[8bd8ea4] | 1 | |
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| 2 | |
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[0997158f] | 3 | ############################################################################ |
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| 4 | #This software was developed by the University of Tennessee as part of the |
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| 5 | #Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
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| 6 | #project funded by the US National Science Foundation. |
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| 7 | #If you use DANSE applications to do scientific research that leads to |
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| 8 | #publication, we ask that you acknowledge the use of the software with the |
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| 9 | #following sentence: |
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| 10 | #This work benefited from DANSE software developed under NSF award DMR-0520547. |
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| 11 | #copyright 2008, University of Tennessee |
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| 12 | ############################################################################# |
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| 13 | |
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[8bd8ea4] | 14 | |
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| 15 | import numpy |
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| 16 | import os |
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| 17 | from DataLoader.data_info import Data1D |
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| 18 | |
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[daa56d0] | 19 | # Check whether we have a converter available |
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[99d1af6] | 20 | has_converter = True |
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| 21 | try: |
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| 22 | from data_util.nxsunit import Converter |
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| 23 | except: |
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| 24 | has_converter = False |
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| 25 | |
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[8bd8ea4] | 26 | class Reader: |
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| 27 | """ |
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[0997158f] | 28 | Class to load ascii files (2, 3 or 4 columns). |
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[8bd8ea4] | 29 | """ |
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[8780e9a] | 30 | ## File type |
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[28caa03] | 31 | type_name = "ASCII" |
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| 32 | |
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| 33 | ## Wildcards |
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[8780e9a] | 34 | type = ["ASCII files (*.txt)|*.txt", |
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[470bf7e] | 35 | "ASCII files (*.dat)|*.dat", |
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| 36 | "ASCII files (*.abs)|*.abs"] |
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[8bd8ea4] | 37 | ## List of allowed extensions |
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[470bf7e] | 38 | ext=['.txt', '.TXT', '.dat', '.DAT', '.abs', '.ABS'] |
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[8bd8ea4] | 39 | |
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[e082e2c] | 40 | ## Flag to bypass extension check |
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| 41 | allow_all = True |
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| 42 | |
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[8bd8ea4] | 43 | def read(self, path): |
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| 44 | """ |
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[0997158f] | 45 | Load data file |
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| 46 | |
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| 47 | :param path: file path |
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| 48 | |
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| 49 | :return: Data1D object, or None |
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| 50 | |
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| 51 | :raise RuntimeError: when the file can't be opened |
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| 52 | :raise ValueError: when the length of the data vectors are inconsistent |
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[8bd8ea4] | 53 | """ |
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| 54 | if os.path.isfile(path): |
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| 55 | basename = os.path.basename(path) |
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| 56 | root, extension = os.path.splitext(basename) |
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[e082e2c] | 57 | if self.allow_all or extension.lower() in self.ext: |
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[8bd8ea4] | 58 | try: |
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| 59 | input_f = open(path,'r') |
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| 60 | except : |
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| 61 | raise RuntimeError, "ascii_reader: cannot open %s" % path |
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| 62 | buff = input_f.read() |
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| 63 | lines = buff.split('\n') |
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[470bf7e] | 64 | |
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[892f246] | 65 | #Jae could not find python universal line spliter: keep the below for now |
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[730c9eb] | 66 | # some ascii data has \r line separator, try it when the data is on only one long line |
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[470bf7e] | 67 | if len(lines) < 2 : |
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| 68 | lines = buff.split('\r') |
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| 69 | |
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[8bd8ea4] | 70 | x = numpy.zeros(0) |
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| 71 | y = numpy.zeros(0) |
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| 72 | dy = numpy.zeros(0) |
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[de1da34] | 73 | dx = numpy.zeros(0) |
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| 74 | |
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| 75 | #temp. space to sort data |
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| 76 | tx = numpy.zeros(0) |
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| 77 | ty = numpy.zeros(0) |
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| 78 | tdy = numpy.zeros(0) |
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| 79 | tdx = numpy.zeros(0) |
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| 80 | |
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| 81 | output = Data1D(x, y, dy=dy, dx=dx) |
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[8bd8ea4] | 82 | self.filename = output.filename = basename |
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[99d1af6] | 83 | |
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| 84 | data_conv_q = None |
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| 85 | data_conv_i = None |
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| 86 | |
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[ca10d8e] | 87 | if has_converter == True and output.x_unit != '1/A': |
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| 88 | data_conv_q = Converter('1/A') |
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[99d1af6] | 89 | # Test it |
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| 90 | data_conv_q(1.0, output.x_unit) |
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| 91 | |
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[ca10d8e] | 92 | if has_converter == True and output.y_unit != '1/cm': |
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| 93 | data_conv_i = Converter('1/cm') |
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[99d1af6] | 94 | # Test it |
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| 95 | data_conv_i(1.0, output.y_unit) |
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| 96 | |
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[8bd8ea4] | 97 | |
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| 98 | # The first good line of data will define whether |
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| 99 | # we have 2-column or 3-column ascii |
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[de1da34] | 100 | has_error_dx = None |
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| 101 | has_error_dy = None |
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[8bd8ea4] | 102 | |
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[892f246] | 103 | #Initialize counters for data lines and header lines. |
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[0e5e586] | 104 | is_data = False #Has more than 5 lines |
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| 105 | mum_data_lines = 5 # More than "5" lines of data is considered as actual data unless that is the only data |
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[892f246] | 106 | |
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[d508be9] | 107 | i=-1 # To count # of current data candidate lines |
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| 108 | i1=-1 # To count total # of previous data candidate lines |
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| 109 | j=-1 # To count # of header lines |
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| 110 | j1=-1 # Helps to count # of header lines |
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| 111 | lentoks = 2 # minimum required number of columns of data; ( <= 4). |
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[892f246] | 112 | |
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[8bd8ea4] | 113 | for line in lines: |
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| 114 | toks = line.split() |
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[892f246] | 115 | |
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[8bd8ea4] | 116 | try: |
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[5f2d3c78] | 117 | #Make sure that all columns are numbers. |
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| 118 | for colnum in range(len(toks)): |
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| 119 | float(toks[colnum]) |
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| 120 | |
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[8bd8ea4] | 121 | _x = float(toks[0]) |
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| 122 | _y = float(toks[1]) |
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| 123 | |
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[892f246] | 124 | #Reset the header line counters |
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| 125 | if j == j1: |
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| 126 | j = 0 |
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| 127 | j1 = 0 |
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| 128 | |
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| 129 | if i > 1: |
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| 130 | is_data = True |
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[d508be9] | 131 | |
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[99d1af6] | 132 | if data_conv_q is not None: |
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| 133 | _x = data_conv_q(_x, units=output.x_unit) |
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| 134 | |
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| 135 | if data_conv_i is not None: |
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| 136 | _y = data_conv_i(_y, units=output.y_unit) |
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| 137 | |
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[8bd8ea4] | 138 | # If we have an extra token, check |
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| 139 | # whether it can be interpreted as a |
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| 140 | # third column. |
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| 141 | _dy = None |
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| 142 | if len(toks)>2: |
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| 143 | try: |
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| 144 | _dy = float(toks[2]) |
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[99d1af6] | 145 | |
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| 146 | if data_conv_i is not None: |
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| 147 | _dy = data_conv_i(_dy, units=output.y_unit) |
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| 148 | |
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[8bd8ea4] | 149 | except: |
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| 150 | # The third column is not a float, skip it. |
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| 151 | pass |
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| 152 | |
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| 153 | # If we haven't set the 3rd column |
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| 154 | # flag, set it now. |
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[de1da34] | 155 | if has_error_dy == None: |
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| 156 | has_error_dy = False if _dy == None else True |
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| 157 | |
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| 158 | #Check for dx |
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| 159 | _dx = None |
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| 160 | if len(toks)>3: |
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| 161 | try: |
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| 162 | _dx = float(toks[3]) |
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| 163 | |
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| 164 | if data_conv_i is not None: |
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| 165 | _dx = data_conv_i(_dx, units=output.x_unit) |
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| 166 | |
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| 167 | except: |
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| 168 | # The 4th column is not a float, skip it. |
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| 169 | pass |
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| 170 | |
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| 171 | # If we haven't set the 3rd column |
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| 172 | # flag, set it now. |
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| 173 | if has_error_dx == None: |
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| 174 | has_error_dx = False if _dx == None else True |
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[892f246] | 175 | |
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[d508be9] | 176 | #After talked with PB, we decided to take care of only 4 columns of data for now. |
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| 177 | #number of columns in the current line |
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[0e5e586] | 178 | #To remember the # of columns in the current line of data |
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| 179 | new_lentoks = len(toks) |
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[d508be9] | 180 | |
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[3aed0eb] | 181 | #If the previous columns not equal to the current, mark the previous as non-data and reset the dependents. |
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[272b107] | 182 | if lentoks != new_lentoks : |
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| 183 | if is_data == True: |
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| 184 | break |
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| 185 | else: |
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[d508be9] | 186 | i = -1 |
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| 187 | i1 = 0 |
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| 188 | j = -1 |
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| 189 | j1 = -1 |
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| 190 | |
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[272b107] | 191 | |
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[892f246] | 192 | #Delete the previously stored lines of data candidates if is not data. |
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| 193 | if i < 0 and -1< i1 < mum_data_lines and is_data == False: |
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| 194 | try: |
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| 195 | x= numpy.zeros(0) |
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| 196 | y= numpy.zeros(0) |
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| 197 | |
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| 198 | except: |
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| 199 | pass |
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| 200 | |
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[8bd8ea4] | 201 | x = numpy.append(x, _x) |
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| 202 | y = numpy.append(y, _y) |
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[892f246] | 203 | |
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[de1da34] | 204 | if has_error_dy == True: |
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[892f246] | 205 | #Delete the previously stored lines of data candidates if is not data. |
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| 206 | if i < 0 and -1< i1 < mum_data_lines and is_data== False: |
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| 207 | try: |
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| 208 | dy = numpy.zeros(0) |
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| 209 | except: |
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| 210 | pass |
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[8bd8ea4] | 211 | dy = numpy.append(dy, _dy) |
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[892f246] | 212 | |
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[de1da34] | 213 | if has_error_dx == True: |
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[892f246] | 214 | #Delete the previously stored lines of data candidates if is not data. |
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| 215 | if i < 0 and -1< i1 < mum_data_lines and is_data== False: |
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| 216 | try: |
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| 217 | dx = numpy.zeros(0) |
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| 218 | except: |
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| 219 | pass |
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[de1da34] | 220 | dx = numpy.append(dx, _dx) |
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| 221 | |
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| 222 | #Same for temp. |
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[892f246] | 223 | #Delete the previously stored lines of data candidates if is not data. |
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| 224 | if i < 0 and -1< i1 < mum_data_lines and is_data== False: |
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| 225 | try: |
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| 226 | tx = numpy.zeros(0) |
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| 227 | ty = numpy.zeros(0) |
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| 228 | except: |
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| 229 | pass |
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| 230 | |
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[de1da34] | 231 | tx = numpy.append(tx, _x) |
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| 232 | ty = numpy.append(ty, _y) |
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[892f246] | 233 | |
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[de1da34] | 234 | if has_error_dy == True: |
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[892f246] | 235 | #Delete the previously stored lines of data candidates if is not data. |
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| 236 | if i < 0 and -1<i1 < mum_data_lines and is_data== False: |
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| 237 | try: |
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| 238 | tdy = numpy.zeros(0) |
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| 239 | except: |
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| 240 | pass |
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[de1da34] | 241 | tdy = numpy.append(tdy, _dy) |
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| 242 | if has_error_dx == True: |
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[892f246] | 243 | #Delete the previously stored lines of data candidates if is not data. |
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| 244 | if i < 0 and -1< i1 < mum_data_lines and is_data== False: |
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| 245 | try: |
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| 246 | tdx = numpy.zeros(0) |
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| 247 | except: |
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| 248 | pass |
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[de1da34] | 249 | tdx = numpy.append(tdx, _dx) |
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[d508be9] | 250 | |
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| 251 | #reset i1 and flag lentoks for the next |
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| 252 | if lentoks < new_lentoks : |
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| 253 | if is_data == False: |
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| 254 | i1 = -1 |
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[0e5e586] | 255 | #To remember the # of columns on the current line for the next line of data |
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| 256 | lentoks = len(toks) |
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[8bd8ea4] | 257 | |
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[892f246] | 258 | #Reset # of header lines and counts # of data candidate lines |
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| 259 | if j == 0 and j1 ==0: |
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| 260 | i1 = i + 1 |
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| 261 | i+=1 |
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| 262 | |
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[8bd8ea4] | 263 | except: |
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[892f246] | 264 | |
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| 265 | # It is data and meet non - number, then stop reading |
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| 266 | if is_data == True: |
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| 267 | break |
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[d508be9] | 268 | lentoks = 2 |
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[892f246] | 269 | #Counting # of header lines |
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| 270 | j+=1 |
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| 271 | if j == j1+1: |
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| 272 | j1 = j |
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| 273 | else: |
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| 274 | j = -1 |
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| 275 | #Reset # of lines of data candidates |
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| 276 | i = -1 |
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| 277 | |
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[8bd8ea4] | 278 | # Couldn't parse this line, skip it |
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| 279 | pass |
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[892f246] | 280 | |
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| 281 | |
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[c7c5ef8] | 282 | input_f.close() |
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[8bd8ea4] | 283 | # Sanity check |
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[de1da34] | 284 | if has_error_dy == True and not len(y) == len(dy): |
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| 285 | raise RuntimeError, "ascii_reader: y and dy have different length" |
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| 286 | if has_error_dx == True and not len(x) == len(dx): |
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[daa56d0] | 287 | raise RuntimeError, "ascii_reader: y and dy have different length" |
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[8bd8ea4] | 288 | |
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| 289 | # If the data length is zero, consider this as |
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| 290 | # though we were not able to read the file. |
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| 291 | if len(x)==0: |
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[daa56d0] | 292 | raise RuntimeError, "ascii_reader: could not load file" |
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[de1da34] | 293 | |
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[470bf7e] | 294 | #Let's re-order the data to make cal. curve look better some cases |
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[de1da34] | 295 | ind = numpy.lexsort((ty,tx)) |
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| 296 | for i in ind: |
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| 297 | x[i] = tx[ind[i]] |
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| 298 | y[i] = ty[ind[i]] |
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| 299 | if has_error_dy == True: |
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| 300 | dy[i] = tdy[ind[i]] |
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| 301 | if has_error_dx == True: |
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| 302 | dx[i] = tdx[ind[i]] |
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[892f246] | 303 | |
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[d508be9] | 304 | |
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[892f246] | 305 | #Data |
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[8bd8ea4] | 306 | output.x = x |
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| 307 | output.y = y |
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[fca90f82] | 308 | output.dy = dy if has_error_dy == True else numpy.zeros(len(y)) |
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| 309 | output.dx = dx if has_error_dx == True else numpy.zeros(len(x)) |
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| 310 | |
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[99d1af6] | 311 | if data_conv_q is not None: |
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| 312 | output.xaxis("\\rm{Q}", output.x_unit) |
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| 313 | else: |
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| 314 | output.xaxis("\\rm{Q}", 'A^{-1}') |
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| 315 | if data_conv_i is not None: |
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[0e2aa40] | 316 | output.yaxis("\\rm{Intensity}", output.y_unit) |
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[99d1af6] | 317 | else: |
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[0e2aa40] | 318 | output.yaxis("\\rm{Intensity}","cm^{-1}") |
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[fe78c7b] | 319 | |
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| 320 | # Store loading process information |
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| 321 | output.meta_data['loader'] = self.type_name |
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| 322 | |
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[8bd8ea4] | 323 | return output |
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[892f246] | 324 | |
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[8bd8ea4] | 325 | else: |
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| 326 | raise RuntimeError, "%s is not a file" % path |
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| 327 | return None |
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| 328 | |
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| 329 | if __name__ == "__main__": |
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| 330 | reader = Reader() |
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| 331 | #print reader.read("../test/test_3_columns.txt") |
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| 332 | print reader.read("../test/empty.txt") |
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| 333 | |
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| 334 | |
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| 335 | |
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