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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Finding a string in files and removing it
Removing #include<malloc.h> in all c files in a structure
Step1:  Quick introduction to chocolate bars and classes
Step2:  Using cython
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Python Code:
%%bash
find . -name "*.c" | xargs sed -i bck "/#include<malloc\.h>/d"
%%bash 
cat ./isis/abs/allocate.cbck
Explanation: Finding a string in files and removing it
Removing #include<malloc.h> in all c files in a structure
End of explanation
from astropy import units as u, constants as const
class SnickersBar(object):
    def __init__(self, w, h, l, weight, energy_density=2460 * u.kJ/ (100 * u.g)):
        self.w = u.Quantity(w, u.cm)
        self.h = u.Quantity(h, u.cm)
        self.l = u.Quantity(l, u.cm)
        self.weight = u.Quantity(weight, u.g)
        self.energy_density = u.Quantity(energy_density, u.kJ / u.g)
    
    def calculate_volume(self):
        return self.w * self.h * self.l
    
    @property
    def volume(self):
        return self.w * self.h * self.l
    
        
my_snickers_bar = SnickersBar(0.5, 0.5, 4, 0.01 * u.kg)
my_snickers_bar.w = 1 * u.cm
my_snickers_bar.volume
Explanation: Quick introduction to chocolate bars and classes
End of explanation
%load_ext Cython
import numpy as np
import numexpr as ne
x1, y1 = np.random.random((2, 1000000))
x2, y2 = np.random.random((2, 1000000))
distance = []
def calculate_distances(x1, y1, x2, y2):
    distances = []
    for i in xrange(len(x1)):
        distances.append(np.sqrt((x1[i] - x2[i])**2 + (y1[i] - y2[i]**2)))
    return distances
def numpy_calculate_distances(x1, y1, x2, y2):
    return np.sqrt((x1 - x2)**2 + (y1-y2)**2)
def ne_calculate_distances(x1, y1, x2, y2):
    return ne.evaluate('sqrt((x1 - x2)**2 + (y1-y2)**2)')
#%timeit calculate_distances(x1, y1, x2, y2)
%timeit ne_calculate_distances(x1, y1, x2, y2)
%%cython -a
import numpy as np
cimport numpy as np
import cython
cdef extern from "math.h":
    cpdef double sqrt(double x)
@cython.boundscheck(False)
def cython_calculate_distances(double [:] x1, double [:] y1, double [:] x2, double [:] y2):
    distances = np.empty(len(x1))
    cdef double [:] distances_view = distances
    cdef int i
    cdef int len_x1=len(x1)
    for i in xrange(len_x1):
        distances_view[i] = sqrt((x1[i] - x2[i])**2 + (y1[i] - y2[i]**2))
    return distances
Explanation: Using cython
End of explanation
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