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 as np
from sas.sascalc.dataloader.data_info import Data2D
from sas.sascalc.dataloader.manipulations import reader2D_converter

logger = logging.getLogger(__name__)

class Reader:
    """
    Example data manipulation
    """
    ## File type
    type_name = "TIF"
    ## Wildcards
    type = ["TIF files (*.tif)|*.tif",
            "TIFF files (*.tiff)|*.tiff",
            ]
    ## Extension
    ext = ['.tif', '.tiff']

    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 = np.zeros([im.size[0], im.size[1]])
        output.err_data = np.zeros([im.size[0], im.size[1]])
        output.mask = np.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:
                logger.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 = np.array(x_vals)
        output.qy_data = np.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