rizer.io.experimental_data.preprocess_data#

Functions#

`plot_diameters`(time_ns, diameter_mm, mean_time_ns, ...)	Plot diameter data and the mean with error bars.
`preprocess_diameters`(→ dict[str, numpy.ndarray])	Preprocess diameter data from a pickle file and save the mean to a .npz file.
`plot_electrical_signals`(times_v, voltages, ...)
`compute_electrical_signals_npz_payload`(→ dict[str, ...)	Compute cumulated Joule energy (+ propagated std) and package the npz payload.
`preprocess_electrical_signals`(→ dict[str, numpy.ndarray])	Preprocess electrical signals from oscilloscope data.
`plot_electron_density`(time, ne, ne_std, r2)	Plot electron density vs time with error bars.
`preprocess_electron_density`(→ dict[str, numpy.ndarray])	Preprocess electron density data from a raw CSV file.

Module Contents#

rizer.io.experimental_data.preprocess_data.plot_diameters(time_ns: numpy.ndarray, diameter_mm: numpy.ndarray, mean_time_ns: numpy.ndarray, mean_diameter_mm: numpy.ndarray, std_diameter_mm: numpy.ndarray)#

Plot diameter data and the mean with error bars.

Parameters:

time_ns (numpy.ndarray) – Time data in nanoseconds.
diameter_mm (numpy.ndarray) – Diameter data in millimeters.
mean_time_ns (numpy.ndarray) – Mean time data in nanoseconds.
mean_diameter_mm (numpy.ndarray) – Mean diameter data in millimeters.
std_diameter_mm (numpy.ndarray) – Standard deviation of diameter data in millimeters.

rizer.io.experimental_data.preprocess_data.preprocess_diameters(input_filepath: pathlib.Path, output_mean_filepath: pathlib.Path, plot: bool = True, offset_time_ns: float = 10000000.0, outlier_threshold: float | None = None) → dict[str, numpy.ndarray]#

Preprocess diameter data from a pickle file and save the mean to a .npz file.

Parameters:

input_filepath (pathlib.Path) – Path to the input pickle file containing diameter data.
output_mean_filepath (pathlib.Path) – Path to the output .npz file where the mean diameter data will be saved.
plot (bool, optional) – If True, plot the diameter data and the mean with error bars, by default True
offset_time_ns (float, optional) – Offset for the time values, by default 1e7
outlier_threshold (float or None, optional) – Threshold for outlier removal in diameter data. If None, no outlier removal is performed

Returns:

The saved payload, keyed by rizer.io.experimental_data.load_experiment_data.SECTION_DIAMETERS_NPZ_KEYS (time [s], d_mean [m], d_std [m], r2 [-]).

Return type:

dict of str to numpy.ndarray

rizer.io.experimental_data.preprocess_data.plot_electrical_signals(times_v: numpy.ndarray, voltages: numpy.ndarray, mean_voltage: numpy.ndarray, std_voltage: numpy.ndarray, times_i: numpy.ndarray, currents: numpy.ndarray, mean_current: numpy.ndarray, std_current: numpy.ndarray)#

rizer.io.experimental_data.preprocess_data.compute_electrical_signals_npz_payload(time_s: numpy.ndarray, voltage_mean: numpy.ndarray, voltage_std: numpy.ndarray, current_mean: numpy.ndarray, current_std: numpy.ndarray) → dict[str, numpy.ndarray]#

Compute cumulated Joule energy (+ propagated std) and package the npz payload.

Uses a left-Riemann cumulative sum (numpy.cumsum), matching rizer.misc.simulation.post_process_quantities.compute_voltage_current_energy’s E_p = cumsum(I_p * V_p * dt) convention exactly.

Parameters:

time_s (numpy.ndarray) – Time, seconds.
voltage_mean (numpy.ndarray) – Mean/std of voltage [V] and current [A], same shape as time_s.
voltage_std (numpy.ndarray) – Mean/std of voltage [V] and current [A], same shape as time_s.
current_mean (numpy.ndarray) – Mean/std of voltage [V] and current [A], same shape as time_s.
current_std (numpy.ndarray) – Mean/std of voltage [V] and current [A], same shape as time_s.

Returns:

Keyed by rizer.io.experimental_data.load_experiment_data.ELECTRICAL_SIGNALS_NPZ_KEYS.

Return type:

dict of str to numpy.ndarray

rizer.io.experimental_data.preprocess_data.preprocess_electrical_signals(data_folder: pathlib.Path, output_filepath: pathlib.Path, pattern: str | None, max_iter: int = 1000, plot: bool = False, offset_time_v: float | None = None, offset_time_i: float | None = None, voltage_channel: str = 'C1', current_channel: str = 'C2') → dict[str, numpy.ndarray]#

Preprocess electrical signals from oscilloscope data.

Parameters:

data_folder (pathlib.Path) – Path to the folder containing the oscilloscope data folders.
output_filepath (pathlib.Path) – Path to the output .npz file where the mean and std data will be saved.
pattern (str or None) – Pattern to match the oscilloscope data folders. If None, all folders in the data_folder will be processed.
max_iter (int, optional) – Maximum number of folders to process, by default 1000
plot (bool, optional) – Whether to plot the processed data, by default False
offset_time_v (float or None, optional) – Offset for voltage time data, by default None
offset_time_i (float or None, optional) – Offset for current time data, by default None
voltage_channel (str, optional) – Channel name for voltage data, by default “C1”
current_channel (str, optional) – Channel name for current data, by default “C2”

Returns:

The saved payload, keyed by rizer.io.experimental_data.load_experiment_data.ELECTRICAL_SIGNALS_NPZ_KEYS.

Return type:

dict of str to numpy.ndarray

Raises:

FileNotFoundError – If the data folder or required files do not exist.

rizer.io.experimental_data.preprocess_data.plot_electron_density(time: numpy.ndarray, ne: numpy.ndarray, ne_std: numpy.ndarray, r2: numpy.ndarray)#

Plot electron density vs time with error bars.

Parameters:

time (numpy.ndarray) – Time data in nanoseconds.
ne (numpy.ndarray) – Electron density data in m^-3.
ne_std (numpy.ndarray) – Standard deviation of electron density data in m^-3.
r2 (numpy.ndarray) – R^2 of the fit.

rizer.io.experimental_data.preprocess_data.preprocess_electron_density(input_filepath: pathlib.Path | list[pathlib.Path], output_filepath: pathlib.Path, plot: bool = True, offset_time_ns: float = 10000000.0) → dict[str, numpy.ndarray]#

Preprocess electron density data from a raw CSV file.

Save the processed data to a .npz file.

Missing values in the input data are replaced with np.nan. For a given time, it is possible that multiple measurements of electron density are available.

Parameters:

input_filepath (pathlib.Path or list of pathlib.Path) – Path to the input CSV file or a list of paths.
output_filepath (pathlib.Path) – Path to the output .npz file.
plot (bool, optional) – Whether to plot the data, by default True
offset_time_ns (float, optional) – Time offset in nanoseconds, by default 1e7

Returns:

The saved payload, keyed by rizer.io.experimental_data.load_experiment_data.ELECTRON_DENSITY_NPZ_KEYS (time [s], ne_mean [m^-3], ne_std [m^-3], r2 [-]).

Return type:

dict of str to numpy.ndarray