New to Python. Given in the code snippet below is a numpy 1d array called randomWalk. Given indices (which can be interpreted as start dates and end dates, both of which may vary from item to item), I want to do take multiple slices from that 1d array randomWalk and arrange the results in a 2d array of given shape.
I am trying to vectorize this. Was able to select the slices I wanted from the 1d array using np.r_, but failed to store these in the format I require for the output (a 2d array with rows representing items and columns representing time from min(startDates) to max(endDates).
Below is the (ugly) code that works.
import numpy as np
numItems = 20
numPeriods = 12
# Data
randomWalk = np.random.normal(loc = 0.0, scale = 0.05, size = (numPeriods,))
startDates = np.random.randint(low = 1, high = 5, size = numItems)
endDates = np.random.randint(low = 5, high = numPeriods + 1, size = numItems)
stochasticItems = np.random.choice([False, True], size=(numItems,), p = [0.9, 0.1])
# Result needs to be in this shape (code snippet is designed to capture that only
# a relatively small fraction of resultMatrix's elements will differ from unity)
resultMatrix = np.ones((numItems, numPeriods))
# Desired result (obtained via brute force)
for i in range(numItems):
if stochasticItems[i]:
resultMatrix[
i, startDates[i]:endDates[i]] = np.cumprod(randomWalk[startDates[i]:endDates[i]] + 1.0)
Inspired by @mozway 's answer, convert irregular slices into regular mask array:
>>> # build all arrays with np.random.seed(0)
>>> x = np.arange(numPeriods)
>>> mask = (startDates[:, None] <= x) & (endDates[:, None] > x)
>>> result = np.where(mask & stochasticItems[:, None], np.where(mask, randomWalk + 1, 1).cumprod(-1), 1)
>>> np.allclose(result, resultMatrix)
True
>>> result
array([[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1.0489369 , 1.16646468, 1.2753867 ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ],
[1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. ]])