maximtrp

Orange Pi PC + Arch Linux ARM: installation guide

This guide is based on multiple guides as well as official instructions for the other boards found on the Internet:

1. https://github.com/RoEdAl/alarm-uboot-sunxi-armv7
2. https://uthings.uniud.it/building-mainline-u-boot-and-linux-kernel-for-orange-pi-boards
3. https://archlinuxarm.org/platforms/armv7/allwinner/pcduino3

I have gone through all these steps recently and got a working board with my favorite Arch Linux ARM. I hope it will be helpful for someone else.

maximtrp

version: "3"

services:
  youtrack:
    image: jetbrains/youtrack:2022.2.52874
    container_name: "youtrack"
    volumes:
      - ./youtrack/data:/opt/youtrack/data
      - ./youtrack/conf:/opt/youtrack/conf
      - ./youtrack/logs:/opt/youtrack/logs

maximtrp

import subprocess as sp
import json

def get_lemma(x):
    lemma = x['analysis'][0].get('lex') if x['analysis'] else x.get('text')
    return lemma

def mylem(x):
    p = sp.Popen(['mystem', '-l', '--format', 'json'], stdin=sp.PIPE, stdout=sp.PIPE, stderr=sp.STDOUT)
    stdout = p.communicate(input=bytes(x, encoding='utf-8'))

maximtrp

#!/usr/bin/bash
pactl load-module module-remap-sink \
    sink_name=reverse-stereo \
    master=0 \
    channels=2 \
    master_channel_map=front-left,front-right \
    channel_map=front-left,front-left

pactl set-default-sink reverse-stereo

maximtrp

#!/usr/bin/bash

# updated packages
echo "[1] Getting updated packages list"
pip list -o | tail -n +3 | cut -d" " -f1 | tr - _ | sort > updated.txt

# pip-managed packages
echo "[2] Getting packages managed by pip"
find /usr/lib/python3.9/site-packages/ -name INSTALLER | grep --color -oE "site-packages/([^-\/]*?)" | sed "s/site-packages\///g" | sort > installed.txt

maximtrp

def ecdf(data, num=100):
    y = np.linspace(0, 100, num)
    x = np.percentile(data, y)
    return x, y

def ecdf_plot(data, ax=None):
    x, y = ecdf(data)
    return ax.plot(x, y) if ax is not None else plt.plot(x, y)

maximtrp

import numpy as np
from sklearn.datasets import make_multilabel_classification
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix

# Dataset init
x, y = make_multilabel_classification(n_samples=1000, n_features=10, n_classes=3, n_labels=1, random_state=0)
y = y.sum(axis=1)
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, test_size=0.33)

maximtrp

import numpy as np
import PIL.Image as pim
import PIL.ImageDraw as pid
import palettable as pal

circles = 10
points = 9
radius = 128
width = 2000
height = 2000