| 1 | """ |
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| 2 | TXT/IGOR 2D Q Map file reader |
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| 3 | """ |
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| 4 | ##################################################################### |
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| 5 | #This software was developed by the University of Tennessee as part of the |
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| 6 | #Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
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| 7 | #project funded by the US National Science Foundation. |
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| 8 | #See the license text in license.txt |
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| 9 | #copyright 2008, University of Tennessee |
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| 10 | ###################################################################### |
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| 11 | import os |
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| 12 | import numpy |
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| 13 | import math |
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| 14 | from sas.sascalc.dataloader.data_info import Data2D, Detector |
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| 15 | |
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| 16 | # Look for unit converter |
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| 17 | has_converter = True |
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| 18 | try: |
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| 19 | from sas.sascalc.data_util.nxsunit import Converter |
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| 20 | except: |
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| 21 | has_converter = False |
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| 22 | |
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| 23 | |
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| 24 | def check_point(x_point): |
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| 25 | """ |
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| 26 | check point validity |
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| 27 | """ |
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| 28 | # set zero for non_floats |
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| 29 | try: |
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| 30 | return float(x_point) |
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| 31 | except: |
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| 32 | return 0 |
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| 33 | |
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| 34 | |
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| 35 | class Reader: |
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| 36 | """ Simple data reader for Igor data files """ |
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| 37 | ## File type |
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| 38 | type_name = "IGOR/DAT 2D Q_map" |
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| 39 | ## Wildcards |
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| 40 | type = ["IGOR/DAT 2D file in Q_map (*.dat)|*.DAT"] |
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| 41 | ## Extension |
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| 42 | ext = ['.DAT', '.dat'] |
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| 43 | |
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| 44 | def write(self, filename, data): |
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| 45 | """ |
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| 46 | Write to .dat |
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| 47 | |
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| 48 | :param filename: file name to write |
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| 49 | :param data: data2D |
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| 50 | """ |
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| 51 | import time |
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| 52 | # Write the file |
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| 53 | fd = open(filename, 'w') |
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| 54 | t = time.localtime() |
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| 55 | time_str = time.strftime("%H:%M on %b %d %y", t) |
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| 56 | |
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| 57 | header_str = "Data columns are Qx - Qy - I(Qx,Qy)\n\nASCII data" |
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| 58 | header_str += " created at %s \n\n" % time_str |
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| 59 | # simple 2D header |
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| 60 | fd.write(header_str) |
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| 61 | # write qx qy I values |
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| 62 | for i in range(len(data.data)): |
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| 63 | fd.write("%g %g %g\n" % (data.qx_data[i], |
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| 64 | data.qy_data[i], |
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| 65 | data.data[i])) |
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| 66 | # close |
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| 67 | fd.close() |
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| 68 | |
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| 69 | def read(self, filename=None): |
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| 70 | """ Read file """ |
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| 71 | if not os.path.isfile(filename): |
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| 72 | raise ValueError, \ |
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| 73 | "Specified file %s is not a regular file" % filename |
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| 74 | |
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| 75 | # Read file |
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| 76 | f = open(filename, 'r') |
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| 77 | buf = f.read() |
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| 78 | f.close() |
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| 79 | # Instantiate data object |
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| 80 | output = Data2D() |
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| 81 | output.filename = os.path.basename(filename) |
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| 82 | detector = Detector() |
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| 83 | if len(output.detector) > 0: |
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| 84 | print str(output.detector[0]) |
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| 85 | output.detector.append(detector) |
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| 86 | |
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| 87 | # Get content |
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| 88 | dataStarted = False |
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| 89 | |
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| 90 | ## Defaults |
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| 91 | lines = buf.split('\n') |
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| 92 | x = [] |
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| 93 | y = [] |
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| 94 | |
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| 95 | wavelength = None |
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| 96 | distance = None |
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| 97 | transmission = None |
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| 98 | |
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| 99 | pixel_x = None |
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| 100 | pixel_y = None |
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| 101 | |
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| 102 | isInfo = False |
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| 103 | isCenter = False |
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| 104 | |
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| 105 | data_conv_q = None |
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| 106 | data_conv_i = None |
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| 107 | |
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| 108 | # Set units: This is the unit assumed for Q and I in the data file. |
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| 109 | if has_converter == True and output.Q_unit != '1/A': |
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| 110 | data_conv_q = Converter('1/A') |
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| 111 | # Test it |
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| 112 | data_conv_q(1.0, output.Q_unit) |
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| 113 | |
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| 114 | if has_converter == True and output.I_unit != '1/cm': |
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| 115 | data_conv_i = Converter('1/cm') |
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| 116 | # Test it |
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| 117 | data_conv_i(1.0, output.I_unit) |
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| 118 | |
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| 119 | |
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| 120 | # Remove the last lines before the for loop if the lines are empty |
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| 121 | # to calculate the exact number of data points |
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| 122 | count = 0 |
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| 123 | while (len(lines[len(lines) - (count + 1)].lstrip().rstrip()) < 1): |
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| 124 | del lines[len(lines) - (count + 1)] |
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| 125 | count = count + 1 |
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| 126 | |
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| 127 | #Read Header and find the dimensions of 2D data |
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| 128 | line_num = 0 |
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| 129 | # Old version NIST files: 0 |
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| 130 | ver = 0 |
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| 131 | for line in lines: |
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| 132 | line_num += 1 |
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| 133 | ## Reading the header applies only to IGOR/NIST 2D q_map data files |
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| 134 | # Find setup info line |
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| 135 | if isInfo: |
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| 136 | isInfo = False |
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| 137 | line_toks = line.split() |
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| 138 | # Wavelength in Angstrom |
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| 139 | try: |
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| 140 | wavelength = float(line_toks[1]) |
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| 141 | # Units |
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| 142 | if has_converter == True and \ |
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| 143 | output.source.wavelength_unit != 'A': |
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| 144 | conv = Converter('A') |
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| 145 | wavelength = conv(wavelength, |
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| 146 | units=output.source.wavelength_unit) |
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| 147 | except: |
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| 148 | #Not required |
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| 149 | pass |
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| 150 | # Distance in mm |
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| 151 | try: |
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| 152 | distance = float(line_toks[3]) |
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| 153 | # Units |
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| 154 | if has_converter == True and detector.distance_unit != 'm': |
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| 155 | conv = Converter('m') |
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| 156 | distance = conv(distance, units=detector.distance_unit) |
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| 157 | except: |
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| 158 | #Not required |
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| 159 | pass |
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| 160 | |
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| 161 | # Distance in meters |
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| 162 | try: |
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| 163 | transmission = float(line_toks[4]) |
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| 164 | except: |
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| 165 | #Not required |
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| 166 | pass |
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| 167 | |
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| 168 | if line.count("LAMBDA") > 0: |
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| 169 | isInfo = True |
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| 170 | |
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| 171 | # Find center info line |
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| 172 | if isCenter: |
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| 173 | isCenter = False |
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| 174 | line_toks = line.split() |
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| 175 | # Center in bin number |
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| 176 | center_x = float(line_toks[0]) |
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| 177 | center_y = float(line_toks[1]) |
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| 178 | |
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| 179 | if line.count("BCENT") > 0: |
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| 180 | isCenter = True |
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| 181 | # Check version |
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| 182 | if line.count("Data columns") > 0: |
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| 183 | if line.count("err(I)") > 0: |
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| 184 | ver = 1 |
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| 185 | # Find data start |
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| 186 | if line.count("ASCII data") > 0: |
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| 187 | dataStarted = True |
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| 188 | continue |
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| 189 | |
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| 190 | ## Read and get data. |
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| 191 | if dataStarted == True: |
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| 192 | line_toks = line.split() |
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| 193 | if len(line_toks) == 0: |
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| 194 | #empty line |
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| 195 | continue |
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| 196 | # the number of columns must be stayed same |
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| 197 | col_num = len(line_toks) |
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| 198 | break |
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| 199 | # Make numpy array to remove header lines using index |
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| 200 | lines_array = numpy.array(lines) |
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| 201 | |
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| 202 | # index for lines_array |
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| 203 | lines_index = numpy.arange(len(lines)) |
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| 204 | |
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| 205 | # get the data lines |
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| 206 | data_lines = lines_array[lines_index >= (line_num - 1)] |
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| 207 | # Now we get the total number of rows (i.e., # of data points) |
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| 208 | row_num = len(data_lines) |
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| 209 | # make it as list again to control the separators |
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| 210 | data_list = " ".join(data_lines.tolist()) |
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| 211 | # split all data to one big list w/" "separator |
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| 212 | data_list = data_list.split() |
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| 213 | |
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| 214 | # Check if the size is consistent with data, otherwise |
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| 215 | #try the tab(\t) separator |
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| 216 | # (this may be removed once get the confidence |
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| 217 | #the former working all cases). |
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| 218 | if len(data_list) != (len(data_lines)) * col_num: |
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| 219 | data_list = "\t".join(data_lines.tolist()) |
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| 220 | data_list = data_list.split() |
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| 221 | |
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| 222 | # Change it(string) into float |
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| 223 | #data_list = map(float,data_list) |
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| 224 | data_list1 = map(check_point, data_list) |
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| 225 | |
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| 226 | # numpy array form |
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| 227 | data_array = numpy.array(data_list1) |
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| 228 | # Redimesion based on the row_num and col_num, |
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| 229 | #otherwise raise an error. |
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| 230 | try: |
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| 231 | data_point = data_array.reshape(row_num, col_num).transpose() |
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| 232 | except: |
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| 233 | msg = "red2d_reader: Can't read this file: Not a proper file format" |
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| 234 | raise ValueError, msg |
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| 235 | ## Get the all data: Let's HARDcoding; Todo find better way |
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| 236 | # Defaults |
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| 237 | dqx_data = numpy.zeros(0) |
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| 238 | dqy_data = numpy.zeros(0) |
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| 239 | err_data = numpy.ones(row_num) |
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| 240 | qz_data = numpy.zeros(row_num) |
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| 241 | mask = numpy.ones(row_num, dtype=bool) |
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| 242 | # Get from the array |
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| 243 | qx_data = data_point[0] |
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| 244 | qy_data = data_point[1] |
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| 245 | data = data_point[2] |
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| 246 | if ver == 1: |
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| 247 | if col_num > (2 + ver): |
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| 248 | err_data = data_point[(2 + ver)] |
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| 249 | if col_num > (3 + ver): |
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| 250 | qz_data = data_point[(3 + ver)] |
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| 251 | if col_num > (4 + ver): |
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| 252 | dqx_data = data_point[(4 + ver)] |
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| 253 | if col_num > (5 + ver): |
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| 254 | dqy_data = data_point[(5 + ver)] |
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| 255 | #if col_num > (6 + ver): mask[data_point[(6 + ver)] < 1] = False |
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| 256 | q_data = numpy.sqrt(qx_data*qx_data+qy_data*qy_data+qz_data*qz_data) |
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| 257 | |
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| 258 | # Extra protection(it is needed for some data files): |
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| 259 | # If all mask elements are False, put all True |
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| 260 | if not mask.any(): |
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| 261 | mask[mask == False] = True |
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| 262 | |
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| 263 | # Store limits of the image in q space |
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| 264 | xmin = numpy.min(qx_data) |
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| 265 | xmax = numpy.max(qx_data) |
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| 266 | ymin = numpy.min(qy_data) |
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| 267 | ymax = numpy.max(qy_data) |
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| 268 | |
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| 269 | # units |
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| 270 | if has_converter == True and output.Q_unit != '1/A': |
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| 271 | xmin = data_conv_q(xmin, units=output.Q_unit) |
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| 272 | xmax = data_conv_q(xmax, units=output.Q_unit) |
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| 273 | ymin = data_conv_q(ymin, units=output.Q_unit) |
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| 274 | ymax = data_conv_q(ymax, units=output.Q_unit) |
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| 275 | |
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| 276 | ## calculate the range of the qx and qy_data |
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| 277 | x_size = math.fabs(xmax - xmin) |
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| 278 | y_size = math.fabs(ymax - ymin) |
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| 279 | |
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| 280 | # calculate the number of pixels in the each axes |
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| 281 | npix_y = math.floor(math.sqrt(len(data))) |
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| 282 | npix_x = math.floor(len(data) / npix_y) |
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| 283 | |
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| 284 | # calculate the size of bins |
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| 285 | xstep = x_size / (npix_x - 1) |
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| 286 | ystep = y_size / (npix_y - 1) |
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| 287 | |
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| 288 | # store x and y axis bin centers in q space |
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| 289 | x_bins = numpy.arange(xmin, xmax + xstep, xstep) |
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| 290 | y_bins = numpy.arange(ymin, ymax + ystep, ystep) |
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| 291 | |
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| 292 | # get the limits of q values |
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| 293 | xmin = xmin - xstep / 2 |
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| 294 | xmax = xmax + xstep / 2 |
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| 295 | ymin = ymin - ystep / 2 |
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| 296 | ymax = ymax + ystep / 2 |
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| 297 | |
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| 298 | #Store data in outputs |
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| 299 | #TODO: Check the lengths |
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| 300 | output.data = data |
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| 301 | if (err_data == 1).all(): |
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| 302 | output.err_data = numpy.sqrt(numpy.abs(data)) |
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| 303 | output.err_data[output.err_data == 0.0] = 1.0 |
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| 304 | else: |
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| 305 | output.err_data = err_data |
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| 306 | |
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| 307 | output.qx_data = qx_data |
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| 308 | output.qy_data = qy_data |
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| 309 | output.q_data = q_data |
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| 310 | output.mask = mask |
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| 311 | |
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| 312 | output.x_bins = x_bins |
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| 313 | output.y_bins = y_bins |
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| 314 | |
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| 315 | output.xmin = xmin |
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| 316 | output.xmax = xmax |
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| 317 | output.ymin = ymin |
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| 318 | output.ymax = ymax |
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| 319 | |
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| 320 | output.source.wavelength = wavelength |
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| 321 | |
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| 322 | # Store pixel size in mm |
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| 323 | detector.pixel_size.x = pixel_x |
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| 324 | detector.pixel_size.y = pixel_y |
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| 325 | |
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| 326 | # Store the sample to detector distance |
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| 327 | detector.distance = distance |
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| 328 | |
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| 329 | # optional data: if all of dq data == 0, do not pass to output |
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| 330 | if len(dqx_data) == len(qx_data) and dqx_data.any() != 0: |
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| 331 | # if no dqx_data, do not pass dqy_data. |
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| 332 | #(1 axis dq is not supported yet). |
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| 333 | if len(dqy_data) == len(qy_data) and dqy_data.any() != 0: |
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| 334 | # Currently we do not support dq parr, perp. |
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| 335 | # tranfer the comp. to cartesian coord. for newer version. |
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| 336 | if ver != 1: |
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| 337 | diag = numpy.sqrt(qx_data * qx_data + qy_data * qy_data) |
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| 338 | cos_th = qx_data / diag |
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| 339 | sin_th = qy_data / diag |
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| 340 | output.dqx_data = numpy.sqrt((dqx_data * cos_th) * \ |
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| 341 | (dqx_data * cos_th) \ |
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| 342 | + (dqy_data * sin_th) * \ |
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| 343 | (dqy_data * sin_th)) |
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| 344 | output.dqy_data = numpy.sqrt((dqx_data * sin_th) * \ |
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| 345 | (dqx_data * sin_th) \ |
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| 346 | + (dqy_data * cos_th) * \ |
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| 347 | (dqy_data * cos_th)) |
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| 348 | else: |
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| 349 | output.dqx_data = dqx_data |
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| 350 | output.dqy_data = dqy_data |
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| 351 | |
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| 352 | # Units of axes |
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| 353 | if data_conv_q is not None: |
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| 354 | output.xaxis("\\rm{Q_{x}}", output.Q_unit) |
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| 355 | output.yaxis("\\rm{Q_{y}}", output.Q_unit) |
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| 356 | else: |
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| 357 | output.xaxis("\\rm{Q_{x}}", 'A^{-1}') |
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| 358 | output.yaxis("\\rm{Q_{y}}", 'A^{-1}') |
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| 359 | if data_conv_i is not None: |
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| 360 | output.zaxis("\\rm{Intensity}", output.I_unit) |
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| 361 | else: |
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| 362 | output.zaxis("\\rm{Intensity}", "cm^{-1}") |
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| 363 | |
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| 364 | # Store loading process information |
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| 365 | output.meta_data['loader'] = self.type_name |
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| 366 | |
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| 367 | return output |
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