source: tools/perf/png.py@ 9317419

#!/usr/bin/python3

import argparse, json, math, sys, time
import multiprocessing
from PIL import Image
import numpy as np

class Timed:
        def __init__(self, text):
                print(text, end='', flush=True)

        def pretty(self, durr):
                seconds = int(durr)
                days, seconds = divmod(seconds, 86400)
                hours, seconds = divmod(seconds, 3600)
                minutes, seconds = divmod(seconds, 60)
                if days > 0:
                        return '%dd%dh%dm%ds' % (days, hours, minutes, seconds)
                elif hours > 0:
                        return '%dh%dm%ds' % (hours, minutes, seconds)
                elif minutes > 0:
                        return '%dm%ds' % (minutes, seconds)
                else:
                        return '%ds' % (seconds,)

        def __enter__(self):
                self.start = time.time()
                return self

        def __exit__(self, *args):
                self.end = time.time()
                print(self.pretty(self.end - self.start))



parser = argparse.ArgumentParser()
parser.add_argument('--infile', type=argparse.FileType('r'), default=sys.stdin)
parser.add_argument('--outfile', type=str, default='out.png')

args = parser.parse_args()

pool = multiprocessing.Pool()

with Timed("Loading json..."):
        obj = json.load(args.infile)

min_tsc = int(obj['min-tsc'])
max_tsc = int(obj['max-tsc'])

def tsc_to_s(tsc):
        return float(tsc - min_tsc)  / 2500000000.0

max_sec = tsc_to_s(max_tsc)
print([min_tsc, max_tsc, max_sec])

min_cpu = int(obj['min-cpu'])
max_cpu = int(obj['max-cpu'])
cnt_cpu = max_cpu - min_cpu + 1
nbins = math.ceil(max_sec * 10)

class Bar:
        def __init__(self):
                pass

with Timed("Creating bins..."):
        bins = []
        for _ in range(0, int(nbins)):
                bar = Bar()
                bins.append([bar, *[*([0] * cnt_cpu), bar] * cnt_cpu])
                # bins.append([0] * cnt_cpu)

        bins = np.array(bins)



def flatten(val):
        secs = tsc_to_s(val[1])
        ratio = secs / max_sec
        b = int(ratio * (nbins - 1))
        ## from/to
        from_ = val[2] - min_cpu
        to_   = val[3] - min_cpu
        idx = int(1 + ((cnt_cpu + 1) * to_) + from_)
        return [b, idx, 1]
        ## val per cpu
        # cnt = val[2]
        # idx = val[3] - min_cpu
        # # idx = from_
        # return [b, idx, cnt]


with Timed("Compressing data..."):
        compress = map(flatten, obj['values'])

highest  = 1
with Timed("Grouping data..."):
        for x in compress:
                bins[x[0]][x[1]] += x[2]
                highest = max(highest, bins[x[0]][x[1]])

print(highest)
# highest  = 10000000000

with Timed("Normalizing data..."):
        def normalize(v):
                if type(v) is Bar:
                        return np.uint32(0xff008000)
                v = v * 255 / float(highest)
                if v > 256:
                        v = 255
                u8 = np.uint8(v)
                u32 = np.uint32(u8)

                return (0xff << 24) | (u32 << 16) | (u32 << 8) | (u32 << 0)
        normalizef = np.vectorize(normalize, [np.uint32])

        bins = normalizef(bins)

print(bins.shape)
with Timed("Saving image..."):
        im = Image.fromarray(bins, mode='RGBA')
        im.show()
        im.save(args.outfile)