I have an array D of variable length,
I want to create a loop that performs a sum based on the value of D corresponding to the number of times looped
i.e. the 5th run through the loop would use the 5th value in my array.
My code is:
period = 63 # can be edited to an input() command for variable periods.
Mrgn_dec = .10 # decimal value of 10%, can be manipulated to produce a 10% increase/decrease
rtn_annual = np.arange(0.00,0.15,0.05) # creates an array ??? not sure if helpful
sig_annual = np.arange(0.01,0.31,0.01) #use .31 as python doesnt include the upper range value.
#functions for variables of daily return and risk.
rtn_daily = (1/252)*rtn_annual
sig_daily = (1/(np.sqrt(252)))*sig_annual
D=np.random.normal(size=period) # unsure of range to use for standard distribution
for i in range(period):
r=(rtn_daily+sig_daily*D)
I'm trying to make it so my for loop is multiplied by the value for D of each step.
So D has a random value for every value of period, where period represents a day.
So for the 8th day I want the loop value for r to be multiplied by the 8th value in my array, is there a way to select the specific value or not?
Does the numpy.cumprod command offer any help, I'm not sure how it works but it has been suggested to help the problem.
You can select element in an iterative object (such as D in your code) simply by choosing its index. Such as:
for i in range(period):
print D[i]
But in your code, rtn_daily and sig_daily are not in the same shape, I assume that you want to add sig_daily multiply by D[i] in each position of rtn. so try this:
# -*- coding:utf-8 -*-
import numpy as np
period = 63 # can be edited to an input() command for variable periods.
Mrgn_dec = .10 # decimal value of 10%, can be manipulated to produce a 10% increase/decrease
rtn_annual = np.repeat(np.arange(0.00,0.15,0.05), 31) # creates an array ??? not sure if helpful
sig_annual = np.repeat(np.arange(0.01,0.31,0.01), 3) #use .31 as python doesnt include the upper range value.
#functions for variables of daily return and risk.
rtn_daily = (float(1)/252)*rtn_annual
sig_daily = (1/(np.sqrt(252)))*sig_annual
D=np.random.normal(size=period) # unsure of range to use for standard distribution
print D
for i in range(period):
r=(rtn_daily[i]+sig_daily[i]*D[i])
print r
Last of all, if you are using python2, the division method is for integer, so that means 1/252 will give you zero as result.
a = 1/252 >-- 0
to solve this you may try to make it float:
rtn_daily = (float(1)/252)*rtn_annual
Right now, D is just a scalar.
I'd suggest reading https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.normal.html to learn about the parameters.
If you change it to:
D=np.random.normal(mean,stdev,period)
you will get a 1D array with period number of samples, where mean and stdev are your mean and standard deviation of the distribution. Then you change the loop to:
for i in range(period):
r=(rtn_daily+sig_daily*D[i])
EDIT: I don't know what I was thinking when I read the code the first time. It was a horribly bad read on my part.
Looking back at the code, a few things need to happen to make it work.
First:
rtn_annual = np.arange(0.00,0.15,0.05)
sig_annual = np.arange(0.01,0.31,0.01)
These two lines need to be fixed so that the dimensions of the resulting matricies are the same.
Then:
rtn_daily = (1/252)*rtn_annual
Needs to be changed so it doesn't zero everything out -- either change 1 to 1.0 or float(1)
Finally:
r=(rtn_daily+sig_daily*D)
needs to be changed to:
r=(rtn_daily+sig_daily*D[i])
I'm not really sure of the intent of the original code, but it appears as though the loop is unnecessary and you could just change the loop to:
r=(rtn_daily+sig_daily*D[day])
where day is the day you're trying to isolate.
Related
I will have an interview with a company which like the hackerearth.com. I don't know how to work and doing the code perfectly. Could you help me with the following example?
This is the example for the .hackerearth.com, however, I don't know that I should consider the constraint in the code? can I use a package like NumPy? or I should only use the basic calculation with my self? Could you check my response and let me know the problem with that? Thank you so much
Input Format:
First line of input consists of an integer N denoting the number of elements in the array A.
Second line consists of N space separated integers denoting the array elements.
Output Format:
The only line of output consists of the value of x.
Input Constraints:
1<N<100
1<A[i]<100
explanation:
An initial sum of array is 1+2+3+4+5=15
When we update all elements to 4, the sum of array which is greater than 15 .
Note that if we had updated the array elements to 3, which is not greater than 15 . So, 4 is the minimum value to which array elements need to be updated.
# Write your code here
import numpy as np
A= [1, 2, 3,4,5]
for i in range(1, max(A)+1):
old = sum(A)
new = sum(i*np.ones(len(A)))
diff = new-old
if diff>0:
print(i)
break
Well this isn't Code Review stack exchange, but:
You don't say how to calculate x. It seems to be something to do with finding an average value, but no-one can judge your code without know what it's trying to do. A web search suggests it is this:
Fredo is assigned a new task today. He is given an array A containing N integers. His task is to update all elements of array to some minimum value x , that is, ; such that sum of this new array is strictly greater than the sum of the initial array. Note that x should be as minimum as possible such that sum of the new array is greater than the sum of the initial array.
Given that the task starts by accepting input, it's important that your program does this part.
N = int(input()) # you can put a prompt string in here, but may conflict with limited output
A = list(map(int,input().split()))
# might need input checks
# might need range checks
# might check that A has exactly N values
you don't need to recalculate old = sum(A) every time around your search loop
calculation of new doesn't need a sum at all - it's just new = i * len(A)
there's no point in checking values of i at or below min(A)
your search will fail if all values of A are the same (try it), because you never look above max(A)
These remarks apply to your approach; a more efficient search would be binary chop, and there is also a mathematical way to go straight to the answer from sum(A) without any searching:
x = sum(A) // len(A) + 1
You don't need numpy or looping for this. Get the average of the array elements, then get the next higher integer from this.
N = 5
A = [1, 2, 3, 4, 5]
total = sum(A)
avg = A/N # not checking for zero-divide because conditions say N > 1
x = floor(avg + 1)
print(x)
Adding 1 is necessary to make the new sum greater than the original sum when the average is an exact integer (e.g. 15/5 == 3).
that's my first time here at Stack and I'm on my first days with Python.
I'm dealing with the HAR-RV model, trying to run this equation but having no success at all to store my operations on the array
Here what I'm trying to calculate:
r_[t,i] = Y_[t,i] - Y_[t,i-1]
https://cdn1.imggmi.com/uploads/2019/8/30/299a4ab026de7db33c4222b30f3ed70a-full.png
Im using the first relation here, where "r" means return and "Y" the stock prices
t = 10 # daily intervals
i = 30 # number of days
s = 1
# (Here I've just created some fake numbers, intending to simulate some stock prices)
Y = abs(np.random.random((10,30)) + 1)
# initializing my return array
return = np.array([])
# (I also tried to initialize it as a Matrix before..) :
return = np.zeros((10,30))
# here is my for loop to store each daily return at its position on the "return" Array. I wanted an Array but got just "() size"
for t in range(0,9):
for i in range(1,29):
return = np.array( Y.item((t,i)) - Y.item((t,i-1)) )
... so, I was expecting something like this:
return = [first difference, second difference, third difference...]
how can I do that ?
first don't use return as a variable name in python as it is a python keyword (it signifies the result of a function). I have changed your variable return to ret_val.
You are wanting to make a change at each position in your array, so make the following change to your for loop:
for t in range(0,10):
for i in range(1,30):
ret_val[t][i] = Y[t][i] - Y[t][i-1]
print(ret_val)
This is saying to change the value at index ret_val[t][i] with the result of subtracting the values at that specific index in Y. You should see an array of the same shape when you print.
Also, the range function in python does not include the upper number. So when you say, for i in range(0,9) you are saying to include numbers 0-8. For your array, you'll want to do for i in range(0,10) to include all values in your array. Correspondingly, you'll want to do the same for i in range(1,30).
I've recently "taught" myself python in order to analyze data for my experiments. As such I'm pretty clueless on many aspects. I've managed to make my analysis work for certain files but in some cases it breaks down and I imagine it is a result of faulty programming.
Currently I export a file containing 3 numpy arrays. One of these arrays is my signal (float values from -10 to 10). What I wish to do is to normalize every datum in this array to a range of values that preceed it. (i.e. the 30001st value must have the average of the preceeding 3000 values subtracted from it and then the difference must then be divided by thisvery same average (the preceeding 3000 values). My data is collected at a rate of 100Hz thus to get a normalization of the alst 30s i must use the preceeding 3000values.
As it stand this is how I've managed to make it work:
this stores the signal into the variable photosignal
photosignal = np.array(seg.analogsignals[0], ndmin=1)
now this the part I use to get the delta F/F over a moving window of 30s
normalizedphotosignal = [(uu-(np.mean(photosignal[uu-3000:uu])))/abs(np.mean(photosignal[uu-3000:uu])) for uu in photosignal[3000:]]
The following adds 3000 values to the beginning to keep the array the same length since later on i must time lock it to another list that is the same length
holder =list(range(3000))
normalizedphotosignal = holder + normalizedphotosignal
What I have noticed is that in certain files this code gives me an error because it says that the"slice" is empty and therefore it cannot create a mean.
I think maybe there is a better way to program this that could avoid this problem altogether. Or this a correct way to approach this problem?
So i tried the solution but it is quite slow and it nevertheless still gives me the "empty slice error".
I went over the moving average post and found this method:
def running_mean(x, N):
cumsum = np.cumsum(np.insert(x, 0, 0))
return (cumsum[N:] - cumsum[:-N]) / N
however I'm having trouble accommodating it to my desired output. namely (x-running average)/running average
Allright so I finally figured it out thanks to your help and the posts you referred me to.
The calculation for my entire data (300 000 +) takes about a second!
I used the following code:
def runningmean(x,N):
cumsum =np.cumsum(np.insert(x,0,0))
return (cumsum[N:] -cumsum[:-N])/N
photosignal = np.array(seg.analogsignal[0], ndmin =1)
photosignalaverage = runningmean(photosignal, 3000)
holder = np.zeros(2999)
photosignalaverage = np.append(holder,photosignalaverage)
detalfsignal = (photosignal-photosignalaverage)/abs(photosignalaverage)
Photosignal stores my raw signal in a numpy array.
Photosignalaverage uses cumsum to calculate the running average of every datapoint in photosignal. I then add the first 2999 values as 0, to maintian the same list size as my photosignal.
I then use basic numpy calculations to get my delta F/F signal.
Thank you once more for the feedback, was truly helpful!
Your approach goes in the right direction. However, you made a mistake in your list comprehension: you are using uu as your index whereas uu are the elements of your input data photosignal.
You want something like this:
normalizedphotosignal2 = np.zeros((photosignal.shape[0]-3000))
for i, uu in enumerate(photosignal[3000:]):
normalizedphotosignal2 = (uu - (np.mean(photosignal[i-3000:i]))) / abs(np.mean(photosignal[i-3000:i]))
Keep in mind that for-loops are relatively slow in python. If performance is an issue here, you could try avoiding the for loop and use numpy methods instead (e.g. have a look at Moving average or running mean).
Hope this helps.
I am new in python and I am trying to learn it by myself. I am currently working on a code, which gives me index error because somehow for loop does not populate my data. I am supposed to iterate a value and with it, I depend on the previous value to produce the new value. Normally this was easy with matlab, only with x(:,k) but python does not work the same way and I will really be grateful for any help that does not judge my level of knowledge in python. Here how it goes:
x = np.matrix([[1.2],[.2]]) # prior knowledge
A = np.matrix([[1, 1], [0, 1]])
B = np.matrix([[.5], [1]])
U = -9
t1 = range(1,100,1)
for k, val in enumerate(t1):
x[:,k] = A*x[:,k-1] + B*U
To my understanding, the error 'IndexError: index 1 is out of bounds for axis 1 with size 1' pops up because the for loop does not populate the data 'x' and therefore, there is no value for neither 'k-1' nor 'k'.
What I should do is to iterate and store 'x' values and pick the relevant previous value each time to obtain new value with given equation till the end of loop. As you can see, I have a column matrix and I should have a column matrix each time. I hope I could make myself clear.
Thank you
The first line is the initial value of x, the second, third, fourth and fifth lines are the values that are used in for loop to calculate iterations for x.
What I am trying to implement is code for kaman filter in general. In this system, the current value x(k) is calculated with previous value x(k-1) with given equation x(k) = Ax(k-1) + BU. Each x(k) value becomes x(k-1) in next iteration until loop is executed. Here, I am expecting to have (2,k) matrix after every loop because record of values are essential for other calculations. And to use the previous value in current value, I need to access to (k-1)th value.
The question was solved by juanpa.arrivillaga (https://stackoverflow.com/users/5014455/juanpa-arrivillaga) Thank you.
Today my task is to make a histogram to represent the operation of A^n where A is a matrix, but only for specific entries in the matrix.
For example, say I have a matrix where the rows sum to one. The first entry is some specific decimal number. However, if I raise that matrix to the 2nd power, that first entry becomes something else, and if I raise that matrix to the 3rd power, it changes again, etc - ad nauseum, and that's what I need to plot.
Right now my attempt is to create an empty list, and then use a for loop to add the entries that result from matrix multiplication to the list. However, all that it does is print the result from the final matrix multiplication into the list, rather than printing its value at each iteration.
Here's the specific bit of code that I'm talking about:
print("The intial probability matrix.")
print(tabulate(matrix))
baseprob = []
for i in range(1000):
matrix_n = numpy.linalg.matrix_power(matrix, s)
baseprob.append(matrix_n.item(0))
print(baseprob)
print("The final probability matrix.")
print(tabulate(matrix_n))
Here is the full code, as well as the output I got.
http://pastebin.com/EkfQX2Hu
Of course it only prints the final value, you are doing the same operation, matrix^s, 1000 times. You need to have s change each of those 1000 times.
If you want to calculate all values in location matrix(0) for matrix^i where i is each value from 1 to s (your final power) do:
baseprob = []
for i in range(1,s): #changed to do a range 1-s instead of 1000
#must use the loop variable here, not s (s is always the same)
matrix_n = numpy.linalg.matrix_power(matrix, i)
baseprob.append(matrix_n.item(0))
Then baseprob will hold matrix(0) for matrix^1, matrix^2, etc. all the way to matrix^s.