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screenlog.0
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bash: /ascldap/users/merbrow/.bashrc: line 33: syntax error in conditional expression
bash: /ascldap/users/merbrow/.bashrc: line 34: syntax error near `then'
bash: /ascldap/users/merbrow/.bashrc: line 34: `then'
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(base) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(base) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(base) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(base) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(base) [merbrow@cee-compute005 RFR-CLDERA]$ conda activate py37
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ python [K[K[K[K[K[K[Kls -tr
plot_weighted_graph.ipynb CreateSystemEqs.ipynb
[0m[01;35mFullConnect.png[0m GlobalHSW_AOD_T050_T1000_weights.txt
RF_Run_Netcdf.ipynb GlobalHSW_AOD_T050_T1000_weights.xls
RF_Run_SOE-MULTIVARATE.ipynb GlobalHSW_AOD_T050_T1000_weights.xlsm
test.ipynb Global_LV_all.csv
RF_Run_SyntheticSpatial-MULTIVARATE.ipynb Global_LV_all.xlsm
RF_Run_Synthetic-MULTIVARATE.ipynb [01;35mHSW_Zonal_Mean.png[0m
Zonal_RFR_meanT1000.csv [01;35mHSW_Zonal_Mean_NORM.png[0m
Zonal_RFR_6hourmeans_T1000.csv [01;35mHSW_Zonal_Mean_NORM_Pruned.png[0m
Zonal_RFR_dailymeans_T1000.csv [01;35mHSW_Zonal_Mean_Pruned.png[0m
ZonalHSW_AOD_T050_dailymax.csv LVstats.xlsx
Zonal_RFR-Copy1.ipynb MeanFeatureImportance.ipynb
ZonalHSW_T1000_dailymax.csv README.md
ZonalHSW_T050_dailymax.csv RFR.py
ZonalHSW_AOD_dailymax.csv RFR_mglb.py
ZonalHSW_AOD_dailymax_global.csv RF_Run_SOE.ipynb
ZonalHSW_T050_dailymax_global.csv RF_Run_Synthetic.ipynb
ZonalHSW_T1000_dailymax_global.csv RF_Run_SyntheticSpatial.ipynb
untitled.txt Simple.txt
[01;34mPapers[0m ZonalFunctions.py
untitled1.txt ZonalHSW_AOD_T050_T1000_dailymax.csv
HSWpreprocessing-global.ipynb ZonalHSW_AOD_T050_T1000_dailymax_global.csv
HSWpreproc-Copy1.py ZonalHSW_AOD_T050_T1000_dailymax_norm.csv
Untitled.ipynb ZonalHSW_LV_AOD_T050_T1000.csv
HSWpreproc.py~ ZonalHSW_LV_AOD_T050_T1000.csv~
HSWpreproc.py ZonalHSW_LV_AOD_T050_weights.xlsx
[01;34mpreproc_output[0m ZonalHSW_LV_AOD_dailymax.csv
Zonal_RFR.mglb.230519.ipynb Zonal_RFR-Individual-EnsembleAvg.ipynb
~$ZonalHSW_LV_AOD_T050_weights.xlsx Zonal_RFR-Individual.ipynb
[01;34mGlobalFigs[0m Zonal_RFR.ipynb
[01;34mZonalFigs[0m Zonal_RFR_iterate.py
~$GlobalHSW_AOD_T050_T1000_weights.xlsm [01;34mZonalHSW_LV_weights[0m
[01;34mOLD_code[0m Zonal_RFR-new.ipynb
[01;34mNormalized230608[0m HSW_preproc_LV_run.py
[01;34mSHAPoutputs[0m HSWpreprocessing.ipynb
[01;34m__pycache__[0m screenlog.0
[01;35mAllNodes.png[0m
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ python HSWP[K_preproc_LV_run.py
^Z
[1]+ Stopped python HSW_preproc_LV_run.py
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ python HSW_preproc_LV_run.py &
[2] 730288
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ screen --h/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/backends/plugins.py:65: RuntimeWarning: Engine 'rasterio' loading failed:
libcrypto.so.1.0.0: cannot open shared object file: No such file or directory
warnings.warn(f"Engine {name!r} loading failed:\n{ex}", RuntimeWarning)
elp
Use: screen [-opts] [cmd [args]]
or: screen -r [host.tty]
Options:
-4 Resolve hostnames only to IPv4 addresses.
-6 Resolve hostnames only to IPv6 addresses.
-a Force all capabilities into each window's termcap.
-A -[r|R] Adapt all windows to the new display width & height.
-c file Read configuration file instead of '.screenrc'.
-d (-r) Detach the elsewhere running screen (and reattach here).
-dmS name Start as daemon: Screen session in detached mode.
-D (-r) Detach and logout remote (and reattach here).
-D -RR Do whatever is needed to get a screen session.
-e xy Change command characters.
-f Flow control on, -fn = off, -fa = auto.
-h lines Set the size of the scrollback history buffer.
-i Interrupt output sooner when flow control is on.
-l Login mode on (update /var/run/utmp), -ln = off.
-ls [match] or
-list Do nothing, just list our SockDir [on possible matches].
-L Turn on output logging.
-m ignore $STY variable, do create a new screen session.
-O Choose optimal output rather than exact vt100 emulation.
-p window Preselect the named window if it exists.
-q Quiet startup. Exits with non-zero return code if unsuccessful.
-Q Commands will send the response to the stdout of the querying process.
-r [session] Reattach to a detached screen process.
-R Reattach if possible, otherwise start a new session.
-s shell Shell to execute rather than $SHELL.
-S sockname Name this session <pid>.sockname instead of <pid>.<tty>.<host>.
-t title Set title. (window's name).
-T term Use term as $TERM for windows, rather than "screen".
-U Tell screen to use UTF-8 encoding.
-v Print "Screen version 4.01.00devel (GNU) 2-May-06".
-wipe [match] Do nothing, just clean up SockDir [on possible matches].
-x Attach to a not detached screen. (Multi display mode).
-X Execute <cmd> as a screen command in the specified session.
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ screen -r
There is a screen on:
720565.pts-11.cee-compute005 (Attached)
There is no screen to be resumed.
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ screen -D
[720565.pts-11.cee-compute005 power detached.]
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ /ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
0%| | 0/20 [00:00<?, ?it/s] 0%| | 0/20 [00:37<?, ?it/s]
Traceback (most recent call last):
File "HSW_preproc_LV_run.py", line 145, in <module>
daily_data['Globe_'+ens+'_'+QOI] = daily_globe[i,].values
NameError: name 'daily_data' is not defined
[K(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\[2]- Segmentation fault python HSW_preproc_LV_run.py
(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ jnb &
[2] 85371
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ usage: jupyter [-h] [--version] [--config-dir] [--data-dir] [--runtime-dir]
[--paths] [--json] [--debug]
[subcommand]
Jupyter: Interactive Computing
positional arguments:
subcommand the subcommand to launch
optional arguments:
-h, --help show this help message and exit
--version show the versions of core jupyter packages and exit
--config-dir show Jupyter config dir
--data-dir show Jupyter data dir
--runtime-dir show Jupyter runtime dir
--paths show all Jupyter paths. Add --json for machine-readable
format.
--json output paths as machine-readable json
--debug output debug information about paths
Available subcommands: kernel kernelspec migrate run troubleshoot
Jupyter command `jupyter-notebook` not found.
exit
exit
There are stopped jobs.
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\[2]- Exit 1 jupyter notebook --no-browser --port 31650
(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ exi[K[K[Kps ux
USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND
merbrow 80065 0.0 0.0 155244 2272 ? S 11:23 0:00 sshd: merbrow@
merbrow 80066 0.1 0.0 115584 3896 pts/538 Ss 11:23 0:00 -bash
merbrow 84541 2.8 0.0 130280 1188 pts/538 S+ 11:24 0:00 screen -r 7205
merbrow 86397 0.0 0.0 153768 1868 pts/77 R+ 11:25 0:00 ps ux
merbrow 720565 0.0 0.0 132220 1600 ? Ss Jul25 0:00 SCREEN -L
merbrow 720566 0.0 0.0 115608 3972 pts/77 Ss Jul25 0:01 /bin/bash
merbrow 729075 0.0 0.0 16995820 79648 pts/77 Tl Jul25 0:09 python HSW_pre
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ [K(py37) [merbrow@cee-compute005 RFR-CLDERA]$ [K(py37) [merbrow@cee-compute005 RFR-CLDERA]$ [K(py37) [merbrow@cee-compute005 RFR-CLDERA]$ [K(py37) [merbrow@cee-compute005 RFR-CLDERA]$ termi
bash: termi: command not found
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ screen -D
[720565.pts-11.cee-compute005 power detached.]
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ [K(py37) [merbrow@cee-compute005 RFR-CLDERA]$ ps ux
USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND
merbrow 682593 0.0 0.0 155244 2404 ? S 14:17 0:00 sshd: merbrow@pts/79
merbrow 682594 0.1 0.0 115584 3888 pts/79 Ss 14:17 0:00 -bash
merbrow 683367 0.0 0.0 155244 2260 ? S 14:17 0:00 sshd: merbrow@pts/85
merbrow 683368 0.0 0.0 115452 3600 pts/85 Ss+ 14:17 0:00 -bash
merbrow 688386 22.0 0.0 130280 1188 pts/79 S+ 14:18 0:01 screen -r 720565
merbrow 688623 0.0 0.0 153768 1876 pts/77 R+ 14:19 0:00 ps ux
merbrow 720565 0.0 0.0 132220 1620 ? Ss Jul25 0:00 SCREEN -L
merbrow 720566 0.0 0.0 115608 3976 pts/77 Ss Jul25 0:01 /bin/bash
merbrow 729075 0.0 0.0 16995820 79648 pts/77 Tl Jul25 0:09 python HSW_preproc_L
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ ls -te[Kr
plot_weighted_graph.ipynb
[0m[01;35mFullConnect.png[0m
RF_Run_Netcdf.ipynb
RF_Run_SOE-MULTIVARATE.ipynb
test.ipynb
RF_Run_SyntheticSpatial-MULTIVARATE.ipynb
RF_Run_Synthetic-MULTIVARATE.ipynb
Zonal_RFR_meanT1000.csv
Zonal_RFR_6hourmeans_T1000.csv
Zonal_RFR_dailymeans_T1000.csv
ZonalHSW_AOD_T050_dailymax.csv
Zonal_RFR-Copy1.ipynb
ZonalHSW_T1000_dailymax.csv
ZonalHSW_T050_dailymax.csv
ZonalHSW_AOD_dailymax.csv
ZonalHSW_AOD_dailymax_global.csv
ZonalHSW_T050_dailymax_global.csv
ZonalHSW_T1000_dailymax_global.csv
untitled.txt
[01;34mPapers[0m
untitled1.txt
HSWpreprocessing-global.ipynb
HSWpreproc-Copy1.py
Untitled.ipynb
HSWpreproc.py~
HSWpreproc.py
[01;34mpreproc_output[0m
Zonal_RFR.mglb.230519.ipynb
~$ZonalHSW_LV_AOD_T050_weights.xlsx
[01;34mGlobalFigs[0m
[01;34mZonalFigs[0m
~$GlobalHSW_AOD_T050_T1000_weights.xlsm
[01;34mOLD_code[0m
[01;34mNormalized230608[0m
[01;34mSHAPoutputs[0m
[01;34m__pycache__[0m
[01;35mAllNodes.png[0m
CreateSystemEqs.ipynb
GlobalHSW_AOD_T050_T1000_weights.txt
GlobalHSW_AOD_T050_T1000_weights.xls
GlobalHSW_AOD_T050_T1000_weights.xlsm
Global_LV_all.csv
Global_LV_all.xlsm
[01;35mHSW_Zonal_Mean.png[0m
[01;35mHSW_Zonal_Mean_NORM.png[0m
[01;35mHSW_Zonal_Mean_NORM_Pruned.png[0m
[01;35mHSW_Zonal_Mean_Pruned.png[0m
LVstats.xlsx
MeanFeatureImportance.ipynb
README.md
RFR.py
RFR_mglb.py
RF_Run_SOE.ipynb
RF_Run_Synthetic.ipynb
RF_Run_SyntheticSpatial.ipynb
Simple.txt
ZonalFunctions.py
ZonalHSW_AOD_T050_T1000_dailymax.csv
ZonalHSW_AOD_T050_T1000_dailymax_global.csv
ZonalHSW_AOD_T050_T1000_dailymax_norm.csv
ZonalHSW_LV_AOD_T050_T1000.csv
ZonalHSW_LV_AOD_T050_T1000.csv~
ZonalHSW_LV_AOD_T050_weights.xlsx
ZonalHSW_LV_AOD_dailymax.csv
Zonal_RFR-Individual-EnsembleAvg.ipynb
Zonal_RFR-Individual.ipynb
Zonal_RFR.ipynb
Zonal_RFR_iterate.py
[01;34mZonalHSW_LV_weights[0m
Zonal_RFR-new.ipynb
HSWpreprocessing.ipynb
HSW_preproc_LV_run.py
screenlog.0
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ la -tr
total 76461
-rw-r--r-- 1 merbrow merbrow 28207 May 3 2022 plot_weighted_graph.ipynb
-rw-r--r-- 1 merbrow merbrow 490176 May 19 2022 [0m[01;35mFullConnect.png[0m
-rw-r--r-- 1 merbrow merbrow 1167764 May 24 2022 RF_Run_Netcdf.ipynb
-rw-r--r-- 1 merbrow merbrow 939310 Jul 27 2022 RF_Run_SOE-MULTIVARATE.ipynb
-rw-rw-r-- 1 merbrow merbrow 0 Nov 30 2022 test.ipynb
-rw-r--r-- 1 merbrow merbrow 1064787 Feb 7 09:02 RF_Run_SyntheticSpatial-MULTIVARATE.ipynb
-rw-r--r-- 1 merbrow merbrow 291480 Feb 9 09:58 RF_Run_Synthetic-MULTIVARATE.ipynb
-rw------- 1 merbrow merbrow 743071 Feb 9 10:39 Zonal_RFR_meanT1000.csv
-rw------- 1 merbrow merbrow 903923 Feb 14 10:49 Zonal_RFR_6hourmeans_T1000.csv
-rw------- 1 merbrow merbrow 225942 Feb 14 10:51 Zonal_RFR_dailymeans_T1000.csv
-rw------- 1 merbrow merbrow 1129729 Mar 8 12:23 ZonalHSW_AOD_T050_dailymax.csv
-rw------- 1 merbrow merbrow 270082 Mar 28 13:32 Zonal_RFR-Copy1.ipynb
-rw------- 1 merbrow merbrow 976924 Apr 4 13:29 ZonalHSW_T1000_dailymax.csv
-rw------- 1 merbrow merbrow 986136 Apr 4 15:35 ZonalHSW_T050_dailymax.csv
-rw------- 1 merbrow merbrow 988284 Apr 5 11:43 ZonalHSW_AOD_dailymax.csv
-rw------- 1 merbrow merbrow 133937 Apr 17 15:12 ZonalHSW_AOD_dailymax_global.csv
-rw------- 1 merbrow merbrow 139647 Apr 17 15:31 ZonalHSW_T050_dailymax_global.csv
-rw------- 1 merbrow merbrow 137361 Apr 17 15:57 ZonalHSW_T1000_dailymax_global.csv
-rw------- 1 merbrow merbrow 0 May 2 10:00 untitled.txt
drwxr-xr-x 3 merbrow merbrow 4096 May 2 14:15 [01;34mPapers[0m
-rw------- 1 merbrow merbrow 0 May 9 09:08 untitled1.txt
-rw------- 1 merbrow merbrow 15384 May 9 10:21 HSWpreprocessing-global.ipynb
-rw------- 1 merbrow merbrow 9313 May 15 10:34 HSWpreproc-Copy1.py
-rw------- 1 merbrow merbrow 8185 May 15 12:19 Untitled.ipynb
-rw------- 1 merbrow merbrow 10665 May 15 13:44 HSWpreproc.py~
-rw------- 1 merbrow merbrow 10666 May 15 14:26 HSWpreproc.py
drwxr-xr-x 2 merbrow merbrow 4096 May 16 10:03 [01;34mpreproc_output[0m
-rw-r--r-- 1 merbrow merbrow 464898 May 19 11:59 Zonal_RFR.mglb.230519.ipynb
-rw-r--r-- 1 merbrow merbrow 165 May 22 11:01 ~$ZonalHSW_LV_AOD_T050_weights.xlsx
drwxr-xr-x 2 merbrow merbrow 4096 May 22 11:34 [01;34mGlobalFigs[0m
drwxr-xr-x 4 merbrow merbrow 4096 May 22 11:35 [01;34mZonalFigs[0m
-rw-r--r-- 1 merbrow merbrow 165 May 23 14:44 ~$GlobalHSW_AOD_T050_T1000_weights.xlsm
drwxr-xr-x 3 merbrow merbrow 4096 Jul 12 11:15 [01;34mOLD_code[0m
drwx------ 2 merbrow merbrow 4096 Jul 12 11:18 [01;34mNormalized230608[0m
drwx------ 2 merbrow merbrow 32768 Jul 12 11:18 [01;34mSHAPoutputs[0m
drwxr-xr-x 2 merbrow merbrow 4096 Jul 12 11:18 [01;34m__pycache__[0m
drwxr-xr-x 2 merbrow merbrow 4096 Jul 12 11:18 [01;34m.ipynb_checkpoints[0m
drwxrwxr-x 14 merbrow merbrow 4096 Jul 12 11:22 [01;34m..[0m
-rw------- 1 merbrow merbrow 8196 Jul 12 11:23 .DS_Store
-rw------- 1 merbrow merbrow 31 Jul 12 11:23 .gitignore
-rw------- 1 merbrow merbrow 15168362 Jul 12 11:23 [01;35mAllNodes.png[0m
-rw------- 1 merbrow merbrow 40056 Jul 12 11:23 CreateSystemEqs.ipynb
-rw------- 1 merbrow merbrow 254324 Jul 12 11:23 GlobalHSW_AOD_T050_T1000_weights.txt
-rw------- 1 merbrow merbrow 898560 Jul 12 11:23 GlobalHSW_AOD_T050_T1000_weights.xls
-rw------- 1 merbrow merbrow 339751 Jul 12 11:23 GlobalHSW_AOD_T050_T1000_weights.xlsm
-rw------- 1 merbrow merbrow 282892 Jul 12 11:23 Global_LV_all.csv
-rw------- 1 merbrow merbrow 40110 Jul 12 11:23 Global_LV_all.xlsm
-rw------- 1 merbrow merbrow 7628535 Jul 12 11:23 [01;35mHSW_Zonal_Mean.png[0m
-rw------- 1 merbrow merbrow 13101142 Jul 12 11:23 [01;35mHSW_Zonal_Mean_NORM.png[0m
-rw------- 1 merbrow merbrow 614299 Jul 12 11:23 [01;35mHSW_Zonal_Mean_NORM_Pruned.png[0m
-rw------- 1 merbrow merbrow 2115372 Jul 12 11:23 [01;35mHSW_Zonal_Mean_Pruned.png[0m
-rw------- 1 merbrow merbrow 21504 Jul 12 11:23 LVstats.xlsx
-rw------- 1 merbrow merbrow 608011 Jul 12 11:23 MeanFeatureImportance.ipynb
-rw------- 1 merbrow merbrow 6222 Jul 12 11:23 README.md
-rw------- 1 merbrow merbrow 13070 Jul 12 11:23 RFR.py
-rw------- 1 merbrow merbrow 8857 Jul 12 11:23 RFR_mglb.py
-rw------- 1 merbrow merbrow 1026525 Jul 12 11:23 RF_Run_SOE.ipynb
-rw------- 1 merbrow merbrow 212806 Jul 12 11:23 RF_Run_Synthetic.ipynb
-rw------- 1 merbrow merbrow 337148 Jul 12 11:23 RF_Run_SyntheticSpatial.ipynb
-rw------- 1 merbrow merbrow 192 Jul 12 11:23 Simple.txt
-rw------- 1 merbrow merbrow 2564 Jul 12 11:23 ZonalFunctions.py
-rw------- 1 merbrow merbrow 1730438 Jul 12 11:23 ZonalHSW_AOD_T050_T1000_dailymax.csv
-rw------- 1 merbrow merbrow 252719 Jul 12 11:23 ZonalHSW_AOD_T050_T1000_dailymax_global.csv
-rw------- 1 merbrow merbrow 1713308 Jul 12 11:23 ZonalHSW_AOD_T050_T1000_dailymax_norm.csv
-rw------- 1 merbrow merbrow 3333671 Jul 12 11:23 ZonalHSW_LV_AOD_T050_T1000.csv
-rw------- 1 merbrow merbrow 3333189 Jul 12 11:23 ZonalHSW_LV_AOD_T050_T1000.csv~
-rw------- 1 merbrow merbrow 2625082 Jul 12 11:23 ZonalHSW_LV_AOD_T050_weights.xlsx
-rw------- 1 merbrow merbrow 1121008 Jul 12 11:23 ZonalHSW_LV_AOD_dailymax.csv
-rw------- 1 merbrow merbrow 2532825 Jul 12 11:23 Zonal_RFR-Individual-EnsembleAvg.ipynb
-rw------- 1 merbrow merbrow 491931 Jul 12 11:23 Zonal_RFR-Individual.ipynb
-rw------- 1 merbrow merbrow 1410535 Jul 12 11:23 Zonal_RFR.ipynb
-rw------- 1 merbrow merbrow 10895 Jul 12 11:23 Zonal_RFR_iterate.py
drwx------ 2 merbrow merbrow 8192 Jul 12 11:27 [01;34mZonalHSW_LV_weights[0m
drwxr-xr-x 8 merbrow merbrow 4096 Jul 12 11:30 [01;34m.git[0m
-rw------- 1 merbrow merbrow 1258968 Jul 12 12:01 Zonal_RFR-new.ipynb
-rw------- 1 merbrow merbrow 46707 Jul 26 14:23 HSWpreprocessing.ipynb
-rw------- 1 merbrow merbrow 6846 Jul 26 14:24 HSW_preproc_LV_run.py
drwxr-xr-x 13 merbrow merbrow 8192 Jul 26 14:24 [01;34m.[0m
-rw------- 1 merbrow merbrow 15425 Jul 26 14:25 screenlog.0
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ python HSW_preproc_LV_run.py
^Z
[2]+ Stopped python HSW_preproc_LV_run.py
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ python HSW_preproc_LV_run.py &
[3] 711095
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ /ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/backends/plugins.py:65: RuntimeWarning: Engine 'rasterio' loading failed:
libcrypto.so.1.0.0: cannot open shared object file: No such file or directory
warnings.warn(f"Engine {name!r} loading failed:\n{ex}", RuntimeWarning)
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ ls -tr
plot_weighted_graph.ipynb
[0m[01;35mFullConnect.png[0m
RF_Run_Netcdf.ipynb
RF_Run_SOE-MULTIVARATE.ipynb
test.ipynb
RF_Run_SyntheticSpatial-MULTIVARATE.ipynb
RF_Run_Synthetic-MULTIVARATE.ipynb
Zonal_RFR_meanT1000.csv
Zonal_RFR_6hourmeans_T1000.csv
Zonal_RFR_dailymeans_T1000.csv
ZonalHSW_AOD_T050_dailymax.csv
Zonal_RFR-Copy1.ipynb
ZonalHSW_T1000_dailymax.csv
ZonalHSW_T050_dailymax.csv
ZonalHSW_AOD_dailymax.csv
ZonalHSW_AOD_dailymax_global.csv
ZonalHSW_T050_dailymax_global.csv
ZonalHSW_T1000_dailymax_global.csv
untitled.txt
[01;34mPapers[0m
untitled1.txt
HSWpreprocessing-global.ipynb
HSWpreproc-Copy1.py
Untitled.ipynb
HSWpreproc.py~
HSWpreproc.py
[01;34mpreproc_output[0m
Zonal_RFR.mglb.230519.ipynb
~$ZonalHSW_LV_AOD_T050_weights.xlsx
[01;34mGlobalFigs[0m
[01;34mZonalFigs[0m
~$GlobalHSW_AOD_T050_T1000_weights.xlsm
[01;34mOLD_code[0m
[01;34mNormalized230608[0m
[01;34mSHAPoutputs[0m
[01;34m__pycache__[0m
[01;35mAllNodes.png[0m
CreateSystemEqs.ipynb
GlobalHSW_AOD_T050_T1000_weights.txt
GlobalHSW_AOD_T050_T1000_weights.xls
GlobalHSW_AOD_T050_T1000_weights.xlsm
Global_LV_all.csv
Global_LV_all.xlsm
[01;35mHSW_Zonal_Mean.png[0m
[01;35mHSW_Zonal_Mean_NORM.png[0m
[01;35mHSW_Zonal_Mean_NORM_Pruned.png[0m
[01;35mHSW_Zonal_Mean_Pruned.png[0m
LVstats.xlsx
MeanFeatureImportance.ipynb
README.md
RFR.py
RFR_mglb.py
RF_Run_SOE.ipynb
RF_Run_Synthetic.ipynb
RF_Run_SyntheticSpatial.ipynb
Simple.txt
ZonalFunctions.py
ZonalHSW_AOD_T050_T1000_dailymax.csv
ZonalHSW_AOD_T050_T1000_dailymax_global.csv
ZonalHSW_AOD_T050_T1000_dailymax_norm.csv
ZonalHSW_LV_AOD_T050_T1000.csv
ZonalHSW_LV_AOD_T050_T1000.csv~
ZonalHSW_LV_AOD_T050_weights.xlsx
ZonalHSW_LV_AOD_dailymax.csv
Zonal_RFR-Individual-EnsembleAvg.ipynb
Zonal_RFR-Individual.ipynb
Zonal_RFR.ipynb
Zonal_RFR_iterate.py
[01;34mZonalHSW_LV_weights[0m
Zonal_RFR-new.ipynb
HSWpreprocessing.ipynb
screenlog.0
HSW_preproc_LV_run.py
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ cat screenlog.0 [K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[Ktail -r[Kf screenlog.0
Zonal_RFR-Individual-EnsembleAvg.ipynb
Zonal_RFR-Individual.ipynb
Zonal_RFR.ipynb
Zonal_RFR_iterate.py
[01;34mZonalHSW_LV_weights[0m
Zonal_RFR-new.ipynb
HSWpreprocessing.ipynb
screenlog.0
HSW_preproc_LV_run.py
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ cat screenlog.0 [K[K[K[K[K[K
[K[K[K[K[K[K[K[K[K[Ktail -r[Kf screenlog.0
Zonal_RFR-Individual-EnsembleAvg.ipynb
Zonal_RFR-Individual.ipynb
Zonal_RFR.ipynb
Zonal_RFR_iterate.py
[01;34mZonalHSW_LV_weights[0m
Zonal_RFR-new.ipynb
HSWpreprocessing.ipynb
screenlog.0
HSW_preproc_LV_run.py
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ cat screenlog.0 [K[K[K[K[K[K
^Z
[4]+ Stopped tail -f screenlog.0
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ c[Kscreen -D
[720565.pts-11.cee-compute005 power detached.]
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ /ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
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[K(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\[3] Segmentation fault python HSW_preproc_LV_run.py
(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ python[K[K[K[K[K[Kcat HSW+[Kpreproc_[K[K[K[K[K[K[K[K_preproc_LV_run.py
import netCDF4 as nc
import os
import datetime
import pandas as pd
import seaborn as sns
import numpy as np
import xarray as xr
from numpy import ma
from tqdm import tqdm
from time import time
from glob import glob
# set variable of interest
# QOIs = ['AOD','T050','T1000']
QOIs = ['T050','T1000']
# QOI = "AOD" # might need to rewrite the clipping function if we do the full vertical temperature profile or SO2, but should be fine for the 2D variables
# do normalization?
dn = 0 # 0 for no, 1 for yes
# use mass ensembles?
massens = 1 # 0 for no, 1 for yes
try:
import clif
except:
import sys
sys.path.append("/gpfs/merbrow/CLDERA/Fingerprinting/") # or wherever you have installed clif
# from eof import fingerprints
import clif
def getZonalmeans(weights, dstack, ubnd, lbnd):
# get weights
lat_lon_weights = weights
# clip latitude only for use in lat lon weighting
clipLatT = clif.preprocessing.ClipTransform(dims=["lat"], bounds=[(lbnd, ubnd)])
lat_lon_weights_new = clipLatT.fit_transform(lat_lon_weights)
# clip the data
clipT = clif.preprocessing.ClipTransform(
dims=["lat"], bounds=[(lbnd, ubnd)]
)
data_new = clipT.fit_transform(dstack)
# get weighted zonal means
intoutT = clif.preprocessing.MarginalizeOutTransform(
dims=["lat", "lon"], lat_lon_weights=lat_lon_weights_new
)
data_new = intoutT.fit_transform(data_new)
return data_new
# # import HSW data
# Load data, specify variable of interest
# dataDir = '/gpfs/cldera/data/HSW/outputs/release_011423/netcdf/ens_members_latlon/' ## full variability
# dataDir = '/gpfs/cldera/data/HSW/outputs/release_030123/netcdf/low_var_ens_latlon/' ## limited variability
dataDir = '/gpfs/cldera/data/HSW/outputs/release_030123/netcdf/low_var_mass_ens_latlon/' ## mass ensembles
ens_members = next(os.walk(dataDir))[1]
thisVar = '*eam.h0*' # 6 hourly means
data = {}
data['comb']={} # initialized combined data.
# Iterate over the directory structure to create a combined xarray dataset that contains
# each ensemble member in a dimension named 'member'
idx = 0
for member in ens_members:
# if massens ==0:
thisVarPaths = glob(dataDir + member + '/' + thisVar, recursive=True)
thisVarData = xr.open_mfdataset(thisVarPaths)
# if massens ==1:
# thisVarPaths = glob(dataDir + member + '/' + thisVar, recursive=True)
# thisVarData = xr.open_mfdataset(thisVarPaths)
if idx == 0:
data['comb']=thisVarData
else:
data['comb']=xr.concat( [data['comb'], thisVarData], dim='member')
idx += 1
wgts = thisVarData.area
# # Loop over QOIs
for QOI in QOIs:
if dn == 1:
# normalize
qmin = data['comb'][QOI].min().values
qmax = data['comb'][QOI].max().values
Qnorm = (data['comb'][QOI]-qmin)/(qmax-qmin)
if dn == 0:
# don't normalize
Qnorm = data['comb'][QOI]
# calculate zonal means
globe = getZonalmeans(wgts[1],Qnorm,90, -90)
polN = getZonalmeans(wgts[1],Qnorm,90, 66.5)
tempN = getZonalmeans(wgts[1],Qnorm,66.5,35)
subtropN = getZonalmeans(wgts[1],Qnorm,35,23.5)
tropical = getZonalmeans(wgts[1],Qnorm,23.5,-23.5)
subtropS = getZonalmeans(wgts[1],Qnorm,-23.5,-35)
tempS = getZonalmeans(wgts[1],Qnorm,-35,-66.5)
polS = getZonalmeans(wgts[1],Qnorm,-66.5,-90)
# calculate daily means for each ensemble
daily_globe = globe.resample(time = "1D").mean()
# daily_globe_mean = daily_globe.mean(dim="member")
daily_polN = polN.resample(time = "1D").mean()
# daily_polN_mean = daily_polN.mean(dim="member")
daily_polS = polS.resample(time = "1D").mean()
# daily_polS_mean = daily_polS.mean(dim="member")
daily_tempN = tempN.resample(time = "1D").mean()
# daily_tempN_mean = daily_tempN.mean(dim="member")
daily_tempS = tempS.resample(time = "1D").mean()
# daily_tempS_mean = daily_tempS.mean(dim="member")
daily_subtropN = subtropN.resample(time = "1D").mean()
# daily_subtropN_mean = daily_subtropN.mean(dim="member")
daily_subtropS = subtropS.resample(time = "1D").mean()
# daily_subtropS_mean = daily_subtropS.mean(dim="member")
daily_tropical = tropical.resample(time = "1D").mean()
# daily_tropical_mean = daily_tropical.mean(dim="member")
# write out zonal daily mean data for all ensembles
# tstart = time.perf_counter()
dailydf=pd.DataFrame()
for i, ens in enumerate(tqdm(ens_members)):
dailydf['Globe_'+ens+'_'+QOI] = daily_globe[i,].values
pd.DataFrame(dailydf).to_csv('Global_HSW_LV_'+QOI+'mass_ens_dailymean.csv')
# print('total time = %0.2f mins'%((time.perf_counter() - tstart)/60))
# tstart = time.perf_counter()
dailydf=pd.DataFrame()
for i, ens in enumerate(tqdm(ens_members)):
dailydf['PolN_'+ens+'_'+QOI] = daily_polN[i,].values
pd.DataFrame(dailydf).to_csv('PolarN_HSW_LV_'+QOI+'mass_ens_dailymean.csv')
# print('total time = %0.2f mins'%((time.perf_counter() - tstart)/60))
# tstart = time.perf_counter()
dailydf=pd.DataFrame()
for i, ens in enumerate(tqdm(ens_members)):
dailydf['PolS_'+ens+'_'+QOI] = daily_polS[i,].values
pd.DataFrame(dailydf).to_csv('PolarS_HSW_LV_'+QOI+'mass_ens_dailymean.csv')
# print('total time = %0.2f mins'%((time.perf_counter() - tstart)/60))
# tstart = time.perf_counter()
dailydf=pd.DataFrame()
for i, ens in enumerate(tqdm(ens_members)):
dailydf['SubtropN_'+ens+'_'+QOI] = daily_subtropN[i,].values
pd.DataFrame(dailydf).to_csv('SubtropN_HSW_LV_'+QOI+'mass_ens_dailymean.csv')
# print('total time = %0.2f mins'%((time.perf_counter() - tstart)/60))
# tstart = time.perf_counter()
dailydf=pd.DataFrame()
for i, ens in enumerate(tqdm(ens_members)):
dailydf['SubtropS_'+ens+'_'+QOI] = daily_subtropS[i,].values
pd.DataFrame(dailydf).to_csv('SubtropS_HSW_LV_'+QOI+'mass_ens_dailymean.csv')
# print('total time = %0.2f mins'%((time.perf_counter() - tstart)/60))
# tstart = time.perf_counter()
dailydf=pd.DataFrame()
for i, ens in enumerate(tqdm(ens_members)):
dailydf['TempN_'+ens+'_'+QOI] = daily_tempN[i,].values
pd.DataFrame(dailydf).to_csv('TempN_HSW_LV_'+QOI+'mass_ens_dailymean.csv')
# print('total time = %0.2f mins'%((time.perf_counter() - tstart)/60))
# tstart = time.perf_counter()
dailydf=pd.DataFrame()
for i, ens in enumerate(tqdm(ens_members)):
dailydf['TempS_'+ens+'_'+QOI] = daily_tempS[i,].values
pd.DataFrame(dailydf).to_csv('TempS_HSW_LV_'+QOI+'mass_ens_dailymean.csv')
# print('total time = %0.2f mins'%((time.perf_counter() - tstart)/60))
# tstart = time.perf_counter()
dailydf=pd.DataFrame()
for i, ens in enumerate(tqdm(ens_members)):
dailydf['Tropcial_'+ens+'_'+QOI] = daily_tropical[i,].values
pd.DataFrame(dailydf).to_csv('Tropical_HSW_LV_'+QOI+'mass_ens_dailymean.csv')
# print('total time = %0.2f mins'%((time.perf_counter() - tstart)/60))
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ [K
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ cat HSW_preproc_LV_run.py [C[1P[1P[1P[1@m[1@o[1@r[1@e
import netCDF4 as nc
import os
import datetime
import pandas as pd
import seaborn as sns
import numpy as np
import xarray as xr
from numpy import ma
from tqdm import tqdm
from time import time
from glob import glob
# set variable of interest
# QOIs = ['AOD','T050','T1000']
QOIs = ['T050','T1000']
# QOI = "AOD" # might need to rewrite the clipping function if we do the full vertical temperature pro
file or SO2, but should be fine for the 2D variables
# do normalization?
dn = 0 # 0 for no, 1 for yes
# use mass ensembles?
massens = 1 # 0 for no, 1 for yes
try:
import clif
except:
import sys
sys.path.append("/gpfs/merbrow/CLDERA/Fingerprinting/") # or wherever you have installed clif
# from eof import fingerprints
[3m--More--(10%)[23m[Kkmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ pyht[K[Kthon HSW_preproc_LV_run.py &
[5] 493504
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ /ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/backends/plugins.py:65: RuntimeWarning: Engine 'rasterio' loading failed:
libcrypto.so.1.0.0: cannot open shared object file: No such file or directory
warnings.warn(f"Engine {name!r} loading failed:\n{ex}", RuntimeWarning)
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ sscr[K[K[Kcreed -d[K[K[K[Kn -d
[720565.pts-11.cee-compute005 detached.]
kmerbrow@cee-compute005:/gpfs/merbrow/CLDERA/RFR-CLDERA\(py37) [merbrow@cee-compute005 RFR-CLDERA]$ /ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
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/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/ascldap/users/merbrow/.conda/envs/py37/lib/python3.7/site-packages/xarray/core/indexing.py:1227: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
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