# Rainfall - August 2016 Community Challenge (Hooray for graphs!)

A solution for the August 2016 Community Challenge (Rainfall).

I've intentionally left out error checking on the file formatting.

This seems like a very obvious graph problem to me - we want to partition some graph based off of relative elevations. I've used networkx 1.9.1 to operate on the graphs, and matplotlib to make it pretty(ish).

Haven't tested this on a large sample, but I suspect that it will go pretty slowly. t.txt refers to a file like this - the name was for brevity's sake, and would be a different one were I making a production application.

3
1 5 2
2 4 7
3 6 9


The general idea is to build up a graph of all of the plots and their neighbors, while also building up a list of plots sorted by elevation (which is fairly efficient due to the binary search insertion) and then starting from the highest elevation and moving down, removing edges that get disqualified. I then do a little extra at the end to set the basin property on each node to the correct value, but that isn't strictly necessary (I'd get the correct answer either way, and the coloring would only require a minor change to work).

I wrote this and ran it on Python 2, but at a first glance it should work on Python 3 without modification (famous last words...).

from collections import namedtuple
import bisect
import functools

import networkx as nx
import matplotlib.pyplot as plt
import numpy as np

@functools.total_ordering
class PlotData(namedtuple('PlotData', 'plot elevation')):

def __lt__(self, other):
return self.elevation < other.elevation

def __eq__(self, other):
return self.elevation == other.elevation

def build_topography(filename):
with open(filename) as elevation_data:
lines = iter(elevation_data)
next(lines)

topography = nx.Graph()
sorted_plots = []

for row_index, row in enumerate(lines):
for plot_index, elevation in enumerate(map(int, row.split())):
plot = (row_index, plot_index)
data = PlotData(plot, elevation)
bisect.insort(sorted_plots, data)
plot,
elevation=elevation,
basin=plot,
parents=[]
)

above_node = (plot[0] - 1, plot[1])
left_node = (plot[0], plot[1] - 1)
if above_node in topography:
if left_node in topography:

def process_topography(topography, sorted_nodes):
for node in sorted_nodes:
node = node.plot
edges = topography[node]
if edges:
min_elevation = topography.node[node]['elevation']
basin = node
parents = topography.node[node]['parents']
for connected_node in edges:
elevation = topography.node[connected_node]['elevation']
if elevation < min_elevation:
min_elevation = elevation
basin = connected_node
topography.node[node]['basin'] = basin
topography.node[basin]['parents'].append(node)
topography.node[basin]['parents'].extend(parents)
edges_to_remove = [connected_node
for connected_node in edges
if connected_node != basin and
connected_node not in parents]
for edge_to_remove in edges_to_remove:
topography.remove_edge(node, edge_to_remove)

fix_basins(topography)

def fix_basins(topography):
for node in topography.nodes():
if is_sink(topography, node):
for parent in topography.node[node]['parents']:
topography.node[parent]['basin'] = node

def is_sink(topography, node):
connected_nodes = topography[node]
parents = topography.node[node]['parents']
return all(connected in parents for connected in connected_nodes)

def get_label(topography, node):
n_dict = topography.node[node]
return "{} - {}".format(n_dict['basin'], n_dict['elevation'])

def get_basin_colors(topography):
connected_components = list(nx.connected_components(topography))
colors = np.linspace(0, 1, len(connected_components))
return {
topography.node[component[0]]['basin'] : color
for component, color in zip(connected_components, colors)
}

def get_basin_sizes(connected_components):
return (
len(component)
for component in sorted(connected_components, key=len, reverse=True)
)

def display_graph(topography, colored=False):
labels = {node : get_label(topography, node) for node in topography.nodes()}
kwargs = {
'with_labels': True,
'layout': nx.shell_layout(topography),
'labels': labels
}
if colored:
color_dict = get_basin_colors(topography)
colors = [color_dict[topography.node[node]['basin']] for node in topography.nodes()]
kwargs['node_color'] = colors

nx.draw(topography, **kwargs)
plt.draw()
plt.show()

if __name__ == '__main__':
topography, sorted_plots = build_topography('t.txt')

display_graph(topography)

process_topography(topography, sorted_plots)

display_graph(topography, colored=True)

for basin in get_basin_sizes(nx.connected_components(topography)):
print basin,


This displays these graphs:

And then this output:

7 2


NetworkX is nice, but you really aren't using a lot of its features, and the features you are using end up not being that pivotal; your implementation won't be changed (much) if you remove NetworkX and use some homegrown features.

Also - your current method of interspersing drawing with doing calculations is kind of annoying, and it makes it hard to tell what the slow parts of your calculation are - that should be separated out into its own method. You can do that better as well - while the general problem of drawing a graph is hard, drawing a 2D matrix is not - NetworkX doesn't know we have a 2D matrix, but we do.

I've rewritten the display functionality. You'll notice that it is now an instance method - while rewriting it to not use NetworkX I encapsulated everything in a class like a good little object oriented programmer, and I've also referenced a few data members that I wasn't using previously. I'll talk about that a bit more in a second.

def display(self):
colors = plt.cm.rainbow(np.linspace(0, 1, len(self.sinks)))
color_dict = {}
labels = {}

for index, sink in enumerate(self.sinks):
color_dict[sink] = colors[index]
labels[sink] = self.cells[sink]['elevation']
for parent in self.cells[sink]['parents']:
color_dict[parent] = colors[index]
labels[parent] = self.cells[parent]['elevation']

segments = []
for key, values in self.neighbors.iteritems():
for value in values:
segments.append((key, value))

fig=plt.figure()
plt.xlim(-.5, self.size - .5)
plt.ylim(-.5, self.size - .5)
for s in segments:
first_point = s[0][1], self.size - s[0][0] - 1
second_point = s[1][1], self.size - s[1][0] - 1
plt.plot(*zip(first_point, second_point), marker='o', color=color_dict[s[0]])
plt.annotate(
labels[s[0]],
xy=first_point, xytext=(-5, 5),
textcoords='offset points', ha='right', va='bottom')

plt.show()


We've kept a lot of the logic the same, but in particular we've fixed the positioning of points so that they're in the correct location, we've colored everything (and the edges!) more appropriately, and we've fixed the label positioning (and also removed the pointless basin). If we wanted to we could add something special at the sinks, but this should be sufficient for now. There is still a bit of ickiness here - we could easily break this up into about three or four helper methods, but for demonstrative purposes this should be fine. Running this gives us the follower, much, much prettier graph.

Now lets look at how we can actually reimplement all of this without NetworkX. It starts pretty straightforward by creating a Topology class (as an aside, make sure you use the domain appropriate terminology if possible. I've swapped nodes/plots for cells, edges for neighbors, etc. Also you were using nodes/plots interchangeably, which was confusing). I've made the constructor take data instead of a file, but provided a convenient classmethod to construct it from a file if desired.

import matplotlib.pyplot as plt
import numpy as np

from collections import defaultdict

class Topography(object):

@classmethod
def from_file(cls, filename):
with open(filename) as f:
data = iter(f)
size = int(next(data))
data = (map(int, row.strip().split()) for row in data)

def __init__(self, elevation_data, size):
self.size = size
self.cells = {}
self.neighbors = defaultdict(list)
self.sinks = []
for row_index, row in enumerate(elevation_data):
for cell_index, elevation in enumerate(row):
cell = (row_index, cell_index)
self.cells[cell] = {'elevation': elevation, 'parents': []}

self._clean_topography()

above_cell = (cell[0] - 1, cell[1])

left_cell = (cell[0], cell[1] - 1)

if neighbor in self.cells
self.neighbors[neighbor].append(cell)
self.neighbors[cell].append(neighbor)


You'll notice that the vast majority of this is the exact same as it was before; we've just removed the NetworkX kruft. I also took out the bisect sorted list - I'm not convinced that it was actually more performant, and it impaired the flow of the code for me. If after running benchmarks it turns out to be more performant then by all means put it back in, but until then use the cleaner, more idiomatic sorted (seen in self._clean_topography).

The most substantial change is probably that we're now keeping track of our sinks, and we don't track the 'basin' of a cell anymore. Tracking the basins added overhead, and took up post-processing time to clean things up and wasn't necessary. Remember YAGNI. If you need it you can put it back, but leave it out until then. By keeping track of our sinks, and realizing that we already had the whole list of parents in each cell before anyway, we don't need to muck around with the connected subcomponents (as fun as that was) anymore, and that was really the only reason to be using NetworkX instead of a homegrown solution.

def _clean_topography(self):
cells = sorted(self.cells, key=lambda x: self.cells[x]['elevation'], reverse=True)
for cell in cells:
min_elevation = self.cells[cell]['elevation']
basin = None
for neighbor in self.neighbors[cell]:
if self.cells[neighbor]['elevation'] < min_elevation:
min_elevation = self.cells[neighbor]['elevation']
basin = neighbor

if basin:
self.cells[basin]['parents'].append(cell)
self.cells[basin]['parents'].extend(self.cells[cell]['parents'])
for neighbor in self.neighbors[cell]:
if neighbor not in self.cells[cell]['parents'] and neighbor != basin:
self.remove_neighbor(cell, neighbor)
else:
self.sinks.append(cell)

def remove_neighbor(self, cell, neighbor):
self.neighbors[cell].remove(neighbor)
self.neighbors[neighbor].remove(cell)


Like I said, we just use sorted now instead - immediately obvious what it does to any experienced Pythonista, and until proven otherwise I doubt it will be less performant (my guess is that all of the extra allocations and copies of the bisect method will always be worse than the idiomatic way).

We still keep track of the parents of a cell, and we make sure that goes all the way up the chain. We also track our sinks - this makes it easier later. Otherwise the general algorithm was sound, so we don't change it very much.

Then comes the important part:

def get_basin_sizes(self):
return [
len(self.cells[sink]['parents']) + 1
for sink in self.sinks
]

if __name__ == '__main__':
with open("rainfall_data_simple.txt", 'w') as f:
f.write("""3
1 5 2
2 4 7
3 6 9""")

topo = Topography.from_file("rainfall_data_simple.txt")
basin_sizes = topo.get_basin_sizes()
basin_sizes.sort(reverse=True)

for basin in basin_sizes:
print basin,

topo.display()


It's that easy - by tracking the sinks and their parents getting our answer is really easy. I also pulled out the logic of the order to display the basin sizes in - that seemed more like display logic and not how the computation should work. YMMV.

• The one change I'd make to this is add x-ticks and y-ticks, as the .5th column of a matrix will never exist. – Oscar Smith Aug 8 '16 at 13:09