admercs

Pretty print tables summarizing properties of tensor arrays in numpy, pytorch, jax, etc.

admercs

Keybase proof

I hereby claim:

• I am admercs on github.
• I am admercs (https://keybase.io/admercs) on keybase.
• I have a public key ASDAgyTXQVPrVhUzn0XnSyQ3q3Sn6aV42WXmsDm_OR1Tfgo

To claim this, I am signing this object:

admercs

Default

Powerlevel9k

admercs

Processing LiDAR to extract building heights

Walk through

Detailed walk through of building extraction using postgis

First lets pull a data layer from of openstreetmap. You can do this any which way you’d like, as there are a variety of methods for pulling openstreetmap data from their database. Check the [wiki] (http://wiki.openstreetmap.org/wiki/Downloading_data) for a comprehensive list. My favourite method thus far is pulling the data straight into QGIS using the open layers plugin. For those who may want to explore this method, check [this tutorial] (http://www.qgistutorials.com/en/docs/downloading_osm_data.html). For building extraction you only need building footprints, and include the building tags. Not all polygons are of type building in OSM, so we can download all the polygons, and then filter the layer for only polygons tagged as buildings.

LiDAR data was pulled from USGS via the Earth Explorer site. [Here] (http://earthobservatory.nasa.gov/blogs/ele

admercs

library(ggplot2)
library(scales)

# load data:
log <- data.frame(Date = c("2013/05/25","2013/05/28","2013/05/31","2013/06/01","2013/06/02","2013/06/05","2013/06/07"), 
  Quantity = c(9,1,15,4,5,17,18))
log
str(log)

# convert date variable from factor to date format: