neutron reflectivity

SciFiReaders/readers/spectroscopy/neutron_reflectivity.py

@@ -0,0 +1,72 @@
+# -*- coding: utf-8 -*-
+"""
+Neutron reflectivity captured at SNS comes in this format.
+
+Created on Fri  Jun 21 2024
+
+@author: Rama Vasudevan
+"""
+
+import numpy as np
+import re
+from sidpy import Dataset, Dimension, Reader
+
+class NeutronReflectivity(Reader):
+
+    def read(self):
+        """
+        Reads a single neutron reflectivity curve, acquired at SNS/ORNL.
+
+        Returns
+        -------
+        data_set: sidpy.Dataset object
+            wraps all the raw data and metadata from the input file into a Dataset object
+        
+        """
+        self.file_path = self._input_file_path
+        metadata, headings, data = self._read_data()
+
+        # create the sidpy dataset
+        data_set = Dataset.from_array(data[:,1], name='Neutron Reflectivity')
+
+        data_set.data_type = 'spectrum'
+        data_set.units = headings[1][1]
+        data_set.quantity = headings[1][0]
+
+        # set dimensions
+        data_set.set_dimension(0, Dimension(data[:,0], name=headings[0][0],
+                                                    units = headings[0][1],
+                                                    quantity=headings[0][0],
+                                                    dimension_type='spectral'))
+
+        metadata['column_headings'] = headings
+        metadata['raw_data'] = data
+
+        data_set.metadata = metadata
+
+
+    def _read_data(self):
+
+        with open(self.file_path, 'r') as f:
+            header = []
+            for ind,line in enumerate(f):
+                if '#' in line:
+                    header.append(line[2:])
+
+            column_headings = header[-1]
+            columns = re.split(r'\s{2,}', column_headings.strip())
+
+            columns_with_units = []
+
+            for column in columns:
+                match = re.match(r'([^\[]+)(\s*\[.*\])?', column)
+                name = match.group(1).strip()
+                unit = match.group(2).strip() if match.group(2) else None
+                columns_with_units.append((name, unit))
+
+        data = np.loadtxt(self.file_path)
+        metadata = header
+
+        return metadata, columns_with_units, data
+
+