Formerly PyAl, alp has been trimmed and slimmed to make building Alfred v2 workflows even easier. Not much was lost in the transition---and indeed, some neat things were gained---and so making the transition from PyAl to alp should be relatively easy. All in all, the module is intended to result in fewer lines of repeated code and easy accessibility for newcomers to workflow construction in Alfred and with Python.
alp's primary features include:
However, you can pick and choose from among these by deleting any of the files in the module (except for core.py and the folder core_dependencies); if something is missing, alp will silently ignore it.
To get started, simply download or clone this repository and copy the alp folder into your workflow directory, then import alp. A list of everything alp can do, with appropriate documentation, is below.
These are intended to make some basic rote tasks easier and faster to code. alp defines the following functions:
alp.bundle()Info.plist file.alp.args()unicodedata.)alp.decode(s)s.alp.local(join=None), alp.cache(join=None), alp.storage(join=None)join, you can have a file or folder name appended to the path---however, this file or folder will not be created.alp.readPlist(path), alp.writePlist(path), alp.jsonLoad(path, default=None), alp.jsonDump(path)path. If path is not an absolute path, they will treat it as a filename in the storage directory (so, for example, you could call alp.jsonDump(yourFancyObject, "dump.json") without any problems. jsonLoad has one additional argument, default, specifying the object to be dumped and returned if the JSON file did not already exist. For example, calling alp.jsonLoad("foo.json", default=[]) would load foo.json with an empty list if the file was not found.alp.find(query)mdfind with the arguments given in query, splitting the results into a list and returning it. For more information, see mdfind (1).alp.log(s)s to a file called debug.log in the workflow's main folder.alp uses a robust and complete implementation of Alfred's feedback system, generating and outputting the required XML for the attributes you specify. This breaks down into a class called Item and a function called feedback.
alp.Item(**kwargs)
The Item class is initialized with a list of keys and values, and returns one Item which can then be passed to the feedback function. The following keys are currently understood by alp and Alfred:
title: The title string to show in the feedback list.subtitle: The subtitle string to show below the title.uid: A unique identifier string for Alfred's sorting functions.valid: Either True, False, or a string. Tells Alfred whether the item is actionable.autocomplete: A string to autocomplete Alfred's query to when an invalid item is chosen.icon: A path to an icon image, a path to a file, or a file type (default: icon.png).fileIcon: If you wish to use a particular file's icon, set icon to its path and fileIcon to True.fileType: If you wish to specify a type of file whose icon Alfred should use, set icon to the type and fileType to True.arg: The argument to be passed as {query} if the item is valid and actioned. If it contains newlines, it will be passed as a separate XML key rather than an attribute of <item></item>.type: Currently, can only be set to file, which tells Alfred to treat the result as a file.A nifty trick is to pass a dictionary with some or all of alp.Item's keys and values into the initializer. So you could conceivably do this:
>>> iDict = dict(title="A Title", subtitle="This is only a test.", uid="alp-test", valid=False)
>>> i = alp.Item(**iDict)Item.copy()
Returns one copy of the item, which can then be modified separately and reused.
Item.get()
Returns the current values for the item in the following format. Primarily used with the feedback() function below.
{
"data":
{
"content":
{
"title": self.title,
"subtitle": self.subtitle,
"icon": self.icon,
"fileIcon": self.fileIcon,
"fileType": self.fileType
},
"attrib":
{
"uid": self.uid,
"valid": self.valid
}
}
}If autocomplete or type is set, that value will be added to the "attrib" dictionary, as will an arg that does not contain newlines.
alp.feedback(items)
Takes either an individual item or a list of items for items and prints a UTF-8-encoded XML string for Alfred to interpret.
Github user jlegewie has contributed a stellar fuzzy-searching method to alp. Given a query, a list of strings, dictionaries, tuples, or other lists, and a key function, it returns a ranked list of matching objects. To use it, first get your data into an appropriate list, then call alp.fuzzy_search() with the following parameters: alp.fuzzy_search(query, elements, key, rank, seq). jlegewie has provided the following example:
elements = [{'key': u'ZB7K535R', 'author': u'Reskin 2003', 'title': u'Including Mechanisms in Our Models of Ascriptive Inequality: 2002 Presidential Address'}, {'key': u'DBTD3HQS', 'author': u'Igor & Ronald 2008', 'title': u'Die Zunahme der Lohnungleichheit in der Bundesrepublik. Aktuelle Befunde f\xfcr den Zeitraum von 1998 bis 2005'}, {'key': u'BKTCNEGP', 'author': u'Kirk & Sampson 2013', 'title': u'Juvenile Arrest and Collateral Educational Damage in the Transition to Adulthood'}, {'key': u'9AN4SPKT', 'author': u'Turner 2003', 'title': u'The Structure of Sociological Theory'}, {'key': u'9M92EV6S', 'author': u'Bruhns et al. 1999', 'title': u'Die heimliche Revolution'}, {'key': u'25QBTM5P', 'author': u'Durkheim 1997', 'title': u'The Division of Labor in Society'}, {'key': u'MQ3BHTBJ', 'author': u'Marx 1978', 'title': u'Alienation and Social Class'}, {'key': u'7G4BRU45', 'author': u'Marx 1978', 'title': u'The German Ideology: Part I'}, {'key': u'9ANAZXQB', 'author': u'Llorente 2006', 'title': u'Analytical Marxism and the Division of Labor'}]
query = 'marx'
results = fuzzy_search(query, elements, key=lambda x: '%s - %s' % (x['author'], x['title']))As you can see, the key access function should extract a searchable string from your data set, and the elements argument should be a list that eventually contains strings. The neat thing about this being a fuzzy search is that mrx would also match many of the Marx elements, and if a mistyped or fragmented query is closer to one good candidate (say "Max") than another (like "Marx"), results will be ranked accordingly.
The fact that Alfred's internal workflow workings only make it possible for a workflow to output one string can be frustrating. Enter alp's Notification class. It's used a little something like this:
>>> import alp
>>> n = alp.Notification()
>>> n.notify("Title", "Subtitle", "Informative Text")The title, subtitle, and informative text should be strings. This will pop up a notification that bears Python's logo but is otherwise indistinguishable from any of Alfred's notices. Currently, this is restricted to sending messages to the user---there's not yet a way to know whether the user clicks the notification, for example---but the problem is being worked on.
The modules Requests, requests_cache, and BeautifulSoup are bundled with alp by default. (N.B.: Because they are relatively large, they can be removed from the module by deleting the folder request.) They vastly simplify making and interpreting HTTP requests. The alp Request class provides a quick-and-dirty interface to them, setting up a requests cache, making a request, and passing the returned data to BeautifulSoup for parsing.
alp.Request(url, payload=None, post=False, cache_for=None)request property, which is a Requests object and can be manipulated in any way that a standard Requests object can (see the documentation for more info).Request.souper()Request.clear_cache()requests_cache immediately.The alp Settings object loads and saves settings in your workflow's designated storage path. On initialization, it looks for a settings file, creating it if it fails to find one and loading the predefined settings if it succeeds. It also defines the following methods:
Settings.set(**kwargs)**kwargs into memory and saves them to the settings file.Settings.get(k, default=None)k, optionally returning default if no setting is found.Settings.delete(k)k from memory and deletes it from the settings file.If your workflow needs to store a user's sensitive data, particularly a username--password pair, the safest way to do so is with the Mac OS X Keychain. The Keychain is normally an utter pain in the behind, but alp makes working with it a breeze. Simply initialize alp.Keychain(service) with a service name---for example, your workflow's bundle ID---and then use the following methods:
Keychain.storePassword(account, password)Keychain.retrievePassword(account)Keychain.modifyPassword(account, newPassword)
Changes the saved password for account to newPassword.Keychain.deletePassword(account)
Removes account's password from the Keychain.By request, alp can also send simple plaintext messages over e-mail. Initialize the alp.Mail() object with the following parameters: alp.Mail(host, port, SSL, user, pw, sender, to, mimetype, subject, body). SSL should be set to True or False. mimetype should be a string specifying the MIME subtype under "text/" and defaults to "plain" if None is passed. to can be either a single e-mail address or a list of e-mail addresses. Use Mail.notify() to send your message(s):
>>> e = alp.Mail(host, port, SSL, user, pw, sender, to, mimetype, subject, body)
>>> e.notify()See the list of exceptions that can be raised in the docs.
The Alfred v2 forums are a good place to look for answers, but you can also reach this package's maintenance man, Daniel, at d atsign daniel dot sh or on Twitter at @phyllisstein.
alp and all of its components are free to use and distribute however you see fit. Go hog-wild. The author appreciates getting some credit for his work, and the authors of Requests, requests_cache, BeautifulSoup, six, and biplist probably do as well, so it'd be neat if you'd mention us all somewhere. Additionally, donations are gratefully accepted over at my Alfred website.