def write(self, detector):
"""Print all keys and values from detector structure
they include also a metadata read from binary output file
"""
for name, value in sorted(detector.__dict__.items()):
# be careful not to check for np.array but for np.ndarray!
if name not in {'data', 'data_raw', 'error', 'error_raw', 'counter'}: # skip non-metadata fields
line = "{:24s}: '{:s}'".format(str(name), str(value))
print(line)
# print some data-related statistics
print(75 * "*")
print("Data min: {:g}, max: {:g}".format(detector.data.min(), detector.data.max()))
if self.options.details:
# print data scatter-plot if possible
if detector.dimension == 1:
try:
from hipsterplot import plot
print(75 * "*")
print("Data scatter plot")
plot(detector.data_raw)
except ImportError:
logger.warning("Detailed summary requires installation of hipsterplot package")
# print data histogram if possible
try:
from bashplotlib.histogram import plot_hist
print(75 * "*")
print("Data histogram")
plot_hist(detector.data_raw, bincount=70, xlab=False, showSummary=True)
except ImportError:
logger.warning("Detailed summary requires installation of bashplotlib package")
return 0
def explore_col(df, col, histo=True, nb_uniques=10):
print(f"{'-'*(len(col)+4)}")
print(f"| {col} |")
print(f"{'-'*(len(col)+4)}")
print(f"type: {type(df[col][0])}")
print(df[col][:10])
print(f"\nmin: {df[col].dropna().min()}")
print(f"max: {df[col].dropna().max()}")
if (isinstance(df[col][0], int)
or isinstance(df[col][0], float)
or np.issubdtype(type(df[col][0]), np.integer)
or np.issubdtype(type(df[col][0]), np.floating)
):
print(f"mean: {np.mean(df[col])}")
print("\n---------------------------------")
print("| 25% | 50% | 75% | std |")
print("---------------------------------")
print("|" + "|".join([(str(val)+' ')[:7] for val in df[col].quantile([.25, .5, .75])])
+ "|" + (str(df[col].std())+' ')[:7] + "|"
)
print("---------------------------------")
if histo:
print("histogram:")
if isinstance(df[col][0], bool):
plot_hist(
df[col].dropna().apply(lambda row: 0 if row is False else 1 if row is True else -1),
bincount = 30,
pch = '.',
xlab=True
)
else:
plot_hist(df[col].dropna(),bincount = 30, pch = '.', xlab=True)
uniques = sorted(df[col].dropna().unique())
print(f"contains {len(uniques)} different values")
if len(uniques) < nb_uniques:
print("uniques values: ")
columns = '| '+' | '.join([str(elem) for elem in uniques])+' |'
print('-' * len(columns))
print(columns)
print('-' * len(columns))
numbers = []
print(
'| '+'%| '.join([(str(
df[col].where(
df[col]==elem).dropna().count() /df[col].shape[0] *100
)+ ' '*len(str(elem)) )[:len(str(elem))] for elem in uniques
]) + '%|'
)
print('-' * len(columns))
print(f"contains {df[col].shape[0]- df[col].dropna().shape[0]} NaN values\n")
def display(self):
plot_hist(list(self.knockouts_hist),
title="Knockout size distribution",
colour="blue")
lines = [
"%s, %s" % (x, y)
for x, y in zip(self.solution.solutions['Size'],
self.solution.solutions['Fitness'])
]
plot_scatter(
lines, None, None, 20, "*", "blue",
"Correlation between number of knockouts and fitness")
def plot(clk_json):
try:
# data was writen with: json.dump({'clks': clk_data}, output); so ...
clks = json.load(clk_json)['clks']
except ValueError as e: # In Python 3 we can be more specific
# with json.decoder.JSONDecodeError,
# but that doesn't exist in Python 2.
msg = 'The input is not a valid JSON file.'
raise_from(DescribeError(msg), e)
if len(clks) == 0:
msg = 'No clks found'
raise DescribeError(msg)
popcounts = [deserialize_bitarray(clk).count() for clk in clks]
plot_hist(popcounts, bincount=60, title='popcounts', xlab=True, showSummary=True)
def write(self, estimator):
"""Print all keys and values from estimator structure
they include also a metadata read from binary output file
"""
for name, value in sorted(estimator.__dict__.items()):
# skip non-metadata fields
if name not in {'data', 'data_raw', 'error', 'error_raw', 'counter', 'pages'}:
line = "{:24s}: '{:s}'".format(str(name), str(value))
print(line)
# print some data-related statistics
print(75 * "*")
for page_no, page in enumerate(estimator.pages):
print("Page {} / {}".format(page_no, len(estimator.pages)))
for name, value in sorted(page.__dict__.items()):
# skip non-metadata fields
if name not in {'data', 'data_raw', 'error', 'error_raw'}:
line = "\t{:24s}: '{:s}'".format(str(name), str(value))
print(line)
print("Data min: {:g}, max: {:g}".format(page.data_raw.min(), page.data_raw.max()))
print(75 * "-")
if self.options.details:
# print data scatter-plot if possible
if estimator.dimension == 1 and len(self.pages) == 1:
try:
from hipsterplot import plot
print(75 * "*")
print("Data scatter plot")
plot(estimator.data_raw)
except ImportError:
logger.warning("Detailed summary requires installation of hipsterplot package")
# print data histogram if possible
try:
from bashplotlib.histogram import plot_hist
print(75 * "*")
print("Data histogram")
plot_hist(estimator.data_raw, bincount=70, xlab=False, showSummary=True)
except ImportError:
logger.warning("Detailed summary requires installation of bashplotlib package")
return 0
def _do_dir(target_dir: pathlib.Path):
width_list = []
height_list = []
files = [p for p in target_dir.glob("**/*") if p.is_file()]
for file in tqdm.tqdm(files, ascii=True, ncols=100):
try:
with PIL.Image.open(file) as img:
try:
img = PIL.ImageOps.exif_transpose(img)
except Exception:
pass
width_list.append(img.width)
height_list.append(img.height)
with tqdm.tqdm.external_write_mode():
logger.info(
f"{file.relative_to(target_dir)}: width={img.width} height={img.height}"
)
except Exception:
logger.info(f"{file.relative_to(target_dir)}: skip")
width_list = np.array(width_list)
height_list = np.array(height_list)
plot_hist(width_list, title="width", bincount=20, showSummary=True)
plot_hist(height_list, title="height", bincount=20, showSummary=True)
sizes = np.sqrt(width_list * height_list)
plot_hist(sizes,
title="sqrt(width * height)",
bincount=20,
showSummary=True)
from bashplotlib.scatterplot import plot_scatter
from bashplotlib.histogram import plot_hist
plot_hist(5,
height=20.0,
bincount=None,
pch='0',
colour='white',
title='',
xlab=None)
#!/usr/bin/env python3 # sudo pip3 install bashplotlib # bashplotlib is a python package and command line tool for making basic plots in the terminal. # It’s a quick way to visualize data when you don’t have a GUI. import numpy as np from bashplotlib.histogram import plot_hist arr = np.random.normal(size=10000, loc=0, scale=1) plot_hist(arr, bincount=50) #* 647| oo #* 613| ooooo #* 579| oooooo #* 545| ooooooo o #* 511| oooooooooo #* 477| ooooooooooo #* 443| ooooooooooo #* 409| oooooooooooo #* 375| ooooooooooooo #* 341| ooooooooooooooo #* 307| ooooooooooooooo #* 273| oooooooooooooooo #* 239| ooooooooooooooooo #* 205| oooooooooooooooooo #* 171| ooooooooooooooooooo #* 137| ooooooooooooooooooooo #* 103| ooooooooooooooooooooooo #* 69| oooooooooooooooooooooooooo
def plot_beta(a, b, max=1.0):
sample = beta.rvs(a, b, size=10000) * max
plot_hist(sample, bincount=BINCOUNT, height=10, xlab=True)
#! /home/gregory.ashton/anaconda2/bin/python
import sys
files = sys.argv[1:]
CPU_hours = []
for file in files:
with open(file, 'r') as f:
for line in f:
if 'Total Remote Usage' in line:
string = line.lstrip(' ').split(' ')[2].rstrip(',')
h, m, s = [float(u) for u in string.split(':')]
CPU_hours.append(h + m / 60. + s / (60.**2))
print('\nCPU hours summary')
print(' Total: {}'.format(sum(CPU_hours)))
print(' Shortest: {}'.format(min(CPU_hours)))
print(' Longest: {}'.format(max(CPU_hours)))
print('')
try:
from bashplotlib.histogram import plot_hist
plot_hist(CPU_hours, bincount=50, xlab=True)
except IOError:
pass
def plot_norm(mean, variation):
sample = norm.rvs(mean, variation, size=10000)
plot_hist(sample, bincount=BINCOUNT, height=10, xlab=True)
def plot_beta(a, b, max=1.0):
sample = beta.rvs(a, b, size=10000) * max
plot_hist(sample, bincount=BINCOUNT, height=10, xlab=True)
def plot_gamma(k, theta):
sample = gamma.rvs(k, scale=theta, size=10000)
plot_hist(sample, bincount=BINCOUNT, height=10, xlab=True)
import json
from bashplotlib.histogram import plot_hist
data = json.load(open('lambdaSamples.json'))
plot_hist(data, height=25, bincount=80, xlab=True, title="Lambda samples")
data = json.load(open('muSamples.json'))
plot_hist(data, height=25, bincount=80, xlab=True, title="Mu samples")
def main():
args = Args()
datasets = {
# "train": read_pairs(args.train_file, tuple_separator=" ▁|SEP|▁ ", token_separator=" ", decode=True),
# "train": read_pairs(args.train_file),
"test": read_pairs(args.test_file),
}
train_src, train_tgt = [], []
for train_path in args.train_files.split(","):
cur_src, cur_tgt = read_pairs(train_path)
train_src += cur_src
train_tgt += cur_tgt
datasets["train"] = (train_src, train_tgt)
merged_dataset = sorted([(tgt, idx if key == "test" else -1)
for key, (src_data, tgt_data) in datasets.items()
for idx, tgt in enumerate(tgt_data)])
cnt = 0
progress = tqdm(merged_dataset)
overlap_ids = []
overlap_scores = {}
for idx, (test_sent, key) in enumerate(progress):
if key == -1: continue
candidates = []
left = next(
(i for i in range(idx - 1, -1, -1) if merged_dataset[i][1] == -1),
None)
right = next((i for i in range(idx + 1, len(merged_dataset))
if merged_dataset[i][1] == -1), None)
if left: candidates.append(tokenize(merged_dataset[left][0]))
if right: candidates.append(tokenize(merged_dataset[right][0]))
if len(candidates) == 0:
breakpoint()
continue
test_sent = tokenize(test_sent)
max_overlap, cand_sent = max(
(cotra.utils.lcs(test_sent, cand) / max(len(test_sent), len(cand)),
cand) for cand in candidates)
if max_overlap > 0.8:
# progress.write(" ".join(test_sent))
# progress.write(" ".join(cand_sent))
cnt += 1
progress.set_postfix(cnt=cnt)
overlap_ids.append(key)
overlap_scores[key] = max_overlap
print(cnt)
print(overlap_ids)
if len(overlap_scores) != len(datasets["test"][0]):
breakpoint()
overlap_scores = [
overlap_scores[idx] for idx in range(len(overlap_scores))
]
plot_hist(overlap_scores,
height=10,
pch="x",
xlab=True,
showSummary=True,
bincount=70)
with open(args.output_file, "w") as f:
f.write("\n".join(map(str, overlap_scores)))
def describe(clk_json):
"""show distribution of clk's popcounts using a ascii plot.
"""
clks = json.load(clk_json)['clks']
counts = get_encoding_popcounts([deserialize_bitarray(clk) for clk in clks])
plot_hist(counts, bincount=60, title='popcounts', xlab=True, showSummary=True)
import matplotlib
import firebase_admin
from firebase_admin import credentials
from firebase_admin import db
from bashplotlib.histogram import plot_hist
cred = credentials.Certificate("db-rix.json")
firebase_admin.initialize_app(cred)
ref = db.reference('/traffic_v2', url="https://<your_db_name>.firebaseio.com/")
data = ref.get()
up = [value['upload'] for value in data.values()]
down = [value['download'] for value in data.values()]
up_delta = [up[i] - up[i - 1] for i in range(1, len(up))]
down_delta = [down[i] - down[i - 1] for i in range(1, len(down))]
index = list(range(1, len(up)))
plot_hist(up)
def plot_gamma(k, theta):
sample = gamma.rvs(k, scale=theta, size=10000)
plot_hist(sample, bincount=BINCOUNT, height=10, xlab=True)
def main_work():
#################################################
# ======== Get stuff from command line ==========
a = ArgumentParser()
a.add_argument('-i', dest='infile', required=True)
a.add_argument('-cmp', dest='cmpdir', required=False, default='')
a.add_argument('-maxframes', dest='maxframes', type=int, default=10000000)
a.add_argument('-maxletters',
dest='maxletters',
type=int,
default=10000000)
a.add_argument(
'-phone',
action='store_true',
help=
'Use the 4th field of transcript, containing space-delimited phones')
a.add_argument('-speaker', action='store_true')
a.add_argument('-o', dest='outfile', required=False, default='')
opts = a.parse_args()
# ===============================================
texts = codecs.open(opts.infile, 'r', 'utf-8', errors='ignore').readlines()
texts = [line.strip('\n\r |') for line in texts]
texts = [t for t in texts if t != '']
texts = [line.strip().split("|") for line in texts]
for line in texts:
assert len(line) == len(texts[0]), line
#print prompts
all_frame_lengths = []
all_letter_lengths = []
frame_lengths = []
letter_lengths = []
outlines = []
letter_inventory = {}
speaker_ids = {}
missing_audio = []
for text in tqdm(texts):
base, plaintext, normtext = text[:3]
if opts.phone:
assert len(text) >= 4
symbols = text[3]
symbols = re.split('\s+', symbols)
else:
symbols = list(normtext.lower())
all_letter_lengths.append(len(symbols))
if opts.speaker:
assert len(text) >= 5
speaker_id = text[4]
speaker_ids[speaker_id] = ''
if opts.cmpdir:
cmpfile = os.path.join(opts.cmpdir, base + '.npy')
if not os.path.exists(cmpfile):
missing_audio.append(base)
continue
nframe_audio, ndim = np.load(cmpfile).shape
#print base
all_frame_lengths.append(nframe_audio)
if nframe_audio > opts.maxframes:
continue
frame_lengths.append(nframe_audio)
if len(symbols) > opts.maxletters:
continue
letter_lengths.append(len(symbols))
letter_inventory.update(dict(zip(symbols, symbols)))
#print phones
#outline = '%s||%s'%(base, normtext)
#outlines.append(outline)
print
print '%s of %s sentences processed successfully -- to debug rerun ....' % (
len(outlines), len(texts))
print
print 'missing audio for :'
print missing_audio
print
print
print 'Keep %s of %s letter/phone tokens' % (len(letter_lengths),
len(all_letter_lengths))
print 'Keep %s of %s frames' % (len(frame_lengths), len(all_frame_lengths))
print
if opts.phone:
print '------------------'
print 'Observed phones:'
print '------------------'
print
print sorted([str(k) for k in letter_inventory.keys()])
print '------------------'
junctures = [
p for p in letter_inventory.keys()
if p.startswith(u'<') and p.endswith(u'>')
]
junctures.remove('<_START_>')
junctures.remove('<_END_>')
junctures = [p.strip('<>') for p in junctures]
junctures = [j for j in junctures if j != '']
print
print
print 'Junctures (many of these may be spurious):'
print
print ' '.join(sorted(junctures))
print
else:
print '------------------'
print 'Observed letters:'
print '------------------'
print
print[''.join(sorted(letter_inventory.keys()))]
print '------------------'
print
print 'Letter/phone length max:'
print max(letter_lengths)
if opts.speaker:
print '------------------'
print 'Observed speakers:'
print '------------------'
print
print sorted(speaker_ids.keys())
print '------------------'
if opts.cmpdir:
print 'Frame length max:'
print max(frame_lengths)
## 5% for testing:
# outlines = np.array(outlines) ## for indexing with lists
# n_train = int(len(outlines) * 0.95)
# ixx = range(len(outlines))
# random.seed(12345)
# random.shuffle(ixx) ## TODO seeds everywhere
# train_ixx = sorted(ixx[:n_train])
# test_ixx = sorted(ixx[n_train:])
#writelist(outlines, opts.outfile)
# writelist(outlines[test_ixx], opts.outfile + '.test')
plot_hist(letter_lengths,
xlab=True,
bincount=30,
title='Histogram of sentence length in letters',
height=10,
colour='k')
if opts.cmpdir:
plot_hist(frame_lengths,
xlab=True,
bincount=30,
title='Histogram of sentence length in frames',
height=10,
colour='k')
print
print 'Run again with -maxframes and -maxletters to exclude the tail...'
if opts.outfile:
f = open(opts.outfile, 'w')
for text in tqdm(texts):
base = text[0]
if base not in missing_audio:
f.write('|'.join(text) + '\n')
f.close()
def plot_norm(mean, variation):
sample = norm.rvs(mean, variation, size=10000)
plot_hist(sample, bincount=BINCOUNT, height=10, xlab=True)
# scratch.py
from bashplotlib.horizontal_histogram import plot_horiz_hist
from bashplotlib.histogram import plot_hist
import random
nums = []
for i in range(1, 100):
nums.append(random.randint(1, 100))
height = 20
bincount = 10
binwidth = 1
char = 'o'
color = 'default'
title = 'My Bar Chart'
displayXLabel = True
showSum = True
yStart = True
plot_hist(nums,
height,
bincount,
binwidth,
char,
color,
title,
displayXLabel,
showSum,
yStart,
xtitle="My x",
ytitle="My y")
def display(self):
plot_hist(list(self.knockouts_hist), title="Knockout size distribution", colour="blue")
lines = ["%s, %s" % (x, y) for x, y in
zip(self.solution.solutions['Size'], self.solution.solutions['Fitness'])]
plot_scatter(lines, None, None, 20, "*", "blue", "Correlation between number of knockouts and fitness")
#!/usr/bin/env python3.6
from bashplotlib.histogram import plot_hist
import glob
import re
import argparse
import fi_tools
parser = argparse.ArgumentParser('Run profiling experiments')
parser.add_argument('-t', '--tool', help='tool to run', required=True)
args = parser.parse_args()
bitflips = []
for f in glob.glob('*/' + fi_tools.files[args.tool]['injection']):
with open(f, 'r') as f:
m = re.match('.*bitflip=(\d+).*', f.read())
bitflips.append(m[1])
print(bitflips)
plot_hist(bitflips, title='Distro bitflip', bincount=128, xlab=True)
fi_index = []
for f in glob.glob('*/' + fi_tools.files[args.tool]['target']):
with open(f, 'r') as f:
m = re.match('.*fi_index=(\d+).*', f.read())
fi_index.append(m[1])
plot_hist(fi_index, title='Distro fi_index', bincount=128, xlab=True)
