Sample

pyint-71226d472070-I12

Inline all eligible single-use intermediate local variables

Matched original source

Lines228

Scores

MethodScoreScale
Posnett00–1; higher is more readable
Dorn0.1650–1; higher is more readable
Mi0.5180–1; higher is more readable
LLOC115Logical lines; fewer is more readable
DeepSeek V4 Pro170–20; mean of three runs
GPT-6.1 Sol170–20; mean of three runs
BTTF0.3630–1; 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. if index < 0: 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() capi.get_extent(self.ptr, byref(env), 1) return Envelope(env) @property def name(self): "Return the name of this layer in the Data Source." return force_str((capi.get_fd_name(self._ldefn)), 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: return SpatialReference(srs_api.clone_srs((capi.get_layer_srs(self.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)): if not len(filter) == 4: 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.7569.038−249.778
Source linesThe number of non-empty source lines.228+91.2
Byte entropyHow varied the bytes in the source text are. Repeated bytes give a lower value; a more even mixture gives a higher value.4.402−6.604

Intercept 8.87 + feature contributions = -156.312 (log-odds).
Score = sigmoid(-156.312) = 0.

Feature contributions

FeatureValueContribution
(log-odds)
Comment layout frequencyHow frequently the layout switches between lines containing only comments or whitespace and lines containing code.229−7.072
Indentation frequencyHow rapidly indentation changes from line to line.181+5.523
Number layout frequencyHow rapidly the number of numeric values per line changes through the code.181−3.733
Identifier layout (vertical)How rapidly the visual pattern of identifier characters changes from top to bottom.227.938+2.776
Comment layout (horizontal)How rapidly the visual pattern of comment characters changes from left to right.94.956+0.728
Operator visual areaThe share of the code’s visual area occupied by operators such as +, =, and *.0.052+0.44
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.94.961−0.357

Intercept 0.07 + feature contributions = -1.624 (log-odds).
Score = sigmoid(-1.624) = 0.165.

Score0.518
Score115
Run 1

The code is highly readable with clear structure, logical organization, and consistent patterns. The class is well-documented with descriptive docstrings and comments that explain non-obvious behavior. The code is flat with minimal nesting, using early returns and clear conditional branches. Each method has a clear single purpose, and the action granularity is appropriate—one action per line in most cases. The reading flow is natural, moving from initialization to core methods, then properties, and finally utility methods. The code length is appropriate for the functionality, though some property methods are slightly repetitive. Minor issues include a few overly long lines and some redundant comments (e.g., '# Random access isn't supported, have to increment through'), but overall this is a well-structured, maintainable implementation.

Run 2

The code is well-structured with clear class organization, logical imports, and a cohesive Layer wrapper around OGR. Methods are appropriately grouped with comments and docstrings, with sensible separation of dunder, property, and method sections. Nesting is minimal, mostly single-level conditionals and list comprehensions. Intent is clear throughout—each method has a focused purpose and descriptive naming. Action granularity is appropriate, with one main operation per line. However, a few areas slightly reduce readability: some long lines exceed typical length, especially in the fields property and spatial filter method, and methods like _make_feature and _set_spatial_filter have moderate conditional complexity. The code is verbose in places, and certain expressions mix multiple nested calls, which can be harder to parse. Overall, it is highly readable but not perfect.

Run 3

The code is well-structured and readable. It uses clear class methods, consistent docstrings, and logical property groupings separated by comments. Nesting is minimal, and most methods are short with a single responsibility. Intent is clear, especially in methods like _make_feature, which explains fallback behavior. The use of helper methods and list comprehensions keeps the code concise. Some methods are slightly long, but they remain flat and easy to follow. Reading flow is natural, with properties grouped together and methods following. Minor issues include a slightly redundant blank line in the name property and a long conditional in _set_spatial_filter, but overall it is highly readable.

Run 1

Code Structure: The class is logically organized into initialization, Python protocol methods, properties, and layer operations, with helpful section markers. Nesting: Most methods are flat; indexing, feature lookup, and filter validation have modest, manageable nesting. Clarity of intent: Descriptive method names, docstrings, and explanatory comments make responsibilities clear, though abbreviated C API names and mixed ptr/_ptr usage require extra attention. Code length: The class is substantial but consists mostly of short, focused methods; some repetitive property implementations and redundant comments add bulk. Action granularity: Statements generally perform one action, although nested API calls and comprehensions compress several transformations into individual expressions. Reading flow: Method grouping and consistent patterns support natural reading. Stray blank lines, unnecessary parentheses, one long return statement, and minor docstring errors slightly disrupt polish.

Run 2

Code Structure: The class is logically organized into initialization, Python protocol methods, properties, and layer operations, with focused helpers. Nesting: Mostly shallow; indexing, feature lookup, and spatial filtering introduce only modest branching. Clarity of intent: Descriptive method names, docstrings, and explanatory comments make the wrapper's purpose clear, though abbreviated C API names and inconsistent ptr/_ptr usage require extra attention. Code length: Methods are concise, but repetitive property implementations and some redundant comments add bulk. Action granularity: Statements generally perform one action each; nested API calls and comprehensions occasionally combine several transformations. Reading flow: Organization and consistent method patterns support natural reading, with minor distractions from unnecessary parentheses, stray blank lines, a long return statement, and small documentation errors.

Run 3

Code Structure: Well-organized class with initialization, protocol methods, grouped properties, and utility methods. Nesting: Mostly shallow; feature lookup and spatial-filter validation introduce manageable nested branches. Clarity of intent: Descriptive method names, docstrings, and comments explain behavior and ownership, although abbreviated C API names and inconsistent ptr/_ptr usage require extra attention. Code length: Methods are generally concise, but repetitive field-access properties and some redundant comments add length. Action granularity: Most statements perform one clear action; nested API calls and comprehensions occasionally combine several operations. Reading flow: Logical ordering and consistent patterns make navigation straightforward. Minor distractions include unnecessary parentheses, stray blank lines, a long return statement, docstring typos, and naming a parameter filter, which shadows a built-in.