lleocal

pylleo.lleocal.fit1d(lower, upper)[source]

Fit acceleration data at lower and upper boundaries of gravity

Parameters:
  • lower (pandas dataframe) – slice of lleo datafram containing points at -1g calibration position
  • upper (pandas dataframe) – slice of lleo datafram containing points at -1g calibration position
Returns:

p – Polynomial coefficients, highest power first. If y was 2-D, the coefficients for k-th data set are in p[:,k]. From numpy.polyfit().
Return type:

ndarray

Note

This method should be compared agaist alternate linalg method, which allows for 2d for 2d poly, see - http://stackoverflow.com/a/33966967/943773

A = numpy.vstack(lower, upper).transpose() y = A[:,1] m, c = numpy.linalg.lstsq(A, y)[0]

pylleo.lleocal.get_cal_data(data_df, cal_dict, param)[source]

Get data along specified axis during calibration intervals

Parameters:
  • data_df (pandas.DataFrame) – Pandas dataframe with lleo data
  • cal_dict (dict) – Calibration dictionary
Returns:

  • lower (pandas dataframe) – slice of lleo datafram containing points at -1g calibration position
  • upper (pandas dataframe) – slice of lleo datafram containing points at -1g calibration position

See also

lleoio.read_data()
creates pandas dataframe data_df
read_cal()
creates cal_dict and describes fields
pylleo.lleocal.read_cal(cal_yaml_path)[source]

Load calibration file if exists, else create

Parameters:cal_yaml_path (str) – Path to calibration YAML file
Returns:cal_dict – Key value pairs of calibration meta data
Return type:dict
pylleo.lleocal.update(data_df, cal_dict, param, bound, start, end)[source]

Update calibration times for give parameter and boundary