simcats_datasets.loading.load_ground_truth

Functions for providing ground truth data to be used with the Pytorch Dataset class.

For examples of the different ground truth types, please have a look at the notebook Examples_Pytorch_SimcatsDataset.

Every function must accept a h5 File or path for a simcats_dataset as input, provide an option to use only specific_ids and allow disabling the progress_bar. Output type depends on the ground truth type. Could for example be a pixel mask or defined start end points of lines. Please look at load_zeros_masks for a reference.

@author: f.hader

Module Contents

Functions

load_zeros_masks

Load no/empty ground truth data (arrays with only zeros).

load_tct_masks

Load Total Charge Transition (TCT) masks as ground truth data.

load_tct_by_dot_masks

Load Total Charge Transition (TCT) masks with transitions labeled by affected dot as ground truth data.

load_idt_masks

Load Inter-Dot Transition (IDT) masks as ground truth data.

load_ct_masks

Load Charge Transition (CT) masks as ground truth data.

load_ct_by_dot_masks

Load Charge Transition (CT) masks with transitions labeled by affected dot as ground truth data.

load_tc_region_masks

Load Total Charge (TC) region masks as ground truth data.

load_tc_region_minus_tct_masks

Load Total Charge (TC) region minus Total Charge Transition (TCT) masks as ground truth data (TCTs are basically excluded from the regions).

load_c_region_masks

Load Charge (C) region masks as ground truth data (CTs are basically excluded from the TC regions).

load_sensor_potential

Load the sensor-potential as ground truth.

load_compensated_sensor_potential

Load the compensated sensor-potential as ground truth.

load_charge_transition_sensor_phase_shift

Load the charge-transition-induced sensor phase-shift signal as ground-truth data.

load_sensor_regime_masks

Load sensor regime masks as ground truth data for sensor scans.

load_sensor_peak_center_masks

Load sensor peak center masks as ground truth data for sensor scans.

load_sensor_oscillation_boundaries_masks

Masks for the boundaries of the oscillation area with a triangular basic shape (in 2D).

load_sensor_quadrilateral_oscillation_boundaries_masks

Load quadrilateral oscillation area boundaries masks as ground truth data for sensor scans.

Module Implementation Details

simcats_datasets.loading.load_ground_truth.load_zeros_masks(file, specific_ids=None, progress_bar=True)

Load no/empty ground truth data (arrays with only zeros). Used for loading sets without ground truth. This is helpful to e.g. load experimental datasets without labels with the pytorch SimcatsDataset class to analyze train results with the same Interface as for simulated data.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

List of arrays containing only zeros as ground truth data

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_tct_masks(file, specific_ids=None, progress_bar=True)

Load Total Charge Transition (TCT) masks as ground truth data.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

Total Charge Transition (TCT) masks

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_tct_by_dot_masks(file, specific_ids=None, progress_bar=True, lut_entries=1000)

Load Total Charge Transition (TCT) masks with transitions labeled by affected dot as ground truth data.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

  • lut_entries (int) – Number of lookup-table entries to use for tct_bezier. Default is 1000.

Returns:

Total Charge Transition (TCT) masks

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_idt_masks(file, specific_ids=None, progress_bar=True)

Load Inter-Dot Transition (IDT) masks as ground truth data. In comparison to the Total Charge Transition (TCT) masks, only inter-dot transitions are included.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

Inter-Dot Transition (IDT) masks

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_ct_masks(file, specific_ids=None, progress_bar=True)

Load Charge Transition (CT) masks as ground truth data. In comparison to the Total Charge Transition (TCT) masks, the inter-dot transitions are included.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

Charge Transition (CT) masks

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_ct_by_dot_masks(file, specific_ids=None, progress_bar=True, lut_entries=1000, try_directly_loading_from_file=True)

Load Charge Transition (CT) masks with transitions labeled by affected dot as ground truth data. In comparison to the Total Charge Transition (TCT) masks, the inter-dot transitions are included.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

  • lut_entries (int) – Number of lookup-table entries to use for tct_bezier. Default is 1000.

  • try_directly_loading_from_file (bool) – Specifies if the loader should try to find the masks in the h5 file before falling back to calculating them (not all datasets include these masks). Default is True.

Returns:

Charge Transition (CT) masks

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_tc_region_masks(file, specific_ids=None, progress_bar=True)

Load Total Charge (TC) region masks as ground truth data.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

Total Charge (TC) region masks

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_tc_region_minus_tct_masks(file, specific_ids=None, progress_bar=True)

Load Total Charge (TC) region minus Total Charge Transition (TCT) masks as ground truth data (TCTs are basically excluded from the regions).

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

Total Charge (TC) region minus Total Charge Transition (TCT) masks

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_c_region_masks(file, specific_ids=None, progress_bar=True)

Load Charge (C) region masks as ground truth data (CTs are basically excluded from the TC regions).

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

Charge (C) region masks

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_sensor_potential(file, specific_ids=None, progress_bar=True)

Load the sensor-potential as ground truth.

The sensor potential is reconstructed from the stored sensor metadata and the occupation data. In contrast to the compensated sensor potential, this function keeps the direct gate-voltage contribution, i.e. the original sensor.alpha_gate values are used.

For sensors implementing the SensorScanSensorInterface, only the sensor-dot potential is returned; barrier potentials are ignored.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

List of sensor-potential maps, one per loaded dataset entry.

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_compensated_sensor_potential(file, specific_ids=None, progress_bar=True)

Load the compensated sensor-potential as ground truth.

The compensated sensor potential is reconstructed from the stored sensor metadata and the occupation data after removing the direct coupling between the swept gates and the sensor. This is done by setting sensor.alpha_gate = [0, 0] before calculating the sensor potential.

As a result, the returned potential contains only the occupation-induced contribution to the sensor potential and excludes the smooth background caused directly by the gate voltages.

For sensors implementing the SensorScanSensorInterface, only the sensor-dot potential is returned; barrier potentials are ignored.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

List of compensated sensor-potential maps, one per loaded dataset entry.

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_charge_transition_sensor_phase_shift(file, specific_ids=None, progress_bar=True)

Load the charge-transition-induced sensor phase-shift signal as ground-truth data.

The phase-shift signal is calculated from the reconstructed sensor potential by taking the gradient along the fast-sweeping voltage axis (row-wise).

To isolate the contribution of charge transitions, the direct gate coupling to the sensor is removed by setting sensor.alpha_gate = [0, 0] before calculating the sensor potential. Consequently, the returned signal contains only phase-shift contributions caused by changes in the quantum-dot occupations and not the smooth background induced by gate voltages.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

Phase-shift maps with the same spatial resolution as the corresponding charge stability diagrams.

Return type:

List[np.ndarray]

simcats_datasets.loading.load_ground_truth.load_sensor_regime_masks(file, specific_ids=None, progress_bar=True)

Load sensor regime masks as ground truth data for sensor scans.

In the sensor regime mask, the non-conductive area is marked with 0, the oscillation area with 1 and the fully conductive area with 2.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

Sensor regime masks

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_sensor_peak_center_masks(file, specific_ids=None, progress_bar=True)

Load sensor peak center masks as ground truth data for sensor scans.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

Sensor peak center masks

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_sensor_oscillation_boundaries_masks(file, specific_ids=None, progress_bar=True)

Masks for the boundaries of the oscillation area with a triangular basic shape (in 2D).

The oscillation area boundaries are marked with value 1 for pinch-off lines and 2 for the fully conductive line. The rest of the mask is 0. In 1D data the oscillation area boundaries are points, in 2D data lines. This method uses the metadata from each scan to calculate the fully conductive line in the sensor potential.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

Oscillation area boundaries masks (triangular shape)

Return type:

List[numpy.ndarray]

simcats_datasets.loading.load_ground_truth.load_sensor_quadrilateral_oscillation_boundaries_masks(file, specific_ids=None, progress_bar=True)

Load quadrilateral oscillation area boundaries masks as ground truth data for sensor scans.

Unlike the standard triangular masks from load_sensor_oscillation_boundaries_masks, this method does not combine the sensor dot potential with the barriers to determine full conductivity. Instead, it treats the two barriers separately and draws fully conductive lines where each barrier becomes fully conductive (or effectively vanishes).

The oscillation area boundaries are marked with the values 1 for pinch-off lines and 2 for fully conductive lines. The rest of the mask is 0. In 1D data the oscillation area boundaries are points, in 2D data lines.

Parameters:

  • file (Union[str, h5py.File]) – The file to read the data from. Can either be an object of the type h5py.File or the path to the dataset. If you want to do multiple consecutive loads from the same file (e.g. for using th PyTorch SimcatsDataset without preloading), consider initializing the file object yourself and passing it, to improve the performance.

  • specific_ids (Union[range, List[int], numpy.ndarray, None]) – Determines if only specific ids should be loaded. Using this option, the returned values are sorted according to the specified ids and not necessarily ascending. If set to None, all data is loaded. Default is None.

  • progress_bar (bool) – Determines whether to display a progress bar. Default is True.

Returns:

Oscillation area boundaries masks

Return type:

List[numpy.ndarray]