Python的配件：優化循環

Question

我試圖將多個高斯給定的數據，並在第500個型號達到時，這部分程序是用約3 GB的內存，我需要適應總共〜2000款。下面是我的程序與隨機生成的數據，這不會產生非常適合一個到簡體版本，但它說明了時間的問題：我無法想出一個辦法來優化這個部分，使其

import sys 
sys.setrecursionlimit(5000) 
import matplotlib.pyplot as plt 
import numpy as np 
import random 
from random import uniform 
x=[random.uniform(2200.,3100.) for p in range(0, 1000)] 
y=[random.uniform(1.,1000.) for p in range(0, 1000)] 

import sherpa.ui as ui 
import numpy as np 
ui.load_arrays(1,x,y) # 1 is the first data 
d1=ui.get_data() 
d1.staterror=0.002*d1.y # define error on y just for plotting purpose, not required for fit 
ui.plot_data() 
ui.set_stat("leastsq") # leasr square method for fit 
ui.set_model(ui.powlaw1d.pow1) # fit powerlaw.. pow1 is the shortcut name 
# ui.show_all() will show you all the parameters for the model 
pow1.ref=2500 
ui.fit() 
# fitting templates 
x2=[random.uniform(2200.,3100.) for p in range(0, 1000)] 
y2=[random.uniform(1.,1000.) for p in range(0, 1000)] 

model1="pow1" # initiliaze the model for fitting all the gaussians 
sign="+" 
sigma=45. 
g_pos=x2 
g_ampl=[] # we will store the fit value here 



ui.freeze(model1) # freeze the powerlaw 
for n in range(1,1000): # this excludes the upper limit 
     ui.create_model_component("gauss1d","g{}".format(n)) 
     ui.set_par("g{}.pos".format(n),x2[n],frozen=True) 
     ui.set_par("g{}.ampl".format(n),y2[n]) 
     ui.set_par("g{}.fwhm".format(n),sigma,frozen=True) 
     model1=model1+sign+"g{}".format(n) 
     if y2[n] == 0.: 
      g_ampl.append(0.) # list zero amplitude for this model 
     else: 
      g=ui.create_model_component("gauss1d","g{}".format(n)) # do this to store g_ampl of this model only 
      ui.set_source(model1) # overwriting with actual model 
      ui.fit() 
      ui.fit() 
      ui.fit() 
      g_ampl.append(g.ampl.val) 
     ui.freeze(model1) # freeze the model and go to the next gaussian 

高效且耗時更少。任何想法，以幫助我讓它跑得更快，將不勝感激。

Answer 1

與您的代碼的問題是，它似乎不必要地存儲所有要擬合數據。更好的解決方案是隻存儲擬合結果。我不是很清楚sherpa。這是一個解決方案scipy.optimize

import numpy as np 
import matplotlib.pyplot as plt 
from scipy.optimize import curve_fit 
import glob, os 
plt.ion() 

def gaussian_func(x, a, x0, sigma,c): 
    return a * np.exp(-(x-x0)**2/(2*sigma**2)) + c 

# This is creates two files with random data 
# Don't use for actual program 
xdata = np.linspace(0, 4, 50) 
ydata = gaussian_func(xdata, 2.5, 1.3, 0.5,0) + 0.2 * np.random.normal(size=len(xdata)) 
np.savetxt('example.dat',np.array([xdata,ydata]).T) 
ydata = gaussian_func(xdata, 2.5, 2.3, 0.5,0) + 0.2 * np.random.normal(size=len(xdata)) 
np.savetxt('example2.dat',np.array([xdata,ydata]).T) 

# Create your list of files with the data 
# This examples just loads all files with .dat extension 
filelist = glob.glob("*.dat") 
print(filelist) 

results = [] 

for file in filelist: 
    data = np.loadtxt(file) 
    xdata = data[:,0] 
    ydata = data[:,1] 
    # if the error of the points is included in your file 
    # exchange the following line to sigma = data[:,2] 
    sigma = 0*ydata+0.2 
    initial_guess = [2,1,1,0] 
    popt, pcov = curve_fit(gaussian_func, xdata, ydata,p0=initial_guess,sigma=sigma) 
    results.append({"filename":file,"parameters":popt,"covariance matrix":pcov}) 

    # This plots the result 
    # Should be commented out for the large dataset 
    plt.figure(1) 
    plt.clf() 
    plt.errorbar(xdata,ydata,sigma,fmt='ko') 
    xplot = np.linspace(0,4,100) 
    plt.plot(xplot,gaussian_func(xplot,*popt),'r',linewidth=2) 
    plt.draw()