MSeifert04

thing = """1 ID:K-95 list of data

0 ID:D-56 list of other data"""

idtag = "K-95"

for line in thing.split('\n'):
    if line.startswith('1') and line.startswith(idtag, 5, 5+4):                                                                                            
        print(line)
    

MSeifert04

x = [[1],[1],[1],[-1]]
a = [[-2,-1,-1,-2],[1,-2,2,-1],[-1,-2,-2,-1],[2,-1,1,-2]]
h = 0.1
def K(h,a,x):
    cont = [1,2,3,4]
    k = [h * item for item in cont]
    y = [[item_x[0] + item_k/4] for item_x, item_k in zip(x, k)]
    return y
    
K(h,a,x)

MSeifert04

import io
import pandas as pd

a = """[[ 0.96725219  0.01808783  0.63087793  0.45407222  0.30586779  0.04848813
   0.01797095]
 [ 0.87762897  0.07705762  0.33049588  0.91429797  0.5776607   0.18207652
   0.2355932 ]
 [ 0.68803166  0.31540537  0.92606902  0.83542726  0.43457601  0.44952604
   0.35121332]
 [ 0.14366487  0.23486924  0.16421432  0.27709387  0.19646975  0.8243488

MSeifert04

class A(object):
    def __reversed__(self):
        return [1,2,3]
    
reversed(A())

MSeifert04

%load_ext cython

%%cython

from cpython.sequence cimport PySequence_Check

def is_sequence(val):
    return PySequence_Check(val)
    
class A(object):

MSeifert04

from iteration_utilities import roundrobin

from itertools import chain, cycle, islice

def shuffle_mseifert(L):
    return list(roundrobin(L[:len(L)//2], L[len(L)//2:]))

def shuffle_mitch(l):
    return list(chain.from_iterable(zip(l[:len(l)//2], l[len(l)//2:])))

MSeifert04

# explicit loops
l = []
for a in x:
    for b in a:
        if b == k:
            l.append(b)
     
# list-comprehension
[b 
 for a in x

MSeifert04

class Streams(object):
    def _initialize_attributes(self):
        try:
            from IPython import get_ipython
        except ImportError:
            pass
        try:
            get_ipython()
        except NameError:
            self._OutStream = None

MSeifert04

from itertools import groupby
import math

import numpy as np
import numba as nb


def Divakar(a):
    # Mask of NaNs
    mask = np.concatenate(([False],np.isnan(a),[False]))

MSeifert04

/* Helper to warn about deprecated tp_compare return values.  Return:
   -2 for an exception;
   -1 if v <  w;
    0 if v == w;
    1 if v  > w.
   (This function cannot return 2.)
*/
static int
adjust_tp_compare(int c)
{