No newline at end of file
+host = 'http://127.0.0.1:5000/';
+%host = 'https://hedtools.ucsd.edu/hed_dev';
+errors = testLibraryServices(host);
\ No newline at end of file
From 744f194b0c97121c5d7d086a22c17437975a6a9d Mon Sep 17 00:00:00 2001
From: Kay Robbins <1189050+VisLab@users.noreply.github.com>
Date: Mon, 6 Feb 2023 10:44:16 -0600
Subject: [PATCH 09/13] Corrections to the remodeling file documentation
---
docs/source/FileRemodelingQuickstart.md | 106 +++++++++++-------------
docs/source/FileRemodelingTools.md | 44 ++++++----
2 files changed, 79 insertions(+), 71 deletions(-)
diff --git a/docs/source/FileRemodelingQuickstart.md b/docs/source/FileRemodelingQuickstart.md
index 0e01b36..05c3b17 100644
--- a/docs/source/FileRemodelingQuickstart.md
+++ b/docs/source/FileRemodelingQuickstart.md
@@ -7,7 +7,7 @@ information in experimental logs and for restructuring event files to enable a p
The tools, which are written in Python, are designed to be run on an entire dataset.
This dataset can be in BIDS
-([**Brain Imaging Data Structure**](https://bids.neuroimaging.io/)) format,
+([**Brain Imaging Data Structure**](https://bids.neuroimaging.io/)),
Alternative users can specify files with a particular suffix and extension appearing
in a specified directory tree.
The later format is useful for restructuring that occurs early in the experimental process,
@@ -119,7 +119,7 @@ stored in the `derivatives/remodel/backups` subdirectory of the dataset.
Restructuring applies a sequence of remodeling operations given in a JSON remodeling
file to produce a final result.
By convention, we name these remodeling instruction files `_rmdl.json`
-and try to store them in the `derivatives/remodel/remodeling_files` directory relative
+and store them in the `derivatives/remodel/remodeling_files` directory relative
to the dataset root directory.
The restructuring always proceeds by looking up each data file in the backup
@@ -406,21 +406,51 @@ A full overview of all arguments is available at
The `run_remodel_backup` is usually run only once for a dataset.
It makes the baseline backup of the event files to assure that nothing will be lost.
The remodeling always starts from the backup files.
-The main script is the `run_remodel` which executes a remodeling script and overwrites
-the data files using the corresponding backup files as the starting point.
-An example command line call for `run_remodel` is shown in the following example.
+
+The `run_remodel` restores the data files from the corresponding backup files and then
+executes remodeling operations from a JSON file.
+A sample command line call for `run_remodel` is shown in the following example.
(remodel-run-anchor)=
-````{admonition} Command to run summary on AOMIC dataset.
+````{admonition} Command to run a summary for the AOMIC dataset.
:class: tip
```bash
-python run_remodel.py /data/ds002790 /data/ds002790/derivatives/remodel/remodeling_files/AOMIC_summarize_rmdl.json \
--s .txt -x derivatives -b
+python run_remodel /data/ds002790 /data/ds002790/derivatives/remodel/remodeling_files/AOMIC_summarize_rmdl.json \
+ -b -s .txt -x derivatives
```
````
+The parameters are as follows:
+
+* `data_dir` - (Required first argument) Root directory of the dataset.
+* `model_path` - (Required second argument) Path of JSON file with remodeling operations.
+* `-b` - (Optional) If present, assume BIDS formatted data.
+* `-s` - (Optional) list of formats to save summaries in.
+* `-x` - (Optional) List of directories to exclude from event processing.
+
+There are three types of command line arguments:
+
+[**Positional arguments**](./FileRemodelingTools.md#positional-arguments),
+[**Named arguments**](./FileRemodelingTools.md#named-arguments),
+and [**Named arguments with values**](./FileRemodelingTools.md#named-arguments).
+
+The positional arguments, `data_dir` and `model_path` are not optional and must
+be the first and second arguments to `run_remodel`.
+The named arguments (with and without values) are optional.
+They all have default values if omitted.
+
+The `-b` option is a named argument indicating whether the dataset is in BIDS format.
+If in BIDS format, the remodeling tools can extract information such as the HED schema
+and the HED annotations from the dataset.
+BIDS data file names are unique, which is convenient for reporting summary information.
+Name arguments are flags-- their presence indicates true and absence indicates false.
+
+The `-s` and `-x` options are examples of named arguments with values.
+The `-s .txt` specifies that summaries should be saved in text format.
+The `-x derivatives` indicates that the `derivatives` subdirectory should not
+be processed during remodeling.
This script can be run multiple times without doing backups and restores,
since it always starts with the backed up files.
@@ -432,59 +462,23 @@ A number of optional key-value arguments are also available.
After the `run_remodel` finishes, it overwrites the data files (not the backups)
and writes any requested summaries in `derivatives/remodel/summaries`.
-If we want to run the split rows operation we demonstrated earlier on the full AOMIC dataset,
-we might first want to check whether the response_time exists for all subjects.
-We can do this by running the `summarize_column_headers` operation.
-
-First we prepare the remodeler json file again.
-
-````{admonition} Split rows remodeler json file for the AOMIC stop signal task.
-:class: tip
-
-```json
-[
- {
- "operation": "summarize_column_names",
- "description": "Summarize existing column names across entire AOMIC dataset.",
- "parameters": {
- "summary_name": "AOMIC_column_headers",
- "summary_filename": "AOMIC_column_headers"
- }
- }
-]
-```
-````
-
-All summaries require summary name and summary filename parameters.
-The *summarize_column_names* summary does not require any additional parameters.
-
-To run the summary we have to provide the following arguments:
-
-* *data dir*
-* *model-path*
-* *-s*, *--save-formats*
-* *-b*, *--bids-format*
-* *-x*, *--exclude-dirs*
-
-The exact paths will look different on your computer but the full command-line call should look something like this:
-
-(remodel-run-summary-anchor)=
-````{admonition} Command to run summary on AOMIC dataset.
-:class: tip
-
-```bash
-python run_remodel.py /data/ds002790 /data/ds002790/derivatives/remodel/remodeling_files/AOMIC_summarize_rmdl.json \
--s .txt -x derivatives -b
-
-```
-````
-
The summaries will be written to `/data/ds002790/derivatives/remodel/summaries` folder in text format.
By default, the summary operations will return both.
The [**summary file**](./_static/data/AOMIC_column_names_2022_12_21_T_13_12_35_641062.txt) lists all different column combinations and for each combination, the files with those columns.
Looking at the different column combinations you can see there are three, one for each task that was performed for this dataset.
-All event files for the stop signal task contain the `stop_signal_delay` column and the `response_time` column.
+
+Going back to the [**split rows example**](#more-complex-remodeling) of remodeling,
+we see that splitting the rows into multiple rows only makes sense if the event files have the same columns.
+Only the event files for the stop signal task contain the `stop_signal_delay` column and the `response_time` column.
+The summarizing the column names across the dataset allows users to check whether the column
+names are consistent across the dataset.
+A common use case for BIDS datasets is that the event files have a different structure
+for different tasks.
+The `-t` command-line option allows users to specify which tasks to perform remodeling on.
+Using this option allows users to select only the files that have the specified task names
+in their filenames.
+
Now you can try out the *split_rows* on the full dataset!
diff --git a/docs/source/FileRemodelingTools.md b/docs/source/FileRemodelingTools.md
index 31fa685..1dc70cd 100644
--- a/docs/source/FileRemodelingTools.md
+++ b/docs/source/FileRemodelingTools.md
@@ -368,7 +368,7 @@ In addition to the `backup_root`, the backup directory also contains a dictionar
in the `backup_lock.json` file. This dictionary is used internally by the remodeling tools.
The backup should be created once and not modified by the user.
-The following example shows how to run the `run_remodel_backup.py` program from the command line
+The following example shows how to run the `run_remodel_backup` program from the command line
to back up the dataset located at `/datasets/eeg_ds003654s_hed_remodel`.
(remodel-backup-anchor)=
@@ -376,7 +376,7 @@ to back up the dataset located at `/datasets/eeg_ds003654s_hed_remodel`.
:class: tip
```bash
-python run_remodel_backup.py /datasets/eeg_ds003654s_hed_remodel -x derivatives stimuli
+python run_remodel_backup /datasets/eeg_ds003654s_hed_remodel -x derivatives stimuli
```
````
@@ -452,7 +452,7 @@ The example assumes that the backup has already been created for the dataset.
:class: tip
```bash
-python run_remodel.py /datasets/eeg_ds003654s_hed_remodel /datasets/remove_extra_rmdl.json -x derivatives simuli
+python run_remodel /datasets/eeg_ds003654s_hed_remodel /datasets/remove_extra_rmdl.json -x derivatives simuli
```
````
@@ -504,7 +504,7 @@ The restore operation restores all the files in the specified backup.
:class: tip
```bash
-python run_remodel_restore.py /datasets/eeg_ds003654s_hed_remodel
+python run_remodel_restore /datasets/eeg_ds003654s_hed_remodel
```
````
@@ -601,7 +601,7 @@ the path to the JSON file with the HED annotations.
:class: tip
```bash
-python run_remodel.py /datasets/eeg_ds003654s_hed_remodel /datasets/summarize_conditions_rmdl.json \
+python run_remodel /datasets/eeg_ds003654s_hed_remodel /datasets/summarize_conditions_rmdl.json \
-x derivatives simuli -r 8.1.0 -j /datasets/eeg_ds003654s_hed_remodel/task-FacePerception_events.json
```
@@ -628,7 +628,8 @@ cli_remodel.main(arg_list)
Errors can occur during several stages in during remodeling and how they are
handled depends on the type of error and where the error occurs.
-The underlying
+Except for the validation summary, the underlying remodeling code raises exceptions for most errors.
+
(errors-in-the-remodel-file-anchor)=
### Errors in the remodel file
@@ -998,7 +999,6 @@ The results of executing this *factor_hed-tags* operation on the
| 17.1021 | 0.5083 | unsuccesful_stop | 0.25 | 0.633 | correct | left | male | 0 | 1 |
| 21.6103 | 0.5083 | go | n/a | 0.443 | correct | left | male | 0 | 1 |
````
-**Note**: Put in a table of the assembled HED tags here to explain how these variables were created.
(merge-consecutive-anchor)=
### Merge consecutive
@@ -1181,6 +1181,11 @@ based on the unique values in the combination of columns *response_accuracy* and
}]
```
````
+In this example there are two source columns and one destination column,
+so each entry in *map_list* must be a list with three elements
+two source values and one destination value).
+Since all the values in *map_list* are strings,
+the optional *integer_sources* list is not needed.
The results of executing the previous *remap_column* command on the
[**sample remodel event file**](sample-remodel-event-file-anchor) are:
@@ -1576,8 +1581,9 @@ All summary operations have two required parameters: *summary_name* and *summary
The *summary_name* is the unique key used to identify the
particular incarnation of this summary in the dispatcher.
-Since a particular operation file may use a given operation multiple times,
-care should be taken to make sure that it is unique.
+Care should be taken to make sure that the *summary_name* is unique within
+a given JSON remodeling file if the same summary operation is used more than
+once within the file (e.g. for before and after summary information).
The *summary_filename* should also be unique and is used for saving the summary upon request.
When the remodeler is applied to full datasets rather than single files,
@@ -1700,9 +1706,15 @@ In addition to the standard parameters, *summary_name* and *summary_filename* re
the *summarize_column_values* operation requires two additional lists to be supplied.
The *skip_columns* list specifies the names of columns to skip entirely in the summary.
Typically, the `onset` and `sample` columns are skipped, since they have unique values for
-each row and their values have limited information. Limited information is also gathered for
-those columns are specified as *value_columns*. The unique values in the remaining columns
-are counted.
+each row and their values have limited information.
+
+The *summarize_column_values* is mainly meant for creating summary information about columns
+containing a finite number of distinct values.
+Columns that contain numeric information will usually have distinct entries for
+each row in a tabular file and are not amenable to such summarization.
+These columns could be specified as *skip_columns*, but another option is to
+designate them as *value_columns*. The *value_columns* are reported in the summary,
+but their distinct values are not reported individually.
For datasets that include multiple tasks, the event values for each task may be distinct.
The *summarize_column_values* operation does not separate by task, but expects the
@@ -2157,14 +2169,16 @@ The *summarize_hed_validation* is a HED operation and the calling program must p
and usually a JSON sidecar containing the HED annotations.
The validation process takes place in two stages: first the JSON sidecar is validated.
+This strategy is used because a single error in the JSON sidecar can generate an error message
+for every line in the corresponding data file.
+
If the JSON sidecar has errors (warnings don't count), the validation process is terminated
without validation of the data file and assembled HED annotations.
+
If the JSON sidecar does not have errors,
the validator assembles the annotations for each line in the data files and validates
the assembled HED annotation.
Data file-wide consistency, such as matched onsets and offsets, is also checked.
-This strategy is used because a single error in the JSON sidecar can generate an error message
-for every line in the corresponding data file.
(summarize-hed-validation-example-anchor)=
@@ -2318,7 +2332,7 @@ replaced by `numpy.NaN` values in the incoming dataframe `df`.
The `Dispatcher` class has a static method `prep_data` that does this replacement.
At the end of running all the remodeling operations on a data file `Dispatcher` `run_operations`
method replaces all of the `numpy.NaN` values with `n/a`, the value expected by BIDS.
-This operation is performed by the `Dispatcher` static method `post_proc_data`. replaces
+This operation is performed by the `Dispatcher` static method `post_proc_data`.
(the-do_op-for summarization-anchor)=
### The do_op for summarization
From 005c5490d1a8dd81e16b965aa4e79b9c324ef227 Mon Sep 17 00:00:00 2001
From: Kay Robbins <1189050+VisLab@users.noreply.github.com>
Date: Fri, 10 Feb 2023 09:40:14 -0600
Subject: [PATCH 10/13] Corrected the WH accession number reversal
---
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docs/source/BidsAnnotationQuickstart.md | 6 +-
docs/source/FileRemodelingTools.md | 24 +-
docs/source/HedConditionsAndDesignMatrices.md | 2 +-
docs/source/HedMatlabTools.md | 8 +-
docs/source/HedRepositoryDatasets.md | 4 +-
docs/source/HedSummaryGuide.md | 6 +-
docs/source/HedTestDatasets.md | 26 +-
.../FacePerception_column_value_summary.json | 1270 -----------------
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.../FacePerception_hed_tag_summary.json | 945 ------------
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.../FacePerception_hed_type_summary.json | 967 -------------
.../FacePerception_hed_type_summary.txt | 185 ---
.../bids/bids_generate_sidecar.ipynb | 8 +-
.../bids/bids_generate_sidecar_temp.ipynb | 4 +-
.../bids/bids_merge_sidecar.ipynb | 6 +-
.../bids/bids_sidecar_to_spreadsheet.ipynb | 6 +-
.../bids/bids_summarize_events.ipynb | 8 +-
...s_summarize_events_separated_by_task.ipynb | 4 +-
...bids_validate_dataset_with_libraries.ipynb | 8 +-
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.../remodeling/run_remodel_backup.ipynb | 2 +-
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.../utilities/zero_data_files.ipynb | 2 +-
.../hedtools_wrappers/testScript.m | 2 +-
.../web_services/exampleGenerateSidecar.m | 2 +-
.../matlab_scripts/web_services/getTestData.m | 8 +-
.../matlab_scripts/web_services/runAllTests.m | 2 +-
.../run_bids_dataset_validation.py | 2 +-
.../run_bids_style_file_validate.py | 4 +-
.../run_json_validation_and_conversion.py | 6 +-
1721 files changed, 105 insertions(+), 3672 deletions(-)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/CHANGES (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/README (100%)
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rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u100.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u103.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u106.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u111.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u112.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u113.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u117.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u120.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u123.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u124.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u127.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u128.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u131.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u132.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u134.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u139.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u140.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/stimuli/u143.bmp (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-002/eeg/sub-002_task-FacePerception_run-1_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-002/eeg/sub-002_task-FacePerception_run-1_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-002/eeg/sub-002_task-FacePerception_run-1_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-002/eeg/sub-002_task-FacePerception_run-1_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-002/eeg/sub-002_task-FacePerception_run-2_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-002/eeg/sub-002_task-FacePerception_run-2_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-002/eeg/sub-002_task-FacePerception_run-2_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-002/eeg/sub-002_task-FacePerception_run-2_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-002/eeg/sub-002_task-FacePerception_run-3_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-002/eeg/sub-002_task-FacePerception_run-3_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-002/eeg/sub-002_task-FacePerception_run-3_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-002/eeg/sub-002_task-FacePerception_run-3_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-003/eeg/sub-003_task-FacePerception_run-1_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-003/eeg/sub-003_task-FacePerception_run-1_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-003/eeg/sub-003_task-FacePerception_run-1_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-003/eeg/sub-003_task-FacePerception_run-1_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-003/eeg/sub-003_task-FacePerception_run-2_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-003/eeg/sub-003_task-FacePerception_run-2_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-003/eeg/sub-003_task-FacePerception_run-3_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/sub-003/eeg/sub-003_task-FacePerception_run-3_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed => eeg_ds003645s_hed}/task-FacePerception_events.json (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/CHANGES (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/README (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/code/task-FacePerception_events_after.json (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/code/task-FacePerception_events_before.json (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/dataset_description.json (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/participants.json (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/participants.tsv (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/circle.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/cross.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f004.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f005.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f006.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f009.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f010.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f011.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f012.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f013.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f015.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f017.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f020.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f021.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f022.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f023.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f024.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f025.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f026.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f027.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f028.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f031.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f032.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f033.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f034.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f036.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f037.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f038.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f039.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f040.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f041.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f045.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f046.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f048.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f049.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f051.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f052.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f053.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f054.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f055.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f057.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f058.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f059.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f060.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f062.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f063.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f064.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f065.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f066.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f067.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f068.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f072.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f073.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f075.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f076.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f077.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f078.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f080.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f081.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f082.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f084.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f087.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f089.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f090.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f091.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f093.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f094.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f095.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f096.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f097.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f098.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f099.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f100.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f101.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f102.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f103.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f104.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f105.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f106.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f108.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f109.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f110.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f111.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f112.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f115.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f117.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f119.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f120.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f121.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f123.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f124.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f127.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f128.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f129.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f130.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f131.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f132.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f133.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f135.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f136.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f137.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f138.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f139.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f140.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f141.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f143.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f145.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f146.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f147.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f148.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f149.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/f150.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s001.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s002.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s003.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s005.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s006.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s008.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s009.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s010.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s011.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s012.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s015.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s016.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s017.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s018.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s020.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s021.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s022.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s023.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s024.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s025.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s026.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s027.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s028.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s029.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s030.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s031.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s033.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s034.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s035.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s036.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s037.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s039.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s040.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s041.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s043.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s044.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s045.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s046.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s048.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s049.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s051.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s052.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s053.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s055.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s056.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s057.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s058.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s060.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s062.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s063.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s065.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s066.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s067.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s068.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s071.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s072.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s073.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s076.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s077.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s080.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s081.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s083.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s084.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s085.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s086.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s087.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s088.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s090.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s091.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s092.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s093.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s094.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s095.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s096.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s098.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s099.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s100.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s101.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s102.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s103.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s105.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s106.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s108.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s109.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s110.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s111.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s112.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s113.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s114.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s115.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s116.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s119.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s121.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s122.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s123.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s124.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s125.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s126.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s127.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s128.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s129.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s131.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s132.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s135.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s136.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s137.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s138.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s139.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s140.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s141.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s142.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s143.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s144.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s146.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s148.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s149.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/s150.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u002.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u003.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u004.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u005.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u007.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u008.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u009.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u010.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u011.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u012.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u013.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u014.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u016.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u017.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u018.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u019.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u020.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u021.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u022.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u023.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u024.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u027.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u028.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u029.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u031.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u032.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u034.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u037.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u040.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u041.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u042.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u044.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u045.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u048.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u049.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u050.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u051.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u052.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u054.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u055.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u056.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u057.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u058.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u061.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u063.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u064.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u065.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u066.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u068.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u069.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u070.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u071.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u073.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u077.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u078.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u079.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u081.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u083.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u084.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u086.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u088.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u089.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u090.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u091.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u092.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u093.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u095.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u097.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u098.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u099.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u100.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u101.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u102.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u103.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u104.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u105.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u106.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u107.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u108.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u111.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u112.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u113.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u114.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u116.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u117.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u118.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u120.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u121.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u122.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u123.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u124.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u125.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u127.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u128.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u129.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u130.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u131.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u132.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u134.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u136.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u137.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u138.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u139.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u140.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u141.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u143.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u144.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u145.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u146.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u147.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u148.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u149.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/stimuli/u150.bmp (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-002/eeg/sub-002_task-FacePerception_run-1_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-002/eeg/sub-002_task-FacePerception_run-1_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-002/eeg/sub-002_task-FacePerception_run-1_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-002/eeg/sub-002_task-FacePerception_run-1_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-002/eeg/sub-002_task-FacePerception_run-2_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-002/eeg/sub-002_task-FacePerception_run-2_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-002/eeg/sub-002_task-FacePerception_run-2_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-002/eeg/sub-002_task-FacePerception_run-2_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-002/eeg/sub-002_task-FacePerception_run-3_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-002/eeg/sub-002_task-FacePerception_run-3_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-002/eeg/sub-002_task-FacePerception_run-3_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-002/eeg/sub-002_task-FacePerception_run-3_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-003/eeg/sub-003_task-FacePerception_run-1_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-003/eeg/sub-003_task-FacePerception_run-1_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-003/eeg/sub-003_task-FacePerception_run-1_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-003/eeg/sub-003_task-FacePerception_run-1_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-003/eeg/sub-003_task-FacePerception_run-2_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-003/eeg/sub-003_task-FacePerception_run-2_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-003/eeg/sub-003_task-FacePerception_run-3_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/sub-003/eeg/sub-003_task-FacePerception_run-3_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_column => eeg_ds003645s_hed_column}/task-FacePerception_events.json (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/CHANGES (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/README (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/dataset_description.json (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/participants.json (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/participants.tsv (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/circle.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/cross.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f004.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f005.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f006.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f009.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f010.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f011.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f012.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f013.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f015.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f017.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f020.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f021.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f022.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f023.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f024.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f025.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f026.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f027.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f028.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f031.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f032.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f033.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f034.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f036.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f037.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f038.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f039.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f040.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f041.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f045.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f046.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f048.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f049.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f051.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f052.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f053.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f054.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f055.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f057.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f058.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f059.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f060.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f062.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f063.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f064.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f065.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f066.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f067.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f068.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f072.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f073.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f075.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f076.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f077.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f078.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f080.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f081.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f082.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f084.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f087.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f089.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f090.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f091.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f093.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f094.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f095.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f096.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f097.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f101.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/f149.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s039.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s040.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s041.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s043.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s044.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s045.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s046.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s048.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s049.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s051.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s052.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s053.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s055.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s056.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s057.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s058.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s060.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s062.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s063.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s065.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s066.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s067.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s068.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s071.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s072.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s073.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s076.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s077.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s080.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s081.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s083.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s084.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s085.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s086.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s087.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s088.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s090.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s091.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s092.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s093.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s094.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s095.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s096.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s098.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s099.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s100.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s101.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s102.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s103.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s105.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s106.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s108.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s109.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s110.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s111.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s112.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s113.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s114.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s115.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s116.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s119.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s121.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s122.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s123.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s124.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s125.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s126.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s127.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s128.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s129.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s131.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s132.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s135.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s136.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s137.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s138.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s139.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s140.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s141.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s142.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s143.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s144.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s146.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s148.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s149.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/s150.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u002.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u003.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u004.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u005.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u007.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u008.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u009.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u010.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u011.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u012.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u013.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u014.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u016.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u017.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u018.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u019.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u020.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u021.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u022.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u023.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u024.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u027.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u028.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u029.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u031.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u032.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u034.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u037.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u040.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u041.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u042.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u044.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u045.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u048.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u049.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u050.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u051.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u052.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u054.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u055.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u056.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u057.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u058.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u061.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u063.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u064.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u065.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u066.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u068.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u069.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u070.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u071.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u073.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u077.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u078.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u079.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u081.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u083.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u084.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u086.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u088.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u089.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u090.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u091.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u092.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u093.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u095.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u097.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u098.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u099.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u100.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u101.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u102.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u103.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u104.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u105.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u106.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u107.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u108.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u111.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u112.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u113.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u114.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u116.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u117.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u118.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u120.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u121.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u122.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u123.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u124.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u125.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u127.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u128.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u129.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u130.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u131.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u132.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u134.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u136.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u137.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u138.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u139.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u140.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u141.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u143.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u144.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u145.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u146.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u147.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u148.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u149.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/stimuli/u150.bmp (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-002/eeg/sub-002_task-FacePerception_run-1_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-002/eeg/sub-002_task-FacePerception_run-2_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-002/eeg/sub-002_task-FacePerception_run-3_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-002/sub-002_task-FacePerception_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-002/sub-002_task-FacePerception_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-002/sub-002_task-FacePerception_events.json (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-002/sub-002_task-FacePerception_run-1_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-002/sub-002_task-FacePerception_run-2_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-002/sub-002_task-FacePerception_run-3_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-003/eeg/sub-003_task-FacePerception_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-003/eeg/sub-003_task-FacePerception_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-003/eeg/sub-003_task-FacePerception_run-1_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-003/eeg/sub-003_task-FacePerception_run-1_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-003/eeg/sub-003_task-FacePerception_run-2_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-003/eeg/sub-003_task-FacePerception_run-3_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/sub-003/sub-003_task-FacePerception_events.json (100%)
rename datasets/{eeg_ds003654s_hed_inheritance => eeg_ds003645s_hed_inheritance}/task-FacePerception_events.json (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/CHANGES (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/README (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/dataset_description.json (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/participants.json (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/participants.tsv (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/circle.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/cross.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f004.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f006.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f010.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f020.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f025.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f028.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f033.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f034.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f036.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f038.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f039.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f041.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f051.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f058.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f059.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f063.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f064.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f072.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f075.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f076.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f081.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f089.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f093.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f094.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f098.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f099.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f101.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f102.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f103.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f104.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f105.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f108.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f111.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f112.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f119.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f121.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f123.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f124.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f130.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f132.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f138.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f139.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f141.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f143.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f145.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f146.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f147.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f148.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/f150.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s001.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s005.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s006.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s008.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s015.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s017.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s018.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s025.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s026.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s028.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s033.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s034.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s036.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s037.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s043.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s044.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s051.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s052.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s053.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s056.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s058.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s062.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s066.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s068.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s071.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s076.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s080.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s081.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s083.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s086.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s087.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s088.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s090.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s096.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s100.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s103.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s106.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s111.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s112.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s115.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s121.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s125.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s135.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s137.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s138.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s139.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s141.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s142.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/s150.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u002.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u004.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u005.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u011.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u014.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u018.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u019.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u020.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u021.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u022.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u023.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u024.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u032.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u041.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u044.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u045.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u048.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u055.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u058.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u061.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u066.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u069.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u070.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u077.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u079.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u083.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u084.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u086.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u088.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u089.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u091.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u095.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u097.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u098.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u100.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u103.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u106.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u111.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u112.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u113.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u117.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u120.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u123.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u124.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u127.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u128.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u131.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u132.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u134.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u139.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u140.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/stimuli/u143.bmp (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-002/eeg/sub-002_task-FacePerception_run-1_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-002/eeg/sub-002_task-FacePerception_run-1_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-002/eeg/sub-002_task-FacePerception_run-1_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-002/eeg/sub-002_task-FacePerception_run-1_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-002/eeg/sub-002_task-FacePerception_run-2_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-002/eeg/sub-002_task-FacePerception_run-2_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-002/eeg/sub-002_task-FacePerception_run-2_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-002/eeg/sub-002_task-FacePerception_run-2_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-002/eeg/sub-002_task-FacePerception_run-3_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-002/eeg/sub-002_task-FacePerception_run-3_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-002/eeg/sub-002_task-FacePerception_run-3_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-002/eeg/sub-002_task-FacePerception_run-3_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-003/eeg/sub-003_task-FacePerception_run-1_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-003/eeg/sub-003_task-FacePerception_run-1_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-003/eeg/sub-003_task-FacePerception_run-1_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-003/eeg/sub-003_task-FacePerception_run-1_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-003/eeg/sub-003_task-FacePerception_run-2_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-003/eeg/sub-003_task-FacePerception_run-2_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-003/eeg/sub-003_task-FacePerception_run-3_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/sub-003/eeg/sub-003_task-FacePerception_run-3_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_library => eeg_ds003645s_hed_library}/task-FacePerception_events.json (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/CHANGES (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/README (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/dataset_description.json (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/participants.json (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/participants.tsv (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/circle.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/cross.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f004.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f005.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f006.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f009.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f010.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f011.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f012.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f013.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f015.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f017.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f020.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f024.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f025.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f026.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f027.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f028.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f031.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f032.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f033.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f034.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f036.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f037.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f038.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f039.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f040.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f041.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f045.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f046.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f048.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f049.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f051.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f052.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f053.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f054.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f055.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f057.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f059.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f060.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f062.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f063.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f064.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f065.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f066.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f067.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f068.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f072.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f073.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f075.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f076.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f077.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f078.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f080.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f081.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f082.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f084.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f087.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f089.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f090.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f091.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f093.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f094.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f095.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f096.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f097.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f098.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f099.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f100.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f101.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f105.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f106.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f108.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f109.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f111.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f115.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f127.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f135.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f136.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f139.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f140.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f141.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f145.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/f146.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s044.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s045.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s046.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s048.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s049.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s051.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s052.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s053.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s055.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s056.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s057.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s058.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s060.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s062.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s063.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s065.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s066.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s067.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s068.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s071.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s072.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s073.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s076.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s077.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s080.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s081.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s083.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s084.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s085.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s086.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s087.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s088.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s090.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s091.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s092.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s093.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s094.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s095.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s096.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s098.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s099.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s100.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s101.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s102.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s103.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s105.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s106.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s108.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s109.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s110.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s111.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s112.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s113.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s114.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s115.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s116.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s119.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s121.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s122.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s123.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s124.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s125.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s126.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s127.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s128.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s129.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s131.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s132.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s135.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s136.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s137.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s138.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s139.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s140.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s141.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s142.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s143.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s144.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s146.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s148.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s149.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/s150.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u002.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u003.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u004.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u005.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u007.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u008.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u009.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u010.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u011.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u012.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u013.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u014.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u016.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u017.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u018.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u019.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u020.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u021.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u022.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u023.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u024.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u027.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u028.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u029.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u031.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u032.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u034.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u037.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u040.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u041.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u042.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u044.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u045.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u048.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u049.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u050.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u051.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u052.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u054.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u055.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u056.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u057.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u058.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u061.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u063.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u064.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u065.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u066.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u068.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u069.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u070.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u071.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u073.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u077.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u078.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u079.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u081.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u083.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u084.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u086.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u088.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u089.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u090.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u091.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u092.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u093.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u095.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u097.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u098.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u099.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u100.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u101.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u102.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u103.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u104.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u105.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u106.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u107.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u108.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u111.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u112.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u113.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u114.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u116.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u117.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u118.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u120.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u121.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u122.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u123.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u124.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u125.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u127.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u128.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u129.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u130.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u131.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u132.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u134.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u136.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u137.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u138.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u139.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u140.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u141.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u143.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u144.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u145.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u146.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u147.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u148.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u149.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/stimuli/u150.bmp (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-002/eeg/sub-002_task-FacePerception_run-1_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-002/eeg/sub-002_task-FacePerception_run-1_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-002/eeg/sub-002_task-FacePerception_run-1_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-002/eeg/sub-002_task-FacePerception_run-1_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-002/eeg/sub-002_task-FacePerception_run-2_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-002/eeg/sub-002_task-FacePerception_run-2_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-002/eeg/sub-002_task-FacePerception_run-2_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-002/eeg/sub-002_task-FacePerception_run-2_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-002/eeg/sub-002_task-FacePerception_run-3_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-002/eeg/sub-002_task-FacePerception_run-3_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-002/eeg/sub-002_task-FacePerception_run-3_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-002/eeg/sub-002_task-FacePerception_run-3_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-003/eeg/sub-003_task-FacePerception_run-1_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-003/eeg/sub-003_task-FacePerception_run-1_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-003/eeg/sub-003_task-FacePerception_run-1_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-003/eeg/sub-003_task-FacePerception_run-1_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-003/eeg/sub-003_task-FacePerception_run-2_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-003/eeg/sub-003_task-FacePerception_run-2_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-003/eeg/sub-003_task-FacePerception_run-3_channels.tsv (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.json (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.set (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/sub-003/eeg/sub-003_task-FacePerception_run-3_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_longform => eeg_ds003645s_hed_longform}/task-FacePerception_events.json (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/CHANGES (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/README (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/dataset_description.json (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/derivatives/remodel/backups/default_back/backup_lock.json (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/derivatives/remodel/backups/default_back/backup_root/sub-002/eeg/sub-002_task-FacePerception_run-1_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/derivatives/remodel/backups/default_back/backup_root/sub-002/eeg/sub-002_task-FacePerception_run-2_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/derivatives/remodel/backups/default_back/backup_root/sub-002/eeg/sub-002_task-FacePerception_run-3_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/derivatives/remodel/backups/default_back/backup_root/sub-003/eeg/sub-003_task-FacePerception_run-1_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/derivatives/remodel/backups/default_back/backup_root/sub-003/eeg/sub-003_task-FacePerception_run-2_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/derivatives/remodel/backups/default_back/backup_root/sub-003/eeg/sub-003_task-FacePerception_run-3_events.tsv (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/derivatives/remodel/remodeling_files/remove_extra_rmdl.json (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/derivatives/remodel/remodeling_files/summarize_columns_rmdl.json (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/derivatives/remodel/remodeling_files/summarize_hed_types_rmdl.json (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/participants.json (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/participants.tsv (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/circle.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/cross.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f004.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f006.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f010.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f020.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f025.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f028.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f033.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f034.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f036.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f038.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f039.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f041.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f051.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f058.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f059.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f063.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f064.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f072.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f075.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f076.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f081.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f089.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f093.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f094.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f098.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f099.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f101.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f102.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f103.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f104.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f105.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f108.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f111.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f112.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f119.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f121.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f123.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f124.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f130.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f132.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f138.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f139.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f141.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f143.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f145.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f146.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f147.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f148.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/f150.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s001.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s005.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s006.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s008.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s015.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s017.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s018.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s025.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s028.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s033.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s034.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s037.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s043.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s044.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s051.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s056.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s058.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s062.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s066.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s068.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s071.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s074.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s076.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s080.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s081.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s083.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s086.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s087.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s088.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s090.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s096.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s100.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s103.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s106.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s111.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s112.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s115.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s121.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s125.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s135.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s137.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s138.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s139.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s141.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s142.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/s150.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/u002.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/u004.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/u005.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/u011.bmp (100%)
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rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/u032.bmp (100%)
rename datasets/{eeg_ds003654s_hed_remodel => eeg_ds003645s_hed_remodel}/stimuli/u041.bmp (100%)
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diff --git a/datasets/README.md b/datasets/README.md
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--- a/datasets/README.md
+++ b/datasets/README.md
@@ -23,11 +23,11 @@ The suffix modifier indicates what this dataset is designed to test.
| Dataset | Description |
| ----------------- | ------------|
| eeg_ds002893s_hed_attention_shift | Auditory-visual attention shift data.
Illustrates remapping of multiple event columns. |
-| eeg_ds003654s_hed | Wakeman-Henson data using short form tags and definitions. |
-| eeg_ds003654s_hed_column | Wakeman-Henson data with tags in events.tsv HED column. |
-| eeg_ds003654s_hed_inheritance | Wakeman-Henson data with multiple sidecars. |
-| eeg_ds003654s_hed_library | Wakeman-Henson data using HED libraries. |
-| eeg_ds003654s_hed_longform | Wakeman-Henson data using long form tags and definitions. |
+| eeg_ds003645s_hed | Wakeman-Henson data using short form tags and definitions. |
+| eeg_ds003645s_hed_column | Wakeman-Henson data with tags in events.tsv HED column. |
+| eeg_ds003645s_hed_inheritance | Wakeman-Henson data with multiple sidecars. |
+| eeg_ds003645s_hed_library | Wakeman-Henson data using HED libraries. |
+| eeg_ds003645s_hed_longform | Wakeman-Henson data using long form tags and definitions. |
| eeg_ds004105s_hed | BCIT Driving with auditory cueing data.
Part of a test data corpus for BIDS-MEGA testing. |
| eeg_ds004106s_hed | BCIT Advanced guard duty data.
Part of a test data corpus for BIDS-MEGA testing. |
| eeg_ds004117s_hed_sternberg | Sternberg working memory task.
Chosen as a replication study for [**EEGManyLabs**](https://osf.io/yb3pq/).|
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diff --git a/datasets/eeg_ds003654s_hed_remodel/stimuli/s141.bmp b/datasets/eeg_ds003645s_hed_remodel/stimuli/s141.bmp
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diff --git a/datasets/eeg_ds003654s_hed_remodel/stimuli/u032.bmp b/datasets/eeg_ds003645s_hed_remodel/stimuli/u032.bmp
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diff --git a/datasets/eeg_ds003654s_hed_remodel/stimuli/u041.bmp b/datasets/eeg_ds003645s_hed_remodel/stimuli/u041.bmp
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diff --git a/datasets/eeg_ds003654s_hed_remodel/stimuli/u045.bmp b/datasets/eeg_ds003645s_hed_remodel/stimuli/u045.bmp
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diff --git a/datasets/eeg_ds003654s_hed_remodel/stimuli/u048.bmp b/datasets/eeg_ds003645s_hed_remodel/stimuli/u048.bmp
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diff --git a/datasets/eeg_ds003654s_hed_remodel/stimuli/u055.bmp b/datasets/eeg_ds003645s_hed_remodel/stimuli/u055.bmp
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diff --git a/datasets/eeg_ds003654s_hed_remodel/stimuli/u058.bmp b/datasets/eeg_ds003645s_hed_remodel/stimuli/u058.bmp
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diff --git a/datasets/eeg_ds003654s_hed_remodel/stimuli/u061.bmp b/datasets/eeg_ds003645s_hed_remodel/stimuli/u061.bmp
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diff --git a/datasets/eeg_ds003654s_hed_remodel/stimuli/u066.bmp b/datasets/eeg_ds003645s_hed_remodel/stimuli/u066.bmp
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diff --git a/datasets/eeg_ds003654s_hed_remodel/stimuli/u069.bmp b/datasets/eeg_ds003645s_hed_remodel/stimuli/u069.bmp
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diff --git a/datasets/eeg_ds003654s_hed_remodel/stimuli/u070.bmp b/datasets/eeg_ds003645s_hed_remodel/stimuli/u070.bmp
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diff --git a/datasets/eeg_ds003654s_hed_remodel/stimuli/u074.bmp b/datasets/eeg_ds003645s_hed_remodel/stimuli/u074.bmp
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-1_channels.tsv b/datasets/eeg_ds003645s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-1_channels.tsv
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-1_eeg.set b/datasets/eeg_ds003645s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-1_eeg.set
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-1_events.tsv b/datasets/eeg_ds003645s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-1_events.tsv
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-2_channels.tsv b/datasets/eeg_ds003645s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-2_channels.tsv
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-2_eeg.json b/datasets/eeg_ds003645s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-2_eeg.json
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-2_eeg.set b/datasets/eeg_ds003645s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-2_eeg.set
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-2_events.tsv b/datasets/eeg_ds003645s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-2_events.tsv
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-3_channels.tsv b/datasets/eeg_ds003645s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-3_channels.tsv
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-3_eeg.json b/datasets/eeg_ds003645s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-3_eeg.json
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-3_eeg.set b/datasets/eeg_ds003645s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-3_eeg.set
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-3_events.tsv b/datasets/eeg_ds003645s_hed_remodel/sub-002/eeg/sub-002_task-FacePerception_run-3_events.tsv
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-1_channels.tsv b/datasets/eeg_ds003645s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-1_channels.tsv
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-1_eeg.json b/datasets/eeg_ds003645s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-1_eeg.json
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-1_eeg.set b/datasets/eeg_ds003645s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-1_eeg.set
similarity index 100%
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-1_events.tsv b/datasets/eeg_ds003645s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-1_events.tsv
similarity index 100%
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-2_channels.tsv b/datasets/eeg_ds003645s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-2_channels.tsv
similarity index 100%
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.json b/datasets/eeg_ds003645s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.json
similarity index 100%
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.set b/datasets/eeg_ds003645s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.set
similarity index 100%
rename from datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-2_eeg.set
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-2_events.tsv b/datasets/eeg_ds003645s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-2_events.tsv
similarity index 100%
rename from datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-2_events.tsv
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-3_channels.tsv b/datasets/eeg_ds003645s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-3_channels.tsv
similarity index 100%
rename from datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-3_channels.tsv
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.json b/datasets/eeg_ds003645s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.json
similarity index 100%
rename from datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.json
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.set b/datasets/eeg_ds003645s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-3_eeg.set
similarity index 100%
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diff --git a/datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-3_events.tsv b/datasets/eeg_ds003645s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-3_events.tsv
similarity index 100%
rename from datasets/eeg_ds003654s_hed_remodel/sub-003/eeg/sub-003_task-FacePerception_run-3_events.tsv
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diff --git a/datasets/eeg_ds003654s_hed_remodel/task-FacePerception_events.json b/datasets/eeg_ds003645s_hed_remodel/task-FacePerception_events.json
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rename from datasets/eeg_ds003654s_hed_remodel/task-FacePerception_events.json
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diff --git a/docs/source/BidsAnnotationQuickstart.md b/docs/source/BidsAnnotationQuickstart.md
index 6f43bf5..d51fc02 100644
--- a/docs/source/BidsAnnotationQuickstart.md
+++ b/docs/source/BidsAnnotationQuickstart.md
@@ -38,12 +38,12 @@ to produce a comprehensive JSON sidecar template.
````
The examples in this tutorial use an
-[**abbreviated version**](https://raw.githubusercontent.com/hed-standard/hed-examples/main/datasets/eeg_ds003654s_hed/sub-002/eeg/sub-002_task-FacePerception_run-1_events.tsv)
+[**abbreviated version**](https://raw.githubusercontent.com/hed-standard/hed-examples/main/datasets/eeg_ds003645s_hed/sub-002/eeg/sub-002_task-FacePerception_run-1_events.tsv)
of the `events.tsv`file from subject 002 run 1 from
-[**ds003654**](https://openneuro.org/datasets/ds003645/versions/2.0.0):Face processing MEEG dataset with HED annotation
+[**ds003645**](https://openneuro.org/datasets/ds003645/versions/2.0.0):Face processing MEEG dataset with HED annotation
dataset on OpenNeuro.
A reduced version of this dataset
-[**eeg_ds003654s_hed**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003654s_hed)
+[**eeg_ds003645s_hed**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)
is also available.
(how-hed-works-in-bids-anchor)=
diff --git a/docs/source/FileRemodelingTools.md b/docs/source/FileRemodelingTools.md
index 1dc70cd..48923a7 100644
--- a/docs/source/FileRemodelingTools.md
+++ b/docs/source/FileRemodelingTools.md
@@ -348,7 +348,7 @@ These scripts can be run from the command line or from another Python program us
The `run_remodel_backup` Python program creates a backup of the specified files.
The backup is always created in the `derivatives/remodel/backups` subdirectory
under the dataset root as shown in the following example for the
-sample dataset `eeg_ds003654s_hed_remodel`,
+sample dataset `eeg_ds003645s_hed_remodel`,
which can be found in the `datasets` subdirectory of the
[**hed-examples**](https://github.com/hed-standard/hed-examples) GitHub repository.
@@ -369,14 +369,14 @@ in the `backup_lock.json` file. This dictionary is used internally by the remode
The backup should be created once and not modified by the user.
The following example shows how to run the `run_remodel_backup` program from the command line
-to back up the dataset located at `/datasets/eeg_ds003654s_hed_remodel`.
+to back up the dataset located at `/datasets/eeg_ds003645s_hed_remodel`.
(remodel-backup-anchor)=
````{admonition} Example of calling run_remodel_backup from the command line.
:class: tip
```bash
-python run_remodel_backup /datasets/eeg_ds003654s_hed_remodel -x derivatives stimuli
+python run_remodel_backup /datasets/eeg_ds003645s_hed_remodel -x derivatives stimuli
```
````
@@ -398,7 +398,7 @@ The command-line arguments are given in a list instead of on the command line.
import hed.tools.remodeling.cli.run_remodel_backup as cli_backup
-data_root = '/datasets/eeg_ds003654s_hed_remodel'
+data_root = '/datasets/eeg_ds003645s_hed_remodel'
arg_list = [data_root, '-x', 'derivatives', 'stimuli']
cli_backup.main(arg_list)
@@ -452,7 +452,7 @@ The example assumes that the backup has already been created for the dataset.
:class: tip
```bash
-python run_remodel /datasets/eeg_ds003654s_hed_remodel /datasets/remove_extra_rmdl.json -x derivatives simuli
+python run_remodel /datasets/eeg_ds003645s_hed_remodel /datasets/remove_extra_rmdl.json -x derivatives simuli
```
````
@@ -478,7 +478,7 @@ This code can be used in a Jupyter notebook or in another Python program.
```python
import hed.tools.remodeling.cli.run_remodel as cli_remodel
-data_root = '/datasets/eeg_ds003654s_hed_remodel'
+data_root = '/datasets/eeg_ds003645s_hed_remodel'
model_path = '/datasets/remove_extra_rmdl.json'
arg_list = [data_root, model_path, '-x', 'derivatives', 'stimuli']
cli_remodel.main(arg_list)
@@ -504,7 +504,7 @@ The restore operation restores all the files in the specified backup.
:class: tip
```bash
-python run_remodel_restore /datasets/eeg_ds003654s_hed_remodel
+python run_remodel_restore /datasets/eeg_ds003645s_hed_remodel
```
````
@@ -517,7 +517,7 @@ As with the other command-line programs, `run_remodel_restore` can be also calle
```python
import hed.tools.remodeling.cli.run_restore as cli_remodel
-data_root = '/datasets/eeg_ds003654s_hed_remodel'
+data_root = '/datasets/eeg_ds003645s_hed_remodel'
cli_remodel.main([data_root])
```
@@ -601,8 +601,8 @@ the path to the JSON file with the HED annotations.
:class: tip
```bash
-python run_remodel /datasets/eeg_ds003654s_hed_remodel /datasets/summarize_conditions_rmdl.json \
--x derivatives simuli -r 8.1.0 -j /datasets/eeg_ds003654s_hed_remodel/task-FacePerception_events.json
+python run_remodel /datasets/eeg_ds003645s_hed_remodel /datasets/summarize_conditions_rmdl.json \
+-x derivatives simuli -r 8.1.0 -j /datasets/eeg_ds003645s_hed_remodel/task-FacePerception_events.json
```
````
@@ -614,9 +614,9 @@ python run_remodel /datasets/eeg_ds003654s_hed_remodel /datasets/summarize_condi
```python
import hed.tools.remodeling.cli.run_remodel as cli_remodel
-data_root = '/datasets/eeg_ds003654s_hed_remodel'
+data_root = '/datasets/eeg_ds003645s_hed_remodel'
model_path = '/datasets/summarize_conditions_rmdl.json'
-json_path = '/datasets/eeg_ds003654s_hed_remodel/task-FacePerception_events.json'
+json_path = '/datasets/eeg_ds003645s_hed_remodel/task-FacePerception_events.json'
arg_list = [data_root, model_path, '-x', 'derivatives', 'stimuli', '-r' 8.1.0 '-j' json_path]
cli_remodel.main(arg_list)
diff --git a/docs/source/HedConditionsAndDesignMatrices.md b/docs/source/HedConditionsAndDesignMatrices.md
index c35a549..72e4b4c 100644
--- a/docs/source/HedConditionsAndDesignMatrices.md
+++ b/docs/source/HedConditionsAndDesignMatrices.md
@@ -283,7 +283,7 @@ as `Task` can be summarized and used as factor vectors in a way similar to *Cond
In this section, we look at a more complicated example based on the Wakeman-Henson face-processing dataset.
This dataset, which is available on [OpenNeuro](https://openneuro.org) under accession number
-ds003654, was used in as a case study on HED annotation described in the
+ds003645, was used in as a case study on HED annotation described in the
[Capturing the nature of events paper](https://www.sciencedirect.com/science/article/pii/S1053811921010387).
The experiment is based on a 3 x 3 x 2 experimental design: face type x repetition status x key choice.
diff --git a/docs/source/HedMatlabTools.md b/docs/source/HedMatlabTools.md
index 30ecd4b..2cf7474 100644
--- a/docs/source/HedMatlabTools.md
+++ b/docs/source/HedMatlabTools.md
@@ -175,7 +175,7 @@ versions available in the
`````{admonition} Create a request for the sidecar_validate web service.
:class: tip
```matlab
-jsonText = fileread('../../../datasets/eeg_ds003654s_hed/task-FacePerception_events.json');
+jsonText = fileread('../../../datasets/eeg_ds003645s_hed/task-FacePerception_events.json');
request = struct('service', 'sidecar_validate', ...
'schema_version', '8.0.0', ...
'json_string', jsonText, ...
@@ -720,7 +720,7 @@ function issueString = validateHedInBids(dataPath)
Example MATLAB calling code for this function:
```matlab
-dataPath = 'H:\datasets\eeg_ds003654s_hed';
+dataPath = 'H:\datasets\eeg_ds003645s_hed';
issueString = validateHedInBids(dataPath);
if isempty(issueString)
fprintf('Dataset %s has no HED validation errors\n', dataPath);
@@ -730,8 +730,8 @@ end
```
````
-In above example assumes that the BIDS dataset was located at `H:\datasets\eeg_ds003654s_hed`.
-We tested it with the [**eeg_ds003654s_hed**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003654s_hed) available on GitHub.
+In above example assumes that the BIDS dataset was located at `H:\datasets\eeg_ds003645s_hed`.
+We tested it with the [**eeg_ds003645s_hed**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed) available on GitHub.
You can download and use this test data or set `dataPath` to the root directory of your own dataset.
diff --git a/docs/source/HedRepositoryDatasets.md b/docs/source/HedRepositoryDatasets.md
index cbca904..fcddb40 100644
--- a/docs/source/HedRepositoryDatasets.md
+++ b/docs/source/HedRepositoryDatasets.md
@@ -4,5 +4,5 @@
| Dataset | Description |
| ----------------- | ------------|
-| [eeg_ds003654s_hed](eeg_ds003654s_hed)| Short-form tags with definitions. |
-| [eeg_ds003654s_hed_inheritance](eeg_ds003654s_hed_inheritance) | Multiple sidecars with inheritance. |
+| [eeg_ds003645s_hed](eeg_ds003645s_hed)| Short-form tags with definitions. |
+| [eeg_ds003645s_hed_inheritance](eeg_ds003645s_hed_inheritance) | Multiple sidecars with inheritance. |
diff --git a/docs/source/HedSummaryGuide.md b/docs/source/HedSummaryGuide.md
index 962983b..a624ccc 100644
--- a/docs/source/HedSummaryGuide.md
+++ b/docs/source/HedSummaryGuide.md
@@ -23,10 +23,10 @@ files that have been processed and, if requested, also include an individual sum
for each input file.
The examples in this tutorial use the Wakeman-Hanson Face Processing dataset as an example.
-A [**reduced version**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003654s_hed)
+A [**reduced version**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)
containing 2 subjects and no imaging data is used to produce the summaries in the examples.
The reduced dataset has 6 event files each containing 200 events.
-The full dataset is available on OpenNeuro as [**ds003654**](https://openneuro.org/datasets/ds003645/versions/2.0.0).
+The full dataset is available on OpenNeuro as [**ds003645**](https://openneuro.org/datasets/ds003645/versions/2.0.0).
Each example only shows the overall summary with links to the full summaries that
include individual summaries. The summaries use a **[number events, number files]**
@@ -167,7 +167,7 @@ Tags that don't fit in these three categories will be grouped under "Other tags"
The following excerpt shows the dataset portion of the resulting summary in text format
-when running on the [**reduced version**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003654s_hed) face processing
+when running on the [**reduced version**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed) face processing
dataset, which has 6 event files containing a total of 1200 events.
````{admonition} Text format excerpt with dataset-level summary of hed tag counts
diff --git a/docs/source/HedTestDatasets.md b/docs/source/HedTestDatasets.md
index b020a5e..9cb145f 100644
--- a/docs/source/HedTestDatasets.md
+++ b/docs/source/HedTestDatasets.md
@@ -21,14 +21,14 @@ The suffix modifier indicates what this dataset is designed to test.
| Dataset | Description |
| ----------------- | ------------|
-| [eeg_ds003654s_hed](eeg_ds003654s_hed_anchor)| Short-form tags with definitions. |
-| [eeg_ds003654s_hed_inheritance](eeg_ds003654s_hed_inheritance_anchor) | Multiple sidecars with inheritance. |
-| [eeg_ds003654s_hed_library](eeg_ds003654s_hed_library_anchor) | Multiple HED library schemas. |
-| [eeg_ds003654s_hed_longform](eeg_ds003654s_hed_longform_anchor) | Long-form with definitions. |
+| [eeg_ds003645s_hed](eeg_ds003645s_hed_anchor)| Short-form tags with definitions. |
+| [eeg_ds003645s_hed_inheritance](eeg_ds003645s_hed_inheritance_anchor) | Multiple sidecars with inheritance. |
+| [eeg_ds003645s_hed_library](eeg_ds003645s_hed_library_anchor) | Multiple HED library schemas. |
+| [eeg_ds003645s_hed_longform](eeg_ds003645s_hed_longform_anchor) | Long-form with definitions. |
| [fmri_soccer21_hed](fmri_soccer21_hed_anchor) | Annotation with single column. |
-(eeg_ds003654s_hed_anchor)=
-## eeg_ds003654s_hed
+(eeg_ds003645s_hed_anchor)=
+## eeg_ds003645s_hed
This dataset was originally released as a multi-modal dataset by
@@ -39,13 +39,13 @@ Detailed case study in using HED for tagging:
> BioRxiv, 2021.05.06.442841.
> [https://doi.org/10.1101/2021.05.06.442841](https://doi.org/10.1101/2021.05.06.442841)
-(eeg_ds003654s_hed_inheritance_anchor)=
-## eeg_ds003654s_hed_inheritance
+(eeg_ds003645s_hed_inheritance_anchor)=
+## eeg_ds003645s_hed_inheritance
-This is eeg_ds003654s_hed_inheritance
+This is eeg_ds003645s_hed_inheritance
-(eeg_ds003654s_hed_library_anchor)=
-## eeg_ds003654s_hed_library
+(eeg_ds003645s_hed_library_anchor)=
+## eeg_ds003645s_hed_library
This dataset is designed to test the HED library schema facility.
It uses HED 8.0.0 as a base schema and as the "test" library schema.
@@ -54,8 +54,8 @@ In addition, this dataset uses the SCORE library version 1.0.0 as a library sche
The schema used are specified in the `dataset_description.json` file using the
proposed format.
-(eeg_ds003654s_hed_longform_anchor)=
-## eeg_ds003654s_hed_longform
+(eeg_ds003645s_hed_longform_anchor)=
+## eeg_ds003645s_hed_longform
This dataset is used to test that tools work with either long-form or short-form HED tags.
(fmri_soccer21_hed_anchor)=
diff --git a/docs/source/_static/data/summaries/FacePerception_column_value_summary.json b/docs/source/_static/data/summaries/FacePerception_column_value_summary.json
deleted file mode 100644
index e470cff..0000000
--- a/docs/source/_static/data/summaries/FacePerception_column_value_summary.json
+++ /dev/null
@@ -1,1270 +0,0 @@
-{
- "context_name": "column_values_summary",
- "context_type": "column_values",
- "context_filename": "column_values_summary",
- "summary": {
- "Dataset": {
- "Summary name": "Dataset",
- "Total events": 1200,
- "Total files": 6,
- "Categorical columns": {
- "event_type": {
- "double_press": [
- 1,
- 1
- ],
- "left_press": [
- 83,
- 4
- ],
- "right_press": [
- 168,
- 6
- ],
- "setup_right_sym": [
- 6,
- 6
- ],
- "show_circle": [
- 316,
- 6
- ],
- "show_cross": [
- 310,
- 6
- ],
- "show_face": [
- 310,
- 6
- ],
- "show_face_initial": [
- 6,
- 6
- ]
- },
- "face_type": {
- "famous_face": [
- 108,
- 6
- ],
- "n/a": [
- 884,
- 6
- ],
- "scrambled_face": [
- 103,
- 6
- ],
- "unfamiliar_face": [
- 105,
- 6
- ]
- },
- "rep_lag": {
- "1": [
- 77,
- 6
- ],
- "10": [
- 15,
- 6
- ],
- "11": [
- 13,
- 5
- ],
- "12": [
- 9,
- 5
- ],
- "13": [
- 7,
- 6
- ],
- "14": [
- 6,
- 4
- ],
- "15": [
- 2,
- 2
- ],
- "6": [
- 1,
- 1
- ],
- "7": [
- 2,
- 2
- ],
- "8": [
- 6,
- 4
- ],
- "9": [
- 10,
- 6
- ],
- "n/a": [
- 1052,
- 6
- ]
- },
- "rep_status": {
- "delayed_repeat": [
- 71,
- 6
- ],
- "first_show": [
- 168,
- 6
- ],
- "immediate_repeat": [
- 77,
- 6
- ],
- "n/a": [
- 884,
- 6
- ]
- },
- "value": {
- "0": [
- 316,
- 6
- ],
- "1": [
- 310,
- 6
- ],
- "13": [
- 56,
- 6
- ],
- "14": [
- 24,
- 6
- ],
- "15": [
- 25,
- 6
- ],
- "17": [
- 54,
- 6
- ],
- "18": [
- 32,
- 6
- ],
- "19": [
- 17,
- 6
- ],
- "256": [
- 83,
- 4
- ],
- "3": [
- 6,
- 6
- ],
- "4096": [
- 168,
- 6
- ],
- "4352": [
- 1,
- 1
- ],
- "5": [
- 58,
- 6
- ],
- "6": [
- 21,
- 6
- ],
- "7": [
- 29,
- 6
- ]
- }
- },
- "Value columns": {
- "stim_file": [
- 1200,
- 6
- ],
- "trial": [
- 1200,
- 6
- ]
- }
- },
- "Individual files": {
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-1_events.tsv": {
- "Summary name": "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-1_events.tsv",
- "Total events": 200,
- "Total files": 1,
- "Categorical columns": {
- "event_type": {
- "double_press": [
- 1,
- 1
- ],
- "left_press": [
- 20,
- 1
- ],
- "right_press": [
- 23,
- 1
- ],
- "setup_right_sym": [
- 1,
- 1
- ],
- "show_circle": [
- 52,
- 1
- ],
- "show_cross": [
- 51,
- 1
- ],
- "show_face": [
- 51,
- 1
- ],
- "show_face_initial": [
- 1,
- 1
- ]
- },
- "face_type": {
- "famous_face": [
- 14,
- 1
- ],
- "n/a": [
- 148,
- 1
- ],
- "scrambled_face": [
- 18,
- 1
- ],
- "unfamiliar_face": [
- 20,
- 1
- ]
- },
- "rep_lag": {
- "1": [
- 12,
- 1
- ],
- "10": [
- 2,
- 1
- ],
- "11": [
- 1,
- 1
- ],
- "12": [
- 2,
- 1
- ],
- "13": [
- 1,
- 1
- ],
- "14": [
- 3,
- 1
- ],
- "6": [
- 1,
- 1
- ],
- "7": [
- 1,
- 1
- ],
- "9": [
- 1,
- 1
- ],
- "n/a": [
- 176,
- 1
- ]
- },
- "rep_status": {
- "delayed_repeat": [
- 12,
- 1
- ],
- "first_show": [
- 28,
- 1
- ],
- "immediate_repeat": [
- 12,
- 1
- ],
- "n/a": [
- 148,
- 1
- ]
- },
- "value": {
- "0": [
- 52,
- 1
- ],
- "1": [
- 51,
- 1
- ],
- "13": [
- 10,
- 1
- ],
- "14": [
- 6,
- 1
- ],
- "15": [
- 4,
- 1
- ],
- "17": [
- 10,
- 1
- ],
- "18": [
- 5,
- 1
- ],
- "19": [
- 3,
- 1
- ],
- "256": [
- 20,
- 1
- ],
- "3": [
- 1,
- 1
- ],
- "4096": [
- 23,
- 1
- ],
- "4352": [
- 1,
- 1
- ],
- "5": [
- 8,
- 1
- ],
- "6": [
- 1,
- 1
- ],
- "7": [
- 5,
- 1
- ]
- }
- },
- "Value columns": {
- "stim_file": [
- 200,
- 1
- ],
- "trial": [
- 200,
- 1
- ]
- }
- },
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-2_events.tsv": {
- "Summary name": "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-2_events.tsv",
- "Total events": 200,
- "Total files": 1,
- "Categorical columns": {
- "event_type": {
- "right_press": [
- 29,
- 1
- ],
- "setup_right_sym": [
- 1,
- 1
- ],
- "show_circle": [
- 57,
- 1
- ],
- "show_cross": [
- 56,
- 1
- ],
- "show_face": [
- 56,
- 1
- ],
- "show_face_initial": [
- 1,
- 1
- ]
- },
- "face_type": {
- "famous_face": [
- 26,
- 1
- ],
- "n/a": [
- 143,
- 1
- ],
- "scrambled_face": [
- 17,
- 1
- ],
- "unfamiliar_face": [
- 14,
- 1
- ]
- },
- "rep_lag": {
- "1": [
- 14,
- 1
- ],
- "10": [
- 2,
- 1
- ],
- "11": [
- 2,
- 1
- ],
- "12": [
- 2,
- 1
- ],
- "13": [
- 1,
- 1
- ],
- "14": [
- 1,
- 1
- ],
- "15": [
- 1,
- 1
- ],
- "8": [
- 2,
- 1
- ],
- "9": [
- 2,
- 1
- ],
- "n/a": [
- 173,
- 1
- ]
- },
- "rep_status": {
- "delayed_repeat": [
- 13,
- 1
- ],
- "first_show": [
- 30,
- 1
- ],
- "immediate_repeat": [
- 14,
- 1
- ],
- "n/a": [
- 143,
- 1
- ]
- },
- "value": {
- "0": [
- 57,
- 1
- ],
- "1": [
- 56,
- 1
- ],
- "13": [
- 7,
- 1
- ],
- "14": [
- 2,
- 1
- ],
- "15": [
- 5,
- 1
- ],
- "17": [
- 9,
- 1
- ],
- "18": [
- 7,
- 1
- ],
- "19": [
- 1,
- 1
- ],
- "3": [
- 1,
- 1
- ],
- "4096": [
- 29,
- 1
- ],
- "5": [
- 14,
- 1
- ],
- "6": [
- 5,
- 1
- ],
- "7": [
- 7,
- 1
- ]
- }
- },
- "Value columns": {
- "stim_file": [
- 200,
- 1
- ],
- "trial": [
- 200,
- 1
- ]
- }
- },
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-3_events.tsv": {
- "Summary name": "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-3_events.tsv",
- "Total events": 200,
- "Total files": 1,
- "Categorical columns": {
- "event_type": {
- "right_press": [
- 29,
- 1
- ],
- "setup_right_sym": [
- 1,
- 1
- ],
- "show_circle": [
- 57,
- 1
- ],
- "show_cross": [
- 56,
- 1
- ],
- "show_face": [
- 56,
- 1
- ],
- "show_face_initial": [
- 1,
- 1
- ]
- },
- "face_type": {
- "famous_face": [
- 21,
- 1
- ],
- "n/a": [
- 143,
- 1
- ],
- "scrambled_face": [
- 16,
- 1
- ],
- "unfamiliar_face": [
- 20,
- 1
- ]
- },
- "rep_lag": {
- "1": [
- 14,
- 1
- ],
- "10": [
- 3,
- 1
- ],
- "11": [
- 4,
- 1
- ],
- "12": [
- 2,
- 1
- ],
- "13": [
- 1,
- 1
- ],
- "8": [
- 1,
- 1
- ],
- "9": [
- 2,
- 1
- ],
- "n/a": [
- 173,
- 1
- ]
- },
- "rep_status": {
- "delayed_repeat": [
- 13,
- 1
- ],
- "first_show": [
- 30,
- 1
- ],
- "immediate_repeat": [
- 14,
- 1
- ],
- "n/a": [
- 143,
- 1
- ]
- },
- "value": {
- "0": [
- 57,
- 1
- ],
- "1": [
- 56,
- 1
- ],
- "13": [
- 11,
- 1
- ],
- "14": [
- 4,
- 1
- ],
- "15": [
- 5,
- 1
- ],
- "17": [
- 8,
- 1
- ],
- "18": [
- 4,
- 1
- ],
- "19": [
- 4,
- 1
- ],
- "3": [
- 1,
- 1
- ],
- "4096": [
- 29,
- 1
- ],
- "5": [
- 11,
- 1
- ],
- "6": [
- 6,
- 1
- ],
- "7": [
- 4,
- 1
- ]
- }
- },
- "Value columns": {
- "stim_file": [
- 200,
- 1
- ],
- "trial": [
- 200,
- 1
- ]
- }
- },
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-1_events.tsv": {
- "Summary name": "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-1_events.tsv",
- "Total events": 200,
- "Total files": 1,
- "Categorical columns": {
- "event_type": {
- "left_press": [
- 22,
- 1
- ],
- "right_press": [
- 28,
- 1
- ],
- "setup_right_sym": [
- 1,
- 1
- ],
- "show_circle": [
- 50,
- 1
- ],
- "show_cross": [
- 49,
- 1
- ],
- "show_face": [
- 49,
- 1
- ],
- "show_face_initial": [
- 1,
- 1
- ]
- },
- "face_type": {
- "famous_face": [
- 20,
- 1
- ],
- "n/a": [
- 150,
- 1
- ],
- "scrambled_face": [
- 11,
- 1
- ],
- "unfamiliar_face": [
- 19,
- 1
- ]
- },
- "rep_lag": {
- "1": [
- 13,
- 1
- ],
- "10": [
- 2,
- 1
- ],
- "11": [
- 4,
- 1
- ],
- "13": [
- 2,
- 1
- ],
- "8": [
- 1,
- 1
- ],
- "9": [
- 2,
- 1
- ],
- "n/a": [
- 176,
- 1
- ]
- },
- "rep_status": {
- "delayed_repeat": [
- 11,
- 1
- ],
- "first_show": [
- 26,
- 1
- ],
- "immediate_repeat": [
- 13,
- 1
- ],
- "n/a": [
- 150,
- 1
- ]
- },
- "value": {
- "0": [
- 50,
- 1
- ],
- "1": [
- 49,
- 1
- ],
- "13": [
- 10,
- 1
- ],
- "14": [
- 6,
- 1
- ],
- "15": [
- 3,
- 1
- ],
- "17": [
- 6,
- 1
- ],
- "18": [
- 3,
- 1
- ],
- "19": [
- 2,
- 1
- ],
- "256": [
- 22,
- 1
- ],
- "3": [
- 1,
- 1
- ],
- "4096": [
- 28,
- 1
- ],
- "5": [
- 10,
- 1
- ],
- "6": [
- 4,
- 1
- ],
- "7": [
- 6,
- 1
- ]
- }
- },
- "Value columns": {
- "stim_file": [
- 200,
- 1
- ],
- "trial": [
- 200,
- 1
- ]
- }
- },
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-2_events.tsv": {
- "Summary name": "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-2_events.tsv",
- "Total events": 200,
- "Total files": 1,
- "Categorical columns": {
- "event_type": {
- "left_press": [
- 17,
- 1
- ],
- "right_press": [
- 33,
- 1
- ],
- "setup_right_sym": [
- 1,
- 1
- ],
- "show_circle": [
- 50,
- 1
- ],
- "show_cross": [
- 49,
- 1
- ],
- "show_face": [
- 49,
- 1
- ],
- "show_face_initial": [
- 1,
- 1
- ]
- },
- "face_type": {
- "famous_face": [
- 13,
- 1
- ],
- "n/a": [
- 150,
- 1
- ],
- "scrambled_face": [
- 21,
- 1
- ],
- "unfamiliar_face": [
- 16,
- 1
- ]
- },
- "rep_lag": {
- "1": [
- 12,
- 1
- ],
- "10": [
- 2,
- 1
- ],
- "12": [
- 2,
- 1
- ],
- "13": [
- 1,
- 1
- ],
- "14": [
- 1,
- 1
- ],
- "15": [
- 1,
- 1
- ],
- "7": [
- 1,
- 1
- ],
- "8": [
- 2,
- 1
- ],
- "9": [
- 1,
- 1
- ],
- "n/a": [
- 177,
- 1
- ]
- },
- "rep_status": {
- "delayed_repeat": [
- 11,
- 1
- ],
- "first_show": [
- 27,
- 1
- ],
- "immediate_repeat": [
- 12,
- 1
- ],
- "n/a": [
- 150,
- 1
- ]
- },
- "value": {
- "0": [
- 50,
- 1
- ],
- "1": [
- 49,
- 1
- ],
- "13": [
- 9,
- 1
- ],
- "14": [
- 3,
- 1
- ],
- "15": [
- 4,
- 1
- ],
- "17": [
- 11,
- 1
- ],
- "18": [
- 7,
- 1
- ],
- "19": [
- 3,
- 1
- ],
- "256": [
- 17,
- 1
- ],
- "3": [
- 1,
- 1
- ],
- "4096": [
- 33,
- 1
- ],
- "5": [
- 7,
- 1
- ],
- "6": [
- 2,
- 1
- ],
- "7": [
- 4,
- 1
- ]
- }
- },
- "Value columns": {
- "stim_file": [
- 200,
- 1
- ],
- "trial": [
- 200,
- 1
- ]
- }
- },
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv": {
- "Summary name": "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv",
- "Total events": 200,
- "Total files": 1,
- "Categorical columns": {
- "event_type": {
- "left_press": [
- 24,
- 1
- ],
- "right_press": [
- 26,
- 1
- ],
- "setup_right_sym": [
- 1,
- 1
- ],
- "show_circle": [
- 50,
- 1
- ],
- "show_cross": [
- 49,
- 1
- ],
- "show_face": [
- 49,
- 1
- ],
- "show_face_initial": [
- 1,
- 1
- ]
- },
- "face_type": {
- "famous_face": [
- 14,
- 1
- ],
- "n/a": [
- 150,
- 1
- ],
- "scrambled_face": [
- 20,
- 1
- ],
- "unfamiliar_face": [
- 16,
- 1
- ]
- },
- "rep_lag": {
- "1": [
- 12,
- 1
- ],
- "10": [
- 4,
- 1
- ],
- "11": [
- 2,
- 1
- ],
- "12": [
- 1,
- 1
- ],
- "13": [
- 1,
- 1
- ],
- "14": [
- 1,
- 1
- ],
- "9": [
- 2,
- 1
- ],
- "n/a": [
- 177,
- 1
- ]
- },
- "rep_status": {
- "delayed_repeat": [
- 11,
- 1
- ],
- "first_show": [
- 27,
- 1
- ],
- "immediate_repeat": [
- 12,
- 1
- ],
- "n/a": [
- 150,
- 1
- ]
- },
- "value": {
- "0": [
- 50,
- 1
- ],
- "1": [
- 49,
- 1
- ],
- "13": [
- 9,
- 1
- ],
- "14": [
- 3,
- 1
- ],
- "15": [
- 4,
- 1
- ],
- "17": [
- 10,
- 1
- ],
- "18": [
- 6,
- 1
- ],
- "19": [
- 4,
- 1
- ],
- "256": [
- 24,
- 1
- ],
- "3": [
- 1,
- 1
- ],
- "4096": [
- 26,
- 1
- ],
- "5": [
- 8,
- 1
- ],
- "6": [
- 3,
- 1
- ],
- "7": [
- 3,
- 1
- ]
- }
- },
- "Value columns": {
- "stim_file": [
- 200,
- 1
- ],
- "trial": [
- 200,
- 1
- ]
- }
- }
- }
- }
-}
diff --git a/docs/source/_static/data/summaries/FacePerception_column_value_summary.txt b/docs/source/_static/data/summaries/FacePerception_column_value_summary.txt
deleted file mode 100644
index d5db3dc..0000000
--- a/docs/source/_static/data/summaries/FacePerception_column_value_summary.txt
+++ /dev/null
@@ -1,117 +0,0 @@
-Context name: column_values_summary
-Context type: column_values
-Context filename: column_values_summary
-
-Dataset: Total events=1200 Total files=6
- Categorical column values[Events, Files]:
- event_type:
- double_press[1, 1] left_press[83, 4] right_press[168, 6] setup_right_sym[6, 6] show_circle[316, 6] show_cross[310, 6] show_face[310, 6] show_face_initial[6, 6]
- face_type:
- famous_face[108, 6] n/a[884, 6] scrambled_face[103, 6] unfamiliar_face[105, 6]
- rep_lag:
- 1[77, 6] 10[15, 6] 11[13, 5] 12[9, 5] 13[7, 6] 14[6, 4] 15[2, 2] 6[1, 1] 7[2, 2] 8[6, 4] 9[10, 6] n/a[1052, 6]
- rep_status:
- delayed_repeat[71, 6] first_show[168, 6] immediate_repeat[77, 6] n/a[884, 6]
- value:
- 0[316, 6] 1[310, 6] 13[56, 6] 14[24, 6] 15[25, 6] 17[54, 6] 18[32, 6] 19[17, 6] 256[83, 4] 3[6, 6] 4096[168, 6] 4352[1, 1] 5[58, 6] 6[21, 6] 7[29, 6]
- Value columns[Events, Files]:
- stim_file[1200, 6]
- trial[1200, 6]
-
-Individual files:
-
- H:\HEDExamples\hed-examples\datasets\eeg_ds003654s_hed\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv:
- Categorical column values[Events, Files]:
- event_type:
- double_press[1, 1] left_press[20, 1] right_press[23, 1] setup_right_sym[1, 1] show_circle[52, 1] show_cross[51, 1] show_face[51, 1] show_face_initial[1, 1]
- face_type:
- famous_face[14, 1] n/a[148, 1] scrambled_face[18, 1] unfamiliar_face[20, 1]
- rep_lag:
- 1[12, 1] 10[2, 1] 11[1, 1] 12[2, 1] 13[1, 1] 14[3, 1] 6[1, 1] 7[1, 1] 9[1, 1] n/a[176, 1]
- rep_status:
- delayed_repeat[12, 1] first_show[28, 1] immediate_repeat[12, 1] n/a[148, 1]
- value:
- 0[52, 1] 1[51, 1] 13[10, 1] 14[6, 1] 15[4, 1] 17[10, 1] 18[5, 1] 19[3, 1] 256[20, 1] 3[1, 1] 4096[23, 1] 4352[1, 1] 5[8, 1] 6[1, 1] 7[5, 1]
- Value columns[Events, Files]:
- stim_file[200, 1]
- trial[200, 1]
-
- H:\HEDExamples\hed-examples\datasets\eeg_ds003654s_hed\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv:
- Categorical column values[Events, Files]:
- event_type:
- right_press[29, 1] setup_right_sym[1, 1] show_circle[57, 1] show_cross[56, 1] show_face[56, 1] show_face_initial[1, 1]
- face_type:
- famous_face[26, 1] n/a[143, 1] scrambled_face[17, 1] unfamiliar_face[14, 1]
- rep_lag:
- 1[14, 1] 10[2, 1] 11[2, 1] 12[2, 1] 13[1, 1] 14[1, 1] 15[1, 1] 8[2, 1] 9[2, 1] n/a[173, 1]
- rep_status:
- delayed_repeat[13, 1] first_show[30, 1] immediate_repeat[14, 1] n/a[143, 1]
- value:
- 0[57, 1] 1[56, 1] 13[7, 1] 14[2, 1] 15[5, 1] 17[9, 1] 18[7, 1] 19[1, 1] 3[1, 1] 4096[29, 1] 5[14, 1] 6[5, 1] 7[7, 1]
- Value columns[Events, Files]:
- stim_file[200, 1]
- trial[200, 1]
-
- H:\HEDExamples\hed-examples\datasets\eeg_ds003654s_hed\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv:
- Categorical column values[Events, Files]:
- event_type:
- right_press[29, 1] setup_right_sym[1, 1] show_circle[57, 1] show_cross[56, 1] show_face[56, 1] show_face_initial[1, 1]
- face_type:
- famous_face[21, 1] n/a[143, 1] scrambled_face[16, 1] unfamiliar_face[20, 1]
- rep_lag:
- 1[14, 1] 10[3, 1] 11[4, 1] 12[2, 1] 13[1, 1] 8[1, 1] 9[2, 1] n/a[173, 1]
- rep_status:
- delayed_repeat[13, 1] first_show[30, 1] immediate_repeat[14, 1] n/a[143, 1]
- value:
- 0[57, 1] 1[56, 1] 13[11, 1] 14[4, 1] 15[5, 1] 17[8, 1] 18[4, 1] 19[4, 1] 3[1, 1] 4096[29, 1] 5[11, 1] 6[6, 1] 7[4, 1]
- Value columns[Events, Files]:
- stim_file[200, 1]
- trial[200, 1]
-
- H:\HEDExamples\hed-examples\datasets\eeg_ds003654s_hed\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv:
- Categorical column values[Events, Files]:
- event_type:
- left_press[22, 1] right_press[28, 1] setup_right_sym[1, 1] show_circle[50, 1] show_cross[49, 1] show_face[49, 1] show_face_initial[1, 1]
- face_type:
- famous_face[20, 1] n/a[150, 1] scrambled_face[11, 1] unfamiliar_face[19, 1]
- rep_lag:
- 1[13, 1] 10[2, 1] 11[4, 1] 13[2, 1] 8[1, 1] 9[2, 1] n/a[176, 1]
- rep_status:
- delayed_repeat[11, 1] first_show[26, 1] immediate_repeat[13, 1] n/a[150, 1]
- value:
- 0[50, 1] 1[49, 1] 13[10, 1] 14[6, 1] 15[3, 1] 17[6, 1] 18[3, 1] 19[2, 1] 256[22, 1] 3[1, 1] 4096[28, 1] 5[10, 1] 6[4, 1] 7[6, 1]
- Value columns[Events, Files]:
- stim_file[200, 1]
- trial[200, 1]
-
- H:\HEDExamples\hed-examples\datasets\eeg_ds003654s_hed\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv:
- Categorical column values[Events, Files]:
- event_type:
- left_press[17, 1] right_press[33, 1] setup_right_sym[1, 1] show_circle[50, 1] show_cross[49, 1] show_face[49, 1] show_face_initial[1, 1]
- face_type:
- famous_face[13, 1] n/a[150, 1] scrambled_face[21, 1] unfamiliar_face[16, 1]
- rep_lag:
- 1[12, 1] 10[2, 1] 12[2, 1] 13[1, 1] 14[1, 1] 15[1, 1] 7[1, 1] 8[2, 1] 9[1, 1] n/a[177, 1]
- rep_status:
- delayed_repeat[11, 1] first_show[27, 1] immediate_repeat[12, 1] n/a[150, 1]
- value:
- 0[50, 1] 1[49, 1] 13[9, 1] 14[3, 1] 15[4, 1] 17[11, 1] 18[7, 1] 19[3, 1] 256[17, 1] 3[1, 1] 4096[33, 1] 5[7, 1] 6[2, 1] 7[4, 1]
- Value columns[Events, Files]:
- stim_file[200, 1]
- trial[200, 1]
-
- H:\HEDExamples\hed-examples\datasets\eeg_ds003654s_hed\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv:
- Categorical column values[Events, Files]:
- event_type:
- left_press[24, 1] right_press[26, 1] setup_right_sym[1, 1] show_circle[50, 1] show_cross[49, 1] show_face[49, 1] show_face_initial[1, 1]
- face_type:
- famous_face[14, 1] n/a[150, 1] scrambled_face[20, 1] unfamiliar_face[16, 1]
- rep_lag:
- 1[12, 1] 10[4, 1] 11[2, 1] 12[1, 1] 13[1, 1] 14[1, 1] 9[2, 1] n/a[177, 1]
- rep_status:
- delayed_repeat[11, 1] first_show[27, 1] immediate_repeat[12, 1] n/a[150, 1]
- value:
- 0[50, 1] 1[49, 1] 13[9, 1] 14[3, 1] 15[4, 1] 17[10, 1] 18[6, 1] 19[4, 1] 256[24, 1] 3[1, 1] 4096[26, 1] 5[8, 1] 6[3, 1] 7[3, 1]
- Value columns[Events, Files]:
- stim_file[200, 1]
- trial[200, 1]
\ No newline at end of file
diff --git a/docs/source/_static/data/summaries/FacePerception_hed_tag_summary.json b/docs/source/_static/data/summaries/FacePerception_hed_tag_summary.json
deleted file mode 100644
index 5c308df..0000000
--- a/docs/source/_static/data/summaries/FacePerception_hed_tag_summary.json
+++ /dev/null
@@ -1,945 +0,0 @@
-{
- "context_name": "summarize_hed_tags",
- "context_type": "hed_tag_summary",
- "context_filename": "summarize_hed_tags",
- "summary": {
- "Dataset": {
- "Main tags": {
- "Sensory events": [
- {
- "tag": "Sensory-event",
- "events": 942,
- "files": [
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-1_events.tsv",
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-2_events.tsv",
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-3_events.tsv",
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-1_events.tsv",
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-2_events.tsv",
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv"
- ]
- },
- {
- "tag": "Cue",
- "events": 626,
- "files": [
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-1_events.tsv",
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-2_events.tsv",
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-3_events.tsv",
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-1_events.tsv",
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-2_events.tsv",
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv"
- ]
- },
- {
- "tag": "Experimental-stimulus",
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deleted file mode 100644
index 8411c8f..0000000
--- a/docs/source/_static/data/summaries/FacePerception_hed_tag_summary.txt
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deleted file mode 100644
index 8c93c1f..0000000
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diff --git a/docs/source/_static/data/summaries/FacePerception_hed_type_summary.txt b/docs/source/_static/data/summaries/FacePerception_hed_type_summary.txt
deleted file mode 100644
index b809c92..0000000
--- a/docs/source/_static/data/summaries/FacePerception_hed_type_summary.txt
+++ /dev/null
@@ -1,185 +0,0 @@
-Context name: wh_condition_variables
-Context type: hed_type_summary
-Context filename: wh_condition_variables
-
-Summary details:
-
-Dataset: Type=condition-variable Total events=1200 Total files=6
- key-assignment:
- Files:['H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-1_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-2_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-3_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-1_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-2_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv']
- Events: 1200 out of 1200 total events in 6 files
- Levels[Events,Files]: [Tags]
- right-sym-cond [1200,6]: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
- tDescription: Right index finger key press indicates a face with above average symmetry.
- face-type:
- Files:['H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-1_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-2_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-3_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-1_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-2_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv']
- Events: 316 out of 1200 total events in 6 files
- Levels[Events,Files]: [Tags]
- unfamiliar-face-cond [105,6]: ['Image', 'Face', 'Unfamiliar']
- tDescription: A face that should not be recognized by the participants.
- famous-face-cond [108,6]: ['Image', 'Face', 'Famous']
- tDescription: A face that should be recognized by the participants
- scrambled-face-cond [103,6]: ['Image', 'Face', 'Disordered']
- tDescription: A scrambled face image generated by taking face 2D FFT.
- repetition-type:
- Files:['H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-1_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-2_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-002\\eeg\\sub-002_task-FacePerception_run-3_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-1_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-2_events.tsv', 'H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv']
- Events: 316 out of 1200 total events in 6 files
- Levels[Events,Files]: [Tags]
- first-show-cond [168,6]: ['Item-count', 'Face', 'Item-interval']
- tDescription: Factor level indicating the first display of this face.
- immediate-repeat-cond [77,6]: ['Item-count', 'Face', 'Item-interval']
- tDescription: Factor level indicating this face was the same as previous one.
- delayed-repeat-cond [71,6]: ['Item-count', 'Face', 'Item-interval', 'Greater-than-or-equal-to', 'Item-interval']
- tDescription: Factor level indicating face was seen 5 to 15 trials ago.
-
-Individual files:
-
- H:\HEDExamples\hed-examples\datasets\eeg_ds003654s_hed\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv: Type=condition-variable Total events=200
- key-assignment:
- Levels:1 Events:200
- Levels[Events,Files]: [Tags]
- right-sym-cond [200,1]: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
- tDescription: Right index finger key press indicates a face with above average symmetry.
- face-type:
- Levels:3 Events:52
- Levels[Events,Files]: [Tags]
- unfamiliar-face-cond [20,1]: ['Image', 'Face', 'Unfamiliar']
- tDescription: A face that should not be recognized by the participants.
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- tDescription: A face that should be recognized by the participants
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- tDescription: A scrambled face image generated by taking face 2D FFT.
- repetition-type:
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- tDescription: Factor level indicating this face was the same as previous one.
- delayed-repeat-cond [12,1]: ['Item-count', 'Face', 'Item-interval', 'Greater-than-or-equal-to', 'Item-interval']
- tDescription: Factor level indicating face was seen 5 to 15 trials ago.
-
- H:\HEDExamples\hed-examples\datasets\eeg_ds003654s_hed\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv: Type=condition-variable Total events=200
- key-assignment:
- Levels:1 Events:200
- Levels[Events,Files]: [Tags]
- right-sym-cond [200,1]: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
- tDescription: Right index finger key press indicates a face with above average symmetry.
- face-type:
- Levels:3 Events:57
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- tDescription: A scrambled face image generated by taking face 2D FFT.
- famous-face-cond [26,1]: ['Image', 'Face', 'Famous']
- tDescription: A face that should be recognized by the participants
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- tDescription: Factor level indicating this face was the same as previous one.
- delayed-repeat-cond [13,1]: ['Item-count', 'Face', 'Item-interval', 'Greater-than-or-equal-to', 'Item-interval']
- tDescription: Factor level indicating face was seen 5 to 15 trials ago.
-
- H:\HEDExamples\hed-examples\datasets\eeg_ds003654s_hed\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv: Type=condition-variable Total events=200
- key-assignment:
- Levels:1 Events:200
- Levels[Events,Files]: [Tags]
- right-sym-cond [200,1]: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
- tDescription: Right index finger key press indicates a face with above average symmetry.
- face-type:
- Levels:3 Events:57
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- tDescription: A face that should not be recognized by the participants.
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- tDescription: A face that should be recognized by the participants
- scrambled-face-cond [16,1]: ['Image', 'Face', 'Disordered']
- tDescription: A scrambled face image generated by taking face 2D FFT.
- repetition-type:
- Levels:3 Events:57
- Levels[Events,Files]: [Tags]
- first-show-cond [30,1]: ['Item-count', 'Face', 'Item-interval']
- tDescription: Factor level indicating the first display of this face.
- immediate-repeat-cond [14,1]: ['Item-count', 'Face', 'Item-interval']
- tDescription: Factor level indicating this face was the same as previous one.
- delayed-repeat-cond [13,1]: ['Item-count', 'Face', 'Item-interval', 'Greater-than-or-equal-to', 'Item-interval']
- tDescription: Factor level indicating face was seen 5 to 15 trials ago.
-
- H:\HEDExamples\hed-examples\datasets\eeg_ds003654s_hed\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv: Type=condition-variable Total events=200
- key-assignment:
- Levels:1 Events:200
- Levels[Events,Files]: [Tags]
- right-sym-cond [200,1]: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
- tDescription: Right index finger key press indicates a face with above average symmetry.
- face-type:
- Levels:3 Events:50
- Levels[Events,Files]: [Tags]
- famous-face-cond [20,1]: ['Image', 'Face', 'Famous']
- tDescription: A face that should be recognized by the participants
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- scrambled-face-cond [11,1]: ['Image', 'Face', 'Disordered']
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- tDescription: Factor level indicating face was seen 5 to 15 trials ago.
-
- H:\HEDExamples\hed-examples\datasets\eeg_ds003654s_hed\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv: Type=condition-variable Total events=200
- key-assignment:
- Levels:1 Events:200
- Levels[Events,Files]: [Tags]
- right-sym-cond [200,1]: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
- tDescription: Right index finger key press indicates a face with above average symmetry.
- face-type:
- Levels:3 Events:50
- Levels[Events,Files]: [Tags]
- famous-face-cond [13,1]: ['Image', 'Face', 'Famous']
- tDescription: A face that should be recognized by the participants
- scrambled-face-cond [21,1]: ['Image', 'Face', 'Disordered']
- tDescription: A scrambled face image generated by taking face 2D FFT.
- unfamiliar-face-cond [16,1]: ['Image', 'Face', 'Unfamiliar']
- tDescription: A face that should not be recognized by the participants.
- repetition-type:
- Levels:3 Events:50
- Levels[Events,Files]: [Tags]
- first-show-cond [27,1]: ['Item-count', 'Face', 'Item-interval']
- tDescription: Factor level indicating the first display of this face.
- immediate-repeat-cond [12,1]: ['Item-count', 'Face', 'Item-interval']
- tDescription: Factor level indicating this face was the same as previous one.
- delayed-repeat-cond [11,1]: ['Item-count', 'Face', 'Item-interval', 'Greater-than-or-equal-to', 'Item-interval']
- tDescription: Factor level indicating face was seen 5 to 15 trials ago.
-
- H:\HEDExamples\hed-examples\datasets\eeg_ds003654s_hed\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv: Type=condition-variable Total events=200
- key-assignment:
- Levels:1 Events:200
- Levels[Events,Files]: [Tags]
- right-sym-cond [200,1]: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
- tDescription: Right index finger key press indicates a face with above average symmetry.
- face-type:
- Levels:3 Events:50
- Levels[Events,Files]: [Tags]
- scrambled-face-cond [20,1]: ['Image', 'Face', 'Disordered']
- tDescription: A scrambled face image generated by taking face 2D FFT.
- unfamiliar-face-cond [16,1]: ['Image', 'Face', 'Unfamiliar']
- tDescription: A face that should not be recognized by the participants.
- famous-face-cond [14,1]: ['Image', 'Face', 'Famous']
- tDescription: A face that should be recognized by the participants
- repetition-type:
- Levels:3 Events:50
- Levels[Events,Files]: [Tags]
- first-show-cond [27,1]: ['Item-count', 'Face', 'Item-interval']
- tDescription: Factor level indicating the first display of this face.
- immediate-repeat-cond [12,1]: ['Item-count', 'Face', 'Item-interval']
- tDescription: Factor level indicating this face was the same as previous one.
- delayed-repeat-cond [11,1]: ['Item-count', 'Face', 'Item-interval', 'Greater-than-or-equal-to', 'Item-interval']
- tDescription: Factor level indicating face was seen 5 to 15 trials ago.
diff --git a/hedcode/jupyter_notebooks/bids/bids_generate_sidecar.ipynb b/hedcode/jupyter_notebooks/bids/bids_generate_sidecar.ipynb
index c292705..6005fde 100644
--- a/hedcode/jupyter_notebooks/bids/bids_generate_sidecar.ipynb
+++ b/hedcode/jupyter_notebooks/bids/bids_generate_sidecar.ipynb
@@ -55,9 +55,9 @@
"It then outputs the result as a JSON string representing a JSON sidecar.\n",
"\n",
"The example below uses a\n",
- "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003654s_hed)\n",
+ "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)\n",
"of the Wakeman-Hanson face-processing dataset available on openNeuro as\n",
- "[ds003654](https://openneuro.org/datasets/ds003645/versions/2.0.0).\n"
+ "[ds003645](https://openneuro.org/datasets/ds003645/versions/2.0.0).\n"
]
},
{
@@ -68,10 +68,10 @@
"from hed.tools import BidsTabularDictionary, TabularSummary, get_file_list\n",
"\n",
"# Variables to set for the specific dataset\n",
- "bids_root_path = os.path.realpath('../../../datasets/eeg_ds003654s_hed')\n",
+ "bids_root_path = os.path.realpath('../../../datasets/eeg_ds003645s_hed')\n",
"exclude_dirs = ['stimuli']\n",
"entities = ('sub', 'run')\n",
- "name = 'eeg_ds003654s_hed'\n",
+ "name = 'eeg_ds003645s_hed'\n",
"skip_columns = [\"onset\", \"duration\", \"sample\", \"trial\", \"response_time\"]\n",
"value_columns = [\"rep_lag\", \"stim_file\", \"value\"]\n",
"\n",
diff --git a/hedcode/jupyter_notebooks/bids/bids_generate_sidecar_temp.ipynb b/hedcode/jupyter_notebooks/bids/bids_generate_sidecar_temp.ipynb
index 5de7a44..b0b664d 100644
--- a/hedcode/jupyter_notebooks/bids/bids_generate_sidecar_temp.ipynb
+++ b/hedcode/jupyter_notebooks/bids/bids_generate_sidecar_temp.ipynb
@@ -55,9 +55,9 @@
"It then outputs the result as a JSON string representing a JSON sidecar.\n",
"\n",
"The example below uses a\n",
- "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003654s_hed)\n",
+ "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)\n",
"of the Wakeman-Hanson face-processing dataset available on openNeuro as\n",
- "[ds003654](https://openneuro.org/datasets/ds003645/versions/2.0.0).\n"
+ "[ds003645](https://openneuro.org/datasets/ds003645/versions/2.0.0).\n"
]
},
{
diff --git a/hedcode/jupyter_notebooks/bids/bids_merge_sidecar.ipynb b/hedcode/jupyter_notebooks/bids/bids_merge_sidecar.ipynb
index 920db5c..d57545f 100644
--- a/hedcode/jupyter_notebooks/bids/bids_merge_sidecar.ipynb
+++ b/hedcode/jupyter_notebooks/bids/bids_merge_sidecar.ipynb
@@ -50,9 +50,9 @@
"merged with another JSON sidecar.\n",
"\n",
"The example below uses a\n",
- "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003654s_hed)\n",
+ "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)\n",
"of the Wakeman-Hanson face-processing dataset available on openNeuro as\n",
- "[ds003654](https://openneuro.org/datasets/ds003645/versions/2.0.0)."
+ "[ds003645](https://openneuro.org/datasets/ds003645/versions/2.0.0)."
]
},
{
@@ -195,7 +195,7 @@
"from hed.tools import df_to_hed, hed_to_df, merge_hed_dict\n",
"\n",
"# Create a test spreadsheet for the merge\n",
- "bids_sidecar_path = os.path.realpath('../../../datasets/eeg_ds003654s_hed/task-FacePerception_events.json')\n",
+ "bids_sidecar_path = os.path.realpath('../../../datasets/eeg_ds003645s_hed/task-FacePerception_events.json')\n",
"with open(bids_sidecar_path) as fp:\n",
" sidecar_json = json.load(fp)\n",
"test_spreadsheet = hed_to_df(sidecar_json)\n",
diff --git a/hedcode/jupyter_notebooks/bids/bids_sidecar_to_spreadsheet.ipynb b/hedcode/jupyter_notebooks/bids/bids_sidecar_to_spreadsheet.ipynb
index b084423..ab4e23f 100644
--- a/hedcode/jupyter_notebooks/bids/bids_sidecar_to_spreadsheet.ipynb
+++ b/hedcode/jupyter_notebooks/bids/bids_sidecar_to_spreadsheet.ipynb
@@ -30,9 +30,9 @@
"If you don't wish to save the spreadsheet, assign `spreadsheet_filename` to be None.\n",
"\n",
"The example below uses a\n",
- "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003654s_hed)\n",
+ "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)\n",
"of the Wakeman-Hanson face-processing dataset available on openNeuro as\n",
- "[ds003654](https://openneuro.org/datasets/ds003645/versions/2.0.0)."
+ "[ds003645](https://openneuro.org/datasets/ds003645/versions/2.0.0)."
]
},
{
@@ -52,7 +52,7 @@
"import json\n",
"from hed.tools import hed_to_df\n",
"\n",
- "json_path = os.path.realpath('../../../datasets/eeg_ds003654s_hed/task-FacePerception_events.json')\n",
+ "json_path = os.path.realpath('../../../datasets/eeg_ds003645s_hed/task-FacePerception_events.json')\n",
"spreadsheet_filename = os.path.realpath('../../data/example_spreadsheet.tsv')\n",
"with open(json_path) as fp:\n",
" example_sidecar = json.load(fp)\n",
diff --git a/hedcode/jupyter_notebooks/bids/bids_summarize_events.ipynb b/hedcode/jupyter_notebooks/bids/bids_summarize_events.ipynb
index 8e3eeb4..0dca27f 100644
--- a/hedcode/jupyter_notebooks/bids/bids_summarize_events.ipynb
+++ b/hedcode/jupyter_notebooks/bids/bids_summarize_events.ipynb
@@ -52,9 +52,9 @@
"The notebook creates a `TabularSummary` object to handle the summarization.\n",
"\n",
"The example below uses a\n",
- "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003654s_hed)\n",
+ "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)\n",
"of the Wakeman-Hanson face-processing dataset available on openNeuro as\n",
- "[ds003654](https://openneuro.org/datasets/ds003645/versions/2.0.0)\n",
+ "[ds003645](https://openneuro.org/datasets/ds003645/versions/2.0.0)\n",
"which is distributed as part of this dataset."
]
},
@@ -135,8 +135,8 @@
"from hed.tools import TabularSummary, BidsTabularDictionary, get_file_list\n",
"\n",
"# Variables to set for the specific dataset\n",
- "bids_root_path = os.path.realpath('../../../datasets/eeg_ds003654s_hed')\n",
- "name = 'eeg_ds003654s_hed'\n",
+ "bids_root_path = os.path.realpath('../../../datasets/eeg_ds003645s_hed')\n",
+ "name = 'eeg_ds003645s_hed'\n",
"exclude_dirs = ['stimuli']\n",
"entities = ('sub', 'run')\n",
"skip_columns = [\"onset\", \"duration\", \"sample\", \"stim_file\", \"trial\", \"response_time\"]\n",
diff --git a/hedcode/jupyter_notebooks/bids/bids_summarize_events_separated_by_task.ipynb b/hedcode/jupyter_notebooks/bids/bids_summarize_events_separated_by_task.ipynb
index 3001db2..ab94a9b 100644
--- a/hedcode/jupyter_notebooks/bids/bids_summarize_events_separated_by_task.ipynb
+++ b/hedcode/jupyter_notebooks/bids/bids_summarize_events_separated_by_task.ipynb
@@ -52,9 +52,9 @@
"The notebook creates a `TabularSummary` object to handle the summarization.\n",
"\n",
"The example below uses a\n",
- "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003654s_hed)\n",
+ "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)\n",
"of the Wakeman-Hanson face-processing dataset available on openNeuro as\n",
- "[ds003654](https://openneuro.org/datasets/ds003645/versions/2.0.0)\n",
+ "[ds003645](https://openneuro.org/datasets/ds003645/versions/2.0.0)\n",
"which is distributed as part of this dataset."
]
},
diff --git a/hedcode/jupyter_notebooks/bids/bids_validate_dataset_with_libraries.ipynb b/hedcode/jupyter_notebooks/bids/bids_validate_dataset_with_libraries.ipynb
index 80a1449..b394e3b 100644
--- a/hedcode/jupyter_notebooks/bids/bids_validate_dataset_with_libraries.ipynb
+++ b/hedcode/jupyter_notebooks/bids/bids_validate_dataset_with_libraries.ipynb
@@ -33,9 +33,9 @@
"**Note:** This validation pertains to event files and HED annotation only. It does not do a full BIDS validation.\n",
"\n",
"The example below uses a\n",
- "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003654s_hed)\n",
+ "[small version](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)\n",
"of the Wakeman-Hanson face-processing dataset available on openNeuro as\n",
- "[ds003654](https://openneuro.org/datasets/ds003645/versions/2.0.0).\n",
+ "[ds003645](https://openneuro.org/datasets/ds003645/versions/2.0.0).\n",
"\n",
"This dataset has no validation errors, but since we have set `check_for_warnings` to `True`,\n",
"validation returns warnings that the `sample` column does not have any metadata.\n",
@@ -53,7 +53,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Handling a BIDS data set that uses library schema: ../../../datasets/eeg_ds003654s_hed_library\n",
+ "Handling a BIDS data set that uses library schema: ../../../datasets/eeg_ds003645s_hed_library\n",
"No HED validation errors\n",
"\n",
"Now validating with the prerelease schema.\n",
@@ -68,7 +68,7 @@
"\n",
"## Set the dataset location and the check_for_warnings flag\n",
"check_for_warnings = False\n",
- "bids_path = '../../../datasets/eeg_ds003654s_hed_library'\n",
+ "bids_path = '../../../datasets/eeg_ds003645s_hed_library'\n",
"\n",
"## Validate the dataset using the information from the dataset_description\n",
"print(f\"Handling a BIDS data set that uses library schema: {bids_path}\")\n",
diff --git a/hedcode/jupyter_notebooks/bids/bids_validate_datasets.ipynb b/hedcode/jupyter_notebooks/bids/bids_validate_datasets.ipynb
index a656acc..13018a0 100644
--- a/hedcode/jupyter_notebooks/bids/bids_validate_datasets.ipynb
+++ b/hedcode/jupyter_notebooks/bids/bids_validate_datasets.ipynb
@@ -49,15 +49,15 @@
"text": [
"Using HEDTOOLS version: {'date': '2022-12-20T13:39:39-0600', 'dirty': True, 'error': None, 'full-revisionid': 'b67ae4dbb5914b58662138399caad99f20d2053e', 'version': '0.1.0+166.gb67ae4d.dirty'}\n",
"HED Examples version: {'version': '0.1.0+168.g42df80c.dirty', 'full-revisionid': '42df80c616f4573d29855efd17b93bbfaa3ce1a4', 'dirty': True, 'error': None, 'date': '2022-12-20T13:07:27-0600'}\n",
- "Validating eeg_ds003654s_hed\n",
+ "Validating eeg_ds003645s_hed\n",
"No HED validation errors\n",
- "Validating eeg_ds003654s_hed_column\n",
+ "Validating eeg_ds003645s_hed_column\n",
"No HED validation errors\n",
- "Validating eeg_ds003654s_hed_inheritance\n",
+ "Validating eeg_ds003645s_hed_inheritance\n",
"No HED validation errors\n",
- "Validating eeg_ds003654s_hed_longform\n",
+ "Validating eeg_ds003645s_hed_longform\n",
"No HED validation errors\n",
- "Validating eeg_ds003654s_hed_library\n",
+ "Validating eeg_ds003645s_hed_library\n",
"No HED validation errors\n",
"Validating eeg_ds002893s_hed_attention_shift\n",
"No HED validation errors\n",
@@ -79,9 +79,9 @@
"\n",
"## Set the dataset locations and the check_for_warnings flag\n",
"check_for_warnings = False\n",
- "bids_datasets = ['eeg_ds003654s_hed', 'eeg_ds003654s_hed_column',\n",
- " 'eeg_ds003654s_hed_inheritance', 'eeg_ds003654s_hed_longform',\n",
- " 'eeg_ds003654s_hed_library', 'eeg_ds002893s_hed_attention_shift',\n",
+ "bids_datasets = ['eeg_ds003645s_hed', 'eeg_ds003645s_hed_column',\n",
+ " 'eeg_ds003645s_hed_inheritance', 'eeg_ds003645s_hed_longform',\n",
+ " 'eeg_ds003645s_hed_library', 'eeg_ds002893s_hed_attention_shift',\n",
" 'eeg_ds004117s_hed_sternberg'\n",
" ]\n",
"\n",
diff --git a/hedcode/jupyter_notebooks/remodeling/run_remodel.ipynb b/hedcode/jupyter_notebooks/remodeling/run_remodel.ipynb
index 5949414..c1a29d8 100644
--- a/hedcode/jupyter_notebooks/remodeling/run_remodel.ipynb
+++ b/hedcode/jupyter_notebooks/remodeling/run_remodel.ipynb
@@ -36,7 +36,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003654s_hed_remodel\\derivatives\\remodel\\models\\summarize_columns_rmdl.json\n"
+ "H:\\HEDExamples\\hed-examples\\datasets\\eeg_ds003645s_hed_remodel\\derivatives\\remodel\\models\\summarize_columns_rmdl.json\n"
]
}
],
@@ -44,7 +44,7 @@
"import os\n",
"import hed.tools.remodeling.cli.run_remodel as cli_remodel\n",
"\n",
- "data_root = os.path.realpath('../../../datasets/eeg_ds003654s_hed_remodel')\n",
+ "data_root = os.path.realpath('../../../datasets/eeg_ds003645s_hed_remodel')\n",
"model_path = os.path.join(data_root, 'derivatives', 'remodel', 'models', 'summarize_columns_rmdl.json')\n",
"print(model_path)\n",
"arg_list = [data_root, model_path, '-n', 'default_back', '-x', 'derivatives', 'stimuli',\n",
diff --git a/hedcode/jupyter_notebooks/remodeling/run_remodel_backup.ipynb b/hedcode/jupyter_notebooks/remodeling/run_remodel_backup.ipynb
index 83da48d..1521a5d 100644
--- a/hedcode/jupyter_notebooks/remodeling/run_remodel_backup.ipynb
+++ b/hedcode/jupyter_notebooks/remodeling/run_remodel_backup.ipynb
@@ -34,7 +34,7 @@
"from hed.tools.remodeling.backup_manager import BackupManager\n",
"import hed.tools.remodeling.cli.run_remodel_backup as cli_backup\n",
"\n",
- "data_root = os.path.realpath('../../../datasets/eeg_ds003654s_hed_remodel')\n",
+ "data_root = os.path.realpath('../../../datasets/eeg_ds003645s_hed_remodel')\n",
"print(data_root)\n",
"arg_list = [data_root, '-n', BackupManager.DEFAULT_BACKUP_NAME, '-x', 'derivatives', 'stimuli',\n",
" '-f', 'events', '-e', '.tsv']\n",
diff --git a/hedcode/jupyter_notebooks/remodeling/run_remodel_restore.ipynb b/hedcode/jupyter_notebooks/remodeling/run_remodel_restore.ipynb
index 8ac9a63..1f251dd 100644
--- a/hedcode/jupyter_notebooks/remodeling/run_remodel_restore.ipynb
+++ b/hedcode/jupyter_notebooks/remodeling/run_remodel_restore.ipynb
@@ -29,7 +29,7 @@
"import os\n",
"import hed.tools.remodeling.cli.run_remodel_restore as cli_restore\n",
"\n",
- "data_dir = os.path.realpath('../../../datasets/eeg_ds003654s_hed_remodel')\n",
+ "data_dir = os.path.realpath('../../../datasets/eeg_ds003645s_hed_remodel')\n",
"arg_list = [data_dir, '-n', 'default_back']\n",
"cli_restore.main(arg_list)"
],
diff --git a/hedcode/jupyter_notebooks/utilities/generate_hed_column_test_data.ipynb b/hedcode/jupyter_notebooks/utilities/generate_hed_column_test_data.ipynb
index 5083563..e0874d0 100644
--- a/hedcode/jupyter_notebooks/utilities/generate_hed_column_test_data.ipynb
+++ b/hedcode/jupyter_notebooks/utilities/generate_hed_column_test_data.ipynb
@@ -23,7 +23,7 @@
"from hed.tools import assemble_hed, get_file_list\n",
"\n",
"# Variables to set for the specific dataset\n",
- "bids_root_path = os.path.realpath('../../../datasets/eeg_ds003654s_hed_column')\n",
+ "bids_root_path = os.path.realpath('../../../datasets/eeg_ds003645s_hed_column')\n",
"exclude_dirs = ['sourcedata', 'stimuli', 'code']\n",
"entities = ('sub', 'run')\n",
"hed_version = '8.0.0'\n",
diff --git a/hedcode/jupyter_notebooks/utilities/remove_stim_files.ipynb b/hedcode/jupyter_notebooks/utilities/remove_stim_files.ipynb
index d557c71..def88d3 100644
--- a/hedcode/jupyter_notebooks/utilities/remove_stim_files.ipynb
+++ b/hedcode/jupyter_notebooks/utilities/remove_stim_files.ipynb
@@ -22,20 +22,20 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "f005.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f005.bmp\n",
- "f009.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f009.bmp\n",
- "f011.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f011.bmp\n",
- "f012.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f012.bmp\n",
- "f013.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f013.bmp\n",
- "f015.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f015.bmp\n",
- "f017.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f017.bmp\n",
- "f021.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f021.bmp\n",
- "f022.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f022.bmp\n",
- "f023.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f023.bmp\n",
- "f024.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f024.bmp\n",
+ "f005.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003645s_hed_library\\stimuli\\f005.bmp\n",
+ "f009.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003645s_hed_library\\stimuli\\f009.bmp\n",
+ "f011.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003645s_hed_library\\stimuli\\f011.bmp\n",
+ "f012.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003645s_hed_library\\stimuli\\f012.bmp\n",
+ "f013.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003645s_hed_library\\stimuli\\f013.bmp\n",
+ "f015.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003645s_hed_library\\stimuli\\f015.bmp\n",
+ "f017.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003645s_hed_library\\stimuli\\f017.bmp\n",
+ "f021.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003645s_hed_library\\stimuli\\f021.bmp\n",
+ "f022.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003645s_hed_library\\stimuli\\f022.bmp\n",
+ "f023.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003645s_hed_library\\stimuli\\f023.bmp\n",
+ "f024.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003645s_hed_library\\stimuli\\f024.bmp\n",
"f026.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f026.bmp\n",
- "f027.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f027.bmp\n",
- "f031.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f031.bmp\n",
+ "f027.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003645s_hed_library\\stimuli\\f027.bmp\n",
+ "f031.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003645s_hed_library\\stimuli\\f031.bmp\n",
"f032.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f032.bmp\n",
"f037.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f037.bmp\n",
"f040.bmp: D:\\Research\\HED\\hed-examples\\datasets\\eeg_ds003654s_hed_library\\stimuli\\f040.bmp\n",
diff --git a/hedcode/jupyter_notebooks/utilities/zero_data_files.ipynb b/hedcode/jupyter_notebooks/utilities/zero_data_files.ipynb
index eb58c30..2e23b38 100644
--- a/hedcode/jupyter_notebooks/utilities/zero_data_files.ipynb
+++ b/hedcode/jupyter_notebooks/utilities/zero_data_files.ipynb
@@ -383,7 +383,7 @@
"eeglab_exts = [\".set\", \".fdt\"]\n",
"fmri_exts = [\".nii\", \".nii.gz\"]\n",
"image_exts = [\".bmp\"]\n",
- "bids_root_path = os.path.realpath('../../../datasets/eeg_ds003654s_hed_library')\n",
+ "bids_root_path = os.path.realpath('../../../datasets/eeg_ds003645s_hed_library')\n",
"files_bids = get_file_list(bids_root_path, extensions=image_exts, exclude_dirs=exclude_dirs)\n",
"\n",
"\n",
diff --git a/hedcode/matlab_scripts/hedtools_wrappers/testScript.m b/hedcode/matlab_scripts/hedtools_wrappers/testScript.m
index a5a3e0c..2e673a3 100644
--- a/hedcode/matlab_scripts/hedtools_wrappers/testScript.m
+++ b/hedcode/matlab_scripts/hedtools_wrappers/testScript.m
@@ -1,6 +1,6 @@
%% A test script for a wrapper function to validate HED in a BIDS dataset.
-dataPath = 'H:\datasets\eeg_ds003654s_hed';
+dataPath = 'H:\datasets\eeg_ds003645s_hed';
issueString = validateHedInBids(dataPath);
if isempty(issueString)
fprintf('Dataset %s has no HED validation errors\n', dataPath);
diff --git a/hedcode/matlab_scripts/web_services/exampleGenerateSidecar.m b/hedcode/matlab_scripts/web_services/exampleGenerateSidecar.m
index 1e14fc3..51fe770 100644
--- a/hedcode/matlab_scripts/web_services/exampleGenerateSidecar.m
+++ b/hedcode/matlab_scripts/web_services/exampleGenerateSidecar.m
@@ -1,6 +1,6 @@
host = 'https://hedtools.ucsd.edu/hed';
[servicesUrl, options] = getHostOptions(host); % Setup the options
-pathname = '../../../datasets/eeg_ds003654s_hed/sub-002/eeg/';
+pathname = '../../../datasets/eeg_ds003645s_hed/sub-002/eeg/';
filename = 'sub-002_task-FacePerception_run-1_events.tsv';
eventsText = fileread([pathname filename]); % Read the data
request = struct('service', 'events_generate_sidecar', ...
diff --git a/hedcode/matlab_scripts/web_services/getTestData.m b/hedcode/matlab_scripts/web_services/getTestData.m
index 6a4c409..eb87019 100644
--- a/hedcode/matlab_scripts/web_services/getTestData.m
+++ b/hedcode/matlab_scripts/web_services/getTestData.m
@@ -7,8 +7,8 @@
'schemaUrl', '', 'schemaText', '', ...
'spreadsheetText', '', 'spreadsheetTextInvalid', '');
- remodelPath = '../../../datasets/eeg_ds003654s_hed_remodel/';
- libraryPath = '../../../datasets/eeg_ds003654s_hed_library/';
+ remodelPath = '../../../datasets/eeg_ds003645s_hed_remodel/';
+ libraryPath = '../../../datasets/eeg_ds003645s_hed_library/';
data.jsonLibrary = fileread(...
[libraryPath 'task-FacePerception_events.json']);
data.jsonText = fileread(...
@@ -39,7 +39,7 @@
data.goodStrings = {'Red,Blue', 'Green', 'White, (Black, Image)'};
data.badStrings = {'Red, Blue, Blech', 'Green', 'White, Black, Binge'};
data.jsonUpperText = ...
- fileread('../../../datasets/eeg_ds003654s_hed_inheritance/task-FacePerception_events.json');
+ fileread('../../../datasets/eeg_ds003645s_hed_inheritance/task-FacePerception_events.json');
data.jsonLower2Text = ...
- fileread('../../../datasets/eeg_ds003654s_hed_inheritance/sub-002/sub-002_task-FacePerception_events.json');
+ fileread('../../../datasets/eeg_ds003645s_hed_inheritance/sub-002/sub-002_task-FacePerception_events.json');
end
\ No newline at end of file
diff --git a/hedcode/matlab_scripts/web_services/runAllTests.m b/hedcode/matlab_scripts/web_services/runAllTests.m
index c50499a..a9fb636 100644
--- a/hedcode/matlab_scripts/web_services/runAllTests.m
+++ b/hedcode/matlab_scripts/web_services/runAllTests.m
@@ -1,6 +1,6 @@
host = 'https://hedtools.ucsd.edu/hed_dev';
%host = 'https://hedtools.ucsd.edu/hed';
-host = 'http://127.0.0.1:5000/';
+%host = 'http://127.0.0.1:5000/';
errorMap = containers.Map('KeyType', 'char', 'ValueType', 'any');
errorMap('testGetServices') = testGetServices(host);
errorMap('testEventServices') = testEventServices(host);
diff --git a/hedcode/python_scripts/needs_refactoring/run_bids_dataset_validation.py b/hedcode/python_scripts/needs_refactoring/run_bids_dataset_validation.py
index 05fa629..6476d5c 100644
--- a/hedcode/python_scripts/needs_refactoring/run_bids_dataset_validation.py
+++ b/hedcode/python_scripts/needs_refactoring/run_bids_dataset_validation.py
@@ -5,7 +5,7 @@
if __name__ == '__main__':
path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
- '../../../datasets/eeg_ds003654s_hed_library')
+ '../../../datasets/eeg_ds003645s_hed_library')
bids = BidsDataset(path)
# Validate and include warnings
print("Validating and include warnings...")
diff --git a/hedcode/python_scripts/needs_refactoring/run_bids_style_file_validate.py b/hedcode/python_scripts/needs_refactoring/run_bids_style_file_validate.py
index 4a17d5e..6969cf5 100644
--- a/hedcode/python_scripts/needs_refactoring/run_bids_style_file_validate.py
+++ b/hedcode/python_scripts/needs_refactoring/run_bids_style_file_validate.py
@@ -20,8 +20,8 @@
hed_schema_lib2 = load_schema(hed_library_url2)
hed_schema_lib2.set_library_prefix("test")
events_file = os.path.join(
- '../../../datasets/eeg_ds003654s_hed_library/sub-003/eeg/sub-003_task-FacePerception_run-2_events.tsv')
- json_file = os.path.join('../../../datasets/eeg_ds003654s_hed_library/task-FacePerception_events.json')
+ '../../../datasets/eeg_ds003645s_hed_library/sub-003/eeg/sub-003_task-FacePerception_run-2_events.tsv')
+ json_file = os.path.join('../../../datasets/eeg_ds003645s_hed_library/task-FacePerception_events.json')
schema_group = HedSchemaGroup([hed_schema, hed_schema_lib1, hed_schema_lib2])
validator = HedValidator(hed_schema=schema_group)
diff --git a/hedcode/python_scripts/needs_refactoring/run_json_validation_and_conversion.py b/hedcode/python_scripts/needs_refactoring/run_json_validation_and_conversion.py
index e4e53c6..18f3a4a 100644
--- a/hedcode/python_scripts/needs_refactoring/run_json_validation_and_conversion.py
+++ b/hedcode/python_scripts/needs_refactoring/run_json_validation_and_conversion.py
@@ -23,7 +23,7 @@
if __name__ == '__main__':
hed_xml_url = 'https://raw.githubusercontent.com/hed-standard/hed-specification/master/hedxml/HED8.0.0.xml'
hed_schema = load_schema(hed_xml_url)
- json_filename = "../../../datasets/eeg_ds003654s_hed/task-FacePerception_events.json"
+ json_filename = "../../../datasets/eeg_ds003645s_hed/task-FacePerception_events.json"
# Example 1
print("\nExample 1 demonstrating Sidecar validation....")
@@ -31,9 +31,9 @@
validator = HedValidator(hed_schema)
issues = sidecar.validate_entries(validator, check_for_warnings=True)
if issues:
- print(get_printable_issue_string(issues), "JSON dictionary from eeg_ds003654s_hed has validation errors")
+ print(get_printable_issue_string(issues), "JSON dictionary from eeg_ds003645s_hed has validation errors")
else:
- print("JSON dictionary from eeg_ds003654s_hed has no validation errors")
+ print("JSON dictionary from eeg_ds003645s_hed has no validation errors")
# Example 2: Convert JSON to long and output it.
print("\n\nExample 2 converting a Sidecar to long in place ...")
From 0f6f1fdab12dd0239e1cd84e685916f178fd6e38 Mon Sep 17 00:00:00 2001
From: Kay Robbins <1189050+VisLab@users.noreply.github.com>
Date: Fri, 10 Feb 2023 10:49:15 -0600
Subject: [PATCH 11/13] Updated the file remodeling summaries examples with new
file links
---
docs/source/FileRemodelingTools.md | 24 +
docs/source/HedSummaryGuide.md | 12 +-
.../FacePerception_column_values_summary.json | 864 ++++++++++++++
.../FacePerception_hed_tag_summary.json | 1013 +++++++++++++++++
.../FacePerception_hed_tag_summary.txt | 129 +++
.../FacePerception_hed_type_summary.txt | 199 ++++
.../FacePercepton_column_values_summary.txt | 115 ++
.../FacePercepton_hed_type_summary.json | 967 ++++++++++++++++
.../_static/data/summarize_columns_rmdl.json | 20 +
.../_static/data/summarize_hed_tags_rmdl.json | 36 +
.../data/summarize_hed_types_rmdl.json | 11 +
11 files changed, 3382 insertions(+), 8 deletions(-)
create mode 100644 docs/source/_static/data/summaries/FacePerception_column_values_summary.json
create mode 100644 docs/source/_static/data/summaries/FacePerception_hed_tag_summary.json
create mode 100644 docs/source/_static/data/summaries/FacePerception_hed_tag_summary.txt
create mode 100644 docs/source/_static/data/summaries/FacePerception_hed_type_summary.txt
create mode 100644 docs/source/_static/data/summaries/FacePercepton_column_values_summary.txt
create mode 100644 docs/source/_static/data/summaries/FacePercepton_hed_type_summary.json
create mode 100644 docs/source/_static/data/summarize_columns_rmdl.json
create mode 100644 docs/source/_static/data/summarize_hed_tags_rmdl.json
create mode 100644 docs/source/_static/data/summarize_hed_types_rmdl.json
diff --git a/docs/source/FileRemodelingTools.md b/docs/source/FileRemodelingTools.md
index 48923a7..c69f460 100644
--- a/docs/source/FileRemodelingTools.md
+++ b/docs/source/FileRemodelingTools.md
@@ -1797,6 +1797,15 @@ results from the individual data files are shown after the overall summary.
The individual results are similar to the overall summary because only one data file
was processed.
+For a more extensive example see the
+[**text**](./_static/data/summaries/FacePerception_column_values_summary.txt)
+and [**JSON**](./_static/data/summaries/FacePerception_column_values_summary.json)
+format summaries of the sample dataset
+[**ds003645s_hed**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)
+using the [**summarize_columns_rmdl.json**](./static/data/summarize_columns_rmdl.json)
+remodeling file.
+
+
(summarize-sidecar-from-events-anchor)=
### Summarize sidecar from events
@@ -2052,6 +2061,13 @@ The sample events file had 6 events, including 1 correct action and 2 incorrect
Since only one file was processed, the information for *Dataset* was
similar to that presented under *Individual files*.
+For a more extensive example, see the
+[**text**](./_static/data/summaries/FacePerception_hed_tag_summary.txt)
+and [**JSON**](./_static/data/summaries/FacePerception_hed_tag_summary.json)
+format summaries of the sample dataset
+[**ds003645s_hed**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)
+using the [**summarize_hed_tags_rmdl.json**](./static/data/summarize_hed_tags_rmdl.json)
+remodeling file.
(summarize-hed-type-anchor)=
### Summarize HED type
@@ -2140,6 +2156,14 @@ This summary was produced by using `hed_version="8.1.0"` when creating the `disp
and using the [**sample remodel sidecar file**](sample-remodel-sidecar-file-anchor) in the `do_op`.
The sidecar provides the annotations that use the `condition-variable` tag in the summary.
+For a more extensive example, see the
+[**text**](./_static/data/summaries/FacePerception_hed_type_summary.txt)
+and [**JSON**](./_static/data/summaries/FacePerception_hed_type_summary.json)
+format summaries of the sample dataset
+[**ds003645s_hed**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)
+using the [**summarize_hed_types_rmdl.json**](./static/data/summarize_hed_types_rmdl.json)
+remodeling file.
+
(summarize-hed-validation-anchor)=
### Summarize HED validation
diff --git a/docs/source/HedSummaryGuide.md b/docs/source/HedSummaryGuide.md
index a624ccc..5565e65 100644
--- a/docs/source/HedSummaryGuide.md
+++ b/docs/source/HedSummaryGuide.md
@@ -84,25 +84,21 @@ The following excerpt shows the dataset portion of the resulting summary in text
````{admonition} Text format excerpt with dataset-level summary of column values.
```text
-Context name: column_values_summary
+Context name: column values summary
Context type: column_values
Context filename: column_values_summary
+Overall summary:
Dataset: Total events=1200 Total files=6
Categorical column values[Events, Files]:
event_type:
- double_press[1, 1] left_press[83, 4] right_press[168, 6] setup_right_sym[6, 6]
- show_circle[316, 6] show_cross[310, 6] show_face[310, 6] show_face_initial[6, 6]
+ double_press[1, 1] left_press[83, 4] right_press[168, 6] setup_right_sym[6, 6] show_circle[316, 6] show_cross[310, 6] show_face[310, 6] show_face_initial[6, 6]
face_type:
famous_face[108, 6] n/a[884, 6] scrambled_face[103, 6] unfamiliar_face[105, 6]
rep_lag:
- 1[77, 6] 10[15, 6] 11[13, 5] 12[9, 5] 13[7, 6] 14[6, 4] 15[2, 2] 6[1, 1] 7[2, 2]
- 8[6, 4] 9[10, 6] n/a[1052, 6]
+ 1[77, 6] 10[15, 6] 11[13, 5] 12[9, 5] 13[7, 6] 14[6, 4] 15[2, 2] 6[1, 1] 7[2, 2] 8[6, 4] 9[10, 6] n/a[1052, 6]
rep_status:
delayed_repeat[71, 6] first_show[168, 6] immediate_repeat[77, 6] n/a[884, 6]
- value:
- 0[316, 6] 1[310, 6] 13[56, 6] 14[24, 6] 15[25, 6] 17[54, 6] 18[32, 6] 19[17, 6]
- 256[83, 4] 3[6, 6] 4096[168, 6] 4352[1, 1] 5[58, 6] 6[21, 6] 7[29, 6]
Value columns[Events, Files]:
stim_file[1200, 6]
trial[1200, 6]
diff --git a/docs/source/_static/data/summaries/FacePerception_column_values_summary.json b/docs/source/_static/data/summaries/FacePerception_column_values_summary.json
new file mode 100644
index 0000000..1a7297b
--- /dev/null
+++ b/docs/source/_static/data/summaries/FacePerception_column_values_summary.json
@@ -0,0 +1,864 @@
+{
+ "Dataset": {
+ "Context name": "column values summary",
+ "Context type": "column_values",
+ "Context filename": "column_values_summary",
+ "Overall summary": {
+ "Summary name": "Dataset",
+ "Total events": 1200,
+ "Total files": 6,
+ "Categorical columns": {
+ "event_type": {
+ "double_press": [
+ 1,
+ 1
+ ],
+ "left_press": [
+ 83,
+ 4
+ ],
+ "right_press": [
+ 168,
+ 6
+ ],
+ "setup_right_sym": [
+ 6,
+ 6
+ ],
+ "show_circle": [
+ 316,
+ 6
+ ],
+ "show_cross": [
+ 310,
+ 6
+ ],
+ "show_face": [
+ 310,
+ 6
+ ],
+ "show_face_initial": [
+ 6,
+ 6
+ ]
+ },
+ "face_type": {
+ "famous_face": [
+ 108,
+ 6
+ ],
+ "n/a": [
+ 884,
+ 6
+ ],
+ "scrambled_face": [
+ 103,
+ 6
+ ],
+ "unfamiliar_face": [
+ 105,
+ 6
+ ]
+ },
+ "rep_lag": {
+ "1": [
+ 77,
+ 6
+ ],
+ "10": [
+ 15,
+ 6
+ ],
+ "11": [
+ 13,
+ 5
+ ],
+ "12": [
+ 9,
+ 5
+ ],
+ "13": [
+ 7,
+ 6
+ ],
+ "14": [
+ 6,
+ 4
+ ],
+ "15": [
+ 2,
+ 2
+ ],
+ "6": [
+ 1,
+ 1
+ ],
+ "7": [
+ 2,
+ 2
+ ],
+ "8": [
+ 6,
+ 4
+ ],
+ "9": [
+ 10,
+ 6
+ ],
+ "n/a": [
+ 1052,
+ 6
+ ]
+ },
+ "rep_status": {
+ "delayed_repeat": [
+ 71,
+ 6
+ ],
+ "first_show": [
+ 168,
+ 6
+ ],
+ "immediate_repeat": [
+ 77,
+ 6
+ ],
+ "n/a": [
+ 884,
+ 6
+ ]
+ }
+ },
+ "Value columns": {
+ "stim_file": [
+ 1200,
+ 6
+ ],
+ "trial": [
+ 1200,
+ 6
+ ]
+ }
+ }
+ },
+ "Individual files": {
+ "g:\\eeg_ds003645s\\sub-002\\eeg\\sub-002_task-FacePerception_run-1_events.tsv": {
+ "Summary name": "g:\\eeg_ds003645s\\sub-002\\eeg\\sub-002_task-FacePerception_run-1_events.tsv",
+ "Total events": 200,
+ "Total files": 1,
+ "Categorical columns": {
+ "event_type": {
+ "double_press": [
+ 1,
+ 1
+ ],
+ "left_press": [
+ 20,
+ 1
+ ],
+ "right_press": [
+ 23,
+ 1
+ ],
+ "setup_right_sym": [
+ 1,
+ 1
+ ],
+ "show_circle": [
+ 52,
+ 1
+ ],
+ "show_cross": [
+ 51,
+ 1
+ ],
+ "show_face": [
+ 51,
+ 1
+ ],
+ "show_face_initial": [
+ 1,
+ 1
+ ]
+ },
+ "face_type": {
+ "famous_face": [
+ 14,
+ 1
+ ],
+ "n/a": [
+ 148,
+ 1
+ ],
+ "scrambled_face": [
+ 18,
+ 1
+ ],
+ "unfamiliar_face": [
+ 20,
+ 1
+ ]
+ },
+ "rep_lag": {
+ "1": [
+ 12,
+ 1
+ ],
+ "10": [
+ 2,
+ 1
+ ],
+ "11": [
+ 1,
+ 1
+ ],
+ "12": [
+ 2,
+ 1
+ ],
+ "13": [
+ 1,
+ 1
+ ],
+ "14": [
+ 3,
+ 1
+ ],
+ "6": [
+ 1,
+ 1
+ ],
+ "7": [
+ 1,
+ 1
+ ],
+ "9": [
+ 1,
+ 1
+ ],
+ "n/a": [
+ 176,
+ 1
+ ]
+ },
+ "rep_status": {
+ "delayed_repeat": [
+ 12,
+ 1
+ ],
+ "first_show": [
+ 28,
+ 1
+ ],
+ "immediate_repeat": [
+ 12,
+ 1
+ ],
+ "n/a": [
+ 148,
+ 1
+ ]
+ }
+ },
+ "Value columns": {
+ "stim_file": [
+ 200,
+ 1
+ ],
+ "trial": [
+ 200,
+ 1
+ ]
+ }
+ },
+ "g:\\eeg_ds003645s\\sub-002\\eeg\\sub-002_task-FacePerception_run-2_events.tsv": {
+ "Summary name": "g:\\eeg_ds003645s\\sub-002\\eeg\\sub-002_task-FacePerception_run-2_events.tsv",
+ "Total events": 200,
+ "Total files": 1,
+ "Categorical columns": {
+ "event_type": {
+ "right_press": [
+ 29,
+ 1
+ ],
+ "setup_right_sym": [
+ 1,
+ 1
+ ],
+ "show_circle": [
+ 57,
+ 1
+ ],
+ "show_cross": [
+ 56,
+ 1
+ ],
+ "show_face": [
+ 56,
+ 1
+ ],
+ "show_face_initial": [
+ 1,
+ 1
+ ]
+ },
+ "face_type": {
+ "famous_face": [
+ 26,
+ 1
+ ],
+ "n/a": [
+ 143,
+ 1
+ ],
+ "scrambled_face": [
+ 17,
+ 1
+ ],
+ "unfamiliar_face": [
+ 14,
+ 1
+ ]
+ },
+ "rep_lag": {
+ "1": [
+ 14,
+ 1
+ ],
+ "10": [
+ 2,
+ 1
+ ],
+ "11": [
+ 2,
+ 1
+ ],
+ "12": [
+ 2,
+ 1
+ ],
+ "13": [
+ 1,
+ 1
+ ],
+ "14": [
+ 1,
+ 1
+ ],
+ "15": [
+ 1,
+ 1
+ ],
+ "8": [
+ 2,
+ 1
+ ],
+ "9": [
+ 2,
+ 1
+ ],
+ "n/a": [
+ 173,
+ 1
+ ]
+ },
+ "rep_status": {
+ "delayed_repeat": [
+ 13,
+ 1
+ ],
+ "first_show": [
+ 30,
+ 1
+ ],
+ "immediate_repeat": [
+ 14,
+ 1
+ ],
+ "n/a": [
+ 143,
+ 1
+ ]
+ }
+ },
+ "Value columns": {
+ "stim_file": [
+ 200,
+ 1
+ ],
+ "trial": [
+ 200,
+ 1
+ ]
+ }
+ },
+ "g:\\eeg_ds003645s\\sub-002\\eeg\\sub-002_task-FacePerception_run-3_events.tsv": {
+ "Summary name": "g:\\eeg_ds003645s\\sub-002\\eeg\\sub-002_task-FacePerception_run-3_events.tsv",
+ "Total events": 200,
+ "Total files": 1,
+ "Categorical columns": {
+ "event_type": {
+ "right_press": [
+ 29,
+ 1
+ ],
+ "setup_right_sym": [
+ 1,
+ 1
+ ],
+ "show_circle": [
+ 57,
+ 1
+ ],
+ "show_cross": [
+ 56,
+ 1
+ ],
+ "show_face": [
+ 56,
+ 1
+ ],
+ "show_face_initial": [
+ 1,
+ 1
+ ]
+ },
+ "face_type": {
+ "famous_face": [
+ 21,
+ 1
+ ],
+ "n/a": [
+ 143,
+ 1
+ ],
+ "scrambled_face": [
+ 16,
+ 1
+ ],
+ "unfamiliar_face": [
+ 20,
+ 1
+ ]
+ },
+ "rep_lag": {
+ "1": [
+ 14,
+ 1
+ ],
+ "10": [
+ 3,
+ 1
+ ],
+ "11": [
+ 4,
+ 1
+ ],
+ "12": [
+ 2,
+ 1
+ ],
+ "13": [
+ 1,
+ 1
+ ],
+ "8": [
+ 1,
+ 1
+ ],
+ "9": [
+ 2,
+ 1
+ ],
+ "n/a": [
+ 173,
+ 1
+ ]
+ },
+ "rep_status": {
+ "delayed_repeat": [
+ 13,
+ 1
+ ],
+ "first_show": [
+ 30,
+ 1
+ ],
+ "immediate_repeat": [
+ 14,
+ 1
+ ],
+ "n/a": [
+ 143,
+ 1
+ ]
+ }
+ },
+ "Value columns": {
+ "stim_file": [
+ 200,
+ 1
+ ],
+ "trial": [
+ 200,
+ 1
+ ]
+ }
+ },
+ "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-1_events.tsv": {
+ "Summary name": "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-1_events.tsv",
+ "Total events": 200,
+ "Total files": 1,
+ "Categorical columns": {
+ "event_type": {
+ "left_press": [
+ 22,
+ 1
+ ],
+ "right_press": [
+ 28,
+ 1
+ ],
+ "setup_right_sym": [
+ 1,
+ 1
+ ],
+ "show_circle": [
+ 50,
+ 1
+ ],
+ "show_cross": [
+ 49,
+ 1
+ ],
+ "show_face": [
+ 49,
+ 1
+ ],
+ "show_face_initial": [
+ 1,
+ 1
+ ]
+ },
+ "face_type": {
+ "famous_face": [
+ 20,
+ 1
+ ],
+ "n/a": [
+ 150,
+ 1
+ ],
+ "scrambled_face": [
+ 11,
+ 1
+ ],
+ "unfamiliar_face": [
+ 19,
+ 1
+ ]
+ },
+ "rep_lag": {
+ "1": [
+ 13,
+ 1
+ ],
+ "10": [
+ 2,
+ 1
+ ],
+ "11": [
+ 4,
+ 1
+ ],
+ "13": [
+ 2,
+ 1
+ ],
+ "8": [
+ 1,
+ 1
+ ],
+ "9": [
+ 2,
+ 1
+ ],
+ "n/a": [
+ 176,
+ 1
+ ]
+ },
+ "rep_status": {
+ "delayed_repeat": [
+ 11,
+ 1
+ ],
+ "first_show": [
+ 26,
+ 1
+ ],
+ "immediate_repeat": [
+ 13,
+ 1
+ ],
+ "n/a": [
+ 150,
+ 1
+ ]
+ }
+ },
+ "Value columns": {
+ "stim_file": [
+ 200,
+ 1
+ ],
+ "trial": [
+ 200,
+ 1
+ ]
+ }
+ },
+ "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-2_events.tsv": {
+ "Summary name": "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-2_events.tsv",
+ "Total events": 200,
+ "Total files": 1,
+ "Categorical columns": {
+ "event_type": {
+ "left_press": [
+ 17,
+ 1
+ ],
+ "right_press": [
+ 33,
+ 1
+ ],
+ "setup_right_sym": [
+ 1,
+ 1
+ ],
+ "show_circle": [
+ 50,
+ 1
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+
+
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv:
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv: 200 events
+
+ Main tags[events,files]:
+ Sensory events:
+ Sensory-event[149,1] Cue[99,1] Experimental-stimulus[50,1]
+ Agent actions:
+ Agent-action[50,1] Participant-response[50,1]
+ Task properties:
+ Intended-effect[99,1]
+ Objects:
+ Image[149,1] Face[24,1]
+ Properties:
+ Def[200,1] Onset[150,1] Experimental-trial[199,1] Pathname[149,1] Offset[148,1] Item-interval[24,1]
+ Other tags[events,files]:
+ Experiment-structure[1,1]
+
+
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv:
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv: 200 events
+
+ Main tags[events,files]:
+ Sensory events:
+ Sensory-event[149,1] Cue[99,1] Experimental-stimulus[50,1]
+ Agent actions:
+ Agent-action[50,1] Participant-response[50,1]
+ Task properties:
+ Intended-effect[99,1]
+ Objects:
+ Image[149,1] Face[23,1]
+ Properties:
+ Def[200,1] Onset[150,1] Experimental-trial[199,1] Pathname[149,1] Offset[148,1] Item-interval[23,1]
+ Other tags[events,files]:
+ Experiment-structure[1,1]
+
+
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv:
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv: 200 events
+
+ Main tags[events,files]:
+ Sensory events:
+ Sensory-event[149,1] Cue[99,1] Experimental-stimulus[50,1]
+ Agent actions:
+ Agent-action[50,1] Participant-response[50,1]
+ Task properties:
+ Intended-effect[99,1]
+ Objects:
+ Image[149,1] Face[23,1]
+ Properties:
+ Def[200,1] Onset[150,1] Experimental-trial[199,1] Pathname[149,1] Offset[148,1] Item-interval[23,1]
+ Other tags[events,files]:
+ Experiment-structure[1,1]
diff --git a/docs/source/_static/data/summaries/FacePerception_hed_type_summary.txt b/docs/source/_static/data/summaries/FacePerception_hed_type_summary.txt
new file mode 100644
index 0000000..54541bc
--- /dev/null
+++ b/docs/source/_static/data/summaries/FacePerception_hed_type_summary.txt
@@ -0,0 +1,199 @@
+Context name: hed type summary
+Context type: hed_type_summary
+Context filename: hed_type_summary
+
+Overall summary:
+Dataset: 3 condition-variable types in 6 files with a total of 1200
+ key-assignment: 1 levels in 1200 events out of 1200 total events in 6 files
+ right-sym-cond [1200 events, 6 files]:
+ Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
+ Description: Right index finger key press indicates a face with above average symmetry.
+ face-type: 3 levels in 316 events out of 1200 total events in 6 files
+ unfamiliar-face-cond [105 events, 6 files]:
+ Tags: ['Image', 'Face', 'Unfamiliar']
+ Description: A face that should not be recognized by the participants.
+ famous-face-cond [108 events, 6 files]:
+ Tags: ['Image', 'Face', 'Famous']
+ Description: A face that should be recognized by the participants
+ scrambled-face-cond [103 events, 6 files]:
+ Tags: ['Image', 'Face', 'Disordered']
+ Description: A scrambled face image generated by taking face 2D FFT.
+ repetition-type: 3 levels in 316 events out of 1200 total events in 6 files
+ first-show-cond [168 events, 6 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating the first display of this face.
+ immediate-repeat-cond [77 events, 6 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating this face was the same as previous one.
+ delayed-repeat-cond [71 events, 6 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval', 'Greater-than-or-equal-to', 'Item-interval']
+ Description: Factor level indicating face was seen 5 to 15 trials ago.
+
+Individual files:
+
+g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv:
+ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv: Type=condition-variable Total events=200
+ key-assignment: 1 levels in 200 events
+ right-sym-cond [200 events, 1 files]:
+ Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
+ Description: Right index finger key press indicates a face with above average symmetry.
+ face-type: 3 levels in 52 events
+ unfamiliar-face-cond [20 events, 1 files]:
+ Tags: ['Image', 'Face', 'Unfamiliar']
+ Description: A face that should not be recognized by the participants.
+ famous-face-cond [14 events, 1 files]:
+ Tags: ['Image', 'Face', 'Famous']
+ Description: A face that should be recognized by the participants
+ scrambled-face-cond [18 events, 1 files]:
+ Tags: ['Image', 'Face', 'Disordered']
+ Description: A scrambled face image generated by taking face 2D FFT.
+ repetition-type: 3 levels in 52 events
+ first-show-cond [28 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating the first display of this face.
+ immediate-repeat-cond [12 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating this face was the same as previous one.
+ delayed-repeat-cond [12 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval', 'Greater-than-or-equal-to', 'Item-interval']
+ Description: Factor level indicating face was seen 5 to 15 trials ago.
+
+
+g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv:
+ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv: Type=condition-variable Total events=200
+ key-assignment: 1 levels in 200 events
+ right-sym-cond [200 events, 1 files]:
+ Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
+ Description: Right index finger key press indicates a face with above average symmetry.
+ face-type: 3 levels in 57 events
+ scrambled-face-cond [17 events, 1 files]:
+ Tags: ['Image', 'Face', 'Disordered']
+ Description: A scrambled face image generated by taking face 2D FFT.
+ famous-face-cond [26 events, 1 files]:
+ Tags: ['Image', 'Face', 'Famous']
+ Description: A face that should be recognized by the participants
+ unfamiliar-face-cond [14 events, 1 files]:
+ Tags: ['Image', 'Face', 'Unfamiliar']
+ Description: A face that should not be recognized by the participants.
+ repetition-type: 3 levels in 57 events
+ first-show-cond [30 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating the first display of this face.
+ immediate-repeat-cond [14 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating this face was the same as previous one.
+ delayed-repeat-cond [13 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval', 'Greater-than-or-equal-to', 'Item-interval']
+ Description: Factor level indicating face was seen 5 to 15 trials ago.
+
+
+g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv:
+ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv: Type=condition-variable Total events=200
+ key-assignment: 1 levels in 200 events
+ right-sym-cond [200 events, 1 files]:
+ Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
+ Description: Right index finger key press indicates a face with above average symmetry.
+ face-type: 3 levels in 57 events
+ unfamiliar-face-cond [20 events, 1 files]:
+ Tags: ['Image', 'Face', 'Unfamiliar']
+ Description: A face that should not be recognized by the participants.
+ famous-face-cond [21 events, 1 files]:
+ Tags: ['Image', 'Face', 'Famous']
+ Description: A face that should be recognized by the participants
+ scrambled-face-cond [16 events, 1 files]:
+ Tags: ['Image', 'Face', 'Disordered']
+ Description: A scrambled face image generated by taking face 2D FFT.
+ repetition-type: 3 levels in 57 events
+ first-show-cond [30 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating the first display of this face.
+ immediate-repeat-cond [14 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating this face was the same as previous one.
+ delayed-repeat-cond [13 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval', 'Greater-than-or-equal-to', 'Item-interval']
+ Description: Factor level indicating face was seen 5 to 15 trials ago.
+
+
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv:
+ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv: Type=condition-variable Total events=200
+ key-assignment: 1 levels in 200 events
+ right-sym-cond [200 events, 1 files]:
+ Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
+ Description: Right index finger key press indicates a face with above average symmetry.
+ face-type: 3 levels in 50 events
+ famous-face-cond [20 events, 1 files]:
+ Tags: ['Image', 'Face', 'Famous']
+ Description: A face that should be recognized by the participants
+ unfamiliar-face-cond [19 events, 1 files]:
+ Tags: ['Image', 'Face', 'Unfamiliar']
+ Description: A face that should not be recognized by the participants.
+ scrambled-face-cond [11 events, 1 files]:
+ Tags: ['Image', 'Face', 'Disordered']
+ Description: A scrambled face image generated by taking face 2D FFT.
+ repetition-type: 3 levels in 50 events
+ first-show-cond [26 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating the first display of this face.
+ immediate-repeat-cond [13 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating this face was the same as previous one.
+ delayed-repeat-cond [11 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval', 'Greater-than-or-equal-to', 'Item-interval']
+ Description: Factor level indicating face was seen 5 to 15 trials ago.
+
+
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv:
+ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv: Type=condition-variable Total events=200
+ key-assignment: 1 levels in 200 events
+ right-sym-cond [200 events, 1 files]:
+ Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
+ Description: Right index finger key press indicates a face with above average symmetry.
+ face-type: 3 levels in 50 events
+ famous-face-cond [13 events, 1 files]:
+ Tags: ['Image', 'Face', 'Famous']
+ Description: A face that should be recognized by the participants
+ scrambled-face-cond [21 events, 1 files]:
+ Tags: ['Image', 'Face', 'Disordered']
+ Description: A scrambled face image generated by taking face 2D FFT.
+ unfamiliar-face-cond [16 events, 1 files]:
+ Tags: ['Image', 'Face', 'Unfamiliar']
+ Description: A face that should not be recognized by the participants.
+ repetition-type: 3 levels in 50 events
+ first-show-cond [27 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating the first display of this face.
+ immediate-repeat-cond [12 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating this face was the same as previous one.
+ delayed-repeat-cond [11 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval', 'Greater-than-or-equal-to', 'Item-interval']
+ Description: Factor level indicating face was seen 5 to 15 trials ago.
+
+
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv:
+ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv: Type=condition-variable Total events=200
+ key-assignment: 1 levels in 200 events
+ right-sym-cond [200 events, 1 files]:
+ Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
+ Description: Right index finger key press indicates a face with above average symmetry.
+ face-type: 3 levels in 50 events
+ scrambled-face-cond [20 events, 1 files]:
+ Tags: ['Image', 'Face', 'Disordered']
+ Description: A scrambled face image generated by taking face 2D FFT.
+ unfamiliar-face-cond [16 events, 1 files]:
+ Tags: ['Image', 'Face', 'Unfamiliar']
+ Description: A face that should not be recognized by the participants.
+ famous-face-cond [14 events, 1 files]:
+ Tags: ['Image', 'Face', 'Famous']
+ Description: A face that should be recognized by the participants
+ repetition-type: 3 levels in 50 events
+ first-show-cond [27 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating the first display of this face.
+ immediate-repeat-cond [12 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval']
+ Description: Factor level indicating this face was the same as previous one.
+ delayed-repeat-cond [11 events, 1 files]:
+ Tags: ['Item-count', 'Face', 'Item-interval', 'Greater-than-or-equal-to', 'Item-interval']
+ Description: Factor level indicating face was seen 5 to 15 trials ago.
diff --git a/docs/source/_static/data/summaries/FacePercepton_column_values_summary.txt b/docs/source/_static/data/summaries/FacePercepton_column_values_summary.txt
new file mode 100644
index 0000000..86a9c7d
--- /dev/null
+++ b/docs/source/_static/data/summaries/FacePercepton_column_values_summary.txt
@@ -0,0 +1,115 @@
+Context name: column values summary
+Context type: column_values
+Context filename: column_values_summary
+
+Overall summary:
+Dataset: Total events=1200 Total files=6
+ Categorical column values[Events, Files]:
+ event_type:
+ double_press[1, 1] left_press[83, 4] right_press[168, 6] setup_right_sym[6, 6] show_circle[316, 6] show_cross[310, 6] show_face[310, 6] show_face_initial[6, 6]
+ face_type:
+ famous_face[108, 6] n/a[884, 6] scrambled_face[103, 6] unfamiliar_face[105, 6]
+ rep_lag:
+ 1[77, 6] 10[15, 6] 11[13, 5] 12[9, 5] 13[7, 6] 14[6, 4] 15[2, 2] 6[1, 1] 7[2, 2] 8[6, 4] 9[10, 6] n/a[1052, 6]
+ rep_status:
+ delayed_repeat[71, 6] first_show[168, 6] immediate_repeat[77, 6] n/a[884, 6]
+ Value columns[Events, Files]:
+ stim_file[1200, 6]
+ trial[1200, 6]
+
+Individual files:
+
+g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv:
+ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv:
+ Categorical column values[Events, Files]:
+ event_type:
+ double_press[1, 1] left_press[20, 1] right_press[23, 1] setup_right_sym[1, 1] show_circle[52, 1] show_cross[51, 1] show_face[51, 1] show_face_initial[1, 1]
+ face_type:
+ famous_face[14, 1] n/a[148, 1] scrambled_face[18, 1] unfamiliar_face[20, 1]
+ rep_lag:
+ 1[12, 1] 10[2, 1] 11[1, 1] 12[2, 1] 13[1, 1] 14[3, 1] 6[1, 1] 7[1, 1] 9[1, 1] n/a[176, 1]
+ rep_status:
+ delayed_repeat[12, 1] first_show[28, 1] immediate_repeat[12, 1] n/a[148, 1]
+ Value columns[Events, Files]:
+ stim_file[200, 1]
+ trial[200, 1]
+
+
+g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv:
+ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv:
+ Categorical column values[Events, Files]:
+ event_type:
+ right_press[29, 1] setup_right_sym[1, 1] show_circle[57, 1] show_cross[56, 1] show_face[56, 1] show_face_initial[1, 1]
+ face_type:
+ famous_face[26, 1] n/a[143, 1] scrambled_face[17, 1] unfamiliar_face[14, 1]
+ rep_lag:
+ 1[14, 1] 10[2, 1] 11[2, 1] 12[2, 1] 13[1, 1] 14[1, 1] 15[1, 1] 8[2, 1] 9[2, 1] n/a[173, 1]
+ rep_status:
+ delayed_repeat[13, 1] first_show[30, 1] immediate_repeat[14, 1] n/a[143, 1]
+ Value columns[Events, Files]:
+ stim_file[200, 1]
+ trial[200, 1]
+
+
+g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv:
+ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv:
+ Categorical column values[Events, Files]:
+ event_type:
+ right_press[29, 1] setup_right_sym[1, 1] show_circle[57, 1] show_cross[56, 1] show_face[56, 1] show_face_initial[1, 1]
+ face_type:
+ famous_face[21, 1] n/a[143, 1] scrambled_face[16, 1] unfamiliar_face[20, 1]
+ rep_lag:
+ 1[14, 1] 10[3, 1] 11[4, 1] 12[2, 1] 13[1, 1] 8[1, 1] 9[2, 1] n/a[173, 1]
+ rep_status:
+ delayed_repeat[13, 1] first_show[30, 1] immediate_repeat[14, 1] n/a[143, 1]
+ Value columns[Events, Files]:
+ stim_file[200, 1]
+ trial[200, 1]
+
+
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv:
+ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv:
+ Categorical column values[Events, Files]:
+ event_type:
+ left_press[22, 1] right_press[28, 1] setup_right_sym[1, 1] show_circle[50, 1] show_cross[49, 1] show_face[49, 1] show_face_initial[1, 1]
+ face_type:
+ famous_face[20, 1] n/a[150, 1] scrambled_face[11, 1] unfamiliar_face[19, 1]
+ rep_lag:
+ 1[13, 1] 10[2, 1] 11[4, 1] 13[2, 1] 8[1, 1] 9[2, 1] n/a[176, 1]
+ rep_status:
+ delayed_repeat[11, 1] first_show[26, 1] immediate_repeat[13, 1] n/a[150, 1]
+ Value columns[Events, Files]:
+ stim_file[200, 1]
+ trial[200, 1]
+
+
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv:
+ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv:
+ Categorical column values[Events, Files]:
+ event_type:
+ left_press[17, 1] right_press[33, 1] setup_right_sym[1, 1] show_circle[50, 1] show_cross[49, 1] show_face[49, 1] show_face_initial[1, 1]
+ face_type:
+ famous_face[13, 1] n/a[150, 1] scrambled_face[21, 1] unfamiliar_face[16, 1]
+ rep_lag:
+ 1[12, 1] 10[2, 1] 12[2, 1] 13[1, 1] 14[1, 1] 15[1, 1] 7[1, 1] 8[2, 1] 9[1, 1] n/a[177, 1]
+ rep_status:
+ delayed_repeat[11, 1] first_show[27, 1] immediate_repeat[12, 1] n/a[150, 1]
+ Value columns[Events, Files]:
+ stim_file[200, 1]
+ trial[200, 1]
+
+
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv:
+ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv:
+ Categorical column values[Events, Files]:
+ event_type:
+ left_press[24, 1] right_press[26, 1] setup_right_sym[1, 1] show_circle[50, 1] show_cross[49, 1] show_face[49, 1] show_face_initial[1, 1]
+ face_type:
+ famous_face[14, 1] n/a[150, 1] scrambled_face[20, 1] unfamiliar_face[16, 1]
+ rep_lag:
+ 1[12, 1] 10[4, 1] 11[2, 1] 12[1, 1] 13[1, 1] 14[1, 1] 9[2, 1] n/a[177, 1]
+ rep_status:
+ delayed_repeat[11, 1] first_show[27, 1] immediate_repeat[12, 1] n/a[150, 1]
+ Value columns[Events, Files]:
+ stim_file[200, 1]
+ trial[200, 1]
diff --git a/docs/source/_static/data/summaries/FacePercepton_hed_type_summary.json b/docs/source/_static/data/summaries/FacePercepton_hed_type_summary.json
new file mode 100644
index 0000000..66bae4c
--- /dev/null
+++ b/docs/source/_static/data/summaries/FacePercepton_hed_type_summary.json
@@ -0,0 +1,967 @@
+{
+ "Dataset": {
+ "Context name": "hed type summary",
+ "Context type": "hed_type_summary",
+ "Context filename": "hed_type_summary",
+ "Overall summary": {
+ "name": "Dataset",
+ "type_tag": "condition-variable",
+ "files": [
+ "g:\\eeg_ds003645s\\sub-002\\eeg\\sub-002_task-FacePerception_run-1_events.tsv",
+ "g:\\eeg_ds003645s\\sub-002\\eeg\\sub-002_task-FacePerception_run-2_events.tsv",
+ "g:\\eeg_ds003645s\\sub-002\\eeg\\sub-002_task-FacePerception_run-3_events.tsv",
+ "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-1_events.tsv",
+ "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-2_events.tsv",
+ "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv"
+ ],
+ "total_events": 1200,
+ "details": {
+ "key-assignment": {
+ "type_value": "key-assignment",
+ "type_tag": "condition-variable",
+ "levels": 1,
+ "direct_references": 0,
+ "total_events": 1200,
+ "events": 1200,
+ "events_with_multiple_refs": 0,
+ "max_refs_per_event": 1,
+ "files": [
+ "g:\\eeg_ds003645s\\sub-002\\eeg\\sub-002_task-FacePerception_run-1_events.tsv",
+ "g:\\eeg_ds003645s\\sub-002\\eeg\\sub-002_task-FacePerception_run-2_events.tsv",
+ "g:\\eeg_ds003645s\\sub-002\\eeg\\sub-002_task-FacePerception_run-3_events.tsv",
+ "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-1_events.tsv",
+ "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-2_events.tsv",
+ "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv"
+ ],
+ "level_counts": {
+ "right-sym-cond": {
+ "files": 6,
+ "events": 1200,
+ "tags": [
+ "Index-finger",
+ "Right-side-of",
+ "Experiment-participant",
+ "Behavioral-evidence",
+ "Symmetrical",
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+ "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv": {
+ "name": "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv",
+ "type_tag": "condition-variable",
+ "files": [
+ "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv"
+ ],
+ "total_events": 200,
+ "details": {
+ "key-assignment": {
+ "type_value": "key-assignment",
+ "type_tag": "condition-variable",
+ "levels": 1,
+ "direct_references": 0,
+ "total_events": 200,
+ "events": 200,
+ "events_with_multiple_refs": 0,
+ "max_refs_per_event": 1,
+ "files": [
+ "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv"
+ ],
+ "level_counts": {
+ "right-sym-cond": {
+ "files": 1,
+ "events": 200,
+ "tags": [
+ "Index-finger",
+ "Right-side-of",
+ "Experiment-participant",
+ "Behavioral-evidence",
+ "Symmetrical",
+ "Index-finger",
+ "Left-side-of",
+ "Experiment-participant",
+ "Behavioral-evidence",
+ "Asymmetrical"
+ ],
+ "description": "Right index finger key press indicates a face with above average symmetry."
+ }
+ }
+ },
+ "face-type": {
+ "type_value": "face-type",
+ "type_tag": "condition-variable",
+ "levels": 3,
+ "direct_references": 0,
+ "total_events": 200,
+ "events": 50,
+ "events_with_multiple_refs": 0,
+ "max_refs_per_event": 1,
+ "files": [
+ "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv"
+ ],
+ "level_counts": {
+ "scrambled-face-cond": {
+ "files": 1,
+ "events": 20,
+ "tags": [
+ "Image",
+ "Face",
+ "Disordered"
+ ],
+ "description": "A scrambled face image generated by taking face 2D FFT."
+ },
+ "unfamiliar-face-cond": {
+ "files": 1,
+ "events": 16,
+ "tags": [
+ "Image",
+ "Face",
+ "Unfamiliar"
+ ],
+ "description": "A face that should not be recognized by the participants."
+ },
+ "famous-face-cond": {
+ "files": 1,
+ "events": 14,
+ "tags": [
+ "Image",
+ "Face",
+ "Famous"
+ ],
+ "description": "A face that should be recognized by the participants"
+ }
+ }
+ },
+ "repetition-type": {
+ "type_value": "repetition-type",
+ "type_tag": "condition-variable",
+ "levels": 3,
+ "direct_references": 0,
+ "total_events": 200,
+ "events": 50,
+ "events_with_multiple_refs": 0,
+ "max_refs_per_event": 1,
+ "files": [
+ "g:\\eeg_ds003645s\\sub-003\\eeg\\sub-003_task-FacePerception_run-3_events.tsv"
+ ],
+ "level_counts": {
+ "first-show-cond": {
+ "files": 1,
+ "events": 27,
+ "tags": [
+ "Item-count",
+ "Face",
+ "Item-interval"
+ ],
+ "description": "Factor level indicating the first display of this face."
+ },
+ "immediate-repeat-cond": {
+ "files": 1,
+ "events": 12,
+ "tags": [
+ "Item-count",
+ "Face",
+ "Item-interval"
+ ],
+ "description": "Factor level indicating this face was the same as previous one."
+ },
+ "delayed-repeat-cond": {
+ "files": 1,
+ "events": 11,
+ "tags": [
+ "Item-count",
+ "Face",
+ "Item-interval",
+ "Greater-than-or-equal-to",
+ "Item-interval"
+ ],
+ "description": "Factor level indicating face was seen 5 to 15 trials ago."
+ }
+ }
+ }
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/docs/source/_static/data/summarize_columns_rmdl.json b/docs/source/_static/data/summarize_columns_rmdl.json
new file mode 100644
index 0000000..fedeb93
--- /dev/null
+++ b/docs/source/_static/data/summarize_columns_rmdl.json
@@ -0,0 +1,20 @@
+[
+ {
+ "operation": "summarize_column_names",
+ "description": "Summarize the unique orders of column names.",
+ "parameters": {
+ "summary_name": "column name summary",
+ "summary_filename": "column_name_summary"
+ }
+ },
+ {
+ "operation": "summarize_column_values",
+ "description": "Summarize column values.",
+ "parameters": {
+ "summary_name": "column values summary",
+ "summary_filename": "column_values_summary",
+ "skip_columns": ["onset", "duration", "sample", "value"],
+ "value_columns": ["stim_file", "trial"]
+ }
+ }
+]
\ No newline at end of file
diff --git a/docs/source/_static/data/summarize_hed_tags_rmdl.json b/docs/source/_static/data/summarize_hed_tags_rmdl.json
new file mode 100644
index 0000000..e7f183a
--- /dev/null
+++ b/docs/source/_static/data/summarize_hed_tags_rmdl.json
@@ -0,0 +1,36 @@
+ [
+ {
+ "operation": "summarize_hed_tags",
+ "description": "Produce a summary of HED tags.",
+ "parameters": {
+ "summary_name": "hed_tag_summary",
+ "summary_filename": "hed_tag_summary",
+ "tags": {
+ "Sensory events": [
+ "Sensory-event",
+ "Sensory-presentation",
+ "Task-stimulus-role",
+ "Experimental-stimulus"
+ ],
+ "Agent actions": [
+ "Agent-action",
+ "Agent",
+ "Action",
+ "Agent-task-role",
+ "Task-action-type",
+ "Participant-response"
+ ],
+ "Task properties": [
+ "Task-property"
+ ],
+ "Objects": [
+ "Item"
+ ],
+ "Properties": [
+ "Property"
+ ]
+ },
+ "expand_context": false
+ }
+ }
+]
\ No newline at end of file
diff --git a/docs/source/_static/data/summarize_hed_types_rmdl.json b/docs/source/_static/data/summarize_hed_types_rmdl.json
new file mode 100644
index 0000000..fdae65e
--- /dev/null
+++ b/docs/source/_static/data/summarize_hed_types_rmdl.json
@@ -0,0 +1,11 @@
+[
+ {
+ "operation": "summarize_hed_type",
+ "description": "Summarize a hed type tag such as condition-variable",
+ "parameters": {
+ "summary_name": "hed type summary",
+ "summary_filename": "hed_type_summary",
+ "type_tag": "condition-variable"
+ }
+ }
+]
From b183309957222652969171a8bb5ce6a430393872 Mon Sep 17 00:00:00 2001
From: Kay Robbins <1189050+VisLab@users.noreply.github.com>
Date: Fri, 10 Feb 2023 13:11:06 -0600
Subject: [PATCH 12/13] Added event count on column value summary
---
docs/source/FileRemodelingTools.md | 3 +--
....txt => FacePerception_column_values_summary.txt} | 12 ++++++------
...ary.json => FacePerception_hed_type_summary.json} | 0
3 files changed, 7 insertions(+), 8 deletions(-)
rename docs/source/_static/data/summaries/{FacePercepton_column_values_summary.txt => FacePerception_column_values_summary.txt} (88%)
rename docs/source/_static/data/summaries/{FacePercepton_hed_type_summary.json => FacePerception_hed_type_summary.json} (100%)
diff --git a/docs/source/FileRemodelingTools.md b/docs/source/FileRemodelingTools.md
index c69f460..1c31adb 100644
--- a/docs/source/FileRemodelingTools.md
+++ b/docs/source/FileRemodelingTools.md
@@ -1754,8 +1754,7 @@ Context name: AOMIC_column_values
Context type: column_values
Context filename: AOMIC_column_values
-Summary details:
-
+Overall summary:
Dataset: Total events=6 Total files=1
Categorical column values[Events, Files]:
response_accuracy:
diff --git a/docs/source/_static/data/summaries/FacePercepton_column_values_summary.txt b/docs/source/_static/data/summaries/FacePerception_column_values_summary.txt
similarity index 88%
rename from docs/source/_static/data/summaries/FacePercepton_column_values_summary.txt
rename to docs/source/_static/data/summaries/FacePerception_column_values_summary.txt
index 86a9c7d..0662392 100644
--- a/docs/source/_static/data/summaries/FacePercepton_column_values_summary.txt
+++ b/docs/source/_static/data/summaries/FacePerception_column_values_summary.txt
@@ -20,7 +20,7 @@ Dataset: Total events=1200 Total files=6
Individual files:
g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv:
- g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv:
+g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv: 200 events
Categorical column values[Events, Files]:
event_type:
double_press[1, 1] left_press[20, 1] right_press[23, 1] setup_right_sym[1, 1] show_circle[52, 1] show_cross[51, 1] show_face[51, 1] show_face_initial[1, 1]
@@ -36,7 +36,7 @@ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv:
g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv:
- g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv:
+g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv: 200 events
Categorical column values[Events, Files]:
event_type:
right_press[29, 1] setup_right_sym[1, 1] show_circle[57, 1] show_cross[56, 1] show_face[56, 1] show_face_initial[1, 1]
@@ -52,7 +52,7 @@ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv:
g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv:
- g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv:
+g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv: 200 events
Categorical column values[Events, Files]:
event_type:
right_press[29, 1] setup_right_sym[1, 1] show_circle[57, 1] show_cross[56, 1] show_face[56, 1] show_face_initial[1, 1]
@@ -68,7 +68,7 @@ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv:
g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv:
- g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv:
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv: 200 events
Categorical column values[Events, Files]:
event_type:
left_press[22, 1] right_press[28, 1] setup_right_sym[1, 1] show_circle[50, 1] show_cross[49, 1] show_face[49, 1] show_face_initial[1, 1]
@@ -84,7 +84,7 @@ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv:
g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv:
- g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv:
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv: 200 events
Categorical column values[Events, Files]:
event_type:
left_press[17, 1] right_press[33, 1] setup_right_sym[1, 1] show_circle[50, 1] show_cross[49, 1] show_face[49, 1] show_face_initial[1, 1]
@@ -100,7 +100,7 @@ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv:
g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv:
- g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv:
+g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv: 200 events
Categorical column values[Events, Files]:
event_type:
left_press[24, 1] right_press[26, 1] setup_right_sym[1, 1] show_circle[50, 1] show_cross[49, 1] show_face[49, 1] show_face_initial[1, 1]
diff --git a/docs/source/_static/data/summaries/FacePercepton_hed_type_summary.json b/docs/source/_static/data/summaries/FacePerception_hed_type_summary.json
similarity index 100%
rename from docs/source/_static/data/summaries/FacePercepton_hed_type_summary.json
rename to docs/source/_static/data/summaries/FacePerception_hed_type_summary.json
From f8e6db4cc598dcaa3c0c0cbedaec19869eafd740 Mon Sep 17 00:00:00 2001
From: Kay Robbins <1189050+VisLab@users.noreply.github.com>
Date: Fri, 10 Feb 2023 19:05:49 -0600
Subject: [PATCH 13/13] Minor adjustments in remodeling file format
---
docs/source/FileRemodelingTools.md | 66 +++++++++----------
.../FacePerception_column_values_summary.txt | 12 ++--
.../FacePerception_hed_tag_summary.txt | 14 ++--
.../FacePerception_hed_type_summary.txt | 20 +++---
.../summaries/summarize_columns_rmdl.json | 20 ++++++
.../summaries/summarize_hed_tags_rmdl.json | 36 ++++++++++
.../summaries/summarize_hed_types_rmdl.json | 11 ++++
7 files changed, 123 insertions(+), 56 deletions(-)
create mode 100644 docs/source/_static/data/summaries/summarize_columns_rmdl.json
create mode 100644 docs/source/_static/data/summaries/summarize_hed_tags_rmdl.json
create mode 100644 docs/source/_static/data/summaries/summarize_hed_types_rmdl.json
diff --git a/docs/source/FileRemodelingTools.md b/docs/source/FileRemodelingTools.md
index 1c31adb..41d560b 100644
--- a/docs/source/FileRemodelingTools.md
+++ b/docs/source/FileRemodelingTools.md
@@ -1664,7 +1664,8 @@ Dataset: Number of files=1
Individual files:
- sub-0013_task-stopsignal_acq-seq_events.tsv: ['onset', 'duration', 'trial_type', 'stop_signal_delay', 'response_time', 'response_accuracy', 'response_hand', 'sex']
+sub-0013_task-stopsignal_acq-seq_events.tsv:
+ ['onset', 'duration', 'trial_type', 'stop_signal_delay', 'response_time', 'response_accuracy', 'response_hand', 'sex']
```
````
@@ -1771,7 +1772,8 @@ Dataset: Total events=6 Total files=1
Individual files:
- sub-0013_task-stopsignal_acq-seq_events.tsv:
+sub-0013_task-stopsignal_acq-seq_events.tsv:
+Total events=200
Categorical column values[Events, Files]:
response_accuracy:
correct[5, 1] n/a[1, 1]
@@ -1801,7 +1803,7 @@ For a more extensive example see the
and [**JSON**](./_static/data/summaries/FacePerception_column_values_summary.json)
format summaries of the sample dataset
[**ds003645s_hed**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)
-using the [**summarize_columns_rmdl.json**](./static/data/summarize_columns_rmdl.json)
+using the [**summarize_columns_rmdl.json**](./static/data/summaries/summarize_columns_rmdl.json)
remodeling file.
@@ -2022,9 +2024,8 @@ Context name: summarize_hed_tags
Context type: hed_tag_summary
Context filename: summarize_hed_tags
-Summary details:
-
-Dataset: Total events=6 Total files=1
+Overall summary:
+Dataset: Total events=1200 Total1 file=6
Main tags[events,files]:
Sensory events:
Sensory-presentation[6,1] Visual-presentation[6,1] Auditory-presentation[3,1]
@@ -2036,17 +2037,18 @@ Dataset: Total events=6 Total files=1
Label[6,1] Def[6,1] Delay[3,1]
Individual files:
-
- aomic_sub-0013_excerpt_events.tsv: Type=condition-variable Total events=6
- Main tags[events,files]:
- Sensory events:
- Sensory-presentation[6,1] Visual-presentation[6,1] Auditory-presentation[3,1]
- Agent actions:
- Incorrect-action[2,1] Correct-action[1,1]
- Objects:
- Image[6,1]
- Other tags[events,files]:
- Label[6,1] Def[6,1] Delay[3,1]
+
+aomic_sub-0013_excerpt_events.tsv:
+Total events=6
+ Main tags[events,files]:
+ Sensory events:
+ Sensory-presentation[6,1] Visual-presentation[6,1] Auditory-presentation[3,1]
+ Agent actions:
+ Incorrect-action[2,1] Correct-action[1,1]
+ Objects:
+ Image[6,1]
+ Other tags[events,files]:
+ Label[6,1] Def[6,1] Delay[3,1]
```
````
@@ -2065,7 +2067,7 @@ For a more extensive example, see the
and [**JSON**](./_static/data/summaries/FacePerception_hed_tag_summary.json)
format summaries of the sample dataset
[**ds003645s_hed**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)
-using the [**summarize_hed_tags_rmdl.json**](./static/data/summarize_hed_tags_rmdl.json)
+using the [**summarize_hed_tags_rmdl.json**](./static/data/summaries/summarize_hed_tags_rmdl.json)
remodeling file.
(summarize-hed-type-anchor)=
@@ -2128,24 +2130,22 @@ Context name: AOMIC_condition_variables
Context type: hed_type_summary
Context filename: AOMIC_condition_variables
-Summary details:
+Overall summary:
-Dataset: Type=condition-variable Total events=6 Total files=1
- image-sex:
- Files:['aomic_sub-0013_excerpt_events.tsv']
- Events: 6 out of 6 total events in 1 files
- Levels[Events,Files]: [Tags]
- female-image-cond [4,1]: ['Female', 'Image', 'Face']
- male-image-cond [2,1]: ['Male', 'Image', 'Face']
+Dataset: Type=condition-variable Type values=1 Total events=6 Total files=1
+ image-sex: 2 levels in 6 event(s)s out of 6 total events in 1 file(s)
+ female-image-cond [4,1]: ['Female', 'Image', 'Face']
+ male-image-cond [2,1]: ['Male', 'Image', 'Face']
Individual files:
- aomic_sub-0013_excerpt_events.tsv: Type=condition-variable Total events=6
- image-sex:
- Levels:2 Events:6
- Levels[Events,Files]: [Tags]
- female-image-cond [4,1]: ['Female', 'Image', 'Face']
- male-image-cond [2,1]: ['Male', 'Image', 'Face']
+aomic_sub-0013_excerpt_events.tsv:
+Type=condition-variable Total events=6
+ image-sex: 2 levels in 6 events
+ female-image-cond [4 events, 1 files]:
+ Tags: ['Female', 'Image', 'Face']
+ male-image-cond [2 events, 1 files]:
+ Tags: ['Male', 'Image', 'Face']
```
````
@@ -2160,7 +2160,7 @@ For a more extensive example, see the
and [**JSON**](./_static/data/summaries/FacePerception_hed_type_summary.json)
format summaries of the sample dataset
[**ds003645s_hed**](https://github.com/hed-standard/hed-examples/tree/main/datasets/eeg_ds003645s_hed)
-using the [**summarize_hed_types_rmdl.json**](./static/data/summarize_hed_types_rmdl.json)
+using the [**summarize_hed_types_rmdl.json**](./static/data/summaries/summarize_hed_types_rmdl.json)
remodeling file.
(summarize-hed-validation-anchor)=
diff --git a/docs/source/_static/data/summaries/FacePerception_column_values_summary.txt b/docs/source/_static/data/summaries/FacePerception_column_values_summary.txt
index 0662392..08ebbac 100644
--- a/docs/source/_static/data/summaries/FacePerception_column_values_summary.txt
+++ b/docs/source/_static/data/summaries/FacePerception_column_values_summary.txt
@@ -20,7 +20,7 @@ Dataset: Total events=1200 Total files=6
Individual files:
g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv:
-g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv: 200 events
+Total events=200
Categorical column values[Events, Files]:
event_type:
double_press[1, 1] left_press[20, 1] right_press[23, 1] setup_right_sym[1, 1] show_circle[52, 1] show_cross[51, 1] show_face[51, 1] show_face_initial[1, 1]
@@ -36,7 +36,7 @@ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv: 200 e
g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv:
-g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv: 200 events
+Total events=200
Categorical column values[Events, Files]:
event_type:
right_press[29, 1] setup_right_sym[1, 1] show_circle[57, 1] show_cross[56, 1] show_face[56, 1] show_face_initial[1, 1]
@@ -52,7 +52,7 @@ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv: 200 e
g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv:
-g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv: 200 events
+Total events=200
Categorical column values[Events, Files]:
event_type:
right_press[29, 1] setup_right_sym[1, 1] show_circle[57, 1] show_cross[56, 1] show_face[56, 1] show_face_initial[1, 1]
@@ -68,7 +68,7 @@ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv: 200 e
g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv:
-g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv: 200 events
+Total events=200
Categorical column values[Events, Files]:
event_type:
left_press[22, 1] right_press[28, 1] setup_right_sym[1, 1] show_circle[50, 1] show_cross[49, 1] show_face[49, 1] show_face_initial[1, 1]
@@ -84,7 +84,7 @@ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv: 200 e
g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv:
-g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv: 200 events
+Total events=200
Categorical column values[Events, Files]:
event_type:
left_press[17, 1] right_press[33, 1] setup_right_sym[1, 1] show_circle[50, 1] show_cross[49, 1] show_face[49, 1] show_face_initial[1, 1]
@@ -100,7 +100,7 @@ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv: 200 e
g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv:
-g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv: 200 events
+Total events=200
Categorical column values[Events, Files]:
event_type:
left_press[24, 1] right_press[26, 1] setup_right_sym[1, 1] show_circle[50, 1] show_cross[49, 1] show_face[49, 1] show_face_initial[1, 1]
diff --git a/docs/source/_static/data/summaries/FacePerception_hed_tag_summary.txt b/docs/source/_static/data/summaries/FacePerception_hed_tag_summary.txt
index d447f10..6b2bc0f 100644
--- a/docs/source/_static/data/summaries/FacePerception_hed_tag_summary.txt
+++ b/docs/source/_static/data/summaries/FacePerception_hed_tag_summary.txt
@@ -3,7 +3,7 @@ Context type: hed_tag_summary
Context filename: hed_tag_summary
Overall summary:
-Dataset: 6 files with a total of 1200 events
+Dataset: Total events=1200 Total files=6
Main tags[events,files]:
Sensory events:
@@ -22,7 +22,7 @@ Dataset: 6 files with a total of 1200 events
Individual files:
g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv:
-g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv: 200 events
+Total events=200
Main tags[events,files]:
Sensory events:
@@ -40,7 +40,7 @@ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv: 200 e
g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv:
-g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv: 200 events
+Total events=200
Main tags[events,files]:
Sensory events:
@@ -58,7 +58,7 @@ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv: 200 e
g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv:
-g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv: 200 events
+Total events=200
Main tags[events,files]:
Sensory events:
@@ -76,7 +76,7 @@ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv: 200 e
g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv:
-g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv: 200 events
+Total events=200
Main tags[events,files]:
Sensory events:
@@ -94,7 +94,7 @@ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv: 200 e
g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv:
-g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv: 200 events
+Total events=200
Main tags[events,files]:
Sensory events:
@@ -112,7 +112,7 @@ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv: 200 e
g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv:
-g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv: 200 events
+Total events=200
Main tags[events,files]:
Sensory events:
diff --git a/docs/source/_static/data/summaries/FacePerception_hed_type_summary.txt b/docs/source/_static/data/summaries/FacePerception_hed_type_summary.txt
index 54541bc..0386c10 100644
--- a/docs/source/_static/data/summaries/FacePerception_hed_type_summary.txt
+++ b/docs/source/_static/data/summaries/FacePerception_hed_type_summary.txt
@@ -3,12 +3,12 @@ Context type: hed_type_summary
Context filename: hed_type_summary
Overall summary:
-Dataset: 3 condition-variable types in 6 files with a total of 1200
- key-assignment: 1 levels in 1200 events out of 1200 total events in 6 files
+Dataset: Type=condition-variable Type values=3 Total events=1200 Total files=6
+ key-assignment: 1 levels in 1200 event(s) out of 1200 total events in 6 file(s)
right-sym-cond [1200 events, 6 files]:
Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
Description: Right index finger key press indicates a face with above average symmetry.
- face-type: 3 levels in 316 events out of 1200 total events in 6 files
+ face-type: 3 levels in 316 event(s) out of 1200 total events in 6 file(s)
unfamiliar-face-cond [105 events, 6 files]:
Tags: ['Image', 'Face', 'Unfamiliar']
Description: A face that should not be recognized by the participants.
@@ -18,7 +18,7 @@ Dataset: 3 condition-variable types in 6 files with a total of 1200
scrambled-face-cond [103 events, 6 files]:
Tags: ['Image', 'Face', 'Disordered']
Description: A scrambled face image generated by taking face 2D FFT.
- repetition-type: 3 levels in 316 events out of 1200 total events in 6 files
+ repetition-type: 3 levels in 316 event(s) out of 1200 total events in 6 file(s)
first-show-cond [168 events, 6 files]:
Tags: ['Item-count', 'Face', 'Item-interval']
Description: Factor level indicating the first display of this face.
@@ -32,7 +32,7 @@ Dataset: 3 condition-variable types in 6 files with a total of 1200
Individual files:
g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv:
- g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv: Type=condition-variable Total events=200
+Type=condition-variable Type values=3 Total events=200
key-assignment: 1 levels in 200 events
right-sym-cond [200 events, 1 files]:
Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
@@ -60,7 +60,7 @@ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-1_events.tsv:
g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv:
- g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv: Type=condition-variable Total events=200
+Type=condition-variable Type values=3 Total events=200
key-assignment: 1 levels in 200 events
right-sym-cond [200 events, 1 files]:
Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
@@ -88,7 +88,7 @@ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-2_events.tsv:
g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv:
- g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv: Type=condition-variable Total events=200
+Type=condition-variable Type values=3 Total events=200
key-assignment: 1 levels in 200 events
right-sym-cond [200 events, 1 files]:
Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
@@ -116,7 +116,7 @@ g:\eeg_ds003645s\sub-002\eeg\sub-002_task-FacePerception_run-3_events.tsv:
g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv:
- g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv: Type=condition-variable Total events=200
+Type=condition-variable Type values=3 Total events=200
key-assignment: 1 levels in 200 events
right-sym-cond [200 events, 1 files]:
Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
@@ -144,7 +144,7 @@ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-1_events.tsv:
g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv:
- g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv: Type=condition-variable Total events=200
+Type=condition-variable Type values=3 Total events=200
key-assignment: 1 levels in 200 events
right-sym-cond [200 events, 1 files]:
Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
@@ -172,7 +172,7 @@ g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-2_events.tsv:
g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv:
- g:\eeg_ds003645s\sub-003\eeg\sub-003_task-FacePerception_run-3_events.tsv: Type=condition-variable Total events=200
+Type=condition-variable Type values=3 Total events=200
key-assignment: 1 levels in 200 events
right-sym-cond [200 events, 1 files]:
Tags: ['Index-finger', 'Right-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Symmetrical', 'Index-finger', 'Left-side-of', 'Experiment-participant', 'Behavioral-evidence', 'Asymmetrical']
diff --git a/docs/source/_static/data/summaries/summarize_columns_rmdl.json b/docs/source/_static/data/summaries/summarize_columns_rmdl.json
new file mode 100644
index 0000000..fedeb93
--- /dev/null
+++ b/docs/source/_static/data/summaries/summarize_columns_rmdl.json
@@ -0,0 +1,20 @@
+[
+ {
+ "operation": "summarize_column_names",
+ "description": "Summarize the unique orders of column names.",
+ "parameters": {
+ "summary_name": "column name summary",
+ "summary_filename": "column_name_summary"
+ }
+ },
+ {
+ "operation": "summarize_column_values",
+ "description": "Summarize column values.",
+ "parameters": {
+ "summary_name": "column values summary",
+ "summary_filename": "column_values_summary",
+ "skip_columns": ["onset", "duration", "sample", "value"],
+ "value_columns": ["stim_file", "trial"]
+ }
+ }
+]
\ No newline at end of file
diff --git a/docs/source/_static/data/summaries/summarize_hed_tags_rmdl.json b/docs/source/_static/data/summaries/summarize_hed_tags_rmdl.json
new file mode 100644
index 0000000..e7f183a
--- /dev/null
+++ b/docs/source/_static/data/summaries/summarize_hed_tags_rmdl.json
@@ -0,0 +1,36 @@
+ [
+ {
+ "operation": "summarize_hed_tags",
+ "description": "Produce a summary of HED tags.",
+ "parameters": {
+ "summary_name": "hed_tag_summary",
+ "summary_filename": "hed_tag_summary",
+ "tags": {
+ "Sensory events": [
+ "Sensory-event",
+ "Sensory-presentation",
+ "Task-stimulus-role",
+ "Experimental-stimulus"
+ ],
+ "Agent actions": [
+ "Agent-action",
+ "Agent",
+ "Action",
+ "Agent-task-role",
+ "Task-action-type",
+ "Participant-response"
+ ],
+ "Task properties": [
+ "Task-property"
+ ],
+ "Objects": [
+ "Item"
+ ],
+ "Properties": [
+ "Property"
+ ]
+ },
+ "expand_context": false
+ }
+ }
+]
\ No newline at end of file
diff --git a/docs/source/_static/data/summaries/summarize_hed_types_rmdl.json b/docs/source/_static/data/summaries/summarize_hed_types_rmdl.json
new file mode 100644
index 0000000..fdae65e
--- /dev/null
+++ b/docs/source/_static/data/summaries/summarize_hed_types_rmdl.json
@@ -0,0 +1,11 @@
+[
+ {
+ "operation": "summarize_hed_type",
+ "description": "Summarize a hed type tag such as condition-variable",
+ "parameters": {
+ "summary_name": "hed type summary",
+ "summary_filename": "hed_type_summary",
+ "type_tag": "condition-variable"
+ }
+ }
+]