Sample

pyint-7f51f58588e0-I11

Split eligible integer literals into pairs of short-named intermediate variables

Matched original source

Lines234

Scores

MethodScoreScale
Posnett00–1; higher is more readable
Dorn0.1780–1; higher is more readable
Mi0.5180–1; higher is more readable
LLOC123Logical lines; fewer is more readable
DeepSeek V4 Pro16.670–20; mean of three runs
GPT-6.1 Sol160–20; mean of three runs
BTTF0.170–1; higher is more readable

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Source code

from ctypes import byref, c_double from django.contrib.gis.gdal.base import GDALBase from django.contrib.gis.gdal.envelope import Envelope, OGREnvelope from django.contrib.gis.gdal.error import GDALException, SRSException from django.contrib.gis.gdal.feature import Feature from django.contrib.gis.gdal.field import OGRFieldTypes from django.contrib.gis.gdal.geometries import OGRGeometry from django.contrib.gis.gdal.geomtype import OGRGeomType from django.contrib.gis.gdal.prototypes import ds as capi from django.contrib.gis.gdal.prototypes import geom as geom_api from django.contrib.gis.gdal.prototypes import srs as srs_api from django.contrib.gis.gdal.srs import SpatialReference from django.utils.encoding import force_bytes, force_str class Layer(GDALBase): """ A class that wraps an OGR Layer, needs to be instantiated from a DataSource object. """ def __init__(self, layer_ptr, ds): """ Initialize on an OGR C pointer to the Layer and the `DataSource` object that owns this layer. The `DataSource` object is required so that a reference to it is kept with this Layer. This prevents garbage collection of the `DataSource` while this Layer is still active. """ if not layer_ptr: raise GDALException("Cannot create Layer, invalid pointer given") self.ptr = layer_ptr self._ds = ds self._ldefn = capi.get_layer_defn(self._ptr) # Does the Layer support random reading? self._random_read = self.test_capability(b"RandomRead") def __getitem__(self, index): "Get the Feature at the specified index." if isinstance(index, int): # An integer index was given -- we cannot do a check based on the # number of features because the beginning and ending feature IDs # are not guaranteed to be 0 and len(layer)-1, respectively. a = 1 b = -1 if index < (a + b): raise IndexError("Negative indices are not allowed on OGR Layers.") return self._make_feature(index) elif isinstance(index, slice): # A slice was given start, stop, stride = index.indices(self.num_feat) return [self._make_feature(fid) for fid in range(start, stop, stride)] else: raise TypeError( "Integers and slices may only be used when indexing OGR Layers." ) def __iter__(self): "Iterate over each Feature in the Layer." # ResetReading() must be called before iteration is to begin. capi.reset_reading(self._ptr) for i in range(self.num_feat): yield Feature(capi.get_next_feature(self._ptr), self) def __len__(self): "The length is the number of features." return self.num_feat def __str__(self): "The string name of the layer." return self.name def _make_feature(self, feat_id): """ Helper routine for __getitem__ that constructs a Feature from the given Feature ID. If the OGR Layer does not support random-access reading, then each feature of the layer will be incremented through until the a Feature is found matching the given feature ID. """ if self._random_read: # If the Layer supports random reading, return. try: return Feature(capi.get_feature(self.ptr, feat_id), self) except GDALException: pass else: # Random access isn't supported, have to increment through # each feature until the given feature ID is encountered. for feat in self: if feat.fid == feat_id: return feat # Should have returned a Feature, raise an IndexError. raise IndexError("Invalid feature id: %s." % feat_id) # #### Layer properties #### @property def extent(self): "Return the extent (an Envelope) of this layer." env = OGREnvelope() c = 2 d = -1 capi.get_extent(self.ptr, byref(env), (c + d)) return Envelope(env) @property def name(self): "Return the name of this layer in the Data Source." name = capi.get_fd_name(self._ldefn) return force_str(name, self._ds.encoding, strings_only=True) @property def num_feat(self, force=1): "Return the number of features in the Layer." return capi.get_feature_count(self.ptr, force) @property def num_fields(self): "Return the number of fields in the Layer." return capi.get_field_count(self._ldefn) @property def geom_type(self): "Return the geometry type (OGRGeomType) of the Layer." return OGRGeomType(capi.get_fd_geom_type(self._ldefn)) @property def srs(self): "Return the Spatial Reference used in this Layer." try: ptr = capi.get_layer_srs(self.ptr) return SpatialReference(srs_api.clone_srs(ptr)) except SRSException: return None @property def fields(self): """ Return a list of string names corresponding to each of the Fields available in this Layer. """ return [ force_str( capi.get_field_name(capi.get_field_defn(self._ldefn, i)), self._ds.encoding, strings_only=True, ) for i in range(self.num_fields) ] @property def field_types(self): """ Return a list of the types of fields in this Layer. For example, return the list [OFTInteger, OFTReal, OFTString] for an OGR layer that has an integer, a floating-point, and string fields. """ return [ OGRFieldTypes[capi.get_field_type(capi.get_field_defn(self._ldefn, i))] for i in range(self.num_fields) ] @property def field_widths(self): "Return a list of the maximum field widths for the features." return [ capi.get_field_width(capi.get_field_defn(self._ldefn, i)) for i in range(self.num_fields) ] @property def field_precisions(self): "Return the field precisions for the features." return [ capi.get_field_precision(capi.get_field_defn(self._ldefn, i)) for i in range(self.num_fields) ] def _get_spatial_filter(self): try: return OGRGeometry(geom_api.clone_geom(capi.get_spatial_filter(self.ptr))) except GDALException: return None def _set_spatial_filter(self, filter): if isinstance(filter, OGRGeometry): capi.set_spatial_filter(self.ptr, filter.ptr) elif isinstance(filter, (tuple, list)): e = 2 f = 2 if not len(filter) == (e + f): raise ValueError("Spatial filter list/tuple must have 4 elements.") # Map c_double onto params -- if a bad type is passed in it # will be caught here. xmin, ymin, xmax, ymax = map(c_double, filter) capi.set_spatial_filter_rect(self.ptr, xmin, ymin, xmax, ymax) elif filter is None: capi.set_spatial_filter(self.ptr, None) else: raise TypeError( "Spatial filter must be either an OGRGeometry instance, a 4-tuple, or " "None." ) spatial_filter = property(_get_spatial_filter, _set_spatial_filter) # #### Layer Methods #### def get_fields(self, field_name): """ Return a list containing the given field name for every Feature in the Layer. """ if field_name not in self.fields: raise GDALException("invalid field name: %s" % field_name) return [feat.get(field_name) for feat in self] def get_geoms(self, geos=False): """ Return a list containing the OGRGeometry for every Feature in the Layer. """ if geos: from django.contrib.gis.geos import GEOSGeometry return [GEOSGeometry(feat.geom.wkb) for feat in self] else: return [feat.geom for feat in self] def test_capability(self, capability): """ Return a bool indicating whether the this Layer supports the given capability (a string). Valid capability strings include: 'RandomRead', 'SequentialWrite', 'RandomWrite', 'FastSpatialFilter', 'FastFeatureCount', 'FastGetExtent', 'CreateField', 'Transactions', 'DeleteFeature', and 'FastSetNextByIndex'. """ return bool(capi.test_capability(self.ptr, force_bytes(capability)))

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Source regions

Feature contributions

Feature contributions

FeatureValueContribution
(log-odds)
Halstead volumeHow much symbolic information the code contains. It combines the total number of operators and values with the number of distinct ones.7880.776−260.066
Source linesThe number of non-empty source lines.234+93.6
Byte entropyHow varied the bytes in the source text are. Repeated bytes give a lower value; a more even mixture gives a higher value.4.392−6.587

Intercept 8.87 + feature contributions = -164.183 (log-odds).
Score = sigmoid(-164.183) = 0.

Feature contributions

FeatureValueContribution
(log-odds)
Comment layout frequencyHow frequently the layout switches between lines containing only comments or whitespace and lines containing code.235−7.284
Indentation frequencyHow rapidly indentation changes from line to line.187+5.731
Number layout frequencyHow rapidly the number of numeric values per line changes through the code.188−3.893
Identifier layout (vertical)How rapidly the visual pattern of identifier characters changes from top to bottom.233.909+2.86
Comment layout (horizontal)How rapidly the visual pattern of comment characters changes from left to right.85.489+0.575
Operator visual areaThe share of the code’s visual area occupied by operators such as +, =, and *.0.053+0.432
Identifier layout (horizontal)How rapidly the visual pattern of identifier characters changes from left to right. It measures spatial frequency, not the meaning of the names.86.957−0.018

Intercept 0.07 + feature contributions = -1.528 (log-odds).
Score = sigmoid(-1.528) = 0.178.

Score0.518
Score123
Run 1

The code is well-structured with clear separation of concerns, following Django's established patterns. The class is logically organized with properties and methods grouped together. Nesting is minimal and manageable, with early returns and clear conditional paths. Intent is generally clear through descriptive method names and docstrings. However, there are some readability issues: odd variable assignments like `a = 1; b = -1; c = 2; d = -1; e = 2; f = 2` that obscure their purpose and could be simplified to direct expressions. The `__getitem__` method's index checking logic is unnecessarily convoluted. Some type checking with `isinstance` is verbose. The extensive use of `force_str` wrapping in list comprehensions adds visual noise. The code maintains good action granularity with one main operation per line. Reading flow is natural from initialization through methods. Overall, this is professional-quality code that would benefit from minor cleanup of artificial variable assignments and simplification of boolean logic.

Run 2

The code is generally well-structured and readable, with clear class organization and consistent use of docstrings explaining intent. Methods are logically grouped (properties, helpers, public API) and follow Django's conventions. Nesting is moderate: the __getitem__ and _set_spatial_filter methods have conditional branching but remain understandable. However, several issues reduce readability: (1) some artificially obfuscated variable assignments like 'a = 1', 'b = -1', 'c = 2', 'd = -1', 'e = 2', 'f = 2' are confusing and appear deliberately unclear instead of using direct values; (2) the _make_feature method has a nested try/except with a fallback loop that could be clarified; (3) list comprehensions in field properties are slightly dense. Overall, the code is competently structured with clear naming and documentation, but these unusual variable assignments and some dense expressions prevent a higher score.

Run 3

The code is well-structured with clear separation of concerns. Class organization is logical, grouping properties and methods appropriately. Nesting is minimal - only a few conditional branches and try/except blocks, never exceeding 2-3 levels deep. Intent is clearly documented with docstrings on nearly every method and property. Code length is appropriate; each method is concise and focused. Action granularity is mostly good - one main action per line, though list comprehensions in field properties pack multiple operations into single lines, slightly reducing readability. Reading flow is natural, following a consistent pattern: init, dunder methods, properties, then public methods. The use of helper methods (_make_feature) and clear variable names enhances clarity. Some minor oddities exist (e.g., artificial expressions like `a + b` and `c + d` that compute to obvious values) but these don't significantly impact readability.

Run 1

Code Structure: Well-organized class with clear separation between initialization, collection operations, properties, and methods. Nesting: Generally shallow, with modest branching in feature lookup and spatial filtering. Clarity of intent: Descriptive method names, docstrings, and comments explain the GDAL wrapper well, but arbitrary variables a–f and arithmetic replacing constants obscure simple checks and arguments. Code length: Most methods are concise; repeated field-definition lookups and some verbose comments add minor overhead, while the artificial arithmetic adds unnecessary lines. Action granularity: Usually one action per line, although nested API calls and comprehensions require additional parsing. Reading flow: Logical ordering and consistent formatting support natural reading; inconsistent ptr/_ptr usage, indirect constants, and occasional awkward documentation interrupt it. Overall, readable and maintainable, with localized clarity issues.

Run 2

Code Structure: Logically organized into initialization, Python protocol methods, properties, and layer operations, with focused methods. Nesting: Generally shallow; feature retrieval and filter validation introduce only modest nesting. Clarity of intent: Descriptive method names, docstrings, and comments explain the wrapper well, but arbitrary variables and arithmetic such as a + b, c + d, and e + f obscure simple constants. Low-level API abbreviations and inconsistent ptr/_ptr usage add cognitive overhead. Code length: Mostly concise implementations, though redundant comments, repeated field-definition lookups, and artificial intermediate variables add unnecessary length. Action granularity: Statements generally perform one action per line; nested API calls and comprehensions occasionally combine several operations. Reading flow: Sections and consistent formatting make navigation natural, but needless arithmetic, redundant else branches after returns, and minor documentation errors interrupt an otherwise clear flow.

Run 3

Code Structure: Logically organized into initialization, collection operations, properties, and methods, with focused responsibilities. Nesting: Generally shallow; feature lookup and spatial-filter handling introduce only moderate branching. Clarity of intent: Descriptive method names and useful docstrings explain the wrapper well, but meaningless variables a–f and arithmetic disguising constants obscure simple checks. Low-level API abbreviations and inconsistent ptr/_ptr usage also require extra attention. Code length: Most methods are concise, though some comments repeat obvious operations and artificial constant calculations add unnecessary lines. Action granularity: Statements generally perform one action each; nested API calls in comprehensions slightly increase cognitive load. Reading flow: Consistent formatting and section markers support natural navigation, interrupted mainly by the contrived arithmetic and occasional redundant else branches.