dokato

## precomputed.R
library(nat)
library(jsonlite)
library(float)

#' Based on specification
#' https://github.com/google/neuroglancer/tree/master/src/neuroglancer/datasource/precomputed

# write mesh to single res precomputed format
#meshes = read.neurons("../registration/dns_objs21_reg/")

## badges.tex
\documentclass[a4paper,10pt]{letter}
\usepackage[utf8]{inputenc}
\usepackage[freepin,crossmark]{ticket}
\usepackage{graphicx}

\unitlength=1mm
\ticketSize{52}{78}
\ticketNumbers{2}{3}

\renewcommand{\ticketdefault}{

## doughnut_chart.html
<!DOCTYPE html>
<html>
<head>
  <script src="https://cdn.jsdelivr.net/npm/chart.js@2.9.3/dist/Chart.min.js"></script>
</head>

<body>
    <div width="100px" height="100px">
        <canvas id="myChart"></canvas>
    </div>

## datget.py
import pickle
import itertools
import numpy as np
import tensorflow.keras as keras

with open('HackathonMetadata.pkl','rb') as ff:
    metad = pickle.load(ff)

with open('HackathonData_Homology0.pkl','rb') as ff:
    homol = pickle.load(ff)

## phaseplane.py
import numpy as np
import matplotlib.pyplot as plt

__author__ = 'Dominik Krzeminski (dokato)'

tau   = 2
I_ext = 0.5
a     = 0.8
b     = 0.7

## delayed-checkbox.R
library(shiny)
library(magrittr)
library(ggplot2)

ui <- fluidPage(
  checkboxGroupInput("checkbox", "Variables to show:",
                     c("psavert", "uempmed", "unemploy")),
  plotOutput("plot")
)

## resting_state_inverse2.py
import os
import mne
from mne.datasets.brainstorm import bst_resting
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as ss

########################## INPUT:
folder = os.path.join(bst_resting.data_path(), 'MEG/bst_resting')
file = 'subj002_spontaneous_20111102_01_AUX_raw.fif'

## resting_state_example.py
import os
import mne
from mne.datasets.brainstorm import bst_resting
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as ss

folder = os.path.join(bst_resting.data_path(), 'MEG/bst_resting')
file = 'subj002_spontaneous_20111102_01_AUX_raw.fif'

## app_get.R
library(shiny)
library(shiny.router)

options(shiny.router.debug = T)
# This generates menu in user interface with links.
menu <- (
  tags$ul(
    tags$li(a(class = "item", href = "/", "Page")),
    tags$li(a(class = "item", href = route_link("other"), "Other page"))
  )

## orthogonalization.py
import numpy as np

def find_closest_orthogonal_matrix(A):
    '''
    Find closest orthogonal matrix to *A* using iterative method.

    Bases on the code from REMOVE_SOURCE_LEAKAGE function from OSL Matlab package.

    Args:
        A (numpy.array): array shaped k, n, where k is number of channels, n - data points
	library(nat)
	library(jsonlite)
	library(float)

	#' Based on specification
	#' https://github.com/google/neuroglancer/tree/master/src/neuroglancer/datasource/precomputed

	# write mesh to single res precomputed format
	#meshes = read.neurons("../registration/dns_objs21_reg/")
	\documentclass[a4paper,10pt]{letter}
	\usepackage[utf8]{inputenc}
	\usepackage[freepin,crossmark]{ticket}
	\usepackage{graphicx}

	\unitlength=1mm
	\ticketSize{52}{78}
	\ticketNumbers{2}{3}

	\renewcommand{\ticketdefault}{
	<!DOCTYPE html>
	<html>
	<head>
	<script src="https://cdn.jsdelivr.net/npm/chart.js@2.9.3/dist/Chart.min.js"></script>
	</head>

	<body>
	<div width="100px" height="100px">
	<canvas id="myChart"></canvas>
	</div>
	import pickle
	import itertools
	import numpy as np
	import tensorflow.keras as keras

	with open('HackathonMetadata.pkl','rb') as ff:
	metad = pickle.load(ff)

	with open('HackathonData_Homology0.pkl','rb') as ff:
	homol = pickle.load(ff)
	import numpy as np
	import matplotlib.pyplot as plt

	__author__ = 'Dominik Krzeminski (dokato)'

	tau = 2
	I_ext = 0.5
	a = 0.8
	b = 0.7
	library(shiny)
	library(magrittr)
	library(ggplot2)

	ui <- fluidPage(
	checkboxGroupInput("checkbox", "Variables to show:",
	c("psavert", "uempmed", "unemploy")),
	plotOutput("plot")
	)
	import os
	import mne
	from mne.datasets.brainstorm import bst_resting
	import numpy as np
	import matplotlib.pyplot as plt
	import scipy.signal as ss

	########################## INPUT:
	folder = os.path.join(bst_resting.data_path(), 'MEG/bst_resting')
	file = 'subj002_spontaneous_20111102_01_AUX_raw.fif'
	library(shiny)
	library(shiny.router)

	options(shiny.router.debug = T)
	# This generates menu in user interface with links.
	menu <- (
	tags$ul(
	tags$li(a(class = "item", href = "/", "Page")),
	tags$li(a(class = "item", href = route_link("other"), "Other page"))
	)
	import numpy as np

	def find_closest_orthogonal_matrix(A):
	'''
	Find closest orthogonal matrix to A using iterative method.

	Bases on the code from REMOVE_SOURCE_LEAKAGE function from OSL Matlab package.

	Args:
	A (numpy.array): array shaped k, n, where k is number of channels, n - data points