uweziegenhagen.de

import datetime
 
now = datetime.datetime.now()
 
print(now.strftime('%Y-%m-%d %H:%M'))
print(now.isoformat())

# -*- coding: utf-8 -*-
import re
 
def parseEmaceOrgmode(s):
    r = '^([\*]+)?\s?(TODO|PROGRESSING|FEEDBACK|VERIFY|POSTPONED|DELEGATED|CANCELLED|DONE)?\s?(\[#[A|B|C]\])?\s?(.*?)\s*(:(.*):)?$'    
    m = re.search(r,s)
    level = m.group(1)
    if (level is not None):
        level = len(level)
    prio = m.group(3)
    if (prio is not None):
        prio = prio[2:3]
    tags = []
    a = m.group(5)
    if a != None:
        b = len(a)-1
        a= a[1:b]
        a = a.split(':')
    tags.append(a)
    return(level, m.group(2), prio, m.group(4), tags)
 
with open("../orgmode.org", "r") as ins:
    for line in ins:
        level, status, priority, task, tags = parseEmaceOrgmode(line)
        if level is not None:        
            print('Level:', level)
            print('Status:', status)
            print('Priority:', priority)
            print('Task:', task)
            print('Tags:',tags,'\n\n')

# -*- coding: utf-8 -*-
import math as m
 
class Point:
 
    def __init__(self,x,y):
        self.x = x
        self.y = y
 
    def calcEuclidDistanceToPoint(self,x,y):
        return m.sqrt(m.pow(self.x-x,2) + m.pow(self.y-y,2))
 
p1 = Point(0,0)
p2 = Point(1,1)
print(p2.calcEuclidDistanceToPoint(p1.x,p1.y))

import numpy as np
 
a = np.array([[3,1], [1,2]])
b = np.array([9,8])
x = np.linalg.solve(a, b)
print(x) # gibt [ 2.  3.]

# -*- coding: utf-8 -*-
"""
Created on 2016-07-06
Uwe Ziegenhagen
based on http://stackoverflow.com/questions/16181121/python-very-simple-multithreading-parallel-url-fetching-without-queue
"""
 
from multiprocessing.pool import ThreadPool
from time import time as timer
import os
 
files = ['test-01.tex','test-02.tex','test-03.tex','test-04.tex','test-05.tex',
'test-06.tex','test-07.tex','test-08.tex','test-09.tex','test-10.tex']
 
def compile_file(cfile):
	try:
		result = os.system('pdflatex -interaction=batchmode ' + cfile)
		return cfile, None
	except Exception as e:
		return cfile, e	
 
start = timer()
results = ThreadPool(8).imap_unordered(compile_file, files)
for cfile, error in results:
	if error is None:
		print("%r compiled in %ss" % (cfile, timer() - start))
	else:
		print("Error compiling %r: %s" % (cfile, error))
		print("Elapsed Time: %s" % (timer() - start,))
 
print('Gesamtzeit',timer() - start)

def splitFileOneColumn(inputFile,outputFile,columnSeparator,column):
    with open(inputFile, 'r') as infile:
        with open(outputFile, 'w') as outfile:
            for line in infile:
                s = line.split(columnSeparator)
                outfile.write(s[column]+os.linesep) # '\r\n' on Windows, '\n' on Unix/Linux/Mac
            outfile.close()
    infile.close()

# -*- coding: utf-8 -*-
import pandas as pd
 
def unique(liste):
    """ takes a list of elements, separated by comma and returns sorted string of unique items separated by comma """
    a = liste.split(',')
    b = sorted(set(a))
    return ','.join(b)
 
df = pd.read_excel('groupb_Beispiel.xlsx')
print(df)
 
grouped = df.groupby(['Key1','Key2'],as_index=False)['Bar'].agg(lambda col: ','.join(col))
grouped = pd.DataFrame(grouped)
 
grouped['Unique'] = grouped['Bar'].apply(unique)
 
print(grouped)
 
grouped.to_excel('result.xlsx')

import pandas as pd
 
# Lade die Daten
daten = pd.read_excel('combine.xlsx')
# Erstelle leeren Dataframe mit den Spaltennamen aus den Excelzeilen 
verarbeitet = pd.DataFrame(columns=['ColA','ColB','ColC'])
 
# Iteriere über die Daten
for i, row in daten.iterrows():
    # ganzzahliges Teilen, um die Zeile zu bestimmen
    # in die die Zelle gehört, Spalte ergibt sich aus dem Wert in 'Spalte'
    verarbeitet.loc[i // 3,row['Spalte']] = row['Wert']
 
print(verarbeitet)

import pandas as pd
 
data = {'A': ["cola", "colb", "colc", "cola", "colb", "colc"], "B": [1, 2, 3, 4, 5, 6]}
data = pd.DataFrame(data)
gb = data.groupby('A')
res = pd.DataFrame()
for key in gb.groups:
    res[key] = gb.get_group(key)['B'].values.flatten()
 
print(res)