sabrooks

import osmnx as ox
import geopandas as gpd
import matplotlib.pyplot as plt
from typing import Tuple

MAJOR = {
    "motorway","trunk","primary","secondary",
    "motorway_link","trunk_link","primary_link","secondary_link",
}

sabrooks

[{"current": 86.51745, "time": 298.8232, "fuse": "40T", "curve": "Melting "}, {"current": 86.95113, "time": 278.8996, "fuse": "40T", "curve": "Melting "}, {"current": 87.64952, "time": 252.3587, "fuse": "40T", "curve": "Melting "}, {"current": 88.35352, "time": 226.0716, "fuse": "40T", "curve": "Melting "}, {"current": 89.06317, "time": 203.9452, "fuse": "40T", "curve": "Melting "}, {"current": 90.40918, "time": 167.981, "fuse": "40T", "curve": "Melting "}, {"current": 91.59217, "time": 143.8614, "fuse": "40T", "curve": "Melting "}, {"current": 92.88347, "time": 121.3708, "fuse": "40T", "curve": "Melting "}, {"current": 94.28725, "time": 103.2186, "fuse": "40T", "curve": "Melting "}, {"current": 95.90382, "time": 86.21543, "fuse": "40T", "curve": "Melting "}, {"current": 97.54814, "time": 72.22954, "fuse": "40T", "curve": "Melting "}, {"current": 99.31992, "time": 61.05951, "fuse": "40T", "curve": "Melting "}, {"current": 101.225, "time": 51.15437, "fuse": "40T", "curve": "Melting "}, {"current": 102.8576, "t

sabrooks

var height = 450,
   width = 960,
   margin = {top: 20, right:20, bottom: 30, left: 50},
   layers = 4;

var parseTime = d3.timeParse("%m/%d/%Y");

var layerArray = d3.range(1, layers+1);

var x = d3.scaleTime()

sabrooks

var height = 450,
   width = 960,
   layers = 4;

var layerArray = d3.range(layers);


var values =[
        {"x": 1,  "y": 28}, {"x": 2,  "y": 55},
        {"x": 3,  "y": 43}, {"x": 4,  "y": 91},

sabrooks

//From Wide and Short to Narrow and Skinny, 
var headers = ["hydro", "thermal"];

var layer = headers.map(function(header){
  return data.map(d => 
    ({date: d.date, type: header, value:d[header]}) 
  );
});

sabrooks

//vega-lite example of compile to vega spec and then parse the vega spec.
function parse(spec) {
  vg.parse.spec(spec, function(chart) {
   chart({el:"#vis"}).update(); });
}

var vlspec = {
      "data": {
        "values": [
          {"a":"A", "b":28}, {"a":"B", "b":55}, {"a":"C", "b":43},

sabrooks

# Data a dataframe of Confidence Intervals around parameter estimates
params <- bind_rows(lm_for%>%
            confint()%>%
            as.data.frame(., rownames = rownames(.))%>%
            add_rownames(var = "parameter")%>%
            mutate(model = "Forecast"),
          lm_back%>%
            confint()%>%
            as.data.frame(., rownames = rownames(.))%>%
            add_rownames(var = "parameter")%>%

sabrooks

params <- bind_rows(lm_for%>%
            confint()%>%
            as.data.frame(., rownames = rownames(.))%>% # convert matrix to dataframe
            add_rownames(var = "parameter")%>% # convert rownames to variable
            mutate(model = "Forecast"),
          lm_back%>%
            confint()%>%
            as.data.frame(., rownames = rownames(.))%>%
            add_rownames(var = "parameter")%>%
            mutate(model = "Backcast"))

sabrooks

df.int <- df.start.end$start[1]%--%df.start.end$end[1] # define interval

time.points <- int_start(df.int)+minutes(seq(0, df.int%/%minutes(1), by = 5 ))# 5 minutes

sabrooks

#Classify workdays and weekends
cs.df<- coldstorage%>%
  mutate(dofw = wday(Date.Time, label = TRUE),
         day.type = as.factor(ifelse(dofw == "Sun"|dofw == "Sat", "weekend", "workweek")))%>%
  dplyr::select(-dofw)

#Train Model
model <- train(day.type ~., data = cs.df,
               'nb', trControl=trainControl(method='cv',number=10))