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| 2 | ##################################################################### |
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| 3 | #This software was developed by the University of Tennessee as part of the |
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| 4 | #Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
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| 5 | #project funded by the US National Science Foundation. |
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| 6 | #See the license text in license.txt |
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| 7 | #copyright 2008, University of Tennessee |
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| 8 | ###################################################################### |
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| 9 | |
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| 10 | """ |
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| 11 | TXT/IGOR 2D Q Map file reader |
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| 12 | """ |
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| 13 | |
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| 14 | |
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| 15 | import os |
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| 16 | #import sys |
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| 17 | import numpy |
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| 18 | import math |
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| 19 | #import logging |
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| 20 | from DataLoader.data_info import Data2D, Detector |
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| 21 | |
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| 22 | # Look for unit converter |
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| 23 | has_converter = True |
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| 24 | try: |
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| 25 | from data_util.nxsunit import Converter |
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| 26 | except: |
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| 27 | has_converter = False |
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| 28 | |
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| 29 | def check_point(x_point): |
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| 30 | """ |
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| 31 | check point validity |
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| 32 | """ |
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| 33 | # set zero for non_floats |
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| 34 | try: |
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| 35 | return float(x_point) |
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| 36 | except: |
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| 37 | return 0 |
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| 38 | |
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| 39 | class Reader: |
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| 40 | """ Simple data reader for Igor data files """ |
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| 41 | ## File type |
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| 42 | type_name = "IGOR/DAT 2D Q_map" |
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| 43 | ## Wildcards |
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| 44 | type = ["IGOR/DAT 2D file in Q_map (*.dat)|*.DAT"] |
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| 45 | ## Extension |
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| 46 | ext=['.DAT', '.dat'] |
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| 47 | |
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| 48 | def read(self,filename=None): |
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| 49 | """ Read file """ |
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| 50 | if not os.path.isfile(filename): |
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| 51 | raise ValueError, \ |
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| 52 | "Specified file %s is not a regular file" % filename |
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| 53 | |
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| 54 | # Read file |
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| 55 | f = open(filename,'r') |
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| 56 | buf = f.read() |
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| 57 | f.close() |
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| 58 | # Instantiate data object |
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| 59 | output = Data2D() |
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| 60 | output.filename = os.path.basename(filename) |
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| 61 | detector = Detector() |
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| 62 | if len(output.detector)>0: print str(output.detector[0]) |
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| 63 | output.detector.append(detector) |
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| 64 | |
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| 65 | # Get content |
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| 66 | dataStarted = False |
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| 67 | |
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| 68 | ## Defaults |
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| 69 | lines = buf.split('\n') |
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| 70 | itot = 0 |
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| 71 | x = [] |
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| 72 | y = [] |
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| 73 | |
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| 74 | ncounts = 0 |
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| 75 | |
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| 76 | wavelength = None |
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| 77 | distance = None |
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| 78 | transmission = None |
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| 79 | |
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| 80 | pixel_x = None |
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| 81 | pixel_y = None |
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| 82 | |
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| 83 | i_x = 0 |
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| 84 | i_y = -1 |
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| 85 | pixels = 0 |
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| 86 | |
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| 87 | isInfo = False |
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| 88 | isCenter = False |
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| 89 | |
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| 90 | data_conv_q = None |
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| 91 | data_conv_i = None |
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| 92 | |
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| 93 | # Set units: This is the unit assumed for Q and I in the data file. |
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| 94 | if has_converter == True and output.Q_unit != '1/A': |
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| 95 | data_conv_q = Converter('1/A') |
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| 96 | # Test it |
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| 97 | data_conv_q(1.0, output.Q_unit) |
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| 98 | |
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| 99 | if has_converter == True and output.I_unit != '1/cm': |
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| 100 | data_conv_i = Converter('1/cm') |
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| 101 | # Test it |
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| 102 | data_conv_i(1.0, output.I_unit) |
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| 103 | |
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| 104 | |
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| 105 | # Remove the last lines before the for loop if the lines are empty |
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| 106 | # to calculate the exact number of data points |
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| 107 | count = 0 |
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| 108 | while (len(lines[len(lines)-(count+1)].lstrip().rstrip()) < 1): |
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| 109 | del lines[len(lines)-(count+1)] |
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| 110 | count = count + 1 |
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| 111 | |
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| 112 | #Read Header and find the dimensions of 2D data |
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| 113 | line_num = 0 |
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| 114 | for line in lines: |
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| 115 | line_num += 1 |
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| 116 | ## Reading the header applies only to IGOR/NIST 2D q_map data files |
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| 117 | # Find setup info line |
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| 118 | if isInfo: |
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| 119 | isInfo = False |
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| 120 | line_toks = line.split() |
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| 121 | # Wavelength in Angstrom |
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| 122 | try: |
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| 123 | wavelength = float(line_toks[1]) |
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| 124 | # Units |
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| 125 | if has_converter == True and \ |
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| 126 | output.source.wavelength_unit != 'A': |
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| 127 | conv = Converter('A') |
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| 128 | wavelength = conv(wavelength, |
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| 129 | units=output.source.wavelength_unit) |
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| 130 | except: |
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| 131 | #Not required |
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| 132 | pass |
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| 133 | # Distance in mm |
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| 134 | try: |
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| 135 | distance = float(line_toks[3]) |
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| 136 | # Units |
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| 137 | if has_converter == True and detector.distance_unit != 'm': |
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| 138 | conv = Converter('m') |
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| 139 | distance = conv(distance, units=detector.distance_unit) |
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| 140 | except: |
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| 141 | #Not required |
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| 142 | pass |
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| 143 | |
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| 144 | # Distance in meters |
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| 145 | try: |
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| 146 | transmission = float(line_toks[4]) |
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| 147 | except: |
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| 148 | #Not required |
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| 149 | pass |
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| 150 | |
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| 151 | if line.count("LAMBDA") > 0: |
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| 152 | isInfo = True |
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| 153 | |
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| 154 | # Find center info line |
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| 155 | if isCenter: |
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| 156 | isCenter = False |
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| 157 | line_toks = line.split() |
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| 158 | # Center in bin number |
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| 159 | center_x = float(line_toks[0]) |
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| 160 | center_y = float(line_toks[1]) |
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| 161 | |
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| 162 | if line.count("BCENT") > 0: |
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| 163 | isCenter = True |
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| 164 | |
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| 165 | # Find data start |
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| 166 | if line.count("Data columns") or line.count("ASCII data") > 0: |
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| 167 | dataStarted = True |
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| 168 | continue |
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| 169 | |
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| 170 | ## Read and get data. |
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| 171 | if dataStarted == True: |
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| 172 | line_toks = line.split() |
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| 173 | if len(line_toks) == 0: |
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| 174 | #empty line |
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| 175 | continue |
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| 176 | # the number of columns must be stayed same |
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| 177 | col_num = len(line_toks) |
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| 178 | break |
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| 179 | # Make numpy array to remove header lines using index |
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| 180 | lines_array = numpy.array(lines) |
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| 181 | |
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| 182 | # index for lines_array |
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| 183 | lines_index = numpy.arange(len(lines)) |
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| 184 | |
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| 185 | # get the data lines |
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| 186 | data_lines = lines_array[lines_index >= (line_num - 1)] |
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| 187 | # Now we get the total number of rows (i.e., # of data points) |
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| 188 | row_num = len(data_lines) |
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| 189 | # make it as list again to control the separators |
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| 190 | data_list = " ".join(data_lines.tolist()) |
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| 191 | # split all data to one big list w/" "separator |
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| 192 | data_list = data_list.split() |
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| 193 | |
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| 194 | # Check if the size is consistent with data, otherwise |
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| 195 | #try the tab(\t) separator |
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| 196 | # (this may be removed once get the confidence |
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| 197 | #the former working all cases). |
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| 198 | if len(data_list) != (len(data_lines)) * col_num: |
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| 199 | data_list = "\t".join(data_lines.tolist()) |
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| 200 | data_list = data_list.split() |
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| 201 | |
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| 202 | # Change it(string) into float |
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| 203 | #data_list = map(float,data_list) |
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| 204 | data_list1 = map(check_point,data_list) |
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| 205 | |
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| 206 | # numpy array form |
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| 207 | data_array = numpy.array(data_list1) |
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| 208 | # Redimesion based on the row_num and col_num, |
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| 209 | #otherwise raise an error. |
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| 210 | try: |
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| 211 | data_point = data_array.reshape(row_num, col_num).transpose() |
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| 212 | except: |
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| 213 | msg = "red2d_reader: Can't read this file: Not a proper file format" |
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| 214 | raise ValueError, msg |
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| 215 | |
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| 216 | ## Get the all data: Let's HARDcoding; Todo find better way |
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| 217 | # Defaults |
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| 218 | dqx_data = numpy.zeros(0) |
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| 219 | dqy_data = numpy.zeros(0) |
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| 220 | qz_data = numpy.zeros(row_num) |
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| 221 | mask = numpy.ones(row_num,dtype=bool) |
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| 222 | # Get from the array |
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| 223 | qx_data = data_point[0] |
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| 224 | qy_data = data_point[1] |
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| 225 | data = data_point[2] |
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| 226 | if col_num > 3: qz_data = data_point[3] |
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| 227 | if col_num > 4: dqx_data = data_point[4] |
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| 228 | if col_num > 5: dqy_data = data_point[5] |
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| 229 | if col_num > 6: mask[data_point[6] < 1] = False |
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| 230 | q_data = numpy.sqrt(qx_data*qx_data+qy_data*qy_data+qz_data*qz_data) |
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| 231 | |
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| 232 | # Extra protection(it is needed for some data files): |
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| 233 | # If all mask elements are False, put all True |
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| 234 | if not mask.any(): mask[mask==False] = True |
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| 235 | |
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| 236 | # Store limits of the image in q space |
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| 237 | xmin = numpy.min(qx_data) |
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| 238 | xmax = numpy.max(qx_data) |
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| 239 | ymin = numpy.min(qy_data) |
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| 240 | ymax = numpy.max(qy_data) |
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| 241 | |
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| 242 | # units |
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| 243 | if has_converter == True and output.Q_unit != '1/A': |
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| 244 | xmin = data_conv_q(xmin, units=output.Q_unit) |
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| 245 | xmax = data_conv_q(xmax, units=output.Q_unit) |
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| 246 | ymin = data_conv_q(ymin, units=output.Q_unit) |
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| 247 | ymax = data_conv_q(ymax, units=output.Q_unit) |
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| 248 | |
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| 249 | ## calculate the range of the qx and qy_data |
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| 250 | x_size = math.fabs(xmax - xmin) |
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| 251 | y_size = math.fabs(ymax - ymin) |
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| 252 | |
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| 253 | # calculate the number of pixels in the each axes |
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| 254 | npix_y = math.floor(math.sqrt(len(data))) |
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| 255 | npix_x = math.floor(len(data)/npix_y) |
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| 256 | |
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| 257 | # calculate the size of bins |
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| 258 | xstep = x_size/(npix_x-1) |
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| 259 | ystep = y_size/(npix_y-1) |
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| 260 | |
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| 261 | # store x and y axis bin centers in q space |
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| 262 | x_bins = numpy.arange(xmin,xmax+xstep,xstep) |
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| 263 | y_bins = numpy.arange(ymin,ymax+ystep,ystep) |
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| 264 | |
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| 265 | # get the limits of q values |
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| 266 | xmin = xmin - xstep/2 |
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| 267 | xmax = xmax + xstep/2 |
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| 268 | ymin = ymin - ystep/2 |
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| 269 | ymax = ymax + ystep/2 |
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| 270 | |
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| 271 | #Store data in outputs |
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| 272 | #TODO: Check the lengths |
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| 273 | output.data = data |
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| 274 | output.err_data = numpy.sqrt(numpy.abs(data)) |
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| 275 | output.qx_data = qx_data |
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| 276 | output.qy_data = qy_data |
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| 277 | output.q_data = q_data |
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| 278 | output.mask = mask |
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| 279 | |
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| 280 | output.x_bins = x_bins |
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| 281 | output.y_bins = y_bins |
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| 282 | |
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| 283 | output.xmin = xmin |
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| 284 | output.xmax = xmax |
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| 285 | output.ymin = ymin |
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| 286 | output.ymax = ymax |
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| 287 | |
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| 288 | output.source.wavelength = wavelength |
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| 289 | |
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| 290 | # Store pixel size in mm |
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| 291 | detector.pixel_size.x = pixel_x |
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| 292 | detector.pixel_size.y = pixel_y |
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| 293 | |
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| 294 | # Store the sample to detector distance |
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| 295 | detector.distance = distance |
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| 296 | |
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| 297 | # optional data: if all of dq data == 0, do not pass to output |
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| 298 | if len(dqx_data) == len(qx_data) and dqx_data.any()!=0: |
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| 299 | # if no dqx_data, do not pass dqy_data. |
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| 300 | #(1 axis dq is not supported yet). |
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| 301 | if len(dqy_data) == len(qy_data) and dqy_data.any()!=0: |
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| 302 | output.dqx_data = dqx_data |
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| 303 | output.dqy_data = dqy_data |
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| 304 | |
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| 305 | # Units of axes |
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| 306 | if data_conv_q is not None: |
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| 307 | output.xaxis("\\rm{Q_{x}}", output.Q_unit) |
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| 308 | output.yaxis("\\rm{Q_{y}}", output.Q_unit) |
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| 309 | else: |
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| 310 | output.xaxis("\\rm{Q_{x}}", 'A^{-1}') |
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| 311 | output.yaxis("\\rm{Q_{y}}", 'A^{-1}') |
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| 312 | if data_conv_i is not None: |
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| 313 | output.zaxis("\\rm{Intensity}", output.I_unit) |
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| 314 | else: |
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| 315 | output.zaxis("\\rm{Intensity}","cm^{-1}") |
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| 316 | |
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| 317 | # Store loading process information |
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| 318 | output.meta_data['loader'] = self.type_name |
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| 319 | |
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| 320 | return output |
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| 321 | |
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| 322 | if __name__ == "__main__": |
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| 323 | reader = Reader() |
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| 324 | print reader.read("../test/exp18_14_igor_2dqxqy.dat") |
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| 325 | |
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| 326 | |
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