ahue

#!/bin/bash

# Create InfluxDB and Grafana 4.4.3 (latest)

source /etc/os-release
OPSYS=${ID^^}
curl -sL https://repos.influxdata.com/influxdb.key | sudo apt-key add -
test $VERSION_ID = "8" && echo "deb https://repos.influxdata.com/debian jessie stable" | sudo tee /etc/apt/sources.list.d/influxdb.list
test $VERSION_ID = "9" && echo "deb https://repos.influxdata.com/debian jessie stable" | sudo tee /etc/apt/sources.list.d/influxdb.list
curl https://bintray.com/user/downloadSubjectPublicKey?username=bintray | sudo apt-key add -

ahue

#!/bin/bash

# create new folder for the day
DATE=`date +%Y-%m-%d_%H-%M-%S`
BACKUP_PATH=~/openhab2-backup

# backup metadata in ./meta
mkdir -p $BACKUP_PATH

sh /usr/share/openhab2/runtime/bin/backup $BACKUP_PATH/openhab2-backup-$DATE.zip

ahue

#! /usr/bin/python3
try:
    # For Python 3.0 and later
    from urllib.request import urlopen
    from urllib.parse import quote
except ImportError:
    # Fall back to Python 2's urllib2
    from urllib2 import urlopen
    from urllib2 import quote

ahue

#!/usr/bin/env bash

set -e
url="https://web.getbring.com/locale/articles.de-DE.json"

echo "GET ${url}" >&2
curl -X GET \
    -H 'Content-Type: application/json' \
    "${url}"  | \
    jq --raw-output 'to_entries[] as $e | "\($e.value)"' | \

ahue

from typing import Union

def scale(x: Union[int|float], 
    min_x: Union[int|float], max_x: Union[int|float],  
    a: Union[int|float], b: Union[int|float]) -> float: 
    """
    Scale a number x relative to domain [min_x, max_x] to a domain [a,b]

    Parameters:
            x (int|float): The value to scale

ahue

import numpy as np
from typing import Union

def cyclical_feature(x: Union(float|np.array), max_x: float):
    """
    Transforms a linear scale feature to a feature pair that represent a cyclical one.
    Assumes that the feature is in interval [0, max_x]
    
    See also https://towardsdatascience.com/cyclical-features-encoding-its-about-time-ce23581845ca
    

ahue

# Rename this distribution example file to motion.conf
#
# This config file was generated by motion 4.1.1
# Documentation:  /usr/share/doc/motion/motion_guide.html

############################################################
# Daemon
############################################################

# Start in daemon (background) mode and release terminal (default: off)

ahue

import numpy as np

def filter_tensor_by_mx(tensor, mx):
  tshape = tensor.shape
  row_ix = np.arange(mx.shape[0])
  col_ix = np.arange(mx.shape[1])
  ix = np.array(np.meshgrid(row_ix, col_ix)).T.reshape(-1,2).T 
  pane = rnd_ix_mx.ravel()
  res = tensor[pane, ix[0], ix[1]].reshape(mx.shape)
  return res

ahue

import pandas as pd
import seaborn as sns
def cardinalities(df: pd.DataFrame, plot: bool = False):
    """
    Analyses cardinalities in a data frame

    df DataFrame the data frame to be analyized
    plot Plot whether to plot rather than return the result DataFrame

    returns (DataFrame, Plot) of a matrix M(i,j) that reads row-wise, 

ahue

import scipy
import scipy.cluster.hierarchy as sch

def cluster_corr(corr_array, inplace=False, impute_nan=False):
    """
    Original author: @WYegelwel (https://wil.yegelwel.com/cluster-correlation-matrix/)
    
    Rearranges the correlation matrix, corr_array, so that groups of highly 
    correlated variables are next to eachother