Noise.algorithm() - Code Metrics - HexDecimal/python-tdl - Measure and Improve Code Quality continuously with Scrutinizer

Noise.algorithm() A
last analyzed 2018-06-24 08:14 UTC

↳ Parent: Noise

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes

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

"""
The :any:`Noise.sample_mgrid` and :any:`Noise.sample_ogrid` methods are
multi-threaded operations when the Python runtime supports OpenMP.
Even when single threaded these methods will perform much better than
multiple calls to :any:`Noise.get_point`.

Example::

    import numpy as np
    import tcod
    import tcod.noise

    noise = tcod.noise.Noise(
        dimensions=2,
        algorithm=tcod.NOISE_SIMPLEX,
        implementation=tcod.noise.TURBULENCE,
        hurst=0.5,
        lacunarity=2.0,
        octaves=4,
        seed=None,
        )

    # Create a 5x5 open multi-dimensional mesh-grid.
    ogrid = [np.arange(5, dtype=np.float32),
             np.arange(5, dtype=np.float32)]
    print(ogrid)

    # Scale the grid.
    ogrid[0] *= 0.25
    ogrid[1] *= 0.25

    # Return the sampled noise from this grid of points.
    samples = noise.sample_ogrid(ogrid)
    print(samples)
"""
from __future__ import absolute_import

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

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.
        seed (Optional[Random]): A Random instance, or None.

    Attributes:
        noise_c (CData): A cffi pointer to a TCOD_noise_t object.
    """

    def __init__(self, dimensions, algorithm=2, implementation=SIMPLE,
                 hurst=0.5, lacunarity=2.0, octaves=4, seed=None):
        if not 0 < dimensions <= 4:
            raise ValueError('dimensions must be in range 0 < n <= 4, got %r' %
                             (dimensions,))
        self._random = seed
        _random_c = seed.random_c if seed else ffi.NULL
        self._algorithm = algorithm
        self.noise_c = ffi.gc(
            ffi.cast(
                'perlin_data_t*',
                lib.TCOD_noise_new(dimensions, hurst, lacunarity,
                                   _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 `numpy.float32` is preferred.

        Returns:
            numpy.ndarray: An array of sampled points.

                This array has the shape: ``mgrid.shape[:-1]``.
                The ``dtype`` is `numpy.float32`.
        """
        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[Sequence[float]]): 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 `numpy.float32`.
        """
        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):
        state = self.__dict__.copy()
        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 = list(self.noise_c.waveletTileData[0:32*32*32])

            state['_waveletTileData'] = waveletTileData

        state['noise_c'] = {
            'ndim': self.noise_c.ndim,
            'map': list(self.noise_c.map),
            'buffer': [list(sub_buffer) for sub_buffer in self.noise_c.buffer],
            'H': self.noise_c.H,
            'lacunarity': self.noise_c.lacunarity,
            'exponent': list(self.noise_c.exponent),
            'waveletTileData': waveletTileData,
            'noise_type': self.noise_c.noise_type,
            }
        state['_tdl_noise_c'] = {
            'dimensions': self._tdl_noise_c.dimensions,
            'implementation': self._tdl_noise_c.implementation,
            'octaves': self._tdl_noise_c.octaves,
            }
        return state

    def __setstate__(self, state):
        if isinstance(state, tuple): # deprecated format
            return self._setstate_old(state)
        # unpack wavelet tile data if it exists
        if '_waveletTileData' in state:
            state['_waveletTileData'] = ffi.new('float[]',
                                                state['_waveletTileData'])
            state['noise_c']['waveletTileData'] = state['_waveletTileData']
        else:
            state['noise_c']['waveletTileData'] = ffi.NULL

        # unpack perlin_data_t and link to Random instance
        state['noise_c']['rand'] = state['_random'].random_c
        state['noise_c'] = ffi.new('perlin_data_t*', state['noise_c'])

        # unpack TDLNoise and link to libtcod noise
        state['_tdl_noise_c']['noise'] = state['noise_c']
        state['_tdl_noise_c'] = ffi.new('TDLNoise*', state['_tdl_noise_c'])
        self.__dict__.update(state)

    def _setstate_old(self, state):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        self._random = state[0]
        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]))


Noise.algorithm() A
last analyzed 2018-06-24 08:14 UTC

Complexity

Size

Duplication

Importance

1. Missing Dependencies

2. Missing init.py files

1			"""
2			The :any:`Noise.sample_mgrid` and :any:`Noise.sample_ogrid` methods are
3			multi-threaded operations when the Python runtime supports OpenMP.
4			Even when single threaded these methods will perform much better than
5			multiple calls to :any:`Noise.get_point`.
6
7			Example::
8
9			import numpy as np
10			import tcod
11			import tcod.noise
12
13			noise = tcod.noise.Noise(
14			dimensions=2,
15			algorithm=tcod.NOISE_SIMPLEX,
16			implementation=tcod.noise.TURBULENCE,
17			hurst=0.5,
18			lacunarity=2.0,
19			octaves=4,
20			seed=None,
21			)
22
23			# Create a 5x5 open multi-dimensional mesh-grid.
24			ogrid = [np.arange(5, dtype=np.float32),
25			np.arange(5, dtype=np.float32)]
26			print(ogrid)
27
28			# Scale the grid.
29			ogrid[0] *= 0.25
30			ogrid[1] *= 0.25
31
32			# Return the sampled noise from this grid of points.
33			samples = noise.sample_ogrid(ogrid)
34			print(samples)
35			"""
36			from __future__ import absolute_import
37
38			import numpy as np
			0 ignored issues – show Configuration introduced 2017-07-05 00:27 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...
39
40			from tcod.libtcod import ffi, lib
41			import tcod.libtcod
42
43			"""Noise implementation constants"""
			0 ignored issues – show Unused Code introduced 2017-07-05 00:27 UTC by Report Bug Copy Issue Report This string statement has no effect and could be removed. Loading history...
44			SIMPLE = 0
45			FBM = 1
46			TURBULENCE = 2
47
48			class Noise(object):
49			"""
50
51			The ``hurst`` exponent describes the raggedness of the resultant noise,
52			with a higher value leading to a smoother noise.
53			Not used with tcod.noise.SIMPLE.
54
55			``lacunarity`` is a multiplier that determines how fast the noise
56			frequency increases for each successive octave.
57			Not used with tcod.noise.SIMPLE.
58
59			Args:
60			dimensions (int): Must be from 1 to 4.
61			algorithm (int): Defaults to NOISE_SIMPLEX
62			implementation (int): Defaults to tcod.noise.SIMPLE
63			hurst (float): The hurst exponent. Should be in the 0.0-1.0 range.
64			lacunarity (float): The noise lacunarity.
65			octaves (float): The level of detail on fBm and turbulence
66			implementations.
67			seed (Optional[Random]): A Random instance, or None.
68
69			Attributes:
70			noise_c (CData): A cffi pointer to a TCOD_noise_t object.
71			"""
72
73			def __init__(self, dimensions, algorithm=2, implementation=SIMPLE,
74			hurst=0.5, lacunarity=2.0, octaves=4, seed=None):
75			if not 0 < dimensions <= 4:
76			raise ValueError('dimensions must be in range 0 < n <= 4, got %r' %
77			(dimensions,))
78			self._random = seed
79			_random_c = seed.random_c if seed else ffi.NULL
80			self._algorithm = algorithm
81			self.noise_c = ffi.gc(
82			ffi.cast(
83			'perlin_data_t*',
84			lib.TCOD_noise_new(dimensions, hurst, lacunarity,
85			_random_c),
86			),
87			lib.TCOD_noise_delete)
88			self._tdl_noise_c = ffi.new('TDLNoise*', (self.noise_c,
89			dimensions,
90			0,
91			octaves))
92			self.implementation = implementation # sanity check
93
94			@property
95			def dimensions(self):
			0 ignored issues – show Coding Style introduced 2017-07-05 00:27 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.dimensions
97
98			@property
99			def dimentions(self): # deprecated
			0 ignored issues – show Coding Style introduced 2017-07-05 00:27 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...
100			return self.dimensions
101
102			@property
103			def algorithm(self):
			0 ignored issues – show Coding Style introduced 2017-07-05 00:27 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...
104			return self.noise_c.noise_type
105			@algorithm.setter
106			def algorithm(self, value):
			0 ignored issues – show Coding Style introduced 2017-07-05 00:27 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...
107			lib.TCOD_noise_set_type(self.noise_c, value)
108
109			@property
110			def implementation(self):
			0 ignored issues – show Coding Style introduced 2017-07-05 00:27 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...
111			return self._tdl_noise_c.implementation
112			@implementation.setter
113			def implementation(self, value):
			0 ignored issues – show Coding Style introduced 2017-07-05 00:27 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...
114			if not 0 <= value < 3:
115			raise ValueError('%r is not a valid implementation. ' % (value,))
116			self._tdl_noise_c.implementation = value
117
118			@property
119			def hurst(self):
			0 ignored issues – show Coding Style introduced 2017-07-05 00:27 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...
120			return self.noise_c.H
121
122			@property
123			def lacunarity(self):
			0 ignored issues – show Coding Style introduced 2017-07-05 00:27 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...
124			return self.noise_c.lacunarity
125
126			@property
127			def octaves(self):
			0 ignored issues – show Coding Style introduced 2017-07-05 00:27 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...
128			return self._tdl_noise_c.octaves
129			@octaves.setter
130			def octaves(self, value):
			0 ignored issues – show Coding Style introduced 2017-07-05 00:27 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...
131			self._tdl_noise_c.octaves = value
132
133			def get_point(self, x=0, y=0, z=0, w=0):
			0 ignored issues – show Coding Style Naming introduced 2017-07-05 00:27 UTC by Report Bug Copy Issue Report The name `x` does not conform to the argument naming conventions (`[a-z_][a-z0-9_]{2,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history... Coding Style Naming introduced 2017-07-05 00:27 UTC by Report Bug Copy Issue Report The name `y` does not conform to the argument naming conventions (`[a-z_][a-z0-9_]{2,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history... Coding Style Naming introduced 2017-07-05 00:27 UTC by Report Bug Copy Issue Report The name `z` does not conform to the argument naming conventions (`[a-z_][a-z0-9_]{2,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history... Coding Style Naming introduced 2017-07-05 00:27 UTC by Report Bug Copy Issue Report The name `w` does not conform to the argument naming conventions (`[a-z_][a-z0-9_]{2,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
134			"""Return the noise value at the (x, y, z, w) point.
135
136			Args:
137			x (float): The position on the 1st axis.
138			y (float): The position on the 2nd axis.
139			z (float): The position on the 3rd axis.
140			w (float): The position on the 4th axis.
141			"""
142			return lib.NoiseGetSample(self._tdl_noise_c, (x, y, z, w))
143
144			def sample_mgrid(self, mgrid):
145			"""Sample a mesh-grid array and return the result.
146
147			The :any:`sample_ogrid` method performs better as there is a lot of
148			overhead when working with large mesh-grids.
149
150			Args:
151			mgrid (numpy.ndarray): A mesh-grid array of points to sample.
152			A contiguous array of type `numpy.float32` is preferred.
153
154			Returns:
155			numpy.ndarray: An array of sampled points.
156
157			This array has the shape: ``mgrid.shape[:-1]``.
158			The ``dtype`` is `numpy.float32`.
159			"""
160			mgrid = np.ascontiguousarray(mgrid, np.float32)
161			if mgrid.shape[0] != self.dimensions:
162			raise ValueError('mgrid.shape[0] must equal self.dimensions, '
163			'%r[0] != %r' % (mgrid.shape, self.dimensions))
164			out = np.ndarray(mgrid.shape[1:], np.float32)
165			if mgrid.shape[1:] != out.shape:
166			raise ValueError('mgrid.shape[1:] must equal out.shape, '
167			'%r[1:] != %r' % (mgrid.shape, out.shape))
168			lib.NoiseSampleMeshGrid(self._tdl_noise_c, out.size,
169			ffi.cast('float*', mgrid.ctypes.data),
170			ffi.cast('float*', out.ctypes.data))
171			return out
172
173			def sample_ogrid(self, ogrid):
174			"""Sample an open mesh-grid array and return the result.
175
176			Args
177			ogrid (Sequence[Sequence[float]]): An open mesh-grid.
178
179			Returns:
180			numpy.ndarray: An array of sampled points.
181
182			The ``shape`` is based on the lengths of the open mesh-grid
183			arrays.
184			The ``dtype`` is `numpy.float32`.
185			"""
186			if len(ogrid) != self.dimensions:
187			raise ValueError('len(ogrid) must equal self.dimensions, '
188			'%r != %r' % (len(ogrid), self.dimensions))
189			ogrids = [np.ascontiguousarray(array, np.float32) for array in ogrid]
190			out = np.ndarray([array.size for array in ogrids], np.float32)
191			lib.NoiseSampleOpenMeshGrid(
192			self._tdl_noise_c,
193			len(ogrids),
194			out.shape,
195			[ffi.cast('float*', array.ctypes.data) for array in ogrids],
196			ffi.cast('float*', out.ctypes.data),
197			)
198			return out
199
200			def __getstate__(self):
201			state = self.__dict__.copy()
202			if self.dimensions < 4 and self.noise_c.waveletTileData == ffi.NULL:
203			# Trigger a side effect of wavelet, so that copies will be synced.
204			saved_algo = self.algorithm
205			self.algorithm = tcod.libtcod.NOISE_WAVELET
			0 ignored issues – show Bug introduced 2017-07-05 00:27 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...
206			self.get_point()
207			self.algorithm = saved_algo
208
209			waveletTileData = None
			0 ignored issues – show Coding Style Naming introduced 2017-07-05 00:27 UTC by Report Bug Copy Issue Report The name `waveletTileData` does not conform to the variable naming conventions (`[a-z_][a-z0-9_]{2,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
210			if self.noise_c.waveletTileData != ffi.NULL:
211			waveletTileData = list(self.noise_c.waveletTileData[0:323232])
			0 ignored issues – show Coding Style Naming introduced 2017-07-05 00:27 UTC by Report Bug Copy Issue Report The name `waveletTileData` does not conform to the variable naming conventions (`[a-z_][a-z0-9_]{2,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
212			state['_waveletTileData'] = waveletTileData
213
214			state['noise_c'] = {
215			'ndim': self.noise_c.ndim,
216			'map': list(self.noise_c.map),
217			'buffer': [list(sub_buffer) for sub_buffer in self.noise_c.buffer],
218			'H': self.noise_c.H,
219			'lacunarity': self.noise_c.lacunarity,
220			'exponent': list(self.noise_c.exponent),
221			'waveletTileData': waveletTileData,
222			'noise_type': self.noise_c.noise_type,
223			}
224			state['_tdl_noise_c'] = {
225			'dimensions': self._tdl_noise_c.dimensions,
226			'implementation': self._tdl_noise_c.implementation,
227			'octaves': self._tdl_noise_c.octaves,
228			}
229			return state
230
231			def __setstate__(self, state):
232			if isinstance(state, tuple): # deprecated format
233			return self._setstate_old(state)
234			# unpack wavelet tile data if it exists
235			if '_waveletTileData' in state:
236			state['_waveletTileData'] = ffi.new('float[]',
237			state['_waveletTileData'])
238			state['noise_c']['waveletTileData'] = state['_waveletTileData']
239			else:
240			state['noise_c']['waveletTileData'] = ffi.NULL
241
242			# unpack perlin_data_t and link to Random instance
243			state['noise_c']['rand'] = state['_random'].random_c
244			state['noise_c'] = ffi.new('perlin_data_t*', state['noise_c'])
245
246			# unpack TDLNoise and link to libtcod noise
247			state['_tdl_noise_c']['noise'] = state['noise_c']
248			state['_tdl_noise_c'] = ffi.new('TDLNoise*', state['_tdl_noise_c'])
249			self.__dict__.update(state)
250
251			def _setstate_old(self, state):
			0 ignored issues – show Coding Style introduced 2017-07-05 00:27 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...
252			self._random = state[0]
253			self.noise_c = ffi.new('perlin_data_t*')
254			self.noise_c.ndim = state[3]
255			ffi.buffer(self.noise_c.map)[:] = state[4]
256			ffi.buffer(self.noise_c.buffer)[:] = state[5]
257			self.noise_c.H = state[6]
258			self.noise_c.lacunarity = state[7]
259			ffi.buffer(self.noise_c.exponent)[:] = state[8]
260			if state[9]:
261			# high change of this being prematurely garbage collected!
262			self.__waveletTileData = ffi.new('float[]', 323232)
			0 ignored issues – show Coding Style Naming introduced 2017-07-05 00:27 UTC by Report Bug Copy Issue Report The name `__waveletTileData` does not conform to the attribute naming conventions (`[a-z_][a-z0-9_]{2,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history... Coding Style introduced 2017-07-05 00:27 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...
263			ffi.buffer(self.__waveletTileData)[:] = state[9]
264			self.noise_c.noise_type = state[10]
265			self._tdl_noise_c = ffi.new('TDLNoise*',
266			(self.noise_c, self.noise_c.ndim,
267			state[1], state[2]))
268

HexDecimal / python-tdl

Noise.algorithm() A last analyzed 2018-06-24 08:14 UTC

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1. Missing Dependencies

2. Missing __init__.py files

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Noise.algorithm() A
last analyzed 2018-06-24 08:14 UTC

2. Missing init.py files