quoracle/quorums/viz.py

from . import distribution
from . import geometry
from .distribution import Distribution
from .expr import Node
from .geometry import Point, Segment
from .strategy import Strategy
from typing import Dict, List, Optional, Set, Tuple, TypeVar
import collections
import matplotlib
import matplotlib.pyplot as plt
import numpy as np


T = TypeVar('T')


def plot_node_load(filename: str,
                   strategy: Strategy[T],
                   nodes: Optional[List[Node[T]]] = None,
                   read_fraction: Optional[Distribution] = None,
                   write_fraction: Optional[Distribution] = None):
    fig, ax = plt.subplots()
    plot_node_load_on(ax, strategy, nodes, read_fraction, write_fraction)
    ax.set_xlabel('Node')
    ax.set_ylabel('Load')
    fig.tight_layout()
    fig.savefig(filename)


def plot_node_load_on(ax: plt.Axes,
                      strategy: Strategy[T],
                      nodes: Optional[List[Node[T]]] = None,
                      read_fraction: Optional[Distribution] = None,
                      write_fraction: Optional[Distribution] = None):
    _plot_node_load_on(ax,
                       strategy,
                       nodes or list(strategy.nodes),
                       scale=1,
                       scale_by_node_capacity=True,
                       read_fraction=read_fraction,
                       write_fraction=write_fraction)


def plot_node_utilization(filename: str,
                          strategy: Strategy[T],
                          nodes: Optional[List[Node[T]]] = None,
                          read_fraction: Optional[Distribution] = None,
                          write_fraction: Optional[Distribution] = None):
    fig, ax = plt.subplots()
    plot_node_utilization_on(ax, strategy, nodes, read_fraction, write_fraction)
    ax.set_xlabel('Node')
    ax.set_ylabel('Utilization')
    fig.tight_layout()
    fig.savefig(filename)


def plot_node_utilization_on(ax: plt.Axes,
                      strategy: Strategy[T],
                      nodes: Optional[List[Node[T]]] = None,
                      read_fraction: Optional[Distribution] = None,
                      write_fraction: Optional[Distribution] = None):
    _plot_node_load_on(ax,
                       strategy,
                       nodes or list(strategy.nodes),
                       scale=strategy.capacity(read_fraction, write_fraction),
                       scale_by_node_capacity=True,
                       read_fraction=read_fraction,
                       write_fraction=write_fraction)


def plot_node_throughput(filename: str,
                   strategy: Strategy[T],
                   nodes: Optional[List[Node[T]]] = None,
                   read_fraction: Optional[Distribution] = None,
                   write_fraction: Optional[Distribution] = None):
    fig, ax = plt.subplots()
    plot_node_throughput_on(ax, strategy, nodes, read_fraction, write_fraction)
    ax.set_xlabel('Node')
    ax.set_ylabel('Throughput')
    fig.tight_layout()
    fig.savefig(filename)


def plot_node_throughput_on(ax: plt.Axes,
                            strategy: Strategy[T],
                            nodes: Optional[List[Node[T]]] = None,
                            read_fraction: Optional[Distribution] = None,
                            write_fraction: Optional[Distribution] = None):
    _plot_node_load_on(ax,
                       strategy,
                       nodes or list(strategy.nodes),
                       scale=strategy.capacity(read_fraction, write_fraction),
                       scale_by_node_capacity=False,
                       read_fraction=read_fraction,
                       write_fraction=write_fraction)


def _plot_node_load_on(ax: plt.Axes,
                       sigma: Strategy[T],
                       nodes: List[Node[T]],
                       scale: float,
                       scale_by_node_capacity: bool,
                       read_fraction: Optional[Distribution] = None,
                       write_fraction: Optional[Distribution] = None):
    d = distribution.canonicalize_rw(read_fraction, write_fraction)
    x_list = [node.x for node in nodes]
    x_index = {x: i for (i, x) in enumerate(x_list)}
    x_ticks = list(range(len(x_list)))

    def one_hot(quorum: Set[T]) -> np.array:
        bar_heights = np.zeros(len(x_list))
        for x in quorum:
            bar_heights[x_index[x]] = 1
        return bar_heights

    def plot_quorums(quorums: List[Set[T]],
                     weights: List[float],
                     fraction: float,
                     bottoms: np.array,
                     capacities: np.array,
                     cmap: matplotlib.colors.Colormap):
        for (i, (quorum, weight)) in enumerate(zip(quorums, weights)):
            bar_heights = scale * fraction * weight * one_hot(quorum)
            if scale_by_node_capacity:
                bar_heights /= capacities

            ax.bar(x_ticks,
                   bar_heights,
                   bottom=bottoms,
                   color=cmap(0.75 - i * 0.5 / len(quorums)),
                   edgecolor='white', width=0.8)

            for j, (bar_height, bottom) in enumerate(zip(bar_heights, bottoms)):
                # TODO(mwhittaker): Fix the unhappy typechecker.
                text = ''.join(str(x) for x in sorted(list(quorum)))
                if bar_height != 0:
                    ax.text(x_ticks[j], bottom + bar_height / 2, text,
                            ha='center', va='center')
            bottoms += bar_heights

    fr = sum(weight * fr for (fr, weight) in d.items())
    fw = 1 - fr
    read_capacities = np.array([node.read_capacity for node in nodes])
    write_capacities = np.array([node.write_capacity for node in nodes])
    bottoms = np.zeros(len(x_list))
    plot_quorums(sigma.reads, sigma.read_weights, fr, bottoms, read_capacities,
                 matplotlib.cm.get_cmap('Reds'))
    plot_quorums(sigma.writes, sigma.write_weights, fw, bottoms,
                 write_capacities, matplotlib.cm.get_cmap('Blues'))
    ax.set_xticks(x_ticks)
    ax.set_xticklabels(str(x) for x in x_list)


def plot_load_distribution(filename: str,
                           strategy: Strategy[T],
                           nodes: Optional[List[Node[T]]] = None):
    fig, ax = plt.subplots()
    plot_load_distribution_on(ax, strategy, nodes)
    ax.set_xlabel('Read Fraction')
    ax.set_ylabel('Load')
    fig.tight_layout()
    fig.savefig(filename)


def _group(segments: Dict[T, Segment]) -> Dict[Segment, List[T]]:
    groups: Dict[Segment, List[T]] = collections.defaultdict(list)
    for x, segment in segments.items():
        matches = (s for s in groups if segment.approximately_equal(s))
        groups[next(matches, segment)].append(x)
    return groups


def plot_load_distribution_on(ax: plt.Axes,
                              strategy: Strategy[T],
                              nodes: Optional[List[Node[T]]] = None):
    nodes = nodes or list(strategy.nodes)

    # We want to plot every node's load distribution. Multiple nodes might
    # have the same load distribution, so we group the nodes by their
    # distribution. The grouping is a little annoying because two floats
    # might not be exactly equal but pretty close.
    groups = _group({
        node.x: Segment(
            Point(0, strategy.node_load(node, read_fraction=0)),
            Point(1, strategy.node_load(node, read_fraction=1))
        )
        for node in nodes
    })

    # Compute and plot the max of all segments. We plot the load first so that
    # it lies underneath the node loads.
    path = geometry.max_of_segments(list(groups.keys()))
    ax.plot([p[0] for p in path],
            [p[1] for p in path],
            label='load',
            linewidth=4)

    # We plot the node loads second so that they appear above the load.
    for segment, xs in groups.items():
        ax.plot([segment.l.x, segment.r.x],
                [segment.l.y, segment.r.y],
                '--',
                label=','.join(str(x) for x in xs),
                linewidth=2,
                alpha=0.75)