def data2tiles_southtrac():
tag = 'stratospheric'
df = southtrac.read(strat=1).set_index(target).sort_index()
relative_count, total_count = group(df)
return relative_count, total_count, tag
res=res
)
ace = xr.open_dataset(dircInput2+f'ACE_OCS_{target}_{postfix}.nc').to_dataframe()
ace, _ = group(
ace[ace['air'] >= 1].reset_index(),
group='OCS',
groupby_v=target,
vmin=vmin,
vmax=vmax,
res=res
)
southtrac = southtrac.read(strat=1)
southtrac, _ = group(
southtrac.reset_index(),
group='OCS',
groupby_v=target,
vmin=vmin,
vmax=vmax,
res=res
)
###############################################################################
colors = {'MIPAS' : 'firebrick',
'ACE': 'orangered',
'SOUTHTRAC': 'teal'
}
fig = plt.figure(figsize=(10,50))
import clean.clean_03 as southtrac
from matplotlib import gridspec
from scipy import stats
import plotly.io as pio
import plotly.express as px
pio.renderers.default='browser'
def group(df):
df = df[['ALT', 'LAT', 'OCS']]
alt_range = np.arange(9, 14+0.5, 0.5)
lat_range = [-70, -60, -50, -40]
output = df['OCS'].groupby([pd.cut(df['ALT'], alt_range), pd.cut(df['LAT'], lat_range)]).agg(['count', 'mean', 'std'])
output = output[output['count']>=3]
return output
df = southtrac.read(strat=1, local=1)
plot_data_p1 = group(df[(df.index.month==9) | (df.index.month==10)])
plot_data_p2 = group(df[(df.index.month==11)])
plot_data_dif = plot_data_p1['mean'] - plot_data_p2['mean']
index = [pd.Interval(-70, -60, closed='right'),
pd.Interval(-60, -50, closed='right'),
pd.Interval(-50, -40, closed='right'),]
tro_hs_v = [
dict(sep = 10.4, nov = 9.1),
dict(sep = 9.8, nov = 9.5),
dict(sep = 10.1, nov = 10.6),
]
trp_hs = dict(zip(index, tro_hs_v))
# 'p75': group.quantile(0.75),
# 'p90': group.quantile(0.9)}
groupby = 'LAT'
vmin = -90#-90
vmax = 90#90
res = 5#5
def group(df, group='OCS', groupby=groupby, vmin=vmin, vmax=vmax, res=res):
vrange = np.arange(vmin, vmax+res, res)
output = df[group].groupby([pd.cut(df[groupby], vrange)]).describe().reset_index() #apply(get_stats) .unstack()
output[groupby.casefold()] = output.apply(lambda x: x[groupby].left+res/2,axis=1)
return output
# amica
df = southtrac.read(strat=0,LAT=[-90, 90], ALT=9)
df_9oct = df[(df['flight']=='2019-10-09') | (df['flight']=='2019-10-09')] #SAL - OPH super high
ocs_9oct = group(df_9oct)
df_2nov = df[df['flight']=='2019-11-02'] #OPH - SAL super low
ocs_2nov = group(df_2nov)
def plot(df=None, ax=None, shift=0, **kwargs):
if ax is None:
ax = plt.gca()
ax.errorbar(df[groupby.casefold()]+shift,
df['mean'],
yerr=df['std'],
capsize=4,
# 'SD': group.std(),
# 'p10': group.quantile(0.1),
# 'p25': group.quantile(0.25),
# 'p75': group.quantile(0.75),
# 'p90': group.quantile(0.9)}
def group(df):
lat_range = np.arange(-60, 60+10, 10)
output = df['OCS'].groupby([pd.cut(df['LAT'], lat_range)]).describe().reset_index() #apply(get_stats) .unstack()
print(output.head())
output['lat'] = output.apply(lambda x: x['LAT'].left+5/2,axis=1)
return output
# amica
df = southtrac.read(strat=0, transfer=1, ALT=11)
def plot(label=None, df=None, ax=None, shift=0, **kwargs):
if ax is None:
ax = plt.gca()
ax.errorbar(y=df['mean'],
x=df['lat']+shift,
yerr=df['std'],
capsize=4,
label=label,
fmt='s',
#ecolor='#BDBDBD',
**kwargs
)
return ax
target = 'AGE'
vmin = 0
vmax = 60
res = 5
ace = xr.open_dataset(dircInput2+f'ACE_N2O_{target}_{postfix}.nc').to_dataframe()
ace, _ = group(
ace.reset_index(),
group='N2O',
groupby_v=target,
vmin=vmin,
vmax=vmax,
res=res
)
southtrac = southtrac.read(local=1)
southtrac, _ = group(
southtrac[southtrac.air >= 1].reset_index(),
group='UMAQS_N2O',
groupby_v=target,
vmin=vmin,
vmax=vmax,
res=res
)
###############################################################################
fig = plt.figure(figsize=(10,50))
spec = gridspec.GridSpec(nrows=2, ncols=2,height_ratios=[15,1],width_ratios=[9,1])#,height_ratios=[15,1]) width_ratios=[9,1]
ax = fig.add_subplot(spec[0,0])
# x = df.OCS
# y = df[target]
plt.rc('font', **font)
ax1 = fig.add_subplot()
plot(label='AMICA', df=stats_amica)
plot(label='MIPAS', df=stats_mipas)
plot(label='ACE', df=stats_ace)
ax1.set_xlim((0, 600) if species == 'OCS' else (0, 350))
ax1.set_ylim((vmin,vmax))
ax1.set_xlabel('OCS [ppt]' if species == 'OCS' else 'N2O [ppb]', fontweight='bold')
ax1.set_ylabel(f'{target} [month]' if target in ('AGE', 'p50') else f'{target} [%]', fontweight='bold')
plt.legend()
#plt.title(f'{tag}')
plt.show()
##########plotting##########
for target in np.intersect1d(c.ALL_AGEV_SOUTHTRAC, c.ALL_AGEV):
vrange = c.VRANGE_AGE if target == 'AGE' else c.VRANGE
vmin, vmax, res = c.VMIN, c.VMAX_AGE if target== 'AGE' else c.VMAX, c.VRES
##########data processing##########
stats_ace, tag = data2plot('ACE')
stats_mipas, tag = data2plot('MIPAS')
data_southtrac = southtrac.read(strat=1).set_index(target)[species]
data_southtrac.dropna(inplace=True)
data_southtrac.index = pd.IntervalIndex(pd.cut(data_southtrac.index, vrange))
data_southtrac.rename_axis(target, inplace=True)
stats_amica = data_southtrac.groupby(target).agg({'mean', 'std', 'count'})
plot_age()
winsound.Beep(freq, duration)