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machine-learning
MLAir
Commits
2bb2a975
Commit
2bb2a975
authored
May 25, 2023
by
leufen1
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data origin era5 loads now data from TOARDB, era5_local uses former data loader from local path
parent
a435c7a3
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3 merge requests
!522
filter can now combine obs, forecast, and apriori for first iteration. Further...
,
!521
Resolve "release v2.4.0"
,
!514
Resolve "load era5 data from toar db"
Pipeline
#140225
canceled
May 25, 2023
Stage: test
Stage: docs
Stage: pages
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1
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1 changed file
mlair/data_handler/data_handler_single_station.py
+19
-19
19 additions, 19 deletions
mlair/data_handler/data_handler_single_station.py
with
19 additions
and
19 deletions
mlair/data_handler/data_handler_single_station.py
+
19
−
19
View file @
2bb2a975
...
...
@@ -375,37 +375,37 @@ class DataHandlerSingleStation(AbstractDataHandler):
:return: downloaded data and its meta data
"""
df_all
=
{}
df_era5
,
df_toar
=
None
,
None
meta_era5
,
meta_toar
=
None
,
None
df_era5
_local
,
df_toar
=
None
,
None
meta_era5
_local
,
meta_toar
=
None
,
None
if
data_origin
is
not
None
:
era5_origin
=
filter_dict_by_value
(
data_origin
,
"
era5
"
,
True
)
era5_stats
=
select_from_dict
(
statistics_per_var
,
era5_origin
.
keys
())
toar_origin
=
filter_dict_by_value
(
data_origin
,
"
era5
"
,
False
)
toar_stats
=
select_from_dict
(
statistics_per_var
,
era5_origin
.
keys
(),
filter_cond
=
False
)
assert
len
(
era5_origin
)
+
len
(
toar_origin
)
==
len
(
data_origin
)
assert
len
(
era5_stats
)
+
len
(
toar_stats
)
==
len
(
statistics_per_var
)
era5_
local_
origin
=
filter_dict_by_value
(
data_origin
,
"
era5
_local
"
,
True
)
era5_
local_
stats
=
select_from_dict
(
statistics_per_var
,
era5_
local_
origin
.
keys
())
toar_origin
=
filter_dict_by_value
(
data_origin
,
"
era5
_local
"
,
False
)
toar_stats
=
select_from_dict
(
statistics_per_var
,
era5_
local_
origin
.
keys
(),
filter_cond
=
False
)
assert
len
(
era5_
local_
origin
)
+
len
(
toar_origin
)
==
len
(
data_origin
)
assert
len
(
era5_
local_
stats
)
+
len
(
toar_stats
)
==
len
(
statistics_per_var
)
else
:
era5_origin
,
toar_origin
=
None
,
None
era5_stats
,
toar_stats
=
statistics_per_var
,
statistics_per_var
era5_
local_
origin
,
toar_origin
=
None
,
None
era5_
local_
stats
,
toar_stats
=
statistics_per_var
,
statistics_per_var
# load data
if
era5_origin
is
not
None
and
len
(
era5_stats
)
>
0
:
if
era5_
local_
origin
is
not
None
and
len
(
era5_
local_
stats
)
>
0
:
# load era5 data
df_era5
,
meta_era5
=
data_sources
.
era5
.
load_era5
(
station_name
=
station
,
stat_var
=
era5_stats
,
sampling
=
sampling
,
data_origin
=
era5_origin
)
df_era5
_local
,
meta_era5
_local
=
data_sources
.
era5
.
load_era5
(
station_name
=
station
,
stat_var
=
era5_local_stats
,
sampling
=
sampling
,
data_origin
=
era5_
local_
origin
)
if
toar_origin
is
None
or
len
(
toar_stats
)
>
0
:
# load combined data from toar-data (v2 & v1)
df_toar
,
meta_toar
=
data_sources
.
toar_data
.
download_toar
(
station
=
station
,
toar_stats
=
toar_stats
,
sampling
=
sampling
,
data_origin
=
toar_origin
)
if
df_era5
is
None
and
df_toar
is
None
:
raise
data_sources
.
toar_data
.
EmptyQueryResult
(
f
"
No data available for era5 and toar-data
"
)
if
df_era5
_local
is
None
and
df_toar
is
None
:
raise
data_sources
.
toar_data
.
EmptyQueryResult
(
f
"
No data available for era5
_local
and toar-data
"
)
df
=
pd
.
concat
([
df_era5
,
df_toar
],
axis
=
1
,
sort
=
True
)
if
meta_era5
is
not
None
and
meta_toar
is
not
None
:
meta
=
meta_era5
.
combine_first
(
meta_toar
)
df
=
pd
.
concat
([
df_era5
_local
,
df_toar
],
axis
=
1
,
sort
=
True
)
if
meta_era5
_local
is
not
None
and
meta_toar
is
not
None
:
meta
=
meta_era5
_local
.
combine_first
(
meta_toar
)
else
:
meta
=
meta_era5
if
meta_era5
is
not
None
else
meta_toar
meta
=
meta_era5
_local
if
meta_era5
_local
is
not
None
else
meta_toar
meta
.
loc
[
"
data_origin
"
]
=
str
(
data_origin
)
meta
.
loc
[
"
statistics_per_var
"
]
=
str
(
statistics_per_var
)
...
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