Protomix.Icoshift class

class Icoshift[source]

Bases: object

A class to perform Icoshift alignment on spectral data.

The Icoshift algorithm aligns signals (e.g., NMR spectra) to a reference signal by shifting them to maximize the correlation with the reference. The alignment can be performed on the entire signal or in defined intervals. The class supports various modes for determining the reference signal, alignment modes, and shift correction strategies.

Properties:

  • name (str): The name of the Icoshift instance.

  • global_pre_align (bool): Flag to enable or disable global pre-alignment.

  • unit_vector (np.ndarray): Vector mapping units to sample points.

  • signal_names (List[str]): List of names corresponding to the signals.

  • input_type (str): Type of input (datapoints or units).

  • result (np.ndarray): The aligned signals after running Icoshift.

  • signals (np.ndarray): The input signals to be aligned.

  • inter (List[Tuple[Union[int, float], Union[int, float]]]): User-defined intervals for alignment.

  • target (Tuple[str, Union[np.ndarray, list]]): The target signal used for alignment.

  • avg2factor (int): Factor used in the ‘average2’ target mode.

  • max_shift (Tuple[str, int]): Maximum shift correction mode and value.

  • fill_mode (str): Mode used to fill missing data after shifting (‘nan’, ‘zero’, ‘adjacent’).

  • loglvl (str): Logging level for debugging and information.

Methods:
run(): Executes the Icoshift alignment process.
The run method performs the main alignment process using the following steps:

  1. Optionally performs a global pre-alignment of the entire dataset.

  2. Splits the signals into intervals based on the selected alignment mode.

  3. Calculates the target signal based on the chosen target mode.

  4. Aligns the signals within each interval to the target signal.

  5. Reconstructs the aligned signals from the intervals.

Example:
icoshift = px.Icoshift()
icoshift.signals = nvz_df.values
icoshift.signal_names = list(nvz_df.index.values)
icoshift.inter = ('n_intervals', 100)
icoshift.target = 'median'
icoshift.global_pre_align = True
icoshift.max_shift = 'best'

icoshift.run()
run()[source]

Main function to run the actual icoshift process.

The steps involved are:

  1. Pre-run coshift on the whole dataset:

    • Co-shift or no co-shift.

  2. Split the data into intervals.

  3. Co-shift each interval.

  4. Reconstruct the data from intervals.