def loadPointCorrespondences(filename):
"""Loads and returns the corresponding points in world (first 2 lines) and image spaces (last 2 lines)"""
from numpy.lib.npyio import loadtxt
from numpy import float32
points = loadtxt(filename, dtype=float32)
return (points[:2, :].T, points[2:, :].T) # (world points, image points)
def makeUserSED(filename):
"""
Returns a model of an SED supplied by the user
"""
from numpy.lib.io import loadtxt
SED = loadtxt(filename)
return SED[:,0],SED[:,1]
def matlab2PointCorrespondences(filename):
'''Loads and converts the point correspondences saved
by the matlab camera calibration tool'''
from numpy.lib.io import loadtxt, savetxt
from numpy.lib.function_base import append
points = loadtxt(filename, delimiter=',')
savetxt(utils.removeExtension(filename)+'-point-correspondences.txt',append(points[:,:2].T, points[:,3:].T, axis=0))
def makeUserSED(filename):
"""
Returns a model of an SED supplied by the user
"""
from numpy.lib.io import loadtxt
SED = loadtxt(filename)
return SED[:, 0], SED[:, 1]
def getSED(name):
"""
Returns a model of the SED, a tuple of (wave,data)
"""
try:
from numpy.lib.io import loadtxt
except:
from numpy import loadtxt
f = open(SEDpath + name + ".sed")
SED = loadtxt(f)
f.close()
return SED[:, 0], SED[:, 1]
def getSED(name):
"""
Returns a model of the SED, a tuple of (wave,data)
"""
try:
from numpy.lib.io import loadtxt
except:
from numpy import loadtxt
f = open(SEDpath+name+".sed")
SED = loadtxt(f)
f.close()
return SED[:,0],SED[:,1]
def filterfromfile(file):
"""
Create a filter model from a file.
"""
try:
from numpy.lib.io import loadtxt
except:
from numpy import loadtxt
from scipy.interpolate import splrep
import scipy
f = open(filterpath + file + ".res")
filter = loadtxt(f)
f.close()
return splrep(filter[:, 0], filter[:, 1], k=1, s=0)
def filterfromfile(file):
"""
Create a filter model from a file.
"""
try:
from numpy.lib.io import loadtxt
except:
from numpy import loadtxt
from scipy.interpolate import splrep
import scipy
f = open(filterpath+file+".res")
filter = loadtxt(f)
f.close()
return splrep(filter[:,0],filter[:,1],k=1,s=0)
import os.path
import numpy.lib.io as io
import numpy.core.multiarray as ma
import matplotlib.pyplot as plt
######################
### Main body
######################
# base name of files
base = "N16_dr10_fr10_br1_bd5"
# plot average throughput
x, y = io.loadtxt(base + ".thr", usecols=[0,1], unpack=True)
plt.close()
plt.plot(x, y, 'o-', linewidth=3)
#plt.axis([0, 100, 0, 40])
plt.xlabel("Number of Sessions per ONU ($n$)")
plt.ylabel("Average Throughput [Mbps]")
#plt.yticks(ma.arange(0, 50, 10))
plt.grid(linestyle='-')
plt.minorticks_on()
plt.show()
plt.savefig(base + ".thr.png", format='png')
# raw_input("Press ENTER to continue ...")
# # plot ECR
# x, y = io.loadtxt(base + ".ecr", usecols=[0,1], unpack=True)
import os.path
import numpy.lib.io as io
import numpy.core.multiarray as ma
import matplotlib.pyplot as plt
######################
### Main body
######################
# base name of files
base = "N1_br1_bd5"
# plot web page delay
x, y = io.loadtxt(base + "_n5.dly.org", usecols=[0,1], unpack=True)
plt.close()
plt.plot(x, y, linewidth=3)
plt.axis([0, 10, 0, 4])
plt.xlabel("Access Rate ($R_D$=$R_F$) [Mbps]")
plt.ylabel("$D_W$ [s]")
plt.xticks(ma.arange(0, 11))
#plt.yticks(ma.arange(0, 50, 10))
plt.grid(linestyle='-')
plt.minorticks_on()
plt.show()
plt.savefig(base + "_n5.dly.png", format='png')
import numpy.core.multiarray as ma
import numpy.lib.function_base as num_base
from scipy import interpolate, optimize
######################
### Main body
######################
# base name of files
base1 = "N16_dr10"
base2 = "br1_bd5"
# get data
plt.close()
x1, y1 = io.loadtxt("../TdmPonWithHttp/N16_dr10_fr10_br1_bd5.ecr", usecols=[0,1], unpack=True)
x2, y2 = io.loadtxt(base1 + "_fr20_" + base2 + ".ecr.new", usecols=[0,1], unpack=True)
x3, y3 = io.loadtxt(base1 + "_fr30_" + base2 + ".ecr.new", usecols=[0,1], unpack=True)
x4, y4 = io.loadtxt(base1 + "_fr40_" + base2 + ".ecr.new", usecols=[0,1], unpack=True)
# # obain 1-d spline curves
# smoothing = 2
#
# tck = interpolate.splrep(x1, y1, s=smoothing)
# x1_new = num_base.linspace(0.01, 100, 100)
# y1_new = interpolate.splev(x1_new, tck, der=0)
#
# tck = interpolate.splrep(x2, y2, s=smoothing)
# x2_new = num_base.linspace(0.01, 100, 100)
# y2_new = interpolate.splev(x2_new, tck, der=0)
#
c='orange'
elif 'Other Pathogen' in d[n]:
c='m'
colorList.append(c)
if s.has_key(n):
sizeList.append(int(s[n]))
else:
sizeList.append(0)
return colorList, sizeList
fileName1="MCL_1.6_OutputModified.txt" % (projectDir1) #output file from MCL clustering
fileName2="phiBase_version4.0_content.txt" % (projectDir1)#file created with script: gettingHostTaxa_fromPHIbase_4.0.py
fileName3="pathClassOnGeneId_PHIid_phiBase_4.0.csv.csv" % (projectDir1) #file created with script: analysisOfPHIbase_v4.0.py
#Read the cluster information into an array
data1=loadtxt(fileName1, dtype='S')
data2=loadtxt(fileName2, dtype='S', delimiter=';')
#d=dict([(e.split(';')[0], e.split(';')[7]) for e in data2])
d=dict()
for e in data2:
k='PHI:'+e[0]
v=e[7]
d.setdefault(k, set()).add(v)
data3=loadtxt(fileName3, dtype='S', delimiter=';')
s=dict([('PHI:'+e[0], e[1]) for e in data3])
#Create an empty dictionary
cluster=dict()
phi=dict()
G=nx.Graph()
#For each element of the array, that is for each line of the file
for row in data1:
# returns a single-argument function which returns interpolated delay given a rate
def f(delay):
return lambda x: interpolate.splev(x, tck, der=0) - delay
######################
### Main loop
######################
# get file name of delay data from the user
in_name = raw_input("The name of the delay data file: ")
# extract the number of sessions & web page delay to X & Y
X, Y = io.loadtxt(in_name, usecols=[0, 1], unpack=True)
# DEBUG
plt.close()
for i in range(0, len(X)):
# calculate ECR for a given pair of the number of sessions (n) & web page delay (web_page_delay)
n = X[i]
delay = Y[i]
# DEBUG
#print "for n = %d and delay = %f:" % (n, delay)
# extract rate & delay to x & y from the data file for ECR reference model
ecr_basename = "../EcrReferenceWithHttp/N1_br1_bd5_n"
ecr_name = ecr_basename + str(int(n)) + ".dly"
import os.path
import numpy.lib.io as io
import numpy.core.multiarray as ma
import matplotlib.pyplot as plt
######################
### Main body
######################
# base name of files
base = "N1_br1_bd5"
# plot web page delay
x1, y1 = io.loadtxt(base + "_n1.dly", usecols=[0,1], unpack=True)
x2, y2 = io.loadtxt(base + "_n10.dly", usecols=[0,1], unpack=True)
x3, y3 = io.loadtxt(base + "_n100.dly", usecols=[0,1], unpack=True)
x4, y4 = io.loadtxt(base + "_n400.dly", usecols=[0,1], unpack=True)
plt.close()
l1, l2, l3, l4 = plt.plot(x1, y1, 'bo-', x2, y2, 'r*-', x3, y3, 'gD-', x4, y4, 'c^-', linewidth=3)
plt.legend((l1, l2, l3, l4), ('$n$=1', '$n$=10', '$n$=100', '$n$=400'), 'center right')
plt.axis([0, 10, 0, 100])
plt.xlabel("Access Rate ($R_D$=$R_F$) [Mbps]")
plt.ylabel("$D_W$ [s]")
plt.xticks(ma.arange(0, 11))
#plt.yticks(ma.arange(0, 50, 10))
plt.grid(linestyle='-')
plt.minorticks_on()
plt.show()
import numpy.core.multiarray as ma
import numpy.lib.function_base as num_base
from scipy import interpolate, optimize
######################
### Main body
######################
# base name of files
base1 = "N16_dr10"
base2 = "br1_bd5"
# get data
plt.close()
x1, y1 = io.loadtxt("../TdmPonWithHttp/N16_dr10_fr10_br1_bd5.ecr",
usecols=[0, 1],
unpack=True)
x2, y2 = io.loadtxt(base1 + "_fr20_" + base2 + ".ecr.new",
usecols=[0, 1],
unpack=True)
x3, y3 = io.loadtxt(base1 + "_fr30_" + base2 + ".ecr.new",
usecols=[0, 1],
unpack=True)
x4, y4 = io.loadtxt(base1 + "_fr40_" + base2 + ".ecr.new",
usecols=[0, 1],
unpack=True)
# # obain 1-d spline curves
# smoothing = 2
#
# tck = interpolate.splrep(x1, y1, s=smoothing)
def loadPointCorrespondences(filename):
'''Loads and returns the corresponding points in world (first 2 lines) and image spaces (last 2 lines)'''
from numpy import loadtxt, float32
points = loadtxt(filename, dtype=float32)
return (points[:2,:].T, points[2:,:].T) # (world points, image points)
# (C) 2009 Kyeong Soo (Joseph) Kim
import os.path
import numpy.lib.io as io
import numpy.core.multiarray as ma
import matplotlib.pyplot as plt
######################
### Main body
######################
# base name of files
base = "N16_dr10_fr10_br1_bd5"
# plot average throughput
x, y = io.loadtxt(base + ".thr", usecols=[0, 1], unpack=True)
plt.close()
plt.plot(x, y, 'o-', linewidth=3)
#plt.axis([0, 100, 0, 40])
plt.xlabel("Number of Sessions per ONU ($n$)")
plt.ylabel("Average Throughput [Mbps]")
#plt.yticks(ma.arange(0, 50, 10))
plt.grid(linestyle='-')
plt.minorticks_on()
plt.show()
plt.savefig(base + ".thr.png", format='png')
# raw_input("Press ENTER to continue ...")
# # plot ECR
# x, y = io.loadtxt(base + ".ecr", usecols=[0,1], unpack=True)
if s.has_key(n):
sizeList.append(int(s[n]))
else:
sizeList.append(0)
return colorList, sizeList
fileName1="MCL_1.6_Output.txt" #% (projectDir1)
fileName2="phiBaseFile_withHostTaxaGrouping_noPHIid.txt" #% (projectDir1)
fileName3="pathClassOnGeneId.csv" #% (projectDir1)
#Read the cluster information into an array
data1=loadtxt(fileName1, dtype='S')
data2=loadtxt(fileName2, dtype='S', delimiter=';')
#d=dict([(e.split(';')[0], e.split(';')[7]) for e in data2])
d=dict()
for e in data2:
k='PHI:'+e[0]
v=e[7]
d.setdefault(k, set()).add(v)
data3=loadtxt(fileName3, dtype='S', delimiter=';')
s=dict([('PHI:'+e[0], e[1]) for e in data3])
#Create an empty dictionary
cluster=dict()
tck = ()
# returns a single-argument function which returns interpolated delay given a rate
def f(delay):
return lambda x: interpolate.splev(x, tck, der=0) - delay
######################
### Main loop
######################
# get file name of delay data from the user
in_name = raw_input("The name of the delay data file: ")
# extract the number of sessions & web page delay to X & Y
X, Y = io.loadtxt(in_name, usecols=[0,1], unpack=True)
# DEBUG
plt.close()
for i in range(0, len(X)):
# calculate ECR for a given pair of the number of sessions (n) & web page delay (web_page_delay)
n = X[i]
delay = Y[i]
# DEBUG
#print "for n = %d and delay = %f:" % (n, delay)
# extract rate & delay to x & y from the data file for ECR reference model
ecr_basename = "../EcrReferenceWithHttp/N1_br1_bd5_n"
ecr_name = ecr_basename + str(int(n)) + ".dly"
import os.path
import numpy.lib.io as io
import numpy.core.multiarray as ma
import matplotlib.pyplot as plt
######################
### Main body
######################
# base name of files
base = "N16_dr10_fr10_br1_bd5"
# plot web page delay
x, y = io.loadtxt(base + ".dly", usecols=[0,1], unpack=True)
plt.close()
plt.plot(x, y, 'o-', linewidth=3)
plt.axis([0, 100, 0, 40])
plt.xlabel("Number of Sessions per ONU ($n$)")
plt.ylabel("$D_W$ [s]")
plt.yticks(ma.arange(0, 50, 10))
plt.grid(linestyle='-')
plt.minorticks_on()
plt.show()
plt.savefig(base + ".dly.png", format='png')
raw_input("Press ENTER to continue ...")
# plot ECR
x, y = io.loadtxt(base + ".ecr", usecols=[0,1], unpack=True)
