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plotSound.py
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plotSound.py
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"""
Plot NOAA balloon soundings.
Files have extension tsv
Raul Valenzuela
August, 2015
"""
import pandas as pd
import metpy.plots as metplt
# import metpy.calc as metcal
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import os
import datetime as dt
# import sys
import Thermodyn as thermo
import Meteoframes as mf
# from scipy.interpolate import interp1d
# from metpy.units import units, concatenate
''' set color codes in seaborn '''
sns.set_color_codes()
rc = {'axes.titlesize': 24,
'axes.labelsize': 18,
'ytick.labelsize': 13,
'xtick.labelsize': 13}
# mpl.rcParams.update(rc)
sns.set(rc=rc)
''' set directory and input files '''
# base_directory='/Users/raulv/Desktop/SOUNDING'
base_directory = '/home/rvalenzuela/BALLOON'
print base_directory
usr_case = raw_input('\nIndicate case number (i.e. 1): ')
case = 'case'+usr_case.zfill(2)
casedir = base_directory+'/'+case
out = os.listdir(casedir)
file_sound = []
for f in out:
if f[-3:] == 'tsv':
file_sound.append(casedir+'/'+f)
file_sound.sort()
def main():
for f in file_sound:
print f
df = mf.parse_sounding2(f)
fname = os.path.basename(f)
''' removes file extension and split date '''
raw_date = fname[:-4].split('_')
''' some files have preffix, so I take only datetime'''
raw_date = raw_date[-2:]
if len(raw_date[1]) == 6:
raw_date = raw_date[0]+raw_date[1]
date = dt.datetime.strptime(raw_date, "%Y%m%d%H%M%S")
else:
raw_date = raw_date[0]+raw_date[1]
date = dt.datetime.strptime(raw_date, "%Y%m%d%H%M")
# plot_skew(df,date)
plot_thermo(df, date, top=5.)
# compare_potential_temp(df,date)
# break
# print df
plt.show()
# plt.show(block=False)
def compare_potential_temp(sounding, date):
theta1 = thermo.theta1(K=sounding.TE, hPa=sounding.P)
theta2 = thermo.theta2(
K=sounding.TE, hPa=sounding.P, mixing_ratio=sounding.MR/1000)
thetaeq1 = thermo.theta_equiv1(K=sounding.TE, hPa=sounding.P)
thetaeq2 = thermo.theta_equiv2(K=sounding.TE, hPa=sounding.P,
relh=sounding.RH, mixing_ratio=sounding.MR/1000)
foo = pd.DataFrame(
{'theta1': theta1, 'thetaeq1': thetaeq1, 'theta2': theta2, 'thetaeq2': thetaeq2})
y = foo.index.values
fig, ax = plt.subplots(figsize=(8.5, 11))
ln1 = ax.plot(theta1, y, label='theta=f(temp,press) - W&H')
ln2 = ax.plot(theta2, y, label='theta=f(temp,press, w) - Bolton 1980')
ln3 = ax.plot(thetaeq1, y, label='thetaeq=f(temp,press,ws) - W&H')
ln4 = ax.plot(
thetaeq2, y, label='thetaeq=f(temp,press, w, rh) - Bolton 1980')
ax.set_xlim([280, 320])
ax.set_ylim([0, 5000])
ax.set_xlabel('Temperature [K]')
ax.set_ylabel('Altitude [m]')
ax2 = ax.twiny()
ln5 = ax2.plot(sounding.MR, y, sns.xkcd_rgb["amber"], label='mixing ratio')
ax2.set_xlim([0, 8])
ax2.set_ylim([0, 5000])
ax2.grid(False)
ax2.set_xlabel('Mixing ratio [g kg-1]')
ax3 = ax.twiny()
ln6 = ax3.plot(
sounding.RH, y, sns.xkcd_rgb["plum purple"], label='relative humidity')
ax3.set_xlim([0, 100])
ax3.set_ylim([0, 5000])
ax3.grid(False)
ax3.set_xlabel('Relative humidity [%]')
ax3.xaxis.set_label_coords(0.5, 1.07)
ax3.tick_params(direction='out', pad=35)
lns = ln1+ln2+ln3+ln4+ln5+ln6
labs = [l.get_label() for l in lns]
ax3.legend(lns, labs, loc=0)
plt.subplots_adjust(bottom=0.05, top=0.89)
datestr = date.strftime('%Y%m%d_%H%M%S')
plt.title(
'Comparison of Potential Temperature BBY sounding '+datestr, y=0.99)
plt.draw()
def plot_skew(sounding, date):
hgt = sounding.index # [m]
pres = sounding.P # [hPa]
TE = sounding.TE - 273.15 # [C]
TD = sounding.TD - 273.15 # [C]
U = sounding.u.values
V = sounding.v.values
freq = 20
U2 = np.empty(len(U))
U2[:] = np.NAN
U2[::freq] = U[::freq]
V2 = np.empty(len(V))
V2[:] = np.NAN
V2[::freq] = V[::freq]
fig = plt.figure(figsize=(9, 9))
skew = metplt.SkewT(fig)
skew.plot(pres, TE, 'r')
skew.plot(pres, TD, 'g', linestyle='--')
skew.plot_barbs(pres, U2, V2)
p0 = np.asarray([1000, 900, 800, 700, 600])*units.hPa
t0 = np.asarray([0, 5, 10, 15, 20, 25]) * units.degC
skew.plot_dry_adiabats(t0=t0, p=p0, linestyle='-', linewidth=0.8)
skew.plot_moist_adiabats(t0=t0, p=p0, linestyle='-', linewidth=0.8)
skew.ax.set_ylim(1014, 600)
skew.ax.set_xlim(-5, 15)
skew.ax.set_ylabel('Pressure [hPa]')
skew.ax.set_xlabel('Temperature [degC]')
l1 = 'Balloon sounding at Bodega Bay'
l2 = '\nDate: ' + date.strftime('%Y-%b-%d %H:%M:%S UTC')
plt.title(l1+l2)
plt.draw()
def plot_thermo(sounding, date, **kwarg):
hgt = sounding.index/1000. # [m]
pres = sounding.P # [hPa]
TE = sounding.TE - 273.15 # [C]
TD = sounding.TD - 273.15 # [C]
relh = sounding.RH
mixr = sounding.MR
theta = sounding.theta
thetaeq = sounding.thetaeq
U = sounding.u.values
V = sounding.v.values
BVFd = sounding.bvf_dry
BVFm = sounding.bvf_moist
fig, ax = plt.subplots(1, 5, sharey=True, figsize=(13, 8.5))
hgt_lim = kwarg['top']
n = 0
ax[n].plot(TE, hgt, label='Temp')
ax[n].plot(TD, hgt, label='Dewp')
ax[n].legend()
ax[n].set_xlim([-30, 20])
ax[n].set_ylim([0, hgt_lim])
add_minor_grid(ax[n])
ax[n].set_xlabel('Temperature [C]')
ax[n].set_ylabel('Altitude [km]')
n = 1
ln1 = ax[n].plot(mixr, hgt, label='mixr')
ax[n].set_xlim([0, 8])
ax[n].set_ylim([0, hgt_lim])
add_minor_grid(ax[n])
for label in ax[n].xaxis.get_ticklabels()[::2]:
label.set_visible(False)
ax[n].set_xlabel('Mixing Ratio [g kg-1]')
axt = ax[n].twiny()
ln2 = axt.plot(relh, hgt, 'g', label='relh')
axt.set_xlim([0, 100])
axt.set_ylim([0, hgt_lim])
axt.set_xlabel('Relative humidity [%]')
axt.xaxis.set_label_coords(0.5, 1.04)
axt.grid(False)
lns = ln1+ln2
labs = [l.get_label() for l in lns]
axt.legend(lns, labs, loc=0)
n = 2
ax[n].plot(theta, hgt, label='Theta')
ax[n].plot(thetaeq, hgt, label='ThetaEq')
ax[n].legend()
ax[n].set_xlim([280, 320])
ax[n].set_ylim([0, hgt_lim])
add_minor_grid(ax[n])
for label in ax[n].xaxis.get_ticklabels()[::2]:
label.set_visible(False)
ax[n].set_xlabel('Theta [K]')
n = 3
ax[n].plot(U, hgt, label='u')
ax[n].plot(V, hgt, label='v')
ax[n].axvline(x=0, linestyle=':', color='r')
ax[n].legend()
ax[n].set_xlim([-10, 40])
ax[n].set_ylim([0, hgt_lim])
add_minor_grid(ax[n])
ax[n].set_xlabel('Wind Speed [ms-1]')
n = 4
bvfd = BVFd*10000.
bvfm = BVFm*10000.
ax[n].plot(bvfd, hgt, label='dry')
ax[n].plot(bvfm, hgt, label='moist')
ax[n].axvline(x=0, linestyle=':', color='r')
ax[n].legend(loc=2)
ax[n].set_xlim([-6, 6])
ax[n].set_ylim([0, hgt_lim])
add_minor_grid(ax[n])
ax[n].set_xlabel('BVF (x10^-4) [s-2]')
# f=interp1d(hgt, range(len(hgt)))
# idx=int(np.ceil(f(1.)))
# dfslice=bvfm.iloc[0:idx+1]
# mean = np.nanmean(dfslice)
# median = np.nanmedian(dfslice)
# minn = np.amin(dfslice)
# maxx = np.amax(dfslice)
# strout='N^2 moist (e-4) lowest 1000 mts: mean={:1.1f}, median={:1.1f}, min={:1.1f}, max={:1.1f}'
# print strout.format(mean,median,minn,maxx)
# dfslice=bvfd.iloc[0:idx+1]
# mean = np.nanmean(dfslice)
# median = np.nanmedian(dfslice)
# minn = np.amin(dfslice)
# maxx = np.amax(dfslice)
# strout='N^2 dry (e-4) lowest 1000 mts: mean={:1.1f}, median={:1.1f}, min={:1.1f}, max={:1.1f}\n'
# print strout.format(mean,median,minn,maxx)
# idx=int(np.ceil(f(0.5)))
# print sounding.iloc[0:idx+1]
l1 = 'Profile at BBY from sounding '
l2 = date.strftime('%Y-%m-%d %H:%M:%S UTC')
plt.suptitle(l1+l2)
plt.subplots_adjust(bottom=0.08, top=0.89, left=0.06, right=0.98)
plt.draw()
def add_minor_grid(ax):
# ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(b=True, which='major', color='w', linewidth=1.0)
ax.grid(b=True, which='minor', color='w', linewidth=0.5)
def find_nearest2(array, target):
""" See stackoverflow answer from Bi Rico """
''' array must be sorted '''
idx = array.searchsorted(target)
idx = np.clip(idx, 1, len(array)-1)
left = array[idx-1]
right = array[idx]
idx -= target - left < right - target
return idx
''' start '''
main()