diff --git a/.github/workflows/cta_data_downloads.yml b/.github/workflows/cta_data_downloads.yml
new file mode 100644
index 0000000..d98520b
--- /dev/null
+++ b/.github/workflows/cta_data_downloads.yml
@@ -0,0 +1,68 @@
+name: Automate CTA schedule and realtime downloads
+
+on:
+
+ schedule:
+ # Run every day at 12:30pm CST which is 5:30pm UTC
+ - cron: 30 17 * * *
+
+env:
+ PYTHON_VERSION: 3.10.6
+ AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
+ AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+
+jobs:
+ download-cta-schedule-data:
+ runs-on: ubuntu-latest
+
+ steps:
+ - uses: actions/checkout@v3
+
+ - uses: actions/setup-python@v4
+ with:
+ python-version: ${{ env.PYTHON_VERSION }}
+
+ - name: Download and save CTA schedule data
+
+ run: |
+ pip install -r requirements.txt
+ python -c 'from scrape_data.cta_data_downloads import save_cta_zip; \
+ save_cta_zip()' \
+ $AWS_ACCESS_KEY_ID $AWS_SECRET_ACCESS_KEY
+
+
+ save-schedule-daily-summary:
+ runs-on: ubuntu-latest
+
+ steps:
+ - uses: actions/checkout@v3
+
+ - uses: actions/setup-python@v4
+ with:
+ python-version: ${{ env.PYTHON_VERSION }}
+
+ - name: 'Save schedule summaries'
+ run: |
+ pip install -r requirements.txt
+ python -c 'from scrape_data.cta_data_downloads import save_sched_daily_summary; \
+ save_sched_daily_summary()' $AWS_ACCESS_KEY_ID $AWS_SECRET_ACCESS_KEY
+
+
+ save-realtime-daily-summary:
+ runs-on: ubuntu-latest
+
+ steps:
+ - uses: actions/checkout@v3
+
+ - uses: actions/setup-python@v4
+ with:
+ python-version: ${{ env.PYTHON_VERSION }}
+
+ - name: 'Save realtime summaries'
+
+ run: |
+ pip install -r requirements.txt
+
+ python -c 'from scrape_data.cta_data_downloads import save_realtime_daily_summary; \
+ save_realtime_daily_summary()' $AWS_ACCESS_KEY_ID $AWS_SECRET_ACCESS_KEY
+
diff --git a/data_analysis/static_gtfs_analysis.py b/data_analysis/static_gtfs_analysis.py
index 119366c..65bba88 100644
--- a/data_analysis/static_gtfs_analysis.py
+++ b/data_analysis/static_gtfs_analysis.py
@@ -13,7 +13,7 @@
import os
from pathlib import Path
from dataclasses import dataclass
-from typing import List
+from typing import Tuple
import logging
import calendar
@@ -38,7 +38,6 @@
datefmt='%m/%d/%Y %I:%M:%S %p'
)
-
@dataclass
class GTFSFeed:
"""Class for storing GTFSFeed data.
@@ -53,14 +52,14 @@ class GTFSFeed:
@classmethod
def extract_data(cls, gtfs_zipfile: zipfile.ZipFile,
- version_id: str = None) -> GTFSFeed:
+ version_id: str = None, cta_download: bool = True) -> GTFSFeed:
"""Load each text file in zipfile into a DataFrame
Args:
gtfs_zipfile (zipfile.ZipFile): Zipfile downloaded from
- CTA transit feeds e.g.
+ transitfeeds.com or transitchicago.com e.g.
https://transitfeeds.com/p/chicago-transit-authority/
- 165/20220718/download"
+ 165/20220718/download or https://www.transitchicago.com/downloads/sch_data/
version_id (str, optional): The schedule version in use.
Defaults to None.
@@ -68,9 +67,17 @@ def extract_data(cls, gtfs_zipfile: zipfile.ZipFile,
GTFSFeed: A GTFSFeed object containing multiple DataFrames
accessible by name.
"""
- if version_id is None:
- version_id = VERSION_ID
- logging.info(f"Extracting data from CTA zipfile version {version_id}")
+ if cta_download:
+ if version_id is not None:
+ raise ValueError("version_id is not used for downloads directly from CTA")
+ else:
+ logging.info(f"Extracting data from transitchicago.com zipfile")
+
+ else:
+ if version_id is None:
+ version_id = VERSION_ID
+ logging.info(f"Extracting data from transitfeeds.com zipfile version {version_id}")
+
data_dict = {}
pbar = tqdm(cls.__annotations__.keys())
for txt_file in pbar:
@@ -140,14 +147,16 @@ def format_dates_hours(data: GTFSFeed) -> GTFSFeed:
def make_trip_summary(
data: GTFSFeed,
- feed_start_date: pendulum.datetime,
- feed_end_date: pendulum.datetime) -> pd.DataFrame:
+ feed_start_date: pendulum.datetime = None,
+ feed_end_date: pendulum.datetime = None) -> pd.DataFrame:
"""Create a summary of trips with one row per date
Args:
data (GTFSFeed): GTFS data from CTA
- feed_start_date (datetime): Date from which this feed is valid (inclusive)
- feed_end_date (datetime): Date until which this feed is valid (inclusive)
+ feed_start_date (datetime): Date from which this feed is valid (inclusive).
+ Defaults to None
+ feed_end_date (datetime): Date until which this feed is valid (inclusive).
+ Defaults to None
Returns:
pd.DataFrame: A DataFrame with each trip that occurred per row.
@@ -161,7 +170,7 @@ def make_trip_summary(
),
columns=["raw_date"],
)
-
+
# cross join calendar index with actual calendar to get all combos of
# possible dates & services
calendar_cross = calendar_date_range.merge(data.calendar, how="cross")
@@ -244,9 +253,10 @@ def make_trip_summary(
trip_stop_hours, how="left", on="trip_id")
# filter to only the rows for the period where this specific feed version was in effect
- trip_summary = trip_summary.loc[
- (trip_summary['raw_date'] >= feed_start_date)
- & (trip_summary['raw_date'] <= feed_end_date), :]
+ if feed_start_date is not None and feed_end_date is not None:
+ trip_summary = trip_summary.loc[
+ (trip_summary['raw_date'] >= feed_start_date)
+ & (trip_summary['raw_date'] <= feed_end_date), :]
return trip_summary
@@ -321,6 +331,23 @@ def make_linestring_of_points(
return shapely.geometry.LineString(list(sorted_df["pt"]))
+def download_cta_zip() -> Tuple[zipfile.ZipFile, BytesIO]:
+ """Download CTA schedule data from transitchicago.com
+
+ Returns:
+ zipfile.ZipFile: A zipfile of the latest GTFS schedule data from transitchicago.com
+ """
+ logger.info('Downloading CTA data')
+ zip_bytes_io = BytesIO(
+ requests.get("https://www.transitchicago.com/downloads/sch_data/google_transit.zip"
+ ).content
+ )
+ CTA_GTFS = zipfile.ZipFile(zip_bytes_io)
+ logging.info('Download complete')
+ return CTA_GTFS, zip_bytes_io
+
+
+
def download_zip(version_id: str) -> zipfile.ZipFile:
"""Download a version schedule from transitfeeds.com
@@ -344,17 +371,22 @@ def download_zip(version_id: str) -> zipfile.ZipFile:
return CTA_GTFS
-def download_extract_format(version_id: str) -> GTFSFeed:
+def download_extract_format(version_id: str = None) -> GTFSFeed:
"""Download a zipfile of GTFS data for a given version_id,
extract data, and format date column.
Args:
- version_id (str): The version of the GTFS schedule data to download
+ version_id (str): The version of the GTFS schedule data to download. Defaults to None
+ If version_id is None, data will be downloaded from the CTA directly (transitchicag.com)
+ instead of transitfeeds.com
Returns:
GTFSFeed: A GTFSFeed object with formated dates
"""
- CTA_GTFS = download_zip(version_id)
+ if version_id is None:
+ CTA_GTFS, _ = download_cta_zip()
+ else:
+ CTA_GTFS = download_zip(version_id)
data = GTFSFeed.extract_data(CTA_GTFS, version_id=version_id)
data = format_dates_hours(data)
return data
diff --git a/requirements.txt b/requirements.txt
index a709e5d..9e3218d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,7 +9,7 @@ python-dotenv==0.20.0
seaborn==0.12.0
PyQt5==5.15.7
folium==0.12.1.post1
-mapclassify==2.4.2+55.g0155c6e
+mapclassify>=2.4.2+55.g0155c6e
plotly==5.11.0
kaleido==0.2.1
pre-commit==2.20.0
diff --git a/scrape_data/cta_data_downloads.py b/scrape_data/cta_data_downloads.py
new file mode 100644
index 0000000..75f10d3
--- /dev/null
+++ b/scrape_data/cta_data_downloads.py
@@ -0,0 +1,115 @@
+import boto3
+import sys
+import data_analysis.static_gtfs_analysis as sga
+import data_analysis.compare_scheduled_and_rt as csrt
+import pendulum
+from io import StringIO
+import pandas as pd
+
+
+ACCESS_KEY = sys.argv[1]
+SECRET_KEY = sys.argv[2]
+
+client = boto3.client(
+ 's3',
+ aws_access_key_id=ACCESS_KEY,
+ aws_secret_access_key=SECRET_KEY
+)
+
+s3 = boto3.resource(
+ 's3',
+ region_name='us-east-1',
+ aws_access_key_id=ACCESS_KEY,
+ aws_secret_access_key=SECRET_KEY
+)
+
+today = pendulum.now('America/Chicago').to_date_string()
+
+CTA_GTFS, zipfile_bytes_io = sga.download_cta_zip()
+
+def save_cta_zip() -> None:
+ print(f'Saving zipfile available at '
+ f'https://www.transitchicago.com/downloads/sch_data/google_transit.zip '
+ f'on {today} to public bucket')
+ filename = f'cta_schedule_zipfiles_raw/google_transit_{today}.zip'
+ zipfile_bytes_io.seek(0)
+ client.upload_fileobj(
+ zipfile_bytes_io,
+ csrt.BUCKET_PUBLIC,
+ filename
+ )
+ print(f'Confirm that {filename} exists in bucket')
+ keys('chn-ghost-buses-public', [filename])
+
+
+def save_csv_to_bucket(df: pd.DataFrame, filename: str) -> None:
+ """Save pandas DataFrame to csv in s3
+
+ Args:
+ df (pd.DataFrame): DataFrame to be saved
+ filename (str): Name of the saved filename in s3.
+ Should contain the .csv suffix.
+ """
+ csv_buffer = StringIO()
+ df.to_csv(csv_buffer)
+
+ print(f'Saving {filename} to public bucket')
+ s3.Object(
+ csrt.BUCKET_PUBLIC,
+ f'{filename}')\
+ .put(Body=csv_buffer.getvalue())
+
+
+def save_sched_daily_summary() -> None:
+ data = sga.GTFSFeed.extract_data(CTA_GTFS)
+ data = sga.format_dates_hours(data)
+ trip_summary = sga.make_trip_summary(data)
+
+ route_daily_summary = (
+ sga.summarize_date_rt(trip_summary)
+ )
+ route_daily_summary['date'] = route_daily_summary['date'].astype(str)
+ route_daily_summary_today = route_daily_summary.loc[route_daily_summary['date'] == today]
+
+ print(f'Saving cta_route_daily_summary_{today}.csv to public bucket')
+ filename = f'schedule_summaries/daily_job/cta_route_daily_summary_{today}.csv'
+ save_csv_to_bucket(
+ route_daily_summary_today,
+ filename=filename
+ )
+ print(f'Confirm that {filename} exists in bucket')
+ keys(csrt.BUCKET_PUBLIC, [filename])
+
+
+def save_realtime_daily_summary() -> None:
+ if pendulum.now("America/Chicago").hour >= 11:
+ end_date = pendulum.yesterday("America/Chicago")
+ else:
+ end_date = pendulum.now("America/Chicago").subtract(days=2)
+
+ end_date = end_date.to_date_string()
+
+ daily_data = pd.read_csv(
+ (csrt.BASE_PATH / f"bus_full_day_data_v2/{end_date}.csv")
+ .as_uri(),
+ low_memory=False
+ )
+
+ daily_data = csrt.make_daily_summary(daily_data)
+ filename = f'realtime_summaries/daily_job/bus_full_day_data_v2/{end_date}.csv'
+ save_csv_to_bucket(daily_data, filename=filename)
+
+ print(f'Confirm that {filename} exists in bucket')
+ keys(csrt.BUCKET_PUBLIC, [filename])
+
+# https://stackoverflow.com/questions/30249069/listing-contents-of-a-bucket-with-boto3
+def keys(bucket_name: str, filenames: list,
+ prefix: str='/', delimiter: str='/',
+ start_after: str='') -> None:
+ s3_paginator = client.get_paginator('list_objects_v2')
+ prefix = prefix.lstrip(delimiter)
+ start_after = (start_after or prefix) if prefix.endswith(delimiter) else start_after
+ for page in s3_paginator.paginate(Bucket=bucket_name, Prefix=prefix, StartAfter=start_after):
+ for content in page.get('Contents', ()):
+ if content['Key'] in filenames:
+ print(f"{content['Key']} exists")