Add pre commit configuration (original, reworking)

.flake8

@@ -0,0 +1,8 @@
+[flake8]
+# Exceptions below are ignored because their solution was not clear to the author of the PR,
+# but they should be solved and taken out from the ignore.
+ignore = E203, E501, W503, B950, F821, B007, E402, E722, F401, F811, B001, B008, C901, E731, E231, B009, B303, E731, B903, B011
+max-line-length = 100
+max-complexity = 18
+select = A,B,C,E,F,W,T4,B9
+exclude = .git,__pycache__,misc

.gitlab-ci.yml

@@ -0,0 +1,30 @@
+stages:
+- test
+
+linting:
+  stage: test
+  image: python:3.5-slim
+  before_script:
+  - apt-get update
+  - apt-get install -y git
+  - pip3 install -U pre-commit==1.21.0
+  script:
+  - pre-commit run --all-files
+
+test:
+  stage: test
+  image: python:3.5
+  variables:
+    PYTHONPATH: $PWD:$PYTHONPATH
+  script:
+  - ./setup.py test
+
+security:
+  stage: test
+  image: python:3.5-slim
+  allow_failure: true
+  before_script:
+  - pip install .
+  - pip install safety
+  script:
+  - safety check

.pre-commit-config.yaml

@@ -0,0 +1,24 @@
+repos:
+  - repo: https://github.com/ambv/black
+    rev: 19.10b0
+    hooks:
+      - id: black
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.4.0
+    hooks:
+      - id: flake8
+        exclude: misc
+        additional_dependencies: [
+          'flake8==3.7.9',
+          'flake8-builtins==1.4.2',
+          'flake8-bugbear==20.1.2',
+        ]
+  - repo: https://github.com/pycqa/bandit
+    rev: 1.6.2
+    hooks:
+      - id: bandit
+        args: [-lll]
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v0.761
+    hooks:
+      - id: mypy

.travis.yml

@@ -2,16 +2,34 @@ language: python
 python:
   - "3.8"
 
-# command to install dependencies
+install: "pip install Cython && pip install . pre-commit"
+
+jobs:
+  include:
+
+    - python: 3.6
+      stage: test
+
+    - python: 3.7
+      stage: test
+
+    - python: 3.8
+      stage: test
 
-install: "pip install ."
 # command to run tests
 services:
   - xvfb
 before_script: # configure a headless display for testing plot generation
 - "export DISPLAY=:99.0"
 
-script: nosetests .
+script:
+  - pre-commit run --all-files
+  - nosetests .
+
+cache:
+  directories:
+    - $HOME/.cache/pre-commit
+    - $HOME/.cache/pip
 
 notifications:
   email:

examples/example_export.py

@@ -10,24 +10,21 @@
 
 
 # get elementary bus events (connections) taking place within a given time interval:
-all_events = networks.temporal_network(g,
-                                       start_time_ut=start_ut,
-                                       end_time_ut=end_ut
-                                       )
+all_events = networks.temporal_network(g, start_time_ut=start_ut, end_time_ut=end_ut)
 print("Number of elementary PT events during rush hour in Kuopio: ", len(all_events))
 
 # get  elementary bus events (connections) taking place within a given time interval:
-tram_events = networks.temporal_network(g,
-                                        start_time_ut=start_ut,
-                                        end_time_ut=end_ut,
-                                        route_type=route_types.TRAM
-                                        )
-assert(len(tram_events) == 0) # there should be no trams in our example city (Kuopio, Finland)
+tram_events = networks.temporal_network(
+    g, start_time_ut=start_ut, end_time_ut=end_ut, route_type=route_types.TRAM
+)
+assert len(tram_events) == 0  # there should be no trams in our example city (Kuopio, Finland)
 
 # construct a networkx graph
 print("\nConstructing a combined stop_to_stop_network")
 
-graph = networks.combined_stop_to_stop_transit_network(g, start_time_ut=start_ut, end_time_ut=end_ut)
+graph = networks.combined_stop_to_stop_transit_network(
+    g, start_time_ut=start_ut, end_time_ut=end_ut
+)
 print("Number of edges: ", len(graph.edges()))
 print("Number of nodes: ", len(graph.nodes()))
 print("Example edge: ", list(graph.edges(data=True))[0])
@@ -37,4 +34,3 @@
 #################################################
 # See also other functions in gtfspy.networks ! #
 #################################################
-

examples/example_filter.py

@@ -16,16 +16,24 @@
 # filter by time and 3 kilometers from the city center
 week_start = G.get_weekly_extract_start_date()
 week_end = week_start + datetime.timedelta(days=7)
-fe = FilterExtract(G, filtered_database_path, start_date=week_start, end_date=week_end,
-                   buffer_lat=62.8930796, buffer_lon=27.6671316, buffer_distance_km=3)
+fe = FilterExtract(
+    G,
+    filtered_database_path,
+    start_date=week_start,
+    end_date=week_end,
+    buffer_lat=62.8930796,
+    buffer_lon=27.6671316,
+    buffer_distance_km=3,
+)
 
 fe.create_filtered_copy()
-assert (os.path.exists(filtered_database_path))
+assert os.path.exists(filtered_database_path)
 
 G = GTFS(filtered_database_path)
 
 # visualize the routes of the filtered database
 from gtfspy import mapviz
 from matplotlib import pyplot as plt
+
 mapviz.plot_route_network_from_gtfs(G)
-plt.show()
+plt.show()

examples/example_import.py

@@ -7,25 +7,31 @@
 
 def load_or_import_example_gtfs(verbose=False):
     imported_database_path = "test_db_kuopio.sqlite"
-    if not os.path.exists(imported_database_path):  # reimport only if the imported database does not already exist
+    if not os.path.exists(
+        imported_database_path
+    ):  # reimport only if the imported database does not already exist
         print("Importing gtfs zip file")
-        import_gtfs.import_gtfs(["data/gtfs_kuopio_finland.zip"],  # input: list of GTFS zip files (or directories)
-                                imported_database_path,  # output: where to create the new sqlite3 database
-                                print_progress=verbose,  # whether to print progress when importing data
-                                location_name="Kuopio")
+        import_gtfs.import_gtfs(
+            ["data/gtfs_kuopio_finland.zip"],  # input: list of GTFS zip files (or directories)
+            imported_database_path,  # output: where to create the new sqlite3 database
+            print_progress=verbose,  # whether to print progress when importing data
+            location_name="Kuopio",
+        )
 
         # Not this is an optional step, which is not necessary for many things.
         print("Computing walking paths using OSM")
         G = gtfs.GTFS(imported_database_path)
-        G.meta['download_date'] = "2017-03-15"
+        G.meta["download_date"] = "2017-03-15"
 
         osm_path = "data/kuopio_extract_mapzen_2017_03_15.osm.pbf"
 
         # when using with the Kuopio test data set,
         # this should raise a warning due to no nearby OSM nodes for one of the stops.
         osm_transfers.add_walk_distances_to_db_python(imported_database_path, osm_path)
 
-        print("Note: for large cities we have also a faster option for computing footpaths that uses Java.)")
+        print(
+            "Note: for large cities we have also a faster option for computing footpaths that uses Java.)"
+        )
         dir_path = os.path.dirname(os.path.realpath(__file__))
         java_path = os.path.join(dir_path, "../java_routing/")
         print("Please see the contents of " + java_path + " for more details.")
@@ -35,7 +41,10 @@ def load_or_import_example_gtfs(verbose=False):
 
     if verbose:
         print("Location name:" + G.get_location_name())  # should print Kuopio
-        print("Time span of the data in unixtime: " + str(G.get_approximate_schedule_time_span_in_ut()))
+        print(
+            "Time span of the data in unixtime: "
+            + str(G.get_approximate_schedule_time_span_in_ut())
+        )
         # prints the time span in unix time
     return G
 

examples/example_map_visualization.py

@@ -14,4 +14,4 @@
 
 # ax_thumbnail.figure.savefig("test_thumbnail.jpg")
 
-plt.show()
+plt.show()

examples/example_plot_trip_counts.py

@@ -1,20 +1,20 @@
-import functools
 import os
 
 from example_import import load_or_import_example_gtfs
 from matplotlib import pyplot as plt
+
 from gtfspy.gtfs import GTFS
 
 G = load_or_import_example_gtfs()
 
 daily_trip_counts = G.get_trip_counts_per_day()
 f, ax = plt.subplots()
 
-datetimes = [date.to_pydatetime() for date in daily_trip_counts['date']]
-trip_counts = daily_trip_counts['trip_counts']
+datetimes = [date.to_pydatetime() for date in daily_trip_counts["date"]]
+trip_counts = daily_trip_counts["trip_counts"]
 
 ax.bar(datetimes, trip_counts)
-ax.axvline(G.meta['download_date'], color="red")
+ax.axvline(G.meta["download_date"], color="red")
 threshold = 0.96
 ax.axhline(trip_counts.max() * threshold, color="red")
 ax.axvline(G.get_weekly_extract_start_date(weekdays_at_least_of_max=threshold), color="yellow")
@@ -24,18 +24,17 @@
     G = GTFS(weekly_db_path)
     f, ax = plt.subplots()
     daily_trip_counts = G.get_trip_counts_per_day()
-    datetimes = [date.to_pydatetime() for date in daily_trip_counts['date']]
-    trip_counts = daily_trip_counts['trip_counts']
+    datetimes = [date.to_pydatetime() for date in daily_trip_counts["date"]]
+    trip_counts = daily_trip_counts["trip_counts"]
     ax.bar(datetimes, trip_counts)
 
-    events = list(G.generate_routable_transit_events(0, G.get_approximate_schedule_time_span_in_ut()[0]))
-    min_ut = float('inf')
+    events = list(
+        G.generate_routable_transit_events(0, G.get_approximate_schedule_time_span_in_ut()[0])
+    )
+    min_ut = float("inf")
     for e in events:
         min_ut = min(e.dep_time_ut, min_ut)
 
     print(G.get_approximate_schedule_time_span_in_ut())
 
 plt.show()
-
-
-

examples/example_temporal_distance_profile.py

@@ -3,7 +3,9 @@
 
 import example_import
 from gtfspy.routing.helpers import get_transit_connections, get_walk_network
-from gtfspy.routing.multi_objective_pseudo_connection_scan_profiler import MultiObjectivePseudoCSAProfiler
+from gtfspy.routing.multi_objective_pseudo_connection_scan_profiler import (
+    MultiObjectivePseudoCSAProfiler,
+)
 from gtfspy.routing.node_profile_analyzer_time_and_veh_legs import NodeProfileAnalyzerTimeAndVehLegs
 
 G = example_import.load_or_import_example_gtfs()
@@ -14,12 +16,12 @@
 to_stop_I = None
 stop_dict = G.stops().to_dict("index")
 for stop_I, data in stop_dict.items():
-    if data['name'] == from_stop_name:
+    if data["name"] == from_stop_name:
         from_stop_I = stop_I
-    if data['name'] == to_stop_name:
+    if data["name"] == to_stop_name:
         to_stop_I = stop_I
-assert (from_stop_I is not None)
-assert (to_stop_I is not None)
+assert from_stop_I is not None
+assert to_stop_I is not None
 
 # The start and end times between which PT operations (and footpaths) are scanned:
 ANALYSIS_START_TIME_UT = G.get_suitable_date_for_daily_extract(ut=True) + 10 * 3600
@@ -40,17 +42,18 @@
 # gtfspy.osm_transfers.add_walk_distances_to_db_python(..., cutoff_distance_m=2000).
 
 
-
-mpCSA = MultiObjectivePseudoCSAProfiler(connections,
-                                        targets=[to_stop_I],
-                                        start_time_ut=CONNECTION_SCAN_START_TIME_UT,
-                                        end_time_ut=CONNECTION_SCAN_END_TIME_UT,
-                                        transfer_margin=120,  # seconds
-                                        walk_network=walk_network,
-                                        walk_speed=1.5,  # meters per second
-                                        verbose=True,
-                                        track_vehicle_legs=True,
-                                        track_time=True)
+mpCSA = MultiObjectivePseudoCSAProfiler(
+    connections,
+    targets=[to_stop_I],
+    start_time_ut=CONNECTION_SCAN_START_TIME_UT,
+    end_time_ut=CONNECTION_SCAN_END_TIME_UT,
+    transfer_margin=120,  # seconds
+    walk_network=walk_network,
+    walk_speed=1.5,  # meters per second
+    verbose=True,
+    track_vehicle_legs=True,
+    track_time=True,
+)
 
 mpCSA.run()
 profiles = mpCSA.stop_profiles
@@ -60,19 +63,21 @@
 direct_walk_duration = departure_stop_profile.get_walk_to_target_duration()
 # This equals inf, if walking distance between the departure_stop (from_stop_I) and target_stop (to_stop_I)
 # is longer than MAX_WALK_LENGTH
-analyzer = NodeProfileAnalyzerTimeAndVehLegs(departure_stop_profile.get_final_optimal_labels(),
-                                             direct_walk_duration,
-                                             ANALYSIS_START_TIME_UT,
-                                             ANALYSIS_END_TIME_UT)
+analyzer = NodeProfileAnalyzerTimeAndVehLegs(
+    departure_stop_profile.get_final_optimal_labels(),
+    direct_walk_duration,
+    ANALYSIS_START_TIME_UT,
+    ANALYSIS_END_TIME_UT,
+)
 
 # Print out results:
 stop_dict = G.stops().to_dict("index")
 print("Origin: ", stop_dict[from_stop_I])
 print("Destination: ", stop_dict[to_stop_I])
-print("Minimum temporal distance: ", analyzer.min_temporal_distance() / 60., " minutes")
-print("Mean temporal distance: ", analyzer.mean_temporal_distance() / 60., " minutes")
-print("Medan temporal distance: ", analyzer.median_temporal_distance() / 60., " minutes")
-print("Maximum temporal distance: ", analyzer.max_temporal_distance() / 60., " minutes")
+print("Minimum temporal distance: ", analyzer.min_temporal_distance() / 60.0, " minutes")
+print("Mean temporal distance: ", analyzer.mean_temporal_distance() / 60.0, " minutes")
+print("Medan temporal distance: ", analyzer.median_temporal_distance() / 60.0, " minutes")
+print("Maximum temporal distance: ", analyzer.max_temporal_distance() / 60.0, " minutes")
 # Note that the mean and max temporal distances have the value of `direct_walk_duration`,
 # if there are no journey alternatives departing after (or at the same time as) `ANALYSIS_END_TIME_UT`.
 # Thus, if you obtain a float('inf') value for some of the temporal distance measures, it could probably be
@@ -85,8 +90,9 @@
 
 # use tex in plotting
 rc("text", usetex=True)
-fig1 = analyzer.plot_new_transfer_temporal_distance_profile(timezone=timezone_pytz,
-                                                            format_string="%H:%M")
+fig1 = analyzer.plot_new_transfer_temporal_distance_profile(
+    timezone=timezone_pytz, format_string="%H:%M"
+)
 fig2 = analyzer.plot_temporal_distance_pdf_horizontal(use_minutes=True)
 print("Showing...")
 plt.show()