Running pre-commit, reformatting all files

.gitignore

@@ -2,4 +2,4 @@
 *.pyc
 __pycache__/
 ptenv/
-.cache/
+.cache/

.pre-commit-config.yaml

@@ -1,8 +1,8 @@
 # Docs @ http://pre-commit.com/
 
 repos:
-- repo: git://github.com/pre-commit/pre-commit-hooks
-  sha: v0.7.1  # Use the ref you want to point at
+- repo: https://github.com/kuanb/pre-commit-hooks.git
+  sha: 61d0735ca8b0ce5ab8df867bc974798ba8316ee5
   hooks:
   - id: autopep8-wrapper
   - id: check-added-large-files
@@ -24,7 +24,7 @@ repos:
   - id: flake8
   - id: trailing-whitespace
 
-- repo: https://github.com/CalthorpeAnalytics/pre-commit-python-sorter.git
+- repo: https://github.com/kuanb/pre-commit-python-sorter.git
   sha: 1.0.8
   hooks:
-  - id: python-import-sorter
+  - id: python-import-sorter

.travis.yml

@@ -21,4 +21,4 @@ install:
   - pip install -r requirements.txt
 
 script:
-  - PYTHONPATH=. py.test
+  - PYTHONPATH=. py.test

Makefile

@@ -1,2 +1,2 @@
 test:
-	PYTHONPATH=. py.test
+	PYTHONPATH=. py.test

README.rst

@@ -29,7 +29,7 @@ Usage
     # use to summarize impedance
     start = 7*60*60  # 7:00 AM
     end = 10*60*60  # 10:00 AM
-    
+
     # Converts feed subset into a directed
     # network multigraph
     G = pt.load_feed_as_graph(feed, start, end)
@@ -42,4 +42,3 @@ Note: Still a WIP. Current version on PyPI not good...
 .. code:: console
 
     pip install peartree
-

peartree/__version__.py

@@ -1 +1 @@
-__version__ = '0.1.2'
+__version__ = '0.1.2'

peartree/graph.py

@@ -1,20 +1,14 @@
 from typing import Dict
 
-import networkx as nx
-import numpy as np
-import pandas as pd
-import random
-
-from fiona import crs
 import networkx as nx
 import pandas as pd
 import partridge as ptg
+from fiona import crs
 
 from .settings import WGS84
 from .summarizer import (generate_edge_and_wait_values,
                          generate_summary_edge_costs,
                          generate_summary_wait_times)
-from .utilities import log
 
 
 def generate_empty_md_graph(name: str,
@@ -35,10 +29,10 @@ def generate_summary_graph_elements(feed: ptg.gtfs.feed,
      all_wait_times) = generate_edge_and_wait_values(feed,
                                                      target_time_start,
                                                      target_time_end)
-    
+
     summary_edge_costs = generate_summary_edge_costs(all_edge_costs)
     wait_times_by_stop = generate_summary_wait_times(all_wait_times)
-    
+
     return (summary_edge_costs, wait_times_by_stop)
 
 
@@ -53,7 +47,7 @@ def populate_graph(G: nx.MultiDiGraph,
 
         # TODO: Join tables before hand to make
         #       this part go faster
-        id_mask = (feed.stops.stop_id==sid)
+        id_mask = (feed.stops.stop_id == sid)
         stop_data = feed.stops[id_mask].head(1).T.squeeze()
 
         G.add_node(full_sid,

peartree/paths.py

@@ -1,11 +1,10 @@
-import string
 import random
+import string
 
 import networkx as nx
 import partridge as ptg
 
-from .graph import (generate_empty_md_graph,
-                    generate_summary_graph_elements,
+from .graph import (generate_empty_md_graph, generate_summary_graph_elements,
                     populate_graph)
 from .utilities import log
 
@@ -26,14 +25,15 @@ def _generate_random_name(N: int=5):
 
 def get_representative_feed(file_loc: str,
                             day_type: str='busiest'):
-    
+
     service_ids_by_date = ptg.read_service_ids_by_date(file_loc)
     trip_counts_by_date = ptg.read_trip_counts_by_date(file_loc)
 
     # Make sure we have some valid values returned in trips
     if not len(trip_counts_by_date.items()):
         # Otherwise, error out
-        raise InvalidGTFS('No valid trip counts by date were identified in GTFS.')
+        raise InvalidGTFS('No valid trip counts by date '
+                          'were identified in GTFS.')
 
     # At this point, different methods can be implemented to help select how
     # to pick which date/schedule id to use
@@ -43,14 +43,14 @@ def get_representative_feed(file_loc: str,
     else:
         raise NotImplementedError('Unsupported day type string supplied.')
 
-    log('Selected_date: {}'.format(selected_date))
-    log('Number of trips on that date: {}'.format(trip_count))
+    log(f'Selected_date: {selected_date}')
+    log(f'Number of trips on that date: {trip_count}')
 
     all_service_ids = '\n\t'.join(service_ids_by_date[selected_date])
-    log('\nAll related service IDs: \n\t{}'.format(all_service_ids))
+    log(f'\nAll related service IDs: \n\t{all_service_ids}')
 
-    selected_service_ids = service_ids_by_date[selected_date]
-    feed_query = {'trips.txt': {'service_id': selected_service_ids}}
+    sub = service_ids_by_date[selected_date]
+    feed_query = {'trips.txt': {'service_id': sub}}
     return ptg.feed(file_loc, view=feed_query)
 
 
@@ -68,10 +68,13 @@ def load_feed_as_graph(feed: ptg.gtfs.feed,
         raise InvalidTimeBracket('Invalid start or end target times provided.')
 
     if end_time < start_time:
-        raise InvalidTimeBracket('Invalid ordering: Start time is greater than end time.')
+        raise InvalidTimeBracket('Invalid ordering: Start time '
+                                 'is greater than end time.')
 
     (summary_edge_costs,
-     wait_times_by_stop) = generate_summary_graph_elements(feed, start_time, end_time)
+     wait_times_by_stop) = generate_summary_graph_elements(feed,
+                                                           start_time,
+                                                           end_time)
 
     # This is a flag used to check if we need to run any additional steps
     # after the feed is returned to ensure that new nodes and edge can connect
@@ -80,11 +83,15 @@ def load_feed_as_graph(feed: ptg.gtfs.feed,
 
     # G is either a new MultiDiGraph or one pass from before
     if existing_graph_supplied:
-        # TODO: If passed from before we should run some checks to ensure it is valid
-        #       as well as set a flag to create join points with other feeds so that
-        #       they can be linked when the next is added.
+        # TODO: If passed from before we should run some checks to ensure
+        #       it is valid as well as set a flag to create join points with
+        #       other feeds so that they can be linked when the next is added.
         G = existing_graph
     else:
         G = generate_empty_md_graph(name)
-
-    return populate_graph(G, name, feed, wait_times_by_stop, summary_edge_costs)
+
+    return populate_graph(G,
+                          name,
+                          feed,
+                          wait_times_by_stop,
+                          summary_edge_costs)

peartree/plot.py

@@ -1,6 +1,11 @@
+import matplotlib
 import networkx as nx
 import osmnx as ox
 
+# Force matplotlib to not use any Xwindows backend.
+matplotlib.use('Agg')
+
+
 def generate_plot(G: nx.MultiDiGraph):
     # TODO: Build out custom plotting configurations but,
     #       in the meantime, use OSMnx's plotting configurations
@@ -16,4 +21,4 @@ def generate_plot(G: nx.MultiDiGraph):
                             edge_color='#e2dede',
                             edge_alpha=0.25,
                             bgcolor='black')
-    return (fig, ax)
+    return (fig, ax)

peartree/settings.py

@@ -6,7 +6,7 @@
 log_console = False
 log_level = lg.INFO
 
-# Set some globals here that 
+# Set some globals here that
 # are used as reference throughout lib
 WGS84 = 4326  # degree measurement
-WEB_MERCATOR = 3857  # meter-based
+WEB_MERCATOR = 3857  # meter-based