Sample

pyint-3d07708e0cff-I10

Deterministically remove some optional blank lines and bracketed line breaks

Matched original source

Lines210

Scores

MethodScoreScale
Posnett00–1; higher is more readable
Dorn0.0780–1; higher is more readable
Mi0.5160–1; higher is more readable
LLOC117Logical lines; fewer is more readable
DeepSeek V4 Pro12.330–20; mean of three runs
GPT-6.1 Sol160–20; mean of three runs
BTTF0.3210–1; higher is more readable

Views

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." 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)): 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.7584.101−250.275
Source linesThe number of non-empty source lines.210+84
Byte entropyHow varied the bytes in the source text are. Repeated bytes give a lower value; a more even mixture gives a higher value.4.398−6.597

Intercept 8.87 + feature contributions = -164.002 (log-odds).
Score = sigmoid(-164.002) = 0.

Feature contributions

FeatureValueContribution
(log-odds)
Comment layout frequencyHow frequently the layout switches between lines containing only comments or whitespace and lines containing code.211−6.435
Indentation frequencyHow rapidly indentation changes from line to line.163+4.901
Number layout frequencyHow rapidly the number of numeric values per line changes through the code.163−3.32
Identifier layout (vertical)How rapidly the visual pattern of identifier characters changes from top to bottom.209.904+2.524
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.133.981−2.01
Comment layout (horizontal)How rapidly the visual pattern of comment characters changes from left to right.133.991+1.357
Operator visual areaThe share of the code’s visual area occupied by operators such as +, =, and *.0.051+0.445

Intercept 0.07 + feature contributions = -2.466 (log-odds).
Score = sigmoid(-2.466) = 0.078.

Score0.516
Score117
Run 1

The code is generally well-structured with clear class organization and docstrings, but readability suffers from several issues. The class follows a logical pattern with properties and methods grouped together, and comments explain complex logic like random access fallback. However, there are multiple formatting problems: missing blank lines between methods, inconsistent indentation in list comprehensions (particularly in fields, field_types, and field_precisions methods), and awkward line breaks that disrupt reading flow. Some lines try to accomplish too much, especially in the list comprehensions where function calls are split across lines with odd spacing. The __getitem__ method handles three cases cleanly but the slice handling line is dense. The spatial filter setter has reasonable validation logic but could be broken into smaller helper methods. Overall, the code is functional and the intent is clear, but it needs consistent formatting and better line organization to be truly readable.

Run 2

The code is generally clear and well-structured, with logical class organization and good use of docstrings and comments. However, several readability issues exist: inconsistent blank lines between methods, occasional long or misaligned lines (e.g., in fields and field_types properties), and inconsistent indentation/line breaks in list comprehensions. Some methods are slightly dense, and the overall flow feels uneven due to these formatting inconsistencies. The purpose of each method is clear, and naming is descriptive, which improves clarity. The class is not deeply nested and each action is mostly separated per line, but a few comprehensions pack multiple operations into one line. Overall readable but would benefit from more consistent PEP 8 formatting and cleaner line breaks.

Run 3

While the code follows a logical class structure with docstrings and grouped sections, readability is significantly compromised by several factors. Deep nesting occurs in methods like __getitem__ and _set_spatial_filter, adding cognitive load. There is inconsistent code formatting and alignment, particularly in list comprehensions within the properties for fields, field_types, and field_precisions, which disrupts the reading flow. Some lines are excessively long and contain multiple actions, violating the principle of action granularity. Additionally, the code has unnecessary mixed-case abstractions and a dense use of C API calls, making it harder to discern intent at a glance. Though variable names are descriptive and organization is decent, the inconsistency and clutter reduce overall clarity. The code is maintainable but not highly readable in its current form.

Run 1

Code Structure: The class is logically organized into initialization, iteration/indexing, properties, and methods, with focused responsibilities. Nesting: Generally shallow; feature lookup and spatial-filter validation introduce only moderate nesting. Clarity of intent: Descriptive method names, docstrings, and explanatory comments make behavior clear, although C API abbreviations and inconsistent ptr/_ptr usage add cognitive overhead. Code length: Appropriate for the wrapper's functionality, with concise methods and comprehensions; some documentation is slightly verbose. Action granularity: Most lines perform one action, but nested API calls and crowded comprehensions combine several operations. Reading flow: The overall sequence is natural, but missing blank lines between methods, irregular spacing, and malformed-looking line wrapping noticeably disrupt scanning.

Run 2

Code Structure: The class is logically organized into initialization, protocol methods, properties, and layer operations, with focused helper methods. Nesting: Generally shallow; feature lookup and spatial-filter validation introduce only moderate nesting. Clarity of intent: Descriptive method names, docstrings, and explanatory comments make the wrapper's purpose clear, although abbreviated C API names and inconsistent ptr/_ptr usage require additional context. Code length: Most methods are concise; some comments repeat obvious operations, but overall length is appropriate for the functionality. Action granularity: Statements generally perform one action, though nested API calls and compressed comprehensions increase parsing effort. Reading flow: The overall sequence is natural, but missing blank lines between methods, irregular spacing, and poorly formatted multiline expressions noticeably interrupt scanning.

Run 3

Code Structure: Logically organized into a cohesive Layer class with initialization, iteration, properties, and utility methods. Nesting: Generally shallow; feature lookup and spatial-filter validation introduce only moderate nesting. Clarity of intent: Descriptive method names and helpful docstrings explain the wrapper's purpose and important GDAL behavior, although abbreviated C API names require domain knowledge. Code length: Most methods are concise, with some repetitive property implementations and verbose comments. Action granularity: Statements generally perform one action each, though nested API calls and crowded comprehensions require extra parsing. Reading flow: The overall progression is natural, but inconsistent blank lines, excessive inline whitespace, and awkwardly formatted comprehensions interrupt scanning. Consistent formatting and clearer separation between methods would substantially improve readability.