rsignell-usgs

edsu

#!/usr/bin/env python

import sys

"""
For each character c, if its value is:
    #13: treat as newline
    #128, #160: treat as space ' '
    #169..#171, #173, #174: treat as dash '-'
    #192..#236: skip ahead and ignore all characters until another occurrence

chrisRedwine

# From https://coderwall.com/p/-wbo5q/pip-install-a-specific-github-repo-tag-or-branch
pip install -e git://github.com/{ username }/{ reponame }.git@{ tag name }#egg={ desired egg name }

magnetikonline

AWS CLI JMESPath cheatsheet

Random query recipes of JMESPath for the AWS CLI tools that I might have written or stumbled upon.

• Examples
  • List available AWS regions
  • List Lambda functions within AWS account region
  • Test if specific Lambda function exists
  • List Route 53 record names and their type for a zone
  • List CloudWatch log groups

wgurecky

#!/usr/bin/python3
#!/usr/bin/env python
#-*- coding: utf-8 -*-
#==============================================================================
# Copyright (c) 2018, William Gurecky
# All rights reserved.
#
# DESCRIPTION:
# Inspired from stack overflow question:
# https://stackoverflow.com/questions/52227599/interpolate-griddata-uses-only-one-core

parente

Summary

IPython shell assignment (the ! operator) evaluates a command using the local shell (e.g., bash) and returns a string list (IPython.utils.text.SList). An SList is a list-like object containing "chunks" of stdout and stderr, properties for accessing those chunks in different forms, and convenience methods for operating on them.

Example

The SList.ipynb notebook below uses SList properties to access the output of a shell command as a list-like of strings, a newline-separated string, a space-separated string, and a list of pathlib.Path objects. The notebook then uses the SList.fields() and SList.grep() methods to extract columns from and search command output.

jakelevi1996

Running TensorFlow Sessions inside sub-processes

In some applications of machine learning/TensorFlow, it is desirable to start multiple processes, and have separate training procedures running concurrently in each of those processes. A useful Python method for achieving this is the multiprocessing.pool.Pool.map() method (or the equivalent starmap() method when the target function takes multiple arguments; see the section "Process Pools" from the description of the multiprocessing module in the Python Library Reference).

The Pool.map() method takes a target-function and a list (or more generally an iterable) of arguments, and returns an equivalent iterable of the results of the function evaluated on each member of the argument-list (which is similar to the [built-in Python function map()

jacobtomlinson

jesserobertson

import pathlib
from itertools import product

from tqdm import tqdm
import rasterio

import geosoft
import geosoft.gxpy.gx as gx
import geosoft.gxpy.coordinate_system as gxcs
import geosoft.gxpy.grid as gxgrid

andrewdhicks