Robin Kraft robinkraft

## domino_event_log_5c777df946e0fb0008ada92c_condensed.json
"Request to deploy app with id 5c777df946e0fb0008ada92c"
"Deploying app with id 5c777df946e0fb0008ada92c"
"Deployment created: run-5c777df946e0fb0008ada92c"
"Pod created: run-5c777df946e0fb0008ada92c-6794d486b8-sxsp4"
"Scaled up replica set run-5c777df946e0fb0008ada92c-6794d486b8 to 1"
"Successfully assigned run-5c777df946e0fb0008ada92c-6794d486b8-sxsp4 to ip-10-0-175-133.us-west-2.compute.internal"
"Pod updated: run-5c777df946e0fb0008ada92c-6794d486b8-sxsp4"
"Deployment updated: run-5c777df946e0fb0008ada92c"
"Deployment updated: run-5c777df946e0fb0008ada92c"
"Pod updated: run-5c777df946e0fb0008ada92c-6794d486b8-sxsp4"

## minikube_uninstall.sh
minikube stop; minikube delete &&
docker stop $(docker ps -aq) &&
rm -rf ~/.kube ~/.minikube &&
sudo rm -rf /usr/local/bin/localkube /usr/local/bin/minikube &&
launchctl stop '*kubelet*.mount' &&
launchctl stop localkube.service &&
launchctl disable localkube.service &&
sudo rm -rf /etc/kubernetes/ &&
docker system prune -af --volumes

## upload.py
from mapbox import Uploader

u = Uploader()  # handles authentication

tileset = 'username.tileset_name'  # name your tileset
url = u.stage(open('mosaic_final.tif'))  # upload happens here
u.create(url, tileset, name=fname)  # starts the tiling job

## download.py
#!/usr/bin/env python

import argparse
import os
import requests
import json
import sys
import logging
import datetime

## firemap.html
<iframe src="https://api.mapbox.com/styles/v1/robinkraft/cj8nn4lvp7yoq2ro1klhjltw8.html?title=true&access_token=pk.eyJ1Ijoicm9iaW5rcmFmdCIsImEiOiJQLUp2RU9NIn0.B20c6fiHx0NCgfSOE3HYbw#13.15/38.4816/-122.7019" width="640" height="480”>

## toa_reflectance.py
import os
import argparse

from bs4 import BeautifulSoup
import rasterio


SUFFIX = '_BGRN_Analytic'
BASEPATH = '/Users/robinkraft/gitlab/change/hackathon/robin/data/'

## learning_rates.ipynb

      
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                robinkraft
                / learning_rates.ipynb
            
            
              Last active
              November 29, 2016 03:17
            
              
                Showing difference between different methods for changing learning rates in Keras.
              
          
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## prep_data.py
import os
from glob import glob
import shutil

import pandas as pd


def choose_subjects(df, count):
    # group the frame by the subject in the image
    subjects = df.groupby('subject')

## prep_data.py
import os
import random
import glob
import shutil


def get_dc_paths(path):
    """Given a path filled with dog & cat files, make separate lists of both."""
    cats = []
    dogs = []

## f250-cdm.clj
(use 'forma.hadoop.jobs.postprocess)
(in-ns 'forma.hadoop.jobs.postprocess)

(defn f250->cdm
  [src t-res zoom]
  (let [epoch (date/datetime->period t-res "2000-01-01")]
    (<- [?x ?y ?z ?min-period]
        (src ?line)
        (clojure.string/split ?line #"\t" :> ?lat-str ?lon-str ?date-str)
        (read-string ?lat-str :> ?lat)
	"Request to deploy app with id 5c777df946e0fb0008ada92c"
	"Deploying app with id 5c777df946e0fb0008ada92c"
	"Deployment created: run-5c777df946e0fb0008ada92c"
	"Pod created: run-5c777df946e0fb0008ada92c-6794d486b8-sxsp4"
	"Scaled up replica set run-5c777df946e0fb0008ada92c-6794d486b8 to 1"
	"Successfully assigned run-5c777df946e0fb0008ada92c-6794d486b8-sxsp4 to ip-10-0-175-133.us-west-2.compute.internal"
	"Pod updated: run-5c777df946e0fb0008ada92c-6794d486b8-sxsp4"
	"Deployment updated: run-5c777df946e0fb0008ada92c"
	"Deployment updated: run-5c777df946e0fb0008ada92c"
	"Pod updated: run-5c777df946e0fb0008ada92c-6794d486b8-sxsp4"
	minikube stop; minikube delete &&
	docker stop $(docker ps -aq) &&
	rm -rf ~/.kube ~/.minikube &&
	sudo rm -rf /usr/local/bin/localkube /usr/local/bin/minikube &&
	launchctl stop 'kubelet.mount' &&
	launchctl stop localkube.service &&
	launchctl disable localkube.service &&
	sudo rm -rf /etc/kubernetes/ &&
	docker system prune -af --volumes
	from mapbox import Uploader

	u = Uploader() # handles authentication

	tileset = 'username.tileset_name' # name your tileset
	url = u.stage(open('mosaic_final.tif')) # upload happens here
	u.create(url, tileset, name=fname) # starts the tiling job
	#!/usr/bin/env python

	import argparse
	import os
	import requests
	import json
	import sys
	import logging
	import datetime
	import os
	import argparse

	from bs4 import BeautifulSoup
	import rasterio


	SUFFIX = '_BGRN_Analytic'
	BASEPATH = '/Users/robinkraft/gitlab/change/hackathon/robin/data/'
	import os
	from glob import glob
	import shutil

	import pandas as pd


	def choose_subjects(df, count):
	# group the frame by the subject in the image
	subjects = df.groupby('subject')
	import os
	import random
	import glob
	import shutil


	def get_dc_paths(path):
	"""Given a path filled with dog & cat files, make separate lists of both."""
	cats = []
	dogs = []
	(use 'forma.hadoop.jobs.postprocess)
	(in-ns 'forma.hadoop.jobs.postprocess)

	(defn f250->cdm
	[src t-res zoom]
	(let [epoch (date/datetime->period t-res "2000-01-01")]
	(<- [?x ?y ?z ?min-period]
	(src ?line)
	(clojure.string/split ?line #"\t" :> ?lat-str ?lon-str ?date-str)
	(read-string ?lat-str :> ?lat)