Consider creating rasters for these data sets, each of which will be stored using integers. What pixel depth, in bytes, would be
1 answer:
Answer:
(a) 2 bytes
(b) 1 byte
(c) 1 byte
(d) 2 bytes
Explanation:
The pixel depth, in bytes, that would be most efficient choice for each type of data are:
(a) elevation in meters; 2 bytes.
(b) crown canopy percent; 1 bytes.
(c) temperature in degrees for a thermal camera used by firefighters; 1 bytes.
(d) median county income in dollars; 2 bytes.
Pixel depth also known as bit depth, determines the number of bits used to hold a screen pixel or the range of values that a particular raster file can store.
Pixel depth is calculated with the formula where n is the pixel depth.
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Answer:
Explanation:
This program is written in Python. It is a function that takes in two arrays. Then it loops through the first array twice, adding each element with the others and comparing the sum to the values in the second array. If a similar value is found in the second array, the program prints out the value and returns True to the user. Otherwise, it returns False. A test case has been created with two array variables that print out False, the output can be seen in the attached image below.
def sumArray(arr1, arr2):
for i in range(len(arr1)):
for x in range(len(arr1)):
sum = 0
if i != x:
sum = arr1[i] + arr1[x]
if sum in arr2:
print(str(sum) + "Found in Second Array")
return True
return False
arr1 = [1, 9, 10, 44]
arr2 = [2, 4, 8, 14]
print(sumArray(arr1, arr2))
