def test_vertical_velocity_pressure_moist_air():
"""Test conversion of w to omega assuming moist air."""
w = -1 * units('cm/s')
omega_truth = 1.032100858 * units('microbar/second')
omega_test = vertical_velocity_pressure(w, 850. * units.mbar, 280. * units.K,
8 * units('g/kg'))
assert_almost_equal(omega_test, omega_truth, 6)
def test_basic3(self):
'Basic test of advection'
u = np.array([1, 2, 3]) * units('m/s')
s = np.array([1, 2, 3]) * units('Pa')
a = advection(s, u, (1 * units.meter,))
truth = np.array([-1, -2, -3]) * units('Pa/sec')
assert_array_equal(a, truth)
def test_nws_layout():
"""Test metpy's NWS layout for station plots."""
fig = plt.figure(figsize=(3, 3))
# testing data
x = np.array([1])
y = np.array([2])
data = dict()
data["air_temperature"] = np.array([77]) * units.degF
data["dew_point_temperature"] = np.array([71]) * units.degF
data["air_pressure_at_sea_level"] = np.array([999.8]) * units("mbar")
data["eastward_wind"] = np.array([15.0]) * units.knots
data["northward_wind"] = np.array([15.0]) * units.knots
data["cloud_coverage"] = [7]
data["present_weather"] = [80]
data["high_cloud_type"] = [1]
data["medium_cloud_type"] = [3]
data["low_cloud_type"] = [2]
data["visibility_in_air"] = np.array([5.0]) * units.mile
data["tendency_of_air_pressure"] = np.array([-0.3]) * units("mbar")
data["tendency_of_air_pressure_symbol"] = [8]
# Make the plot
sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=12, spacing=16)
nws_layout.plot(sp, data)
sp.ax.set_xlim(0, 3)
sp.ax.set_ylim(0, 3)
return fig
def test_nws_layout():
'Test metpy\'s NWS layout for station plots'
setup_font()
fig = make_figure(figsize=(3, 3))
# testing data
x = np.array([1])
y = np.array([2])
data = dict()
data['air_temperature'] = np.array([77]) * units.degF
data['dew_point_temperature'] = np.array([71]) * units.degF
data['air_pressure_at_sea_level'] = np.array([999.8]) * units('mbar')
data['eastward_wind'] = np.array([15.]) * units.knots
data['northward_wind'] = np.array([15.]) * units.knots
data['cloud_coverage'] = [7]
data['present_weather'] = [80]
data['high_cloud_type'] = [1]
data['medium_cloud_type'] = [3]
data['low_cloud_type'] = [2]
data['visibility_in_air'] = np.array([5.]) * units.mile
data['tendency_of_air_pressure'] = np.array([-0.3]) * units('mbar')
data['tendency_of_air_pressure_symbol'] = [8]
# Make the plot
sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=12, spacing=16)
nws_layout.plot(sp, data)
sp.ax.set_xlim(0, 3)
sp.ax.set_ylim(0, 3)
hide_tick_labels(sp.ax)
return fig
def test_basic(self):
'Basic braindead test of advection'
u = np.ones((3,)) * units('m/s')
s = np.ones_like(u) * units.kelvin
a = advection(s, u, (1 * units.meter,))
truth = np.zeros_like(u) * units('K/sec')
assert_array_equal(a, truth)
def test_basic2(self):
'Basic test of advection'
u = np.ones((3,)) * units('m/s')
s = np.array([1, 2, 3]) * units('kg')
a = advection(s, u, (1 * units.meter,))
truth = -np.ones_like(u) * units('kg/sec')
assert_array_equal(a, truth)
def test_grid_deltas_from_dataarray_xy(test_da_xy):
"""Test grid_deltas_from_dataarray with a xy grid."""
dx, dy = grid_deltas_from_dataarray(test_da_xy)
true_dx = np.array([[[[500] * 3]]]) * units('km')
true_dy = np.array([[[[500]] * 3]]) * units('km')
assert_array_almost_equal(dx, true_dx, 5)
assert_array_almost_equal(dy, true_dy, 5)
def test_advection_2d_uniform():
"""Test advection for uniform 2D field."""
u = np.ones((3, 3)) * units('m/s')
s = np.ones_like(u) * units.kelvin
a = advection(s, [u, u], (1 * units.meter, 1 * units.meter))
truth = np.zeros_like(u) * units('K/sec')
assert_array_equal(a, truth)
def test_advection_uniform():
"""Test advection calculation for a uniform 1D field."""
u = np.ones((3,)) * units('m/s')
s = np.ones_like(u) * units.kelvin
a = advection(s, u, (1 * units.meter,))
truth = np.zeros_like(u) * units('K/sec')
assert_array_equal(a, truth)
def test_advection_1d():
"""Test advection calculation with varying wind and field."""
u = np.array([1, 2, 3]) * units('m/s')
s = np.array([1, 2, 3]) * units('Pa')
a = advection(s, u, (1 * units.meter,))
truth = np.array([-1, -2, -3]) * units('Pa/sec')
assert_array_equal(a, truth)
def test_advection_1d_uniform_wind():
"""Test advection for simple 1D case with uniform wind."""
u = np.ones((3,)) * units('m/s')
s = np.array([1, 2, 3]) * units('kg')
a = advection(s, u, (1 * units.meter,))
truth = -np.ones_like(u) * units('kg/sec')
assert_array_equal(a, truth)
def test_nws_layout():
"""Test metpy's NWS layout for station plots."""
fig = plt.figure(figsize=(3, 3))
# testing data
x = np.array([1])
y = np.array([2])
data = {'air_temperature': np.array([77]) * units.degF,
'dew_point_temperature': np.array([71]) * units.degF,
'air_pressure_at_sea_level': np.array([999.8]) * units('mbar'),
'eastward_wind': np.array([15.]) * units.knots,
'northward_wind': np.array([15.]) * units.knots, 'cloud_coverage': [7],
'present_weather': [80], 'high_cloud_type': [1], 'medium_cloud_type': [3],
'low_cloud_type': [2], 'visibility_in_air': np.array([5.]) * units.mile,
'tendency_of_air_pressure': np.array([-0.3]) * units('mbar'),
'tendency_of_air_pressure_symbol': [8]}
# Make the plot
sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=12, spacing=16)
nws_layout.plot(sp, data)
sp.ax.set_xlim(0, 3)
sp.ax.set_ylim(0, 3)
return fig
def main():
"""Go Main Go"""
pgconn = get_dbconn('scan')
for station in ['S2004', 'S2196', 'S2002', 'S2072', 'S2068',
'S2031', 'S2001', 'S2047']:
df = read_sql("""
select extract(year from valid + '2 months'::interval) as wy,
tmpf, dwpf from alldata where station = %s and tmpf is not null
and dwpf is not null
""", pgconn, params=(station, ), index_col=None)
df['mixingratio'] = meteorology.mixing_ratio(
temperature(df['dwpf'].values, 'F')).value('KG/KG')
df['vapor_pressure'] = mcalc.vapor_pressure(
1000. * units.mbar,
df['mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['saturation_mixingratio'] = (
meteorology.mixing_ratio(
temperature(df['tmpf'].values, 'F')).value('KG/KG'))
df['saturation_vapor_pressure'] = mcalc.vapor_pressure(
1000. * units.mbar,
df['saturation_mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['vpd'] = df['saturation_vapor_pressure'] - df['vapor_pressure']
means = df.groupby('wy').mean()
counts = df.groupby('wy').count()
for yr, row in means.iterrows():
print(("%s,%s,%.0f,%.3f"
) % (yr, station, counts.at[yr, 'vpd'], row['vpd']))
def test_supercell_composite_scalar():
"""Test supercell composite function with a single value."""
mucape = 2000. * units('J/kg')
esrh = 400. * units('m^2/s^2')
ebwd = 30. * units('m/s')
truth = 16.
supercell_comp = supercell_composite(mucape, esrh, ebwd)
assert_almost_equal(supercell_comp, truth, 6)
def test_advection_2d():
"""Test advection in varying 2D field."""
u = np.ones((3, 3)) * units('m/s')
v = 2 * np.ones((3, 3)) * units('m/s')
s = np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]]) * units.kelvin
a = advection(s, [u, v], (1 * units.meter, 1 * units.meter))
truth = np.array([[-3, -2, 1], [-4, 0, 4], [-1, 2, 3]]) * units('K/sec')
assert_array_equal(a, truth)
def test_supercell_composite():
"""Test supercell composite function."""
mucape = [2000., 1000., 500., 2000.] * units('J/kg')
esrh = [400., 150., 45., 45.] * units('m^2/s^2')
ebwd = [30., 15., 5., 5.] * units('m/s')
truth = [16., 2.25, 0., 0.]
supercell_comp = supercell_composite(mucape, esrh, ebwd)
assert_array_equal(supercell_comp, truth)
def test_surface_based_cape_cin():
"""Test the surface-based CAPE and CIN calculation."""
p = np.array([959., 779.2, 751.3, 724.3, 700., 269.]) * units.mbar
temperature = np.array([22.2, 14.6, 12., 9.4, 7., -38.]) * units.celsius
dewpoint = np.array([19., -11.2, -10.8, -10.4, -10., -53.2]) * units.celsius
cape, cin = surface_based_cape_cin(p, temperature, dewpoint)
assert_almost_equal(cape, 58.0368212 * units('joule / kilogram'), 6)
assert_almost_equal(cin, -89.8073512 * units('joule / kilogram'), 6)
def test_2dbasic2(self):
'Basic 2D test of advection'
u = np.ones((3, 3)) * units('m/s')
v = 2 * np.ones((3, 3)) * units('m/s')
s = np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]]) * units.kelvin
a = advection(s, [u, v], (1 * units.meter, 1 * units.meter))
truth = np.array([[-3, -2, 1], [-4, 0, 4], [-1, 2, 3]]) * units('K/sec')
assert_array_equal(a, truth)
def test_geopotential(self):
'Test of geostrophic wind calculation with geopotential'
z = np.array([[48, 49, 48], [49, 50, 49], [48, 49, 48]]) * 100. * units('m^2/s^2')
ug, vg = geostrophic_wind(z, 1 / units.sec, 100. * units.meter, 100. * units.meter)
true_u = np.array([[-1, 0, 1]] * 3) * units('m/s')
true_v = -true_u.T
assert_array_equal(ug, true_u)
assert_array_equal(vg, true_v)
def bv_data():
"""Return height and potential temperature data for testing Brunt-Vaisala functions."""
heights = [1000., 1500., 2000., 2500.] * units('m')
potential_temperatures = [[290., 290., 290., 290.],
[292., 293., 293., 292.],
[294., 296., 293., 293.],
[296., 295., 293., 296.]] * units('K')
return heights, potential_temperatures
def test_most_unstable_cape_cin():
"""Test the most unstable CAPE/CIN calculation."""
pressure = np.array([1000., 959., 867.9, 850., 825., 800.]) * units.mbar
temperature = np.array([18.2, 22.2, 17.4, 10., 0., 15]) * units.celsius
dewpoint = np.array([19., 19., 14.3, 0., -10., 0.]) * units.celsius
mucape, mucin = most_unstable_cape_cin(pressure, temperature, dewpoint)
assert_almost_equal(mucape, 157.07111 * units('joule / kilogram'), 4)
assert_almost_equal(mucin, -15.74772 * units('joule / kilogram'), 4)
def test_most_unstable_cape_cin_surface():
"""Test the most unstable CAPE/CIN calculation when surface is most unstable."""
pressure = np.array([959., 779.2, 751.3, 724.3, 700., 269.]) * units.mbar
temperature = np.array([22.2, 14.6, 12., 9.4, 7., -38.]) * units.celsius
dewpoint = np.array([19., -11.2, -10.8, -10.4, -10., -53.2]) * units.celsius
mucape, mucin = most_unstable_cape_cin(pressure, temperature, dewpoint)
assert_almost_equal(mucape, 58.0368212 * units('joule / kilogram'), 6)
assert_almost_equal(mucin, -89.8073512 * units('joule / kilogram'), 6)
def test_critical_angle():
"""Test critical angle with observed sounding."""
data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC')
ca = critical_angle(data['pressure'], data['u_wind'],
data['v_wind'], data['height'],
stormu=0 * units('m/s'), stormv=0 * units('m/s'))
truth = [140.0626637513269] * units('degrees')
assert_almost_equal(ca, truth, 8)
def test_cape_cin_no_lfc():
"""Test that CAPE is zero with no LFC."""
p = np.array([959., 779.2, 751.3, 724.3, 700., 269.]) * units.mbar
temperature = np.array([22.2, 24.6, 22., 20.4, 18., -10.]) * units.celsius
dewpoint = np.array([19., -11.2, -10.8, -10.4, -10., -53.2]) * units.celsius
parcel_prof = parcel_profile(p, temperature[0], dewpoint[0]).to('degC')
cape, cin = cape_cin(p, temperature, dewpoint, parcel_prof)
assert_almost_equal(cape, 0.0 * units('joule / kilogram'), 6)
assert_almost_equal(cin, 0.0 * units('joule / kilogram'), 6)
def test_cape_cin_custom_profile():
"""Test the CAPE and CIN calculation with a custom profile passed to LFC and EL."""
p = np.array([959., 779.2, 751.3, 724.3, 700., 269.]) * units.mbar
temperature = np.array([22.2, 14.6, 12., 9.4, 7., -38.]) * units.celsius
dewpoint = np.array([19., -11.2, -10.8, -10.4, -10., -53.2]) * units.celsius
parcel_prof = parcel_profile(p, temperature[0], dewpoint[0]) + 5 * units.delta_degC
cape, cin = cape_cin(p, temperature, dewpoint, parcel_prof)
assert_almost_equal(cape, 1443.505086499895 * units('joule / kilogram'), 6)
assert_almost_equal(cin, 0.0 * units('joule / kilogram'), 6)
def test_sigtor():
"""Test significant tornado parameter function."""
sbcape = [2000., 2000., 2000., 2000., 3000, 4000] * units('J/kg')
sblcl = [3000., 1500., 500., 1500., 1500, 800] * units('meter')
srh1 = [200., 200., 200., 200., 300, 400] * units('m^2/s^2')
shr6 = [20., 5., 20., 35., 20., 35] * units('m/s')
truth = [0., 0, 1.777778, 1.333333, 2., 10.666667]
sigtor = significant_tornado(sbcape, sblcl, srh1, shr6)
assert_almost_equal(sigtor, truth, 6)
def test_cape_cin():
"""Test the basic CAPE and CIN calculation."""
p = np.array([959., 779.2, 751.3, 724.3, 700., 269.]) * units.mbar
temperature = np.array([22.2, 14.6, 12., 9.4, 7., -38.]) * units.celsius
dewpoint = np.array([19., -11.2, -10.8, -10.4, -10., -53.2]) * units.celsius
parcel_prof = parcel_profile(p, temperature[0], dewpoint[0])
cape, cin = cape_cin(p, temperature, dewpoint, parcel_prof)
assert_almost_equal(cape, 58.0368212 * units('joule / kilogram'), 6)
assert_almost_equal(cin, -89.8073512 * units('joule / kilogram'), 6)
def test_sigtor_scalar():
"""Test significant tornado parameter function with a single value."""
sbcape = 4000 * units('J/kg')
sblcl = 800 * units('meter')
srh1 = 400 * units('m^2/s^2')
shr6 = 35 * units('m/s')
truth = 10.666667
sigtor = significant_tornado(sbcape, sblcl, srh1, shr6)
assert_almost_equal(sigtor, truth, 6)
def test_bulk_shear():
"""Test bulk shear with observed sounding."""
data = get_upper_air_data(datetime(2016, 5, 22, 0), 'DDC')
u, v = bulk_shear(data['pressure'], data['u_wind'],
data['v_wind'], heights=data['height'],
depth=6000 * units('meter'))
truth = [29.899581266946115, -14.389225800205509] * units('knots')
assert_almost_equal(u.to('knots'), truth[0], 8)
assert_almost_equal(v.to('knots'), truth[1], 8)
def test_cape_cin_no_el():
"""Test that CAPE works with no EL."""
p = np.array([959., 779.2, 751.3, 724.3]) * units.mbar
temperature = np.array([22.2, 14.6, 12., 9.4]) * units.celsius
dewpoint = np.array([19., -11.2, -10.8, -10.4]) * units.celsius
parcel_prof = parcel_profile(p, temperature[0], dewpoint[0]).to('degC')
cape, cin = cape_cin(p, temperature, dewpoint, parcel_prof)
assert_almost_equal(cape, 0.08750805 * units('joule / kilogram'), 6)
assert_almost_equal(cin, -89.8073512 * units('joule / kilogram'), 6)
def test_get_wind_dir():
"""Test that get_wind_dir wrapper works (deprecated in 0.9)."""
with pytest.warns(MetpyDeprecationWarning):
d = get_wind_dir(3. * units('m/s'), 4. * units('m/s'))
assert_almost_equal(d, 216.870 * units.deg, 3)
def test_windchill_scalar():
"""Test wind chill with scalars."""
wc = windchill(-5 * units.degC, 35 * units('m/s'))
assert_almost_equal(wc, -18.9357 * units.degC, 0)
def test_wind_comps_scalar():
"""Test wind components calculation with scalars."""
u, v = wind_components(8 * units('m/s'), 150 * units.deg)
assert_almost_equal(u, -4 * units('m/s'), 3)
assert_almost_equal(v, 6.9282 * units('m/s'), 3)
x.strftime('%Y-%m-%d %H:%M')
for x in rrule.rrule(rrule.HOURLY, dtstart=date_1, until=date_2)
]
metar_file = main + '/wyoming/' + date[0:6] + '/AbuDhabi_surf_' + date[
0:8] + '.csv'
outFile = main + '/wyoming/' + date[0:6] + '/AbuDhabi_surf_mr' + date[
0:8] + '.csv'
metar_data = pd.read_csv(metar_file)
metar_data = metar_data[[
'STN', 'TIME', 'ALTM', 'TMP', 'DEW', 'RH', 'DIR', 'SPD', 'VIS'
]]
#metar_data=metar_data.drop('Unnamed: 9',axis=1)
metar_data = metar_data.drop(metar_data.index[0])
metar_data['TIME'] = date_list
tmp = np.array((metar_data.iloc[:, 3]).apply(pd.to_numeric, errors='coerce') +
273.15) * units('K')
rh = np.array(
(metar_data.iloc[:, 5]).apply(pd.to_numeric, errors='coerce') / 100.0)
press = np.array((metar_data.iloc[:, 2]).apply(
pd.to_numeric, errors='coerce')) * units('millibar')
mrio = mcalc.mixing_ratio_from_relative_humidity(rh, tmp, press)
metar_data['mrio'] = mrio
metar_data.to_csv(outFile)
def test_warning_dir():
"""Test that warning is raised wind direction > 2Pi."""
with pytest.warns(UserWarning):
wind_components(3. * units('m/s'), 270)
def test_direction_dimensions():
"""Verify wind_direction returns degrees."""
d = wind_direction(3. * units('m/s'), 4. * units('m/s'))
assert str(d.units) == 'degree'
def test_scalar_direction():
"""Test wind direction with scalars."""
d = wind_direction(3. * units('m/s'), 4. * units('m/s'))
assert_almost_equal(d, 216.870 * units.deg, 3)
def test_assign_y_x_dataarray_outside_tolerance(test_coord_helper_da_latlon):
"""Test assign_y_x raises ValueError when tolerance is exceeded on DataArray."""
with pytest.raises(ValueError) as exc:
test_coord_helper_da_latlon.metpy.assign_y_x(tolerance=1 * units('um'))
assert 'cannot be collapsed to 1D within tolerance' in str(exc)
def test_units_percent():
"""Test that '%' is converted to 'percent'."""
test_var_percent = xr.open_dataset(
get_test_data('irma_gfs_example.nc',
as_file_obj=False))['Relative_humidity_isobaric']
assert test_var_percent.metpy.units == units('percent')
sl_vars_this = sl_vars.sel(time=this_time)
vtime = pd.to_datetime(iso_vars_this.time.values)
print("working on "+vtime.strftime("%H%M UTC %d %b %Y"))
## pull out needed variables
lon = iso_vars_this.longitude.data
lat = iso_vars_this.latitude.data
## get 500-mb data to start
hght_500 = iso_vars_this.z.sel(level=500)/9.81 ## this was geopotential, convert to geopotential height
uwnd_500 = iso_vars_this.u.sel(level=500)
vwnd_500 = iso_vars_this.v.sel(level=500)
# less smooth the height a bit
hght_500 = ndimage.gaussian_filter(hght_500, sigma=1, order=0) * units('m')
# Combine 1D latitude and longitudes into a 2D grid of locations
lon_2d, lat_2d = np.meshgrid(lon, lat)
# Gridshift for barbs
lon_2d[lon_2d > 180] = lon_2d[lon_2d > 180] - 360
if plot_500vort:
#Begin Data Calculations
dx, dy = mpcalc.lat_lon_grid_deltas(lon, lat)
f = mpcalc.coriolis_parameter(np.deg2rad(lat_2d)).to(units('1/sec'))
## need to set the lats this way since GFS is 1-d
#avor = mpcalc.absolute_vorticity(uwnd_500,vwnd_500,dx,dy,lat[None,:,None]*units('degrees'))
def test_streamfunc():
"""Test of Montgomery Streamfunction calculation."""
t = 287. * units.kelvin
hgt = 5000. * units.meter
msf = montgomery_streamfunction(hgt, t)
assert_almost_equal(msf, 337468.2500 * units('m^2 s^-2'), 4)
def test_default_order_warns():
"""Test that using the default array ordering issues a warning."""
u = np.ones((3, 3)) * units('m/s')
with pytest.warns(UserWarning):
vorticity(u, u, 1 * units.meter, 1 * units.meter)
def test_get_wind_speed():
"""Test that get_wind_speed wrapper works (deprecated in 0.9)."""
with pytest.warns(MetpyDeprecationWarning):
s = get_wind_speed(-3. * units('m/s'), -4. * units('m/s'))
assert_almost_equal(s, 5. * units('m/s'), 3)
def test_units(test_var):
"""Test the units property on the accessor."""
assert test_var.metpy.units == units('kelvin')
def test_get_wind_components():
"""Test that get_wind_components wrapper works (deprecated in 0.9)."""
with pytest.warns(MetpyDeprecationWarning):
u, v = get_wind_components(8 * units('m/s'), 150 * units.deg)
assert_almost_equal(u, -4 * units('m/s'), 3)
assert_almost_equal(v, 6.9282 * units('m/s'), 3)
def process(ncfn):
"""Process this file """
pgconn = get_dbconn("iem")
icursor = pgconn.cursor()
xref = {}
icursor.execute("""
SELECT id, network from stations where
network ~* 'ASOS' or network = 'AWOS' and country = 'US'
""")
for row in icursor:
xref[row[0]] = row[1]
icursor.close()
nc = ncopen(ncfn)
data = {}
for vname in [
"stationId",
"observationTime",
"temperature",
"dewpoint",
"altimeter", # Pa
"windDir",
"windSpeed", # mps
"windGust", # mps
"visibility", # m
"precipAccum",
"presWx",
"skyCvr",
"skyCovLayerBase",
"autoRemark",
"operatorRemark",
]:
data[vname] = nc.variables[vname][:]
for qc in ["QCR", "QCD"]:
vname2 = vname + qc
if vname2 in nc.variables:
data[vname2] = nc.variables[vname2][:]
for vname in ["temperature", "dewpoint"]:
data[vname + "C"] = temperature(data[vname], "K").value("C")
data[vname] = temperature(data[vname], "K").value("F")
for vname in ["windSpeed", "windGust"]:
data[vname] = (masked_array(data[vname], units("meter / second")).to(
units("knots")).magnitude)
data["altimeter"] = pressure(data["altimeter"], "PA").value("IN")
data["skyCovLayerBase"] = distance(data["skyCovLayerBase"],
"M").value("FT")
data["visibility"] = distance(data["visibility"], "M").value("MI")
data["precipAccum"] = distance(data["precipAccum"], "MM").value("IN")
stations = chartostring(data["stationId"][:])
presentwxs = chartostring(data["presWx"][:])
skycs = chartostring(data["skyCvr"][:])
autoremarks = chartostring(data["autoRemark"][:])
opremarks = chartostring(data["operatorRemark"][:])
def decision(i, fieldname, tolerance):
"""Our decision if we are going to take a HFMETAR value or not"""
if data[fieldname][i] is np.ma.masked:
return None
if data["%sQCR" % (fieldname, )][i] == 0:
return data[fieldname][i]
# Now we have work to do
departure = np.ma.max(np.ma.abs(data["%sQCD" % (fieldname, )][i, :]))
# print("departure: %s tolerance: %s" % (departure, tolerance))
if departure <= tolerance:
return data[fieldname][i]
return None
for i, sid in tqdm(
enumerate(stations),
total=len(stations),
disable=(not sys.stdout.isatty()),
):
sid3 = sid[1:] if sid[0] == "K" else sid
ts = datetime.datetime(1970, 1, 1) + datetime.timedelta(
seconds=data["observationTime"][i])
ts = ts.replace(tzinfo=pytz.UTC)
mtr = "%s %sZ AUTO " % (sid, ts.strftime("%d%H%M"))
network = xref.get(sid3, "ASOS")
iem = Observation(sid3, network, ts)
# 06019G23KT
val = decision(i, "windDir", 15)
if val is not None:
iem.data["drct"] = int(val)
mtr += "%03i" % (iem.data["drct"], )
else:
mtr += "///"
val = decision(i, "windSpeed", 10)
if val is not None:
iem.data["sknt"] = int(val)
mtr += "%02i" % (iem.data["sknt"], )
else:
mtr += "//"
val = decision(i, "windGust", 10)
if val is not None and val > 0:
iem.data["gust"] = int(val)
mtr += "G%02i" % (iem.data["gust"], )
mtr += "KT "
val = decision(i, "visibility", 4)
if val is not None:
iem.data["vsby"] = float(val)
mtr += "%sSM " % (vsbyfmt(iem.data["vsby"]), )
presentwx = presentwxs[i]
if presentwx != "":
# database storage is comma delimited
iem.data["wxcodes"] = presentwx.split(" ")
mtr += "%s " % (presentwx, )
for _i, (skyc,
_l) in enumerate(zip(skycs[i], data["skyCovLayerBase"][i])):
if skyc != "":
iem.data["skyc%s" % (_i + 1, )] = skyc
if skyc != "CLR":
iem.data["skyl%s" % (_i + 1, )] = int(_l)
mtr += "%s%03i " % (skyc, int(_l) / 100)
else:
mtr += "CLR "
t = ""
tgroup = "T"
val = decision(i, "temperature", 10)
if val is not None:
# Recall the pain enabling this
# iem.data['tmpf'] = float(data['temperature'][i])
tmpc = float(data["temperatureC"][i])
t = "%s%02i/" % (
"M" if tmpc < 0 else "",
tmpc if tmpc > 0 else (0 - tmpc),
)
tgroup += "%s%03i" % (
"1" if tmpc < 0 else "0",
(tmpc if tmpc > 0 else (0 - tmpc)) * 10.0,
)
val = decision(i, "dewpoint", 10)
if val is not None:
# iem.data['dwpf'] = float(data['dewpoint'][i])
tmpc = float(data["dewpointC"][i])
if t != "":
t = "%s%s%02i " % (
t,
"M" if tmpc < 0 else "",
tmpc if tmpc > 0 else 0 - tmpc,
)
tgroup += "%s%03i" % (
"1" if tmpc < 0 else "0",
(tmpc if tmpc > 0 else (0 - tmpc)) * 10.0,
)
if len(t) > 4:
mtr += t
val = decision(i, "altimeter", 20)
if val is not None:
iem.data["alti"] = float(round(val, 2))
mtr += "A%4i " % (iem.data["alti"] * 100.0, )
mtr += "RMK "
val = decision(i, "precipAccum", 25)
if val is not None:
if val >= 0.01:
iem.data["phour"] = float(round(val, 2))
mtr += "P%04i " % (iem.data["phour"] * 100.0, )
elif val > 0:
# Trace
mtr += "P0000 "
iem.data["phour"] = TRACE_VALUE
if tgroup != "T":
mtr += "%s " % (tgroup, )
if autoremarks[i] != "" or opremarks[i] != "":
mtr += "%s %s " % (autoremarks[i], opremarks[i])
mtr += "MADISHF"
# Eat our own dogfood
try:
Metar.Metar(mtr)
iem.data["raw"] = mtr
except Exception as exp:
print("dogfooding extract_hfmetar %s resulted in %s" % (mtr, exp))
continue
for key in iem.data:
if isinstance(iem.data[key], np.float32):
print("key: %s type: %s" % (key, type(iem.data[key])))
icursor = pgconn.cursor()
if not iem.save(icursor, force_current_log=True, skip_current=True):
print(("extract_hfmetar: unknown station? %s %s %s\n%s") %
(sid3, network, ts, mtr))
icursor.close()
pgconn.commit()
def test_scalar_speed():
"""Test wind speed with scalars."""
s = wind_speed(-3. * units('m/s'), -4. * units('m/s'))
assert_almost_equal(s, 5. * units('m/s'), 3)
def zdrarc(zdrc, ZDRmasked, CC, REF, grad_ffd, grad_mag, KDP, forest_loaded,
ax, f, time_start, month, d_beg, h_beg, min_beg, sec_beg, d_end,
h_end, min_end, sec_end, rlons, rlats, max_lons_c, max_lats_c,
zdrlev, proj, storm_relative_dir, Outer_r, Inner_r, tracking_ind):
#Inputs,
#zdrc: Contour of differential reflectivity at zdrlev (typically 1.5 dB)
#ZDRmasked: Masked array ZDRmasked1 in regions outside the forward flank (grad_ffd) and below 0.6 CC
#CC: 1km Correlation Coefficient (CC) grid
#REF: 1km Reflectivity grid
#grad_ffd: Angle (degrees) used to indicate angular region of supercell containing the forward flank
#grad_mag: Array of wind velocity magnitude along reflectivity gradient
#KDP: 1km Specific Differential Phase (KDP) grid
#forest_loaded: Random forest pickle file for Zdr arcs
#ax: Subplot object to be built on with each contour
#f: Placefile, edited throughout the program
#time_start: Radar file date and time of scan
#month: Month of case, supplied by user
#d_beg,h_beg,min_beg,sec_beg,d_end,h_end,min_end,sec_end: Day, hour, minute, second of the beginning and end of a scan
#rlons,rlats: Full volume geographic coordinates, longitude and latitude respectively
#max_lons_c,max_lats_c: Centroid coordinates of storm objects
#zdrlev: User defined value for Zdr contour of arcs
#proj: Projection of Earth's surface to be used for accurate area and distance calculations
#storm_relative_dir: Vector direction along the reflectivity gradient in the forward flank
#Outer_r,Inner_r: Outer and Inner limit of radar range effective for analysis
#tracking_ind: Index for storm of interest
zdr_areas = []
zdr_centroid_lon = []
zdr_centroid_lat = []
zdr_mean = []
zdr_cc_mean = []
zdr_max = []
zdr_storm_lon = []
zdr_storm_lat = []
zdr_dist = []
zdr_forw = []
zdr_back = []
zdr_masks = []
zdr_outlines = []
if np.max(ZDRmasked) > zdrlev:
#Break contours into polygons using the same method as for reflectivity
for level in zdrc.collections:
for contour_poly in level.get_paths():
for n_contour, contour in enumerate(
contour_poly.to_polygons()):
contour_a = np.asarray(contour[:])
xa = contour_a[:, 0]
ya = contour_a[:, 1]
polygon_new = geometry.Polygon([(i[0], i[1])
for i in zip(xa, ya)])
if n_contour == 0:
polygon = polygon_new
else:
polygon = polygon.difference(polygon_new)
try:
pr_area = (transform(proj, polygon).area *
units('m^2')).to('km^2')
except:
continue
boundary = np.asarray(polygon.boundary.xy)
polypath = Path(boundary.transpose())
coord_map = np.vstack(
(rlons[0, :, :].flatten(), rlats[0, :, :].flatten())).T
mask = polypath.contains_points(coord_map).reshape(
rlons[0, :, :].shape)
mean = np.mean(ZDRmasked[mask])
mean_cc = np.mean(CC[mask])
mean_Z = np.mean(REF[mask])
mean_graddir = np.mean(grad_ffd[mask])
mean_grad = np.mean(grad_mag[mask])
mean_kdp = np.mean(KDP[mask])
#Only select out objects larger than 1 km^2 with high enough CC
if pr_area > 1 * units(
'km^2') and mean > zdrlev[0] and mean_cc > .88:
g = Geod(ellps='sphere')
dist = np.zeros((np.asarray(max_lons_c).shape[0]))
forw = np.zeros((np.asarray(max_lons_c).shape[0]))
rawangle = np.zeros((np.asarray(max_lons_c).shape[0]))
back = np.zeros((np.asarray(max_lons_c).shape[0]))
zdr_polypath = polypath
#Assign ZDR arc objects to the nearest acceptable storm object
for i in range(dist.shape[0]):
distance_1 = g.inv(polygon.centroid.x,
polygon.centroid.y, max_lons_c[i],
max_lats_c[i])
back[i] = distance_1[1]
if distance_1[1] < 0:
back[i] = distance_1[1] + 360
forw[i] = np.abs(back[i] - storm_relative_dir)
rawangle[i] = back[i] - storm_relative_dir
#Account for weird angles
if forw[i] > 180:
forw[i] = 360 - forw[i]
rawangle[i] = (360 - forw[i]) * (-1)
dist[i] = distance_1[2] / 1000.
rawangle[i] = rawangle[i] * (-1)
#Pick out only ZDR arc objects with a reasonable probability of actually being in the FFD region
#using their location relative to the storm centroid
if (forw[np.where(dist == np.min(dist))[0][0]] < 180
and np.min(dist) < Outer_r
) or (forw[np.where(dist == np.min(dist))[0][0]] < 140
and np.min(dist) < Inner_r):
#Use ML algorithm to eliminate non-arc objects
#Get x and y components
if (rawangle[np.where(dist == np.min(dist))[0][0]] >
0):
directions_raw = 360 - rawangle[np.where(
dist == np.min(dist))[0][0]]
else:
directions_raw = (-1) * rawangle[np.where(
dist == np.min(dist))[0][0]]
xc, yc = wind_components(
np.min(dist) * units('m/s'),
directions_raw * units('degree'))
ARC_X = np.zeros((1, 12))
ARC_X[:, 0] = pr_area.magnitude
ARC_X[:, 1] = np.min(dist)
ARC_X[:, 2] = np.max(ZDRmasked[mask]) / mean
ARC_X[:, 3] = (np.max(ZDRmasked[mask]) /
mean) * pr_area.magnitude
ARC_X[:, 4] = mean_cc
ARC_X[:, 5] = mean_kdp
ARC_X[:, 6] = mean_Z
ARC_X[:, 7] = mean_graddir
ARC_X[:, 8] = mean_grad
ARC_X[:, 9] = rawangle[np.where(
dist == np.min(dist))[0][0]]
ARC_X[:, 10] = xc
ARC_X[:, 11] = yc
pred_zdr = forest_loaded.predict(ARC_X)
if (pred_zdr[0] == 1):
zdr_storm_lon.append((max_lons_c[np.where(
dist == np.min(dist))[0][0]]))
zdr_storm_lat.append((max_lats_c[np.where(
dist == np.min(dist))[0][0]]))
zdr_dist.append(np.min(dist))
zdr_forw.append(
forw[np.where(dist == np.min(dist))[0][0]])
zdr_back.append(
back[np.where(dist == np.min(dist))[0][0]])
zdr_areas.append((pr_area))
zdr_centroid_lon.append((polygon.centroid.x))
zdr_centroid_lat.append((polygon.centroid.y))
zdr_mean.append((mean))
zdr_cc_mean.append((mean_cc))
zdr_max.append((np.max(ZDRmasked[mask])))
zdr_masks.append(mask)
patch = PathPatch(polypath,
facecolor='blue',
alpha=.6,
edgecolor='blue',
linewidth=3)
ax.add_patch(patch)
#Add polygon to placefile
f.write('TimeRange: ' + str(time_start.year) +
'-' + str(month) + '-' + str(d_beg) + 'T' +
str(h_beg) + ':' + str(min_beg) + ':' +
str(sec_beg) + 'Z ' +
str(time_start.year) + '-' + str(month) +
'-' + str(d_end) + 'T' + str(h_end) + ':' +
str(min_end) + ':' + str(sec_end) + 'Z')
f.write('\n')
f.write("Color: 000 000 139 \n")
f.write('Line: 3, 0, "ZDR Arc Outline" \n')
for i in range(len(zdr_polypath.vertices)):
f.write("%.5f" % (zdr_polypath.vertices[i][1]))
f.write(", ")
f.write("%.5f" % (zdr_polypath.vertices[i][0]))
f.write('\n')
f.write("End: \n \n")
f.flush()
if (((max_lons_c[np.where(
dist == np.min(dist))[0][0]])
in max_lons_c[tracking_ind])
and ((max_lats_c[np.where(
dist == np.min(dist))[0][0]])
in max_lats_c[tracking_ind])):
zdr_outlines.append(polypath)
#Returning Variables,
#zdr_storm_lon,zdr_storm_lat: Storm object centroids associated with Zdr arcs
#zdr_dist: Zdr arc centroid distance from associated storm object centroid
#zdr_forw,zdr_back: Forward and rear angles about the zdr arc region
#zdr_areas: Zdr arc area
#zdr_centroid_lon,zdr_centroid_lat: Zdr arc centroid coordinates
#zdr_mean: Mean value of Zdr within arc
#zdr_cc_mean: Mean value of CC within arc
#zdr_max: Maximum value of Zdr within arc
#zdr_masks: Mask array of zdr arc
#zdr_outlines: Zdr arc contour array
#ax: Subplot object to be built on with each contour
#f: Placefile, edited throughout the program
return zdr_storm_lon, zdr_storm_lat, zdr_dist, zdr_forw, zdr_back, zdr_areas, zdr_centroid_lon, zdr_centroid_lat, zdr_mean, zdr_cc_mean, zdr_max, zdr_masks, zdr_outlines, ax, f
# Grab lat/lon values (GFS will be 1D)
lat = ds.lat.data
lon = ds.lon.data
# Set subset slice for the geographic extent of data to limit download
lon_slice = slice(400, 701)
lat_slice = slice(10, 160)
# Subset lat/lon values
lons = lon[lon_slice]
lats = lat[lat_slice]
# Grab the pressure levels and select the data to be imported
# Need all pressure levels for Temperatures, U and V Wind, and Rel. Humidity
# Smooth with the gaussian filter from scipy
pres = ds['isobaric3'].data[:] * units('Pa')
tmpk_var = ds['Temperature_isobaric'].data[0, :, lat_slice, lon_slice]
tmpk = gaussian_filter(tmpk_var, sigma=1.0) * units.K
thta = mpcalc.potential_temperature(pres[:, None, None], tmpk)
uwnd_var = ds['u-component_of_wind_isobaric'].data[0, :, lat_slice, lon_slice]
vwnd_var = ds['v-component_of_wind_isobaric'].data[0, :, lat_slice, lon_slice]
uwnd = gaussian_filter(uwnd_var, sigma=1.0) * units('m/s')
vwnd = gaussian_filter(vwnd_var, sigma=1.0) * units('m/s')
# Create a clean datetime object for plotting based on time of Geopotential heights
vtime = datetime.strptime(str(ds.time.data[0].astype('datetime64[ms]')),
'%Y-%m-%dT%H:%M:%S.%f')
######################################################################
# Remove all missing data from pressure
x_masked, y_masked, pres = remove_nan_observations(xp, yp, data['slp'].values)
###########################################
# Interpolate pressure using Cressman interpolation
slpgridx, slpgridy, slp = interpolate_to_grid(x_masked,
y_masked,
pres,
interp_type='cressman',
minimum_neighbors=1,
search_radius=400000,
hres=100000)
##########################################
# Get wind information and mask where either speed or direction is unavailable
wind_speed = (data['wind_speed'].values * units('m/s')).to('knots')
wind_dir = data['wind_dir'].values * units.degree
good_indices = np.where((~np.isnan(wind_dir)) & (~np.isnan(wind_speed)))
x_masked = xp[good_indices]
y_masked = yp[good_indices]
wind_speed = wind_speed[good_indices]
wind_dir = wind_dir[good_indices]
###########################################
# Calculate u and v components of wind and then interpolate both.
#
# Both will have the same underlying grid so throw away grid returned from v interpolation.
u, v = wind_components(wind_speed, wind_dir)
#!/home/twixtrom/miniconda3/envs/wrfpost/bin/python
import sys
from PWPP import wrfpost
import numpy as np
from metpy.units import units
# import xarray as xr
infile = sys.argv[1]
outfile = sys.argv[2]
variables = [
'temp', 'dewpt', 'uwnd', 'vwnd', 'wwnd', 'avor', 'height', 'temp_2m',
'dewpt_2m', 'mslp', 'q_2m', 'u_10m', 'v_10m', 'timestep_pcp', 'UH', 'cape',
'refl'
]
plevs = np.array([1000, 925, 850, 700, 500, 300, 250]) * units('hPa')
# chunks = {'Time': 1}
wrfpost(infile,
outfile,
variables,
plevs=plevs,
compression=True,
complevel=4,
format='NETCDF4')
interruptor = 'on'
if interruptor == 'on':
for dd in range(0, 10):
######################## LEYENDO LOS DATOS #########################
## Se cargan los datos de la pagina
datos = Dataset(enlace, 'r')
lat = datos.variables[u'lat'][120:300]
lon = datos.variables[u'lon'][1080:1320]
hgt = datos.variables['hgtprs'][dd, 12, 120:300, 1080:1320]
ugrd = datos.variables['ugrdprs'][dd, 12, 120:300, 1080:1320]
vgrd = datos.variables['vgrdprs'][dd, 12, 120:300, 1080:1320]
datos.close()
## calculo la vorticidad
dx, dy = mpcalc.lat_lon_grid_deltas(lon, lat)
f = mpcalc.coriolis_parameter(np.deg2rad(lat)).to(units('1/sec'))
vor = mpcalc.vorticity(ugrd, vgrd, dx, dy, dim_order='yx')
#vor = ndimage.gaussian_filter(vor, sigma=3, order=0) * units('1/sec')
####### GRAFICADO ########
fig = plt.figure(figsize=(15, 12))
ax1 = plt.subplot(1, 1, 1, projection=ccrs.PlateCarree())
ax1.set_extent([lon[0], lon[len(lon) - 1], lat[0], lat[len(lat) - 1]],
crs=ccrs.PlateCarree())
# en contornos el geopotencial
cs_1 = ax1.contour(lon,
lat,
hgt,
30,
colors='black',
transform=ccrs.PlateCarree())
data = fulldata[reduce_point_density(point_locs, 300000.)]
###########################################
# Now that we have the data we want, we need to perform some conversions:
#
# - Get a list of strings for the station IDs
# - Get wind components from speed and direction
# - Convert cloud fraction values to integer codes [0 - 8]
# - Map METAR weather codes to WMO codes for weather symbols
# Get all of the station IDs as a list of strings
stid = [s.decode('ascii') for s in data['stid']]
# Get the wind components, converting from m/s to knots as will be appropriate
# for the station plot
u, v = get_wind_components((data['wind_speed'] * units('m/s')).to('knots'),
data['wind_dir'] * units.degree)
# Convert the fraction value into a code of 0-8 and compensate for NaN values,
# which can be used to pull out the appropriate symbol
cloud_frac = (8 * data['cloud_fraction'])
cloud_frac[np.isnan(cloud_frac)] = 10
cloud_frac = cloud_frac.astype(int)
# Map weather strings to WMO codes, which we can use to convert to symbols
# Only use the first symbol if there are multiple
wx_text = [s.decode('ascii') for s in data['weather']]
wx_codes = {
'': 0,
'HZ': 5,
'BR': 10,
# Our import for numpy and metpy
import numpy as np
import matplotlib.pyplot as plt
from metpy.units import units
# Here's the "data"
np.random.seed(19990503) # Make sure we all have the same data
temp = (20 * np.cos(np.linspace(0, 2 * np.pi, 100)) +
80 + 2 * np.random.randn(100)) * units.degF
rh = (np.abs(20 * np.cos(np.linspace(0, 4 * np.pi, 100)) +
50 + 5 * np.random.randn(100))) * units('percent')
# Create a mask for the two conditions described above
good_heat_index = (temp >= 80 * units.degF) & (rh >= 40 * units.percent)
# Use this mask to grab the temperature and relative humidity values that together
# will give good heat index values
hi_temps = temp[good_heat_index]
hi_rh = rh[good_heat_index]
# BONUS POINTS: Plot only the data where heat index is defined by
# inverting the mask (using `~mask`) and setting invalid values to np.nan
temp[~good_heat_index] = np.nan
rh[~good_heat_index] = np.nan
plt.plot(temp.m, color='tab:red')
plt.plot(rh.m, color='tab:green')
def hail_objects(hailc, REF_Hail2, ax, f, time_start, month, d_beg, h_beg,
min_beg, sec_beg, d_end, h_end, min_end, sec_end, rlons,
rlats, max_lons_c, max_lats_c, proj):
#Inputs,
#REF_Hail2: REFmasked masked where Zdr and CC greater than 1.0
#hailc: Contour of REF_Hail2 where reflectivity greater than 50.0 dBz
#ax: Subplot object to be built on with each contour
#f: Placefile, edited throughout the program
#time_start: Radar file date and time of scan
#month: Month of case, supplied by user
#d_beg,h_beg,min_beg,sec_beg,d_end,h_end,min_end,sec_end: Day, hour, minute, second of the beginning and end of a scan
#rlons,rlats: Full volume geographic coordinates, longitude and latitude respectively
#max_lons_c,max_lats_c: Centroid coordinates of storm objects
#proj: Projection of Earth's surface to be used for accurate area and distance calculations
hail_areas = []
hail_centroid_lon = []
hail_centroid_lat = []
hail_storm_lon = []
hail_storm_lat = []
if np.max(REF_Hail2) > 50.0:
for level in hailc.collections:
for contour_poly in level.get_paths():
for n_contour, contour in enumerate(
contour_poly.to_polygons()):
contour_a = np.asarray(contour[:])
xa = contour_a[:, 0]
ya = contour_a[:, 1]
polygon_new = geometry.Polygon([(i[0], i[1])
for i in zip(xa, ya)])
if n_contour == 0:
polygon = polygon_new
else:
polygon = polygon.difference(polygon_new)
pr_area = (transform(proj, polygon).area *
units('m^2')).to('km^2')
boundary = np.asarray(polygon.boundary.xy)
polypath = Path(boundary.transpose())
coord_map = np.vstack(
(rlons[0, :, :].flatten(), rlats[0, :, :].flatten())).T
#Create an Mx2 array listing all the coordinates in field
mask_hail = polypath.contains_points(coord_map).reshape(
rlons[0, :, :].shape)
if pr_area > 2 * units('km^2'):
g = Geod(ellps='sphere')
dist_hail = np.zeros((np.asarray(max_lons_c).shape[0]))
for i in range(dist_hail.shape[0]):
distance_hail = g.inv(polygon.centroid.x,
polygon.centroid.y,
max_lons_c[i], max_lats_c[i])
dist_hail[i] = distance_hail[2] / 1000.
if np.min(np.asarray(dist_hail)) < 15.0:
hail_path = polypath
hail_areas.append((pr_area))
hail_centroid_lon.append((polygon.centroid.x))
hail_centroid_lat.append((polygon.centroid.y))
hail_storm_lon.append((max_lons_c[np.where(
dist_hail == np.min(dist_hail))[0][0]]))
hail_storm_lat.append((max_lats_c[np.where(
dist_hail == np.min(dist_hail))[0][0]]))
patch = PathPatch(polypath,
facecolor='gold',
alpha=.7,
edgecolor='gold',
linewidth=4)
ax.add_patch(patch)
#Add polygon to placefile
f.write('TimeRange: ' + str(time_start.year) + '-' +
str(month) + '-' + str(d_beg) + 'T' +
str(h_beg) + ':' + str(min_beg) + ':' +
str(sec_beg) + 'Z ' + str(time_start.year) +
'-' + str(month) + '-' + str(d_end) + 'T' +
str(h_end) + ':' + str(min_end) + ':' +
str(sec_end) + 'Z')
f.write('\n')
f.write("Color: 245 242 066 \n")
f.write('Line: 3, 0, "Hail Core Outline" \n')
for i in range(len(hail_path.vertices)):
f.write("%.5f" % (hail_path.vertices[i][1]))
f.write(", ")
f.write("%.5f" % (hail_path.vertices[i][0]))
f.write('\n')
f.write("End: \n \n")
#Returning Variables,
#hail_areas: Hail core area
#hail_centroid_lon,hail_centroid_lat: Hail core centroid coordinates
#hail_storm_lon,hail_storm_lat: Storm object centroids associated with the hail core
#ax: Subplot object to be built on with each contour
#f: Placefile, edited throughout the program
return hail_areas, hail_centroid_lon, hail_centroid_lat, hail_storm_lon, hail_storm_lat, ax, f
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
month = ctx["month"]
if month == "all":
months = range(1, 13)
elif month == "fall":
months = [9, 10, 11]
elif month == "winter":
months = [12, 1, 2]
elif month == "spring":
months = [3, 4, 5]
elif month == "summer":
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", "%Y-%b-%d")
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
df = read_sql(
"""
SELECT tmpf::int as tmpf, dwpf, relh,
coalesce(mslp, alti * 33.8639, 1013.25) as slp
from alldata where station = %s
and drct is not null and dwpf is not null and dwpf <= tmpf
and sknt > 3 and drct::int %% 10 = 0
and extract(month from valid) in %s
and report_type = 2
""",
pgconn,
params=(station, tuple(months)),
)
if df.empty:
raise NoDataFound("No Data Found.")
# Convert sea level pressure to station pressure
df["pressure"] = mcalc.add_height_to_pressure(
df["slp"].values * units("millibars"),
ctx["_nt"].sts[station]["elevation"] * units("m"),
).to(units("millibar"))
# compute mixing ratio
df["mixingratio"] = mcalc.mixing_ratio_from_relative_humidity(
df["relh"].values * units("percent"),
df["tmpf"].values * units("degF"),
df["pressure"].values * units("millibars"),
)
# compute pressure
df["vapor_pressure"] = mcalc.vapor_pressure(
df["pressure"].values * units("millibars"),
df["mixingratio"].values * units("kg/kg"),
).to(units("kPa"))
means = df.groupby("tmpf").mean().copy()
# compute dewpoint now
means["dwpf"] = (
mcalc.dewpoint(means["vapor_pressure"].values * units("kPa"))
.to(units("degF"))
.m
)
means.reset_index(inplace=True)
# compute RH again
means["relh"] = (
mcalc.relative_humidity_from_dewpoint(
means["tmpf"].values * units("degF"),
means["dwpf"].values * units("degF"),
)
* 100.0
)
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
ax.bar(
means["tmpf"].values - 0.5,
means["dwpf"].values - 0.5,
ec="green",
fc="green",
width=1,
)
ax.grid(True, zorder=11)
ab = ctx["_nt"].sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
ax.set_title(
(
"%s [%s]\nAverage Dew Point by Air Temperature (month=%s) "
"(%s-%s)\n"
"(must have 3+ hourly observations at the given temperature)"
)
% (
ctx["_nt"].sts[station]["name"],
station,
month.upper(),
ab.year,
datetime.datetime.now().year,
),
size=10,
)
ax.plot([0, 140], [0, 140], color="b")
ax.set_ylabel("Dew Point [F]")
y2 = ax.twinx()
y2.plot(means["tmpf"].values, means["relh"].values, color="k")
y2.set_ylabel("Relative Humidity [%] (black line)")
y2.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
y2.set_ylim(0, 100)
ax.set_ylim(0, means["tmpf"].max() + 2)
ax.set_xlim(0, means["tmpf"].max() + 2)
ax.set_xlabel(r"Air Temperature $^\circ$F")
return fig, means[["tmpf", "dwpf", "relh"]]
def test_windchill_kelvin():
"""Test wind chill when given Kelvin temperatures."""
wc = windchill(268.15 * units.kelvin, 35 * units('m/s'))
assert_almost_equal(wc, -18.9357 * units.degC, 0)
def test_coriolis_units():
"""Test that coriolis returns units of 1/second."""
f = coriolis_parameter(50 * units.degrees)
assert f.units == units('1/second')
simple_layout.names()
###########################################
# Next grab the simple variables out of the data we have (attaching correct units), and
# put them into a dictionary that we will hand the plotting function later:
# This is our container for the data
data = dict()
# Copy out to stage everything together. In an ideal world, this would happen on
# the data reading side of things, but we're not there yet.
data['longitude'] = data_arr['lon']
data['latitude'] = data_arr['lat']
data['air_temperature'] = data_arr['air_temperature'] * units.degC
data['dew_point_temperature'] = data_arr['dew_point_temperature'] * units.degC
data['air_pressure_at_sea_level'] = data_arr['slp'] * units('mbar')
###########################################
# Notice that the names (the keys) in the dictionary are the same as those that the
# layout is expecting.
#
# Now perform a few conversions:
#
# - Get wind components from speed and direction
# - Convert cloud fraction values to integer codes [0 - 8]
# - Map METAR weather codes to WMO codes for weather symbols
# Get the wind components, converting from m/s to knots as will be appropriate
# for the station plot
u, v = get_wind_components(data_arr['wind_speed'] * units('m/s'),
data_arr['wind_dir'] * units.degree)
# Add the MetPy logo!
fig = plt.gcf()
add_metpy_logo(fig, 115, 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')
# Get indexes of values closest to defined interval
ix = mpcalc.resample_nn_1d(p, my_interval)
# Plot only values nearest to defined interval values
skew.plot_barbs(p[ix], u[ix], v[ix])
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
skew.ax.set_ylim(1000, 100)
# Add the MetPy logo!
fig = plt.gcf()
