fsync_network.__oscillator_property() - Code Metrics - Inspection of "#168: [pyclustering.nnet.fsync] Sync implementatio..." - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

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Push — 0.7.dev ( 2e222e...d9bef7 )

by Andrei

created 2017-08-21 16:18 UTC

fsync_network.__oscillator_property() A

↳ Parent: fsync_network

Complexity

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Lines	0
Ratio	0 %

Importance

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cc	1
dl	0
loc	11
rs	9.4285
c	0
b	0
f	0

"""!

@brief Oscillatory Neural Network based on Kuramoto model in frequency domain.
@details Based on article description:
         - Y.Kuramoto. Chemical Oscillations, Waves, and Turbulence. 1984.

@authors Andrei Novikov ([email protected])
@date 2014-2017
@copyright GNU Public License

@cond GNU_PUBLIC_LICENSE
    PyClustering is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.
    
    PyClustering is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.
    
    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
@endcond

"""


import numpy;
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
import random;
import pyclustering.utils;

from scipy.integrate import odeint;
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3

from pyclustering.nnet import network, conn_type, conn_represent;


class fsync_dynamic:
    """!
    @brief Represents output dynamic of Sync in frequency domain.
    
    """


    def __init__(self, amplitude, time):
        """!
        @brief Constructor of Sync dynamic in frequency domain.
        
        @param[in] amplitude (list): Dynamic of oscillators on each step of simulation.
        @param[in] time (list): Simulation time where each time-point corresponds to amplitude-point.
        
        """

        self.__amplitude = amplitude;
        self.__time = time;


    @property
    def output(self):
        """!
        @brief (list) Returns output dynamic of the Sync network (amplitudes of each oscillator in the network) during simulation.
        
        """

        return self.__amplitude;


    @property
    def time(self):
        """!
        @brief (list) Returns time-points corresponds to dynamic-points points.
        
        """

        return self.__time;


    def __len__(self):
        """!
        @brief (uint) Returns number of simulation steps that are stored in dynamic.
        
        """
        
        return len(self.__amplitude);


    def __getitem__(self, index):
        """!
        @brief Indexing of the dynamic.
        
        """
        if (index is 0):
            return self.__time;
        
        elif (index is 1):
            return self.__amplitude;
        
        else:
            raise NameError('Out of range ' + index + ': only indexes 0 and 1 are supported.');


    def allocate_sync_ensembles(self, tolerance = 0.1):
        """!
        @brief Allocate clusters in line with ensembles of synchronous oscillators where each synchronous ensemble corresponds to only one cluster.
        
        @param[in] tolerance (double): Maximum error for allocation of synchronous ensemble oscillators.
        
        @return (list) Grours of indexes of synchronous oscillators, for example, [ [index_osc1, index_osc3], [index_osc2], [index_osc4, index_osc5] ].
        
        """
        
        return pyclustering.utils.allocate_sync_ensembles(self.__amplitude, tolerance);


    def extract_number_oscillations(self, index, amplitude_threshold):
        """!
        @brief Extracts number of oscillations of specified oscillator.
        
        @param[in] index (uint): Index of oscillator whose dynamic is considered.
        @param[in] amplitude_threshold (double): Amplitude threshold when oscillation is taken into account, for example,
                    when oscillator amplitude is greater than threshold then oscillation is incremented.
        
        @return (uint) Number of oscillations of specified oscillator.
        
        """
        
        return pyclustering.utils.extract_number_oscillations(self.__amplitude, index, amplitude_threshold);



class fsync_visualizer:
    """!
    @brief Visualizer of output dynamic of sync network in frequency domain.
    
    """

    @staticmethod
    def show_output_dynamic(fsync_output_dynamic):
        """!
        @brief Shows output dynamic (output of each oscillator) during simulation.
        
        @param[in] fsync_output_dynamic (fsync_dynamic): Output dynamic of the fSync network.
        
        @see show_output_dynamics
        
        """
        
        pyclustering.utils.draw_dynamics(fsync_output_dynamic.time, fsync_output_dynamic.output, x_title = "t", y_title = "amplitude");


    @staticmethod
    def show_output_dynamics(fsync_output_dynamics):
        """!
        @brief Shows several output dynamics (output of each oscillator) during simulation.
        @details Each dynamic is presented on separate plot.
        
        @param[in] fsync_output_dynamics (list): list of output dynamics 'fsync_dynamic' of the fSync network.
        
        @see show_output_dynamic
        
        """
        
        pyclustering.utils.draw_dynamics_set(fsync_output_dynamics, "t", "amplitude", None, None, False, False);



class fsync_network(network):
    """!

    @brief Model of oscillatory network that uses Landau-Stuart oscillator and Kuramoto model as a synchronization mechanism.
    @details Dynamic of each oscillator in the network is described by following differential Landau-Stuart equation with feedback:
    
    \f[
    \dot{z}_{i} = (i\omega_{i} + \rho^{2}_{i} - |z_{i}|^{2} )z_{i} + \frac{1}{N}\sum_{j=0}^{N}k_{ij}(z_{j} - z_{i});
    \f]
    
    Where left part of the equation is Landau-Stuart equation and the right is a Kuramoto model for synchronization.
    For solving this equation Runge-Kutta 4 method is used by default.
    
    Example:
    @code
        # Prepare oscillatory network parameters.
        amount_oscillators = 3;
        frequency = 1.0;
        radiuses = [1.0, 2.0, 3.0];
        coupling_strength = 1.0;
        
        # Create oscillatory network
        oscillatory_network = fsync_network(amount_oscillators, frequency, radiuses, coupling_strength);
        
        # Simulate network during 200 steps on 10 time-units of time-axis.
        output_dynamic = oscillatory_network.simulate(200, 10, True);    # True is to collect whole output dynamic.
        
        # Visualize output result
        fsync_visualizer.show_output_dynamic(output_dynamic);
    @endcode
    
    Example of output dynamic of the network:
    @image html fsync_sync_examples.png
    
    """
    
    __DEFAULT_FREQUENCY_VALUE = 1.0;
    __DEFAULT_RADIUS_VALUE = 1.0;
    __DEFAULT_COUPLING_STRENGTH = 1.0;


    def __init__(self, num_osc, factor_frequency = 1.0, factor_radius = 1.0, factor_coupling = 1.0, type_conn = conn_type.ALL_TO_ALL, representation = conn_represent.MATRIX):
        """!
        @brief Constructor of oscillatory network based on synchronization Kuramoto model and Landau-Stuart oscillator.
        
        @param[in] num_osc (uint): Amount oscillators in the network.
        @param[in] factor_frequency (double|list): Frequency of oscillators, it can be specified as common value for all oscillators by
                    single double value and for each separately by list.
        @param[in] factor_radius (double|list): Radius of oscillators that affects amplitude, it can be specified as common value for all oscillators by
                    single double value and for each separately by list.
        @param[in] factor_coupling (double): Coupling strength between oscillators.
        @param[in] type_conn (conn_type): Type of connection between oscillators in the network (all-to-all, grid, bidirectional list, etc.).
        @param[in] representation (conn_represent): Internal representation of connection in the network: matrix or list.
        
        """
        
        super().__init__(num_osc, type_conn, representation);
        
        self.__frequency = factor_frequency if isinstance(factor_frequency, list) else [ fsync_network.__DEFAULT_FREQUENCY_VALUE * factor_frequency for _ in range(num_osc) ];
        self.__radius = factor_radius if isinstance(factor_radius, list) else [ fsync_network.__DEFAULT_RADIUS_VALUE * factor_radius for _ in range(num_osc) ];
        self.__coupling_strength = fsync_network.__DEFAULT_COUPLING_STRENGTH * factor_coupling;
        self.__properties = [ self.__oscillator_property(index) for index in range(self._num_osc) ];
        
        random.seed();
        self.__amplitude = [ random.random() for _ in range(num_osc) ];


    def simulate(self, steps, time, collect_dynamic = False):
        """!
        @brief Performs static simulation of oscillatory network.
        
        @param[in] steps (uint): Number simulation steps.
        @param[in] time (double): Time of simulation.
        @param[in] collect_dynamic (bool): If True - returns whole dynamic of oscillatory network, otherwise returns only last values of dynamics.
        
        @return (list) Dynamic of oscillatory network. If argument 'collect_dynamic' is True, than return dynamic for the whole simulation time,
                 otherwise returns only last values (last step of simulation) of output dynamic.
        
        @see simulate()
        @see simulate_dynamic()
        
        """
        
        dynamic_amplitude, dynamic_time = ([], []) if collect_dynamic is False else ([self.__amplitude], [0]);
        
        step = time / steps;
        int_step = step / 10.0;
        
        for t in numpy.arange(step, time + step, step):
            self.__amplitude = self.__calculate(t, step, int_step);
            
            if (collect_dynamic == True):
                dynamic_amplitude.append([ numpy.real(amplitude)[0] for amplitude in self.__amplitude ]);
                dynamic_time.append(t);
        
        if (collect_dynamic != True):
            dynamic_amplitude.append([ numpy.real(amplitude)[0] for amplitude in self.__amplitude ]);
            dynamic_time.append(time);

        output_sync_dynamic = fsync_dynamic(dynamic_amplitude, dynamic_time);
        return output_sync_dynamic;


    def __calculate(self, t, step, int_step):
        """!
        @brief Calculates new amplitudes for oscillators in the network in line with current step.
        
        @param[in] t (double): Time of simulation.
        @param[in] step (double): Step of solution at the end of which states of oscillators should be calculated.
        @param[in] int_step (double): Step differentiation that is used for solving differential equation.
        
        @return (list) New states (phases) for oscillators.
        
        """
        
        next_amplitudes = [0.0] * self._num_osc;
        
        for index in range (0, self._num_osc, 1):
            z = numpy.array(self.__amplitude[index], dtype = numpy.complex128, ndmin = 1);
            result = odeint(self.__calculate_amplitude, z.view(numpy.float64), numpy.arange(t - step, t, int_step), (index , ));
            next_amplitudes[index] = (result[len(result) - 1]).view(numpy.complex128);
        
        return next_amplitudes;


    def __oscillator_property(self, index):
        """!
        @brief Calculate Landau-Stuart oscillator constant property that is based on frequency and radius.
        
        @param[in] index (uint): Oscillator index whose property is calculated.
        
        @return (double) Oscillator property.
        
        """
        
        return numpy.array(1j * self.__frequency[index] + self.__radius[index]**2, dtype = numpy.complex128, ndmin = 1);


    def __landau_stuart(self, amplitude, index):
        """!
        @brief Calculate Landau-Stuart state.
        
        @param[in] amplitude (double): Current amplitude of oscillator.
        @param[in] index (uint): Oscillator index whose state is calculated. 
        
        @return (double) Landau-Stuart state.
        
        """
        
        return (self.__properties[index] - numpy.absolute(amplitude) ** 2) * amplitude;


    def __synchronization_mechanism(self, amplitude, index):
        """!
        @brief Calculate synchronization part using Kuramoto synchronization mechanism.
        
        @param[in] amplitude (double): Current amplitude of oscillator.
        @param[in] index (uint): Oscillator index whose synchronization influence is calculated.
        
        @return (double) Synchronization influence for the specified oscillator.
        
        """
        
        sync_influence = 0.0;
        
        for k in range(self._num_osc):
            if (self.has_connection(index, k) == True):
                amplitude_neighbor = numpy.array(self.__amplitude[k], dtype = numpy.complex128, ndmin = 1);
                sync_influence += amplitude_neighbor - amplitude;
        
        return sync_influence * self.__coupling_strength / self._num_osc;


    def __calculate_amplitude(self, amplitude, t, argv):

        """!
        @brief Returns new amplitude value for particular oscillator that is defined by index that is in 'argv' argument.
        @details The method is used for differential calculation.
        
        @param[in] amplitude (double): Current amplitude of oscillator.
        @param[in] t (double): Current time of simulation.
        @param[in] argv (uint): Index of the current oscillator.
        
        @return (double) New amplitude of the oscillator.
        
        """
        
        z = amplitude.view(numpy.complex);
        dzdt = self.__landau_stuart(z, argv) + self.__synchronization_mechanism(z, argv);
        
        return dzdt.view(numpy.float64);

Push — 0.7.dev ( 2e222e...d9bef7 )

fsync_network.__oscillator_property() A

Complexity

Size

Duplication

Importance

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1			"""!
2
3			@brief Oscillatory Neural Network based on Kuramoto model in frequency domain.
4			@details Based on article description:
5			- Y.Kuramoto. Chemical Oscillations, Waves, and Turbulence. 1984.
6
7			@authors Andrei Novikov ([email protected])
8			@date 2014-2017
9			@copyright GNU Public License
10
11			@cond GNU_PUBLIC_LICENSE
12			PyClustering is free software: you can redistribute it and/or modify
13			it under the terms of the GNU General Public License as published by
14			the Free Software Foundation, either version 3 of the License, or
15			(at your option) any later version.
16
17			PyClustering is distributed in the hope that it will be useful,
18			but WITHOUT ANY WARRANTY; without even the implied warranty of
19			MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
20			GNU General Public License for more details.
21
22			You should have received a copy of the GNU General Public License
23			along with this program. If not, see <http://www.gnu.org/licenses/>.
24			@endcond
25
26			"""
27
28
29			import numpy;
			0 ignored issues – show Configuration introduced 2017-08-21 16:19 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...
30			import random;
31			import pyclustering.utils;
32
33			from scipy.integrate import odeint;
			0 ignored issues – show Configuration introduced 2016-03-31 09:26 UTC by Report Bug Copy Issue Report The import `scipy.integrate` 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...
34
35			from pyclustering.nnet import network, conn_type, conn_represent;
36
37
38			class fsync_dynamic:
39			"""!
40			@brief Represents output dynamic of Sync in frequency domain.
41
42			"""
43
44
45			def __init__(self, amplitude, time):
46			"""!
47			@brief Constructor of Sync dynamic in frequency domain.
48
49			@param[in] amplitude (list): Dynamic of oscillators on each step of simulation.
50			@param[in] time (list): Simulation time where each time-point corresponds to amplitude-point.
51
52			"""
53
54			self.__amplitude = amplitude;
55			self.__time = time;
56
57
58			@property
59			def output(self):
60			"""!
61			@brief (list) Returns output dynamic of the Sync network (amplitudes of each oscillator in the network) during simulation.
62
63			"""
64
65			return self.__amplitude;
66
67
68			@property
69			def time(self):
70			"""!
71			@brief (list) Returns time-points corresponds to dynamic-points points.
72
73			"""
74
75			return self.__time;
76
77
78			def __len__(self):
79			"""!
80			@brief (uint) Returns number of simulation steps that are stored in dynamic.
81
82			"""
83
84			return len(self.__amplitude);
85
86
87			def __getitem__(self, index):
88			"""!
89			@brief Indexing of the dynamic.
90
91			"""
92			if (index is 0):
93			return self.__time;
94
95			elif (index is 1):
96			return self.__amplitude;
97
98			else:
99			raise NameError('Out of range ' + index + ': only indexes 0 and 1 are supported.');
100
101
102			def allocate_sync_ensembles(self, tolerance = 0.1):
103			"""!
104			@brief Allocate clusters in line with ensembles of synchronous oscillators where each synchronous ensemble corresponds to only one cluster.
105
106			@param[in] tolerance (double): Maximum error for allocation of synchronous ensemble oscillators.
107
108			@return (list) Grours of indexes of synchronous oscillators, for example, [ [index_osc1, index_osc3], [index_osc2], [index_osc4, index_osc5] ].
109
110			"""
111
112			return pyclustering.utils.allocate_sync_ensembles(self.__amplitude, tolerance);
113
114
115			def extract_number_oscillations(self, index, amplitude_threshold):
116			"""!
117			@brief Extracts number of oscillations of specified oscillator.
118
119			@param[in] index (uint): Index of oscillator whose dynamic is considered.
120			@param[in] amplitude_threshold (double): Amplitude threshold when oscillation is taken into account, for example,
121			when oscillator amplitude is greater than threshold then oscillation is incremented.
122
123			@return (uint) Number of oscillations of specified oscillator.
124
125			"""
126
127			return pyclustering.utils.extract_number_oscillations(self.__amplitude, index, amplitude_threshold);
128
129
130
131			class fsync_visualizer:
132			"""!
133			@brief Visualizer of output dynamic of sync network in frequency domain.
134
135			"""
136
137			@staticmethod
138			def show_output_dynamic(fsync_output_dynamic):
139			"""!
140			@brief Shows output dynamic (output of each oscillator) during simulation.
141
142			@param[in] fsync_output_dynamic (fsync_dynamic): Output dynamic of the fSync network.
143
144			@see show_output_dynamics
145
146			"""
147
148			pyclustering.utils.draw_dynamics(fsync_output_dynamic.time, fsync_output_dynamic.output, x_title = "t", y_title = "amplitude");
149
150
151			@staticmethod
152			def show_output_dynamics(fsync_output_dynamics):
153			"""!
154			@brief Shows several output dynamics (output of each oscillator) during simulation.
155			@details Each dynamic is presented on separate plot.
156
157			@param[in] fsync_output_dynamics (list): list of output dynamics 'fsync_dynamic' of the fSync network.
158
159			@see show_output_dynamic
160
161			"""
162
163			pyclustering.utils.draw_dynamics_set(fsync_output_dynamics, "t", "amplitude", None, None, False, False);
164
165
166
167			class fsync_network(network):
168			"""!
			0 ignored issues – show Bug introduced 2017-08-16 14:12 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\d` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history... Bug introduced 2017-08-16 14:12 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\o` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history... Bug introduced 2017-08-16 14:12 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\s` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history...
169			@brief Model of oscillatory network that uses Landau-Stuart oscillator and Kuramoto model as a synchronization mechanism.
170			@details Dynamic of each oscillator in the network is described by following differential Landau-Stuart equation with feedback:
171
172			\f[
173			\dot{z}_{i} = (i\omega_{i} + \rho^{2}_{i} - \|z_{i}\|^{2} )z_{i} + \frac{1}{N}\sum_{j=0}^{N}k_{ij}(z_{j} - z_{i});
174			\f]
175
176			Where left part of the equation is Landau-Stuart equation and the right is a Kuramoto model for synchronization.
177			For solving this equation Runge-Kutta 4 method is used by default.
178
179			Example:
180			@code
181			# Prepare oscillatory network parameters.
182			amount_oscillators = 3;
183			frequency = 1.0;
184			radiuses = [1.0, 2.0, 3.0];
185			coupling_strength = 1.0;
186
187			# Create oscillatory network
188			oscillatory_network = fsync_network(amount_oscillators, frequency, radiuses, coupling_strength);
189
190			# Simulate network during 200 steps on 10 time-units of time-axis.
191			output_dynamic = oscillatory_network.simulate(200, 10, True); # True is to collect whole output dynamic.
192
193			# Visualize output result
194			fsync_visualizer.show_output_dynamic(output_dynamic);
195			@endcode
196
197			Example of output dynamic of the network:
198			@image html fsync_sync_examples.png
199
200			"""
201
202			__DEFAULT_FREQUENCY_VALUE = 1.0;
203			__DEFAULT_RADIUS_VALUE = 1.0;
204			__DEFAULT_COUPLING_STRENGTH = 1.0;
205
206
207			def __init__(self, num_osc, factor_frequency = 1.0, factor_radius = 1.0, factor_coupling = 1.0, type_conn = conn_type.ALL_TO_ALL, representation = conn_represent.MATRIX):
208			"""!
209			@brief Constructor of oscillatory network based on synchronization Kuramoto model and Landau-Stuart oscillator.
210
211			@param[in] num_osc (uint): Amount oscillators in the network.
212			@param[in] factor_frequency (double\|list): Frequency of oscillators, it can be specified as common value for all oscillators by
213			single double value and for each separately by list.
214			@param[in] factor_radius (double\|list): Radius of oscillators that affects amplitude, it can be specified as common value for all oscillators by
215			single double value and for each separately by list.
216			@param[in] factor_coupling (double): Coupling strength between oscillators.
217			@param[in] type_conn (conn_type): Type of connection between oscillators in the network (all-to-all, grid, bidirectional list, etc.).
218			@param[in] representation (conn_represent): Internal representation of connection in the network: matrix or list.
219
220			"""
221
222			super().__init__(num_osc, type_conn, representation);
223
224			self.__frequency = factor_frequency if isinstance(factor_frequency, list) else [ fsync_network.__DEFAULT_FREQUENCY_VALUE * factor_frequency for _ in range(num_osc) ];
225			self.__radius = factor_radius if isinstance(factor_radius, list) else [ fsync_network.__DEFAULT_RADIUS_VALUE * factor_radius for _ in range(num_osc) ];
226			self.__coupling_strength = fsync_network.__DEFAULT_COUPLING_STRENGTH * factor_coupling;
227			self.__properties = [ self.__oscillator_property(index) for index in range(self._num_osc) ];
228
229			random.seed();
230			self.__amplitude = [ random.random() for _ in range(num_osc) ];
231
232
233			def simulate(self, steps, time, collect_dynamic = False):
234			"""!
235			@brief Performs static simulation of oscillatory network.
236
237			@param[in] steps (uint): Number simulation steps.
238			@param[in] time (double): Time of simulation.
239			@param[in] collect_dynamic (bool): If True - returns whole dynamic of oscillatory network, otherwise returns only last values of dynamics.
240
241			@return (list) Dynamic of oscillatory network. If argument 'collect_dynamic' is True, than return dynamic for the whole simulation time,
242			otherwise returns only last values (last step of simulation) of output dynamic.
243
244			@see simulate()
245			@see simulate_dynamic()
246
247			"""
248
249			dynamic_amplitude, dynamic_time = ([], []) if collect_dynamic is False else ([self.__amplitude], [0]);
250
251			step = time / steps;
252			int_step = step / 10.0;
253
254			for t in numpy.arange(step, time + step, step):
255			self.__amplitude = self.__calculate(t, step, int_step);
256
257			if (collect_dynamic == True):
258			dynamic_amplitude.append([ numpy.real(amplitude)[0] for amplitude in self.__amplitude ]);
259			dynamic_time.append(t);
260
261			if (collect_dynamic != True):
262			dynamic_amplitude.append([ numpy.real(amplitude)[0] for amplitude in self.__amplitude ]);
263			dynamic_time.append(time);
264
265			output_sync_dynamic = fsync_dynamic(dynamic_amplitude, dynamic_time);
266			return output_sync_dynamic;
267
268
269			def __calculate(self, t, step, int_step):
270			"""!
271			@brief Calculates new amplitudes for oscillators in the network in line with current step.
272
273			@param[in] t (double): Time of simulation.
274			@param[in] step (double): Step of solution at the end of which states of oscillators should be calculated.
275			@param[in] int_step (double): Step differentiation that is used for solving differential equation.
276
277			@return (list) New states (phases) for oscillators.
278
279			"""
280
281			next_amplitudes = [0.0] * self._num_osc;
282
283			for index in range (0, self._num_osc, 1):
284			z = numpy.array(self.__amplitude[index], dtype = numpy.complex128, ndmin = 1);
285			result = odeint(self.__calculate_amplitude, z.view(numpy.float64), numpy.arange(t - step, t, int_step), (index , ));
286			next_amplitudes[index] = (result[len(result) - 1]).view(numpy.complex128);
287
288			return next_amplitudes;
289
290
291			def __oscillator_property(self, index):
292			"""!
293			@brief Calculate Landau-Stuart oscillator constant property that is based on frequency and radius.
294
295			@param[in] index (uint): Oscillator index whose property is calculated.
296
297			@return (double) Oscillator property.
298
299			"""
300
301			return numpy.array(1j * self.__frequency[index] + self.__radius[index]**2, dtype = numpy.complex128, ndmin = 1);
302
303
304			def __landau_stuart(self, amplitude, index):
305			"""!
306			@brief Calculate Landau-Stuart state.
307
308			@param[in] amplitude (double): Current amplitude of oscillator.
309			@param[in] index (uint): Oscillator index whose state is calculated.
310
311			@return (double) Landau-Stuart state.
312
313			"""
314
315			return (self.__properties[index] - numpy.absolute(amplitude) ** 2) * amplitude;
316
317
318			def __synchronization_mechanism(self, amplitude, index):
319			"""!
320			@brief Calculate synchronization part using Kuramoto synchronization mechanism.
321
322			@param[in] amplitude (double): Current amplitude of oscillator.
323			@param[in] index (uint): Oscillator index whose synchronization influence is calculated.
324
325			@return (double) Synchronization influence for the specified oscillator.
326
327			"""
328
329			sync_influence = 0.0;
330
331			for k in range(self._num_osc):
332			if (self.has_connection(index, k) == True):
333			amplitude_neighbor = numpy.array(self.__amplitude[k], dtype = numpy.complex128, ndmin = 1);
334			sync_influence += amplitude_neighbor - amplitude;
335
336			return sync_influence * self.__coupling_strength / self._num_osc;
337
338
339			def __calculate_amplitude(self, amplitude, t, argv):
			0 ignored issues – show Unused Code introduced 2017-08-16 14:12 UTC by Report Bug Copy Issue Report The argument `t` seems to be unused. Loading history...
340			"""!
341			@brief Returns new amplitude value for particular oscillator that is defined by index that is in 'argv' argument.
342			@details The method is used for differential calculation.
343
344			@param[in] amplitude (double): Current amplitude of oscillator.
345			@param[in] t (double): Current time of simulation.
346			@param[in] argv (uint): Index of the current oscillator.
347
348			@return (double) New amplitude of the oscillator.
349
350			"""
351
352			z = amplitude.view(numpy.complex);
353			dzdt = self.__landau_stuart(z, argv) + self.__synchronization_mechanism(z, argv);
354
355			return dzdt.view(numpy.float64);

annoviko / pyclustering

Push — 0.7.dev ( 2e222e...d9bef7 )

fsync_network.__oscillator_property() A

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