os.makedirs(SAVE)
print "created:", SAVE
# then link the photo viewer
photo_viewer = '/uufs/chpc.utah.edu/common/home/u0553130/public_html/Brian_Blaylock/photo_viewer/photo_viewer2.php'
os.link(photo_viewer, SAVE + 'photo_viewer2.php')
# Title over map
figs[locName][1].set_title('UTC: %s' % (TS_dates[hh]))
figs[locName][2].set_title(
' Run (UTC): %s f%02d\nValid (UTC): %s' %
(H['anlys'].strftime('%Y %b %d, %H:%M'), fxx,
H['valid'].strftime('%Y %b %d, %H:%M')))
#
# Project on map
X, Y = maps[locName](H['lon'], H['lat']) # HRRR grid
# Trim the data
cut_v, cut_h = pluck_point_new(l['latitude'], l['longitude'], H['lat'],
H['lon'])
bfr = 15
trim_X = X[cut_v - bfr:cut_v + bfr, cut_h - bfr:cut_h + bfr]
trim_Y = Y[cut_v - bfr:cut_v + bfr, cut_h - bfr:cut_h + bfr]
trim_dBZ = dBZ[cut_v - bfr:cut_v + bfr, cut_h - bfr:cut_h + bfr]
trim_H_U = H_U['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
trim_H_V = H_V['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
#
# Overlay Simulated Radar Reflectivity
ctable = 'NWSReflectivity'
norm, cmap = ctables.registry.get_with_steps(ctable, -0, 5)
radar = figs[locName][1].pcolormesh(trim_X,
trim_Y,
trim_dBZ,
def get_OSG_stats(locs, variable, stats, months=range(1,13), hours=range(0,24), extra=True, fxx=0):
"""
Returns a dictionary of values plucked from the OSG HRRR statistics files.
Input:
locs - a dictionary with the latitude and longitude of each location
e.g. {'NAME':{'latitude':40, 'longitude':-111}}
variable - the HRRR variable name (e.g. 'TMP:2 m' or 'WIND:10 m')
stats - a list of the requested statistics. May include:
['MAX', 'MIN', 'MEAN', 'P01', 'P05', 'P10', 'P90', 'P95', 'P99']
(the more you ask for, the longer it takes)
months - a list of months
hours - a list of hours
extra - returns other data if True,
count: sample size of statistics
cores: number of cores used by the OSG node to compute the statistics
timer: length of time to create the file on the OSG
fxx - the forecast hour. So far only fxx is available
"""
# The variable with underscores replacing the special characters in the
# HRRR variable name
var = variable.replace(':', '_').replace(' ', '_')
# Define an offset for temperature
if variable == 'TMP:2 m' or variable == 'DPT:2 m':
offset = 273.15
else:
offset = 0
# Open the first file, so we can pluck the points and store the position
A = '/uufs/chpc.utah.edu/common/home/horel-group/archive/'
B = 'HRRR/OSG_hourbymonth_stats_20150418-2017731/'
C = '%s/' % var
DIR = A+B+C
FILE = 'OSG_HRRR_%s_m%02d_h%02d_f%02d.nc' % (var, 1, 0, fxx)
# Handle for every netCDF file we want:
# Starting with all hours for first month first, then all hours the second month, etc.
a = [Dataset(DIR+'OSG_HRRR_%s_m%02d_h%02d_f%02d.nc' % (var, i, j, fxx), 'r') for i in months for j in hours]
print 'Got the file handles'
# Initalize the return dictionary
return_this = {'month':np.array([i for i in months for j in hours]),
'hour':np.array([j for i in months for j in hours]),
'monthhour':np.array([i+j/24. for i in months for j in hours])}
# Store the requested values in the return dictionary
if extra==True:
return_this['count'] = np.array([i.variables['count'][0] for i in a])
return_this['cores'] = np.array([i.variables['cores'][0] for i in a])
return_this['timer'] = np.array([''.join(i.variables['timer'][:]) for i in a])
print 'Got the count, cores, and timer'
# Get the pluck indexes for each requested location
Hlat = a[0].variables['latitude'][:] # HRRR latitude grid
Hlon = a[0].variables['longitude'][:] # HRRR longitude grid
for L in locs:
print 'working on L...',
Slat = locs[L]['latitude'] # Station latitude point
Slon = locs[L]['longitude'] # Statin longitude point
x, y = pluck_point_new(Slat, Slon, Hlat, Hlon)
print 'got the plucked points...',
return_this[L] = {'stn_latlon':[Slat, Slon],
'hrrr_latlon':[Hlat[x[0],y[0]], Hlon[x[0],y[0]]]}
if 'MAX' in stats:
return_this[L]['MAX'] = np.array([i.variables['max_'+var][x[0],y[0]]-offset for i in a])
print 'got the max...',
if 'MIN' in stats:
return_this[L]['MIN'] = np.array([i.variables['min_'+var][x[0],y[0]]-offset for i in a])
print 'got the min...',
if 'MEAN' in stats:
return_this[L]['MEAN'] = np.array([i.variables['mean_'+var][x[0],y[0]]-offset for i in a])
print 'got the mean...',
if 'P01' in stats:
return_this[L]['P01'] = np.array([i.variables['percentile'][0][x[0],y[0]]-offset for i in a])
print 'got the P01...',
if 'P05' in stats:
return_this[L]['P05'] = np.array([i.variables['percentile'][1][x[0],y[0]]-offset for i in a])
print 'got the P05...',
if 'P10' in stats:
return_this[L]['P10'] = np.array([i.variables['percentile'][2][x[0],y[0]]-offset for i in a])
print 'got the P10...',
if 'P90' in stats:
return_this[L]['P90'] = np.array([i.variables['percentile'][3][x[0],y[0]]-offset for i in a])
print 'got the P90...',
if 'P95' in stats:
return_this[L]['P95'] = np.array([i.variables['percentile'][4][x[0],y[0]]-offset for i in a])
print 'got the P95...',
if 'P99' in stats:
return_this[L]['P99'] = np.array([i.variables['percentile'][5][x[0],y[0]]-offset for i in a])
print 'got the P99...',
# Close the NetCDF files
for close in a:
close.close()
print 'done!'
return return_this
def make_plots(inputs):
VALIDDATE, fxx = inputs
print 'working on %s f%02d' % (VALIDDATE, fxx)
plt.clf()
plt.cla()
print fxx, VALIDDATE
# === Some housekeeping variables =============================================
# Convert Valid Date to Run Date, adjusted by the forecast
DATE = VALIDDATE - timedelta(hours=fxx)
# Parse Location lat/lon
if ',' in location:
# User put inputted a lat/lon point request
lat, lon = location.split(',')
lat = float(lat)
lon = float(lon)
else:
# User requested a MesoWest station
stninfo = get_station_info([location])
lat = stninfo['LAT']
lon = stninfo['LON']
# Preload the latitude and longitude grid
latlonpath = '/uufs/chpc.utah.edu/common/home/horel-group7/Pando/hrrr/HRRR_latlon.h5'
latlonh5 = h5py.File(latlonpath, 'r')
gridlat = latlonh5['latitude'][:]
gridlon = latlonh5['longitude'][:]
# === Create map of the domain ================================================
if dsize == 'conus' and model != 'hrrrAK':
barb_thin = 70
alpha = 1
t1 = datetime.now()
#m = draw_CONUS_HRRR_map(res=map_res)
m = np.load(
'/uufs/chpc.utah.edu/common/home/u0553130/public_html/Brian_Blaylock/cgi-bin/HRRR_CONUS_map_object_'
+ map_res + '.npy').item()
m.drawcountries(zorder=500)
m.drawstates(zorder=500)
m.drawcoastlines(zorder=500)
m.fillcontinents(color='tan', lake_color='lightblue', zorder=0)
m.drawmapboundary(fill_color='lightblue')
t2 = datetime.now()
else:
# configure some setting based on the requested domain size
if dsize == 'small':
plus_minus_latlon = .27 # +/- latlon box around center point
barb_thin = 1 # Thin out excessive wind barbs
arcgis_res = 1000 # ArcGIS image resolution
bfr = 15 # trim domain buffer
alpha = .75 # Alpha (pcolormesh transparency)
elif dsize == 'medium':
plus_minus_latlon = .75
barb_thin = 2
arcgis_res = 800
bfr = 35
alpha = .75
elif dsize == 'large':
plus_minus_latlon = 2.5
barb_thin = 6
arcgis_res = 800
bfr = 110
alpha = .75
elif dsize == 'xlarge':
plus_minus_latlon = 5
barb_thin = 12
arcgis_res = 700
bfr = 210
alpha = .75
elif dsize == 'xxlarge': # If domain runs into HRRR boundary, then it'll fail
plus_minus_latlon = 10
barb_thin = 25
arcgis_res = 700
bfr = 430
alpha = .75
elif dsize == 'xxxlarge':
plus_minus_latlon = 15
barb_thin = 35
arcgis_res = 1000
bfr = 700
alpha = .75
m = Basemap(resolution=map_res, projection='cyl',\
area_thresh=3000,\
llcrnrlon=lon-plus_minus_latlon, llcrnrlat=lat-plus_minus_latlon,\
urcrnrlon=lon+plus_minus_latlon, urcrnrlat=lat+plus_minus_latlon,)
m.drawstates(zorder=500)
m.drawcountries(zorder=500)
m.drawcoastlines(zorder=500)
#if dsize == 'small' or dsize == 'medium':
# m.drawcounties()
# === Add a Background image ==================================================
# Start the map image
plt.figure(1)
if background == 'arcgis' and dsize != 'conus':
m.arcgisimage(service='World_Shaded_Relief',
xpixels=arcgis_res,
verbose=False)
elif background == 'arcgisSat' and dsize != 'conus':
m.arcgisimage(service='ESRI_Imagery_World_2D',
xpixels=arcgis_res,
verbose=False)
elif background == 'arcgisRoad' and dsize != 'conus':
m.arcgisimage(service='NatGeo_World_Map',
xpixels=arcgis_res,
verbose=False)
elif background == 'terrain':
# Get data
H_ter = get_hrrr_variable(
DATE,
'HGT:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
H_land = get_hrrr_variable(
DATE,
'LAND:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Plot the terrain
m.contourf(gridlon,
gridlat,
H_ter['value'],
levels=range(0, 4000, 200),
cmap='Greys_r',
zorder=1,
latlon=True)
# Plot Water area
m.contour(gridlon,
gridlat,
H_land['value'],
levels=[0, 1],
colors='b',
zorder=1,
latlon=True)
elif background == 'landuse':
# Get data
from BB_cmap.landuse_colormap import LU_MODIS21
if model == 'hrrr':
VGTYP = 'VGTYP:surface'
else:
VGTYP = 'var discipline=2 center=59 local_table=1 parmcat=0 parm=198'
H_LU = get_hrrr_variable(
DATE,
VGTYP,
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Plot the terrain
cm, labels = LU_MODIS21()
m.pcolormesh(gridlon,
gridlat,
H_LU['value'],
cmap=cm,
vmin=1,
vmax=len(labels) + 1,
zorder=1,
latlon=True)
# Add SHAPEFILE
if is_FIRE:
plt.gca().add_collection(
PatchCollection(patches,
facecolor='indianred',
alpha=.65,
edgecolor='k',
linewidths=1,
zorder=1))
# === Figure Title ============================================================
if dsize != 'conus':
m.scatter(lon, lat, marker='+', c='r', s=100, zorder=1000, latlon=True)
plt.title('Center: %s\n%s' % (location, model.upper()),
fontweight='bold')
else:
plt.title('%s' % (model.upper()), fontweight='bold')
plt.title('Run: %s F%02d' % (DATE.strftime('%Y-%m-%d %H:%M UTC'), fxx),
loc='left')
plt.title('Valid: %s' %
(DATE + timedelta(hours=fxx)).strftime('%Y-%m-%d %H:%M UTC'),
loc='right')
# =============================================================================
if '10mWind_Fill' in plotcode or '10mWind_Shade' in plotcode or '10mWind_Barb' in plotcode or '10mWind_Quiver' in plotcode or '10mWind_p95_fill' in plotcode:
# Get data
H_u = get_hrrr_variable(
DATE,
'UGRD:10 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
H_v = get_hrrr_variable(
DATE,
'VGRD:10 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
spd = wind_uv_to_spd(H_u['value'], H_v['value'])
if '10mWind_Fill' in plotcode:
m.pcolormesh(gridlon,
gridlat,
spd,
latlon=True,
cmap=cm_wind(),
vmin=0,
vmax=60,
alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'10 m Wind Speed (m s$\mathregular{^{-1}}$)')
if '10mWind_Shade' in plotcode:
m.contourf(gridlon,
gridlat,
spd,
levels=[10, 15, 20, 25],
colors=('yellow', 'orange', 'red'),
alpha=alpha,
extend='max',
zorder=1,
latlon=True)
cb = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb.set_label(r'10 m Wind Speed (ms$\mathregular{^{-1}}$)')
if '10mWind_Barb' in plotcode or '10mWind_Quiver' in plotcode:
# For small domain plots, trimming the edges significantly reduces barb plotting time
if barb_thin < 20:
cut_v, cut_h = pluck_point_new(lat, lon, gridlat, gridlon)
Cgridlat = gridlat[cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
Cgridlon = gridlon[cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
H_u['value'] = H_u['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
H_v['value'] = H_v['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
else:
Cgridlat = gridlat
Cgridlon = gridlon
thin = barb_thin
# Add to plot
if '10mWind_Barb' in plotcode:
m.barbs(Cgridlon[::thin, ::thin],
Cgridlat[::thin, ::thin],
H_u['value'][::thin, ::thin],
H_v['value'][::thin, ::thin],
zorder=200,
length=5.5,
barb_increments={
'half': 2.5,
'full': 5,
'flag': 25
},
latlon=True)
if '10mWind_Quiver' in plotcode:
Q = m.quiver(Cgridlon[::thin, ::thin],
Cgridlat[::thin, ::thin],
H_u['value'][::thin, ::thin],
H_v['value'][::thin, ::thin],
zorder=350,
latlon=True)
qk = plt.quiverkey(Q,
.92,
0.07,
10,
r'10 m s$^{-1}$',
labelpos='S',
coordinates='axes',
color='darkgreen')
qk.text.set_backgroundcolor('w')
if '10mWind_p95_fill' in plotcode:
DIR = '/uufs/chpc.utah.edu/common/home/horel-group8/blaylock/HRRR_OSG/hourly30/UVGRD_10_m/'
FILE = 'OSG_HRRR_%s_m%02d_d%02d_h%02d_f00.h5' % (
('UVGRD_10_m', VALIDDATE.month, VALIDDATE.day, VALIDDATE.hour))
with h5py.File(DIR + FILE, 'r') as f:
spd_p95 = f["p95"][:]
masked = spd - spd_p95
masked = np.ma.array(masked)
masked[masked < 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
masked,
vmax=10,
vmin=0,
latlon=True,
cmap='viridis',
alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(
r'10 m Wind Speed exceeding 95th Percentile (m s$\mathregular{^{-1}}$)'
)
if '80mWind_Fill' in plotcode or '80mWind_Shade' in plotcode or '80mWind_Barb' in plotcode:
# Get data
H_u = get_hrrr_variable(
DATE,
'UGRD:80 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
H_v = get_hrrr_variable(
DATE,
'VGRD:80 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
spd = wind_uv_to_spd(H_u['value'], H_v['value'])
if '80mWind_Fill' in plotcode:
m.pcolormesh(gridlon,
gridlat,
spd,
latlon=True,
cmap=cm_wind(),
vmin=0,
vmax=60,
alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'10 m Wind Speed (m s$\mathregular{^{-1}}$)')
if '80mWind_Shade' in plotcode:
m.contourf(gridlon,
gridlat,
spd,
levels=[10, 15, 20, 25],
colors=('yellow', 'orange', 'red'),
alpha=alpha,
extend='max',
zorder=10,
latlon=True)
cb = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb.set_label(r'10 m Wind Speed (ms$\mathregular{^{-1}}$)')
if '80mWind_Barb' in plotcode:
# For small domain plots, trimming the edges significantly reduces barb plotting time
if barb_thin < 20:
cut_v, cut_h = pluck_point_new(lat, lon, gridlat, gridlon)
Cgridlat = gridlat[cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
Cgridlon = gridlon[cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
H_u['value'] = H_u['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
H_v['value'] = H_v['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
else:
Cgridlat = gridlat
Cgridlon = gridlon
# Add to plot
thin = barb_thin
m.barbs(Cgridlon[::thin, ::thin],
Cgridlat[::thin, ::thin],
H_u['value'][::thin, ::thin],
H_v['value'][::thin, ::thin],
zorder=200,
length=5.5,
color='darkred',
barb_increments={
'half': 2.5,
'full': 5,
'flag': 25
},
latlon=True)
if 'Fill80mWind' in plotcode:
m.pcolormesh(gridlon,
gridlat,
spd,
latlon=True,
cmap=cm_wind(),
vmin=0,
vmax=60,
alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'80 m Wind Speed (m s$\mathregular{^{-1}}$)')
if 'Gust_Hatch' in plotcode:
H_gust = get_hrrr_variable(
DATE,
'GUST:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Add to plot
m.contourf(gridlon,
gridlat,
H_gust['value'],
levels=[0, 10, 15, 20, 25],
hatches=[None, '.', '\\\\', '*'],
colors='none',
extend='max',
zorder=10,
latlon=True)
cb = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb.set_label(r'Surface Wind Gust (ms$\mathregular{^{-1}}$)')
m.contour(gridlon,
gridlat,
H_gust['value'],
levels=[10, 15, 20, 25],
colors='k',
zorder=10,
latlon=True)
if 'dBZ_Fill' in plotcode or 'dBZ_Contour' in plotcode:
from BB_cmap.reflectivity_colormap import reflect_ncdc
# Get Data
if model == 'hrrr':
REFC = 'REFC:entire'
elif model == 'hrrrX' or model == 'hrrrAK':
REFC = 'var discipline=0 center=59 local_table=1 parmcat=16 parm=196'
H_ref = get_hrrr_variable(
DATE,
REFC,
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Mask values
dBZ = H_ref['value']
dBZ = np.ma.array(dBZ)
dBZ[dBZ == -10] = np.ma.masked
# Add Contour to plot
if 'dBZ_Contour' in plotcode:
cREF = m.contour(gridlon,
gridlat,
dBZ,
cmap=reflect_ncdc(),
levels=range(10, 80, 10),
latlon=True,
zorder=50)
plt.clabel(cREF,
cREF.levels[::2],
fmt='%2.0f',
colors='k',
fontsize=9)
#cb2 = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
#cb2.set_label('Simulated Composite Reflectivity (dBZ)')
# Add fill to plot
if 'dBZ_Fill' in plotcode:
m.pcolormesh(gridlon,
gridlat,
dBZ,
cmap=reflect_ncdc(),
vmax=80,
vmin=0,
alpha=alpha,
latlon=True)
cb2 = plt.colorbar(orientation='horizontal',
shrink=shrink,
pad=pad)
cb2.set_label('Simulated Composite Reflectivity (dBZ)')
if '2mDPT_p95p05_fill' in plotcode:
H_dpt = get_hrrr_variable(
DATE,
'DPT:2 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
DIR = '/uufs/chpc.utah.edu/common/home/horel-group8/blaylock/HRRR_OSG/hourly30/DPT_2_m/'
FILE = 'OSG_HRRR_%s_m%02d_d%02d_h%02d_f00.h5' % (
('DPT_2_m', VALIDDATE.month, VALIDDATE.day, VALIDDATE.hour))
### Plot Dew Point Depression
with h5py.File(DIR + FILE, 'r') as f:
dpt_p05 = f["p05"][:]
masked = H_dpt[
'value'] - dpt_p05 # both these datasets are in Kelvin, but when we take the difference it is in Celsius
masked = np.ma.array(masked)
masked[masked > 0] = np.ma.masked
mesh_depression = m.pcolormesh(gridlon,
gridlat,
masked,
vmax=10,
vmin=-10,
latlon=True,
cmap='BrBG')
### Plot Dew Point Exceedance
with h5py.File(DIR + FILE, 'r') as f:
dpt_p95 = f["p95"][:]
masked = H_dpt[
'value'] - dpt_p95 # both these datasets are in Kelvin, but when we take the difference it is in Celsius
masked = np.ma.array(masked)
masked[masked < 0] = np.ma.masked
mesh_exceedance = m.pcolormesh(gridlon,
gridlat,
masked,
vmax=10,
vmin=-10,
latlon=True,
cmap='BrBG')
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(
r'5$\mathregular{^{th}}$/95$\mathregular{^{th}}$ percentile 2 m Dew Point Depression/Exceedance (C)'
)
if '2mTemp_Fill' in plotcode or '2mTemp_Freeze' in plotcode or '2mTemp_p95p05_fill' in plotcode:
# Get Data
H_temp = get_hrrr_variable(
DATE,
'TMP:2 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
TMP = H_temp['value'] - 273.15
# Add fill to plot
if '2mTemp_Fill' in plotcode:
m.pcolormesh(gridlon,
gridlat,
TMP,
cmap=cm_temp(),
vmax=50,
vmin=-50,
alpha=alpha,
zorder=3,
latlon=True)
cbT = plt.colorbar(orientation='horizontal',
shrink=shrink,
pad=pad)
cbT.set_label('2 m Temperature (C)')
# Add freezing contour to plot
if '2mTemp_Freeze' in plotcode:
m.contour(gridlon,
gridlat,
TMP,
colors='b',
levels=[0],
zorder=400,
latlon=True)
if '2mTemp_p95p05_fill' in plotcode:
DIR = '/uufs/chpc.utah.edu/common/home/horel-group8/blaylock/HRRR_OSG/hourly30/TMP_2_m/'
FILE = 'OSG_HRRR_%s_m%02d_d%02d_h%02d_f00.h5' % (
('TMP_2_m', VALIDDATE.month, VALIDDATE.day, VALIDDATE.hour))
### Plot Temperature Depression
with h5py.File(DIR + FILE, 'r') as f:
tmp_p05 = f["p05"][:]
masked = H_temp[
'value'] - tmp_p05 # both these datasets are in Kelvin, but when we take the difference it is in Celsius
masked = np.ma.array(masked)
masked[masked > 0] = np.ma.masked
mesh_depression = m.pcolormesh(gridlon,
gridlat,
masked,
vmax=10,
vmin=-10,
latlon=True,
cmap='bwr')
### Plot Temperature Exceedance
with h5py.File(DIR + FILE, 'r') as f:
tmp_p95 = f["p95"][:]
masked = H_temp[
'value'] - tmp_p95 # both these datasets are in Kelvin, but when we take the difference it is in Celsius
masked = np.ma.array(masked)
masked[masked < 0] = np.ma.masked
mesh_exceedance = m.pcolormesh(gridlon,
gridlat,
masked,
vmax=10,
vmin=-10,
latlon=True,
cmap='bwr')
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(
r'5$\mathregular{^{th}}$/95$\mathregular{^{th}}$ percentile 2 m Temperature Depression/Exceedance (C)'
)
if '2mRH_Fill' in plotcode:
# Get Data
try:
H_RH = get_hrrr_variable(
DATE,
'RH:2 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Add fill to plot
m.pcolormesh(gridlon,
gridlat,
H_RH['value'],
cmap=cm_rh(),
vmin=5,
vmax=90,
zorder=3,
latlon=True)
cbT = plt.colorbar(orientation='horizontal',
pad=pad,
shrink=shrink)
cbT.set_label('2m Relative Humidity (%)')
except:
print "!! Some errors getting the RH value."
print "!! If you requested an old date, from HRRR version 1, there isn't a RH variable,"
print "!! and this code doesn't get the dwpt and convert it to RH yet."
if '700Temp_Fill' in plotcode or '700Temp_-12c' in plotcode:
# Get Data
H_temp = get_hrrr_variable(
DATE,
'TMP:700 mb',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
TMP = H_temp['value'] - 273.15
# Add fill to plot
if '700Temp_Fill' in plotcode:
m.pcolormesh(gridlon,
gridlat,
TMP,
cmap=cm_temp(),
vmax=50,
vmin=-50,
alpha=alpha,
zorder=3,
latlon=True)
cbT = plt.colorbar(orientation='horizontal',
shrink=shrink,
pad=pad)
cbT.set_label('700 mb Temperature (C)')
# Add -12 C contour to plot
if '700Temp_-12c' in plotcode:
m.contour(gridlon,
gridlat,
TMP,
colors='b',
levels=[-12],
latlon=True,
zorder=400)
if '500HGT_Contour' in plotcode:
H_500 = get_hrrr_variable(
DATE,
'HGT:500 mb',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
CS = m.contour(gridlon,
gridlat,
H_500['value'],
levels=range(5040, 6181, 60),
linewidths=1.7,
colors='k',
latlon=True,
zorder=400)
plt.clabel(CS, inline=1, fmt='%2.f')
if '500Wind_Fill' in plotcode or '500Wind_Barb' in plotcode or '500Vort_Fill' in plotcode or '500Conv_Fill' in plotcode:
H_u = get_hrrr_variable(
DATE,
'UGRD:500 mb',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
H_v = get_hrrr_variable(
DATE,
'VGRD:500 mb',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
if '500Wind_Fill' in plotcode:
spd = wind_uv_to_spd(H_u['value'], H_v['value'])
m.pcolormesh(gridlon,
gridlat,
spd,
latlon=True,
cmap=cm_wind(),
vmin=0,
vmax=60)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'500 mb Wind Speed (m s$\mathregular{^{-1}}$)')
if '500Conv_Fill' in plotcode or '500Vort_Fill' in plotcode:
dudx, dudy = np.gradient(H_u['value'], 3, 3)
dvdx, dvdy = np.gradient(H_v['value'], 3, 3)
if '500Vort_Fill' in plotcode:
vorticity = dvdx - dudy
# Mask values
vort = vorticity
vort = np.ma.array(vort)
vort[np.logical_and(vort < .05, vort > -.05)] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
vort,
latlon=True,
cmap='bwr',
vmax=np.max(vort),
vmin=-np.max(vort))
cb = plt.colorbar(orientation='horizontal',
pad=pad,
shrink=shrink)
cb.set_label(r'500 mb Vorticity (s$\mathregular{^{-1}}$)')
if '500Conv_Fill' in plotcode:
convergence = dudx + dvdy
# Mask values
conv = convergence
conv = np.ma.array(conv)
conv[np.logical_and(conv < .05, conv > -.05)] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
conv,
latlon=True,
cmap='bwr',
vmax=np.max(conv),
vmin=-np.max(conv))
cb = plt.colorbar(orientation='horizontal',
pad=pad,
shrink=shrink)
cb.set_label(r'500 mb Convergence (s$\mathregular{^{-1}}$)')
if '500Wind_Barb' in plotcode:
# For small domain plots, trimming the edges significantly reduces barb plotting time
if barb_thin < 20:
cut_v, cut_h = pluck_point_new(lat, lon, gridlat, gridlon)
Cgridlat = gridlat[cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
Cgridlon = gridlon[cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
H_u['value'] = H_u['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
H_v['value'] = H_v['value'][cut_v - bfr:cut_v + bfr,
cut_h - bfr:cut_h + bfr]
else:
Cgridlat = gridlat
Cgridlon = gridlon
thin = barb_thin
m.barbs(Cgridlon[::thin, ::thin],
Cgridlat[::thin, ::thin],
H_u['value'][::thin, ::thin],
H_v['value'][::thin, ::thin],
zorder=200,
length=6,
color='navy',
barb_increments={
'half': 2.5,
'full': 5,
'flag': 25
},
latlon=True)
#plt.ylabel(r'Barbs: half=2.5, full=5, flag=25 (ms$\mathregular{^{-1}}$)')
if 'MSLP_Contour' in plotcode or 'MSLP_Fill' in plotcode:
H = get_hrrr_variable(
DATE,
'MSLMA:mean sea level',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
if 'MSLP_Contour' in plotcode:
CS = m.contour(gridlon,
gridlat,
H['value'] / 100.,
latlon=True,
levels=range(952, 1200, 4),
colors='k',
zorder=400)
CS.clabel(inline=1, fmt='%2.f', zorder=400)
if 'MSLP_Fill' in plotcode:
m.pcolormesh(gridlon,
gridlat,
H['value'] / 100.,
latlon=True,
cmap='viridis')
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label('Mean Sea Level Pressure (hPa)')
if '2mPOT_Fill' in plotcode:
# Get Data
H_temp = get_hrrr_variable(
DATE,
'POT:2 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
TMP = H_temp['value'] - 273.15
m.pcolormesh(gridlon,
gridlat,
TMP,
cmap="Oranges",
alpha=alpha,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('2 m Potential Temperature (C)')
if 'SkinTemp_Fill' in plotcode:
# Get Data
H_temp = get_hrrr_variable(
DATE,
'TMP:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
TMP = H_temp['value'] - 273.15
m.pcolormesh(gridlon,
gridlat,
TMP,
cmap="Spectral_r",
alpha=alpha,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('Skin Temperature (C)')
if 'AccumPrecip_Fill' in plotcode or '1hrPrecip_Fill' in plotcode:
if 'AccumPrecip_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(
DATE,
'APCP:surface:0',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Mask values
prec = H['value']
prec = np.ma.array(prec)
prec[prec == 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
prec,
cmap=cm_precip(),
alpha=alpha,
vmin=0,
vmax=762,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal',
shrink=shrink,
pad=pad)
cbS.set_label('Accumulated Precipitation since F00 (mm)')
if '1hrPrecip_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(
DATE,
'APCP:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Mask values
prec = H['value']
prec = np.ma.array(prec)
prec[prec == 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
prec,
cmap=cm_precip(),
alpha=alpha,
vmin=0,
vmax=762,
zorder=3,
latlon=True)
cbS = plt.colorbar(
orientation='horizontal',
shrink=shrink,
pad=pad,
extend="max",
)
cbS.set_label('1 hour Accumulated Precipitation (mm)')
if 'SnowCover_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(
DATE,
'SNOWC',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Mask values
snow = H['value']
snow = np.ma.array(snow)
snow[snow == 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
snow,
cmap="Blues",
alpha=alpha,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('Snow Cover (%)')
if 'PWAT_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(
DATE,
'PWAT:entire',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
m.pcolormesh(gridlon,
gridlat,
H['value'],
cmap="RdYlGn",
alpha=alpha,
vmin=0,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label(
r'Vertically Integrated Liquid Water (kg m$\mathregular{^{-2}}$)')
if 'CAPE_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(
DATE,
'CAPE:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Mask values
cape = H['value']
cape = np.ma.array(cape)
cape[cape == 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
cape,
cmap="Oranges",
alpha=alpha,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label(r'Surface CAPE (J kg$\mathregular{^{-1}}$)')
if 'CIN_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(
DATE,
'CIN:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Mask values
cin = H['value']
cin = np.ma.array(cin)
cin[cin == 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
cin,
cmap="BuPu",
alpha=alpha,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label(r'Surface CIN (J kg$\mathregular{^{-1}}$)')
if 'RedFlag_Fill' in plotcode or 'RedFlag_Contour' in plotcode or 'RedFlagPot_Fill' in plotcode:
# generalized criteria for red flag warning
# Winds (gusts) greater than 6.7 m/s and RH < 25%
rf_RH = 25
rf_WIND = 6.7
# Get Data
H_gust = get_hrrr_variable(
DATE,
'GUST:surface',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
H_rh = get_hrrr_variable(
DATE,
'RH:2 m',
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
RedFlag = np.logical_and(H_gust['value'] > rf_WIND,
H_rh['value'] < rf_RH)
if 'RedFlag_Contour' in plotcode:
try:
CS = m.contour(gridlon,
gridlat,
RedFlag,
latlon=True,
colors='maroon',
zorder=400)
except:
# maybe there isn't any contours in this domain
pass
if 'RedFlag_Fill' in plotcode:
RedFlag = np.ma.array(RedFlag)
RedFlag[RedFlag == 0] = np.ma.masked
m.pcolormesh(gridlon,
gridlat,
RedFlag,
cmap="YlOrRd_r",
alpha=alpha,
zorder=4,
latlon=True)
if 'RedFlagPot_Fill' in plotcode:
cdict3 = {
'red': ((0.0, 1.0, 1.0), (0.5, 0.5, 0.5), (0.5, 1.0, 1.0),
(1.0, 0.4, 0.4)),
'green': ((0.0, 1.0, 1.0), (0.5, 0.5, 0.5), (0.5, 0.4, 0.4),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 1.0, 1.0), (0.5, 0.5, 0.5), (0.5, 0.0, 0.0),
(1.0, 0.0, 0.0))
}
plt.register_cmap(name='FirePot', data=cdict3)
# Definate Red Flag Area:
RED_FLAG = np.logical_and(H_rh['value'] < rf_RH,
H_gust['value'] > rf_WIND)
# Linear Equation
b = (rf_RH - rf_WIND) * (rf_RH / rf_WIND)
z = -(rf_RH / rf_WIND) * (H_rh['value'] - H_gust['value']) + b
m.pcolormesh(gridlon,
gridlat,
z,
cmap="FirePot",
alpha=alpha,
vmax=200,
vmin=-200,
zorder=3,
latlon=True)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'Red Flag Potential')
m.contour(gridlon,
gridlat,
z,
colors='k',
levels=[0],
zorder=3,
latlon=True)
m.contour(gridlon,
gridlat,
RED_FLAG,
colors='darkred',
levels=[0],
zorder=3,
latlon=True)
plt.xlabel(
r'Red Flag Criteria: Winds > 6.7 m s$\mathregular{^{-1}}$ and RH < 25%'
)
# =============================================================================
# Hack! Plot an extra HRRR variable not listed on the webpage hrrr_custom.html
# This extra argument will let you attempt to plot a different variable for
# a quicklook.
try:
# Must be a variable from a line in the .idx file
hrrrVAR = form['extraVAR'].value
extraHRRR = get_hrrr_variable(
DATE,
hrrrVAR,
model=model,
fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
m.pcolormesh(gridlon,
gridlat,
extraHRRR['value'],
cmap='viridis',
alpha=alpha,
zorder=3,
latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label(hrrrVAR + ' (units)')
except:
pass
# =============================================================================
#plt.ylabel('Section Timer:%s\nFull Timer:%s' % (t2-t1, datetime.now()-firsttimer))
SAVEDIR = '/uufs/chpc.utah.edu/common/home/u0553130/public_html/oper/HRRR_yesterday/%s/%s/' % (
MAP_DIR, DOMAIN)
if not os.path.exists(SAVEDIR):
os.makedirs(SAVEDIR)
SAVEFIG = SAVEDIR + '%s_f%02d' % (VALIDDATE.strftime('h%H'), fxx)
plt.savefig(SAVEFIG) # Plot standard output.
print 'SAVED:', SAVEFIG
edgecolor='k',
zorder=100)
# Overlay Simulated Radar Reflectivity
ctable = 'NWSReflectivity'
norm, cmap = ctables.registry.get_with_steps(ctable, -0, 5)
maps[loc].pcolormesh(X, Y, dBZ, norm=norm, cmap=cmap, alpha=.5)
cb = plt.colorbar(orientation='horizontal', pad=0.01, shrink=0.75)
cb.set_label(
'Simulated Radar Reflectivity (dBZ)\n\nBarbs: Half=5 mph, Full=10 mph, Flag=50 mph'
)
# Overlay wind barbs (need to trim this array before we plot it)
# First need to trim the array
cut_vertical, cut_horizontal = pluck_point_new(l['latitude'],
l['longitude'],
H['lat'], H['lon'])
maps[loc].barbs(X[cut_vertical - 5:cut_vertical + 5,
cut_horizontal - 5:cut_horizontal + 5],
Y[cut_vertical - 5:cut_vertical + 5,
cut_horizontal - 5:cut_horizontal + 5],
H_U['value'][cut_vertical - 5:cut_vertical + 5,
cut_horizontal - 5:cut_horizontal + 5],
H_V['value'][cut_vertical - 5:cut_vertical + 5,
cut_horizontal - 5:cut_horizontal + 5],
zorder=200)
# Overlay Utah Roads
BASE = '/uufs/chpc.utah.edu/common/home/u0553130/'
maps[loc].readshapefile(
BASE +
def make_plots(inputs):
VALID_DATE, fxx = inputs
print 'working on %s f%02d' % (VALID_DATE, fxx)
plt.clf(); plt.cla()
print fxx, VALID_DATE
# === Some housekeeping variables =============================================
# Convert Valid Date to Run Date, adjusted by the forecast
DATE = VALID_DATE - timedelta(hours=fxx)
# Parse Location lat/lon
if ',' in location:
# User put inputted a lat/lon point request
lat, lon = location.split(',')
lat = float(lat)
lon = float(lon)
else:
# User requested a MesoWest station
stninfo = get_station_info([location])
lat = stninfo['LAT']
lon = stninfo['LON']
# Preload the latitude and longitude grid
latlonpath = '/uufs/chpc.utah.edu/common/home/horel-group/archive/HRRR/oper_HRRR_latlon.h5'
latlonh5 = h5py.File(latlonpath, 'r')
gridlat = latlonh5['latitude'][:]
gridlon = latlonh5['longitude'][:]
# === Create map of the domain ================================================
if dsize == 'conus' and model != 'hrrrAK':
barb_thin = 70
alpha = 1
t1= datetime.now()
#m = draw_CONUS_HRRR_map(res=map_res)
m = np.load('/uufs/chpc.utah.edu/common/home/u0553130/public_html/Brian_Blaylock/cgi-bin/HRRR_CONUS_map_object_'+map_res+'.npy').item()
m.drawcountries(zorder=500)
m.drawstates(zorder=500)
m.drawcoastlines(zorder=500)
m.fillcontinents(color='tan',lake_color='lightblue', zorder=0)
m.drawmapboundary(fill_color='lightblue')
t2 = datetime.now()
else:
# configure some setting based on the requested domain size
if dsize == 'small':
plus_minus_latlon = .27 # +/- latlon box around center point
barb_thin = 1 # Thin out excessive wind barbs
arcgis_res = 1000 # ArcGIS image resolution
bfr = 15 # trim domain buffer
alpha = .75 # Alpha (pcolormesh transparency)
elif dsize == 'medium':
plus_minus_latlon = .75
barb_thin = 2
arcgis_res = 800
bfr = 35
alpha = .75
elif dsize == 'large':
plus_minus_latlon = 2.5
barb_thin = 6
arcgis_res = 800
bfr = 110
alpha = .75
elif dsize == 'xlarge':
plus_minus_latlon = 5
barb_thin = 12
arcgis_res = 700
bfr = 210
alpha = .75
elif dsize == 'xxlarge': # If domain runs into HRRR boundary, then it'll fail
plus_minus_latlon = 10
barb_thin = 25
arcgis_res = 700
bfr = 430
alpha = .75
elif dsize == 'xxxlarge':
plus_minus_latlon = 15
barb_thin = 35
arcgis_res = 1000
bfr = 700
alpha = .75
m = Basemap(resolution=map_res, projection='cyl',\
area_thresh=3000,\
llcrnrlon=lon-plus_minus_latlon, llcrnrlat=lat-plus_minus_latlon,\
urcrnrlon=lon+plus_minus_latlon, urcrnrlat=lat+plus_minus_latlon,)
m.drawstates(zorder=500)
m.drawcountries(zorder=500)
m.drawcoastlines(zorder=500)
#if dsize == 'small' or dsize == 'medium':
# m.drawcounties()
# === Add a Background image ==================================================
# Start the map image
plt.figure(1)
if background == 'arcgis' and dsize != 'conus':
m.arcgisimage(service='World_Shaded_Relief', xpixels=arcgis_res, verbose=False)
elif background == 'arcgisSat' and dsize != 'conus':
m.arcgisimage(service='ESRI_Imagery_World_2D', xpixels=arcgis_res, verbose=False)
elif background == 'arcgisRoad' and dsize != 'conus':
m.arcgisimage(service='NatGeo_World_Map', xpixels=arcgis_res, verbose=False)
elif background == 'terrain':
# Get data
H_ter = get_hrrr_variable(DATE, 'HGT:surface',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
H_land = get_hrrr_variable(DATE, 'LAND:surface',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Plot the terrain
m.contourf(gridlon, gridlat, H_ter['value'],
levels=range(0, 4000, 200),
cmap='Greys_r',
zorder=1,
latlon=True)
# Plot Water area
m.contour(gridlon, gridlat, H_land['value'],
levels=[0, 1],
colors='b',
zorder=1,
latlon=True)
elif background == 'landuse':
# Get data
from BB_cmap.landuse_colormap import LU_MODIS21
if model=='hrrr':
VGTYP = 'VGTYP:surface'
else:
VGTYP = 'var discipline=2 center=59 local_table=1 parmcat=0 parm=198'
H_LU = get_hrrr_variable(DATE, VGTYP,
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False,
value_only=True)
# Plot the terrain
cm, labels = LU_MODIS21()
m.pcolormesh(gridlon, gridlat, H_LU['value'],
cmap=cm, vmin=1, vmax=len(labels) + 1,
zorder=1,
latlon=True)
# Add SHAPEFILE
if is_FIRE:
plt.gca().add_collection(PatchCollection(patches,
facecolor='indianred',
alpha=.65,
edgecolor='k',
linewidths=1,
zorder=1))
# === Figure Title ============================================================
if dsize != 'conus':
m.scatter(lon, lat, marker='+', c='r', s=100, zorder=1000, latlon=True)
plt.title('Center: %s\n%s' % (location, model.upper()), fontweight='bold')
else:
plt.title('%s' % (model.upper()), fontweight='bold')
plt.title('Run: %s F%02d' % (DATE.strftime('%Y-%m-%d %H:%M UTC'), fxx), loc='left')
plt.title('Valid: %s' % (DATE+timedelta(hours=fxx)).strftime('%Y-%m-%d %H:%M UTC') , loc='right')
# =============================================================================
if '10mWind_Fill' in plotcode or '10mWind_Shade' in plotcode or '10mWind_Barb' in plotcode:
# Get data
H_u = get_hrrr_variable(DATE, 'UGRD:10 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
H_v = get_hrrr_variable(DATE, 'VGRD:10 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
spd = wind_uv_to_spd(H_u['value'], H_v['value'])
if '10mWind_Fill' in plotcode:
m.pcolormesh(gridlon, gridlat, spd,
latlon=True,
cmap='magma_r',
vmin=0, alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'10 m Wind Speed (m s$\mathregular{^{-1}}$)')
if '10mWind_Shade' in plotcode:
m.contourf(gridlon, gridlat, spd,
levels=[10, 15, 20, 25],
colors=('yellow', 'orange', 'red'),
alpha=alpha,
extend='max',
zorder=1,
latlon=True)
cb = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb.set_label(r'10 m Wind Speed (ms$\mathregular{^{-1}}$)')
if '10mWind_Barb' in plotcode:
# For small domain plots, trimming the edges significantly reduces barb plotting time
if barb_thin < 20:
cut_v, cut_h = pluck_point_new(lat, lon, gridlat, gridlon)
Cgridlat = gridlat[cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
Cgridlon = gridlon[cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
H_u['value'] = H_u['value'][cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
H_v['value'] = H_v['value'][cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
else:
Cgridlat = gridlat
Cgridlon = gridlon
# Add to plot
thin = barb_thin
m.barbs(Cgridlon[::thin,::thin], Cgridlat[::thin,::thin],
H_u['value'][::thin,::thin], H_v['value'][::thin,::thin],
zorder=200, length=5.5,
barb_increments={'half':2.5, 'full':5,'flag':25},
latlon=True)
if '80mWind_Fill' in plotcode or '80mWind_Shade' in plotcode or '80mWind_Barb' in plotcode:
# Get data
H_u = get_hrrr_variable(DATE, 'UGRD:80 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
H_v = get_hrrr_variable(DATE, 'VGRD:80 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
spd = wind_uv_to_spd(H_u['value'], H_v['value'])
if '80mWind_Fill' in plotcode:
m.pcolormesh(gridlon, gridlat, spd,
latlon=True,
cmap='magma_r',
vmin=0, alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'10 m Wind Speed (m s$\mathregular{^{-1}}$)')
if '80mWind_Shade' in plotcode:
m.contourf(gridlon, gridlat, spd,
levels=[10, 15, 20, 25],
colors=('yellow', 'orange', 'red'),
alpha=alpha,
extend='max',
zorder=10,
latlon=True)
cb = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb.set_label(r'10 m Wind Speed (ms$\mathregular{^{-1}}$)')
if '80mWind_Barb' in plotcode:
# For small domain plots, trimming the edges significantly reduces barb plotting time
if barb_thin < 20:
cut_v, cut_h = pluck_point_new(lat, lon, gridlat, gridlon)
Cgridlat = gridlat[cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
Cgridlon = gridlon[cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
H_u['value'] = H_u['value'][cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
H_v['value'] = H_v['value'][cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
else:
Cgridlat = gridlat
Cgridlon = gridlon
# Add to plot
thin = barb_thin
m.barbs(Cgridlon[::thin,::thin], Cgridlat[::thin,::thin],
H_u['value'][::thin,::thin], H_v['value'][::thin,::thin],
zorder=200, length=5.5, color='darkred',
barb_increments={'half':2.5, 'full':5,'flag':25},
latlon=True)
if 'Fill80mWind' in plotcode:
m.pcolormesh(gridlon, gridlat, spd,
latlon=True,
cmap='plasma_r',
vmin=0,
alpha=alpha)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'80 m Wind Speed (m s$\mathregular{^{-1}}$)')
if 'Gust_Hatch' in plotcode:
H_gust = get_hrrr_variable(DATE, 'GUST:surface',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Add to plot
m.contourf(gridlon, gridlat, H_gust['value'],
levels=[0, 10, 15, 20, 25],
hatches=[None, '.', '\\\\', '*'],
colors='none',
extend='max',
zorder=10,
latlon=True)
cb = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb.set_label(r'Surface Wind Gust (ms$\mathregular{^{-1}}$)')
m.contour(gridlon, gridlat, H_gust['value'],
levels=[10, 15, 20, 25],
colors='k',
zorder=10,
latlon=True)
if 'dBZ_Fill' in plotcode or 'dBZ_Contour' in plotcode:
from BB_cmap.reflectivity_colormap import reflect_ncdc
# Get Data
if model == 'hrrr':
REFC = 'REFC:entire'
elif model == 'hrrrX' or model == 'hrrrAK':
REFC = 'var discipline=0 center=59 local_table=1 parmcat=16 parm=196'
H_ref = get_hrrr_variable(DATE, REFC,
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Mask values
dBZ = H_ref['value']
dBZ = np.ma.array(dBZ)
dBZ[dBZ == -10] = np.ma.masked
# Add Contour to plot
if 'dBZ_Contour' in plotcode:
cREF = m.contour(gridlon, gridlat, dBZ,
cmap=reflect_ncdc(),
levels=range(10, 80, 10),
latlon=True,
zorder=50)
plt.clabel(cREF, cREF.levels[::2], fmt='%2.0f', colors='k', fontsize=9)
#cb2 = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
#cb2.set_label('Simulated Composite Reflectivity (dBZ)')
# Add fill to plot
if 'dBZ_Fill' in plotcode:
m.pcolormesh(gridlon, gridlat, dBZ,
cmap=reflect_ncdc(),
vmax=80, vmin=0,
alpha=alpha,
latlon=True)
cb2 = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cb2.set_label('Simulated Composite Reflectivity (dBZ)')
if '2mTemp_Fill' in plotcode or '2mTemp_Freeze' in plotcode:
# Get Data
H_temp = get_hrrr_variable(DATE, 'TMP:2 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
TMP = H_temp['value']-273.15
# Add fill to plot
if '2mTemp_Fill' in plotcode:
m.pcolormesh(gridlon, gridlat, TMP,
cmap="Spectral_r",
alpha=alpha,
zorder=3, latlon=True)
cbT = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbT.set_label('2m Temperature (C)')
# Add freezing contour to plot
if '2mTemp_Freeze' in plotcode:
m.contour(gridlon, gridlat, TMP,
colors='b',
levels=[0],
zorder=400,
latlon=True)
if '2mRH_Fill' in plotcode:
# Get Data
try:
H_RH = get_hrrr_variable(DATE, 'RH:2 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Add fill to plot
m.pcolormesh(gridlon, gridlat, H_RH['value'], cmap="BrBG",
vmin=0, vmax=100,
zorder=3,
latlon=True)
cbT = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cbT.set_label('2m Relative Humidity (%)')
except:
print "!! Some errors getting the RH value."
print "!! If you requested an old date, from HRRR version 1, there isn't a RH variable,"
print "!! and this code doesn't get the dwpt and convert it to RH yet."
if '700Temp_Fill' in plotcode or '700Temp_-12c' in plotcode:
# Get Data
H_temp = get_hrrr_variable(DATE, 'TMP:700 mb',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
TMP = H_temp['value']-273.15
# Add fill to plot
if '700Temp_Fill' in plotcode:
m.pcolormesh(gridlon, gridlat, TMP,
cmap="Spectral_r",
alpha=alpha,
zorder=3, latlon=True)
cbT = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbT.set_label('700 mb Temperature (C)')
# Add -12 C contour to plot
if '700Temp_-12c' in plotcode:
m.contour(gridlon, gridlat, TMP,
colors='b',
levels=[-12],
latlon=True,
zorder=400)
if '500HGT_Contour' in plotcode:
H_500 = get_hrrr_variable(DATE, 'HGT:500 mb',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
CS = m.contour(gridlon, gridlat, H_500['value'],
levels=range(5040, 6181, 60),
linewidths=1.7,
colors='k',
latlon=True,
zorder=400)
plt.clabel(CS, inline=1, fmt='%2.f')
if '500Wind_Fill' in plotcode or '500Wind_Barb' in plotcode or '500Vort_Fill' in plotcode or '500Conv_Fill' in plotcode:
H_u = get_hrrr_variable(DATE, 'UGRD:500 mb',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
H_v = get_hrrr_variable(DATE, 'VGRD:500 mb',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
if '500Wind_Fill' in plotcode:
spd = wind_uv_to_spd(H_u['value'], H_v['value'])
m.pcolormesh(gridlon, gridlat, spd,
latlon=True, cmap='BuPu', vmin=0)
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'500 mb Wind Speed (m s$\mathregular{^{-1}}$)')
if '500Conv_Fill' in plotcode or '500Vort_Fill' in plotcode:
dudx, dudy = np.gradient(H_u['value'], 3, 3)
dvdx, dvdy = np.gradient(H_v['value'], 3, 3)
if '500Vort_Fill' in plotcode:
vorticity = dvdx - dudy
# Mask values
vort = vorticity
vort = np.ma.array(vort)
vort[np.logical_and(vort < .05, vort > -.05) ] = np.ma.masked
m.pcolormesh(gridlon, gridlat, vort,
latlon=True, cmap='bwr',
vmax=np.max(vort),
vmin=-np.max(vort))
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'500 mb Vorticity (s$\mathregular{^{-1}}$)')
if '500Conv_Fill' in plotcode:
convergence = dudx + dvdy
# Mask values
conv = convergence
conv = np.ma.array(conv)
conv[np.logical_and(conv < .05, conv > -.05) ] = np.ma.masked
m.pcolormesh(gridlon, gridlat, conv,
latlon=True, cmap='bwr',
vmax=np.max(conv),
vmin=-np.max(conv))
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label(r'500 mb Convergence (s$\mathregular{^{-1}}$)')
if '500Wind_Barb' in plotcode:
# For small domain plots, trimming the edges significantly reduces barb plotting time
if barb_thin < 20:
cut_v, cut_h = pluck_point_new(lat, lon, gridlat, gridlon)
Cgridlat = gridlat[cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
Cgridlon = gridlon[cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
H_u['value'] = H_u['value'][cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
H_v['value'] = H_v['value'][cut_v-bfr:cut_v+bfr, cut_h-bfr:cut_h+bfr]
else:
Cgridlat = gridlat
Cgridlon = gridlon
thin = barb_thin
m.barbs(Cgridlon[::thin, ::thin], Cgridlat[::thin, ::thin], H_u['value'][::thin, ::thin], H_v['value'][::thin, ::thin],
zorder=200, length=6, color='navy',
barb_increments={'half':2.5, 'full':5,'flag':25},
latlon=True)
#plt.ylabel(r'Barbs: half=2.5, full=5, flag=25 (ms$\mathregular{^{-1}}$)')
if 'MSLP_Contour' in plotcode or 'MSLP_Fill' in plotcode:
H = get_hrrr_variable(DATE, 'MSLMA:mean sea level',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
if 'MSLP_Contour' in plotcode:
CS = m.contour(gridlon, gridlat, H['value']/100.,
latlon=True,
levels=range(952, 1200, 4),
colors='k',
zorder=400)
CS.clabel(inline=1, fmt='%2.f',
zorder=400)
if 'MSLP_Fill' in plotcode:
m.pcolormesh(gridlon, gridlat, H['value']/100.,
latlon=True,
cmap='viridis')
cb = plt.colorbar(orientation='horizontal', pad=pad, shrink=shrink)
cb.set_label('Mean Sea Level Pressure (hPa)')
if '2mPOT_Fill' in plotcode:
# Get Data
H_temp = get_hrrr_variable(DATE, 'POT:2 m',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
TMP = H_temp['value']-273.15
m.pcolormesh(gridlon, gridlat, TMP,
cmap="Oranges",
alpha=alpha,
zorder=3, latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('2 m Potential Temperature (C)')
if 'SkinTemp_Fill' in plotcode:
# Get Data
H_temp = get_hrrr_variable(DATE, 'TMP:surface',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
TMP = H_temp['value']-273.15
m.pcolormesh(gridlon, gridlat, TMP,
cmap="Spectral_r",
alpha=alpha,
zorder=3, latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('Skin Temperature (C)')
if 'AccumPrecip_Fill' in plotcode or '1hrPrecip_Fill' in plotcode:
#import matplotlib.colors
#cmap = matplotlib.colors.LinearSegmentedColormap.from_list("Precip", ["#00db16", "blue", "#d10000", 'black'])
cdict3 = {'red': ((0.0, 0.0, 0.0),
(0.25, 1.0, 1.0),
(0.5, 0.0, 0.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.7, 0.7),
(0.25, 1.0, 1.0),
(0.5, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.18, 0.18),
(0.25, 1.0, 1.0),
(0.5, 1.0, 1.0),
(1.0, 0.0, 0.0))
}
plt.register_cmap(name='BlueRed3', data=cdict3)
cmap = 'BlueRed3'
if 'AccumPrecip_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(DATE, 'APCP:surface:0',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Mask values
prec = H['value']
prec = np.ma.array(prec)
prec[prec == 0] = np.ma.masked
m.pcolormesh(gridlon, gridlat, prec,
cmap='BlueRed3',
alpha=alpha,
vmin=.25,
zorder=3, latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('Accumulated Precipitation since F00 (mm)')
if '1hrPrecip_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(DATE, 'APCP:surface',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Mask values
prec = H['value']
prec = np.ma.array(prec)
prec[prec == 0] = np.ma.masked
m.pcolormesh(gridlon, gridlat, prec,
cmap='BlueRed3',
alpha=alpha,
vmin=.25, vmax=20,
zorder=3, latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad, extend="max",)
cbS.set_label('1 hour Accumulated Precipitation (mm)')
if 'SnowCover_Fill' in plotcode:
# Get Data
H = get_hrrr_variable(DATE, 'SNOWC',
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
# Mask values
snow = H['value']
snow = np.ma.array(snow)
snow[snow == 0] = np.ma.masked
m.pcolormesh(gridlon, gridlat, snow,
cmap="Blues",
alpha=alpha,
zorder=3, latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label('Snow Cover (%)')
# =============================================================================
# Hack! Plot an extra HRRR variable not listed on the webpage hrrr_custom.html
# This extra argument will let you attempt to plot a different variable for
# a quicklook.
try:
# Must be a variable from a line in the .idx file
hrrrVAR = form['extraVAR'].value
extraHRRR = get_hrrr_variable(DATE, hrrrVAR,
model=model, fxx=fxx,
outDIR='/uufs/chpc.utah.edu/common/home/u0553130/temp/',
verbose=False, value_only=True)
m.pcolormesh(gridlon, gridlat, extraHRRR['value'],
cmap='viridis',
alpha=alpha,
zorder=3, latlon=True)
cbS = plt.colorbar(orientation='horizontal', shrink=shrink, pad=pad)
cbS.set_label(hrrrVAR+' (units)')
except:
pass
# =============================================================================
#plt.ylabel('Section Timer:%s\nFull Timer:%s' % (t2-t1, datetime.now()-firsttimer))
SAVEDIR = '/uufs/chpc.utah.edu/common/home/u0553130/public_html/PhD/HRRR/Events_Day/%s/' % EVENT
if not os.path.exists(SAVEDIR):
os.makedirs(SAVEDIR)
SAVEFIG = SAVEDIR + '%s_f%02d' % (VALID_DATE.strftime('%Y-%m-%d_h%H'), fxx)
plt.savefig(SAVEFIG) # Plot standard output.
print 'SAVED:', SAVEFIG
else:
MWlat, MWlon = stn.split(',')
MWlat = float(MWlat)
MWlon = float(MWlon)
# List of percentiles computed in the percentiles file
percentiles = [0,1,2,3,4,5,10,25,33,50,66,75,90,95,96,97,98,99,100]
# Load HRRR lat/lon Grid
DIR = '/uufs/chpc.utah.edu/common/home/horel-group2/blaylock/HRRR_OSG/'
latlon_file = h5py.File(DIR+'OSG_HRRR_latlon.h5', 'r')
lat = latlon_file['latitude'].value
lon = latlon_file['longitude'].value
# Pluck point of MesoWest station location in HRRR grid
point = pluck_point_new(MWlat, MWlon, lat, lon)
x = point[0][0]
y = point[1][0]
print ' MesoWest | HRRR Nearest Point '
print 'lat: %s | %s' % (MWlat, lat[x,y])
print 'lon: %s | %s' % (MWlon, lon[x,y])
# Bounding box around the point
LATS_BOX = lat[x-box_radius:x+box_radius+1,y-box_radius:y+box_radius+1]
LONS_BOX = lon[x-box_radius:x+box_radius+1,y-box_radius:y+box_radius+1]
top_right_lat = LATS_BOX.max()
top_right_lon = LONS_BOX.min()
bot_left_lat = LATS_BOX.min()
bot_left_lon = LONS_BOX.max()
for i in range(len(stns)):
STN[a['STNID'][i]]['LAT'] = a['LAT'][i]
STN[a['STNID'][i]]['LON'] = a['LON'][i]
STN[a['STNID'][i]]['NAME'] = a['NAME'][i]
for m in range(1, 13):
for h in range(0, 24):
FILE = 'OSG_HRRR_%s_m%02d_h%02d_f%02d.nc' % (var, m, h, fxx)
print FILE
nc = Dataset(FILE, 'r')
#
for S in STN.keys():
# Pluck value from each location
point = pluck_point_new(STN[S]['LAT'], STN[S]['LON'],
nc.variables['latitude'][:],
nc.variables['longitude'][:])
#
# append the data to the list
STN[S]['count'] = np.append(STN[S]['count'],
nc.variables['count'][0])
STN[S]['cores'] = np.append(STN[S]['cores'],
nc.variables['cores'][0])
STN[S]['month'] = np.append(STN[S]['month'], m)
STN[S]['hour'] = np.append(STN[S]['hour'], h)
STN[S]['monthhour'] = np.append(STN[S]['monthhour'], m + h / 24.)
STN[S]['MAX'] = np.append(
STN[S]['MAX'], nc.variables['max_' + var][point[0], point[1]])
STN[S]['MIN'] = np.append(
STN[S]['MIN'], nc.variables['min_' + var][point[0], point[1]])
STN[S]['MEAN'] = np.append(
## ============================================================================
# Pluck a point from HRRR grid
latlon = h5py.File(
'/uufs/chpc.utah.edu/common/home/horel-group2/blaylock/HRRR_OSG/daily30_20150418-20170801/OSG_HRRR_latlon.h5',
'r')
lat = latlon['latitude'].value
lon = latlon['longitude'].value
wbb_lat = 40.76623
wbb_lon = -111.84755
from BB_data.grid_manager import pluck_point_new
from pylab import poly_between
from BB_rose.windrose import WindroseAxes
I, J = pluck_point_new(wbb_lat, wbb_lon, lat, lon)
i = I[0]
j = J[0]
wbb_rose_table = rose[i, j]
## ============================================================================
# Plot a rose for the point
new_bins = bins[0:-1]
new_nbins = num_spd_bins
new_nsector = num_dir_sectors
new_cmap = mpl.cm.jet
new_colors = [new_cmap(i) for i in np.linspace(0.0, 1.0, new_nbins)]
new_angles = np.arange(0, -2 * np.pi, -2 * np.pi / new_nsector) + np.pi / 2
plot_angles = np.hstack((new_angles, new_angles[-1] - 2 * np.pi / new_nsector))
ROW = 10
COL = 10
timer = datetime.now()
HTS = np.array([get_point(DIR+'OSG_HRRR_%s_m%02d_d%02d_h%02d_f00.h5' % \
(variable, month, day, hour), 'mean', ROW, COL) \
for month in months \
for day in range(1,days[month-1]+1) \
for hour in hours])
print datetime.now()-timer
'''
# MesoWest Station Info
stn = ['WBB']
a = get_station_info(stn)
# Pluck point
point = pluck_point_new(a['LAT'][0], a['LON'][0], lat, lon)
# Multiprocessing :)
args_p90 = [[DIR+'OSG_HRRR_%s_m%02d_d%02d_h%02d_f00.h5' % \
(variable, month, day, hour), 'p90', point[0][0], point[1][0]] \
for month in months for day in range(1,days[month-1]+1) for hour in hours]
num_proc = multiprocessing.cpu_count()
p = multiprocessing.Pool(num_proc)
HTS_p90 = p.map(get_point_MP, args_p90)
p.close()
# Multiprocessing :)
args_p10 = [[DIR+'OSG_HRRR_%s_m%02d_d%02d_h%02d_f00.h5' % \
(variable, month, day, hour), 'p10', point[0][0], point[1][0]] \
for month in months for day in range(1,days[month-1]+1) for hour in hours]
num_proc = multiprocessing.cpu_count()
result = p.map(HRRR_error, date_list)
p.close()
# Remove anny nan arrays
samples_requested = len(result)
result = np.array([i for i in result if not np.isnan(np.sum(i))])
samples = len(result)
# Calculate error statistics
RMSE = np.sqrt(np.nanmean(result**2, axis=0))
mean_error = np.nanmean(result, axis=0)
for f in range(1,19):
for h in range(24):
for i in locations.keys():
# Pluck point
x, y = pluck_point_new(locations[i]['latitude'], locations[i]['longitude'], H['lat'], H['lon'])
locations[i]['mean error'][f-1, h] = mean_error[x[0], y[0]]
locations[i]['RMSE'][f-1, h] = RMSE[x[0], y[0]]
for i in locations.keys():
fig, [ax1, ax2] = plt.subplots(nrows=1, ncols=2)
plt.sca(ax1)
plt.pcolormesh(locations[i]['mean error'])
plt.title('Mean Error')
plt.sca(ax2)
plt.pcolormesh(locations[i]['RMSE'])
plt.title('RMSE')
plt.show()
