tlogan2000

from shapely.geometry import Point
import xarray as xr
import geopandas as gpd
from geopandas.tools import sjoin
import numpy as np
import pandas as pd


def create_mask_from_shape(ds, poly):
    ds = ds.drop(ds.data_vars)

emmanuelnk

How to install PyGrib with ECCodes (Python 3.6.5, Ubuntu 19, 18, 16, RHEL, Lambda, Linux AMI, Docker)

[ last_updated: 07-JAN-2020 ]

Recently I've had to work with Meterological data of the GRIB format. Needless to say, if you're reading this, you probably know how hard it is to get a library that can read them and even harder, setting up those libraries. Pygrib for python is by far the best library for reading grib files but the installation can leave you dizzy

In this tutorial I use Ubuntu 18 and python 3.6.5 virtualenv. Note: You don't have to use virtualenv Python. You can just as well use your system Python 3 however I personally find virtualenv a clean way to deal with these kind of complicated setups (prevents me mucking up my system's Python). This setup should also work with lower and higher Ubuntu versions (as someone below mentioned) and other Linux distros (like RHEL - AWS Linux AMI etc). The key to this installation is to build eccodes correctly (for your machine architecture) and succe

danwild

# setup
from cdo import *
cdo = Cdo()
cdo.debug = True
in_file = '/path/to/in_file.nc'
out_file = '/path/to/out_file.nc'

date1 = '2018-03-01T00:00:00'
date2 = '2018-03-01T02:00:00'
# my target is in meters

dyerrington

import os, sys
from time import sleep

## We could update this to all files in all subdirectories
watching = [__file__]
watched_mtimes = [(f, os.path.getmtime(f)) for f in watching]

while True:
    print("Idle...")
    sleep(2) ## So not to kill our CPU cycles

Hermanio

#!/bin/bash

targettemp=90
clockpct=70
while true
do
  currenttemp=$(sensors -u | awk '/temp1_input/ {print $2; exit}' )
  compare=$(echo $currenttemp'>'$targettemp | bc -l)
  turbo=`cat /sys/devices/system/cpu/intel_pstate/no_turbo`
  if [ "$turbo" -eq 0 ]

miguelgrinberg

import asyncio
loop = asyncio.get_event_loop()

async def hello():
    await asyncio.sleep(3)
    print('Hello!')
    
if __name__ == '__main__':
    loop.run_until_complete(hello())
    

bdjackson

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

class MidpointNormalize(mpl.colors.Normalize):
    """
    class to help renormalize the color scale
    """
    def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
        self.midpoint = midpoint

guziy

# -*- coding: utf-8 -*-
from netCDF4 import Dataset

#input file
dsin = Dataset("crop.nc")

#output file
dsout = Dataset("crop.nc3", "w", format="NETCDF3_CLASSIC")

#Copy dimensions

jwass

import fiona
import fiona.crs

def convert(f_in, f_out):
    with fiona.open(f_in) as source:
        with fiona.open(
                f_out,
                'w',
                driver='GeoJSON',
                crs = fiona.crs.from_epsg(4326),

benbalter

# Bulk convert shapefiles to geojson using ogr2ogr
# For more information, see http://ben.balter.com/2013/06/26/how-to-convert-shapefiles-to-geojson-for-use-on-github/

# Note: Assumes you're in a folder with one or more zip files containing shape files
# and Outputs as geojson with the crs:84 SRS (for use on GitHub or elsewhere)

#geojson conversion
function shp2geojson() {
  ogr2ogr -f GeoJSON -t_srs crs:84 "$1.geojson" "$1.shp"
}