Repository: hed-standard/hed-python (language: python)

Issue:
validator would fail on "legit" "n/a" value
on "spacetop" dataset (which is yet to be made public) there is .tsv files with `n/a` in some .tsv's, which is the way BIDS mandates to code "missing values".  See https://bids-specification.readthedocs.io/en/stable/common-principles.html#tabular-files which states

> String values containing tabs MUST be escaped using double quotes. Missing and non-applicable values MUST be coded as `n/a`. Numerical values MUST employ the dot (`.`) as decimal separator and MAY be specified in scientific notation, using `e` or `E` to separate the significand from the exponent. TSV files MUST be in UTF-8 encoding.

I filed 
- https://github.com/bids-standard/bids-specification/issues/1938
though.

ATM for my sample dataset (not yet public on openneuro, attn @jungheejung - remind me if we do have it somewhere public meanwhile), a version of `hed-validator` (#1025) blows with

```
(deno) yoh@typhon:/mnt/DATA/data/yoh/1076_spacetop$ ~/bin/hed-validator .
Using HEDTOOLS version: {'date': '2024-06-14T17:02:33-0500', 'dirty': False, 'error': None, 'full-revisionid': '940e75ddcedd5a14910098b60277413edc3c024e', 'version': '0.5.0'}
Traceback (most recent call last):
  File "/home/yoh/bin/hed-validator", line 71, in <module>
    main()
  File "/home/yoh/bin/hed-validator", line 37, in main
    issue_list = bids.validate(check_for_warnings=args.check_for_warnings)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yoh/miniconda3/envs/deno/lib/python3.12/site-packages/hed/tools/bids/bids_dataset.py", line 84, in validate
    issues += files.validate_datafiles(self.schema, check_for_warnings=check_for_warnings)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yoh/miniconda3/envs/deno/lib/python3.12/site-packages/hed/tools/bids/bids_file_group.py", line 157, in validate_datafiles
    issues += data_obj.contents.validate(hed_schema, extra_def_dicts=extra_def_dicts, name=name,
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yoh/miniconda3/envs/deno/lib/python3.12/site-packages/hed/models/base_input.py", line 359, in validate
    validation_issues = tab_validator.validate(self, self._mapper.get_def_dict(hed_schema, extra_def_dicts), name,
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yoh/miniconda3/envs/deno/lib/python3.12/site-packages/hed/validator/spreadsheet_validator.py", line 63, in validate
    data_new._dataframe = df_util.sort_dataframe_by_onsets(data.dataframe)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yoh/miniconda3/envs/deno/lib/python3.12/site-packages/hed/models/df_util.py", line 118, in sort_dataframe_by_onsets
    df_copy['_temp_onset_sort'] = df_copy['onset'].astype(float)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yoh/miniconda3/envs/deno/lib/python3.12/site-packages/pandas/core/generic.py", line 6643, in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yoh/miniconda3/envs/deno/lib/python3.12/site-packages/pandas/core/internals/managers.py", line 430, in astype
    return self.apply(
           ^^^^^^^^^^^
  File "/home/yoh/miniconda3/envs/deno/lib/python3.12/site-packages/pandas/core/internals/managers.py", line 363, in apply
    applied = getattr(b, f)(**kwargs)
              ^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yoh/miniconda3/envs/deno/lib/python3.12/site-packages/pandas/core/internals/blocks.py", line 758, in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yoh/miniconda3/envs/deno/lib/python3.12/site-packages/pandas/core/dtypes/astype.py", line 237, in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yoh/miniconda3/envs/deno/lib/python3.12/site-packages/pandas/core/dtypes/astype.py", line 182, in astype_array
    values = _astype_nansafe(values, dtype, copy=copy)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/yoh/miniconda3/envs/deno/lib/python3.12/site-packages/pandas/core/dtypes/astype.py", line 133, in _astype_nansafe
    return arr.astype(dtype, copy=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: could not convert string to float: 'n/a'
```

note that it is also unclear on which file it blows -- so might be worth providing some feedback (logging ERROR level or just catch/enhance exception while working on a specific file) on which file it happens.

I think it is likely on `n/a` in onset field in some `_events.tsv`, e.g.

```
(deno) yoh@typhon:/mnt/DATA/data/yoh/1076_spacetop$ git grep '^n/a' | grep _events.tsv | head
sub-0001/ses-02/func/sub-0001_ses-02_task-faces_acq-mb8_run-01_events.tsv:n/a   n/a     rating_mouse_trajectory n/a     intensity       disgust male    African old     n/a
sub-0001/ses-02/func/sub-0001_ses-02_task-faces_acq-mb8_run-01_events.tsv:n/a   n/a     rating_mouse_trajectory n/a     intensity       happy   male    EA      young   n/a
sub-0001/ses-02/func/sub-0001_ses-02_task-faces_acq-mb8_run-02_events.tsv:n/a   n/a     rating_mouse_trajectory n/a     sex     happy   male    WC      old     n/a
```

FWIW -- we did not see similar crash while running deno bids-validator (I guess uses JS version?).

Modified validator to handle n/a in onset column



Discussion / hints:

Fix patch:
diff --git a/hed/errors/error_messages.py b/hed/errors/error_messages.py
index 06c9c651..bef3a420 100644
--- a/hed/errors/error_messages.py
+++ b/hed/errors/error_messages.py
@@ -25,9 +25,9 @@ def val_error_empty_group(tag):
     return f"HED tags cannot be empty.  Extra delimiters found: '{tag}'"
 
 
-@hed_tag_error(TemporalErrors.HED_ONSET_WITH_NO_COLUMN, actual_code=ValidationErrors.TEMPORAL_TAG_ERROR)
-def val_error_hed_onset_with_no_column(tag):
-    return f"Cannot have Temporal tags without an 'Onset' column.  Found tag: '{tag}'"
+@hed_tag_error(TemporalErrors.TEMPORAL_TAG_NO_TIME, actual_code=ValidationErrors.TEMPORAL_TAG_ERROR)
+def val_error_temporal_tag_no_time(tag):
+    return f"Cannot have Temporal tags without an 'Onset' column and a time.  Found tag: '{tag}'"
 
 
 @hed_tag_error(ValidationErrors.TAG_EXTENDED, has_sub_tag=True, default_severity=ErrorSeverity.WARNING)
@@ -77,9 +77,9 @@ def val_error_CURLY_BRACE_UNSUPPORTED_HERE(tag, problem_tag):
             f"Invalid character '{problem_tag}' in tag '{tag}'")
 
 
-@hed_error(ValidationErrors.ONSETS_OUT_OF_ORDER, default_severity=ErrorSeverity.WARNING)
-def val_error_ONSETS_OUT_OF_ORDER():
-    return "Onsets need to be temporally increasing for most downstream tools to work."
+@hed_error(ValidationErrors.ONSETS_UNORDERED, default_severity=ErrorSeverity.WARNING)
+def val_error_ONSETS_UNORDERED():
+    return "Onsets need to be temporally increasing and defined for many downstream tools to work."
 
 
 @hed_error(ValidationErrors.COMMA_MISSING)
diff --git a/hed/errors/error_reporter.py b/hed/errors/error_reporter.py
index 42b12842..b108c61b 100644
--- a/hed/errors/error_reporter.py
+++ b/hed/errors/error_reporter.py
@@ -1,686 +1,686 @@
-"""
-Support functions for reporting validation errors.
-
-You can scope the formatted errors with calls to push_error_context and pop_error_context.
-"""
-
-from functools import wraps
-import xml.etree.ElementTree as ET
-
-from hed.errors.error_types import ErrorContext, ErrorSeverity
-from hed.errors.known_error_codes import known_error_codes
-
-error_functions = {}
-
-# Controls if the default issue printing skips adding indentation for this context.
-no_tab_context = {ErrorContext.HED_STRING, ErrorContext.SCHEMA_ATTRIBUTE}
-
-# Default sort ordering for issues list.
-default_sort_list = [
-    ErrorContext.CUSTOM_TITLE,
-    ErrorContext.FILE_NAME,
-    ErrorContext.SIDECAR_COLUMN_NAME,
-    ErrorContext.SIDECAR_KEY_NAME,
-    ErrorContext.ROW,
-    ErrorContext.COLUMN,
-    ErrorContext.LINE,
-    ErrorContext.SCHEMA_SECTION,
-    ErrorContext.SCHEMA_TAG,
-    ErrorContext.SCHEMA_ATTRIBUTE,
-]
-
-# ErrorContext which is expected to be int based.
-int_sort_list = [
-    ErrorContext.ROW
-]
-
-
-def _register_error_function(error_type, wrapper_func):
-    if error_type in error_functions:
-        raise KeyError(f"{error_type} defined more than once.")
-
-    error_functions[error_type] = wrapper_func
-
-
-def hed_error(error_type, default_severity=ErrorSeverity.ERROR, actual_code=None):
-    """ Decorator for errors in error handler or inherited classes.
-
-    Parameters:
-        error_type (str): A value from error_types or optionally another value.
-        default_severity (ErrorSeverity): The default severity for the decorated error.
-        actual_code (str): The actual error to report to the outside world.
-
-    """
-    if actual_code is None:
-        actual_code = error_type
-
-    def inner_decorator(func):
-        @wraps(func)
-        def wrapper(*args, severity=default_severity, **kwargs):
-            """ Wrapper function for error handling non-tag errors.
-
-            Parameters:
-                args (args): non keyword args.
-                severity (ErrorSeverity): Will override the default error value if passed.
-                kwargs (**kwargs): Any keyword args to be passed down to error message function.
-
-            Returns:
-                list: A list of dict with the errors.
-            """
-            base_message = func(*args, **kwargs)
-            error_object = ErrorHandler._create_error_object(actual_code, base_message, severity)
-            return error_object
-
-        _register_error_function(error_type, wrapper_func=wrapper)
-        return wrapper
-
-    return inner_decorator
-
-
-def hed_tag_error(error_type, default_severity=ErrorSeverity.ERROR, has_sub_tag=False, actual_code=None):
-    """  Decorator for errors in error handler or inherited classes.
-
-    Parameters:
-        error_type (str): A value from error_types or optionally another value.
-        default_severity (ErrorSeverity): The default severity for the decorated error.
-        has_sub_tag (bool): If True, this error message also wants a sub_tag passed down.  eg "This" in "This/Is/A/Tag"
-        actual_code (str): The actual error to report to the outside world.
-
-    """
-    if actual_code is None:
-        actual_code = error_type
-
-    def inner_decorator(func):
-        if has_sub_tag:
-            @wraps(func)
-            def wrapper(tag, index_in_tag, index_in_tag_end, *args, severity=default_severity, **kwargs):
-                """ Wrapper function for error handling tag errors with sub tags.
-
-                Parameters:
-                    tag (HedTag): The HED tag object with the problem.
-                    index_in_tag (int): The index into the tag with a problem(usually 0).
-                    index_in_tag_end (int): The last index into the tag with a problem - usually len(tag).
-                    args (args): Any other non keyword args.
-                    severity (ErrorSeverity): Used to include warnings as well as errors.
-                    kwargs (**kwargs): Any keyword args to be passed down to error message function.
-
-                Returns:
-                    list: A list of dict with the errors.
-
-                """
-                try:
-                    tag_as_string = tag.tag
-                except AttributeError:
-                    tag_as_string = str(tag)
-
-                if index_in_tag_end is None:
-                    index_in_tag_end = len(tag_as_string)
-                problem_sub_tag = tag_as_string[index_in_tag: index_in_tag_end]
-                try:
-                    org_tag_text = tag.org_tag
-                except AttributeError:
-                    org_tag_text = str(tag)
-
-                base_message = func(org_tag_text, problem_sub_tag, *args, **kwargs)
-                error_object = ErrorHandler._create_error_object(actual_code, base_message, severity,
-                                                                 index_in_tag=index_in_tag,
-                                                                 index_in_tag_end=index_in_tag_end, source_tag=tag)
-
-                return error_object
-
-            _register_error_function(error_type, wrapper_func=wrapper)
-            return wrapper
-        else:
-            @wraps(func)
-            def wrapper(tag, *args, severity=default_severity, **kwargs):
-                """ Wrapper function for error handling tag errors.
-
-                Parameters:
-                    tag (HedTag or HedGroup): The HED tag object with the problem.
-                    args (non keyword args): Any other non keyword args.
-                    severity (ErrorSeverity): For including warnings.
-                    kwargs (keyword args): Any keyword args to be passed down to error message function.
-
-                Returns:
-                    list: A list of dict with the errors.
-
-                """
-                from hed.models.hed_tag import HedTag
-                from hed.models.hed_group import HedGroup
-                if isinstance(tag, HedTag):
-                    org_tag_text = tag.org_tag
-                elif isinstance(tag, HedGroup):
-                    org_tag_text = tag.get_original_hed_string()
-                else:
-                    org_tag_text = str(tag)
-                base_message = func(org_tag_text, *args, **kwargs)
-                error_object = ErrorHandler._create_error_object(actual_code, base_message, severity,
-                                                                 source_tag=tag)
-
-                return error_object
-
-            _register_error_function(error_type, wrapper_func=wrapper)
-            return wrapper
-
-    return inner_decorator
-
-
-# Import after hed_error decorators are defined.
-from hed.errors import error_messages  # noqa:E402
-from hed.errors import schema_error_messages  # noqa:E402
-
-# Intentional to make sure tools don't think the import is unused
-error_messages.mark_as_used = True
-schema_error_messages.mark_as_used = True
-
-
-class ErrorHandler:
-    """Class to hold error context and having general error functions."""
-    def __init__(self, check_for_warnings=True):
-        # The current (ordered) dictionary of contexts.
-        self.error_context = []
-        self._check_for_warnings = check_for_warnings
-
-    def push_error_context(self, context_type, context):
-        """ Push a new error context to narrow down error scope.
-
-        Parameters:
-            context_type (ErrorContext): A value from ErrorContext representing the type of scope.
-            context (str, int, or HedString): The main value for the context_type.
-
-        Notes:
-            The context depends on the context_type. For ErrorContext.FILE_NAME this would be the actual filename.
-
-        """
-        if context is None:
-            if context_type in int_sort_list:
-                context = 0
-            else:
-                context = ""
-        self.error_context.append((context_type, context))
-
-    def pop_error_context(self):
-        """ Remove the last scope from the error context.
-
-        Notes:
-            Modifies the error context of this reporter.
-
-        """
-
-        self.error_context.pop(-1)
-
-    def reset_error_context(self):
-        """ Reset all error context information to defaults.
-
-        Notes:
-            This function is mainly for testing and should not be needed with proper usage.
-
-        """
-        self.error_context = []
-
-    def format_error_with_context(self, *args, **kwargs):
-        error_object = ErrorHandler.format_error(*args, **kwargs)
-        if self is not None:
-            actual_error = error_object[0]
-            # # Filter out warning errors
-            if not self._check_for_warnings and actual_error['severity'] >= ErrorSeverity.WARNING:
-                return []
-            self._add_context_to_errors(actual_error, self.error_context)
-            self._update_error_with_char_pos(actual_error)
-
-        return error_object
-
-    @staticmethod
-    def format_error(error_type, *args, actual_error=None, **kwargs):
-        """ Format an error based on the parameters, which vary based on what type of error this is.
-
-        Parameters:
-            error_type (str): The type of error for this.  Registered with @hed_error or @hed_tag_error.
-            args (args): Any remaining non keyword args after those required by the error type.
-            actual_error (str or None): Code to actually add to report out.
-            kwargs (kwargs): The other keyword args to pass down to the error handling func.
-
-        Returns:
-            list:   A list containing a single dictionary representing a single error.
-
-        Notes:
-            The actual error is useful for errors that are shared like invalid character.
-
-        """
-        error_func = error_functions.get(error_type)
-        if not error_func:
-            error_object = ErrorHandler.val_error_unknown(*args, **kwargs)
-            error_object['code'] = error_type
-        else:
-            error_object = error_func(*args, **kwargs)
-
-        if actual_error:
-            error_object['code'] = actual_error
-
-        return [error_object]
-
-    def add_context_and_filter(self, issues):
-        """ Filter out warnings if requested, while adding context to issues.
-
-            issues(list):
-                list:   A list containing a single dictionary representing a single error.
-        """
-        if not self._check_for_warnings:
-            issues[:] = self.filter_issues_by_severity(issues, ErrorSeverity.ERROR)
-
-        for error_object in issues:
-            self._add_context_to_errors(error_object, self.error_context)
-            self._update_error_with_char_pos(error_object)
-
-    @staticmethod
-    def format_error_from_context(error_type, error_context, *args, actual_error=None, **kwargs):
-        """ Format an error based on the error type.
-
-        Parameters:
-            error_type (str): The type of error.  Registered with @hed_error or @hed_tag_error.
-            error_context (list): Contains the error context to use for this error.
-            args (args): Any remaining non keyword args.
-            actual_error (str or None): Error code to actually add to report out.
-            kwargs (kwargs): Keyword parameters to pass down to the error handling func.
-
-        Returns:
-            list:  A list containing a single dictionary.
-
-        Notes:
-            - Generally the error_context is returned from _add_context_to_errors.
-            - The actual_error is useful for errors that are shared like invalid character.
-            - This can't filter out warnings like the other ones.
-
-        """
-        error_list = ErrorHandler.format_error(error_type, *args, actual_error=actual_error, **kwargs)
-
-        ErrorHandler._add_context_to_errors(error_list[0], error_context)
-        ErrorHandler._update_error_with_char_pos(error_list[0])
-        return error_list
-
-    @staticmethod
-    def _add_context_to_errors(error_object, error_context_to_add):
-        """ Add relevant context such as row number or column name around an error object.
-
-        Parameters:
-            error_object (dict): Generated error containing at least a code and message entry.
-            error_context_to_add (list): Source context to use.  If none, the error handler context is used.
-
-        Returns:
-            list: A list of dict with needed context strings added at the beginning of the list.
-
-        """
-        for (context_type, context) in error_context_to_add:
-            error_object[context_type] = context
-
-        return error_object
-
-    @staticmethod
-    def _create_error_object(error_type, base_message, severity, **kwargs):
-        error_object = {'code': error_type,
-                        'message': base_message,
-                        'severity': severity
-                        }
-
-        for key, value in kwargs.items():
-            error_object.setdefault(key, value)
-
-        return error_object
-
-    @staticmethod
-    def _get_tag_span_to_error_object(error_object):
-        if ErrorContext.HED_STRING not in error_object:
-            return None, None
-
-        if 'source_tag' in error_object:
-            source_tag = error_object['source_tag']
-            if isinstance(source_tag, int):
-                return None, None
-        else:
-            return None, None
-
-        hed_string = error_object[ErrorContext.HED_STRING]
-        span = hed_string._get_org_span(source_tag)
-        return span
-
-    @staticmethod
-    def _update_error_with_char_pos(error_object):
-        # This part is optional as you can always generate these as needed.
-        start, end = ErrorHandler._get_tag_span_to_error_object(error_object)
-        if start is not None:
-            # silence warning in pycharm
-            start = int(start)
-            source_tag = error_object.get('source_tag', None)
-            # Todo: Move this functionality somewhere more centralized.
-            # If the tag has been modified from the original, don't try to use sub indexing.
-            if source_tag and source_tag._tag:
-                new_start, new_end = start, end
-            else:
-                new_start = start + error_object.get('index_in_tag', 0)
-                index_in_tag_end = end
-                if 'index_in_tag_end' in error_object:
-                    index_in_tag_end = start + error_object['index_in_tag_end']
-                new_end = index_in_tag_end
-            error_object['char_index'], error_object['char_index_end'] = new_start, new_end
-            error_object['message'] += f"  Problem spans string indexes: {new_start}, {new_end}"
-
-    @hed_error("Unknown")
-    def val_error_unknown(*args, **kwargs):
-        """ Default error handler if no error of this type was registered.
-
-        Parameters:
-            args (args):  List of non-keyword parameters (varies).
-            kwargs (kwargs): Keyword parameters (varies)
-
-        Returns:
-            str: The error message.
-
-        """
-        return f"Unknown error.  Args: {str(args), str(kwargs)}"
-
-    @staticmethod
-    def filter_issues_by_severity(issues_list, severity):
-        """ Gather all issues matching or below a given severity.
-
-        Parameters:
-            issues_list (list): A list of dictionaries containing the full issue list.
-            severity (int): The level of issues to keep.
-
-        Returns:
-            list: A list of dictionaries containing the issue list after filtering by severity.
-
-        """
-        return [issue for issue in issues_list if issue['severity'] <= severity]
-
-
-def sort_issues(issues, reverse=False):
-    """Sort a list of issues by the error context values.
-
-    Parameters:
-        issues (list): A list of dictionaries representing the issues to be sorted.
-        reverse (bool, optional): If True, sorts the list in descending order. Default is False.
-
-    Returns:
-        list: The sorted list of issues."""
-    def _get_keys(d):
-        result = []
-        for key in default_sort_list:
-            if key in int_sort_list:
-                result.append(d.get(key, -1))
-            else:
-                result.append(d.get(key, ""))
-        return tuple(result)
-
-    issues = sorted(issues, key=_get_keys, reverse=reverse)
-
-    return issues
-
-
-def check_for_any_errors(issues_list):
-    """ Return True if there are any errors with a severity of warning. """
-    for issue in issues_list:
-        if issue['severity'] < ErrorSeverity.WARNING:
-            return True
-
-    return False
-
-
-def get_printable_issue_string(issues, title=None, severity=None, skip_filename=True, add_link=False):
-    """ Return a string with issues list flatted into single string, one per line.
-
-    Parameters:
-        issues (list):  Issues to print.
-        title (str):  Optional title that will always show up first if present(even if there are no validation issues).
-        severity (int):        Return only warnings >= severity.
-        skip_filename (bool):  If True, don't add the filename context to the printable string.
-        add_link (bool): Add a link at the end of message to the appropriate error if True
-    Returns:
-        str:   A string containing printable version of the issues or ''.
-
-    """
-    if severity is not None:
-        issues = ErrorHandler.filter_issues_by_severity(issues, severity)
-
-    output_dict = _build_error_context_dict(issues, skip_filename)
-    issue_string = _error_dict_to_string(output_dict, add_link=add_link)
-
-    if title:
-        issue_string = title + '\n' + issue_string
-    return issue_string
-
-
-def get_printable_issue_string_html(issues, title=None, severity=None, skip_filename=True):
-    """ Return a string with issues list as an HTML tree.
-
-    Parameters:
-        issues (list):  Issues to print.
-        title (str):  Optional title that will always show up first if present.
-        severity (int): Return only warnings >= severity.
-        skip_filename (bool): If True, don't add the filename context to the printable string.
-
-    Returns:
-        str: An HTML string containing the issues or ''.
-    """
-    if severity is not None:
-        issues = ErrorHandler.filter_issues_by_severity(issues, severity)
-
-    output_dict = _build_error_context_dict(issues, skip_filename)
-
-    root_element = _create_error_tree(output_dict)
-    if title:
-        title_element = ET.Element("h1")
-        title_element.text = title
-        root_element.insert(0, title_element)
-    return ET.tostring(root_element, encoding='unicode')
-
-
-def create_doc_link(error_code):
-    """If error code is a known code, return a documentation url for it.
-
-    Parameters:
-        error_code(str): A HED error code.
-
-    Returns:
-        url(str or None): The URL if it's a valid code.
-    """
-    if error_code in known_error_codes["hed_validation_errors"] \
-            or error_code in known_error_codes["schema_validation_errors"]:
-        modified_error_code = error_code.replace("_", "-").lower()
-        return f"https://hed-specification.readthedocs.io/en/latest/Appendix_B.html#{modified_error_code}"
-    return None
-
-
-def _build_error_context_dict(issues, skip_filename):
-    """Build the context -> error dictionary for an entire list of issues.
-
-    Returns:
-        dict: A nested dictionary structure with a "children" key at each level for unrelated children.
-    """
-    output_dict = None
-    for single_issue in issues:
-        single_issue_context = _get_context_from_issue(single_issue, skip_filename)
-        output_dict = _add_single_error_to_dict(single_issue_context, output_dict, single_issue)
-
-    return output_dict
-
-
-def _add_single_error_to_dict(items, root=None, issue_to_add=None):
-    """ Build a nested dictionary out of the context lists.
-
-    Parameters:
-        items (list): A list of error contexts
-        root (dict, optional): An existing nested dictionary structure to update.
-        issue_to_add (dict, optional): The issue to add at this level of context.
-
-    Returns:
-        dict: A nested dictionary structure with a "children" key at each level for unrelated children.
-    """
-    if root is None:
-        root = {"children": []}
-
-    current_dict = root
-    for item in items:
-        # Navigate to the next level if the item already exists, or create a new level
-        next_dict = current_dict.get(item, {"children": []})
-        current_dict[item] = next_dict
-        current_dict = next_dict
-
-    if issue_to_add:
-        current_dict["children"].append(issue_to_add)
-
-    return root
-
-
-def _error_dict_to_string(print_dict, add_link=True, level=0):
-    output = ""
-    if print_dict is None:
-        return output
-    for context, value in print_dict.items():
-        if context == "children":
-            for child in value:
-                single_issue_message = child["message"]
-                issue_string = level * "\t" + _get_error_prefix(child)
-                issue_string += f"{single_issue_message}\n"
-                if add_link:
-                    link_url = create_doc_link(child['code'])
-                    if link_url:
-                        single_issue_message += f"   See... {link_url}"
-                output += issue_string
-            continue
-        output += _format_single_context_string(context[0], context[1], level)
-        output += _error_dict_to_string(value, add_link, level + 1)
-
-    return output
-
-
-def _get_context_from_issue(val_issue, skip_filename=True):
-    """ Extract all the context values from the given issue.
-
-    Parameters:
-        val_issue (dict): A dictionary a representing a single error.
-        skip_filename (bool): If True, don't gather the filename context.
-
-    Returns:
-        list: A list of tuples containing the context_type and context for the given issue.
-
-    """
-    single_issue_context = []
-    for key, value in val_issue.items():
-        if skip_filename and key == ErrorContext.FILE_NAME:
-            continue
-        if key == ErrorContext.HED_STRING:
-            value = value.get_original_hed_string()
-        if key.startswith("ec_"):
-            single_issue_context.append((key, str(value)))
-
-    return single_issue_context
-
-
-def _get_error_prefix(single_issue):
-    """Return the prefix for the error message based on severity and error code.
-
-    Parameters:
-        single_issue(dict): A single issue object.
-
-    Returns:
-        error_prefix(str):  the prefix to use.
-    """
-    severity = single_issue.get('severity', ErrorSeverity.ERROR)
-    error_code = single_issue['code']
-
-    if severity == ErrorSeverity.ERROR:
-        error_prefix = f"{error_code}: "
-    else:
-        error_prefix = f"{error_code}: (Warning) "
-    return error_prefix
-
-
-def _format_single_context_string(context_type, context, tab_count=0):
-    """ Return the human-readable form of a single context tuple.
-
-    Parameters:
-        context_type (str): The context type of this entry.
-        context (str or HedString): The value of this context.
-        tab_count (int): Number of tabs to name_prefix each line with.
-
-    Returns:
-        str: A string containing the context, including tabs.
-
-    """
-    tab_string = tab_count * '\t'
-    error_types = {
-        ErrorContext.FILE_NAME: f"\nErrors in file '{context}'",
-        ErrorContext.SIDECAR_COLUMN_NAME: f"Column '{context}':",
-        ErrorContext.SIDECAR_KEY_NAME: f"Key: {context}",
-        ErrorContext.ROW: f'Issues in row {context}:',
-        ErrorContext.COLUMN: f'Issues in column {context}:',
-        ErrorContext.CUSTOM_TITLE: context,
-        ErrorContext.LINE: f"Line: {context}",
-        ErrorContext.HED_STRING: f"hed string: {context}",
-        ErrorContext.SCHEMA_SECTION: f"Schema Section: {context}",
-        ErrorContext.SCHEMA_TAG: f"Source tag: {context}",
-        ErrorContext.SCHEMA_ATTRIBUTE: f"Source Attribute: {context}",
-    }
-    context_portion = error_types[context_type]
-    context_string = f"{tab_string}{context_portion}\n"
-    return context_string
-
-
-def _create_error_tree(error_dict, parent_element=None, add_link=True):
-    if parent_element is None:
-        parent_element = ET.Element("ul")
-
-    for context, value in error_dict.items():
-        if context == "children":
-            for child in value:
-                child_li = ET.SubElement(parent_element, "li")
-                error_prefix = _get_error_prefix(child)
-                single_issue_message = child["message"]
-
-                # Create a link for the error prefix if add_link is True.
-                if add_link:
-                    link_url = create_doc_link(child['code'])
-                    if link_url:
-                        a_element = ET.SubElement(child_li, "a", href=link_url)
-                        a_element.text = error_prefix
-                        a_element.tail = " " + single_issue_message
-                    else:
-                        child_li.text = error_prefix + " " + single_issue_message
-                else:
-                    child_li.text = error_prefix + " " + single_issue_message
-            continue
-
-        context_li = ET.SubElement(parent_element, "li")
-        context_li.text = _format_single_context_string(context[0], context[1])
-        context_ul = ET.SubElement(context_li, "ul")
-        _create_error_tree(value, context_ul, add_link)
-
-    return parent_element
-
-
-def replace_tag_references(list_or_dict):
-    """ Utility function to remove any references to tags, strings, etc. from any type of nested list or dict.
-
-       Use this if you want to save out issues to a file.
-
-       If you'd prefer a copy returned, use replace_tag_references(list_or_dict.copy()).
-
-    Parameters:
-       list_or_dict(list or dict): An arbitrarily nested list/dict structure
-    """
-    if isinstance(list_or_dict, dict):
-        for key, value in list_or_dict.items():
-            if isinstance(value, (dict, list)):
-                replace_tag_references(value)
-            elif isinstance(value, (bool, float, int)):
-                list_or_dict[key] = value
-            else:
-                list_or_dict[key] = str(value)
-    elif isinstance(list_or_dict, list):
-        for key, value in enumerate(list_or_dict):
-            if isinstance(value, (dict, list)):
-                replace_tag_references(value)
-            elif isinstance(value, (bool, float, int)):
-                list_or_dict[key] = value
-            else:
-                list_or_dict[key] = str(value)
+""""
+Support functions for reporting validation errors.
+
+You can scope the formatted errors with calls to push_error_context and pop_error_context.
+"""
+
+from functools import wraps
+import xml.etree.ElementTree as ET
+
+from hed.errors.error_types import ErrorContext, ErrorSeverity
+from hed.errors.known_error_codes import known_error_codes
+
+error_functions = {}
+
+# Controls if the default issue printing skips adding indentation for this context.
+no_tab_context = {ErrorContext.HED_STRING, ErrorContext.SCHEMA_ATTRIBUTE}
+
+# Default sort ordering for issues list.
+default_sort_list = [
+    ErrorContext.CUSTOM_TITLE,
+    ErrorContext.FILE_NAME,
+    ErrorContext.SIDECAR_COLUMN_NAME,
+    ErrorContext.SIDECAR_KEY_NAME,
+    ErrorContext.ROW,
+    ErrorContext.COLUMN,
+    ErrorContext.LINE,
+    ErrorContext.SCHEMA_SECTION,
+    ErrorContext.SCHEMA_TAG,
+    ErrorContext.SCHEMA_ATTRIBUTE,
+]
+
+# ErrorContext which is expected to be int based.
+int_sort_list = [
+    ErrorContext.ROW
+]
+
+
+def _register_error_function(error_type, wrapper_func):
+    if error_type in error_functions:
+        raise KeyError(f"{error_type} defined more than once.")
+
+    error_functions[error_type] = wrapper_func
+
+
+def hed_error(error_type, default_severity=ErrorSeverity.ERROR, actual_code=None):
+    """ Decorator for errors in error handler or inherited classes.
+
+    Parameters:
+        error_type (str): A value from error_types or optionally another value.
+        default_severity (ErrorSeverity): The default severity for the decorated error.
+        actual_code (str): The actual error to report to the outside world.
+
+    """
+    if actual_code is None:
+        actual_code = error_type
+
+    def inner_decorator(func):
+        @wraps(func)
+        def wrapper(*args, severity=default_severity, **kwargs):
+            """ Wrapper function for error handling non-tag errors.
+
+            Parameters:
+                args (args): non keyword args.
+                severity (ErrorSeverity): Will override the default error value if passed.
+                kwargs (**kwargs): Any keyword args to be passed down to error message function.
+
+            Returns:
+                list: A list of dict with the errors.
+            """
+            base_message = func(*args, **kwargs)
+            error_object = ErrorHandler._create_error_object(actual_code, base_message, severity)
+            return error_object
+
+        _register_error_function(error_type, wrapper_func=wrapper)
+        return wrapper
+
+    return inner_decorator
+
+
+def hed_tag_error(error_type, default_severity=ErrorSeverity.ERROR, has_sub_tag=False, actual_code=None):
+    """  Decorator for errors in error handler or inherited classes.
+
+    Parameters:
+        error_type (str): A value from error_types or optionally another value.
+        default_severity (ErrorSeverity): The default severity for the decorated error.
+        has_sub_tag (bool): If True, this error message also wants a sub_tag passed down.  eg "This" in "This/Is/A/Tag"
+        actual_code (str): The actual error to report to the outside world.
+
+    """
+    if actual_code is None:
+        actual_code = error_type
+
+    def inner_decorator(func):
+        if has_sub_tag:
+            @wraps(func)
+            def wrapper(tag, index_in_tag, index_in_tag_end, *args, severity=default_severity, **kwargs):
+                """ Wrapper function for error handling tag errors with sub tags.
+
+                Parameters:
+                    tag (HedTag): The HED tag object with the problem.
+                    index_in_tag (int): The index into the tag with a problem(usually 0).
+                    index_in_tag_end (int): The last index into the tag with a problem - usually len(tag).
+                    args (args): Any other non keyword args.
+                    severity (ErrorSeverity): Used to include warnings as well as errors.
+                    kwargs (**kwargs): Any keyword args to be passed down to error message function.
+
+                Returns:
+                    list: A list of dict with the errors.
+
+                """
+                try:
+                    tag_as_string = tag.tag
+                except AttributeError:
+                    tag_as_string = str(tag)
+
+                if index_in_tag_end is None:
+                    index_in_tag_end = len(tag_as_string)
+                problem_sub_tag = tag_as_string[index_in_tag: index_in_tag_end]
+                try:
+                    org_tag_text = tag.org_tag
+                except AttributeError:
+                    org_tag_text = str(tag)
+
+                base_message = func(org_tag_text, problem_sub_tag, *args, **kwargs)
+                error_object = ErrorHandler._create_error_object(actual_code, base_message, severity,
+                                                                 index_in_tag=index_in_tag,
+                                                                 index_in_tag_end=index_in_tag_end, source_tag=tag)
+
+                return error_object
+
+            _register_error_function(error_type, wrapper_func=wrapper)
+            return wrapper
+        else:
+            @wraps(func)
+            def wrapper(tag, *args, severity=default_severity, **kwargs):
+                """ Wrapper function for error handling tag errors.
+
+                Parameters:
+                    tag (HedTag or HedGroup): The HED tag object with the problem.
+                    args (non keyword args): Any other non keyword args.
+                    severity (ErrorSeverity): For including warnings.
+                    kwargs (keyword args): Any keyword args to be passed down to error message function.
+
+                Returns:
+                    list: A list of dict with the errors.
+
+                """
+                from hed.models.hed_tag import HedTag
+                from hed.models.hed_group import HedGroup
+                if isinstance(tag, HedTag):
+                    org_tag_text = tag.org_tag
+                elif isinstance(tag, HedGroup):
+                    org_tag_text = tag.get_original_hed_string()
+                else:
+                    org_tag_text = str(tag)
+                base_message = func(org_tag_text, *args, **kwargs)
+                error_object = ErrorHandler._create_error_object(actual_code, base_message, severity,
+                                                                 source_tag=tag)
+
+                return error_object
+
+            _register_error_function(error_type, wrapper_func=wrapper)
+            return wrapper
+
+    return inner_decorator
+
+
+# Import after hed_error decorators are defined.
+from hed.errors import error_messages  # noqa:E402
+from hed.errors import schema_error_messages  # noqa:E402
+
+# Intentional to make sure tools don't think the import is unused
+error_messages.mark_as_used = True
+schema_error_messages.mark_as_used = True
+
+
+class ErrorHandler:
+    """Class to hold error context and having general error functions."""
+    def __init__(self, check_for_warnings=True):
+        # The current (ordered) dictionary of contexts.
+        self.error_context = []
+        self._check_for_warnings = check_for_warnings
+
+    def push_error_context(self, context_type, context):
+        """ Push a new error context to narrow down error scope.
+
+        Parameters:
+            context_type (ErrorContext): A value from ErrorContext representing the type of scope.
+            context (str, int, or HedString): The main value for the context_type.
+
+        Notes:
+            The context depends on the context_type. For ErrorContext.FILE_NAME this would be the actual filename.
+
+        """
+        if context is None:
+            if context_type in int_sort_list:
+                context = 0
+            else:
+                context = ""
+        self.error_context.append((context_type, context))
+
+    def pop_error_context(self):
+        """ Remove the last scope from the error context.
+
+        Notes:
+            Modifies the error context of this reporter.
+
+        """
+
+        self.error_context.pop(-1)
+
+    def reset_error_context(self):
+        """ Reset all error context information to defaults.
+
+        Notes:
+            This function is mainly for testing and should not be needed with proper usage.
+
+        """
+        self.error_context = []
+
+    def format_error_with_context(self, *args, **kwargs):
+        error_object = ErrorHandler.format_error(*args, **kwargs)
+        if self is not None:
+            actual_error = error_object[0]
+            # # Filter out warning errors
+            if not self._check_for_warnings and actual_error['severity'] >= ErrorSeverity.WARNING:
+                return []
+            self._add_context_to_errors(actual_error, self.error_context)
+            self._update_error_with_char_pos(actual_error)
+
+        return error_object
+
+    @staticmethod
+    def format_error(error_type, *args, actual_error=None, **kwargs):
+        """ Format an error based on the parameters, which vary based on what type of error this is.
+
+        Parameters:
+            error_type (str): The type of error for this.  Registered with @hed_error or @hed_tag_error.
+            args (args): Any remaining non keyword args after those required by the error type.
+            actual_error (str or None): Code to actually add to report out.
+            kwargs (kwargs): The other keyword args to pass down to the error handling func.
+
+        Returns:
+            list:   A list containing a single dictionary representing a single error.
+
+        Notes:
+            The actual error is useful for errors that are shared like invalid character.
+
+        """
+        error_func = error_functions.get(error_type)
+        if not error_func:
+            error_object = ErrorHandler.val_error_unknown(*args, **kwargs)
+            error_object['code'] = error_type
+        else:
+            error_object = error_func(*args, **kwargs)
+
+        if actual_error:
+            error_object['code'] = actual_error
+
+        return [error_object]
+
+    def add_context_and_filter(self, issues):
+        """ Filter out warnings if requested, while adding context to issues.
+
+            issues(list):
+                list:   A list containing a single dictionary representing a single error.
+        """
+        if not self._check_for_warnings:
+            issues[:] = self.filter_issues_by_severity(issues, ErrorSeverity.ERROR)
+
+        for error_object in issues:
+            self._add_context_to_errors(error_object, self.error_context)
+            self._update_error_with_char_pos(error_object)
+
+    @staticmethod
+    def format_error_from_context(error_type, error_context, *args, actual_error=None, **kwargs):
+        """ Format an error based on the error type.
+
+        Parameters:
+            error_type (str): The type of error.  Registered with @hed_error or @hed_tag_error.
+            error_context (list): Contains the error context to use for this error.
+            args (args): Any remaining non keyword args.
+            actual_error (str or None): Error code to actually add to report out.
+            kwargs (kwargs): Keyword parameters to pass down to the error handling func.
+
+        Returns:
+            list:  A list containing a single dictionary.
+
+        Notes:
+            - Generally the error_context is returned from _add_context_to_errors.
+            - The actual_error is useful for errors that are shared like invalid character.
+            - This can't filter out warnings like the other ones.
+
+        """
+        error_list = ErrorHandler.format_error(error_type, *args, actual_error=actual_error, **kwargs)
+
+        ErrorHandler._add_context_to_errors(error_list[0], error_context)
+        ErrorHandler._update_error_with_char_pos(error_list[0])
+        return error_list
+
+    @staticmethod
+    def _add_context_to_errors(error_object, error_context_to_add):
+        """ Add relevant context such as row number or column name around an error object.
+
+        Parameters:
+            error_object (dict): Generated error containing at least a code and message entry.
+            error_context_to_add (list): Source context to use.  If none, the error handler context is used.
+
+        Returns:
+            list: A list of dict with needed context strings added at the beginning of the list.
+
+        """
+        for (context_type, context) in error_context_to_add:
+            error_object[context_type] = context
+
+        return error_object
+
+    @staticmethod
+    def _create_error_object(error_type, base_message, severity, **kwargs):
+        error_object = {'code': error_type,
+                        'message': base_message,
+                        'severity': severity
+                        }
+
+        for key, value in kwargs.items():
+            error_object.setdefault(key, value)
+
+        return error_object
+
+    @staticmethod
+    def _get_tag_span_to_error_object(error_object):
+        if ErrorContext.HED_STRING not in error_object:
+            return None, None
+
+        if 'source_tag' in error_object:
+            source_tag = error_object['source_tag']
+            if isinstance(source_tag, int):
+                return None, None
+        else:
+            return None, None
+
+        hed_string = error_object[ErrorContext.HED_STRING]
+        span = hed_string._get_org_span(source_tag)
+        return span
+
+    @staticmethod
+    def _update_error_with_char_pos(error_object):
+        # This part is optional as you can always generate these as needed.
+        start, end = ErrorHandler._get_tag_span_to_error_object(error_object)
+        if start is not None:
+            # silence warning in pycharm
+            start = int(start)
+            source_tag = error_object.get('source_tag', None)
+            # Todo: Move this functionality somewhere more centralized.
+            # If the tag has been modified from the original, don't try to use sub indexing.
+            if source_tag and source_tag._tag:
+                new_start, new_end = start, end
+            else:
+                new_start = start + error_object.get('index_in_tag', 0)
+                index_in_tag_end = end
+                if 'index_in_tag_end' in error_object:
+                    index_in_tag_end = start + error_object['index_in_tag_end']
+                new_end = index_in_tag_end
+            error_object['char_index'], error_object['char_index_end'] = new_start, new_end
+            error_object['message'] += f"  Problem spans string indexes: {new_start}, {new_end}"
+
+    @hed_error("Unknown")
+    def val_error_unknown(*args, **kwargs):
+        """ Default error handler if no error of this type was registered.
+
+        Parameters:
+            args (args):  List of non-keyword parameters (varies).
+            kwargs (kwargs): Keyword parameters (varies)
+
+        Returns:
+            str: The error message.
+
+        """
+        return f"Unknown error.  Args: {str(args), str(kwargs)}"
+
+    @staticmethod
+    def filter_issues_by_severity(issues_list, severity):
+        """ Gather all issues matching or below a given severity.
+
+        Parameters:
+            issues_list (list): A list of dictionaries containing the full issue list.
+            severity (int): The level of issues to keep.
+
+        Returns:
+            list: A list of dictionaries containing the issue list after filtering by severity.
+
+        """
+        return [issue for issue in issues_list if issue['severity'] <= severity]
+
+
+def sort_issues(issues, reverse=False):
+    """Sort a list of issues by the error context values.
+
+    Parameters:
+        issues (list): A list of dictionaries representing the issues to be sorted.
+        reverse (bool, optional): If True, sorts the list in descending order. Default is False.
+
+    Returns:
+        list: The sorted list of issues."""
+    def _get_keys(d):
+        result = []
+        for key in default_sort_list:
+            if key in int_sort_list:
+                result.append(d.get(key, -1))
+            else:
+                result.append(d.get(key, ""))
+        return tuple(result)
+
+    issues = sorted(issues, key=_get_keys, reverse=reverse)
+
+    return issues
+
+
+def check_for_any_errors(issues_list):
+    """ Return True if there are any errors with a severity of warning. """
+    for issue in issues_list:
+        if issue['severity'] < ErrorSeverity.WARNING:
+            return True
+
+    return False
+
+
+def get_printable_issue_string(issues, title=None, severity=None, skip_filename=True, add_link=False):
+    """ Return a string with issues list flatted into single string, one per line.
+
+    Parameters:
+        issues (list):  Issues to print.
+        title (str):  Optional title that will always show up first if present(even if there are no validation issues).
+        severity (int):        Return only warnings >= severity.
+        skip_filename (bool):  If True, don't add the filename context to the printable string.
+        add_link (bool): Add a link at the end of message to the appropriate error if True
+    Returns:
+        str:   A string containing printable version of the issues or ''.
+
+    """
+    if severity is not None:
+        issues = ErrorHandler.filter_issues_by_severity(issues, severity)
+
+    output_dict = _build_error_context_dict(issues, skip_filename)
+    issue_string = _error_dict_to_string(output_dict, add_link=add_link)
+
+    if title:
+        issue_string = title + '\n' + issue_string
+    return issue_string
+
+
+def get_printable_issue_string_html(issues, title=None, severity=None, skip_filename=True):
+    """ Return a string with issues list as an HTML tree.
+
+    Parameters:
+        issues (list):  Issues to print.
+        title (str):  Optional title that will always show up first if present.
+        severity (int): Return only warnings >= severity.
+        skip_filename (bool): If True, don't add the filename context to the printable string.
+
+    Returns:
+        str: An HTML string containing the issues or ''.
+    """
+    if severity is not None:
+        issues = ErrorHandler.filter_issues_by_severity(issues, severity)
+
+    output_dict = _build_error_context_dict(issues, skip_filename)
+
+    root_element = _create_error_tree(output_dict)
+    if title:
+        title_element = ET.Element("h1")
+        title_element.text = title
+        root_element.insert(0, title_element)
+    return ET.tostring(root_element, encoding='unicode')
+
+
+def create_doc_link(error_code):
+    """If error code is a known code, return a documentation url for it.
+
+    Parameters:
+        error_code(str): A HED error code.
+
+    Returns:
+        url(str or None): The URL if it's a valid code.
+    """
+    if error_code in known_error_codes["hed_validation_errors"] \
+            or error_code in known_error_codes["schema_validation_errors"]:
+        modified_error_code = error_code.replace("_", "-").lower()
+        return f"https://hed-specification.readthedocs.io/en/latest/Appendix_B.html#{modified_error_code}"
+    return None
+
+
+def _build_error_context_dict(issues, skip_filename):
+    """Build the context -> error dictionary for an entire list of issues.
+
+    Returns:
+        dict: A nested dictionary structure with a "children" key at each level for unrelated children.
+    """
+    output_dict = None
+    for single_issue in issues:
+        single_issue_context = _get_context_from_issue(single_issue, skip_filename)
+        output_dict = _add_single_error_to_dict(single_issue_context, output_dict, single_issue)
+
+    return output_dict
+
+
+def _add_single_error_to_dict(items, root=None, issue_to_add=None):
+    """ Build a nested dictionary out of the context lists.
+
+    Parameters:
+        items (list): A list of error contexts
+        root (dict, optional): An existing nested dictionary structure to update.
+        issue_to_add (dict, optional): The issue to add at this level of context.
+
+    Returns:
+        dict: A nested dictionary structure with a "children" key at each level for unrelated children.
+    """
+    if root is None:
+        root = {"children": []}
+
+    current_dict = root
+    for item in items:
+        # Navigate to the next level if the item already exists, or create a new level
+        next_dict = current_dict.get(item, {"children": []})
+        current_dict[item] = next_dict
+        current_dict = next_dict
+
+    if issue_to_add:
+        current_dict["children"].append(issue_to_add)
+
+    return root
+
+
+def _error_dict_to_string(print_dict, add_link=True, level=0):
+    output = ""
+    if print_dict is None:
+        return output
+    for context, value in print_dict.items():
+        if context == "children":
+            for child in value:
+                single_issue_message = child["message"]
+                issue_string = level * "\t" + _get_error_prefix(child)
+                issue_string += f"{single_issue_message}\n"
+                if add_link:
+                    link_url = create_doc_link(child['code'])
+                    if link_url:
+                        single_issue_message += f"   See... {link_url}"
+                output += issue_string
+            continue
+        output += _format_single_context_string(context[0], context[1], level)
+        output += _error_dict_to_string(value, add_link, level + 1)
+
+    return output
+
+
+def _get_context_from_issue(val_issue, skip_filename=True):
+    """ Extract all the context values from the given issue.
+
+    Parameters:
+        val_issue (dict): A dictionary a representing a single error.
+        skip_filename (bool): If True, don't gather the filename context.
+
+    Returns:
+        list: A list of tuples containing the context_type and context for the given issue.
+
+    """
+    single_issue_context = []
+    for key, value in val_issue.items():
+        if skip_filename and key == ErrorContext.FILE_NAME:
+            continue
+        if key == ErrorContext.HED_STRING:
+            value = value.get_original_hed_string()
+        if key.startswith("ec_"):
+            single_issue_context.append((key, str(value)))
+
+    return single_issue_context
+
+
+def _get_error_prefix(single_issue):
+    """Return the prefix for the error message based on severity and error code.
+
+    Parameters:
+        single_issue(dict): A single issue object.
+
+    Returns:
+        error_prefix(str):  the prefix to use.
+    """
+    severity = single_issue.get('severity', ErrorSeverity.ERROR)
+    error_code = single_issue['code']
+
+    if severity == ErrorSeverity.ERROR:
+        error_prefix = f"{error_code}: "
+    else:
+        error_prefix = f"{error_code}: (Warning) "
+    return error_prefix
+
+
+def _format_single_context_string(context_type, context, tab_count=0):
+    """ Return the human-readable form of a single context tuple.
+
+    Parameters:
+        context_type (str): The context type of this entry.
+        context (str or HedString): The value of this context.
+        tab_count (int): Number of tabs to name_prefix each line with.
+
+    Returns:
+        str: A string containing the context, including tabs.
+
+    """
+    tab_string = tab_count * '\t'
+    error_types = {
+        ErrorContext.FILE_NAME: f"\nErrors in file '{context}'",
+        ErrorContext.SIDECAR_COLUMN_NAME: f"Column '{context}':",
+        ErrorContext.SIDECAR_KEY_NAME: f"Key: {context}",
+        ErrorContext.ROW: f'Issues in row {context}:',
+        ErrorContext.COLUMN: f'Issues in column {context}:',
+        ErrorContext.CUSTOM_TITLE: context,
+        ErrorContext.LINE: f"Line: {context}",
+        ErrorContext.HED_STRING: f"hed string: {context}",
+        ErrorContext.SCHEMA_SECTION: f"Schema Section: {context}",
+        ErrorContext.SCHEMA_TAG: f"Source tag: {context}",
+        ErrorContext.SCHEMA_ATTRIBUTE: f"Source Attribute: {context}",
+    }
+    context_portion = error_types[context_type]
+    context_string = f"{tab_string}{context_portion}\n"
+    return context_string
+
+
+def _create_error_tree(error_dict, parent_element=None, add_link=True):
+    if parent_element is None:
+        parent_element = ET.Element("ul")
+
+    for context, value in error_dict.items():
+        if context == "children":
+            for child in value:
+                child_li = ET.SubElement(parent_element, "li")
+                error_prefix = _get_error_prefix(child)
+                single_issue_message = child["message"]
+
+                # Create a link for the error prefix if add_link is True.
+                if add_link:
+                    link_url = create_doc_link(child['code'])
+                    if link_url:
+                        a_element = ET.SubElement(child_li, "a", href=link_url)
+                        a_element.text = error_prefix
+                        a_element.tail = " " + single_issue_message
+                    else:
+                        child_li.text = error_prefix + " " + single_issue_message
+                else:
+                    child_li.text = error_prefix + " " + single_issue_message
+            continue
+
+        context_li = ET.SubElement(parent_element, "li")
+        context_li.text = _format_single_context_string(context[0], context[1])
+        context_ul = ET.SubElement(context_li, "ul")
+        _create_error_tree(value, context_ul, add_link)
+
+    return parent_element
+
+
+def replace_tag_references(list_or_dict):
+    """ Utility function to remove any references to tags, strings, etc. from any type of nested list or dict.
+
+       Use this if you want to save out issues to a file.
+
+       If you'd prefer a copy returned, use replace_tag_references(list_or_dict.copy()).
+
+    Parameters:
+       list_or_dict(list or dict): An arbitrarily nested list/dict structure
+    """
+    if isinstance(list_or_dict, dict):
+        for key, value in list_or_dict.items():
+            if isinstance(value, (dict, list)):
+                replace_tag_references(value)
+            elif isinstance(value, (bool, float, int)):
+                list_or_dict[key] = value
+            else:
+                list_or_dict[key] = str(value)
+    elif isinstance(list_or_dict, list):
+        for key, value in enumerate(list_or_dict):
+            if isinstance(value, (dict, list)):
+                replace_tag_references(value)
+            elif isinstance(value, (bool, float, int)):
+                list_or_dict[key] = value
+            else:
+                list_or_dict[key] = str(value)
diff --git a/hed/errors/error_types.py b/hed/errors/error_types.py
index b07e3544..1ca1e047 100644
--- a/hed/errors/error_types.py
+++ b/hed/errors/error_types.py
@@ -93,7 +93,7 @@ class ValidationErrors:
     INVALID_TAG_CHARACTER = 'invalidTagCharacter'
 
     CURLY_BRACE_UNSUPPORTED_HERE = "CURLY_BRACE_UNSUPPORTED_HERE"
-    ONSETS_OUT_OF_ORDER = "ONSETS_OUT_OF_ORDER"
+    ONSETS_UNORDERED = "ONSETS_UNORDERED"
 
 
 class SidecarErrors:
@@ -176,7 +176,7 @@ class TemporalErrors:
     ONSET_TAG_OUTSIDE_OF_GROUP = "ONSET_TAG_OUTSIDE_OF_GROUP"
     INSET_BEFORE_ONSET = "INSET_BEFORE_ONSET"
     ONSET_SAME_DEFS_ONE_ROW = "ONSET_SAME_DEFS_ONE_ROW"
-    HED_ONSET_WITH_NO_COLUMN = 'HED_ONSET_WITH_NO_COLUMN'
+    TEMPORAL_TAG_NO_TIME = 'TEMPORAL_TAG_NO_TIME'
 
     DURATION_HAS_OTHER_TAGS = "DURATION_HAS_OTHER_TAGS"
     DURATION_WRONG_NUMBER_GROUPS = "DURATION_WRONG_NUMBER_GROUPS"
diff --git a/hed/models/base_input.py b/hed/models/base_input.py
index 24dc033c..f02ffe62 100644
--- a/hed/models/base_input.py
+++ b/hed/models/base_input.py
@@ -1,477 +1,480 @@
-"""
-Superclass representing a basic columnar file.
-"""
-import os
-
-import openpyxl
-import pandas as pd
-
-from hed.models.column_mapper import ColumnMapper
-from hed.errors.exceptions import HedFileError, HedExceptions
-
-from hed.models.df_util import _handle_curly_braces_refs, filter_series_by_onset
-
-
-class BaseInput:
-    """ Superclass representing a basic columnar file. """
-
-    TEXT_EXTENSION = ['.tsv', '.txt']
-    EXCEL_EXTENSION = ['.xlsx']
-
-    def __init__(self, file, file_type=None, worksheet_name=None, has_column_names=True, mapper=None, name=None,
-                 allow_blank_names=True):
-        """ Constructor for the BaseInput class.
-
-        Parameters:
-            file (str or file-like or pd.Dataframe): An xlsx/tsv file to open.
-            file_type (str or None): ".xlsx" (Excel), ".tsv" or ".txt" (tab-separated text).
-                Derived from file if file is a filename.  Ignored if pandas dataframe.
-            worksheet_name (str or None): Name of Excel workbook worksheet name to use.
-                (Not applicable to tsv files.)
-            has_column_names (bool): True if file has column names.
-                This value is ignored if you pass in a pandas dataframe.
-            mapper (ColumnMapper or None):  Indicates which columns have HED tags.
-                See SpreadsheetInput or TabularInput for examples of how to use built-in a ColumnMapper.
-            name (str or None): Optional field for how this file will report errors.
-            allow_blank_names(bool): If True, column names can be blank
-
-        :raises HedFileError:
-            - file is blank.
-            - An invalid dataframe was passed with size 0.
-            - An invalid extension was provided.
-            - A duplicate or empty column name appears.
-            - Cannot open the indicated file.
-            - The specified worksheet name does not exist.
-            - If the sidecar file or tabular file had invalid format and could not be read.
-
-         """
-        if mapper is None:
-            mapper = ColumnMapper()
-        self._mapper = mapper
-        self._has_column_names = has_column_names
-        self._name = name
-        # This is the loaded workbook if we loaded originally from an Excel file.
-        self._loaded_workbook = None
-        self._worksheet_name = worksheet_name
-        self._dataframe = None
-
-        input_type = file_type
-        if isinstance(file, str):
-            if file_type is None:
-                _, input_type = os.path.splitext(file)
-            if self.name is None:
-                self._name = file
-
-        self._open_dataframe_file(file, has_column_names, input_type)
-
-        column_issues = ColumnMapper.check_for_blank_names(self.columns, allow_blank_names=allow_blank_names)
-        if column_issues:
-            raise HedFileError(HedExceptions.BAD_COLUMN_NAMES, "Duplicate or blank columns found. See issues.",
-                               self.name, issues=column_issues)
-
-        self.reset_mapper(mapper)
-
-    def reset_mapper(self, new_mapper):
-        """ Set mapper to a different view of the file.
-
-        Parameters:
-            new_mapper (ColumnMapper): A column mapper to be associated with this base input.
-        """
-        self._mapper = new_mapper
-        if not self._mapper:
-            self._mapper = ColumnMapper()
-
-        if self._dataframe is not None and self._has_column_names:
-            columns = self._dataframe.columns
-            self._mapper.set_column_map(columns)
-
-    @property
-    def dataframe(self):
-        """ The underlying dataframe. """
-        return self._dataframe
-
-    @property
-    def dataframe_a(self):
-        """Return the assembled dataframe Probably a placeholder name.
-
-        Returns:
-            Dataframe: the assembled dataframe"""
-        return self.assemble()
-
-    @property
-    def series_a(self):
-        """Return the assembled dataframe as a series.
-
-        Returns:
-            Series: the assembled dataframe with columns merged.
-        """
-        return self.combine_dataframe(self.assemble())
-
-    @property
-    def series_filtered(self):
-        """Return the assembled dataframe as a series, with rows that have the same onset combined.
-
-        Returns:
-            Series or None: the assembled dataframe with columns merged, and the rows filtered together.
-        """
-        if self.onsets is not None:
-            return filter_series_by_onset(self.series_a, self.onsets)
-
-    @property
-    def onsets(self):
-        """Return the onset column if it exists. """
-        if "onset" in self.columns:
-            return self._dataframe["onset"]
-
-    @property
-    def needs_sorting(self):
-        """Return True if this both has an onset column, and it needs sorting."""
-        onsets = self.onsets
-        if onsets is not None:
-            onsets = pd.to_numeric(self.dataframe['onset'], errors='coerce')
-            return not onsets.is_monotonic_increasing
-
-    @property
-    def name(self):
-        """ Name of the data. """
-        return self._name
-
-    @property
-    def has_column_names(self):
-        """ True if dataframe has column names. """
-        return self._has_column_names
-
-    @property
-    def loaded_workbook(self):
-        """ The underlying loaded workbooks. """
-        return self._loaded_workbook
-
-    @property
-    def worksheet_name(self):
-        """ The worksheet name. """
-        return self._worksheet_name
-
-    def convert_to_form(self, hed_schema, tag_form):
-        """ Convert all tags in underlying dataframe to the specified form.
-
-        Parameters:
-            hed_schema (HedSchema): The schema to use to convert tags.
-            tag_form(str): HedTag property to convert tags to.
-                Most cases should use convert_to_short or convert_to_long below.
-        """
-        from hed.models.df_util import convert_to_form
-        convert_to_form(self._dataframe, hed_schema, tag_form, self._mapper.get_tag_columns())
-
-    def convert_to_short(self, hed_schema):
-        """ Convert all tags in underlying dataframe to short form.
-
-        Parameters:
-            hed_schema (HedSchema): The schema to use to convert tags.
-        """
-        return self.convert_to_form(hed_schema, "short_tag")
-
-    def convert_to_long(self, hed_schema):
-        """ Convert all tags in underlying dataframe to long form.
-
-        Parameters:
-            hed_schema (HedSchema or None): The schema to use to convert tags.
-        """
-        return self.convert_to_form(hed_schema, "long_tag")
-
-    def shrink_defs(self, hed_schema):
-        """ Shrinks any def-expand found in the underlying dataframe.
-
-        Parameters:
-            hed_schema (HedSchema or None): The schema to use to identify defs.
-        """
-        from df_util import shrink_defs
-        shrink_defs(self._dataframe, hed_schema=hed_schema, columns=self._mapper.get_tag_columns())
-
-    def expand_defs(self, hed_schema, def_dict):
-        """ Shrinks any def-expand found in the underlying dataframe.
-
-        Parameters:
-            hed_schema (HedSchema or None): The schema to use to identify defs.
-            def_dict (DefinitionDict): The definitions to expand.
-        """
-        from df_util import expand_defs
-        expand_defs(self._dataframe, hed_schema=hed_schema, def_dict=def_dict, columns=self._mapper.get_tag_columns())
-
-    def to_excel(self, file):
-        """ Output to an Excel file.
-
-        Parameters:
-            file (str or file-like): Location to save this base input.
-
-        :raises ValueError:
-            - If empty file object was passed.
-
-        :raises OSError:
-            - Cannot open the indicated file.
-        """
-        if not file:
-            raise ValueError("Empty file name or object passed in to BaseInput.save.")
-
-        dataframe = self._dataframe
-        if self._loaded_workbook:
-            old_worksheet = self.get_worksheet(self._worksheet_name)
-            # Excel spreadsheets are 1 based, then add another 1 for column names if present
-            adj_row_for_col_names = 1
-            if self._has_column_names:
-                adj_row_for_col_names += 1
-            adj_for_one_based_cols = 1
-            for row_number, text_file_row in dataframe.iterrows():
-                for column_number, column_text in enumerate(text_file_row):
-                    cell_value = dataframe.iloc[row_number, column_number]
-                    old_worksheet.cell(row_number + adj_row_for_col_names,
-                                       column_number + adj_for_one_based_cols).value = cell_value
-
-            self._loaded_workbook.save(file)
-        else:
-            dataframe.to_excel(file, header=self._has_column_names)
-
-    def to_csv(self, file=None):
-        """ Write to file or return as a string.
-
-        Parameters:
-            file (str, file-like, or None): Location to save this file. If None, return as string.
-        Returns:
-            None or str:  None if file is given or the contents as a str if file is None.
-
-        :raises OSError:
-            - Cannot open the indicated file.
-        """
-        dataframe = self._dataframe
-        csv_string_if_filename_none = dataframe.to_csv(file, sep='\t', index=False, header=self._has_column_names)
-        return csv_string_if_filename_none
-
-    @property
-    def columns(self):
-        """ Returns a list of the column names.
-
-            Empty if no column names.
-
-        Returns:
-            columns(list): The column names.
-        """
-        columns = []
-        if self._dataframe is not None and self._has_column_names:
-            columns = list(self._dataframe.columns)
-        return columns
-
-    def column_metadata(self):
-        """ Return the metadata for each column.
-
-        Returns:
-            dict: Number/ColumnMeta pairs.
-        """
-        if self._mapper:
-            return self._mapper._final_column_map
-        return {}
-
-    def set_cell(self, row_number, column_number, new_string_obj, tag_form="short_tag"):
-        """ Replace the specified cell with transformed text.
-
-        Parameters:
-            row_number (int):    The row number of the spreadsheet to set.
-            column_number (int): The column number of the spreadsheet to set.
-            new_string_obj (HedString): Object with text to put in the given cell.
-            tag_form (str): Version of the tags (short_tag, long_tag, base_tag, etc)
-
-        Notes:
-             Any attribute of a HedTag that returns a string is a valid value of tag_form.
-
-        :raises ValueError:
-            - There is not a loaded dataframe.
-
-        :raises KeyError:
-            - The indicated row/column does not exist.
-
-        :raises AttributeError:
-            - The indicated tag_form is not an attribute of HedTag.
-        """
-        if self._dataframe is None:
-            raise ValueError("No data frame loaded")
-
-        new_text = new_string_obj.get_as_form(tag_form)
-        self._dataframe.iloc[row_number, column_number] = new_text
-
-    def get_worksheet(self, worksheet_name=None):
-        """ Get the requested worksheet.
-
-        Parameters:
-            worksheet_name (str or None): The name of the requested worksheet by name or the first one if None.
-
-        Returns:
-            openpyxl.workbook.Workbook: The workbook request.
-
-        Notes:
-            If None, returns the first worksheet.
-
-        :raises KeyError:
-            - The specified worksheet name does not exist.
-        """
-        if worksheet_name and self._loaded_workbook:
-            # return self._loaded_workbook.get_sheet_by_name(worksheet_name)
-            return self._loaded_workbook[worksheet_name]
-        elif self._loaded_workbook:
-            return self._loaded_workbook.worksheets[0]
-        else:
-            return None
-
-    @staticmethod
-    def _get_dataframe_from_worksheet(worksheet, has_headers):
-        """ Create a dataframe from the worksheet.
-
-        Parameters:
-            worksheet (Worksheet): The loaded worksheet to convert.
-            has_headers (bool): True if this worksheet has column headers.
-
-        Returns:
-            DataFrame: The converted data frame.
-
-        """
-        if has_headers:
-            data = worksheet.values
-            # first row is columns
-            cols = next(data)
-            data = list(data)
-            return pd.DataFrame(data, columns=cols, dtype=str)
-        else:
-            return pd.DataFrame(worksheet.values, dtype=str)
-
-    def validate(self, hed_schema, extra_def_dicts=None, name=None, error_handler=None):
-        """Creates a SpreadsheetValidator and returns all issues with this file.
-
-        Parameters:
-            hed_schema(HedSchema): The schema to use for validation.
-            extra_def_dicts(list of DefDict or DefDict): All definitions to use for validation.
-            name(str): The name to report errors from this file as.
-            error_handler (ErrorHandler): Error context to use.  Creates a new one if None.
-
-        Returns:
-            issues (list of dict): A list of issues for a HED string.
-        """
-        from hed.validator.spreadsheet_validator import SpreadsheetValidator
-        if not name:
-            name = self.name
-        tab_validator = SpreadsheetValidator(hed_schema)
-        validation_issues = tab_validator.validate(self, self._mapper.get_def_dict(hed_schema, extra_def_dicts), name,
-                                                   error_handler=error_handler)
-        return validation_issues
-
-    @staticmethod
-    def _dataframe_has_names(dataframe):
-        for column in dataframe.columns:
-            if isinstance(column, str):
-                return True
-        return False
-
-    def assemble(self, mapper=None, skip_curly_braces=False):
-        """ Assembles the HED strings.
-
-        Parameters:
-            mapper(ColumnMapper or None): Generally pass none here unless you want special behavior.
-            skip_curly_braces (bool): If True, don't plug in curly brace values into columns.
-        Returns:
-            Dataframe: The assembled dataframe.
-        """
-        if mapper is None:
-            mapper = self._mapper
-
-        all_columns = self._handle_transforms(mapper)
-        if skip_curly_braces:
-            return all_columns
-        transformers, _ = mapper.get_transformers()
-        refs = self.get_column_refs()
-        column_names = list(transformers)
-        return _handle_curly_braces_refs(all_columns, refs, column_names)
-
-    def _handle_transforms(self, mapper):
-        transformers, need_categorical = mapper.get_transformers()
-        if transformers:
-            all_columns = self._dataframe
-            if need_categorical:
-                all_columns[need_categorical] = all_columns[need_categorical].astype('category')
-
-            all_columns = all_columns.transform(transformers)
-
-            if need_categorical:
-                all_columns[need_categorical] = all_columns[need_categorical].astype('str')
-        else:
-            all_columns = self._dataframe
-
-        return all_columns
-
-    @staticmethod
-    def combine_dataframe(dataframe):
-        """ Combine all columns in the given dataframe into a single HED string series,
-            skipping empty columns and columns with empty strings.
-
-        Parameters:
-            dataframe(Dataframe): The dataframe to combine
-
-        Returns:
-            Series: The assembled series.
-        """
-        dataframe = dataframe.apply(
-            lambda x: ', '.join(filter(lambda e: bool(e) and e != "n/a", map(str, x))),
-            axis=1
-        )
-        return dataframe
-
-    def get_def_dict(self, hed_schema, extra_def_dicts=None):
-        """ Return the definition dict for this file.
-
-        Note: Baseclass implementation returns just extra_def_dicts.
-
-        Parameters:
-            hed_schema(HedSchema): Identifies tags to find definitions(if needed).
-            extra_def_dicts (list, DefinitionDict, or None): Extra dicts to add to the list.
-
-        Returns:
-            DefinitionDict:   A single definition dict representing all the data(and extra def dicts).
-        """
-        from hed.models.definition_dict import DefinitionDict
-        return DefinitionDict(extra_def_dicts, hed_schema)
-
-    def get_column_refs(self):
-        """ Return a list of column refs for this file.
-
-            Default implementation returns none.
-
-        Returns:
-            column_refs(list): A list of unique column refs found.
-        """
-        return []
-
-    def _open_dataframe_file(self, file, has_column_names, input_type):
-        pandas_header = 0
-        if not has_column_names:
-            pandas_header = None
-
-        if isinstance(file, pd.DataFrame):
-            self._dataframe = file.astype(str)
-            self._has_column_names = self._dataframe_has_names(self._dataframe)
-        elif not file:
-            raise HedFileError(HedExceptions.FILE_NOT_FOUND, "Empty file passed to BaseInput.", file)
-        elif input_type in self.TEXT_EXTENSION:
-            try:
-                self._dataframe = pd.read_csv(file, delimiter='\t', header=pandas_header,
-                                              dtype=str, keep_default_na=True, na_values=("", "null"))
-            except Exception as e:
-                raise HedFileError(HedExceptions.INVALID_FILE_FORMAT, str(e), self.name) from e
-            # Convert nan values to a known value
-            self._dataframe = self._dataframe.fillna("n/a")
-        elif input_type in self.EXCEL_EXTENSION:
-            try:
-                self._loaded_workbook = openpyxl.load_workbook(file)
-                loaded_worksheet = self.get_worksheet(self._worksheet_name)
-                self._dataframe = self._get_dataframe_from_worksheet(loaded_worksheet, has_column_names)
-            except Exception as e:
-                raise HedFileError(HedExceptions.GENERIC_ERROR, str(e), self.name) from e
-        else:
-            raise HedFileError(HedExceptions.INVALID_EXTENSION, "", file)
-
-        if self._dataframe.size == 0:
-            raise HedFileError(HedExceptions.INVALID_DATAFRAME, "Invalid dataframe(malformed datafile, etc)", file)
+"""
+Superclass representing a basic columnar file.
+"""
+import os
+
+import openpyxl
+import pandas as pd
+
+from hed.models.column_mapper import ColumnMapper
+from hed.errors.exceptions import HedFileError, HedExceptions
+
+from hed.models.df_util import _handle_curly_braces_refs, filter_series_by_onset
+
+
+class BaseInput:
+    """ Superclass representing a basic columnar file. """
+
+    TEXT_EXTENSION = ['.tsv', '.txt']
+    EXCEL_EXTENSION = ['.xlsx']
+
+    def __init__(self, file, file_type=None, worksheet_name=None, has_column_names=True, mapper=None, name=None,
+                 allow_blank_names=True):
+        """ Constructor for the BaseInput class.
+
+        Parameters:
+            file (str or file-like or pd.Dataframe): An xlsx/tsv file to open.
+            file_type (str or None): ".xlsx" (Excel), ".tsv" or ".txt" (tab-separated text).
+                Derived from file if file is a filename.  Ignored if pandas dataframe.
+            worksheet_name (str or None): Name of Excel workbook worksheet name to use.
+                (Not applicable to tsv files.)
+            has_column_names (bool): True if file has column names.
+                This value is ignored if you pass in a pandas dataframe.
+            mapper (ColumnMapper or None):  Indicates which columns have HED tags.
+                See SpreadsheetInput or TabularInput for examples of how to use built-in a ColumnMapper.
+            name (str or None): Optional field for how this file will report errors.
+            allow_blank_names(bool): If True, column names can be blank
+
+        :raises HedFileError:
+            - file is blank.
+            - An invalid dataframe was passed with size 0.
+            - An invalid extension was provided.
+            - A duplicate or empty column name appears.
+            - Cannot open the indicated file.
+            - The specified worksheet name does not exist.
+            - If the sidecar file or tabular file had invalid format and could not be read.
+
+         """
+        if mapper is None:
+            mapper = ColumnMapper()
+        self._mapper = mapper
+        self._has_column_names = has_column_names
+        self._name = name
+        # This is the loaded workbook if we loaded originally from an Excel file.
+        self._loaded_workbook = None
+        self._worksheet_name = worksheet_name
+        self._dataframe = None
+
+        input_type = file_type
+        if isinstance(file, str):
+            if file_type is None:
+                _, input_type = os.path.splitext(file)
+            if self.name is None:
+                self._name = file
+
+        self._open_dataframe_file(file, has_column_names, input_type)
+
+        column_issues = ColumnMapper.check_for_blank_names(self.columns, allow_blank_names=allow_blank_names)
+        if column_issues:
+            raise HedFileError(HedExceptions.BAD_COLUMN_NAMES, "Duplicate or blank columns found. See issues.",
+                               self.name, issues=column_issues)
+
+        self.reset_mapper(mapper)
+
+    def reset_mapper(self, new_mapper):
+        """ Set mapper to a different view of the file.
+
+        Parameters:
+            new_mapper (ColumnMapper): A column mapper to be associated with this base input.
+        """
+        self._mapper = new_mapper
+        if not self._mapper:
+            self._mapper = ColumnMapper()
+
+        if self._dataframe is not None and self._has_column_names:
+            columns = self._dataframe.columns
+            self._mapper.set_column_map(columns)
+
+    @property
+    def dataframe(self):
+        """ The underlying dataframe. """
+        return self._dataframe
+
+    @property
+    def dataframe_a(self):
+        """Return the assembled dataframe Probably a placeholder name.
+
+        Returns:
+            Dataframe: the assembled dataframe"""
+        return self.assemble()
+
+    @property
+    def series_a(self):
+        """Return the assembled dataframe as a series.
+
+        Returns:
+            Series: the assembled dataframe with columns merged.
+        """
+
+        return self.combine_dataframe(self.assemble())
+
+    @property
+    def series_filtered(self):
+        """Return the assembled dataframe as a series, with rows that have the same onset combined.
+
+        Returns:
+            Series or None: the assembled dataframe with columns merged, and the rows filtered together.
+        """
+        if self.onsets is not None:
+            return filter_series_by_onset(self.series_a, self.onsets)
+
+    @property
+    def onsets(self):
+        """Return the onset column if it exists. """
+        if "onset" in self.columns:
+            return self._dataframe["onset"]
+
+    @property
+    def needs_sorting(self):
+        """Return True if this both has an onset column, and it needs sorting."""
+        onsets = self.onsets
+        if onsets is not None:
+            onsets = pd.to_numeric(self.dataframe['onset'], errors='coerce')
+            return not onsets.is_monotonic_increasing
+        else:
+            return False
+
+    @property
+    def name(self):
+        """ Name of the data. """
+        return self._name
+
+    @property
+    def has_column_names(self):
+        """ True if dataframe has column names. """
+        return self._has_column_names
+
+    @property
+    def loaded_workbook(self):
+        """ The underlying loaded workbooks. """
+        return self._loaded_workbook
+
+    @property
+    def worksheet_name(self):
+        """ The worksheet name. """
+        return self._worksheet_name
+
+    def convert_to_form(self, hed_schema, tag_form):
+        """ Convert all tags in underlying dataframe to the specified form.
+
+        Parameters:
+            hed_schema (HedSchema): The schema to use to convert tags.
+            tag_form(str): HedTag property to convert tags to.
+                Most cases should use convert_to_short or convert_to_long below.
+        """
+        from hed.models.df_util import convert_to_form
+        convert_to_form(self._dataframe, hed_schema, tag_form, self._mapper.get_tag_columns())
+
+    def convert_to_short(self, hed_schema):
+        """ Convert all tags in underlying dataframe to short form.
+
+        Parameters:
+            hed_schema (HedSchema): The schema to use to convert tags.
+        """
+        return self.convert_to_form(hed_schema, "short_tag")
+
+    def convert_to_long(self, hed_schema):
+        """ Convert all tags in underlying dataframe to long form.
+
+        Parameters:
+            hed_schema (HedSchema or None): The schema to use to convert tags.
+        """
+        return self.convert_to_form(hed_schema, "long_tag")
+
+    def shrink_defs(self, hed_schema):
+        """ Shrinks any def-expand found in the underlying dataframe.
+
+        Parameters:
+            hed_schema (HedSchema or None): The schema to use to identify defs.
+        """
+        from df_util import shrink_defs
+        shrink_defs(self._dataframe, hed_schema=hed_schema, columns=self._mapper.get_tag_columns())
+
+    def expand_defs(self, hed_schema, def_dict):
+        """ Shrinks any def-expand found in the underlying dataframe.
+
+        Parameters:
+            hed_schema (HedSchema or None): The schema to use to identify defs.
+            def_dict (DefinitionDict): The definitions to expand.
+        """
+        from df_util import expand_defs
+        expand_defs(self._dataframe, hed_schema=hed_schema, def_dict=def_dict, columns=self._mapper.get_tag_columns())
+
+    def to_excel(self, file):
+        """ Output to an Excel file.
+
+        Parameters:
+            file (str or file-like): Location to save this base input.
+
+        :raises ValueError:
+            - If empty file object was passed.
+
+        :raises OSError:
+            - Cannot open the indicated file.
+        """
+        if not file:
+            raise ValueError("Empty file name or object passed in to BaseInput.save.")
+
+        dataframe = self._dataframe
+        if self._loaded_workbook:
+            old_worksheet = self.get_worksheet(self._worksheet_name)
+            # Excel spreadsheets are 1 based, then add another 1 for column names if present
+            adj_row_for_col_names = 1
+            if self._has_column_names:
+                adj_row_for_col_names += 1
+            adj_for_one_based_cols = 1
+            for row_number, text_file_row in dataframe.iterrows():
+                for column_number, column_text in enumerate(text_file_row):
+                    cell_value = dataframe.iloc[row_number, column_number]
+                    old_worksheet.cell(row_number + adj_row_for_col_names,
+                                       column_number + adj_for_one_based_cols).value = cell_value
+
+            self._loaded_workbook.save(file)
+        else:
+            dataframe.to_excel(file, header=self._has_column_names)
+
+    def to_csv(self, file=None):
+        """ Write to file or return as a string.
+
+        Parameters:
+            file (str, file-like, or None): Location to save this file. If None, return as string.
+        Returns:
+            None or str:  None if file is given or the contents as a str if file is None.
+
+        :raises OSError:
+            - Cannot open the indicated file.
+        """
+        dataframe = self._dataframe
+        csv_string_if_filename_none = dataframe.to_csv(file, sep='\t', index=False, header=self._has_column_names)
+        return csv_string_if_filename_none
+
+    @property
+    def columns(self):
+        """ Returns a list of the column names.
+
+            Empty if no column names.
+
+        Returns:
+            columns(list): The column names.
+        """
+        columns = []
+        if self._dataframe is not None and self._has_column_names:
+            columns = list(self._dataframe.columns)
+        return columns
+
+    def column_metadata(self):
+        """ Return the metadata for each column.
+
+        Returns:
+            dict: Number/ColumnMeta pairs.
+        """
+        if self._mapper:
+            return self._mapper._final_column_map
+        return {}
+
+    def set_cell(self, row_number, column_number, new_string_obj, tag_form="short_tag"):
+        """ Replace the specified cell with transformed text.
+
+        Parameters:
+            row_number (int):    The row number of the spreadsheet to set.
+            column_number (int): The column number of the spreadsheet to set.
+            new_string_obj (HedString): Object with text to put in the given cell.
+            tag_form (str): Version of the tags (short_tag, long_tag, base_tag, etc.)
+
+        Notes:
+             Any attribute of a HedTag that returns a string is a valid value of tag_form.
+
+        :raises ValueError:
+            - There is not a loaded dataframe.
+
+        :raises KeyError:
+            - The indicated row/column does not exist.
+
+        :raises AttributeError:
+            - The indicated tag_form is not an attribute of HedTag.
+        """
+        if self._dataframe is None:
+            raise ValueError("No data frame loaded")
+
+        new_text = new_string_obj.get_as_form(tag_form)
+        self._dataframe.iloc[row_number, column_number] = new_text
+
+    def get_worksheet(self, worksheet_name=None):
+        """ Get the requested worksheet.
+
+        Parameters:
+            worksheet_name (str or None): The name of the requested worksheet by name or the first one if None.
+
+        Returns:
+            openpyxl.workbook.Workbook: The workbook request.
+
+        Notes:
+            If None, returns the first worksheet.
+
+        :raises KeyError:
+            - The specified worksheet name does not exist.
+        """
+        if worksheet_name and self._loaded_workbook:
+            # return self._loaded_workbook.get_sheet_by_name(worksheet_name)
+            return self._loaded_workbook[worksheet_name]
+        elif self._loaded_workbook:
+            return self._loaded_workbook.worksheets[0]
+        else:
+            return None
+
+    @staticmethod
+    def _get_dataframe_from_worksheet(worksheet, has_headers):
+        """ Create a dataframe from the worksheet.
+
+        Parameters:
+            worksheet (Worksheet): The loaded worksheet to convert.
+            has_headers (bool): True if this worksheet has column headers.
+
+        Returns:
+            DataFrame: The converted data frame.
+
+        """
+        if has_headers:
+            data = worksheet.values
+            # first row is columns
+            cols = next(data)
+            data = list(data)
+            return pd.DataFrame(data, columns=cols, dtype=str)
+        else:
+            return pd.DataFrame(worksheet.values, dtype=str)
+
+    def validate(self, hed_schema, extra_def_dicts=None, name=None, error_handler=None):
+        """Creates a SpreadsheetValidator and returns all issues with this file.
+
+        Parameters:
+            hed_schema(HedSchema): The schema to use for validation.
+            extra_def_dicts(list of DefDict or DefDict): All definitions to use for validation.
+            name(str): The name to report errors from this file as.
+            error_handler (ErrorHandler): Error context to use.  Creates a new one if None.
+
+        Returns:
+            issues (list of dict): A list of issues for a HED string.
+        """
+        from hed.validator.spreadsheet_validator import SpreadsheetValidator
+        if not name:
+            name = self.name
+        tab_validator = SpreadsheetValidator(hed_schema)
+        validation_issues = tab_validator.validate(self, self._mapper.get_def_dict(hed_schema, extra_def_dicts), name,
+                                                   error_handler=error_handler)
+        return validation_issues
+
+    @staticmethod
+    def _dataframe_has_names(dataframe):
+        for column in dataframe.columns:
+            if isinstance(column, str):
+                return True
+        return False
+
+    def assemble(self, mapper=None, skip_curly_braces=False):
+        """ Assembles the HED strings.
+
+        Parameters:
+            mapper(ColumnMapper or None): Generally pass none here unless you want special behavior.
+            skip_curly_braces (bool): If True, don't plug in curly brace values into columns.
+        Returns:
+            Dataframe: The assembled dataframe.
+        """
+        if mapper is None:
+            mapper = self._mapper
+
+        all_columns = self._handle_transforms(mapper)
+        if skip_curly_braces:
+            return all_columns
+        transformers, _ = mapper.get_transformers()
+        refs = self.get_column_refs()
+        column_names = list(transformers)
+        return _handle_curly_braces_refs(all_columns, refs, column_names)
+
+    def _handle_transforms(self, mapper):
+        transformers, need_categorical = mapper.get_transformers()
+        if transformers:
+            all_columns = self._dataframe
+            if need_categorical:
+                all_columns[need_categorical] = all_columns[need_categorical].astype('category')
+
+            all_columns = all_columns.transform(transformers)
+
+            if need_categorical:
+                all_columns[need_categorical] = all_columns[need_categorical].astype('str')
+        else:
+            all_columns = self._dataframe
+
+        return all_columns
+
+    @staticmethod
+    def combine_dataframe(dataframe):
+        """ Combine all columns in the given dataframe into a single HED string series,
+            skipping empty columns and columns with empty strings.
+
+        Parameters:
+            dataframe(Dataframe): The dataframe to combine
+
+        Returns:
+            Series: The assembled series.
+        """
+        dataframe = dataframe.apply(
+            lambda x: ', '.join(filter(lambda e: bool(e) and e != "n/a", map(str, x))),
+            axis=1
+        )
+        return dataframe
+
+    def get_def_dict(self, hed_schema, extra_def_dicts=None):
+        """ Return the definition dict for this file.
+
+        Note: Baseclass implementation returns just extra_def_dicts.
+
+        Parameters:
+            hed_schema(HedSchema): Identifies tags to find definitions(if needed).
+            extra_def_dicts (list, DefinitionDict, or None): Extra dicts to add to the list.
+
+        Returns:
+            DefinitionDict:   A single definition dict representing all the data(and extra def dicts).
+        """
+        from hed.models.definition_dict import DefinitionDict
+        return DefinitionDict(extra_def_dicts, hed_schema)
+
+    def get_column_refs(self):
+        """ Return a list of column refs for this file.
+
+            Default implementation returns none.
+
+        Returns:
+            column_refs(list): A list of unique column refs found.
+        """
+        return []
+
+    def _open_dataframe_file(self, file, has_column_names, input_type):
+        pandas_header = 0
+        if not has_column_names:
+            pandas_header = None
+
+        if isinstance(file, pd.DataFrame):
+            self._dataframe = file.astype(str)
+            self._has_column_names = self._dataframe_has_names(self._dataframe)
+        elif not file:
+            raise HedFileError(HedExceptions.FILE_NOT_FOUND, "Empty file passed to BaseInput.", file)
+        elif input_type in self.TEXT_EXTENSION:
+            try:
+                self._dataframe = pd.read_csv(file, delimiter='\t', header=pandas_header,
+                                              dtype=str, keep_default_na=True, na_values=("", "null"))
+            except Exception as e:
+                raise HedFileError(HedExceptions.INVALID_FILE_FORMAT, str(e), self.name) from e
+            # Convert nan values to a known value
+            self._dataframe = self._dataframe.fillna("n/a")
+        elif input_type in self.EXCEL_EXTENSION:
+            try:
+                self._loaded_workbook = openpyxl.load_workbook(file)
+                loaded_worksheet = self.get_worksheet(self._worksheet_name)
+                self._dataframe = self._get_dataframe_from_worksheet(loaded_worksheet, has_column_names)
+            except Exception as e:
+                raise HedFileError(HedExceptions.GENERIC_ERROR, str(e), self.name) from e
+        else:
+            raise HedFileError(HedExceptions.INVALID_EXTENSION, "", file)
+
+        if self._dataframe.size == 0:
+            raise HedFileError(HedExceptions.INVALID_DATAFRAME, "Invalid dataframe(malformed datafile, etc)", file)
diff --git a/hed/models/column_mapper.py b/hed/models/column_mapper.py
index d9fc51f8..203ee05f 100644
--- a/hed/models/column_mapper.py
+++ b/hed/models/column_mapper.py
@@ -1,421 +1,422 @@
-"""
-Mapping of a base input file columns into HED tags.
-"""
-from hed.models.column_metadata import ColumnMetadata, ColumnType
-from hed.errors.error_reporter import ErrorHandler
-from hed.errors.error_types import ValidationErrors
-from hed.models.definition_dict import DefinitionDict
-
-import copy
-from collections import Counter
-
-PANDAS_COLUMN_PREFIX_TO_IGNORE = "Unnamed: "
-
-
-class ColumnMapper:
-    """ Mapping of a base input file columns into HED tags.
-
-    Notes:
-        - All column numbers are 0 based.
-    """
-
-    def __init__(self, sidecar=None, tag_columns=None, column_prefix_dictionary=None,
-                 optional_tag_columns=None, warn_on_missing_column=False):
-        """ Constructor for ColumnMapper.
-
-        Parameters:
-            sidecar (Sidecar): A sidecar to gather column data from.
-            tag_columns: (list):  A list of ints or strings containing the columns that contain the HED tags.
-                Sidecar column definitions will take precedent if there is a conflict with tag_columns.
-            column_prefix_dictionary (dict): Dictionary with keys that are column numbers/names and values are HED tag
-                prefixes to prepend to the tags in that column before processing.
-            optional_tag_columns (list): A list of ints or strings containing the columns that contain
-                the HED tags. If the column is otherwise unspecified, convert this column type to HEDTags.
-            warn_on_missing_column (bool): If True, issue mapping warnings on column names that are missing from
-                                            the sidecar.
-
-        Notes:
-            - All column numbers are 0 based.
-            - The column_prefix_dictionary may be deprecated/renamed in the future.
-                - These are no longer prefixes, but rather converted to value columns:
-                  {"key": "Description", 1: "Label/"} will turn into value columns as
-                  {"key": "Description/#", 1: "Label/#"}
-                  It will be a validation issue if column 1 is called "key" in the above example.
-                  This means it no longer accepts anything but the value portion only in the columns.
-
-        """
-
-        # Maps column number to column_entry.  This is what's actually used by most code.
-        self._final_column_map = {}
-        self._no_mapping_info = True
-
-        self._column_map = {}
-        self._reverse_column_map = {}
-        self._warn_on_missing_column = warn_on_missing_column
-        if tag_columns is None:
-            tag_columns = []
-        self._tag_columns = tag_columns
-        if optional_tag_columns is None:
-            optional_tag_columns = []
-        self._optional_tag_columns = optional_tag_columns
-        if column_prefix_dictionary is None:
-            column_prefix_dictionary = {}
-        self._column_prefix_dictionary = column_prefix_dictionary
-
-        self._na_patterns = ["n/a", "nan"]
-        self._sidecar = None
-        self._set_sidecar(sidecar)
-
-        # finalize the column map based on initial settings with no header
-        self._finalize_mapping()
-
-    @property
-    def tag_columns(self):
-        """ Return the known tag and optional tag columns with numbers as names when possible.
-
-            Returns:
-                tag_columns(list of str or int): A list of all tag and optional tag columns as labels.
-        """
-        joined_list = self._tag_columns + self._optional_tag_columns
-        return list(set(self._convert_to_names(self._column_map, joined_list)))
-
-    @property
-    def column_prefix_dictionary(self):
-        """ Return the column_prefix_dictionary with numbers turned into names where possible.
-
-            Returns:
-                column_prefix_dictionary(list of str or int): A column_prefix_dictionary with column labels as keys.
-        """
-        return self._convert_to_names_dict(self._column_map, self._column_prefix_dictionary)
-
-    def get_transformers(self):
-        """ Return the transformers to use on a dataframe.
-
-            Returns:
-                tuple(dict, list):
-                    dict({str or int: func}): The functions to use to transform each column.
-                    need_categorical(list of int): A list of columns to treat as categorical.
-        """
-        final_transformers = {}
-        need_categorical = []
-        for column in self._final_column_map.values():
-            assign_to_column = column.column_name
-            if isinstance(assign_to_column, int):
-                if self._column_map:
-                    assign_to_column = self._column_map[assign_to_column]
-                else:
-                    assign_to_column = assign_to_column
-            if column.column_type == ColumnType.Ignore:
-                continue
-            elif column.column_type == ColumnType.Value:
-                value_str = column.hed_dict
-                from functools import partial
-                final_transformers[assign_to_column] = partial(self._value_handler, value_str)
-            elif column.column_type == ColumnType.Categorical:
-                need_categorical.append(column.column_name)
-                category_values = column.hed_dict
-                from functools import partial
-                final_transformers[assign_to_column] = partial(self._category_handler, category_values)
-            else:
-                final_transformers[assign_to_column] = lambda x: x
-
-        return final_transformers, need_categorical
-
-    @staticmethod
-    def check_for_blank_names(column_map, allow_blank_names):
-        """ Validate there are no blank column names.
-
-        Parameters:
-            column_map(iterable): A list of column names.
-            allow_blank_names(bool): Only find issues if True.
-
-        Returns:
-            issues(list): A list of dicts, one per issue.
-        """
-        # We don't have any checks right now if blank/duplicate is allowed
-        if allow_blank_names:
-            return []
-
-        issues = []
-
-        for column_number, name in enumerate(column_map):
-            if name is None or not name or name.startswith(PANDAS_COLUMN_PREFIX_TO_IGNORE):
-                issues += ErrorHandler.format_error(ValidationErrors.HED_BLANK_COLUMN, column_number)
-                continue
-
-        return issues
-
-    def _set_sidecar(self, sidecar):
-        """ Set the sidecar this column mapper uses.
-
-        Parameters:
-            sidecar (Sidecar or None): The sidecar to use.
-
-        :raises ValueError:
-            - A sidecar was previously set.
-        """
-        if self._sidecar:
-            raise ValueError("Trying to set a second sidecar on a column mapper.")
-        if not sidecar:
-            return None
-
-        self._sidecar = sidecar
-
-    @property
-    def sidecar_column_data(self):
-        """ Pass through to get the sidecar ColumnMetadata.
-
-        Returns:
-            dict({str:ColumnMetadata}): The column metadata defined by this sidecar.
-        """
-        if self._sidecar:
-            return self._sidecar.column_data
-
-        return {}
-
-    def get_tag_columns(self):
-        """ Return the column numbers or names that are mapped to be HedTags.
-
-            Note: This is NOT the tag_columns or optional_tag_columns parameter, though they set it.
-
-        Returns:
-            column_identifiers(list): A list of column numbers or names that are ColumnType.HedTags.
-                0-based if integer-based, otherwise column name.
-        """
-        return [column_entry.column_name for number, column_entry in self._final_column_map.items()
-                if column_entry.column_type == ColumnType.HEDTags]
-
-    def set_tag_columns(self, tag_columns=None, optional_tag_columns=None, finalize_mapping=True):
-        """ Set tag columns and optional tag columns.
-
-        Parameters:
-            tag_columns (list): A list of ints or strings containing the columns that contain the HED tags.
-                                If None, clears existing tag_columns
-            optional_tag_columns (list): A list of ints or strings containing the columns that contain the HED tags,
-                                         but not an error if missing.
-                                         If None, clears existing tag_columns
-            finalize_mapping (bool): Re-generate the internal mapping if True, otherwise no effect until finalize.
-        """
-        if tag_columns is None:
-            tag_columns = []
-        if optional_tag_columns is None:
-            optional_tag_columns = []
-        self._tag_columns = tag_columns
-        self._optional_tag_columns = optional_tag_columns
-        if finalize_mapping:
-            self._finalize_mapping()
-
-    def set_column_map(self, new_column_map=None):
-        """ Set the column number to name mapping.
-
-        Parameters:
-            new_column_map (list or dict):  Either an ordered list of the column names or column_number:column name.
-                dictionary. In both cases, column numbers start at 0.
-
-        Returns:
-            list: List of issues. Each issue is a dictionary.
-
-        """
-        if new_column_map is None:
-            new_column_map = {}
-        if isinstance(new_column_map, dict):
-            column_map = new_column_map
-        # List like
-        else:
-            column_map = {column_number: column_name for column_number, column_name in enumerate(new_column_map)}
-        self._column_map = column_map
-        self._reverse_column_map = {column_name: column_number for column_number, column_name in column_map.items()}
-        self._finalize_mapping()
-
-    def set_column_prefix_dictionary(self, column_prefix_dictionary, finalize_mapping=True):
-        """Set the column prefix dictionary. """
-        self._column_prefix_dictionary = column_prefix_dictionary
-        if finalize_mapping:
-            self._finalize_mapping()
-
-    @staticmethod
-    def _get_sidecar_basic_map(column_map, column_data):
-        basic_final_map = {}
-        unhandled_cols = []
-        if column_map:
-            for column_number, column_name in column_map.items():
-                if column_name is None:
-                    continue
-                if column_name in column_data:
-                    column_entry = copy.deepcopy(column_data[column_name])
-                    column_entry.column_name = column_name
-                    basic_final_map[column_name] = column_entry
-                    continue
-                elif isinstance(column_name, str) and column_name.startswith(PANDAS_COLUMN_PREFIX_TO_IGNORE):
-                    continue
-                unhandled_cols.append(column_name)
-
-        return basic_final_map, unhandled_cols
-
-    @staticmethod
-    def _convert_to_names(column_to_name_map, column_list):
-        converted_names = []
-        for index in column_list:
-            if isinstance(index, int):
-                if not column_to_name_map:
-                    converted_names.append(index)
-                elif index in column_to_name_map:
-                    converted_names.append(column_to_name_map[index])
-            else:
-                if index in column_to_name_map.values():
-                    converted_names.append(index)
-        return converted_names
-
-    @staticmethod
-    def _convert_to_names_dict(column_to_name_map, column_dict):
-        converted_dict = {}
-        for index, column_data in column_dict.items():
-            if isinstance(index, int):
-                if not column_to_name_map:
-                    converted_dict[index] = column_data
-                elif index in column_to_name_map:
-                    converted_dict[column_to_name_map[index]] = column_data
-            else:
-                if index in column_to_name_map.values():
-                    converted_dict[index] = column_data
-        return converted_dict
-
-    @staticmethod
-    def _add_value_columns(final_map, column_prefix_dictionary):
-        for col, prefix in column_prefix_dictionary.items():
-            if prefix.endswith("/"):
-                prefix = prefix + "#"
-            else:
-                prefix = prefix + "/#"
-            new_def = ColumnMetadata(ColumnType.Value, col, source=prefix)
-            final_map[col] = new_def
-
-    @staticmethod
-    def _add_tag_columns(final_map, tag_columns):
-        for col in tag_columns:
-            new_def = ColumnMetadata(ColumnType.HEDTags, col)
-            final_map[col] = new_def
-
-    def _get_column_lists(self):
-        column_lists = self._tag_columns, self._optional_tag_columns, self._column_prefix_dictionary
-        list_names = ["tag_columns", "optional_tag_columns", "column_prefix_dictionary"]
-
-        if not any(column for column in column_lists):
-            return column_lists, list_names
-        # Filter out empty lists from the above
-        column_lists, list_names = zip(*[(col_list, list_name) for col_list, list_name in zip(column_lists, list_names)
-                                         if col_list])
-
-        return column_lists, list_names
-
-    def _check_for_duplicates_and_required(self, list_names, column_lists):
-        issues = []
-        for list_name, col_list in zip(list_names, column_lists):
-            # Convert all known strings to ints, then check for duplicates
-            converted_list = [item if isinstance(item, int) else self._reverse_column_map.get(item, item)
-                              for item in col_list]
-
-            if col_list != self._optional_tag_columns:
-                for test_col in converted_list:
-                    if isinstance(test_col, str) and test_col not in self._reverse_column_map:
-                        issues += ErrorHandler.format_error(ValidationErrors.HED_MISSING_REQUIRED_COLUMN,
-                                                            test_col, list_name)
-
-            issues += self._check_for_duplicates_between_lists(converted_list, list_name,
-                                                               ValidationErrors.DUPLICATE_COLUMN_IN_LIST)
-
-        return issues
-
-    def _check_for_duplicates_between_lists(self, checking_list, list_names, error_type):
-        issues = []
-        duplicates = [item for item, count in Counter(checking_list).items() if count > 1]
-        for duplicate in duplicates:
-            issues += ErrorHandler.format_error(error_type, duplicate,
-                                                self._column_map.get(duplicate), list_names)
-        return issues
-
-    def check_for_mapping_issues(self, allow_blank_names=False):
-        """ Find all issues given the current column_map, tag_columns, etc.
-
-        Parameters:
-            allow_blank_names(bool): Only flag blank names if False.
-
-        Returns:
-            issue_list(list of dict): All issues found as a list of dicts.
-        """
-        # 1. Get the lists with entries
-        column_lists, list_names = self._get_column_lists()
-        # 2. Verify column_prefix columns and tag columns are present, and check for duplicates
-        issues = self._check_for_duplicates_and_required(list_names, column_lists)
-
-        combined_list = self.tag_columns + list(self.column_prefix_dictionary)
-        # 3. Verify prefix and tag columns do not conflict.
-        issues += self._check_for_duplicates_between_lists(combined_list, list_names,
-                                                           ValidationErrors.DUPLICATE_COLUMN_BETWEEN_SOURCES)
-
-        # 4. Verify we didn't get both a sidecar and a tag column list
-        if self._sidecar and combined_list and combined_list != ["HED"]:
-            issues += ErrorHandler.format_error(ValidationErrors.SIDECAR_AND_OTHER_COLUMNS, column_names=combined_list)
-
-        # 5. Verify we handled all columns
-        if self._warn_on_missing_column:
-            fully_combined_list = list(self.sidecar_column_data) + combined_list
-            for column in self._column_map.values():
-                if column not in fully_combined_list:
-                    issues += ErrorHandler.format_error(ValidationErrors.HED_UNKNOWN_COLUMN, column)
-
-        issues += self.check_for_blank_names(self._column_map.values(), allow_blank_names=allow_blank_names)
-        return issues
-
-    def _finalize_mapping(self):
-        final_map, unhandled_cols = self._get_sidecar_basic_map(self._column_map, self.sidecar_column_data)
-
-        self._add_tag_columns(final_map, self.tag_columns)
-        self._remove_from_list(unhandled_cols, self.tag_columns)
-
-        self._add_value_columns(final_map, self.column_prefix_dictionary)
-        self._remove_from_list(unhandled_cols, self.column_prefix_dictionary)
-
-        self._final_column_map = dict(sorted(final_map.items()))
-
-    @staticmethod
-    def _remove_from_list(list_to_alter, to_remove):
-        return [item for item in list_to_alter if item not in to_remove]
-
-    def get_def_dict(self, hed_schema, extra_def_dicts=None):
-        """ Return def dicts from every column description.
-
-        Parameters:
-            hed_schema (Schema): A HED schema object to use for extracting definitions.
-            extra_def_dicts (list, DefinitionDict, or None): Extra dicts to add to the list.
-
-        Returns:
-           DefinitionDict:   A single definition dict representing all the data(and extra def dicts).
-        """
-        if self._sidecar:
-            return self._sidecar.get_def_dict(hed_schema=hed_schema, extra_def_dicts=extra_def_dicts)
-
-        return DefinitionDict(extra_def_dicts, hed_schema=hed_schema)
-
-    def get_column_mapping_issues(self):
-        """ Get all the issues with finalizing column mapping(duplicate columns, missing required, etc.).
-
-        Notes:
-            - This is deprecated and now a wrapper for "check_for_mapping_issues()".
-
-        Returns:
-            list: A list dictionaries of all issues found from mapping column names to numbers.
-
-        """
-        return self.check_for_mapping_issues()
-
-    @staticmethod
-    def _category_handler(category_values, x):
-        return category_values.get(x, "")
-
-    @staticmethod
-    def _value_handler(value_str, x):
-        if x == "n/a":
-            return "n/a"
-
-        return value_str.replace("#", str(x))
+"""
+Mapping of a base input file columns into HED tags.
+"""
+from hed.models.column_metadata import ColumnMetadata, ColumnType
+from hed.errors.error_reporter import ErrorHandler
+from hed.errors.error_types import ValidationErrors
+from hed.models.definition_dict import DefinitionDict
+
+import copy
+from collections import Counter
+
+PANDAS_COLUMN_PREFIX_TO_IGNORE = "Unnamed: "
+NO_WARN_COLUMNS = ['onset', 'duration']
+
+
+class ColumnMapper:
+    """ Mapping of a base input file columns into HED tags.
+
+    Notes:
+        - All column numbers are 0 based.
+    """
+
+    def __init__(self, sidecar=None, tag_columns=None, column_prefix_dictionary=None,
+                 optional_tag_columns=None, warn_on_missing_column=False):
+        """ Constructor for ColumnMapper.
+
+        Parameters:
+            sidecar (Sidecar): A sidecar to gather column data from.
+            tag_columns: (list):  A list of ints or strings containing the columns that contain the HED tags.
+                Sidecar column definitions will take precedent if there is a conflict with tag_columns.
+            column_prefix_dictionary (dict): Dictionary with keys that are column numbers/names and values are HED tag
+                prefixes to prepend to the tags in that column before processing.
+            optional_tag_columns (list): A list of ints or strings containing the columns that contain
+                the HED tags. If the column is otherwise unspecified, convert this column type to HEDTags.
+            warn_on_missing_column (bool): If True, issue mapping warnings on column names that are missing from
+                                            the sidecar.
+
+        Notes:
+            - All column numbers are 0 based.
+            - The column_prefix_dictionary may be deprecated/renamed in the future.
+                - These are no longer prefixes, but rather converted to value columns:
+                  {"key": "Description", 1: "Label/"} will turn into value columns as
+                  {"key": "Description/#", 1: "Label/#"}
+                  It will be a validation issue if column 1 is called "key" in the above example.
+                  This means it no longer accepts anything but the value portion only in the columns.
+
+        """
+
+        # Maps column number to column_entry.  This is what's actually used by most code.
+        self._final_column_map = {}
+        self._no_mapping_info = True
+
+        self._column_map = {}
+        self._reverse_column_map = {}
+        self._warn_on_missing_column = warn_on_missing_column
+        if tag_columns is None:
+            tag_columns = []
+        self._tag_columns = tag_columns
+        if optional_tag_columns is None:
+            optional_tag_columns = []
+        self._optional_tag_columns = optional_tag_columns
+        if column_prefix_dictionary is None:
+            column_prefix_dictionary = {}
+        self._column_prefix_dictionary = column_prefix_dictionary
+
+        self._na_patterns = ["n/a", "nan"]
+        self._sidecar = None
+        self._set_sidecar(sidecar)
+
+        # finalize the column map based on initial settings with no header
+        self._finalize_mapping()
+
+    @property
+    def tag_columns(self):
+        """ Return the known tag and optional tag columns with numbers as names when possible.
+
+            Returns:
+                tag_columns(list of str or int): A list of all tag and optional tag columns as labels.
+        """
+        joined_list = self._tag_columns + self._optional_tag_columns
+        return list(set(self._convert_to_names(self._column_map, joined_list)))
+
+    @property
+    def column_prefix_dictionary(self):
+        """ Return the column_prefix_dictionary with numbers turned into names where possible.
+
+            Returns:
+                column_prefix_dictionary(list of str or int): A column_prefix_dictionary with column labels as keys.
+        """
+        return self._convert_to_names_dict(self._column_map, self._column_prefix_dictionary)
+
+    def get_transformers(self):
+        """ Return the transformers to use on a dataframe.
+
+            Returns:
+                tuple(dict, list):
+                    dict({str or int: func}): The functions to use to transform each column.
+                    need_categorical(list of int): A list of columns to treat as categorical.
+        """
+        final_transformers = {}
+        need_categorical = []
+        for column in self._final_column_map.values():
+            assign_to_column = column.column_name
+            if isinstance(assign_to_column, int):
+                if self._column_map:
+                    assign_to_column = self._column_map[assign_to_column]
+                else:
+                    assign_to_column = assign_to_column
+            if column.column_type == ColumnType.Ignore:
+                continue
+            elif column.column_type == ColumnType.Value:
+                value_str = column.hed_dict
+                from functools import partial
+                final_transformers[assign_to_column] = partial(self._value_handler, value_str)
+            elif column.column_type == ColumnType.Categorical:
+                need_categorical.append(column.column_name)
+                category_values = column.hed_dict
+                from functools import partial
+                final_transformers[assign_to_column] = partial(self._category_handler, category_values)
+            else:
+                final_transformers[assign_to_column] = lambda x: x
+
+        return final_transformers, need_categorical
+
+    @staticmethod
+    def check_for_blank_names(column_map, allow_blank_names):
+        """ Validate there are no blank column names.
+
+        Parameters:
+            column_map(iterable): A list of column names.
+            allow_blank_names(bool): Only find issues if True.
+
+        Returns:
+            issues(list): A list of dicts, one per issue.
+        """
+        # We don't have any checks right now if blank/duplicate is allowed
+        if allow_blank_names:
+            return []
+
+        issues = []
+
+        for column_number, name in enumerate(column_map):
+            if name is None or not name or name.startswith(PANDAS_COLUMN_PREFIX_TO_IGNORE):
+                issues += ErrorHandler.format_error(ValidationErrors.HED_BLANK_COLUMN, column_number)
+                continue
+
+        return issues
+
+    def _set_sidecar(self, sidecar):
+        """ Set the sidecar this column mapper uses.
+
+        Parameters:
+            sidecar (Sidecar or None): The sidecar to use.
+
+        :raises ValueError:
+            - A sidecar was previously set.
+        """
+        if self._sidecar:
+            raise ValueError("Trying to set a second sidecar on a column mapper.")
+        if not sidecar:
+            return None
+
+        self._sidecar = sidecar
+
+    @property
+    def sidecar_column_data(self):
+        """ Pass through to get the sidecar ColumnMetadata.
+
+        Returns:
+            dict({str:ColumnMetadata}): The column metadata defined by this sidecar.
+        """
+        if self._sidecar:
+            return self._sidecar.column_data
+
+        return {}
+
+    def get_tag_columns(self):
+        """ Return the column numbers or names that are mapped to be HedTags.
+
+            Note: This is NOT the tag_columns or optional_tag_columns parameter, though they set it.
+
+        Returns:
+            column_identifiers(list): A list of column numbers or names that are ColumnType.HedTags.
+                0-based if integer-based, otherwise column name.
+        """
+        return [column_entry.column_name for number, column_entry in self._final_column_map.items()
+                if column_entry.column_type == ColumnType.HEDTags]
+
+    def set_tag_columns(self, tag_columns=None, optional_tag_columns=None, finalize_mapping=True):
+        """ Set tag columns and optional tag columns.
+
+        Parameters:
+            tag_columns (list): A list of ints or strings containing the columns that contain the HED tags.
+                                If None, clears existing tag_columns
+            optional_tag_columns (list): A list of ints or strings containing the columns that contain the HED tags,
+                                         but not an error if missing.
+                                         If None, clears existing tag_columns
+            finalize_mapping (bool): Re-generate the internal mapping if True, otherwise no effect until finalize.
+        """
+        if tag_columns is None:
+            tag_columns = []
+        if optional_tag_columns is None:
+            optional_tag_columns = []
+        self._tag_columns = tag_columns
+        self._optional_tag_columns = optional_tag_columns
+        if finalize_mapping:
+            self._finalize_mapping()
+
+    def set_column_map(self, new_column_map=None):
+        """ Set the column number to name mapping.
+
+        Parameters:
+            new_column_map (list or dict):  Either an ordered list of the column names or column_number:column name.
+                dictionary. In both cases, column numbers start at 0.
+
+        Returns:
+            list: List of issues. Each issue is a dictionary.
+
+        """
+        if new_column_map is None:
+            new_column_map = {}
+        if isinstance(new_column_map, dict):
+            column_map = new_column_map
+        # List like
+        else:
+            column_map = {column_number: column_name for column_number, column_name in enumerate(new_column_map)}
+        self._column_map = column_map
+        self._reverse_column_map = {column_name: column_number for column_number, column_name in column_map.items()}
+        self._finalize_mapping()
+
+    def set_column_prefix_dictionary(self, column_prefix_dictionary, finalize_mapping=True):
+        """Set the column prefix dictionary. """
+        self._column_prefix_dictionary = column_prefix_dictionary
+        if finalize_mapping:
+            self._finalize_mapping()
+
+    @staticmethod
+    def _get_sidecar_basic_map(column_map, column_data):
+        basic_final_map = {}
+        unhandled_cols = []
+        if column_map:
+            for column_number, column_name in column_map.items():
+                if column_name is None:
+                    continue
+                if column_name in column_data:
+                    column_entry = copy.deepcopy(column_data[column_name])
+                    column_entry.column_name = column_name
+                    basic_final_map[column_name] = column_entry
+                    continue
+                elif isinstance(column_name, str) and column_name.startswith(PANDAS_COLUMN_PREFIX_TO_IGNORE):
+                    continue
+                unhandled_cols.append(column_name)
+
+        return basic_final_map, unhandled_cols
+
+    @staticmethod
+    def _convert_to_names(column_to_name_map, column_list):
+        converted_names = []
+        for index in column_list:
+            if isinstance(index, int):
+                if not column_to_name_map:
+                    converted_names.append(index)
+                elif index in column_to_name_map:
+                    converted_names.append(column_to_name_map[index])
+            else:
+                if index in column_to_name_map.values():
+                    converted_names.append(index)
+        return converted_names
+
+    @staticmethod
+    def _convert_to_names_dict(column_to_name_map, column_dict):
+        converted_dict = {}
+        for index, column_data in column_dict.items():
+            if isinstance(index, int):
+                if not column_to_name_map:
+                    converted_dict[index] = column_data
+                elif index in column_to_name_map:
+                    converted_dict[column_to_name_map[index]] = column_data
+            else:
+                if index in column_to_name_map.values():
+                    converted_dict[index] = column_data
+        return converted_dict
+
+    @staticmethod
+    def _add_value_columns(final_map, column_prefix_dictionary):
+        for col, prefix in column_prefix_dictionary.items():
+            if prefix.endswith("/"):
+                prefix = prefix + "#"
+            else:
+                prefix = prefix + "/#"
+            new_def = ColumnMetadata(ColumnType.Value, col, source=prefix)
+            final_map[col] = new_def
+
+    @staticmethod
+    def _add_tag_columns(final_map, tag_columns):
+        for col in tag_columns:
+            new_def = ColumnMetadata(ColumnType.HEDTags, col)
+            final_map[col] = new_def
+
+    def _get_column_lists(self):
+        column_lists = self._tag_columns, self._optional_tag_columns, self._column_prefix_dictionary
+        list_names = ["tag_columns", "optional_tag_columns", "column_prefix_dictionary"]
+
+        if not any(column for column in column_lists):
+            return column_lists, list_names
+        # Filter out empty lists from the above
+        column_lists, list_names = zip(*[(col_list, list_name) for col_list, list_name in zip(column_lists, list_names)
+                                         if col_list])
+
+        return column_lists, list_names
+
+    def _check_for_duplicates_and_required(self, list_names, column_lists):
+        issues = []
+        for list_name, col_list in zip(list_names, column_lists):
+            # Convert all known strings to ints, then check for duplicates
+            converted_list = [item if isinstance(item, int) else self._reverse_column_map.get(item, item)
+                              for item in col_list]
+
+            if col_list != self._optional_tag_columns:
+                for test_col in converted_list:
+                    if isinstance(test_col, str) and test_col not in self._reverse_column_map:
+                        issues += ErrorHandler.format_error(ValidationErrors.HED_MISSING_REQUIRED_COLUMN,
+                                                            test_col, list_name)
+
+            issues += self._check_for_duplicates_between_lists(converted_list, list_name,
+                                                               ValidationErrors.DUPLICATE_COLUMN_IN_LIST)
+
+        return issues
+
+    def _check_for_duplicates_between_lists(self, checking_list, list_names, error_type):
+        issues = []
+        duplicates = [item for item, count in Counter(checking_list).items() if count > 1]
+        for duplicate in duplicates:
+            issues += ErrorHandler.format_error(error_type, duplicate,
+                                                self._column_map.get(duplicate), list_names)
+        return issues
+
+    def check_for_mapping_issues(self, allow_blank_names=False):
+        """ Find all issues given the current column_map, tag_columns, etc.
+
+        Parameters:
+            allow_blank_names(bool): Only flag blank names if False.
+
+        Returns:
+            issue_list(list of dict): All issues found as a list of dicts.
+        """
+        # 1. Get the lists with entries
+        column_lists, list_names = self._get_column_lists()
+        # 2. Verify column_prefix columns and tag columns are present, and check for duplicates
+        issues = self._check_for_duplicates_and_required(list_names, column_lists)
+
+        combined_list = self.tag_columns + list(self.column_prefix_dictionary)
+        # 3. Verify prefix and tag columns do not conflict.
+        issues += self._check_for_duplicates_between_lists(combined_list, list_names,
+                                                           ValidationErrors.DUPLICATE_COLUMN_BETWEEN_SOURCES)
+
+        # 4. Verify we didn't get both a sidecar and a tag column list
+        if self._sidecar and combined_list and combined_list != ["HED"]:
+            issues += ErrorHandler.format_error(ValidationErrors.SIDECAR_AND_OTHER_COLUMNS, column_names=combined_list)
+
+        # 5. Verify we handled all columns
+        if self._warn_on_missing_column:
+            fully_combined_list = list(self.sidecar_column_data) + combined_list + NO_WARN_COLUMNS
+            for column in self._column_map.values():
+                if column not in fully_combined_list:
+                    issues += ErrorHandler.format_error(ValidationErrors.HED_UNKNOWN_COLUMN, column)
+
+        issues += self.check_for_blank_names(self._column_map.values(), allow_blank_names=allow_blank_names)
+        return issues
+
+    def _finalize_mapping(self):
+        final_map, unhandled_cols = self._get_sidecar_basic_map(self._column_map, self.sidecar_column_data)
+
+        self._add_tag_columns(final_map, self.tag_columns)
+        self._remove_from_list(unhandled_cols, self.tag_columns)
+
+        self._add_value_columns(final_map, self.column_prefix_dictionary)
+        self._remove_from_list(unhandled_cols, self.column_prefix_dictionary)
+
+        self._final_column_map = dict(sorted(final_map.items()))
+
+    @staticmethod
+    def _remove_from_list(list_to_alter, to_remove):
+        return [item for item in list_to_alter if item not in to_remove]
+
+    def get_def_dict(self, hed_schema, extra_def_dicts=None):
+        """ Return def dicts from every column description.
+
+        Parameters:
+            hed_schema (Schema): A HED schema object to use for extracting definitions.
+            extra_def_dicts (list, DefinitionDict, or None): Extra dicts to add to the list.
+
+        Returns:
+           DefinitionDict:   A single definition dict representing all the data(and extra def dicts).
+        """
+        if self._sidecar:
+            return self._sidecar.get_def_dict(hed_schema=hed_schema, extra_def_dicts=extra_def_dicts)
+
+        return DefinitionDict(extra_def_dicts, hed_schema=hed_schema)
+
+    def get_column_mapping_issues(self):
+        """ Get all the issues with finalizing column mapping(duplicate columns, missing required, etc.).
+
+        Notes:
+            - This is deprecated and now a wrapper for "check_for_mapping_issues()".
+
+        Returns:
+            list: A list dictionaries of all issues found from mapping column names to numbers.
+
+        """
+        return self.check_for_mapping_issues()
+
+    @staticmethod
+    def _category_handler(category_values, x):
+        return category_values.get(x, "")
+
+    @staticmethod
+    def _value_handler(value_str, x):
+        if x == "n/a":
+            return "n/a"
+
+        return value_str.replace("#", str(x))
diff --git a/hed/models/df_util.py b/hed/models/df_util.py
index 39aa979b..c7caf16a 100644
--- a/hed/models/df_util.py
+++ b/hed/models/df_util.py
@@ -1,5 +1,7 @@
 """ Utilities for assembly and conversion of HED strings to different forms. """
 import re
+import math
+from collections import defaultdict
 from functools import partial
 import pandas as pd
 from hed.models.hed_string import HedString
@@ -115,7 +117,7 @@ def sort_dataframe_by_onsets(df):
     if "onset" in df.columns:
         # Create a copy and sort by onsets as floats(if needed), but continue to keep the string version.
         df_copy = df.copy()
-        df_copy['_temp_onset_sort'] = df_copy['onset'].astype(float)
+        df_copy['_temp_onset_sort'] = pd.to_numeric(df_copy['onset'], errors='coerce')
         df_copy.sort_values(by='_temp_onset_sort', inplace=True)
         df_copy.drop(columns=['_temp_onset_sort'], inplace=True)
 
@@ -215,7 +217,7 @@ def split_delay_tags(series, hed_schema, onsets):
     Note: This dataframe may be longer than the original series, but it will never be shorter.
     """
     if series is None or onsets is None:
-        return
+        return None
     split_df = pd.DataFrame({"onset": onsets, "HED": series, "original_index": series.index})
     delay_strings = [(i, HedString(hed_string, hed_schema)) for (i, hed_string) in series.items() if
                      "delay/" in hed_string.casefold()]
@@ -251,23 +253,42 @@ def filter_series_by_onset(series, onsets):
     Returns:
         Series or Dataframe: the series with rows filtered together.
     """
-    indexed_dict = _indexed_dict_from_onsets(onsets.astype(float))
-    return _filter_by_index_list(series, indexed_dict=indexed_dict)
+    #indexed_dict = _indexed_dict_from_onsets(pd.to_numeric(onsets, errors='coerce'))
+    #return _filter_by_index_list(series, indexed_dict=indexed_dict)
+    indexed_dict = _indexed_dict_from_onsets(pd.to_numeric(onsets, errors='coerce'))
+    y =  _filter_by_index_list(series, indexed_dict=indexed_dict)
+    return y
+    # return _filter_by_index_list(series, indexed_dict=indexed_dict)
 
 
 def _indexed_dict_from_onsets(onsets):
-    """Finds series of consecutive lines with the same(or close enough) onset"""
+    """Finds series of consecutive lines with the same (or close enough) onset."""
     current_onset = -1000000.0
     tol = 1e-9
-    from collections import defaultdict
     indexed_dict = defaultdict(list)
+
     for i, onset in enumerate(onsets):
+        if math.isnan(onset): # Ignore NaNs
+            continue
         if abs(onset - current_onset) > tol:
             current_onset = onset
         indexed_dict[current_onset].append(i)
 
     return indexed_dict
 
+# def _indexed_dict_from_onsets(onsets):
+#     """Finds series of consecutive lines with the same(or close enough) onset"""
+#     current_onset = -1000000.0
+#     tol = 1e-9
+#     from collections import defaultdict
+#     indexed_dict = defaultdict(list)
+#     for i, onset in enumerate(onsets):
+#         if abs(onset - current_onset) > tol:
+#             current_onset = onset
+#         indexed_dict[current_onset].append(i)
+#
+#     return indexed_dict
+
 
 def _filter_by_index_list(original_data, indexed_dict):
     """Filters a series or dataframe by the indexed_dict, joining lines as indicated"""
diff --git a/hed/models/tabular_input.py b/hed/models/tabular_input.py
index 61bf90ed..3fea5195 100644
--- a/hed/models/tabular_input.py
+++ b/hed/models/tabular_input.py
@@ -1,88 +1,88 @@
-""" A BIDS tabular file with sidecar. """
-from hed.models.column_mapper import ColumnMapper
-from hed.models.base_input import BaseInput
-from hed.models.sidecar import Sidecar
-
-
-class TabularInput(BaseInput):
-    """ A BIDS tabular file with sidecar. """
-
-    HED_COLUMN_NAME = "HED"
-
-    def __init__(self, file=None, sidecar=None, name=None):
-
-        """ Constructor for the TabularInput class.
-
-        Parameters:
-            file (str or FileLike): A tsv file to open.
-            sidecar (str or Sidecar or FileLike): A Sidecar or source file/filename.
-            name (str): The name to display for this file for error purposes.
-
-        :raises HedFileError:
-            - The file is blank.
-            - An invalid dataframe was passed with size 0.
-            - An invalid extension was provided.
-            - A duplicate or empty column name appears.
-
-        :raises OSError:
-            - Cannot open the indicated file.
-
-        :raises ValueError:
-            - This file has no column names.
-        """
-        if sidecar and not isinstance(sidecar, Sidecar):
-            sidecar = Sidecar(sidecar)
-        new_mapper = ColumnMapper(sidecar=sidecar, optional_tag_columns=[self.HED_COLUMN_NAME],
-                                  warn_on_missing_column=True)
-
-        self._sidecar = sidecar
-
-        super().__init__(file, file_type=".tsv", worksheet_name=None, has_column_names=True, mapper=new_mapper,
-                         name=name, allow_blank_names=False, )
-
-        if not self._has_column_names:
-            raise ValueError("You are attempting to open a bids_old style file with no column headers provided.\n"
-                             "This is probably not intended.")
-
-    def reset_column_mapper(self, sidecar=None):
-        """ Change the sidecars and settings.
-
-        Parameters:
-            sidecar (str or [str] or Sidecar or [Sidecar]): A list of json filenames to pull sidecar info from.
-
-        """
-        new_mapper = ColumnMapper(sidecar=sidecar, optional_tag_columns=[self.HED_COLUMN_NAME])
-        self._sidecar = sidecar
-
-        self.reset_mapper(new_mapper)
-
-    def get_def_dict(self, hed_schema, extra_def_dicts=None):
-        """ Return the definition dict for this sidecar.
-
-        Parameters:
-            hed_schema(HedSchema): Used to identify tags to find definitions.
-            extra_def_dicts (list, DefinitionDict, or None): Extra dicts to add to the list.
-
-        Returns:
-            DefinitionDict:   A single definition dict representing all the data(and extra def dicts).
-        """
-        if self._sidecar:
-            return self._sidecar.get_def_dict(hed_schema, extra_def_dicts)
-        else:
-            return super().get_def_dict(hed_schema, extra_def_dicts)
-
-    def get_column_refs(self):
-        """ Return a list of column refs for this file.
-
-            Default implementation returns none.
-
-        Returns:
-            column_refs(list): A list of unique column refs found.
-        """
-        if self._sidecar:
-            return self._sidecar.get_column_refs()
-        return []
-
-    def get_sidecar(self):
-        """Return the sidecar associated with this TabularInput."""
-        return self._sidecar
+""" A BIDS tabular file with sidecar. """
+from hed.models.column_mapper import ColumnMapper
+from hed.models.base_input import BaseInput
+from hed.models.sidecar import Sidecar
+
+
+class TabularInput(BaseInput):
+    """ A BIDS tabular file with sidecar. """
+
+    HED_COLUMN_NAME = "HED"
+
+    def __init__(self, file=None, sidecar=None, name=None):
+
+        """ Constructor for the TabularInput class.
+
+        Parameters:
+            file (str or FileLike or pd.Dataframe): A tsv file to open.
+            sidecar (str or Sidecar or FileLike): A Sidecar or source file/filename.
+            name (str): The name to display for this file for error purposes.
+
+        :raises HedFileError:
+            - The file is blank.
+            - An invalid dataframe was passed with size 0.
+            - An invalid extension was provided.
+            - A duplicate or empty column name appears.
+
+        :raises OSError:
+            - Cannot open the indicated file.
+
+        :raises ValueError:
+            - This file has no column names.
+        """
+        if sidecar and not isinstance(sidecar, Sidecar):
+            sidecar = Sidecar(sidecar)
+        new_mapper = ColumnMapper(sidecar=sidecar, optional_tag_columns=[self.HED_COLUMN_NAME],
+                                  warn_on_missing_column=True)
+
+        self._sidecar = sidecar
+
+        super().__init__(file, file_type=".tsv", worksheet_name=None, has_column_names=True, mapper=new_mapper,
+                         name=name, allow_blank_names=False, )
+
+        if not self._has_column_names:
+            raise ValueError("You are attempting to open a bids_old style file with no column headers provided.\n"
+                             "This is probably not intended.")
+
+    def reset_column_mapper(self, sidecar=None):
+        """ Change the sidecars and settings.
+
+        Parameters:
+            sidecar (str or [str] or Sidecar or [Sidecar]): A list of json filenames to pull sidecar info from.
+
+        """
+        new_mapper = ColumnMapper(sidecar=sidecar, optional_tag_columns=[self.HED_COLUMN_NAME])
+        self._sidecar = sidecar
+
+        self.reset_mapper(new_mapper)
+
+    def get_def_dict(self, hed_schema, extra_def_dicts=None):
+        """ Return the definition dict for this sidecar.
+
+        Parameters:
+            hed_schema(HedSchema): Used to identify tags to find definitions.
+            extra_def_dicts (list, DefinitionDict, or None): Extra dicts to add to the list.
+
+        Returns:
+            DefinitionDict:   A single definition dict representing all the data(and extra def dicts).
+        """
+        if self._sidecar:
+            return self._sidecar.get_def_dict(hed_schema, extra_def_dicts)
+        else:
+            return super().get_def_dict(hed_schema, extra_def_dicts)
+
+    def get_column_refs(self):
+        """ Return a list of column refs for this file.
+
+            Default implementation returns none.
+
+        Returns:
+            column_refs(list): A list of unique column refs found.
+        """
+        if self._sidecar:
+            return self._sidecar.get_column_refs()
+        return []
+
+    def get_sidecar(self):
+        """Return the sidecar associated with this TabularInput."""
+        return self._sidecar
diff --git a/hed/tools/analysis/event_manager.py b/hed/tools/analysis/event_manager.py
index d898b1f9..2cbad4bf 100644
--- a/hed/tools/analysis/event_manager.py
+++ b/hed/tools/analysis/event_manager.py
@@ -29,13 +29,15 @@ def __init__(self, input_data, hed_schema, extra_defs=None):
         are separated from the rest of the annotations, which are contained in self.hed_strings.
 
         """
+        if input_data.onsets is not None and input_data.needs_sorting:
+            raise HedFileError("OnsetsNotOrdered", "Events must have numeric non-decreasing onset values", "")
         self.hed_schema = hed_schema
         self.input_data = input_data
         self.def_dict = input_data.get_def_dict(hed_schema, extra_def_dicts=extra_defs)
-        if self.input_data.needs_sorting:
-            raise HedFileError("OnsetsNotOrdered", "The onset values must be non-decreasing", "")
-        self.onsets = None
-        self.hed_strings = None
+        self.onsets = None  # list of onset times or None if not an events file
+        self.base = None  # list of strings containing the starts of event processes
+        self.context = None  # list of strings containing the contexts of event processes
+        self.hed_strings = None  # list of HedString objects without the temporal events
         self.event_list = None
         self._create_event_list(input_data)
 
@@ -53,6 +55,9 @@ def _create_event_list(self, input_data):
         """
         hed_strings = input_data.series_a
         df_util.shrink_defs(hed_strings, self.hed_schema)
+        if input_data.onsets is None:
+            self.hed_strings = [HedString(hed_string, self.hed_schema) for hed_string in hed_strings]
+            return
         delay_df = df_util.split_delay_tags(hed_strings, self.hed_schema, input_data.onsets)
 
         hed_strings = [HedString(hed_string, self.hed_schema) for hed_string in delay_df.HED]
@@ -66,6 +71,7 @@ def _create_event_list(self, input_data):
         for item in onset_dict.values():
             item.set_end(len(self.onsets), None)
         self.hed_strings = hed_strings
+        self._extract_context()
 
     def _extract_duration_events(self, hed, event_index):
         groups = hed.find_top_level_tags(anchor_tags={DefTagNames.DURATION_KEY})
@@ -120,31 +126,42 @@ def unfold_context(self, remove_types=[]):
 
         Returns:
             list of str or HedString representing the information without the events of temporal extent.
-            list of str or HedString representing the onsets of the events of temporal extent.
-            list of str or HedString representing the ongoing context information.
+            list of str or HedString or None representing the onsets of the events of temporal extent.
+            list of str or HedString or None representing the ongoing context information.
 
+        If the
         """
-        placeholder = ""
+
         remove_defs = self.get_type_defs(remove_types)  # definitions corresponding to remove types to be filtered out
-        new_hed = [placeholder for _ in range(len(self.hed_strings))]
-        new_base = [placeholder for _ in range(len(self.hed_strings))]
-        new_contexts = [placeholder for _ in range(len(self.hed_strings))]
-        base, contexts = self._expand_context()
+        new_hed = ["" for _ in range(len(self.hed_strings))]
         for index, item in enumerate(self.hed_strings):
             new_hed[index] = self._filter_hed(item, remove_types=remove_types,
                                               remove_defs=remove_defs, remove_group=False)
-            new_base[index] = self._filter_hed(base[index], remove_types=remove_types,
+        if self.onsets is None:
+            return new_hed, None, None
+        new_base, new_contexts = self._get_base_contexts(remove_types, remove_defs)
+        return new_hed, new_base, new_contexts
+
+    def _get_base_contexts(self, remove_types, remove_defs):
+        """ Expand the context and filter to remove specified types.
+
+        Parameters:
+            remove_types (list):  List of types to remove.
+            remove_defs (list):  List of definitions to remove.
+
+        """
+        new_base = ["" for _ in range(len(self.hed_strings))]
+        new_contexts = ["" for _ in range(len(self.hed_strings))]
+        for index, item in enumerate(self.hed_strings):
+            new_base[index] = self._filter_hed(self.base[index], remove_types=remove_types,
                                                remove_defs=remove_defs, remove_group=True)
-            new_contexts[index] = self._filter_hed(contexts[index], remove_types=remove_types,
+            new_contexts[index] = self._filter_hed(self.contexts[index], remove_types=remove_types,
                                                    remove_defs=remove_defs, remove_group=True)
-        return new_hed, new_base, new_contexts   # these are each a list of strings
+        return new_base, new_contexts   # these are each a list of strings
 
-    def _expand_context(self):
+    def _extract_context(self):
         """ Expand the onset and the ongoing context for additional processing.
 
-        Returns:
-            tuple of lists: (base list of str, context list of str).
-
         Notes: For each event, the Onset goes in the base list and the remainder of the times go in the contexts list.
 
         """
@@ -156,8 +173,8 @@ def _expand_context(self):
                 base[event.start_index].append(this_str)
                 for i in range(event.start_index + 1, event.end_index):
                     contexts[i].append(this_str)
-
-        return self.compress_strings(base), self.compress_strings(contexts)
+        self.base = self.compress_strings(base)
+        self.contexts = self.compress_strings(contexts)
 
     def _filter_hed(self, hed, remove_types=[], remove_defs=[], remove_group=False):
         """ Remove types and definitions from a HED string.
diff --git a/hed/tools/analysis/hed_tag_counts.py b/hed/tools/analysis/hed_tag_counts.py
index 24133f42..542abc25 100644
--- a/hed/tools/analysis/hed_tag_counts.py
+++ b/hed/tools/analysis/hed_tag_counts.py
@@ -85,7 +85,7 @@ def __init__(self, name, total_events=0):
         self.files = {}
         self.total_events = total_events
 
-    def update_event_counts(self, hed_string_obj, file_name):
+    def update_tag_counts(self, hed_string_obj, file_name):
         """ Update the tag counts based on a HedString object.
 
         Parameters:
diff --git a/hed/tools/analysis/hed_tag_manager.py b/hed/tools/analysis/hed_tag_manager.py
index d8cd0529..d1553f3a 100644
--- a/hed/tools/analysis/hed_tag_manager.py
+++ b/hed/tools/analysis/hed_tag_manager.py
@@ -2,12 +2,13 @@
 
 from hed.models.hed_string import HedString
 from hed.models import string_util
+from hed.tools.analysis.event_manager import EventManager
 
 
 class HedTagManager:
     """ Manager for the HED tags from a columnar file. """
 
-    def __init__(self, event_manager, remove_types=[]):
+    def __init__(self, event_manager, remove_types=[], extra_defs=None):
         """ Create a tag manager for one tabular file.
 
         Parameters:
diff --git a/hed/tools/analysis/hed_type.py b/hed/tools/analysis/hed_type.py
index d6c64943..96e25dc3 100644
--- a/hed/tools/analysis/hed_type.py
+++ b/hed/tools/analysis/hed_type.py
@@ -14,7 +14,7 @@ def __init__(self, event_manager, name, type_tag="condition-variable"):
         """ Create a variable manager for one type-variable for one tabular file.
 
         Parameters:
-            event_manager (EventManager): An event manager for the tabular file.
+            event_manager (EventManager): Event manager instance
             name (str): Name of the tabular file as a unique identifier.
             type_tag (str): Lowercase short form of the tag to be managed.
 
@@ -25,7 +25,7 @@ def __init__(self, event_manager, name, type_tag="condition-variable"):
         self.name = name
         self.type_tag = type_tag.casefold()
         self.event_manager = event_manager
-        self.type_defs = HedTypeDefs(event_manager.def_dict, type_tag=type_tag)
+        self.type_defs = HedTypeDefs(self.event_manager.def_dict, type_tag=type_tag)
         self._type_map = {}  # Dictionary of type tags versus dictionary with keys being definition names.
         self._extract_variables()
 
diff --git a/hed/tools/analysis/hed_type_manager.py b/hed/tools/analysis/hed_type_manager.py
index 402d45d1..6d510fd7 100644
--- a/hed/tools/analysis/hed_type_manager.py
+++ b/hed/tools/analysis/hed_type_manager.py
@@ -3,6 +3,7 @@
 import pandas as pd
 import json
 from hed.tools.analysis.hed_type import HedType
+from hed.tools.analysis.event_manager import EventManager
 
 
 class HedTypeManager:
diff --git a/hed/tools/remodeling/operations/factor_hed_tags_op.py b/hed/tools/remodeling/operations/factor_hed_tags_op.py
index 69a1464d..7222c2a4 100644
--- a/hed/tools/remodeling/operations/factor_hed_tags_op.py
+++ b/hed/tools/remodeling/operations/factor_hed_tags_op.py
@@ -119,8 +119,7 @@ def do_op(self, dispatcher, df, name, sidecar=None):
                 raise ValueError("QueryNameAlreadyColumn",
                                  f"Query [{query_name}]: is already a column name of the data frame")
         df_list = [input_data.dataframe]
-        tag_man = HedTagManager(EventManager(input_data, dispatcher.hed_schema),
-                                remove_types=self.remove_types)
+        tag_man = HedTagManager(EventManager(input_data, dispatcher.hed_schema), remove_types=self.remove_types)
         hed_objs = tag_man.get_hed_objs(include_context=self.expand_context, replace_defs=self.replace_defs)
         df_factors = query_service.search_hed_objs(hed_objs, self.query_handlers, query_names=self.query_names)
         if len(df_factors.columns) > 0:
diff --git a/hed/tools/remodeling/operations/factor_hed_type_op.py b/hed/tools/remodeling/operations/factor_hed_type_op.py
index 3d6f523f..8a3246c5 100644
--- a/hed/tools/remodeling/operations/factor_hed_type_op.py
+++ b/hed/tools/remodeling/operations/factor_hed_type_op.py
@@ -73,8 +73,7 @@ def do_op(self, dispatcher, df, name, sidecar=None):
 
         input_data = TabularInput(df.copy().fillna('n/a'), sidecar=sidecar, name=name)
         df_list = [input_data.dataframe]
-        var_manager = HedTypeManager(
-            EventManager(input_data, dispatcher.hed_schema))
+        var_manager = HedTypeManager(EventManager(input_data, dispatcher.hed_schema))
         var_manager.add_type(self.type_tag.casefold())
 
         df_factors = var_manager.get_factor_vectors(
diff --git a/hed/tools/remodeling/operations/merge_consecutive_op.py b/hed/tools/remodeling/operations/merge_consecutive_op.py
index 8041864e..8d193db2 100644
--- a/hed/tools/remodeling/operations/merge_consecutive_op.py
+++ b/hed/tools/remodeling/operations/merge_consecutive_op.py
@@ -115,7 +115,7 @@ def do_op(self, dispatcher, df, name, sidecar=None):
 
         df_new = df.copy()
         code_mask = df_new[self.column_name] == self.event_code
-        if sum(code_mask.astype(int)) == 0:
+        if not code_mask.any():
             return df_new
         match_columns.append(self.column_name)
         match_df = df_new.loc[:, match_columns]
diff --git a/hed/tools/remodeling/operations/summarize_column_names_op.py b/hed/tools/remodeling/operations/summarize_column_names_op.py
index 8dbdb15b..03fed614 100644
--- a/hed/tools/remodeling/operations/summarize_column_names_op.py
+++ b/hed/tools/remodeling/operations/summarize_column_names_op.py
@@ -94,7 +94,7 @@ def __init__(self, sum_op):
         """ Constructor for column name summary manager.
 
         Parameters:
-            sum_op (BaseOp): Operation associated with this summary.
+            sum_op (SummarizeColumnNamesOp): Operation associated with this summary.
 
         """
         super().__init__(sum_op)
diff --git a/hed/tools/remodeling/operations/summarize_column_values_op.py b/hed/tools/remodeling/operations/summarize_column_values_op.py
index 612e4b5e..5fa5ddff 100644
--- a/hed/tools/remodeling/operations/summarize_column_values_op.py
+++ b/hed/tools/remodeling/operations/summarize_column_values_op.py
@@ -133,7 +133,7 @@ def __init__(self, sum_op):
         """ Constructor for column value summary manager.
 
         Parameters:
-            sum_op (BaseOp): Operation associated with this summary.
+            sum_op (SummarizeColumnValuesOp): Operation associated with this summary.
 
         """
         super().__init__(sum_op)
diff --git a/hed/tools/remodeling/operations/summarize_definitions_op.py b/hed/tools/remodeling/operations/summarize_definitions_op.py
index f953d593..65503fce 100644
--- a/hed/tools/remodeling/operations/summarize_definitions_op.py
+++ b/hed/tools/remodeling/operations/summarize_definitions_op.py
@@ -94,7 +94,7 @@ def __init__(self, sum_op, hed_schema, known_defs=None):
         """ Constructor for the summary of definitions.
 
         Parameters:
-            sum_op (BaseOp): Summary operation class for gathering definitions.
+            sum_op (SummarizeDefinitionsOp): Summary operation class for gathering definitions.
             hed_schema (HedSchema or HedSchemaGroup):  Schema used for the dataset.
             known_defs (str or list or DefinitionDict): Definitions already known to be used.
 
diff --git a/hed/tools/remodeling/operations/summarize_hed_tags_op.py b/hed/tools/remodeling/operations/summarize_hed_tags_op.py
index 89b678ee..5e426bae 100644
--- a/hed/tools/remodeling/operations/summarize_hed_tags_op.py
+++ b/hed/tools/remodeling/operations/summarize_hed_tags_op.py
@@ -187,7 +187,7 @@ def __init__(self, parameters):
             }
             if self.word_cloud["use_mask"] and not self.word_cloud["mask_path"]:
                 self.word_cloud["mask_path"] = os.path.realpath(
-                    os.path.join(os.path.dirname(__file__),  '../../../resources/word_cloud_brain_mask.png'))
+                    os.path.join(os.path.dirname(__file__), '../../../resources/word_cloud_brain_mask.png'))
             if self.word_cloud["font_path"]:
                 self.word_cloud["font_path"] = os.path.realpath(self.word_cloud["font_path"])
 
@@ -224,11 +224,12 @@ def validate_input_data(parameters):
 
 class HedTagSummary(BaseSummary):
     """ Manager of the HED tag summaries. """
+
     def __init__(self, sum_op):
         """ Constructor for HED tag summary manager.
 
         Parameters:
-            sum_op (BaseOp): Operation associated with this summary.
+            sum_op (SummarizeHedTagsOp): Operation associated with this summary.
 
         """
 
@@ -249,12 +250,11 @@ def update_summary(self, new_info):
             new_info['name'], total_events=len(new_info['df']))
         input_data = TabularInput(
             new_info['df'], sidecar=new_info['sidecar'], name=new_info['name'])
-        tag_man = HedTagManager(EventManager(input_data, new_info['schema']),
-                                remove_types=self.sum_op.remove_types)
+        tag_man = HedTagManager(EventManager(input_data, new_info['schema']), remove_types=self.sum_op.remove_types)
         hed_objs = tag_man.get_hed_objs(include_context=self.sum_op.include_context,
                                         replace_defs=self.sum_op.replace_defs)
         for hed in hed_objs:
-            counts.update_event_counts(hed, new_info['name'])
+            counts.update_tag_counts(hed, new_info['name'])
         self.summary_dict[new_info["name"]] = counts
 
     def get_details_dict(self, tag_counts):
@@ -393,8 +393,7 @@ def _get_dataset_string(result, indent=BaseSummary.DISPLAY_INDENT):
         """
         sum_list = [f"Dataset: Total events={result.get('Total events', 0)} "
                     f"Total files={len(result.get('Files', []))}"]
-        sum_list = sum_list + \
-            HedTagSummary._get_tag_list(result, indent=indent)
+        sum_list = sum_list + HedTagSummary._get_tag_list(result, indent=indent)
         return "\n".join(sum_list)
 
     @staticmethod
@@ -410,8 +409,7 @@ def _get_individual_string(result, indent=BaseSummary.DISPLAY_INDENT):
 
         """
         sum_list = [f"Total events={result.get('Total events', 0)}"]
-        sum_list = sum_list + \
-            HedTagSummary._get_tag_list(result, indent=indent)
+        sum_list = sum_list + HedTagSummary._get_tag_list(result, indent=indent)
         return "\n".join(sum_list)
 
     @staticmethod
diff --git a/hed/tools/remodeling/operations/summarize_hed_type_op.py b/hed/tools/remodeling/operations/summarize_hed_type_op.py
index dbd09e57..36a9667f 100644
--- a/hed/tools/remodeling/operations/summarize_hed_type_op.py
+++ b/hed/tools/remodeling/operations/summarize_hed_type_op.py
@@ -108,7 +108,7 @@ def __init__(self, sum_op):
         """ Constructor for HED type summary manager.
 
         Parameters:
-            sum_op (BaseOp): Operation associated with this summary.
+            sum_op (SummarizeHedTypeOp): Operation associated with this summary.
 
         """
         super().__init__(sum_op)
@@ -130,8 +130,7 @@ def update_summary(self, new_info):
             sidecar = Sidecar(sidecar)
         input_data = TabularInput(
             new_info['df'], sidecar=sidecar, name=new_info['name'])
-        type_values = HedType(EventManager(
-            input_data, new_info['schema']), new_info['name'], type_tag=self.type_tag)
+        type_values = HedType(EventManager(input_data, new_info['schema']), new_info['name'], type_tag=self.type_tag)
         counts = HedTypeCounts(new_info['name'], self.type_tag)
         counts.update_summary(type_values.get_summary(),
                               type_values.total_events, new_info['name'])
diff --git a/hed/tools/remodeling/operations/summarize_hed_validation_op.py b/hed/tools/remodeling/operations/summarize_hed_validation_op.py
index 3f720740..6549c4c6 100644
--- a/hed/tools/remodeling/operations/summarize_hed_validation_op.py
+++ b/hed/tools/remodeling/operations/summarize_hed_validation_op.py
@@ -106,7 +106,7 @@ def __init__(self, sum_op):
         """ Constructor for validation issue manager.
 
         Parameters:
-            sum_op (BaseOp): Operation associated with this summary.
+            sum_op (SummarizeHedValidationOp): Operation associated with this summary.
 
         """
         super().__init__(sum_op)
@@ -153,8 +153,7 @@ def update_summary(self, new_info):
 
         sidecar = new_info.get('sidecar', None)
         if sidecar and not isinstance(sidecar, Sidecar):
-            sidecar = Sidecar(
-                files=new_info['sidecar'], name=os.path.basename(sidecar))
+            sidecar = Sidecar(files=new_info['sidecar'], name=os.path.basename(sidecar))
         results = self._get_sidecar_results(
             sidecar, new_info, self.sum_op.check_for_warnings)
         if not results['sidecar_had_issues']:
diff --git a/hed/validator/onset_validator.py b/hed/validator/onset_validator.py
index 1983f409..1d7a04dd 100644
--- a/hed/validator/onset_validator.py
+++ b/hed/validator/onset_validator.py
@@ -76,5 +76,5 @@ def check_for_banned_tags(hed_string):
         issues = []
         for tag in hed_string.get_all_tags():
             if tag.short_base_tag in banned_tag_list:
-                issues += ErrorHandler.format_error(TemporalErrors.HED_ONSET_WITH_NO_COLUMN, tag)
+                issues += ErrorHandler.format_error(TemporalErrors.TEMPORAL_TAG_NO_TIME, tag)
         return issues
diff --git a/hed/validator/spreadsheet_validator.py b/hed/validator/spreadsheet_validator.py
index 0e283b2a..9fd47443 100644
--- a/hed/validator/spreadsheet_validator.py
+++ b/hed/validator/spreadsheet_validator.py
@@ -1,6 +1,6 @@
 """ Validates spreadsheet tabular data. """
 import copy
-
+import pandas as pd
 from hed.models.base_input import BaseInput
 from hed.errors.error_types import ColumnErrors, ErrorContext, ValidationErrors
 from hed.errors.error_reporter import ErrorHandler
@@ -61,7 +61,7 @@ def validate(self, data, def_dicts=None, name=None, error_handler=None):
         if data.needs_sorting:
             data_new = copy.deepcopy(data)
             data_new._dataframe = df_util.sort_dataframe_by_onsets(data.dataframe)
-            issues += error_handler.format_error_with_context(ValidationErrors.ONSETS_OUT_OF_ORDER)
+            issues += error_handler.format_error_with_context(ValidationErrors.ONSETS_UNORDERED)
             data = data_new
 
         onsets = df_util.split_delay_tags(data.series_a, self._schema, data.onsets)
@@ -70,12 +70,13 @@ def validate(self, data, def_dicts=None, name=None, error_handler=None):
         self._hed_validator = HedValidator(self._schema, def_dicts=def_dicts)
         if data.onsets is not None:
             self._onset_validator = OnsetValidator()
+            onset_mask = ~pd.isna(pd.to_numeric(onsets['onset'], errors='coerce'))
         else:
             self._onset_validator = None
+            onset_mask = None
 
         # Check the rows of the input data
-        issues += self._run_checks(df, error_handler=error_handler, row_adj=row_adj,
-                                   has_onsets=bool(self._onset_validator))
+        issues += self._run_checks(df, error_handler=error_handler, row_adj=row_adj, onset_mask=onset_mask)
         if self._onset_validator:
             issues += self._run_onset_checks(onsets, error_handler=error_handler, row_adj=row_adj)
         error_handler.pop_error_context()
@@ -83,7 +84,7 @@ def validate(self, data, def_dicts=None, name=None, error_handler=None):
         issues = sort_issues(issues)
         return issues
 
-    def _run_checks(self, hed_df, error_handler, row_adj, has_onsets):
+    def _run_checks(self, hed_df, error_handler, row_adj, onset_mask=None):
         issues = []
         columns = list(hed_df.columns)
         self.invalid_original_rows = set()
@@ -113,7 +114,7 @@ def _run_checks(self, hed_df, error_handler, row_adj, has_onsets):
                 error_handler.pop_error_context()  # Row
                 continue
 
-            if has_onsets or not row_strings:
+            if not row_strings or (onset_mask is not None and onset_mask.iloc[row_number]):
                 error_handler.pop_error_context()  # Row
                 continue
 
@@ -148,6 +149,9 @@ def _run_onset_checks(self, onset_filtered, error_handler, row_adj):
             error_handler.pop_error_context()  # Row
         return issues
 
+    def _run_onset_nan_checks(self, onsets, error_handler, row_adj):
+        return
+
     def _validate_column_structure(self, base_input, error_handler, row_adj):
         """
         Validate that each column in the input data has valid values.
@@ -160,7 +164,7 @@ def _validate_column_structure(self, base_input, error_handler, row_adj):
             List of issues associated with each invalid value. Each issue is a dictionary.
         """
         issues = []
-        col_issues = base_input._mapper.check_for_mapping_issues(base_input)
+        col_issues = base_input._mapper.check_for_mapping_issues()
         error_handler.add_context_and_filter(col_issues)
         issues += col_issues
         for column in base_input.column_metadata().values():
diff --git a/hed/validator/util/class_regex.json b/hed/validator/util/class_regex.json
index 81f49f02..303983da 100644
--- a/hed/validator/util/class_regex.json
+++ b/hed/validator/util/class_regex.json
@@ -22,9 +22,9 @@
     "less-than": "<",
     "letters": "[A-Za-z]",
     "lowercase": "[a-z]",
-    "name": "[\\w\\-\\u0080-\\uFFFF]",
+    "name": "[\\w\\-\\u00A0-\\uFFFF]",
     "newline": "\\n",
-    "nonascii": "[\\u0080-\\uFFFF]",
+    "nonascii": "[\\u00A0-\\uFFFF]",
     "number-sign": "#",
     "numeric": "[0-9.\\-+^Ee]",
     "percent-sign": "%",
@@ -37,7 +37,7 @@
     "single-quote": "'",
     "forward-slash": "/",
     "tab": "\\t",
-    "text": "[^\\x00-\\x1F\\x7F,{}]",
+    "text": "[^\\x00-\\x1F\\x7F-\\x9F,{}]",
     "tilde": "~",
     "underscore": "_",
     "uppercase": "[A-Z]",


Test patch:
diff --git a/spec_tests/hed-examples b/spec_tests/hed-examples
index f117c4d6..ca307b99 160000
--- a/spec_tests/hed-examples
+++ b/spec_tests/hed-examples
@@ -1,1 +1,1 @@
-Subproject commit f117c4d669ea034053011e6d3d152b0e906c1914
+Subproject commit ca307b99fa12e75335bf7a8b1d3313d85ad5d9e5
diff --git a/tests/models/test_base_input.py b/tests/models/test_base_input.py
index dba97140..54b75795 100644
--- a/tests/models/test_base_input.py
+++ b/tests/models/test_base_input.py
@@ -101,7 +101,7 @@ def test_needs_sort(self):
         self.assertFalse(opened_file.needs_sorting)
 
         issues = opened_file.validate(load_schema_version("8.3.0"))
-        self.assertEqual(issues[1][ErrorContext.ROW], 5)
+        self.assertEqual(issues[0][ErrorContext.ROW], 5)
         df.at[3, "onset"] = 1.5
         opened_file = TabularInput(df)
         self.assertFalse(opened_file.needs_sorting)
@@ -111,8 +111,8 @@ def test_needs_sort(self):
         self.assertTrue(opened_file.needs_sorting)
         issues = opened_file.validate(load_schema_version("8.3.0"))
         # Should still report the same issue row despite needing sorting for validation
-        self.assertEqual(issues[1]['code'], ValidationErrors.ONSETS_OUT_OF_ORDER)
-        self.assertEqual(issues[2][ErrorContext.ROW], 5)
+        self.assertEqual(issues[0]['code'], ValidationErrors.ONSETS_UNORDERED)
+        self.assertEqual(issues[1][ErrorContext.ROW], 5)
 
     def test_sort(self):
         from hed.models.df_util import sort_dataframe_by_onsets
diff --git a/tests/tools/analysis/test_event_manager.py b/tests/tools/analysis/test_event_manager.py
index 29e661e4..8a4024d5 100644
--- a/tests/tools/analysis/test_event_manager.py
+++ b/tests/tools/analysis/test_event_manager.py
@@ -1,5 +1,7 @@
 import os
 import unittest
+import json
+import io
 import numpy as np
 import pandas as pd
 
@@ -8,6 +10,7 @@
 from hed.models.tabular_input import TabularInput
 from hed.schema.hed_schema_io import load_schema_version
 from hed.tools.analysis.event_manager import EventManager
+from hed.validator import SpreadsheetValidator
 
 
 class Test(unittest.TestCase):
@@ -22,9 +25,26 @@ def setUpClass(cls):
         sidecar_path = os.path.realpath(os.path.join(bids_root_path, 'task-FacePerception_events.json'))
         sidecar1 = Sidecar(sidecar_path, name='face_sub1_json')
         cls.input_data = TabularInput(events_path, sidecar=sidecar1, name="face_sub1_events")
-        cls.events_path = events_path
+        sidecar_dict = {
+            "event_code": {
+                "HED": {
+                    "show": "Sensory-event,Visual-presentation",
+                    "respond": "Press",
+                    "whatever": "Black",
+                    "whatelse": "Purple"
+                }
+            },
+            "defs": {
+                "HED": {
+                    "defs1": "(Definition/Con1, (Condition-variable/Cond-one)), (Definition/Con2, (Condition-variable/Cond-one))"
+                }
+            }
+        }
+
+        cls.sidecar2 = Sidecar(io.StringIO(json.dumps(sidecar_dict)))
         cls.sidecar = sidecar1
         cls.schema = schema
+        cls.def_dict = "(Definition/Def1, (Event))"
 
     def test_constructor(self):
         manager1 = EventManager(self.input_data, self.schema)
@@ -45,6 +65,63 @@ def test_constructor(self):
                 if not event.end_time:
                     self.assertEqual(event.end_index, len(manager1.input_data.dataframe))
 
+    def test_no_onset_constructor(self):
+        # No onsets --- has an event manager
+        tsv = {
+            "event_code": ["show", "respond", "show", "respond", "whatever", "show", "whatelse", "respond"],
+            "HED": ["Age/100", "n/a", "n/a", "n/a", "Green", "n/a", "Female", "n/a"],
+        }
+
+        tab = TabularInput(pd.DataFrame(tsv), sidecar=self.sidecar2)
+        eman = EventManager(tab, self.schema)
+        self.assertIsNone(eman.onsets)
+        self.assertEqual(str(eman.hed_strings[0]), "Age/100,Sensory-event,Visual-presentation")
+
+        # No onsets and an n/a entry
+        tsv["event_code"][2] = "n/a"
+        tab2 = TabularInput(pd.DataFrame(tsv), sidecar=self.sidecar2)
+        eman2 = EventManager(tab2, self.schema)
+        self.assertIsNone(eman2.onsets)
+        self.assertEqual(str(eman2.hed_strings[0]), "Age/100,Sensory-event,Visual-presentation")
+        self.assertFalse(str(eman2.hed_strings[2]))
+        self.assertIsNone(eman2.base)
+        self.assertIsNone(eman2.context)
+
+    def test_bad_onset_constructor(self):
+        tsv = {
+            "onset": [0.0, 1.2, 1.2, 3.0, 5, 3.5, 4, 6],
+            "duration": [0.5, "n/a", "n/a", "n/a", "n/a", "n/a", "n/a", "n/a"],
+            "event_code": ["show", "respond", "show", "respond", "whatever", "show", "whatelse", "respond"],
+            "HED": ["Age/100", "n/a", "n/a", "n/a", "Green", "n/a", "Female", "n/a"],
+        }
+
+        tab = TabularInput(pd.DataFrame(tsv), sidecar=self.sidecar2)
+        with self.assertRaises(HedFileError):
+            EventManager(tab, self.schema)
+
+        tsv = {
+            "onset": [0.0, 1.2, 1.2, 3.0, "n/a", 3.5, "n/a", 6],
+            "duration": [0.5, "n/a", "n/a", "n/a", "n/a", "n/a", "n/a", "n/a"],
+            "event_code": ["show", "respond", "show", "respond", "whatever", "show", "whatelse", "respond"],
+            "HED": ["Age/100", "n/a", "n/a", "n/a", "Green", "n/a", "Female", "n/a"],
+        }
+        tab = TabularInput(pd.DataFrame(tsv), sidecar=self.sidecar2)
+        with self.assertRaises(HedFileError):
+            EventManager(tab, self.schema)
+
+    def test_unfold_no_onset(self):
+        tsv = {
+            "event_code": ["show", "respond", "show", "respond", "whatever", "show", "whatelse", "respond"],
+            "HED": ["Age/100,Condition-variable/Temp", "Def/Con1", "Def/Con2", "n/a", "Green", "n/a", "Female", "n/a"],
+        }
+        tab = TabularInput(pd.DataFrame(tsv), sidecar=self.sidecar2)
+        defs = self.sidecar2.get_def_dict(self.schema)
+        manager1 = EventManager(tab, self.schema)
+        hed1, base1, context1 = manager1.unfold_context()
+        hed2, base2, context2 = manager1.unfold_context(remove_types=["Condition-variable"])
+        self.assertEqual(hed1[1], "Def/Con1,Press")
+        self.assertEqual(hed2[1], "Press")
+
     def test_unfold_context_no_remove(self):
         manager1 = EventManager(self.input_data, self.schema)
         hed, base, context = manager1.unfold_context()
@@ -52,6 +129,8 @@ def test_unfold_context_no_remove(self):
             self.assertIsInstance(hed[index], str)
             self.assertIsInstance(base[index], str)
 
+
+
     def test_unfold_context_remove(self):
         manager1 = EventManager(self.input_data, self.schema)
         hed, base, context = manager1.unfold_context(remove_types=['Condition-variable', 'Task'])
diff --git a/tests/tools/analysis/test_hed_tag_counts.py b/tests/tools/analysis/test_hed_tag_counts.py
index d70ba1d0..3bcbb0d3 100644
--- a/tests/tools/analysis/test_hed_tag_counts.py
+++ b/tests/tools/analysis/test_hed_tag_counts.py
@@ -44,8 +44,8 @@ def test_constructor(self):
         self.assertIsInstance(counts, HedTagCounts)
         self.assertFalse(counts.tag_dict)
         for k in range(6):
-            counts.update_event_counts(HedString(self.input_df.iloc[k]['HED_assembled'], self.hed_schema),
-                                       file_name='Base_name')
+            counts.update_tag_counts(HedString(self.input_df.iloc[k]['HED_assembled'], self.hed_schema),
+                                     file_name='Base_name')
         self.assertIsInstance(counts.tag_dict, dict)
         self.assertEqual(14, len(counts.tag_dict))
 
@@ -53,10 +53,10 @@ def test_merge_tag_dicts(self):
         counts1 = HedTagCounts('Base_name1', 50)
         counts2 = HedTagCounts('Base_name2', 100)
         for k in range(6):
-            counts1.update_event_counts(HedString(self.input_df.iloc[k]['HED_assembled'], self.hed_schema),
-                                        file_name='Base_name1')
-            counts2.update_event_counts(HedString(self.input_df.iloc[k]['HED_assembled'], self.hed_schema),
-                                        file_name='Base_name2')
+            counts1.update_tag_counts(HedString(self.input_df.iloc[k]['HED_assembled'], self.hed_schema),
+                                      file_name='Base_name1')
+            counts2.update_tag_counts(HedString(self.input_df.iloc[k]['HED_assembled'], self.hed_schema),
+                                      file_name='Base_name2')
         counts3 = HedTagCounts("All", 0)
         counts3.merge_tag_dicts(counts1.tag_dict)
         counts3.merge_tag_dicts(counts2.tag_dict)
@@ -68,8 +68,8 @@ def test_merge_tag_dicts(self):
     def test_hed_tag_count(self):
         name = 'Base_name1'
         counts1 = HedTagCounts(name, 0)
-        counts1.update_event_counts(HedString(self.input_df.iloc[0]['HED_assembled'], self.hed_schema),
-                                    file_name=name)
+        counts1.update_tag_counts(HedString(self.input_df.iloc[0]['HED_assembled'], self.hed_schema),
+                                  file_name=name)
         self.assertIsInstance(counts1, HedTagCounts)
 
     def test_organize_tags(self):
@@ -80,7 +80,7 @@ def test_organize_tags(self):
 
         # type_defs = input_data.get_definitions().gathered_defs
         for hed in df["HED_assembled"]:
-            counts.update_event_counts(HedString(hed, self.hed_schema), 'run-1')
+            counts.update_tag_counts(HedString(hed, self.hed_schema), 'run-1')
         self.assertIsInstance(counts.tag_dict, dict)
         self.assertEqual(46, len(counts.tag_dict))
         org_tags, leftovers = counts.organize_tags(self.tag_template)
diff --git a/tests/tools/analysis/test_hed_tag_manager.py b/tests/tools/analysis/test_hed_tag_manager.py
index c7cb40ea..b539bc6b 100644
--- a/tests/tools/analysis/test_hed_tag_manager.py
+++ b/tests/tools/analysis/test_hed_tag_manager.py
@@ -109,7 +109,7 @@ def test_get_hed_objs(self):
         self.assertIsInstance(tag_man, HedTagManager)
         hed_objs = tag_man.get_hed_objs()
         self.assertIsInstance(hed_objs, list)
-        self.assertEqual(len(hed_objs), len(event_man.onsets))
+        self.assertEqual(len(hed_objs), len(tag_man.event_manager.onsets))
 
     # def test_constructor_variable_caps(self):
     #     sidecar1 = Sidecar(self.sidecar_path, name='face_sub1_json')
diff --git a/tests/tools/analysis/test_hed_type.py b/tests/tools/analysis/test_hed_type.py
index 662b82cc..08cede6c 100644
--- a/tests/tools/analysis/test_hed_type.py
+++ b/tests/tools/analysis/test_hed_type.py
@@ -7,8 +7,8 @@
 from hed.models.sidecar import Sidecar
 from hed.models.tabular_input import TabularInput
 from hed.schema.hed_schema_io import load_schema_version
-from hed.tools.analysis.event_manager import EventManager
 from hed.tools.analysis.hed_type import HedType
+from hed.tools.analysis.event_manager import EventManager
 
 
 class Test(unittest.TestCase):
@@ -42,8 +42,7 @@ def setUpClass(cls):
         test_onsets1 = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]
         df1 = DataFrame(test_onsets1, columns=['onset'])
         df1['HED'] = test_strings1
-        input_data1 = TabularInput(df1)
-        cls.event_man1 = EventManager(input_data1, schema, extra_defs=def_dict)
+        cls.input_data1 = TabularInput(df1)
         test_strings2 = ["Def/Cond2, (Def/Cond6/4, Onset), (Def/Cond6/7.8, Onset), Def/Cond6/Alpha",
                          "Yellow",
                          "Def/Cond2, (Def/Cond6/4, Onset)",
@@ -52,8 +51,7 @@ def setUpClass(cls):
         test_onsets2 = [0.0, 1.0, 2.0, 3.0, 4.0]
         df2 = DataFrame(test_onsets2, columns=['onset'])
         df2['HED'] = test_strings2
-        input_data2 = TabularInput(df2)
-        cls.event_man2 = EventManager(input_data2, schema, extra_defs=def_dict)
+        cls.input_data2 = TabularInput(df2)
         test_strings3 = ['(Def/Cond3, Offset)']
         test_onsets3 = [0.0]
         df3 = DataFrame(test_onsets3, columns=['onset'])
@@ -68,7 +66,7 @@ def setUpClass(cls):
         cls.def_dict = def_dict
 
     def test_constructor(self):
-        type_var = HedType(self.event_man1, 'test-it')
+        type_var = HedType(EventManager(self.input_data1, self.schema, extra_defs=self.def_dict), 'test-it')
         self.assertIsInstance(type_var, HedType, "Constructor should create a HedType from an event manager")
         self.assertEqual(len(type_var._type_map), 8,
                          "Constructor ConditionVariables should have the right length")
@@ -76,34 +74,30 @@ def test_constructor(self):
     def test_constructor_from_tabular_input(self):
         sidecar1 = Sidecar(self.sidecar_path, name='face_sub1_json')
         input_data = TabularInput(self.events_path, sidecar=sidecar1, name="face_sub1_events")
-        event_man = EventManager(input_data, self.schema)
-        var_man = HedType(event_man, 'face')
+        var_man = HedType(EventManager(input_data, self.schema), 'face')
         self.assertIsInstance(var_man, HedType, "Constructor should create a HedTypeManager from a tabular input")
 
     def test_constructor_variable_caps(self):
         sidecar1 = Sidecar(self.sidecar_path, name='face_sub1_json')
         input_data = TabularInput(self.events_path, sidecar1, name="face_sub1_events")
-        event_man = EventManager(input_data, self.schema)
-        var_manager = HedType(event_man, 'run-01')
+        var_manager = HedType(EventManager(input_data, self.schema), 'run-01')
         self.assertIsInstance(var_manager, HedType, "Constructor should create a HedTypeManager variable caps")
 
     def test_constructor_multiple_values(self):
-        type_var = HedType(self.event_man2, 'test-it')
+        type_var = HedType(EventManager(self.input_data2, self.schema, extra_defs=self.def_dict), 'test-it')
         self.assertIsInstance(type_var, HedType, "Constructor should create a HedType from an event manager")
         self.assertEqual(len(type_var._type_map), 3,
                          "Constructor should have right number of type_variables if multiple")
 
     def test_constructor_unmatched(self):
         with self.assertRaises(KeyError) as context:
-            event_man = EventManager(self.input_data3, self.schema, extra_defs=self.def_dict)
-            HedType(event_man, 'run-01')
+            HedType(EventManager(self.input_data3, self.schema, extra_defs=self.def_dict), 'run-01')
         self.assertEqual(context.exception.args[0], 'cond3')
 
     def test_get_variable_factors(self):
         sidecar1 = Sidecar(self.sidecar_path, name='face_sub1_json')
         input_data = TabularInput(self.events_path, sidecar1, name="face_sub1_events")
-        event_man = EventManager(input_data, self.schema)
-        var_manager = HedType(event_man, 'run-01')
+        var_manager = HedType(EventManager(input_data, self.schema), 'run-01')
         df_new1 = var_manager.get_type_factors()
         self.assertIsInstance(df_new1, DataFrame)
         self.assertEqual(len(df_new1), 200)
@@ -117,23 +111,21 @@ def test_get_variable_factors(self):
     def test_str(self):
         sidecar1 = Sidecar(self.sidecar_path, name='face_sub1_json')
         input_data = TabularInput(self.events_path, sidecar1, name="face_sub1_events")
-        event_man = EventManager(input_data, self.schema)
-        var_manager = HedType(event_man, 'run-01')
+        var_manager = HedType(EventManager(input_data, self.schema), 'run-01')
         new_str = str(var_manager)
         self.assertIsInstance(new_str, str)
 
     def test_summarize_variables(self):
         sidecar1 = Sidecar(self.sidecar_path, name='face_sub1_json')
         input_data = TabularInput(self.events_path, sidecar1, name="face_sub1_events")
-        event_man = EventManager(input_data, self.schema)
-        var_manager = HedType(event_man, 'run-01')
+        var_manager = HedType(EventManager(input_data, self.schema), 'run-01')
         summary = var_manager.get_summary()
         self.assertIsInstance(summary, dict, "get_summary produces a dictionary if not json")
         self.assertEqual(len(summary), 3, "Summarize_variables has right number of condition type_variables")
         self.assertIn("key-assignment", summary, "get_summary has a correct key")
 
     def test_extract_definition_variables(self):
-        var_manager = HedType(self.event_man1, 'run-01')
+        var_manager = HedType(EventManager(self.input_data1, self.schema, extra_defs=self.def_dict), 'run-01')
         var_levels = var_manager._type_map['var3'].levels
         self.assertNotIn('cond3/7', var_levels,
                          "_extract_definition_variables before extraction def/cond3/7 not in levels")
@@ -143,12 +135,12 @@ def test_extract_definition_variables(self):
                       "_extract_definition_variables after extraction def/cond3/7 not in levels")
 
     def test_get_variable_names(self):
-        conditions1 = HedType(self.event_man1, 'run-01')
+        conditions1 = HedType(EventManager(self.input_data1, self.schema, extra_defs=self.def_dict), 'run-01')
         list1 = conditions1.get_type_value_names()
         self.assertEqual(len(list1), 8, "get_variable_tags list should have the right length")
 
     def test_get_variable_def_names(self):
-        conditions1 = HedType(self.event_man1, 'run-01')
+        conditions1 = HedType(EventManager(self.input_data1, self.schema, extra_defs=self.def_dict), 'run-01')
         list1 = conditions1.get_type_def_names()
         self.assertEqual(len(list1), 5, "get_type_def_names list should have the right length")
 
diff --git a/tests/tools/remodeling/operations/test_summarize_hed_tags_op.py b/tests/tools/remodeling/operations/test_summarize_hed_tags_op.py
index 5303b576..e93e5e3d 100644
--- a/tests/tools/remodeling/operations/test_summarize_hed_tags_op.py
+++ b/tests/tools/remodeling/operations/test_summarize_hed_tags_op.py
@@ -153,7 +153,7 @@ def test_quick3(self):
         self.assertIsInstance(tag_man, HedTagManager)
         # hed_objs = tag_man.get_hed_objs(include_context=include_context, replace_defs=replace_defs)
         # for hed in hed_objs:
-        #     counts.update_event_counts(hed, 'myName')
+        #     counts.update_tag_counts(hed, 'myName')
         # summary_dict['myName'] = counts
 
     def test_quick4(self):
@@ -172,7 +172,7 @@ def test_quick4(self):
 
         # type_defs = input_data.get_definitions().gathered_defs
         for hed in df["HED_assembled"]:
-            counts.update_event_counts(HedString(hed, schema), 'myName')
+            counts.update_tag_counts(HedString(hed, schema), 'myName')
         summary_dict['myName'] = counts
 
     def test_get_summary_details(self):
diff --git a/tests/validator/test_spreadsheet_validator.py b/tests/validator/test_spreadsheet_validator.py
index 3b6e7e32..6c9b08ac 100644
--- a/tests/validator/test_spreadsheet_validator.py
+++ b/tests/validator/test_spreadsheet_validator.py
@@ -1,11 +1,13 @@
 import pandas as pd
 import os
 import shutil
+import json
+import io
 
 import unittest
 from hed import load_schema_version, load_schema
 from hed.validator import SpreadsheetValidator
-from hed import TabularInput, SpreadsheetInput
+from hed import TabularInput, SpreadsheetInput, Sidecar
 from hed.errors.error_types import ValidationErrors
 
 
@@ -62,8 +64,7 @@ def test_invalid_onset_invalid_column(self):
         self.assertEqual(len(issues), 0)
 
         issues = self.validator.validate(TabularInput(self.df_with_onset), def_dicts=def_dict)
-        self.assertEqual(len(issues), 1)
-        self.assertEqual(issues[0]['code'], ValidationErrors.HED_UNKNOWN_COLUMN)
+        self.assertEqual(len(issues), 0)
 
         base_has_tags_df = pd.DataFrame({
             'HED': ["(Onset, Def/DefaultOnset)", "(Inset, Def/DefaultOnset), (Event, Age/2)",
@@ -78,8 +79,7 @@ def test_invalid_onset_invalid_column(self):
         self.assertEqual(len(issues), 3)
         self.assertEqual(issues[0]['code'], ValidationErrors.TEMPORAL_TAG_ERROR)
         issues = self.validator.validate(TabularInput(self.df_with_onset_has_tags), def_dicts=def_dict)
-        self.assertEqual(len(issues), 1)
-        self.assertEqual(issues[0]['code'], ValidationErrors.HED_UNKNOWN_COLUMN)
+        self.assertEqual(len(issues), 0)
 
         base_has_tags_unordered_df = pd.DataFrame({
             'HED': ["(Onset, Def/DefaultOnset)", "(Offset, Def/DefaultOnset), (Age/4)",
@@ -93,6 +93,44 @@ def test_invalid_onset_invalid_column(self):
         self.assertEqual(len(issues), 3)
         self.assertEqual(issues[0]['code'], ValidationErrors.TEMPORAL_TAG_ERROR)
         issues = self.validator.validate(TabularInput(self.df_with_onset_has_tags_unordered), def_dicts=def_dict)
-        self.assertEqual(len(issues), 2)
-        self.assertEqual(issues[0]['code'], ValidationErrors.HED_UNKNOWN_COLUMN)
-        self.assertEqual(issues[1]['code'], ValidationErrors.TEMPORAL_TAG_ERROR)
+        self.assertEqual(len(issues), 1)
+        self.assertEqual(issues[0]['code'], ValidationErrors.TEMPORAL_TAG_ERROR)
+
+    def test_onset_na(self):
+        # Test with no sidecar
+        def_dict = "(Definition/Def1, (Event))"
+        tsv = {
+            "onset": [0.0, 1.2, 1.2, 3.0, "n/a", 3.5, "n/a", 6],
+            "duration": [0.5, "n/a", "n/a", "n/a", "n/a", "n/a", "n/a", "n/a"],
+            "event_code": ["show", "respond", "show", "respond", "whatever", "show", "whatelse", "respond"],
+            "HED": ["Age/100", "(Def/Def1, Onset)", "Red", "n/a", "Green", "(Def/Def1, Offset)",
+                    "Female,(Def/Def1,Onset)", "n/a"],
+        }
+        df_with_nans = pd.DataFrame(tsv)
+        issues = self.validator.validate(TabularInput(df_with_nans), def_dicts=def_dict)
+        self.assertEqual(len(issues), 3)
+        self.assertEqual(issues[2]['code'], ValidationErrors.TEMPORAL_TAG_ERROR)
+
+        # Test with sidecar
+        sidecar_dict = {
+            "event_code": {
+                "HED": {
+                    "show": "Sensory-event,Visual-presentation",
+                    "respond": "Press",
+                    "whatever": "Black",
+                    "whatelse": "Purple"
+                }
+            }
+        }
+
+        sidecar1 = Sidecar(io.StringIO(json.dumps(sidecar_dict)))
+        issues1 = self.validator.validate(TabularInput(df_with_nans, sidecar=sidecar1), def_dicts=def_dict)
+        self.assertEqual(len(issues1), 2)
+        self.assertEqual(issues1[1]['code'], ValidationErrors.TEMPORAL_TAG_ERROR)
+
+        # The whatelse does not use the bad HED columns and HED column appears in {} so only when assembled is used.
+        sidecar_dict["event_code"]["HED"]["whatever"] = "Black, {HED}"
+        sidecar2 = Sidecar(io.StringIO(json.dumps(sidecar_dict)))
+        issues2 = self.validator.validate(TabularInput(df_with_nans, sidecar=sidecar2), def_dicts=def_dict)
+        self.assertEqual(len(issues2), 1)
+        self.assertEqual(issues1[0]['code'], ValidationErrors.ONSETS_UNORDERED)