bSvlist = list()
for n,par in enumerate(params):
" synth data "
out = vitas.main(par[0])
p3u = out[0]
p3v = out[1]
p3wU = out[2] # from 230deg
p3z = out[3]
p3ulist.extend(p3u[:16])
p3vlist.extend(p3v[:16])
" balloon data "
infiles3,_ = so.get_sounding_files(par[1], homedir='/localdata')
infiles3.sort()
df = mf.parse_sounding2(infiles3[par[2]])
bSu = df.u.values
bSv = df.v.values
bSz = df.index.values
""" reduce resolution of bS by averaging 100-m layer
centered at p3 altitude """
bot = 25
top = 26
for z in p3z[:16]:
center_idx = np.where(bSz==z*1000.)[0]
if not center_idx:
bSulist.append(np.nan)
bSvlist.append(np.nan)
rcParams['xtick.labelsize'] = 15
rcParams['ytick.labelsize'] = 15
rcParams['legend.fontsize'] = 15
rcParams['axes.labelsize'] = 15
rcParams['legend.handletextpad'] = 0.2
rcParams['mathtext.default'] = 'sf'
scale = 1.2
fig, axes = plt.subplots(2, 1, sharex=True, figsize=(5 * scale, 10 * scale))
axes[0].set_gid('(a) 23-24Jan01')
axes[1].set_gid('(b) 17Feb01')
nobs = ('n=7', 'n=11')
infiles3, _ = so.get_sounding_files('3', homedir='/localdata')
infiles7, _ = so.get_sounding_files('7', homedir='/localdata')
cmap = discrete_cmap(7, base_cmap='Set1')
color = (cmap(0), cmap(1))
infiles = (infiles3, infiles7)
for n, ax in enumerate(axes):
first = True
for f in infiles[n]:
df = mf.parse_sounding2(f)
x = np.expand_dims(df.bvf_moist.values, axis=1) * 10000
y = np.expand_dims(df.index.values, axis=1)
ax.plot(x, y, color=color[n], lw=0.5)
2,
sharex=True,
sharey=True,
figsize=(8.5 * scale, 11 * scale))
axes = axes.flatten()
axes[0].set_gid('(a) 12-14Jan03 (n={})')
axes[1].set_gid('(b) 21-23Jan03 (n={})')
axes[2].set_gid('(c) 15-16Feb03 (n={})')
axes[3].set_gid('(d) 09Jan04 (n={})')
axes[4].set_gid('(e) 02Feb04 (n={})')
axes[5].set_gid('(f) 16-18Feb04 (n={})')
axes[6].set_gid('(g) 25Feb04 (n={})')
fig.delaxes(axes[-1])
infiles08, _ = so.get_sounding_files('8', homedir='/localdata')
infiles09, _ = so.get_sounding_files('9', homedir='/localdata')
infiles10, _ = so.get_sounding_files('10', homedir='/localdata')
infiles11, _ = so.get_sounding_files('11', homedir='/localdata')
infiles12, _ = so.get_sounding_files('12', homedir='/localdata')
infiles13, _ = so.get_sounding_files('13', homedir='/localdata')
infiles14, _ = so.get_sounding_files('14', homedir='/localdata')
cmap = discrete_cmap(7, base_cmap='Set1')
color = cmap(0)
infiles = (infiles08, infiles09, infiles10, infiles11, infiles12, infiles13,
infiles14)
for ax, infile in zip(axes, infiles):
rcParams['xtick.labelsize'] = 15
rcParams['ytick.labelsize'] = 15
rcParams['legend.fontsize'] = 15
rcParams['axes.labelsize'] = 15
rcParams['legend.handletextpad'] = 0.2
rcParams['mathtext.default'] = 'sf'
scale=1.2
fig,axes = plt.subplots(2,1,sharex=True,figsize=(5*scale,10*scale))
axes[0].set_gid('(a) 23-24Jan01')
axes[1].set_gid('(b) 17Feb01')
nobs=('n=7','n=11')
infiles3,_ = so.get_sounding_files('3', homedir='/localdata')
infiles7,_ = so.get_sounding_files('7', homedir='/localdata')
cmap = discrete_cmap(7, base_cmap='Set1')
color=(cmap(0),cmap(1))
infiles=(infiles3,infiles7)
for n,ax in enumerate(axes):
first = True
for f in infiles[n]:
df = mf.parse_sounding2(f)
x = np.expand_dims(df.bvf_moist.values,axis=1)*10000
y = np.expand_dims(df.index.values,axis=1)
import matplotlib.pyplot as plt
import numpy as np
import sounding as so
from scipy.ndimage.filters import gaussian_filter
# homedir = '/Users/raulv/Documents'
homedir = '/home/rvalenzuela'
usr_case = None
file_sound, usc = so.get_sounding_files(usr_case, homedir=homedir)
''' raw soundings vertically-interpolated '''
# # soundarray,_,_,_,_ = get_raw_array('thetaeq', file_sound)
out = so.get_raw_array('bvf_moist', file_sound)
soundarray, _, _, y, x, raw_dates = out
title = 'BVFm raw'
# make_imshow(soundarray,title,x,y,raw_dates)
''' time-interpolated '''
# soundarray2,_,_ = get_interp_array('u',files=file_sound)
# soundarray2,_,_ = get_interp_array('v',files=file_sound)
# soundarray2,_,_ = get_interp_array('DD',files=file_sound)
# soundarray2,_,_ = get_interp_array('thetaeq',files=file_sound)
out = so.get_interp_array('bvf_moist', files=file_sound)
soundarray2, hgt, timestamp, raw_dates = out
Raul Valenzuela
September, 2015
"""
import matplotlib.pyplot as plt
import numpy as np
import sounding as so
from scipy.ndimage.filters import gaussian_filter
# homedir = '/Users/raulv/Documents'
homedir = '/home/rvalenzuela'
usr_case = None
file_sound, usc = so.get_sounding_files(usr_case, homedir=homedir)
''' raw soundings vertically-interpolated '''
# # soundarray,_,_,_,_ = get_raw_array('thetaeq', file_sound)
out = so.get_raw_array('bvf_moist', file_sound)
soundarray, _, _, y, x, raw_dates = out
title = 'BVFm raw'
# make_imshow(soundarray,title,x,y,raw_dates)
''' time-interpolated '''
# soundarray2,_,_ = get_interp_array('u',files=file_sound)
# soundarray2,_,_ = get_interp_array('v',files=file_sound)
# soundarray2,_,_ = get_interp_array('DD',files=file_sound)
# soundarray2,_,_ = get_interp_array('thetaeq',files=file_sound)
out = so.get_interp_array('bvf_moist', files=file_sound)
soundarray2, hgt, timestamp, raw_dates = out
# make_imshow(soundarray2,'')
