Valid keywords: uh,precip,prate,pmax,snow,covp6. Valid accum for precip & snow: 1,3,6
Default Models: gfs,fv3href,nam,hrrr,rrfsp1,nssl. order determines position (in default mrms is at the top), lower case only, and separated by commas
# Copyright (c) 2014,2015,2016,2017,2019 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
#from js import document
#from pyodide import create_proxy
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
from datetime import datetime, timedelta
from pyodide.http import open_url
from js import console, document
gp_int = [0,1,10,50,100,500,1000,5000,10000,50000,100000,500000,1000000]
gmap = plt.cm.tab20c
gnorm = mcolors.BoundaryNorm(gp_int, gmap.N)
rate_int = [0.00,0.01, 0.1,.5,1.0,2.0,3.0,4.0,5.0,10.,20.,30,40,50,100]
rmap = plt.cm.tab20c
rnorm = mcolors.BoundaryNorm(rate_int, rmap.N)
uh_int = [0,10,25,50,75,100,150,200,300,400,500,750,1000]
umap = plt.cm.tab20c
unorm = mcolors.BoundaryNorm(uh_int, umap.N)
snow_int = [0,.1,1,2,3,4,6,8,12,18,24,30,36,48,60,72,96,120]
scolors = [(1.0, 1.0, 1.0),
(189./255., 215./255., 231./255.),
(107./255., 174./255., 214./255.),
(49./255., 130./255., 189./255.),
(8./255., 81./255., 156./255.),
(8./255., 38./255., 148./255.),
(1.0, 1.0, 150./255.),
(1.0, 196./255., 0./255.),
(1.0, 135./255., 0./255.),
(219./255., 20./255., 0./255.),
(158./255., 0.0, 0./255),
(105./255., 0.0, 0./255.),
(54./255., 0.0, 0.0),
(204./255., 204./255., 255./255.),
(159./255., 140./255., 216./255.),
(124./255., 82./255., 165./255.),
(86./255., 28./255., 114./255.),
(46/255., 0./255., 51./255.) ]
smap = mcolors.ListedColormap(scolors, 'snows')
snorm = mcolors.BoundaryNorm(snow_int, smap.N,clip=False)
smap.set_over('darkviolet')
precip_int = np.array([0.00,0.01, 0.1, 0.25, 0.50, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.5, 3.0, 4.0, 5.0, 7.0, 10., 15., 20.])
colors = [(1.0, 1.0, 1.0),
(199./255., 231./255., 192./255.),
(161./255., 227./255., 155./255.),
(116./255., 196./255., 118./255.),
(49./255., 163./255., 83./255.),
(0.0, 109./255., 44./255.),
(1.0, 250./255., 138./255.),
(1.0, 204./255., 79./255.),
(254./255., 141./255., 60./255.),
(252./255., 78./255., 42./255.),
(214./255., 26./255., 28./255),
(173./255., 0.0, 38./255.),
(105./255., 0.0, 0.0),
(1.0, 170./255., 250./255.),
(1.0, 131./255., 249./255.),
(1.0, 87./255., 247./255.),
(230./255., 25./255., 249./255.),
(148./255., 0.0, 204./255.)]
pmap = mcolors.ListedColormap(colors, 'precipitation')
pnorm = mcolors.BoundaryNorm(precip_int, pmap.N)
def model_hr(model):
hrd = {'gfs':85, 'fv3href':61, 'nam':61,'hrrr':48,'rrfsp1':61, 'rrfsp3':85,'rrfsp4':85,'nssl':48, 'mrms':85}
return hrd[model]
def get_nvar(*args, **kwargs):
nvar = Element('test-input').element.value
input(nvar)
def input(nvar):
date = Element('date').element.value
vm = int(Element('vmax').element.value)
print(date)
if len(date) > 3:
console.log(f'text: {date}')
today = Element('date').element.value
else:
today = datetime.strftime(datetime.utcnow(),'%Y%m%d')
now = datetime.strftime(datetime.utcnow(),'%Y%m%d %H%M')
# MODELS
try:
modela = Element('model').element.value
#console.log(f"empty?: {modela}")
except:
modela = None
#print('no models entered')
if modela == '':
models = np.array(['gfs','fv3href','nssl','nam','hrrr','rrfsp1','rrfsp3','rrfsp4','mrms'])
else:
models = modela.split(',')
models.append('mrms')
models = np.array(models)
console.log(f"Models: {models}")
# need a dict for the models run time
hours = []
for q,model in enumerate(models):
hours.append(model_hr(model))
hours = np.array(hours)
console.log(hours)
cycles = np.array(['00','06','12','18'])
#nvar = 'pmax'
if (nvar == 'prate' or nvar == 'pmax' or nvar == 'uh'):
accum = '1'
elif nvar == 'covp6':
accum = '6'
else:
accum = Element('naccum').element.value
console.log(f'text: {nvar,accum}')
if nvar == 'prate':
#vm = 25
lab = 'In/hr'
cmap = rmap
norm = rnorm
intervals = rate_int
vals = rate_int
elif nvar == 'precip':
#vm = 20
lab = 'In'
cmap = pmap
intervals = precip_int
vals = precip_int
elif nvar == 'snow':
#vm = 20
lab = 'In'
cmap = smap
intervals = snow_int
vals = snow_int
elif nvar == 'uh':
#vm = 1000
lab = r'$m^{2}s^{-2}$'
cmap = umap
norm = unorm
intervals = uh_int
vals = uh_int
elif nvar == 'pmax':
#vm = 25
lab = "In/hr"
cmap = rmap
norm = rnorm
intervals = rate_int
vals = rate_int
elif nvar == 'covp6':
lab = "Grid pts"
cmap = gmap
norm = gnorm
intervals = gp_int
vals = gp_int
arr = np.zeros([np.size(cycles),np.size(models),np.max(hours)])
for cc,c in enumerate(cycles):
try:
fil = f"dash_mrms.csv"
fh = open_url(f"https://www.wpc.ncep.noaa.gov/hmt/hmt_webpages/drawingtools/dash/{fil}").readlines()
console.log(f'MRMS Cycle: {c}, successful read')
except:
continue
#convert the date and time into cycle relative forecast hours
for j,i in enumerate(fh):
try:
#console.log(f'text: {i}')
temp = i.split(',')
tem = datetime.strptime(temp[0]+temp[1],'%Y%m%d%H')
dt = int((tem-datetime.strptime(today+c,'%Y%m%d%H')).total_seconds()/(60*60))
#console.log(f'text: {tem},{dt},{temp}')
if (dt >= 0 and dt < np.max(hours)):
if (nvar == temp[4] and accum == temp[5]):
jj = np.where(models==temp[3])[0]
arr[cc,-1,dt] = float(temp[6])
except:
continue
for m in models[:-1]:
console.log(f'Model read: {m}')
try:
fil = f"dash_{m}_{today}.csv"
fh = open_url(f"https://www.wpc.ncep.noaa.gov/hmt/hmt_webpages/drawingtools/dash/{fil}").readlines()
xx = len(fh)
#print('file:',fil,'x:',x)
except:
continue
try:
for j,i in enumerate(fh): #which one
#console.log(f'text: {i}')
temp = i.split(',')
x = np.where(cycles == temp[1])[0][0]
#print(j,i,x,m)
if (nvar == temp[4] and temp[0][0:8] == today and accum == temp[5]):
k = int(temp[2])
#print(j,i,x,m,k)
jj = np.where(models==temp[3].lower())[0]
arr[x,jj,k] = float(temp[6])
except:
print('missing:',m)
arr[arr==0] = np.nan
#mc = np.nanmax(arr)
# Generate image dashboard for the 00z runs
fig, axs = plt.subplot_mosaic([['a', 'b'], ['c', 'd']],figsize=(12,6),sharex=True,sharey=True) # ,constrained_layout=True)
fig.suptitle(nvar.upper()+' '+accum+' hr, CONUS Max Values @ '+now )
im = axs['a'].imshow(arr[0],interpolation='none',origin='lower',aspect=5,vmin=0,vmax=vm)
axs['b'].imshow(arr[1],interpolation='none',origin='lower',aspect=5,vmin=0,vmax=vm)
axs['c'].imshow(arr[2],interpolation='none',origin='lower',aspect=5,vmin=0,vmax=vm)
axs['d'].imshow(arr[3],interpolation='none',origin='lower',aspect=5,vmin=0,vmax=vm)
axs['a'].axvline(12,ls='--',color='white')
axs['a'].axvline(24,ls='--',color='white')
axs['a'].axvline(36,ls='--',color='white')
axs['a'].axvline(48,ls='--',color='white')
axs['a'].axvline(60,ls='--',color='white')
axs['a'].axvline(72,ls='--',color='white')
axs['b'].axvline(6,ls='--',color='white')
axs['b'].axvline(18,ls='--',color='white')
axs['b'].axvline(30,ls='--',color='white')
axs['b'].axvline(42,ls='--',color='white')
axs['b'].axvline(54,ls='--',color='white')
axs['b'].axvline(66,ls='--',color='white')
axs['b'].axvline(78,ls='--',color='white')
axs['c'].axvline(12,ls='--',color='white')
axs['c'].axvline(24,ls='--',color='white')
axs['c'].axvline(36,ls='--',color='white')
axs['c'].axvline(48,ls='--',color='white')
axs['c'].axvline(60,ls='--',color='white')
axs['c'].axvline(72,ls='--',color='white')
axs['d'].axvline(18,ls='--',color='white')
axs['d'].axvline(30,ls='--',color='white')
axs['d'].axvline(42,ls='--',color='white')
axs['d'].axvline(54,ls='--',color='white')
axs['d'].axvline(66,ls='--',color='white')
axs['d'].axvline(78,ls='--',color='white')
axs['a'].set_title('00 UTC '+today)
axs['b'].set_title('06 UTC '+today)
axs['c'].set_title('12 UTC '+today)
axs['d'].set_title('18 UTC '+today)
mc = np.nanmax(arr[0])
axs['a'].set_title(str(round(mc,1)),loc='left')
mc = np.nanmax(arr[1])
axs['b'].set_title(str(round(mc,1)),loc='left')
mc = np.nanmax(arr[2])
axs['c'].set_title(str(round(mc,1)),loc='left')
mc = np.nanmax(arr[3])
axs['d'].set_title(str(round(mc,1)),loc='left')
for y,i in enumerate(arr[0]):
mc = np.nanmax(i)
axs['a'].text(78,y-0.25,str(round(mc,1)),color='r')
for y,i in enumerate(arr[1]):
mc = np.nanmax(i)
axs['b'].text(78,y-0.25,str(round(mc,1)),color='r')
for y,i in enumerate(arr[2]):
mc = np.nanmax(i)
axs['c'].text(78,y-0.25,str(round(mc,1)),color='r')
for y,i in enumerate(arr[3]):
mc = np.nanmax(i)
axs['d'].text(78,y-0.25,str(round(mc,1)),color='r')
axs['c'].set_xlabel('forecast hrs')
axs['d'].set_xlabel('forecast hrs')
axs['a'].set_yticks(np.arange(0,np.size(models),1),labels=models)
axs['b'].set_yticks(np.arange(0,np.size(models),1),labels=models)
axs['c'].set_yticks(np.arange(0,np.size(models),1),labels=models)
axs['d'].set_yticks(np.arange(0,np.size(models),1),labels=models)
cbar_ax = fig.add_axes([0.92, 0.1, 0.025, 0.8])
fig.colorbar(im, cax=cbar_ax,orientation='vertical',extend='max',label=lab)
pyscript.write("plot",fig)