| 1 | #!/usr/bin/python3
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| 2 | """
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| 3 | Python Script to plot values obtained by the rmit.py script
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| 4 | Runs a R.I.P.L.
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| 5 |
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| 6 | ./plot.py
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| 7 | -t trials
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| 8 | -o option:values
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| 9 | """
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| 10 |
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| 11 | import argparse
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| 12 | import itertools
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| 13 | import json
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| 14 | import math
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| 15 | import numpy
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| 16 | import re
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| 17 | import statistics
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| 18 | import sys
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| 19 |
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| 20 | import matplotlib.pyplot as plt
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| 21 | from matplotlib.ticker import EngFormatter
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| 22 |
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| 23 | class Field:
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| 24 | def __init__(self, unit, _min):
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| 25 | self.unit = unit
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| 26 | self.min = _min
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| 27 |
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| 28 | field_names = {
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| 29 | "ns per ops" : Field('ns' , 0),
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| 30 | "Number of processors" : Field('' , 1),
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| 31 | "Ops per procs" : Field('Ops' , 0),
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| 32 | "Ops per threads" : Field('Ops' , 0),
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| 33 | "ns per ops/procs" : Field('ns' , 0),
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| 34 | "Number of threads" : Field('thrd' , 1),
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| 35 | "Total Operations(ops)": Field('Ops' , 0),
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| 36 | "Ops/sec/procs" : Field('Ops' , 0),
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| 37 | "Total blocks" : Field('Blocks', 0),
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| 38 | "Ops per second" : Field('Ops' , 0),
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| 39 | "Cycle size (# thrds)" : Field('thrd' , 1),
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| 40 | "Duration (ms)" : Field('ms' , 0),
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| 41 | }
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| 42 |
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| 43 | def plot(in_data, x, y, out):
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| 44 | fig, ax = plt.subplots()
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| 45 | colors = itertools.cycle(['#0095e3','#006cb4','#69df00','#0aa000','#fb0300','#e30002','#fd8f00','#ff7f00','#8f00d6','#4b009a','#ffff00','#b13f00'])
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| 46 | series = {} # scatter data for each individual data point
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| 47 | groups = {} # data points for x value
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| 48 | for entry in in_data:
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| 49 | name = entry[0]
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| 50 | if not name in series:
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| 51 | series[name] = {'x':[], 'y':[]}
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| 52 |
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| 53 | if not name in groups:
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| 54 | groups[name] = {}
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| 55 |
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| 56 | if x in entry[2] and y in entry[2]:
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| 57 | xval = entry[2][x]
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| 58 | yval = entry[2][y]
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| 59 | series[name]['x'].append(xval)
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| 60 | series[name]['y'].append(yval)
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| 61 |
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| 62 | if not xval in groups[name]:
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| 63 | groups[name][xval] = []
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| 64 |
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| 65 | groups[name][xval].append(yval)
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| 66 |
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| 67 | lines = {} # lines from groups with min, max, median, etc.
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| 68 | for name, data in groups.items():
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| 69 | if not name in lines:
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| 70 | lines[name] = { 'x': [], 'min':[], 'max':[], 'med':[], 'avg':[] }
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| 71 |
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| 72 | for xkey in sorted(data):
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| 73 | ys = data[xkey]
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| 74 | lines[name]['x'] .append(xkey)
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| 75 | lines[name]['min'].append(min(ys))
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| 76 | lines[name]['max'].append(max(ys))
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| 77 | lines[name]['med'].append(statistics.median(ys))
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| 78 | lines[name]['avg'].append(statistics.mean(ys))
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| 79 |
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| 80 | for name, data in series.items():
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| 81 | _col = next(colors)
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| 82 | plt.scatter(data['x'], data['y'], color=_col, label=name, marker='x')
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| 83 | plt.plot(lines[name]['x'], lines[name]['min'], '--', color=_col)
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| 84 | plt.plot(lines[name]['x'], lines[name]['max'], '--', color=_col)
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| 85 | plt.plot(lines[name]['x'], lines[name]['med'], '-', color=_col)
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| 86 |
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| 87 | mx = max([max(s['x']) for s in series.values()])
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| 88 | my = max([max(s['y']) for s in series.values()])
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| 89 |
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| 90 | plt.ylabel(y)
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| 91 | plt.xlim(field_names[x].min, mx + 0.25)
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| 92 | plt.xticks(range(1, math.ceil(mx) + 1))
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| 93 | plt.xlabel(x)
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| 94 | plt.ylim(field_names[y].min, my*1.2)
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| 95 | plt.grid(b = True)
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| 96 | ax.xaxis.set_major_formatter( EngFormatter(unit=field_names[x].unit) )
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| 97 | ax.yaxis.set_major_formatter( EngFormatter(unit=field_names[y].unit) )
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| 98 | plt.legend(loc='upper left')
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| 99 | if out:
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| 100 | plt.savefig(out)
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| 101 | else:
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| 102 | plt.show()
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| 103 |
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| 104 |
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| 105 | if __name__ == "__main__":
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| 106 | # ================================================================================
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| 107 | # parse command line arguments
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| 108 | parser = parser = argparse.ArgumentParser(description='Python Script to draw R.M.I.T. results')
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| 109 | parser.add_argument('-f', '--file', nargs='?', type=argparse.FileType('r'), default=sys.stdin)
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| 110 | parser.add_argument('-o', '--out', nargs='?', type=str, default=None)
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| 111 | parser.add_argument('-y', nargs='?', type=str, default="")
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| 112 |
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| 113 | try:
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| 114 | options = parser.parse_args()
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| 115 | except:
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| 116 | print('ERROR: invalid arguments', file=sys.stderr)
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| 117 | parser.print_help(sys.stderr)
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| 118 | sys.exit(1)
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| 119 |
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| 120 | # ================================================================================
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| 121 | # load data
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| 122 | try :
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| 123 | data = json.load(options.file)
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| 124 | except :
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| 125 | print('ERROR: could not read input', file=sys.stderr)
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| 126 | parser.print_help(sys.stderr)
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| 127 | sys.exit(1)
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| 128 |
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| 129 | # ================================================================================
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| 130 | # identify the keys
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| 131 |
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| 132 | series = set()
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| 133 | fields = set()
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| 134 |
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| 135 | for entry in data:
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| 136 | series.add(entry[0])
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| 137 | for label in entry[2].keys():
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| 138 | fields.add(label)
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| 139 |
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| 140 | if not options.out :
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| 141 | print(series)
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| 142 | print("fields")
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| 143 | for f in fields:
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| 144 | print("{}".format(f))
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| 145 |
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| 146 | if options.y and options.y in field_names.keys():
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| 147 | plot(data, "Number of processors", options.y, options.out)
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| 148 | else:
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| 149 | if options.y:
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| 150 | print("Could not find key '{}', defaulting to 'ns per ops'".format(options.y))
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| 151 | plot(data, "Number of processors", "ns per ops", options.out)
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