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from datetime import datetime, timedelta
base = datetime.today()
numdays = 5
date_list = [base - timedelta(days=x) for x in range(0,numdays)]

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from datetime import datetime

dates = ['01/01/2016 04:50','01/01/2016 05:50','01/01/2016 06:50','01/01/2016 07:50']
DATE = [datetime.strptime(x,'%m/%d/%Y %H:%M') for x in dates]

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DATES = np.array([])
for i in dates:
     DATES = np.append(DATES,datetime.strptime(i,'%m/%d/%Y %H%M'))

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# where dates is a list or numpy array of epoch dates...
DATE = [datetime.utcfromtimestamp(x) for x in dates]

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import numpy as np
import datetime
def datestr_to_datetime(x):
    return datetime.datetime.strptime(x,'%Y-%m-%dT%H:%M:%SZ')

# where dates is some list of dates as a string... 
# vectorize the function
converttime = np.vectorize(datestr_to_datetime)
DATES = converttime(dates)

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# To untar just about everything
tar -xzvf models.tar.gz 20160101/models/hrrr/

# To untar just the HRRR pressure fields for analysis hours
tar -zxvf models.tar.gz --wildcards --no-anchored 'hrrr.t*z.wrfprsf00.grib2'

$ to untar just the HRRR surface fields for analysis hours
tar -zxvf models.tar.gz --wildcards --no-anchored 'hrrr.t*z.wrfsfcf00.grib2'

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# Brian Blaylock
# ATMOS 6910 Homework
# 10 November 2016

# Look at the figures here: 
# http://kbkb-wx-python.blogspot.com/2016/11/verifying-gfs-dewpoint-data-with.html

"""
[X] Reads a GRiB forecast file from the GFS (15%)
[X] Extracts Temperature and Relative Humidity from ingest (5%)

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ffmpeg -f image2 -pattern_type glob -framerate 6 -i '*.png' foo.avi


# Retain high resolution
ffmpeg -f image2 -pattern_type glob -framerate 6 -i '*.png' -vb 20M foo.avi


# Convert .mov to .avi
ffmpeg -i file.mov file.avi

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import os
OUTDIR = '/this/path/will/be/created/'
if not os.path.exists(OUTDIR):
    os.makedirs(OUTDIR)

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import multiprocessing
from multiprocessing.dummy import Pool as ThreadPool

import numpy as np


def my_multipro(items, func, max_cpus=12):
    """Do an embarrassingly parallel task using multiprocessing.

    Use this for CPU bound tasks.