ndricca

## fastapi_error_example.py
from typing import Optional, List, Union
from typing_extensions import Literal

import uvicorn
from pydantic import BaseModel, Field
from fastapi import FastAPI, Body


class Payload(BaseModel):
    start: int

## find_notebook_by_mem_usage.md

      
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                ndricca
                / find_notebook_by_mem_usage.md
            
            
              Last active
              July 5, 2022 09:35
            
              
                Find which jupyter notebook is using more memory on server
              
          
    How to find out which jupyter notebook is using most of memory on a server


UPDATE 2022-07: see python script attached


find PIDs responsibles of most memory usage:
ps aux --sort=-%mem | head


look at the name of the JSON used inside the jupyter launcher command, usually is kernel-${kernel_id}.json and get `${kernel_id}


go at the jupyter server endpoint /api/sessions


## fizz_buzz.py


MULTIPLES_DICT = {
    3: 'Fizz',
    5: 'Buzz',
    7: 'Wolf',
}

def _is_by_num(num: int, multiple: int) -> bool:
    return num % multiple == 0

## css-parallax-w3s-sample.markdown

      
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                ndricca
                / css-parallax-w3s-sample.markdown
            
            
              Created
              June 11, 2020 12:53
            
              
                CSS-Parallax-W3S-Sample
              
          
    CSS-Parallax-W3S-Sample

A Pen by ndricca on CodePen.
License.

  
## covid_effect_pollution_milan.html
<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />

<title>covid_effect_pollution_milan</title>

<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>


## join_coalesce.R
library(tidyverse)

(a <- data_frame(ID = 1:20,
                D1 = sample(0:1,size = 20,replace = T,prob = c(0.8,0.2)),
                D2 = sample(0:1,size = 20,replace = T,prob = c(0.7,0.3)))
)
(b <- data_frame(ID = 1:20,
                D1 = sample(0:1,size = 20,replace = T,prob = c(0.9,0.1)),
                D2 = sample(0:1,size = 20,replace = T,prob = c(0.9,0.1)))
)

## gist:95dca0530e4a1be6ab5be52b7753e8a5
rm(list = ls())
library(dplyr)

df <- data_frame(y = rnorm(10,2),
                 x1 = as.factor(rbinom(10,1,0.5)),
                 x2 = as.factor(rep(letters[1:2],5)))
df

df %>%
  add_count() %>%
	from typing import Optional, List, Union
	from typing_extensions import Literal

	import uvicorn
	from pydantic import BaseModel, Field
	from fastapi import FastAPI, Body


	class Payload(BaseModel):
	start: int


	MULTIPLES_DICT = {
	3: 'Fizz',
	5: 'Buzz',
	7: 'Wolf',
	}

	def _is_by_num(num: int, multiple: int) -> bool:
	return num % multiple == 0
	<!DOCTYPE html>
	<html>
	<head><meta charset="utf-8" />

	<title>covid_effect_pollution_milan</title>

	<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
	library(tidyverse)

	(a <- data_frame(ID = 1:20,
	D1 = sample(0:1,size = 20,replace = T,prob = c(0.8,0.2)),
	D2 = sample(0:1,size = 20,replace = T,prob = c(0.7,0.3)))
	)
	(b <- data_frame(ID = 1:20,
	D1 = sample(0:1,size = 20,replace = T,prob = c(0.9,0.1)),
	D2 = sample(0:1,size = 20,replace = T,prob = c(0.9,0.1)))
	)
	rm(list = ls())
	library(dplyr)

	df <- data_frame(y = rnorm(10,2),
	x1 = as.factor(rbinom(10,1,0.5)),
	x2 = as.factor(rep(letters[1:2],5)))
	df

	df %>%
	add_count() %>%