Don't catch SystemExit from a subprocess

nilmrun/filters/__init__.py

@@ -1 +0,0 @@
-# Filters

nilmrun/filters/trainola.py

@@ -1,260 +0,0 @@
-#!/usr/bin/python
-
-from nilmdb.utils.printf import *
-import nilmdb.client
-import nilmtools.filter
-from nilmdb.utils.time import (timestamp_to_human,
-                               timestamp_to_seconds,
-                               seconds_to_timestamp)
-from nilmdb.utils.interval import Interval
-
-import numpy as np
-import scipy
-import scipy.signal
-from numpy.core.umath_tests import inner1d
-import nilmrun
-from collections import OrderedDict
-
-class DataError(ValueError):
-    pass
-
-class Data(object):
-    def __init__(self, name, url, stream, start, end, columns):
-        """Initialize, get stream info, check columns"""
-        self.name = name
-        self.url = url
-        self.stream = stream
-        self.start = start
-        self.end = end
-
-        # Get stream info
-        self.client = nilmdb.client.numpyclient.NumpyClient(url)
-        self.info = nilmtools.filter.get_stream_info(self.client, stream)
-
-        # Build up name => index mapping for the columns
-        self.columns = OrderedDict()
-        for c in columns:
-            if (c['name'] in self.columns.keys() or
-                c['index'] in self.columns.values()):
-                raise DataError("duplicated columns")
-            if (c['index'] < 0 or c['index'] >= self.info.layout_count):
-                raise DataError("bad column number")
-            self.columns[c['name']] = c['index']
-        if not len(self.columns):
-            raise DataError("no columns")
-
-        # Count points
-        self.count = self.client.stream_count(self.stream, self.start, self.end)
-
-    def __str__(self):
-        return sprintf("%-20s: %s%s, %s rows",
-                       self.name, self.stream, str(self.columns.keys()),
-                       self.count)
-
-    def fetch(self, min_rows = 10, max_rows = 100000):
-        """Fetch all the data into self.data.  This is intended for
-        exemplars, and can only handle a relatively small number of
-        rows"""
-        # Verify count
-        if self.count == 0:
-            raise DataError("No data in this exemplar!")
-        if self.count < min_rows:
-            raise DataError("Too few data points: " + str(self.count))
-        if self.count > max_rows:
-            raise DataError("Too many data points: " + str(self.count))
-
-        # Extract the data
-        datagen = self.client.stream_extract_numpy(self.stream,
-                                                   self.start, self.end,
-                                                   self.info.layout,
-                                                   maxrows = self.count)
-        self.data = list(datagen)[0]
-
-        # Discard timestamp
-        self.data = self.data[:,1:]
-
-        # Subtract the mean from each column
-        self.data = self.data - self.data.mean(axis=0)
-
-        # Get scale factors for each column by computing dot product
-        # of each column with itself.
-        self.scale = inner1d(self.data.T, self.data.T)
-
-        # Ensure a minimum (nonzero) scale and convert to list
-        self.scale = np.maximum(self.scale, [1e-9]).tolist()
-
-def process(main, function, args = None, rows = 200000):
-    """Process through the data; similar to nilmtools.Filter.process_numpy"""
-    if args is None:
-        args = []
-
-    extractor = main.client.stream_extract_numpy
-    old_array = np.array([])
-    for new_array in extractor(main.stream, main.start, main.end,
-                               layout = main.info.layout, maxrows = rows):
-        # If we still had old data left, combine it
-        if old_array.shape[0] != 0:
-            array = np.vstack((old_array, new_array))
-        else:
-            array = new_array
-
-        # Process it
-        processed = function(array, args)
-
-        # Save the unprocessed parts
-        if processed >= 0:
-            old_array = array[processed:]
-        else:
-            raise Exception(sprintf("%s return value %s must be >= 0",
-                                    str(function), str(processed)))
-
-        # Warn if there's too much data remaining
-        if old_array.shape[0] > 3 * rows:
-            printf("warning: %d unprocessed rows in buffer\n",
-                   old_array.shape[0])
-
-    # Handle leftover data
-    if old_array.shape[0] != 0:
-        processed = function(array, args)
-
-def peak_detect(data, delta):
-    """Simple min/max peak detection algorithm, taken from my code
-    in the disagg.m from the 10-8-5 paper"""
-    mins = [];
-    maxs = [];
-    cur_min = (None, np.inf)
-    cur_max = (None, -np.inf)
-    lookformax = False
-    for (n, p) in enumerate(data):
-        if p > cur_max[1]:
-            cur_max = (n, p)
-        if p < cur_min[1]:
-            cur_min = (n, p)
-        if lookformax:
-            if p < (cur_max[1] - delta):
-                maxs.append(cur_max)
-                cur_min = (n, p)
-                lookformax = False
-        else:
-            if p > (cur_min[1] + delta):
-                mins.append(cur_min)
-                cur_max = (n, p)
-                lookformax = True
-    return (mins, maxs)
-
-def match(data, args):
-    """Perform cross-correlation match"""
-    ( columns, exemplars ) = args
-    nrows = data.shape[0]
-
-    # We want at least 10% more points than the widest exemplar.
-    widest = max([ x.count for x in exemplars ])
-    if (widest * 1.1) > nrows:
-        return 0
-
-    # This is how many points we'll consider valid in the
-    # cross-correlation.
-    valid = nrows + 1 - widest
-    matches = []
-
-    # Try matching against each of the exemplars
-    for e_num, e in enumerate(exemplars):
-        corrs = []
-
-        # Compute cross-correlation for each column
-        for c in e.columns:
-            a = data[:,columns[c] + 1]
-            b = e.data[:,e.columns[c]]
-            corr = scipy.signal.fftconvolve(a, np.flipud(b), 'valid')[0:valid]
-
-            # Scale by the norm of the exemplar
-            corr = corr / e.scale[columns[c]]
-            corrs.append(corr)
-
-        # Find the peaks using the column with the largest amplitude
-        biggest = e.scale.index(max(e.scale))
-        peaks_minmax = peak_detect(corrs[biggest], 0.1)
-        peaks = [ p[0] for p in peaks_minmax[1] ]
-
-        # Now look at every peak
-        for p in peaks:
-            # Correlation for each column must be close enough to 1.
-            for (corr, scale) in zip(corrs, e.scale):
-                # The accepted distance from 1 is based on the relative
-                # amplitude of the column.  Use a linear mapping:
-                # scale 1.0 -> distance 0.1
-                # scale 0.0 -> distance 1.0
-                distance = 1 - 0.9 * (scale / e.scale[biggest])
-                if abs(corr[p] - 1) > distance:
-                    # No match
-                    break
-            else:
-                # Successful match
-                matches.append((p, e_num))
-
-    # Print matches
-    for (point, e_num) in sorted(matches):
-        # Ignore matches that showed up at the very tail of the window,
-        # and shorten the window accordingly.  This is an attempt to avoid
-        # problems at chunk boundaries.
-        if point > (valid - 50):
-            valid -= 50
-            break
-        print "matched", data[point,0], "exemplar", exemplars[e_num].name
-
-    #from matplotlib import pyplot as p
-    #p.plot(data[:,1:3])
-    #p.show()
-
-    return max(valid, 0)
-
-def trainola(conf):
-    # Load main stream data
-    print "Loading stream data"
-    main = Data(None, conf['url'], conf['stream'],
-                conf['start'], conf['end'], conf['columns'])
-
-    # Pull in the exemplar data
-    exemplars = []
-    for n, e in enumerate(conf['exemplars']):
-        print sprintf("Loading exemplar %d: %s", n, e['name'])
-        ex = Data(e['name'], e['url'], e['stream'],
-                  e['start'], e['end'], e['columns'])
-        ex.fetch()
-        exemplars.append(ex)
-
-    # Verify that the exemplar columns are all represented in the main data
-    for n, ex in enumerate(exemplars):
-        for col in ex.columns:
-            if col not in main.columns:
-                raise DataError(sprintf("Exemplar %d column %s is not "
-                                        "available in main data", n, col))
-
-    # Process the main data
-    process(main, match, (main.columns, exemplars))
-
-    return "done"
-
-filterfunc = trainola
-
-def main(argv = None):
-    import simplejson as json
-    import argparse
-    import sys
-
-    parser = argparse.ArgumentParser(
-        formatter_class = argparse.RawDescriptionHelpFormatter,
-        version = nilmrun.__version__,
-        description = """Run Trainola using parameters passed in as
-        JSON-formatted data.""")
-    parser.add_argument("file", metavar="FILE", nargs="?",
-                        type=argparse.FileType('r'), default=sys.stdin)
-    args = parser.parse_args(argv)
-
-    conf = json.loads(args.file.read())
-    result = trainola(conf)
-    print json.dumps(result, sort_keys = True, indent = 2 * ' ')
-
-if __name__ == "__main__":
-    main()
-

nilmrun/processmanager.py

@@ -173,25 +173,30 @@ def _exec_user_code(codeargs): # pragma: no cover (runs in subprocess)
         codeobj = compile(code, '<user-code>', 'exec',
                           flags = 0, dont_inherit = 1)
         exec(codeobj, module.__dict__, {})
-    except:
+    except Exception:
-        # Pull out the exception
+        try:
-        info = sys.exc_info()
+            # Pull out the exception
-        tblist = traceback.extract_tb(info[2])
+            info = sys.exc_info()
+            tblist = traceback.extract_tb(info[2])
 
-        # First entry is probably this code; get rid of it
+            # First entry is probably this code; get rid of it
-        if len(tblist) and tblist[0][2] == '_exec_user_code':
+            if len(tblist) and tblist[0][2] == '_exec_user_code':
-            tblist = tblist[1:]
+                tblist = tblist[1:]
 
-        # Add the user's source code to every line that's missing it
+            # Add the user's source code to every line that's missing it
-        lines = code.splitlines()
+            lines = code.splitlines()
-        for (n, (name, line, func, text)) in enumerate(tblist):
+            maxline = len(lines)
-            if name == '<user-code>' and text is None and line <= len(lines):
+            for (n, (name, line, func, text)) in enumerate(tblist):
-                tblist[n] = (name, line, func, lines[line-1].strip())
+                if name == '<user-code>' and text is None and line <= maxline:
+                    tblist[n] = (name, line, func, lines[line-1].strip())
 
-        # Print it to stderr in the usual format
+            # Format it in the usual manner
-        out = ['Traceback (most recent call last):\n']
+            out = ['Traceback (most recent call last):\n']
-        out.extend(traceback.format_list(tblist))
+            out.extend(traceback.format_list(tblist))
-        out.extend(traceback.format_exception_only(info[0], info[1]))
+            out.extend(traceback.format_exception_only(info[0], info[1]))
+        finally:
+            # Need to explicitly delete traceback object to avoid ref cycle
+            del info
         sys.stderr.write("".join(out))
         sys.stderr.flush()
         sys.exit(1)

nilmrun/server.py

@@ -25,8 +25,7 @@ from nilmdb.server.serverutil import (
         cherrypy_stop,
         )
 import nilmrun
-import nilmrun.filters.trainola
+import nilmrun.testfilter
-import nilmrun.filters.dummy
 
 # Add CORS_allow tool
 cherrypy.tools.CORS_allow = cherrypy.Tool('on_start_resource', CORS_allow)
@@ -124,6 +123,7 @@ class AppRun(object):
         and 'argv[1:]' are arguments"""
         return self.manager.run_command("command", argv)
 
+    # /run/code
     @cherrypy.expose
     @cherrypy.tools.json_in()
     @cherrypy.tools.json_out()
@@ -135,25 +135,15 @@ class AppRun(object):
         (i.e., they end up in sys.argv[1:])"""
         return self.manager.run_code("usercode", code, args)
 
-    # /run/trainola
+    # /run/testfilter
     @cherrypy.expose
     @cherrypy.tools.json_in()
     @cherrypy.tools.json_out()
     @exception_to_httperror(KeyError, ValueError)
     @cherrypy.tools.CORS_allow(methods = ["POST"])
-    def trainola(self, data):
+    def testfilter(self, data):
         return self.manager.run_function(
-            "trainola", nilmrun.filters.trainola.filterfunc, data)
+            "dummy", nilmrun.testfilter.test, data)
-
-    # /run/dummy
-    @cherrypy.expose
-    @cherrypy.tools.json_in()
-    @cherrypy.tools.json_out()
-    @exception_to_httperror(KeyError, ValueError)
-    @cherrypy.tools.CORS_allow(methods = ["POST"])
-    def dummy(self, data):
-        return self.manager.run_function(
-            "dummy", nilmrun.filters.dummy.filterfunc, data)
 
 class Server(object):
     def __init__(self, host = '127.0.0.1', port = 8080,

nilmrun/testfilter.py

@@ -6,7 +6,7 @@ import signal
 import sys
 
 # This is just for testing the process management.
-def filterfunc(n):
+def test(n):
     n = int(n)
     if n < 0: # raise an exception
         raise Exception("test exception")

tests/test.order

@@ -1,3 +1,3 @@
-test_client.py
+test_nilmrun.py
 
 test_*.py

tests/test_nilmrun.py

@@ -98,7 +98,7 @@ class TestClient(object):
         client = HTTPClient(baseurl = testurl, post_json = True)
 
         # start dummy filter
-        pid = client.post("/run/dummy", { "data": 30 })
+        pid = client.post("/run/testfilter", { "data": 30 })
         eq_(client.get("/process/list"), [pid])
         time.sleep(1)
 
@@ -132,7 +132,7 @@ class TestClient(object):
         client = HTTPClient(baseurl = testurl, post_json = True)
 
         # Trigger exception in filter
-        pid = client.post("/run/dummy", { "data": -1 })
+        pid = client.post("/run/testfilter", { "data": -1 })
         time.sleep(0.5)
         status = client.get("/process/status", { "pid": pid })
         eq_(status["alive"], False)
@@ -141,7 +141,7 @@ class TestClient(object):
         client.post("/process/remove", { "pid": pid })
 
         # Kill a running filter by removing it early
-        newpid = client.post("/run/dummy", { "data": 50 })
+        newpid = client.post("/run/testfilter", { "data": 50 })
         ne_(newpid, pid)
         time.sleep(0.5)
         start = time.time()
@@ -156,7 +156,7 @@ class TestClient(object):
         eq_(client.get("/process/list"), [])
 
         # Try to remove a running filter that ignored SIGTERM
-        pid = client.post("/run/dummy", { "data": 0 })
+        pid = client.post("/run/testfilter", { "data": 0 })
         start = time.time()
         status = client.post("/process/remove", { "pid": pid })
         elapsed = time.time() - start
@@ -165,6 +165,7 @@ class TestClient(object):
         eq_(status["alive"], False)
         ne_(status["exitcode"], 0)
 
+    @unittest.skip("trainola moving to nilmtools")
     def test_client_05_trainola_simple(self):
         client = HTTPClient(baseurl = testurl, post_json = True)
         pid = client.post("/run/trainola", { "data": {} })
@@ -172,7 +173,7 @@ class TestClient(object):
         ne_(status["exitcode"], 0)
         status = client.post("/process/remove", { "pid": pid })
 
-    @unittest.skip("needs a running nilmdb")
+    @unittest.skip("needs a running nilmdb; trainola moved to nilmtools")
     def test_client_06_trainola(self):
         client = HTTPClient(baseurl = testurl, post_json = True)
 
@@ -306,6 +307,7 @@ class TestClient(object):
         code=textwrap.dedent("""
         import sys
         print sys.argv[1].encode('ascii'), sys.argv[2]
+        sys.exit(0)  # also test raising SystemExit
         """)
         status = do(code, ["hello", 123], False)
         eq_(status["log"], "hello 123\n")