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"""Enumerate and export a bunch of structured or scalar objects from a Python module to YaML

This enumerates everything in the module in the globals namespace automatically, skipping
a few things (like things starting with `__`)

Useful when you have a ton of constants (including some that are lists, tuples, dicts, etc) in a
Python module and you want to put them into YaML to maintain separately from the Python code. It's
not a difficult task, but it becomes painful when there are 10-20 (or more) different objects. This
just automates it

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#!/bin/bash
#
# WARNING: YOU WILL LOSE THE FILE IF YOU SCREW UP BE CAREFUL
# If UNSAFE is set to YES, no backup will occur; you better not screw up!
# grep args or pattern because your file will be gone :>
#
# - AG
set -Eu

declare -r UNSAFE=YES

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#!/bin/bash
#
# (C) 2021, AG ([email protected])
# BSD 3-Clause License
#
# Logging and tracing skeleton for Bash shell scripts
#
# Features
# --------
#   - Verbose execution tracing to stderr or to a file; optional, via start_trace()

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from collections import ChainMap

class NoneChainMap(ChainMap):
    """Modified ChainMap to allow items to be treated as unset if they have a specific value (e.g. None)

    (C) 2021, [email protected], BSD-3-Clause
    LICENSE: https://opensource.org/licenses/BSD-3-Clause

    Problem
    -------

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Hardening the USB Armory

As a good crypto nerd, I usually use an entirely encrypted linux FS: / but also /boot using grub LUKS support. It's a good setup but it's not perfect, the BIOS and the bootloader are not protected.

I recently got a USBArmory and I wanted to apply the same (or a better) setup.

I found some useful links but no clear howto. So this is my setup.

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from subprocess import Popen, PIPE
import pty
import os
from select import select
import sys
import tty

master, slave = pty.openpty()
p = Popen(['python'], stdin=slave, stdout=PIPE, stderr=PIPE)
pin = os.fdopen(master, 'w')

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# Call with /path/to/python python-config.py to check a specific version of Python, obviously
import sysconfig
for k, v in sysconfig.get_config_vars().items():
    print('%s = %s' % (k, v))

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def psycopg2_diag_summarize(err: psycopg2.Error, formatted_lines: bool = False) -> Union[Dict, List[str]]:
    """Return a dict representation of a psycopg2.extensions.Diagnostics object

     - Dynamically produce a dict for all public members of the object using dir() to get the
       attribute names
     - Filter out some undesirables (None, callables, non-localized versions of strings)
     - Rename a fixed list of keys to make it more conducive to pretty-printing
    
    Uses f-strings, so needs Python >= 3.6
    psycopg2 started exposing the Diagnostic object some time >= 2.5

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This small subclass of the Pandas sqlalchemy-based SQL support for reading/storing tables uses the Postgres-specific "COPY FROM" method to insert large amounts of data to the database. It is much faster that using INSERT. To acheive this, the table is created in the normal way using sqlalchemy but no data is inserted. Instead the data is saved to a temporary CSV file (using Pandas' mature CSV support) then read back to Postgres using Psychopg2 support for COPY FROM STDIN.

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"""
Patched version to support PostgreSQL 
(original version: https://github.com/pydata/pandas/blob/v0.13.1/pandas/io/sql.py)

Adapted functions are:
- added _write_postgresql
- updated table_exist
- updated get_sqltype
- updated get_schema