[7d6351e] | 1 | """ |
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| 2 | ASCII reader |
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| 3 | """ |
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[0997158f] | 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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[7d6351e] | 8 | #If you use DANSE applications to do scientific research that leads to |
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| 9 | #publication, we ask that you acknowledge the use of the software with the |
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[0997158f] | 10 | #following sentence: |
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[7d6351e] | 11 | #This work benefited from DANSE software developed under NSF award DMR-0520547. |
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[0997158f] | 12 | #copyright 2008, University of Tennessee |
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| 13 | ############################################################################# |
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| 14 | |
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[8bd8ea4] | 15 | |
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| 16 | import numpy |
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| 17 | import os |
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[ad8034f] | 18 | from sans.dataloader.data_info import Data1D |
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[8bd8ea4] | 19 | |
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[daa56d0] | 20 | # Check whether we have a converter available |
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[99d1af6] | 21 | has_converter = True |
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| 22 | try: |
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| 23 | from data_util.nxsunit import Converter |
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| 24 | except: |
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| 25 | has_converter = False |
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[da96629] | 26 | _ZERO = 1e-16 |
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[99d1af6] | 27 | |
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[7d6351e] | 28 | |
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[8bd8ea4] | 29 | class Reader: |
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| 30 | """ |
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[0997158f] | 31 | Class to load ascii files (2, 3 or 4 columns). |
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[8bd8ea4] | 32 | """ |
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[8780e9a] | 33 | ## File type |
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[28caa03] | 34 | type_name = "ASCII" |
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| 35 | |
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| 36 | ## Wildcards |
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[8780e9a] | 37 | type = ["ASCII files (*.txt)|*.txt", |
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[470bf7e] | 38 | "ASCII files (*.dat)|*.dat", |
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[ef9d209] | 39 | "ASCII files (*.abs)|*.abs", |
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| 40 | "CSV files (*.csv)|*.csv"] |
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[8bd8ea4] | 41 | ## List of allowed extensions |
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[7d6351e] | 42 | ext = ['.txt', '.TXT', '.dat', '.DAT', '.abs', '.ABS', 'csv', 'CSV'] |
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[8bd8ea4] | 43 | |
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[e082e2c] | 44 | ## Flag to bypass extension check |
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| 45 | allow_all = True |
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| 46 | |
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[8bd8ea4] | 47 | def read(self, path): |
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[7d6351e] | 48 | """ |
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[0997158f] | 49 | Load data file |
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| 50 | |
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| 51 | :param path: file path |
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| 52 | |
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| 53 | :return: Data1D object, or None |
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| 54 | |
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| 55 | :raise RuntimeError: when the file can't be opened |
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| 56 | :raise ValueError: when the length of the data vectors are inconsistent |
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[8bd8ea4] | 57 | """ |
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| 58 | if os.path.isfile(path): |
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[7d6351e] | 59 | basename = os.path.basename(path) |
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[a7a5886] | 60 | _, extension = os.path.splitext(basename) |
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[e082e2c] | 61 | if self.allow_all or extension.lower() in self.ext: |
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[8bd8ea4] | 62 | try: |
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[9cd0baa] | 63 | # Read in binary mode since GRASP frequently has no-ascii |
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| 64 | # characters that brakes the open operation |
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[7d6351e] | 65 | input_f = open(path,'rb') |
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| 66 | except: |
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[8bd8ea4] | 67 | raise RuntimeError, "ascii_reader: cannot open %s" % path |
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| 68 | buff = input_f.read() |
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| 69 | lines = buff.split('\n') |
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[470bf7e] | 70 | |
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[7d6351e] | 71 | #Jae could not find python universal line spliter: |
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[a7a5886] | 72 | #keep the below for now |
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| 73 | # some ascii data has \r line separator, |
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| 74 | # try it when the data is on only one long line |
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[7d6351e] | 75 | if len(lines) < 2 : |
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[470bf7e] | 76 | lines = buff.split('\r') |
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| 77 | |
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[8bd8ea4] | 78 | x = numpy.zeros(0) |
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| 79 | y = numpy.zeros(0) |
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| 80 | dy = numpy.zeros(0) |
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[de1da34] | 81 | dx = numpy.zeros(0) |
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| 82 | |
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| 83 | #temp. space to sort data |
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| 84 | tx = numpy.zeros(0) |
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| 85 | ty = numpy.zeros(0) |
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| 86 | tdy = numpy.zeros(0) |
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| 87 | tdx = numpy.zeros(0) |
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| 88 | |
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| 89 | output = Data1D(x, y, dy=dy, dx=dx) |
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[8bd8ea4] | 90 | self.filename = output.filename = basename |
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[99d1af6] | 91 | |
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| 92 | data_conv_q = None |
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| 93 | data_conv_i = None |
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| 94 | |
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[ca10d8e] | 95 | if has_converter == True and output.x_unit != '1/A': |
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| 96 | data_conv_q = Converter('1/A') |
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[99d1af6] | 97 | # Test it |
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| 98 | data_conv_q(1.0, output.x_unit) |
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| 99 | |
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[ca10d8e] | 100 | if has_converter == True and output.y_unit != '1/cm': |
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| 101 | data_conv_i = Converter('1/cm') |
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[99d1af6] | 102 | # Test it |
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| 103 | data_conv_i(1.0, output.y_unit) |
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| 104 | |
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[8bd8ea4] | 105 | # The first good line of data will define whether |
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| 106 | # we have 2-column or 3-column ascii |
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[de1da34] | 107 | has_error_dx = None |
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| 108 | has_error_dy = None |
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[8bd8ea4] | 109 | |
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[892f246] | 110 | #Initialize counters for data lines and header lines. |
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[7d6351e] | 111 | is_data = False # Has more than 5 lines |
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[a7a5886] | 112 | # More than "5" lines of data is considered as actual |
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| 113 | # data unless that is the only data |
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[7d6351e] | 114 | mum_data_lines = 5 |
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[a7a5886] | 115 | # To count # of current data candidate lines |
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[7d6351e] | 116 | i = -1 |
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| 117 | # To count total # of previous data candidate lines |
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| 118 | i1 = -1 |
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| 119 | # To count # of header lines |
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[a7a5886] | 120 | j = -1 |
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[7d6351e] | 121 | # Helps to count # of header lines |
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[a7a5886] | 122 | j1 = -1 |
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[7d6351e] | 123 | #minimum required number of columns of data; ( <= 4). |
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| 124 | lentoks = 2 |
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[8bd8ea4] | 125 | for line in lines: |
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[ef9d209] | 126 | toks = line.split(',') |
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| 127 | if len(toks) < 2: |
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| 128 | toks = line.split() |
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[8bd8ea4] | 129 | try: |
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[5f2d3c78] | 130 | #Make sure that all columns are numbers. |
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| 131 | for colnum in range(len(toks)): |
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| 132 | float(toks[colnum]) |
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| 133 | |
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[8bd8ea4] | 134 | _x = float(toks[0]) |
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| 135 | _y = float(toks[1]) |
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| 136 | |
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[892f246] | 137 | #Reset the header line counters |
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| 138 | if j == j1: |
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| 139 | j = 0 |
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| 140 | j1 = 0 |
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| 141 | |
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| 142 | if i > 1: |
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| 143 | is_data = True |
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[d508be9] | 144 | |
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[99d1af6] | 145 | if data_conv_q is not None: |
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| 146 | _x = data_conv_q(_x, units=output.x_unit) |
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| 147 | |
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| 148 | if data_conv_i is not None: |
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[7d6351e] | 149 | _y = data_conv_i(_y, units=output.y_unit) |
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[99d1af6] | 150 | |
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[8bd8ea4] | 151 | # If we have an extra token, check |
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| 152 | # whether it can be interpreted as a |
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| 153 | # third column. |
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| 154 | _dy = None |
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[a7a5886] | 155 | if len(toks) > 2: |
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[8bd8ea4] | 156 | try: |
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| 157 | _dy = float(toks[2]) |
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[99d1af6] | 158 | |
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| 159 | if data_conv_i is not None: |
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| 160 | _dy = data_conv_i(_dy, units=output.y_unit) |
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| 161 | |
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[8bd8ea4] | 162 | except: |
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| 163 | # The third column is not a float, skip it. |
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| 164 | pass |
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| 165 | |
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| 166 | # If we haven't set the 3rd column |
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| 167 | # flag, set it now. |
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[de1da34] | 168 | if has_error_dy == None: |
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| 169 | has_error_dy = False if _dy == None else True |
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| 170 | |
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| 171 | #Check for dx |
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| 172 | _dx = None |
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[a7a5886] | 173 | if len(toks) > 3: |
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[de1da34] | 174 | try: |
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| 175 | _dx = float(toks[3]) |
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| 176 | |
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| 177 | if data_conv_i is not None: |
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| 178 | _dx = data_conv_i(_dx, units=output.x_unit) |
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| 179 | |
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| 180 | except: |
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| 181 | # The 4th column is not a float, skip it. |
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| 182 | pass |
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| 183 | |
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| 184 | # If we haven't set the 3rd column |
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| 185 | # flag, set it now. |
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| 186 | if has_error_dx == None: |
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| 187 | has_error_dx = False if _dx == None else True |
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[892f246] | 188 | |
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[7d6351e] | 189 | #After talked with PB, we decided to take care of only |
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[a7a5886] | 190 | # 4 columns of data for now. |
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[d508be9] | 191 | #number of columns in the current line |
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[7d6351e] | 192 | #To remember the # of columns in the current |
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[a7a5886] | 193 | #line of data |
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[0e5e586] | 194 | new_lentoks = len(toks) |
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[d508be9] | 195 | |
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[7d6351e] | 196 | #If the previous columns not equal to the current, |
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| 197 | #mark the previous as non-data and reset the dependents. |
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| 198 | if lentoks != new_lentoks: |
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[272b107] | 199 | if is_data == True: |
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| 200 | break |
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| 201 | else: |
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[d508be9] | 202 | i = -1 |
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| 203 | i1 = 0 |
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| 204 | j = -1 |
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| 205 | j1 = -1 |
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| 206 | |
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[a7a5886] | 207 | #Delete the previously stored lines of data candidates |
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| 208 | # if is not data. |
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| 209 | if i < 0 and -1 < i1 < mum_data_lines and \ |
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| 210 | is_data == False: |
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[892f246] | 211 | try: |
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[a7a5886] | 212 | x = numpy.zeros(0) |
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| 213 | y = numpy.zeros(0) |
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[892f246] | 214 | except: |
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| 215 | pass |
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| 216 | |
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[7d6351e] | 217 | x = numpy.append(x, _x) |
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| 218 | y = numpy.append(y, _y) |
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[892f246] | 219 | |
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[de1da34] | 220 | if has_error_dy == True: |
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[a7a5886] | 221 | #Delete the previously stored lines of |
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| 222 | # data candidates if is not data. |
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| 223 | if i < 0 and -1 < i1 < mum_data_lines and \ |
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| 224 | is_data == False: |
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[892f246] | 225 | try: |
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[7d6351e] | 226 | dy = numpy.zeros(0) |
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[892f246] | 227 | except: |
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[7d6351e] | 228 | pass |
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[8bd8ea4] | 229 | dy = numpy.append(dy, _dy) |
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[892f246] | 230 | |
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[de1da34] | 231 | if has_error_dx == True: |
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[a7a5886] | 232 | #Delete the previously stored lines of |
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| 233 | # data candidates if is not data. |
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| 234 | if i < 0 and -1 < i1 < mum_data_lines and \ |
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| 235 | is_data == False: |
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[892f246] | 236 | try: |
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[7d6351e] | 237 | dx = numpy.zeros(0) |
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[892f246] | 238 | except: |
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[7d6351e] | 239 | pass |
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[de1da34] | 240 | dx = numpy.append(dx, _dx) |
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| 241 | |
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| 242 | #Same for temp. |
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[a7a5886] | 243 | #Delete the previously stored lines of data candidates |
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| 244 | # if is not data. |
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| 245 | if i < 0 and -1 < i1 < mum_data_lines and\ |
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| 246 | is_data == False: |
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[892f246] | 247 | try: |
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| 248 | tx = numpy.zeros(0) |
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| 249 | ty = numpy.zeros(0) |
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| 250 | except: |
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[7d6351e] | 251 | pass |
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[892f246] | 252 | |
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[7d6351e] | 253 | tx = numpy.append(tx, _x) |
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| 254 | ty = numpy.append(ty, _y) |
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[892f246] | 255 | |
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[de1da34] | 256 | if has_error_dy == True: |
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[a7a5886] | 257 | #Delete the previously stored lines of |
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| 258 | # data candidates if is not data. |
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| 259 | if i < 0 and -1 < i1 < mum_data_lines and \ |
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| 260 | is_data == False: |
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[892f246] | 261 | try: |
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| 262 | tdy = numpy.zeros(0) |
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| 263 | except: |
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[7d6351e] | 264 | pass |
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[de1da34] | 265 | tdy = numpy.append(tdy, _dy) |
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| 266 | if has_error_dx == True: |
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[a7a5886] | 267 | #Delete the previously stored lines of |
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| 268 | # data candidates if is not data. |
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| 269 | if i < 0 and -1 < i1 < mum_data_lines and \ |
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| 270 | is_data == False: |
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[892f246] | 271 | try: |
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| 272 | tdx = numpy.zeros(0) |
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| 273 | except: |
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[7d6351e] | 274 | pass |
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[de1da34] | 275 | tdx = numpy.append(tdx, _dx) |
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[d508be9] | 276 | |
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| 277 | #reset i1 and flag lentoks for the next |
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[a7a5886] | 278 | if lentoks < new_lentoks: |
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[d508be9] | 279 | if is_data == False: |
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[7d6351e] | 280 | i1 = -1 |
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[a7a5886] | 281 | #To remember the # of columns on the current line |
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| 282 | # for the next line of data |
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[0e5e586] | 283 | lentoks = len(toks) |
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[8bd8ea4] | 284 | |
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[7d6351e] | 285 | #Reset # of header lines and counts # |
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| 286 | # of data candidate lines |
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[a7a5886] | 287 | if j == 0 and j1 == 0: |
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[7d6351e] | 288 | i1 = i + 1 |
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[a7a5886] | 289 | i += 1 |
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[8bd8ea4] | 290 | except: |
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[892f246] | 291 | |
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| 292 | # It is data and meet non - number, then stop reading |
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| 293 | if is_data == True: |
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[7d6351e] | 294 | break |
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[d508be9] | 295 | lentoks = 2 |
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[7d6351e] | 296 | #Counting # of header lines |
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[a7a5886] | 297 | j += 1 |
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| 298 | if j == j1 + 1: |
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[7d6351e] | 299 | j1 = j |
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| 300 | else: |
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[892f246] | 301 | j = -1 |
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| 302 | #Reset # of lines of data candidates |
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| 303 | i = -1 |
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| 304 | |
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[7d6351e] | 305 | # Couldn't parse this line, skip it |
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[8bd8ea4] | 306 | pass |
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[892f246] | 307 | |
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[7d6351e] | 308 | input_f.close() |
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[8bd8ea4] | 309 | # Sanity check |
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[de1da34] | 310 | if has_error_dy == True and not len(y) == len(dy): |
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[a7a5886] | 311 | msg = "ascii_reader: y and dy have different length" |
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| 312 | raise RuntimeError, msg |
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[de1da34] | 313 | if has_error_dx == True and not len(x) == len(dx): |
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[a7a5886] | 314 | msg = "ascii_reader: y and dy have different length" |
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| 315 | raise RuntimeError, msg |
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[8bd8ea4] | 316 | # If the data length is zero, consider this as |
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| 317 | # though we were not able to read the file. |
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[a7a5886] | 318 | if len(x) == 0: |
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[daa56d0] | 319 | raise RuntimeError, "ascii_reader: could not load file" |
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[de1da34] | 320 | |
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[a7a5886] | 321 | #Let's re-order the data to make cal. |
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| 322 | # curve look better some cases |
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| 323 | ind = numpy.lexsort((ty, tx)) |
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[de1da34] | 324 | for i in ind: |
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| 325 | x[i] = tx[ind[i]] |
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| 326 | y[i] = ty[ind[i]] |
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| 327 | if has_error_dy == True: |
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| 328 | dy[i] = tdy[ind[i]] |
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| 329 | if has_error_dx == True: |
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| 330 | dx[i] = tdx[ind[i]] |
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[7d6351e] | 331 | # Zeros in dx, dy |
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[da96629] | 332 | if has_error_dx: |
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[7d6351e] | 333 | dx[dx == 0] = _ZERO |
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[da96629] | 334 | if has_error_dy: |
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[7d6351e] | 335 | dy[dy == 0] = _ZERO |
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| 336 | #Data |
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| 337 | output.x = x[x != 0] |
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| 338 | output.y = y[x != 0] |
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| 339 | output.dy = dy[x != 0] if has_error_dy == True\ |
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| 340 | else numpy.zeros(len(output.y)) |
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| 341 | output.dx = dx[x != 0] if has_error_dx == True\ |
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| 342 | else numpy.zeros(len(output.x)) |
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[fca90f82] | 343 | |
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[99d1af6] | 344 | if data_conv_q is not None: |
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| 345 | output.xaxis("\\rm{Q}", output.x_unit) |
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| 346 | else: |
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| 347 | output.xaxis("\\rm{Q}", 'A^{-1}') |
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| 348 | if data_conv_i is not None: |
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[0e2aa40] | 349 | output.yaxis("\\rm{Intensity}", output.y_unit) |
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[99d1af6] | 350 | else: |
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[7d6351e] | 351 | output.yaxis("\\rm{Intensity}", "cm^{-1}") |
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[fe78c7b] | 352 | |
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| 353 | # Store loading process information |
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[7d6351e] | 354 | output.meta_data['loader'] = self.type_name |
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[fe78c7b] | 355 | |
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[8bd8ea4] | 356 | return output |
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[892f246] | 357 | |
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[8bd8ea4] | 358 | else: |
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| 359 | raise RuntimeError, "%s is not a file" % path |
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| 360 | return None |
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