software

everything that is linked to software

GitHub accelerator

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Initialising

Go into your local folder and make it suitable for git.

git init

Build a connection between local and remote repository.

git remote add origin https://github.com/wofuerGitHub/Fundamentals_Overview.git

Create a remote git-repository – easiest to remember – with the same folder name.

Add all local files.

git add .

Create a first commit called “init”.

git commit -m "init"

With the first upload define the standard branch.

git push --force --set-upstream origin master

Information about the repository-status

Getting all available information about the repository.

git status

Push local changes

To push your local changes to the repository.

git add .
git commit -m "message"
git push

Pull remote changes

To get the newest changes in the repository to your local storage.

git pull

Clone repository

To get a complete repository locally installed.

git clone https://github.com/wofuerGitHub/Fundamentals_Overview.git

Further information

Special thanks to Roger Dudler and Thomas Leister for their instructions.

Neo4j learning accelerator

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Introduction

Neo4j is a graph database and queried via CYPHER. An Commercial Version incl. the Neo4j-Browser you can download here. Neo4j Enterprise as open source under the AGPLv3 open source license created by the Free software foundation you can find here.

Further Examples are described on Windows platform.

Start “Neo4j Desktop” and just great a new graph. The APOC procedures makes the world much more easy and just has to be downloaded from GitHub here.

Helpful Queries

Playing with e-Mails

Find all nodes, cut the relationships and delete them.

MATCH (n) DETACH DELETE n;

Create a node – relationship – node with properties.

CREATE (m:Instance {name: "Sven"})-[n:SENT {subject: "Likes"}]->(o:Instance {name: "Anna"});

Delete a node where a certain property does not exist.

MATCH (n) WHERE NOT exists(n.name) DETACH DELETE n;

Delete a special constellation (sender/receiver according subject without direction of the mail).

MATCH (m)--(n:SENT)--(o) where n.subject="Hates" DETACH DELETE m,n,o

Import data from an CSV-File (to be stored in the import folder) and create some relations. In the following example and export of Outlook Inbox. The sender/receiver is created with the label “Instance” and the property “name”. The relationship is created with the label “SENT” and the property “subject”

LOAD CSV FROM 'file:///Inbox.csv' as line 
CREATE (:Instance {name: line[1]})-[:SENT {subject: line[0]}]->(:Instance {name: line[2]});

Find all nodes with the label “Instance” with the property “name” and build nodelist that will gone through until there is only one node remaining. Before that all nodes with the same node-property “name” are merged into one. 

MATCH (n:Instance) WITH n.name AS name, COLLECT(n) AS nodelist, COUNT(*) AS count WHERE count > 1
CALL apoc.refactor.mergeNodes(nodelist) YIELD node RETURN node;

Split a node according the string of an property and create multiple nodes out of it with the same relationship (point out every receiver of a mail individually).

MATCH (l)-[m]->(n:Instance) WHERE n.name CONTAINS ";" WITH l,m,n, SPLIT(n.name, ";") as oneInstance
UNWIND RANGE (0,SIZE(oneInstance)-1) as i MERGE (p:Instance {name: l.name})-[q:SENT {subject: m.subject}]->(o:Instance {name: oneInstance[i]}) RETURN p,q,n,o;

Delete nodes that contain a certain substring within the property “name”.

MATCH (n) where n.name CONTAINS ";" DETACH DELETE n;

Finally as one executable block:

MATCH (n) DETACH DELETE n;
LOAD CSV FROM 'file:///Inbox.csv' as line 
CREATE (:Instance {name: line[1]})-[:SENT {subject: line[0]}]->(:Instance {name: line[2]});
MATCH (l)-[m]->(n:Instance) WHERE n.name CONTAINS ";" WITH l,m,n, SPLIT(n.name, ";") as oneInstance
UNWIND RANGE (0,SIZE(oneInstance)-1) as i MERGE (p:Instance {name: l.name})-[q:SENT {subject: m.subject}]->(o:Instance {name: oneInstance[i]}) RETURN p,q,n,o;
MATCH (n:Instance) WITH n.name AS name, COLLECT(n) AS nodelist, COUNT(*) AS count WHERE count > 1
CALL apoc.refactor.mergeNodes(nodelist) YIELD node RETURN node;
MATCH (n) where n.name CONTAINS ";" DETACH DELETE n;
MATCH (n) WHERE NOT exists(n.name) DETACH DELETE n;

Analyzing Timeseries with Python

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Small demo of the needed elements to load, analyze and print a time series in Python. Format of the data-source:

...
2019-08-28,11701.019531,EUR
2019-08-29,11838.879883,EUR
2019-08-30,11939.280273,EUR
2019-09-02,11953.780273,EUR
2019-09-03,11910.860352,EUR
2019-09-04,12025.040039,EUR
2019-09-05,12126.780273,EUR
2019-09-06,12191.730469,EUR
2019-09-09,12226.099609,EUR
2019-09-10,12268.709961,EUR

Generates based on the complete data, the annual average performance and volatility

Annual Performance:  8.026639312445006
Annual Vola:  19.100050116208784

… and the plot of the analyzed data:

… based on the following code:

#!/usr/bin/python3 

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, date

# some variables
now = datetime.now() # date/time of software execution
endDate = date(year = now.year, month = now.month, day = now.day) # date of software execution (end of analytics period)
startDate = date(year = now.year-10, month = now.month, day = now.day) # 1 year earlies (begin of analytics period)
deltaYears = (endDate-startDate).days/365.2425 # difference of startDate and endDate in years

# read csv file & build timeseries
# raw = pd.read_csv("./Reference/stocks_2/JP3942600002.EUR.csv", header=None)
raw = pd.read_csv("./DAX.EUR.csv", header=None)

ts = pd.DataFrame(columns=['datetime','value']) # generate timeframe
ts['datetime'] = pd.to_datetime(raw[0]) # load column-datetime with the raw-timestamps
ts['value'] = raw[1] # load column-value with the values
ts = ts.set_index('datetime') # index based on datetime
# print(ts)

# reduction of timeseries to the choosen period and cleaning for weekdays
ts = ts.resample('D').ffill() # generate sample size one-day and fill missing elements
selection = pd.date_range(startDate, endDate, freq='B') # generat selection from startDate to endDate with weekdays
ts = ts.asof(selection) # get subset of ts according selection and interpolate remaining ()
# print(ts)

# some calculation
val = np.array(ts['value'])
res = np.log(val[1:]/val[:(len(val)-1)])
# r = (np.power(1+np.mean(res),len(res))-1)*100 # performance
r = (np.power(np.power(1+np.mean(res),len(res)),1/deltaYears)-1)*100 # performance (annual)
v = np.std(res)*np.sqrt(len(res))*100/np.sqrt(deltaYears) # vola (annual)
print('Annual Performance: ',r)
print('Annual Vola: ',v)

# check
ts.head()

# plot 
ts.plot()
plt.show()