install awscli for linux (python)

Try to do these command


>pip install awscli --upgrade --user

>sudo apt install awscli



Thank you.

www.marearts.com

Get file list in s3 bucket folder (example python code)

 refer to source code

..

import boto3

import botocore

ACCESS_KEY = '**'

SECRET_KEY = '**'

s3_client = boto3.client(

's3',

aws_access_key_id=ACCESS_KEY,

aws_secret_access_key=SECRET_KEY,

region_name = 'eu-west-1'

)

bucket_name = '***'

folder_name = '***'

#check folder exist

try:

result = s3_client.list_objects_v2(Bucket=bucket_name, Prefix=folder_name )

for o in result['Contents']:

print(o['Key']) #file list in folder

except botocore.exceptions.ClientError as e:

print('no folder exist')

..

Thank you 

www.marearts.com

aws dynamodb scan all item using paginate

 

Refer to code, Thank you!

..

import boto3

ACCESS_KEY = '***'

SECRET_KEY = '***'

dynamo_client = boto3.client(

'dynamodb',

aws_access_key_id=ACCESS_KEY,

aws_secret_access_key=SECRET_KEY,

region_name = 'eu-west-1'

)

def Search_Item(TABLE_NAME):

paginator = dynamo_client.get_paginator('scan')

operation_parameters = {'TableName': TABLE_NAME,}

page_iterator = paginator.paginate(**operation_parameters)

return page_iterator

TABLE_NAME ='****'

page_iterator = Search_Item(TABLE_NAME)

for page in page_iterator:

for item in page['Items']:

print(item['UID']['S'])

..

www.marearts.com

🙇🏻‍♂️

Docker container stop by image name

 Stop docker container by image name:

imagename='mydockerimage'
docker stop $(docker ps | awk '{split($2,image,":"); print $1, image[1]}' | awk -v image=$imagename '$2 == image {print $1}')

Stop docker container by image name and tag:

imagename='mydockerimage:latest'
docker stop $(docker ps | awk -v image=$imagename '$2 == image {print $1}')

docker swarm simple command

*install virtualbox

-+-

#install virtualbox on terminal

#osx

brew install virtualbox

#linux

sudo apt install virtualbox

-+-

 

* install docker-machine

...

#osx

curl -L https://github.com/docker/machine/releases/download/v0.16.2/docker-machine-`uname -s`-`uname -m` >/usr/local/bin/docker-machine && chmod +x /usr/local/bin/docker-machine

#linux

curl -L https://github.com/docker/machine/releases/download/v0.16.2/docker-machine-`uname -s`-`uname -m` >/tmp/docker-machine && chmod +x /tmp/docker-machine && sudo cp /tmp/docker-machine /usr/local/bin/docker-machine

...

*docker-machine

...

#docker-machine basic cmd

docker-machine ls

docker-machine rm name

docker-machine kill name

docker-machine create name

...

python save & load argparse

 

refer to code:

..

from argparse import ArgumentParser

import json

parser = ArgumentParser()

parser.add_argument('--seed', type=int, default=8)

parser.add_argument('--resume', type=str, default='a/b/c.ckpt')

parser.add_argument('--surgery', type=str, default='190', choices=['190', '417'])

args = parser.parse_args()

with open('commandline_args.txt', 'w') as f:

json.dump(args.__dict__, f, indent=2)

parser = ArgumentParser()

args = parser.parse_args()

with open('commandline_args.txt', 'r') as f:

args.__dict__ = json.load(f)

print(args)

..

Thank you!

🙇🏻‍♂️

1D sequence (signal) data resampling (python)

 import numpy as np

import matplotlib.pyplot as plt

from scipy.signal import resample

from scipy.interpolate import interp1d

def ResampleLinear1D(original, targetLen):

original = np.array(original, dtype=np.float)

index_arr = np.linspace(0, len(original)-1, num=targetLen, dtype=np.float)

index_floor = np.array(index_arr, dtype=np.int) #Round down

index_ceil = index_floor + 1

index_rem = index_arr - index_floor #Remain

val1 = original[index_floor]

val2 = original[index_ceil % len(original)]

interp = val1 * (1.0-index_rem) + val2 * index_rem

assert(len(interp) == targetLen)

return interp

if __name__=="__main__":

original = np.sin(np.arange(256)/10.0)

targetLen = 100

# Method 1: Use scipy interp1d (linear interpolation)

# This is the simplest conceptually as it just uses linear interpolation. Scipy

# also offers a range of other interpolation methods.

f = interp1d(np.arange(256), original, 'linear')

plt.plot(np.apply_along_axis(f, 0, np.linspace(0, 255, num=targetLen)))

# Method 2: Use numpy to do linear interpolation

# If you don't have scipy, you can do it in numpy with the above function

plt.plot(ResampleLinear1D(original, targetLen))

# Method 3: Use scipy's resample

# Converts the signal to frequency space (Fourier method), then back. This

# works efficiently on periodic functions but poorly on non-periodic functions.

plt.plot(resample(original, targetLen))

plt.show()