def __init__(self, filename=None, delimiter=None):
self._e = 0
self._adj = dict()
if filename is not None:
instream = InStream(filename)
while instream.hasNextLine():
line = instream.readLine()
names = line.split(delimiter)
for i in range(1, len(names)):
self.addEdge(names[0], names[i])
def __init__(self, filename=None, delimiter=None):
self._e = 0
self._adj = dict()
if filename is not None:
instream = InStream(filename)
while instream.hasNextLine():
line = instream.readLine()
names = line.split(delimiter)
for i in range(1, len(names)):
self.addEdge(names[0], names[i])
fileName = sys.argv[1]
fieldCount = int(sys.argv[2])
# Create the input stream.
inStream = InStream(fileName + '.csv')
# Create output streams.
outStreams = stdarray.create1D(fieldCount)
for i in range(fieldCount):
file = OutStream(fileName + str(i) + '.txt')
outStreams[i] = file
# Read lines from the input stream and write them to the
# output stream.
while inStream.hasNextLine():
line = inStream.readLine()
fields = line.split(DELIM)
for i in range(fieldCount):
outStreams[i].writeln(fields[i])
#-----------------------------------------------------------------------
# more djia.csv
# Date,Open,High,Low,Close,Volume,Adj. Close*
# 17-Mar-06,11294.94,11294.94,11253.23,11279.65,2549619968,11279.65
# 16-Mar-06,11210.97,11324.80,11176.07,11253.24,2292179968,11253.24
# 15-Mar-06,11149.76,11258.28,11097.23,11209.77,2292999936,11209.77
# ...
# python split.py djia 3
from graph import Graph
from instream import InStream
# Accept the name of a movie-cast file and a delimiter as command-line
# arguments and create the associated performer-performer graph. Write
# to standard output the number of vertices, the average degree,
# the average path length, and the clustering coefficient of the graph.
# Assume that the performer-performer graph is connected so that the
# average page length is defined.
file = sys.argv[1]
delimiter = sys.argv[2]
graph = Graph()
instream = InStream(file)
while instream.hasNextLine():
line = instream.readLine()
names = line.split(delimiter)
for i in range(1, len(names)):
for j in range(i+1, len(names)):
graph.addEdge(names[i], names[j])
degree = smallworld.averageDegree(graph)
length = smallworld.averagePathLength(graph)
cluster = smallworld.clusteringCoefficient(graph)
stdio.writef('number of vertices = %d\n', graph.countV())
stdio.writef('average degree = %7.3f\n', degree)
stdio.writef('average path length = %7.3f\n', length)
stdio.writef('clustering coefficient = %7.3f\n', cluster)
from graph import Graph
from instream import InStream
# Accept the name of a movie-cast file and a delimiter as command-line
# arguments and create the associated performer-performer graph. Write
# to standard output the number of vertices, the average degree,
# the average path length, and the clustering coefficient of the graph.
# Assume that the performer-performer graph is connected so that the
# average page length is defined.
file = sys.argv[1]
delimiter = sys.argv[2]
graph = Graph()
instream = InStream(file)
while instream.hasNextLine():
line = instream.readLine()
names = line.split(delimiter)
for i in range(1, len(names)):
for j in range(i + 1, len(names)):
graph.addEdge(names[i], names[j])
degree = smallworld.averageDegree(graph)
length = smallworld.averagePathLength(graph)
cluster = smallworld.clusteringCoefficient(graph)
stdio.writef('number of vertices = %d\n', graph.countV())
stdio.writef('average degree = %7.3f\n', degree)
stdio.writef('average path length = %7.3f\n', length)
stdio.writef('clustering coefficient = %7.3f\n', cluster)
