source: sasview/src/sas/sascalc/dataloader/readers/ascii_reader.py @ b09095a

"""
    Generic multi-column ASCII data reader
"""
############################################################################
# This software was developed by the University of Tennessee as part of the
# Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
# project funded by the US National Science Foundation.
# If you use DANSE applications to do scientific research that leads to
# publication, we ask that you acknowledge the use of the software with the
# following sentence:
# This work benefited from DANSE software developed under NSF award DMR-0520547.
# copyright 2008, University of Tennessee
#############################################################################

import logging
import numpy as np
from sas.sascalc.dataloader.file_reader_base_class import FileReader
from sas.sascalc.dataloader.data_info import DataInfo, plottable_1D

logger = logging.getLogger(__name__)


class Reader(FileReader):
    """
    Class to load ascii files (2, 3 or 4 columns).
    """
    # File type
    type_name = "ASCII"
    # Wildcards
    type = ["ASCII files (*.txt)|*.txt",
            "ASCII files (*.dat)|*.dat",
            "ASCII files (*.abs)|*.abs",
            "CSV files (*.csv)|*.csv"]
    # List of allowed extensions
    ext = ['.txt', '.dat', '.abs', '.csv']
    # Flag to bypass extension check
    allow_all = True
    # data unless that is the only data
    min_data_pts = 5

    def get_file_contents(self):
        """
        Get the contents of the file
        """

        buff = self.f_open.read()
        filepath = self.f_open.name
        lines = buff.splitlines()
        self.output = []
        self.current_datainfo = DataInfo()
        self.current_datainfo.filename = filepath
        self.reset_data_list(len(lines))

        # The first good line of data will define whether
        # we have 2-column or 3-column ascii
        has_error_dx = None
        has_error_dy = None

        # Initialize counters for data lines and header lines.
        is_data = False
        # More than "5" lines of data is considered as actual
        # To count # of current data candidate lines
        candidate_lines = 0
        # To count total # of previous data candidate lines
        candidate_lines_previous = 0
        # Current line number
        line_no = 0
        # minimum required number of columns of data
        lentoks = 2
        for line in lines:
            toks = self.splitline(line.strip())
            # To remember the number of columns in the current line of data
            new_lentoks = len(toks)
            try:
                if new_lentoks == 0:
                    # If the line is blank, skip and continue on
                    # In case of breaks within data sets.
                    continue
                elif new_lentoks != lentoks and is_data:
                    # If a footer is found, break the loop and save the data
                    break
                elif new_lentoks != lentoks and not is_data:
                    # If header lines are numerical
                    candidate_lines = 0
                    self.reset_data_list(len(lines) - line_no)

                candidate_lines += 1
                # If 5 or more lines, this is considering the set data
                if candidate_lines >= self.min_data_pts:
                    is_data = True

                self.current_dataset.x[candidate_lines - 1] = float(toks[0])
                self.current_dataset.y[candidate_lines - 1] = float(toks[1])

                # If a 3rd row is present, consider it dy
                if new_lentoks > 2:
                    self.current_dataset.dy[candidate_lines - 1] = \
                        float(toks[2])
                    has_error_dy = True

                # If a 4th row is present, consider it dx
                if new_lentoks > 3:
                    self.current_dataset.dx[candidate_lines - 1] = \
                        float(toks[3])
                    has_error_dx = True

                # To remember the # of columns on the current line
                # for the next line of data
                lentoks = new_lentoks
                line_no += 1
            except ValueError:
                # It is data and meet non - number, then stop reading
                if is_data:
                    break
                # Delete the previously stored lines of data candidates if
                # the list is not data
                self.reset_data_list(len(lines) - line_no)
                lentoks = 2
                has_error_dx = None
                has_error_dy = None
                # Reset # of lines of data candidates
                candidate_lines = 0
            except Exception:
                # Handle any unexpected exceptions
                raise

        if not is_data:
            # TODO: Check file extension - primary reader, throw error.
            # TODO: Secondary check, pass and try next reader
            msg = "ascii_reader: x has no data"
            raise RuntimeError(msg)
        # Sanity check
        if has_error_dy and not len(self.current_dataset.y) == \
                len(self.current_dataset.dy):
            msg = "ascii_reader: y and dy have different length"
            raise RuntimeError(msg)
        if has_error_dx and not len(self.current_dataset.x) == \
                len(self.current_dataset.dx):
            msg = "ascii_reader: y and dy have different length"
            raise RuntimeError(msg)
        # If the data length is zero, consider this as
        # though we were not able to read the file.
        if len(self.current_dataset.x) < 1:
            raise RuntimeError("ascii_reader: could not load file")
            return None

        # Data
        self.current_dataset.x = \
            self.current_dataset.x[self.current_dataset.x != 0]
        self.current_dataset.y = \
            self.current_dataset.y[self.current_dataset.x != 0]
        self.current_dataset.dy = \
            self.current_dataset.dy[self.current_dataset.x != 0] if \
                has_error_dy else np.zeros(len(self.current_dataset.y))
        self.current_dataset.dx = \
            self.current_dataset.dx[self.current_dataset.x != 0] if \
                has_error_dx else np.zeros(len(self.current_dataset.x))

        self.current_dataset.xaxis("\\rm{Q}", 'A^{-1}')
        self.current_dataset.yaxis("\\rm{Intensity}", "cm^{-1}")

        # Store loading process information
        self.current_datainfo.meta_data['loader'] = self.type_name
        self.send_to_output()

    def reset_data_list(self, no_lines):
        """
        Reset the plottable_1D object
        """
        # Initialize data sets with arrays the maximum possible size
        x = np.zeros(no_lines)
        y = np.zeros(no_lines)
        dy = np.zeros(no_lines)
        dx = np.zeros(no_lines)
        self.current_dataset = plottable_1D(x, y, dx, dy)