def __init__(self):
# ============================================#
# initiate a new instance of thermocalcs class#
# ============================================#
self.tC = ThermoCalcs()
# ===========================================#
# initiate a new instance of shearcalcs class#
# ===========================================#
self.sC = ShearCalcs()
# Global height arrays for wind shear
self.u_3km = []
self.v_3km = []
self.u_6km = []
self.v_6km = []
self.u_9km = []
self.v_9km = []
self.u_12km = []
self.v_12km = []
def __init__(self):
# ============================================#
# initiate a new instance of thermocalcs class#
# ============================================#
self.tC = ThermoCalcs()
# ===========================================#
# initiate a new instance of shearcalcs class#
# ===========================================#
self.sC = ShearCalcs()
# Global height arrays for wind shear
self.u_3km = []
self.v_3km = []
self.u_6km = []
self.v_6km = []
self.u_9km = []
self.v_9km = []
self.u_12km = []
self.v_12km = []
class DropSondes(object):
'''
Description:
class to analyze dropsonde and sounding data
#==========================
requirements:
thermodynaic calculations
windshear calculations
skew x axis class
#==========================
'''
def __init__(self):
# ============================================#
# initiate a new instance of thermocalcs class#
# ============================================#
self.tC = ThermoCalcs()
# ===========================================#
# initiate a new instance of shearcalcs class#
# ===========================================#
self.sC = ShearCalcs()
# Global height arrays for wind shear
self.u_3km = []
self.v_3km = []
self.u_6km = []
self.v_6km = []
self.u_9km = []
self.v_9km = []
self.u_12km = []
self.v_12km = []
def get_sounding_data(self, filePath):
'''
method to retrieve standard sounding data from noaa radiosonde
and U of Wyoming repository.
Inputs:
file path
# ============
output:
# Dictionary with variable for use in plotting
'''
data = filePath
fp = open(data, 'r')
lines = fp.readlines()
header = lines[1]
fp.close()
fp = open(data, 'r')
sounding_data = fp
p, h, T, Td, RH, MIXR, wd, ws = np.genfromtxt(
sounding_data,
skip_header=8,
usecols=range(0, 8),
dtype=float,
delimiter=None,
autostrip=True,
missing_values='-9999.00',
unpack=True,
usemask=True)
u = -ws * np.sin(np.radians(wd))
v = -ws * np.cos(np.radians(wd))
# RH = self.tC._dewpoint_to_RH(T+273.15, Td+273.15)
mask = T.mask
T = T[~mask]
TD = Td[~mask]
P = p[~mask]
H = h[~mask]
RH = RH[~mask]
mask = u.mask
U = u[~mask]
V = v[~mask]
data = dict()
data['Header'] = header
data['Temperature'] = T
data['Dewpoint'] = Td
data['Pressure'] = p
data['Relative Humidity'] = RH
data['U Component'] = U
data['V Component'] = V
data['Height'] = h
data['Type'] = 'radioSonde'
fp.close()
# self.type = 'radioSonde'
return data
def get_dropsonde_data(self, filePath):
'''
method to retrieve dropsonde data
Inputs:
file path
#============
output:
#Dictionary with variable for use in plotting
'''
data = filePath
fp = open(data, 'r')
lines = fp.readlines()
header = lines[1]
fp.close()
fp = open(data, 'r')
sounding_data = fp
p, T, Td, RH, u, v, h = np.genfromtxt(sounding_data,
skip_header=15,
usecols=(1, 2, 3, 4, 5, 6, 14),
dtype=float,
missing_values='9999.0',
unpack=True,
usemask=True)
# mask incoming T and dewpoint data
T = np.ma.masked_greater_equal(T, 999.0)
Td = np.ma.masked_greater_equal(Td, 999.0)
RH = np.ma.masked_greater_equal(RH, 999.0)
h = np.ma.masked_greater_equal(h, 99999.0)
mask = T.mask
T = T[~mask]
TD = Td[~mask]
P = p[~mask]
H = h[~mask]
RH = RH[~mask]
mask = u.mask
U = u[~mask]
V = v[~mask]
data = dict()
data['Header'] = header
data['Temperature'] = T
data['Dewpoint'] = TD
data['Pressure'] = P
data['Relative Humidity'] = RH
data['U Component'] = U
data['V Component'] = V
data['Height'] = H
data['Type'] = 'dropsonde'
return data
def plot_skewtlogp(self, data, **kwargs):
'''
method to plot sounding or dropsonde data
Inputs:
dictionary of sounding data
keyword arguments min, max, titles, and label.
#============
output:
#Image
'''
T = data['Temperature']
TD = data['Dewpoint']
P = data['Pressure']
self.fig = plt.figure(figsize=(10, 8))
self.ax1 = self.fig.add_axes([0.05, 0.1, 0.6, 0.8], projection='skewx')
plt.grid(True)
self.plot_dryadiabats()
self.ax1.semilogy(T, P, 'r-', linewidth=1.5)
self.ax1.semilogy(TD, P, 'g-', linewidth=1.5)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
# Disables the log-formatting that comes with semilogy
# set bounds for data on Skew T chart
self.ax1.yaxis.set_major_formatter(ScalarFormatter())
self.ax1.set_yticks(np.linspace(100, 1000, 10))
self.ax1.set_ylim(1050, 100)
self.ax1.set_ylabel('Pressure mb')
self.ax1.set_xlabel('Temperature C')
self.ax1.xaxis.set_major_locator(MultipleLocator(10))
self.ax1.set_title('Skew T log P')
self.y_min = kwargs.get('y_min')
self.y_max = kwargs.get('y_max')
self.x_min = kwargs.get('x_min')
self.x_max = kwargs.get('x_max')
# data label, title and font sizes value assignments
# default values above.
y_label = kwargs.get('y_label')
x_label = kwargs.get('x_label')
title = kwargs.get('title')
font_size = kwargs.get('font_size')
for item in ([self.ax1.xaxis.label, self.ax1.yaxis.label] +
self.ax1.get_xticklabels() + self.ax1.get_yticklabels()):
item.set_fontsize(font_size)
# set the labels and titles defaults to 'none'
self.ax1.set_ylabel(y_label)
self.ax1.set_xlabel(x_label)
self.ax1.set_title(title)
self.ax1.set_ylim(self.y_max, self.y_min)
self.ax1.set_xlim(self.x_min, self.x_max)
def plot_hodograph(self, data):
'''
method to plot wind data on a hodograph
Inputs:
dictionary containing wind data.
file path
# ============
output:
# Image
'''
self.ax2 = self.fig.add_axes([.05, 0.6, 0.25, 0.3])
# create axis and invert masks and assign values
# for U, V, and h coordinates
U = data['U Component']
V = data['V Component']
H = data['Height']
# =====================================================#
# loop to place hodograph data into proper height array
# 0-3km
# 3-6km
# 6-9km
# 9-12km
# ======#
if data['Type'] == 'radioSonde':
for unew, vnew, hnew in zip(U, V, H):
if hnew <= 3001.0:
self.u_3km.append(unew)
self.v_3km.append(vnew)
elif hnew <= 6001.0:
self.u_6km.append(unew)
self.v_6km.append(vnew)
elif hnew <= 9000.0:
self.u_9km.append(unew)
self.v_9km.append(vnew)
elif hnew <= 12000.0:
self.u_12km.append(unew)
self.v_12km.append(vnew)
# print(len(self.u_3km))
# print(len(self.v_3km))
# =============================================================#
# take the first point of the next height level and attach it
# to the last point of the previous line segment to
# connect hodograph segments
# should only be done for soundings and non dense data
# type determines how the data is placed in the array.
# Soundings require endpoints be joined so that
# the hodograph is continuous.
# =============================================================#
if data['Type'] == 'radioSonde':
self.u_3km.append(self.u_6km[0])
self.u_6km.append(self.u_9km[0])
self.u_9km.append(self.u_12km[0])
self.v_3km.append(self.v_6km[0])
self.v_6km.append(self.v_9km[0])
self.v_9km.append(self.v_12km[0])
# ==============================#
# Mask the arrays U and V coords
# ==============================#
self.v_3km = np.ma.asarray(self.v_3km)
self.v_6km = np.ma.asarray(self.v_6km)
self.v_9km = np.ma.asarray(self.v_9km)
self.v_12km = np.ma.asarray(self.v_12km)
self.u_3km = np.ma.asarray(self.u_3km)
self.u_6km = np.ma.asarray(self.u_6km)
self.u_9km = np.ma.asarray(self.u_9km)
self.u_12km = np.ma.asarray(self.u_12km)
# plotting of the hodograph information
# Different colors are sued to represent different height levels
# r = 0-3km
# y = 3-6km
# g = 6-9km
# b = 9-12km
self.ax2.plot(self.u_3km, self.v_3km, 'r-', linewidth=3)
self.ax2.plot(
self.u_6km,
self.v_6km,
'y-',
linewidth=3,
)
self.ax2.plot(self.u_9km, self.v_9km, 'g-', linewidth=3)
self.ax2.plot(self.u_12km, self.v_12km, 'b-', linewidth=3)
# set the default limits on the hodo axes
# remove spines and set the axes to be invisible.
self.ax2.spines['left'].set_position('zero')
self.ax2.spines['right'].set_color('none')
self.ax2.spines['bottom'].set_position('zero')
self.ax2.spines['top'].set_color('none')
self.ax2.xaxis.set_ticks_position('bottom')
self.ax2.yaxis.set_ticks_position('left')
self.ax2.set_ylim(-50, 50)
self.ax2.set_xlim(-50, 50)
# Draw hodograph range rings
# 10 20 30 40 m/s circles
circ1 = patches.Circle((0, 0), 10, fc='white')
circ2 = patches.Circle((0, 0), 20, fc='white')
circ3 = patches.Circle((0, 0), 30, fc='white')
circ4 = patches.Circle((0, 0), 40, fc='white')
# Add circles to plot as patches#
# descending order to make sure they layer ontop of each other.
self.ax2.add_patch(circ4)
self.ax2.add_patch(circ3)
self.ax2.add_patch(circ2)
self.ax2.add_patch(circ1)
def plot_aux_graph(self, x_value, y_value, **kwargs):
'''
method to plot an auxiliary graph of user defined data
Inputs:
# sounding data (User specified)
# kwargs to adjust plot dimensions scales label, and title.
# ============
output:
# Image
'''
# define axes fro figure.
# set grid and plot user specified data
self.ax3 = self.fig.add_axes([.7, 0.7, .29, .24])
plt.grid(True)
self.ax3.plot(x_value, y_value)
# Max and min graph bonds value assignment. Defaults to autoscale.
y_min = kwargs.get('y_min')
y_max = kwargs.get('y_max')
x_min = kwargs.get('x_min')
x_max = kwargs.get('x_max')
# data label, title and font size value assignments .
y_lable = kwargs.get('y_lable')
x_lable = kwargs.get('x_lable')
title = kwargs.get('title')
font_size = kwargs.get('font_size')
# keyword arguments for minimum and maximum values
self.ax3.set_ylim(y_min, y_max)
self.ax3.set_xlim(x_min, x_max)
# set label and tick fontsizes to user defined value. Defaults to 10pt.
for item in ([self.ax3.xaxis.label, self.ax3.yaxis.label] +
self.ax3.get_xticklabels() + self.ax3.get_yticklabels()):
item.set_fontsize(font_size)
# set the labels and title from keyword arguments. Defaults to 'none'
self.ax3.set_ylabel(y_lable)
self.ax3.set_xlabel(x_lable)
self.ax3.set_title(title)
def generate_parameter_list(self):
'''
method to generate a list of parameters calculated
from the sounding data
Inputs:
# None
# ============
output:
# Blank axis for plotting
'''
# Set axes position and set both axes invisible
self.ax4 = self.fig.add_axes([.65, 0.1, .35, .54])
self.ax4.xaxis.set_visible(False)
self.ax4.yaxis.set_visible(False)
def run_thermo_calcs(self, data):
'''
method to calculate thermodynamic parameters from dropsonde data
Inputs:
# Dictionary of sounding data.
# Uses calculations from thermocalcs.py
# ============
output:
# multiple arrays containing thermodynamic information
'''
T = data['Temperature']
Td = data['Dewpoint']
p = data['Pressure']
RH = data['Relative Humidity']
u = data['U Component']
v = data['V Component']
h = data['Height']
self.LCLT = round(
(self.tC._LCL_Temperature(h, T + 273.15, Td + 273.15) - 273.15), 2)
self.LCLP = round(
(self.tC._LCL_Pressure(h, p, T + 273.15, Td + 273.15)), 0)
self.LCLZ = round((self.tC._LCL_Height(h, p, T + 273.15, Td + 273.15)),
0)
self.THETA = self.tC._PTk2_Theta(p, T + 273.15)
self.MIXR = self.tC._RH_2_MixR(RH, p, T + 273.15)
self.THETAE = self.tC._Tk_RH_MixR_2_ThetaE(p, T + 273.15, RH,
self.MIXR / 1000.)
self.ESAT = self.tC._esat(T + 273.15)
def plot_thermo_calcs(self):
'''
method to plot the thermodynamic parameters on the parameter list.
Inputs:
# None
# ============
output:
# Image
'''
# plot the parameters on the list generated.
self.ax4.text(.01, .01, 'LCL Pressure: ' + str(self.LCLP) + (' hPa'))
self.ax4.text(.01, .04, 'LCL Temp: ' + str(self.LCLT) + ' c')
self.ax4.text(.01, .07, 'LCL Height: ' + str(self.LCLZ) + ' m')
def plot_dryadiabats(self, **kwargs):
'''
method to plot the dry adibats. Used in the plotskewtlogp method.
Inputs:
# kwargs (does not function)
# ============
output:
# Image
'''
# test = self.shear1km
# temperature array and pressure array
t0 = np.linspace(200, 430, 17)
press = np.linspace(100, 1000.)
# retrieve desired line style from user kwargs
line_style = kwargs.get('line_style')
if line_style is None:
line_style = '-'
# loop to calculate using Poisson's equation the
# dry adiabats for the skew t.
for temp in t0:
theta = temp * (press / 1000.)**(2. / 7.)
# plot the dry adiabats given a specified color
self.ax1.semilogy((theta - 273.15),
press,
line_style,
color='#7F4B10',
linewidth=0.5)
def plot_wind_barbs(self, data, **kwargs):
P = data['Pressure']
U = data['U Component']
V = data['V Component']
H = data['Height']
# Copy y axis to plot wind barbs
# Set x lim for windpbarbs
# adjust location of barbs from x =0
# plot every 30th windbarb
# set axis to invisible
self.ax1_copy = self.ax1.twiny()
self.ax1_copy.set_xlim(self.x_min, self.x_max)
self.ax1_copy.set_ylim(self.y_min, self.y_max)
x_const = np.zeros(P.shape) + (self.x_max - 2)
self.ax1_copy.xaxis.set_visible(False)
if data['Type'] == 'radioSonde':
mask = U.mask
U = U[~mask]
V = V[~mask]
P = P[~mask]
self.ax1_copy.barbs(x_const[::3], P[::3], U[::3], V[::3])
else:
self.ax1_copy.barbs(x_const[::40], P[::40], U[::40], V[::40])
def run_shear_calcs(self, data):
'''
method to calculate thermodynamic parameters from dropsonde data
Inputs:
# Dictionary of sounding data.
# Uses calculations from thermocalcs.py
# ============
output:
# multiple arrays containing thermodynamic information
'''
T = data['Temperature']
Td = data['Dewpoint']
p = data['Pressure']
RH = data['Relative Humidity']
u = data['U Component']
v = data['V Component']
h = data['Height']
mask = h.mask
u = u[~mask]
v = v[~mask]
h = h[~mask]
self.SHEAR1KM = self.sC._VertShear_Sfc_to_1km(h, u, v)
self.SHEAR3KM = self.sC._VertShear_Sfc_to_3km(h, u, v)
self.SHEAR6KM = self.sC._VertShear_Sfc_to_6km(h, u, v)
self.BULKSHEAR1km = round(self.sC._bulkshear_sfc_1km(h, u, v), 2)
self.BULKSHEAR3km = round(self.sC._bulkshear_sfc_3km(h, u, v), 2)
self.BULKSHEAR6km = round(self.sC._bulkshear_sfc_6km(h, u, v), 2)
# self.ax4.text(.01, .1, '0-1 km shear: '+str(self.SHEAR1KM)+(' 1/s'))
def plot_shear_calcs(self):
'''
method to plot the thermodynamic parameters on the parameter list.
Inputs:
# None
# ============
output:
# Image
'''
# bitch = self.SHEAR1KM
# plot the parameters on the list generated.
self.ax4.text(.01, .1,
'0-1 km shear: ' + str(self.SHEAR1KM) + (' 1/s'))
self.ax4.text(.01, .14, '0-3 km shear: ' + str(self.SHEAR3KM) + ' 1/s')
self.ax4.text(.01, .18, '0-6 km shear: ' + str(self.SHEAR6KM) + ' 1/s')
self.ax4.text(.01, .22,
'0-1km Bulk Shear: ' + str(self.BULKSHEAR1km) + ' m/s')
self.ax4.text(.01, .26,
'0-3km Bulk Shear: ' + str(self.BULKSHEAR3km) + ' m/s')
self.ax4.text(.01, .3,
'0-6km Bulk Shear: ' + str(self.BULKSHEAR6km) + ' m/s')
def dry_lift(self, data):
T = data['Temperature']
Td = data['Dewpoint']
p = data['Pressure']
RH = data['Relative Humidity']
u = data['U Component']
v = data['V Component']
h = data['Height']
t_parcel, p_parcel = self.tC.dry_lift(T, p, self.LCLT, self.LCLP)
# print(t_parcel,p_parcel)
self.ax1.semilogy(t_parcel, p_parcel, 'k--', ms=1)
class DropSondes(object):
'''
Description:
class to analyze dropsonde and sounding data
#==========================
requirements:
thermodynaic calculations
windshear calculations
skew x axis class
#==========================
'''
def __init__(self):
# ============================================#
# initiate a new instance of thermocalcs class#
# ============================================#
self.tC = ThermoCalcs()
# ===========================================#
# initiate a new instance of shearcalcs class#
# ===========================================#
self.sC = ShearCalcs()
# Global height arrays for wind shear
self.u_3km = []
self.v_3km = []
self.u_6km = []
self.v_6km = []
self.u_9km = []
self.v_9km = []
self.u_12km = []
self.v_12km = []
def get_sounding_data(self, filePath):
'''
method to retrieve standard sounding data from noaa radiosonde
and U of Wyoming repository.
Inputs:
file path
# ============
output:
# Dictionary with variable for use in plotting
'''
data = filePath
fp = open(data, 'r')
lines = fp.readlines()
header = lines[1]
fp.close()
fp = open(data, 'r')
sounding_data = fp
p, h, T, Td, RH, MIXR, wd, ws = np.genfromtxt(
sounding_data, skip_header=8, usecols=range(0, 8),
dtype=float, delimiter=None, autostrip=True,
missing_values='-9999.00', unpack=True, usemask=True)
u = -ws*np.sin(np.radians(wd))
v = -ws*np.cos(np.radians(wd))
# RH = self.tC._dewpoint_to_RH(T+273.15, Td+273.15)
mask = T.mask
T = T[~mask]
TD = Td[~mask]
P = p[~mask]
H = h[~mask]
RH = RH[~mask]
mask = u.mask
U = u[~mask]
V = v[~mask]
data = dict()
data['Header'] = header
data['Temperature'] = T
data['Dewpoint'] = Td
data['Pressure'] = p
data['Relative Humidity'] = RH
data['U Component'] = U
data['V Component'] = V
data['Height'] = h
data['Type'] = 'radioSonde'
fp.close()
# self.type = 'radioSonde'
return data
def get_dropsonde_data(self, filePath):
'''
method to retrieve dropsonde data
Inputs:
file path
#============
output:
#Dictionary with variable for use in plotting
'''
data = filePath
fp = open(data, 'r')
lines = fp.readlines()
header = lines[1]
fp.close()
fp = open(data, 'r')
sounding_data = fp
p, T, Td, RH, u, v, h = np.genfromtxt(
sounding_data, skip_header=15, usecols=(1, 2, 3, 4, 5, 6, 14),
dtype=float, missing_values='9999.0', unpack=True, usemask=True)
# mask incoming T and dewpoint data
T = np.ma.masked_greater_equal(T, 999.0)
Td = np.ma.masked_greater_equal(Td, 999.0)
RH = np.ma.masked_greater_equal(RH, 999.0)
h = np.ma.masked_greater_equal(h, 99999.0)
mask = T.mask
T = T[~mask]
TD = Td[~mask]
P = p[~mask]
H = h[~mask]
RH = RH[~mask]
mask = u.mask
U = u[~mask]
V = v[~mask]
data = dict()
data['Header'] = header
data['Temperature'] = T
data['Dewpoint'] = TD
data['Pressure'] = P
data['Relative Humidity'] = RH
data['U Component'] = U
data['V Component'] = V
data['Height'] = H
data['Type'] = 'dropsonde'
return data
def plot_skewtlogp(self, data, **kwargs):
'''
method to plot sounding or dropsonde data
Inputs:
dictionary of sounding data
keyword arguments min, max, titles, and label.
#============
output:
#Image
'''
T = data['Temperature']
TD = data['Dewpoint']
P = data['Pressure']
self.fig = plt.figure(figsize=(10, 8))
self.ax1 = self.fig.add_axes([0.05, 0.1, 0.6, 0.8],
projection='skewx')
plt.grid(True)
self.plot_dryadiabats()
self.ax1.semilogy(T, P, 'r-', linewidth=1.5)
self.ax1.semilogy(TD, P, 'g-', linewidth=1.5)
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
# Disables the log-formatting that comes with semilogy
# set bounds for data on Skew T chart
self.ax1.yaxis.set_major_formatter(ScalarFormatter())
self.ax1.set_yticks(np.linspace(100, 1000, 10))
self.ax1.set_ylim(1050, 100)
self.ax1.set_ylabel('Pressure mb')
self.ax1.set_xlabel('Temperature C')
self.ax1.xaxis.set_major_locator(MultipleLocator(10))
self.ax1.set_title('Skew T log P')
self.y_min = kwargs.get('y_min')
self.y_max = kwargs.get('y_max')
self.x_min = kwargs.get('x_min')
self.x_max = kwargs.get('x_max')
# data label, title and font sizes value assignments
# default values above.
y_label = kwargs.get('y_label')
x_label = kwargs.get('x_label')
title = kwargs.get('title')
font_size = kwargs.get('font_size')
for item in ([self.ax1.xaxis.label, self.ax1.yaxis.label] +
self.ax1.get_xticklabels()+self.ax1.get_yticklabels()):
item.set_fontsize(font_size)
# set the labels and titles defaults to 'none'
self.ax1.set_ylabel(y_label)
self.ax1.set_xlabel(x_label)
self.ax1.set_title(title)
self.ax1.set_ylim(self.y_max, self.y_min)
self.ax1.set_xlim(self.x_min, self.x_max)
def plot_hodograph(self, data):
'''
method to plot wind data on a hodograph
Inputs:
dictionary containing wind data.
file path
# ============
output:
# Image
'''
self.ax2 = self.fig.add_axes([.05, 0.6, 0.25, 0.3])
# create axis and invert masks and assign values
# for U, V, and h coordinates
U = data['U Component']
V = data['V Component']
H = data['Height']
# =====================================================#
# loop to place hodograph data into proper height array
# 0-3km
# 3-6km
# 6-9km
# 9-12km
# ======#
if data['Type'] == 'radioSonde':
for unew, vnew, hnew in zip(U, V, H):
if hnew <= 3001.0:
self.u_3km.append(unew)
self.v_3km.append(vnew)
elif hnew <= 6001.0:
self.u_6km.append(unew)
self.v_6km.append(vnew)
elif hnew <= 9000.0:
self.u_9km.append(unew)
self.v_9km.append(vnew)
elif hnew <= 12000.0:
self.u_12km.append(unew)
self.v_12km.append(vnew)
# print(len(self.u_3km))
# print(len(self.v_3km))
# =============================================================#
# take the first point of the next height level and attach it
# to the last point of the previous line segment to
# connect hodograph segments
# should only be done for soundings and non dense data
# type determines how the data is placed in the array.
# Soundings require endpoints be joined so that
# the hodograph is continuous.
# =============================================================#
if data['Type'] == 'radioSonde':
self.u_3km.append(self.u_6km[0])
self.u_6km.append(self.u_9km[0])
self.u_9km.append(self.u_12km[0])
self.v_3km.append(self.v_6km[0])
self.v_6km.append(self.v_9km[0])
self.v_9km.append(self.v_12km[0])
# ==============================#
# Mask the arrays U and V coords
# ==============================#
self.v_3km = np.ma.asarray(self.v_3km)
self.v_6km = np.ma.asarray(self.v_6km)
self.v_9km = np.ma.asarray(self.v_9km)
self.v_12km = np.ma.asarray(self.v_12km)
self.u_3km = np.ma.asarray(self.u_3km)
self.u_6km = np.ma.asarray(self.u_6km)
self.u_9km = np.ma.asarray(self.u_9km)
self.u_12km = np.ma.asarray(self.u_12km)
# plotting of the hodograph information
# Different colors are sued to represent different height levels
# r = 0-3km
# y = 3-6km
# g = 6-9km
# b = 9-12km
self.ax2.plot(self.u_3km, self.v_3km, 'r-', linewidth=3)
self.ax2.plot(self.u_6km, self.v_6km, 'y-', linewidth=3,)
self.ax2.plot(self.u_9km, self.v_9km, 'g-', linewidth=3)
self.ax2.plot(self.u_12km, self.v_12km, 'b-', linewidth=3)
# set the default limits on the hodo axes
# remove spines and set the axes to be invisible.
self.ax2.spines['left'].set_position('zero')
self.ax2.spines['right'].set_color('none')
self.ax2.spines['bottom'].set_position('zero')
self.ax2.spines['top'].set_color('none')
self.ax2.xaxis.set_ticks_position('bottom')
self.ax2.yaxis.set_ticks_position('left')
self.ax2.set_ylim(-50, 50)
self.ax2.set_xlim(-50, 50)
# Draw hodograph range rings
# 10 20 30 40 m/s circles
circ1 = patches.Circle((0, 0), 10, fc='white')
circ2 = patches.Circle((0, 0), 20, fc='white')
circ3 = patches.Circle((0, 0), 30, fc='white')
circ4 = patches.Circle((0, 0), 40, fc='white')
# Add circles to plot as patches#
# descending order to make sure they layer ontop of each other.
self.ax2.add_patch(circ4)
self.ax2.add_patch(circ3)
self.ax2.add_patch(circ2)
self.ax2.add_patch(circ1)
def plot_aux_graph(self, x_value, y_value, **kwargs):
'''
method to plot an auxiliary graph of user defined data
Inputs:
# sounding data (User specified)
# kwargs to adjust plot dimensions scales label, and title.
# ============
output:
# Image
'''
# define axes fro figure.
# set grid and plot user specified data
self.ax3 = self.fig.add_axes([.7, 0.7, .29, .24])
plt.grid(True)
self.ax3.plot(x_value, y_value)
# Max and min graph bonds value assignment. Defaults to autoscale.
y_min = kwargs.get('y_min')
y_max = kwargs.get('y_max')
x_min = kwargs.get('x_min')
x_max = kwargs.get('x_max')
# data label, title and font size value assignments .
y_lable = kwargs.get('y_lable')
x_lable = kwargs.get('x_lable')
title = kwargs.get('title')
font_size = kwargs.get('font_size')
# keyword arguments for minimum and maximum values
self.ax3.set_ylim(y_min, y_max)
self.ax3.set_xlim(x_min, x_max)
# set label and tick fontsizes to user defined value. Defaults to 10pt.
for item in ([self.ax3.xaxis.label, self.ax3.yaxis.label] +
self.ax3.get_xticklabels()+self.ax3.get_yticklabels()):
item.set_fontsize(font_size)
# set the labels and title from keyword arguments. Defaults to 'none'
self.ax3.set_ylabel(y_lable)
self.ax3.set_xlabel(x_lable)
self.ax3.set_title(title)
def generate_parameter_list(self):
'''
method to generate a list of parameters calculated
from the sounding data
Inputs:
# None
# ============
output:
# Blank axis for plotting
'''
# Set axes position and set both axes invisible
self.ax4 = self.fig.add_axes([.65, 0.1, .35, .54])
self.ax4.xaxis.set_visible(False)
self.ax4.yaxis.set_visible(False)
def run_thermo_calcs(self, data):
'''
method to calculate thermodynamic parameters from dropsonde data
Inputs:
# Dictionary of sounding data.
# Uses calculations from thermocalcs.py
# ============
output:
# multiple arrays containing thermodynamic information
'''
T = data['Temperature']
Td = data['Dewpoint']
p = data['Pressure']
RH = data['Relative Humidity']
u = data['U Component']
v = data['V Component']
h = data['Height']
self.LCLT = round((
self.tC._LCL_Temperature(h, T+273.15, Td+273.15)-273.15), 2)
self.LCLP = round((
self.tC._LCL_Pressure(h, p, T+273.15, Td+273.15)), 0)
self.LCLZ = round((
self.tC._LCL_Height(h, p, T+273.15, Td+273.15)), 0)
self.THETA = self.tC._PTk2_Theta(p, T+273.15)
self.MIXR = self.tC._RH_2_MixR(RH, p, T+273.15)
self.THETAE = self.tC._Tk_RH_MixR_2_ThetaE(
p, T+273.15, RH, self.MIXR/1000.)
self.ESAT = self.tC._esat(T+273.15)
def plot_thermo_calcs(self):
'''
method to plot the thermodynamic parameters on the parameter list.
Inputs:
# None
# ============
output:
# Image
'''
# plot the parameters on the list generated.
self.ax4.text(.01, .01, 'LCL Pressure: ' + str(self.LCLP) + (' hPa'))
self.ax4.text(.01, .04, 'LCL Temp: ' + str(self.LCLT) + ' c')
self.ax4.text(.01, .07, 'LCL Height: ' + str(self.LCLZ) + ' m')
def plot_dryadiabats(self, **kwargs):
'''
method to plot the dry adibats. Used in the plotskewtlogp method.
Inputs:
# kwargs (does not function)
# ============
output:
# Image
'''
# test = self.shear1km
# temperature array and pressure array
t0 = np.linspace(200, 430, 17)
press = np.linspace(100, 1000.)
# retrieve desired line style from user kwargs
line_style = kwargs.get('line_style')
if line_style is None:
line_style = '-'
# loop to calculate using Poisson's equation the
# dry adiabats for the skew t.
for temp in t0:
theta = temp * (press/1000.)**(2./7.)
# plot the dry adiabats given a specified color
self.ax1.semilogy((theta-273.15), press, line_style,
color='#7F4B10', linewidth=0.5)
def plot_wind_barbs(self, data, **kwargs):
P = data['Pressure']
U = data['U Component']
V = data['V Component']
H = data['Height']
# Copy y axis to plot wind barbs
# Set x lim for windpbarbs
# adjust location of barbs from x =0
# plot every 30th windbarb
# set axis to invisible
self.ax1_copy = self.ax1.twiny()
self.ax1_copy.set_xlim(self.x_min, self.x_max)
self.ax1_copy.set_ylim(self.y_min, self.y_max)
x_const = np.zeros(P.shape) + (self.x_max - 2)
self.ax1_copy.xaxis.set_visible(False)
if data['Type'] == 'radioSonde':
mask = U.mask
U = U[~mask]
V = V[~mask]
P = P[~mask]
self.ax1_copy.barbs(x_const[::3], P[::3], U[::3], V[::3])
else:
self.ax1_copy.barbs(x_const[::40], P[::40], U[::40], V[::40])
def run_shear_calcs(self, data):
'''
method to calculate thermodynamic parameters from dropsonde data
Inputs:
# Dictionary of sounding data.
# Uses calculations from thermocalcs.py
# ============
output:
# multiple arrays containing thermodynamic information
'''
T = data['Temperature']
Td = data['Dewpoint']
p = data['Pressure']
RH = data['Relative Humidity']
u = data['U Component']
v = data['V Component']
h = data['Height']
mask = h.mask
u = u[~mask]
v = v[~mask]
h = h[~mask]
self.SHEAR1KM = self.sC._VertShear_Sfc_to_1km(h, u, v)
self.SHEAR3KM = self.sC._VertShear_Sfc_to_3km(h, u, v)
self.SHEAR6KM = self.sC._VertShear_Sfc_to_6km(h, u, v)
self.BULKSHEAR1km = round(self.sC._bulkshear_sfc_1km(h, u, v), 2)
self.BULKSHEAR3km = round(self.sC._bulkshear_sfc_3km(h, u, v), 2)
self.BULKSHEAR6km = round(self.sC._bulkshear_sfc_6km(h, u, v), 2)
# self.ax4.text(.01, .1, '0-1 km shear: '+str(self.SHEAR1KM)+(' 1/s'))
def plot_shear_calcs(self):
'''
method to plot the thermodynamic parameters on the parameter list.
Inputs:
# None
# ============
output:
# Image
'''
# bitch = self.SHEAR1KM
# plot the parameters on the list generated.
self.ax4.text(.01, .1, '0-1 km shear: '+str(self.SHEAR1KM)+(' 1/s'))
self.ax4.text(.01, .14, '0-3 km shear: '+str(self.SHEAR3KM)+' 1/s')
self.ax4.text(.01, .18, '0-6 km shear: '+str(self.SHEAR6KM)+' 1/s')
self.ax4.text(
.01, .22, '0-1km Bulk Shear: '+str(self.BULKSHEAR1km)+' m/s')
self.ax4.text(
.01, .26, '0-3km Bulk Shear: '+str(self.BULKSHEAR3km)+' m/s')
self.ax4.text(
.01, .3, '0-6km Bulk Shear: '+str(self.BULKSHEAR6km)+' m/s')
def dry_lift(self, data):
T = data['Temperature']
Td = data['Dewpoint']
p = data['Pressure']
RH = data['Relative Humidity']
u = data['U Component']
v = data['V Component']
h = data['Height']
t_parcel, p_parcel = self.tC.dry_lift(T, p, self.LCLT, self.LCLP)
# print(t_parcel,p_parcel)
self.ax1.semilogy(t_parcel, p_parcel, 'k--', ms=1)
