scbrown86

# Code from https://www.uva.nl/en/profile/l/o/e.e.vanloon/e.e.vanloon.html

# Implementation of the MapCurves algorithm (DOI 10.1007/s10109-006-0025-x)

mapcurves <- function(A, B, plotGOF = TRUE) {
  ## R implementation of the mapcurves goodness of fit measure
  ## (Hargrove et al. 20006, see full reference below) for comparing
  ## two categorical maps
  ##
  ## usage:

merriam

Emerging LIDAR Concepts and Sensor Technologies for AV

• SensorFusion MeetUp (https://www.meetup.com/Sunnyvale-Sensor-Fusion-Meetup/)
• Wednesday, July 25, 2018, presentation 6:30pm to 8pm
• Hosted at Plug-n-play (https://www.plugandplaytechcenter.com/) with over 100 attendees.
• Speaker: Jake Li, Business Development Manager for Hamamatsu Corporation. ([email protected])
• Slides available (https://drive.google.com/file/d/1RBGGernLF3DwAf9eQKFi9o8rXA7Te9hb/view?usp=sharing)

Summary

scbrown86

# Convert a GeoJSON object to a geopackage
# Add a second geoJSON object in as a second layer

# https://gis.stackexchange.com/q/223240/24249

library(rgdal)
library(sf)

# Here I open a shapefile, but readOGR can be used to read geoJSON files
nc <- st_as_sf(readOGR(system.file("shape/nc.shp", package = "sf")))

ocoyawale

This page is a curated collection of Jupyter/IPython notebooks that are notable for some reason. Feel free to add new content here, but please try to only include links to notebooks that include interesting visual or technical content; this should not simply be a dump of a Google search on every ipynb file out there.

Important contribution instructions: If you add new content, please ensure that for any notebook you link to, the link is to the rendered version using nbviewer, rather than the raw file. Simply paste the notebook URL in the nbviewer box and copy the resulting URL of the rendered version. This will make it much easier for visitors to be able to immediately access the new content.

Note that Matt Davis has conveniently written a set of bookmarklets and extensions to make it a one-click affair to load a Notebook URL into your browser of choice, directly opening into nbviewer.

sgillies

scbrown86

stratified <- function(df, group, size, select = NULL, 
                       replace = TRUE, bothSets = FALSE) {
  if (is.null(select)) {
    df <- df
  } else {
    if (is.null(names(select))) stop("'select' must be a named list")
    if (!all(names(select) %in% names(df)))
      stop("Please verify your 'select' argument")
    temp <- sapply(names(select),
                   function(x) df[[x]] %in% select[[x]])

connormanning

Houston LiDAR

Links to source data files for Houston LiDAR data - viewable with Potree and Plasio.

See https://twitter.com/howardbutler/status/9029370196592353

scbrown86

# Script from a density to color quantiles area

# Packages ---------------------------------------------------------------------
library(ggplot2)
library(dplyr)

# Data -------------------------------------------------------------------------
r_data = rnorm(100)  # Computes 100 values

r_quant = quantile(r_data, probs = seq(0, 1, 0.1))  # Computes quantiles

RooseveltAdvisors

This page is a curated collection of Jupyter/IPython notebooks that are notable for some reason. Feel free to add new content here, but please try to only include links to notebooks that include interesting visual or technical content; this should not simply be a dump of a Google search on every ipynb file out there.

Important contribution instructions: If you add new content, please ensure that for any notebook you link to, the link is to the rendered version using nbviewer, rather than the raw file. Simply paste the notebook URL in the nbviewer box and copy the resulting URL of the rendered version. This will make it much easier for visitors to be able to immediately access the new content.

Note that Matt Davis has conveniently written a set of bookmarklets and extensions to make it a one-click affair to load a Notebook URL into your browser of choice, directly opening into nbviewer.

scbrown86

diverge0 <- function(p, ramp) {
  # p: a trellis object resulting from rasterVis::levelplot
  # ramp: the name of an RColorBrewer palette (as character), a character 
  #       vector of colour names to interpolate, or a colorRampPalette.
  require(RColorBrewer)
  require(rasterVis)
  if(length(ramp)==1 && is.character(ramp) && ramp %in% 
     row.names(brewer.pal.info)) {
    ramp <- suppressWarnings(colorRampPalette(brewer.pal(11, ramp)))
  } else if(length(ramp) > 1 && is.character(ramp) && all(ramp %in% colors())) {