def get_data(usr_case=None):
file_met, usr_case = get_files(usr_case)
index_field, name_field = get_index_field(usr_case)
dfBBY = []
dfCZD = []
dfFRS = []
# period = get_request_dates(usr_case)
for f in file_met:
loc = f[-12:-9]
if loc == "bby":
# elev = 15
# dfBBY.append(mf.parse_surface(
# f, index_field[loc], name_field, elev))
dfBBY.append(mf.parse_surface(f))
elif loc == "czc":
# elev = 462
# dfCZD.append(mf.parse_surface(
# f, index_field[loc], name_field, elev))
dfCZD.append(mf.parse_surface(f))
elif loc == "frs":
# elev = 462
# dfFRS.append(mf.parse_surface(
# f, index_field[loc], name_field, elev))
dfFRS.append(mf.parse_surface(f))
if len(dfBBY) > 1:
meteoBBY = pd.concat(dfBBY)
else:
meteoBBY = dfBBY[0]
if len(dfCZD) > 1:
meteoCZD = pd.concat(dfCZD)
else:
meteoCZD = dfCZD[0]
if len(dfFRS) > 1:
meteoFRS = pd.concat(dfFRS)
else:
try:
meteoFRS = dfFRS[0]
except IndexError:
index = meteoBBY.index
columns = meteoBBY.columns
meteoFRS = pd.DataFrame(index=index, columns=columns)
return meteoBBY, meteoCZD, meteoFRS, usr_case
def get_data(usr_case=None):
file_met, usr_case = get_files(usr_case)
index_field, name_field = get_index_field(usr_case)
dfBBY = []
dfCZD = []
dfFRS = []
# period = get_request_dates(usr_case)
for f in file_met:
loc = f[-12:-9]
if loc == 'bby':
# elev = 15
# dfBBY.append(mf.parse_surface(
# f, index_field[loc], name_field, elev))
dfBBY.append(mf.parse_surface(f))
elif loc == 'czc':
# elev = 462
# dfCZD.append(mf.parse_surface(
# f, index_field[loc], name_field, elev))
dfCZD.append(mf.parse_surface(f))
elif loc == 'frs':
# elev = 462
# dfFRS.append(mf.parse_surface(
# f, index_field[loc], name_field, elev))
dfFRS.append(mf.parse_surface(f))
if len(dfBBY) > 1:
meteoBBY = pd.concat(dfBBY)
else:
meteoBBY = dfBBY[0]
if len(dfCZD) > 1:
meteoCZD = pd.concat(dfCZD)
else:
meteoCZD = dfCZD[0]
if len(dfFRS) > 1:
meteoFRS = pd.concat(dfFRS)
else:
try:
meteoFRS = dfFRS[0]
except IndexError:
index = meteoBBY.index
columns = meteoBBY.columns
meteoFRS = pd.DataFrame(index=index, columns=columns)
return meteoBBY, meteoCZD, meteoFRS, usr_case
def get_surface_data(usr_case, homedir=None):
''' set directory and input files '''
case = 'case' + usr_case.zfill(2)
casedir = homedir + '/' + case
out = os.listdir(casedir)
out.sort()
files = []
for f in out:
if f[-3:] in ['met', 'urf']:
files.append(f)
file_met = []
for f in files:
if f[:3] == 'bby':
file_met.append(casedir + '/' + f)
df = []
for f in file_met:
meteo = mf.parse_surface(f)
df.append(meteo)
if len(df) > 1:
surface = pd.concat(df)
else:
surface = df[0]
return surface
def get_buoy(f=None, target_index=None):
buoy = mf.parse_buoy(f)
bucomp = -buoy.SPD * np.sin(np.radians(buoy.DIR))
bvcomp = -buoy.SPD * np.cos(np.radians(buoy.DIR))
buoy['U'] = bucomp
buoy['V'] = bvcomp
buoy.drop(buoy.columns[[0, 1, 2, 3, 4]], axis=1, inplace=True)
buoy = buoy.loc[target_index]
return buoy
def get_buoy(f=None, target_index=None):
buoy = mf.parse_buoy(f)
bucomp = -buoy.SPD * np.sin(np.radians(buoy.DIR))
bvcomp = -buoy.SPD * np.cos(np.radians(buoy.DIR))
buoy['U'] = bucomp
buoy['V'] = bvcomp
buoy.drop(buoy.columns[[0, 1, 2, 3, 4]], axis=1, inplace=True)
buoy = buoy.loc[target_index]
return buoy
def main(option):
if option == 'compare':
dfBBY = []
dfCZD = []
for f in file_met:
loc = f[-12:-9]
if loc == 'bby':
dfBBY.append(mf.parse_surface(f))
elif loc == 'czc':
dfCZD.append(mf.parse_surface(f))
if len(dfBBY) > 1:
meteoBBY = pd.concat(dfBBY)
else:
meteoBBY = dfBBY[0]
if len(dfCZD) > 1:
meteoCZD = pd.concat(dfCZD)
else:
meteoCZD = dfCZD[0]
make_compare(BBY=meteoBBY, CZD=meteoCZD)
else:
df = []
for f in file_met:
# df.append(mf.parse_surface(f,index_field[usr_loc],name_field,elev[usr_loc]))
df.append(mf.parse_surface(f))
if len(df) > 1:
meteo = pd.concat(df)
else:
meteo = df[0]
make_meteo(meteo)
make_thermo(meteo)
plt.show()
def get_surf(f=None):
surf = mf.parse_surface(f)
sucomp = -surf.wspd * np.sin(np.radians(surf.wdir))
svcomp = -surf.wspd * np.cos(np.radians(surf.wdir))
surf['U'] = sucomp
surf['V'] = svcomp
surf.drop(surf.columns[[0, 1, 2, 5, 6, 7, 8, 9, 10]], axis=1, inplace=True)
g = pd.TimeGrouper('10T')
surfU = surf.U.groupby(g).mean()
surfV = surf.V.groupby(g).mean()
surf10 = pd.DataFrame(data={'U': surfU, 'V': surfV})
return surf10
def get_surf(f=None):
surf = mf.parse_surface(f)
sucomp = -surf.wspd * np.sin(np.radians(surf.wdir))
svcomp = -surf.wspd * np.cos(np.radians(surf.wdir))
surf['U'] = sucomp
surf['V'] = svcomp
surf.drop(surf.columns[[0, 1, 2, 5, 6, 7, 8, 9, 10]], axis=1, inplace=True)
g = pd.TimeGrouper('10T')
surfU = surf.U.groupby(g).mean()
surfV = surf.V.groupby(g).mean()
surf10 = pd.DataFrame(data={'U': surfU, 'V': surfV})
return surf10
def make_quiver(ax):
""" make quiver plot for each file """
for i, f in enumerate(buoyfiles):
df = mf.parse_buoy(f)
df2 = df[st:en]
time = df2.index[::2]
wspd = df2.SPD[::2]
wdir = df2.DIR[::2]
X = np.asarray(range(len(wspd)))
Y = np.zeros(len(wspd))
U = np.asarray(-wspd*np.sin(wdir*np.pi/180.))
V = np.asarray(-wspd*np.cos(wdir*np.pi/180.))
if len(time) > 0:
ntime = len(time)
xticks = range(0, ntime, 18)
xticklabels = time[xticks]
date_fmt = '%d\n%H'
xtlabels = [t.strftime(date_fmt) for t in xticklabels]
Q = ax[i].quiver(
X, Y, U, V, width=0.002, facecolor='b', edgecolor='k', headwidth=3)
if i == 0:
ax[i].quiverkey(Q, 0.9, 0.1, 10,
r'$10 \frac{m}{s}$',
fontproperties={'weight': 'bold', 'size': 14})
ax[i].set_xticks(xticks)
ax[i].set_xticklabels(xtlabels)
ax[i].invert_xaxis()
ax[i].set_xlim([ntime+5, -5])
ax[i].set_ylim([-0.02, 0.02])
filename = os.path.basename(f)
buoyname = 'B'+filename[3:5]
degree_sign = u'\N{DEGREE SIGN}'
atext = buoyname+' - '+buoylats[buoyname]+degree_sign+'N'
ax[i].text(0.05, 0.1, atext, transform=ax[i].transAxes, fontsize=14,
verticalalignment='bottom')
else:
ax[i].text(
0.5, 0.5, 'NO DATA', weight='bold', transform=ax[i].transAxes)
filename = os.path.basename(f)
buoyname = 'B'+filename[3:5]
atext = buoyname+' - '+buoylats[buoyname]+degree_sign+'N'
ax[i].text(0.05, 0.1, atext, transform=ax[i].transAxes, fontsize=14,
verticalalignment='bottom')
ax[i].set_yticklabels([''])
plt.draw()
def main():
''' raf files are created using matlab function
W:/MATLAB/p3_matlab/convert_to_raf_ascii.m '''
f = '/home/rvalenzuela/P3_stdtape/ascii/010123I.std.ascii.raf'
ini, end = get_sounding_times()
m = 0
for i, e in zip(ini, end):
if m >= 0:
sound = mf.parse_acft_sounding(f, i, e, return_interp=True)
# print sound
temp = sound.AIR_TEMP.values #[C]
dewp = sound.DEW_POINT.values #[C]
wspd = sound.WIND_SPD.values
wdir = sound.WIND_DIR.values
u, v = get_components(wspd, wdir)
pres = sound.AIR_PRESS.values
lats = sound.LAT.values
lons = sound.LON.values
hgt = sound.index.values
theta = sound.theta.values
thetaeq = sound.thetaeq.values
bvf_dry = sound.bvf_dry.values
bvf_moist = sound.bvf_moist.values
location = [np.average(lats), np.average(lons)]
# plot_skew1(temp=temp, dewp=dewp, u=u, v=v, press=pres, date=[i, e], loc=location)
plot_skew2(temp=temp,
dewp=dewp,
u=u,
v=v,
press=pres,
date=[i, e],
loc=location,
hgt=hgt)
# plot_thermo(temp=temp, dewp=dewp, u=u, v=v, hgt=hgt,theta=theta, thetaeq=thetaeq,
# bvf_dry=bvf_dry, bvf_moist=bvf_moist,press=pres, date=[i, e], loc=location,top=5000)
m += 1
# compare_potential_temp(df,date)
# break
plt.show()
def get_pressure(case=None, homedir=None):
from glob import glob
fpath = homedir + '/SURFACE/case{}/bby*'
surf_files = glob(fpath.format(str(case).zfill(2)))
surf_files.sort()
df_list = []
for f in surf_files:
df_list.append(mf.parse_surface(f))
if len(df_list) > 1:
df = pd.concat(df_list)
else:
df = df_list[0]
g = pd.TimeGrouper('60T')
dfg = df['press'].groupby(g).mean()
return dfg
def main():
case=7
mesocase=get_mesocase(case)
mesofiles=mesocase['files']
st = mesocase['dates'][0]
en = mesocase['dates'][1]
stations=mesocase['stations']
var=['TMP','RELH','PMSL','DRCT']
hl=[]
xticks = pd.date_range(start=st,end=en, freq='3H')
fig,ax = plt.subplots(len(var),1,figsize=(13,10),sharex=True)
for i,f in enumerate(mesofiles):
df=mf.parse_mesowest_excel(f)
df2=df[st:en]
for j in range(len(var)):
l=ax[j].plot(df2.index,df2[var[j]],'-o')
ax[j].invert_xaxis()
ax[j].set_xticks(xticks)
ax[j].set_xlim([en,st])
if j == 0: hl.append(l)
deg_sign= u'\N{DEGREE SIGN}'
lns = [line[0] for line in hl]
ax[0].legend(lns, stations, loc=0)
ax[0].set_ylabel('Temperature [' +deg_sign+'C]')
ax[1].set_ylim([40,105])
ax[1].set_ylabel('Relative humidity [%]')
ax[2].set_ylim([1015,1025])
ax[2].set_ylabel('Pressure MSL [hPa]')
ax[3].set_yticks(range(0,360+60,60))
ax[3].set_ylabel('Wind direction [deg]')
datefmt = dates.DateFormatter('%d\n%H')
ax[3].xaxis.set_major_formatter(datefmt)
ax[3].set_xlabel(r'$\Leftarrow$'+' Time [UTC]')
t1='METAR surface observations (source: Mesowest)'
t2='\nDate: ' + st.strftime('%b-%Y')
plt.suptitle(t1+t2)
plt.show(block=False)
def main(): ''' raf files are created using matlab function W:/MATLAB/p3_matlab/convert_to_raf_ascii.m ''' f='/home/rvalenzuela/P3_stdtape/ascii/010123I.std.ascii.raf' ini,end = get_sounding_times() m=0 for i,e in zip(ini,end): if m>=0: sound=mf.parse_acft_sounding(f, i, e, return_interp=True) # print sound temp=sound.AIR_TEMP.values #[C] dewp=sound.DEW_POINT.values #[C] wspd=sound.WIND_SPD.values wdir=sound.WIND_DIR.values u,v = get_components(wspd,wdir) pres=sound.AIR_PRESS.values lats=sound.LAT.values lons=sound.LON.values hgt=sound.index.values theta=sound.theta.values thetaeq=sound.thetaeq.values bvf_dry=sound.bvf_dry.values bvf_moist=sound.bvf_moist.values location=[np.average(lats),np.average(lons)] # plot_skew1(temp=temp, dewp=dewp, u=u, v=v, press=pres, date=[i, e], loc=location) plot_skew2(temp=temp, dewp=dewp, u=u, v=v, press=pres, date=[i, e], loc=location, hgt=hgt) # plot_thermo(temp=temp, dewp=dewp, u=u, v=v, hgt=hgt,theta=theta, thetaeq=thetaeq, # bvf_dry=bvf_dry, bvf_moist=bvf_moist,press=pres, date=[i, e], loc=location,top=5000) m+=1 # compare_potential_temp(df,date) # break plt.show()
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()
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()
def run(case, tta=None, plot_theory=False, grid=True, ax=None, homedir=None,
color_surf=(0, 0, 0.5, 0.5),color_wp=(0, 0.5, 0, 0.5),
add_date=False, wprof_hgt=None):
" process gapflow analysis"
" first index is mesowest file, second index is \
BBY (1 or more files) "
f = get_filenames(case,homedir)
" parse mesowest data "
meso = mf.parse_mesowest_excel(f[0])
t = get_times(case)
mpress = meso.loc[t[0]: t[1]]['PMSL'].values
mesoidx = meso.loc[t[0]: t[1]].index
" parse surface BBY data "
if len(f[1]) > 1:
' more than one day of obs '
surf = mf.parse_surface(f[1][0])
for ff in f[1][1:]:
surf = surf.append(mf.parse_surface(ff))
else:
' only one day '
surf = mf.parse_surface(f[1][0])
" resample to 1min so we can find \
mesowest index "
surf = surf.resample('1T').interpolate()
" adjust bias is mesowest in case 1 and 2 "
if case in [1, 2]:
bias = 9
else:
bias = 0
spress = surf.loc[mesoidx]['press'].values - bias
" BBY and mesowest pressure difference "
pressDiff = spress - mpress
" BBY surface winds "
swspd = surf.loc[mesoidx]['wspd'].values
swdir = surf.loc[mesoidx]['wdir'].values
ucomp = -swspd*np.sin(np.radians(swdir))
" gapflow dataframe "
d = {'ucomp': ucomp, 'wspd': swspd, 'wdir': swdir,
'pdiff': pressDiff, 'Bpress': spress, 'Kpress': mpress}
gapflow = pd.DataFrame(data=d, index=mesoidx)
" removes rows with NaN "
gapflow = gapflow[np.isfinite(gapflow['Kpress'])]
" add wind profiler data at target altitude "
out = get_windprof(case,
gapflow_time=gapflow.index,
top_hgt_km=wprof_hgt,
homedir=homedir)
wp_wspd, wp_wdir = out
wp_ucomp = -wp_wspd*np.sin(np.radians(wp_wdir))
gapflow['wp_ws'] = wp_wspd
gapflow['wp_wd'] = wp_wdir
gapflow['wp_ucomp'] = wp_ucomp
" Mass etal 95 equation "
# blh = get_BLH(case)
blh = 500 #[m]
massPa, massU = mass_eq(air_density=1.24, BLH=blh)
path = make_polygon(massPa, massU)
gapflow = check_polygon(gapflow, path)
# gapflow['gapflow'] = ((gapflow.poly is True) & (gapflow.wdir <= 120))
# sub = gapflow[(gapflow.poly is True) & (gapflow.wdir <= 120)]
" theoretical lines "
if plot_theory:
if ax is None:
fig, ax = plt.subplots(figsize=(8, 7))
ax.set_xlabel('Pressure difference, BBY-SCK [hPa]')
ax.set_ylabel('BBY zonal wind [m s-1]')
# ax.scatter(gapflow['pdiff'], gapflow['ucomp'],
# color=color_surf, label='surf')
# ax.scatter(gapflow['pdiff'], gapflow['wp_ucomp'],
# color=color_wp, label='wp')
# ax.scatter(sub['pdiff'], sub['ucomp'], color='r')
ax.plot(massPa/100, massU[0], marker=None)
ax.plot(massPa/100, massU[1], linestyle='--', color='r')
ax.plot(massPa/100, massU[2], linestyle='--', color='r')
if grid:
ax.grid(True)
ax.set_xlim([-12, 1])
ax.set_ylim([-20, 15])
ini = mesoidx[0]
end = mesoidx[-1]
date = ini.strftime('%b-%Y ')
beg = ini.strftime('%d')
end = end.strftime('%d')
# ax.text(0.03, 0.76, timetxt.format(str(case).zfill(2),
# date, beg, end),
if add_date is True:
if beg == end:
ax.text(0.03, 0.85,'{} {}'.format(beg,date),
fontsize=12,
transform=ax.transAxes)
else:
ax.text(0.03, 0.85,'{}-{} {}'.format(beg,end,date),
fontsize=12,
transform=ax.transAxes)
return gapflow
def get_raw_array(soundvar, file_sound):
file_sound.sort()
''' height grid with 10 m resolution'''
var = [] # 2D list
timestamps = []
top_limit = 10000 # [m]
hgtgrid = np.asarray(range(10, 4010, 10))
for f in file_sound:
''' sounding timestamps approached to
the nearest 20 minute grid point'''
fname = os.path.basename(f)
raw_date = fname[:-4].split('_')[-2:]
y = raw_date[0][:4]
m = raw_date[0][4:6]
d = raw_date[0][6:8]
hh = int(raw_date[1][0:2])
mm = int(raw_date[1][2:4])
nearest = round_to_nearest(mm, 20)
if nearest == 60:
hh += 1
mm = 0
else:
mm = nearest
if hh == 24:
d = str(int(d) + 1)
hh = 0
raw_date = y+'-'+m+'-'+d+'T' + \
str(hh).zfill(2)+':'+str(mm).zfill(2)+':'+str(0).zfill(2)
timestamps.append(np.datetime64(raw_date))
''' variable '''
df = mf.parse_sounding2(f)
# print df
sv = df[soundvar][df.index < top_limit].values
Z = df.index[df.index < top_limit].values
''' interpolate to a common vertical grid '''
f = interp1d(Z, sv)
svinterp = np.asarray([])
for h in hgtgrid:
try:
svinterp = np.append(svinterp, f(h))
except ValueError:
svinterp = np.append(svinterp, np.nan)
''' take care of vertical nan values in raw soundings;
more gaps can show up if the top of the vertical grid
is increased (i.e. higher balloon altitudes show more
missing data)
'''
if np.any(np.isnan(svinterp)):
nanidx = np.where(np.isnan(svinterp))[0]
diff = np.diff(nanidx)
idxjump = np.where(diff > 1)[0]
ngaps = len(idxjump) + 1
if ngaps > 1 and nanidx[-1] != svinterp.size - 1:
gapidx = np.split(nanidx, idxjump + 1)
for g in gapidx:
first = g[0]
last = g[-1]
''' pick good values between nans and
make a linear interpolation (hopefully there
few nans) '''
if last + 1 == svinterp.size:
x = [hgtgrid[first - 1], hgtgrid[last]]
y = [svinterp[first - 1], svinterp[last]]
elif first == 0:
x = [hgtgrid[first], hgtgrid[last + 1]]
y = [svinterp[first], svinterp[last + 1]]
else:
x = [hgtgrid[first - 1], hgtgrid[last + 1]]
y = [svinterp[first - 1], svinterp[last + 1]]
f = interp1d(x, y)
svinterp[g] = [f(h) for h in hgtgrid[g]]
elif ngaps == 1 and nanidx[-1] != svinterp.size - 1:
first = nanidx[0]
last = nanidx[-1]
if last + 1 == svinterp.size:
x = [hgtgrid[first - 1], hgtgrid[last]]
y = [svinterp[first - 1], svinterp[last]]
elif first == 0:
x = [hgtgrid[first], hgtgrid[last + 1]]
y = [svinterp[first], svinterp[last + 1]]
else:
x = [hgtgrid[first - 1], hgtgrid[last + 1]]
y = [svinterp[first - 1], svinterp[last + 1]]
f = interp1d(x, y)
svinterp[nanidx] = [f(h) for h in hgtgrid[nanidx]]
var.append(svinterp)
else:
''' add column to list '''
var.append(svinterp)
''' time grid with 20 minute spacing'''
t1 = timestamps[0] - np.timedelta64(20, 'm')
t2 = timestamps[-1] + np.timedelta64(20, 'm')
dates = np.arange(t1, t2, dtype='datetime64[20m]')
''' get index of each sounding date '''
tidx = []
for t in timestamps:
tidx.append(np.where(dates == t)[0][0])
''' assign soundings to 2D array '''
soundarray = np.full((hgtgrid.size, dates.size), np.nan)
for n, i in enumerate(tidx):
soundarray[:, i] = var[n]
return soundarray, var, tidx, hgtgrid, dates, timestamps
def get_raw_array(soundvar, file_sound):
file_sound.sort()
''' height grid with 10 m resolution'''
var = [] # 2D list
timestamps = []
top_limit = 10000 # [m]
hgtgrid = np.asarray(range(10, 4010, 10))
for f in file_sound:
''' sounding timestamps approached to
the nearest 20 minute grid point'''
fname = os.path.basename(f)
raw_date = fname[:-4].split('_')[-2:]
y = raw_date[0][:4]
m = raw_date[0][4:6]
d = raw_date[0][6:8]
hh = int(raw_date[1][0:2])
mm = int(raw_date[1][2:4])
nearest = round_to_nearest(mm, 20)
if nearest == 60:
hh += 1
mm = 0
else:
mm = nearest
if hh == 24:
d = str(int(d)+1)
hh = 0
raw_date = y+'-'+m+'-'+d+'T' + \
str(hh).zfill(2)+':'+str(mm).zfill(2)+':'+str(0).zfill(2)
timestamps.append(np.datetime64(raw_date))
''' variable '''
df = mf.parse_sounding2(f)
# print df
sv = df[soundvar][df.index < top_limit].values
Z = df.index[df.index < top_limit].values
''' interpolate to a common vertical grid '''
f = interp1d(Z, sv)
svinterp = np.asarray([])
for h in hgtgrid:
try:
svinterp = np.append(svinterp, f(h))
except ValueError:
svinterp = np.append(svinterp, np.nan)
''' take care of vertical nan values in raw soundings;
more gaps can show up if the top of the vertical grid
is increased (i.e. higher balloon altitudes show more
missing data)
'''
if np.any(np.isnan(svinterp)):
nanidx = np.where(np.isnan(svinterp))[0]
diff = np.diff(nanidx)
idxjump = np.where(diff > 1)[0]
ngaps = len(idxjump) + 1
if ngaps > 1 and nanidx[-1] != svinterp.size-1:
gapidx = np.split(nanidx, idxjump+1)
for g in gapidx:
first = g[0]
last = g[-1]
''' pick good values between nans and
make a linear interpolation (hopefully there
few nans) '''
if last+1 == svinterp.size:
x = [hgtgrid[first-1], hgtgrid[last]]
y = [svinterp[first-1], svinterp[last]]
elif first == 0:
x = [hgtgrid[first], hgtgrid[last+1]]
y = [svinterp[first], svinterp[last+1]]
else:
x = [hgtgrid[first-1], hgtgrid[last+1]]
y = [svinterp[first-1], svinterp[last+1]]
f = interp1d(x, y)
svinterp[g] = [f(h) for h in hgtgrid[g]]
elif ngaps == 1 and nanidx[-1] != svinterp.size-1:
first = nanidx[0]
last = nanidx[-1]
if last+1 == svinterp.size:
x = [hgtgrid[first-1], hgtgrid[last]]
y = [svinterp[first-1], svinterp[last]]
elif first == 0:
x = [hgtgrid[first], hgtgrid[last+1]]
y = [svinterp[first], svinterp[last+1]]
else:
x = [hgtgrid[first-1], hgtgrid[last+1]]
y = [svinterp[first-1], svinterp[last+1]]
f = interp1d(x, y)
svinterp[nanidx] = [f(h) for h in hgtgrid[nanidx]]
var.append(svinterp)
else:
''' add column to list '''
var.append(svinterp)
''' time grid with 20 minute spacing'''
t1 = timestamps[0]-np.timedelta64(20, 'm')
t2 = timestamps[-1]+np.timedelta64(20, 'm')
dates = np.arange(t1, t2, dtype='datetime64[20m]')
''' get index of each sounding date '''
tidx = []
for t in timestamps:
tidx.append(np.where(dates == t)[0][0])
''' assign soundings to 2D array '''
soundarray = np.full((hgtgrid.size, dates.size), np.nan)
for n, i in enumerate(tidx):
soundarray[:, i] = var[n]
return soundarray, var, tidx, hgtgrid, dates, timestamps
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)
top = 2000 # [m]
top_idx = np.where(y == top)[0]
if first is True:
prof = x[:top_idx]
first = False
else:
prof = np.hstack((prof, x[:top_idx]))
meanx = np.expand_dims(np.nanmean(prof, axis=1), axis=1)
y2 = y[:top_idx]
ax.plot(meanx, y2, color=color[n], lw=3)
xpos = 0.08
ax.text(
name = {"bby": "BodegaBay", "czc": "Cazadero"}
elev = {"bby": 15, "czc": 462}
if usr_case in ["1", "2"]:
index_field = {"bby": [3, 4, 10, 5, 6, 11, 13], "czc": [3, 4, 10, 5, 6, 11, 13]}
elif usr_case in ["3", "4", "5", "6", "7"]:
index_field = {"bby": [3, 6, 9, 10, 12, 17, 26], "czc": [3, 4, 5, 6, 8, 13, 22]}
else:
index_field = {"bby": [3, 4, 5, 6, 8, 13, 15], "czc": [3, 4, 5, 6, 8, 13, 15]}
""" make dataframe for each station """
dfBBY = []
dfCZD = []
for f in file_met:
loc = f[-12:-9]
if loc == "bby":
dfBBY.append(mf.parse_surface(f, index_field[loc], name_field, elev[loc]))
elif loc == "czc":
dfCZD.append(mf.parse_surface(f, index_field[loc], name_field, elev[loc]))
""" concatenate dataframes when there is
more than one day of data """
if len(dfBBY) > 1:
meteoBBY = pd.concat(dfBBY)
else:
meteoBBY = dfBBY[0]
if len(dfCZD) > 1:
meteoCZD = pd.concat(dfCZD)
else:
meteoCZD = dfCZD[0]
rcParams['mathtext.default'] = 'sf'
def cosd(array):
return np.cos(np.radians(array))
homedir = '/localdata'
topdf = False
case = range(13, 14)
res = 'coarse'
o = 'case{}_total_wind_{}.pdf'
surf_file = '/Users/raulvalenzuela/Data/SURFACE/case13/czc04047.met'
czd = mf.parse_surface(surf_file)
surf_file = '/Users/raulvalenzuela/Data/SURFACE/case13/bby04047.met'
bby = mf.parse_surface(surf_file)
czdh = czd.preciph[~czd.preciph.isnull()]
bbyh = bby.preciph[~bby.preciph.isnull()]
''' creates plot with seaborn style '''
# with sns.axes_style("white"):
# sns.set_style('ticks',
# {'xtick.direction': u'in',
# 'ytick.direction': u'in'}
# )
scale = 1.3
plt.figure(figsize=(8 * scale, 6 * scale))
ax=None,
homedir=None,
color_surf=(0, 0, 0.5, 0.5),
color_wp=(0, 0.5, 0, 0.5),
add_date=False,
wprof_hgt=None):
" process gapflow analysis"
" first index is mesowest file, second index is \
BBY (1 or more files) "
f = get_filenames(case, homedir)
" parse mesowest data "
meso = mf.parse_mesowest_excel(f[0])
t = get_times(case)
mpress = meso.loc[t[0]:t[1]]['PMSL'].values
mesoidx = meso.loc[t[0]:t[1]].index
" parse surface BBY data "
if len(f[1]) > 1:
' more than one day of obs '
surf = mf.parse_surface(f[1][0])
for ff in f[1][1:]:
surf = surf.append(mf.parse_surface(ff))
else:
' only one day '
surf = mf.parse_surface(f[1][0])
" resample to 1min so we can find \
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)
top = 2000 # [m]
top_idx = np.where(y == top)[0]
if first is True:
prof = x[:top_idx]
first = False
else:
prof = np.hstack((prof,x[:top_idx]))
meanx = np.expand_dims(np.nanmean(prof,axis=1),axis=1)
y2 = y[:top_idx]
ax.plot(meanx,y2,color=color[n],lw=3)
xpos = 0.08
ax.text(xpos,0.9,ax.get_gid(),
import Meteoframes as mf
import matplotlib.pyplot as plt
from matplotlib import dates
import datetime
import seaborn as sns
import pandas as pd
f='/home/rvalenzuela/GPS_IWV/case03/bby_gpsiwv_010123-24.dat'
df = mf.parse_gps_iwv(f)
st = datetime.datetime(2001, 1, 23, 0, 0)
en = datetime.datetime(2001, 1, 25, 0, 0)
xticks = pd.date_range(start=st,end=en, freq='3H')
fig,ax=plt.subplots()
ax.plot(df.index, df['IPW'],'-o')
ax.set_xticks(xticks)
ax.invert_xaxis()
datefmt = dates.DateFormatter('%H\n%d')
ax.xaxis.set_major_formatter(datefmt)
ax.set_xlabel(r'$\Leftarrow$'+' Time [UTC]')
ax.set_ylabel('IWV [cm]')
t1='GPS-IWV at Bodega Bay'
t2='\nDate: ' + st.strftime('%b-%Y')
plt.title(t1+t2)
plt.show(block=False)
rcParams['mathtext.default'] = 'sf'
def cosd(array):
return np.cos(np.radians(array))
homedir = '/localdata'
topdf = False
case = range(13,14)
res = 'coarse'
o = 'case{}_total_wind_{}.pdf'
# surf_file = '/Users/raulvalenzuela/Data/SURFACE/case13/czc04047.met'
surf_file = '/localdata/SURFACE/case13/czc04047.met'
czd = mf.parse_surface(surf_file)
czdh = czd.preciph[~czd.preciph.isnull()]
surf_file = '/localdata/SURFACE/case13/bby04047.met'
bby = mf.parse_surface(surf_file)
bbyh = bby.preciph[~czd.preciph.isnull()]
''' creates plot with seaborn style '''
# with sns.axes_style("white"):
# sns.set_style('ticks',
# {'xtick.direction': u'in',
# 'ytick.direction': u'in'}
# )
scale=1.3
plt.figure(figsize=(8*scale, 6*scale))
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)
else:
c = center_idx[0]
return surface
def make_arrays(resolution='coarse',
surface=False,
case=None,
period=False,
homedir=None):
wpfiles = get_filenames(case, homedir=homedir + '/WINDPROF')
wp = []
ncols = 0 # number of timestamps
for f in wpfiles:
if resolution == 'fine':
wp.append(mf.parse_windprof(f, 'fine'))
elif resolution == 'coarse':
wp.append(mf.parse_windprof(f, 'coarse'))
else:
print 'Error: resolution has to be "fine" or "coarse"'
ncols += 1
''' creates 2D arrays with spd and dir '''
nrows = len(
wp[0].HT.values) # number of altitude gates (fine same as coarse)
hgt = wp[0].HT.values
# print len(hgt)
wspd = np.empty([nrows, ncols])
wdir = np.empty([nrows, ncols])
timestamp = []
for i, p in enumerate(wp):
timestamp.append(p.timestamp)
from rass_thetav import get_thetav
from glob import glob
from rv_utilities import add_colorbar, format_xaxis, format_yaxis
homedir = os.path.expanduser('~')
for r in range(8, 14):
''' case 14 does not have RASS obs '''
print 'case '+str(r)
print '-----------------'
fs = glob(homedir+'/RASS/c'+str(r).zfill(2)+'/*.cns')
fs.sort()
for n, f in enumerate(fs):
T, Tc, Hgt, timestamp = mf.parse_rass(f)
if n == 0:
Tstack = T
Hgtstack = Hgt
ts = np.array(timestamp)
else:
Tstack = np.hstack((Tstack, T))
Hgtstack = np.hstack((Hgtstack, Hgt))
ts = np.hstack((ts, np.array(timestamp)))
thetav = get_thetav(case=r, Tv_array=Tstack,
hgt_array=Hgtstack, homedir=homedir)
titletxt = 'Case{} ({})'
fig, ax = plt.subplots(2, 1, sharex=True)
im = ax[0].imshow(Tstack, interpolation='none', origin='lower')
ax[0].set_title(titletxt.format(str(r), ts[0].strftime('%Y-%b')))
'bby': [3, 6, 9, 10, 12, 17, 26],
'czc': [3, 4, 5, 6, 8, 13, 22]
}
else:
index_field = {
'bby': [3, 4, 5, 6, 8, 13, 15],
'czc': [3, 4, 5, 6, 8, 13, 15]
}
''' make dataframe for each station '''
dfBBY = []
dfCZD = []
for f in file_met:
loc = f[-12:-9]
if loc == 'bby':
dfBBY.append(
mf.parse_surface(f, index_field[loc], name_field, elev[loc]))
elif loc == 'czc':
dfCZD.append(
mf.parse_surface(f, index_field[loc], name_field, elev[loc]))
''' concatenate dataframes when there is
more than one day of data '''
if len(dfBBY) > 1:
meteoBBY = pd.concat(dfBBY)
else:
meteoBBY = dfBBY[0]
if len(dfCZD) > 1:
meteoCZD = pd.concat(dfCZD)
else:
meteoCZD = dfCZD[0]
