Analysis of the new datasets¶
In [1]:
import xarray as xr
import xarray.ufuncs as xrf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import glob
from matplotlib import colors
from definitions import * # imports all functions from definitions.py
from bootstrap_sampling import *
xr.set_options(
keep_attrs=True, display_style="html"
) # Attributes of DataArrays will be kept through operations.
plt.rcParams["font.size"] = "22"
alt = [
70,
72,
74,
76,
78,
80,
82,
84,
86,
88,
90,
92,
94,
96,
98,
100,
102,
104,
106,
108,
110,
112,
114,
116,
118,
120,
]
root_path = "/home/gemeinsam_tmp/UA_students/data/PW_GW_analysis/"
dir_path = "/home/hochatmstud/bene/"
In [2]:
station = "Leipzig"
Wind measurements¶
In [3]:
ds_wind = read_group("wind", station)
u = read_var(ds_wind, "u0")
v = read_var(ds_wind, "v0")
u_anomalie = anomalie("dayofyear", u)
v_anomalie = anomalie("dayofyear", v)
sea_u = sea(40, station, u)
sea_u_anomalie = sea(40, station, u_anomalie)
sea_v = sea(40, station, v)
sea_v_anomalie = sea(40, station, v_anomalie)
/home/hochatmstud/.conda/envs/uas/lib/python3.6/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice return np.nanmean(a, axis=axis, dtype=dtype) /home/hochatmstud/.conda/envs/uas/lib/python3.6/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice return np.nanmean(a, axis=axis, dtype=dtype) /home/hochatmstud/.conda/envs/uas/lib/python3.6/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice return np.nanmean(a, axis=axis, dtype=dtype)
Zonal wind component¶
Climatology¶
In [4]:
plotting_routine(sea_u, "u0")
Anomalies¶
In [5]:
# plotting_routine(sea_u_anomalie,'u0')
stat_signific(station, sea_u_anomalie, u_anomalie, "u0")
/home/hochatmstud/.conda/envs/uas/lib/python3.6/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice return np.nanmean(a, axis=axis, dtype=dtype)
Meridional wind component¶
Climatology¶
In [6]:
plotting_routine(sea_v, "v0")
Anomalies¶
In [7]:
# plotting_routine(sea_v_anomalie,'v0')
stat_signific(station, sea_v_anomalie, v_anomalie, "v0")
Wave estimates¶
In [8]:
ds_waves = read_group("waves", station)
gw = read_var(ds_waves, "GW")
pw = read_var(ds_waves, "PW")
gw_anomalie = anomalie("dayofyear", gw)
pw_anomalie = anomalie("dayofyear", pw)
sea_gw = sea(40, station, gw)
# sea_gw.name = 'sea_gw'
sea_gw_anomalie = sea(40, station, gw_anomalie)
sea_gw_anomalie["name"] = "sea_gw_anomalie"
sea_pw = sea(40, station, pw)
# sea_pw.name = 'sea_pw'
sea_pw_anomalie = sea(40, station, pw_anomalie)
sea_pw_anomalie["name"] = "sea_pw_anomalie"
/home/hochatmstud/.conda/envs/uas/lib/python3.6/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice return np.nanmean(a, axis=axis, dtype=dtype) /home/hochatmstud/.conda/envs/uas/lib/python3.6/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice return np.nanmean(a, axis=axis, dtype=dtype)
Gravity waves¶
Gravity wave activity¶
Climatology¶
In [9]:
plotting_routine(sea_gw, "GW", True)
Anomalies¶
In [ ]:
In [10]:
# plotting_routine(sea_gw_anomalie,'GW')
stat_signific(station, sea_gw_anomalie, gw_anomalie, "GW")
Gravity wave drag¶
In [11]:
directory = glob.glob(f"{root_path}GWD*_{station}.nc")[0]
ds = xr.open_dataset(directory)
gwd = ds["GWD"]
gwd_anomalie = anomalie("dayofyear", gwd)
sea_gwd = sea(40, station, gwd)
# sea_gwd.name = 'sea_gwd'
sea_gwd_anomalie = sea(40, station, gwd_anomalie)
/home/hochatmstud/.conda/envs/uas/lib/python3.6/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice return np.nanmean(a, axis=axis, dtype=dtype) /home/hochatmstud/.conda/envs/uas/lib/python3.6/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice return np.nanmean(a, axis=axis, dtype=dtype)
Climatology¶
In [12]:
plotting_routine(sea_gwd, "GWD")
Anomalies¶
In [13]:
sea_gwd_anomalie.sel(alt=slice(80, 100))["GWD_mean"].dropna("days", how="all").plot(
x="days"
)
Out[13]:
<matplotlib.collections.QuadMesh at 0x7f9698ba7c50>
In [14]:
gwd_anomalie
Out[14]:
xarray.DataArray
'GWD'
- time: 143928
- alt: 26
- nan nan nan nan nan nan 204.8 166.7 ... nan nan nan nan nan nan nan
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - alt(alt)int6470 72 74 76 78 ... 114 116 118 120
- units :
- km
array([ 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120]) - month(time)int648 8 8 8 8 8 8 ... 12 12 12 12 12 12
array([ 8, 8, 8, ..., 12, 12, 12])
- time(time)datetime64[ns]2004-08-01 ... 2020-12-31T23:00:00
array(['2004-08-01T00:00:00.000000000', '2004-08-01T01:00:00.000000000', '2004-08-01T02:00:00.000000000', ..., '2020-12-31T21:00:00.000000000', '2020-12-31T22:00:00.000000000', '2020-12-31T23:00:00.000000000'], dtype='datetime64[ns]') - dayofyear(time)int64214 214 214 214 ... 366 366 366 366
array([214, 214, 214, ..., 366, 366, 366])
- units :
- m/s/day
- long_name :
- zonal GWF from fluxes
In [19]:
from resampling import _resample_iterations_idx
In [89]:
rnd_arr = _resample_iterations_idx(
u_anomalie, 10000, "time", replace=True, chunk=False, dim_max=7
)
/home/hochatmstud/.conda/envs/uas/lib/python3.6/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice return np.nanmean(a, axis=axis, dtype=dtype)
In [91]:
bins = rnd_arr.sel(alt=90).plot.hist(density=True, bins=21)
x = bins[1]
plt.plot(x, kde(x), lw=4)
Out[91]:
[<matplotlib.lines.Line2D at 0x7f9695f0b390>]
In [96]:
gwd_anomalie.sel(alt=90).dropna("time")
Out[96]:
xarray.DataArray
'GWD'
- time: 141351
- 112.4 117.4 118.0 118.7 124.6 ... -651.1 -652.0 -657.7 -663.9 -666.1
array([ 112.40663859, 117.38937383, 118.0472012 , ..., -657.68522347, -663.94046979, -666.08866837]) - alt()int6490
- units :
- km
array(90)
- month(time)int648 8 8 8 8 8 8 ... 10 10 10 10 10 10
array([ 8, 8, 8, ..., 10, 10, 10])
- time(time)datetime64[ns]2004-08-01 ... 2020-10-18T20:00:00
array(['2004-08-01T00:00:00.000000000', '2004-08-01T01:00:00.000000000', '2004-08-01T02:00:00.000000000', ..., '2020-10-18T18:00:00.000000000', '2020-10-18T19:00:00.000000000', '2020-10-18T20:00:00.000000000'], dtype='datetime64[ns]') - dayofyear(time)int64214 214 214 214 ... 292 292 292 292
array([214, 214, 214, ..., 292, 292, 292])
- units :
- m/s/day
- long_name :
- zonal GWF from fluxes
In [126]:
mask = np.isin(gwd_anomalie.time.dt.month, [12, 1, 2])
mask
Out[126]:
array([False, False, False, ..., True, True, True])
In [139]:
temp = gwd_anomalie.sel(alt=90, time=mask).dropna("time")
temp.plot.hist(density=True, bins=21)
Out[139]:
(array([4.11376124e-05, 1.39410798e-04, 3.03504162e-04, 2.75164918e-04,
4.92280095e-04, 6.59573052e-04, 1.10523052e-03, 1.93849571e-03,
1.96957746e-03, 1.63636280e-03, 1.50152285e-03, 1.79725658e-03,
1.19801869e-03, 9.04113303e-04, 5.68613220e-04, 3.37328422e-04,
3.65667666e-04, 2.81107018e-04, 1.57237096e-04, 1.19299076e-04,
4.98222194e-05]),
array([-621.54987583, -558.42144523, -495.29301463, -432.16458402,
-369.03615342, -305.90772282, -242.77929222, -179.65086161,
-116.52243101, -53.39400041, 9.7344302 , 72.8628608 ,
135.9912914 , 199.119722 , 262.24815261, 325.37658321,
388.50501381, 451.63344441, 514.76187502, 577.89030562,
641.01873622, 704.14716683]),
<a list of 21 Patch objects>)
In [140]:
temp.std()
Out[140]:
xarray.DataArray
'GWD'
- 226.3
array(226.26871603)
- alt()int6490
- units :
- km
array(90)
- units :
- m/s/day
- long_name :
- zonal GWF from fluxes
In [143]:
temp.median()
Out[143]:
xarray.DataArray
'GWD'
- -16.66
array(-16.66032727)
- alt()int6490
- units :
- km
array(90)
- units :
- m/s/day
- long_name :
- zonal GWF from fluxes
In [137]:
gwd_anomalie.sel(alt=90, time=mask).dropna("time").min()
Out[137]:
xarray.DataArray
'GWD'
- -621.5
array(-621.54987583)
- alt()int6490
- units :
- km
array(90)
- units :
- m/s/day
- long_name :
- zonal GWF from fluxes
In [129]:
rnd_arr = _resample_iterations_idx(
gwd_anomalie.sel(alt=90, time=mask).dropna("time"),
10000,
"time",
replace=True,
chunk=False,
dim_max=7,
)
In [130]:
bins = rnd_arr.plot.hist(density=True, bins=121)
x = bins[1]
# plt.plot(x, kde(x), lw = 4)
In [108]:
plt.plot(sorted(rnd_arr.values))
Out[108]:
[<matplotlib.lines.Line2D at 0x7f96951ff080>]
In [109]:
sorted(rnd_arr.values)[950]
Out[109]:
159.34056796834292
In [68]:
sum(kde(x))
Out[68]:
0.03681878805905249
In [119]:
density = bins[0]
widths = bins[1][1:] - bins[1][:-1]
(density * widths).sum()
Out[119]:
0.9999999999999999
In [63]:
kde(bins[1])
Out[63]:
array([4.60158876e-05, 8.39539678e-05, 1.77245661e-04, 3.17387348e-04,
6.27326636e-04, 1.12910039e-03, 1.51600026e-03, 1.91703774e-03,
2.59364554e-03, 3.39852309e-03, 3.87467384e-03, 3.89062727e-03,
3.72382960e-03, 3.55480080e-03, 3.00138476e-03, 2.30099679e-03,
1.82564957e-03, 1.36982865e-03, 8.38975235e-04, 3.95073172e-04,
1.68956118e-04, 6.77557316e-05])
In [49]:
y = rnd_arr.sel(alt=90)
mask = np.isnan(y)
np.count_nonzero(mask)
Out[49]:
0
In [52]:
rnd_arr.sel(alt=90).dropna("iteration")
Out[52]:
xarray.DataArray
- iteration: 1000
- -37.72 29.35 -151.8 -37.07 -9.832 ... 98.35 141.6 61.61 29.44 37.2
array([-3.77157959e+01, 2.93520800e+01, -1.51821136e+02, -3.70703403e+01, -9.83205540e+00, 5.72697300e+01, -1.49033552e+02, 4.68576514e+01, -8.01606144e+00, 4.71952455e+01, -3.78311338e+01, -1.03581313e+01, 7.78780905e+01, -3.88187467e+01, 1.18626733e+02, -2.21664879e+01, 6.50756482e+01, 5.01509457e+01, 1.95985758e+02, -6.66767980e+01, 2.50835459e+01, 1.29671828e+02, -6.85277481e+01, -2.50005599e+01, 1.91769195e+01, -1.19299767e+02, -1.31149367e+02, -3.33073892e+01, -3.64576928e+01, -1.94433086e+01, -4.79939025e+01, -1.36533786e+02, 1.53493514e+02, 2.46966417e+01, 6.26461567e+01, -3.15024323e+01, -1.71216127e+02, -4.37510544e+00, -7.06674075e+01, -3.05612224e+02, -1.76323401e+02, -1.05799045e+02, 1.61429865e+02, -1.01952969e+02, 1.23965181e+02, 4.19997534e+01, -5.23669609e+01, 4.40450362e+00, -1.20618643e+02, -9.07368011e+01, 5.21253149e+01, 2.01738569e+01, -1.44100034e+02, 1.39786294e+01, 1.74427485e+00, -4.51973326e+01, 1.96206422e+02, 4.27260497e+01, -1.27002712e+00, 1.67648225e+02, -6.10914796e+01, 6.54617746e+00, -1.18859497e+02, -1.54150559e+02, 1.40328833e+02, -5.13645571e+01, 4.80098415e+01, -1.35695907e+01, -4.70011084e+01, -6.63231295e+01, -1.72982401e+02, -2.12878033e+02, 1.13447863e+01, -2.05397669e+02, -6.56500729e+01, 6.72478691e+01, -4.89531163e+01, -7.12718935e+01, -8.06036798e+01, -1.39105458e+02, -9.54756384e+01, 2.27132102e+01, 5.59043978e+01, 1.06900612e+02, 2.06994366e+01, -1.02632081e+02, -2.78589612e+01, -1.69780167e+01, 1.89127561e+02, 1.85201507e+02, 1.87790468e+02, -1.73868596e+02, -7.68428841e+01, -4.81521954e+01, 9.34312542e+00, 7.92205764e+01, 1.33203776e+01, 7.56670272e+00, -2.35497678e+01, 6.10499579e+01, 1.99716299e+01, 2.46663091e+02, 7.09807285e+00, 5.58204222e+01, -1.39344692e+02, 2.54856634e+01, -7.81228823e+01, 5.53387378e+01, -1.78919468e+02, 1.65135697e+02, -1.61834514e+00, 2.35180981e+02, 1.04539183e+02, 3.98127593e+00, 1.03299026e+02, -1.73498539e+01, 1.47939161e+02, 4.17781278e+01, -1.61297816e+01, -1.43161942e+01, -1.99842564e+02, -5.35210407e+01, -1.67553042e+02, -4.89073200e+01, -6.69276671e+01, 1.41549365e+02, -7.63807608e+01, 1.31495438e+02, -1.86258099e+02, -2.27948748e+00, 9.49742567e+01, -2.88412069e+01, -3.29873769e+01, -5.35247662e+01, -7.97614340e+01, 2.97216132e+01, 2.48978095e+01, -9.46683757e+01, 6.02615884e+01, 1.92747498e+01, -3.59043462e+01, -1.67056174e+02, 1.27243075e+02, -1.89113287e+02, 1.91896007e+01, -8.11622845e+01, -9.48382194e+00, 2.01130913e+01, -2.50635696e+01, 3.29228361e+00, -7.81579169e+01, -1.90580286e+01, 1.46475540e+02, -1.78189580e+01, 4.68922769e+01, 8.07519554e+01, 1.24397410e+02, 5.22693426e+01, 6.93435894e+01, -1.26321170e+01, 1.72338450e+02, -4.52453297e+00, 2.38933864e+02, 1.90495229e+02, -1.09068596e+01, -5.19044792e+01, -3.05864696e+01, -1.14987276e+02, -5.32251008e+01, 7.96205355e+01, 1.29062460e+02, -1.19297084e+01, -1.08003327e+02, -1.40581968e+01, 4.53003077e+01, -1.84903392e+01, -1.14254056e+02, -5.72805778e+00, 1.02047922e+02, 1.76161673e+02, 7.13269731e+01, -8.89701232e+01, 1.19142153e+02, -1.07473012e+01, -4.61078183e+01, 2.18638293e+01, -5.30480905e+01, -4.05545320e+00, 3.44943747e+01, -1.05466644e+01, -5.17940327e+01, 1.73091929e+01, -1.14797373e+02, 4.60024303e+01, 2.52223500e+01, 1.50856957e+01, -3.98701428e+01, 1.93189559e+01, -8.96845444e+01, 1.26005687e+01, 8.76302611e+01, -1.43361640e+02, 1.35912416e+01, -1.47598864e+02, -2.40013592e+01, -1.23096236e+02, -9.06933507e+01, -9.65461639e+00, 9.71474275e-01, 8.88055758e+01, -1.37449766e+02, 7.61669661e+01, -6.40674989e+01, 1.31995589e+02, -7.69696877e+01, -7.07856264e+01, 1.18611159e+02, 1.19422610e+02, -5.65887255e+01, -1.88954917e+01, -4.86872495e+01, -8.02992324e+01, -7.91350991e+01, 4.16180028e+01, 2.50029665e+01, 5.07563192e+01, -6.54457775e+01, -8.41176740e+01, -3.30515040e+01, 7.76505449e+01, 1.17214975e+02, -4.11627694e+01, -1.80781477e+01, 3.66098948e+01, 1.48793758e-01, -9.97901057e+01, -5.44018792e-01, -1.00821527e+01, -6.85639629e+01, 1.32575095e+02, 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- units :
- km
array(90)
- iteration(iteration)int640 1 2 3 4 5 ... 995 996 997 998 999
array([ 0, 1, 2, ..., 997, 998, 999])
In [50]:
mask
Out[50]:
xarray.DataArray
- iteration: 1000
- False False False False False False ... False False False False False
array([False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, 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False]) - alt()int6490
- units :
- km
array(90)
- iteration(iteration)int640 1 2 3 4 5 ... 995 996 997 998 999
array([ 0, 1, 2, ..., 997, 998, 999])
In [32]:
rnd_arr.sel(alt=90).quantile(0.95)
Out[32]:
xarray.DataArray
- 157.7
array(157.72614045)
- quantile()float640.95
array(0.95)
In [46]:
rnd_arr.sel(alt=90).quantile(0.05)
Out[46]:
xarray.DataArray
- -167.1
array(-167.08101786)
- quantile()float640.05
array(0.05)
In [45]:
sea_gwd_anomalie.sel(alt=90, days=0)["GWD_mean"]
Out[45]:
xarray.DataArray
'GWD_mean'
- -11.59
array(-11.58637319)
- alt()int6490
- units :
- km
array(90)
- days()int640
array(0)
- units :
- m/s/day
- long_name :
- zonal GWF from fluxes
In [30]:
sea_gwd_anomalie.sel(alt=90)["GWD_mean"].plot()
Out[30]:
[<matplotlib.lines.Line2D at 0x7f969658e048>]
In [34]:
rnd_arr
Out[34]:
xarray.DataArray
- alt: 26
- iteration: 1000
- nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]) - alt(alt)int6470 72 74 76 78 ... 114 116 118 120
- units :
- km
array([ 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120]) - iteration(iteration)int640 1 2 3 4 5 ... 995 996 997 998 999
array([ 0, 1, 2, ..., 997, 998, 999])
In [37]:
sea_gwd_anomalie.sel(days=0, alt=slice(80, 100))["GWD_mean"]
Out[37]:
xarray.DataArray
'GWD_mean'
- alt: 11
- -101.7 33.3 85.9 67.76 11.7 -11.59 -67.31 -199.1 -174.3 -326.7 nan
array([-101.71251428, 33.29621084, 85.89718485, 67.76356449, 11.70194394, -11.58637319, -67.30789868, -199.12337665, -174.34816066, -326.70354413, nan]) - alt(alt)int6480 82 84 86 88 90 92 94 96 98 100
- units :
- km
array([ 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100])
- days()int640
array(0)
- units :
- m/s/day
- long_name :
- zonal GWF from fluxes
In [38]:
da_kde = xr.apply_ufunc(
g_kde,
rnd_arr.sel(alt=slice(80, 100)),
sea_gwd_anomalie.sel(days=0, alt=slice(80, 100))["GWD_mean"],
input_core_dims=[["iteration"], []],
vectorize=True,
dask="parallelized",
exclude_dims=set(("iteration",)),
output_core_dims=[[]],
output_dtypes=[rnd_arr.dtype],
)
In [42]:
rnd_arr.sel(alt=90)
Out[42]:
xarray.DataArray
- iteration: 1000
- -37.72 29.35 -151.8 -37.07 -9.832 ... 98.35 141.6 61.61 29.44 37.2
array([-3.77157959e+01, 2.93520800e+01, -1.51821136e+02, -3.70703403e+01, -9.83205540e+00, 5.72697300e+01, -1.49033552e+02, 4.68576514e+01, -8.01606144e+00, 4.71952455e+01, -3.78311338e+01, -1.03581313e+01, 7.78780905e+01, -3.88187467e+01, 1.18626733e+02, -2.21664879e+01, 6.50756482e+01, 5.01509457e+01, 1.95985758e+02, -6.66767980e+01, 2.50835459e+01, 1.29671828e+02, -6.85277481e+01, -2.50005599e+01, 1.91769195e+01, -1.19299767e+02, -1.31149367e+02, -3.33073892e+01, -3.64576928e+01, -1.94433086e+01, -4.79939025e+01, -1.36533786e+02, 1.53493514e+02, 2.46966417e+01, 6.26461567e+01, -3.15024323e+01, -1.71216127e+02, -4.37510544e+00, -7.06674075e+01, -3.05612224e+02, -1.76323401e+02, -1.05799045e+02, 1.61429865e+02, -1.01952969e+02, 1.23965181e+02, 4.19997534e+01, -5.23669609e+01, 4.40450362e+00, -1.20618643e+02, -9.07368011e+01, 5.21253149e+01, 2.01738569e+01, -1.44100034e+02, 1.39786294e+01, 1.74427485e+00, -4.51973326e+01, 1.96206422e+02, 4.27260497e+01, -1.27002712e+00, 1.67648225e+02, 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4.55030135e+01, -8.38865400e+01, -3.75587454e+01, 2.03607920e+02, -2.15961971e+02, 1.17889211e+02, -7.89811870e+01, -1.53581650e+02, -3.26915156e+01, -7.67387695e+01, -1.65416182e+02, -1.64367325e+02, 2.43955592e+01, -1.97416798e+01, 6.81781797e+01, -9.34934542e+00, -2.75692657e+02, -1.59926320e+02, 2.16304718e+02, 1.62861266e+02, -2.31223730e+01, 1.02034314e+02, 9.04996587e+01, -1.15103425e+02, 4.35393435e+01, -8.58685587e+00, 1.36472557e+02, -1.18917109e+02, -1.75408741e+02, 1.90443093e+02, -4.50704398e+01, -6.85546839e+01, -1.63963006e+01, 4.79760578e+01, 9.83525317e+01, 1.41562198e+02, 6.16072017e+01, 2.94351245e+01, 3.71995161e+01]) - alt()int6490
- units :
- km
array(90)
- iteration(iteration)int640 1 2 3 4 5 ... 995 996 997 998 999
array([ 0, 1, 2, ..., 997, 998, 999])
In [47]:
da_kde.sel(alt=90)
Out[47]:
xarray.DataArray
- 0.003912
array(0.00391162)
- alt()int6490
- units :
- km
array(90)
- days()int640
array(0)
In [41]:
da_kde.plot(y="alt")
Out[41]:
[<matplotlib.lines.Line2D at 0x7f9696122748>]
In [74]:
rnd_arr.sel(alt=90).dropna("iteration")
Out[74]:
xarray.DataArray
- iteration: 1000
- -37.72 29.35 -151.8 -37.07 -9.832 ... 98.35 141.6 61.61 29.44 37.2
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-7.02261404e+01, -2.43934216e+02, 4.22577992e+00, 1.17027932e+00, 5.78603868e+01, -7.88810493e+01, 9.06382019e+01, 6.31374008e+01, 6.42352348e+01, 6.50100731e+01, -6.78147888e+01, 5.70443770e+01, 1.72479621e+02, 5.96254187e+01, -1.15879014e+02, 3.60901767e+01, -5.84817260e+01, -7.18963682e+01, 1.14006360e+01, -2.92764783e+01, 8.09544968e+01, -2.25479636e+02, 3.85035239e+01, -3.81820356e-01, 9.28112393e+01, 2.36220904e+02, 5.68498536e+01, 1.05302698e+02, -4.56765061e+01, -1.44775965e+02, 9.62694407e+01, -2.07984371e+01, -6.23686366e+01, -3.01172271e+01, 9.85831723e+01, 4.61725075e+00, -2.39198853e+02, 2.06468104e+01, 1.22749763e+02, -3.17667129e+01, -6.78691367e+00, 7.52465346e+01, -1.15903943e+02, -4.07250497e+01, 6.22801247e+01, -1.01164052e+02, 4.86891359e+01, -4.34017127e+01, 2.20647031e+02, 4.98874783e+01, -1.80501378e+02, -2.95339890e+01, -3.10178742e+01, -1.71574749e+02, 3.68061729e+01, 7.70320419e+01, -4.88841325e+01, -5.75240901e+00, 6.55382907e+01, -8.80849439e+00, 4.55030135e+01, -8.38865400e+01, -3.75587454e+01, 2.03607920e+02, -2.15961971e+02, 1.17889211e+02, -7.89811870e+01, -1.53581650e+02, -3.26915156e+01, -7.67387695e+01, -1.65416182e+02, -1.64367325e+02, 2.43955592e+01, -1.97416798e+01, 6.81781797e+01, -9.34934542e+00, -2.75692657e+02, -1.59926320e+02, 2.16304718e+02, 1.62861266e+02, -2.31223730e+01, 1.02034314e+02, 9.04996587e+01, -1.15103425e+02, 4.35393435e+01, -8.58685587e+00, 1.36472557e+02, -1.18917109e+02, -1.75408741e+02, 1.90443093e+02, -4.50704398e+01, -6.85546839e+01, -1.63963006e+01, 4.79760578e+01, 9.83525317e+01, 1.41562198e+02, 6.16072017e+01, 2.94351245e+01, 3.71995161e+01]) - alt()int6490
- units :
- km
array(90)
- iteration(iteration)int640 1 2 3 4 5 ... 995 996 997 998 999
array([ 0, 1, 2, ..., 997, 998, 999])
In [77]:
import seaborn as sns
from scipy.stats import norm
In [79]:
sns.kdeplot(
data=rnd_arr.sel(alt=90),
cumulative=True,
)
Out[79]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f9695a140f0>
In [85]:
sns.kdeplot(data=rnd_arr.sel(alt=90), cumulative=False)
Out[85]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f9695871d30>
In [78]:
sns.distplot(rnd_arr.sel(alt=90), hist=False, rug=True, kde=False, fit=norm)
Out[78]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f9695ad59e8>
In [76]:
sns.distplot(
rnd_arr.sel(alt=90),
hist=False,
rug=True,
axlabel="Something ?",
kde_kws=dict(label="kde"),
rug_kws=dict(height=0.2, linewidth=2, color="C1", label="data"),
)
plt.legend()
In [70]:
kde = stats.gaussian_kde(rnd_arr.sel(alt=90).values, bw_method="silverman")
out = kde(sea_gwd_anomalie.sel(days=0, alt=90)["GWD_mean"])
In [69]:
kde.pdf(sea_gwd_anomalie.sel(days=0, alt=90)["GWD_mean"])
Out[69]:
array([0.00391162])
In [71]:
out
Out[71]:
array([0.00389903])
In [15]:
# plotting_routine(sea_gwd_anomalie,'GWD')
stat_signific(station, sea_gwd_anomalie, gwd_anomalie, "GWD")
Planetary waves¶
Climatology¶
In [16]:
plotting_routine(sea_pw, "PW", True)
Anomalies¶
In [17]:
# plotting_routine(sea_pw_anomalie,'PW')
stat_signific(station, sea_pw_anomalie, pw_anomalie, "PW")
