ronaldokun

## execute_nb.py
#!/usr/bin/env python

import nbformat,fire
from nbconvert.preprocessors import ExecutePreprocessor

def run_notebook(path):
    nb = nbformat.read(open(path), as_version=nbformat.NO_CONVERT)
    ExecutePreprocessor(timeout=600).preprocess(nb, {})
    print('done')

## History\-10e19d43\entries.json
{"version":1,"resource":"file:///c%3A/Users/rsilva/code/anateldb/extracao/reading.py","entries":[{"id":"aEyo.py","timestamp":1675198943147},{"id":"gZqW.py","timestamp":1675342978085},{"id":"fsOp.py","timestamp":1675342988883}]}

## cloudSettings
{"lastUpload":"2021-06-18T02:34:22.337Z","extensionVersion":"v3.4.3"}

## selenium.py
# install chromium, its driver, and selenium
!apt update
!apt install chromium-chromedriver
!pip install selenium
# set options to be headless, ..
from selenium import webdriver
options = webdriver.ChromeOptions()
options.add_argument('--headless')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')

## jupyter_ngrok.md

      
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                ronaldokun
                / jupyter_ngrok.md
            
            
              Created
              July 22, 2020 16:41
                — forked from artificialsoph/jupyter_ngrok.md
            
              
                Quickest way to get Jupyter notebook running on a remote server.
              
          
    Log into your server with ssh, something like
ssh -i "my_secret.pem" ubuntu@12.123.12.123
If it's a new server, you'll need to install a few things.
Install conda with

  
## pandas_crossjoin_example.py
"""
Shows how to do a cross join (i.e. cartesian product) between two pandas DataFrames using an example on
calculating the distances between origin and destination cities.

Tested with pandas 0.17.1 and 0.18 on Python 3.4 and Python 3.5

Best run this with Spyder (see https://github.com/spyder-ide/spyder)
Author: Markus Konrad <post@mkonrad.net>

April 2016

## Change HOMEDRIVE HOMEPATH and HOMESHARE
c:\> set HOME
HOME=C:\Users\rsilva
HOMEDRIVE=U:
HOMEPATH=\
HOMESHARE=\\WIFSSP01\EstruturaFS\usu\rsilva

The Drive U: is a network drive defined by the Group Policy in the organization. It's very slow.

1. I had a similar problem, which caused problems with msysgit. Here is the solution I used, and it definately worked for me. This answer is similar to this and that SO post.If you are on Windows 7, you can skip this step. If you are on Windows XP, download and install Windows XP Service Pack 2 Support Tools which contains SETX, a utility, described on SS64 and technet, that lets you set permanent system and user variables. You must have administrative rights to set global system variables. The basic usage is SETX <variable> "<value>" [-m].
2. Add the following script to your startup folder - W7: "C:\Users\<username>\Start Menu\Programs\Startup" and XP: "C:\Documents and Settings\<username>\Start Menu\Programs\Startup".

## Enabling GPU support of xgboost
To support GPU. The xgboost library must be build from the github code.

The instructions on https://xgboost.readthedocs.io/en/latest/build.html are straightforward.

Except for a little detail:

we must use:

sudo -s
python setup.pu install

## subplots.py
##
# Create a figure space matrix consisting of 3 columns and 2 rows
#
# Here is a useful template to use for working with subplots.
#
##################################################################
fig, ax = plt.subplots(figsize=(10,5), ncols=3, nrows=2)

left   =  0.125  # the left side of the subplots of the figure
right  =  0.9    # the right side of the subplots of the figure

## How to install Cuda 8.0 work on Geforce GT 750M and Ubuntu 16.04
#sudo dpkg --install cuda-repo-<distro>-<version>.<architecture>.deb
#sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/<distro>/<architecture>/7fa2af80.pub

$ sudo apt-get update
$ sudo apt-get install cuda
$ sudo apt-get install nvidia-cuda-toolkit

Reboot the system to load the NVIDIA drivers.
Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
	#!/usr/bin/env python

	import nbformat,fire
	from nbconvert.preprocessors import ExecutePreprocessor

	def run_notebook(path):
	nb = nbformat.read(open(path), as_version=nbformat.NO_CONVERT)
	ExecutePreprocessor(timeout=600).preprocess(nb, {})
	print('done')
	# install chromium, its driver, and selenium
	!apt update
	!apt install chromium-chromedriver
	!pip install selenium
	# set options to be headless, ..
	from selenium import webdriver
	options = webdriver.ChromeOptions()
	options.add_argument('--headless')
	options.add_argument('--no-sandbox')
	options.add_argument('--disable-dev-shm-usage')
	"""
	Shows how to do a cross join (i.e. cartesian product) between two pandas DataFrames using an example on
	calculating the distances between origin and destination cities.

	Tested with pandas 0.17.1 and 0.18 on Python 3.4 and Python 3.5

	Best run this with Spyder (see https://github.com/spyder-ide/spyder)
	Author: Markus Konrad <post@mkonrad.net>

	April 2016
	c:\> set HOME
	HOME=C:\Users\rsilva
	HOMEDRIVE=U:
	HOMEPATH=\
	HOMESHARE=\\WIFSSP01\EstruturaFS\usu\rsilva

	The Drive U: is a network drive defined by the Group Policy in the organization. It's very slow.

	1. I had a similar problem, which caused problems with msysgit. Here is the solution I used, and it definately worked for me. This answer is similar to this and that SO post.If you are on Windows 7, you can skip this step. If you are on Windows XP, download and install Windows XP Service Pack 2 Support Tools which contains SETX, a utility, described on SS64 and technet, that lets you set permanent system and user variables. You must have administrative rights to set global system variables. The basic usage is SETX <variable> "<value>" [-m].
	2. Add the following script to your startup folder - W7: "C:\Users\<username>\Start Menu\Programs\Startup" and XP: "C:\Documents and Settings\<username>\Start Menu\Programs\Startup".
	To support GPU. The xgboost library must be build from the github code.

	The instructions on https://xgboost.readthedocs.io/en/latest/build.html are straightforward.

	Except for a little detail:

	we must use:

	sudo -s
	python setup.pu install
	##
	# Create a figure space matrix consisting of 3 columns and 2 rows
	#
	# Here is a useful template to use for working with subplots.
	#
	##################################################################
	fig, ax = plt.subplots(figsize=(10,5), ncols=3, nrows=2)

	left = 0.125 # the left side of the subplots of the figure
	right = 0.9 # the right side of the subplots of the figure
	#sudo dpkg --install cuda-repo-<distro>-<version>.<architecture>.deb
	#sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/<distro>/<architecture>/7fa2af80.pub

	$ sudo apt-get update
	$ sudo apt-get install cuda
	$ sudo apt-get install nvidia-cuda-toolkit

	Reboot the system to load the NVIDIA drivers.
	Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables: