source: sasview/DataLoader/readers/red2d_reader.py @ 6450d8c

#####################################################################
#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
######################################################################

"""
    TXT/IGOR 2D Q Map file reader
"""


import os, sys
import numpy
import math, logging
from DataLoader.data_info import Data2D, Detector

# Look for unit converter
has_converter = True
try:
    from data_util.nxsunit import Converter
except:
    has_converter = False

class Reader:
    """ Simple data reader for Igor data files """
    ## File type
    type_name = "IGOR/DAT 2D Q_map"   
    ## Wildcards
    type = ["IGOR/DAT 2D file in Q_map (*.dat)|*.DAT"]
    ## Extension
    ext=['.DAT', '.dat']

    def read(self,filename=None):
        """ Read file """
        if not os.path.isfile(filename):
            raise ValueError, \
            "Specified file %s is not a regular file" % filename

        # Read file
        f = open(filename,'r')
        buf = f.read()
        f.close()     
        # Instantiate data object
        output = Data2D()
        output.filename = os.path.basename(filename)
        detector = Detector()
        if len(output.detector)>0: print str(output.detector[0])
        output.detector.append(detector)
                
        # Get content
        dataStarted = False
        
        ## Defaults      
        lines = buf.split('\n')
        itot = 0
        x = []
        y = []
        
        ncounts = 0
        
        wavelength   = None
        distance     = None
        transmission = None
        
        pixel_x = None
        pixel_y = None
        
        i_x    = 0
        i_y    = -1
        pixels = 0
        
        isInfo   = False
        isCenter = False

        data_conv_q = None
        data_conv_i = None
        
        # Set units: This is the unit assumed for Q and I in the data file.
        if has_converter == True and output.Q_unit != '1/A':
            data_conv_q = Converter('1/A')
            # Test it
            data_conv_q(1.0, output.Q_unit)
            
        if has_converter == True and output.I_unit != '1/cm':
            data_conv_i = Converter('1/cm')
            # Test it
            data_conv_i(1.0, output.I_unit)            
        
              
        # Remove the last lines before the for loop if the lines are empty
        # to calculate the exact number of data points
        count = 0
        while (len(lines[len(lines)-(count+1)].lstrip().rstrip()) < 1):
            del lines[len(lines)-(count+1)]
            count = count + 1

        #Read Header and find the dimensions of 2D data
        line_num = 0
        for line in lines:      
            line_num += 1
            ## Reading the header applies only to IGOR/NIST 2D q_map data files
            # Find setup info line
            if isInfo:
                isInfo = False
                line_toks = line.split()
                # Wavelength in Angstrom
                try:
                    wavelength = float(line_toks[1])
                    # Units
                    if has_converter==True and output.source.wavelength_unit != 'A':
                        conv = Converter('A')
                        wavelength = conv(wavelength, units=output.source.wavelength_unit)
                except:
                    #Not required
                    pass
                # Distance in mm
                try:
                    distance = float(line_toks[3])
                    # Units
                    if has_converter==True and detector.distance_unit != 'm':
                        conv = Converter('m')
                        distance = conv(distance, units=detector.distance_unit)
                except:
                    #Not required
                    pass
                
                # Distance in meters
                try:
                    transmission = float(line_toks[4])
                except:
                    #Not required
                    pass
                                            
            if line.count("LAMBDA")>0:
                isInfo = True
                
            # Find center info line
            if isCenter:
                isCenter = False                
                line_toks = line.split()
                # Center in bin number
                center_x = float(line_toks[0])
                center_y = float(line_toks[1])

            if line.count("BCENT")>0:
                isCenter = True
                       
            # Find data start
            if line.count("Data columns") or line.count("ASCII data")>0:
                dataStarted = True
                continue

            ## Read and get data.   
            if dataStarted == True:
                line_toks = line.split()              
                if len(line_toks) == 0:
                    #empty line
                    continue
                # the number of columns must be stayed same 
                col_num = len(line_toks)
                break

        
        # Make numpy array to remove header lines using index
        lines_array = numpy.array(lines)

        # index for lines_array
        lines_index = numpy.arange(len(lines))
        
        # get the data lines
        data_lines = lines_array[lines_index>=(line_num-1)]
        # Now we get the total number of rows (i.e., # of data points)
        row_num = len(data_lines)
        # make it as list again to control the separators
        data_list = " ".join(data_lines.tolist())
        # split all data to one big list w/" "separator
        data_list = data_list.split()
 
        # Check if the size is consistent with data, otherwise try the tab(\t) separator
        # (this may be removed once get the confidence the former working all cases).
        if len(data_list) != (len(data_lines)) * col_num:
            data_list = "\t".join(data_lines.tolist())
            data_list = data_list.split()
            
        # Change it(string) into float
        data_list = map(float,data_list)
        # numpy array form
        data_array = numpy.array(data_list)

        # Redimesion based on the row_num and col_num, otherwise raise an error.
        try:
            data_point = data_array.reshape(row_num,col_num).transpose()
        except:
            raise ValueError, "red2d_reader: Can't read this file: Not a proper file format"
        
        ## Get the all data: Let's HARDcoding; Todo find better way
        # Defaults
        dqx_data = numpy.zeros(0)
        dqy_data = numpy.zeros(0)
        qz_data = numpy.zeros(row_num)
        mask = numpy.ones(row_num,dtype=bool)
        # Get from the array
        qx_data = data_point[0]
        qy_data = data_point[1]
        data = data_point[2]
        if col_num >3: qz_data = data_point[3]
        if col_num >4: dqx_data = data_point[4]
        if col_num >5: dqy_data = data_point[5]
        if col_num >6: mask[data_point[6]<1] = False
        q_data = numpy.sqrt(qx_data*qx_data+qy_data*qy_data+qz_data*qz_data)
           
        # Extra protection(it is needed for some data files): 
        # If all mask elements are False, put all True
        if not mask.any(): mask[mask==False] = True   
  
        # Store limits of the image in q space
        xmin    = numpy.min(qx_data)
        xmax    = numpy.max(qx_data)
        ymin    = numpy.min(qy_data)
        ymax    = numpy.max(qy_data)

        # units
        if has_converter == True and output.Q_unit != '1/A':
            xmin = data_conv_q(xmin, units=output.Q_unit)
            xmax = data_conv_q(xmax, units=output.Q_unit)
            ymin = data_conv_q(ymin, units=output.Q_unit)
            ymax = data_conv_q(ymax, units=output.Q_unit)
            
        ## calculate the range of the qx and qy_data
        x_size = math.fabs(xmax - xmin)
        y_size = math.fabs(ymax - ymin)
        
        # calculate the number of pixels in the each axes
        npix_y = math.floor(math.sqrt(len(data)))
        npix_x = math.floor(len(data)/npix_y)
        
        # calculate the size of bins      
        xstep = x_size/(npix_x-1)
        ystep = y_size/(npix_y-1)
        
        # store x and y axis bin centers in q space
        x_bins  = numpy.arange(xmin,xmax+xstep,xstep)
        y_bins  = numpy.arange(ymin,ymax+ystep,ystep)
       
        # get the limits of q values
        xmin = xmin - xstep/2
        xmax = xmax + xstep/2
        ymin = ymin - ystep/2
        ymax = ymax + ystep/2
        
        #Store data in outputs  
        #TODO: Check the lengths 
        output.data     = data
        output.err_data = numpy.sqrt(numpy.abs(data))
        output.qx_data  = qx_data
        output.qy_data  = qy_data             
        output.q_data   = q_data
        output.mask     = mask 
        
        output.x_bins = x_bins
        output.y_bins = y_bins
               
        output.xmin = xmin
        output.xmax = xmax
        output.ymin = ymin
        output.ymax = ymax
        
        output.source.wavelength = wavelength
        
        # Store pixel size in mm
        detector.pixel_size.x = pixel_x
        detector.pixel_size.y = pixel_y
        
        # Store the sample to detector distance
        detector.distance = distance
        
        # optional data: if all of dq data == 0, do not pass to output
        if len(dqx_data) == len(qx_data) and dqx_data.any()!=0: 
            # if no dqx_data, do not pass dqy_data.(1 axis dq is not supported yet).
            if len(dqy_data) == len(qy_data) and dqy_data.any()!=0:
                output.dqx_data = dqx_data
                output.dqy_data = dqy_data
        
        # Units of axes
        if data_conv_q is not None:
            output.xaxis("\\rm{Q_{x}}", output.Q_unit)
            output.yaxis("\\rm{Q_{y}}", output.Q_unit)
        else:
            output.xaxis("\\rm{Q_{x}}", 'A^{-1}')
            output.yaxis("\\rm{Q_{y}}", 'A^{-1}')            
        if data_conv_i is not None:
            output.zaxis("\\rm{Intensity}", output.I_unit)
        else:
            output.zaxis("\\rm{Intensity}","cm^{-1}")
    
        # Store loading process information
        output.meta_data['loader'] = self.type_name

        return output
    
if __name__ == "__main__": 
    reader = Reader()
    print reader.read("../test/exp18_14_igor_2dqxqy.dat")