Noise.dimentions() - Code Metrics - Inspection of "Prepare v2.2.0 release" - HexDecimal/libtcod-cffi - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( a3685b...e66410 )

by Kyle

created 2017-02-19 06:17 UTC

Noise.dimentions() A

↳ Parent: Noise

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes	5
Bugs	0	Features	0

Metric	Value
cc	1
c	5
b	0
f	0
dl	0
loc	3
rs	10


class SomeClass:
    def some_method(self):
        """Do x and return foo."""
from __future__ import absolute_import

import operator


import numpy as np
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3

from tcod.tcod import _cdata
from tcod.libtcod import ffi, lib
import tcod.libtcod

"""Noise implementation constants"""

SIMPLE = 0
FBM = 1
TURBULENCE = 2

class Noise(object):
    """

    The ``hurst`` exponent describes the raggedness of the resultant noise,
    with a higher value leading to a smoother noise.
    Not used with tcod.noise.SIMPLE.

    ``lacunarity`` is a multiplier that determines how fast the noise
    frequency increases for each successive octave.
    Not used with tcod.noise.SIMPLE.

    Args:
        dimensions (int): Must be from 1 to 4.
        algorithm (int): Defaults to NOISE_SIMPLEX
        implementation (int): Defaults to tcod.noise.SIMPLE
        hurst (float): The hurst exponent.  Should be in the 0.0-1.0 range.
        lacunarity (float): The noise lacunarity.
        octaves (float): The level of detail on fBm and turbulence
                         implementations.
        rand (Optional[Random]): A Random instance, or None.

    .. versionadded:: 2.0
    """

    def __init__(self, dimensions, algorithm=2, implementation=SIMPLE,
                 hurst=0.5, lacunarity=2.0, octaves=4, rand=None):
        if not 0 < dimensions <= 4:
            raise ValueError('dimensions must be in range 0 < n <= 4, got %r' %
                             (dimensions,))
        self._random = rand
        self._random_c = _cdata(rand)
        self._algorithm = algorithm
        self.noise_c = ffi.gc(
            ffi.cast(
                'perlin_data_t*',
                lib.TCOD_noise_new(dimensions, hurst, lacunarity,
                                   self._random_c),
                ),
            lib.TCOD_noise_delete)
        self._tdl_noise_c = ffi.new('TDLNoise*', (self.noise_c,
                                                  dimensions,
                                                  0,
                                                  octaves))
        self.implementation = implementation # sanity check

    @property
    def dimensions(self):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        return self._tdl_noise_c.dimensions

    @property
    def dimentions(self): # deprecated
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        return self.dimensions

    @property
    def algorithm(self):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        return self.noise_c.noise_type
    @algorithm.setter
    def algorithm(self, value):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        lib.TCOD_noise_set_type(self.noise_c, value)

    @property
    def implementation(self):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        return self._tdl_noise_c.implementation
    @implementation.setter
    def implementation(self, value):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        if not 0 <= value < 3:
            raise ValueError('%r is not a valid implementation. ' % (value,))
        self._tdl_noise_c.implementation = value

    @property
    def hurst(self):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        return self.noise_c.H

    @property
    def lacunarity(self):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        return self.noise_c.lacunarity

    @property
    def octaves(self):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        return self._tdl_noise_c.octaves
    @octaves.setter
    def octaves(self, value):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        self._tdl_noise_c.octaves = value

    def get_point(self, x=0, y=0, z=0, w=0):
        """Return the noise value at the (x, y, z, w) point.

        Args:
            x (float): The position on the 1st axis.
            y (float): The position on the 2nd axis.
            z (float): The position on the 3rd axis.
            w (float): The position on the 4th axis.
        """
        return lib.NoiseGetSample(self._tdl_noise_c, (x, y, z, w))

    def sample_mgrid(self, mgrid):
        """Sample a mesh-grid array and return the result.

        The :any:`sample_ogrid` method performs better as there is a lot of
        overhead when working with large mesh-grids.

        Args:
            mgrid (numpy.ndarray): A mesh-grid array of points to sample.
                A contiguous array of type :any:`numpy.float32` is preferred.

        Returns:
            numpy.ndarray: An array of sampled points
                           with the shape: ``mgrid.shape[:-1]``.
                           The ``dtype`` is :any:`numpy.float32`.

        .. versionadded:: 2.2
        """
        mgrid = np.ascontiguousarray(mgrid, np.float32)
        if mgrid.shape[0] != self.dimensions:
            raise ValueError('mgrid.shape[0] must equal self.dimensions, '
                             '%r[0] != %r' % (mgrid.shape, self.dimensions))
        out = np.ndarray(mgrid.shape[1:], np.float32)
        if mgrid.shape[1:] != out.shape:
            raise ValueError('mgrid.shape[1:] must equal out.shape, '
                             '%r[1:] != %r' % (mgrid.shape, out.shape))
        lib.NoiseSampleMeshGrid(self._tdl_noise_c, out.size,
                                ffi.cast('float*', mgrid.ctypes.data),
                                ffi.cast('float*', out.ctypes.data))
        return out

    def sample_ogrid(self, ogrid):
        """Sample an open mesh-grid array and return the result.

        Args
            ogrid (Sequence[numpy.ndarray]): An open mesh-grid.

        Returns:
            numpy.ndarray: An array of sampled points.  The ``shape`` is based
                           on the lengths of the open mesh-grid arrays.
                           The ``dtype`` is :any:`numpy.float32`.

        .. versionadded:: 2.2
        """
        if len(ogrid) != self.dimensions:
            raise ValueError('len(ogrid) must equal self.dimensions, '
                             '%r != %r' % (len(ogrid), self.dimensions))
        ogrids = [np.ascontiguousarray(array, np.float32) for array in ogrid]
        out = np.ndarray([array.size for array in ogrids], np.float32)
        lib.NoiseSampleOpenMeshGrid(
            self._tdl_noise_c,
            len(ogrids),
            out.shape,
            [ffi.cast('float*', array.ctypes.data) for array in ogrids],
            ffi.cast('float*', out.ctypes.data),
            )
        return out

    def __getstate__(self):
        if self.dimensions < 4 and self.noise_c.waveletTileData == ffi.NULL:
            # Trigger a side effect of wavelet, so that copies will be synced.
            saved_algo = self.algorithm
            self.algorithm = tcod.libtcod.NOISE_WAVELET

            self.get_point()
            self.algorithm = saved_algo

        waveletTileData = None
        if self.noise_c.waveletTileData != ffi.NULL:
            waveletTileData = ffi.buffer(
                self.noise_c.waveletTileData[0:32*32*32])[:]
        return (
            self._random,
            self.implementation,
            self.octaves,
            self.noise_c.ndim,
            ffi.buffer(self.noise_c.map)[:],
            ffi.buffer(self.noise_c.buffer)[:],
            self.noise_c.H,
            self.noise_c.lacunarity,
            ffi.buffer(self.noise_c.exponent)[:],
            waveletTileData,
            self.noise_c.noise_type,
            )

    def __setstate__(self, state):
        self._random = state[0]
        self._random_c = _cdata(self._random)
        self.noise_c = ffi.new('perlin_data_t*')
        self.noise_c.ndim = state[3]
        ffi.buffer(self.noise_c.map)[:] = state[4]
        ffi.buffer(self.noise_c.buffer)[:] = state[5]
        self.noise_c.H = state[6]
        self.noise_c.lacunarity = state[7]
        ffi.buffer(self.noise_c.exponent)[:] = state[8]
        if state[9]:
            # high change of this being prematurely garbage collected!
            self.__waveletTileData = ffi.new('float[]', 32*32*32)
class Foo:
    def __init__(self, x=None):
        self.x = x
            ffi.buffer(self.__waveletTileData)[:] = state[9]
        self.noise_c.noise_type = state[10]
        self._tdl_noise_c = ffi.new('TDLNoise*',
                                    (self.noise_c, self.noise_c.ndim,
                                     state[1], state[2]))


Push — master ( a3685b...e66410 )

Noise.dimentions() A

Complexity

Size

Duplication

Importance

1. Missing Dependencies

2. Missing init.py files

1
			0 ignored issues – show Coding Style introduced 2017-01-19 12:56 UTC by Report Bug Copy Issue Report This module should have a docstring. The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods: class SomeClass: def some_method(self): """Do x and return foo.""" If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions. Loading history...
2			from __future__ import absolute_import
3
4			import operator
			0 ignored issues – show Unused Code introduced 2017-02-19 06:18 UTC by Report Bug Copy Issue Report The import `operator` seems to be unused. Loading history...
5
6			import numpy as np
			0 ignored issues – show Configuration introduced 2017-02-19 06:18 UTC by Report Bug Copy Issue Report The import `numpy` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
7
8			from tcod.tcod import _cdata
9			from tcod.libtcod import ffi, lib
10			import tcod.libtcod
11
12			"""Noise implementation constants"""
			0 ignored issues – show Unused Code introduced 2017-02-19 06:18 UTC by Report Bug Copy Issue Report This string statement has no effect and could be removed. Loading history...
13			SIMPLE = 0
14			FBM = 1
15			TURBULENCE = 2
16
17			class Noise(object):
18			"""
19
20			The ``hurst`` exponent describes the raggedness of the resultant noise,
21			with a higher value leading to a smoother noise.
22			Not used with tcod.noise.SIMPLE.
23
24			``lacunarity`` is a multiplier that determines how fast the noise
25			frequency increases for each successive octave.
26			Not used with tcod.noise.SIMPLE.
27
28			Args:
29			dimensions (int): Must be from 1 to 4.
30			algorithm (int): Defaults to NOISE_SIMPLEX
31			implementation (int): Defaults to tcod.noise.SIMPLE
32			hurst (float): The hurst exponent. Should be in the 0.0-1.0 range.
33			lacunarity (float): The noise lacunarity.
34			octaves (float): The level of detail on fBm and turbulence
35			implementations.
36			rand (Optional[Random]): A Random instance, or None.
37
38			.. versionadded:: 2.0
39			"""
40
41			def __init__(self, dimensions, algorithm=2, implementation=SIMPLE,
42			hurst=0.5, lacunarity=2.0, octaves=4, rand=None):
43			if not 0 < dimensions <= 4:
44			raise ValueError('dimensions must be in range 0 < n <= 4, got %r' %
45			(dimensions,))
46			self._random = rand
47			self._random_c = _cdata(rand)
48			self._algorithm = algorithm
49			self.noise_c = ffi.gc(
50			ffi.cast(
51			'perlin_data_t*',
52			lib.TCOD_noise_new(dimensions, hurst, lacunarity,
53			self._random_c),
54			),
55			lib.TCOD_noise_delete)
56			self._tdl_noise_c = ffi.new('TDLNoise*', (self.noise_c,
57			dimensions,
58			0,
59			octaves))
60			self.implementation = implementation # sanity check
61
62			@property
63			def dimensions(self):
			0 ignored issues – show Coding Style introduced 2016-11-19 10:43 UTC by Report Bug Copy Issue Report This method should have a docstring. The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods: class SomeClass: def some_method(self): """Do x and return foo.""" If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions. Loading history...
64			return self._tdl_noise_c.dimensions
65
66			@property
67			def dimentions(self): # deprecated
			0 ignored issues – show Coding Style introduced 2017-02-19 06:18 UTC by Report Bug Copy Issue Report This method should have a docstring. The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods: class SomeClass: def some_method(self): """Do x and return foo.""" If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions. Loading history...
68			return self.dimensions
69
70			@property
71			def algorithm(self):
			0 ignored issues – show Coding Style introduced 2016-12-31 08:28 UTC by Report Bug Copy Issue Report This method should have a docstring. The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods: class SomeClass: def some_method(self): """Do x and return foo.""" If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions. Loading history...
72			return self.noise_c.noise_type
73			@algorithm.setter
74			def algorithm(self, value):
			0 ignored issues – show Coding Style introduced 2016-12-31 08:28 UTC by Report Bug Copy Issue Report This method should have a docstring. The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods: class SomeClass: def some_method(self): """Do x and return foo.""" If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions. Loading history...
75			lib.TCOD_noise_set_type(self.noise_c, value)
76
77			@property
78			def implementation(self):
			0 ignored issues – show Coding Style introduced 2017-02-19 06:18 UTC by Report Bug Copy Issue Report This method should have a docstring. The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods: class SomeClass: def some_method(self): """Do x and return foo.""" If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions. Loading history...
79			return self._tdl_noise_c.implementation
80			@implementation.setter
81			def implementation(self, value):
			0 ignored issues – show Coding Style introduced 2017-02-19 06:18 UTC by Report Bug Copy Issue Report This method should have a docstring. The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods: class SomeClass: def some_method(self): """Do x and return foo.""" If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions. Loading history...
82			if not 0 <= value < 3:
83			raise ValueError('%r is not a valid implementation. ' % (value,))
84			self._tdl_noise_c.implementation = value
85
86			@property
87			def hurst(self):
			0 ignored issues – show Coding Style introduced 2016-11-19 10:43 UTC by Report Bug Copy Issue Report This method should have a docstring. The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods: class SomeClass: def some_method(self): """Do x and return foo.""" If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions. Loading history...
88			return self.noise_c.H
89
90			@property
91			def lacunarity(self):
			0 ignored issues – show Coding Style introduced 2016-11-19 10:43 UTC by Report Bug Copy Issue Report This method should have a docstring. The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods: class SomeClass: def some_method(self): """Do x and return foo.""" If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions. Loading history...
92			return self.noise_c.lacunarity
93
94			@property
95			def octaves(self):
			0 ignored issues – show Coding Style introduced 2017-02-19 06:18 UTC by Report Bug Copy Issue Report This method should have a docstring. The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods: class SomeClass: def some_method(self): """Do x and return foo.""" If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions. Loading history...
96			return self._tdl_noise_c.octaves
97			@octaves.setter
98			def octaves(self, value):
			0 ignored issues – show Coding Style introduced 2017-02-19 06:18 UTC by Report Bug Copy Issue Report This method should have a docstring. The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods: class SomeClass: def some_method(self): """Do x and return foo.""" If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions. Loading history...
99			self._tdl_noise_c.octaves = value
100
101			def get_point(self, x=0, y=0, z=0, w=0):
102			"""Return the noise value at the (x, y, z, w) point.
103
104			Args:
105			x (float): The position on the 1st axis.
106			y (float): The position on the 2nd axis.
107			z (float): The position on the 3rd axis.
108			w (float): The position on the 4th axis.
109			"""
110			return lib.NoiseGetSample(self._tdl_noise_c, (x, y, z, w))
111
112			def sample_mgrid(self, mgrid):
113			"""Sample a mesh-grid array and return the result.
114
115			The :any:`sample_ogrid` method performs better as there is a lot of
116			overhead when working with large mesh-grids.
117
118			Args:
119			mgrid (numpy.ndarray): A mesh-grid array of points to sample.
120			A contiguous array of type :any:`numpy.float32` is preferred.
121
122			Returns:
123			numpy.ndarray: An array of sampled points
124			with the shape: ``mgrid.shape[:-1]``.
125			The ``dtype`` is :any:`numpy.float32`.
126
127			.. versionadded:: 2.2
128			"""
129			mgrid = np.ascontiguousarray(mgrid, np.float32)
130			if mgrid.shape[0] != self.dimensions:
131			raise ValueError('mgrid.shape[0] must equal self.dimensions, '
132			'%r[0] != %r' % (mgrid.shape, self.dimensions))
133			out = np.ndarray(mgrid.shape[1:], np.float32)
134			if mgrid.shape[1:] != out.shape:
135			raise ValueError('mgrid.shape[1:] must equal out.shape, '
136			'%r[1:] != %r' % (mgrid.shape, out.shape))
137			lib.NoiseSampleMeshGrid(self._tdl_noise_c, out.size,
138			ffi.cast('float*', mgrid.ctypes.data),
139			ffi.cast('float*', out.ctypes.data))
140			return out
141
142			def sample_ogrid(self, ogrid):
143			"""Sample an open mesh-grid array and return the result.
144
145			Args
146			ogrid (Sequence[numpy.ndarray]): An open mesh-grid.
147
148			Returns:
149			numpy.ndarray: An array of sampled points. The ``shape`` is based
150			on the lengths of the open mesh-grid arrays.
151			The ``dtype`` is :any:`numpy.float32`.
152
153			.. versionadded:: 2.2
154			"""
155			if len(ogrid) != self.dimensions:
156			raise ValueError('len(ogrid) must equal self.dimensions, '
157			'%r != %r' % (len(ogrid), self.dimensions))
158			ogrids = [np.ascontiguousarray(array, np.float32) for array in ogrid]
159			out = np.ndarray([array.size for array in ogrids], np.float32)
160			lib.NoiseSampleOpenMeshGrid(
161			self._tdl_noise_c,
162			len(ogrids),
163			out.shape,
164			[ffi.cast('float*', array.ctypes.data) for array in ogrids],
165			ffi.cast('float*', out.ctypes.data),
166			)
167			return out
168
169			def __getstate__(self):
170			if self.dimensions < 4 and self.noise_c.waveletTileData == ffi.NULL:
171			# Trigger a side effect of wavelet, so that copies will be synced.
172			saved_algo = self.algorithm
173			self.algorithm = tcod.libtcod.NOISE_WAVELET
			0 ignored issues – show Bug introduced 2017-02-19 06:18 UTC by Report Bug Copy Issue Report The Module `tcod.libtcod` does not seem to have a member named `NOISE_WAVELET`. This check looks for calls to members that are non-existent. These calls will fail. The member could have been renamed or removed. Loading history...
174			self.get_point()
175			self.algorithm = saved_algo
176
177			waveletTileData = None
178			if self.noise_c.waveletTileData != ffi.NULL:
179			waveletTileData = ffi.buffer(
180			self.noise_c.waveletTileData[0:323232])[:]
181			return (
182			self._random,
183			self.implementation,
184			self.octaves,
185			self.noise_c.ndim,
186			ffi.buffer(self.noise_c.map)[:],
187			ffi.buffer(self.noise_c.buffer)[:],
188			self.noise_c.H,
189			self.noise_c.lacunarity,
190			ffi.buffer(self.noise_c.exponent)[:],
191			waveletTileData,
192			self.noise_c.noise_type,
193			)
194
195			def __setstate__(self, state):
196			self._random = state[0]
197			self._random_c = _cdata(self._random)
198			self.noise_c = ffi.new('perlin_data_t*')
199			self.noise_c.ndim = state[3]
200			ffi.buffer(self.noise_c.map)[:] = state[4]
201			ffi.buffer(self.noise_c.buffer)[:] = state[5]
202			self.noise_c.H = state[6]
203			self.noise_c.lacunarity = state[7]
204			ffi.buffer(self.noise_c.exponent)[:] = state[8]
205			if state[9]:
206			# high change of this being prematurely garbage collected!
207			self.__waveletTileData = ffi.new('float[]', 323232)
			0 ignored issues – show Coding Style introduced 2017-02-19 06:18 UTC by Report Bug Copy Issue Report The attribute `__waveletTileData` was defined outside `__init__`. It is generally a good practice to initialize all attributes to default values in the `__init__` method: class Foo: def __init__(self, x=None): self.x = x Loading history...
208			ffi.buffer(self.__waveletTileData)[:] = state[9]
209			self.noise_c.noise_type = state[10]
210			self._tdl_noise_c = ffi.new('TDLNoise*',
211			(self.noise_c, self.noise_c.ndim,
212			state[1], state[2]))
213

HexDecimal / libtcod-cffi

Push — master ( a3685b...e66410 )

Noise.dimentions() A

Complexity

Size

Duplication

Importance

1. Missing Dependencies

2. Missing __init__.py files

Duplication Side-by-Side

Filter issues like

2. Missing init.py files