def plot_sounding(date, station):
p, T, Td, u, v, windspeed = get_sounding_data(date, station)
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
lfc_pressure, lfc_temperature = mpcalc.lfc(p, T, Td)
parcel_path = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(8, 8))
skew = SkewT(fig)
# Plot the data
temperature_line, = skew.plot(p, T, color='tab:red')
dewpoint_line, = skew.plot(p, Td, color='blue')
cursor = mplcursors.cursor([temperature_line, dewpoint_line])
# Plot thermodynamic parameters and parcel path
skew.plot(p, parcel_path, color='black')
if lcl_pressure:
skew.ax.axhline(lcl_pressure, color='black')
if lfc_pressure:
skew.ax.axhline(lfc_pressure, color='0.7')
# Add the relevant special lines
skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
# Shade areas representing CAPE and CIN
skew.shade_cin(p, T, parcel_path)
skew.shade_cape(p, T, parcel_path)
# Add wind barbs
skew.plot_barbs(p, u, v)
# Add an axes to the plot
ax_hod = inset_axes(skew.ax, '30%', '30%', loc=1, borderpad=3)
# Plot the hodograph
h = Hodograph(ax_hod, component_range=100.)
# Grid the hodograph
h.add_grid(increment=20)
# Plot the data on the hodograph
mask = (p >= 100 * units.mbar)
h.plot_colormapped(u[mask], v[mask], windspeed[mask]) # Plot a line colored by wind speed
# Set some sensible axis limits
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
return fig, skew
def test_skewt_api():
"""Test the SkewT API."""
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
p = np.linspace(1000, 100, 10)
t = np.linspace(20, -20, 10)
u = np.linspace(-10, 10, 10)
skew.plot(p, t, 'r')
skew.plot_barbs(p, u, u)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
return fig
def test_skewt_api():
"""Test the SkewT API."""
with matplotlib.rc_context({'axes.autolimit_mode': 'data'}):
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
p = np.linspace(1000, 100, 10)
t = np.linspace(20, -20, 10)
u = np.linspace(-10, 10, 10)
skew.plot(p, t, 'r')
skew.plot_barbs(p, u, u)
skew.ax.set_xlim(-20, 30)
skew.ax.set_ylim(1000, 100)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
return fig
Td = dataset.variables['dewpoint'][:]
u = dataset.variables['u_wind'][:]
v = dataset.variables['v_wind'][:]
###########################################
skew = SkewT()
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
skew.plot_barbs(p, u, v)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
skew.ax.set_ylim(1000, 100)
###########################################
# Example of defining your own vertical barb spacing
skew = SkewT()
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
# Set spacing interval--Every 50 mb from 1000 to 100 mb
my_interval = np.arange(100, 1000, 50) * units('mbar')
def main():
img_dir = Path("hail_plots/soundings/")
if not img_dir.exists():
img_dir.mkdir(parents=True)
data_dir = Path("/HOME/huziy/skynet3_rech1/hail/soundings_from_erai/")
# dates = [datetime(1991, 9, 7), datetime(1991, 9, 7, 6), datetime(1991, 9, 7, 12), datetime(1991, 9, 7, 18),
# datetime(1991, 9, 8, 0), datetime(1991, 9, 8, 18)]
#
# dates.extend([datetime(1991, 9, 6, 0), datetime(1991, 9, 6, 6), datetime(1991, 9, 6, 12), datetime(1991, 9, 6, 18)])
#
# dates = [datetime(1990, 7, 7), datetime(2010, 7, 12), datetime(1991, 9, 8, 0)]
dates_s = """
- 07/09/1991 12:00
- 07/09/1991 18:00
- 08/09/1991 00:00
- 08/09/1991 06:00
- 08/09/1991 12:00
- 13/09/1991 12:00
- 13/09/1991 18:00
- 14/09/1991 00:00
- 14/09/1991 06:00
- 14/09/1991 12:00
"""
dates = [datetime.strptime(line.strip()[1:].strip(), "%d/%m/%Y %H:%M") for line in dates_s.split("\n") if line.strip() != ""]
def __date_parser(s):
return pd.datetime.strptime(s, '%Y-%m-%d %H:%M:%S')
tt = pd.read_csv(data_dir.joinpath("TT.csv"), index_col=0, parse_dates=['Time'])
uu = pd.read_csv(data_dir.joinpath("UU.csv"), index_col=0, parse_dates=['Time'])
vv = pd.read_csv(data_dir.joinpath("VV.csv"), index_col=0, parse_dates=['Time'])
hu = pd.read_csv(data_dir.joinpath("HU.csv"), index_col=0, parse_dates=['Time'])
print(tt.head())
print([c for c in tt])
print(list(tt.columns.values))
temp_perturbation_degc = 0
for the_date in dates:
p = np.array([float(c) for c in tt])
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig)
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
tsel = tt.select(lambda d: d == the_date)
usel = uu.select(lambda d: d == the_date)
vsel = vv.select(lambda d: d == the_date)
husel = hu.select(lambda d: d == the_date)
tvals = tsel.values.mean(axis=0)
uvals = usel.values.mean(axis=0) * mul_mpers_per_knot
vvals = vsel.values.mean(axis=0) * mul_mpers_per_knot
huvals = husel.values.mean(axis=0) * units("g/kg")
# ignore the lowest level
all_vars = [p, tvals, uvals, vvals, huvals]
for i in range(len(all_vars)):
all_vars[i] = all_vars[i][:-5]
p, tvals, uvals, vvals, huvals = all_vars
assert len(p) == len(huvals)
tdvals = calc.dewpoint(calc.vapor_pressure(p * units.mbar, huvals).to(units.mbar))
print(tvals, tdvals)
# Calculate full parcel profile and add to plot as black line
parcel_profile = calc.parcel_profile(p[::-1] * units.mbar, (tvals[-1] + temp_perturbation_degc) * units.degC, tdvals[-1]).to('degC')
parcel_profile = parcel_profile[::-1]
skew.plot(p, parcel_profile, 'k', linewidth=2)
# Example of coloring area between profiles
greater = tvals * units.degC >= parcel_profile
skew.ax.fill_betweenx(p, tvals, parcel_profile, where=greater, facecolor='blue', alpha=0.4)
skew.ax.fill_betweenx(p, tvals, parcel_profile, where=~greater, facecolor='red', alpha=0.4)
skew.plot(p, tvals, "r")
skew.plot(p, tdvals, "g")
skew.plot_barbs(p, uvals, vvals)
# Plot a zero degree isotherm
l = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
plt.title("{} (dT={})".format(the_date, temp_perturbation_degc))
img_path = "{}_dT={}.png".format(the_date.strftime("%Y%m%d_%H%M%S"), temp_perturbation_degc)
img_path = img_dir.joinpath(img_path)
fig.savefig(str(img_path), bbox_inches="tight")
plt.close(fig)
def cape(filelist,storm,track,show):
#Sort filelist.
filelist=np.sort(filelist)
# Get sampling periods (this will be a dictionary). See the toolbox
print('Retrieving sampling periods')
sampleperiods=getsamplingperiods(filelist,3.)
# Iterate over all sampling periods.
for sampindex,periodskey in enumerate(sampleperiods):
#Allocate starting (stdt) and ending date (endt). Remeber dt is the convetional short-name for date.
stdt=periodskey
endt=sampleperiods[periodskey]
# Define sampling period string
period=str(stdt.hour)+'_'+str(stdt.day)+'-'+str(endt.hour)+'_'+str(endt.day)
# Create new-empty lists.
lats=[]
lons=[]
xs=[]
ys=[]
capes=[]
cins=[]
distfig = plt.figure(figsize=(13, 9))
ax=distfig.add_subplot(111)
print('start filelist loop')
# Iterate over all files.
for filename in filelist:
# Select end-name of file by inspecting filename string. Notice how filename can change how file is read.
if 'radazm' in filename.split('/')[-1] or 'eol' in filename.split('/')[-1]:
end='radazm'
else:
end='avp'
# Obtain properties of file, i.e., launch time and location into a dictionary (dicc).
dicc=findproperties(filename,end)
# Condition to see if current file is in sampling period.
# Notice how if structure is constructed, condition finds times outside of sampling period and
# if found outside the sampling period, continue to next file.
if dicc['Launch Time']<stdt or dicc['Launch Time'] > endt:
continue
nump=np.genfromtxt(filename,skip_header=16,skip_footer=0)
temperature=clean1(nump[:,5])
pressure=clean1(nump[:,4])
Height=clean1(nump[:,13])
if np.nanmax(Height)<3500:
continue
#Clean for cape
RelH=clean1(nump[:,7])
lon=clean1(nump[:,14])
lat=clean1(nump[:,15])
lon=clean1(lon)
lat=clean1(lat)
mlon=np.nanmean(lon)
mlat=np.nanmean(lat)
RH=RelH/100
T,P,rh,dz=cleanforcape(temperature,pressure,RH,Height)
#Metpy set-up
T=np.flip(T,0)
rh=np.flip(rh,0)
p=np.flip(P,0)
dz=np.flip(dz,0)
p=p*units.hPa
T=T*units.celsius
mixing=rh*mpcalc.saturation_mixing_ratio(p,T)
epsilon=0.6219800858985514
Tv=mpcalc.virtual_temperature(T, mixing,
molecular_weight_ratio=epsilon)
dwpoint=mpcalc.dewpoint_rh(T, rh)
blh_indx=np.where(dz<500)
try:
parcelprofile=mpcalc.parcel_profile(p,np.nanmean(T[blh_indx])*units.celsius,mpcalc.dewpoint_rh(np.nanmean(T[blh_indx])*units.celsius, np.nanmean(rh[blh_indx]))).to('degC')
Tv_parcelprofile=mpcalc.virtual_temperature(parcelprofile, mixing,
molecular_weight_ratio=epsilon)
cape,cin=cape_cin(p,Tv,dwpoint,Tv_parcelprofile,dz,T)
except:
continue
plotskewT=True
if plotskewT==True:
os.system('mkdir figs/skewt')
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig, rotation=45)
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
skew.plot(p, dwpoint, 'g',label=r'$T_{dp}$')
skew.plot(p, Tv, 'r',label=r'$T_v$')
plt.text(-120,120,str(np.around(cape,2)),fontsize=14,fontweight='bold')
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p,Tv_parcelprofile,'k',label=r'$T_{v env}$')
skew.shade_cin(p, T, parcelprofile,label='CIN')
skew.shade_cape(p, Tv, Tv_parcelprofile,label='CAPE')
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
plt.legend()
plt.title(storm + ' on' + period,fontsize=14)
plt.savefig('figs/skewt/'+storm+str(dicc['Launch Time'].time())+'.png')
#plt.show()
plt.close()
r,theta=cart_to_cylindr(mlon,mlat,track,dicc['Launch Time'])
if not(np.isnan(r)) and not(np.isnan(theta)) and not(np.isnan(cape.magnitude)):
xs.append(r*np.cos(theta))
ys.append(r*np.sin(theta))
capes.append(cape.magnitude)
cins.append(cin)
cs=ax.scatter(xs,ys,c=np.asarray(capes),cmap='jet')
for i,xi in enumerate(xs):
ax.text(xi,ys[i]+10,str(np.around(capes[i],1)))
plt.colorbar(cs)
ax.scatter(0,0,marker='v',s=100,color='black')
ax.grid()
ax.set_xlabel('X distance [km]')
ax.set_ylabel('Y distance [km]')
ax.set_title('CAPE distribution for '+storm+' on '+period,fontsize=14)
distfig.savefig('figs/cape'+storm+period+'.png')
if show:
plt.show()
# Recreate stack of wind barbs
s = []
bot=2000.
# Space out wind barbs evenly on log axis.
for ind, i in enumerate(prof.pres):
if i < 100: break
if np.log(bot/i) > 0.04:
s.append(ind)
bot = i
b = skew.plot_barbs(prof.pres[s], prof.u[s], prof.v[s], linewidth=0.4, length=6)
# 'knots' label under wind barb stack
kts = ax.text(1.0, 0, 'knots', clip_on=False, ha='center',va='bottom',size=7,zorder=2)
# Tried drawing adiabats and mixing lines right after creating SkewT object but got errors.
draw_adiabats = skew.plot_dry_adiabats(color='r', alpha=0.2, linestyle="solid")
moist_adiabats = skew.plot_moist_adiabats(linewidth=0.5, color='black', alpha=0.2)
mixing_lines = skew.plot_mixing_lines(color='g', alpha=0.35, linestyle="dotted")
string = "created "+str(datetime.datetime.now(tz=None)).split('.')[0]
plt.annotate(s=string, xy=(10,2), xycoords='figure pixels', fontsize=5)
res = plt.savefig(ofile,dpi=125)
skew.ax.clear()
ax.clear()
if verbose:
print 'created', os.path.realpath(ofile)
mapax.clear()
hodo_ax.clear()
# Copy to web server
class Window(QtGui.QMainWindow):
r""" A mainwindow object for the GUI display. Inherits from QMainWindow."""
def __init__(self):
super(Window, self).__init__()
self.interface()
def interface(self):
r""" Contains the main window interface generation functionality. Commented where needed."""
# Get the screen width and height and set the main window to that size
screen = QtGui.QDesktopWidget().screenGeometry()
self.setGeometry(0, 0, 800, screen.height())
self.setMaximumSize(QtCore.QSize(800, 2000))
# Set the window title and icon
self.setWindowTitle("WAVE: Weather Analysis and Visualization Environment")
self.setWindowIcon(QtGui.QIcon('./img/wave_64px.png'))
# Import the stylesheet for this window and set it to the window
stylesheet = "css/MainWindow.css"
with open(stylesheet, "r") as ssh:
self.setStyleSheet(ssh.read())
self.setAutoFillBackground(True)
self.setBackgroundRole(QtGui.QPalette.Highlight)
# Create actions for menus and toolbar
exit_action = QtGui.QAction(QtGui.QIcon('./img/exit_64px.png'), 'Exit', self)
exit_action.setShortcut('Ctrl+Q')
exit_action.setStatusTip('Exit application')
exit_action.triggered.connect(self.close)
clear_action = QtGui.QAction(QtGui.QIcon('./img/clear_64px.png'), 'Clear the display', self)
clear_action.setShortcut('Ctrl+C')
clear_action.setStatusTip('Clear the display')
clear_action.triggered.connect(self.clear_canvas)
skewt_action = QtGui.QAction(QtGui.QIcon('./img/skewt_64px.png'), 'Open the skew-T dialog', self)
skewt_action.setShortcut('Ctrl+S')
skewt_action.setStatusTip('Open the skew-T dialog')
skewt_action.triggered.connect(self.skewt_dialog)
radar_action = QtGui.QAction(QtGui.QIcon('./img/radar_64px.png'), 'Radar', self)
radar_action.setShortcut('Ctrl+R')
radar_action.setStatusTip('Open Radar Dialog Box')
radar_action.triggered.connect(self.radar_dialog)
# Create the top menubar, setting native to false (for OS) and add actions to the menus
menubar = self.menuBar()
menubar.setNativeMenuBar(False)
filemenu = menubar.addMenu('&File')
editmenu = menubar.addMenu('&Edit')
helpmenu = menubar.addMenu('&Help')
filemenu.addAction(exit_action)
# Create the toolbar, place it on the left of the GUI and add actions to toolbar
left_tb = QtGui.QToolBar()
self.addToolBar(QtCore.Qt.LeftToolBarArea, left_tb)
left_tb.setMovable(False)
left_tb.addAction(clear_action)
left_tb.addAction(skewt_action)
left_tb.addAction(radar_action)
self.setIconSize(QtCore.QSize(30, 30))
# Create the toolbar, place it on the left of the GUI and add actions to toolbar
right_tb = QtGui.QToolBar()
self.addToolBar(QtCore.Qt.RightToolBarArea, right_tb)
right_tb.setMovable(False)
right_tb.addAction(clear_action)
right_tb.addAction(skewt_action)
right_tb.addAction(radar_action)
# Create the status bar with a default display
self.statusBar().showMessage('Ready')
# Figure and canvas widgets that display the figure in the GUI
self.figure = plt.figure(facecolor='#2B2B2B')
self.canvas = FigureCanvas(self.figure)
# Add subclassed matplotlib navbar to GUI
# spacer widgets for left and right of buttons
left_spacer = QtGui.QWidget()
left_spacer.setSizePolicy(QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Expanding)
right_spacer = QtGui.QWidget()
right_spacer.setSizePolicy(QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Expanding)
self.mpltb = QtGui.QToolBar()
self.mpltb.addWidget(left_spacer)
self.mpltb.addWidget(MplToolbar(self.canvas, self))
self.mpltb.addWidget(right_spacer)
self.mpltb.setMovable(False)
self.addToolBar(QtCore.Qt.TopToolBarArea, self.mpltb)
# Set the figure as the central widget and show the GUI
self.setCentralWidget(self.canvas)
self.show()
def skewt_dialog(self):
r""" When the toolbar icon for the Skew-T dialog is clicked, this function is executed. Creates an instance of
the SkewTDialog object which is the dialog box. If the submit button on the dialog is clicked, get the user
inputted values and pass them into the sounding retrieval call (DataAccessor.get_sounding) to fetch the data.
Finally, plot the returned data via self.plot.
Args:
None.
Returns:
None.
Raises:
None.
"""
dialog = SkewTDialog()
if dialog.exec_():
source, lat, long = dialog.get_values()
t, td, p, u, v, lat, long, time = DataAccessor.get_sounding(source, lat, long)
self.plot(t, td, p, u, v, lat, long, time)
def plot(self, t, td, p, u, v, lat, long, time):
r"""Displays the Skew-T data on a matplotlib figure.
Args:
t (array-like): A list of temperature values.
td (array-like): A list of dewpoint values.
p (array-like): A list of pressure values.
u (array-like): A list of u-wind component values.
v (array-like): A list of v-wind component values.
lat (string): A string containing the requested latitude value.
long (string): A string containing the requested longitude value.
time (string): A string containing the UTC time requested with seconds truncated.
Returns:
None.
Raises:
None.
"""
# Create a new figure. The dimensions here give a good aspect ratio
self.skew = SkewT(self.figure, rotation=40)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
self.skew.plot(p, t, 'r')
self.skew.plot(p, td, 'g')
self.skew.plot_barbs(p, u, v, barbcolor='#FF0000', flagcolor='#FF0000')
self.skew.ax.set_ylim(1000, 100)
self.skew.ax.set_xlim(-40, 60)
# Axis colors
self.skew.ax.tick_params(axis='x', colors='#A3A3A4')
self.skew.ax.tick_params(axis='y', colors='#A3A3A4')
# Calculate LCL height and plot as black dot
l = lcl(p[0], t[0], td[0])
lcl_temp = dry_lapse(concatenate((p[0], l)), t[0])[-1].to('degC')
self.skew.plot(l, lcl_temp, 'ko', markerfacecolor='black')
# Calculate full parcel profile and add to plot as black line
prof = parcel_profile(p, t[0], td[0]).to('degC')
self.skew.plot(p, prof, 'k', linewidth=2)
# Color shade areas between profiles
self.skew.ax.fill_betweenx(p, t, prof, where=t >= prof, facecolor='#5D8C53', alpha=0.7)
self.skew.ax.fill_betweenx(p, t, prof, where=t < prof, facecolor='#CD6659', alpha=0.7)
# Add the relevant special lines
self.skew.plot_dry_adiabats()
self.skew.plot_moist_adiabats()
self.skew.plot_mixing_lines()
# Set title
deg = u'\N{DEGREE SIGN}'
self.skew.ax.set_title('Sounding for ' + lat + deg + ', ' + long + deg + ' at ' + time + 'z', y=1.02,
color='#A3A3A4')
# Discards old graph, works poorly though
# skew.ax.hold(False)
# Figure and canvas widgets that display the figure in the GUI
# set canvas size to display Skew-T appropriately
self.canvas.setMaximumSize(QtCore.QSize(800, 2000))
# refresh canvas
self.canvas.draw()
def radar_dialog(self):
r""" When the toolbar icon for the Skew-T dialog is clicked, this function is executed. Creates an instance of
the SkewTDialog object which is the dialog box. If the submit button on the dialog is clicked, get the user
inputted values and pass them into the sounding retrieval call (DataAccessor.get_sounding) to fetch the data.
Finally, plot the returned data via self.plot.
Args:
None.
Returns:
None.
Raises:
None.
"""
radar_dialog = RadarDialog()
if radar_dialog.exec_():
station, product = radar_dialog.get_radarvals()
x, y, ref = DataAccessor.get_radar(station, product)
self.plot_radar(x, y, ref)
def plot_radar(self, x, y, ref):
r"""Displays the Skew-T data on a matplotlib figure.
Args:
t (array-like): A list of temperature values.
td (array-like): A list of dewpoint values.
p (array-like): A list of pressure values.
u (array-like): A list of u-wind component values.
v (array-like): A list of v-wind component values.
lat (string): A string containing the requested latitude value.
long (string): A string containing the requested longitude value.
time (string): A string containing the UTC time requested with seconds truncated.
Returns:
None.
Raises:
None.
"""
self.ax = self.figure.add_subplot(111)
self.ax.pcolormesh(x, y, ref)
self.ax.set_aspect('equal', 'datalim')
self.ax.set_xlim(-460, 460)
self.ax.set_ylim(-460, 460)
self.ax.tick_params(axis='x', colors='#A3A3A4')
self.ax.tick_params(axis='y', colors='#A3A3A4')
# set canvas size to display Skew-T appropriately
self.canvas.setMaximumSize(QtCore.QSize(800, 2000))
# refresh canvas
self.canvas.draw()
def clear_canvas(self):
self.canvas.close()
self.figure = plt.figure(facecolor='#2B2B2B')
self.canvas = FigureCanvas(self.figure)
self.setCentralWidget(self.canvas)
def main():
args = get_args()
setup_logging(args['verbose'])
# Define input file
file = args['inputfile']
output = args['outputfile']
ds = xr.open_dataset(file)
ds_sel = ds.isel({'sounding': 0})
ds_sel = ds_sel.sortby(ds_sel.pressure, ascending=False)
p = ds_sel.pressure.values
T = ds_sel.temperature.values
Td = ds_sel.dewPoint.values
wind_speed = ds_sel.windSpeed.values
wind_dir = ds_sel.windDirection.values
# Filter nans
idx = np.where((np.isnan(T) + np.isnan(Td) + np.isnan(p) +
np.isnan(wind_speed) + np.isnan(wind_dir)) == False, True,
False)
p = p[idx]
T = T[idx]
Td = Td[idx]
wind_speed = wind_speed[idx]
wind_dir = wind_dir[idx]
# Add units
p = p * units.hPa
T = T * units.degC
Td = Td * units.degC
wind_speed = wind_speed * (units.meter / units.second)
wind_dir = wind_dir * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
parcel_prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig, rotation=30)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
# Plot only specific barbs to increase visibility
pressure_levels_barbs = np.logspace(0.1, 1, 50) * 100
def find_nearest(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return array[idx]
# Search for levels by providing pressures
# (levels is the coordinate not pressure)
pres_vals = ds_sel.pressure.values[idx]
closest_pressure_levels = np.unique(
[find_nearest(pres_vals, p_) for p_ in pressure_levels_barbs])
_, closest_pressure_levels_idx, _ = np.intersect1d(pres_vals,
closest_pressure_levels,
return_indices=True)
p_barbs = ds_sel.pressure.isel({
'levels': closest_pressure_levels_idx
}).values * units.hPa
wind_speed_barbs = ds_sel.windSpeed.isel({
'levels':
closest_pressure_levels_idx
}).values * (units.meter / units.second)
wind_dir_barbs = ds_sel.windDirection.isel({
'levels':
closest_pressure_levels_idx
}).values * units.degrees
u_barbs, v_barbs = mpcalc.wind_components(wind_speed_barbs, wind_dir_barbs)
# Find nans in pressure
# p_non_nan_idx = np.where(~np.isnan(pres_vals))
skew.plot_barbs(p_barbs, u_barbs, v_barbs)
skew.ax.set_ylim(1020, 100)
skew.ax.set_xlim(-50, 40)
# Plot LCL as black dot
skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')
# Plot the parcel profile as a black line
skew.plot(pres_vals, parcel_prof, 'k', linewidth=2)
# Shade areas of CAPE and CIN
skew.shade_cin(pres_vals, T, parcel_prof)
skew.shade_cape(pres_vals, T, parcel_prof)
# Plot a zero degree isotherm
skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
# Create a hodograph
# Create an inset axes object that is 40% width and height of the
# figure and put it in the upper right hand corner.
ax_hod = inset_axes(skew.ax, '40%', '40%', loc=1)
h = Hodograph(ax_hod, component_range=80.)
h.add_grid(increment=20)
h.plot_colormapped(u, v, wind_speed) # Plot a line colored by wind speed
# Set title
sounding_name = ds_sel.sounding.values
sounding_name_str = str(sounding_name.astype('str'))
skew.ax.set_title('{sounding}'.format(sounding=sounding_name_str))
if output is None:
output = str(os.path.basename(file).split('.')[0]) + '.pdf'
logging.info('Write output to {}'.format(output))
plt.savefig(output)
def fmi2skewt(station, time, img_name):
apikey = 'e72a2917-1e71-4d6f-8f29-ff4abfb8f290'
url = 'http://data.fmi.fi/fmi-apikey/' + str(
apikey
) + '/wfs?request=getFeature&storedquery_id=fmi::observations::weather::sounding::multipointcoverage&fmisid=' + str(
station) + '&starttime=' + str(time) + '&endtime=' + str(time) + '&'
req = requests.get(url)
xmlstring = req.content
tree = ET.ElementTree(ET.fromstring(xmlstring))
root = tree.getroot()
#reading location and time data to "positions" from XML
positions = ""
for elem in root.getiterator(
tag='{http://www.opengis.net/gmlcov/1.0}positions'):
positions = elem.text
#'positions' is string type variable
#--> split positions into a list by " "
#then remove empty chars and "\n"
# from pos_split --> data into positions_data
try:
pos_split = positions.split(' ')
except NameError:
return "Sounding data not found: stationid " + station + " time " + time
pos_split = positions.split(' ')
positions_data = []
for i in range(0, len(pos_split)):
if not (pos_split[i] == "" or pos_split[i] == "\n"):
positions_data.append(pos_split[i])
#index for height: 2,6,10 etc in positions_data
height = []
myList = range(2, len(positions_data))
for i in myList[::4]:
height.append(positions_data[i])
p = []
for i in range(0, len(height)):
p.append(height2pressure(float(height[i])))
#reading wind speed, wind direction, air temperature and dew point data to 'values'
values = ""
for elem in root.getiterator(
tag='{http://www.opengis.net/gml/3.2}doubleOrNilReasonTupleList'):
values = elem.text
#split 'values' into a list by " "
#then remove empty chars and "\n"
val_split = values.split(' ')
values_data = []
for i in range(0, len(val_split)):
if not (val_split[i] == "" or val_split[i] == "\n"):
values_data.append(val_split[i])
#data in values_data: w_speed, w_dir, t_air, t_dew
wind_speed = []
wind_dir = []
T = []
Td = []
myList = range(0, len(values_data))
for i in myList[::4]:
wind_speed.append(float(values_data[i]))
wind_dir.append(float(values_data[i + 1]))
T.append(float(values_data[i + 2]))
Td.append(float(values_data[i + 3]))
if stationid == "101104":
loc_time = "Jokioinen Ilmala " + time1
elif stationid == "101932":
loc_time = "Sodankyla Tahtela " + time1
else:
return None
#calculate wind components u,v:
u = []
v = []
for i in range(0, len(wind_speed)):
u1, v1 = getWindComponent(wind_speed[i], wind_dir[i])
u.append(u1)
v.append(v1)
#find index for pressure < 100hPa (for number of wind bars)
if min(p) > 100:
wthin = len(p) / 20
u_plot = u
v_plot = v
p_plot = p
else:
for i in range(0, len(p)):
if p[i] - 100 <= 0:
wthin = i / 20
u_plot = u[0:i]
v_plot = v[0:i]
p_plot = p[0:i]
break
#units
wind_speed = wind_speed * units("m/s")
wind_dir = wind_dir * units.deg
T = T * units.degC
Td = Td * units.degC
p = p * units("hPa")
#calculate pwat, lcl, cape, cin and plot cape
pwat = mpcalc.precipitable_water(Td, p, bottom=None, top=None)
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])
prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')
try:
cape, cin = mpcalc.cape_cin(p, T, Td, prof)
except IndexError:
cape = 0 * units("J/kg")
cin = 0 * units("J/kg")
#__________________plotting__________________
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig, rotation=45)
font_par = {
'family': 'monospace',
'color': 'darkred',
'weight': 'normal',
'size': 10,
}
font_title = {
'family': 'monospace',
'color': 'black',
'weight': 'normal',
'size': 20,
}
font_axis = {
'family': 'monospace',
'color': 'black',
'weight': 'normal',
'size': 10,
}
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'k')
skew.plot(p, Td, 'b')
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
skew.plot_barbs(p_plot[0::wthin], u_plot[0::wthin], v_plot[0::wthin])
skew.plot_dry_adiabats(alpha=0.4)
skew.plot_moist_adiabats(alpha=0.4)
skew.plot_mixing_lines(alpha=0.4)
skew.shade_cape(p, T, prof, color="orangered")
plt.title(loc_time, fontdict=font_title)
plt.xlabel("T (C)", fontdict=font_axis)
plt.ylabel("P (hPa)", fontdict=font_axis)
#round and remove units from cape,cin,plcl,tlcl,pwat
if cape.magnitude > 0:
capestr = str(np.round(cape.magnitude))
else:
capestr = "NaN"
if cin.magnitude > 0:
cinstr = str(np.round(cin.magnitude))
else:
cinstr = "NaN"
lclpstr = str(np.round(lcl_pressure.magnitude))
lclTstr = str(np.round(lcl_temperature.magnitude))
pwatstr = str(np.round(pwat.magnitude))
str_par = "CAPE[J/kg]=" + capestr + " CIN[J/kg]=" + cinstr + " Plcl[hPa]=" + lclpstr + " Tlcl[C]=" + lclTstr + " pwat[mm]=" + pwatstr
font = {
'family': 'monospace',
'color': 'darkred',
'weight': 'normal',
'size': 10,
}
plt.text(-20, 1250, str_par, fontdict=font_par)
save_file = "figures/" + img_name + ".png"
plt.savefig(save_file)
# An example of a slanted line at constant T -- in this case the 0
# isotherm
skew.ax.axvline(0, color='c', linestyle='-', linewidth=1)
for i in range(23):
for j in range(2, 9, 2):
skew.ax.axvline(i * 10 - 160 + j,
color='c',
linestyle='--',
linewidth=0.3)
#print (i*10-40+j)
# Add the relevant special lines
skew.plot_dry_adiabats(color='green', linestyle='-')
#skew.plot_dry_adiabats([1, 2], color='green', linestyle='-', linewidth=1)
skew.plot_moist_adiabats(color='brown', linestyle='-')
skew.plot_mixing_lines(color='blue', linestyle='--', linewidth=0.3)
fig.savefig('{0}student_{1}.png'.format(dir_name, selected_time[:13]),
dpi=None,
facecolor='w',
edgecolor='w',
orientation='portrait',
papertype=None,
format=None,
transparent=False,
bbox_inches=None,
pad_inches=0.1,
frameon=None,
metadata=None)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
skew.plot_barbs(p[::3], u[::3], v[::3], y_clip_radius=0.03)
# Set some appropriate axes limits for x and y
skew.ax.set_xlim(-30, 40)
skew.ax.set_ylim(1020, 100)
# Add the relevant special lines to plot throughout the figure
skew.plot_dry_adiabats(t0=np.arange(233, 533, 10) * units.K,
alpha=0.25,
color='orangered')
skew.plot_moist_adiabats(t0=np.arange(233, 400, 5) * units.K,
alpha=0.25,
color='tab:green')
# does not work for me, unclear why:
# skew.plot_mixing_lines(p=np.arange(1000, 99, -20) * units.hPa,
# linestyle='dotted', color='tab:blue')
# this does:
skew.plot_mixing_lines(linestyle='dotted', color='tab:blue')
# Add some descriptive titles
plt.title('{} Sounding'.format(station), loc='left')
plt.title('Valid Time: {}'.format(dt), loc='right')
plt.show()
# Create a new figure. The dimensions here give a good aspect ratio fig = plt.figure(figsize=(9, 9)) # Grid for plots skew = SkewT(fig, rotation=45) # Plot the data using normal plotting functions, in this case using # log scaling in Y, as dictated by the typical meteorological plot skew.plot(p, T, 'r') skew.plot(p, Td, 'g') skew.plot_barbs(p, u, v) skew.ax.set_ylim(1000, 100) # Add the relevant special lines skew.plot_dry_adiabats() skew.plot_moist_adiabats() skew.plot_mixing_lines() # Good bounds for aspect ratio skew.ax.set_xlim(-50, 60) # Create a hodograph ax_hod = inset_axes(skew.ax, '40%', '40%', loc=1) h = Hodograph(ax_hod, component_range=80.) h.add_grid(increment=20) h.plot_colormapped(u, v, np.hypot(u, v)) # Show the plot plt.show()
def cape(filelist,storm,track):
#Sort filelist.
filelist=np.sort(filelist)
# Get sampling periods (this will be a dictionary). See the toolbox
print('Retrieving sampling periods')
sampleperiods=getsamplingperiods(filelist,3.)
# Iterate over all sampling periods.
for sampindex,periodskey in enumerate(sampleperiods):
#Allocate starting (stdt) and ending date (endt). Remeber dt is the convetional short-name for date.
stdt=periodskey
endt=sampleperiods[periodskey]
# Define sampling period string
period=str(stdt.hour)+'_'+str(stdt.day)+'-'+str(endt.hour)+'_'+str(endt.day)
# Create new-empty lists.
lats=[]
lons=[]
xs=[]
ys=[]
capes=[]
cins=[]
print('start filelist loop')
# Iterate over all files.
for filename in filelist:
# Select end-name of file by inspecting filename string. Notice how filename can change how file is read.
if 'radazm' in filename.split('/')[-1] or 'eol' in filename.split('/')[-1]:
end='radazm'
else:
end='avp'
# Obtain properties of file, i.e., launch time and location into a dictionary (dicc).
dicc=findproperties(filename,end)
# Condition to see if current file is in sampling period.
# Notice how if structure is constructed, condition finds times outside of sampling period and
# if found outside the sampling period, continue to next file.
if dicc['Launch Time']<stdt or dicc['Launch Time'] > endt:
continue
nump=np.genfromtxt(filename,skip_header=16,skip_footer=0)
temperature=clean1(nump[:,5])
pressure=clean1(nump[:,4])
Height=clean1(nump[:,13])
if np.nanmax(Height)<3500:
continue
#Clean for cape
RelH=clean1(nump[:,7])
lon=clean1(nump[:,14])
lat=clean1(nump[:,15])
lon=clean1(lon)
lat=clean1(lat)
mlon=np.nanmean(lon)
mlat=np.nanmean(lat)
RH=RelH/100
T,P,rh,dz=cleanforcape(temperature,pressure,RH,Height)
#Metpy set-up
T=np.flip(T,0)
rh=np.flip(rh,0)
p=np.flip(P,0)
dz=np.flip(dz,0)
p=p*units.hPa
T=T*units.celsius
mixing=rh*mpcalc.saturation_mixing_ratio(p,T)
epsilon=0.6219800858985514
Tv=mpcalc.virtual_temperature(T, mixing,
molecular_weight_ratio=epsilon)
dwpoint=mpcalc.dewpoint_rh(T, rh)
blh_indx=np.where(dz<500)
try:
parcelprofile=mpcalc.parcel_profile(p,np.nanmean(T[blh_indx])*units.celsius,mpcalc.dewpoint_rh(np.nanmean(T[blh_indx])*units.celsius, np.nanmean(rh[blh_indx]))).to('degC')
Tv_parcelprofile=mpcalc.virtual_temperature(parcelprofile, mixing,
molecular_weight_ratio=epsilon)
cape,cin=cape_cin(p,Tv,dwpoint,Tv_parcelprofile,dz,T)
except:
continue
plotskewT=True
if plotskewT==True:
os.system('mkdir figs/skewt')
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig, rotation=45)
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 60)
skew.plot(p, dwpoint, 'g',label=r'$T_{dp}$')
skew.plot(p, Tv, 'r',label=r'$T_v$')
plt.text(-120,120,str(np.around(cape,2)),fontsize=14,fontweight='bold')
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p,Tv_parcelprofile,'k',label=r'$T_{v env}$')
skew.shade_cin(p, T, parcelprofile,label='CIN')
skew.shade_cape(p, Tv, Tv_parcelprofile,label='CAPE')
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
plt.legend()
plt.title(storm + ' on' + period,fontsize=14)
plt.savefig('figs/skewt/'+storm+str(dicc['Launch Time'].time())+'.png')
#plt.show()
plt.close()
r,theta=cart_to_cylindr(mlon,mlat,track,dicc['Launch Time'])
if not(np.isnan(r)) and not(np.isnan(theta)) and not(np.isnan(cape.magnitude)):
xs.append(r*np.cos(theta))
ys.append(r*np.sin(theta))
capes.append(cape.magnitude)
cins.append(cin)
fig = plt.figure(figsize=(13, 9))
plt.scatter(xs,ys,c=np.asarray(capes),cmap='jet')
for i,xi in enumerate(xs):
plt.text(xi,ys[i]+10,str(np.around(capes[i],1)))
plt.colorbar(label=r"$J/kg$')
plt.scatter(0,0,marker='v',s=100,color='black')
plt.grid()
plt.xlabel('X distance [km]')
plt.ylabel('Y distance [km]')
plt.title('CAPE distribution for '+storm+' on '+period,fontsize=14)
plt.savefig('figs/cape'+storm+period+'.png')
