Module: fetch¶
The fetch module starts at your server where you're logged in and searches a hashtag. After it gets all the toots your server knows about, then it starts looking at where they came from. For each server that it finds mentioned, it calls fetch_remote()
. Each time that it connects to a new server, it fetches every toot that server knows about the hashtag. Then it looks at the servers that are mentioned and adds any new ones to a list of servers to contact.
Public API¶
This code depends on Mastodon.py and uses it to connect to servers that are mentioned. If you know anything about the fediverse, you know that there's more than just Mastodon servers out there. There's Pleroma, Akkoma, and various other ActivityPub-compatible servers. Some are derived from Mastodon and implement the same APIs. Others don't. Some Mastodon servers offer public read APIs, others don't. So servers that allow public read of their APIs will send you the details on their toots. Servers that don't allow public read, or that don't implement a Mastodon-compatible timeline API will be quietly skipped.
Module for fetching toots for a hashtag.
check_journaldir(dir_name)
¶
Check if a directory exists and create it if it doesn't.
Parameters¶
- directory (str): The name of the directory to check/create.
Returns:¶
bool: True if the directory already existed or was created, False means we tried to create it and failed.
Source code in mastoscore/fetch.py
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create_journal_directory(base_dir, year, month, day)
¶
Create a hierarchical directory structure for journal files.
Parameters¶
- base_dir: Base directory for journal files
- year: Year as string (YYYY)
- month: Month as string (MM)
- day: Day as string (DD)
Returns¶
Full path to the created directory, or None if creation failed
Source code in mastoscore/fetch.py
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fetch(config)
¶
This is the top-level function that will download toots and store them in a JSON cache. This
function will create a tooter and login to the server named in the cred_file
.
Parameters¶
- config: A ConfigParser object from the config module
Config Parameters Used¶
- fetch:lookback: Number of days to look back in time. Toots older than that are ignored
- fetch:botusername: Name of our bot. Toots from our bot are dropped from the data set
- fetch:max: Max number of toots to pull from a server (default: 2000)
- fetch:hashtag: Hashtag to search for
- fetch:dry_run: If True, we contact our home server, but make no remote connections. If False, do it for real.
- fetch:api_base_url: Starting server for our first connection
- fetch:cred_file: Implicitly used when we create our Tooter
- mastoscore:event_year: Year of the event (YYYY)
- mastoscore:event_month: Month of the event (MM)
- mastoscore:event_day: Day of the event (DD)
Returns¶
None
Source code in mastoscore/fetch.py
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fetch_hashtag_remote(config, server)
¶
Given a uri of a toot, (like from Mastodon.status), create a Tooter for that URI. Connect and fetch the statuses. Return a few fields, but not all.
Parameters¶
- config: A ConfigParser object from the config module
- server: The api_base_url of a server to fetch from
Config Parameters Used¶
- fetch:lookback: Number of days to look back in time. Toots older than that are ignored
- fetch:botusername: Name of our bot. Toots from our bot are dropped from the data set
- fetch:max: Max number of toots to pull from a server (default: 2000)
- fetch:hashtag: Hashtag to search for
Returns¶
Dictionary of statuses in the raw JSON format from the API. Fields are not normalised or converted in any way. Since not all ActivityPub servers are exactly the same, it's not even sure which fields you get.
Source code in mastoscore/fetch.py
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toots2df(toots, api_base_url)
¶
Take in a json dict of toots from a tooter object, turn it into a pandas dataframe with a bunch of data normalized.
Parameters¶
- toots: dict. A dictionary of toots in the same format as returned by the read_timeline() API
- api_base_url: string. Expected to include protocol, like
https://server.example.com
.
Returns¶
A Pandas DataFrame that contains all the toots normalised. Normalisation includes:
- Converting date fields like created_at
to timezone-aware datetime
objects
- Converting integer fields like reblogs_count
to integers
- Adding some columns (see below)
- Deleting about 40 different columns we don't use in the analysis
Synthetic columns added:¶
- server: The server part of
api_base_url
:server.example.com
if theapi_base_url
ishttps://server.example.com
- userid: The user's name in
person@server.example.com
format. Note it does not have the leading@
because tagging people is optional. - local: Boolean that is True if the toot comes from the
api_base_url
server. False otherwise. - source: The server part of the server who owns the toot. I might be talking to
server.example.com
, but they've sent me a copy of a toot fromother.example.social
.
Source code in mastoscore/fetch.py
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write_journal(config, df, server)
¶
Take dataframe and the url it represents, and calls
pandas.DataFrame.to_json()
to write it to a corresponding json journal file. Writes it to a file in a hierarchical
directory structure: journaldir/year/month/day/journalfile-server.json
.
Parameters¶
- config: A ConfigParser object from the config module
- df: A Pandas DataFrame full of toots to write out.
- server: The api_base_url of a server to fetch from
Config Parameters Used¶
- fetch:journaldir: Base directory to write journal files into
- fetch:journalfile: Journal file template
- mastoscore:event_year: Year of the event (YYYY)
- mastoscore:event_month: Month of the event (MM)
- mastoscore:event_day: Day of the event (DD)
Returns¶
True if successful, False otherwise
Source code in mastoscore/fetch.py
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