dataset_analyzer: add get_0_180_radios and get_angles functions

nabu/resources/dataset_analyzer.py

@@ -2,6 +2,7 @@ import os
 import posixpath
 from tempfile import mkdtemp
 import numpy as np
+from silx.io import get_data
 from silx.io.url import DataUrl
 from tomoscan.esrf.edfscan import EDFTomoScan
 from tomoscan.esrf.hdf5scan import HDF5TomoScan
@@ -385,3 +386,71 @@ def analyze_dataset(dataset_path, processes_file=None, extra_options=None, logge
     )
     return dataset_structure
 
+
+
+def get_0_180_radios(dataset_info, angles=None):
+    """
+    Get the radios at 0 degres and 180 degrees.
+
+    Parameters
+    ----------
+    dataset_info: `dataset_infos`
+        Data structure with the dataset information
+    angles: array, optional
+        Array with the rotation angles. If provided, it overwrites the information from 'dataset_info', if any.
+    """
+
+    if angles is None:
+        angles = dataset_info.rotation_angles
+    radios_indices = []
+    radios_indices = sorted(dataset_info.projections.keys())
+    # Take angles 0 and 180 degrees. It might not work of there is an offset
+    i_0 = np.argmin(np.abs(angles))
+    i_180 = np.argmin(np.abs(angles - np.pi))
+    _min_indices = [i_0, i_180]
+    radios_indices = [
+        radios_indices[i_0],
+        radios_indices[i_180]
+    ]
+    n_radios = 2
+    radios = np.zeros((n_radios, ) + dataset_info.radio_dims[::-1], "f")
+    for i in range(n_radios):
+        radio_idx = radios_indices[i]
+        radios[i] = get_data(dataset_info.projections[radio_idx]).astype("f")
+    return radios
+
+
+def get_angles(dataset_info, logger=None):
+    """
+    Get rotation angles (in radians) from a dataset_info data structure.
+    If no such information is available, generates an array with standard angles.
+
+    Parameters
+    ----------
+    dataset_info: `dataset_infos`
+        Data structure with the dataset information
+    logger: Logger
+        Logger object
+    """
+    logger = LoggerOrPrint(logger)
+    dataset_angles = dataset_info.rotation_angles
+    if dataset_angles is not None:
+        return dataset_angles
+    theta_min, theta_max = 0, np.pi
+    msg = "no information on angles was found for this dataset. Using default range "
+    endpoint = False
+    if dataset_info.is_halftomo:
+        theta_max *= 2
+        endpoint = True
+        msg += "[0, 360]"
+    else:
+        msg += "[0, 180["
+    logger.warning(msg)
+    angles = np.linspace(
+        theta_min, theta_max, len(dataset_info.projections),
+        endpoint=endpoint
+    )
+    return angles
+
+
+