profile.py 5.85 KB
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from pylab import *
import scipy
from scipy.weave import *
from scipy.special import *
from matplotlib import *
PI = math.pi

class profile:
    def __init__(self, N, x):
        self.shift = 0.
        self.xmin = 0.
        self.xmax = N*x
        self.stepsize = x
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        self.data = zeros(N)
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        self.size = N
        self.contam = 0.
        
    def axisdepth(self):
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        return arange(self.contam, self.size*self.stepsize+self.contam, self.stepsize)
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    def clear(self):
        self.data = zeros(self.size)

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    def soma(self, qt):
        for j in range(self.size):
            if self.data[j] > 0: 
                self.data[j] = self.data[j] + qt

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    def resize(self, size):
        N = self.size
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        M = size
        self.data = list(self.data)
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        if M > N:
            for j in range(M-N):
                self.data.insert(N+j,0)
        elif N > M:
            for j in range(N-M):
                self.data.pop()
        self.size = M
        self.xmax = M*self.stepsize

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    def shiftx(self, x, x2):
        self.data = list(self.data)
        if x < x2:
            for y in arange(int(x/self.stepsize), int(x2/self.stepsize)):
                self.data.insert(y,0)
                self.data.pop()
        elif x > x2: # funciona mal
            for y in arange(int(x2/self.stepsize), int(self.xmax/self.stepsize)):
                if y < self.size-1:
                    self.data[y] = self.data[y+1]
                self.data[self.size-1] = 0

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    def setcontam(self, newcontam):
        delta = newcontam-self.contam
        dx = math.ceil(delta/self.stepsize)
        if (delta < 0):
            for i in arange(math.ceil(newcontam*self.stepsize),self.xmax):
                self.data[i] = self.data[i-dx]
            resize(self.size+delta)
        elif (delta > 0):
            self.resize(self.size + delta)
            datatemp = profile(self.xmax,self.stepsize)
            datatemp.data = self.data
            for i in arange(math.floor(newcontam/self.stepsize),self.xmax):
                self.data[i] = datatemp.data[i-dx]
        contam = newcontam
        for i in range(math.ceil(newcontam/self.stepsize)):
            data[i]=0

    def drawline(self, xa, xb, ca, cb):
        contam = self.contam
        stepsize = self.stepsize
        if (xb >= self.xmax):
            xb = self.xmax 
        if (xa < 0):
            xa = 0.
        if (xa >= self.xmax):
            xa = self.xmax
        if (xb < 0):
            xb = 0
        if (xa < xb):
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            for i in arange(math.floor(xa/stepsize), math.floor(xb/stepsize),1):
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                if (i > math.floor(contam/stepsize)):
                    a = (cb - ca) / (xb - xa)
                    b = -a*xa + ca
                    conc = a*(1.*i*stepsize) + b
                    if (conc < 0):
                        conc = 0.
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                    self.data[int(i)] = conc;
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        if (xb < xa):
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            for i in arange(math.floor(xb/stepsize), math.floor(xa/stepsize),1):
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                if (i > math.floor(contam/stepsize)):
                    a = (ca - cb) / (xa - xb)
                    b = -a*xb + cb
                    conc = a*(1.*i*stepsize) + b
                    if(conc < 0):
                        conc = 0
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                    self.data[int(i)] = conc
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    def adderfc(self, qtd, sigma):
        xsigma = self.xmax
        ratio = self.contam/self.stepsize
        if (4.*sigma/self.stepsize < self.xmax):
            xsigma = 4.*sigma/self.stepsize
        for i in arange(math.ceil(ratio)+1, xsigma+ratio):
            j = i - math.floor(ratio)
            self.data[i] = self.data[i] + qtd*scipy.special.erfc(j*self.stepsize/sqrt(2.)/sigma);

    def remerfc(self, qtd, sigma):
        xsigma = self.xmax
        ratio = self.contam/self.stepsize
        if (4.*sigma/self.stepsize < xmax):
            xsigma = 4.*sigma/self.stepsize
        for i in arange(math.ceil(ratio)+1, xsigma+ratio):
            j = i - math.floor(ratio)
            self.data[i] = self.data[i] - qtd*scipy.special.erfc(j*self.stepsize/sqrt(2.)/sigma)

    def erfc(self, x):
        passo = .005
        valor = 0.
        for i in range(x/passo + 1):
            valor += exp(-(i*passo)**2)
            erf = 1. - 2.*valor*passo/sqrt(PI)
        if erf<0:
            erf = 0.
        return erf

    def quant(self):
        soma = 0.
        for i in range(self.size):
            soma = soma + self.data[i]
        soma = soma*self.stepsize
        return  soma

    def Kn(self, n, m, tltcorr): #Nao compila
        tltcorr = 1.
        stepsize = self.stepsize
        data = tuple(self.data)
        xmax = self.xmax
        datasize = int(self.size)
        code = """
            long double fatorial(int a)
            {
            int aa;
            long double valor=1.0;
            for(aa=1;aa<=a;aa++)
                valor=valor*aa;
            return valor;
            }

            double datab[datasize];
            for (int k=0;k<datasize;k++)
                datab[k] = data[k];
            long double kk=0.0;
            if(n==0){
               for(int x=0;x<xmax;x++) 
                   kk = kk + 1.0*exp(-1.0*M*x*stepsize*tltcorr)*datab[x];
               kk = kk*pow(stepsize,1.0);}
            else{
                for(int x=0;x<xmax;x++)
                    kk = kk + 1.0*powl(1.0*x*tltcorr/(1.0/stepsize),n)*exp(-1.0*M*x*tltcorr/(1.0/stepsize))*datab[x];
                kk = kk*1.0*(1.0*powl(1.0*M,n)/(1.0*fatorial(n)*1.0/stepsize));}
            return_val =  kk/stepsize*1000.0;}
            """
        return inline_tools.inline(code,['n','m','tltcorr','stepsize','data','xmax', 'datasize'],type_converters=converters.blitz,compiler = 'gcc')

    def smooth(self, qts):
        for n in range(qts):
            for i in arange(math.ceil(self.contam/self.stepsize)+4,self.xmax-6):
                self.data[i] = (self.data[i-3]+self.data[i-2]+self.data[i-1]+self.data[i]+self.data[i+1]+self.data[i+2]+self.data[i+3])/7.