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machine-learning
MLAir
Commits
d31c0176
Commit
d31c0176
authored
4 years ago
by
leufen1
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solved lazy error, some station removals have to be fixed still
parent
9e4c6db8
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5 merge requests
!319
add all changes of dev into release v1.4.0 branch
,
!318
Resolve "release v1.4.0"
,
!317
enabled window_lead_time=1
,
!295
Resolve "data handler FIR filter"
,
!259
Draft: Resolve "WRF-Datahandler should inherit from SingleStationDatahandler"
Pipeline
#68018
passed
4 years ago
Stage: test
Stage: docs
Stage: pages
Stage: deploy
Changes
2
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1
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2 changed files
mlair/data_handler/data_handler_mixed_sampling.py
+1
-1
1 addition, 1 deletion
mlair/data_handler/data_handler_mixed_sampling.py
mlair/helpers/filter.py
+7
-7
7 additions, 7 deletions
mlair/helpers/filter.py
with
8 additions
and
8 deletions
mlair/data_handler/data_handler_mixed_sampling.py
+
1
−
1
View file @
d31c0176
...
@@ -236,7 +236,7 @@ class DataHandlerMixedSamplingWithClimateFirFilterSingleStation(DataHandlerMixed
...
@@ -236,7 +236,7 @@ class DataHandlerMixedSamplingWithClimateFirFilterSingleStation(DataHandlerMixed
def
_extract_lazy
(
self
,
lazy_data
):
def
_extract_lazy
(
self
,
lazy_data
):
_data
,
_meta
,
_input_data
,
_target_data
,
self
.
climate_filter_coeff
,
self
.
apriori
,
self
.
all_apriori
=
lazy_data
_data
,
_meta
,
_input_data
,
_target_data
,
self
.
climate_filter_coeff
,
self
.
apriori
,
self
.
all_apriori
=
lazy_data
DataHandlerSingleStation
.
_extract_lazy
(
self
,
(
_data
,
_meta
,
_input_data
,
_target_data
))
DataHandler
MixedSamplingWithFilter
SingleStation
.
_extract_lazy
(
self
,
(
_data
,
_meta
,
_input_data
,
_target_data
))
@staticmethod
@staticmethod
def
_get_fs
(
**
kwargs
):
def
_get_fs
(
**
kwargs
):
...
...
This diff is collapsed.
Click to expand it.
mlair/helpers/filter.py
+
7
−
7
View file @
d31c0176
...
@@ -397,7 +397,7 @@ class ClimateFIRFilter:
...
@@ -397,7 +397,7 @@ class ClimateFIRFilter:
# filt = xr.apply_ufunc(fir_filter_vectorized, filter_input_data, time_axis,
# filt = xr.apply_ufunc(fir_filter_vectorized, filter_input_data, time_axis,
# input_core_dims=[[new_dim], []], output_core_dims=[[new_dim]], vectorize=True,
# input_core_dims=[[new_dim], []], output_core_dims=[[new_dim]], vectorize=True,
# kwargs=kwargs)
# kwargs=kwargs)
#
with TimeTracking(name="convolve"):
with
TimeTracking
(
name
=
"
convolve
"
):
slicer
=
slice
(
int
(
-
(
length
-
1
)
/
2
),
int
((
length
-
1
)
/
2
))
slicer
=
slice
(
int
(
-
(
length
-
1
)
/
2
),
int
((
length
-
1
)
/
2
))
filt
=
xr
.
apply_ufunc
(
fir_filter_convolve_vectorized
,
filter_input_data
.
sel
({
new_dim
:
slicer
}),
filt
=
xr
.
apply_ufunc
(
fir_filter_convolve_vectorized
,
filter_input_data
.
sel
({
new_dim
:
slicer
}),
input_core_dims
=
[[
new_dim
]],
input_core_dims
=
[[
new_dim
]],
...
...
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Click to expand it.
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