#from collections import OrderedDict
#import math
#import operator
import pysal as ps
import numpy as np


def compute_length(v0, v1):
    """
    Compute the euclidean distance between two points.

    Parameters
    ----------
    v0:         sequence in the form x, y
    
    vq:         sequence in the form x, y

    Returns
    --------
    Euclidean distance
    """
    #  Is there a reason we are using `math` as opposed to `numpy` is this function?
    #  Moreover, we are not using `OrderedDict` or `operator` for anything currently in
    #  any of the defined functions in `utils.py`. 
    
    #return math.sqrt((v0[0] - v1[0])**2 + (v0[1] - v1[1])**2)
    return np.sqrt((v0[0] - v1[0])**2 + (v0[1] - v1[1])**2)
    

def get_neighbor_distances(ntw, v0, l):
    edges = ntw.enum_links_node(v0)
    neighbors = {}
    for e in edges:
        if e[0] != v0:
            neighbors[e[0]] = l[e]
        else:
            neighbors[e[1]] = l[e]
    return neighbors


def generatetree(pred):
    tree = {}
    for i, p in enumerate(pred):
        if p == -1:
            #root node
            tree[i] = [i]
            continue
        idx = p
        path = [idx]
        while idx >= 0:
            nextnode = pred[idx]
            idx = nextnode
            if idx >= 0:
                path.append(nextnode)
        tree[i] = path
    return tree


def dijkstra(ntw, cost, node, n=float('inf')):
    """
    Compute the shortest path between a start node and all other nodes in the web.
    
    Parameters
    ----------
    ntw:        object
                PySAL network object
                
    cost:       dict
                key:    tuple
                        (start node, end node)
                value:  float
                        Cost per edge to travel, e.g. distance
    
    node:       int
                Start node ID
    
    n:          float('inf')
                integer break point to stop iteration and return n neighbors
    
    Returns
    -------
    distance:   list
                List of distances from node to all other nodes.
                
    pred:       list
                List of preceeding nodes for traversal route.
    """

    v0 = node
    distance = [float('inf') for x in ntw.node_list]
    idx = ntw.node_list.index(v0)
    distance[ntw.node_list.index(v0)] = 0
    pred = [-1 for x in ntw.node_list]
    a = set()
    a.add(v0)
    while len(a) > 0:
        # Get node with the lowest value from distance.
        dist = float('inf')
        for node in a:
            if distance[node] < dist:
                dist = distance[node]
                v = node
        # Remove that node from the set.
        a.remove(v)
        last = v
        #4. Get the neighbors to the current node.
        neighbors = get_neighbor_distances(ntw, v, cost)
        for v1, indiv_cost in neighbors.items():
            if distance[v1] > distance[v] + indiv_cost:
                distance[v1] = distance[v] + indiv_cost
                pred[v1] = v
                a.add(v1)
    return distance, np.array(pred, dtype=np.int)


def dijkstra_mp(xxx_todo_changeme):
    """
    Compute the shortest path between a start node and all other nodes in the web 
    utilizing multiple cores upon request.
    
    Parameters
    ----------
    ntw:        object
                PySAL network object
                
    cost:       dict
                key:    tuple
                        (start node, end node)
                value:  float
                        Cost per edge to travel, e.g. distance
    
    node:       int
                Start node ID
    
    n:          float('inf')
                integer break point to stop iteration and return n neighbors
    
    Returns
    -------
    distance:   list
                List of distances from node to all other nodes.
    
    pred:       list
                List of preceeding nodes for traversal route.
    """
    (ntw, cost, node) = xxx_todo_changeme
    return dijkstra(ntw, cost, node)
       
    
def squaredDistancePointSegment(point, segment):
    """Find the squared distance between a point and a segment
    
    Parameters
    ---------
    point:      tuple 
                (x,y)
    
    segment:    list 
                List of 2 tuples [(x0,y0), (x1,y1)]
    
    Returns
    -------
    tuple:      2 elements:
                    1. distance squared between point and segment
                    2. array(xb, yb): the nearest point on the segment
    """
    p0,p1 = [np.array(p) for p in segment]
    v = p1 - p0
    p = np.array(point)
    w = p - p0
    c1 = np.dot(w,v)
    if c1 <= 0.:
        # Print 'before p0'
        return np.dot(w.T,w), p0
    c2 = np.dot(v,v)
    if c2 <= c1:
        dp1 = p - p1
        # Print 'after p1'
        return np.dot(dp1.T,dp1), p1
    
    b = c1 / c2
    bv = np.dot(b,v)
    pb = p0 + bv
    d2 = p - pb
    
    return np.dot(d2,d2), pb

    
def snapPointsOnSegments(points, segments):
    """Place points onto closet segment in a set of segments
    
    Arguments
    ---------
    points:     dict
                Point id as key and (x,y) coordinate as value
    
    segments:   list
                Elements are of type pysal.cg.shapes.Chain 
                ** Note **
                        each element is a segment represented as a chain with 
                        *one head and one tail node*, in other words one link only.
              
    Returns
    ------- 
    p2s:        dict
                key:    point id (see points in arguments)
         
                value:  a 2-tuple: ((head, tail), point)
                        where (head, tail) is the target segment, and point is the snapped
                        location on the segment
    """
    
    # Put segments in an Rtree.
    rt = ps.cg.Rtree()
    SMALL = 0.01
    node2segs = {}
    
    for segment in segments:
        head,tail = segment.vertices
        x0,y0 = head
        x1,y1 = tail
        if (x0,y0) not in node2segs:
            node2segs[(x0,y0)] = []
        if (x1,y1) not in node2segs:
            node2segs[(x1,y1)] = []
        node2segs[(x0,y0)].append(segment)
        node2segs[(x1,y1)].append(segment)
        x0,y0,x1,y1 =  segment.bounding_box
        x0 -= SMALL
        y0 -= SMALL
        x1 += SMALL
        y1 += SMALL
        r = ps.cg.Rect(x0,y0,x1,y1)
        rt.insert(segment, r)
         
    # Build a KDtree on segment nodes.
    kt = ps.cg.KDTree(list(node2segs.keys()))
    p2s = {}

    for ptIdx, point in points.items():
        # First, find nearest neighbor segment node for the point.
        dmin, node = kt.query(point, k=1)
        node = tuple(kt.data[node])
        closest = node2segs[node][0].vertices
        
        # Use this segment as the candidate closest segment:  closest
        # Use the distance as the distance to beat:           dmin
        p2s[ptIdx] = (closest, node) # sna
        x0 = point[0] - dmin
        y0 = point[1] - dmin
        x1 = point[0] + dmin
        y1 = point[1] + dmin
        
        # Find all segments with bounding boxes that intersect
        # a query rectangle centered on the point with sides of length 2*dmin.
        candidates = [ cand for cand in rt.intersection([x0,y0,x1,y1])]
        dmin += SMALL
        dmin2 = dmin * dmin
        
        # Of the candidate segments, find the nearest to the query point.
        for candidate in candidates:
            dnc, p2b = squaredDistancePointSegment(point, candidate.vertices)
            if dnc <= dmin2:
                closest = candidate.vertices
                dmin2 = dnc
                p2s[ptIdx] = (closest, p2b)
        
    return p2s