[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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[ad8034f] | 17 | from sans.dataloader.data_info import Data1D |
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[8bd8ea4] | 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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[da96629] | 25 | _ZERO = 1e-16 |
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[99d1af6] | 26 | |
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[8bd8ea4] | 27 | class Reader: |
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| 28 | """ |
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[0997158f] | 29 | Class to load ascii files (2, 3 or 4 columns). |
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[8bd8ea4] | 30 | """ |
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[8780e9a] | 31 | ## File type |
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[28caa03] | 32 | type_name = "ASCII" |
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| 33 | |
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| 34 | ## Wildcards |
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[8780e9a] | 35 | type = ["ASCII files (*.txt)|*.txt", |
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[470bf7e] | 36 | "ASCII files (*.dat)|*.dat", |
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[ef9d209] | 37 | "ASCII files (*.abs)|*.abs", |
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| 38 | "CSV files (*.csv)|*.csv"] |
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[8bd8ea4] | 39 | ## List of allowed extensions |
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[ef9d209] | 40 | ext = ['.txt', '.TXT', '.dat', '.DAT', '.abs', '.ABS', 'csv', 'CSV'] |
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[8bd8ea4] | 41 | |
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[e082e2c] | 42 | ## Flag to bypass extension check |
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| 43 | allow_all = True |
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| 44 | |
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[8bd8ea4] | 45 | def read(self, path): |
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| 46 | """ |
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[0997158f] | 47 | Load data file |
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| 48 | |
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| 49 | :param path: file path |
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| 50 | |
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| 51 | :return: Data1D object, or None |
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| 52 | |
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| 53 | :raise RuntimeError: when the file can't be opened |
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| 54 | :raise ValueError: when the length of the data vectors are inconsistent |
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[8bd8ea4] | 55 | """ |
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| 56 | if os.path.isfile(path): |
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| 57 | basename = os.path.basename(path) |
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[a7a5886] | 58 | _, extension = os.path.splitext(basename) |
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[e082e2c] | 59 | if self.allow_all or extension.lower() in self.ext: |
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[8bd8ea4] | 60 | try: |
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| 61 | input_f = open(path,'r') |
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| 62 | except : |
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| 63 | raise RuntimeError, "ascii_reader: cannot open %s" % path |
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| 64 | buff = input_f.read() |
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| 65 | lines = buff.split('\n') |
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[470bf7e] | 66 | |
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[a7a5886] | 67 | #Jae could not find python universal line spliter: |
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| 68 | #keep the below for now |
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| 69 | # some ascii data has \r line separator, |
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| 70 | # try it when the data is on only one long line |
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[470bf7e] | 71 | if len(lines) < 2 : |
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| 72 | lines = buff.split('\r') |
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| 73 | |
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[8bd8ea4] | 74 | x = numpy.zeros(0) |
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| 75 | y = numpy.zeros(0) |
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| 76 | dy = numpy.zeros(0) |
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[de1da34] | 77 | dx = numpy.zeros(0) |
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| 78 | |
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| 79 | #temp. space to sort data |
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| 80 | tx = numpy.zeros(0) |
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| 81 | ty = numpy.zeros(0) |
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| 82 | tdy = numpy.zeros(0) |
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| 83 | tdx = numpy.zeros(0) |
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| 84 | |
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| 85 | output = Data1D(x, y, dy=dy, dx=dx) |
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[8bd8ea4] | 86 | self.filename = output.filename = basename |
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[99d1af6] | 87 | |
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| 88 | data_conv_q = None |
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| 89 | data_conv_i = None |
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| 90 | |
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[ca10d8e] | 91 | if has_converter == True and output.x_unit != '1/A': |
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| 92 | data_conv_q = Converter('1/A') |
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[99d1af6] | 93 | # Test it |
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| 94 | data_conv_q(1.0, output.x_unit) |
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| 95 | |
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[ca10d8e] | 96 | if has_converter == True and output.y_unit != '1/cm': |
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| 97 | data_conv_i = Converter('1/cm') |
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[99d1af6] | 98 | # Test it |
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| 99 | data_conv_i(1.0, output.y_unit) |
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| 100 | |
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[8bd8ea4] | 101 | |
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| 102 | # The first good line of data will define whether |
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| 103 | # we have 2-column or 3-column ascii |
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[de1da34] | 104 | has_error_dx = None |
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| 105 | has_error_dy = None |
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[8bd8ea4] | 106 | |
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[892f246] | 107 | #Initialize counters for data lines and header lines. |
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[0e5e586] | 108 | is_data = False #Has more than 5 lines |
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[a7a5886] | 109 | # More than "5" lines of data is considered as actual |
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| 110 | # data unless that is the only data |
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| 111 | mum_data_lines = 5 |
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| 112 | # To count # of current data candidate lines |
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| 113 | i = -1 |
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| 114 | # To count total # of previous data candidate lines |
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| 115 | i1 = -1 |
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| 116 | # To count # of header lines |
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| 117 | j = -1 |
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| 118 | # Helps to count # of header lines |
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| 119 | j1 = -1 |
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| 120 | #minimum required number of columns of data; ( <= 4). |
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| 121 | lentoks = 2 |
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[8bd8ea4] | 122 | for line in lines: |
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[ef9d209] | 123 | toks = line.split(',') |
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| 124 | if len(toks) < 2: |
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| 125 | toks = line.split() |
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[8bd8ea4] | 126 | try: |
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[5f2d3c78] | 127 | #Make sure that all columns are numbers. |
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| 128 | for colnum in range(len(toks)): |
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| 129 | float(toks[colnum]) |
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| 130 | |
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[8bd8ea4] | 131 | _x = float(toks[0]) |
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| 132 | _y = float(toks[1]) |
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| 133 | |
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[892f246] | 134 | #Reset the header line counters |
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| 135 | if j == j1: |
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| 136 | j = 0 |
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| 137 | j1 = 0 |
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| 138 | |
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| 139 | if i > 1: |
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| 140 | is_data = True |
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[d508be9] | 141 | |
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[99d1af6] | 142 | if data_conv_q is not None: |
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| 143 | _x = data_conv_q(_x, units=output.x_unit) |
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| 144 | |
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| 145 | if data_conv_i is not None: |
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| 146 | _y = data_conv_i(_y, units=output.y_unit) |
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| 147 | |
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[8bd8ea4] | 148 | # If we have an extra token, check |
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| 149 | # whether it can be interpreted as a |
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| 150 | # third column. |
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| 151 | _dy = None |
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[a7a5886] | 152 | if len(toks) > 2: |
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[8bd8ea4] | 153 | try: |
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| 154 | _dy = float(toks[2]) |
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[99d1af6] | 155 | |
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| 156 | if data_conv_i is not None: |
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| 157 | _dy = data_conv_i(_dy, units=output.y_unit) |
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| 158 | |
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[8bd8ea4] | 159 | except: |
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| 160 | # The third column is not a float, skip it. |
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| 161 | pass |
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| 162 | |
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| 163 | # If we haven't set the 3rd column |
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| 164 | # flag, set it now. |
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[de1da34] | 165 | if has_error_dy == None: |
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| 166 | has_error_dy = False if _dy == None else True |
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| 167 | |
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| 168 | #Check for dx |
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| 169 | _dx = None |
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[a7a5886] | 170 | if len(toks) > 3: |
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[de1da34] | 171 | try: |
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| 172 | _dx = float(toks[3]) |
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| 173 | |
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| 174 | if data_conv_i is not None: |
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| 175 | _dx = data_conv_i(_dx, units=output.x_unit) |
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| 176 | |
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| 177 | except: |
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| 178 | # The 4th column is not a float, skip it. |
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| 179 | pass |
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| 180 | |
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| 181 | # If we haven't set the 3rd column |
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| 182 | # flag, set it now. |
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| 183 | if has_error_dx == None: |
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| 184 | has_error_dx = False if _dx == None else True |
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[892f246] | 185 | |
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[a7a5886] | 186 | #After talked with PB, we decided to take care of only |
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| 187 | # 4 columns of data for now. |
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[d508be9] | 188 | #number of columns in the current line |
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[a7a5886] | 189 | #To remember the # of columns in the current |
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| 190 | #line of data |
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[0e5e586] | 191 | new_lentoks = len(toks) |
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[d508be9] | 192 | |
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[a7a5886] | 193 | #If the previous columns not equal to the current, |
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| 194 | #mark the previous as non-data and reset the dependents. |
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[272b107] | 195 | if lentoks != new_lentoks : |
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| 196 | if is_data == True: |
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| 197 | break |
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| 198 | else: |
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[d508be9] | 199 | i = -1 |
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| 200 | i1 = 0 |
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| 201 | j = -1 |
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| 202 | j1 = -1 |
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| 203 | |
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[272b107] | 204 | |
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[a7a5886] | 205 | #Delete the previously stored lines of data candidates |
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| 206 | # if is not data. |
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| 207 | if i < 0 and -1 < i1 < mum_data_lines and \ |
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| 208 | is_data == False: |
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[892f246] | 209 | try: |
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[a7a5886] | 210 | x = numpy.zeros(0) |
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| 211 | y = numpy.zeros(0) |
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[892f246] | 212 | except: |
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| 213 | pass |
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| 214 | |
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[8bd8ea4] | 215 | x = numpy.append(x, _x) |
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| 216 | y = numpy.append(y, _y) |
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[892f246] | 217 | |
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[de1da34] | 218 | if has_error_dy == True: |
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[a7a5886] | 219 | #Delete the previously stored lines of |
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| 220 | # data candidates if is not data. |
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| 221 | if i < 0 and -1 < i1 < mum_data_lines and \ |
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| 222 | is_data == False: |
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[892f246] | 223 | try: |
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| 224 | dy = numpy.zeros(0) |
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| 225 | except: |
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| 226 | pass |
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[8bd8ea4] | 227 | dy = numpy.append(dy, _dy) |
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[892f246] | 228 | |
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[de1da34] | 229 | if has_error_dx == True: |
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[a7a5886] | 230 | #Delete the previously stored lines of |
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| 231 | # data candidates if is not data. |
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| 232 | if i < 0 and -1 < i1 < mum_data_lines and \ |
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| 233 | is_data == False: |
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[892f246] | 234 | try: |
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| 235 | dx = numpy.zeros(0) |
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| 236 | except: |
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| 237 | pass |
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[de1da34] | 238 | dx = numpy.append(dx, _dx) |
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| 239 | |
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| 240 | #Same for temp. |
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[a7a5886] | 241 | #Delete the previously stored lines of data candidates |
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| 242 | # if is not data. |
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| 243 | if i < 0 and -1 < i1 < mum_data_lines and\ |
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| 244 | is_data == False: |
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[892f246] | 245 | try: |
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| 246 | tx = numpy.zeros(0) |
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| 247 | ty = numpy.zeros(0) |
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| 248 | except: |
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[a7a5886] | 249 | pass |
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[892f246] | 250 | |
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[de1da34] | 251 | tx = numpy.append(tx, _x) |
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| 252 | ty = numpy.append(ty, _y) |
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[892f246] | 253 | |
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[de1da34] | 254 | if has_error_dy == True: |
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[a7a5886] | 255 | #Delete the previously stored lines of |
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| 256 | # data candidates if is not data. |
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| 257 | if i < 0 and -1 < i1 < mum_data_lines and \ |
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| 258 | is_data == False: |
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[892f246] | 259 | try: |
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| 260 | tdy = numpy.zeros(0) |
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| 261 | except: |
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| 262 | pass |
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[de1da34] | 263 | tdy = numpy.append(tdy, _dy) |
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| 264 | if has_error_dx == True: |
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[a7a5886] | 265 | #Delete the previously stored lines of |
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| 266 | # data candidates if is not data. |
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| 267 | if i < 0 and -1 < i1 < mum_data_lines and \ |
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| 268 | is_data == False: |
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[892f246] | 269 | try: |
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| 270 | tdx = numpy.zeros(0) |
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| 271 | except: |
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[a7a5886] | 272 | pass |
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[de1da34] | 273 | tdx = numpy.append(tdx, _dx) |
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[d508be9] | 274 | |
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| 275 | #reset i1 and flag lentoks for the next |
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[a7a5886] | 276 | if lentoks < new_lentoks: |
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[d508be9] | 277 | if is_data == False: |
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| 278 | i1 = -1 |
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[a7a5886] | 279 | #To remember the # of columns on the current line |
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| 280 | # for the next line of data |
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[0e5e586] | 281 | lentoks = len(toks) |
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[8bd8ea4] | 282 | |
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[a7a5886] | 283 | #Reset # of header lines and counts # |
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| 284 | # of data candidate lines |
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| 285 | if j == 0 and j1 == 0: |
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[892f246] | 286 | i1 = i + 1 |
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[a7a5886] | 287 | i += 1 |
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[8bd8ea4] | 288 | except: |
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[892f246] | 289 | |
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| 290 | # It is data and meet non - number, then stop reading |
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| 291 | if is_data == True: |
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| 292 | break |
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[d508be9] | 293 | lentoks = 2 |
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[892f246] | 294 | #Counting # of header lines |
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[a7a5886] | 295 | j += 1 |
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| 296 | if j == j1 + 1: |
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[892f246] | 297 | j1 = j |
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| 298 | else: |
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| 299 | j = -1 |
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| 300 | #Reset # of lines of data candidates |
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| 301 | i = -1 |
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| 302 | |
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[8bd8ea4] | 303 | # Couldn't parse this line, skip it |
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| 304 | pass |
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[892f246] | 305 | |
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[c7c5ef8] | 306 | input_f.close() |
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[8bd8ea4] | 307 | # Sanity check |
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[de1da34] | 308 | if has_error_dy == True and not len(y) == len(dy): |
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[a7a5886] | 309 | msg = "ascii_reader: y and dy have different length" |
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| 310 | raise RuntimeError, msg |
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[de1da34] | 311 | if has_error_dx == True and not len(x) == len(dx): |
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[a7a5886] | 312 | msg = "ascii_reader: y and dy have different length" |
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| 313 | raise RuntimeError, msg |
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[8bd8ea4] | 314 | # If the data length is zero, consider this as |
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| 315 | # though we were not able to read the file. |
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[a7a5886] | 316 | if len(x) == 0: |
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[daa56d0] | 317 | raise RuntimeError, "ascii_reader: could not load file" |
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[de1da34] | 318 | |
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[a7a5886] | 319 | #Let's re-order the data to make cal. |
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| 320 | # curve look better some cases |
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| 321 | ind = numpy.lexsort((ty, tx)) |
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[de1da34] | 322 | for i in ind: |
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| 323 | x[i] = tx[ind[i]] |
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| 324 | y[i] = ty[ind[i]] |
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| 325 | if has_error_dy == True: |
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| 326 | dy[i] = tdy[ind[i]] |
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| 327 | if has_error_dx == True: |
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| 328 | dx[i] = tdx[ind[i]] |
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[da96629] | 329 | # Zeros in dx, dy |
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| 330 | if has_error_dx: |
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| 331 | dx[dx==0] = _ZERO |
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| 332 | if has_error_dy: |
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| 333 | dy[dy==0] = _ZERO |
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[892f246] | 334 | #Data |
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[121c224] | 335 | output.x = x[x!=0] |
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| 336 | output.y = y[x!=0] |
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[5f03524] | 337 | output.dy = dy[x!=0] if has_error_dy == True else numpy.zeros(len(output.y)) |
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| 338 | output.dx = dx[x!=0] if has_error_dx == True else numpy.zeros(len(output.x)) |
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[fca90f82] | 339 | |
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[99d1af6] | 340 | if data_conv_q is not None: |
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| 341 | output.xaxis("\\rm{Q}", output.x_unit) |
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| 342 | else: |
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| 343 | output.xaxis("\\rm{Q}", 'A^{-1}') |
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| 344 | if data_conv_i is not None: |
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[0e2aa40] | 345 | output.yaxis("\\rm{Intensity}", output.y_unit) |
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[99d1af6] | 346 | else: |
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[0e2aa40] | 347 | output.yaxis("\\rm{Intensity}","cm^{-1}") |
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[fe78c7b] | 348 | |
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| 349 | # Store loading process information |
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| 350 | output.meta_data['loader'] = self.type_name |
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| 351 | |
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[8bd8ea4] | 352 | return output |
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[892f246] | 353 | |
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[8bd8ea4] | 354 | else: |
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| 355 | raise RuntimeError, "%s is not a file" % path |
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| 356 | return None |
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| 357 | |
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| 358 | if __name__ == "__main__": |
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| 359 | reader = Reader() |
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| 360 | #print reader.read("../test/test_3_columns.txt") |
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| 361 | print reader.read("../test/empty.txt") |
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| 362 | |
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| 363 | |
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| 364 | |
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