--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/cpt_intron_detect/gff3.py	Fri May 13 05:08:54 2022 +0000
@@ -0,0 +1,346 @@
+import copy
+import logging
+
+log = logging.getLogger()
+log.setLevel(logging.WARN)
+
+
+def feature_lambda(
+    feature_list,
+    test,
+    test_kwargs,
+    subfeatures=True,
+    parent=None,
+    invert=False,
+    recurse=True,
+):
+    """Recursively search through features, testing each with a test function, yielding matches.
+
+    GFF3 is a hierachical data structure, so we need to be able to recursively
+    search through features. E.g. if you're looking for a feature with
+    ID='bob.42', you can't just do a simple list comprehension with a test
+    case. You don't know how deeply burried bob.42 will be in the feature tree. This is where feature_lambda steps in.
+
+    :type feature_list: list
+    :param feature_list: an iterable of features
+
+    :type test: function reference
+    :param test: a closure with the method signature (feature, **kwargs) where
+                 the kwargs are those passed in the next argument. This
+                 function should return True or False, True if the feature is
+                 to be yielded as part of the main feature_lambda function, or
+                 False if it is to be ignored. This function CAN mutate the
+                 features passed to it (think "apply").
+
+    :type test_kwargs: dictionary
+    :param test_kwargs: kwargs to pass to your closure when it is called.
+
+    :type subfeatures: boolean
+    :param subfeatures: when a feature is matched, should just that feature be
+                        yielded to the caller, or should the entire sub_feature
+                        tree for that feature be included? subfeatures=True is
+                        useful in cases such as searching for a gene feature,
+                        and wanting to know what RBS/Shine_Dalgarno_sequences
+                        are in the sub_feature tree (which can be accomplished
+                        with two feature_lambda calls). subfeatures=False is
+                        useful in cases when you want to process (and possibly
+                        return) the entire feature tree, such as applying a
+                        qualifier to every single feature.
+
+    :type invert: boolean
+    :param invert: Negate/invert the result of the filter.
+
+    :rtype: yielded list
+    :return: Yields a list of matching features.
+    """
+    # Either the top level set of [features] or the subfeature attribute
+    for feature in feature_list:
+        feature._parent = parent
+        if not parent:
+            # Set to self so we cannot go above root.
+            feature._parent = feature
+        test_result = test(feature, **test_kwargs)
+        # if (not invert and test_result) or (invert and not test_result):
+        if invert ^ test_result:
+            if not subfeatures:
+                feature_copy = copy.deepcopy(feature)
+                feature_copy.sub_features = list()
+                yield feature_copy
+            else:
+                yield feature
+
+        if recurse and hasattr(feature, "sub_features"):
+            for x in feature_lambda(
+                feature.sub_features,
+                test,
+                test_kwargs,
+                subfeatures=subfeatures,
+                parent=feature,
+                invert=invert,
+                recurse=recurse,
+            ):
+                yield x
+
+
+def fetchParent(feature):
+    if not hasattr(feature, "_parent") or feature._parent is None:
+        return feature
+    else:
+        return fetchParent(feature._parent)
+
+
+def feature_test_true(feature, **kwargs):
+    return True
+
+
+def feature_test_type(feature, **kwargs):
+    if "type" in kwargs:
+        return str(feature.type).upper() == str(kwargs["type"]).upper()
+    elif "types" in kwargs:
+      for x in kwargs["types"]:
+        if str(feature.type).upper() == str(x).upper():
+          return True
+      return False
+    raise Exception("Incorrect feature_test_type call, need type or types")
+
+
+def feature_test_qual_value(feature, **kwargs):
+    """Test qualifier values.
+
+    For every feature, check that at least one value in
+    feature.quailfiers(kwargs['qualifier']) is in kwargs['attribute_list']
+    """
+    if isinstance(kwargs["qualifier"], list):
+        for qualifier in kwargs["qualifier"]:
+            for attribute_value in feature.qualifiers.get(qualifier, []):
+                if attribute_value in kwargs["attribute_list"]:
+                    return True
+    else:
+        for attribute_value in feature.qualifiers.get(kwargs["qualifier"], []):
+            if attribute_value in kwargs["attribute_list"]:
+                return True
+    return False
+
+
+def feature_test_location(feature, **kwargs):
+    if "strand" in kwargs:
+        if feature.location.strand != kwargs["strand"]:
+            return False
+
+    return feature.location.start <= kwargs["loc"] <= feature.location.end
+
+
+def feature_test_quals(feature, **kwargs):
+    """
+    Example::
+
+        a = Feature(qualifiers={'Note': ['Some notes', 'Aasdf']})
+
+        # Check if a contains a Note
+        feature_test_quals(a, {'Note': None})  # Returns True
+        feature_test_quals(a, {'Product': None})  # Returns False
+
+        # Check if a contains a note with specific value
+        feature_test_quals(a, {'Note': ['ome']})  # Returns True
+
+        # Check if a contains a note with specific value
+        feature_test_quals(a, {'Note': ['other']})  # Returns False
+    """
+    for key in kwargs:
+        if key not in feature.qualifiers:
+            return False
+
+        # Key is present, no value specified
+        if kwargs[key] is None:
+            return True
+
+        # Otherwise there is a key value we're looking for.
+        # so we make a list of matches
+        matches = []
+        # And check all of the feature qualifier valuse
+        for value in feature.qualifiers[key]:
+            # For that kwargs[key] value
+            for x in kwargs[key]:
+                matches.append(x in value)
+
+        # If none matched, then we return false.
+        if not any(matches):
+            return False
+
+    return True
+
+
+def feature_test_contains(feature, **kwargs):
+    if "index" in kwargs:
+        return feature.location.start < kwargs["index"] < feature.location.end
+    elif "range" in kwargs:
+        return (
+            feature.location.start < kwargs["range"]["start"] < feature.location.end
+            and feature.location.start < kwargs["range"]["end"] < feature.location.end
+        )
+    else:
+        raise RuntimeError("Must use index or range keyword")
+
+
+def get_id(feature=None, parent_prefix=None):
+    result = ""
+    if parent_prefix is not None:
+        result += parent_prefix + "|"
+    if "locus_tag" in feature.qualifiers:
+        result += feature.qualifiers["locus_tag"][0]
+    elif "gene" in feature.qualifiers:
+        result += feature.qualifiers["gene"][0]
+    elif "Gene" in feature.qualifiers:
+        result += feature.qualifiers["Gene"][0]
+    elif "product" in feature.qualifiers:
+        result += feature.qualifiers["product"][0]
+    elif "Product" in feature.qualifiers:
+        result += feature.qualifiers["Product"][0]
+    elif "Name" in feature.qualifiers:
+        result += feature.qualifiers["Name"][0]
+    else:
+        return feature.id
+        # Leaving in case bad things happen.
+        # result += '%s_%s_%s_%s' % (
+        # feature.id,
+        # feature.location.start,
+        # feature.location.end,
+        # feature.location.strand
+        # )
+    return result
+
+
+def get_gff3_id(gene):
+    return gene.qualifiers.get("Name", [gene.id])[0]
+
+
+def ensure_location_in_bounds(start=0, end=0, parent_length=0):
+    # This prevents frameshift errors
+    while start < 0:
+        start += 3
+    while end < 0:
+        end += 3
+    while start > parent_length:
+        start -= 3
+    while end > parent_length:
+        end -= 3
+    return (start, end)
+
+
+def coding_genes(feature_list):
+    for x in genes(feature_list):
+        if (
+            len(
+                list(
+                    feature_lambda(
+                        x.sub_features,
+                        feature_test_type,
+                        {"type": "CDS"},
+                        subfeatures=False,
+                    )
+                )
+            )
+            > 0
+        ):
+            yield x
+
+
+def genes(feature_list, feature_type="gene", sort=False):
+    """
+    Simple filter to extract gene features from the feature set.
+    """
+
+    if not sort:
+        for x in feature_lambda(
+            feature_list, feature_test_type, {"type": feature_type}, subfeatures=True
+        ):
+            yield x
+    else:
+        data = list(genes(feature_list, feature_type=feature_type, sort=False))
+        data = sorted(data, key=lambda feature: feature.location.start)
+        for x in data:
+            yield x
+
+
+def wa_unified_product_name(feature):
+    """
+    Try and figure out a name. We gave conflicting instructions, so
+    this isn't as trivial as it should be. Sometimes it will be in
+    'product' or 'Product', othertimes in 'Name'
+    """
+    # Manually applied tags.
+    protein_product = feature.qualifiers.get(
+        "product", feature.qualifiers.get("Product", [None])
+    )[0]
+
+    # If neither of those are available ...
+    if protein_product is None:
+        # And there's a name...
+        if "Name" in feature.qualifiers:
+            if not is_uuid(feature.qualifiers["Name"][0]):
+                protein_product = feature.qualifiers["Name"][0]
+
+    return protein_product
+
+
+def is_uuid(name):
+    return name.count("-") == 4 and len(name) == 36
+
+
+def get_rbs_from(gene):
+    # Normal RBS annotation types
+    rbs_rbs = list(
+        feature_lambda(
+            gene.sub_features, feature_test_type, {"type": "RBS"}, subfeatures=False
+        )
+    )
+    rbs_sds = list(
+        feature_lambda(
+            gene.sub_features,
+            feature_test_type,
+            {"type": "Shine_Dalgarno_sequence"},
+            subfeatures=False,
+        )
+    )
+    # Fraking apollo
+    apollo_exons = list(
+        feature_lambda(
+            gene.sub_features, feature_test_type, {"type": "exon"}, subfeatures=False
+        )
+    )
+    apollo_exons = [x for x in apollo_exons if len(x) < 10]
+    # These are more NCBI's style
+    regulatory_elements = list(
+        feature_lambda(
+            gene.sub_features,
+            feature_test_type,
+            {"type": "regulatory"},
+            subfeatures=False,
+        )
+    )
+    rbs_regulatory = list(
+        feature_lambda(
+            regulatory_elements,
+            feature_test_quals,
+            {"regulatory_class": ["ribosome_binding_site"]},
+            subfeatures=False,
+        )
+    )
+    # Here's hoping you find just one ;)
+    return rbs_rbs + rbs_sds + rbs_regulatory + apollo_exons
+
+
+def nice_name(record):
+    """
+    get the real name rather than NCBI IDs and so on. If fails, will return record.id
+    """
+    name = record.id
+    likely_parental_contig = list(genes(record.features, feature_type="contig"))
+    if len(likely_parental_contig) == 1:
+        name = likely_parental_contig[0].qualifiers.get("organism", [name])[0]
+    return name
+
+
+def fsort(it):
+    for i in sorted(it, key=lambda x: int(x.location.start)):
+        yield i