Mat G. matheustguimaraes

## gist:f9cdcbfda24022e03c347b49ce04f03d
def load_dcm(img_path):
  dcm = pydicom.read_file(img_path)
  RescaleIntercept = int(dcm.data_element('RescaleIntercept').value)
  return np.array(dcm.pixel_array,dtype=np.int16)+RescaleIntercept

## cuda_11.8_installation_on_Ubuntu_22.04
#!/bin/bash

### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###

### to verify your gpu is cuda enable check

## user.js
// Get all users
var url  = "http://localhost:8080/api/v1/users";
var xhr  = new XMLHttpRequest()
xhr.open('GET', url, true)
xhr.onload = function () {
	var users = JSON.parse(xhr.responseText);
	if (xhr.readyState == 4 && xhr.status == "200") {
		console.table(users);
	} else {
		console.error(users);

## README-Template.md

      
              1 file
            
          
              4765 forks
            
          
              1703 comments
            
          
              14009 stars
            
          
                PurpleBooth
                / README-Template.md
            
            
              Last active
              May 22, 2024 13:17
            
              
                A template to make good README.md
              
          
    Project Title

One Paragraph of project description goes here
Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Prerequisites


## serialize.py
import pandas as pd
df = pd.DataFrame({'text': [str(i % 1000) for i in range(1000000)],
                   'numbers': range(1000000)})

import pickle
import cPickle
import json
from functools import partial
from time import time

## gist:7360908

      
              1 file
            
          
              3648 forks
            
          
              1022 comments
            
          
              16684 stars
            
          
                rxaviers
                / gist:7360908
            
            
              Last active
              May 24, 2024 03:53
            
              
                Complete list of github markdown emoji markup
              
          
    People


 :bowtie:
😄 :smile:
😆 :laughing:


😊 :blush:
😃 :smiley:
☺️ :relaxed:


😏 :smirk:
😍 :heart_eyes:
😘 :kissing_heart:


😚 :kissing_closed_eyes:
😳 :flushed:
😌 :relieved:


😆 :satisfied:
😁 :grin:
😉 :wink:


😜 :stuck_out_tongue_winking_eye:
😝 :stuck_out_tongue_closed_eyes:
😀 :grinning:


😗 :kissing:
😙 :kissing_smiling_eyes:
😛 :stuck_out_tongue:
	def load_dcm(img_path):
	dcm = pydicom.read_file(img_path)
	RescaleIntercept = int(dcm.data_element('RescaleIntercept').value)
	return np.array(dcm.pixel_array,dtype=np.int16)+RescaleIntercept
	#!/bin/bash

	### steps ####
	# Verify the system has a cuda-capable gpu
	# Download and install the nvidia cuda toolkit and cudnn
	# Setup environmental variables
	# Verify the installation
	###

	### to verify your gpu is cuda enable check
	// Get all users
	var url = "http://localhost:8080/api/v1/users";
	var xhr = new XMLHttpRequest()
	xhr.open('GET', url, true)
	xhr.onload = function () {
	var users = JSON.parse(xhr.responseText);
	if (xhr.readyState == 4 && xhr.status == "200") {
	console.table(users);
	} else {
	console.error(users);
	import pandas as pd
	df = pd.DataFrame({'text': [str(i % 1000) for i in range(1000000)],
	'numbers': range(1000000)})

	import pickle
	import cPickle
	import json
	from functools import partial
	from time import time
`:bowtie:`	😄 `:smile:`	😆 `:laughing:`
😊 `:blush:`	😃 `:smiley:`	☺️ `:relaxed:`
😏 `:smirk:`	😍 `:heart_eyes:`	😘 `:kissing_heart:`
😚 `:kissing_closed_eyes:`	😳 `:flushed:`	😌 `:relieved:`
😆 `:satisfied:`	😁 `:grin:`	😉 `:wink:`
😜 `:stuck_out_tongue_winking_eye:`	😝 `:stuck_out_tongue_closed_eyes:`	😀 `:grinning:`
😗 `:kissing:`	😙 `:kissing_smiling_eyes:`	😛 `:stuck_out_tongue:`