minimal working version refs #12233

tools/output/fcdDiff.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python
+# Eclipse SUMO, Simulation of Urban MObility; see https://eclipse.org/sumo
+# Copyright (C) 2012-2022 German Aerospace Center (DLR) and others.
+# This program and the accompanying materials are made available under the
+# terms of the Eclipse Public License 2.0 which is available at
+# https://www.eclipse.org/legal/epl-2.0/
+# This Source Code may also be made available under the following Secondary
+# Licenses when the conditions for such availability set forth in the Eclipse
+# Public License 2.0 are satisfied: GNU General Public License, version 2
+# or later which is available at
+# https://www.gnu.org/licenses/old-licenses/gpl-2.0-standalone.html
+# SPDX-License-Identifier: EPL-2.0 OR GPL-2.0-or-later
+
+# @file    scheduleStats.py
+# @author  Jakob Erdmann
+# @date    2022-12-06
+
+"""
+Compare two fcd output files with regard to spatial difference
+"""
+
+from __future__ import absolute_import
+from __future__ import print_function
+
+import os
+import sys
+from math import sqrt
+
+import pandas as pd
+
+if 'SUMO_HOME' in os.environ:
+    sys.path.append(os.path.join(os.environ['SUMO_HOME'], 'tools'))
+import sumolib  # noqa
+from sumolib.miscutils import parseTime, humanReadableTime  # noqa
+from sumolib.statistics import Statistics  # noqa
+from sumolib.xml import parse_fast_nested  # noqa
+
+pd.options.display.width = 0  # auto-detect terminal width
+
+GROUPSTATS = {
+    'mean': lambda s: s.avg(),
+    'median': lambda s: s.median(),
+    'min': lambda s: s.min,
+    'max': lambda s: s.max,
+}
+
+
+def get_options(args=None):
+    parser = sumolib.options.ArgumentParser(description="Compare route-file stop timing with stop-output")
+    parser.add_argument("old", help="the first fcd file")
+    parser.add_argument("new", help="the second fcd file")
+    parser.add_argument("-o", "--xml-output", dest="output",
+                        help="xml output file")
+    parser.add_option("--csv-output", dest="csv_output", help="write plot as csv", metavar="FILE")
+    parser.add_option("--filter-ids", dest="filterIDs", help="only plot data points from the given list of ids")
+    parser.add_argument("-H", "--human-readable-time", dest="hrTime", action="store_true", default=False,
+                        help="Write time values as hour:minute:second or day:hour:minute:second rathern than seconds")
+    parser.add_argument("-t", "--tripid", dest="tripId", action="store_true", default=False,
+                        help="use attr tripId for matching instead of vehicle id")
+    parser.add_argument("-i", "--histogram", dest="histogram", type=float,
+                        help="histogram bin size")
+    parser.add_argument("-v", "--verbose", action="store_true",
+                        default=False, help="tell me what you are doing")
+
+    options = parser.parse_args(args=args)
+    if options.old is None or options.new is None:
+        parser.print_help()
+        sys.exit()
+
+    return options
+
+
+ATTR_CONVERSIONS = {
+    'time': parseTime,
+}
+
+
+def key(row):
+    return "%s_%s" % (row[0], humanReadableTime(row['t']))
+
+
+def getDataFrame(fname, attrs, columns, tripId):
+    data = []
+    if tripId:
+        orderedAttrs = attrs[2:] + [attrs[0]]
+    else:
+        orderedAttrs = [attrs[0]] + attrs[2:]
+
+    for ts, v in parse_fast_nested(fname, 'timestep', ['time'], 'vehicle', orderedAttrs):
+            data.append([getattr(v, attrs[0]), parseTime(ts.time)] + [float(getattr(v, a)) for a in attrs[2:]])
+    return pd.DataFrame.from_records(data, columns=columns)
+
+
+def main(options):
+    nan = float("nan")
+    idAttr = 'tripId' if options.tripId else 'id'
+    attrs = [
+        idAttr,
+        't', # time
+        'x',
+        'y',
+    ]
+    cols2 = [
+        idAttr,
+        't', # time
+        'x2',
+        'y2',
+    ]
+
+    df1 = getDataFrame(options.old, attrs, attrs, options.tripId)
+    print("read", options.old)
+    df2 = getDataFrame(options.new, attrs, cols2, options.tripId)
+    print("new", options.old)
+
+    # merge on common columns id, time
+    df = pd.merge(df1, df2, on=attrs[:2], how="inner")
+    # outer merge to count missing entries
+    dfOuter = pd.merge(df1, df2, on=attrs[:2], how="outer")
+
+    if options.verbose:
+        print("Found %s matches" % len(df))
+        print("Found %s records" % len(dfOuter))
+        print("missing in old: %s" % dfOuter['x'].isna().sum())
+        print("missing in new: %s" % dfOuter['x2'].isna().sum())
+
+    useHist = options.histogram is not None
+    fun = (lambda r, s: s.add(sqrt((r['x'] - r['x2']) ** 2 + (r['y'] - r['y2']) ** 2) , key(r)))
+    s = Statistics("euclidian_error", histogram=useHist, scale=options.histogram)
+    df.apply(fun, axis=1, args=(s,))
+
+    print(s)
+    #print(dfOuter)
+
+if __name__ == "__main__":
+    main(get_options())