Sample

pyint-4c5b4a4b5dfd-SRC

Unmodified source class

Lines228

Scores

MethodScoreScale
Posnett00–1; higher is more readable
Dorn0.1930–1; higher is more readable
Mi0.5180–1; higher is more readable
LLOC117Logical lines; fewer is more readable
DeepSeek V4 Pro17.670–20; mean of three runs
GPT-6.1 Sol17.670–20; mean of three runs
BTTF0.3670–1; 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." 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.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.4.402−6.603

Intercept 8.87 + feature contributions = -156.808 (log-odds).
Score = sigmoid(-156.808) = 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.227.898+2.776
Comment layout (horizontal)How rapidly the visual pattern of comment characters changes from left to right.85.86+0.581
Operator visual areaThe share of the code’s visual area occupied by operators such as +, =, and *.0.051+0.444
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.86.952−0.018

Intercept 0.07 + feature contributions = -1.428 (log-odds).
Score = sigmoid(-1.428) = 0.193.

Score0.518
Score117
Run 1

The code is highly readable. It follows a clear class structure with logical grouping: initialization, dunder methods, properties, and methods are well-organized. Each method/property has a docstring explaining its purpose. Nesting is minimal—only at necessary points like isinstance checks and try/except blocks. Lines are concise and focused, with one main action per line. The code makes good use of list comprehensions and helper methods to avoid repetition. Variable names are descriptive, and the code uses appropriate whitespace. The reading flow is natural, progressing from simple accessors to more complex operations. Minor deductions: some complex one-liners in list comprehensions could be broken up, and a few method implementations rely on understanding the GDAL C API. Overall, this is an excellent example of readable, maintainable code.

Run 2

The code is highly readable with clear structure and intent. It organizes a complex GDAL wrapper into logical sections (properties, methods, helpers) and uses consistent docstrings and naming conventions. Nesting is generally shallow, though the `__getitem__` method has moderate branching. Action granularity is mostly one action per line, with some list comprehensions that remain clear. The code leverages Python idioms like properties and iteration, improving readability. A few minor issues slightly reduce clarity: some comments repeat what the code already shows, and methods like `_make_feature` have nested conditionals that require careful reading. Overall, the code is maintainable and easy to follow.

Run 3

The code is highly readable with clear structure, consistent patterns, and excellent documentation. It follows logical organization with properties grouped together, private helper methods separated, and public methods at the end. Nesting is minimal—only a few conditional branches and list comprehensions. Intent is clear through descriptive method names, docstrings, and inline comments. Code length is appropriate; each method is focused and concise without unnecessary repetition. Action granularity is generally one action per line, with list comprehensions used effectively for simple patterns. The reading flow is natural: imports → class definition → init → dunder methods → helper methods → properties → public methods. Minor deductions: some list comprehensions could be slightly more readable if extracted to helper methods, and a few methods (like _make_feature) have slightly longer logical paths. The use of type hints is absent, but this is typical for older Django code. Overall, exemplary readability with strong documentation throughout.

Run 1

Code Structure: Well-organized class with initialization, Python protocol methods, properties, and layer operations clearly grouped. Nesting: Mostly shallow; feature lookup and spatial-filter validation introduce only moderate nesting. Clarity of intent: Descriptive method names, docstrings, and explanatory comments make responsibilities clear, though abbreviated C API names, inconsistent ptr/_ptr usage, and nested API calls require domain knowledge. Code length: Appropriate for the functionality, with small methods; some repetitive property implementations and redundant comments add minor verbosity. Action granularity: Statements generally perform one action per line, although nested calls and comprehensions combine retrieval, conversion, and construction. Reading flow: Natural progression and consistent formatting support easy scanning. Minor distractions include legacy property construction, unnecessary else branches after returns, and a force parameter on a property getter that looks caller-configurable but is not accessible through normal property usage.

Run 2

Code Structure: Well-organized class with focused methods and clearly grouped properties and operations. Nesting: Generally shallow; indexing, feature lookup, and filter validation introduce only modest nesting. 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: Most methods are concise, but repetitive field-property implementations and some redundant comments add length. Action granularity: Statements generally perform one action each; nested API calls in comprehensions compress several operations and slightly reduce readability. Reading flow: Initialization, Python protocol methods, properties, and public operations follow a natural progression. Minor distractions include mixing property declaration styles, shadowing the built-in filter name, and exposing a force parameter on a property getter.

Run 3

Code Structure: Well-organized class with focused methods and clearly grouped properties and operations. Nesting: Generally shallow; indexing, feature lookup, and spatial-filter validation introduce only modest nesting. Clarity of intent: Descriptive method names, docstrings, and useful comments explain both behavior and GDAL-specific constraints. Abbreviations such as _ldefn and capi, inconsistent ptr/_ptr usage, and nested C API calls slightly reduce clarity. Code length: Appropriately concise for the functionality, though some comments repeat obvious actions and field-access properties contain repetitive patterns. Action granularity: Most statements perform one clear action; comprehensions and nested calls occasionally combine retrieval, conversion, and wrapping. Reading flow: Imports, initialization, special methods, properties, and public operations follow a natural progression. Minor distractions include redundant else branches after returns, the parameter named filter shadowing a built-in, and the unusual optional argument on the num_feat property.