Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.
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library("shiny") | |
library("tidyverse") | |
library("rnaturalearthdata") | |
library("sf") | |
# Using Africa instead of whole world as countries110 contains | |
# 3 invalid geometries. Also, too many examples use the USA everytime | |
africa_sf <- countries110 %>% | |
st_as_sf() %>% | |
st_transform(4326) %>% |
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library(googledrive) | |
# authenticate yourself | |
drive_auth() | |
gdoc_to_rmd <- function(drive_id, output, verbose = TRUE, overwrite = FALSE) { | |
# get the doc | |
doc <- drive_get(as_id(drive_id)) | |
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--- | |
title: "Tweet thread" | |
author: "Elio Campitelli" | |
output: github_document | |
--- | |
```{r} | |
knitr::opts_chunk$set(dev = "png", | |
tweet_this = TRUE) |
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import cdstoolbox as ct | |
@ct.application(title='PV Power Potential', | |
description = 'Computation of PV Power potential for a specific day using the dimensionless described in Mavromatakis et al. https://doi.org/10.1016/j.renene.2009.11.010', | |
abstract = 'Lorem Ipsum') | |
@ct.input.dropdown('show_year', values=range(2010, 2017)) | |
@ct.input.dropdown('region', values=['Europe', 'China']) | |
@ct.input.text('month', type = int, label='Month', default=7) | |
@ct.input.text('day', type = int, label='Day', default=1) |
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# This example demonstrates running furrr code distributed on 2 AWS instances ("nodes"). | |
# The instances have already been created. | |
library(future) | |
library(furrr) | |
# Two t2.micro AWS instances | |
# Created from http://www.louisaslett.com/RStudio_AMI/ | |
public_ip <- c("34.205.155.182", "34.201.26.217") |
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a = read.delim("~/Accuracy_as_a_function_of_sketch_size_and_set_size.txt") | |
head(a) | |
a$rel_error = sqrt(a$Mean.squared.error) / a$Exact.size | |
a$p = factor(a$Sketch.size..log2.) | |
library(cowplot) | |
library(ggplot2) | |
plot1 = ggplot(a, aes(x=Exact.size, sqrt(Mean.squared.error) / Exact.size, |
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--- | |
title: "albatross_breeding_cycle" | |
author: "Jaimie Cleeland jaimie.cleeland@utas.edu.au" | |
date: "12/12/2016" | |
output: html_document | |
--- | |
Load libraries | |
```{r} | |
library(dplyr) |
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If, like me, your are booting with UEFI (because having a triple boot ubuntu-windows-mac or because UEFI is the most modern type of bootloader and successor of EFI :p), you have to sign the proprietary modules each time they are recompiled (or upgrade kernel version) so that they are allowed to be loaded in the kernel. | |
1) Step one, create a self-signed certificate to sign nvidia driver: | |
sudo openssl req -new -x509 -newkey rsa:2048 -keyout UEFI.key -outform DER -out UEFI.der -nodes -days 36500 -subj "/CN=rambou_nvidia/" | |
2) step two load and store certificate in a supplementary key database MOC | |
sudo mokutil --import UEFI.der | |
3) step three reboot your system | |
At this step after reboot you will be prompted to select your certificate to import in in key database. If you have inserted a password at certificate creation you'll be prompted to insert it. If you are not prompted, you may have to enter the BIOS by using function keys at boot time. |
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library(doSNOW) | |
library(tcltk) | |
cl <- makeSOCKcluster(2) | |
registerDoSNOW(cl) | |
pb <- txtProgressBar(max=100, style=3) | |
progress <- function(n) setTxtProgressBar(pb, n) | |
opts <- list(progress=progress) | |
r <- foreach(i=1:100, .options.snow=opts) %dopar% { |
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