def test_unjsonify(): attributes, dialect = parser._split_keyvals('transcript_id "mRNA1"') assert attributes == {'transcript_id': ['mRNA1']}, attributes s = helpers._jsonify(attributes) assert s == '{"transcript_id":["mRNA1"]}', s d = helpers._unjsonify(s, isattributes=True) assert d == attributes
def derived_feature_generator(): """ Generator of items from the file that was just created... """ keys = ["parent", "seqid", "start", "end", "strand", "featuretype", "bin", "attributes"] for line in open(fout.name): d = dict(list(zip(keys, line.strip().split("\t")))) d.pop("parent") d["score"] = "." d["source"] = "gffutils_derived" d["frame"] = "." d["extra"] = [] d["attributes"] = helpers._unjsonify(d["attributes"]) f = feature.Feature(**d) f.id = self._id_handler(f) yield f
def derived_feature_generator(): """ Generator of items from the file that was just created... """ keys = ['parent', 'seqid', 'start', 'end', 'strand', 'featuretype', 'bin', 'attributes'] for line in open(fout.name): d = dict(list(zip(keys, line.strip().split('\t')))) d.pop('parent') d['score'] = '.' d['source'] = 'gffutils_derived' d['frame'] = '.' d['extra'] = [] d['attributes'] = helpers._unjsonify(d['attributes']) f = feature.Feature(**d) f.id = self._id_handler(f) yield f
def __init__(self, dbfn, default_encoding='utf-8', keep_order=False, pragmas=constants.default_pragmas, sort_attribute_values=False, text_factory=sqlite3.OptimizedUnicode): """ Connect to a database created by :func:`gffutils.create_db`. Parameters ---------- dbfn : str Path to a database created by :func:`gffutils.create_db`. text_factory : callable Optionally set the way sqlite3 handles strings. Default is sqlite3.OptimizedUnicode, which returns ascii when possible, unicode otherwise encoding : str When non-ASCII characters are encountered, assume they are in this encoding. keep_order : bool If True, all features returned from this instance will have the order of their attributes maintained. This can be turned on or off database-wide by setting the `keep_order` attribute or with this kwarg, or on a feature-by-feature basis by setting the `keep_order` attribute of an individual feature. Default is False, since this includes a sorting step that can get time-consuming for many features. pragmas : dict Dictionary of pragmas to use when connecting to the database. See http://www.sqlite.org/pragma.html for the full list of possibilities, and constants.default_pragmas for the defaults. These can be changed later using the :meth:`FeatureDB.set_pragmas` method. Notes ----- `dbfn` can also be a subclass of :class:`_DBCreator`, useful for when :func:`gffutils.create_db` is provided the ``dbfn=":memory:"`` kwarg. """ # Since specifying the string ":memory:" will actually try to connect # to a new, separate (and empty) db in memory, we can alternatively # pass in a sqlite connection instance to use its existing, in-memory # db. from gffutils import create if isinstance(dbfn, create._DBCreator): self.conn = dbfn.conn self.dbfn = dbfn.dbfn elif isinstance(dbfn, sqlite3.Connection): self.conn = dbfn self.dbfn = dbfn # otherwise assume dbfn is a string. elif dbfn == ':memory:': raise ValueError( "cannot connect to memory db; please provide the connection") else: if not os.path.exists(dbfn): raise ValueError("Database file %s does not exist" % dbfn) self.dbfn = dbfn self.conn = sqlite3.connect(self.dbfn) if text_factory is not None: self.conn.text_factory = text_factory self.conn.row_factory = sqlite3.Row self.default_encoding = default_encoding self.keep_order = keep_order self.sort_attribute_values = sort_attribute_values c = self.conn.cursor() # Load some meta info # TODO: this is a good place to check for previous versions, and offer # to upgrade... c.execute( ''' SELECT version, dialect FROM meta ''') version, dialect = c.fetchone() self.version = version self.dialect = helpers._unjsonify(dialect) # Load directives from db c.execute( ''' SELECT directive FROM directives ''') self.directives = [directive[0] for directive in c if directive] # Load autoincrements so that when we add new features, we can start # autoincrementing from where we last left off (instead of from 1, # which would cause name collisions) c.execute( ''' SELECT base, n FROM autoincrements ''') self._autoincrements = collections.defaultdict(int, c) self.set_pragmas(pragmas) if not self._analyzed(): warnings.warn( "It appears that this database has not had the ANALYZE " "sqlite3 command run on it. Doing so can dramatically " "speed up queries, and is done by default for databases " "created with gffutils >0.8.7.1 (this database was " "created with version %s) Consider calling the analyze() " "method of this object." % self.version)
def __init__(self, dbfn, default_encoding='utf-8', keep_order=False, pragmas=constants.default_pragmas, sort_attribute_values=False, text_factory=sqlite3.OptimizedUnicode): """ Connect to a database created by :func:`gffutils.create_db`. Parameters ---------- dbfn : str Path to a database created by :func:`gffutils.create_db`. text_factory : callable Optionally set the way sqlite3 handles strings. Default is sqlite3.OptimizedUnicode, which returns ascii when possible, unicode otherwise encoding : str When non-ASCII characters are encountered, assume they are in this encoding. keep_order : bool If True, all features returned from this instance will have the order of their attributes maintained. This can be turned on or off database-wide by setting the `keep_order` attribute or with this kwarg, or on a feature-by-feature basis by setting the `keep_order` attribute of an individual feature. Default is False, since this includes a sorting step that can get time-consuming for many features. pragmas : dict Dictionary of pragmas to use when connecting to the database. See http://www.sqlite.org/pragma.html for the full list of possibilities, and constants.default_pragmas for the defaults. These can be changed later using the :meth:`FeatureDB.set_pragmas` method. Notes ----- `dbfn` can also be a subclass of :class:`_DBCreator`, useful for when :func:`gffutils.create_db` is provided the ``dbfn=":memory:"`` kwarg. """ # Since specifying the string ":memory:" will actually try to connect # to a new, separate (and empty) db in memory, we can alternatively # pass in a sqlite connection instance to use its existing, in-memory # db. from gffutils import create if isinstance(dbfn, create._DBCreator): self.conn = dbfn.conn self.dbfn = dbfn.dbfn elif isinstance(dbfn, sqlite3.Connection): self.conn = dbfn self.dbfn = dbfn # otherwise assume dbfn is a string. elif dbfn == ':memory:': raise ValueError( "cannot connect to memory db; please provide the connection") else: if not os.path.exists(dbfn): raise ValueError("Database file %s does not exist" % dbfn) self.dbfn = dbfn self.conn = sqlite3.connect(self.dbfn) if text_factory is not None: self.conn.text_factory = text_factory self.conn.row_factory = sqlite3.Row self.default_encoding = default_encoding self.keep_order = keep_order self.sort_attribute_values = sort_attribute_values c = self.conn.cursor() # Load some meta info # TODO: this is a good place to check for previous versions, and offer # to upgrade... c.execute( ''' SELECT version, dialect FROM meta ''') version, dialect = c.fetchone() self.version = version self.dialect = helpers._unjsonify(dialect) # Load directives from db c.execute( ''' SELECT directive FROM directives ''') self.directives = [directive[0] for directive in c if directive] # Load autoincrements so that when we add new features, we can start # autoincrementing from where we last left off (instead of from 1, # which would cause name collisions) c.execute( ''' SELECT base, n FROM autoincrements ''') self._autoincrements = dict(c) self.set_pragmas(pragmas) if not self._analyzed(): warnings.warn( "It appears that this database has not had the ANALYZE " "sqlite3 command run on it. Doing so can dramatically " "speed up queries, and is done by default for databases " "created with gffutils >0.8.7.1 (this database was " "created with version %s) Consider calling the analyze() " "method of this object." % self.version)
def __init__(self, seqid=".", source=".", featuretype=".", start=".", end=".", score=".", strand=".", frame=".", attributes=None, extra=None, bin=None, id=None, dialect=None, file_order=None, keep_order=False, sort_attribute_values=False): """ Represents a feature from the database. Usually you won't want to use this directly, since it has various implementation details needed for operating in the context of FeatureDB objects. Instead, try the :func:`gffutils.feature.feature_from_line` function. When printed, reproduces the original line from the file as faithfully as possible using `dialect`. Parameters ---------- seqid : string Name of the sequence (often chromosome) source : string Source of the feature; typically the originating database or program that predicted the feature featuretype : string Type of feature. For example "gene", "exon", "TSS", etc start, end : int or "." 1-based coordinates; start must be <= end. If "." (the default placeholder for GFF files), then the corresponding attribute will be None. score : string Stored as a string. strand : "+" | "-" | "." Strand of the feature; "." when strand is not relevant. frame : "0" | "1" | "2" Coding frame. 0 means in-frame; 1 means there is one extra base at the beginning, so the first codon starts at the second base; 2 means two extra bases at the beginning. Interpretation is strand specific; "beginning" for a minus-strand feature is at the end coordinate. attributes : string or dict If a string, first assume it is serialized JSON; if this fails then assume it's the original key/vals string. If it's a dictionary already, then use as-is. The end result is that this instance's `attributes` attribute will always be a dictionary. Upon printing, the attributes will be reconstructed based on this dictionary and the dialect -- except if the original attributes string was provided, in which case that will be used directly. extra : string or list Additional fields after the canonical 9 fields for GFF/GTF. If a string, then first assume it's serialized JSON; if this fails then assume it's a tab-delimited string of additional fields. If it's a list already, then use as-is. bin : int UCSC genomic bin. If None, will be created based on provided start/end; if start or end is "." then bin will be None. id : None or string Database-specific primary key for this feature. The only time this should not be None is if this feature is coming from a database, in which case it will be filled in automatically. dialect : dict or None The dialect to use when reconstructing attribute strings; defaults to the GFF3 spec. :class:`FeatureDB` objects will automatically attach the dialect from the original file. file_order : int This is the `rowid` special field used in a sqlite3 database; this is provided by FeatureDB. keep_order : bool If True, then the attributes in the printed string will be in the order specified in the dialect. Disabled by default, since this sorting step is time-consuming over many features. sort_attribute_values : bool If True, then the values of each attribute will be sorted when the feature is printed. Mostly useful for testing, where the order is important for checking against expected values. Disabled by default, since it can be time-consuming over many features. """ # start/end can be provided as int-like, ".", or None, but will be # converted to int or None if start == ".": start = None elif start is not None: start = int(start) if end == ".": end = None elif end is not None: end = int(end) # Flexible handling of attributes: # If dict, then use that; otherwise assume JSON and convert to a dict; # otherwise assume original string and convert to a dict. # # dict_class is set at the module level above...this is so you can swap # in and out different dict implementations (ordered, defaultdict, etc) # for testing. attributes = attributes or dict_class() if isinstance(attributes, six.string_types): try: attributes = helpers._unjsonify(attributes, isattributes=True) # it's a string but not JSON: assume original attributes string. except simplejson.JSONDecodeError: # But Feature.attributes is still a dict attributes, _dialect = parser._split_keyvals(attributes) # Use this dialect if none provided. dialect = dialect or _dialect # If string, then try un-JSONifying it into a list; if that doesn't # work then assume it's tab-delimited and convert to a list. extra = extra or [] if isinstance(extra, six.string_types): try: extra = helpers._unjsonify(extra) except simplejson.JSONDecodeError: extra = extra.split('\t') self.seqid = seqid self.source = source self.featuretype = featuretype self.start = start self.end = end self.score = score self.strand = strand self.frame = frame self.attributes = attributes self.extra = extra self.bin = self.calc_bin(bin) self.id = id self.dialect = dialect or constants.dialect self.file_order = file_order self.keep_order = keep_order self.sort_attribute_values = sort_attribute_values