| 1 | """ |
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| 2 | This is the base file reader class most file readers should inherit from. |
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| 3 | All generic functionality required for a file loader/reader is built into this |
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| 4 | class |
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| 5 | """ |
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| 6 | |
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| 7 | import os |
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| 8 | import sys |
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| 9 | import math |
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| 10 | import logging |
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| 11 | from abc import abstractmethod |
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| 12 | |
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| 13 | import numpy as np |
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| 14 | from .loader_exceptions import NoKnownLoaderException, FileContentsException,\ |
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| 15 | DataReaderException, DefaultReaderException |
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| 16 | from .data_info import Data1D, Data2D, DataInfo, plottable_1D, plottable_2D,\ |
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| 17 | combine_data_info_with_plottable |
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| 18 | |
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| 19 | logger = logging.getLogger(__name__) |
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| 20 | |
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| 21 | if sys.version_info[0] < 3: |
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| 22 | def decode(s): |
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| 23 | return s |
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| 24 | else: |
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| 25 | def decode(s): |
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| 26 | return s.decode() if isinstance(s, bytes) else s |
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| 27 | |
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| 28 | # Data 1D fields for iterative purposes |
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| 29 | FIELDS_1D = ('x', 'y', 'dx', 'dy', 'dxl', 'dxw') |
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| 30 | # Data 2D fields for iterative purposes |
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| 31 | FIELDS_2D = ('data', 'qx_data', 'qy_data', 'q_data', 'err_data', |
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| 32 | 'dqx_data', 'dqy_data', 'mask') |
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| 33 | DEPRECATION_MESSAGE = ("\rThe extension of this file suggests the data set migh" |
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| 34 | "t not be fully reduced. Support for the reader associat" |
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| 35 | "ed with this file type has been removed. An attempt to " |
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| 36 | "load the file was made, but SasView cannot guarantee th" |
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| 37 | "e accuracy of the data.") |
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| 38 | |
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| 39 | |
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| 40 | class FileReader(object): |
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| 41 | # String to describe the type of data this reader can load |
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| 42 | type_name = "ASCII" |
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| 43 | # Wildcards to display |
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| 44 | type = ["Text files (*.txt|*.TXT)"] |
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| 45 | # List of allowed extensions |
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| 46 | ext = ['.txt'] |
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| 47 | # Deprecated extensions |
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| 48 | deprecated_extensions = ['.asc', '.nxs'] |
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| 49 | # Bypass extension check and try to load anyway |
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| 50 | allow_all = False |
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| 51 | # Able to import the unit converter |
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| 52 | has_converter = True |
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| 53 | # Default value of zero |
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| 54 | _ZERO = 1e-16 |
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| 55 | |
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| 56 | def __init__(self): |
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| 57 | # List of Data1D and Data2D objects to be sent back to data_loader |
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| 58 | self.output = [] |
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| 59 | # Current plottable_(1D/2D) object being loaded in |
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| 60 | self.current_dataset = None |
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| 61 | # Current DataInfo object being loaded in |
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| 62 | self.current_datainfo = None |
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| 63 | # File path sent to reader |
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| 64 | self.filepath = None |
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| 65 | # Open file handle |
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| 66 | self.f_open = None |
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| 67 | |
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| 68 | def read(self, filepath): |
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| 69 | """ |
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| 70 | Basic file reader |
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| 71 | |
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| 72 | :param filepath: The full or relative path to a file to be loaded |
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| 73 | """ |
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| 74 | self.filepath = filepath |
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| 75 | if os.path.isfile(filepath): |
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| 76 | basename, extension = os.path.splitext(os.path.basename(filepath)) |
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| 77 | self.extension = extension.lower() |
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| 78 | # If the file type is not allowed, return nothing |
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| 79 | if self.extension in self.ext or self.allow_all: |
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| 80 | # Try to load the file, but raise an error if unable to. |
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| 81 | try: |
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| 82 | self.f_open = open(filepath, 'rb') |
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| 83 | self.get_file_contents() |
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| 84 | |
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| 85 | except DataReaderException as e: |
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| 86 | self.handle_error_message(e.message) |
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| 87 | except OSError as e: |
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| 88 | # If the file cannot be opened |
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| 89 | msg = "Unable to open file: {}\n".format(filepath) |
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| 90 | msg += e.message |
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| 91 | self.handle_error_message(msg) |
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| 92 | finally: |
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| 93 | # Close the file handle if it is open |
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| 94 | if not self.f_open.closed: |
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| 95 | self.f_open.close() |
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| 96 | if len(self.output) > 0: |
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| 97 | if any(filepath.lower().endswith(ext) for ext in |
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| 98 | self.deprecated_extensions): |
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| 99 | self.handle_error_message(DEPRECATION_MESSAGE) |
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| 100 | # Sort the data that's been loaded |
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| 101 | self.sort_one_d_data() |
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| 102 | self.sort_two_d_data() |
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| 103 | else: |
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| 104 | msg = "Unable to find file at: {}\n".format(filepath) |
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| 105 | msg += "Please check your file path and try again." |
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| 106 | self.handle_error_message(msg) |
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| 107 | |
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| 108 | # Return a list of parsed entries that data_loader can manage |
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| 109 | final_data = self.output |
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| 110 | self.reset_state() |
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| 111 | return final_data |
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| 112 | |
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| 113 | def reset_state(self): |
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| 114 | """ |
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| 115 | Resets the class state to a base case when loading a new data file so previous |
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| 116 | data files do not appear a second time |
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| 117 | """ |
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| 118 | self.current_datainfo = None |
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| 119 | self.current_dataset = None |
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| 120 | self.filepath = None |
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| 121 | self.ind = None |
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| 122 | self.output = [] |
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| 123 | |
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| 124 | def nextline(self): |
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| 125 | """ |
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| 126 | Returns the next line in the file as a string. |
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| 127 | """ |
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| 128 | #return self.f_open.readline() |
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| 129 | return decode(self.f_open.readline()) |
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| 130 | |
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| 131 | def nextlines(self): |
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| 132 | """ |
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| 133 | Returns the next line in the file as a string. |
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| 134 | """ |
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| 135 | for line in self.f_open: |
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| 136 | #yield line |
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| 137 | yield decode(line) |
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| 138 | |
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| 139 | def readall(self): |
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| 140 | """ |
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| 141 | Returns the entire file as a string. |
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| 142 | """ |
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| 143 | #return self.f_open.read() |
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| 144 | return decode(self.f_open.read()) |
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| 145 | |
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| 146 | def handle_error_message(self, msg): |
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| 147 | """ |
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| 148 | Generic error handler to add an error to the current datainfo to |
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| 149 | propagate the error up the error chain. |
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| 150 | :param msg: Error message |
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| 151 | """ |
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| 152 | if len(self.output) > 0: |
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| 153 | self.output[-1].errors.append(msg) |
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| 154 | elif isinstance(self.current_datainfo, DataInfo): |
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| 155 | self.current_datainfo.errors.append(msg) |
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| 156 | else: |
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| 157 | logger.warning(msg) |
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| 158 | |
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| 159 | def send_to_output(self): |
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| 160 | """ |
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| 161 | Helper that automatically combines the info and set and then appends it |
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| 162 | to output |
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| 163 | """ |
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| 164 | data_obj = combine_data_info_with_plottable(self.current_dataset, |
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| 165 | self.current_datainfo) |
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| 166 | self.output.append(data_obj) |
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| 167 | |
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| 168 | def sort_one_d_data(self): |
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| 169 | """ |
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| 170 | Sort 1D data along the X axis for consistency |
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| 171 | """ |
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| 172 | for data in self.output: |
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| 173 | if isinstance(data, Data1D): |
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| 174 | # Normalize the units for |
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| 175 | data.x_unit = self.format_unit(data.x_unit) |
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| 176 | data.y_unit = self.format_unit(data.y_unit) |
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| 177 | # Sort data by increasing x and remove 1st point |
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| 178 | ind = np.lexsort((data.y, data.x)) |
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| 179 | data.x = self._reorder_1d_array(data.x, ind) |
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| 180 | data.y = self._reorder_1d_array(data.y, ind) |
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| 181 | if data.dx is not None: |
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| 182 | if len(data.dx) == 0: |
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| 183 | data.dx = None |
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| 184 | continue |
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| 185 | data.dx = self._reorder_1d_array(data.dx, ind) |
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| 186 | if data.dxl is not None: |
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| 187 | data.dxl = self._reorder_1d_array(data.dxl, ind) |
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| 188 | if data.dxw is not None: |
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| 189 | data.dxw = self._reorder_1d_array(data.dxw, ind) |
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| 190 | if data.dy is not None: |
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| 191 | if len(data.dy) == 0: |
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| 192 | data.dy = None |
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| 193 | continue |
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| 194 | data.dy = self._reorder_1d_array(data.dy, ind) |
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| 195 | if data.lam is not None: |
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| 196 | data.lam = self._reorder_1d_array(data.lam, ind) |
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| 197 | if data.dlam is not None: |
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| 198 | data.dlam = self._reorder_1d_array(data.dlam, ind) |
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| 199 | data = self._remove_nans_in_data(data) |
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| 200 | if len(data.x) > 0: |
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| 201 | data.xmin = np.min(data.x) |
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| 202 | data.xmax = np.max(data.x) |
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| 203 | data.ymin = np.min(data.y) |
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| 204 | data.ymax = np.max(data.y) |
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| 205 | |
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| 206 | @staticmethod |
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| 207 | def _reorder_1d_array(array, ind): |
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| 208 | """ |
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| 209 | Reorders a 1D array based on the indices passed as ind |
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| 210 | :param array: Array to be reordered |
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| 211 | :param ind: Indices used to reorder array |
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| 212 | :return: reordered array |
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| 213 | """ |
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| 214 | array = np.asarray(array, dtype=np.float64) |
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| 215 | return array[ind] |
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| 216 | |
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| 217 | @staticmethod |
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| 218 | def _remove_nans_in_data(data): |
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| 219 | """ |
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| 220 | Remove data points where nan is loaded |
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| 221 | :param data: 1D or 2D data object |
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| 222 | :return: data with nan points removed |
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| 223 | """ |
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| 224 | if isinstance(data, Data1D): |
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| 225 | fields = FIELDS_1D |
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| 226 | elif isinstance(data, Data2D): |
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| 227 | fields = FIELDS_2D |
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| 228 | else: |
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| 229 | return data |
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| 230 | # Make array of good points - all others will be removed |
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| 231 | good = np.isfinite(getattr(data, fields[0])) |
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| 232 | for name in fields[1:]: |
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| 233 | array = getattr(data, name) |
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| 234 | if array is not None: |
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| 235 | # Update good points only if not already changed |
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| 236 | good &= np.isfinite(array) |
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| 237 | if not np.all(good): |
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| 238 | for name in fields: |
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| 239 | array = getattr(data, name) |
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| 240 | if array is not None: |
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| 241 | setattr(data, name, array[good]) |
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| 242 | return data |
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| 243 | |
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| 244 | def sort_two_d_data(self): |
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| 245 | for dataset in self.output: |
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| 246 | if isinstance(dataset, Data2D): |
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| 247 | # Normalize the units for |
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| 248 | dataset.x_unit = self.format_unit(dataset.Q_unit) |
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| 249 | dataset.y_unit = self.format_unit(dataset.I_unit) |
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| 250 | dataset.data = dataset.data.astype(np.float64) |
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| 251 | dataset.qx_data = dataset.qx_data.astype(np.float64) |
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| 252 | dataset.xmin = np.min(dataset.qx_data) |
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| 253 | dataset.xmax = np.max(dataset.qx_data) |
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| 254 | dataset.qy_data = dataset.qy_data.astype(np.float64) |
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| 255 | dataset.ymin = np.min(dataset.qy_data) |
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| 256 | dataset.ymax = np.max(dataset.qy_data) |
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| 257 | dataset.q_data = np.sqrt(dataset.qx_data * dataset.qx_data |
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| 258 | + dataset.qy_data * dataset.qy_data) |
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| 259 | if dataset.err_data is not None: |
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| 260 | dataset.err_data = dataset.err_data.astype(np.float64) |
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| 261 | if dataset.dqx_data is not None: |
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| 262 | dataset.dqx_data = dataset.dqx_data.astype(np.float64) |
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| 263 | if dataset.dqy_data is not None: |
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| 264 | dataset.dqy_data = dataset.dqy_data.astype(np.float64) |
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| 265 | if dataset.mask is not None: |
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| 266 | dataset.mask = dataset.mask.astype(dtype=bool) |
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| 267 | |
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| 268 | if len(dataset.data.shape) == 2: |
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| 269 | n_rows, n_cols = dataset.data.shape |
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| 270 | dataset.y_bins = dataset.qy_data[0::int(n_cols)] |
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| 271 | dataset.x_bins = dataset.qx_data[:int(n_cols)] |
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| 272 | dataset.data = dataset.data.flatten() |
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| 273 | dataset = self._remove_nans_in_data(dataset) |
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| 274 | if len(dataset.data) > 0: |
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| 275 | dataset.xmin = np.min(dataset.qx_data) |
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| 276 | dataset.xmax = np.max(dataset.qx_data) |
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| 277 | dataset.ymin = np.min(dataset.qy_data) |
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| 278 | dataset.ymax = np.max(dataset.qx_data) |
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| 279 | |
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| 280 | def format_unit(self, unit=None): |
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| 281 | """ |
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| 282 | Format units a common way |
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| 283 | :param unit: |
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| 284 | :return: |
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| 285 | """ |
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| 286 | if unit: |
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| 287 | split = unit.split("/") |
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| 288 | if len(split) == 1: |
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| 289 | return unit |
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| 290 | elif split[0] == '1': |
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| 291 | return "{0}^".format(split[1]) + "{-1}" |
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| 292 | else: |
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| 293 | return "{0}*{1}^".format(split[0], split[1]) + "{-1}" |
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| 294 | |
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| 295 | def set_all_to_none(self): |
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| 296 | """ |
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| 297 | Set all mutable values to None for error handling purposes |
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| 298 | """ |
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| 299 | self.current_dataset = None |
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| 300 | self.current_datainfo = None |
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| 301 | self.output = [] |
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| 302 | |
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| 303 | def data_cleanup(self): |
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| 304 | """ |
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| 305 | Clean up the data sets and refresh everything |
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| 306 | :return: None |
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| 307 | """ |
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| 308 | self.remove_empty_q_values() |
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| 309 | self.send_to_output() # Combine datasets with DataInfo |
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| 310 | self.current_datainfo = DataInfo() # Reset DataInfo |
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| 311 | |
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| 312 | def remove_empty_q_values(self): |
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| 313 | """ |
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| 314 | Remove any point where Q == 0 |
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| 315 | """ |
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| 316 | if isinstance(self.current_dataset, plottable_1D): |
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| 317 | # Booleans for resolutions |
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| 318 | has_error_dx = self.current_dataset.dx is not None |
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| 319 | has_error_dxl = self.current_dataset.dxl is not None |
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| 320 | has_error_dxw = self.current_dataset.dxw is not None |
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| 321 | has_error_dy = self.current_dataset.dy is not None |
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| 322 | # Create arrays of zeros for non-existent resolutions |
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| 323 | if has_error_dxw and not has_error_dxl: |
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| 324 | array_size = self.current_dataset.dxw.size - 1 |
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| 325 | self.current_dataset.dxl = np.append(self.current_dataset.dxl, |
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| 326 | np.zeros([array_size])) |
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| 327 | has_error_dxl = True |
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| 328 | elif has_error_dxl and not has_error_dxw: |
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| 329 | array_size = self.current_dataset.dxl.size - 1 |
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| 330 | self.current_dataset.dxw = np.append(self.current_dataset.dxw, |
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| 331 | np.zeros([array_size])) |
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| 332 | has_error_dxw = True |
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| 333 | elif not has_error_dxl and not has_error_dxw and not has_error_dx: |
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| 334 | array_size = self.current_dataset.x.size - 1 |
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| 335 | self.current_dataset.dx = np.append(self.current_dataset.dx, |
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| 336 | np.zeros([array_size])) |
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| 337 | has_error_dx = True |
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| 338 | if not has_error_dy: |
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| 339 | array_size = self.current_dataset.y.size - 1 |
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| 340 | self.current_dataset.dy = np.append(self.current_dataset.dy, |
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| 341 | np.zeros([array_size])) |
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| 342 | has_error_dy = True |
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| 343 | |
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| 344 | # Remove points where q = 0 |
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| 345 | x = self.current_dataset.x |
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| 346 | self.current_dataset.x = self.current_dataset.x[x != 0] |
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| 347 | self.current_dataset.y = self.current_dataset.y[x != 0] |
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| 348 | if has_error_dy: |
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| 349 | self.current_dataset.dy = self.current_dataset.dy[x != 0] |
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| 350 | if has_error_dx: |
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| 351 | self.current_dataset.dx = self.current_dataset.dx[x != 0] |
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| 352 | if has_error_dxl: |
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| 353 | self.current_dataset.dxl = self.current_dataset.dxl[x != 0] |
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| 354 | if has_error_dxw: |
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| 355 | self.current_dataset.dxw = self.current_dataset.dxw[x != 0] |
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| 356 | elif isinstance(self.current_dataset, plottable_2D): |
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| 357 | has_error_dqx = self.current_dataset.dqx_data is not None |
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| 358 | has_error_dqy = self.current_dataset.dqy_data is not None |
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| 359 | has_error_dy = self.current_dataset.err_data is not None |
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| 360 | has_mask = self.current_dataset.mask is not None |
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| 361 | x = self.current_dataset.qx_data |
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| 362 | self.current_dataset.data = self.current_dataset.data[x != 0] |
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| 363 | self.current_dataset.qx_data = self.current_dataset.qx_data[x != 0] |
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| 364 | self.current_dataset.qy_data = self.current_dataset.qy_data[x != 0] |
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| 365 | self.current_dataset.q_data = np.sqrt( |
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| 366 | np.square(self.current_dataset.qx_data) + np.square( |
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| 367 | self.current_dataset.qy_data)) |
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| 368 | if has_error_dy: |
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| 369 | self.current_dataset.err_data = self.current_dataset.err_data[x != 0] |
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| 370 | if has_error_dqx: |
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| 371 | self.current_dataset.dqx_data = self.current_dataset.dqx_data[x != 0] |
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| 372 | if has_error_dqy: |
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| 373 | self.current_dataset.dqy_data = self.current_dataset.dqy_data[x != 0] |
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| 374 | if has_mask: |
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| 375 | self.current_dataset.mask = self.current_dataset.mask[x != 0] |
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| 376 | |
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| 377 | def reset_data_list(self, no_lines=0): |
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| 378 | """ |
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| 379 | Reset the plottable_1D object |
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| 380 | """ |
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| 381 | # Initialize data sets with arrays the maximum possible size |
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| 382 | x = np.zeros(no_lines) |
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| 383 | y = np.zeros(no_lines) |
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| 384 | dx = np.zeros(no_lines) |
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| 385 | dy = np.zeros(no_lines) |
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| 386 | self.current_dataset = plottable_1D(x, y, dx, dy) |
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| 387 | |
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| 388 | @staticmethod |
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| 389 | def splitline(line): |
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| 390 | """ |
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| 391 | Splits a line into pieces based on common delimiters |
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| 392 | :param line: A single line of text |
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| 393 | :return: list of values |
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| 394 | """ |
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| 395 | # Initial try for CSV (split on ,) |
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| 396 | toks = line.split(',') |
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| 397 | # Now try SCSV (split on ;) |
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| 398 | if len(toks) < 2: |
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| 399 | toks = line.split(';') |
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| 400 | # Now go for whitespace |
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| 401 | if len(toks) < 2: |
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| 402 | toks = line.split() |
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| 403 | return toks |
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| 404 | |
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| 405 | @abstractmethod |
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| 406 | def get_file_contents(self): |
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| 407 | """ |
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| 408 | Reader specific class to access the contents of the file |
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| 409 | All reader classes that inherit from FileReader must implement |
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| 410 | """ |
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| 411 | pass |
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