nilm/nilmtools
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Finish trainola testing and porting to Python 3

Browse Source
This commit is contained in:
Jim Paris 2020-08-06 13:36:28 -04:00
parent 2c892a9df7
commit 8c7713076b
17 changed files with 343 additions and 23 deletions

8
extras/trainola-test-param-3.js
View File

@ -1,5 +1,5 @@
{
"url": "http://bucket/nilmdb",
"url": "http://bucket.mit.edu/nilmdb",
"stream": "/sharon/prep-a",
"dest_stream": "/test/jim",
"start": 1364184839901599,
@ -11,7 +11,7 @@
{
"name": "A - True DBL Freezer ON",
"dest_column": 0,
"url": "http://bucket/nilmdb",
"url": "http://bucket.mit.edu/nilmdb",
"stream": "/sharon/prep-a",
"columns": [ { "index": 0, "name": "P1" } ],
"start": 1365277707649000,
@ -20,7 +20,7 @@
{
"name": "A - Boiler 1 Fan OFF",
"dest_column": 1,
"url": "http://bucket/nilmdb",
"url": "http://bucket.mit.edu/nilmdb",
"stream": "/sharon/prep-a",
"columns": [ { "index": 0, "name": "P1" } ],
"start": 1364188370735000,
@ -29,7 +29,7 @@
{
"name": "A - True DBL Freezer OFF",
"dest_column": 2,
"url": "http://bucket/nilmdb",
"url": "http://bucket.mit.edu/nilmdb",
"stream": "/sharon/prep-a",
"columns": [ { "index": 0, "name": "P1" } ],
"start": 1365278087982000,

2
nilmtools/trainola.py
View File

@ -233,6 +233,8 @@ def trainola(conf):
# Pull in the exemplar data
exemplars = []
if 'exemplars' not in conf:
raise DataError("missing exemplars")
for n, exinfo in enumerate(conf['exemplars']):
printf("Loading exemplar %d:\n", n)
e = Exemplar(exinfo)

7
tests/data/trainola-bad1.js Normal file
View File

@ -0,0 +1,7 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/train/matches",
"stream": "/train/data",
"start": 0,
"end": 100000000,
"columns": [ ]
}

17
tests/data/trainola-bad10.js Normal file
View File

@ -0,0 +1,17 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/train/matches",
"stream": "/train/data",
"start": 0,
"end": 100000000,
"columns": [ { "name": "P1", "index": 0 } ],
"exemplars": [
{ "name": "Big ON",
"url": "http://localhost:32182/",
"stream": "/train/data",
"start": 34000000,
"end": 34000001,
"dest_column": 0,
"columns": [ { "name": "P1", "index": 0 } ]
}
]
}

17
tests/data/trainola-bad11.js Normal file
View File

@ -0,0 +1,17 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/train/matches",
"stream": "/train/data",
"start": 0,
"end": 100000000,
"columns": [ { "name": "P1", "index": 0 } ],
"exemplars": [
{ "name": "Big ON",
"url": "http://localhost:32182/",
"stream": "/train/big",
"start": 0,
"end": 110000,
"dest_column": 0,
"columns": [ { "name": "P1", "index": 0 } ]
}
]
}

17
tests/data/trainola-bad12.js Normal file
View File

@ -0,0 +1,17 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/train/matches",
"stream": "/train/data",
"start": 0,
"end": 100000000,
"columns": [ { "name": "P1", "index": 0 } ],
"exemplars": [
{ "name": "Big ON",
"url": "http://localhost:32182/",
"stream": "/train/data",
"start": 34000000,
"end": 36000000,
"dest_column": 0,
"columns": [ { "name": "FOO", "index": 0 } ]
}
]
}

8
tests/data/trainola-bad2.js Normal file
View File

@ -0,0 +1,8 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/train/matches",
"stream": "/train/data",
"start": 0,
"end": 100000000,
"columns": [ { "name": "P1", "index": 0 },
{ "name": "P1", "index": 1 } ]
}

7
tests/data/trainola-bad3.js Normal file
View File

@ -0,0 +1,7 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/train/matches",
"stream": "/train/data",
"start": 0,
"end": 100000000,
"columns": [ { "name": "P1", "index": 10 } ]
}

7
tests/data/trainola-bad4.js Normal file
View File

@ -0,0 +1,7 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/a/b",
"stream": "/c/d",
"start": 0,
"end": 100000000,
"columns": [ { "name": "P1", "index": 0 } ]
}

7
tests/data/trainola-bad5.js Normal file
View File

@ -0,0 +1,7 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/a/b",
"stream": "/train/data",
"start": 0,
"end": 100000000,
"columns": [ { "name": "P1", "index": 0 } ]
}

7
tests/data/trainola-bad6.js Normal file
View File

@ -0,0 +1,7 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/train/matches",
"stream": "/train/data",
"start": 0,
"end": 100000000,
"columns": [ { "name": "P1", "index": 0 } ]
}

8
tests/data/trainola-bad7.js Normal file
View File

@ -0,0 +1,8 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/train/matches",
"stream": "/train/data",
"start": 0,
"end": 100000000,
"columns": [ { "name": "P1", "index": 0 } ],
"exemplars": [ ]
}

17
tests/data/trainola-bad8.js Normal file
View File

@ -0,0 +1,17 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/train/matches",
"stream": "/train/data",
"start": 0,
"end": 100000000,
"columns": [ { "name": "P1", "index": 0 } ],
"exemplars": [
{ "name": "Big ON",
"url": "http://localhost:32182/",
"stream": "/e/f",
"start": 34000000,
"end": 36000000,
"dest_column": 0,
"columns": [ { "name": "P1", "index": 0 } ]
}
]
}

17
tests/data/trainola-bad9.js Normal file
View File

@ -0,0 +1,17 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/train/matches",
"stream": "/train/data",
"start": 0,
"end": 100000000,
"columns": [ { "name": "P1", "index": 0 } ],
"exemplars": [
{ "name": "Big ON",
"url": "http://localhost:32182/",
"stream": "/train/data",
"start": 10034000000,
"end": 10035000000,
"dest_column": 0,
"columns": [ { "name": "P1", "index": 0 } ]
}
]
}

25
tests/data/trainola1.js Normal file
View File

@ -0,0 +1,25 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/train/matches",
"stream": "/train/data",
"start": 0,
"end": 100000000,
"columns": [ { "name": "P1", "index": 0 } ],
"exemplars": [
{ "name": "Big ON",
"url": "http://localhost:32182/",
"stream": "/train/data",
"start": 34000000,
"end": 36000000,
"dest_column": 0,
"columns": [ { "name": "P1", "index": 0 } ]
},
{ "name": "Big OFF",
"url": "http://localhost:32182/",
"stream": "/train/data",
"start": 36000000,
"end": 38000000,
"dest_column": 1,
"columns": [ { "name": "P1", "index": 0 } ]
}
]
}

17
tests/data/trainola2.js Normal file
View File

@ -0,0 +1,17 @@
{ "url": "http://localhost:32182/",
"dest_stream": "/train/matches2",
"stream": "/train/data",
"start": 0,
"end": 100000000,
"columns": [ { "name": "P1", "index": 0 } ],
"exemplars": [
{ "name": "a",
"url": "http://localhost:32182/",
"stream": "/train/data",
"start": 1000000,
"end": 2000000,
"dest_column": 0,
"columns": [ { "name": "P1", "index": 0 } ]
}
]
}

178
tests/test.py
View File

@ -18,10 +18,16 @@ from nilmdb.utils.interval import Interval
from nose.tools import assert_raises
import unittest
import numpy
import math
import json
import random
from testutil.helpers import *
import multiprocessing
import subprocess
import traceback
import os
import atexit
import signal
from urllib.request import urlopen
from nilmtools.filter import ArgumentError
@ -40,28 +46,50 @@ class CommandTester():
@classmethod
def setup_class(cls):
# Use multiprocessing with "spawn" method, so that we can
# start two fully independent cherrypy instances
# (needed for copy-wildcard)
multiprocessing.set_start_method('spawn')
events = []
# We need two servers running for "copy_multiple", but
# cherrypy uses globals and can only run once per process.
# Using multiprocessing with "spawn" method should work in
# theory, but is hard to get working when the test suite is
# spawned directly by nosetests (rather than ./run-tests.py).
# Instead, just run the real nilmdb-server that got installed
# along with our nilmdb dependency.
def terminate_servers():
for p in cls.servers:
p.terminate()
atexit.register(terminate_servers)
cls.servers = []
for (path, port) in (("tests/testdb1", 32182),
("tests/testdb2", 32183)):
def listening():
try:
urlopen(f"http://127.0.0.1:{port}/", timeout=0.1)
return True
except Exception as e:
return False
if listening():
raise Exception(f"another server already running on {port}")
recursive_unlink(path)
event = multiprocessing.Event()
proc = multiprocessing.Process(target=run_cherrypy_server,
args=(path, port, event))
proc.start()
events.append(event)
for event in events:
if not event.wait(timeout = 10):
raise AssertionError("server didn't start")
p = subprocess.Popen(["nilmdb-server",
"--address", "127.0.0.1",
"--database", path,
"--port", str(port),
"--quiet",
"--traceback"],
stdin=subprocess.DEVNULL,
stdout=subprocess.DEVNULL)
for i in range(50):
if listening():
break
time.sleep(0.1)
else:
raise Exception(f"server didn't start on port {port}")
@classmethod
def teardown_class(cls):
urlopen("http://127.0.0.1:32182/exit/", timeout = 1)
urlopen("http://127.0.0.1:32183/exit/", timeout = 1)
for p in cls.servers:
p.terminate()
def run(self, arg_string, infile=None, outfile=None):
"""Run a cmdline client with the specified argument string,
@ -613,15 +641,127 @@ class TestAllCommands(CommandTester):
self.ok(f"-c 2 /sf/raw /sf/out-empty /prep/out-empty")
self.contain("warning: no periods found; skipping")
def generate_trainola_data(self):
# Build some fake data for trainola, which is just pulses of varying
# length.
client = nilmdb.client.Client(url=self.url)
total_sec = 100
fs = 100
rg = numpy.random.Generator(numpy.random.MT19937(1234567))
path = "/train/data"
# Just build up some random pulses. This uses seeded random numbers,
# so any changes here will affect the success/failures of tests later.
client.stream_create(path, "float32_1")
with client.stream_insert_context(path) as ctx:
remaining = 0
for n in range(fs * total_sec):
t = n / fs
data = rg.normal(100) / 100 - 1
if remaining > 0:
remaining -= 1
data += 1
else:
if rg.integers(fs * 10 * total_sec) < fs:
if rg.integers(3) < 2:
remaining = fs*2
else:
remaining = fs/2
line = b"%d %f\n" % (t * 1e6, data)
ctx.insert(line)
# To view what was made, try:
if 0:
subprocess.call(f"nilmtool -u {self.url} extract -s min -e max " +
f"{path} > /tmp/data", shell=True)
# then in Octave: a=load("/tmp/data"); plot(a(:,2));
if 0:
for (s, e) in client.stream_intervals(path):
print(Interval(s,e).human_string())
# Also generate something with more than 100k data points
client.stream_create("/train/big", "uint8_1")
with client.stream_insert_context("/train/big") as ctx:
for n in range(110000):
ctx.insert(b"%d 0\n" % n)
def test_09_trainola(self):
self.main = nilmtools.trainola.main
client = nilmdb.client.numpyclient.NumpyClient(url=self.url)
self.fail(f"")
self.ok(f"--help")
self.ok(f"--version")
self.ok(f"-v")
self.generate_trainola_data()
self.dump()
def get_json(path):
with open(path) as f:
js = f.read().replace('\n', ' ')
return f"'{js}'"
# pass a dict as argv[0]
with assert_raises(KeyError):
saved_stdout = sys.stdout
try:
with open(os.devnull, 'w') as sys.stdout:
nilmtools.trainola.main([{ "url": self.url }])
finally:
sys.stdout = saved_stdout
# pass no args and they come from sys.argv
saved_argv = sys.argv
try:
sys.argv = [ "prog", "bad-json," ]
with assert_raises(json.decoder.JSONDecodeError):
nilmtools.trainola.main()
finally:
sys.argv = saved_argv
# catch a bunch of errors based on different json input
client.stream_create("/train/matches", "uint8_1")
for (num, error) in [ (1, "no columns"),
(2, "duplicated columns"),
(3, "bad column number"),
(4, "source path '/c/d' does not exist"),
(5, "destination path '/a/b' does not exist"),
(6, "missing exemplars"),
(7, "missing exemplars"),
(8, "exemplar stream '/e/f' does not exist"),
(9, "No data in this exemplar"),
(10, "Too few data points"),
(11, "Too many data points"),
(12, "column FOO is not available in source") ]:
self.fail(get_json(f"tests/data/trainola-bad{num}.js"))
self.contain(error)
# not enough columns in dest
self.fail(get_json("tests/data/trainola1.js"))
self.contain("bad destination column number")
# run normally
client.stream_destroy("/train/matches")
client.stream_create("/train/matches", "uint8_2")
self.ok(get_json("tests/data/trainola1.js"))
self.contain("matched 10 exemplars")
# check actual matches, since we made up the data
matches = list(client.stream_extract_numpy("/train/matches"))
eq_(matches[0].tolist(), [[34000000, 1, 0],
[36000000, 0, 1],
[40800000, 1, 0],
[42800000, 0, 1],
[60310000, 1, 0],
[62310000, 0, 1],
[69290000, 1, 0],
[71290000, 0, 1],
[91210000, 1, 0],
[93210000, 0, 1]])
# another run using random noise as an exemplar, to get better coverage
client.stream_create("/train/matches2", "uint8_1")
self.ok(get_json("tests/data/trainola2.js"))
def test_10_pipewatch(self):
self.main = nilmtools.pipewatch.main