tylermorganwall

library(geojsonsf)
library(sf)
library(rayrender)

#Data source: https://github.com/telegeography/www.submarinecablemap.com
cables = geojson_sf("cable-geo.json")

cablescene = list()
counter = 1
for(i in 1:length(cables$geometry)) {

tylermorganwall

library(rayshader)
library(rayrender)

popdata = raster::raster("gpw_v4_population_density_rev11_2020_15_min.tif")

population_mat = rayshader:::flipud(raster_to_matrix(popdata))

above1 = population_mat > 1
above5 = population_mat > 5
above10 = population_mat > 10

tylermorganwall

library(raster)
library(rayshader)
#Load QGIS georeference image (see https://www.qgistutorials.com/en/docs/3/georeferencing_basics.html)
testindia = raster::stack("1870_southern-india_modified.tif")

#Set bounding box for final map (cut off edges without data, introduced via reprojection)
india_bb = raster::extent(c(68,92,1,20))
cropped_india = raster::crop(testindia, india_bb)

#Convert to RGB array

lucdangelis

#Calibration and Holdouts periods for data split
date_start = ['2013-04-01','2014-04-01','2015-04-01','2016-04-01']
calibration_period_end = ['2015-03-31','2016-03-31','2017-03-31','2018-03-31']
date_end = ['2016-03-31','2017-03-31','2018-03-31','2019-03-31']

#Arrays where to store the results of cross validation
accuracies_1y = []
holdouts_1y = []
predictions_1y = []

lucdangelis

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import poisson,expon,nbinom

poisson_lambda = 4.3
p_arr = []

distribution = poisson(poisson_lambda)
for transactions in range(0,10):
     p_arr.append(distribution.pmf(transactions))

MLWhiz

# taken from https://medium.com/@pouryaayria/k-fold-target-encoding-dfe9a594874b
from sklearn import base
from sklearn.model_selection import KFold

class KFoldTargetEncoderTrain(base.BaseEstimator,
                               base.TransformerMixin):
    def __init__(self,colnames,targetName,
                  n_fold=5, verbosity=True,
                  discardOriginal_col=False):
        self.colnames = colnames

walkerke

library(tigris)
library(tidycensus)
library(tidyverse)
library(sf)

ma_income <- get_acs(geography = "county subdivision", 
                     variables = "B19013_001", 
                     state = "MA")

ma_subs <- county_subdivisions(state = "MA", cb = TRUE, class = "sf") %>%

hrbrmstr

Only including this file so the title of the gist isn't `.gitignore`

FinanceData

sjsrey