diff options
Diffstat (limited to 'docs/user_guide')
| -rw-r--r-- | docs/user_guide/gaze_analysis/gaze_position.md | 19 | ||||
| -rw-r--r-- | docs/user_guide/timestamped_data/pandas_dataframe_conversion.md | 8 |
2 files changed, 26 insertions, 1 deletions
diff --git a/docs/user_guide/gaze_analysis/gaze_position.md b/docs/user_guide/gaze_analysis/gaze_position.md index 9cc7f85..8856bec 100644 --- a/docs/user_guide/gaze_analysis/gaze_position.md +++ b/docs/user_guide/gaze_analysis/gaze_position.md @@ -66,3 +66,22 @@ if gaze_position_A.overlap(gaze_position_B, both=True): # Do something if A overlaps B AND B overlaps A ... ``` + +## Timestamped gaze positions + +[TimeStampedGazePositions](../../../argaze/#argaze.GazeFeatures.TimeStampedGazePositions) inherits from [TimeStampedBuffer](../../../argaze/#argaze.DataStructures.TimeStampedBuffer) class to handle especially gaze positions. + +### Import from dataframe + +It is possible to load timestamped gaze positions from a [Pandas DataFrame](https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html#min-tut-01-tableoriented) object. + +```python +import pandas + +# Load gaze positions from a CSV file into Panda Dataframe +dataframe = pandas.read_csv('gaze_positions.csv', delimiter="\t", low_memory=False) + +# Convert Panda dataframe into TimestampedGazePositions buffer precising the use of each specific column labels +ts_gaze_positions = GazeFeatures.TimeStampedGazePositions.from_dataframe(dataframe, timestamp = 'Recording timestamp [ms]', x = 'Gaze point X [px]', y = 'Gaze point Y [px]') + +``` diff --git a/docs/user_guide/timestamped_data/pandas_dataframe_conversion.md b/docs/user_guide/timestamped_data/pandas_dataframe_conversion.md index 839460a..eaa7b69 100644 --- a/docs/user_guide/timestamped_data/pandas_dataframe_conversion.md +++ b/docs/user_guide/timestamped_data/pandas_dataframe_conversion.md @@ -7,6 +7,8 @@ Pandas DataFrame conversion A [Pandas DataFrame](https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html#min-tut-01-tableoriented) is a python data structure allowing powerful table processings. +## Export as dataframe + [TimeStampedBuffer](../../../argaze/#argaze.DataStructures.TimeStampedBuffer) instance can be converted into dataframe provided that data values are stored as dictionaries. ```python @@ -32,4 +34,8 @@ ts_buffer_dataframe would look like: |10 |0 |0.123| |20 |4 |0.567| |30 |8 |0.901| -|... |... |... |
\ No newline at end of file +|... |... |... | + +## Import from dataframe + +Reversely, [TimeStampedBuffer](../../../argaze/#argaze.DataStructures.TimeStampedBuffer) instance can be created from dataframe, as a result of which each dataframe columns label will become a key of data value dictionary. Notice that the column containing timestamp values have to be called 'timestamp'. |