import sys
import numpy
import scipy.spatial
import matplotlib.pyplot as pyplot
import math

HOURS_PER_COLUMN = 2

def neighbors(triangulation, k):
    vnv = triangulation.vertex_neighbor_vertices
    indices = vnv[0]
    indptr = vnv[1]
    return indptr[indices[k]:indices[k+1]]

print sys.stdin.readline().rstrip(), "\tdev_time"
r8_coords = []
split_lines = []
for line in sys.stdin:
    fields = line.split()
    split_lines.append(fields)
    if not fields[2] == 'r8': continue
    r8_coords.append((float(fields[3]), float(fields[4])))
r8_coords = numpy.array(r8_coords)
tri = scipy.spatial.Delaunay(r8_coords)
c_distances = []
for r8_i in xrange(tri.points.shape[0]):
    for x in neighbors(tri, r8_i):
        angle = math.atan(
            math.fabs((r8_coords[x,1]-r8_coords[r8_i,1]))/
            math.fabs((r8_coords[x,0]-r8_coords[r8_i,0])))
        # 0.52 radians = 30 deg, 1.04 radians = 60 deg
        if angle > 0.52 and angle < 1.04:
            c_distances.append(math.fabs((r8_coords[x,0]-r8_coords[r8_i,0])))
mean_column_distance = numpy.mean(c_distances)
anterior_most_column = numpy.min(r8_coords[:,0])

for split_line in split_lines:
    centroid_x = float(split_line[3])
    distance_from_anterior = centroid_x - anterior_most_column
    time =  distance_from_anterior * HOURS_PER_COLUMN/mean_column_distance
    print "\t".join(split_line + [str(time)])