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{
    "imports": [
        "scientifik.plotly.*",
        "scientifik.plotly.models.*",
        "hep.dataforge.meta.*",
        "kotlinx.html.*"
    ],
    "repositories": [
        "*mavenLocal",
        "https://dl.bintray.com/mipt-npm/dataforge",

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import math
import scipy.signal
import numpy as np
import plotly.graph_objects as go # I prefer ployly over matplotlib
from scipy.ndimage import gaussian_filter1d

step = 0.01 # Defining the grid step for all further operations

# gaussian kernel on its own grid with the same step
def gauss(pos, sigma):

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package ru.mipt.npm.demo.react

import kotlinx.coroutines.*
import kotlinx.coroutines.channels.Channel
import kotlinx.coroutines.channels.ReceiveChannel
import kotlinx.html.dom.append
import kotlinx.html.js.*
import react.*
import react.dom.div
import react.dom.h1

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import com.github.javafaker.Faker
import javafx.beans.InvalidationListener
import javafx.beans.binding.ListBinding
import javafx.beans.property.SimpleIntegerProperty
import javafx.collections.FXCollections
import javafx.collections.ObservableList
import javafx.geometry.Pos
import javafx.scene.layout.Priority
import tornadofx.*
import java.util.function.Predicate

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