def insertLevels(prof, zeroHt, zeroPres, level):
prof.dwpc = np.insert(prof.dwpc,level,
[interp.dwpt(prof,zeroPres)])
prof.vtmp = np.insert(prof.vtmp,level,
[interp.vtmp(prof,zeroPres)])
prof.thetae = np.insert(prof.thetae,level,
[interp.thetae(prof,zeroPres)])
#prof.wetbulb = np.insert(prof.wetbulb,level,
# [interp.generic_interp_pres(np.log10(zeroPres), prof.logp[::-1], prof.wetbulb[::-1])])
try:
dir,mag = interp.vec(prof,zeroPres)
prof.wdir = np.insert(prof.wdir,level,[dir])
prof.wspd = np.insert(prof.wspd,level,[mag])
prof.u, prof.v = utils.vec2comp(prof.wdir, prof.wspd)
except:
prof.wdir = np.insert(prof.wdir,level,[0])
prof.wspd = np.insert(prof.wspd,level,[0])
prof.u, prof.v = utils.vec2comp(prof.wdir, prof.wspd)
prof.hght = np.insert(prof.hght,level,[zeroHt])
prof.pres = np.insert(prof.pres,level,[zeroPres])
prof.logp = np.log10(prof.pres.copy())
return prof
def max_wind(prof, lower, upper, all=False):
'''
Finds the maximum wind speed of the layer given by lower and upper levels.
In the event of the maximum wind speed occurring at multiple levels, the
lowest level it occurs is returned by default.
Parameters
----------
prof : profile object
Profile Object
lower : number
Bottom level of layer (m, AGL)
upper : number
Top level of layer (m, AGL)
all : Boolean
Switch to change the output to sorted wind levels or maximum level.
Returns
-------
p : number, numpy array
Pressure level (hPa) of max wind speed
maxu : number, numpy array
Maximum Wind Speed U-component (kts)
maxv : number, numpy array
Maximum Wind Speed V-component (kts)
'''
if prof.wdir.count() == 0 or not utils.QC(lower) or not utils.QC(upper):
return ma.masked, ma.masked, ma.masked
lower = interp.to_msl(prof, lower)
upper = interp.to_msl(prof, upper)
plower = interp.pres(prof, lower)
pupper = interp.pres(prof, upper)
if np.ma.is_masked(plower) or np.ma.is_masked(pupper):
warnings.warn(
"winds.max_wind() was unable to interpolate between height and pressure correctly. This may be due to a data integrity issue."
)
return ma.masked, ma.masked, ma.masked
#print(lower, upper, plower, pupper, prof.pres)
ind1 = np.where((plower > prof.pres)
| (np.isclose(plower, prof.pres)))[0][0]
ind2 = np.where((pupper < prof.pres)
| (np.isclose(pupper, prof.pres)))[0][-1]
if len(prof.wspd[ind1:ind2 + 1]) == 0 or ind1 == ind2:
maxu, maxv = utils.vec2comp([prof.wdir[ind1]], [prof.wspd[ind1]])
return maxu, maxv, prof.pres[ind1]
arr = prof.wspd[ind1:ind2 + 1]
inds = np.ma.argsort(arr)
inds = inds[~arr[inds].mask][::-1]
maxu, maxv = utils.vec2comp(prof.wdir[ind1:ind2 + 1][inds],
prof.wspd[ind1:ind2 + 1][inds])
if all:
return maxu, maxv, prof.pres[inds]
else:
return maxu[0], maxv[0], prof.pres[inds[0]]
def max_wind(prof, lower, upper, all=False):
'''
Finds the maximum wind speed of the layer given by lower and upper levels.
In the event of the maximum wind speed occurring at multiple levels, the
lowest level it occurs is returned by default.
Parameters
----------
prof : profile object
Profile Object
lower : number
Bottom level of layer (m, AGL)
upper : number
Top level of layer (m, AGL)
all : Boolean
Switch to change the output to sorted wind levels or maximum level.
Returns
-------
p : number, numpy array
Pressure level (hPa) of max wind speed
maxu : number, numpy array
Maximum Wind Speed U-component
maxv : number, numpy array
Maximum Wind Speed V-component
'''
lower = interp.to_msl(prof, lower)
upper = interp.to_msl(prof, upper)
plower = interp.pres(prof, lower)
pupper = interp.pres(prof, upper)
ind1 = np.where((plower > prof.pres)
| (np.isclose(plower, prof.pres)))[0][0]
ind2 = np.where((pupper < prof.pres)
| (np.isclose(pupper, prof.pres)))[0][-1]
if len(prof.wspd[ind1:ind2 + 1]) == 0 or ind1 == ind2:
maxu, maxv = utils.vec2comp([prof.wdir[ind1]], [prof.wspd[ind1]])
return maxu, maxv, prof.pres[ind1]
arr = prof.wspd[ind1:ind2 + 1]
inds = np.ma.argsort(arr)
inds = inds[~arr[inds].mask][::-1]
maxu, maxv = utils.vec2comp(prof.wdir[ind1:ind2 + 1][inds],
prof.wspd[ind1:ind2 + 1][inds])
if all:
return maxu, maxv, prof.pres[inds]
else:
return maxu[0], maxv[0], prof.pres[inds[0]]
def test_comp2vec_user_missing_val_single():
missing = 50
input_u = missing
input_v = 30
returned_wdir, returned_wspd = utils.vec2comp(input_u, input_v, missing)
npt.assert_equal(type(returned_wdir), type(ma.masked))
npt.assert_equal(type(returned_wspd), type(ma.masked))
def test_vec2comp_user_missing_val_single():
missing = 50
input_wdir = missing
input_wspd = 30
returned_u, returned_v = utils.vec2comp(input_wdir, input_wspd, missing)
npt.assert_(type(returned_u), type(ma.masked))
npt.assert_(type(returned_v), type(ma.masked))
def test_comp2vec_user_missing_val_single():
missing = 50
input_u = missing
input_v = 30
returned_wdir, returned_wspd = utils.vec2comp(input_u, input_v, missing)
npt.assert_(type(returned_wdir), type(ma.masked))
npt.assert_(type(returned_wspd), type(ma.masked))
def test_vec2comp_user_missing_val_single():
missing = 50
input_wdir = missing
input_wspd = 30
returned_u, returned_v = utils.vec2comp(input_wdir, input_wspd, missing)
npt.assert_equal(type(returned_u), type(ma.masked))
npt.assert_equal(type(returned_v), type(ma.masked))
def max_wind(prof, lower, upper, all=False):
'''
Finds the maximum wind speed of the layer given by lower and upper levels.
In the event of the maximum wind speed occurring at multiple levels, the
lowest level it occurs is returned by default.
Parameters
----------
prof : profile object
Profile Object
lower : number
Bottom level of layer (m, AGL)
upper : number
Top level of layer (m, AGL)
all : Boolean
Switch to change the output to sorted wind levels or maximum level.
Returns
-------
p : number, numpy array
Pressure level (hPa) of max wind speed
maxu : number, numpy array
Maximum Wind Speed U-component
maxv : number, numpy array
Maximum Wind Speed V-component
'''
lower = interp.to_msl(prof, lower)
upper = interp.to_msl(prof, upper)
plower = interp.pres(prof, lower)
pupper = interp.pres(prof, upper)
ind1 = np.where((plower > prof.pres) | (np.isclose(plower, prof.pres)))[0][0]
ind2 = np.where((pupper < prof.pres) | (np.isclose(pupper, prof.pres)))[0][-1]
if len(prof.wspd[ind1:ind2+1]) == 0 or ind1 == ind2:
maxu, maxv = utils.vec2comp([prof.wdir[ind1]], [prof.wspd[ind1]])
return maxu, maxv, prof.pres[ind1]
arr = prof.wspd[ind1:ind2+1]
inds = np.ma.argsort(arr)
inds = inds[~arr[inds].mask][::-1]
maxu, maxv = utils.vec2comp(prof.wdir[ind1:ind2+1][inds], prof.wspd[ind1:ind2+1][inds])
if all:
return maxu, maxv, prof.pres[inds]
else:
return maxu[0], maxv[0], prof.pres[inds[0]]
def test_vec2comp_single():
input_wdir = 225
input_wspd = 7.0710678118654755
correct_u = 5
correct_v = 5
returned_u, returned_v = utils.vec2comp(input_wdir, input_wspd)
npt.assert_almost_equal(returned_u, correct_u)
npt.assert_almost_equal(returned_v, correct_v)
def test_vec2comp_single():
input_wdir = 225
input_wspd = 7.0710678118654755
correct_u = 5
correct_v = 5
returned_u, returned_v = utils.vec2comp(input_wdir, input_wspd)
npt.assert_almost_equal(returned_u, correct_u)
npt.assert_almost_equal(returned_v, correct_v)
def test_vec2comp_zeros():
input_wdir = [0, 90, 180, 270, 360]
input_wspd = [10, 20, 30, 40, 50]
correct_u = [0, -20, 0, 40, 0]
correct_v = [-10, 0, 30, 0, -50]
correct_u = np.asanyarray(correct_u).astype(np.float64)
correct_v = np.asanyarray(correct_v).astype(np.float64)
returned_u, returned_v = utils.vec2comp(input_wdir, input_wspd)
npt.assert_equal(returned_u, correct_u)
npt.assert_equal(returned_v, correct_v)
def test_vec2comp_zeros():
input_wdir = [0, 90, 180, 270, 360]
input_wspd = [10, 20, 30, 40, 50]
correct_u = [0, -20, 0, 40, 0]
correct_v = [-10, 0, 30, 0, -50]
correct_u = np.asanyarray(correct_u).astype(np.float64)
correct_v = np.asanyarray(correct_v).astype(np.float64)
returned_u, returned_v = utils.vec2comp(input_wdir, input_wspd)
npt.assert_equal(returned_u, correct_u)
npt.assert_equal(returned_v, correct_v)
def test_vec2comp_array_like():
input_wdir = [0, 45, 90, 135, 180, 225, 270, 315, 360]
input_wspd = [5, 10, 15, 20, 25, 30, 35, 40, 45]
correct_u = [0, -7.0710678118654746, -15, -14.142135623730951, 0,
21.213203435596423, 35, 28.284271247461909, 0]
correct_v = [-5, -7.0710678118654746, 0, 14.142135623730951, 25,
21.213203435596423, 0, -28.284271247461909, -45]
correct_u = np.asanyarray(correct_u).astype(np.float64)
correct_v = np.asanyarray(correct_v).astype(np.float64)
returned_u, returned_v = utils.vec2comp(input_wdir, input_wspd)
npt.assert_almost_equal(returned_u, correct_u)
npt.assert_almost_equal(returned_v, correct_v)
def test_vec2comp_default_missing_val_array():
input_wdir = [0, 90, 180, MISSING]
input_wspd = [MISSING, 10, 20, 30]
correct_u = [MISSING, -10, 0, MISSING]
correct_v = [MISSING, 0, 20, MISSING]
correct_u = ma.asanyarray(correct_u).astype(np.float64)
correct_v = ma.asanyarray(correct_v).astype(np.float64)
correct_u[correct_u == MISSING] = ma.masked
correct_v[correct_v == MISSING] = ma.masked
correct_u[correct_v.mask] = ma.masked
correct_v[correct_u.mask] = ma.masked
correct_u.set_fill_value(MISSING)
correct_v.set_fill_value(MISSING)
returned_u, returned_v = utils.vec2comp(input_wdir, input_wspd)
npt.assert_almost_equal(returned_u, correct_u)
npt.assert_almost_equal(returned_v, correct_v)
def test_vec2comp_default_missing_val_array():
input_wdir = [0, 90, 180, MISSING]
input_wspd = [MISSING, 10, 20, 30]
correct_u = [MISSING, -10, 0, MISSING]
correct_v= [MISSING, 0, 20, MISSING]
correct_u = ma.asanyarray(correct_u).astype(np.float64)
correct_v = ma.asanyarray(correct_v).astype(np.float64)
correct_u[correct_u == MISSING] = ma.masked
correct_v[correct_v == MISSING] = ma.masked
correct_u[correct_v.mask] = ma.masked
correct_v[correct_u.mask] = ma.masked
correct_u.set_fill_value(MISSING)
correct_v.set_fill_value(MISSING)
returned_u, returned_v = utils.vec2comp(input_wdir, input_wspd)
npt.assert_almost_equal(returned_u, correct_u)
npt.assert_almost_equal(returned_v, correct_v)
def test_vec2comp_user_missing_val_array():
missing = 50
input_wdir = [0, 90, 180, missing]
input_wspd = [missing, 10, 20, 30]
correct_u = [missing, -10, 0, missing]
correct_v= [missing, 0, 20, missing]
correct_u = ma.asanyarray(correct_u).astype(np.float64)
correct_v = ma.asanyarray(correct_v).astype(np.float64)
correct_u[correct_u == missing] = ma.masked
correct_v[correct_v == missing] = ma.masked
correct_u[correct_v.mask] = ma.masked
correct_v[correct_u.mask] = ma.masked
correct_u.set_fill_value(missing)
correct_v.set_fill_value(missing)
returned_u, returned_v = utils.vec2comp(input_wdir, input_wspd, missing)
npt.assert_almost_equal(returned_u, correct_u)
npt.assert_almost_equal(returned_v, correct_v)
def test_vec2comp_user_missing_val_array():
missing = 50
input_wdir = [0, 90, 180, missing]
input_wspd = [missing, 10, 20, 30]
correct_u = [missing, -10, 0, missing]
correct_v = [missing, 0, 20, missing]
correct_u = ma.asanyarray(correct_u).astype(np.float64)
correct_v = ma.asanyarray(correct_v).astype(np.float64)
correct_u[correct_u == missing] = ma.masked
correct_v[correct_v == missing] = ma.masked
correct_u[correct_v.mask] = ma.masked
correct_v[correct_u.mask] = ma.masked
correct_u.set_fill_value(missing)
correct_v.set_fill_value(missing)
returned_u, returned_v = utils.vec2comp(input_wdir, input_wspd, missing)
npt.assert_almost_equal(returned_u, correct_u)
npt.assert_almost_equal(returned_v, correct_v)
def max_wind(prof, lower, upper, all=False):
'''
Finds the maximum wind speed of the layer given by lower and upper levels.
In the event of the maximum wind speed occurring at multiple levels, the
lowest level it occurs is returned by default.
Parameters
----------
prof : profile object
Profile Object
lower : number
Bottom level of layer (m, AGL)
upper : number
Top level of layer (m, AGL)
Returns
-------
p : number, numpy array
Pressure level (hPa) of max wind speed
maxu : number, numpy array
Maximum Wind Speed U-component
maxv : number, numpy array
Maximum Wind Speed V-component
'''
lower = interp.to_msl(prof, lower)
upper = interp.to_msl(prof, upper)
plower = interp.pres(prof, lower)
pupper = interp.pres(prof, upper)
ind1 = np.where(plower > prof.pres)[0].min()
ind2 = np.where(pupper < prof.pres)[0].max()
inds = np.where(
np.fabs(prof.wspd[ind1:ind2 + 1] -
prof.wspd[ind1:ind2 + 1].max()) < TOL)[0]
inds += ind1
inds.sort()
maxu, maxv = utils.vec2comp(prof.wdir[inds], prof.wspd[inds])
if all:
return maxu, maxv, prof.pres[inds]
else:
return maxu[0], maxv[0], prof.pres[inds[0]]
def max_wind(prof, lower, upper, all=False):
'''
Finds the maximum wind speed of the layer given by lower and upper levels.
In the event of the maximum wind speed occurring at multiple levels, the
lowest level it occurs is returned by default.
Parameters
----------
prof : profile object
Profile Object
lower : number
Bottom level of layer (m, AGL)
upper : number
Top level of layer (m, AGL)
Returns
-------
p : number, numpy array
Pressure level (hPa) of max wind speed
maxu : number, numpy array
Maximum Wind Speed U-component
maxv : number, numpy array
Maximum Wind Speed V-component
'''
lower = interp.to_msl(prof, lower)
upper = interp.to_msl(prof, upper)
plower = interp.pres(prof, lower)
pupper = interp.pres(prof, upper)
ind1 = np.where(plower > prof.pres)[0].min()
ind2 = np.where(pupper < prof.pres)[0].max()
inds = np.where(np.fabs(prof.wspd[ind1:ind2+1] -
prof.wspd[ind1:ind2+1].max()) < TOL)[0]
inds += ind1
inds.sort()
maxu, maxv = utils.vec2comp(prof.wdir[inds], prof.wspd[inds])
if all:
return maxu, maxv, prof.pres[inds]
else:
return maxu[0], maxv[0], prof.pres[inds[0]]
''' Create the Sounding (Profile) Object '''
def test_vec2comp_default_missing_val_single():
input_wdir = MISSING
input_wspd = 30
returned_u, returned_v = utils.vec2comp(input_wdir, input_wspd)
npt.assert_(type(returned_u), type(ma.masked))
npt.assert_(type(returned_v), type(ma.masked))
def __init__(self, **kwargs):
'''
Create the sounding data object
Parameters
----------
Mandatory Keywords
pres : array_like
The pressure values (Hectopaschals)
hght : array_like
The corresponding height values (Meters)
tmpc : array_like
The corresponding temperature values (Celsius)
dwpc : array_like
The corresponding dewpoint temperature values (Celsius)
Optional Keyword Pairs (must use one or the other)
wdir : array_like
The direction from which the wind is blowing in
meteorological degrees
wspd : array_like
The speed of the wind
OR
u : array_like
The U-component of the direction from which the wind
is blowing
v : array_like
The V-component of the direction from which the wind
is blowing.
Optional Keywords
missing : number (default: sharppy.sharptab.constants.MISSING)
The value of the missing flag
location : string (default: None)
The 3 character station identifier or 4 character
WMO station ID for radiosonde locations. Used for
the PWV database.
strictQC : boolean
A flag that indicates whether or not the strict quality control
routines should be run on the profile upon construction.
Returns
-------
prof: Profile object
'''
super(BasicProfile, self).__init__(**kwargs)
strictQC = kwargs.get('strictQC', True)
assert len(self.pres) == len(self.hght) == len(self.tmpc) == len(self.dwpc),\
"Length of pres, hght, tmpc, or dwpc arrays passed to constructor are not the same."
## did the user provide the wind in vector form?
if self.wdir is not None:
assert len(self.wdir) == len(self.wspd) == len(self.pres), "Length of wdir and wspd arrays passed to constructor are not the same length as the pres array."
self.wdir[self.wdir == self.missing] = ma.masked
self.wspd[self.wspd == self.missing] = ma.masked
self.wdir[self.wspd.mask] = ma.masked
self.wspd[self.wdir.mask] = ma.masked
self.u, self.v = utils.vec2comp(self.wdir, self.wspd)
## did the user provide the wind in u,v form?
elif self.u is not None:
assert len(self.u) == len(self.v) == len(self.pres), "Length of u and v arrays passed to constructor are not the same length as the pres array."
self.u[self.u == self.missing] = ma.masked
self.v[self.v == self.missing] = ma.masked
self.u[self.v.mask] = ma.masked
self.v[self.u.mask] = ma.masked
self.wdir, self.wspd = utils.comp2vec(self.u, self.v)
## check if any standard deviation data was supplied
if self.tmp_stdev is not None:
self.dew_stdev[self.dew_stdev == self.missing] = ma.masked
self.tmp_stdev[self.tmp_stdev == self.missing] = ma.masked
self.dew_stdev.set_fill_value(self.missing)
self.tmp_stdev.set_fill_value(self.missing)
if self.omeg is not None:
## get the omega data and turn into arrays
assert len(self.omeg) == len(self.pres), "Length of omeg array passed to constructor is not the same length as the pres array."
self.omeg[self.omeg == self.missing] = ma.masked
else:
self.omeg = ma.masked_all(len(self.hght))
# QC Checks on the arrays passed to the constructor.
qc_tools.areProfileArrayLengthEqual(self)
## mask the missing values
self.pres[self.pres == self.missing] = ma.masked
self.hght[self.hght == self.missing] = ma.masked
self.tmpc[self.tmpc == self.missing] = ma.masked
self.dwpc[self.dwpc == self.missing] = ma.masked
#if not qc_tools.isPRESValid(self.pres):
## qc_tools.raiseError("Incorrect order of pressure array (or repeat values) or pressure array is of length <= 1.", ValueError)
if not qc_tools.isHGHTValid(self.hght) and strictQC:
qc_tools.raiseError("Incorrect order of height (or repeat values) array or height array is of length <= 1.", ValueError)
if not qc_tools.isTMPCValid(self.tmpc):
qc_tools.raiseError("Invalid temperature array. Array contains a value < 273.15 Celsius.", ValueError)
if not qc_tools.isDWPCValid(self.dwpc):
qc_tools.raiseError("Invalid dewpoint array. Array contains a value < 273.15 Celsius.", ValueError)
if not qc_tools.isWSPDValid(self.wspd) and strictQC:
qc_tools.raiseError("Invalid wind speed array. Array contains a value < 0 knots.", ValueError)
if not qc_tools.isWDIRValid(self.wdir) and strictQC:
qc_tools.raiseError("Invalid wind direction array. Array contains a value < 0 degrees or value >= 360 degrees.", ValueError)
self.logp = np.log10(self.pres.copy())
self.vtmp = thermo.virtemp( self.pres, self.tmpc, self.dwpc )
idx = np.ma.where(self.pres > 0)[0]
self.vtmp[self.dwpc.mask[idx]] = self.tmpc[self.dwpc.mask[idx]] # Masking any virtual temperature
## get the index of the top and bottom of the profile
self.sfc = self.get_sfc()
self.top = self.get_top()
## generate the wetbulb profile
self.wetbulb = self.get_wetbulb_profile()
## generate theta-e profile
self.thetae = self.get_thetae_profile()
def __init__(self, **kwargs):
'''
Create the sounding data object
Parameters
----------
Mandatory Keywords
hght : array_like
The corresponding height values (Meters)
Optional Keyword Pairs (must use one or the other)
wdir : array_like
The direction from which the wind is blowing in
meteorological degrees
wspd : array_like
The speed of the wind
rms : array_like
The RMS from the VAD algorithm.
OR
u : array_like
The U-component of the direction from which the wind
is blowing
v : array_like
The V-component of the direction from which the wind
is blowing.
Optional Keywords
missing : number (default: sharppy.sharptab.constants.MISSING)
The value of the missing flag
location : string (default: None)
The 3 character station identifier or 4 character
WMO station ID for radiosonde locations. Used for
the PWV database.
Returns
-------
prof: Profile object
'''
#Dummy variables so Profile doesn't crash
wspd = kwargs.get('wspd')
kwargs['tmpc'] = np.ones(len(wspd))
kwargs['dwpc'] = np.ones(len(wspd))
kwargs['pres'] = np.arange(1,len(wspd)+1,1)[::-1]
super(VADProfile, self).__init__(**kwargs)
self.rms = kwargs.get('rms')
assert len(self.hght) == len(self.tmpc) == len(self.dwpc),\
"Length of pres, hght, tmpc, or dwpc arrays passed to constructor are not the same."
## did the user provide the wind in vector form?
if self.wdir is not None:
assert len(self.wdir) == len(self.wspd) == len(self.hght), "Length of wdir and wspd arrays passed to constructor are not the same length as the pres array."
self.wdir[self.wdir == self.missing] = ma.masked
self.wspd[self.wspd == self.missing] = ma.masked
self.wdir[self.wspd.mask] = ma.masked
self.wspd[self.wdir.mask] = ma.masked
self.u, self.v = utils.vec2comp(self.wdir, self.wspd)
## did the user provide the wind in u,v form?
elif self.u is not None:
assert len(self.u) == len(self.v) == len(self.hght), "Length of u and v arrays passed to constructor are not the same length as the pres array."
self.u[self.u == self.missing] = ma.masked
self.v[self.v == self.missing] = ma.masked
self.u[self.v.mask] = ma.masked
self.v[self.u.mask] = ma.masked
self.wdir, self.wspd = utils.comp2vec(self.u, self.v)
## mask the missing values
self.hght[self.hght == self.missing] = ma.masked
strictQC = False
#if not qc_tools.isHGHTValid(self.hght):
# qc_tools.raiseError("Incorrect order of height (or repeat values) array or height array is of length <= 1.", ValueError)
if not qc_tools.isWSPDValid(self.wspd) and strictQC:
qc_tools.raiseError("Invalid wind speed array. Array contains a value < 0 knots.", ValueError)
if not qc_tools.isWDIRValid(self.wdir) and strictQC:
qc_tools.raiseError("Invalid wind direction array. Array contains a value < 0 degrees or value >= 360 degrees.", ValueError)
print """Using the VADDecoder...something to keep in mind is that all interp.py routines
use the prof.logp variable to perform the pressure based interpolation. Because VAD
and radar wind profiler data does not contain pressure data, this will break
any interpolation routines and any routines that require pressure.
Will have to find a way around this."""
self.logp = np.log10(self.pres.copy())
self.vtmp = thermo.virtemp( self.pres, self.tmpc, self.dwpc )
idx = np.ma.where(self.pres > 0)[0]
#self.vtmp[slf.dwpc.mask[idx]] = self.tmpc[self.dwpc.mask[idx]] # Masking any virtual temperature
## get the index of the top and bottom of the profile
self.sfc = self.get_sfc()
self.top = self.get_top()
def __init__(self, **kwargs):
'''
Create the sounding data object
Parameters
----------
Mandatory Keywords
pres : array_like
The pressure values (Hectopaschals)
hght : array_like
The corresponding height values (Meters)
tmpc : array_like
The corresponding temperature values (Celsius)
dwpc : array_like
The corresponding dewpoint temperature values (Celsius)
Optional Keyword Pairs (must use one or the other)
wdir : array_like
The direction from which the wind is blowing in
meteorological degrees
wspd : array_like
The speed of the wind
OR
u : array_like
The U-component of the direction from which the wind
is blowing
v : array_like
The V-component of the direction from which the wind
is blowing.
Optional Keywords
missing : number (default: sharppy.sharptab.constants.MISSING)
The value of the missing flag
Returns
-------
A profile object
'''
self.missing = kwargs.get('missing', MISSING)
self.masked = ma.masked
self.pres = ma.asanyarray(kwargs.get('pres'))
self.hght = ma.asanyarray(kwargs.get('hght'))
self.tmpc = ma.asanyarray(kwargs.get('tmpc'))
self.dwpc = ma.asanyarray(kwargs.get('dwpc'))
self.pres[self.pres == self.missing] = ma.masked
self.hght[self.hght == self.missing] = ma.masked
self.tmpc[self.tmpc == self.missing] = ma.masked
self.dwpc[self.dwpc == self.missing] = ma.masked
self.logp = np.log10(self.pres.copy())
if 'wdir' in kwargs:
self.wdir = ma.asanyarray(kwargs.get('wdir'))
self.wspd = ma.asanyarray(kwargs.get('wspd'))
self.wdir[self.wdir == self.missing] = ma.masked
self.wspd[self.wspd == self.missing] = ma.masked
self.wdir[self.wspd.mask] = ma.masked
self.wspd[self.wdir.mask] = ma.masked
self.u, self.v = utils.vec2comp(self.wdir, self.wspd)
elif 'u' in kwargs:
self.u = ma.asanyarray(kwargs.get('u'))
self.v = ma.asanyarray(kwargs.get('v'))
self.u[self.u == self.missing] = ma.masked
self.v[self.v == self.missing] = ma.masked
self.u[self.v.mask] = ma.masked
self.v[self.u.mask] = ma.masked
self.wdir, self.wspd = utils.comp2vec(self.u, self.v)
self.pres.set_fill_value(self.missing)
self.hght.set_fill_value(self.missing)
self.tmpc.set_fill_value(self.missing)
self.dwpc.set_fill_value(self.missing)
self.wdir.set_fill_value(self.missing)
self.wspd.set_fill_value(self.missing)
self.u.set_fill_value(self.missing)
self.v.set_fill_value(self.missing)
self.sfc = self.get_sfc()
def plot_wof(prof, members, figname, xlat, xlon, idateobj, vdateobj, **kwargs):
# '''
# Plots SHARPpy SPC window as .png
#
# Parameters
# ----------
# prof : a Profile Object from sharppy.sharptab.profile
#
# kwargs
# ------
# parcel_type: Parcel choice for plotting. 'sfc','ml','mu','fcst' Default is 'ml'
# filename: Filename as a string. Default is 'sounding.png'
# logo: Logo for upper-left portion of the skew-t. Default is 'None' and does not plot a logo.
# logo_dxdy: Size of logo. Actual dimensions are dT and dp as it is plotted on the skewT. Default is (20,13) This worked for a 489x132 pixel image.
# '''
#kwargs
parcel_type = kwargs.get('parcel_type', 'ml')
xpts = kwargs.get('x_pts')
ypts = kwargs.get('y_pts')
#Figure User Input
p_grid_labels = [
'1000', '', '', '850', '', '', '700', '', '', '', '500', '', '', '',
'300', '', '200', '', '100'
] #labels for the pressure ticks. Standard.
p_grid = [1000, 850, 700, 500, 300, 200,
100] #where horizontal lines go across the skew-T
my_dpi = 55 #dots per inch for the plot. This is a pretty hi-res image.
pmax = 1050 #lowest pressure on the skew-T
pmin = 100 #highest pressure on the skew-T
dp = -10 #pressure spacing for creating skew-T background lines
presvals = np.arange(
int(pmax),
int(pmin) + dp,
dp) #pressure values used for creating skew-T background lines
# Colors for wind speed bars and hodograph
hgt_list_bar = [0, 1000, 3000, 6000, 9000, 20000]
hgt_list_hodo = [0, 1000, 3000, 6000, 9000, 10000]
hodo_color = [
cb_colors.orange6, cb_colors.green6, cb_colors.blue6,
cb_colors.purple6, cb_colors.red6
]
hodo_label = ['0-1km', '1-3km', '3-6km', '6-9km', '9-10km']
#convoluted mess to get the title to be aligned how I wanted. This should be changed for others...
spaces = 10 #22
# title_text_1 = '' #site + ' ' + dt.strftime('%Y/%m/%d %H:%M UTC ' + data_type)
# title_text_3 = 'Sounding Powered by SHARPpy'
sharptext = 'Sounding Powered by SHARPpy'
# title_text_2 = title_text_3 = ''
title_text_3 = 'WoFS Sounding {}N, {}W'.format(
xlat, xlon) + (' ' * spaces) + 'Init: {} Valid: {}'.format(
idateobj.strftime('%Y-%m-%d %H%M UTC'),
vdateobj.strftime('%Y-%m-%d %H%M UTC'))
#xlat, xlon, initdate, validdate
title_text = title_text_3 #title_text_1 + (' '*spaces) +title_text_2 + (' '*spaces) + title_text_3
#Figures out where at which height the sounding reached in the list of h_new
#Then interpolates pressure to height levels
h_new = [0, 1000, 3000, 6000, 9000, 12000, 15000]
for i in range(len(h_new)):
if np.max(prof.hght) > h_new[i]:
index = i
h_new_labels = ['0 km', '1 km', '3 km', '6 km', '9 km', '12 km', '15 km']
h_new_labels = h_new_labels[0:index + 1]
#p_interped_func = interpolate.interp1d(prof.hght, prof.pres)
p_interped = sharptab.interp.pres(
prof, sharptab.interp.to_msl(prof, h_new[0:index + 1]))
#Thin out the winds for better plotting (significant level data points seem to bunch together to closely
minimum_separation = 250. #minimum spacing between wind barbs (meters)
h_barb = np.array(prof.hght).tolist()
p_barb = np.array(prof.pres).tolist()
spd_barb = np.array(prof.wspd).tolist()
direc_barb = np.array(prof.wdir).tolist()
#adds units to our newly created pressure, speed, and direction arrays for wind barb plotting
#p_barb = p_barb * units.mbar
#spd_barb = spd_barb * units.knot
#direc_barb = direc_barb * units.deg
# Convert wind speed and direction to components
#u, v = get_wind_components(prof.wspd * units.knot, prof.wdir * units.deg)
u, v = utils.vec2comp(prof.wdir, prof.wspd)
u_barb, v_barb = utils.vec2comp(prof.wdir, prof.wspd)
#SELECT PARCEL AND GET PARCEL DATA FROM SPC_UTILS
sfcpcl = prof.sfcpcl #params.parcelx( prof, flag=1 )
fcstpcl = prof.fcstpcl #params.parcelx( prof, flag=2)
mupcl = prof.mupcl #params.parcelx( prof, flag=3 )
mlpcl = prof.mlpcl #params.parcelx( prof, flag=4 )
if parcel_type == 'sfc':
pcl = sfcpcl
pcl_box_level = -0.065
pcl_type = 1
elif parcel_type == 'fcst':
pcl = fcstpcl
pcl_box_level = -0.0875
pcl_type = 2
elif parcel_type == 'mu':
pcl = mupcl
pcl_box_level = -0.1325
pcl_type = 4
elif parcel_type == 'ml':
pcl = mlpcl
pcl_box_level = -0.11
pcl_type = 3
else:
print(
"ERROR! Select 'sfc', 'fcst', 'mu', or 'ml' for parcel type. (plot_spc(prof,parcel_type)"
)
print("Defaulting to surface parcel...")
pcl = sfcpcl
pcl_box_level = -0.065
#PLOTTING *************************************************************************************************************
#Create full figure
fig = plt.figure(figsize=(1180 / my_dpi, 800 / my_dpi),
dpi=my_dpi,
frameon=False)
#SKEW T ***************************************************
ax = fig.add_subplot(111, projection='skewx',
facecolor="w") #skewed x-axis
# plot dashed temperature lines
ax.xaxis.grid(color='k',
linestyle='--',
dashes=(3, 3),
alpha=0.5,
zorder=0)
# plot the moist-adiabats
for temp in np.arange(-10, 45, 5):
tw = []
for pres in presvals:
tw.append(thermo.wetlift(1050., temp, pres))
ax.semilogy(tw,
presvals,
color=cb_colors.purple6,
linestyle='--',
dashes=(5, 2),
alpha=.3) #cb_colors.purple6
# plot the dry adiabats
for t in np.arange(-50, 80, 20):
thetas = ((t + thermo.ZEROCNK) / (np.power(
(1000. / presvals), thermo.ROCP))) - thermo.ZEROCNK
ax.semilogy(thetas, presvals, 'k', alpha=.3)
#plot mixing ratio lines
mixing_ratio_list = range(6, 36, 4)
for mr in mixing_ratio_list:
plt.plot((thermo.temp_at_mixrat(mr, 1050) - 273,
thermo.temp_at_mixrat(mr, 600) - 273), (1050, 600),
'g-',
lw=1.0,
zorder=3,
alpha=0.6)
ax.annotate(str(mr),
xy=((thermo.temp_at_mixrat(mr, 600) - 273), (600 - 3)),
xytext=((thermo.temp_at_mixrat(mr, 600) - 273), (600 - 3)),
ha='center',
color='g',
family='sans-serif',
weight='bold',
zorder=3,
fontsize=10,
alpha=0.6)
#plot horizontal lines at standard pressure levels
for p_loc in p_grid:
ax.axhline(y=p_loc, ls='-', c='k', alpha=0.5, linewidth=1.5, zorder=3)
# PLOT THE DATA ON THE SKEW-T
# Plot the data using normal plotting functions, in this case using log scaling in Y, as dicatated by the typical meteorological plot
ax.semilogy(prof.wetbulb,
prof.pres,
c="c",
linestyle='-',
lw=1,
alpha=1.0,
zorder=3) # Plot the wetbulb profile
ax.annotate(str(int(round(thermo.ctof(prof.wetbulb[prof.sfc])))),
xy=(prof.wetbulb[prof.sfc], prof.pres[prof.sfc] + 30),
xytext=(prof.wetbulb[prof.sfc], prof.pres[prof.sfc] + 30),
ha='left',
color="c",
family='sans-serif',
weight='normal',
zorder=7,
fontsize=12,
alpha=1.0) # annotate the sfc wetbulb in F
ax.semilogy(prof.dwpc,
prof.pres,
c=cb_colors.blue6,
linestyle='-',
lw=3,
zorder=3) # plot the dewpoint profile
ax.annotate(str(int(round(thermo.ctof(prof.dwpc[prof.sfc])))),
xy=(prof.dwpc[prof.sfc], prof.pres[prof.sfc] + 30),
xytext=(prof.dwpc[prof.sfc], prof.pres[prof.sfc] + 30),
ha='left',
color=cb_colors.blue6,
family='sans-serif',
weight='bold',
zorder=7,
fontsize=12,
alpha=1.0) # annotate the sfc dewpoint in F
ax.semilogy(prof.tmpc,
prof.pres,
c=cb_colors.orange6,
linestyle='-',
lw=3,
zorder=3) # Plot the temperature profile
ax.semilogy(prof.vtmp,
prof.pres,
c=cb_colors.orange6,
linestyle='--',
lw=3,
zorder=3) # Plot the temperature profile
ax.annotate(str(int(round(thermo.ctof(prof.tmpc[prof.sfc])))),
xy=(prof.tmpc[prof.sfc], prof.pres[prof.sfc] + 30),
xytext=(prof.tmpc[prof.sfc], prof.pres[prof.sfc] + 30),
ha='left',
color=cb_colors.orange6,
family='sans-serif',
weight='bold',
zorder=7,
fontsize=12,
alpha=1.0) # annotate the sfc temp in F
ax.semilogy(pcl.ttrace,
pcl.ptrace,
c=cb_colors.gray6,
linestyle='--',
dashes=(3, 3),
lw=1.5,
alpha=1.0,
zorder=3) # plot the parcel trace
#member_cape = []
if members is not None:
# print( "Plotting members...")
for m_idx in range(len(members['tmpc'])):
ax.semilogy(members['dwpc'][m_idx],
members['pres'][m_idx],
c=cb_colors.blue6,
linestyle='-',
lw=1.,
zorder=1,
alpha=.6) # plot the dewpoint profile
ax.semilogy(members['tmpc'][m_idx],
members['pres'][m_idx],
c=cb_colors.orange6,
linestyle='-',
lw=1.,
zorder=1,
alpha=.6) # Plot the temperature profile
# set label and tick marks for pressure and temperature
ax.xaxis.set_major_locator(plt.MultipleLocator(10))
ax.set_xticks(np.arange(-100, 60, 10))
ax.set_xticklabels([str(i) for i in np.arange(-100, 60, 10)],
color=cb_colors.gray7,
fontsize=12)
ax.set_xlim(-50, 50)
ax.yaxis.set_major_formatter(plt.ScalarFormatter())
ax.set_yticks(np.linspace(1000, 100, 19))
ax.set_yticklabels(p_grid_labels, color=cb_colors.gray7, fontsize=12)
ax.set_ylim(1050, 100)
#plot the title text
plt.text(0.05,
0.97,
title_text,
fontsize=15,
color=cb_colors.gray8,
weight='bold',
ha='left',
transform=fig.transFigure)
#x_hodo.annotate(sharptext, xy=(0.95, 0.95), xytext=(0.95, 0.95),xycoords='axes fraction',textcoords='axes fraction',ha='center', va='bottom', color=cb_colors.gray7, family='sans-serif', weight='bold', zorder=3,fontsize=14)
plt.text(0.8,
0.97,
sharptext,
fontsize=15,
color=cb_colors.gray8,
weight='bold',
ha='left',
transform=fig.transFigure)
#adjust the skew-T plot to make room for the rest of the figures. This was important to make everything line up.
plt.subplots_adjust(left=0.05, right=0.55, top=0.96, bottom=0.15)
#Plot the height labels on the left axis
ax2 = ax.twinx(
) #makes a twin of the skew-T subplot that's not skewed at 45 degrees
plt.yscale('log', nonposy='clip')
plt.yticks(p_interped, h_new_labels, color=cb_colors.green4, ha='left')
ax2.yaxis.tick_left()
ax2.tick_params(direction='in',
pad=-15,
axis='both',
which='major',
colors=cb_colors.green4,
length=10,
width=1.5)
ax2.set_yticklabels(h_new_labels,
fontsize=12,
weight='bold',
color=cb_colors.green4)
x = np.random.uniform(0.0, 10.0, 15)
y = np.random.uniform(0.0, 10.0, 15)
# Plot LCL and LFC levels
plt.plot((38, 42), (pcl.lfcpres, pcl.lfcpres),
c="darkgreen",
lw=2.0,
zorder=3)
ax2.annotate('LFC',
xy=(40, pcl.lfcpres),
xytext=(40, pcl.lfcpres),
ha='center',
va='bottom',
color=cb_colors.green5,
family='sans-serif',
weight='bold',
zorder=3,
fontsize=12)
plt.plot((38, 42), (pcl.lclpres, pcl.lclpres),
c="r",
linestyle='-',
lw=2.0,
zorder=3)
ax2.annotate('LCL',
xy=(40, pcl.lclpres + 5.),
xytext=(40, pcl.lclpres + 5.),
ha='center',
va='top',
color=cb_colors.red5,
family='sans-serif',
weight='bold',
zorder=3,
fontsize=12)
plt.plot((38, 42), (pcl.elpres, pcl.elpres),
c="m",
linestyle='-',
lw=2.0,
zorder=3)
ax2.annotate('EL',
xy=(40, pcl.elpres),
xytext=(40, pcl.elpres),
ha='center',
va='bottom',
color=cb_colors.purple5,
family='sans-serif',
weight='bold',
zorder=3,
fontsize=12)
# Plot Eff Inflow Layer
eff_inflow = (prof.ebottom, prof.etop)
eff_inflow_top = sharptab.interp.to_agl(
prof, sharptab.interp.hght(prof, eff_inflow[1]))
eff_inflow_bottom = sharptab.interp.to_agl(
prof, sharptab.interp.hght(prof, eff_inflow[0]))
bunkers = prof.srwind
effective_srh = prof.right_esrh
plt.plot((-25, -20), (eff_inflow[0], eff_inflow[0]),
c=cb_colors.red5,
linestyle='-',
lw=1.75,
zorder=3)
plt.plot((-25, -20), (eff_inflow[1], eff_inflow[1]),
c=cb_colors.red5,
linestyle='-',
lw=1.75,
zorder=3)
plt.plot((-22.5, -22.5), (eff_inflow[0], eff_inflow[1]),
c=cb_colors.red5,
linestyle='-',
lw=1.75,
zorder=3)
try:
plt.annotate(str(int(eff_inflow_bottom)) + 'm ',
xy=(-25, eff_inflow[0]),
xytext=(-25, eff_inflow[0]),
ha='right',
va='center',
color=cb_colors.red5,
zorder=3,
fontsize=12,
weight='bold')
plt.annotate(str(int(eff_inflow_top)) + 'm ',
xy=(-25, eff_inflow[1]),
xytext=(-25, eff_inflow[1]),
ha='right',
va='center',
color=cb_colors.red5,
zorder=3,
fontsize=12,
weight='bold')
plt.annotate(str(int(effective_srh[0])) + ' m$^2$/s$^2$',
xy=(-22.5, eff_inflow[1] - 10),
xytext=(-22.5, eff_inflow[1] - 10),
ha='center',
va='bottom',
color=cb_colors.red5,
zorder=3,
fontsize=12,
weight='bold')
except:
print("NO EFF INFLOW")
# PLOT WINDBARBS
p_barb = np.asarray(p_barb)
pidx = idx = np.where(np.asarray(p_barb) >= 100.)[0]
wind_barbs = ax2.barbs(55 * np.ones(len(p_barb[idx])),
p_barb[idx],
u_barb[idx],
v_barb[idx],
barbcolor=cb_colors.gray7,
flagcolor='None',
zorder=10,
lw=1.0,
length=7)
wind_barbs.set_clip_on(False)
ax2.invert_yaxis()
ax2.set_xlim(-50, 50)
ax2.set_ylim(1050, 100)
spd_barb = np.asarray(spd_barb)
# Create hodograph ********************************************************************************************
ax_hod = fig.add_axes([0.60, 0.45, 0.38, 0.475],
frameon=False) #, facecolor='k')
# Set the characteristics of the tick marks
for tick in ax_hod.xaxis.get_major_ticks():
tick.label.set_fontsize(12)
tick.label.set_color(cb_colors.gray7)
tick.label.set_weight('bold')
for tick in ax_hod.yaxis.get_major_ticks():
tick.label.set_fontsize(12)
tick.label.set_color(cb_colors.gray7)
tick.label.set_weight('bold')
for i in range(10, 90, 10):
# Draw the range rings around the hodograph.
circle = plt.Circle((0, 0),
i,
linestyle='--',
color='k',
alpha=.3,
fill=False)
ax_hod.add_artist(circle)
# Set the tick parameters to displace the tick labels from the hodograph axes
ax_hod.tick_params(axis='x',
which='major',
labelsize=10,
color=cb_colors.gray7,
pad=-235,
length=0)
ax_hod.tick_params(axis='y',
which='major',
labelsize=10,
color=cb_colors.gray7,
pad=-315,
length=0)
# Plot the hodograph axes
ax_hod.axvline(0, color=cb_colors.gray7, linestyle='-', linewidth=2.)
ax_hod.axhline(0, color=cb_colors.gray7, linestyle='-', linewidth=2.)
ax_hod.set_yticks(range(-60, 65, 10))
ax_hod.set_xticks(range(-70, 75, 10))
hod_yticklabels = [str(abs(i)) for i in range(-60, 65, 10)]
#hod_yticklabels[len(hod_yticklabels)/2] = ''
hod_xticklabels = [str(abs(i)) for i in range(-70, 75, 10)]
#hod_xticklabels[len(hod_xticklabels)/2] = ''
ax_hod.set_yticklabels(hod_yticklabels,
fontsize=12,
weight='bold',
color=cb_colors.gray7)
ax_hod.set_xticklabels(hod_xticklabels,
fontsize=12,
weight='bold',
color=cb_colors.gray7)
# Plot the hodograph using the color scheme for different layers (0-3, 3-6, etc.)
bounds = [0, 1000, 3000, 6000, 9000, 12000]
for i in range(1, len(bounds), 1):
subset_idxs = np.where(
(prof.hght <= sharptab.interp.to_msl(prof, bounds[i]))
& (prof.hght >= sharptab.interp.to_msl(prof, bounds[i - 1])))
subset_hghts = np.ma.concatenate(
([sharptab.interp.to_msl(prof, bounds[i - 1])],
prof.hght[subset_idxs], [sharptab.interp.to_msl(prof,
bounds[i])]))
u, v = sharptab.interp.components(
prof, sharptab.interp.pres(prof, subset_hghts))
ax_hod.plot(u,
v,
c=hodo_color[i - 1],
linewidth=2.5,
label=hodo_label[i - 1],
zorder=3)
if members is not None:
for mprof in members['member_profs']:
for i in range(1, len(bounds), 1):
subset_idxs = np.where(
(mprof.hght <= sharptab.interp.to_msl(mprof, bounds[i]))
& (mprof.hght >= sharptab.interp.to_msl(
mprof, bounds[i - 1])))
subset_hghts = np.ma.concatenate(
([sharptab.interp.to_msl(mprof, bounds[i - 1])
], mprof.hght[subset_idxs],
[sharptab.interp.to_msl(mprof, bounds[i])]))
u, v = sharptab.interp.components(
mprof, sharptab.interp.pres(mprof, subset_hghts))
ax_hod.plot(u,
v,
c=hodo_color[i - 1],
linewidth=1.25,
alpha=0.6,
label=hodo_label[i - 1],
zorder=1)
# Get the Bunkers storm motions and convert them to strings to plot
bunkers = srwind = prof.srwind
bunkers_rt = utils.comp2vec(bunkers[0], bunkers[1])
bunkers_lf = utils.comp2vec(bunkers[2], bunkers[3])
bunkers_rt_str = str(int(np.ma.around(bunkers_rt[0], 0))) + "/" + str(
int(np.ma.around(bunkers_rt[1], 0)))
bunkers_lf_str = str(int(np.ma.around(bunkers_lf[0], 0))) + "/" + str(
int(np.ma.around(bunkers_lf[1], 0)))
# Plot the effective inflow layer on the hodograph
effubot, effvbot = sharptab.interp.components(prof, eff_inflow[0])
effutop, effvtop = sharptab.interp.components(prof, eff_inflow[1])
ax_hod.plot([effubot, srwind[0]], [effvbot, srwind[1]],
c='c',
linewidth=1.5)
ax_hod.plot([effutop, srwind[0]], [effvtop, srwind[1]],
c='c',
linewidth=1.5)
# Annotate where the Bunkers storm motion vectors are on the hodograph
ax_hod.plot(srwind[0],
srwind[1],
marker='o',
fillstyle='none',
markeredgecolor=cb_colors.blue8,
markeredgewidth=1.5,
markersize=11)
ax_hod.annotate(bunkers_rt_str + ' RM', (srwind[0] + 1.5, srwind[1] - 1.5),
fontsize=12,
va="top",
ha="left",
color='k',
weight='bold',
zorder=10)
ax_hod.plot(srwind[2],
srwind[3],
marker='o',
fillstyle='none',
markeredgecolor=cb_colors.blue8,
markeredgewidth=1.5,
markersize=11)
ax_hod.annotate(bunkers_lf_str + ' LM', (srwind[2] + 1.5, srwind[3] - 1.5),
fontsize=12,
va="top",
ha="left",
color='k',
weight='bold',
zorder=10)
# Annotate where the Corfidi MBE vectors are on the hodograph
corfidi = prof.upshear_downshear
corfidi_up = utils.comp2vec(corfidi[0], corfidi[1])
corfidi_dn = utils.comp2vec(corfidi[2], corfidi[3])
c = 'k' #'#0A74C6'
ax_hod.plot(corfidi[0],
corfidi[1],
marker='o',
fillstyle='none',
markeredgecolor=c,
markeredgewidth=1.5,
markersize=9)
ax_hod.annotate(str(int(corfidi_up[0])) + '/' + str(int(corfidi_up[1])) +
' UP', (corfidi[0] + 1.5, corfidi[1] - 1.5),
fontsize=12,
va="top",
ha="left",
color=cb_colors.purple8,
weight='bold',
zorder=10)
ax_hod.plot(corfidi[2],
corfidi[3],
marker='o',
fillstyle='none',
markeredgecolor=c,
markeredgewidth=1.5,
markersize=9)
ax_hod.annotate(str(int(corfidi_dn[0])) + '/' + str(int(corfidi_dn[1])) +
' DN', (corfidi[2] + 1.5, corfidi[3] - 1.5),
fontsize=12,
va="top",
ha="left",
color=cb_colors.purple8,
weight='bold',
zorder=10)
# Get the cloud-layer mean wind
mean_cloudlayer = winds.mean_wind(prof, pbot=pcl.lclpres, ptop=pcl.elpres)
mean_cloudlayer_comp = utils.comp2vec(mean_cloudlayer[0],
mean_cloudlayer[1])
try:
mean_cloudlayer_str = str(int(np.ma.around(
mean_cloudlayer_comp[0], 0))) + "/" + str(
int(np.ma.around(mean_cloudlayer_comp[1], 0)))
except:
mean_cloudlayer_str = 'M/M'
# Write the critical angle to the hodograph.
if eff_inflow[0] == prof.pres[prof.sfc]:
ax_hod.annotate('Critical Angle = ' + str(int(prof.critical_angle)),
(-65, -50),
fontsize=12,
va="bottom",
ha="left",
color=cb_colors.green6,
weight='bold',
zorder=10)
ax_hod.set_xlim(-80, 80)
ax_hod.set_ylim(-70, 70)
#BELOW IS STUFF FOR BOXES/BORDERS ******************************************************************************
ax3 = ax2.twinx()
ax3.axes.get_xaxis().set_visible(False)
ax3.axes.get_yaxis().set_visible(False)
ax3.set_yticks([])
ax3.set_yticklabels([])
#Big Thick Box around Skew-T
#box = ax3.add_patch(patches.Rectangle((-50, 0), 110.0, 1.0,fill=False,linewidth=2,edgecolor="w",zorder=3))
#box.set_clip_on(False)
#box around hodograph
#box = ax_hod.add_patch(patches.Rectangle((-80., -70.), 160., 140.,fill=False,linewidth=2,edgecolor="w",zorder=4))
#box.set_clip_on(False)
inset_color = cb_colors.gray7
#THICK TEXT BOX around Thermodynamics Text
box = ax3.add_patch(
patches.Rectangle((-58.0, -0.035),
54,
-0.12,
fill=False,
linewidth=2,
edgecolor=inset_color,
zorder=4))
box.set_clip_on(False)
#THICK TEXT BOX around Kinematics Text
box = ax3.add_patch(
patches.Rectangle((-3.0, -0.035),
60,
-0.12,
fill=False,
linewidth=2,
edgecolor=inset_color,
zorder=4))
box.set_clip_on(False)
#THICK TEXT BOX Around Dynamics Text
# box = ax3.add_patch(patches.Rectangle((9.0, -0.04), 60, -0.38,fill=False,linewidth=2,edgecolor=inset_color,zorder=4))
# box.set_clip_on(False)
#THICK TEXT BOX Around SARS Text
# box = ax3.add_patch(patches.Rectangle((69.0, -0.04), 55, -0.38,fill=False,linewidth=2,edgecolor=inset_color,zorder=4))
# box.set_clip_on(False)
#Thermodynamics
#box = ax3.add_patch(patches.Rectangle((-55.0, -0.04), 64.0, -0.12,fill=False,linewidth=1,edgecolor=inset_color,zorder=4))
#box.set_clip_on(False)
#box = ax3.add_patch(patches.Rectangle((-55.0, -0.04), 64.0, -0.025,fill=False,linewidth=1,edgecolor=inset_color,zorder=4))
#box.set_clip_on(False)
# Write the parcel properties to the inset.
#x_list = [0, 0.08, 0.17, 0.25, 0.33, 0.39, 0.47]
x_list = np.array([0, 0.08, 0.17, 0.25, 0.33, 0.39, 0.47]) - 0.075
y_list = [-0.045, -0.07, -0.0925, -0.115, -0.1375]
A = [
"SFC", prof.sfcpcl.bplus,
int(prof.sfcpcl.bminus), prof.sfcpcl.lclhght, prof.sfcpcl.li5,
prof.sfcpcl.lfchght, prof.sfcpcl.elhght
]
# B = ["FCST", prof.fcstpcl.bplus, int(prof.fcstpcl.bminus), prof.fcstpcl.lclhght, prof.fcstpcl.li5, prof.fcstpcl.lfchght, prof.fcstpcl.elhght]
C = [
"ML", prof.mlpcl.bplus,
int(prof.mlpcl.bminus), prof.mlpcl.lclhght, prof.mlpcl.li5,
prof.mlpcl.lfchght, prof.mlpcl.elhght
]
D = [
"MU", prof.mupcl.bplus,
int(prof.mupcl.bminus), prof.mupcl.lclhght, prof.mupcl.li5,
prof.mupcl.lfchght, prof.mupcl.elhght
]
#mlcape = C[1]
#print('mlcape',mlcape)
data = np.array([["PCL", "CAPE", "CINH", "LCL", "LI", "LFC", "EL"],
[
str(int(np.ma.around(A[i], 0))) if
(type(A[i]) == np.float64) else str(A[i])
for i in range(len(A))
],
[
str(int(np.ma.around(C[i], 0))) if
(type(C[i]) == np.float64) else str(C[i])
for i in range(len(C))
],
[
str(int(np.ma.around(D[i], 0))) if
(type(D[i]) == np.float64) else str(D[i])
for i in range(len(D))
]])
for i in range(data.shape[0]):
for j in range(data.shape[1]):
ax2.annotate(data[i, j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="left",
color=cb_colors.gray7,
weight='bold')
# Draw a box around the selected parcel being shown in the Skew-T
box = ax3.add_patch(
patches.Rectangle((-57.7, pcl_box_level),
53.,
0.0225,
fill=False,
linewidth=1,
edgecolor=cb_colors.purple4,
zorder=4))
box.set_clip_on(False)
# Write the lapse rates to the inset.
#box = ax3.add_patch(patches.Rectangle((-55.0, -0.305), 43.0, -0.115,fill=False,linewidth=1,edgecolor="w",zorder=4))
#box.set_clip_on(False)
x_list = [0.15, 0.16]
y_list = np.arange(-0.315, -.40, -0.0225)
data = np.array([[
"0-3km AGL LR =",
str(np.ma.around(prof.lapserate_3km, 1)) + " C/km"
], [
"3-6km AGL LR =",
str(np.ma.around(prof.lapserate_3_6km, 1)) + " C/km"
],
[
"850-500mb LR =",
str(np.ma.around(prof.lapserate_850_500, 1)) + " C/km"
],
[
"700-500mb LR =",
str(np.ma.around(prof.lapserate_700_500, 1)) + " C/km"
]])
for i in range(data.shape[0]):
for j in range(data.shape[1]):
if (j % 2 == 0):
ax2.annotate(data[i, j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="right",
color='k',
weight='bold')
else:
ax2.annotate(data[i, j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="left",
color='k',
weight='bold')
#Severe Indices
#box = ax3.add_patch(patches.Rectangle((-12.0, -0.305), 21.0, -0.115,fill=False,linewidth=1,edgecolor="w",zorder=4))
#box.set_clip_on(False)
x_list = [0.52, 0.53]
y_list = np.arange(-0.315, -.40, -0.0225)
# This looks lifted from the Profile class. Don't need this.
sfc = prof.pres[prof.sfc]
p6km = sharptab.interp.pres(prof, sharptab.interp.to_msl(prof, 6000.))
p8km = sharptab.interp.pres(prof, sharptab.interp.to_msl(prof, 8000.))
# ebot_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[0]))
# etop_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[1]))
# Mean winds
#mean_1km = winds.mean_wind(prof, pbot=sfc, ptop=p1km)
mean_1km_comp = prof.mean_1km #utils.comp2vec(mean_1km[0],mean_1km[1])
#mean_3km = winds.mean_wind(prof, pbot=sfc, ptop=p3km)
mean_3km_comp = prof.mean_3km #utils.comp2vec(mean_3km[0],mean_3km[1])
#mean_eff = winds.mean_wind(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
mean_eff_comp = utils.comp2vec(
*prof.mean_eff) #utils.comp2vec(mean_eff[0],mean_eff[1])
if type(eff_inflow[0]) != np.float64:
mean_eff_comp = ['---', '--']
mean_6km = winds.mean_wind(prof, pbot=sfc, ptop=p6km)
mean_6km_comp = prof.mean_6km #utils.comp2vec(mean_6km[0],mean_6km[1])
mean_8km = winds.mean_wind(prof, pbot=sfc, ptop=p8km)
mean_8km_comp = prof.mean_8km #utils.comp2vec(mean_8km[0],mean_8km[1])
mean_cloudlayer = winds.mean_wind(prof, pbot=pcl.lclpres, ptop=pcl.elpres)
mean_cloudlayer_comp = prof.mean_lcl_el #utils.comp2vec(mean_cloudlayer[0],mean_cloudlayer[1])
mean_ebwd = winds.mean_wind(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
mean_ebwd_comp = utils.comp2vec(
*prof.mean_ebw) #utils.comp2vec(mean_ebwd[0],mean_ebwd[1])
if type(eff_inflow[0]) != np.float64:
mean_ebwd_comp = ['---', '--']
bunkers_rt = utils.comp2vec(bunkers[0], bunkers[1])
bunkers_lf = utils.comp2vec(bunkers[2], bunkers[3])
corfidi_up = utils.comp2vec(corfidi[0], corfidi[1])
corfidi_dn = utils.comp2vec(corfidi[2], corfidi[3])
srw_1km = prof.srw_1km #utils.comp2vec(*winds.sr_wind(prof, pbot=sfc, ptop=p1km, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_3km = prof.srw_3km #utils.comp2vec(*winds.sr_wind(prof, pbot=sfc, ptop=p3km, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_eff = utils.comp2vec(
*prof.srw_eff
) #utils.comp2vec(*winds.sr_wind(prof, pbot=eff_inflow[0], ptop=eff_inflow[1], stu=prof.srwind[0], stv=prof.srwind[1]))
if type(eff_inflow[0]) != np.float64:
srw_eff = ['---', '--']
srw_6km = prof.srw_6km #utils.comp2vec(*winds.sr_wind(prof, pbot=sfc, ptop=p6km, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_8km = prof.srw_8km #utils.comp2vec(*winds.sr_wind(prof, pbot=sfc, ptop=p8km, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_cloudlayer = prof.srw_lcl_el #utils.comp2vec(*winds.sr_wind(prof, pbot=pcl.lclpres, ptop=pcl.elpres, stu=prof.srwind[0], stv=prof.srwind[1]))
srw_ebwd = utils.comp2vec(
*prof.srw_ebw
) #utils.comp2vec(*winds.sr_wind(prof, pbot=eff_inflow[0], ptop=eff_inflow[1], stu=prof.srwind[0], stv=prof.srwind[1]))
if type(eff_inflow[0]) != np.float64:
srw_ebwd = ['---', '--']
srw_46km = utils.comp2vec(
*prof.srw_4_6km
) #utils.comp2vec(*winds.sr_wind(prof, pbot=p4km, ptop=p6km, stu=prof.srwind[0], stv=prof.srwind[1]))
sfc_8km_shear = prof.sfc_8km_shear #winds.wind_shear(prof, pbot=sfc, ptop=p8km)
sfc_6km_shear = prof.sfc_6km_shear #winds.wind_shear(prof, pbot=sfc, ptop=p6km)
sfc_3km_shear = prof.sfc_3km_shear #winds.wind_shear(prof, pbot=sfc, ptop=p3km)
sfc_1km_shear = prof.sfc_1km_shear #winds.wind_shear(prof, pbot=sfc, ptop=p1km)
effective_shear = prof.eff_shear #winds.wind_shear(prof, pbot=eff_inflow[0], ptop=etop_hght)
cloudlayer_shear = prof.lcl_el_shear #winds.wind_shear(prof,pbot= pcl.lclpres, ptop=pcl.elpres)
srh3km = prof.srh3km #winds.helicity(prof, 0, 3000., stu = bunkers[0], stv = bunkers[1])
srh1km = prof.srh1km #winds.helicity(prof, 0, 1000., stu = bunkers[0], stv = bunkers[1])
stp_fixed = prof.stp_fixed #params.stp_fixed(pcl.bplus, pcl.lclhght, srh1km[0], utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1])
ship = prof.ship
effective_srh = prof.right_esrh #winds.helicity(prof, ebot_hght, etop_hght, stu = bunkers[0], stv = bunkers[1])
ebwd = prof.ebwd #winds.wind_shear(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
ebwspd = prof.ebwspd
scp = prof.right_scp
stp_cin = prof.stp_cin #params.stp_cin(pcl.bplus, effective_srh[0], ebwspd, pcl.lclhght, pcl.bminus)
brn_shear = pcl.brnshear
# Draw the SCP, STP, SHIP indices to the plot
# TODO: Include the color variations on this to denote intensity of the index.
# if prof.stp_fixed is ma.masked:
# temp_stp_fixed = '0.0'
# else:
# temp_stp_fixed = str(round(prof.stp_fixed,1))
# if prof.stp_cin is ma.masked:
# temp_stp_cin = '0.0'
# else:
# temp_stp_cin = str(round(prof.stp_cin,1))
# if prof.right_scp is ma.masked:
# temp_right_scp = '0.0'
# else:
# temp_right_scp = str(round(prof.right_scp,1))
# data = np.array([["Supercell =",temp_right_scp],
# ["STP (cin) =",temp_stp_cin],
# ["STP (fix) =",temp_stp_fixed]])
data = np.array([["Supercell =",
str(np.ma.around(prof.right_scp, 1))],
["STP (cin) =",
str(np.ma.around(prof.stp_cin, 1))],
["STP (fix) =",
str(np.ma.around(prof.stp_fixed, 1))]])
data = np.array([["Supercell =",
str(np.ma.around(prof.right_scp, 1))],
["STP (cin) =",
str(np.ma.around(prof.stp_cin, 1))],
["STP (fix) =",
str(np.ma.around(prof.stp_fixed, 1))],
["SHIP =", str(np.ma.around(prof.ship, 1))]])
'''
for i in range(data.shape[0]):
for j in range(data.shape[1]):
d = float(data[i,1])
if i == 0:
if d >= 19.95:
c = MAGENTA
elif d >= 11.95:
c = RED
elif d >= 1.95:
c = YELLOW
elif d >= .45:
c = WHITE
elif d >= -.45:
c = LBROWN
elif d < -0.45:
c = CYAN
elif i == 1:
if d >= 8:
c = MAGENTA
elif d >= 4:
c = RED
elif d >= 2:
c = YELLOW
elif d >= 1:
c = WHITE
elif d >= .5:
c = LBROWN
elif d < .5:
c = DBROWN
stpCinColor = c
elif i == 2:
if d >= 7:
c = MAGENTA
elif d >= 5:
c = RED
elif d >= 2:
c = YELLOW
elif d >= 1:
c = WHITE
elif d >= 0.5:
c = LBROWN
else:
c = DBROWN
elif i == 3:
if d >= 5:
c = MAGENTA
elif d >= 2:
c = RED
elif d >= 1:
c = YELLOW
elif d >= .5:
c = WHITE
else:
c = DBROWN
if (j % 2 == 0):
ax2.annotate(data[i,j], (x_list[j], y_list[i]), xycoords="axes fraction",
fontsize=12, va="top", ha="right", color=c, weight='bold')
else:
ax2.annotate(data[i,j], (x_list[j], y_list[i]), xycoords="axes fraction",
fontsize=12, va="top", ha="left", color=cb_colors.gray7, weight='bold')
'''
# Draw the kinematic inset on the plot
# box = ax3.add_patch(patches.Rectangle((9.0, -0.04), 60.0, -0.025,fill=False,linewidth=1,edgecolor="w",zorder=4))
# box.set_clip_on(False)
x_list = np.array([0.60, 0.84, 0.97, 1.08, 1.18]) - 0.12
y_list = [-0.045]
y_list.extend(np.arange(-.07, -0.12, -.0225).tolist())
y_list.extend(np.arange(-.145, -0.22, -.0225).tolist())
y_list.extend(np.arange(-.2425, -0.27, -.0225).tolist())
y_list.extend(np.arange(-0.295, -.4, -0.0225).tolist())
A = [
"SFC-1km", srh1km[0],
utils.comp2vec(sfc_1km_shear[0], sfc_1km_shear[1])[1]
]
A2 = [mean_1km_comp, srw_1km]
B = [
"SFC-3km", srh3km[0],
utils.comp2vec(sfc_3km_shear[0], sfc_3km_shear[1])[1]
]
B2 = [mean_3km_comp, srw_3km]
C = [
"Eff Inflow Layer", effective_srh[0],
utils.comp2vec(effective_shear[0], effective_shear[1])[1]
]
C2 = [mean_eff_comp, srw_eff]
# D = ["SFC-6km", "", utils.comp2vec(sfc_6km_shear[0],sfc_6km_shear[1])[1]]
# D2 = [mean_6km_comp, srw_6km]
# E = ["SFC-8km", "", utils.comp2vec(sfc_8km_shear[0],sfc_8km_shear[1])[1]]
# E2 = [mean_8km_comp, srw_8km]
# F = ["LCL-EL (CLoud Layer)", "", utils.comp2vec(cloudlayer_shear[0],cloudlayer_shear[1])[1]]
# F2 = [mean_cloudlayer_comp, srw_cloudlayer]
# G = ["Eff Shear (EBWD)", "", utils.comp2vec(ebwd[0],ebwd[1])[1]]
# G2 = [mean_ebwd_comp, srw_ebwd]
# H = ["BRN Shear (m2/s2)", "", brn_shear, "", ""]
# I = ["4-6km SR Wind", ""]
# I2 = [str(int(round(srw_46km[0],0)))+"/"+str(int(round(srw_46km[1],0)))]
# I3 = ["", ""]
# J = ["...Storm Motion Vectors...", "", "", "", ""]
# K = ["Bunkers Right", ""]
# K2 = [str(int(round(bunkers_rt[0],0)))+"/"+str(int(round(bunkers_rt[1],0)))]
# K3 = ["", ""]
# L = ["Bunkers Left", ""]
# L2 = [str(int(round(bunkers_lf[0],0)))+"/"+str(int(round(bunkers_lf[1],0)))]
# L3 = ["", ""]
# M = ["Corfidi Downshear", ""]
# M2 = [str(int(round(corfidi_dn[0],0)))+"/"+str(int(round(corfidi_dn[1],0)))]
# M3 = ["", ""]
# N = ["Corfidi Upshear", ""]
# N2 = [str(int(round(corfidi_up[0],0)))+"/"+str(int(round(corfidi_up[1],0)))]
# N3 = ["", ""]
data = np.array([np.array(["", "SRH (m2/s2)", "Shear (kt)", "MnWind", "SRW"]),
np.array([ str(int(round(A[i],0))) if (type(A[i])== np.float64) else str(A[i]) for i in range(len(A)) ]+\
[ str(int(np.ma.around(A2[i][0],0)))+"/"+str(int(np.ma.around(A2[i][1],0))) if (type(A2[i][0])== np.ma.core.MaskedArray) else str(A2[i][0])+"/"+str(A2[i][1]) for i in range(len(A2)) ]),
np.array([ str(int(round(B[i],0))) if (type(B[i])== np.float64) else str(B[i]) for i in range(len(B)) ]+\
[ str(int(np.ma.around(B2[i][0],0)))+"/"+str(int(np.ma.around(B2[i][1],0))) if (type(B2[i][0])== np.ma.core.MaskedArray) else str(B2[i][0])+"/"+str(B2[i][1]) for i in range(len(B2)) ]),
np.array([ str(int(np.ma.around(C[i],0))) if (type(C[i])== np.float64) else str(C[i]) for i in range(len(C)) ]+\
[ str(int(np.ma.around(C2[i][0],0)))+"/"+str(int(np.ma.around(C2[i][1],0))) if (type(C2[i][0])== np.ma.core.MaskedArray) else str(C2[i][0])+"/"+str(C2[i][1]) for i in range(len(C2)) ])]) #,
# np.array([ str(int(round(D[i],0))) if (type(D[i])== np.float64) else str(D[i]) for i in range(len(D)) ]+\
# [ str(int(round(D2[i][0],0)))+"/"+str(int(round(D2[i][1],0))) if (type(D2[i][0])== np.ma.core.MaskedArray) else str(D2[i][0])+"/"+str(D2[i][1]) for i in range(len(D2)) ]),
# np.array([ str(int(round(E[i],0))) if (type(E[i])== np.float64) else str(E[i]) for i in range(len(E)) ]+\
# [ str(int(round(E2[i][0],0)))+"/"+str(int(round(E2[i][1],0))) if (type(E2[i][0])== np.ma.core.MaskedArray) else str(E2[i][0])+"/"+str(E2[i][1]) for i in range(len(E2)) ]),
# np.array([ str(int(round(F[i],0))) if (type(F[i])== np.float64) else str(F[i]) for i in range(len(F)) ]+\
# [ str(int(round(F2[i][0],0)))+"/"+str(int(round(F2[i][1],0))) if (type(F2[i][0])== np.ma.core.MaskedArray) else str(F2[i][0])+"/"+str(F2[i][1]) for i in range(len(F2)) ]),
# np.array([ str(int(round(G[i],0))) if (type(G[i])== np.float64) else str(G[i]) for i in range(len(G)) ]+\
# [ str(int(round(G2[i][0],0)))+"/"+str(int(round(G2[i][1],0))) if (type(G2[i][0])== np.ma.core.MaskedArray) else str(G2[i][0])+"/"+str(G2[i][1]) for i in range(len(G2)) ]),
# np.array([ str(int(round(H[i],0))) if (type(H[i])== np.float64) else str(H[i]) for i in range(len(H)) ]),
# np.array(I+I2+I3),
# np.array(J),
# np.array(K+K2+K3),
# np.array(L+L2+L3),
# np.array(M+M2+M3),
# np.array(N+N2+N3)])
#x_list = np.array([0, 0.08, 0.17, 0.25, 0.33, 0.39, 0.47])-0.05
y_list = [-0.045, -0.07, -0.0925, -0.115, -0.1375]
for i in range(data.shape[0]):
for j in range(data[0].shape[0]):
if j > 0:
ax2.annotate(data[i][j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="right",
color=cb_colors.gray7,
weight='bold')
else:
ax2.annotate(data[i][j], (x_list[j], y_list[i]),
xycoords="axes fraction",
fontsize=12,
va="top",
ha="left",
color=cb_colors.gray7,
weight='bold')
# wind_1km = utils.vec2comp(prof.wind1km[0], prof.wind1km[1])
# wind_6km = utils.vec2comp(prof.wind6km[0], prof.wind6km[1])
# wind_barbs = ax2.barbs(58, 2200, wind_1km[0], wind_1km[1], color='#AA0000', zorder=3, lw=1.25,length=9)
# wind_barbs.set_clip_on(False)
# wind_barbs = ax2.barbs(58, 2200, wind_6km[0], wind_6km[1], color='#0A74C6', zorder=3, lw=1.25,length=9)
# wind_barbs.set_clip_on(False)
# ax2.annotate("1km & 6km AGL\nWind Barbs", (1.08,-0.37), xycoords="axes fraction", fontsize=12, va="top", ha="center", color='#0A74C6', weight='bold')
# Draw the CAPE vs. SRH Scatter
#ax_EFSTP = fig.add_axes([0.74625, 0.0229, 0.20375, 0.2507], frameon=False)
#x_EFSTP = fig.add_axes([0.72625, 0.0229, 0.20375, 0.2507], frameon=False)
ax_EFSTP = fig.add_axes([0.625, 0.05, 0.35, 0.3], frameon=False)
for member_no, c in zip(
np.arange(1, 19, 1),
np.tile([
cb_colors.orange6, cb_colors.orange6, cb_colors.green6,
cb_colors.green6, cb_colors.purple6, cb_colors.purple6
], (3, 1))):
memidx = [0, 10, 11, 12, 13, 14, 15, 16, 17, 1, 2, 3, 4, 5, 6, 7, 8, 9]
ax_EFSTP.scatter(xpts[memidx[member_no - 1], :, :].ravel(),
ypts[memidx[member_no - 1], :, :].ravel(),
color=c,
marker='o',
s=5,
alpha=0.7)
ax_EFSTP.set_xticks(np.arange(0, 5001, 1000))
ax_EFSTP.set_yticks(np.arange(0, 601, 100))
#ax_EFSTP.set_xticklabels(np.arange(0,5001,1000),color='k',fontsize=12)
#ax_EFSTP.set_yticklabels(np.arange(0,601,100),color='k',fontsize=12)
ax_EFSTP.set_xlabel('MLCAPE', weight='bold', fontsize=14)
ax_EFSTP.set_ylabel('0-1km SRH', weight='bold', fontsize=14)
ax_EFSTP.tick_params(axis='both', labelsize=12, labelcolor='k')
ax_EFSTP.set_xlim(-200, 5000)
ax_EFSTP.set_ylim(-100, 600)
#ax_EFSTP.tick_params(direction='in', axis='x', which='major', colors=cb_colors.gray4,length=0,width=1.5,size=12)#pad=-10
#ax_EFSTP.tick_params(direction='in', axis='y', which='major', colors=cb_colors.gray4,length=0,width=1.5,size=12)#pad=-23
ax_EFSTP.grid(color=cb_colors.gray4,
linestyle='--',
dashes=(3, 3),
alpha=0.75,
zorder=0,
linewidth=1.25)
ax_EFSTP.text(0.8,
0.95,
'YSU',
color=cb_colors.orange6,
transform=ax_EFSTP.transAxes,
fontsize=16,
weight='bold')
ax_EFSTP.text(0.8,
0.89,
'MYJ',
color=cb_colors.green6,
transform=ax_EFSTP.transAxes,
fontsize=16,
weight='bold')
ax_EFSTP.text(0.8,
0.83,
'MYNN',
color=cb_colors.purple6,
transform=ax_EFSTP.transAxes,
fontsize=16,
weight='bold')
box = ax_EFSTP.add_patch(
patches.Rectangle((0, -1),
13,
13,
fill=False,
linewidth=2,
edgecolor=cb_colors.gray4,
zorder=10))
box.set_clip_on(False)
ax_EFSTP.annotate("0-1 km SRH vs. 100-mb MLCAPE", (0.5, 1.075),
xycoords="axes fraction",
fontsize=14,
va="center",
ha="center",
color=cb_colors.gray7,
weight='bold')
ax_EFSTP.annotate("(9 km neighborhood)", (0.5, 1.025),
xycoords="axes fraction",
fontsize=12,
va="center",
ha="center",
color=cb_colors.gray7,
weight='bold')
plt.savefig(figname, facecolor=fig.get_facecolor()) #, edgecolor=None)
def __init__(self, **kwargs):
'''
Create the sounding data object
Parameters
----------
Mandatory Keywords
pres : array_like
The pressure values (Hectopaschals)
hght : array_like
The corresponding height values (Meters)
tmpc : array_like
The corresponding temperature values (Celsius)
dwpc : array_like
The corresponding dewpoint temperature values (Celsius)
Optional Keyword Pairs (must use one or the other)
wdir : array_like
The direction from which the wind is blowing in
meteorological degrees
wspd : array_like
The speed of the wind (kts)
OR
u : array_like
The U-component of the direction from which the wind
is blowing (kts)
v : array_like
The V-component of the direction from which the wind
is blowing. (kts)
Optional Keywords
missing : number (default: sharppy.sharptab.constants.MISSING)
The value of the missing flag
location : string (default: None)
The 3 character station identifier or 4 character
WMO station ID for radiosonde locations. Used for
the PWV database.
strictQC : boolean
A flag that indicates whether or not the strict quality control
routines should be run on the profile upon construction.
Returns
-------
prof: Profile object
'''
super(BasicProfile, self).__init__(**kwargs)
self.strictQC = kwargs.get('strictQC', True)
## did the user provide the wind in vector form?
if self.wdir is not None:
self.wdir[self.wdir == self.missing] = ma.masked
self.wspd[self.wspd == self.missing] = ma.masked
self.wdir[self.wspd.mask] = ma.masked
self.wspd[self.wdir.mask] = ma.masked
self.u, self.v = utils.vec2comp(self.wdir, self.wspd)
## did the user provide the wind in u,v form?
elif self.u is not None:
self.u[self.u == self.missing] = ma.masked
self.v[self.v == self.missing] = ma.masked
self.u[self.v.mask] = ma.masked
self.v[self.u.mask] = ma.masked
self.wdir, self.wspd = utils.comp2vec(self.u, self.v)
## check if any standard deviation data was supplied
if self.tmp_stdev is not None:
self.dew_stdev[self.dew_stdev == self.missing] = ma.masked
self.tmp_stdev[self.tmp_stdev == self.missing] = ma.masked
self.dew_stdev.set_fill_value(self.missing)
self.tmp_stdev.set_fill_value(self.missing)
if self.omeg is not None:
## get the omega data and turn into arrays
self.omeg[self.omeg == self.missing] = ma.masked
else:
self.omeg = ma.masked_all(len(self.hght))
# QC Checks on the arrays passed to the constructor.
qc_tools.areProfileArrayLengthEqual(self)
## mask the missing values
self.pres[self.pres == self.missing] = ma.masked
self.hght[self.hght == self.missing] = ma.masked
self.tmpc[self.tmpc == self.missing] = ma.masked
self.dwpc[self.dwpc == self.missing] = ma.masked
self.logp = np.log10(self.pres.copy())
self.vtmp = thermo.virtemp( self.pres, self.tmpc, self.dwpc )
idx = np.ma.where(self.pres > 0)[0]
self.vtmp[self.dwpc.mask[idx]] = self.tmpc[self.dwpc.mask[idx]] # Masking any virtual temperature
## get the index of the top and bottom of the profile
self.sfc = self.get_sfc()
self.top = self.get_top()
if self.strictQC is True:
self.checkDataIntegrity()
## generate the wetbulb profile
self.wetbulb = self.get_wetbulb_profile()
## generate theta-e profile
self.thetae = self.get_thetae_profile()
## generate theta profile
self.theta = self.get_theta_profile()
## generate water vapor mixing ratio profile
self.wvmr = self.get_wvmr_profile()
## generate rh profile
self.relh = self.get_rh_profile()
def __init__(self, **kwargs):
'''
Create the sounding data object
Parameters
----------
Mandatory Keywords
pres : array_like
The pressure values (Hectopaschals)
hght : array_like
The corresponding height values (Meters)
tmpc : array_like
The corresponding temperature values (Celsius)
dwpc : array_like
The corresponding dewpoint temperature values (Celsius)
Optional Keyword Pairs (must use one or the other)
wdir : array_like
The direction from which the wind is blowing in
meteorological degrees
wspd : array_like
The speed of the wind
OR
u : array_like
The U-component of the direction from which the wind
is blowing
v : array_like
The V-component of the direction from which the wind
is blowing.
Optional Keywords
missing : number (default: sharppy.sharptab.constants.MISSING)
The value of the missing flag
location : string (default: None)
The 3 character station identifier or 4 character
WMO station ID for radiosonde locations. Used for
the PWV database.
strictQC : boolean
A flag that indicates whether or not the strict quality control
routines should be run on the profile upon construction.
Returns
-------
prof: Profile object
'''
super(BasicProfile, self).__init__(**kwargs)
strictQC = kwargs.get('strictQC', True)
assert len(self.pres) == len(self.hght) == len(self.tmpc) == len(self.dwpc),\
"Length of pres, hght, tmpc, or dwpc arrays passed to constructor are not the same."
## did the user provide the wind in vector form?
if self.wdir is not None:
assert len(self.wdir) == len(self.wspd) == len(
self.pres
), "Length of wdir and wspd arrays passed to constructor are not the same length as the pres array."
self.wdir[self.wdir == self.missing] = ma.masked
self.wspd[self.wspd == self.missing] = ma.masked
self.wdir[self.wspd.mask] = ma.masked
self.wspd[self.wdir.mask] = ma.masked
self.u, self.v = utils.vec2comp(self.wdir, self.wspd)
## did the user provide the wind in u,v form?
elif self.u is not None:
assert len(self.u) == len(self.v) == len(
self.pres
), "Length of u and v arrays passed to constructor are not the same length as the pres array."
self.u[self.u == self.missing] = ma.masked
self.v[self.v == self.missing] = ma.masked
self.u[self.v.mask] = ma.masked
self.v[self.u.mask] = ma.masked
self.wdir, self.wspd = utils.comp2vec(self.u, self.v)
## check if any standard deviation data was supplied
if self.tmp_stdev is not None:
self.dew_stdev[self.dew_stdev == self.missing] = ma.masked
self.tmp_stdev[self.tmp_stdev == self.missing] = ma.masked
self.dew_stdev.set_fill_value(self.missing)
self.tmp_stdev.set_fill_value(self.missing)
if self.omeg is not None:
## get the omega data and turn into arrays
assert len(self.omeg) == len(
self.pres
), "Length of omeg array passed to constructor is not the same length as the pres array."
self.omeg[self.omeg == self.missing] = ma.masked
else:
self.omeg = ma.masked_all(len(self.hght))
# QC Checks on the arrays passed to the constructor.
qc_tools.areProfileArrayLengthEqual(self)
## mask the missing values
self.pres[self.pres == self.missing] = ma.masked
self.hght[self.hght == self.missing] = ma.masked
self.tmpc[self.tmpc == self.missing] = ma.masked
self.dwpc[self.dwpc == self.missing] = ma.masked
#if not qc_tools.isPRESValid(self.pres):
## qc_tools.raiseError("Incorrect order of pressure array (or repeat values) or pressure array is of length <= 1.", ValueError)
if not qc_tools.isHGHTValid(self.hght) and strictQC:
qc_tools.raiseError(
"Incorrect order of height (or repeat values) array or height array is of length <= 1.",
ValueError)
if not qc_tools.isTMPCValid(self.tmpc):
qc_tools.raiseError(
"Invalid temperature array. Array contains a value < -273.15 Celsius.",
ValueError)
if not qc_tools.isDWPCValid(self.dwpc):
qc_tools.raiseError(
"Invalid dewpoint array. Array contains a value < -273.15 Celsius.",
ValueError)
if not qc_tools.isWSPDValid(self.wspd) and strictQC:
qc_tools.raiseError(
"Invalid wind speed array. Array contains a value < 0 knots.",
ValueError)
if not qc_tools.isWDIRValid(self.wdir) and strictQC:
qc_tools.raiseError(
"Invalid wind direction array. Array contains a value < 0 degrees or value > 360 degrees.",
ValueError)
self.logp = np.log10(self.pres.copy())
self.vtmp = thermo.virtemp(self.pres, self.tmpc, self.dwpc)
idx = np.ma.where(self.pres > 0)[0]
self.vtmp[self.dwpc.mask[idx]] = self.tmpc[
self.dwpc.mask[idx]] # Masking any virtual temperature
## get the index of the top and bottom of the profile
self.sfc = self.get_sfc()
self.top = self.get_top()
## generate the wetbulb profile
self.wetbulb = self.get_wetbulb_profile()
## generate theta-e profile
self.thetae = self.get_thetae_profile()
def test_vec2comp_default_missing_val_single():
input_wdir = MISSING
input_wspd = 30
returned_u, returned_v = utils.vec2comp(input_wdir, input_wspd)
npt.assert_equal(type(returned_u), type(ma.masked))
npt.assert_equal(type(returned_v), type(ma.masked))