代写Python作业，完成缺失的函数，对地表进行计算。
Requirement
Please read this document very carefully. Follow instructions exactly. If you
have any questions please post them to the course Piazza page.
Consider a section of the Earth’s surface from a birds-eye-view. The elevation
may vary greatly across this section. See the Figures below as an example. If
one were interested in analysing this area based on elevation, one could
represent the section of Earth as a matrix of numerical values, where each
cell of the matrix would indicate the elevation of the Earth at that
particular location. For example, an area the size of 1 square kilometer of
Earth could be encoded as a 1000x1000 matrix, where each cell is the elevation
of a single square meter; here, the value at cell (500,500) of the matrix,
would be the elevation at approximately the middle of the original 1 square
kilometer section.
For this assignment, you will implement several functions which will allow
someone with such a matrix of values to perform meaningful analysis on the
area of the Earth’s surface the matrix represents. You are given a file,
assignment2.py, with six incomplete functions. For this assignment, you’re
required to complete these functions. A description regarding the intended
behaviour of these functions is given later in this document. Further
documentation and examples for these functions are given in the docstrings
within the starter code: assignment2.py.
For the purposes of this assignment we will use the following definitions.
An elevation map is of the type List[List[int]], and moreover, the length of
an elevation map equals the length of all elements within the elevation map.
An elevation map will only contain positive numbers. An example of an
elevation map is:
valid_map = [[1,2,3],[4,5,6],[7,8,9]]
—|—
It may be more intuitive to view the map as:
valid_map = [[1,2,3],
[4,5,6],
[7,8,9]]
—|—
The following two examples are not elevation maps (note the length of all the
lists in each):
invalid_map1 = [[1,2,3],[4,5],[6,7,8,9]]
invalid_map2 = [[1,2,3],[4,5,6],[7,8,9],[10,11,12]]
—|—
A cell is of the type List[int] and has a length of 2. All values in a cell
will be greater than or equal to 0. Within an elevation map m, we say cell [i,
j] as a shorthand for m[i][j]. We also say cell [i, j] is adjacent to cell [n,
m] if and only if [n, m] equals one.
Within an elevation map m, cell [i, j] is a sink if for all adjacent cells.
With the physical interpretation of an elevation map in mind, water would
collect in sinks, since there is no less elevated area in the immediate
vicinity for the water to flow to.
Functions
You are required to implement all of the functions below. Pay attention to
parameters of each function, for example, if it is said an input will be an
elevation map, you can trust your function will never be tested on input which
isn’t an elevation map. For further examples of how these functions are
intended to operate, view the docstrings of the starter code for this
assignment.
get_average_elevation(List[List[int]]) -> float
The first parameter is an elevation map, m. Returns the average elevation
across all the land in m.
find_peak(List[List[int]]) -> List[int]
The first parameter is an elevation map, m. Returns the cell which contains
the highest elevation point in m. For the purposes of us testing this
function, you may assume that all values of m are unique (no two locations
have equal elevations).
is_sink(List[List[int]], List[int]) -> bool
The first parameter is an elevation map, m, the second parameter is a cell, c.
Returns True if and only if c is a sink in m. Note if c does not exist in m
(the values are outside m’s dimensions), this function returns False. See the
previous section for the definition of a sink.
find_local_sink(List[List[int]], List[int]) -> List[int]
The first parameter is an elevation map, m, the second parameter is a cell, c,
which exists in m. Returns the local sink of c. A local sink of c is the cell
which water would flow to if it started at c. Assume if the current location
isn’t a sink, water will always flow to the adjacent cell with the lowest
elevation. You may also assume for the purposes of us testing this function,
that all values of m are unique (no two locations have equal elevations). See
the docstring for some examples.
can_hike_to(List[List[int]], List[int], List[int], int) -> bool
The first parameter is an elevation map, m, the second is start cell, s which
exists in m, the third is a destination cell, d, which exists in m, and the
forth is the amount of available supplies. Under the interpretation that the
top of the elevation map is north, you may assume that d is to the south-east
of s (this means it could also be directly south, or directly east). The idea
is, if a hiker started at s with a given amount of supplies could they reach f
if they used the following strategy. The hiker looks at the cell directly to
the south and the cell directly to the east, and then travels to the cell with
the lower change in elevation. They keep repeating this stratagem until they
reach d (return True) or they run out of supplies (return False). Assume to
move from one cell to another takes an amount of supplies equal to the change
in elevation between the cells. See the docstring for some examples. If the
change in elevation is the same between going East and going South, the hiker
will always go East. Also, the hiker will never choose to travel South, or
East of d (they won’t overshoot their destination). That is, if d is directly
to the East of them, they will only travel East, and if d is directly South,
they will only travel South.
rotate_map(List[List[int]]) -> None
The parameter is an elevation map, m. Under the interpretation that the top of
m is north, the function mutates m such that the top of m would now be viewed
as east. See the docstring for some examples.
Submitting and Grading
This assignment will be submitted electronically via MarkUs. Please find the
MarkUs link on the course website. Note, to avoid potential confusion and
submitting to the wrong location, there will be nowhere on MarkUs to submit
Assignment 2 until the Assignment 1 resubmit is past due.
This assignment is worth 10% of your final grade. Grading is done completely
automatically. That is, a program calls your function, passes it certain
arguments, and checks to see if it returns the expected output. Each function
is worth 20% of assignment grade, with the exception of get average elevation
and find peak which are each worth 10% of the assignment’s grade. For any one
function, if you pass n of the m tests we run on that function, your grade for
that function will be n/m.
Shortly after the deadline, you will receive your grade. If you are not
content with this grade, you may resubmit your assignment up to 48 hours after
the original deadline with a 20% penalty. If you choose to resubmit, your
final grade on the assignment will be the higher of the two grades (the
original submission, and the re-submission with a 20% penalty). Good luck!
Additional Material
You will note the starter code also has a create real map() function. This
will allow you to create an elevation map from the real world data found in
the data.csv file. To properly generate the map, make sure data.csv is in the
same directory as assignment2.py when you run the function.
This assignment was developed with the aid of the GIS department here at UTM.
They’ve been kind enough to give a little background regarding the
applications and source of the data we’re using. If this kind of stuff
interests you, I highly suggest you look into the program here at UTM; it is
very popular with students who take 108.
Near the turn of the millennium, an international research effort was
undertaken to acquire the most complete and high-resolution digital
topographic database of Earth. During an 11 day mission, the Space Shuttle
Endeavor was fitted with a synthetic aperture altimeter capable of resolving
the elevation of the Earth’s surface at 30m resolution. The data collected
during the Shuttle Radar Topography Mission (SRTM) formed the first high
resolution digital elevation model (DEM) of the globe that was homogeneous in
data quality and freely available. The SRTM DEM has been used by over 1
million users from 221 countries in applications ranging from agricultural
planning, dam breakage/flooding risk assessment, natural hazard assessment,
and countless others. The DEM data in data.csv, and seen in the figures
previously given in this document is a 1200x1200 grid where each pixel
(80mx80m) represents the elevation in meters. The area is the northeastern US
near the Wallowa-Whitman National Forest which straddles Washington and Idaho.
The space around us shapes our daily lives in more ways than we can imagine.
Hospitals route ambulances through network analysis, accounting for the
slope/curvature of roads to safe time and reduce the strain on paramedics as
they treat patients. Many institutions are incorporating spatial analysis and
geographic information systems (GIS) into their strategic and everyday
business planning. If you would like to know more about GIS, please see the
courses offered by the Department of Geography and Programs in Environment.
Good luck!
