blaylockbk

masked = H['value']
masked = np.ma.array(masked)
masked[masked == -10] = np.ma.masked

blaylockbk

from datetime import datetime
import numpy as np

def to_datetime(date):
    """
    Converts a numpy datetime64 object to a python datetime object 
    Input:
      date - a np.datetime64 object
    Output:
      DATE - a python datetime object

blaylockbk

# Download single variable from single day
DATE = date(2017, 3, 10)   # Model run date
variable = 'HGT:500 mb'       # Must be part of a line in the .idx file              
download_HRRR_variable_from_pando(DATE, variable,
                                  hours=range(0, 24), fxx=[0],
                                  model='hrrr', field='sfc',
                                  more_vars=4,
                                  outdir='./')

blaylockbk

# Fast download of HRRR grib2 files (single variable) with multithreading
# import some additional modules
from queue import Queue 
from threading import Thread

def worker():
    # This is where the main download function is run.
    # Change the hour and fxx parameters here if needed.
    while True:
        item = q.get()

blaylockbk

# === User change these parameters ================================
# date range
sDATE = date(2017, 3, 10)   # Start date
eDATE = date(2017, 3, 13)   # End date (exclusive)
# list of variables
variables = ['TMP:2 m', 'DPT:2 m', 'UGRD:10 m', 'VGRD:10 m'] 
# =================================================================

days = (eDATE-sDATE).days
DATES = [sDATE + timedelta(days=d) for d in range(days)]

blaylockbk

# === User change these parameters ============================
# date range
sDATE = date(2017, 3, 10) # Start date
eDATE = date(2017, 3, 13) # End date (exclusive)
# variable string
variable = 'TMP:2 m'      # Must be part of a line in the .idx file
# =============================================================

days = (eDATE-sDATE).days
DATES = [sDATE + timedelta(days=d) for d in range(days)]

blaylockbk

# Download single variable from single day
DATE = date(2017, 3, 10)   # Model run date
variable = 'TMP:2 m'       # Must be part of a line in the .idx file              
download_HRRR_variable_from_pando(DATE, variable,
                                  hours=range(0, 24), fxx=[0],
                                  model='hrrr', field='sfc',
                                  outdir='./')

blaylockbk

#########################################
# ✨ Use the new Herbie Package instead: 
#   https://github.com/blaylockbk/Herbie
#########################################

# Brian Blaylock 
# February 13, 2018

# Updated December 10, 2018 for Python 3 (Thanks J.Matt for sharing)

blaylockbk

import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable

data1 = np.random.random([5,5])
data2 = np.random.random([5,5])

fig = plt.figure(1, figsize=[16, 4])
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
this_plot = ax1.pcolormesh(data1, cmap='magma')

blaylockbk

# Method 1: when you are using plt
from matplotlib.dates import DateFormatter
formatter = DateFormatter('%Y-%m-%d %H:%M:%S')
plt.gcf().axes[0].xaxis.set_major_formatter(formatter)

# Method 2: when you have already axessed the axes
from matplotlib.dates import DateFormatter
formatter = DateFormatter('%Y-%m-%d %H:%M:%S')
ax1.xaxis.set_major_formatter(formatter)