canimus

def format_sequence(sequence: str):
    """Format a DNA sequence as genbank file"""
    counter = 1
    print(f"{counter}".rjust(4, " "), end=" ")
    for a,b in zip(range(0,len(sequence)+1, 10), range(10, len(sequence)+1, 10)):
        print(sequence[a:b], end="")
        if ((counter % 6) == 0) and (b < (len(sequence)-1)):
            print("")
            print(f'{((counter*10) + 1)}'.rjust(4, " "), end=" ")
        else:

canimus

cat /proc/cpuinfo | grep MHz | cut -d":" -f2 | tr -d " " | tr "\n" "," | sed 's/[^0-9\.,]//g' | rev | cut -c2- | rev | (echo -n "cpu," && cat) | termgraph

canimus

from pyspark.sql import SparkSession
from pyspark import SparkConf

conf = (
    SparkConf() 
    .setAppName("Connect AWS")
    .setMaster("local[*]")
    )

conf.set("spark.jars.packages","org.apache.hadoop:hadoop-aws:3.3.2")

canimus

sed -n '/^\^SAMPLE/,/sample_table_end/p' GSE27219_family.soft > out2.txt

canimus

series = []
d = {}
for row in raw:
    try:
        if len(row) == 0:
            continue
        if row.startswith("!sample_table_end"):
            for k in arr:
                if k in d:
                    d[k] = [d[k]]

canimus

import asyncio
from random import randint


async def download(code):
    wait_time = randint(1, 3)
    print('downloading {} will take {} second(s)'.format(code, wait_time))
    await asyncio.sleep(wait_time)  # I/O, context will switch to main function
    print('downloaded {}'.format(code))

canimus

openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -days 365 -nodes

canimus

from IPython.core.display import HTML
display(HTML("<style>pre { white-space: pre !important; }</style>"))

canimus

df.select(
     F.array(
         F.create_map(
             F.lit("k1"), F.col("c1"), F.lit("k2"), F.col("c2"), F.lit("k3"), F.col("c3")
         )
     ).alias("losing_bids")
 ).select(
     F.transform(
         "losing_bids",
         lambda m: F.transform_values(m, lambda k, v: v.isNull().cast("integer")),

canimus

import pyspark.sql.functions as F
from pyspark.sql import DataFrame
from pyspark.sql import Window as W
from pyspark.sql.window import WindowSpec

__all__ = ["forward_fill"]


def _window_all_previous_rows(partition, order) -> WindowSpec:
    """Select the window on which values are filled in a forward manner."""