def skewt(data, splots, ranges, temp=None, rel_hum=None, **kwargs):
from metpy.plots import SkewT
if temp is None:
temp = 'Temperature'
if rel_hum is None:
rel_hum = 'Relative Humidity'
# convert range (m) to hectopascals
#hpascals = 1013.25 * np.exp(-data.coords['Range'] / 7)
hpascals = 1013.25 * np.exp(-ranges / 7)
# convert temperature from Kelvins to Celsius
tempC = data[0] - 273.15
# estimate dewpoint from relative humidity
dewpoints = data[0] - ((100 - data[1]) / 5) - 273.15
# get info about the current figure
# fshape = plt.gcf().axes.shape
# skew = SkewT(fig=plt.gcf(), subplot=(fshape[0], fshape[1], splots[0]))
skew = SkewT(fig=plt.gcf(), subplot=splots[0])
#plt.gca().axis('off')
splots.pop(0)
skew.plot(hpascals, tempC, 'r')
skew.plot(hpascals, dewpoints, 'g')
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
if data.shape[0] == 4:
u = data[2]
v = data[3]
skew.plot_barbs(hpascals, u, v, xloc=.9)
def create_skewt(self, rdat):
""" Create the SkewT plot inside the figure instance """
# Extract pressure from data
P = rdat['PRES'].values * units.hPa
# Extract temperature from data
T = rdat['TEMP'].values * units.degC
# Extract dewpt from data
Td = rdat['DWPT'].values * units.degC
skew = SkewT(self.fig, rotation=45)
# Change to read in min/max from data arrays??
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-40, 80)
skew.ax.set_title(self.title)
skew.plot(P, T, 'r', linewidth=2)
skew.plot(P, Td, 'g', linewidth=2)
# 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()
def plot_metpy(data, title="", saveplot=None, showplot=True):
# Convert data into a suitable format for metpy.
_altitude = data[:,0] * units('m')
p = mpcalc.height_to_pressure_std(_altitude)
T = data[:,3] * units.degC
Td = data[:,4] * units.degC
wind_speed = data[:,1] * units('m/s')
wind_direction = data[:,2] * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_direction)
fig = plt.figure(figsize=(6,8))
skew = SkewT(fig=fig)
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
my_interval = np.arange(300, 1000, 50) * units('mbar')
ix = mpcalc.resample_nn_1d(p, my_interval)
skew.plot_barbs(p[ix], u[ix], v[ix])
skew.ax.set_ylim(1000,300)
skew.ax.set_xlim(-40, 30)
skew.plot_dry_adiabats()
heights = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) * units.km
std_pressures = mpcalc.height_to_pressure_std(heights)
for height_tick, p_tick in zip(heights, std_pressures):
trans, _, _ = skew.ax.get_yaxis_text1_transform(0)
skew.ax.text(0.02, p_tick, '---{:~d}'.format(height_tick), transform=trans)
plt.title("Sounding: " + title)
if saveplot != None:
fig.savefig(saveplot, bbox_inches='tight')
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 plot_skewt_icon(sounding, parcel=None, base=1000, top=100, skew=45):
model_time = np.datetime_as_string(sounding.metadata.model_time, unit='m')
valid_time = np.datetime_as_string(sounding.metadata.valid_time, unit='m')
top_idx = find_closest_model_level(sounding.p * units.Pa, top * units("hPa"))
fig = plt.figure(figsize=(11, 11), constrained_layout=True)
skew = SkewT(fig, rotation=skew)
skew.plot(sounding.p * units.Pa, sounding.T * units.K, 'r')
skew.plot(sounding.p * units.Pa, sounding.Td, 'b')
skew.plot_barbs(sounding.p[:top_idx] * units.Pa, sounding.U[:top_idx] * units.mps,
sounding.V[:top_idx] * units.mps, plot_units=units.knot, alpha=0.6, xloc=1.13, x_clip_radius=0.3)
if parcel == "surface-based":
prof = mpcalc.parcel_profile(sounding.p * units.Pa, sounding.T[0] * units.K, sounding.Td[0]).to('degC')
skew.plot(sounding.p * units.Pa, prof, 'y', linewidth=2)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
skew.plot(sounding.p * units.Pa, np.zeros(len(sounding.p)) * units.degC, "#03d3fc", linewidth=1)
skew.ax.set_ylim(base, top)
plt.title(f"Model run: {model_time}Z", loc='left')
plt.title(f"Valid time: {valid_time}Z", fontweight='bold', loc='right')
plt.xlabel("Temperature [°C]")
plt.ylabel("Pressure [hPa]")
fig.suptitle(f"ICON-EU Model for {sounding.latitude_pretty}, {sounding.longitude_pretty}", fontsize=14)
ax1 = plt.gca()
ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
color = '#333333'
ax2.set_ylabel('Geometric Altitude [kft]', color=color) # we already handled the x-label with ax1
ax2_data = (sounding.p * units.Pa).to('hPa')
ax2.plot(np.zeros(len(ax2_data)), ax2_data, color=color, alpha=0.0)
ax2.tick_params(axis='y', labelcolor=color)
ax2.set_yscale('log')
ax2.set_ylim((base, top))
ticks = np.linspace(base, top, num=10)
ideal_ticks = np.geomspace(base, top, 20)
real_tick_idxs = [find_closest_model_level(sounding.p * units.Pa, p_level * units("hPa")) for p_level in
ideal_ticks]
ticks = (sounding.p * units.Pa).to("hPa")[real_tick_idxs]
full_levels = [full_level_height(sounding.HHL, idx) for idx in real_tick_idxs]
tick_labels = np.around((full_levels * units.m).m_as("kft"), decimals=1)
ax2.set_yticks(ticks)
ax2.set_yticklabels(tick_labels)
ax2.minorticks_off()
return fig
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_adiabat_units():
"""Test adiabats and mixing lines can handle different units."""
with matplotlib.rc_context({'axes.autolimit_mode': 'data'}):
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig)
p = np.linspace(950, 100, 10) * units.hPa
t = np.linspace(18, -20, 10) * units.degC
skew.plot(p, t, 'r')
# Add lines with units different to the xaxis
t0 = (np.linspace(-20, 20, 5) * units.degC).to(units.degK)
skew.plot_dry_adiabats(t0=t0)
# add lines with no units
t0 = np.linspace(-20, 20, 5)
skew.plot_moist_adiabats(t0=t0)
skew.plot_mixing_lines()
return fig
def test_skewt_adiabat_kelvin_base():
"""Test adiabats and mixing lines can handle different units."""
with matplotlib.rc_context({'axes.autolimit_mode': 'data'}):
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig, rotation=45)
p = np.linspace(950, 100, 10) * units.hPa
t = (np.linspace(18, -30, 10) * units.degC).to(units.degK)
skew.plot(p, t, 'r')
# At this point the xaxis is actually degC
# Add lines using kelvin base
t0 = (np.linspace(-20, 40, 5) * units.degC).to(units.degK)
skew.plot_dry_adiabats(t0=t0)
# add lines with no units (but using kelvin)
t0 = np.linspace(253.15, 313.15, 5)
skew.plot_moist_adiabats(t0=t0)
skew.plot_mixing_lines()
return fig
def plot(self, savename=None):
# p in hPa, T and Td in K, qv in kg/kg.
# u and v (optional) in m/s.
# All inputs are 1-D arrays.
from matplotlib import pyplot as plt
from metpy.units import units
from metpy.plots import SkewT
import numpy as np
plt.rcParams['figure.figsize'] = (6, 8)
# Set lower limit for plotting on p-axis.
maxp = np.max(self.p)
skew = SkewT()
# Plot data.
skew.plot(self.p * units.hPa, self.T * units.K, 'r')
skew.plot(self.p * units.hPa, self.Td * units.K, 'g')
if self.u is not None and self.v is not None:
skew.plot_barbs((self.p * units.hPa)[::100],
(self.u * units.meters / units.seconds)[::100],
(self.v * units.meters / units.seconds)[::100])
# Add some lines and labels.
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
skew.ax.set_ylabel('Pressure (hPa)')
skew.ax.set_xlabel(
r'Temperature ($^{\circ}$C), Mixing Ratio (g kg$^{-1}$)')
# Set lower limit for plotting on p-axis.
skew.ax.set_ylim(max(maxp, 1000), 100)
# Save plot to a file based on input name.
if savename is not None:
fig = plt.gcf()
fig.savefig(savename)
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
T = dataset.variables['temperature'][:] 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
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()
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)
