Source code for sas.dataloader.readers.red2d_reader

"""
    TXT/IGOR 2D Q Map 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. 
#See the license text in license.txt
#copyright 2008, University of Tennessee
######################################################################
import os
import numpy
import math
from sas.dataloader.data_info import Data2D, Detector

# Look for unit converter
has_converter = True
try:
    from sas.data_util.nxsunit import Converter
except:
    has_converter = False
    
    
[docs]def check_point(x_point): """ check point validity """ # set zero for non_floats try: return float(x_point) except: return 0
[docs]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']
[docs] def write(self, filename, data): """ Write to .dat :param filename: file name to write :param data: data2D """ import time # Write the file fd = open(filename, 'w') t = time.localtime() time_str = time.strftime("%H:%M on %b %d %y", t) header_str = "Data columns are Qx - Qy - I(Qx,Qy)\n\nASCII data" header_str += " created at %s \n\n" % time_str # simple 2D header fd.write(header_str) # write qx qy I values for i in range(len(data.data)): fd.write("%g %g %g\n" % (data.qx_data[i], data.qy_data[i], data.data[i])) # close fd.close()
[docs] 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') x = [] y = [] wavelength = None distance = None transmission = None pixel_x = None pixel_y = None 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 # Old version NIST files: 0 ver = 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 # Check version if line.count("Data columns") > 0: if line.count("err(I)") > 0: ver = 1 # Find data start if 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) data_list1 = map(check_point, data_list) # numpy array form data_array = numpy.array(data_list1) # 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: msg = "red2d_reader: Can't read this file: Not a proper file format" raise ValueError, msg ## Get the all data: Let's HARDcoding; Todo find better way # Defaults dqx_data = numpy.zeros(0) dqy_data = numpy.zeros(0) err_data = numpy.ones(row_num) 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 ver == 1: if col_num > (2 + ver): err_data = data_point[(2 + ver)] if col_num > (3 + ver): qz_data = data_point[(3 + ver)] if col_num > (4 + ver): dqx_data = data_point[(4 + ver)] if col_num > (5 + ver): dqy_data = data_point[(5 + ver)] #if col_num > (6 + ver): mask[data_point[(6 + ver)] < 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 if (err_data == 1).all(): output.err_data = numpy.sqrt(numpy.abs(data)) output.err_data[output.err_data == 0.0] = 1.0 else: output.err_data = err_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: # Currently we do not support dq parr, perp. # tranfer the comp. to cartesian coord. for newer version. if ver != 1: diag = numpy.sqrt(qx_data * qx_data + qy_data * qy_data) cos_th = qx_data / diag sin_th = qy_data / diag output.dqx_data = numpy.sqrt((dqx_data * cos_th) * \ (dqx_data * cos_th) \ + (dqy_data * sin_th) * \ (dqy_data * sin_th)) output.dqy_data = numpy.sqrt((dqx_data * sin_th) * \ (dqx_data * sin_th) \ + (dqy_data * cos_th) * \ (dqy_data * cos_th)) else: 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