import numpy as np
from Orange.data import Domain, DiscreteVariable
from Orange.data.util import get_unique_names

ANNOTATED_DATA_SIGNAL_NAME = "Data"
ANNOTATED_DATA_FEATURE_NAME = "Selected"


def add_columns(domain, attributes=(), class_vars=(), metas=()):
    """Construct a new domain with new columns added to the specified place

    Parameters
    ----------
    domain : Domain
        source domain
    attributes
        list of variables to append to attributes from source domain
    class_vars
        list of variables to append to class_vars from source domain
    metas
        list of variables to append to metas from source domain

    Returns
    -------
    Domain
    """
    attributes = domain.attributes + tuple(attributes)
    class_vars = domain.class_vars + tuple(class_vars)
    metas = domain.metas + tuple(metas)
    return Domain(attributes, class_vars, metas)


def _table_with_annotation_column(data, values, column_data, var_name):
    var = DiscreteVariable(get_unique_names(data.domain, var_name), values)
    class_vars, metas = data.domain.class_vars, data.domain.metas
    if not data.domain.class_vars:
        class_vars += (var, )
    else:
        metas += (var, )
    domain = Domain(data.domain.attributes, class_vars, metas)
    table = data.transform(domain)
    table[:, var] = column_data.reshape((len(data), 1))
    return table


def create_annotated_table(data, selected_indices):
    """
    Returns data with concatenated flag column. Flag column represents
    whether data instance has been selected (Yes) or not (No), which is
    determined in selected_indices parameter.

    :param data: Table
    :param selected_indices: list or ndarray
    :return: Table
    """
    if data is None:
        return None
    annotated = np.zeros((len(data), 1))
    if selected_indices is not None:
        annotated[selected_indices] = 1
    return _table_with_annotation_column(
        data, ("No", "Yes"), annotated, ANNOTATED_DATA_FEATURE_NAME)


def create_groups_table(data, selection,
                        include_unselected=True,
                        var_name=ANNOTATED_DATA_FEATURE_NAME,
                        values=None):
    if data is None:
        return None
    max_sel = np.max(selection)
    if values is None:
        values = ["G{}".format(i + 1) for i in range(max_sel)]
        if include_unselected:
            values.append("Unselected")
    if include_unselected:
        # Place Unselected instances in the "last group", so that the group
        # colors and scatter diagram marker colors will match
        mask = (selection != 0)
        selection = selection.copy()
        selection[mask] = selection[mask] - 1
        selection[~mask] = selection[~mask] = max_sel
    else:
        mask = np.flatnonzero(selection)
        data = data[mask]
        selection = selection[mask] - 1
    return _table_with_annotation_column(data, values, selection, var_name)
