## Analysis points
anal_dates = np.array([])
for i in range(0,len(f00[station])):
D = datetime.strptime(f00[station][i],'%Y-%m-%d %H:%M')
anal_dates = np.append(anal_dates,D)
anal_temp = f00['temp']
anal_dwpt = f00['dwpt']
anal_speed = f00['speed']
date_start_index = 24+24+6
file_start_date = datetime.strptime(f00[station][date_start_index],'%Y-%m-%d %H:%M')
# First grab the MesoWest Data.
MW_start = file_start_date-timedelta(hours=1)
MW_end = file_start_date+timedelta(days=1)
a = get_mesowest_ts(station,MW_start,MW_end)
# plot on a graph
fig = plt.figure(1)
ax = fig.add_subplot(111)
plt.plot(a['datetimes'],a['temperature'],color='k',linewidth=3,label='MesoWest')
anal_color=[.2,.2,.2]
anal_hours = range(0,13,3)
color = ['red','blue','green','darkorange','cornflowerblue','gold','yellowgreen']
lw = np.linspace(2,1.5,len(anal_hours))
for i in range(0,len(anal_hours)):
file_start_date = datetime.strptime(f00[station][date_start_index+anal_hours[i]],'%Y-%m-%d %H:%M')
print file_start_date
Aug5_00UTC_line = np.array([])
Aug5_00UTC_line = np.append(Aug5_00UTC_line,f00['temp'][date_start_index+anal_hours[i]])
Aug5_00UTC_line = np.append(Aug5_00UTC_line,f01['temp'][date_start_index+1+anal_hours[i]])
figs[locName][4].xaxis.set_major_locator(
mdates.HourLocator(range(0, 24, 6)))
figs[locName][4].xaxis.set_minor_locator(
mdates.HourLocator(range(0, 24, 1)))
figs[locName][4].xaxis.set_major_formatter(
mdates.DateFormatter('%b-%d\n%H:%M'))
leg4 = figs[locName][4].legend()
leg4.get_frame().set_linewidth(0)
figs[locName][4].grid()
figs[locName][4].set_ylabel('Height (m)')
#
# Finally, add MesoWest data if it is available
if l['is MesoWest'] is True:
a = get_mesowest_ts(
locName,
TS_dates[0],
TS_dates[-1],
variables='air_temp,dew_point_temperature,wind_direction,wind_speed'
)
if a != 'ERROR':
figs[locName][2].plot(a['DATETIME'], a['air_temp'], c='k', ls='--')
figs[locName][2].plot(a['DATETIME'],
a['dew_point_temperature'],
c='k',
ls='--')
figs[locName][3].plot(a['DATETIME'],
a['wind_speed'],
c='k',
ls='--')
# Now add the element that changes, save the figure, and remove elements from plot.
# Only download the HRRR grid once per forecast hour.
figs[locName][4].xaxis.set_major_locator(
mdates.HourLocator(range(0, 24, 6)))
figs[locName][4].xaxis.set_minor_locator(
mdates.HourLocator(range(0, 24, 1)))
figs[locName][4].xaxis.set_major_formatter(
mdates.DateFormatter('%b-%d\n%H:%M'))
leg4 = figs[locName][4].legend()
leg4.get_frame().set_linewidth(0)
figs[locName][4].grid()
figs[locName][4].set_ylabel('Height (m)')
#
# Finally, add MesoWest data if it is available
if l['is MesoWest'] is True:
a = get_mesowest_ts(
locName,
sDATE,
eDATE,
variables='air_temp,wind_speed,dew_point_temperature')
if a != 'ERROR':
figs[locName][2].plot(a['DATETIME'], a['air_temp'], c='k', ls='--')
figs[locName][2].plot(a['DATETIME'],
a['dew_point_temperature'],
c='k',
ls='--')
figs[locName][3].plot(a['DATETIME'],
a['wind_speed'],
c='k',
ls='--')
# Now add the element that changes, save the figure, and remove elements from plot.
# Only download the HRRR grid once per forecast hour.
HRRR_var = 'DPT:2 m'
MW_var = 'dew_point_temperature'
title_var = "Dew Point Temperature"
#==============================================================================
# Set colormap
if MW_var == 'wind_speed':
cmap = 'magma_r'
elif MW_var == 'dew_point_temperature':
cmap = 'BrBG'
elif MW_var == 'air_temp':
cmap = 'Spectral_r'
else:
cmap = 'viridis'
# Get data from the MesoWest Station
a = get_mesowest_ts(stn, sDATE, eDATE, variables=MW_var)
# Make a time series of HRRR forecasts for each forecast hour.
loc = {stn:{'latitude':a['LAT'],'longitude':a['LON']}}
data = {}
for f in range(19):
sOffset = sDATE - timedelta(hours=f)
eOffset = eDATE - timedelta(hours=f)
data[f] = point_hrrr_time_series_multi(sOffset, eOffset, loc,
variable=HRRR_var,
fxx=f)
# combine the data arrays into a hovmoller array
if MW_var == 'air_temp' or MW_var == 'dew_point_temperature':
hovmoller = np.array([data[i][stn]-273.15 for i in range(19)])
else:
dwptF = TS_dwpt[loc]
ax2.plot(TS_temp['DATETIME'],
tempF,
c='r',
lw='1.5',
label='Temperature')
ax2.scatter(TS_temp['DATETIME'][hh], tempF[hh], c='r', s=60)
ax2.plot(TS_dwpt['DATETIME'],
dwptF,
c='g',
lw='1.5',
label='Dew Point')
ax2.scatter(TS_dwpt['DATETIME'][hh], dwptF[hh], c='g', s=60)
if l['is MesoWest'] is True:
a = get_mesowest_ts(loc,
sDATE,
eDATE,
variables='air_temp,wind_speed')
b = get_mesowest_radius(TS_dates[hh],
15,
extra='&radius=%s,30' % (loc),
variables='wind_speed,wind_direction')
MW_u, MW_v = wind_spddir_to_uv(b['wind_speed'],
b['wind_direction'])
MW_u = mps_to_MPH(MW_u)
MW_v = mps_to_MPH(MW_v)
ax2.plot(a['DATETIME'], CtoF(a['air_temp']), c='k', ls='--')
MWx, MWy = maps[loc](b['LON'], b['LAT'])
ax1.barbs(MWx, MWy, MW_u, MW_v, color='r')
ax2.legend()
ax2.grid()
def plot_pollywog(p, SAVEDIR, custom_end_date=False):
"""
Plots the pollywogs for all the variables in the pollywog dictionary.
Plots the pollywog with the MesoWest observation
inputs:
p - p is a pollywog dictionary
SAVEDIR - a string name for the save directory
output:
none
global:
station
analysis vectors (i.e. Adates, Atemp, Au, ...)
width - the figure width (barb figure height is higher)
"""
Acolor = [.2, .2, .2] #color of the analysis points
barbs_colors = []
hours = mpl.dates.HourLocator([0, 3, 6, 9, 12, 15, 18, 21])
hours_each = mpl.dates.HourLocator()
# find the first time in the p dictionary
fv = p.keys()[0] # first variable key
num_v = len(p.keys()) # number of variables
num_h = len(p[fv]) # number of hours
# p[variable][hour][dates array][position]
first_date = p[fv][0][1][0]
last_date = p[fv][len(p[fv]) - 1][1][-1]
# First grab the MesoWest Data.
MW_start = first_date - timedelta(hours=1)
if custom_end_date != False:
MW_end = custom_end_date
else:
MW_end = last_date
a = get_mesowest_ts(station, MW_start, MW_end)
print "got MesoWest"
for i in range(0, num_v): # for each variable
#%%
if p.keys()[i] == 'temp':
print 'working on temp'
fig = plt.figure(i + 1)
ax = fig.add_subplot(111)
# Add a grid
plt.grid()
# Plot MesoWest
plt.plot(a['datetimes'], a['temperature'], color='k', linewidth=3)
# Plot each pollywog
for h in range(0, num_h):
lw = np.linspace(2, 1.5, num_h)
z = plt.plot(p['temp'][h][1], p['temp'][h][0], linewidth=lw[h])
# store the line color and plot head with same color
color = z[0]._get_rgba_ln_color()
barbs_colors = barbs_colors + [color]
# Plot the Pollywog head, the analysis time, with same color
plt.scatter(p['temp'][h][1][0],
p['temp'][h][0][0],
color=color,
s=80)
# Plot the analysis hours
plt.scatter(Adates, Atemp, color=Acolor, s=30, zorder=10)
plt.title(station)
plt.ylabel(r'2-m Temperature (C)')
#plt.ylim([0,15])
plt.xlim(MW_start, MW_end)
ax.xaxis.set_major_locator(hours)
ax.xaxis.set_minor_locator(hours_each)
ax.xaxis.set_major_formatter(
mpl.dates.DateFormatter('%m/%d\n%H:%M'))
plt.savefig(SAVEDIR + station + '_temp_2m.png')
#%%
elif p.keys()[i] == 'dwpt':
print 'working on dwpt'
fig = plt.figure(i + 1)
ax = fig.add_subplot(111)
# Add a grid
plt.grid()
# Plot MesoWest
plt.plot(a['datetimes'], a['dew point'], color='k', linewidth=3)
# Plot each pollywog
for h in range(0, num_h):
lw = np.linspace(2, 1.5, num_h)
z = plt.plot(p['dwpt'][h][1], p['dwpt'][h][0], linewidth=lw[h])
# store the line color and plot head with same color
color = z[0]._get_rgba_ln_color()
# store the line color and plot head with same color
barbs_colors = barbs_colors + [color]
# Plot the Pollywog head, the analysis time, with same color
plt.scatter(p['dwpt'][h][1][0],
p['dwpt'][h][0][0],
color=color,
s=80)
# Plot the analysis hours
plt.scatter(Adates, Adwpt, color=Acolor, s=30, zorder=10)
plt.title(station)
plt.ylabel(r'2-m Dew Point (C)')
#plt.ylim([0,15])
plt.xlim(MW_start, MW_end)
ax.xaxis.set_major_locator(hours)
ax.xaxis.set_minor_locator(hours_each)
ax.xaxis.set_major_formatter(
mpl.dates.DateFormatter('%m/%d\n%H:%M'))
plt.savefig(SAVEDIR + station + '_dwpt_2m.png')
#%%
elif p.keys()[i] == 'speed':
print 'working on speed'
fig = plt.figure(i + 1)
ax = fig.add_subplot(111)
# Add a grid
plt.grid()
# Plot MesoWest
plt.plot(a['datetimes'],
a['wind speed'],
color='k',
linewidth=3,
zorder=1)
# Plot each pollywog
for h in range(0, num_h):
lw = np.linspace(2, 1.5, num_h)
z = plt.plot(p['speed'][h][1],
p['speed'][h][0],
linewidth=lw[h],
zorder=2)
# store the line color and plot head with same color
color = z[0]._get_rgba_ln_color()
# store the line color and plot head with same color
barbs_colors = barbs_colors + [color]
# Plot the Pollywog head, the analysis time, with same color
plt.scatter(p['speed'][h][1][0],
p['speed'][h][0][0],
color=color,
s=80,
zorder=2)
# Plot the analysis hours
plt.scatter(Adates, Aspeed, color=Acolor, s=30, zorder=10)
plt.title(station)
plt.ylabel(r'10-m Wind Speed (ms$\mathregular{^{-1}}$)')
plt.ylim([0, 15])
plt.xlim(MW_start, MW_end)
ax.xaxis.set_major_locator(hours)
ax.xaxis.set_minor_locator(hours_each)
ax.xaxis.set_major_formatter(
mpl.dates.DateFormatter('%m/%d\n%H:%M'))
plt.savefig(SAVEDIR + station + '_speed_10m.png')
#%%
## for u and v variables, plot the wind barbs
elif p.keys()[i] == 'u':
print 'working on u'
fig = plt.figure(i + 1, [width, 6])
ax = fig.add_subplot(111)
# Add a grid
plt.grid()
# Plot MesoWest
MW_u, MW_v = wind_spddir_to_uv(a['wind speed'],
a['wind direction'])
# If u or v is nan, then set to zero. We got a nan origianlly
# becuase no direction was reported. Speed is zero in these instances.
MW_u[np.isnan(MW_u)] = 0
MW_v[np.isnan(MW_v)] = 0
idx = mpl.dates.date2num(a['datetimes'])
y = np.ones_like(MW_u) * -.5
# ONLY PLOT EVERY THIRD MESOWEST OBSERVATION!!!!
plt.barbs(idx[::3],
y[::3],
MW_u[::3],
MW_v[::3],
barb_increments=dict(half=1, full=2, flag=10),
length=5.25)
#print barbs_colors
yticks = [-0.5]
ylabels = ['MesoWest']
for h in range(0, num_h):
idx = mpl.dates.date2num(p['u'][h][1])
plt.barbs(idx,
np.ones_like(idx) * h + 1,
p['u'][h][0],
p['v'][h][0],
color=barbs_colors[h],
barb_increments=dict(half=1, full=2, flag=10),
length=5.25)
yticks.append(h + 1)
ylabels.append('%02d UTC' % (p['u'][h][1][0].hour))
plt.yticks(yticks, ylabels)
plt.title(station)
plt.ylabel(r'10-m Wind Speed (ms$\mathregular{^{-1}}$)')
#plt.ylim([0,15])
plt.xlim(MW_start, MW_end)
ax.xaxis.set_major_locator(hours)
ax.xaxis.set_minor_locator(hours_each)
ax.xaxis.set_major_formatter(
mpl.dates.DateFormatter('%m/%d\n%H:%M'))
plt.savefig(SAVEDIR + station + '_barbs_10m_full2_half1.png')
#%%
return {
'fv': fv,
'num_v': num_v,
'first_date': first_date,
'last_date': last_date,
'mesowest': a,
'plots': z,
'barbs_colors': barbs_colors
}
ax1.set_title(' UTC: %s\nLocal Time: %s' %
(DATE + timedelta(hours=fxx),
DATE + timedelta(hours=fxx) - timedelta(hours=tz)))
# 3.2) Temperature/Dew Point
ax2 = plt.subplot(3, 2, 2)
tempF = P_temp[loc]
dwptF = P_dwpt[loc]
ax2.plot(P_temp['DATETIME'], tempF, c='r', label='Temperature')
ax2.scatter(P_temp['DATETIME'][fxx], tempF[fxx], c='r', s=60)
ax2.plot(P_dwpt['DATETIME'], dwptF, c='g', label='Dew Point')
ax2.scatter(P_dwpt['DATETIME'][fxx], dwptF[fxx], c='g', s=60)
if l['is MesoWest'] is True:
a = get_mesowest_ts(
loc,
DATE,
datetime.utcnow(),
variables='air_temp,wind_speed,dew_point_temperature')
if a != 'ERROR':
ax2.plot(a['DATETIME'], CtoF(a['air_temp']), c='k', ls='--')
ax2.plot(a['DATETIME'],
CtoF(a['dew_point_temperature']),
c='k',
ls='--')
leg2 = ax2.legend()
leg2.get_frame().set_linewidth(0)
ax2.grid()
ax2.set_ylabel('Degrees (F)')
ax2.set_xlim([P_temp['DATETIME'][0], P_temp['DATETIME'][-1]])
if l['is MesoWest'] is True and a != 'ERROR':
forecasts = [0, 6, 12, 16, 18]
# =============================================================================
# =============================================================================
# Create the hourly date list
base = DATE
hours = (eDATE - DATE).days * 24
date_list = [base + timedelta(hours=x) for x in range(0, hours)]
# Create time series plot overlaying HRRR and MesoWest data for each station
# and each forecast hour.
for stn in stations:
for fxx in forecasts:
# Get MesoWest data
a = get_mesowest_ts(stn, DATE, eDATE)
# Get HRRR data
Hvar = 'MSLMA:mean sea level'
validDate, value = point_hrrr_time_series(DATE,
eDATE,
variable=Hvar,
lat=a['LAT'],
lon=a['LON'],
fxx=fxx,
model='hrrr',
field='sfc',
reduce_CPUs=0)
# Create the plot
fig, ax = plt.subplots(1)
#
first_mw_attempt = S['MW var']
for stn in locations.keys():
print "\nWorking on %s %s" % (stn, s)
SAVE = SAVE_dir + '%s/' % stn
if not os.path.exists(SAVE):
# make the SAVE directory if it doesn't already exist
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_viewer.php'
os.link(photo_viewer, SAVE+'photo_viewer.php')
#
# Get data from the MesoWest Station
#
a = get_mesowest_ts(stn, sDATE, eDATE, variables=S['MW var'])
# Aak, so many different precipitatin variables. Try each one until we
# find one that works...
if a == 'ERROR':
# the variable probably isn't available (i.e. CAPE), so fill with nans
dates_1d = hovmoller['valid_1d+'][:-1]
a = {'DATETIME':dates_1d,
S['MW var']:[np.nan for i in range(len(dates_1d))]}
#
# Apply offset to data if necessary
if s == 'Air Temperature' or s == 'Dew Point Temperature':
hovmoller[stn]['max'] = hovmoller[stn]['max']-273.15
hovmoller[stn]['min'] = hovmoller[stn]['min']-273.15
hovmoller[stn]['box center'] = hovmoller[stn]['box center']-273.15
#
#
first_mw_attempt = S['MW var']
for stn in locations.keys():
print "\nWorking on %s %s" % (stn, s)
SAVE = SAVE_dir + '%s/' % stn
if not os.path.exists(SAVE):
# make the SAVE directory if it doesn't already exist
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_viewer.php'
os.link(photo_viewer, SAVE + 'photo_viewer.php')
#
# Get data from the MesoWest Station
#
S['MW var'] = first_mw_attempt
a = get_mesowest_ts(stn, sDATE, eDATE, variables=first_mw_attempt)
# Aak, so many different precipitatin variables. Try each one until we
# find one that works...
if a == 'ERROR':
a = get_mesowest_ts(stn,
sDATE,
eDATE,
variables='precip_accum_one_hour')
S['MW var'] = 'precip_accum_one_hour'
if a == 'ERROR':
a = get_mesowest_ts(stn,
sDATE,
eDATE,
variables='precip_accum')
S['MW var'] = 'precip_accum'
if a == 'ERROR':
import sys
sys.path.append('B:/pyBKB_v2')
sys.path.append('/uufs/chpc.utah.edu/common/home/u0553130/pyBKB_v2')
from BB_MesoWest.MesoWest_timeseries import get_mesowest_ts
DATE_START = datetime(2017, 1, 4)
DATE_END = datetime.now()
save_date = DATE_START.strftime('%Y%m%d') + '-' + DATE_END.strftime('%Y%m%d')
stations = ['PSINK', 'KLGU', 'UFD09', 'WBB']
data = OrderedDict()
for s in stations:
data[s] = get_mesowest_ts(s, DATE_START, DATE_END)
"""
Plot
"""
fig, ax1 = plt.subplots(1, 1)
for s in data.keys():
ax1.plot(data[s]['DATETIME'], data[s]['air_temp'], label=s)
plt.grid()
ax1.legend()
ax1.set_title('MesoWest Air Temperature')
ax1.set_ylabel('Temperature (C)')
ax1.xaxis.set_major_locator(HourLocator(byhour=[0, 12]))
style_path + 'dpi_medium.mplstyle'
])
from matplotlib.dates import DateFormatter, HourLocator
from datetime import datetime
import sys
sys.path.append('B:/pyBKB_v2')
sys.path.append('/uufs/chpc.utah.edu/common/home/u0553130/pyBKB_v2')
from BB_MesoWest.MesoWest_timeseries import get_mesowest_ts
DATE_START = datetime(2017, 1, 4)
DATE_END = datetime.now()
save_date = DATE_START.strftime('%Y%m%d') + '-' + DATE_END.strftime('%Y%m%d')
valley = get_mesowest_ts('PSINK', DATE_START, DATE_END)
rim = get_mesowest_ts('PSRIM', DATE_START, DATE_END)
"""
Plot
"""
fig, ax1 = plt.subplots(1, 1)
ax1.plot(rim['DATETIME'], rim['air_temp'], color='r', label='rim')
ax1.plot(valley['DATETIME'], valley['air_temp'], color='b', label='valley')
plt.grid()
ax1.legend()
ax1.set_title('Peter Sinks')
ax1.set_ylabel('Temperature (C)')
# the nearest neighbor which isn't water.
bflat_temps = temps[:, 3]
# Air temperatures where also returned
buoy_2m = temps[:, 4]
ufd09_2m = temps[:, 5]
ufd09_2m2 = temps[:, 6] # Remember, these are the 1 grid point west of
# the nearest neighbor which isn't water.
bflat_2m = temps[:, 7]
# Get MesoWest observations.
try:
a = get_buoy_ts(date_list[-1], date_list[0])
except:
print "no buoy observations"
MW_bflat = get_mesowest_ts('BFLAT', date_list[-1], date_list[0])
MW_ufd09 = get_mesowest_ts('UFD09',
date_list[-1],
date_list[0],
variables='air_temp,surface_temp')
plt.plot(a['Datetimes'], a['T_water1'])
plt.plto(date_list, buoy_temps)
"""
Plot time series:
- HRRR surface temperature and
- Buoy observed -0.4 meter water temperature.
- UFD09 surface temperature
"""
"""
