phys2bids.physio_obj.BlueprintInput¶
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class
phys2bids.physio_obj.
BlueprintInput
(timeseries, freq, ch_name, units, trigger_idx, num_timepoints_found=None, thr=None, time_offset=0)[source]¶ Main input object for phys2bids.
Contains the blueprint to be populated. !!! Pay attention: there’s rules on how to populate this object. See below (“Attention”) !!!
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timeseries
¶ List of numpy 1d arrays - one for channel, plus one for time. Time channel has to be the first. Contains all the timeseries recorded. Supports different frequencies!
- Type
(ch, [tps]) list
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freq
¶ List of floats - one per channel. Contains all the frequencies of the recorded channel. Support different frequencies!
- Type
(ch) list of floats
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ch_name
¶ List of names of the channels - can be the header of the columns in the output files.
- Type
(ch) list of strings
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units
¶ List of the units of the channels.
- Type
(ch) list of strings
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num_timepoints_found
¶ Amount of timepoints found in the automatic count. This is initialised as “None” and then computed internally, if check_trigger_amount() is run.
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thr
¶ Threshold used by check_trigger_amount() to detect trigger points. This is initialised as “None” and then computed internally, if check_trigger_amount() is run.
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time_offset
¶ Time offset found by check_trigger_amount(). This is initialised as 0 and then computed internally, if check_trigger_amount() is run.
- Type
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ch_amount:
Property. Returns number of channels (ch).
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rename_channels:
Changes the list “ch_name” in a controlled way.
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return_index:
Returns the proper list entry of all the properties of the object, given an index.
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delete_at_index:
Returns all the proper list entry of the properties of the object, given an index.
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check_trigger_amount:
Counts the amounts of triggers and corrects time offset in “time” ndarray. Also adds property ch_amount.
Notes
The timeseries (and as a consequence, all the other properties) should start with an entry for time. It should have the same length of the trigger - hence same sampling. Meaning: - timeseries[0] → ndarray representing time - timeseries[chtrig] → ndarray representing trigger - timeseries[0].shape == timeseries[chtrig].shape
As a consequence: - freq[0] == freq[chtrig] - ch_name[0] = ‘time’ - units[0] = ‘s’ - Actual number of channels +1 <= ch_amount
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