![]() Property = ua.get("usableDisplayWidth") # This will look up data from all the databases in the chain Automatic database installers Ua = sniffer.sniff(request) # Sniff HTTP_USER_AGENT, HTTP_PROFILE and many other fields Finally if the handset is unknown, but it publishes WAP profile information, the profile is downloaded and analyzed and saved for further requests.Įxample: from import ChainedSnifferįrom import ApexVertexSnifferįrom import WAPProfileSnifferįrom import DeviceAtlasSniffer Matching is done on property level - if one data source lacks the property information the next data source is tried. Use all available handset information sources to accurately get device data. User agent record is retrofitted with the information how accurate the match was If match is found and threshold is exceed return this user agent record If there was no match in the first pass, do the second pass using the search algorithm Match this entry against incoming User-Agent using the search algorithmįirst search pass is doing using exact string matches (no algorithm involved). The default search algorithm is JaroWinkler from Lehvenstein Python package. The search algorithm is initialized with certain match threshold - all matches below this threshold To make this to use lazy initialization pattern Wurlf database is usually loaded during the start-up (slow operation) - it is possible Since Wurfl is the default backend the process of finding UA record is explained more carefully If ua.get("is_wireless_device") and ua.getCertainty() > certainty_threshold: # We use JaroWinkler as the default algorithm # float 0.1, the actual value is UA search algorithm specific add pywurfl and python-Lehvenstein to buildout.cfg eggs section")ĭef request: Request can be Django, WSGI or Zope HTTPRequest object Logger.error("Could not import Wurlf sniffer. This example will work out of the box with the included pywurlf database.įrom import WurlfSniffer # User agent header is missing from HTTP request # Redirect the visitor from a web site to a mobile site # Get HTTP_USER_AGENT from HTTP request object Whether the HTTP request was made by a mobile phone.Įxample: from import detect_mobile_browserįrom import get_user_agent Redirection exampleĭetect_mobile_browser(user_agent) will return True of False There is no single standard to name properties queried from the handset database.įor legacy reasons, we use DeviceAtlas database column names (keys)Īnd then map them to database-dependent keys. WAP profiles: Django (for database abstraction) and rdflibĪpex Vertex: Django (for database abstraction) You might need to install additional libraries depending on what handset database you use Python package comes with a copy of Wurfl database which dates around the release. You can install it using standard Python egg installation methods Mobile.sniffer is distributed as Python egg in PyPi repository. (note: as WAP is deprecating protocol these are not supported on newer smartphones) User agents post a link to their WAP profile data, which is an XML fileĪnd maintained by the handset manufacturer. The code is Django, WSGI and Zope/Plone compatible. Very convenient Python API designed by professionals FeaturesĮasily plug-in mobile redirects to your Python based web sitesĪble to source data from multiple sniffing backends leading better handset coverageĪutomatically download, parse and cache complex RDF based WAP profiles Mobile handset featureĮxtraction always requires a some sort of database of mobile phone entries and mobile.sniffer framework Mobile detection can be done with a fast regular expression match. ![]() If a database entry is found, withĬertain match accuracy, it’s records like device screen width and height are madeĪvailable to the web server so that it can tailor HTML, image and video output suitable Strings, heurestics are applied to the string matching. Since there might be version changes, local varieties, etc. Mobile handset feature extraction - the handset database is looked for a mobile web browser Visitors from a web site to a mobile site if they are using a mobile phone to arrive on your web site. This is done in mobile/sniffer/detect.py module. Mobile detection - this simply detects whether a browser is a mobile phone based or not. ![]() Mobile.sniffer provides two phase mobile phone detection (a.k.a sniffing) When rendering web pages for mobile phones one must deal with varying handset features:ĭifferent screen sizes and shapes, different supported file formats, different sets of web browser features.Īlso, the fact that you know the user is browsing on a mobile phone is most critical for building Mobile.sniffer is Python framework for abstracting mobile browser detection and feature database access.
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