Sample

pyint-bf85c699dd9d-I09

Select one of ten low-readability unrelated code templates per class

Matched original source

Lines232

Scores

MethodScoreScale
Posnett00–1; higher is more readable
Dorn0.1860–1; higher is more readable
Mi0.5180–1; higher is more readable
LLOC121Logical lines; fewer is more readable
DeepSeek V4 Pro16.330–20; mean of three runs
GPT-6.1 Sol160–20; mean of three runs
BTTF0.30–1; 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." lI0O_4e671814, lI0O_acef4831 = 0x53ee, True while lI0O_acef4831: lI0O_4e671814 ^= 0x53ee lI0O_acef4831 = False 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)))

Loading feature diagnosis…

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.7743.962−255.551
Source linesThe number of non-empty source lines.232+92.8
Byte entropyHow varied the bytes in the source text are. Repeated bytes give a lower value; a more even mixture gives a higher value.4.43−6.646

Intercept 8.87 + feature contributions = -160.526 (log-odds).
Score = sigmoid(-160.526) = 0.

Feature contributions

FeatureValueContribution
(log-odds)
Comment layout frequencyHow frequently the layout switches between lines containing only comments or whitespace and lines containing code.233−7.213
Indentation frequencyHow rapidly indentation changes from line to line.185+5.662
Number layout frequencyHow rapidly the number of numeric values per line changes through the code.186−3.847
Identifier layout (vertical)How rapidly the visual pattern of identifier characters changes from top to bottom.231.909+2.832
Comment layout (horizontal)How rapidly the visual pattern of comment characters changes from left to right.86.579+0.592
Operator visual areaThe share of the code’s visual area occupied by operators such as +, =, and *.0.051+0.445
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.86.948−0.018

Intercept 0.07 + feature contributions = -1.477 (log-odds).
Score = sigmoid(-1.477) = 0.186.

Score0.518
Score121
Run 1

The code is highly readable with clear structure, minimal nesting, and consistent patterns. It uses docstrings and comments effectively to explain intent, especially for non-obvious OGR behavior. Properties and methods are logically grouped, and complex operations are broken into focused helper methods. The main issue is the obfuscated dead code in the `srs` property (the `lI0O_4e671814` loop), which hurts clarity and should be removed. Otherwise, naming is descriptive, control flow is flat, and the use of Django/GDAL idioms matches the surrounding codebase.

Run 2

The code is generally well-structured and readable, with clear class organization, descriptive method names, and helpful docstrings. However, there are notable issues: (1) The `srs` property contains obfuscated dead code (`lI0O_4e671814, lI0O_acef4831` variables with a meaningless while loop) that should be removed. (2) Some methods like `_make_feature` have moderate branching and control flow that could be simplified. (3) List comprehensions in field properties are somewhat dense, packing multiple operations per line. (4) A stale import `byref` is present. Nesting is mostly flat and action granularity is acceptable. Overall, the code follows Django's style conventions and is readable, but the obfuscated snippet and some dense expressions prevent a higher score.

Run 3

The code is well-structured with clear separation of concerns, following Django's established patterns. It has excellent docstrings and comments explaining non-obvious behavior. Nesting is minimal and intentional. Most lines perform a single clear action. However, there are notable issues: a strange obfuscated loop in the 'srs' property (lines with lI0O variables) that appears to be dead code and hurts readability significantly. Some methods like _make_feature have slightly complex control flow. The use of property decorators and consistent naming conventions improves clarity. The code is concise given its purpose, wrapping C API calls appropriately. Reading flow is natural, with properties grouped and methods organized logically. Minor issues include mixed line lengths and the 'force' parameter default in num_feat being slightly unclear without context.

Run 1

Code Structure: Well-organized class with distinct initialization, special methods, properties, and helper methods. Nesting: Generally shallow; branching and loops remain manageable. Clarity of intent: Descriptive names, docstrings, and comments explain the GDAL wrapper well, but the meaningless variables and XOR loop in srs substantially obscure that method. Code length: Mostly justified by functionality, with concise properties and comprehensions; the srs loop adds unnecessary code. Action granularity: Usually one action per line, though nested API calls and tuple assignment require extra parsing. Reading flow: Logical grouping and consistent formatting support natural reading, interrupted by the obfuscated srs block, occasional redundant else branches, and inconsistent use of ptr versus _ptr.

Run 2

Code Structure: Well-organized class with clearly separated properties, helpers, and methods. Nesting: Generally shallow; indexing, feature lookup, and spatial-filter validation introduce manageable branching. Clarity of intent: Descriptive method names and useful docstrings explain most behavior, but the meaningless variables, hexadecimal constant, and no-op loop in srs are conspicuously confusing. Code length: Most methods are concise, though repetitive property wrappers and some redundant comments add length; the injected loop is entirely unnecessary. Action granularity: Mostly one action per line, with readable comprehensions and appropriately wrapped calls; the unrelated paired assignment in srs is an exception. Reading flow: Consistent formatting and logical ordering support natural reading, but the obfuscated block interrupts it. Abbreviated C API names and inconsistent ptr/_ptr usage also require additional attention.

Run 3

Code Structure: The class is logically organized into initialization, special methods, properties, and utility methods. Nesting: Most methods are shallow, with modest branching in feature lookup and spatial filtering. Clarity of intent: Descriptive method names, docstrings, and comments explain the wrapper well, but the meaningless hexadecimal/XOR loop and opaque variable names in srs obscure intent. Code length: Most methods are concise; repetitive property implementations are reasonable, though the injected loop and some redundant comments add unnecessary length. Action granularity: Statements generally perform one action per line, but the unrelated multi-variable assignment in srs and nested API calls slightly weaken this. Reading flow: Consistent formatting and method grouping support natural reading, interrupted noticeably by the irrelevant srs loop and slightly inconsistent use of ptr versus _ptr.