<commit_before># coding: utf-8
# Copyright 2015
# <NAME> <<EMAIL>> and <NAME> <<EMAIL>>
import warnings

import pandas as pd
import numpy as np

from . import INKSCAPE_NSMAP
from .draw import draw_lines_svg_layer as _draw_lines_svg_layer


def extend_shapes(df_shapes, axis, distance):
    '''
    Extend shape/polygon outline away from polygon center point by absolute
    distance.
    '''
    df_shapes_i = df_shapes.copy()
    offsets = df_shapes_i[axis + '_center_offset'].copy()
    offsets[offsets < 0] -= distance
    offsets[offsets >= 0] += distance
    df_shapes_i[axis] = df_shapes_i[axis + '_center'] + offsets
    return df_shapes_i


def extract_adjacent_shapes(df_shapes, shape_i_column, extend=.5):
    '''
    Generate list of connections between "adjacent" polygon shapes based on
    geometrical "closeness".

    Arguments
    ---------

     - `df_shapes`: Table of polygon shape vertices (one row per vertex).
         * Table rows with the same value in the `path_id` column are grouped
           together as a polygon.
    '''
    # Find corners of each solid shape outline.
    # Extend x coords by abs units
    df_scaled_x = extend_shapes(df_shapes, 'x', extend)
    # Extend y coords by abs units
    df_scaled_y = extend_shapes(df_shapes, 'y', extend)

    df_corners = df_shapes.groupby(shape_i_column).agg({'x': ['min', 'max'],
                                                        'y': ['min', 'max']})

    # Find adjacent electrodes
    row_list = []

    for shapeNumber in df_shapes[shape_i_column].drop_duplicates():
        df_stretched = df_scaled_x[df_scaled_x[shape_i_column]
                                   .isin([shapeNumber])]
        xmin_x, xmax_x, ymin_x, ymax_x = (df_stretched.x.min(),
                                          df_stretched.x.max(),
                                          df_stretched.y.min(),
                                          df_stretched.y.max())
        df_stretched = df_scaled_y[df_scaled_y[shape_i_column]
                                   .isin([shapeNumber])]
        xmin_y, xmax_y, ymin_y, ymax_y = (df_stretched.x.min(),
                                          df_stretched.x.max(),
                                          df_stretched.y.min(),
                                          df_stretched.y.max())

        #Some conditions unnecessary if it is assumed that electrodes don't overlap
        adjacent = df_corners[
            ((df_corners.x['min'] < xmax_x) & (df_corners.x['max'] >= xmax_x)
            # Check in x stretched direction
            |(df_corners.x['min'] < xmin_x) & (df_corners.x['max'] >= xmin_x))
            # Check if y is within bounds
            & (df_corners.y['min'] < ymax_x) & (df_corners.y['max'] > ymin_x) |

            #maybe do ymax_x - df_corners.y['min'] > threshold &
            #  df_corners.y['max'] - ymin_x > threshold

            ((df_corners.y['min'] < ymax_y) & (df_corners.y['max'] >= ymax_y)
             # Checks in y stretched direction
             |(df_corners.y['min'] < ymin_y) & (df_corners.y['max'] >= ymin_y))
             # Check if x in within bounds
            & ((df_corners.x['min'] < xmax_y) & (df_corners.x['max'] > xmin_y))
        ].index.values

        for shape in adjacent:
            temp_dict = {}
            reverse_dict = {}

            temp_dict ['source'] = shapeNumber
            reverse_dict['source'] = shape
            temp_dict ['target'] = shape
            reverse_dict['target'] = shapeNumber

            if(reverse_dict not in row_list):
                row_list.append(temp_dict)

    df_connected = (pd.DataFrame(row_list)[['source', 'target']]
                    .sort(axis=1, ascending=True).sort(['source', 'target']))
    return df_connected


def get_adjacency_matrix(df_connected):
    '''
    Return matrix where $a_{i,j} = 1$ indicates polygon $i$ is connected to
    polygon $j$.

    Also, return mapping (and reverse mapping) from original keys in
    `df_connected` to zero-based integer index used for matrix rows and
    columns.
    '''
    sorted_path_keys = np.sort(np.unique(df_connected[['source', 'target']]
                                         .values.ravel()))
    indexed_paths = pd.Series(sorted_path_keys)
    path_indexes = pd.Series(indexed_paths.index, index=sorted_path_keys)

    adjacency_matrix = np.zeros((path_indexes.shape[0], ) * 2, dtype=int)
    for i_key, j_key in df_connected[['source', 'target']].values:
        i, j = path_indexes.loc[[i_key, j_key]]
        adjacency_matrix[i, j] = 1
        adjacency_matrix[j, i] = 1
    return adjacency_matrix, indexed_paths, path_indexes


def extract_connections(svg_source, shapes_canvas, line_layer='Connections',
                        line_xpath=None, namespaces=None):
    '''
    Load all lines from a layer of an SVG source.  For each line, if endpoints
    overlap distinct shapes in `shapes_canvas`, add connection between
    overlapped shapes.<commit_msg>[FIX] Update to new pandas sort API<commit_after># coding: utf-8
# Copyright 2015
# <NAME> <<EMAIL>> and <NAME> <<EMAIL>>
import warnings

import pandas as pd
import numpy as np

from . import INKSCAPE_NSMAP
from .draw import draw_lines_svg_layer as _draw_lines_svg_layer


def extend_shapes(df_shapes, axis, distance):
    '''
    Extend shape/polygon outline away from polygon center point by absolute
    distance.
    '''
    df_shapes_i = df_shapes.copy()
    offsets = df_shapes_i[axis + '_center_offset'].copy()
    offsets[offsets < 0] -= distance
    offsets[offsets >= 0] += distance
    df_shapes_i[axis] = df_shapes_i[axis + '_center'] + offsets
    return df_shapes_i


def extract_adjacent_shapes(df_shapes, shape_i_column, extend=.5):
    '''
    Generate list of connections between "adjacent" polygon shapes based on
    geometrical "closeness".

    Arguments
    ---------

     - `df_shapes`: Table of polygon shape vertices (one row per vertex).
         * Table rows with the same value in the `path_id` column are grouped
           together as a polygon.
    '''
    # Find corners of each solid shape outline.
    # Extend x coords by abs units
    df_scaled_x = extend_shapes(df_shapes, 'x', extend)
    # Extend y coords by abs units
    df_scaled_y = extend_shapes(df_shapes, 'y', extend)

    df_corners = df_shapes.groupby(shape_i_column).agg({'x': ['min', 'max'],
                                                        'y': ['min', 'max']})

    # Find adjacent electrodes
    row_list = []

    for shapeNumber in df_shapes[shape_i_column].drop_duplicates():
        df_stretched = df_scaled_x[df_scaled_x[shape_i_column]
                                   .isin([shapeNumber])]
        xmin_x, xmax_x, ymin_x, ymax_x = (df_stretched.x.min(),
                                          df_stretched.x.max(),
                                          df_stretched.y.min(),
                                          df_stretched.y.max())
        df_stretched = df_scaled_y[df_scaled_y[shape_i_column]
                                   .isin([shapeNumber])]
        xmin_y, xmax_y, ymin_y, ymax_y = (df_stretched.x.min(),
                                          df_stretched.x.max(),
                                          df_stretched.y.min(),
                                          df_stretched.y.max())

        #Some conditions unnecessary if it is assumed that electrodes don't overlap
        adjacent = df_corners[
            ((df_corners.x['min'] < xmax_x) & (df_corners.x['max'] >= xmax_x)
            # Check in x stretched direction
            |(df_corners.x['min'] < xmin_x) & (df_corners.x['max'] >= xmin_x))
            # Check if y is within bounds
            & (df_corners.y['min'] < ymax_x) & (df_corners.y['max'] > ymin_x) |

            #maybe do ymax_x - df_corners.y['min'] > threshold &
            #  df_corners.y['max'] - ymin_x > threshold

            ((df_corners.y['min'] < ymax_y) & (df_corners.y['max'] >= ymax_y)
             # Checks in y stretched direction
             |(df_corners.y['min'] < ymin_y) & (df_corners.y['max'] >= ymin_y))
             # Check if x in within bounds
            & ((df_corners.x['min'] < xmax_y) & (df_corners.x['max'] > xmin_y))
        ].index.values

        for shape in adjacent:
            temp_dict = {}
            reverse_dict = {}

            temp_dict ['source'] = shapeNumber
            reverse_dict['source'] = shape
            temp_dict ['target'] = shape
            reverse_dict['target'] = shapeNumber

            if(reverse_dict not in row_list):
                row_list.append(temp_dict)

    df_connected = (pd.DataFrame(row_list)[['source', 'target']]
                    .sort_index(axis=1, ascending=True)
                    .sort_values(['source', 'target']))
    return df_connected


def get_adjacency_matrix(df_connected):
    '''
    Return matrix where $a_{i,j} = 1$ indicates polygon $i$ is connected to
    polygon $j$.

    Also, return mapping (and reverse mapping) from original keys in
    `df_connected` to zero-based integer index used for matrix rows and
    columns.
    '''
    sorted_path_keys = np.sort(np.unique(df_connected[['source', 'target']]
                                         .values.ravel()))
    indexed_paths = pd.Series(sorted_path_keys)
    path_indexes = pd.Series(indexed_paths.index, index=sorted_path_keys)

    adjacency_matrix = np.zeros((path_indexes.shape[0], ) * 2, dtype=int)
    for i_key, j_key in df_connected[['source', 'target']].values:
        i, j = path_indexes.loc[[i_key, j_key]]
        adjacency_matrix[i, j] = 1
        adjacency_matrix[j, i] = 1
    return adjacency_matrix, indexed_paths, path_indexes


def extract_connections(svg_source, shapes_canvas, line_layer='Connections',
                        line_xpath=None, namespaces=None):
    '''
    Load all lines from a layer of an SVG source.  For each line, if endpoints
    overlap distinct shapes in `shapes_canvas`, add connection between
    overlapped shapes.
{'fraction_non_alphanumeric': 0.11342685370741483, 'fraction_numerical': 0.0028056112224448897, 'mean_word_length': 2.174618320610687, 'pattern_counts': {'":': 0, '<': 29, '<?xml version=': 0, '>': 33, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 36, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6056593', 'n_tokens_mistral': 3187, 'n_tokens_neox': 3039, 'n_words': 736}