cb = figs[locName][0].colorbar(radar,
cax=cax,
orientation='horizontal')
cb.set_label(
'Simulated Radar Reflectivity (dBZ)\n\nBarbs: Half=2.5 m/s, Full=5 m/s, Flag=25 m/s'
)
#
# Add nearby MesoWest
MW_date = TS_temp['DATETIME'][hh]
b = get_mesowest_radius(MW_date,
15,
extra='&radius=%s,%s,60' %
(l['latitude'], l['longitude']),
variables='wind_speed,wind_direction')
if len(b['NAME']) > 0:
MW_u, MW_v = wind_spddir_to_uv(b['wind_speed'],
b['wind_direction'])
MWx, MWy = maps[locName](b['LON'], b['LAT'])
MW_barbs = figs[locName][1].barbs(MWx,
MWy,
MW_u,
MW_v,
color='r',
barb_increments=dict(half=2.5,
full=5,
flag=25))
#
# Wind Barbs
# Overlay wind barbs (need to trim this array before we plot it)
# First need to trim the array
barbs = figs[locName][1].barbs(trim_X,
trim_Y,
# Get SLC sounding for Salt Lake City
balloon = get_wyoming_sounding(DATE)
# Get WRF from TS list profile data
station = 'KSLC'
wrf_dir = '/uufs/chpc.utah.edu/common/home/horel-group4/model/bblaylock/WRF3.8.1_MYNN/DATA/'
tsfile = station + '.d02.TS'
model_start = datetime(2015, 6, 14, 0)
wrf = get_vert_data(wrf_dir+tsfile,model_start,DATE)
# Get HRRR analysis profile from *modified* BUFR file
# (modified so all data was on one row for easy reading)
hrrr_file = 'kslc_' + DATE.strftime('%Y%m%d%H') + '.txt'
HRRR = np.genfromtxt(hrrr_file, delimiter=' ', names=True)
HRRR_theta = TempPress_to_PotTemp(HRRR['TMPC'], HRRR['PRES'])
HRRR_u, HRRR_v = wind_calcs.wind_spddir_to_uv(HRRR['SKNT']*0.51444, HRRR['DRCT'])
HRRR_mixr = DwptPress_to_MixRatio(HRRR['DWPC'], HRRR['PRES'])
# Get HRRR 1-hour forecast
f = 'F01'
hrrr_file = 'kslc_'+datetime(2015,6,18,23).strftime('%Y%m%d%H')+'_'+f+'.txt'
HRRR_f01 = np.genfromtxt(hrrr_file, delimiter=' ', names=True)
HRRR_f01_theta = TempPress_to_PotTemp(HRRR_f01['TMPC'], HRRR_f01['PRES'])
HRRR_f01_u, HRRR_f01_v = wind_calcs.wind_spddir_to_uv(HRRR_f01['SKNT']*0.51444, HRRR_f01['DRCT'])
HRRR_f01_mixr = DwptPress_to_MixRatio(HRRR_f01['DWPC'], HRRR_f01['PRES'])
"""
Create 3 subplots for
1. Potential Temperature
2. Mixing Ratio
3. Vector Wind
def plot_pm25_map(mobile,
other_mobile=False,
auto_map_boundaries=True,
background='WRF'):
print "--plot map--"
"""
makes a map of the helicopter observations. Also plots MesoWest
input: mobile=ksl observations from the get moblie function
other_mobile: if set to true will plot truck and trax data if available
auto_map_boundaries: will find the location of the helicopter and will set auto boundaries
else, will map the salt lake valley or you can change the lat/lon manually
background: 'WRF' will use a wrf file to contour the boundary
'sat' will use an old satellite image
'topo' will use a topographical image
return: a string of the URL the figure is saved
"""
plt.cla()
plt.clf()
plt.close()
width = 4
height = 4.5
fig, (ax1) = plt.subplots(1, 1, figsize=(width, height))
## ---- Draw map
top_right_lat = 40.9 + .1
top_right_lon = -111.60
bot_left_lat = 40.4 + .05
bot_left_lon = -112.19785 - .05
# Might need to make the map bigger if the chopper flew out of the valley
try:
if np.nanmax(mobile['latitude']) > top_right_lat:
top_right_lat = np.nanmax(mobile['latitude']) + .01
if np.nanmin(
mobile['latitude'][mobile['latitude'] > -9999]) < bot_left_lat:
bot_left_lat = np.nanmin(
mobile['latitude'][mobile['latitude'] > -9999]) - .01
if np.nanmax(mobile['longitude']) > top_right_lon:
top_right_lon = np.nanmax(mobile['longitude']) + .01
if np.nanmin(mobile['longitude'][
mobile['longitude'] > -9999]) < bot_left_lon:
bot_left_lon = np.nanmin(
mobile['longitude'][mobile['longitude'] > -9999]) - .01
except:
# something funny with the lat/lon data not having a max or min
# must have ozone or pm25 data but no lat lon data
pass
## Map in cylindrical projection (data points may apear skewed)
m = Basemap(resolution='i',projection='cyl',\
llcrnrlon=bot_left_lon,llcrnrlat=bot_left_lat,\
urcrnrlon=top_right_lon,urcrnrlat=top_right_lat,)
# -----Set Background images
if background == 'WRF':
## ---- Grab terrain data
directory = '/uufs/chpc.utah.edu/common/home/horel-group4/model/bblaylock/WRF3.7_spinup/DATA/FULL_RUN_June14-19/'
spat = 'auxhist23_d02_2015-06-14_00:00:00'
nc_spatial = netcdf.netcdf_file(directory + spat, 'r')
# This sets the standard grid point structure at full resolution
# Converts WRF lat and long to the maps x an y coordinate
XLONG = nc_spatial.variables['XLONG'][0]
XLAT = nc_spatial.variables['XLAT'][0]
HGT = nc_spatial.variables['HGT'][0, :, :] #topography
landmask = nc_spatial.variables['LANDMASK'][0, :, :]
# Plot Terrain
X, Y = m(XLONG, XLAT)
plt.contourf(X,
Y,
HGT,
levels=np.arange(1000, 4000, 500),
cmap='binary')
# Draw major roads from shapefile
m.readshapefile(
'/uufs/chpc.utah.edu/common/home/u0553130/shape_files/tl_2015_UtahRoads_prisecroads/tl_2015_49_prisecroads',
'roads',
linewidth=.2)
## Contour Lake Outline
plt.contour(X, Y, landmask, [0, 1], linewidths=1, colors="b")
maps = [
'ESRI_Imagery_World_2D', # 0
'ESRI_StreetMap_World_2D', # 1
'NatGeo_World_Map', # 2
'NGS_Topo_US_2D', # 3
'Ocean_Basemap', # 4
'USA_Topo_Maps', # 5
'World_Imagery', # 6
'World_Physical_Map', # 7
'World_Shaded_Relief', # 8
'World_Street_Map', # 9
'World_Terrain_Base', # 10
'World_Topo_Map' # 11
]
if background == 'sat':
## Instead of using WRF terrain fields you can get a high resolution image from ESRI
m.arcgisimage(service=maps[0], xpixels=1000, verbose=True)
if background == 'topo':
## Instead of using WRF terrain fields you can get a high resolution image from ESRI
m.arcgisimage(service=maps[8], xpixels=1000, verbose=True)
# Draw major roads from shapefile
m.readshapefile(
'/uufs/chpc.utah.edu/common/home/u0553130/shape_files/tl_2015_UtahRoads_prisecroads/tl_2015_49_prisecroads',
'roads',
linewidth=.2)
# can use any other map background from maps list
# Plot Other Mobile data
if other_mobile == True:
# Plot Mobile Ozone points (TRAX01)
try:
x, y = m(trax_mobile['longitude'], trax_mobile['latitude'])
m.scatter(x[:],
y[:],
c=trax_mobile['pm25'][:],
vmax=45,
vmin=0.,
lw=.3,
marker='d',
s=30,
cmap=plt.cm.get_cmap('jet'),
zorder=50)
except:
print 'no TRAX01 data'
# Plot Mobile Truck points (UUTK3)
try:
x, y = m(trk3_mobile['longitude'], trk3_mobile['latitude'])
m.scatter(x[:],
y[:],
c=trk3_mobile['pm25'][:],
vmax=45,
vmin=0.,
lw=.3,
marker='p',
s=30,
cmap=plt.cm.get_cmap('jet'),
zorder=50)
except:
print 'no UUTK3 data'
# Plot Mobile Truck points (UUTK1)
try:
x, y = m(trk1_mobile['longitude'], trk1_mobile['latitude'])
m.scatter(x[:],
y[:],
c=trk1_mobile['pm25'][:],
vmax=45,
vmin=0.,
lw=.3,
marker='p',
s=30,
cmap=plt.cm.get_cmap('jet'),
zorder=50)
except:
print 'no UUTK1 data'
## Plot MesoWest (top of the hour, +/- 10 mins)
# Plot MesoWest wind data
mesowest_time = str(request_time.year).zfill(2) + str(
request_time.month).zfill(2) + str(request_time.day).zfill(2) + str(
request_time.hour).zfill(2) + '00'
print 'plotting mesowest observations within 10 minutes of top of the hour', mesowest_time, ' because the request time is:', request_time
a = get_mesowest_radius(request_time, '10')
u, v = wind_spddir_to_uv(a['wind_speed'], a['wind_direction'])
m.barbs(a['LON'],
a['LAT'],
u,
v,
length=4.5,
linewidth=.5,
barb_increments=dict(half=1, full=2, flag=10),
sizes=dict(spacing=0.15, height=0.3, emptybarb=.1))
# ozone
m.scatter(a['LON'],
a['LAT'],
c=a['pm25'],
vmax=45,
vmin=0.,
lw=.3,
s=20,
marker='s',
cmap=plt.cm.get_cmap('jet'),
zorder=50)
## plot KSL on top of everything becuase it is flying
# Plot Mobile Ozone points (KSL)
x, y = m(mobile['longitude'], mobile['latitude'])
# plot every 30 seconds rather than every 10 seconds (thus the [::3] indexes)
m.scatter(x[::3],
y[::3],
c=ksl_mobile['pm25'][::3],
vmax=45,
vmin=0.,
lw=.3,
s=30,
cmap=plt.cm.get_cmap('jet'),
zorder=50)
cbar = plt.colorbar(orientation='horizontal',
extend='both',
shrink=.8,
pad=.03)
cbar.ax.set_xlabel('PM 2.5 (micrograms m-3)', fontsize=10)
cbar.ax.tick_params(labelsize=8)
cbar.set_ticks([0, 5, 10, 15, 20, 25, 30, 35, 40, 45])
# Manually plot other Sensor locations by lat/lon
"""
m.scatter(-111.93,40.9669, s=75, c='w',zorder=40)
m.scatter(-112.01448,40.71152, s=75, c='b',zorder=40) # NAA
m.scatter(-111.93072,40.95733, s=75, c='darkorange',zorder=40) # O3S02
m.scatter(-111.828211,40.766573, s=75, c='r',zorder=40) # MTMET
m.scatter(-111.8717,40.7335, s=75, c='darkgreen',zorder=40) # QHW
m.scatter(-111.96503,40.77069, s=75, c='w',zorder=40) # SLC
m.scatter(-112.34551,40.89068 , s=75, c='w',zorder=40) # GSLBY
"""
## Sidebar Text
# is trax_in_yard, is trax available?
if trax_mobile == 'No Data Available from TRX01 at this time':
trax_time_range = 'n/a'
trax_ozone_text = 'TRAX \n n/a'
else:
trax_in_yard = ''
if trax_mobile['longitude'].max() == -9999:
trax_in_yard = ' -Yard-'
trax_time_range = trax_mobile['DATES'][0].strftime(
'%H:%M - ') + trax_mobile['DATES'][-1].strftime('%H:%M UTC')
trax_ozone_text = 'TRAX' + trax_in_yard + '\n Max: %.2f ug m-3\n Mean: %.2f ug m-3\n Min: %.2f ug m-3' % (
np.nanmax(trax_mobile['pm25']),
np.mean(trax_mobile['pm25'][trax_mobile['pm25'] > 0]),
np.nanmin(trax_mobile['pm25']))
obs_day = mobile['DATES'][-1].strftime('%d %b %Y')
ksl_time_range = mobile['DATES'][0].strftime(
'%H:%M - ') + mobile['DATES'][-1].strftime('%H:%M UTC')
mesowest_time_range = datetime.strptime(mesowest_time,
"%Y%m%d%H%M").strftime('%H:%M UTC')
date_text = '%s\nKSL: %s\nTRAX: %s\nMesoWest: %s' % (
obs_day, ksl_time_range, trax_time_range, mesowest_time_range)
ksl_ozone_text = 'KSL \n Max: %.2f ug m-3\n Mean: %.2f ug m-3\n Min: %.2f ug m-3' % (
np.nanmax(mobile['pm25']), np.mean(
mobile['pm25'][mobile['pm25'] > 0]), np.nanmin(mobile['pm25']))
mesowest_ozone_text = ''
for i in range(0, len(a['pm25'][a['pm25'] > 0])):
station_obs = '%s: %.2f\n' % (a['STID'][a['pm25'] > 0][i],
a['pm25'][a['pm25'] > 0][i])
mesowest_ozone_text += station_obs
all_text = date_text + '\n\n' + ksl_ozone_text + '\n\n' + trax_ozone_text + '\n\n' + mesowest_ozone_text
fig.text(.93,
.8,
all_text,
fontname='monospace',
va='top',
backgroundcolor='white',
fontsize=7)
plt.savefig(FIGDIR + string_time + '_KSL5_map_pm25.png',
dpi=500,
bbox_inches="tight")
print 'saved map', FIGDIR + string_time + '_KSL5_map_pm25.png'
print ""
return 'http://home.chpc.utah.edu/~u0553130/oper/KSL_daily/' + string_time + '_KSL5_map_pm25.png\n'
def get_wyoming_sounding(request_date, station='slc'):
"""
June 13, 2016
Download a sounding file from University of Wyoming Site and
return a dictinary of the values
Input:
request_date - a datetime object in UTC. Hour must be either 0 or 12
station - defaults to slc, the Salt Lake City. Alternativley use a
number for the station identier.
Return:
a dictionary of the data
"""
if station == 'slc':
stn = '72572' # this is the id number for KSLC
else:
stn = str(station)
year = str(request_date.year).zfill(4)
month = str(request_date.month).zfill(2)
day = str(request_date.day).zfill(2)
# hour in UTC, 00 and 12 z usually available
hour = str(request_date.hour).zfill(2)
# Download, process and add to plot the Wyoming Data
# 1)
# Wyoming URL to download Sounding from
url = 'http://weather.uwyo.edu/cgi-bin/sounding?' \
+ 'region=naconf&TYPE=TEXT%3ALIST' \
+ '&YEAR=' + year \
+ '&MONTH=' + month \
+ '&FROM=' + day + hour \
+ '&TO=' + day + hour \
+ '&STNM=' + stn
content = urllib2.urlopen(url).read()
# 2)
# Remove the html tags
soup = BeautifulSoup(content, "html.parser")
data_text = soup.get_text()
# 3)
# Split the content by new line.
splitted = data_text.split("\n", data_text.count("\n"))
# 4)
# Save the processed data as a .txt file to be read in by the skewt module.
# See more here: https://pypi.python.org/pypi/SkewT
Sounding_dir = './'
Sounding_filename = str(stn) + '.' + str(year) + \
str(month) + str(day) + str(hour) + '.txt'
f = open(Sounding_dir + Sounding_filename, 'w')
for line in splitted[4:]:
f.write(line + '\n')
f.close()
# 5) Read the observed sounding file
# Figure out where the footer is so we can skip it in np.genfromtxt
# This is the line the data ends
f = open(Sounding_filename, 'r')
ls = f.readlines()
f.close()
header = 6 # There are at least six header rows
data_in_row = [len(i.split()) for i in ls]
for i in data_in_row[header:]:
# Add a header row if 11 data points don't exist.
if i != 11:
header += 1
else:
break
data_rows = 0 # count number of data rows
for i in data_in_row[header:]:
# as long as there are 11 data points in the row, add to data_rows
if i == 11:
data_rows += 1
else:
break
# calculate number of lines in the footer
last_line = len(ls)
foot = last_line - header - data_rows
sounding = np.genfromtxt(Sounding_filename,
skip_header=header,
skip_footer=foot)
obs_press = sounding[:, 0] # hPa
obs_hght = sounding[:, 1] # m
obs_temp = sounding[:, 2] # C
obs_dwpt = sounding[:, 3] # C
obs_rh = sounding[:, 4] # %
obs_mixing = sounding[:, 5] / 1000 # kg/kg
obs_wdir = sounding[:, 6] # degrees
obs_wspd = sounding[:, 7] * 0.51444 # m/s
obs_theta = sounding[:, 8] # K
obs_u, obs_v = wind_calcs.wind_spddir_to_uv(obs_wspd, obs_wdir) # m/s
# 6)
# Would be nice to return a diction of the station information from the
# sounding file such as the Station identified, latitude, longitude,
# calculated indexes, etc.
data = {
'url': url, # URL the data is retrived from
'file': Sounding_filename, # Path to the file we created
'DATE': request_date, # Date requested (datetime object)
'station': str(station), # Requested station
'press': obs_press,
'height': obs_hght,
'temp': obs_temp,
'dwpt': obs_dwpt,
'rh': obs_rh,
'mixing ratio': obs_mixing,
'wdir': obs_wdir,
'wspd': obs_wspd,
'theta': obs_theta,
'u': obs_u,
'v': obs_v
}
return data