def get_parcels(self):
'''
Function to generate various parcels and parcel
traces.
Returns nothing, but sets the following
variables:
self.mupcl : Most Unstable Parcel
self.sfcpcl : Surface Based Parcel
self.mlpcl : Mixed Layer Parcel
self.fcstpcl : Forecast Surface Parcel
self.ebottom : The bottom pressure level of
the effective inflow layer
self.etop : the top pressure level of
the effective inflow layer
self.ebotm : The bottom, meters (agl), of the
effective inflow layer
self.etopm : The top, meters (agl), of the
effective inflow layer
Parameters
----------
None
Returns
-------
None
'''
self.mupcl = params.parcelx( self, flag=3 )
if self.mupcl.lplvals.pres == self.pres[self.sfc]:
self.sfcpcl = self.mupcl
else:
self.sfcpcl = params.parcelx( self, flag=1 )
self.fcstpcl = params.parcelx( self, flag=2 )
self.mlpcl = params.parcelx( self, flag=4 )
self.usrpcl = params.Parcel()
## get the effective inflow layer data
self.ebottom, self.etop = params.effective_inflow_layer( self, mupcl=self.mupcl )
## if there was no effective inflow layer, set the values to masked
if self.etop is ma.masked or self.ebottom is ma.masked:
self.ebotm = ma.masked; self.etopm = ma.masked
self.effpcl = self.sfcpcl # Default to surface parcel, as in params.DefineProfile().
## otherwise, interpolate the heights given to above ground level
else:
self.ebotm = interp.to_agl(self, interp.hght(self, self.ebottom))
self.etopm = interp.to_agl(self, interp.hght(self, self.etop))
# The below code was adapted from params.DefineProfile()
# Lifting one additional parcel probably won't slow the program too much.
# It's just one more lift compared to all the lifts in the params.effective_inflow_layer() call.
mtha = params.mean_theta(self, self.ebottom, self.etop)
mmr = params.mean_mixratio(self, self.ebottom, self.etop)
effpres = (self.ebottom+self.etop)/2.
efftmpc = thermo.theta(1000., mtha, effpres)
effdwpc = thermo.temp_at_mixrat(mmr, effpres)
self.effpcl = params.parcelx(self, flag=5, pres=effpres, tmpc=efftmpc, dwpc=effdwpc) #This is the effective parcel.
def get_parcels(self):
'''
Function to generate various parcels and parcel
traces.
Returns nothing, but sets the following
variables:
self.mupcl : Most Unstable Parcel
self.sfcpcl : Surface Based Parcel
self.mlpcl : Mixed Layer Parcel
self.fcstpcl : Forecast Surface Parcel
self.ebottom : The bottom pressure level of
the effective inflow layer
self.etop : the top pressure level of
the effective inflow layer
self.ebotm : The bottom, meters (agl), of the
effective inflow layer
self.etopm : The top, meters (agl), of the
effective inflow layer
Parameters
----------
None
Returns
-------
None
'''
self.mupcl = params.parcelx( self, flag=3 )
if self.mupcl.lplvals.pres == self.pres[self.sfc]:
self.sfcpcl = self.mupcl
else:
self.sfcpcl = params.parcelx( self, flag=1 )
self.fcstpcl = params.parcelx( self, flag=2 )
self.mlpcl = params.parcelx( self, flag=4 )
self.usrpcl = params.Parcel()
## get the effective inflow layer data
self.ebottom, self.etop = params.effective_inflow_layer( self, mupcl=self.mupcl )
## if there was no effective inflow layer, set the values to masked
if self.etop is ma.masked or self.ebottom is ma.masked:
self.ebotm = ma.masked; self.etopm = ma.masked
self.effpcl = self.sfcpcl # Default to surface parcel, as in params.DefineProfile().
## otherwise, interpolate the heights given to above ground level
else:
self.ebotm = interp.to_agl(self, interp.hght(self, self.ebottom))
self.etopm = interp.to_agl(self, interp.hght(self, self.etop))
# The below code was adapted from params.DefineProfile()
# Lifting one additional parcel probably won't slow the program too much.
# It's just one more lift compared to all the lifts in the params.effective_inflow_layer() call.
mtha = params.mean_theta(self, self.ebottom, self.etop)
mmr = params.mean_mixratio(self, self.ebottom, self.etop)
effpres = (self.ebottom+self.etop)/2.
efftmpc = thermo.theta(1000., mtha, effpres)
effdwpc = thermo.temp_at_mixrat(mmr, effpres)
self.effpcl = params.parcelx(self, flag=5, pres=effpres, tmpc=efftmpc, dwpc=effdwpc) #This is the effective parcel.
def lift_parcels(prof):
"""Lift all the parcels within a given height interval and return the CAPEs, CINHs, and LFCs"""
## the height bottom, top, and interval
zvals = np.arange(0, 5000, 100)
pvals = interp.pres(prof, interp.to_msl(prof, zvals))
tvals = interp.temp(prof, pvals)
dvals = interp.dwpt(prof, pvals)
hvals = interp.hght(prof, pvals)
hvals = interp.to_agl(prof, hvals)
## empty lists for storing the result
cape_arr = []
cinh_arr = []
lfc_arr = []
## lift each parcel in the vertical profile
for p, t, td, h in zip(pvals, tvals, dvals, hvals):
## use SHARPpy to compute the parcel indices
pcl = params.parcelx(prof, pres=p, tmpc=t, dwpc=td)
## store the parcel indices
cape_arr.append(pcl.bplus)
cinh_arr.append(pcl.bminus)
lfc_arr.append(pcl.lfchght - h)
## return the data
return np.ma.masked_invalid(cape_arr), np.ma.masked_invalid(
cinh_arr), np.ma.masked_invalid(lfc_arr)
def draw_ensemble_point(self, qp, prof):
# Plot the profile index on the scatter plot
if 'pbl_h' not in dir(
prof
): # Make sure a PBL top has been found in the profile object
ppbl_top = params.pbl_top(prof)
setattr(prof, 'pbl_h',
interp.to_agl(prof, interp.hght(prof, ppbl_top)))
if 'sfcpcl' not in dir(
prof
): # Make sure a surface parcel has been lifted in the profile object
setattr(prof, 'sfcpcl', params.parcelx(prof, flag=1))
#x = self.x_to_xpix()
#y = self.y_to_ypix()
color = QtCore.Qt.red
qp.setPen(QtGui.QPen(color))
qp.setBrush(QtGui.QBrush(color))
x = self.x_to_xpix(prof.pbl_h) - 50 / 2.
y = self.y_to_ypix(prof.sfcpcl.bplus) - (self.fsize - 1) / 2
qp.drawEllipse(x, y, 3, 3)
return
def indices(prof, debug=False):
# return a formatted-string list of stability and kinematic indices
sfcpcl = params.parcelx(prof, flag=1)
mupcl = params.parcelx(prof, flag=3) # most unstable
mlpcl = params.parcelx(prof, flag=4) # 100 mb mean layer parcel
pcl = mupcl
sfc = prof.pres[prof.sfc]
p3km = interp.pres(prof, interp.to_msl(prof, 3000.))
p6km = interp.pres(prof, interp.to_msl(prof, 6000.))
p1km = interp.pres(prof, interp.to_msl(prof, 1000.))
mean_3km = winds.mean_wind(prof, pbot=sfc, ptop=p3km)
sfc_6km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p6km)
sfc_3km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p3km)
sfc_1km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p1km)
#print "0-3 km Pressure-Weighted Mean Wind (kt):", utils.comp2vec(mean_3km[0], mean_3km[1])[1]
#print "0-6 km Shear (kt):", utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1]
srwind = params.bunkers_storm_motion(prof)
srh3km = winds.helicity(prof, 0, 3000., stu=srwind[0], stv=srwind[1])
srh1km = winds.helicity(prof, 0, 1000., stu=srwind[0], stv=srwind[1])
#print "0-3 km Storm Relative Helicity [m2/s2]:",srh3km[0]
#### Calculating variables based off of the effective inflow layer:
# The effective inflow layer concept is used to obtain the layer of buoyant parcels that feed a storm's inflow.
# Here are a few examples of how to compute variables that require the effective inflow layer in order to calculate them:
stp_fixed = params.stp_fixed(
sfcpcl.bplus, sfcpcl.lclhght, srh1km[0],
utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1])
ship = params.ship(prof)
# If you get an error about not converting masked constant to python int
# use the round() function instead of int() - Ahijevych May 11 2016
# 2nd element of list is the # of decimal places
indices = {
'SBCAPE': [sfcpcl.bplus, 0, 'J $\mathregular{kg^{-1}}$'],
'SBCIN': [sfcpcl.bminus, 0, 'J $\mathregular{kg^{-1}}$'],
'SBLCL': [sfcpcl.lclhght, 0, 'm AGL'],
'SBLFC': [sfcpcl.lfchght, 0, 'm AGL'],
'SBEL': [sfcpcl.elhght, 0, 'm AGL'],
'SBLI': [sfcpcl.li5, 0, 'C'],
'MLCAPE': [mlpcl.bplus, 0, 'J $\mathregular{kg^{-1}}$'],
'MLCIN': [mlpcl.bminus, 0, 'J $\mathregular{kg^{-1}}$'],
'MLLCL': [mlpcl.lclhght, 0, 'm AGL'],
'MLLFC': [mlpcl.lfchght, 0, 'm AGL'],
'MLEL': [mlpcl.elhght, 0, 'm AGL'],
'MLLI': [mlpcl.li5, 0, 'C'],
'MUCAPE': [mupcl.bplus, 0, 'J $\mathregular{kg^{-1}}$'],
'MUCIN': [mupcl.bminus, 0, 'J $\mathregular{kg^{-1}}$'],
'MULCL': [mupcl.lclhght, 0, 'm AGL'],
'MULFC': [mupcl.lfchght, 0, 'm AGL'],
'MUEL': [mupcl.elhght, 0, 'm AGL'],
'MULI': [mupcl.li5, 0, 'C'],
'0-1 km SRH': [srh1km[0], 0, '$\mathregular{m^{2}s^{-2}}$'],
'0-1 km Shear':
[utils.comp2vec(sfc_1km_shear[0], sfc_1km_shear[1])[1], 0, 'kt'],
'0-3 km SRH': [srh3km[0], 0, '$\mathregular{m^{2}s^{-2}}$'],
'0-6 km Shear':
[utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1], 0, 'kt'],
'PWV': [params.precip_water(prof), 2, 'inch'],
'K-index': [params.k_index(prof), 0, ''],
'STP(fix)': [stp_fixed, 1, ''],
'SHIP': [ship, 1, '']
}
eff_inflow = params.effective_inflow_layer(prof)
if any(eff_inflow):
ebot_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[0]))
etop_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[1]))
#print "Effective Inflow Layer Bottom Height (m AGL):", ebot_hght
#print "Effective Inflow Layer Top Height (m AGL):", etop_hght
effective_srh = winds.helicity(prof,
ebot_hght,
etop_hght,
stu=srwind[0],
stv=srwind[1])
indices['Eff. SRH'] = [
effective_srh[0], 0, '$\mathregular{m^{2}s^{-2}}$'
]
#print "Effective Inflow Layer SRH (m2/s2):", effective_srh[0]
ebwd = winds.wind_shear(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
ebwspd = utils.mag(*ebwd)
indices['EBWD'] = [ebwspd, 0, 'kt']
#print "Effective Bulk Wind Difference:", ebwspd
scp = params.scp(mupcl.bplus, effective_srh[0], ebwspd)
indices['SCP'] = [scp, 1, '']
stp_cin = params.stp_cin(mlpcl.bplus, effective_srh[0], ebwspd,
mlpcl.lclhght, mlpcl.bminus)
indices['STP(cin)'] = [stp_cin, 1, '']
#print "Supercell Composite Parameter:", scp
#print "Significant Tornado Parameter (w/CIN):", stp_cin
#print "Significant Tornado Parameter (fixed):", stp_fixed
# Update the indices within the indices dictionary on the side of the plot.
string = ''
for index, value in sorted(indices.items()):
if np.ma.is_masked(value[0]):
if debug:
print("skipping masked value for index=", index)
continue
if debug:
print("index=", index)
print("value=", value)
format = '%.' + str(value[1]) + 'f'
string += index + ": " + format % value[0] + " " + value[2] + '\n'
return string
''' Create the Sounding (Profile) Object '''
# Add units to the data arrays p = p * units.mbar p_std = p_std * units.mbar T = T * units.degC Td = Td * units.degC spd = spd * units.knot spd_std = spd_std * units.knot direc = direc * units.deg direc_std = direc_std * units.deg # Convert wind speed and direction to components u, v = get_wind_components(spd, direc) u_std, v_std = get_wind_components(spd_std, direc_std) #PARCEL CALCULATIONS with sharppy sfcpcl = params.parcelx(prof, flag=1) # Surface Parcel fcstpcl = params.parcelx(prof, flag=2) # Forecast Parcel mupcl = params.parcelx(prof, flag=3) # Most-Unstable Parcel mlpcl = params.parcelx(prof, flag=4) # 100 mb Mean Layer Parcel sfc = prof.pres[prof.sfc] p3km = interp.pres(prof, interp.to_msl(prof, 3000.)) p6km = interp.pres(prof, interp.to_msl(prof, 6000.)) p1km = interp.pres(prof, interp.to_msl(prof, 1000.)) mean_3km = winds.mean_wind(prof, pbot=sfc, ptop=p3km) sfc_6km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p6km) sfc_3km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p3km) sfc_1km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p1km) srwind = params.bunkers_storm_motion(prof) srh3km = winds.helicity(prof, 0, 3000., stu=srwind[0], stv=srwind[1]) srh1km = winds.helicity(prof, 0, 1000., stu=srwind[0], stv=srwind[1])
def do_sharppy(spc_file):
"""
Based on the tutorial which can be found here: http://nbviewer.ipython.org/github/sharppy/SHARPpy/blob/master/tutorials/SHARPpy_basics.ipynb
SHARPpy can be found here: https://github.com/sharppy/SHARPpy
Credit goes to:
Patrick Marsh (SPC)
Kelton Halbert (OU School of Meteorology)
Greg Blumberg (OU/CIMMS)
Tim Supinie (OU School of Meteorology)
"""
import sharppy
import sharppy.sharptab.profile as profile
import sharppy.sharptab.interp as interp
import sharppy.sharptab.winds as winds
import sharppy.sharptab.utils as utils
import sharppy.sharptab.params as params
import sharppy.sharptab.thermo as thermo
import matplotlib.pyplot as plt
from StringIO import StringIO
from matplotlib.axes import Axes
import matplotlib.transforms as transforms
import matplotlib.axis as maxis
import matplotlib.spines as mspines
import matplotlib.path as mpath
from matplotlib.projections import register_projection
spc_file = open('skewt_data', 'r').read()
def parseSPC(spc_file):
## read in the file
data = np.array([l.strip() for l in spc_file.split('\n')])
## necessary index points
title_idx = np.where(data == '%TITLE%')[0][0]
start_idx = np.where(data == '%RAW%')[0] + 1
finish_idx = np.where(data == '%END%')[0]
## create the plot title
data_header = data[title_idx + 1].split()
location = data_header[0] + ' ' + data_header[1]
time = data_header[2]
title = location + ' ' + time
## put it all together for StringIO
full_data = '\n'.join(data[start_idx:finish_idx][:])
sound_data = StringIO(full_data)
## read the data into arrays
p, h, T, Td, wdir, wspd = np.genfromtxt(sound_data,
delimiter=',',
comments="%",
unpack=True)
return p, h, T, Td, wdir, wspd, title
pres, hght, tmpc, dwpc, wdir, wspd, title = parseSPC(spc_file)
prof = profile.create_profile(profile='default', pres=pres, hght=hght, tmpc=tmpc, \
dwpc=dwpc, wspd=wspd, wdir=wdir, missing=-9999, strictQC=True)
sfcpcl = params.parcelx(prof, flag=1) # Surface Parcel
fcstpcl = params.parcelx(prof, flag=2) # Forecast Parcel
mupcl = params.parcelx(prof, flag=3) # Most-Unstable Parcel
mlpcl = params.parcelx(prof, flag=4) # 100 mb Mean Layer Parcel
msl_hght = prof.hght[prof.sfc] # Grab the surface height value
print "SURFACE HEIGHT (m MSL):", msl_hght
agl_hght = interp.to_agl(prof, msl_hght) # Converts to AGL
print "SURFACE HEIGHT (m AGL):", agl_hght
msl_hght = interp.to_msl(prof, agl_hght) # Converts to MSL
print "SURFACE HEIGHT (m MSL):", msl_hght
print "Most-Unstable CAPE:", mupcl.bplus # J/kg
print "Most-Unstable CIN:", mupcl.bminus # J/kg
print "Most-Unstable LCL:", mupcl.lclhght # meters AGL
print "Most-Unstable LFC:", mupcl.lfchght # meters AGL
print "Most-Unstable EL:", mupcl.elhght # meters AGL
print "Most-Unstable LI:", mupcl.li5 # C
class SkewXTick(maxis.XTick):
def draw(self, renderer):
if not self.get_visible(): return
renderer.open_group(self.__name__)
lower_interval = self.axes.xaxis.lower_interval
upper_interval = self.axes.xaxis.upper_interval
if self.gridOn and transforms.interval_contains(
self.axes.xaxis.get_view_interval(), self.get_loc()):
self.gridline.draw(renderer)
if transforms.interval_contains(lower_interval, self.get_loc()):
if self.tick1On:
self.tick1line.draw(renderer)
if self.label1On:
self.label1.draw(renderer)
if transforms.interval_contains(upper_interval, self.get_loc()):
if self.tick2On:
self.tick2line.draw(renderer)
if self.label2On:
self.label2.draw(renderer)
renderer.close_group(self.__name__)
# This class exists to provide two separate sets of intervals to the tick,
# as well as create instances of the custom tick
class SkewXAxis(maxis.XAxis):
def __init__(self, *args, **kwargs):
maxis.XAxis.__init__(self, *args, **kwargs)
self.upper_interval = 0.0, 1.0
def _get_tick(self, major):
return SkewXTick(self.axes, 0, '', major=major)
@property
def lower_interval(self):
return self.axes.viewLim.intervalx
def get_view_interval(self):
return self.upper_interval[0], self.axes.viewLim.intervalx[1]
# This class exists to calculate the separate data range of the
# upper X-axis and draw the spine there. It also provides this range
# to the X-axis artist for ticking and gridlines
class SkewSpine(mspines.Spine):
def _adjust_location(self):
trans = self.axes.transDataToAxes.inverted()
if self.spine_type == 'top':
yloc = 1.0
else:
yloc = 0.0
left = trans.transform_point((0.0, yloc))[0]
right = trans.transform_point((1.0, yloc))[0]
pts = self._path.vertices
pts[0, 0] = left
pts[1, 0] = right
self.axis.upper_interval = (left, right)
# This class handles registration of the skew-xaxes as a projection as well
# as setting up the appropriate transformations. It also overrides standard
# spines and axes instances as appropriate.
class SkewXAxes(Axes):
# The projection must specify a name. This will be used be the
# user to select the projection, i.e. ``subplot(111,
# projection='skewx')``.
name = 'skewx'
def _init_axis(self):
#Taken from Axes and modified to use our modified X-axis
self.xaxis = SkewXAxis(self)
self.spines['top'].register_axis(self.xaxis)
self.spines['bottom'].register_axis(self.xaxis)
self.yaxis = maxis.YAxis(self)
self.spines['left'].register_axis(self.yaxis)
self.spines['right'].register_axis(self.yaxis)
def _gen_axes_spines(self):
spines = {
'top': SkewSpine.linear_spine(self, 'top'),
'bottom': mspines.Spine.linear_spine(self, 'bottom'),
'left': mspines.Spine.linear_spine(self, 'left'),
'right': mspines.Spine.linear_spine(self, 'right')
}
return spines
def _set_lim_and_transforms(self):
"""
This is called once when the plot is created to set up all the
transforms for the data, text and grids.
"""
rot = 30
#Get the standard transform setup from the Axes base class
Axes._set_lim_and_transforms(self)
# Need to put the skew in the middle, after the scale and limits,
# but before the transAxes. This way, the skew is done in Axes
# coordinates thus performing the transform around the proper origin
# We keep the pre-transAxes transform around for other users, like the
# spines for finding bounds
self.transDataToAxes = self.transScale + (
self.transLimits + transforms.Affine2D().skew_deg(rot, 0))
# Create the full transform from Data to Pixels
self.transData = self.transDataToAxes + self.transAxes
# Blended transforms like this need to have the skewing applied using
# both axes, in axes coords like before.
self._xaxis_transform = (
transforms.blended_transform_factory(
self.transScale + self.transLimits,
transforms.IdentityTransform()) +
transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes
# Now register the projection with matplotlib so the user can select
# it.
register_projection(SkewXAxes)
pcl = mupcl
# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(6.5875, 6.2125))
ax = fig.add_subplot(111, projection='skewx')
ax.grid(True)
pmax = 1000
pmin = 10
dp = -10
presvals = np.arange(int(pmax), int(pmin) + dp, dp)
# plot the moist-adiabats
for t in np.arange(-10, 45, 5):
tw = []
for p in presvals:
tw.append(thermo.wetlift(1000., t, p))
ax.semilogy(tw, presvals, 'k-', alpha=.2)
def thetas(theta, presvals):
return ((theta + thermo.ZEROCNK) / (np.power(
(1000. / presvals), thermo.ROCP))) - thermo.ZEROCNK
# plot the dry adiabats
for t in np.arange(-50, 110, 10):
ax.semilogy(thetas(t, presvals), presvals, 'r-', alpha=.2)
plt.title(title, fontsize=14, loc='left')
# 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.tmpc, prof.pres, 'r', lw=2)
ax.semilogy(prof.dwpc, prof.pres, 'g', lw=2)
ax.semilogy(pcl.ttrace, pcl.ptrace, 'k-.', lw=2)
# An example of a slanted line at constant X
l = ax.axvline(0, color='b', linestyle='--')
l = ax.axvline(-20, color='b', linestyle='--')
# Disables the log-formatting that comes with semilogy
ax.yaxis.set_major_formatter(plt.ScalarFormatter())
ax.set_yticks(np.linspace(100, 1000, 10))
ax.set_ylim(1050, 100)
ax.xaxis.set_major_locator(plt.MultipleLocator(10))
ax.set_xlim(-50, 50)
plt.show()
##PLOTS SKEWT OK ABOVE HERE ##
"""
hght=hght.to('m').m,
tmpc=tmpc.to('degree_Celsius').m,
dwpc=dwpc.to('degree_Celsius').m,
wspd=wspd.to('knots').m,
wdir=wdir,
latitude=latitude,
longitude=longitude,
strictQC=True)
#### Adding a Parcel Trace
sfc_pcl = 1 # Surface Parcel
fcstpcl = 2 # Forecast Parcel
mupcl = 3 # Most-Unstable Parcel in lowest 400 hPa
mlpcl = 4 # 100 mb Mean Layer Parcel
# Set the parcel trace to be plotted as the Most-Unstable parcel.
pcl = params.parcelx(prof, flag=mupcl)
# Temperature, dewpoint, virtual temperature, wetbulb, parcel profiles
temperature_trace, = skew.plot(prof.pres, prof.tmpc, 'r',
linewidth=2) # temperature profile
# annotate temperature in F at bottom of T profile
temperatureF = skew.ax.text(prof.tmpc[0],
prof.pres[0] + 10,
utils.INT2STR(thermo.ctof(prof.tmpc[0])),
verticalalignment='top',
horizontalalignment='center',
size=7,
color=temperature_trace.get_color())
vtemp_trace, = skew.plot(prof.pres, prof.vtmp, 'r',
linewidth=0.5) # Virtual temperature profile
wetbulb_trace, = skew.plot(prof.pres, prof.wetbulb,
def do_sharppy(spc_file):
"""
Based on the tutorial which can be found here: http://nbviewer.ipython.org/github/sharppy/SHARPpy/blob/master/tutorials/SHARPpy_basics.ipynb
SHARPpy can be found here: https://github.com/sharppy/SHARPpy
Credit goes to:
Patrick Marsh (SPC)
Kelton Halbert (OU School of Meteorology)
Greg Blumberg (OU/CIMMS)
Tim Supinie (OU School of Meteorology)
"""
import sharppy
import sharppy.sharptab.profile as profile
import sharppy.sharptab.interp as interp
import sharppy.sharptab.winds as winds
import sharppy.sharptab.utils as utils
import sharppy.sharptab.params as params
import sharppy.sharptab.thermo as thermo
import matplotlib.pyplot as plt
from StringIO import StringIO
from matplotlib.axes import Axes
import matplotlib.transforms as transforms
import matplotlib.axis as maxis
import matplotlib.spines as mspines
import matplotlib.path as mpath
from matplotlib.projections import register_projection
spc_file = open('skewt_data', 'r').read()
def parseSPC(spc_file):
## read in the file
data = np.array([l.strip() for l in spc_file.split('\n')])
## necessary index points
title_idx = np.where( data == '%TITLE%')[0][0]
start_idx = np.where( data == '%RAW%' )[0] + 1
finish_idx = np.where( data == '%END%')[0]
## create the plot title
data_header = data[title_idx + 1].split()
location = data_header[0]+' '+data_header[1]
time = data_header[2]
title = location+' '+time
## put it all together for StringIO
full_data = '\n'.join(data[start_idx : finish_idx][:])
sound_data = StringIO( full_data )
## read the data into arrays
p, h, T, Td, wdir, wspd = np.genfromtxt( sound_data, delimiter=',', comments="%", unpack=True )
return p, h, T, Td, wdir, wspd, title
pres, hght, tmpc, dwpc, wdir, wspd, title = parseSPC(spc_file)
prof = profile.create_profile(profile='default', pres=pres, hght=hght, tmpc=tmpc, \
dwpc=dwpc, wspd=wspd, wdir=wdir, missing=-9999, strictQC=True)
sfcpcl = params.parcelx( prof, flag=1 ) # Surface Parcel
fcstpcl = params.parcelx( prof, flag=2 ) # Forecast Parcel
mupcl = params.parcelx( prof, flag=3 ) # Most-Unstable Parcel
mlpcl = params.parcelx( prof, flag=4 ) # 100 mb Mean Layer Parcel
msl_hght = prof.hght[prof.sfc] # Grab the surface height value
print "SURFACE HEIGHT (m MSL):",msl_hght
agl_hght = interp.to_agl(prof, msl_hght) # Converts to AGL
print "SURFACE HEIGHT (m AGL):", agl_hght
msl_hght = interp.to_msl(prof, agl_hght) # Converts to MSL
print "SURFACE HEIGHT (m MSL):",msl_hght
print "Most-Unstable CAPE:", mupcl.bplus # J/kg
print "Most-Unstable CIN:", mupcl.bminus # J/kg
print "Most-Unstable LCL:", mupcl.lclhght # meters AGL
print "Most-Unstable LFC:", mupcl.lfchght # meters AGL
print "Most-Unstable EL:", mupcl.elhght # meters AGL
print "Most-Unstable LI:", mupcl.li5 # C
class SkewXTick(maxis.XTick):
def draw(self, renderer):
if not self.get_visible(): return
renderer.open_group(self.__name__)
lower_interval = self.axes.xaxis.lower_interval
upper_interval = self.axes.xaxis.upper_interval
if self.gridOn and transforms.interval_contains(
self.axes.xaxis.get_view_interval(), self.get_loc()):
self.gridline.draw(renderer)
if transforms.interval_contains(lower_interval, self.get_loc()):
if self.tick1On:
self.tick1line.draw(renderer)
if self.label1On:
self.label1.draw(renderer)
if transforms.interval_contains(upper_interval, self.get_loc()):
if self.tick2On:
self.tick2line.draw(renderer)
if self.label2On:
self.label2.draw(renderer)
renderer.close_group(self.__name__)
# This class exists to provide two separate sets of intervals to the tick,
# as well as create instances of the custom tick
class SkewXAxis(maxis.XAxis):
def __init__(self, *args, **kwargs):
maxis.XAxis.__init__(self, *args, **kwargs)
self.upper_interval = 0.0, 1.0
def _get_tick(self, major):
return SkewXTick(self.axes, 0, '', major=major)
@property
def lower_interval(self):
return self.axes.viewLim.intervalx
def get_view_interval(self):
return self.upper_interval[0], self.axes.viewLim.intervalx[1]
# This class exists to calculate the separate data range of the
# upper X-axis and draw the spine there. It also provides this range
# to the X-axis artist for ticking and gridlines
class SkewSpine(mspines.Spine):
def _adjust_location(self):
trans = self.axes.transDataToAxes.inverted()
if self.spine_type == 'top':
yloc = 1.0
else:
yloc = 0.0
left = trans.transform_point((0.0, yloc))[0]
right = trans.transform_point((1.0, yloc))[0]
pts = self._path.vertices
pts[0, 0] = left
pts[1, 0] = right
self.axis.upper_interval = (left, right)
# This class handles registration of the skew-xaxes as a projection as well
# as setting up the appropriate transformations. It also overrides standard
# spines and axes instances as appropriate.
class SkewXAxes(Axes):
# The projection must specify a name. This will be used be the
# user to select the projection, i.e. ``subplot(111,
# projection='skewx')``.
name = 'skewx'
def _init_axis(self):
#Taken from Axes and modified to use our modified X-axis
self.xaxis = SkewXAxis(self)
self.spines['top'].register_axis(self.xaxis)
self.spines['bottom'].register_axis(self.xaxis)
self.yaxis = maxis.YAxis(self)
self.spines['left'].register_axis(self.yaxis)
self.spines['right'].register_axis(self.yaxis)
def _gen_axes_spines(self):
spines = {'top':SkewSpine.linear_spine(self, 'top'),
'bottom':mspines.Spine.linear_spine(self, 'bottom'),
'left':mspines.Spine.linear_spine(self, 'left'),
'right':mspines.Spine.linear_spine(self, 'right')}
return spines
def _set_lim_and_transforms(self):
"""
This is called once when the plot is created to set up all the
transforms for the data, text and grids.
"""
rot = 30
#Get the standard transform setup from the Axes base class
Axes._set_lim_and_transforms(self)
# Need to put the skew in the middle, after the scale and limits,
# but before the transAxes. This way, the skew is done in Axes
# coordinates thus performing the transform around the proper origin
# We keep the pre-transAxes transform around for other users, like the
# spines for finding bounds
self.transDataToAxes = self.transScale + (self.transLimits +
transforms.Affine2D().skew_deg(rot, 0))
# Create the full transform from Data to Pixels
self.transData = self.transDataToAxes + self.transAxes
# Blended transforms like this need to have the skewing applied using
# both axes, in axes coords like before.
self._xaxis_transform = (transforms.blended_transform_factory(
self.transScale + self.transLimits,
transforms.IdentityTransform()) +
transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes
# Now register the projection with matplotlib so the user can select
# it.
register_projection(SkewXAxes)
pcl = mupcl
# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(6.5875, 6.2125))
ax = fig.add_subplot(111, projection='skewx')
ax.grid(True)
pmax = 1000
pmin = 10
dp = -10
presvals = np.arange(int(pmax), int(pmin)+dp, dp)
# plot the moist-adiabats
for t in np.arange(-10,45,5):
tw = []
for p in presvals:
tw.append(thermo.wetlift(1000., t, p))
ax.semilogy(tw, presvals, 'k-', alpha=.2)
def thetas(theta, presvals):
return ((theta + thermo.ZEROCNK) / (np.power((1000. / presvals),thermo.ROCP))) - thermo.ZEROCNK
# plot the dry adiabats
for t in np.arange(-50,110,10):
ax.semilogy(thetas(t, presvals), presvals, 'r-', alpha=.2)
plt.title(title, fontsize=14, loc='left')
# 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.tmpc, prof.pres, 'r', lw=2)
ax.semilogy(prof.dwpc, prof.pres, 'g', lw=2)
ax.semilogy(pcl.ttrace, pcl.ptrace, 'k-.', lw=2)
# An example of a slanted line at constant X
l = ax.axvline(0, color='b', linestyle='--')
l = ax.axvline(-20, color='b', linestyle='--')
# Disables the log-formatting that comes with semilogy
ax.yaxis.set_major_formatter(plt.ScalarFormatter())
ax.set_yticks(np.linspace(100,1000,10))
ax.set_ylim(1050,100)
ax.xaxis.set_major_locator(plt.MultipleLocator(10))
ax.set_xlim(-50,50)
plt.show()
##PLOTS SKEWT OK ABOVE HERE ##
"""
start_idx = np.where(data == '%RAW%')[0]
finish_idx = np.where(data == '%END%')[0]
plot_title = data[title_idx + 1]
full_data = '\n'.join(data[start_idx:finish_idx][:])
sound_data = StringIO(full_data)
P, h, T, Td, wdir, wspd = np.genfromtxt(sound_data,
delimiter=',',
comments="%",
unpack=True)
prof = profile.create_profile(pres=P,
hght=h,
tmpc=T,
dwpc=Td,
wdir=wdir,
wspd=wspd)
pcl = parcelx(prof)
sfcpcl = tab.params.parcelx(prof, flag=1) #Surface Parcel
fcstpcl = tab.params.parcelx(prof, flag=2) #Forecast Parcel
mupcl = tab.params.parcelx(prof, flag=3) #Most-Unstable Parcel
mlpcl = tab.params.parcelx(prof, flag=4) #100 mb Mean Layer Parcel
csvfile = open(('Lift_Parcel_' + filename[:-4] + '.txt'), 'wb')
writer = csv.writer(csvfile, delimiter=' ')
writer.writerow([plot_title])
writer.writerow('\r')
loop_idx = range(1)
for idx in loop_idx:
a = [('Surface CAPE = ' + str(sfcpcl.bplus))] #J/Kg
writer.writerow(a)
b = [('Surface CIN = ' + str(sfcpcl.bminus))] #J/Kg
writer.writerow(b)
print "skipping", sfile
continue
skew.ax.set_title(title, horizontalalignment="left", x=0, fontsize=12)
print "reading", sfile
data = open(sfile).read()
pres, hght, tmpc, dwpc, wdir, wspd, latitude, longitude = parseGEMPAK(data)
if wdir.size == 0:
print "no good data lines. empty profile"
continue
prof = profile.create_profile(profile='default', pres=pres, hght=hght, tmpc=tmpc,
dwpc=dwpc, wspd=wspd, wdir=wdir, latitude=latitude, longitude=longitude, missing=-999., strictQC=True)
#### Adding a Parcel Trace
sfcpcl = params.parcelx( prof, flag=1 ) # Surface Parcel
#fcstpcl = params.parcelx( prof, flag=2 ) # Forecast Parcel
mupcl = params.parcelx( prof, flag=3 ) # Most-Unstable Parcel
mlpcl = params.parcelx( prof, flag=4 ) # 100 mb Mean Layer Parcel
# Set the parcel trace to be plotted as the Most-Unstable parcel.
pcl = mupcl
# Temperature, dewpoint, virtual temperature, wetbulb, parcel profiles
temperature_trace, = skew.plot(prof.pres, prof.tmpc, 'r', linewidth=2) # temperature profile
# annotate temperature in F at bottom of T profile
temperatureF = skew.ax.text(prof.tmpc[0], prof.pres[0]+10, utils.INT2STR(thermo.ctof(prof.tmpc[0])),
verticalalignment='top', horizontalalignment='center', size=7, color=temperature_trace.get_color())
skew.plot(prof.pres, prof.vtmp, 'r', linewidth=0.5) # Virtual temperature profile
skew.plot(prof.pres, prof.wetbulb, 'c-') # wetbulb profile
dwpt_trace, = skew.plot(prof.pres, prof.dwpc, 'g', linewidth=2) # dewpoint profile
# annotate dewpoint in F at bottom of dewpoint profile
