diff --git a/pytorch/analyze.py b/pytorch/analyze.py
new file mode 100755
index 0000000..bf1c73d
--- /dev/null
+++ b/pytorch/analyze.py
@@ -0,0 +1,189 @@
+#! /bin/python3
+
+from perf_stat import Stat, CPU
+
+import argparse
+import os, glob
+import re
+import json
+
+from enum import Enum
+
+import math
+import numpy as np
+import matplotlib.pyplot as plt
+import itertools
+
+class Plot(Enum):
+    BOX = 'box'
+    LINE = 'line'
+
+def accumulate(stats_list: list[dict[str, str | int | float]], category: str, value: str):
+    print(category)
+    print(value)
+    category_list = np.array([stats[category] for stats in stats_list if value in stats])
+    value_list = np.array([stats[value] for stats in stats_list if value in stats])
+
+    result: dict[np.ndarray] = dict()
+    for category in np.sort(np.unique(category_list)):
+        result[category] = value_list[category_list == category]
+
+    return result
+
+def box_plot(ax, stats_list: list[dict[str, str | int | float]], x: Stat, y: Stat):
+    data: dict[str, np.ndarray] = accumulate(stats_list, x, y)
+
+    print("Plotted data: " + str(data))
+
+    ax.boxplot(data.values(), tick_labels=data.keys())
+    ax.set_ylabel(y.value)
+
+def line_plot(
+    ax, stats_list: list[dict[str, str | int | float]],
+    x: Stat, y: Stat, color: Stat
+):
+    x_data: dict[str, np.ndarray] = accumulate(stats_list, color, x)
+    y_data: dict[str, np.ndarray] = accumulate(stats_list, color, y)
+
+    for category in x_data.keys():
+        sorted_indices = np.argsort(x_data[category])
+        x_data[category] = x_data[category][sorted_indices]
+        y_data[category] = y_data[category][sorted_indices]
+        ax.plot(x_data[category], y_data[category], label=category)
+        print("Plotted x data: " + str(x_data[category]))
+        print("Plotted y data: " + str(y_data[category]))
+
+    ax.set_ylabel(y)
+    ax.grid(True)
+
+def visualize(
+    stats_list: list[dict[str, str | int | float]],
+    plot: Plot,
+    rows: int,
+    size_multiplier: int,
+    font_size: int,
+    x: Stat,
+    y: Stat,
+    color: Stat,
+    filter_list: list[str] = []
+):
+    # Remove stats entries containing undesired values (like a specific CPU).
+#    stats_list = [stats for stats in stats_list
+#                      if len([stats[key] for key in stats.keys()
+#                                        if stats[key] in filter_list]) == 0]
+
+    #x = Stat.MAXWELL_SIZE
+    #y = Stat.DTLB_MISS_RATE
+    #color = Stat.SOLVER
+
+    if y is None:
+        #ys = [stat for stat in Stat if stat.value in stats_list[0].keys()
+        ys = [stat for stat in stats_list[0].keys() if "power" not in stat]
+                                    #and stat is not x
+                                    #and y != color
+                                    #and y != marker
+                                    #and stat.value not in filter_list]
+        fig, axes = plt.subplots(rows, int(math.ceil(len(ys) / rows)),
+                                 figsize = (16 * size_multiplier, 9 * size_multiplier))
+        match plot:
+            case Plot.BOX:
+                for i, y in enumerate(ys):
+                    box_plot(axes[i % rows][int(i / rows)], stats_list, x, y)
+            case Plot.LINE:
+                for i, y in enumerate(ys):
+                    line_plot(axes[i % rows][int(i / rows)], stats_list, x, y, color)
+
+        handles, labels = axes[i % rows][int(i / rows)].get_legend_handles_labels()
+    else:
+        fig, ax = plt.subplots()
+
+        match plot:
+            case Plot.BOX:
+                box_plot(ax, stats_list, x, y)
+            case Plot.LINE:
+                line_plot(ax, stats_list, x, y, color)
+
+        handles, labels = ax.get_legend_handles_labels()
+
+    #box_plot(ax, stats, x, y)
+    #line_plot(ax, stats, x, y, color)
+
+    match plot:
+        case Plot.BOX:
+            title = f"{plot.value}_plot_of_{y.value.replace(' ', '_')}_vs_{x.value.replace(' ', '_')}_excluding_{filter_list}"
+        case Plot.LINE:
+            #title = f"{plot.value}_plot_of_{y.replace(' ', '_')}_vs_{x.replace(' ', '_')}_by_{color.replace(' ', '_')}_excluding_{filter_list}"
+            title = "altra_spmv"
+    fig.suptitle(title, fontsize = font_size)
+    fig.legend(handles, labels, fontsize = font_size)
+    fig.supxlabel(x, fontsize = font_size)
+
+    plt.savefig(title + ".png", dpi = 100)
+    plt.show()
+
+def main():
+    class Command(Enum):
+        PARSE = 'parse'
+        VISUALIZE = 'visualize'
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('command', choices=[x.value for x in Command])
+    parser.add_argument('filepath',
+                        help='the output for the ' + Command.PARSE.value + ' command or the input for the ' + Command.VISUALIZE.value + ' command')
+    parser.add_argument('-i', '--input_dir',
+                        help='the input directory for the parse command')
+    parser.add_argument('-p', '--plot',
+                        choices=[x.name.lower() for x in Plot],
+                        help = 'the type of plot')
+    parser.add_argument('-r', '--rows', type=int,
+                        help = 'the number of rows to display when -y is not specified',
+                        default = 5)
+    parser.add_argument('-s', '--size', type=int,
+                        help = 'figure size multiplier',
+                        default = 4)
+    parser.add_argument('-fs', '--font_size', type=int,
+                        help = 'font size',
+                        default = 40)
+    parser.add_argument('-x',
+                        #choices=[x.name.lower() for x in Stat],
+                        help = 'the name of the x axis')
+    parser.add_argument('-y',
+                        #choices=[x.name.lower() for x in Stat],
+                        help = 'the name of the y axis')
+    parser.add_argument('-c', '--color',
+                        #choices=[x.name.lower() for x in Stat],
+                        help = 'the name of the color')
+    parser.add_argument('-f', '--filter', nargs = '+',
+                        help = 'a comma-separated string of names and values to filter out.',
+                        default = [])
+
+    args = parser.parse_args()
+
+    stats_list: list[dict] = list()
+    if args.command == Command.PARSE.value:
+        if (args.input_dir) is None:
+            print("An input directory is required with -i")
+            exit(-1)
+
+        for filename in glob.glob(f'{args.input_dir.rstrip("/")}/*.json'):
+            with open(filename, 'r') as file:
+                stats_list.append(json.load(file))
+            print(filename + " loaded.")
+
+        with open(args.filepath, 'w') as file:
+            json.dump(stats_list, file, indent = 2)
+
+    elif args.command == Command.VISUALIZE.value:
+        with open(args.filepath, 'r') as file:
+            stats_list = json.load(file)
+
+        #x = Stat[args.x.upper()] if args.x is not None else None
+        x = args.x
+        #y = Stat[args.y.upper()] if args.y is not None else None
+        y = args.y
+        #color = Stat[args.color.upper()] if args.color is not None else None
+        color = args.color
+        visualize(stats_list, Plot[args.plot.upper()], args.rows, args.size, args.font_size, x, y, color, args.filter)
+
+if __name__ == '__main__':
+    main()
diff --git a/pytorch/output.json b/pytorch/output.json
new file mode 100644
index 0000000..6948648
--- /dev/null
+++ b/pytorch/output.json
@@ -0,0 +1,859 @@
+[
+  {
+    "power_before": [
+      29.76,
+      33.16
+    ],
+    "shape": [
+      22687,
+      22687
+    ],
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+    "% density": 0.00010628522108964806,
+    "time_s": 0.14322686195373535,
+    "power": [
+      22.6,
+      22.6,
+      26.16,
+      29.2
+    ],
+    "power_after": [
+      34.0,
+      30.16
+    ],
+    "task clock (msec)": 64.71,
+    "page faults": 3319,
+    "cycles": 57611295,
+    "instructions": 83148228,
+    "branch mispredictions": 318386,
+    "branches": 19233431,
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+    "ITLB misses": 6375,
+    "DTLB misses": 17290,
+    "DTLB accesses": 36688544,
+    "L1I cache accesses": 32508072,
+    "L1I cache misses": 297568,
+    "L1D cache misses": 477654,
+    "L1D cache accesses": 34044579,
+    "LL cache misses": 549474,
+    "LL cache accesses": 561939,
+    "L2D TLB accesses": 185622,
+    "L2D TLB misses": 23295,
+    "L2D cache misses": 305878,
+    "L2D cache accesses": 1763089,
+    "instructions per cycle": 1.4432626102225268,
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+    "L2D TLB miss rate": 0.12549697772893298,
+    "L1I cache miss rate": 0.009153664972810446,
+    "L1D cache miss rate": 0.014030251336049713,
+    "L2D cache miss rate": 0.17348982382625042,
+    "LL cache miss rate": 0.9778178770293573,
+    "architecture": "altra"
+  },
+  {
+    "power_before": [
+      20.48,
+      20.96
+    ],
+    "shape": [
+      24115,
+      24115
+    ],
+    "nnz": 116056,
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+    ],
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+    ],
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+    "L2D TLB misses": 19847,
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+    "L2D cache miss rate": 0.1771979592967662,
+    "LL cache miss rate": 0.9688380570556218,
+    "architecture": "altra"
+  },
+  {
+    "power_before": [
+      20.28,
+      20.32
+    ],
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+      36692,
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+    "architecture": "altra"
+  },
+  {
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+  },
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+  },
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+    "L2D cache miss rate": 0.1761430104566423,
+    "LL cache miss rate": 0.9780097388689274,
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+    "architecture": "altra"
+  },
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\ No newline at end of file
diff --git a/pytorch/output_HPC.json b/pytorch/output_HPC.json
new file mode 100644
index 0000000..9be58d5
--- /dev/null
+++ b/pytorch/output_HPC.json
@@ -0,0 +1,1406 @@
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\ No newline at end of file