#filenames = glob('./matchup_synop_radval_npp.txt')
filenames = glob('./matchup_synop_all_npp.txt')
this = MatchupAnalysis(mtype='synop')
#this = MatchupAnalysis(mtype='point')
this.get_results(filenames)
res = this.data[np.isfinite(this.data['sat'])]
# Illumination filtering:
# res = sunz_filter(res, [0, 80]) # Daytime
# res = sunz_filter(res, [90, 180]) # Night
# res = sunz_filter(res, [80, 90]) # Twilight
hist1d_plot(res, 'sat', 'PPS cloud cover', color='blue')
#synop_validation(res, './ppsval_nightime.txt')
synop_validation(res, './ppsval.txt')
x = res.date + res.delta_t.apply(lambda d: timedelta(minutes=d))
startdate = x.min()
enddate = x.max()
mydate = startdate
delta_t = timedelta(minutes=60 * 24 * 10) # 10 days
datelist = []
frequency = []
while mydate < enddate:
mydate = mydate + delta_t
a = np.where(np.logical_and(x.astype(datetime).values < mydate,
x.astype(datetime).values > mydate - delta_t),
1, 0)
frequency.append(np.repeat(a, a).sum())
r13 = np.array(res.loc[res.index, 'r13'].tolist())
ciwv = np.array(res.loc[res.index, 'ciwv'].tolist())
mask = np.logical_or(
nodata, np.logical_or(cloudy == False, ppsclear == False))
r13 = np.ma.masked_array(r13, mask=mask)
ciwv = np.ma.masked_array(ciwv, mask=mask)
y = r13
x = ciwv
plt.hexbin(x, y, cmap=mycmap)
plt.axis([xmin, xmax, ymin, ymax])
plt.title('PPS clear and Synop cloudy')
plt.xlabel('ciwv')
plt.ylabel('r13')
plt.ylim(ymax=ymax)
cb = plt.colorbar()
cb.set_label('N')
plt.savefig(plotfile)
# plt.show()
idx = res[res['r13'] > 0.5].index
idx2 = res[res['ciwv'] > 4.0].index
idx3 = res[res['sat'] >= 0].index
index = [x for x in idx if x in idx2]
index = [x for x in index if x in idx3]
save_pps = res.loc[index, 'sat']
res.loc[res[res['r13'] > 0.5].index, 'sat'] = 1
synop_validation(res, './ppsval_daytime_13boost.txt')
r13 = np.array(res.loc[res.index, 'r13'].tolist())
ciwv = np.array(res.loc[res.index, 'ciwv'].tolist())
mask = np.logical_or(nodata,
np.logical_or(cloudy == False, ppsclear == False))
r13 = np.ma.masked_array(r13, mask=mask)
ciwv = np.ma.masked_array(ciwv, mask=mask)
y = r13
x = ciwv
plt.hexbin(x, y, cmap=mycmap)
plt.axis([xmin, xmax, ymin, ymax])
plt.title('PPS clear and Synop cloudy')
plt.xlabel('ciwv')
plt.ylabel('r13')
plt.ylim(ymax=ymax)
cb = plt.colorbar()
cb.set_label('N')
plt.savefig(plotfile)
# plt.show()
idx = res[res['r13'] > 0.5].index
idx2 = res[res['ciwv'] > 4.0].index
idx3 = res[res['sat'] >= 0].index
index = [x for x in idx if x in idx2]
index = [x for x in index if x in idx3]
save_pps = res.loc[index, 'sat']
res.loc[res[res['r13'] > 0.5].index, 'sat'] = 1
synop_validation(res, './ppsval_daytime_13boost.txt')
#filenames = glob('./matchup_synop_radval_npp.txt')
filenames = glob('./matchup_synop_all_npp.txt')
this = MatchupAnalysis(mtype='synop')
#this = MatchupAnalysis(mtype='point')
this.get_results(filenames)
res = this.data[np.isfinite(this.data['sat'])]
# Illumination filtering:
# res = sunz_filter(res, [0, 80]) # Daytime
# res = sunz_filter(res, [90, 180]) # Night
# res = sunz_filter(res, [80, 90]) # Twilight
hist1d_plot(res, 'sat', 'PPS cloud cover', color='blue')
#synop_validation(res, './ppsval_nightime.txt')
synop_validation(res, './ppsval.txt')
x = res.date + res.delta_t.apply(lambda d: timedelta(minutes=d))
startdate = x.min()
enddate = x.max()
mydate = startdate
delta_t = timedelta(minutes=60 * 24 * 10) # 10 days
datelist = []
frequency = []
while mydate < enddate:
mydate = mydate + delta_t
a = np.where(
np.logical_and(
x.astype(datetime).values < mydate,
x.astype(datetime).values > mydate - delta_t), 1, 0)