def snr_mat_f(mchvec, reds, lum_dist, fmin, fmax, fvec, finteg, tobs, sn_f): '''''' mch_fmat=np.transpose(np.tile(mchvec, (len(reds), len(fvec), finteg, 1) ), axes=(0,3,1,2)) z_fmat=np.transpose(np.tile(reds, (len(mchvec), len(fvec), finteg, 1) ),axes=(3,0,1,2)) f_fmat=np.transpose(np.tile(fvec, (len(reds), len(mchvec), finteg, 1) ), axes=(0,1,3,2)) finteg_fmat=np.transpose(np.tile(np.arange(finteg), (len(reds), len(mchvec), len(fvec), 1) ), axes=(0,1,2,3)) stshape=np.shape(z_fmat) #Standard shape of all matrices that I will use. DL_fmat=np.transpose(np.tile(lum_dist, (len(mchvec), len(fvec), finteg, 1) ),axes=(3,0,1,2)) #Luminosity distance in Mpc. flim_fmat=A8.f_cut(1./4., 2.*mch_fmat*2.**(1./5.))*1./(1.+z_fmat) #The symmetric mass ratio is 1/4, since I assume equal masses. flim_det=np.maximum(np.minimum(fmax, flim_fmat), fmin) #The isco frequency limited to the detector window. tlim_fmat=CM.tafter(mch_fmat, f_fmat, flim_fmat, z_fmat) #By construction, f_mat cannot be smaller than fmin or larger than fmax (which are the limits imposed by the detector). fmin_fmat=np.minimum(f_fmat, flim_det) #I impose that the minimum frequency cannot be larger than the fisco. fmaxobs_fmat=flim_det.copy() #fmaxobs_fmat=fmin_fmat.copy() fmaxobs_fmat[tobs<tlim_fmat]=CM.fafter(mch_fmat[tobs<tlim_fmat], z_fmat[tobs<tlim_fmat], f_fmat[tobs<tlim_fmat], tobs) fmax_fmat=np.minimum(fmaxobs_fmat, flim_det) #The maximum frequency (after an observation tobs) cannot exceed fisco or the maximum frequency of the detector. integconst=(np.log10(fmax_fmat)-np.log10(fmin_fmat))*1./(finteg-1) finteg_fmat=fmin_fmat*10**(integconst*finteg_fmat) sn_vec=sn_f(fvec)########## sn_fmat=sn_f(finteg_fmat) #Noise spectral density. #htilde_fmat=A8.htilde_f(1./4., 2.*mch_fmat*2**(1./5.), z_fmat, DL_fmat, f_fmat) htilde_fmat=A8.htilde_f(1./4., 2.*mch_fmat*2**(1./5.), z_fmat, DL_fmat, finteg_fmat) py.loglog(finteg_fmat[0,0,:,0],htilde_fmat[0,0,:,0]**2.) py.loglog(finteg_fmat[0,0,:,0],sn_fmat[0,0,:,0]) snrsq_int_fmat=4.*htilde_fmat**2./sn_fmat #Integrand of the S/N square. snrsq_int_m_fmat=0.5*(snrsq_int_fmat[:,:,:,1:]+snrsq_int_fmat[:,:,:,:-1]) #Integrand at the arithmetic mean of the infinitesimal intervals. df_fmat=np.diff(finteg_fmat, axis=3) #Infinitesimal intervals. snr_full_fmat=np.sqrt(np.sum(snrsq_int_m_fmat*df_fmat,axis=3)) #S/N as a function of redshift, mass and frequency. fopt=fvec[np.argmax(snr_full_fmat, axis=2)] #Frequency at which the S/N is maximum, for each pixel of redshift and mass. snr_opt=np.amax(snr_full_fmat, axis=2) #Maximum S/N at each pixel of redshift and mass. snr_min=snr_full_fmat[:,:,0] return snr_opt
def simulate_model(self):
# simulate the model
# run simulation and determine the temp_in and temp_mat
self.__dict__.update(COMMON.simulate_model(**self.__dict__))
# update data for peers and history records
self.__dict__.update(COMMON.broadcast(**self.__dict__))
# update counter, which counts how long the device is at a certain status
self.__dict__.update(COMMON.adjust_counter(**self.__dict__))
return self.temp_in, self.temp_mat
def decide(self):
# decide to approve or deny the proposed status
# get device priority
self.__dict__.update(COMMON.adjust_priority(**self.__dict__))
# interpret ldc_signal
self.__dict__.update(COMMON.interpret_signal(**self.__dict__))
# adjust local limit
self.__dict__.update(COMMON.adjust_limit(**self.__dict__))
# save old status
self.old_status = self.a_status # save old status
# decide on the next status
self.__dict__.update(COMMON.get_a_status(**self.__dict__))
return self.a_status
def get_gridNode(self):
"""
获取机器或节点
:return:
"""
filepath = os.getcwd()
fig = FIG.ReadConfig(filepath)
Host = fig.getConfigValue('Host')
driver = CM.get_girdDriver(Host)
return driver
def htilde_f(nu, mtot, z, lumdist, fvec): '''''' mch=nu**(3./5.)*mtot fmer=A8.f_mer(nu, mtot)*1./(1.+z) frin=A8.f_rin(nu, mtot)*1./(1.+z) fcut=A8.f_cut(nu, mtot)*1./(1.+z) fsig=A8.f_sig(nu, mtot)*1./(1.+z) camp=CM.htilde_f(mch, z, lumdist, fmer) aeff=np.zeros(np.shape(fvec)) selecti=(fvec<fmer) aeff[selecti]+=A8.aeff_low(fvec[selecti], fmer, camp) selecti=(fvec>=fmer)&(fvec<frin) aeff[selecti]+=A8.aeff_mid(fvec[selecti], fmer, camp) selecti=(fvec>=frin)&(fvec<fcut) aeff[selecti]+=A8.aeff_upp(fvec[selecti], fmer, frin, fsig, camp) return aeff
import COMMON
class Solution:
# @param root, a tree node
# @param sum, an integer
# @return a boolean
def hasPathSum(self, root, sum):
if root==None:
return False;
if root.left==None and root.right==None and root.val==sum:
return True;
if (self.hasPathSum(root.left,sum-root.val)):
return True;
if (self.hasPathSum(root.right,sum-root.val)):
return True;
return False;
s=Solution();
print s.hasPathSum(COMMON.build_tree("{5,4,8,11,#,13,4,7,2,#,#,#,1}"),22);
print s.hasPathSum(COMMON.build_tree("{5,4,8,11,#,13,4,7,2,#,#,#,1}"),21);
def Keyboard(key, x, y):
global TOGGLE_FULLSCREEN,TOGGLE_LIGHTING,TOGGLE_GRID,TOGGLE_WIREFRAME,TOGGLE_BONES,TOGGLE_3D,TOGGLE_ORTHO
global _mode,DIR
global __MODEL_DATA,__BONE_DATA
#//--// need a GUI handler for these
if key == chr(9): #import model
typenames,modules,modnames,ihandlers,decmpr = [],[],[],[],[]; iftypes,isupport = [],[]
for M,D,I in COMMON.__Scripts[0][0]: #get model import scripts
if D[1] != ('',['']): #script has model info (not sure if it's safe to remove this yet)
iftypes+=[(D[1][0],tuple(["*.%s"%T for T in D[1][1]]))]
for T in D[1][1]:
try: isupport.index("*.%s"%T) #is this file type already supported?
except: isupport+=["*.%s"%T] #add the current file type to the supported types list
modnames+=[D[1][0]] #displayed in the GUI or Tk fiter
typenames+=[T] #filetype
modules+=[M] #current script
ihandlers+=[I] #included image handlers
#----- Tkinter dialog (will be replaced)
_in=askopenfilename(title='Import Model', filetypes=[('Supported', " ".join(isupport))]+iftypes)
#-----
if _in=='': pass #action cancelled
else:
COMMON.__functions=[0,0,0,0] #prevent unwanted initialization
#this block will change once I use my own dialog
#Tkinter doesn't return the filter ID
#-----
it = _in.split('.')[-1]
if typenames.count(it)>1:
print '\nThis filetype is used by multiple scripts:\n'
scr = []
for idx,ft in enumerate(typenames):
if ft==it: scr+=[[modnames[idx],modules[idx]]]
for I,NM in enumerate(scr): print ' %i - %s'%(I,NM[0])
print
sid=input('Please enter the script ID here: ')
i=__import__(scr[sid][1])
else:
ti=typenames.index(it)
i=__import__(modules[ti])
COMMON.__ReloadScripts() #check for valid changes to the scripts
#-----
try: #can we get our hands on the file?
COMMON.ImportFile(_in,1) #set the file data
global Libs; __Libs=Libs #remember last session in case of a script error
Libs=[[],[],[],[],[["Def_Scene",[]]],[]] #reset the data for importing
print 'Converting from import format...'
try: #does the script contain any unfound errors?
__LOG('-- importing %s --\n'%_in.split('/')[-1])
i.ImportModel(it,None)
print 'Verifying data...'
glNewList(__MODEL_DATA, GL_COMPILE); __M(); glEndList()
glNewList(__BONE_DATA, GL_COMPILE); __B(); glEndList()
print 'Updating Viewer\n'
glutSetWindowTitle("Universal Model Converter v3.0a (dev5) - %s" % _in.split('/')[-1])
#export UMC session data
l=open('session.ses','w')
l.write(str([1,Libs]))
l.close()
COMMON.__ClearFiles() #clear the file data to be used for writing
except:
Libs=__Libs
print "Error! Check 'session-info.log' for more details.\n"
import traceback
typ,val,tb=sys.exc_info()#;tb=traceback.extract_tb(i[2])[0]
traceback.print_exception(
typ,val,tb#,
#limit=2,
#file=sys.stdout
)
print
__Libs=[] #save memory usage
except: pass #an error should already be thrown
__WLOG(0) #write log
COMMON.__CleanScripts() #remove pyc files
if key == chr(5): #export model
COMMON.__ClearFiles() #clear the file data again... just in case
etypenames,emodules,emodnames,ehandlers = [],[],[],[]; eftypes = []
for M,D,I in COMMON.__Scripts[0][1]:
if D[1] != ('',['']): #has model info
eftypes+=[(D[1][0],tuple(["*.%s"%T for T in D[1][1]]))]
for T in D[1][1]:
emodnames+=[D[1][0]]
etypenames+=[T]
emodules+=[M]
ehandlers+=[I]
#Tkinter dialog (will be replaced)
#-----
_en=asksaveasfilename(title='Export Model', filetypes=eftypes, defaultextension='.ses')
#-----
if _en=='': pass
else:
COMMON.__functions=[0,0,0,0] #prevent unwanted initialization
#this block will change once I use my own dialog
#Tkinter doesn't return the filter ID
#-----
et = _en.split('.')[-1]
if etypenames.count(et)>1:
print '\nThis filetype is used by multiple scripts:\n'
scr = []
for idx,ft in enumerate(etypenames):
if ft==et: scr+=[[emodnames[idx],emodules[idx]]]
for I,NM in enumerate(scr): print ' %i - %s'%(I,NM[0])
print
sid=input('Please enter the script ID here: ')
e=__import__(scr[sid][1])
else:
e=__import__(emodules[etypenames.index(et)])
COMMON.__ReloadScripts() #check for valid changes to the scripts
#-----
'''
try:
COMMON.ExportFile(_en) #add the file to the data space
print 'converting to export format...'
e.ExportModel(et,None)
COMMON.__WriteFiles()
print 'Done!'
except:
print "Error! Check 'session-info.log' for details.\n"
'''
COMMON.ExportFile(_en) #add the file to the data space
print 'converting to export format...'
e.ExportModel(et,None)
COMMON.__WriteFiles()
print 'Refreshing Viewer\n'
#'''
__WLOG(_mode) #write log
COMMON.__CleanScripts() #remove pyc files
def propose_demand(self):
# This function proposes the demand and status of the device for the next time step
try:
# determine if the device is connected
# update n_usage... used as index for list_starts, and list_ends (the schedule)
self.__dict__.update(COMMON.get_n_usage(**self.__dict__))
# get the data about when the device should start and end
self.__dict__.update(COMMON.get_hour(**self.__dict__))
# convert schedules from hours of the day to unix
self.__dict__.update(COMMON.get_unix(**self.__dict__))
# based on unix_start and unix_end determine if the device is connected at current unixtime
self.__dict__.update(COMMON.is_connected(**self.__dict__))
# determine device status
# get flexibility
self.__dict__.update(COMMON.get_flexibility(**self.__dict__))
# get state of charge
self.__dict__.update(COMMON.get_soc(**self.__dict__))
# get job status
self.__dict__.update(COMMON.get_job_status(**self.__dict__))
# determine mode of the device
self.__dict__.update(COMMON.get_mode(**self.__dict__))
# propose a status for the device (i.e., on or off)
self.__dict__.update(COMMON.get_p_status(**self.__dict__))
# determine device demand for this timestep
# propose a power demand of the device
self.__dict__.update(COMMON.get_p_demand(**self.__dict__))
# determine ramping and shedding potential
# determine if can ramp or can shed
self.__dict__.update(COMMON.check_ramp_shed(**self.__dict__))
# calculate ramping power
self.__dict__.update(COMMON.get_ramp_power(**self.__dict__))
# calculate shedding power
self.__dict__.update(COMMON.get_shed_power(**self.__dict__))
except Exception as e:
print("Error propose_demand:", e)
return 0
import pylab as py from COMMON import nanosec,yr,week,grav,msun,light,mpc,hub0,h0,omm,omv import COMMON as CM import USEFUL as UF from matplotlib import colors from scipy import integrate, interpolate import Formulas_AjithEtAl2008 as A8 #Input parameters: from INPUT_PARAMETERS import * outputdir='../data/output/' if cluster=='1': outputdir='/lustre/projects/p002_swin/prosado/horizon/data/output/' outputfile=outputdir+'snr_z_vs_mz_'+detector+'_'+flim_model+'_mat' #File to save data. overwrite=CM.z_vs_mc_check(outputdir, outputfile, overwrite) #Check if output directory and files already exist. #Load detector's sensitivity. fvecd, sn=CM.detector_f(detector, factor) sn_f=interpolate.interp1d(fvecd,sn) fmin, fmax=min(fvecd), max(fvecd) fmin*=1.000001 fmax*=0.99999 #I reduce the upper frequency slightly, so that there are no issues when extrapolating the sensitivity curve close to this value (the issue appears only when evaluating the sensitivity exactly at fmax). if detector in ['EPTA', 'PPTA']: if (1./tobs)>fmin: fmin=1./tobs else: print 'The minimum frequency of the sensitivity curve is larger than 1/T! The noise cannot be extrapolated to lower frequencies. Exiting...' exit() fvec=np.logspace(np.log10(fmin),np.log10(fmax),fbins)
#fmin=min(fvecd)
fmax=max(fvecd)
fvec=np.logspace(np.log10(fmin),np.log10(fmax),fbins)
#fvec=np.arange(fmin,fmax,fbin)
svec=np.interp(fvec,fvecd,sn) #Power spectral density interpolated.
fvec_m=0.5*(fvec[1:]+fvec[:-1]) #Vector of frequencies centred at the arithmetic mean of the bin.
svec_m=np.interp(fvec_m,fvecd,sn) #Power spectral density interpolated at the arithmetic mean of the bin.
#Create vector of chirp mass.
mchvec=np.logspace(minmch, maxmch, mchbins)
#Calculate luminosity distance and similar functions.
reds=np.logspace(np.log10(minreds),np.log10(maxreds),zbins) #Vector of redshifts logarithmically spaced.
reds_m=0.5*(reds[1:]+reds[:-1]) #Vector of redshifts at the arithmetic mean of the bin.
lum_dist=CM.comdist(reds_m)*(1.+reds_m) #Luminosity distance in Mpc.
#Choose plotting options that look optimal for the paper.
fig_width = 3.4039
goldenmean=(np.sqrt(5.)-1.0)/2.0
fig_height = fig_width * goldenmean
sizepoints=8
legendsizepoints=4.5
py.rcParams.update({
'backend': 'ps',
'ps.usedistiller': 'xpdf',
'text.usetex': True,
'figure.figsize': [fig_width, fig_height],
'axes.titlesize': sizepoints,
'axes.labelsize': sizepoints,
'text.fontsize': sizepoints,
mchfiles.append(plotfiles[mi])
indisort=np.array(mchvec).argsort()
mchvec=np.array(mchvec)[indisort]
mchfiles=np.array(mchfiles)[indisort]
#Optimal plotting options.
import PARAMETER_PLOTS
left, right, top, bottom, cb_fraction=0.13, 0.94, 0.96, 0.16, 0.08 #Borders of the plot.
for mi in xrange(len(mchfiles)):
mchi=mchvec[mi]
print 'Plot for chirp mass 10^{%e} msun.' %mchi
print
mch=10**(mchi) #Chirp mass of the BH binary.
m=mch*2**(1./5.) #Mass of each individual BH, assuming equal masses.
nu=CM.nu_f(m,m) #Symmetric mass ratio.
mtot=2.*m #Total mass.
data=np.load(inputdatadir+mchfiles[mi])[()]
snr_final_mat=data['snr_mat']
f_mat=data['f_mat']
z_mat=data['z_mat']
z_app=data['z_app']
xmin,xmax=np.amin(f_mat),np.amax(f_mat) #Edges of the x-axis.
ymin,ymax=np.amin(z_mat),np.amax(z_mat) #Edges of the y-axis.
lso_mat=np.zeros(np.shape(z_mat))
if flim_model=='ISCO':
flim_mat=CM.felso(m, m)*1./(1.+z_mat)
elif flim_model=='A8':
from INPUT_PARAMETERS import * fmin=1./tobs fmax=1./cad fmin=1 fmax=1000 fvec=np.logspace(np.log10(fmin), np.log10(fmax), fbins) m1=10**(1.) m2=m1 dist=1000.*mpc nu=A8.nu_f(m1,m2) mtot=m1+m2 mch=CM.mchirp(m1, m2) print np.log10(mch) fmer=A8.f_mer(nu, mtot) frin=A8.f_rin(nu, mtot) fcut=A8.f_cut(nu, mtot) fsig=A8.f_sig(nu, mtot) flso=CM.felso(m1,m2) py.ion() fvec_low=np.zeros(len(fvec)) fvec_mid=np.zeros(len(fvec)) fvec_upp=np.zeros(len(fvec)) fvec_low[fvec<fmer]=fvec[fvec<fmer] fvec_mid[(fmer<=fvec)&(fvec<frin)]=fvec[(fmer<=fvec)&(fvec<frin)]
#fmin=min(fvecd)
fmax=max(fvecd)
fvec=np.logspace(np.log10(fmin),np.log10(fmax),fbins)
#fvec=np.arange(fmin,fmax,fbin)
svec=np.interp(fvec,fvecd,sn) #Power spectral density interpolated.
fvec_m=0.5*(fvec[1:]+fvec[:-1]) #Vector of frequencies centred at the arithmetic mean of the bin.
svec_m=np.interp(fvec_m,fvecd,sn) #Power spectral density interpolated at the arithmetic mean of the bin.
#Create vector of chirp mass.
mchvec=np.logspace(minmch, maxmch, mchbins)
#Calculate luminosity distance and similar functions.
reds=np.logspace(np.log10(minreds),np.log10(maxreds),zbins) #Vector of redshifts logarithmically spaced.
reds_m=0.5*(reds[1:]+reds[:-1]) #Vector of redshifts at the arithmetic mean of the bin.
lum_dist=CM.comdist(reds_m)*(1.+reds_m) #Luminosity distance in Mpc.
#Choose plotting options that look optimal for the paper.
fig_width = 3.4039
goldenmean=(np.sqrt(5.)-1.0)/2.0
fig_height = fig_width * goldenmean
sizepoints=8
legendsizepoints=4.5
py.rcParams.update({
'backend': 'ps',
'ps.usedistiller': 'xpdf',
'text.usetex': True,
'figure.figsize': [fig_width, fig_height],
'axes.titlesize': sizepoints,
'axes.labelsize': sizepoints,
'text.fontsize': sizepoints,
import COMMON as CM from Formulas_AjithEtAl2008 import apar, bpar, cpar, xpar, ypar, zpar, kpar import Formulas_AjithEtAl2008 as A8 from scipy import interpolate factor=1. detector='ALIGO' tobs=10.*yr mch=10. m=mch*2.**(1./5.) nu=1./4. mtot=2.*m fbins=1000 finteg=100 z=np.array([10.]) DL=CM.comdist(z)*(1.+z) #Luminosity distance in Mpc. fvecd, sn=CM.detector_f(detector, factor) sn_f=interpolate.interp1d(fvecd,sn) fmin=min(fvecd) fmax=max(fvecd) fvec=np.logspace(np.log10(fmin), np.log10(fmax), fbins) sred=sn_f(fvec) def htilde_f(nu, mtot, z, lumdist, fvec): '''''' mch=nu**(3./5.)*mtot fmer=A8.f_mer(nu, mtot)*1./(1.+z) frin=A8.f_rin(nu, mtot)*1./(1.+z) fcut=A8.f_cut(nu, mtot)*1./(1.+z)
if cell==None:
dCount=self.cur.execute("select * from %s"%table)
else:
dCount=self.cur.execute("select * from %s where interfaceId='%s' "%(table,cell))
dDatas=self.cur.fetchmany(dCount)
for i in dDatas:
resultlist.append(list(i))
return resultlist
def update_data(self,table,data,interfaceid,TCNO):
Sqlit=self.cur.execute("update %s set Parameter='%s' where interfaceId='%s' and TCNO=%d" %(table,data,interfaceid,TCNO))
self.cur.close()
self.conn.commit()
self.conn.close()
# for i in MySql().get_data('vq_testcase','Login'):
# print i
# MySql().get_data('VQ_login')
# print aa
# for i in aa:
# for j in i:
# print j
import CONFIG as CFG
import COMMON as cn
import os
dataPath=os.path.join(CFG.prjDir,'BaseCase','GJB','data','10')
b=cn.get_xls_sql('testData.xlsx',dataPath)
for i in b:
print i
import numpy as np import pylab as py from COMMON import grav, light, hub0, yr, mpc, gpc, msun, week, h0, omm, omv import COMMON as CM from Formulas_AjithEtAl2008 import apar, bpar, cpar, xpar, ypar, zpar, kpar import Formulas_AjithEtAl2008 as A8 from scipy import interpolate mch=10**(10.) flow=1.5e-8 fupp=0. zlim=0.2 zmax=1 m=mch*2**(1./5.) print CM.felso(m,m)*1./(1.+zmax) py.ion() #zvecall=np.linspace(0.,1.,1000) #comdistall=CM.comdist(zvecall) zvec1=np.linspace(0.,zlim,10000) comdist1=CM.comdist(zvec1) zvec2=np.linspace(zlim,zmax,10000) comdist2=CM.comdist(zvec2) def te(mch,flow,fupp,z): return 5.*light**5./(256.*np.pi**(8./3.)*(grav*msun*mch)**(5./3.)*(1.+z)**(8./3.))*(flow**(-8./3.)-fupp**(-8./3.))
#antiSD_Ecoli = "UGGA UCACCUCCUU"
# antiSD_Ecoli = "CUGCGGUUGGA UCACCUCCUU A"
# In this codes, all anti-SD sequence is determined by the above sequence
# including positions, lengths
#UTR_LEN=200
#CDS_LEN=300
#RDS_LEN = 37
# import this py file.
# use CalculateRBSScore(utr, cds)
# that's all.
config = COMMON.CONFIG()
# parameters for kinetics
GENE_COPY_NUMBER = 100.0
RNA_SYNTHESIS_RATE = 20.0
RIBOSOMES_PER_RNA = 20.0
FREE_RIBOSOME_NUMBER = 57000.0
RNA_HALF_LIFE = 2.0 # /min
def findSD(seq, atg_index, aSD):
''' finds the index of SD'''
sff = SDFINDER.SDFINDER()
sff.setAll(seq, aSD, atg_index, 37, 1)
sff.setPerl(True)
import COMMON as CM
from Formulas_AjithEtAl2008 import apar, bpar, cpar, xpar, ypar, zpar, kpar
import Formulas_AjithEtAl2008 as A8
#ifile='../data/Eric/pablo_10_10.dat.txt'
#ofile='../data/Eric/pablo_10_10'
#eric=np.loadtxt(ifile)
#tvec=eric[:,0]
#hpvec=eric[:,1]
#hcvec=eric[:,2]
#dicti={'tvec':tvec, 'hpvec':hpvec, 'hcvec':hcvec}
#np.save(ofile, dicti)
m1=10.
m2=10.
nu=CM.nu_f(m1,m2)
mtot=m1+m2
mch=CM.mchirp(m1,m2)
reds=0.
dl=1.
phic=0.
inc=0.
ifile='../data/Eric/pablo_10_10.npy'
eric=np.load(ifile)[()]
tcoal=eric['tvec']
hpvec=eric['hpvec']
hcvec=eric['hcvec']
tvec=tcoal-tcoal[0]
hpinsp=CM.hp(inc, CM.hamp_t(mch, reds, dl, tvec[-1], tvec[:-1]), CM.phase_t(mch, reds, phic, tvec[-1],tvec[:-1]))
#fvec=np.arange(fmin,fmax,fbin)
svec=np.interp(fvec,fvecd,sn) #Power spectral density interpolated.
fvec_m=0.5*(fvec[1:]+fvec[:-1]) #Vector of frequencies centred at the arithmetic mean of the bin.
svec_m=np.interp(fvec_m,fvecd,sn) #Power spectral density interpolated at the arithmetic mean of the bin.
#I take the minimum of S_n as a first approach to have the optimal result assuming white noise.
s0=min(svec_m)
#Create vector of chirp mass.
mchvec=np.logspace(minmch, maxmch, mchbins)
#Calculate luminosity distance and similar functions.
reds=np.logspace(np.log10(minreds),np.log10(maxreds),zbins) #Vector of redshifts logarithmically spaced.
reds_m=0.5*(reds[1:]+reds[:-1]) #Vector of redshifts at the arithmetic mean of the bin.
lum_dist=CM.comdist(reds_m)*(1.+reds_m) #Luminosity distance in Mpc.
#Choose plotting options that look optimal for the paper.
fig_width = 3.4039
goldenmean=(np.sqrt(5.)-1.0)/2.0
fig_height = fig_width * goldenmean
sizepoints=8
legendsizepoints=4.5
py.rcParams.update({
'backend': 'ps',
'ps.usedistiller': 'xpdf',
'text.usetex': True,
'figure.figsize': [fig_width, fig_height],
'axes.titlesize': sizepoints,
'axes.labelsize': sizepoints,
'text.fontsize': sizepoints,
def update_demand(self):
# Update the heat contribution of the TCL considering the aggregators command
# recalculate demand for next timestep
self.__dict__.update(COMMON.get_a_demand(**self.__dict__))
return self.a_demand
import pylab as py from COMMON import nanosec,yr,week,grav,msun,light,mpc,hub0,h0,omm,omv import COMMON as CM from USEFUL import time_estimate from matplotlib import colors from scipy import interpolate import Formulas_AjithEtAl2008 as A8 #Input parameters: from INPUT_PARAMETERS import * outputfile='../data/output/snr_z_vs_mc_'+detector+'_'+flim_model+'_iter' #File to save data. #----------------------------------------------------------------- #Load detector's sensitivity. fvecd, sn=CM.detector_f(detector, factor) sn_f=interpolate.interp1d(fvecd,sn) fmin, fmax=min(fvecd), max(fvecd) if detector in ['EPTA', 'PPTA']: fmin=1./tobs fmax*=0.99999 #I reduce the upper frequency slightly, so that there are no issues when extrapolating the sensitivity curve close to this value (the issue appears only when evaluating the sensitivity exactly at fmax). fvec=np.logspace(np.log10(fmin),np.log10(fmax),fbins) mchvec=np.logspace(minmch, maxmch, mchbins) #Vector of physical chirp mass. reds=np.logspace(np.log10(minreds),np.log10(maxreds),zbins) #Vector of redshifts logarithmically spaced. lum_dist=CM.comdist(reds)*(1.+reds) #Luminosity distance in Mpc. #Calculate S/N for a given physical chirp mass. z_mat=np.tile(reds,(len(mchvec),1)).T #Matrix with z. DL_mat=np.tile(lum_dist,(len(mchvec),1)).T #Matrix with luminosity distance. mch_mat=np.tile(mchvec,(len(reds),1)) #Matrix with chirp mass. m_mat=mch_mat*2**(1./5.)
outputdir='/lustre/projects/p002_swin/prosado/horizon/data/output/SNR_z_vs_mz_PPTA_model_'+flim_model+'/' if len(sys.argv)<2: print 'Input number of pulsar to consider!' exit() pulsi=sys.argv[1] #Number of the pulsar from 0 to 19. #Obtaining noise curves for the different PPTA pulsars. fmini=1./tobs #Minimum frequency. fmaxi=1./cad #Maximum frequency. fvec=np.logspace(np.log10(fmini), np.log10(fmaxi), fbins) Snf=CM.PPTA_red_noise(fvec, cad) #Matrix with the noise of each pulsar. numpul=np.shape(Snf)[0] #Number of pulsars. mchvec=np.logspace(minmch, maxmch, mchbins) #Vector of physical chirp mass. reds=np.logspace(np.log10(minreds),np.log10(maxreds),zbins) #Vector of redshifts logarithmically spaced. lum_dist=CM.comdist(reds)*(1.+reds) #Luminosity distance in Mpc. print 'Obtaining S/N for pulsar %i / %i .' %(int(pulsi)+1, numpul) print outputfile=outputdir+'snr_pulsar'+'%i' %(int(pulsi)+1) #overwrite=CM.z_vs_mc_check(outputdir, outputfile, overwrite) #Check if output directory and files already exist. #Obtaining pulsar red noise curve. sn_f=interpolate.interp1d(fvec, Snf[pulsi,:]) fmin=min(fvec) fmax=0.99999*max(fvec) #I reduce the upper frequency slightly, so that there are no issues when extrapolating the sensitivity curve close to this value (the issue appears only when evaluating the sensitivity exactly at fmax). #Calculate S/N for a given physical chirp mass. mch_mat=np.zeros((len(reds), len(mchvec))) z_mat=np.zeros(np.shape(mch_mat))
class Solution: # @param head, a ListNode # @return a ListNode def insertionSortList(self, head): if(head==None): return None; result=head; p1=head; while(True): # from start to p1 ,it's a sorted List p2=p1.next; if(p2==None): break; if(p2.val>=p1.val): p1=p2; continue; # insert p2 to the old list p1.next=p2.next; if(p2.val<result.val): p2.next=result; result=p2; else: pointer=result; while(pointer.next.val<p2.val): pointer=pointer.next; p2.next=pointer.next; pointer.next=p2; return result; s=Solution(); COMMON.print_list(s.insertionSortList(COMMON.build_list([1,5,2,4,3])));
raw_input('enter')
#Load SBHB candidates.
candi=np.load('../data/Graham/Candidates.npy')[()]
candi_z=candi['z']
candi_mtot=candi['t_mass']
candi_fgw=candi['f_gw_obs']
candi_mch=candi_mtot*2**(-1./5.)*0.5
candi_snr=np.load('../data/Graham/Cand_snr_PPTA_model_'+flim_model+'.npy')[()]['snr']
#Derive some quantities.
reds=z_mat[:,0]
mchvec=mch_mat[0,:]
if flim_model=='ISCO':
flim_mat=CM.felso(mch_mat*2.**(1./5.),mch_mat*2.**(1./5.))*1./(1.+z_mat)
elif flim_model=='A8':
flim_mat=A8.f_cut(1./4., 2.*mch_mat*2.**(1./5.))*1./(1.+z_mat)
zlim_mat=np.ones(np.shape(z_mat))*np.nan
zlim_mat[flim_mat<fmin]=1. #This matrix shows the region of z-Mc that cannot be seen, because the ISCO has already been reached below the minimum observable frequency.
#snr_mat[snr_mat<=0]=np.nan
snr_mat[snr_mat<=0]=-90.
snr_mat[snr_mat>0.]=np.log10(snr_mat[snr_mat>0.])
#Optimal plotting options.
#import PARAMETER_PLOTS
#Choose plotting options that look optimal for the paper.
fig_width = 3.4039*2./3.
#goldenmean=(np.sqrt(5.)-1.0)/2.0
#Input parameters: from INPUT_PARAMETERS import * cb_width=0.014 cb_height=0.765 left, right, top, bottom, cb_fraction=0.063, 0.905, 0.97, 0.205, 0.08 #Borders of the plot. maxstrainlevel=-11 minstrainlevel=-15 strainlevels=10 zbins=1000 fbins=1000 mchvec=np.array([9.8,10.,10.2]) #Array of values of log10(chirp mass/msun). outputplot='../plots/z_vs_f_EPTA_strain_ALL.png' #----------------------------------------------------------------- #Load detector's sensitivity. fvecd, sn=CM.detector_f(detector, factor) sn_f=interpolate.interp1d(fvecd,sn) fmin, fmax=min(fvecd), max(fvecd) if detector in ['EPTA', 'PPTA']: fmin=1./tobs #Load EPTA upper limits data. ifile2='../data/EPTA/upper_fixed_at_maxL_Steve.txt' #Fp with fixed noise to ML values. Fp_ML ul2=np.array(np.loadtxt(ifile2)) fvecd,hvecd=ul2[:,0],ul2[:,1] #fmin=1.02623486508e-09 #In order to have the same limits as the EPTA plots. #fmax=3.15323154356e-07 #In order to have the same limits as the EPTA plots. fmin=min(fvecd) fmax=max(fvecd) fvec=np.logspace(np.log10(fmin),np.log10(fmax),fbins) hvec=np.interp(fvec, fvecd, hvecd)
#freq=7e-8 #Where you do not see the turnover because it is beyond the ISCO. plotxmin=1e-2 plotxmax=1e2 plotymin=1 plotymax=1e3 outputdir='../plots/' snrt=8. snoise=1.*10**(-23) #Noise power spectral density (assumed white). limit_by='isco' #To stop the S/N at the ISCO. #limit_by='coal' #To stop the S/N at the coalescence. #----------------------------------------------------------------- #Calculate luminosity distance and similar functions. reds=np.logspace(np.log10(minreds),np.log10(maxreds),zbins) #Vector of redshifts logarithmically spaced. reds_m=0.5*(reds[1:]+reds[:-1]) #Vector of redshifts at the arithmetic mean of the bin. com_dist=CM.comdist(reds_m) #Vector of comoving distance in Mpc. lum_dist=(1.+reds_m)*com_dist #Vector of luminosity distance in Mpc. #Calculate strains. hamp=CM.hamp_f(mch, reds_m, lum_dist, freq) #Calculate true S/N. m1=mch*2**(1./5.) m2=m1 fisco=CM.felso(m1, m2)*1./(1.+reds_m) tisco=CM.tafter(mch, freq, fisco, reds_m) if limit_by=='isco': zisco=reds_m[abs(tobs-tisco).argmin()] fmaxobs=np.ones(len(reds_m))*fisco fmaxobs[reds_m<zisco]=CM.fafter(mch, reds_m[reds_m<zisco], freq, tobs) snrvec=CM.snr_white(mch, reds_m, lum_dist, freq, fisco, fmaxobs, snoise)
outputdir='../data/output/'
if cluster=='1':
datafile='/lustre/projects/p002_swin/prosado/horizon/data/output/COMBINED_SNR_'+detector+'_'+flim_model+'.npy'
outputdir='/lustre/projects/p002_swin/prosado/horizon/data/output/'
outputfile=outputdir+'DP_'+detector+'_'+flim_model
#-----------------------------------------------------------------
#Get data.
data=np.load(datafile)[()]
snr=data['combined_max_snr']
mch_mat=data['mch_mat']
z_mat=data['z_mat']
f_mat=data['f_max_mat']
tobs, mchbins, fbins, finteg=data['tobs_yr'], data['mchbins'], data['fbins'], data['finteg']
f0t=CM.f0thres(numtem, fap) #Threshold in the Fe-statistic.
snrshape=np.shape(snr)
dpvec=np.zeros(snrshape)
t=time_estimate(snr.size) #A class that prints estimated computation time.
for row in xrange(snrshape[0]):
for column in xrange(snrshape[1]):
if snr[row,column]<=0.:
continue
t.display() #Shows the remaining computation time.
t.increase() #Needed to calculate the remaining computation time.
dpvec[row,column]=CM.dpfun(f0t, snr[row,column], minrange, intpoints)
#Save combined data (only frequencies and S/N at the optimal frequencies).
dicti={'FAP':fap, 'numtem':numtem, 'minrange':minrange, 'dpinteg':intpoints, 'DP':dpvec, 'combined_max_snr':snr, 'f_max_mat':f_mat, 'mch_mat':mch_mat, 'z_mat':z_mat, 'tobs_yr':tobs, 'mchbins':mchbins, 'fbins':fbins, 'finteg':finteg}
np.save(outputfile,dicti)
mchvec=np.array([9.8,10.]) #Array of values of log10(chirp mass/msun). There will be a plot for each value of mch.
#Obtaining noise curves for the different PPTA pulsars.
fmini=1./tobs #Minimum frequency.
fmaxi=1./cad #Maximum frequency.
#fmini=1.02623486508e-09 #In order to have the same limits as the EPTA plots.
#fmaxi=3.15323154356e-07 #In order to have the same limits as the EPTA plots.
fvec=np.logspace(np.log10(fmini), np.log10(fmaxi), fbins)
fmin=min(fvec)
fmax=0.99999*max(fvec) #I reduce the upper frequency slightly, so that there are no issues when extrapolating the sensitivity curve close to this value (the issue appears only when evaluating the sensitivity exactly at fmax).
Snf=CM.PPTA_red_noise(fvec, cad) #Matrix with the noise of each pulsar.
numpul=np.shape(Snf)[0] #Number of pulsars.
reds=np.logspace(np.log10(minreds),np.log10(maxreds),zbins) #Vector of redshifts logarithmically spaced.
lum_dist=CM.comdist(reds)*(1.+reds) #Luminosity distance in Mpc.
#Calculate S/N for a given physical chirp mass.
z_mat=np.tile(reds,(len(fvec),1)).T #Matrix with z.
DL_mat=np.tile(lum_dist,(len(fvec),1)).T #Matrix with luminosity distance.
f_mat=np.tile(fvec,(len(reds),1)) #Matrix with (minimum) frequencies.
fmaxdet_mat=np.ones(np.shape(f_mat))*fmax #Matrix with maximum frequencies allowed by the detector.
for mi in xrange(len(mchvec)):
print 'Plot for chirp mass 10^{%e} msun.' %mchvec[mi]
print
mch=10**(mchvec[mi]) #Chirp mass of the BH binary.
m=mch*2**(1./5.) #Mass of each individual BH, assuming equal masses.
nu=CM.nu_f(m,m) #Symmetric mass ratio.
mtot=2.*m #Total mass.
outputfile=outputdir+'mch%.3e' %mch
import pylab as py
from scipy import integrate, interpolate
import COMMON as CM
import Formulas_AjithEtAl2008 as A8
from INPUT_PARAMETERS import *
outputfile='../data/Graham/Cand_snr_PPTA_model_'+flim_model
#Load candidate data.
cand=np.load('../data/Graham/Candidates.npy')[()]
cand_z=cand['z'] #Redshift.
cand_mtot=cand['t_mass'] #Total mass.
cand_f=cand['f_gw_obs'] #Observed GW frequency.
#Derive some other quantities.
cand_m=cand_mtot*0.5 #Mass of each BH.
cand_mch=CM.mchirp(cand_m, cand_m) #Chirp mass.
cand_DL=CM.comdist(cand_z)*(1.+cand_z)
#Obtaining noise curves for the different PPTA pulsars.
fmini=1./tobs #Minimum frequency.
fmaxi=1./cad #Maximum frequency.
fvec=np.logspace(np.log10(fmini), np.log10(fmaxi), fbins)
fmin=min(fvec)
fmax=0.99999*max(fvec) #I reduce the upper frequency slightly, so that there are no issues when extrapolating the sensitivity curve close to this value (the issue appears only when evaluating the sensitivity exactly at fmax).
Snf=CM.PPTA_red_noise(fvec, cad) #Matrix with the noise of each pulsar.
numpul=np.shape(Snf)[0] #Number of pulsars.
#py.ion()
#py.loglog(cand_f,'o')
#py.ylim(fmini, fmaxi)
#raw_input('enter')
mchfiles.append(plotfiles[mi])
indisort=np.array(mchvec).argsort()
mchvec=np.array(mchvec)[indisort]
mchfiles=np.array(mchfiles)[indisort]
#Optimal plotting options.
import PARAMETER_PLOTS
left, right, top, bottom, cb_fraction=0.125, 0.945, 0.96, 0.17, 0.08 #Borders of the plot.
for mi in xrange(len(mchfiles)):
mchi=mchvec[mi]
print 'Plot for chirp mass 10^{%e} msun.' %mchi
print
mch=10**(mchi) #Chirp mass of the BH binary.
m=mch*2**(1./5.) #Mass of each individual BH, assuming equal masses.
nu=CM.nu_f(m,m) #Symmetric mass ratio.
mtot=2.*m #Total mass.
data=np.load(inputdatadir+mchfiles[mi])[()]
snr_final_mat=data['snr_mat']
f_mat=data['f_mat']
z_mat=data['z_mat']
xmin,xmax=np.amin(f_mat),np.amax(f_mat) #Edges of the x-axis.
ymin,ymax=np.amin(z_mat),np.amax(z_mat) #Edges of the y-axis.
#z_app=np.zeros(np.shape(z_mat))
lso_mat=np.zeros(np.shape(z_mat))
if flim_model=='ISCO':
flim_mat=CM.felso(m, m)*1./(1.+z_mat)
import COMMON;
class Solution:
# @param root, a tree node
# @return a list of lists of integers
def levelOrderBottom(self, root):
if(root==None):
return [];
data=[[root]];
while(True):
next=[];
for node in data[len(data)-1]:
if(node.left!=None):
next+=[node.left];
if(node.right!=None):
next+=[node.right];
if(next!=[]):
data+=[next];
else:
break;
for arr in data:
size=0;
while(size<len(arr)):
arr[size]=arr[size].val;
size+=1;
data.reverse();
return data;
s=Solution();
print s.levelOrderBottom(COMMON.build_tree("{3,9,20,#,#,15,7}"))
fbin=1./tobs
fmin=1./tobs
#fmin=min(fvecd)
fmax=max(fvecd)
fvec=np.logspace(np.log10(fmin),np.log10(fmax),fbins)
#fvec=np.arange(fmin,fmax,fbin)
svec=np.interp(fvec,fvecd,sn) #Power spectral density interpolated.
fvec_m=0.5*(fvec[1:]+fvec[:-1]) #Vector of frequencies centred at the arithmetic mean of the bin.
svec_m=np.interp(fvec_m,fvecd,sn) #Power spectral density interpolated at the arithmetic mean of the bin.
#Calculate luminosity distance and similar functions.
reds=np.logspace(np.log10(minreds),np.log10(maxreds),zbins) #Vector of redshifts logarithmically spaced.
reds_m=0.5*(reds[1:]+reds[:-1]) #Vector of redshifts at the arithmetic mean of the bin.
lum_dist=CM.comdist(reds_m)*(1.+reds_m) #Luminosity distance in Mpc.
#Choose plotting options that look optimal for the paper.
fig_width = 3.4039
goldenmean=(np.sqrt(5.)-1.0)/2.0
fig_height = fig_width * goldenmean
sizepoints=8
legendsizepoints=4.5
py.rcParams.update({
'backend': 'ps',
'ps.usedistiller': 'xpdf',
'text.usetex': True,
'figure.figsize': [fig_width, fig_height],
'axes.titlesize': sizepoints,
'axes.labelsize': sizepoints,
'text.fontsize': sizepoints,
fbin=1./tobs fmin=1./tobs #fmin=min(fvecd) fmax=max(fvecd) fvec=np.logspace(np.log10(fmin),np.log10(fmax),fbins) #fvec=np.arange(fmin,fmax,fbin) svec=np.interp(fvec,fvecd,sn) #Power spectral density interpolated. fvec_m=0.5*(fvec[1:]+fvec[:-1]) #Vector of frequencies centred at the arithmetic mean of the bin. svec_m=np.interp(fvec_m,fvecd,sn) #Power spectral density interpolated at the arithmetic mean of the bin. #Calculate luminosity distance and similar functions. reds=np.logspace(np.log10(minreds),np.log10(maxreds),zbins) #Vector of redshifts logarithmically spaced. reds_m=0.5*(reds[1:]+reds[:-1]) #Vector of redshifts at the arithmetic mean of the bin. lum_dist=CM.comdist(reds_m)*(1.+reds_m) #Luminosity distance in Mpc. #I assume white noise, i.e. the noise has a particular constant value: sn=np.ones(len(sn))*sn[0]##################### mchvec=np.array([8.5, 9., 9.5, 10., 10.5, 11.]) py.ion() for mch in xrange(len(mchvec)): mchi=10**(mchvec[mch]) m1i=mchi*2**(1./5.) m2i=m1i redsi=0.5 lumdisti=lum_dist[abs(redsi-reds_m).argmin()] feisco=CM.felso(m1i, m2i) fisci=feisco*1./(1.+redsi)
reds_text=3. #Redshift at which the text with frequency should appear. factor_right=5.5 factor_left=0.2 flevels=5 #Number of frequency contours. maxsnrlevel=7. #Maximum S/N level in the colorbar. minsnrlevel=np.log10(snrt) #Minimum one. #datafile='../data/output/snr_SENSIT_red_'+detector+'_mat.npy' #datafile='../data/output/snr_z_vs_mc_'+detector+'_'+flim_model+'_iter.npy' #datafile='../data/output/snr_z_vs_mc_'+detector+'_'+flim_model+'_mat.npy' datafile='../data/output/COMBINED_SNR_'+detector+'_'+flim_model+'.npy' if cluster=='1': datafile='/lustre/projects/p002_swin/prosado/horizon/data/output/COMBINED_SNR_'+detector+'_'+flim_model+'.npy' outputplot='../plots/z_vs_mc_'+detector+'_'+flim_model #----------------------------------------------------------------- #Load detector data. fvecd, sn=CM.detector_f(detector, factor) fmin, fmax=min(fvecd), max(fvecd) if detector in ['EPTA', 'PPTA']: fmax*=0.99999 #I reduce the upper frequency slightly, so that there are no issues when extrapolating the sensitivity curve close to this value (the issue appears only when evaluating the sensitivity exactly at fmax). if (1./tobs)>fmin: fmin=1./tobs else: print 'The minimum frequency of the sensitivity curve is larger than 1/T! The noise cannot be extrapolated to lower frequencies. Exiting...' exit() #Load S/N data. data=np.load(datafile)[()] snr_mat=data['combined_max_snr'] mch_mat=data['mch_mat'] z_mat=data['z_mat'] f_mat=data['f_max_mat']
import COMMON
class Solution:
def helper(self,root):
if root==None:
return (True,None,None);
l=self.helper(root.left);
r=self.helper(root.right);
if not (l[0] and r[0]):
return (False,None,None);
res_bool,res_l,res_r=True,root,root;
if l[2]!=None :
if l[2].val >= root.val:
res_bool=False;
res_l=l[1];
if r[2]!=None :
if r[1].val <= root.val:
res_bool=False;
res_r=r[2];
return (res_bool,res_l,res_r);
# @param root, a tree node
# @return a boolean
def isValidBST(self, root):
return self.helper(root)[0];
s=Solution();
print s.isValidBST(COMMON.build_tree("{1,#,2,#,3}"));
print s.isValidBST(COMMON.build_tree("{1,2,3}"));