bids.variables.kollekshuns.BIDSRunVariableCollection¶
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class
BIDSRunVariableCollection
(variables, sampling_rate=None)[source]¶ A container for one or more RunVariables–i.e., Variables that have a temporal dimension.
Parameters: - variables (list) – A list of SparseRunVariable and/or DenseRunVariable.
- sampling_rate (float) – Sampling rate (in Hz) to use when working with dense representations of variables. If None, defaults to 10.
Notes
- Variables in the list must all be at the ‘run’ level. For other levels (session, subject, or dataset), use the BIDSVariableCollection.
Methods
clone
()Returns a shallow copy of the current instance, except that all variables are deep-cloned. from_df
(data[, entities, source])Create a Collection from a pandas DataFrame. match_variables
(pattern[, return_type])Return columns whose names match the provided regex pattern. matches_entities
(entities[, strict])Checks whether current Collection’s entities match the input. merge_variables
(variables, **kwargs)Concatenates Variables along row axis. resample
([sampling_rate, variables, …])Resample all dense variables (and optionally, sparse ones) to the specified sampling rate. to_df
([variables, format, sparse, …])Merge columns into a single pandas DataFrame. -
__init__
(variables, sampling_rate=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(variables[, sampling_rate])Initialize self. clone
()Returns a shallow copy of the current instance, except that all variables are deep-cloned. from_df
(data[, entities, source])Create a Collection from a pandas DataFrame. match_variables
(pattern[, return_type])Return columns whose names match the provided regex pattern. matches_entities
(entities[, strict])Checks whether current Collection’s entities match the input. merge_variables
(variables, **kwargs)Concatenates Variables along row axis. resample
([sampling_rate, variables, …])Resample all dense variables (and optionally, sparse ones) to the specified sampling rate. to_df
([variables, format, sparse, …])Merge columns into a single pandas DataFrame. -
clone
()¶ Returns a shallow copy of the current instance, except that all variables are deep-cloned.
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classmethod
from_df
(data, entities=None, source='contrast')¶ Create a Collection from a pandas DataFrame.
Parameters: - df (DataFrame) – The DataFrame to convert to a Collection. Each column will be converted to a SimpleVariable.
- entities (DataFrame) – An optional second DataFrame containing entity information.
- source (str) – The value to set as the source for all Variables.
Returns: A BIDSVariableCollection.
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match_variables
(pattern, return_type='name')¶ Return columns whose names match the provided regex pattern.
Parameters: - pattern (str) – A regex pattern to match all variable names against.
- return_type (str) – What to return. Must be one of: ‘name’: Returns a list of names of matching variables. ‘variable’: Returns a list of Variable objects whose names match.
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matches_entities
(entities, strict=False)¶ Checks whether current Collection’s entities match the input.
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static
merge_variables
(variables, **kwargs)¶ Concatenates Variables along row axis.
Parameters: variables (list) – List of Variables to merge. Variables can have different names (and all Variables that share a name will be concatenated together). Returns: A list of Variables.
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resample
(sampling_rate=None, variables=None, force_dense=False, in_place=False, kind='linear')[source]¶ Resample all dense variables (and optionally, sparse ones) to the specified sampling rate.
Parameters: - sampling_rate (int, float) – Target sampling rate (in Hz). If None, uses the instance sampling rate.
- variables (list) – Optional list of Variables to resample. If None, all variables are resampled.
- force_dense (bool) – if True, all sparse variables will be forced to dense.
- in_place (bool) – When True, all variables are overwritten in-place. When False, returns resampled versions of all variables.
- kind (str) – Argument to pass to scipy’s interp1d; indicates the kind of interpolation approach to use. See interp1d docs for valid values.
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to_df
(variables=None, format='wide', sparse=True, sampling_rate=None, include_sparse=True, include_dense=True, **kwargs)[source]¶ Merge columns into a single pandas DataFrame.
Parameters: - variables (list) – Optional list of variable names to retain; if None, all variables are written out.
- format (str) – Whether to return a DataFrame in ‘wide’ or ‘long’ format. In ‘wide’ format, each row is defined by a unique onset/duration, and each variable is in a separate column. In ‘long’ format, each row is a unique combination of onset, duration, and variable name, and a single ‘amplitude’ column provides the value.
- sparse (bool) – If True, variables will be kept in a sparse format provided they are all internally represented as such. If False, a dense matrix (i.e., uniform sampling rate for all events) will be exported. Will be ignored if at least one variable is dense.
- sampling_rate (float) – If a dense matrix is written out, the sampling rate (in Hz) to use for downsampling. Defaults to the value currently set in the instance.
- kwargs – Optional keyword arguments to pass onto each Variable’s to_df() call (e.g., condition, entities, and timing).
- include_sparse (bool) – Whether or not to include sparse Variables.
- include_dense (bool) – Whether or not to include dense Variables.
Returns: A pandas DataFrame.