Nikasa1889

 # Run the following command to start jupyter with custom password
 ## The current directory will be mapped to the /home/jovyan in the docker
 ## User is granted root access
 ## New password can be set using its hash, hash of password can be obtained by:
 ### from IPython.lib import passwd
 ### passwd("this_is_a_password")  #Output: 'sha1:9743a64265c5:5775f49cb5577d50234260a661cb520b1dc07ed3'
 docker run -p 8888:8888 --user root -e NB_GID=100 -e GRANT_SUDO=yes -v "$PWD":/home/jovyan jupyter/scipy-notebook:hoangmt start-notebook.sh --NotebookApp.password='sha1:9743a64265c5:5775f49cb5577d50234260a661cb520b1dc07ed3' -d
 # Save changes to the running container by commiting its image
 # get hash of the container to be commited
 docker ls

Nikasa1889

import attr
from datetime import datetime
from attr import fields

@attr.s(slots=True)
class Small:
    a = attr.ib(type=float, metadata={'JSON':'A'})

@attr.s(slots=True)
class TestField:

Nikasa1889

import timeit

setup='''
import random, bisect
from collections import deque

random.seed('slartibartfast')
s = [random.random() for i in range(1000)]
timsort = sorted

Nikasa1889

from datetime import datetime as dt
from multiprocessing import Pool
from tqdm import tqdm
from features import *
from sklearn.metrics import mean_squared_error
import sys

def cv_model(args):
    '''Cross validate a predefined model with a given time series
       Note: receive one args so that it can be call by worker pool easily'''

Nikasa1889

url = "%s/timeseries/%s/typeClass/%d/resolution/%d/" \
      % (endpoint, asset_id, type_class, resolution_id)
params = {'from': from_date, 'to': to_date}
url = url + "?" + urllib.urlencode(params)
# b64Val = base64.b64encode(username+":"+password)
# response.headers['Authorization'] = "Basic %s" %b64Val
resp = requests.get(url, params, auth=(username, password)).json()

Nikasa1889

DATA_DIR = "appleoutput40"
SC_DATA_DIR = "split_appleoutput40"
VAL_PERCENTAGE = 0.2
from os.path import normpath, basename
from shutil import copyfile
import random
import os
subdir_imgs = [(subdir, files) for subdir, dirs, files in os.walk(DATA_DIR) if len(files)>50]

Nikasa1889

[InfluxMeasurement("consumption")]
public class ConsumptionMeasurement:InfluxMeasurementBase
{
    [InfluxTimestamp]
    public override DateTime Timestamp { get; set; }

    [InfluxTag("assetguid")]
    public Guid AssetGuid { get; set; }

    [InfluxTag("consumptiontype")]

Nikasa1889

# [filter size, stride, padding]
#Assume the two dimensions are the same
#Each kernel requires the following parameters:
# - k_i: kernel size
# - s_i: stride
# - p_i: padding (if padding is uneven, right padding will higher than left padding; "SAME" option in tensorflow)
# 
#Each layer i requires the following parameters to be fully represented: 
# - n_i: number of feature (data layer has n_1 = imagesize )
# - j_i: distance (projected to image pixel distance) between center of two adjacent features

Nikasa1889

#!/bin/bash
#List current running docker images
echo "Select one of the following running images:"
echo -e "Number \t Name"
echo $(docker ps | tail -n +2| awk -F ' ' '{printf("%7d %s\n", NR, $2)}')
read -p "Enter the process number:" selectedID
selectedID=$(($selectedID + 1))
#Get Docker process ID of the specified docker image name
DOCKER_ID=$(docker ps | tail -n  +$selectedID | head -1 | awk '{print $1}')
docker exec -it $DOCKER_ID /bin/bash