Instructions for quadrats

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This task will give you practice using nested for loops and indexing on 2D grids. It is ecology-themed, but no understanding of ecology should be required to complete it. The functions here are not necessarily actually used in real ecology, but have been chosen to give you practice with certain important programming concepts.

In ecology, a "quadrat" is a square or rectangular frame used to count things like species abundance or distribution by placing it over an area and then counting organisms within each square of the grid.

SonoranDesertNPS from Tucson, Arizona, CC BY 2.0, via Wikimedia Commons

We can represent counts of a particular species in a quadrat in Python using a list of lists: the outer list holds multiple "row" lists, and each "row" list holds a series of numbers: the count of that species within each cell in that row. For a 5x5 quadrat like the one pictured above, this might look like:

data = [
    [1, 0, 0, 2, 0],
    [2, 1, 0, 0, 0],
    [1, 1, 0, 3, 0],
    [0, 1, 1, 2, 1],
    [0, 0, 0, 2, 1]
]

Here we have a list of 5 sub-lists, with 5 numbers in each sub-list. We've arranged the code into a square pattern but it could also have been written as:

data = [[1, 0, 0, 2, 0], [2, 1, 0, 0, 0], [1, 1, 0, 3, 0], [0, 1, 1, 2, 1], [0, 0, 0, 2, 1]]

Your job in this task is to write a number of Python functions for processing data of this form. You can assume in all cases that there will be at least 1 row with 1 value in it, and that all values will be non-negative integers. You can also assume that each row within a given example will have the same number of entries, that is, you can assume that the grid is rectangular (but not necessarily square).

Although in some cases these problems could be solved without using a nested loop, the point of this task is to practice using nested loops, so you must use a nested loop. in each function you write.

As usual each function must include a docstring.

There are 6 functions to write:

totalCount

The totalCount function simply returns the total sum of each cell count in the entire grid, given a data list-of-lists as described above. These totalCount examples show how it should work.

Note that normally it would be possible to do this using a single loop and the sum function, but in order to practice writing nested loops, you are not allowed to use sum in this function and you must use a nested loop.

inhabitedCells

The inhabitedCells function returns the total number of cells in which the count is above 0. These inhabitedCells snippets give some examples.

highestCount

The highestCount function must return the highest single entry from the grid. These highestCount snippets give examples.

Note that normally it would be possible to do this using a single loop and the max function, but in order to practice writing nested loops, you are not allowed to use max in this function and you must use a nested loop.

highestThree

The highestThree function works like highestCount except that it returns a list containing 3 elements, which are the 3 highest (or tied-for-highest) counts in the grid. These highestThree snippets provide examples.

Note that even if the grid has fewer than 3 cells, there should always be 3 elements in the list returned, with zeroes added to achieve that.

squaresCount

Species that are more fragmented can be more vulnerable than those that are distributed more evenly. One measure of fragmentation is how many 2x2 squares in the grid include at least one member of the target species in all 4 of their cells. For example, these two grids both have 17 total organisms, but in the first grid there is only one 2x2 square that's got at least 1 organism in every cell and in the second there are five such squares (counting overlaps):

fragmented = [
    [3, 0, 2, 4],
    [2, 1, 2, 3],
    [0, 1, 0, 1],
]

consolidated = [
    [2, 1, 2, 4],
    [2, 1, 1, 3],
    [0, 1, 1, 1],
]

The squaresCount function returns the number of 2x2 squares in which all 4 elements are non-zero, including overlapping squares in the count. For example, on a 3x3 grid, there are four 2x2 squares to check. These squaresCount snippets show how it works.

Hint: In general, for a grid of height X and width Y, there are (X - 1) times (Y - 1) total 2x2 squares.

Note that for this function, you will need to use an index loop and so you are required to use the range function (or enumerate as an alternative).

isolatedCount

Another way of looking at fragmentation is the number of isolated cells: cells where at least one organism is counted, but where none of the four orthogonally adjacent cells have any. Counting the number of isolated cells helps assess how fragmented the distribution is. For example, both of these grids have a total population of 11, but the first grid has two isolated cells, while the second grid here has none:

isolated = [
    [1, 0, 3],
    [0, 2, 0],
    [1, 1, 3]
]

joined = [
    [1, 0, 2],
    [1, 1, 1],
    [0, 2, 3]
]

Note that we don't check how many individuals are in an isolated cell, just that the counts are zero in all four neighbors.

The isolatedCount function must return the count of isolated cells in a grid. These isolatedCount snippets give examples of how it should work.

Notes:

1. You will need to use index loops for this problem. You are required to use the range function (or enumerate as an alternative).
2. Cells at the edge of the grid can more easily become isolated because they have fewer neighbors. Your code needs to check for whether or not you are at an edge of the grid before checking the contents of a neighboring cell.
3. Using a helper function can simplify this problem considerably.