fork of https://github.com/oxigraph/rocksdb and https://github.com/facebook/rocksdb for nextgraph and oxigraph
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721 lines
23 KiB
721 lines
23 KiB
#!/usr/bin/env python3
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import csv
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import math
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import os
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import random
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import sys
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.backends.backend_pdf
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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# Make sure a legend has the same color across all generated graphs.
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def get_cmap(n, name="hsv"):
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"""Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
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RGB color; the keyword argument name must be a standard mpl colormap name."""
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return plt.cm.get_cmap(name, n)
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color_index = 0
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bar_color_maps = {}
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colors = []
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n_colors = 360
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linear_colors = get_cmap(n_colors)
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for i in range(n_colors):
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colors.append(linear_colors(i))
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# Shuffle the colors so that adjacent bars in a graph are obvious to differentiate.
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random.shuffle(colors)
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def num_to_gb(n):
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one_gb = 1024 * 1024 * 1024
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if float(n) % one_gb == 0:
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return "{}".format(n / one_gb)
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# Keep two decimal points.
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return "{0:.2f}".format(float(n) / one_gb)
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def plot_miss_stats_graphs(
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csv_result_dir, output_result_dir, file_prefix, file_suffix, ylabel, pdf_file_name
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):
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miss_ratios = {}
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for file in os.listdir(csv_result_dir):
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if not file.startswith(file_prefix):
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continue
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if not file.endswith(file_suffix):
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continue
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print("Processing file {}/{}".format(csv_result_dir, file))
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mrc_file_path = csv_result_dir + "/" + file
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with open(mrc_file_path, "r") as csvfile:
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rows = csv.reader(csvfile, delimiter=",")
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for row in rows:
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cache_name = row[0]
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num_shard_bits = int(row[1])
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ghost_capacity = int(row[2])
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capacity = int(row[3])
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miss_ratio = float(row[4])
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config = "{}-{}-{}".format(cache_name, num_shard_bits, ghost_capacity)
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if config not in miss_ratios:
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miss_ratios[config] = {}
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miss_ratios[config]["x"] = []
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miss_ratios[config]["y"] = []
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miss_ratios[config]["x"].append(capacity)
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miss_ratios[config]["y"].append(miss_ratio)
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fig = plt.figure()
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for config in miss_ratios:
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plt.plot(
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miss_ratios[config]["x"], miss_ratios[config]["y"], label=config
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)
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plt.xlabel("Cache capacity")
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plt.ylabel(ylabel)
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plt.xscale("log", basex=2)
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plt.ylim(ymin=0)
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plt.title("{}".format(file))
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plt.legend()
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fig.savefig(
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output_result_dir + "/{}.pdf".format(pdf_file_name), bbox_inches="tight"
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)
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def plot_miss_stats_diff_lru_graphs(
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csv_result_dir, output_result_dir, file_prefix, file_suffix, ylabel, pdf_file_name
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):
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miss_ratios = {}
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for file in os.listdir(csv_result_dir):
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if not file.startswith(file_prefix):
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continue
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if not file.endswith(file_suffix):
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continue
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print("Processing file {}/{}".format(csv_result_dir, file))
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mrc_file_path = csv_result_dir + "/" + file
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with open(mrc_file_path, "r") as csvfile:
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rows = csv.reader(csvfile, delimiter=",")
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for row in rows:
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cache_name = row[0]
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num_shard_bits = int(row[1])
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ghost_capacity = int(row[2])
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capacity = int(row[3])
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miss_ratio = float(row[4])
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config = "{}-{}-{}".format(cache_name, num_shard_bits, ghost_capacity)
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if config not in miss_ratios:
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miss_ratios[config] = {}
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miss_ratios[config]["x"] = []
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miss_ratios[config]["y"] = []
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miss_ratios[config]["x"].append(capacity)
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miss_ratios[config]["y"].append(miss_ratio)
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if "lru-0-0" not in miss_ratios:
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return
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fig = plt.figure()
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for config in miss_ratios:
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diffs = [0] * len(miss_ratios["lru-0-0"]["x"])
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for i in range(len(miss_ratios["lru-0-0"]["x"])):
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for j in range(len(miss_ratios[config]["x"])):
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if miss_ratios["lru-0-0"]["x"][i] == miss_ratios[config]["x"][j]:
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diffs[i] = (
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miss_ratios[config]["y"][j] - miss_ratios["lru-0-0"]["y"][i]
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)
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break
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plt.plot(miss_ratios["lru-0-0"]["x"], diffs, label=config)
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plt.xlabel("Cache capacity")
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plt.ylabel(ylabel)
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plt.xscale("log", basex=2)
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plt.title("{}".format(file))
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plt.legend()
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fig.savefig(
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output_result_dir + "/{}.pdf".format(pdf_file_name), bbox_inches="tight"
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)
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def sanitize(label):
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# matplotlib cannot plot legends that is prefixed with "_"
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# so we need to remove them here.
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index = 0
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for i in range(len(label)):
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if label[i] == "_":
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index += 1
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else:
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break
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data = label[index:]
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# The value of uint64_max in c++.
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if "18446744073709551615" in data:
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return "max"
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return data
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# Read the csv file vertically, i.e., group the data by columns.
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def read_data_for_plot_vertical(csvfile):
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x = []
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labels = []
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label_stats = {}
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csv_rows = csv.reader(csvfile, delimiter=",")
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data_rows = []
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for row in csv_rows:
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data_rows.append(row)
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# header
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for i in range(1, len(data_rows[0])):
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labels.append(sanitize(data_rows[0][i]))
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label_stats[i - 1] = []
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for i in range(1, len(data_rows)):
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for j in range(len(data_rows[i])):
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if j == 0:
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x.append(sanitize(data_rows[i][j]))
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continue
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label_stats[j - 1].append(float(data_rows[i][j]))
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return x, labels, label_stats
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# Read the csv file horizontally, i.e., group the data by rows.
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def read_data_for_plot_horizontal(csvfile):
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x = []
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labels = []
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label_stats = {}
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csv_rows = csv.reader(csvfile, delimiter=",")
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data_rows = []
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for row in csv_rows:
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data_rows.append(row)
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# header
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for i in range(1, len(data_rows)):
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labels.append(sanitize(data_rows[i][0]))
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label_stats[i - 1] = []
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for i in range(1, len(data_rows[0])):
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x.append(sanitize(data_rows[0][i]))
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for i in range(1, len(data_rows)):
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for j in range(len(data_rows[i])):
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if j == 0:
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# label
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continue
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label_stats[i - 1].append(float(data_rows[i][j]))
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return x, labels, label_stats
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def read_data_for_plot(csvfile, vertical):
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if vertical:
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return read_data_for_plot_vertical(csvfile)
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return read_data_for_plot_horizontal(csvfile)
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def plot_line_charts(
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csv_result_dir,
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output_result_dir,
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filename_prefix,
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filename_suffix,
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pdf_name,
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xlabel,
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ylabel,
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title,
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vertical,
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legend,
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):
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global color_index, bar_color_maps, colors
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pdf = matplotlib.backends.backend_pdf.PdfPages(output_result_dir + "/" + pdf_name)
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for file in os.listdir(csv_result_dir):
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if not file.endswith(filename_suffix):
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continue
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if not file.startswith(filename_prefix):
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continue
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print("Processing file {}/{}".format(csv_result_dir, file))
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with open(csv_result_dir + "/" + file, "r") as csvfile:
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x, labels, label_stats = read_data_for_plot(csvfile, vertical)
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if len(x) == 0 or len(labels) == 0:
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continue
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# plot figure
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fig = plt.figure()
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for label_index in label_stats:
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# Assign a unique color to this label.
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if labels[label_index] not in bar_color_maps:
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bar_color_maps[labels[label_index]] = colors[color_index]
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color_index += 1
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plt.plot(
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[int(x[i]) for i in range(len(x) - 1)],
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label_stats[label_index][:-1],
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label=labels[label_index],
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color=bar_color_maps[labels[label_index]],
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)
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# Translate time unit into x labels.
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if "_60" in file:
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plt.xlabel("{} (Minute)".format(xlabel))
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if "_3600" in file:
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plt.xlabel("{} (Hour)".format(xlabel))
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plt.ylabel(ylabel)
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plt.title("{} {}".format(title, file))
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if legend:
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plt.legend()
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pdf.savefig(fig)
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pdf.close()
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def plot_stacked_bar_charts(
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csv_result_dir,
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output_result_dir,
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filename_suffix,
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pdf_name,
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xlabel,
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ylabel,
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title,
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vertical,
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x_prefix,
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):
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global color_index, bar_color_maps, colors
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pdf = matplotlib.backends.backend_pdf.PdfPages(
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"{}/{}".format(output_result_dir, pdf_name)
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)
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for file in os.listdir(csv_result_dir):
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if not file.endswith(filename_suffix):
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continue
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with open(csv_result_dir + "/" + file, "r") as csvfile:
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print("Processing file {}/{}".format(csv_result_dir, file))
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x, labels, label_stats = read_data_for_plot(csvfile, vertical)
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if len(x) == 0 or len(label_stats) == 0:
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continue
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# Plot figure
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fig = plt.figure()
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ind = np.arange(len(x)) # the x locations for the groups
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width = 0.5 # the width of the bars: can also be len(x) sequence
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bars = []
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bottom_bars = []
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for _i in label_stats[0]:
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bottom_bars.append(0)
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for i in range(0, len(label_stats)):
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# Assign a unique color to this label.
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if labels[i] not in bar_color_maps:
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bar_color_maps[labels[i]] = colors[color_index]
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color_index += 1
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p = plt.bar(
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ind,
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label_stats[i],
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width,
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bottom=bottom_bars,
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color=bar_color_maps[labels[i]],
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)
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bars.append(p[0])
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for j in range(len(label_stats[i])):
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bottom_bars[j] += label_stats[i][j]
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plt.xlabel(xlabel)
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plt.ylabel(ylabel)
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plt.xticks(
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ind, [x_prefix + x[i] for i in range(len(x))], rotation=20, fontsize=8
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)
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plt.legend(bars, labels)
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plt.title("{} filename:{}".format(title, file))
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pdf.savefig(fig)
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pdf.close()
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def plot_heatmap(csv_result_dir, output_result_dir, filename_suffix, pdf_name, title):
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pdf = matplotlib.backends.backend_pdf.PdfPages(
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"{}/{}".format(output_result_dir, pdf_name)
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)
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for file in os.listdir(csv_result_dir):
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if not file.endswith(filename_suffix):
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continue
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csv_file_name = "{}/{}".format(csv_result_dir, file)
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print("Processing file {}/{}".format(csv_result_dir, file))
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corr_table = pd.read_csv(csv_file_name)
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corr_table = corr_table.pivot("label", "corr", "value")
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fig = plt.figure()
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sns.heatmap(corr_table, annot=True, linewidths=0.5, fmt=".2")
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plt.title("{} filename:{}".format(title, file))
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pdf.savefig(fig)
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pdf.close()
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def plot_timeline(csv_result_dir, output_result_dir):
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plot_line_charts(
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csv_result_dir,
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output_result_dir,
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filename_prefix="",
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filename_suffix="access_timeline",
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pdf_name="access_time.pdf",
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xlabel="Time",
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ylabel="Throughput",
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title="Access timeline with group by label",
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vertical=False,
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legend=True,
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)
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def convert_to_0_if_nan(n):
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if math.isnan(n):
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return 0.0
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return n
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def plot_correlation(csv_result_dir, output_result_dir):
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# Processing the correlation input first.
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label_str_file = {}
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for file in os.listdir(csv_result_dir):
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if not file.endswith("correlation_input"):
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continue
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csv_file_name = "{}/{}".format(csv_result_dir, file)
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print("Processing file {}/{}".format(csv_result_dir, file))
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corr_table = pd.read_csv(csv_file_name)
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label_str = file.split("_")[0]
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label = file[len(label_str) + 1 :]
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label = label[: len(label) - len("_correlation_input")]
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output_file = "{}/{}_correlation_output".format(csv_result_dir, label_str)
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if output_file not in label_str_file:
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f = open("{}/{}_correlation_output".format(csv_result_dir, label_str), "w+")
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label_str_file[output_file] = f
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f.write("label,corr,value\n")
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f = label_str_file[output_file]
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f.write(
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"{},{},{}\n".format(
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label,
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"LA+A",
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convert_to_0_if_nan(
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corr_table["num_accesses_since_last_access"].corr(
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corr_table["num_accesses_till_next_access"], method="spearman"
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)
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),
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)
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)
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f.write(
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"{},{},{}\n".format(
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label,
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"PA+A",
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convert_to_0_if_nan(
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corr_table["num_past_accesses"].corr(
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corr_table["num_accesses_till_next_access"], method="spearman"
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)
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),
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)
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)
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f.write(
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"{},{},{}\n".format(
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label,
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"LT+A",
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convert_to_0_if_nan(
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corr_table["elapsed_time_since_last_access"].corr(
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corr_table["num_accesses_till_next_access"], method="spearman"
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)
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),
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)
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)
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f.write(
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"{},{},{}\n".format(
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label,
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"LA+T",
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convert_to_0_if_nan(
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corr_table["num_accesses_since_last_access"].corr(
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corr_table["elapsed_time_till_next_access"], method="spearman"
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)
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),
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)
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)
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f.write(
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"{},{},{}\n".format(
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label,
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"LT+T",
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convert_to_0_if_nan(
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corr_table["elapsed_time_since_last_access"].corr(
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|
corr_table["elapsed_time_till_next_access"], method="spearman"
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)
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),
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)
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)
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|
f.write(
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"{},{},{}\n".format(
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label,
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"PA+T",
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convert_to_0_if_nan(
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corr_table["num_past_accesses"].corr(
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|
corr_table["elapsed_time_till_next_access"], method="spearman"
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)
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),
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)
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)
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|
for label_str in label_str_file:
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label_str_file[label_str].close()
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|
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|
plot_heatmap(
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csv_result_dir,
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output_result_dir,
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"correlation_output",
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"correlation.pdf",
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"Correlation",
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)
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|
|
|
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|
def plot_reuse_graphs(csv_result_dir, output_result_dir):
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|
plot_stacked_bar_charts(
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|
csv_result_dir,
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|
output_result_dir,
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|
filename_suffix="avg_reuse_interval_naccesses",
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|
pdf_name="avg_reuse_interval_naccesses.pdf",
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|
xlabel="",
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|
ylabel="Percentage of accesses",
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|
title="Average reuse interval",
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|
vertical=True,
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|
x_prefix="< ",
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)
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|
plot_stacked_bar_charts(
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csv_result_dir,
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output_result_dir,
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filename_suffix="avg_reuse_interval",
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pdf_name="avg_reuse_interval.pdf",
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xlabel="",
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ylabel="Percentage of blocks",
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title="Average reuse interval",
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vertical=True,
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x_prefix="< ",
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)
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|
plot_stacked_bar_charts(
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csv_result_dir,
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output_result_dir,
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filename_suffix="access_reuse_interval",
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|
pdf_name="reuse_interval.pdf",
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xlabel="Seconds",
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ylabel="Percentage of accesses",
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title="Reuse interval",
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|
vertical=True,
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x_prefix="< ",
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)
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|
plot_stacked_bar_charts(
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csv_result_dir,
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output_result_dir,
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|
filename_suffix="reuse_lifetime",
|
|
pdf_name="reuse_lifetime.pdf",
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|
xlabel="Seconds",
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|
ylabel="Percentage of blocks",
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title="Reuse lifetime",
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|
vertical=True,
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x_prefix="< ",
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)
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|
plot_line_charts(
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csv_result_dir,
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output_result_dir,
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|
filename_prefix="",
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|
filename_suffix="reuse_blocks_timeline",
|
|
pdf_name="reuse_blocks_timeline.pdf",
|
|
xlabel="",
|
|
ylabel="Percentage of blocks",
|
|
title="Reuse blocks timeline",
|
|
vertical=False,
|
|
legend=False,
|
|
)
|
|
|
|
|
|
def plot_percentage_access_summary(csv_result_dir, output_result_dir):
|
|
plot_stacked_bar_charts(
|
|
csv_result_dir,
|
|
output_result_dir,
|
|
filename_suffix="percentage_of_accesses_summary",
|
|
pdf_name="percentage_access.pdf",
|
|
xlabel="",
|
|
ylabel="Percentage of accesses",
|
|
title="",
|
|
vertical=True,
|
|
x_prefix="",
|
|
)
|
|
plot_stacked_bar_charts(
|
|
csv_result_dir,
|
|
output_result_dir,
|
|
filename_suffix="percent_ref_keys",
|
|
pdf_name="percent_ref_keys.pdf",
|
|
xlabel="",
|
|
ylabel="Percentage of blocks",
|
|
title="",
|
|
vertical=True,
|
|
x_prefix="",
|
|
)
|
|
plot_stacked_bar_charts(
|
|
csv_result_dir,
|
|
output_result_dir,
|
|
filename_suffix="percent_data_size_on_ref_keys",
|
|
pdf_name="percent_data_size_on_ref_keys.pdf",
|
|
xlabel="",
|
|
ylabel="Percentage of blocks",
|
|
title="",
|
|
vertical=True,
|
|
x_prefix="",
|
|
)
|
|
plot_stacked_bar_charts(
|
|
csv_result_dir,
|
|
output_result_dir,
|
|
filename_suffix="percent_accesses_on_ref_keys",
|
|
pdf_name="percent_accesses_on_ref_keys.pdf",
|
|
xlabel="",
|
|
ylabel="Percentage of blocks",
|
|
title="",
|
|
vertical=True,
|
|
x_prefix="",
|
|
)
|
|
|
|
|
|
def plot_access_count_summary(csv_result_dir, output_result_dir):
|
|
plot_stacked_bar_charts(
|
|
csv_result_dir,
|
|
output_result_dir,
|
|
filename_suffix="access_count_summary",
|
|
pdf_name="access_count_summary.pdf",
|
|
xlabel="Access count",
|
|
ylabel="Percentage of blocks",
|
|
title="",
|
|
vertical=True,
|
|
x_prefix="< ",
|
|
)
|
|
plot_line_charts(
|
|
csv_result_dir,
|
|
output_result_dir,
|
|
filename_prefix="",
|
|
filename_suffix="skewness",
|
|
pdf_name="skew.pdf",
|
|
xlabel="",
|
|
ylabel="Percentage of accesses",
|
|
title="Skewness",
|
|
vertical=True,
|
|
legend=False,
|
|
)
|
|
|
|
|
|
def plot_miss_ratio_timeline(csv_result_dir, output_result_dir):
|
|
plot_line_charts(
|
|
csv_result_dir,
|
|
output_result_dir,
|
|
filename_prefix="",
|
|
filename_suffix="3600_miss_ratio_timeline",
|
|
pdf_name="miss_ratio_timeline.pdf",
|
|
xlabel="Time",
|
|
ylabel="Miss Ratio (%)",
|
|
title="Miss ratio timeline",
|
|
vertical=False,
|
|
legend=True,
|
|
)
|
|
plot_line_charts(
|
|
csv_result_dir,
|
|
output_result_dir,
|
|
filename_prefix="",
|
|
filename_suffix="3600_miss_timeline",
|
|
pdf_name="miss_timeline.pdf",
|
|
xlabel="Time",
|
|
ylabel="# of misses ",
|
|
title="Miss timeline",
|
|
vertical=False,
|
|
legend=True,
|
|
)
|
|
plot_line_charts(
|
|
csv_result_dir,
|
|
output_result_dir,
|
|
filename_prefix="",
|
|
filename_suffix="3600_miss_timeline",
|
|
pdf_name="miss_timeline.pdf",
|
|
xlabel="Time",
|
|
ylabel="# of misses ",
|
|
title="Miss timeline",
|
|
vertical=False,
|
|
legend=True,
|
|
)
|
|
plot_line_charts(
|
|
csv_result_dir,
|
|
output_result_dir,
|
|
filename_prefix="",
|
|
filename_suffix="3600_policy_timeline",
|
|
pdf_name="policy_timeline.pdf",
|
|
xlabel="Time",
|
|
ylabel="# of times a policy is selected ",
|
|
title="Policy timeline",
|
|
vertical=False,
|
|
legend=True,
|
|
)
|
|
plot_line_charts(
|
|
csv_result_dir,
|
|
output_result_dir,
|
|
filename_prefix="",
|
|
filename_suffix="3600_policy_ratio_timeline",
|
|
pdf_name="policy_ratio_timeline.pdf",
|
|
xlabel="Time",
|
|
ylabel="Percentage of times a policy is selected ",
|
|
title="Policy timeline",
|
|
vertical=False,
|
|
legend=True,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if len(sys.argv) < 3:
|
|
print(
|
|
"Must provide two arguments: \n"
|
|
"1) The directory that saves a list of "
|
|
"directories which contain block cache trace analyzer result files. \n"
|
|
"2) the directory to save plotted graphs. \n"
|
|
)
|
|
exit(1)
|
|
csv_result_dir = sys.argv[1]
|
|
output_result_dir = sys.argv[2]
|
|
print(
|
|
"Processing directory {} and save graphs to {}.".format(
|
|
csv_result_dir, output_result_dir
|
|
)
|
|
)
|
|
for csv_relative_dir in os.listdir(csv_result_dir):
|
|
csv_abs_dir = csv_result_dir + "/" + csv_relative_dir
|
|
result_dir = output_result_dir + "/" + csv_relative_dir
|
|
if not os.path.isdir(csv_abs_dir):
|
|
print("{} is not a directory".format(csv_abs_dir))
|
|
continue
|
|
print("Processing experiment dir: {}".format(csv_relative_dir))
|
|
if not os.path.exists(result_dir):
|
|
os.makedirs(result_dir)
|
|
plot_access_count_summary(csv_abs_dir, result_dir)
|
|
plot_timeline(csv_abs_dir, result_dir)
|
|
plot_miss_ratio_timeline(csv_result_dir, output_result_dir)
|
|
plot_correlation(csv_abs_dir, result_dir)
|
|
plot_reuse_graphs(csv_abs_dir, result_dir)
|
|
plot_percentage_access_summary(csv_abs_dir, result_dir)
|
|
plot_miss_stats_graphs(
|
|
csv_abs_dir,
|
|
result_dir,
|
|
file_prefix="",
|
|
file_suffix="mrc",
|
|
ylabel="Miss ratio (%)",
|
|
pdf_file_name="mrc",
|
|
)
|
|
plot_miss_stats_diff_lru_graphs(
|
|
csv_abs_dir,
|
|
result_dir,
|
|
file_prefix="",
|
|
file_suffix="mrc",
|
|
ylabel="Miss ratio (%)",
|
|
pdf_file_name="mrc_diff_lru",
|
|
)
|
|
# The following stats are only available in pysim.
|
|
for time_unit in ["1", "60", "3600"]:
|
|
plot_miss_stats_graphs(
|
|
csv_abs_dir,
|
|
result_dir,
|
|
file_prefix="ml_{}_".format(time_unit),
|
|
file_suffix="p95mb",
|
|
ylabel="p95 number of byte miss per {} seconds".format(time_unit),
|
|
pdf_file_name="p95mb_per{}_seconds".format(time_unit),
|
|
)
|
|
plot_miss_stats_graphs(
|
|
csv_abs_dir,
|
|
result_dir,
|
|
file_prefix="ml_{}_".format(time_unit),
|
|
file_suffix="avgmb",
|
|
ylabel="Average number of byte miss per {} seconds".format(time_unit),
|
|
pdf_file_name="avgmb_per{}_seconds".format(time_unit),
|
|
)
|
|
plot_miss_stats_diff_lru_graphs(
|
|
csv_abs_dir,
|
|
result_dir,
|
|
file_prefix="ml_{}_".format(time_unit),
|
|
file_suffix="p95mb",
|
|
ylabel="p95 number of byte miss per {} seconds".format(time_unit),
|
|
pdf_file_name="p95mb_per{}_seconds_diff_lru".format(time_unit),
|
|
)
|
|
plot_miss_stats_diff_lru_graphs(
|
|
csv_abs_dir,
|
|
result_dir,
|
|
file_prefix="ml_{}_".format(time_unit),
|
|
file_suffix="avgmb",
|
|
ylabel="Average number of byte miss per {} seconds".format(time_unit),
|
|
pdf_file_name="avgmb_per{}_seconds_diff_lru".format(time_unit),
|
|
)
|
|
|