title: Cython code 3-4 times slower than Python / Numpy code?
body: <p>I am trying to convert my Python / Numpy code to Cython code for speedup purposes. However, Cython is MUCH slower (3-4 times) than the Python / Numpy code. Am I using Cython correctly? Am I passing arguments correctly to myc_rb_etc() in my Cython code? What about when I call the integrate function? Thank you in advance for your help.
  Here is my Python / Numpy code: </p>
  
  <pre><code>from pylab import * 
  import pylab as pl
  from numpy import *
  import numpy as np
  from scipy import integrate
  
  def myc_rb_e2f(y,t,k,d):
  
      M = y[0]
      E = y[1]
      CD = y[2]
      CE = y[3]
      R = y[4]
      RP = y[5] 
      RE = y[6]
  
      S = 0.01
      if t &gt; 300:
          S = 5.0
      #if t &gt; 400
          #S = 0.01
  
      t1 = k[0]*S/(k[7]+S);
      t2 = k[1]*(M/(k[14]+M))*(E/(k[15]+E));
      t3 = k[5]*M/(k[14]+M);
      t4 = k[11]*CD*RE/(k[16]+RE);
      t5 = k[12]*CE*RE/(k[17]+RE);
      t6 = k[2]*M/(k[14]+M);
      t7 = k[3]*S/(k[7]+S);
      t8 = k[6]*E/(k[15]+E);
      t9 = k[13]*RP/(k[18]+RP);
      t10 = k[9]*CD*R/(k[16]+R);
      t11 = k[10]*CE*R/(k[17]+R);
  
      dM = t1-d[0]*M
      dE = t2+t3+t4+t5-k[8]*R*E-d[1]*E
      dCD = t6+t7-d[2]*CD
      dCE = t8-d[3]*CE
      dR = k[4]+t9-k[8]*R*E-t10-t11-d[4]*R
      dRP = t10+t11+t4+t5-t9-d[5]*RP
      dRE = k[8]*R*E-t4-t5-d[6]*RE
  
      dy = [dM,dE,dCD,dCE,dR,dRP,dRE]
  
      return dy
  
  t = np.zeros(10000)
  t = np.linspace(0.,3000.,10000.)
  
  # Initial concentrations of [M,E,CD,CE,R,RP,RE]
  y0 = np.array([0.,0.,0.,0.,0.4,0.,0.25])
  E_simulated = np.zeros([10000,5000])
  E_avg = np.zeros([10000])
  k = np.zeros([19])
  d = np.zeros([7])
  
  for i in range (0,5000):
      k[0] = 1.+0.1*randn(1)
      k[1] = 0.15+0.05*randn(1)
      k[2] = 0.2+0.05*randn(1)
      k[3] = 0.2+0.05*randn(1)
      k[4] = 0.35+0.05*randn(1)
      k[5] = 0.001+0.0001*randn(1)
      k[6] = 0.5+0.05*randn(1)
      k[7] = 0.3+0.05*randn(1)
      k[8] = 30.+5.*randn(1)
      k[9] = 18.+3.*randn(1)
      k[10] = 18.+3.*randn(1)
      k[11] = 18.+3.*randn(1)
      k[12] = 18.+3.*randn(1)
      k[13] = 3.6+0.5*randn(1)
      k[14] = 0.15+0.05*randn(1)
      k[15] = 0.15+0.05*randn(1)
      k[16] = 0.92+0.1*randn(1)
      k[17] = 0.92+0.1*randn(1)
      k[18] = 0.01+0.001*randn(1)
      d[0] = 0.7+0.05*randn(1)
      d[1] = 0.25+0.025*randn(1)
      d[2] = 1.5+0.05*randn(1)
      d[3] = 1.5+0.05*randn(1)
      d[4] = 0.06+0.01*randn(1)
      d[5] = 0.06+0.01*randn(1)
      d[6] = 0.03+0.005*randn(1)
      r = integrate.odeint(myc_rb_e2f,y0,t,args=(k,d))
      E_simulated[:,i] = r[:,1]
  
  for i in range(0,10000):
      E_avg[i] = sum(E_simulated[i,:])/5000.
  
  pl.plot(t,E_avg,'-ro')
  pl.show()
  </code></pre>
  
  <p>Here is the code converted into Cython:</p>
  
  <pre><code>cimport numpy as np
  import numpy as np
  from numpy import *
  import pylab as pl
  from pylab import * 
  from scipy import integrate
  
  def myc_rb_e2f(y,t,k,d):
  
      cdef double M = y[0]
      cdef double E = y[1]
      cdef double CD = y[2]
      cdef double CE = y[3]
      cdef double R = y[4]
      cdef double RP = y[5] 
      cdef double RE = y[6]
  
      cdef double S = 0.01
      if t &gt; 300.0:
          S = 5.0
      #if t &gt; 400
          #S = 0.01
  
      cdef double t1 = k[0]*S/(k[7]+S)
      cdef double t2 = k[1]*(M/(k[14]+M))*(E/(k[15]+E))
      cdef double t3 = k[5]*M/(k[14]+M)
      cdef double t4 = k[11]*CD*RE/(k[16]+RE)
      cdef double t5 = k[12]*CE*RE/(k[17]+RE)
      cdef double t6 = k[2]*M/(k[14]+M)
      cdef double t7 = k[3]*S/(k[7]+S)
      cdef double t8 = k[6]*E/(k[15]+E)
      cdef double t9 = k[13]*RP/(k[18]+RP)
      cdef double t10 = k[9]*CD*R/(k[16]+R)
      cdef double t11 = k[10]*CE*R/(k[17]+R)
  
      cdef double dM = t1-d[0]*M
      cdef double dE = t2+t3+t4+t5-k[8]*R*E-d[1]*E
      cdef double dCD = t6+t7-d[2]*CD
      cdef double dCE = t8-d[3]*CE
      cdef double dR = k[4]+t9-k[8]*R*E-t10-t11-d[4]*R
      cdef double dRP = t10+t11+t4+t5-t9-d[5]*RP
      cdef double dRE = k[8]*R*E-t4-t5-d[6]*RE
  
      dy = [dM,dE,dCD,dCE,dR,dRP,dRE]
  
      return dy
  
  
  def main():
      cdef np.ndarray[double,ndim=1] t = np.zeros(10000)
      t = np.linspace(0.,3000.,10000.)
      # Initial concentrations of [M,E,CD,CE,R,RP,RE]
      cdef np.ndarray[double,ndim=1] y0 = np.array([0.,0.,0.,0.,0.4,0.,0.25])
      cdef np.ndarray[double,ndim=2] E_simulated = np.zeros([10000,5000])
      cdef np.ndarray[double,ndim=2] r = np.zeros([10000,7])
      cdef np.ndarray[double,ndim=1] E_avg = np.zeros([10000])
      cdef np.ndarray[double,ndim=1] k = np.zeros([19])
      cdef np.ndarray[double,ndim=1] d = np.zeros([7])
      cdef int i
      for i in range (0,5000):
          k[0] = 1.+0.1*randn(1)
          k[1] = 0.15+0.05*randn(1)
          k[2] = 0.2+0.05*randn(1)
          k[3] = 0.2+0.05*randn(1)
          k[4] = 0.35+0.05*randn(1)
          k[5] = 0.001+0.0001*randn(1)
          k[6] = 0.5+0.05*randn(1)
          k[7] = 0.3+0.05*randn(1)
          k[8] = 30.+5.*randn(1)
          k[9] = 18.+3.*randn(1)
          k[10] = 18.+3.*randn(1)
          k[11] = 18.+3.*randn(1)
          k[12] = 18.+3.*randn(1)
          k[13] = 3.6+0.5*randn(1)
          k[14] = 0.15+0.05*randn(1)
          k[15] = 0.15+0.05*randn(1)
          k[16] = 0.92+0.1*randn(1)
          k[17] = 0.92+0.1*randn(1)
          k[18] = 0.01+0.001*randn(1)
          d[0] = 0.7+0.05*randn(1)
          d[1] = 0.25+0.025*randn(1)
          d[2] = 1.5+0.05*randn(1)
          d[3] = 1.5+0.05*randn(1)
          d[4] = 0.06+0.01*randn(1)
          d[5] = 0.06+0.01*randn(1)
          d[6] = 0.03+0.005*randn(1)
          r = integrate.odeint(myc_rb_e2f,y0,t,args=(k,d))
          E_simulated[:,i] = r[:,1]
      for i in range(0,10000):
          E_avg[i] = sum(E_simulated[i,:])/5000.
      pl.plot(t,E_avg,'-ro')
      pl.show()
  </code></pre>
  
  <p>Here are some pstats from cProfile on my Python / Numpy code:</p>
  
  <p><code>ncalls tottime   percall  cumtime  percall</code></p>
  
  <p><code>5000   82.505    0.017  236.760    0.047 {scipy.integrate._odepack.odeint}</code></p>
  
  <p><code>1    1.504    1.504  238.949  238.949 myc_rb_e2f.py:1(&lt;module&gt;)</code></p>
  
  <p><code>5000    0.025    0.000  236.855    0.047 C:\Python27\lib\site-packages\scipy\integrate\odepack.py:18(odeint)</code></p>
  
  <p><code>12291237  154.255    0.000  154.255    0.000 myc_rb_e2f.py:7(myc_rb_e2f)</code></p>