Source code for tvb.adapters.visualizers.fourier_spectrum

# -*- coding: utf-8 -*-
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.. moduleauthor:: Dan Pop <>
.. moduleauthor:: Lia Domide <>
.. moduleauthor:: Stuart A. Knock <>

import json
import numpy

from tvb.adapters.datatypes.db.spectral import FourierSpectrumIndex
from tvb.core.adapters.abcadapter import ABCAdapterForm
from tvb.core.adapters.abcdisplayer import ABCDisplayer, URLGenerator
from tvb.core.neotraits.forms import TraitDataTypeSelectField
from tvb.core.neocom import h5
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.datatypes.spectral import FourierSpectrum
from tvb.datatypes.time_series import TimeSeries

[docs]class FourierSpectrumModel(ViewModel): input_data = DataTypeGidAttr( linked_datatype=FourierSpectrum, label='Fourier Result', doc='Fourier Analysis to display' )
[docs]class FourierSpectrumForm(ABCAdapterForm): def __init__(self): super(FourierSpectrumForm, self).__init__() self.input_data = TraitDataTypeSelectField(FourierSpectrumModel.input_data, name='input_data', conditions=self.get_filters())
[docs] @staticmethod def get_view_model(): return FourierSpectrumModel
[docs] @staticmethod def get_input_name(): return "input_data"
[docs] @staticmethod def get_required_datatype(): return FourierSpectrumIndex
[docs] @staticmethod def get_filters(): return None
[docs]class FourierSpectrumDisplay(ABCDisplayer): """ This viewer takes as inputs a result form FFT analysis, and returns required parameters for a MatplotLib representation. """ _ui_name = "Fourier Visualizer" _ui_subsection = "fourier"
[docs] def get_form_class(self): return FourierSpectrumForm
[docs] def get_required_memory_size(self, view_model): # type: (FourierSpectrumModel) -> dict """ Return the required memory to run this algorithm. """ fs_input_index = self.load_entity_by_gid(view_model.input_data) return * 8
[docs] def launch(self, view_model): # type: (FourierSpectrumModel) -> dict self.log.debug("Plot started...") # these partial loads are dangerous for TS and FS instances, but efficient fourier_spectrum = FourierSpectrum() with h5.h5_file_for_gid(view_model.input_data) as input_h5: shape = list(input_h5.array_data.shape) fourier_spectrum.segment_length = input_h5.segment_length.load() fourier_spectrum.windowing_function = input_h5.windowing_function.load() ts_index = self.load_entity_by_gid(input_h5.source.load()) state_list = ts_index.get_labels_for_dimension(1) if len(state_list) == 0: state_list = list(range(shape[1])) fourier_spectrum.source = TimeSeries(sample_period=ts_index.sample_period) mode_list = list(range(shape[3])) available_scales = ["Linear", "Logarithmic"] params = dict(matrix_shape=json.dumps([shape[0], shape[2]]), plotName=ts_index.title, url_base=URLGenerator.build_h5_url(view_model.input_data, "get_fourier_data", parameter=""), xAxisName="Frequency [kHz]", yAxisName="Power", available_scales=available_scales, state_list=state_list, mode_list=mode_list, normalize_list=["no", "yes"], normalize="no", state_variable=state_list[0], mode=mode_list[0], xscale=available_scales[0], yscale=available_scales[0], x_values=json.dumps(fourier_spectrum.frequency[slice(shape[0])].tolist()), xmin=fourier_spectrum.freq_step, xmax=fourier_spectrum.max_freq) return self.build_display_result("fourier_spectrum/view", params)