Orange.data.FileFormats

Complexity

Total Complexity

6

Size/Duplication

Total Lines	25
Duplicated Lines	0 %

2 Methods

Rating	Name	Duplication	Size	Complexity
B	register()	0	17	6
B	f()	0	13	5

import csv
import re
import sys
import pickle
from itertools import chain

import os
from collections import namedtuple

import bottlechest as bn

The import bottlechest could not be resolved.

import numpy as np

The import numpy could not be resolved.

from scipy import sparse

The import scipy could not be resolved.

# We are not loading openpyxl here since it takes some time

from Orange.data import Domain
from Orange.data.variable import *

The usage of wildcard imports like Orange.data.variable should generally be avoided.

floor was imported with wildcard, but is not used.

VariableMeta was imported with wildcard, but is not used.

Value was imported with wildcard, but is not used.

ValueUnknown was imported with wildcard, but is not used.

Real was imported with wildcard, but is not used.

isnan was imported with wildcard, but is not used.

Integral was imported with wildcard, but is not used.

make_variable was imported with wildcard, but is not used.

PickleError was imported with wildcard, but is not used.

collections was imported with wildcard, but is not used.

Unknown was imported with wildcard, but is not used.

# A singleton simulated with a class
class FileFormats:
    formats = []
    names = {}
    writers = {}
    readers = {}
    img_writers = {}
    graph_writers = {}

    @classmethod
    def register(cls, name, extension):
        def f(format):

This seems to re-define the built-in format.

            cls.NAME = name
            cls.formats.append(format)
            cls.names[extension] = name
            if hasattr(format, "write_file"):
                cls.writers[extension] = format
            if hasattr(format, "read_file"):
                cls.readers[extension] = format
            if hasattr(format, "write_image"):
                cls.img_writers[extension] = format
            if hasattr(format, "write_graph"):
                cls.graph_writers[extension] = format
            return format

        return f


class FileReader:
    def prescan_file(self, f, delim, nvars, disc_cols, cont_cols):

This method could be written as a function/class method.

        values = [set() for _ in range(nvars)]
        decimals = [-1] * nvars
        for lne in f:
            lne = lne.split(delim)
            for vs, col in zip(values, disc_cols):
                vs[col].add(lne[col])
            for col in cont_cols:
                val = lne[col]
                if not col in Variable._DefaultUnknownStr and "." in val:

It seems like _DefaultUnknownStr was declared protected and should not be accessed from this context.

                    decs = len(val) - val.find(".") - 1
                    if decs > decimals[col]:
                        decimals[col] = decs
        return values, decimals


@FileFormats.register("Tab-delimited file", ".tab")
class TabDelimFormat:
    non_escaped_spaces = re.compile(r"(?<!\\) +")

    def read_header(self, f):
        f.seek(0)
        names = [x.strip() for x in f.readline().strip("\n\r").split("\t")]
        types = [x.strip() for x in f.readline().strip("\n\r").split("\t")]
        flags = [x.strip() for x in f.readline().strip("\n\r").split("\t")]
        self.n_columns = len(names)

The attribute n_columns was defined outside __init__.

        if len(types) != self.n_columns:
            raise ValueError("File contains %i variable names and %i types" %
                             (len(names), len(types)))
        if len(flags) > self.n_columns:
            raise ValueError("There are more flags than variables")
        else:
            flags += [""] * self.n_columns

        attributes = []
        class_vars = []
        metas = []

        self.attribute_columns = []

The attribute attribute_columns was defined outside __init__.

        self.classvar_columns = []

The attribute classvar_columns was defined outside __init__.

        self.meta_columns = []

The attribute meta_columns was defined outside __init__.

        self.weight_column = -1

The attribute weight_column was defined outside __init__.

        self.basket_column = -1

The attribute basket_column was defined outside __init__.

        for col, (name, tpe, flag) in enumerate(zip(names, types, flags)):
            tpe = tpe.strip()
            flag = self.non_escaped_spaces.split(flag)
            flag = [f.replace("\\ ", " ") for f in flag]
            if "i" in flag or "ignore" in flag:
                continue
            if "b" in flag or "basket" in flag:
                self.basket_column = col

The attribute basket_column was defined outside __init__.

                continue
            is_class = "class" in flag
            is_meta = "m" in flag or "meta" in flag or tpe in ["s", "string"]
            is_weight = "w" in flag or "weight" in flag \
                        or tpe in ["w", "weight"]

            attrs = [f.split("=", 1) for f in flag if "=" in f]

            if is_weight:
                if is_class:
                    raise ValueError("Variable {} (column {}) is marked as "
                                     "class and weight".format(name, col))
                self.weight_column = col

The attribute weight_column was defined outside __init__.

                continue

            if tpe in ["c", "continuous"]:
                var = ContinuousVariable.make(name)
            elif tpe in ["w", "weight"]:
                var = None
            elif tpe in ["d", "discrete"]:
                var = DiscreteVariable()  # no name to bypass caching
                var.name = name
                var.fix_order = True
            elif tpe in ["s", "string"]:
                var = StringVariable.make(name)
            else:
                values = [v.replace("\\ ", " ")
                          for v in self.non_escaped_spaces.split(tpe)]
                var = DiscreteVariable.make(name, values, True)
            var.attributes.update(attrs)

            if is_class:
                if is_meta:
                    raise ValueError(
                        "Variable {} (column {}) is marked as "
                        "class and meta attribute".format(name, col))
                class_vars.append(var)
                self.classvar_columns.append((col, var.val_from_str_add))
            elif is_meta:
                metas.append(var)
                self.meta_columns.append((col, var.val_from_str_add))
            else:
                attributes.append(var)
                self.attribute_columns.append((col, var.val_from_str_add))

        domain = Domain(attributes, class_vars, metas)
        return domain

    def count_lines(self, file):

This method could be written as a function/class method.

        file.seek(0)
        i = -3
        for _ in file:
            i += 1
        return i

    def read_data(self, f, table):
        X, Y = table.X, table._Y

It seems like _Y was declared protected and should not be accessed from this context.

        W = table.W if table.W.shape[-1] else None
        f.seek(0)
        f.readline()
        f.readline()
        f.readline()
        padding = [""] * self.n_columns
        if self.basket_column >= 0:
            # TODO how many columns?!

TODO and FIXME comments should generally be avoided.

            table._Xsparse = sparse.lil_matrix(len(X), 100)

It seems like _Xsparse was declared protected and should not be accessed from this context.

        table.metas = metas = (
            np.empty((len(X), len(self.meta_columns)), dtype=object))
        line_count = 0
        Xr = None
        for lne in f:
            values = lne
            if not values.strip():
                continue
            values = values.split("\t")
            if len(values) > self.n_columns:
                raise ValueError("Too many columns in line {}".
                                 format(4 + line_count))
            elif len(values) < self.n_columns:
                values += padding
            if self.attribute_columns:
                Xr = X[line_count]
                for i, (col, reader) in enumerate(self.attribute_columns):
                    Xr[i] = reader(values[col].strip())
            for i, (col, reader) in enumerate(self.classvar_columns):
                Y[line_count, i] = reader(values[col].strip())
            if W is not None:
                W[line_count] = float(values[self.weight_column])
            for i, (col, reader) in enumerate(self.meta_columns):
                metas[line_count, i] = reader(values[col].strip())
            line_count += 1
        if line_count != len(X):

This code seems to be duplicated in your project.

            del Xr, X, Y, W, metas
            table.X.resize(line_count, len(table.domain.attributes))
            table._Y.resize(line_count, len(table.domain.class_vars))

It seems like _Y was declared protected and should not be accessed from this context.

            if table.W.ndim == 1:
                table.W.resize(line_count)
            else:
                table.W.resize((line_count, 0))
            table.metas.resize((line_count, len(self.meta_columns)))
        table.n_rows = line_count

    def reorder_values_array(self, arr, variables):

This method could be written as a function/class method.

        newvars = []
        for col, var in enumerate(variables):
            if getattr(var, "fix_order", False):
                nvar = var.make(var.name, var.values, var.ordered)
                nvar.attributes = var.attributes
                move = len(var.values)
                if nvar.values != var.values:
                    arr[:, col] += move
                    for i, val in enumerate(var.values):
                        bn.replace(arr[:, col], move + i, nvar.values.index(val))
                var = nvar
            newvars.append(var)
        return newvars

    def reorder_values(self, table):
        attrs = self.reorder_values_array(table.X, table.domain.attributes)
        classes = self.reorder_values_array(table._Y, table.domain.class_vars)

It seems like _Y was declared protected and should not be accessed from this context.

        metas = self.reorder_values_array(table.metas, table.domain.metas)
        table.domain = Domain(attrs, classes, metas=metas)

    def read_file(self, filename, cls=None):
        with open(filename) as file:
            return self._read_file(file, cls)

    def _read_file(self, file, cls=None):
        from ..data import Table

        if cls is None:
            cls = Table
        domain = self.read_header(file)
        nExamples = self.count_lines(file)
        table = cls.from_domain(domain, nExamples, self.weight_column >= 0)
        self.read_data(file, table)
        self.reorder_values(table)
        return table

    @classmethod
    def _write_fast(cls, f, data):
        wa = [var.str_val for var in data.domain.variables + data.domain.metas]
        for Xi, Yi, Mi in zip(data.X, data._Y, data.metas):

It seems like _Y was declared protected and should not be accessed from this context.

            f.write("\t".join(w(val) for val, w in zip(chain(Xi, Yi, Mi), wa)))
            f.write("\n")

    @classmethod
    def write_file(cls, filename, data):
        """
        Save data to file.

        Function uses fast implementation in case of numpy data, and slower
        fall-back for general storage.

        :param filename: the name of the file
        :type filename: str
        :param data: the data to be saved
        :type data: Orange.data.Storage
        """
        if isinstance(filename, str):
            f = open(filename, "w")
        else:
            f = filename
        domain_vars = data.domain.variables + data.domain.metas
        # first line
        f.write("\t".join([str(j.name) for j in domain_vars]))
        f.write("\n")

        # second line
        # TODO Basket column.

TODO and FIXME comments should generally be avoided.

        t = {"ContinuousVariable": "c", "DiscreteVariable": "d",
             "StringVariable": "string", "Basket": "basket"}

        f.write("\t".join([t[type(j).__name__] for j in domain_vars]))
        f.write("\n")

        # third line
        m = list(data.domain.metas)
        c = list(data.domain.class_vars)
        r = []
        for i in domain_vars:
            r1 = ["{}={}".format(k, v).replace(" ", "\\ ")
                  for k, v in i.attributes.items()]
            if i in m:
                r1.append("m")
            elif i in c:
                r1.append("class")
            r.append(" ".join(r1))
        f.write("\t".join(r))
        f.write("\n")

        # data
        # noinspection PyBroadException
        try:
            cls._write_fast(f, data)
        except:

General except handlers without types should be used sparingly.

            domain_vars = [data.domain.index(var) for var in domain_vars]
            for i in data:
                f.write("\t".join(str(i[j]) for j in domain_vars) + "\n")
        f.close()

    def write(self, filename, data):
        self.write_file(filename, data)


@FileFormats.register("Comma-separated file", ".csv")
class TxtFormat:
    MISSING_VALUES = frozenset({"", "NA", "?"})

    @staticmethod
    def read_header(file, delimiter=None):
        first_line = file.readline()
        file.seek(0)
        if delimiter is None:
            for delimiter in "\t,; ":
                if delimiter in first_line:
                    break
            else:
                delimiter = None
        if delimiter == " ":
            delimiter = None
        atoms = first_line.split(delimiter)
        try:
            [float(atom) for atom in set(atoms) - TxtFormat.MISSING_VALUES]

The expression [float(atom) for atom in (set(atoms)) - (TxtFormat.MISSING_VALUES)] does not seem to have sideeffects and its result is not used.

            header_lines = 0
            names = ["Var{:04}".format(i + 1) for i in range(len(atoms))]
        except ValueError:
            names = [atom.strip() for atom in atoms]
            header_lines = 1
        domain = Domain([ContinuousVariable.make(name) for name in names])
        return domain, header_lines, delimiter

    def read_file(self, filename, cls=None):
        from ..data import Table

        if cls is None:
            cls = Table
        with open(filename, "rt") as file:
            domain, header_lines, delimiter = self.read_header(file)
        with open(filename, "rb") as file:
            arr = np.genfromtxt(file, delimiter=delimiter,
                                skip_header=header_lines,
                                missing_values=self.MISSING_VALUES)
        table = cls.from_numpy(domain, arr)
        return table

    @classmethod
    def csv_saver(cls, filename, data, delimiter='\t'):
        with open(filename, 'w') as csvfile:
            writer = csv.writer(csvfile, delimiter=delimiter)
            all_vars = data.domain.variables + data.domain.metas
            writer.writerow([v.name for v in all_vars])  # write variable names
            if delimiter == '\t':
                flags = ([''] * len(data.domain.attributes)) + \
                        (['class'] * len(data.domain.class_vars)) + \
                        (['m'] * len(data.domain.metas))

                for i, var in enumerate(all_vars):
                    attrs = ["{0!s}={1!s}".format(*item).replace(" ", "\\ ")
                             for item in var.attributes.items()]
                    if attrs:
                        flags[i] += (" " if flags[i] else "") + (" ".join(attrs))

                writer.writerow([type(v).__name__.replace("Variable", "").lower()
                                 for v in all_vars])  # write variable types
                writer.writerow(flags)  # write flags
            for ex in data:  # write examples
                writer.writerow(ex)

    @classmethod
    def write_file(cls, filename, data):
        cls.csv_saver(filename, data, ',')

    def write(self, filename, data):
        self.write_file(filename, data)


@FileFormats.register("Basket file", ".basket")
class BasketFormat:
    @classmethod
    def read_file(cls, filename, storage_class=None):
        from Orange.data import _io

        if storage_class is None:
            from ..data import Table as storage_class

        def constr_vars(inds):
            if inds:
                return [ContinuousVariable(x.decode("utf-8")) for _, x in
                        sorted((ind, name) for name, ind in inds.items())]

        X, Y, metas, attr_indices, class_indices, meta_indices = \
            _io.sparse_read_float(filename.encode(sys.getdefaultencoding()))

        attrs = constr_vars(attr_indices)
        classes = constr_vars(class_indices)
        meta_attrs = constr_vars(meta_indices)
        domain = Domain(attrs, classes, meta_attrs)
        return storage_class.from_numpy(
            domain, attrs and X, classes and Y, metas and meta_attrs)


@FileFormats.register("Excel file", ".xlsx")
class ExcelFormat:
    non_escaped_spaces = re.compile(r"(?<!\\) +")

    def __init__(self):
        self.attribute_columns = []
        self.classvar_columns = []
        self.meta_columns = []
        self.weight_column = -1
        self.basket_column = -1

        self.n_columns = self.first_data_row = 0

    def open_workbook(self, f):
        from openpyxl import load_workbook

The import openpyxl could not be resolved.

        if isinstance(f, str) and ":" in f[2:]:
            f, sheet = f.rsplit(":", 1)
        else:
            sheet = None
        wb = load_workbook(f, use_iterators=True,
                           read_only=True, data_only=True)
        ws = wb.get_sheet_by_name(sheet) if sheet else wb.get_active_sheet()
        self.n_columns = ws.get_highest_column()
        return ws

    # noinspection PyBroadException
    def read_header_3(self, worksheet):
        cols = self.n_columns
        try:
            names, types, flags = [
                [cell.value.strip() if cell.value is not None else ""
                 for cell in row]
                for row in worksheet.get_squared_range(1, 1, cols, 3)]
        except:

General except handlers without types should be used sparingly.

            return False
        if not (all(tpe in ("", "c", "d", "s", "continuous", "discrete",
                            "string", "w", "weight") or " " in tpe
                    for tpe in types) and
                    all(flg in ("", "i", "ignore", "m", "meta", "w", "weight",
                                "b", "basket", "class") or "=" in flg
                        for flg in flags)):
            return False
        attributes = []
        class_vars = []
        metas = []
        for col, (name, tpe, flag) in enumerate(zip(names, types, flags)):
            flag = self.non_escaped_spaces.split(flag)
            if "i" in flag or "ignore" in flag:
                continue
            if "b" in flag or "basket" in flag:
                self.basket_column = col
                continue
            is_class = "class" in flag
            is_meta = "m" in flag or "meta" in flag or tpe in ["s", "string"]
            is_weight = "w" in flag or "weight" in flag \
                        or tpe in ["w", "weight"]
            attrs = [f.split("=", 1) for f in flag if "=" in f]
            if is_weight:
                if is_class:
                    raise ValueError("Variable {} (column {}) is marked as "
                                     "class and weight".format(name, col + 1))
                self.weight_column = col
                continue
            if tpe in ["c", "continuous"]:
                var = ContinuousVariable.make(name)
            elif tpe in ["w", "weight"]:
                var = None
            elif tpe in ["d", "discrete"]:
                var = DiscreteVariable.make(name)
                var.fix_order = True
            elif tpe in ["s", "string"]:
                var = StringVariable.make(name)
            else:
                values = [v.replace("\\ ", " ")
                          for v in self.non_escaped_spaces.split(tpe)]
                var = DiscreteVariable.make(name, values, True)
            var.attributes.update(attrs)
            if is_class:
                if is_meta:
                    raise ValueError(
                        "Variable {} (column {}) is marked as "
                        "class and meta attribute".format(name, col))
                class_vars.append(var)
                self.classvar_columns.append((col, var.val_from_str_add))
            elif is_meta:
                metas.append(var)
                self.meta_columns.append((col, var.val_from_str_add))
            else:
                attributes.append(var)
                self.attribute_columns.append((col, var.val_from_str_add))
        self.first_data_row = 4
        return Domain(attributes, class_vars, metas)

    # noinspection PyBroadException
    def read_header_0(self, worksheet):
        try:
            [float(cell.value) if cell.value is not None else None
             for cell in
             worksheet.get_squared_range(1, 1, self.n_columns, 3).__next__()]
        except:

General except handlers without types should be used sparingly.

            return False
        self.first_data_row = 1
        attrs = [ContinuousVariable.make("Var{:04}".format(i + 1))
                 for i in range(self.n_columns)]
        self.attribute_columns = [(i, var.val_from_str_add)
                                  for i, var in enumerate(attrs)]
        return Domain(attrs)

    def read_header_1(self, worksheet):
        import openpyxl.cell.cell

The import openpyxl.cell.cell could not be resolved.

        if worksheet.get_highest_column() < 2 or \
                        worksheet.get_highest_row() < 2:
            return False
        cols = self.n_columns
        names = [cell.value.strip() if cell.value is not None else ""
                 for cell in
                 worksheet.get_squared_range(1, 1, cols, 3).__next__()]
        row2 = list(worksheet.get_squared_range(1, 2, cols, 3).__next__())
        attributes = []
        class_vars = []
        metas = []
        for col, name in enumerate(names):
            if "#" in name:
                flags, name = name.split("#", 1)
            else:
                flags = ""
            if "i" in flags:
                continue
            if "b" in flags:
                self.basket_column = col
                continue
            is_class = "c" in flags
            is_meta = "m" in flags or "s" in flags
            is_weight = "W" in flags or "w" in flags
            if is_weight:
                if is_class:
                    raise ValueError("Variable {} (column {}) is marked as "
                                     "class and weight".format(name, col))
                self.weight_column = col
                continue
            if "C" in flags:
                var = ContinuousVariable.make(name)
            elif is_weight:
                var = None
            elif "D" in flags:
                var = DiscreteVariable.make(name)
                var.fix_order = True
            elif "S" in flags:
                var = StringVariable.make(name)
            elif row2[col].data_type == "n":
                var = ContinuousVariable.make(name)
            else:
                if len(set(row[col].value for row in worksheet.rows)) > 20:
                    var = StringVariable.make(name)
                    is_meta = True
                else:
                    var = DiscreteVariable.make(name)
                    var.fix_order = True
            if is_class:
                if is_meta:
                    raise ValueError(
                        "Variable {} (column {}) is marked as "
                        "class and meta attribute".format(
                            name, openpyxl.cell.cell.get_column_letter(col + 1))
                    )
                class_vars.append(var)
                self.classvar_columns.append((col, var.val_from_str_add))
            elif is_meta:
                metas.append(var)
                self.meta_columns.append((col, var.val_from_str_add))
            else:
                attributes.append(var)
                self.attribute_columns.append((col, var.val_from_str_add))
        if attributes and not class_vars:
            class_vars.append(attributes.pop(-1))
            self.classvar_columns.append(self.attribute_columns.pop(-1))
        self.first_data_row = 2
        return Domain(attributes, class_vars, metas)

    def read_header(self, worksheet):
        domain = self.read_header_3(worksheet) or \
                 self.read_header_0(worksheet) or \
                 self.read_header_1(worksheet)
        if domain is False:
            raise ValueError("Invalid header")
        return domain

    # noinspection PyPep8Naming,PyProtectedMember
    def read_data(self, worksheet, table):
        X, Y = table.X, table._Y

It seems like _Y was declared protected and should not be accessed from this context.

        W = table.W if table.W.shape[-1] else None
        if self.basket_column >= 0:
            # TODO how many columns?!

TODO and FIXME comments should generally be avoided.

            table._Xsparse = sparse.lil_matrix(len(X), 100)

It seems like _Xsparse was declared protected and should not be accessed from this context.

        table.metas = metas = (
            np.empty((len(X), len(self.meta_columns)), dtype=object))
        sheet_rows = worksheet.rows
        for _ in range(1, self.first_data_row):
            sheet_rows.__next__()
        line_count = 0
        Xr = None
        for row in sheet_rows:
            values = [cell.value for cell in row]
            if all(value is None for value in values):
                continue
            if self.attribute_columns:
                Xr = X[line_count]
                for i, (col, reader) in enumerate(self.attribute_columns):
                    v = values[col]
                    Xr[i] = reader(v.strip() if isinstance(v, str) else v)
            for i, (col, reader) in enumerate(self.classvar_columns):
                v = values[col]
                Y[line_count, i] = reader(
                    v.strip() if isinstance(v, str) else v)
            if W is not None:
                W[line_count] = float(values[self.weight_column])
            for i, (col, reader) in enumerate(self.meta_columns):
                v = values[col]
                metas[line_count, i] = reader(
                    v.strip() if isinstance(v, str) else v)
            line_count += 1
        if line_count != len(X):

This code seems to be duplicated in your project.

            del Xr, X, Y, W, metas
            table.X.resize(line_count, len(table.domain.attributes))
            table._Y.resize(line_count, len(table.domain.class_vars))

It seems like _Y was declared protected and should not be accessed from this context.

            if table.W.ndim == 1:
                table.W.resize(line_count)
            else:
                table.W.resize((line_count, 0))
            table.metas.resize((line_count, len(self.meta_columns)))
        table.n_rows = line_count

    # noinspection PyUnresolvedReferences
    @staticmethod
    def reorder_values_array(arr, variables):
        for col, var in enumerate(variables):
            if getattr(var, "fix_order", False) and len(var.values) < 1000:
                new_order = var.ordered_values(var.values)
                if new_order == var.values:
                    continue
                arr[:, col] += 1000
                for i, val in enumerate(var.values):
                    bn.replace(arr[:, col], 1000 + i, new_order.index(val))
                var.values = new_order
                delattr(var, "fix_order")

    # noinspection PyProtectedMember
    def reorder_values(self, table):
        self.reorder_values_array(table.X, table.domain.attributes)
        self.reorder_values_array(table._Y, table.domain.class_vars)

It seems like _Y was declared protected and should not be accessed from this context.

        self.reorder_values_array(table.metas, table.domain.metas)

    def read_file(self, file, cls=None):
        from Orange.data import Table

        if cls is None:
            cls = Table
        worksheet = self.open_workbook(file)
        domain = self.read_header(worksheet)
        table = cls.from_domain(
            domain,
            worksheet.get_highest_row() - self.first_data_row + 1,
            self.weight_column >= 0)
        self.read_data(worksheet, table)
        self.reorder_values(table)
        return table


@FileFormats.register("Pickled table", ".pickle")
class PickleFormat:
    @classmethod
    def read_file(cls, file, _=None):
        with open(file, "rb") as f:
            return pickle.load(f)

    @classmethod
    def write_file(cls, filename, table):
        with open(filename, "wb") as f:
            pickle.dump(table, f)

    def write(self, filename, table):
        self.write_file(filename, table)


@FileFormats.register("Dot Tree File", ".dot")
class DotFormat:
    @classmethod
    def write_graph(cls, filename, graph):
        from sklearn import tree

The import sklearn could not be resolved.

        tree.export_graphviz(graph, out_file=filename)

    def write(self, filename, tree):
        if type(tree) == dict:
            tree = tree['tree']
        self.write_graph(filename, tree)


@FileFormats.register("Fixed width textfile", ".fixed")
class FixedWidthFormat(TabDelimFormat):
    """
    FixedWidthFormat reads tables from files where the columns have a
    fixed width. The cells are space-padded to the left.
    See datasets/glass.fixed and tests/test_fixedwidth_reader.py
    

Trailing whitespace

    It is possible to determine the exact cell location of a specific
    table cell within the file because of the fixed width columns.
    This allows the FixedWidthFormat to be used with the LazyFile
    widget to 'read' extremely large files.
    

Trailing whitespace

    TODO:
    - Add read_row() without reading entire file.
    - Allow spaces in column names and cell values.
    - Ensure compatibility with all tables in the tests directory.
    - Do metas and class properly.
    """
    def read_ends_columns(self, filename):

This method could be written as a function/class method.

        """
        Returns the location where each column ends in a line in the
        file.
        TODO:
        - Cleanup.
        """
        ColumnInfo = namedtuple(
            'ColumnInfo',
            ['name', 'tpe', 'flag', 'start', 'end', 'width', 'index'],
        )
        with open(filename) as f:
            f.seek(0)
            l_names = f.readline()
            l_types = f.readline()
            l_flags = f.readline()
            types = l_types.split()
            ends = []
            for n in types:
                position_start = ends[-1] if len(ends) else 0
                end = (" "+l_types.replace("\n"," ")).find(" "+n+" ", position_start) + len(n)
                ends.append(end)
            info_columns = [
                ColumnInfo(
                    name=l_names[start:end].strip(),
                    flag=l_flags[start:end].strip(),
                    tpe=tpe,
                    start=start,
                    end=end,
                    width=end-start,
                    index=inde,
                ) for (inde, (start, end, tpe)) in enumerate(zip(
                    [0] + ends[:-1],
                    ends,
                    types,
                ))
            ]
            return info_columns
    

Trailing whitespace

    def read_header(self, filename):
        """
        Reads the header of the fixed width file and returns the
        Domain of the table.
        

Trailing whitespace

        TODO:
        - Use read_ends_columns() to determine the width of the
          columns and use that to parse the lines, because this
          will allow the use of spaces in column names.
        """
        ends = self.read_ends_columns(filename)
        names = [end.name for end in ends]
        types = [end.tpe for end in ends]
        flags = [end.flag for end in ends]
        with open(filename) as f:
            # Function based on read_header from TabDelimReader.
            f.seek(0)
            #names = f.readline().strip("\n\r").split()
            #types = f.readline().strip("\n\r").split()
            #flags = f.readline().strip("\n\r").split()
            f.readline()
            f.readline()
            f.readline()
            # Changed split on "\t" to split on spaces.
            self.n_columns = len(names)

The attribute n_columns was defined outside __init__.

            if len(types) != self.n_columns:
                raise ValueError("File contains %i variable names and %i types" %
                                 (len(names), len(types)))
            if len(flags) > self.n_columns:
                raise ValueError("There are more flags than variables")
            else:
                flags += [""] * self.n_columns
            attributes = []
            class_vars = []
            metas = []
            self.attribute_columns = []

The attribute attribute_columns was defined outside __init__.

            self.classvar_columns = []

The attribute classvar_columns was defined outside __init__.

            self.meta_columns = []

The attribute meta_columns was defined outside __init__.

            self.weight_column = -1

The attribute weight_column was defined outside __init__.

            self.basket_column = -1

The attribute basket_column was defined outside __init__.

            for col, (name, tpe, flag) in enumerate(zip(names, types, flags)):
                tpe = tpe.strip()
                flag = flag.split()
                if "i" in flag or "ignore" in flag:
                    continue
                if "b" in flag or "basket" in flag:
                    self.basket_column = col

The attribute basket_column was defined outside __init__.

                    continue
                is_class = "class" in flag
                is_meta = "m" in flag or "meta" in flag or tpe in ["s", "string"]
                is_weight = "w" in flag or "weight" in flag \
                    or tpe in ["w", "weight"]
                if is_weight:
                    if is_class:
                        raise ValueError("Variable {} (column {}) is marked as "
                                         "class and weight".format(name, col))
                    self.weight_column = col

The attribute weight_column was defined outside __init__.

                    continue
                if tpe in ["c", "continuous"]:
                    var = ContinuousVariable.make(name)
                elif tpe in ["w", "weight"]:
                    var = None
                elif tpe in ["d", "discrete"]:
                    var = DiscreteVariable.make(name)
                elif tpe in ["s", "string"]:
                    var = StringVariable.make(name)
                else:
                    values = [v.replace("\\ ", " ")
                              for v in self.non_escaped_spaces.split(tpe)]
                    var = DiscreteVariable.make(name, values, True)
                var.fix_order = (isinstance(var, DiscreteVariable)
                                 and not var.values)
                if is_class:
                    if is_meta:
                        raise ValueError(
                            "Variable {} (column {}) is marked as "
                            "class and meta attribute".format(name, col))
                    class_vars.append(var)
                    self.classvar_columns.append((col, var.val_from_str_add))
                elif is_meta:
                    metas.append(var)
                    self.meta_columns.append((col, var.val_from_str_add))
                else:
                    attributes.append(var)
                    self.attribute_columns.append((col, var.val_from_str_add))
            domain = Domain(attributes, class_vars, metas)
            return domain
    def count_lines(self, filename):
        """
        Counts the number of lines in the file. This can be done
        without reading the entire file because the file
        has fixed width columns.
        """
        len_file = os.stat(filename).st_size
        with open(filename) as f:
            f.seek(0)
            line = f.readline()
            len_line = len(line)
        

Trailing whitespace

        count = int(len_file / len_line) - 3
        return count
    def read_cell(self, filename, index_row, name_attribute):
        """
        Reads one specific cell value without reading the entire file.
        

Trailing whitespace

        TODO:
        - Cleanup this function.
        - Test with discrete and class attributes.
        - Cache the header information.
        """
        info_columns = self.read_ends_columns(filename)
        header = self.read_header(filename)

The variable header seems to be unused.

        with open(filename) as f:
            f.seek(0)
            line = f.readline()
            len_line1 = len(line)

The variable len_line1 seems to be unused.

        len_line = sum(ic.width for ic in info_columns) + 1 # for \n
        col = [ic for ic in info_columns if ic.name == name_attribute][0]
        with open(filename) as f:
            f.seek( (3+index_row) * len_line + col.start )
            value = f.read(col.width)
            value_n = None
            # Parse the string in the correct format. This is a kludge
            # based on code from read_data().
            if self.attribute_columns:
                for i, (coli, reader) in enumerate(self.attribute_columns):

The variable i seems to be unused.

                    if coli == col.index:
                        value_n = reader(value.strip())
            for i, (coli, reader) in enumerate(self.classvar_columns):
                if coli == col.index:
                    value_n = reader(value.strip())
            return value_n
        

Trailing whitespace

    def read_data(self, filename, table):
        """
        Read the data portion of the file.
        

Trailing whitespace

        This function is based on the one in TabDelimFormat.
        TODO:
        - Use the actual known width of the columns instead
          of splitting on space, because that will allow spaces
          to be part of the cell values.
          That is, use read_ends_columns.
        """        

Trailing whitespace

        with open(filename) as f:
            #X, Y = table.X, table.Y
            X, Y = table.X, table._Y

It seems like _Y was declared protected and should not be accessed from this context.

            W = table.W if table.W.shape[-1] else None
            f.seek(0)
            f.readline()
            f.readline()
            f.readline()
            padding = [""] * self.n_columns
            if self.basket_column >= 0:
                # TODO how many columns?!

TODO and FIXME comments should generally be avoided.

                table._Xsparse = sparse.lil_matrix(len(X), 100)

It seems like _Xsparse was declared protected and should not be accessed from this context.

            table.metas = metas = (
                np.empty((len(X), len(self.meta_columns)), dtype=object))
            line_count = 0
            Xr = None
            for lne in f:
                values = lne.strip()
                if not values:
                    continue
                # Only difference with TabDelimReader
                #values = values.split("\t")
                values = values.split()
                if len(values) > self.n_columns:
                    raise ValueError("Too many columns in line {}".
                                     format(4 + line_count))
                elif len(values) < self.n_columns:
                    values += padding
                if self.attribute_columns:
                    Xr = X[line_count]
                    for i, (col, reader) in enumerate(self.attribute_columns):
                        Xr[i] = reader(values[col].strip())
                for i, (col, reader) in enumerate(self.classvar_columns):
                    Y[line_count, i] = reader(values[col].strip())
                if W is not None:
                    W[line_count] = float(values[self.weight_column])
                for i, (col, reader) in enumerate(self.meta_columns):
                    metas[line_count, i] = reader(values[col].strip())
                line_count += 1
            if line_count != len(X):

This code seems to be duplicated in your project.

                del Xr, X, Y, W, metas
                table.X.resize(line_count, len(table.domain.attributes))
                table.Y.resize(line_count, len(table.domain.class_vars))
                if table.W.ndim == 1:
                    table.W.resize(line_count)
                else:
                    table.W.resize((line_count, 0))
                table.metas.resize((line_count, len(self.meta_columns)))
            table.n_rows = line_count
    def read_file(self, filename, cls=None):
        """
        Read a file.
        

Trailing whitespace

        The distinction between read_file and _read_file cannot
        be made because we cannot get the length of a stream etc.
        """
        from ..data import Table
        if cls is None:
            cls = Table
        domain = self.read_header(filename)
        nExamples = self.count_lines(filename)
        table = cls.from_domain(domain, nExamples, self.weight_column >= 0)
        self.read_data(filename, table)
        self.reorder_values(table)
        return table