freeman-lab

package thunder.streaming

import org.apache.spark.{SparkConf, Logging}
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext._
import org.apache.spark.streaming._
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.mllib.clustering.KMeansModel

import scala.util.Random.nextDouble

freeman-lab

principles

• commitment to open source
• scalability
• transparency
• modularity
• standardization
• iteration
• avoiding reinvention

freeman-lab

var redux = require('redux')
var vdom = require('virtual-dom')
var hyperx = require('hyperx')
var hx = hyperx(vdom.h)

var reducer = require('./reducer')
var store = redux.createStore(reducer)

var item = function (state) {
  function onclick () {

freeman-lab

freeman-lab

# neurofinder submission
# local nmf
#
# requirements:
# thunder-python v1.2.0
# thunder-extraction v1.1.0

import json
import thunder as td
from extraction import NMF

freeman-lab

import json
from numpy import zeros, random, asarray, where, ones
from scipy.ndimage.morphology import binary_closing, binary_dilation

datasets = ['00.00.test', '00.01.test', '01.00.test', '01.01.test', '02.00.test', '02.01.test', '03.00.test', '04.00.test', '04.01.test']

dims = [500,500]
margin = 20
n = 200

freeman-lab

import json

datasets = ['00.00.test', '00.01.test', '01.00.test', '01.01.test', '02.00.test', '02.01.test', '03.00.test', '04.00.test', '04.01.test']

submission = []

for dataset in datasets:
  result = {'dataset': dataset, 'regions': [{'coordinates': [[0, 0], [0, 1]]}]}
  submission.append(result)

freeman-lab

const mat4 = require('gl-mat4')
const sphere = require('primitive-icosphere')
const fit = require('canvas-fit')
const normals = require('angle-normals')
const regl = require('regl')()
const camera = require('lookat-camera')()

var mesh = sphere(1, {
  subdivisions: 5
})

freeman-lab

const mat4 = require('gl-mat4')
const sphere = require('primitive-icosphere')
const regl = require('regl')()
const camera = require('lookat-camera')()

var mesh = sphere(1, {subdivisions: 1})

const cube = regl({
  frag: `
    precision mediump float;

freeman-lab

```python
# create shifted copies

from numpy import arange
from scipy.ndimage.interpolation import shift
```

```python
# create shifted copies