""" DANSE/SANS file reader """ ############################################################################ #This software was developed by the University of Tennessee as part of the #Distributed Data Analysis of Neutron Scattering Experiments (DANSE) #project funded by the US National Science Foundation. #If you use DANSE applications to do scientific research that leads to #publication, we ask that you acknowledge the use of the software with the #following sentence: #This work benefited from DANSE software developed under NSF award DMR-0520547. #copyright 2008, University of Tennessee ############################################################################# import math import os import logging import numpy as np from ..data_info import plottable_2D, DataInfo, Detector from ..manipulations import reader2D_converter from ..file_reader_base_class import FileReader from ..loader_exceptions import FileContentsException, DataReaderException logger = logging.getLogger(__name__) # Look for unit converter has_converter = True try: from sas.sascalc.data_util.nxsunit import Converter except: has_converter = False class Reader(FileReader): """ Example data manipulation """ ## File type type_name = "DANSE" ## Wildcards type = ["DANSE files (*.sans)|*.sans"] ## Extension ext = ['.sans', '.SANS'] def get_file_contents(self): self.current_datainfo = DataInfo() self.current_dataset = plottable_2D() self.output = [] loaded_correctly = True error_message = "" # defaults # wavelength in Angstrom wavelength = 10.0 # Distance in meter distance = 11.0 # Pixel number of center in x center_x = 65 # Pixel number of center in y center_y = 65 # Pixel size [mm] pixel = 5.0 # Size in x, in pixels size_x = 128 # Size in y, in pixels size_y = 128 # Format version fversion = 1.0 self.current_datainfo.filename = os.path.basename(self.f_open.name) detector = Detector() self.current_datainfo.detector.append(detector) self.current_dataset.data = np.zeros([size_x, size_y]) self.current_dataset.err_data = np.zeros([size_x, size_y]) read_on = True data_start_line = 1 while read_on: line = self.nextline() data_start_line += 1 if line.find("DATA:") >= 0: read_on = False break toks = line.split(':') try: if toks[0] == "FORMATVERSION": fversion = float(toks[1]) elif toks[0] == "WAVELENGTH": wavelength = float(toks[1]) elif toks[0] == "DISTANCE": distance = float(toks[1]) elif toks[0] == "CENTER_X": center_x = float(toks[1]) elif toks[0] == "CENTER_Y": center_y = float(toks[1]) elif toks[0] == "PIXELSIZE": pixel = float(toks[1]) elif toks[0] == "SIZE_X": size_x = int(toks[1]) elif toks[0] == "SIZE_Y": size_y = int(toks[1]) except ValueError as e: error_message += "Unable to parse {}. Default value used.\n".format(toks[0]) loaded_correctly = False # Read the data data = [] error = [] if not fversion >= 1.0: msg = "danse_reader can't read this file {}".format(self.f_open.name) raise FileContentsException(msg) for line_num, data_str in enumerate(self.nextlines()): toks = data_str.split() try: val = float(toks[0]) err = float(toks[1]) data.append(val) error.append(err) except ValueError as exc: msg = "Unable to parse line {}: {}".format(line_num + data_start_line, data_str.strip()) raise FileContentsException(msg) num_pts = size_x * size_y if len(data) < num_pts: msg = "Not enough data points provided. Expected {} but got {}".format( size_x * size_y, len(data)) raise FileContentsException(msg) elif len(data) > num_pts: error_message += ("Too many data points provided. Expected {0} but" " got {1}. Only the first {0} will be used.\n").format(num_pts, len(data)) loaded_correctly = False data = data[:num_pts] error = error[:num_pts] # Qx and Qy vectors theta = pixel / distance / 100.0 i_x = np.arange(size_x) theta = (i_x - center_x + 1) * pixel / distance / 100.0 x_vals = 4.0 * np.pi / wavelength * np.sin(theta / 2.0) xmin = x_vals.min() xmax = x_vals.max() i_y = np.arange(size_y) theta = (i_y - center_y + 1) * pixel / distance / 100.0 y_vals = 4.0 * np.pi / wavelength * np.sin(theta / 2.0) ymin = y_vals.min() ymax = y_vals.max() self.current_dataset.data = np.array(data, dtype=np.float64).reshape((size_y, size_x)) if fversion > 1.0: self.current_dataset.err_data = np.array(error, dtype=np.float64).reshape((size_y, size_x)) # Store all data # Store wavelength if has_converter and self.current_datainfo.source.wavelength_unit != 'A': conv = Converter('A') wavelength = conv(wavelength, units=self.current_datainfo.source.wavelength_unit) self.current_datainfo.source.wavelength = wavelength # Store distance if has_converter and detector.distance_unit != 'm': conv = Converter('m') distance = conv(distance, units=detector.distance_unit) detector.distance = distance # Store pixel size if has_converter and detector.pixel_size_unit != 'mm': conv = Converter('mm') pixel = conv(pixel, units=detector.pixel_size_unit) detector.pixel_size.x = pixel detector.pixel_size.y = pixel # Store beam center in distance units detector.beam_center.x = center_x * pixel detector.beam_center.y = center_y * pixel self.current_dataset = self.set_default_2d_units(self.current_dataset) self.current_dataset.x_bins = x_vals self.current_dataset.y_bins = y_vals # Reshape data x_vals = np.tile(x_vals, (size_y, 1)).flatten() y_vals = np.tile(y_vals, (size_x, 1)).T.flatten() if (np.all(self.current_dataset.err_data == None) or np.any(self.current_dataset.err_data <= 0)): new_err_data = np.sqrt(np.abs(self.current_dataset.data)) else: new_err_data = self.current_dataset.err_data.flatten() self.current_dataset.err_data = new_err_data self.current_dataset.qx_data = x_vals self.current_dataset.qy_data = y_vals self.current_dataset.q_data = np.sqrt(x_vals**2 + y_vals**2) self.current_dataset.mask = np.ones(len(x_vals), dtype=bool) # Store loading process information self.current_datainfo.meta_data['loader'] = self.type_name self.send_to_output() if not loaded_correctly: raise DataReaderException(error_message)