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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:
 NumPy and J make Sweet Array Love
Import NumPy using the standard naming convention
Step1:  Configure the J Python3 addon
To use the J Python3 addon you must edit path variables in jbase.py so Python can locate the J binaries. On my system I set
Step2:  Character data is passed as bytes.
Step3:  j.j() enters a simple REPL
Running j.j() opens a simple read, execute and reply loop with J.  Exit by typing ....
Step4:  J accepts a subset of NumPy datatypes
Passing datatypes that do not match the types the J addon supports is allowed but does not work as you might expect.
Step5:  As you can see a round trip of numpy booleans generates digital noise.
The only numpy datatypes J natively supports on Win64 systems are
Step6:  Basic Operations
Step7:  Array Processing
Step8:  Indexing and Slicing
Step9:  Passing Larger Arrays
Toy interfaces abound. Useful interfaces scale. The current addon is capable of passing
large enough arrays for serious work. Useful subsets of J and NumPy arrays can be memory mapped. It wouldn't
be difficult to memory map very large (gigabyte sized) NumPy arrays for J.
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Python Code:
import numpy as np
Explanation: NumPy and J make Sweet Array Love
Import NumPy using the standard naming convention
End of explanation
import sys
# local api/python3 path - adjust path for your system
japipath = 'C:\\j64\\j64-807\\addons\\api\\python3'
if japipath not in sys.path:
    sys.path.append(japipath)
sys.path
import jbase as j 
print(j.__doc__)
# start J - only one instance currently allowed
try:
    j.init()
except:
    print('j running')
j.dor("i. 2 3 4")  # run sentence and print output result
rc = j.do(('+a.'))  # run and return error code
print(rc)
j.getr()            # get last output result  
j.do('abc=: i.2 3') # define abc
q= j.get('abc')     # get q as numpy array from J array 
print (q)
j.set('ghi',23+q)   # set J array from numpy array
j.dor('ghi')        # print array (note typo in addon (j.__doc___)
Explanation: Configure the J Python3 addon
To use the J Python3 addon you must edit path variables in jbase.py so Python can locate the J binaries. On my system I set:
# typical for windows install in home
pathbin= 'c:/j64/j64-807/bin'
pathdll= pathbin+'/j.dll'
pathpro= pathbin+'/profile.ijs'
Insure  jbase.py and jcore.py are on Python's search path
End of explanation
j.do("cows =. 'don''t have a cow man'")
j.get('cows')
ido = "I do what I do because I am what I am!"
j.set("ido", ido)
j.dor("ido")
Explanation: Character data is passed as bytes.
End of explanation
# decomment to run REPL 
# j.j()
Explanation: j.j() enters a simple REPL
Running j.j() opens a simple read, execute and reply loop with J.  Exit by typing ....
End of explanation
# boolean numpy array
p = np.array([True, False, True, True]).reshape(2,2)
p
j.set("p", p)
j.dor("p")
Explanation: J accepts a subset of NumPy datatypes
Passing datatypes that do not match the types the J addon supports is allowed but does not work as you might expect.
End of explanation
# numpy
a = np.arange(15).reshape(3, 5)
print(a)
# J
j.do("a =. 3 5 $ i. 15")
j.dor("a")
# numpy
a = np.array([2,3,4])
print(a)
# J
j.do("a =. 2 3 4")
j.dor("a")
# numpy
b = np.array([(1.5,2,3), (4,5,6)])
print(b)
# J
j.do("b =. 1.5 2 3 ,: 4 5 6")
j.dor("b")
# numpy
c = np.array( [ [1,2], [3,4] ], dtype=complex )
print(c)
# J
j.do("c =. 0 j.~ 1 2 ,: 3 4")
j.dor("c")           # does not show as complex
j.dor("datatype c")  # c is complex 
# numpy - make complex numbers with nonzero real and imaginary parts
c + (0+4.7j)
# J - also for J
j.dor("c + 0j4.7")
# numpy
np.zeros( (3,4) )
# J
j.dor("3 4 $ 0")
# numpy - allocates array with whatever is in memory
np.empty( (2,3) )
# J - uses fill - safer but slower than numpy's trust memory method
j.dor("2 3 $ 0.0001") 
Explanation: As you can see a round trip of numpy booleans generates digital noise.
The only numpy datatypes J natively supports on Win64 systems are:
np.int64
np.float64
simple character strings - passed as bytes
To use other types it will be necessary to encode and decode them with Python and J helper functions. 
The limited datatype support is not as limiting as you might expect. The default NumPy array is
np.float64 on 64 bit systems and the majority of NumPy based packages manipulate floating point
and integer arrays.
NumPy and J are derivative Iverson Array Processing Notations
The following NumPy examples are from the SciPy.org's 
NumPy quick start tutorial. For each NumPy statement, I have provided a J equivalent
Creating simple arrays
End of explanation
# numpy
a = np.array( [20,30,40,50] )
b = np.arange( 4 )
c = a - b
print(c)
# J
j.do("a =. 20 30 40 50")
j.do("b =. i. 4")
j.do("c =. a - b")
j.dor("c")
# numpy - uses previously defined (b)
b ** 2
# J
j.dor("b ^ 2")
# numpy - uses previously defined (a)
10 * np.sin(a)
# J
j.dor("10 * 1 o. a")
# numpy - booleans are True and False
a < 35
# J - booleans are 1 and 0
j.dor("a < 35")
Explanation: Basic Operations
End of explanation
# numpy
a = np.array( [[1,1], [0,1]] )
b = np.array( [[2,0], [3,4]] )
# elementwise product
a * b
# J
j.do("a =. 1 1 ,: 0 1")
j.do("b =. 2 0 ,: 3 4")
j.dor("a * b")
# numpy - matrix product
np.dot(a, b)
# J - matrix product
j.dor("a +/ . * b")
# numpy - uniform pseudo random - seeds are different in Python and J processes - results will differ
a = np.random.random( (2,3) )
print(a)
# J - uniform pseudo random
j.dor("?. 2 3 $ 0")
# numpy - sum all array elements - implicit ravel
a = np.arange(100).reshape(20,5)
a.sum()
# j - sum all array elements - explicit ravel
j.dor("+/ , 20 5 $ i.100")
# numpy
b = np.arange(12).reshape(3,4)
print(b)
# sum of each column
print(b.sum(axis=0))
# min of each row
print(b.min(axis=1))
# cumulative sum along each row
print(b.cumsum(axis=1))
# transpose
print(b.T) 
# J 
j.do("b =. 3 4 $ i. 12")
j.dor("b")
# sum of each column
j.dor("+/ b")
# min of each row
j.dor('<./"1 b')
# cumulative sum along each row
j.dor('+/\\"0 1 b')   # must escape \ character to pass +/\"0 1 properly to J
# transpose
j.dor("|: b")
Explanation: Array Processing
End of explanation
# numpy 
a = np.arange(10) ** 3 
print(a[2])
print(a[2:5])
print(a[ : :-1])   # reversal
# J
j.do("a =. (i. 10) ^ 3")
j.dor("2 { a")
j.dor("(2 + i. 3) { a")
j.dor("|. a")
Explanation: Indexing and Slicing
End of explanation
from numpy import pi
x = np.linspace( 0, 2*pi, 100, np.float64) # useful to evaluate function at lots of points
f = np.sin(x)
f
j.set("f", f)
j.get("f")
r = np.random.random((2000,3000))
r = np.asarray(r, dtype=np.float64)
r
j.set("r", r)
j.get("r")
r.shape
j.get("r").shape
j.dor("r=. ,r")
j.get("r").shape
r.sum()
b = np.ones((5,300,4), dtype=np.int64)
j.set("b", b)
b2 = j.get("b")
print(b.sum())
print(b2.sum())
Explanation: Passing Larger Arrays
Toy interfaces abound. Useful interfaces scale. The current addon is capable of passing
large enough arrays for serious work. Useful subsets of J and NumPy arrays can be memory mapped. It wouldn't
be difficult to memory map very large (gigabyte sized) NumPy arrays for J.
End of explanation
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