please please Here's the python script for the lab. Can you answer only 11.a,b,c,d,e? PLeasefrom scipy import randomimport numpy as npimport matplotlib.pyplot as plti = 1 # set a value for current in Amperes (A)B = 2 # set a value for magnetic field in Tesla (T)R = 1 # set length of wire in meters (m)a1 = -R # set left end point of straight wireb1 = R # set right end point of straight wirea2 = 0 # set left end point of curved wireb2 = np.pi # set end point of curved wireN = 1000 # set number of intervals Ndef f(x):return 1 def g(x):return np.sin(x)# generate an array of all zeroesarray1 = np.zeros(N)array2 = np.zeros(N) # change each entry in array to a random number between a and bfor i in range(N):array1[i] = random.uniform(a1,b1)array2[i] = random.uniform(a2,b2)# compute summation over all random numbers in arrayintegral_straight = 0.0integral_semicirc = 0.0for i in array1:integral_straight += f(i) for i in array2:integral_semicirc += g(i)# compute the average value for each# contribution of force from each wirestraight = ((b1-a1)/float(N)) * integral_straightsemicirc = ((b2-a2)/float(N)) * integral_semicirc# calculate the net force by subtractionf_net = - i * B * straight + i * B * R * semicirc print('The net force is %0.06f' % f_net, 'N')11. Lastly, for your conclusion, please summarize the context of this experi-ment. Following this, please make sure you can address the questions below:(a) What happens when we run our script several times?(b) Given that we can run our script multiple times, what is the average?(c) By running this experiment multiple times, we achieved our goal? why?(d) If there is another scenario, to compute integrals, how many times do youthink we should run this experiment to get the answer?(e) Can we say there is a likelihood that if we ran the experiment one time,we would obtain the answer? and by what chance (in percentage)? Page <1> of 5+ZOOMLab #7: Magnetic Force of a Current Carrying WireRecall that the magnetic force on a current carrying wire can be charac-terized by the notion of moving charges (current) along a wire subject to amagnetic field produces a force on the wire. The expression for magnetic forceon a current carrying wire isF = iL x B,assuming that the wire is straight. For a curved wire, small segments of wirecan be portioned into small elements ds such that the force is then expresseddF = i · ds x B.Integrating yields the form for magnetic force with an arc lengthF = iB sin 0ds.aNotice that for a straight wire, we can simply write as F = iLB sin 0 given somewire of length L.Magnetic Force due to Straight and Semi-circular Wireх ВRAs an example, a wire consisting of a straight and semi-circular portions aresubject to a uniform magnetic field. This problem shows in two ways, thatmagnetic force exerted on the wire reaches to a net zero, and that we canexemplify the method of solving this problem analytically two ways. Lets writethis out based on our understanding and application of the Right-Hand Rule,RFnetFstraight + Fsemicircle = –iBdx + iBRsin Od0.-R0. OurUltimately, the above expression results in Fnetgoal for this lab then, is to confirm our solution through means of a numericalexperiment through solving integrals as shown on the right hand side. Fur-thermore, rather than solving our integrals through difficult and cumbersomenumerical integration techniques, we will make use of a numerical method calledthe monte carlo method of integraion.2¿RB – 2iRB-------- Page <2> of 5ZOOM+Integrating through the Monte Carlo MethodIn short, the monte carlo method is a statistical technique used oftento solve problems numerically where it may be deemed too difficult to employ.While we can solve integration problems analytically by hand, a computer maynot be able to do the same. One such example is the famous Guass integral-x²dx = VT.But without the knowledge in solving this integral by hand, is a challengingpiece of effort. This is where computers help us with numerical approximationsthat are used to solve complex integral problems.The monte carlo method makes use of random sampling. That is, byselecting random numbers x;, i = 1, 2, .. on a given interval [a, b] of some functionf (x). We then methodically compute the output values while taking the averageof a sum of outputs to estimate our total area. More specifically, we want theaverage value of an integral over an interval [a, b], or as such, by defining theaverage value of a function as u, we find that9.1| F(x)dx.аaNoting as well, the mean can be represented through a summation of values,NEf (x;)Ni=1By equating the expressions above, we arrive at an approximation between thediscrete and continuous representations,N11| F(x)dr,Ni=1- aand because the left hand side being discrete, we find ourselves using an algo-rithm that computes this. Solving the integral on the right hand side yields theresult of our interest,а| f(2)dar =Σf (:)Ni=1Recall that we will be choosing points x; at random on the interval [a, b] to helpapproximate our integral. Our goal then is to build an algorithm that providesa summation over N many intervals over the a, b. The output values whengiven an input with random numbers will then be added together achieve ouraverage. The average then, in turn will give us an approximation for the areaunder the curve, or the value of integration.---------

Putting our script into a function as given below: and defining a driver function as below and calling it repeatedly in a loop we get:Running it multiple times we get:Hence we notice that the output ranges between positive and negative values, with almost 50% chances for each. To calculate the average we use a slightly modified driver function as shown in the script below: we notice for a 100 run we get an average of -0.00170310063779(c) By running the script multiple times we are able to account for the random variations of the value taken for array1 and array2. (d) for computing integrals the number of times the script run will not make much difference, however the value of N would need to increase (interval size) for better accuracy (e) if we ran the program only once the chance of getting an accurate answer is very low.