Source code for sas.sascalc.dataloader.readers.tiff_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. 
#See the license text in license.txt
#copyright 2008, University of Tennessee
######################################################################
"""
    Image reader. Untested.
"""
#TODO: load and check data and orientation of the image (needs rendering)
import math
import logging
import os
import numpy
from sas.sascalc.dataloader.data_info import Data2D
from sas.sascalc.dataloader.manipulations import reader2D_converter
    
[docs]class Reader: """ Example data manipulation """ ## File type type_name = "TIF" ## Wildcards type = ["TIF files (*.tif)|*.tif", "TIFF files (*.tiff)|*.tiff", ] ## Extension ext = ['.tif', '.tiff']
[docs] def read(self, filename=None): """ Open and read the data in a file :param file: path of the file """ try: import Image import TiffImagePlugin Image._initialized=2 except: msg = "tiff_reader: could not load file. Missing Image module." raise RuntimeError, msg # Instantiate data object output = Data2D() output.filename = os.path.basename(filename) # Read in the image try: im = Image.open(filename) except: raise RuntimeError, "cannot open %s"%(filename) data = im.getdata() # Initiazed the output data object output.data = numpy.zeros([im.size[0], im.size[1]]) output.err_data = numpy.zeros([im.size[0], im.size[1]]) output.mask = numpy.ones([im.size[0], im.size[1]], dtype=bool) # Initialize x_vals = [] y_vals = [] # x and y vectors for i_x in range(im.size[0]): x_vals.append(i_x) itot = 0 for i_y in range(im.size[1]): y_vals.append(i_y) for val in data: try: value = float(val) except: logging.error("tiff_reader: had to skip a non-float point") continue # Get bin number if math.fmod(itot, im.size[0]) == 0: i_x = 0 i_y += 1 else: i_x += 1 output.data[im.size[1] - 1 - i_y][i_x] = value itot += 1 output.xbins = im.size[0] output.ybins = im.size[1] output.x_bins = x_vals output.y_bins = y_vals output.qx_data = numpy.array(x_vals) output.qy_data = numpy.array(y_vals) output.xmin = 0 output.xmax = im.size[0] - 1 output.ymin = 0 output.ymax = im.size[0] - 1 # Store loading process information output.meta_data['loader'] = self.type_name output = reader2D_converter(output) return output