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---
title: "Airport Wi-Fi"
author: ""
date: "`r sprintf('Last run: %s', format(Sys.time(), '%d %B, %Y'))`"
output: 
  html_document:
    code_download: true
---

```{r setup, include=FALSE}

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def joyplot(data, ax=None, 
            flatten = .1, #rescale the height of each distribution to avoid overlap. If large, will flatten out each of the KDEs
            linecolor='k', 
            shadecolor='w',
            alpha=1,
            shade=True, 
            line_kws = None,
            kde_kws=None,
            fig_kws=None,
            shade_kws=None):

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

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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") %>%

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Install dlib and face_recognition on a Raspberry Pi

Instructions tested with a Raspberry Pi 2 with an 8GB memory card. Probably also works fine on a Raspberry Pi 3.

Steps

Download the latest Raspbian Jessie Light image. Earlier versions of Raspbian won't work.

Write it to a memory card using Etcher, put the memory card in the RPi and boot it up.

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# 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

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#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 = []

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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