a42bccaf0c
Some of the code was nasty. I cleaned it up a bit. I also started moving tests into a tests/ directory. You can run python -m unittest to run them. I still need to add more tests to make sure things are working as expected.
203 lines
7.9 KiB
Python
203 lines
7.9 KiB
Python
from . import distribution
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from . import geometry
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from .distribution import Distribution
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from .expr import Node
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from .geometry import Point, Segment
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from .quorum_system import Strategy
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from typing import Dict, FrozenSet, List, Optional, Set, Tuple, TypeVar
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import collections
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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T = TypeVar('T')
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def plot_node_load(filename: str,
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strategy: Strategy[T],
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nodes: Optional[List[Node[T]]] = None,
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read_fraction: Optional[Distribution] = None,
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write_fraction: Optional[Distribution] = None):
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fig, ax = plt.subplots()
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plot_node_load_on(ax, strategy, nodes, read_fraction, write_fraction)
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ax.set_xlabel('Node')
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ax.set_ylabel('Load')
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fig.tight_layout()
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fig.savefig(filename)
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def plot_node_load_on(ax: plt.Axes,
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strategy: Strategy[T],
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nodes: Optional[List[Node[T]]] = None,
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read_fraction: Optional[Distribution] = None,
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write_fraction: Optional[Distribution] = None):
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_plot_node_load_on(ax,
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strategy,
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nodes or list(strategy.qs.nodes()),
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scale=1,
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scale_by_node_capacity=True,
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read_fraction=read_fraction,
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write_fraction=write_fraction)
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def plot_node_utilization(filename: str,
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strategy: Strategy[T],
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nodes: Optional[List[Node[T]]] = None,
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read_fraction: Optional[Distribution] = None,
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write_fraction: Optional[Distribution] = None):
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fig, ax = plt.subplots()
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plot_node_utilization_on(ax, strategy, nodes, read_fraction, write_fraction)
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ax.set_xlabel('Node')
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ax.set_ylabel('Utilization')
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fig.tight_layout()
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fig.savefig(filename)
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def plot_node_utilization_on(ax: plt.Axes,
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strategy: Strategy[T],
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nodes: Optional[List[Node[T]]] = None,
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read_fraction: Optional[Distribution] = None,
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write_fraction: Optional[Distribution] = None):
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_plot_node_load_on(ax,
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strategy,
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nodes or list(strategy.qs.nodes()),
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scale=strategy.capacity(read_fraction, write_fraction),
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scale_by_node_capacity=True,
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read_fraction=read_fraction,
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write_fraction=write_fraction)
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def plot_node_throughput(filename: str,
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strategy: Strategy[T],
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nodes: Optional[List[Node[T]]] = None,
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read_fraction: Optional[Distribution] = None,
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write_fraction: Optional[Distribution] = None):
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fig, ax = plt.subplots()
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plot_node_throughput_on(ax, strategy, nodes, read_fraction, write_fraction)
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ax.set_xlabel('Node')
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ax.set_ylabel('Throughput')
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fig.tight_layout()
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fig.savefig(filename)
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def plot_node_throughput_on(ax: plt.Axes,
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strategy: Strategy[T],
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nodes: Optional[List[Node[T]]] = None,
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read_fraction: Optional[Distribution] = None,
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write_fraction: Optional[Distribution] = None):
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_plot_node_load_on(ax,
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strategy,
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nodes or list(strategy.qs.nodes()),
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scale=strategy.capacity(read_fraction, write_fraction),
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scale_by_node_capacity=False,
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read_fraction=read_fraction,
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write_fraction=write_fraction)
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def _plot_node_load_on(ax: plt.Axes,
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sigma: Strategy[T],
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nodes: List[Node[T]],
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scale: float,
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scale_by_node_capacity: bool,
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read_fraction: Optional[Distribution] = None,
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write_fraction: Optional[Distribution] = None):
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d = distribution.canonicalize_rw(read_fraction, write_fraction)
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x_list = [node.x for node in nodes]
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x_index = {x: i for (i, x) in enumerate(x_list)}
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x_ticks = list(range(len(x_list)))
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def one_hot(quorum: FrozenSet[T]) -> np.array:
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bar_heights = np.zeros(len(x_list))
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for x in quorum:
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bar_heights[x_index[x]] = 1
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return bar_heights
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def plot_quorums(sigma: Dict[FrozenSet[T], float],
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fraction: float,
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bottoms: np.array,
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capacities: np.array,
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cmap: matplotlib.colors.Colormap):
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for (i, (quorum, weight)) in enumerate(sigma.items()):
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bar_heights = scale * fraction * weight * one_hot(quorum)
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if scale_by_node_capacity:
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bar_heights /= capacities
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ax.bar(x_ticks,
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bar_heights,
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bottom=bottoms,
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color=cmap(0.75 - i * 0.5 / len(sigma)),
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edgecolor='white', width=0.8)
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for j, (bar_height, bottom) in enumerate(zip(bar_heights, bottoms)):
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text = ''.join(str(x) for x in sorted(list(quorum))) # type: ignore
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if bar_height != 0:
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ax.text(x_ticks[j], bottom + bar_height / 2, text,
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ha='center', va='center')
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bottoms += bar_heights
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fr = sum(weight * fr for (fr, weight) in d.items())
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fw = 1 - fr
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read_capacities = np.array([node.read_capacity for node in nodes])
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write_capacities = np.array([node.write_capacity for node in nodes])
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bottoms = np.zeros(len(x_list))
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plot_quorums(sigma.sigma_r, fr, bottoms, read_capacities,
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matplotlib.cm.get_cmap('Reds'))
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plot_quorums(sigma.sigma_w, fw, bottoms,
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write_capacities, matplotlib.cm.get_cmap('Blues'))
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ax.set_xticks(x_ticks)
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ax.set_xticklabels(str(x) for x in x_list)
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def plot_load_distribution(filename: str,
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strategy: Strategy[T],
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nodes: Optional[List[Node[T]]] = None):
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fig, ax = plt.subplots()
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plot_load_distribution_on(ax, strategy, nodes)
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ax.set_xlabel('Read Fraction')
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ax.set_ylabel('Load')
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fig.tight_layout()
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fig.savefig(filename)
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def _group(segments: Dict[T, Segment]) -> Dict[Segment, List[T]]:
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groups: Dict[Segment, List[T]] = collections.defaultdict(list)
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for x, segment in segments.items():
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matches = (s for s in groups if segment.approximately_equal(s))
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groups[next(matches, segment)].append(x)
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return groups
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def plot_load_distribution_on(ax: plt.Axes,
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strategy: Strategy[T],
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nodes: Optional[List[Node[T]]] = None):
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nodes = nodes or list(strategy.qs.nodes())
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# We want to plot every node's load distribution. Multiple nodes might
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# have the same load distribution, so we group the nodes by their
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# distribution. The grouping is a little annoying because two floats
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# might not be exactly equal but pretty close.
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groups = _group({
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node.x: Segment(
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Point(0, strategy.node_load(node, read_fraction=0)),
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Point(1, strategy.node_load(node, read_fraction=1))
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)
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for node in nodes
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})
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# Compute and plot the max of all segments. We plot the load first so that
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# it lies underneath the node loads.
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path = geometry.max_of_segments(list(groups.keys()))
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ax.plot([p[0] for p in path],
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[p[1] for p in path],
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label='load',
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linewidth=4)
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# We plot the node loads second so that they appear above the load.
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for segment, xs in groups.items():
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ax.plot([segment.l.x, segment.r.x],
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[segment.l.y, segment.r.y],
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'--',
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label=','.join(str(x) for x in xs),
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linewidth=2,
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alpha=0.75)
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