CDAT Migration: Refactor annual_cycle_zonal_mean set #798
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chengzhuzhang
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Basic driver and plotting scripts are working. Through only with |
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Current results with one variable: https://portal.nersc.gov/cfs/e3sm/cdat-migration-fy24/669-annual_cycle_zonal_mean/viewer/ Other TODO items:
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This issue seems to be related to these:
I'm currently debugging and will push fixes. |
tomvothecoder
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This commit fixes the multiprocessing=True TimeoutError issue in this comment.
RE: #798 (comment)
Current results with one variable: portal.nersc.gov/cfs/e3sm/cdat-migration-fy24/669-annual_cycle_zonal_mean/viewer
Other TODO items:
* refine axis config for plot * fix viewer * Verify all variable runs
I think the only remaining items are the last two bullets.
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| # NOTE: This GitHub issue explains why the "coords" and "compat" args | ||
| # are defined as they are below: https://github.com/xCDAT/xcdat/issues/641 | ||
| args = { | ||
| "paths": filepath, | ||
| "decode_times": False, | ||
| "add_bounds": ["X", "Y"], | ||
| "coords": "minimal", | ||
| "compat": "override", | ||
| "chunks": "auto", | ||
| } |
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Notable change. I am going to remove "chunks": "auto" because we end up loading the dataset into memory. This means downstream computational operations are all serial within the single process.
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| # NOTE: There seems to be an issue with `open_mfdataset()` and | ||
| # using the multiprocessing scheduler defined in e3sm_diags, | ||
| # resulting in timeouts and resource locking. | ||
| # To avoid this, we load the multi-file dataset into memory before | ||
| # performing downstream operations. | ||
| # Related GH issue: https://github.com/pydata/xarray/issues/3781 | ||
| ds.load(scheduler="sync") | ||
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| # -------------------------------------------------------------------------- | ||
| plt.xticks(time, X_TICKS) | ||
| lat_formatter = LatitudeFormatter() # type: ignore | ||
| ax.yaxis.set_major_formatter(lat_formatter) | ||
| ax.tick_params(labelsize=8.0, direction="out", width=1) | ||
| ax.xaxis.set_ticks_position("bottom") | ||
| ax.yaxis.set_ticks_position("left") |
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I added this block of code from the old plotter because it was missing here.
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I think it fixes the "refine axis config for plot" todo item in this comment.
| _save_data_metrics_and_plots( | ||
| parameter, | ||
| plot_func, | ||
| var_key, | ||
| test_zonal_mean.to_dataset(), | ||
| ref_zonal_mean.to_dataset(), | ||
| diff, | ||
| metrics_dict={}, | ||
| metrics_dict=None, |
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metrics_dict can be set to None after removing the metrics_dict arg from the plot function.
| @@ -36,7 +34,6 @@ def plot( | |||
| da_test: xr.DataArray, | |||
| da_ref: xr.DataArray, | |||
| da_diff: xr.DataArray, | |||
| metrics_dict: MetricsDict, | |||
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Removed unused metrics_dict arg.
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| def _open_annual_cycle_climo_dataset(self, filepath: str) -> xr.Dataset: | ||
| """Open 12 monthly mean climatology dataset. | ||
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| Parameters | ||
| ---------- | ||
| filepath : str | ||
| The path to the climatology datasets. | ||
| """ | ||
| args = {"paths": filepath, "decode_times": False, "add_bounds": ["X", "Y"]} | ||
| ds = xc.open_mfdataset(**args) | ||
| return ds | ||
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I also replaced _open_annual_cycle_climo_dataset() with an updated version of _open_climo_dataset() that supports multi-file datasets.
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@chengzhuzhang you can pick this set back up. I did not make any progress since our last meeting on 4/15/24 (notes). Specifically, there is still a problem related to:
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tomvothecoder
modified the milestones:
FY24 Q3 (04/01/24 - 06/30/24),
FY24 Q4 (07/01/24 - 9/30/24)
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When set multi-processing=True, error |
chengzhuzhang
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Jul 10, 2024
| @@ -109,7 +109,8 @@ def _add_colormap( | |||
| # Add the contour plot | |||
| # -------------------------------------------------------------------------- | |||
| ax = fig.add_axes(DEFAULT_PANEL_CFG[subplot_num], projection=None) | |||
| var = var.transpose("lat", "time") | |||
| # var = var.transpose("lat", "time") | |||
| var = var.transpose(var.dims[1], var.dims[0]) | |||
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One SST data set has "latitude" instead of "lat" as dimension name. The code change voided using the dimension name explicitly.
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More update: The TimeOut error came from driver/utils/regrid.py |
chengzhuzhang
commented
Jul 12, 2024
| @@ -413,6 +413,14 @@ def _get_climo_dataset(self, season: str) -> xr.Dataset: | |||
| # ds = ds[[self.var, 'lat_bnds', 'lon_bnds']] | |||
| ds = ds[[self.var] + keep_bnds] | |||
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This line only keeps variable after derivation and bounds related data variables. It helps to remove excessive data for alleviating memory usage. This change in dataset_xr.py may affect other sets.
| # To avoid this, we load the multi-file dataset into memory before | ||
| # performing downstream operations. | ||
| # Related GH issue: https://github.com/pydata/xarray/issues/3781 | ||
| ds.load(scheduler="sync") |
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This is needed to resolve conflicts between multiprocessing and dask. Though we need it to be here to only load variables needed, otherwise, out of memory still occur as @tomvothecoder noted.
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Actually, the easier thing to do is to ignore the decoded time values since they aren't used and to assume the order is 1-12 (Jan to Dec) like what the CDAT code does. The main caveat is that the time coordinates must be in ascending order, which they are when opening the datasets in Xarray/xCDAT with The only change needed is to update e3sm_diags/e3sm_diags/plot/annual_cycle_zonal_mean_plot.py Lines 103 to 108 in 784404b |
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@tomvothecoder I was searching some code example that reads data using open_mfdatasets with specifying order of files: https://stackoverflow.com/questions/75241585/using-xarrays-open-mfdataset-to-open-a-series-of-nc-files Though I think your solution actually works okay, given that |
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@tomvothecoder I'm retesting this set will all variables, and realize that the memory issue came back. Then I tested again with the commit which resolved the memory issue (15811b8). No errors. Some changes between (f2c3568) and 15811b8 brought back the issue. I doubted the |
Besides the recent plotter update, |
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To change back |
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Not sure the best solution to continue troubleshooting, after ruling out the args change for open_mfdataset. But what I did is to swap the dataset_xr.py from commit 15811b8 into latest code. (I do need to edit slightly to make the code work, i.e. change CLIMO_FREQ to Climo_Freq). No memory issue. At least it narrows down the problematic file, and I suspect some changes made in other PRs being merged introduced memory problem. I'm stepping through the differs to see what might be the cause. The file diff for dataset_xr.py is here https://www.diffchecker.com/mTw8AWif/ |
- Due to incorrectly updating `keep_bnds` logic - Add `_encode_time_coords()` to workaround cftime issue `ValueError: "months since" units only allowed for "360_day" calendar`
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I was actually in the middle of debugging here with my comment. I resolved the multiprocessing issue, it was my fault :( Issues I resolved in
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I re-ran the regression test notebook with the latest commit. I am still getting the following diffs: AODVISComparing:
/global/cfs/cdirs/e3sm/www/cdat-migration-fy24/669-annual_cycle_zonal_mean-debug/annual_cycle_zonal_mean/AOD_550/AOD_550-AODVIS-ANNUALCYCLE-global_ref.nc
/global/cfs/cdirs/e3sm/www/cdat-migration-fy24/main/annual_cycle_zonal_mean/AOD_550/AOD_550-AODVIS-Annual-Cycle_test.nc
AODVIS
var_key AODVIS
Not equal to tolerance rtol=1e-05, atol=0
Mismatched elements: 1808 / 2160 (83.7%)
Max absolute difference: 0.12250582
Max relative difference: 91.14554689
x: array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],...
y: array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],...ALBEDO -- It just looks like
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@tomvothecoder this is big relief! I skimed through the file several times and noticed the changed line |
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I added a debug script for I think the max relative diff is large because the values are close to 0. import numpy as np
import xcdat as xc
dev_path = "/global/cfs/cdirs/e3sm/www/cdat-migration-fy24/669-annual_cycle_zonal_mean-debug/annual_cycle_zonal_mean/AOD_550/AOD_550-AODVIS-ANNUALCYCLE-global_ref.nc"
main_path = "/global/cfs/cdirs/e3sm/www/cdat-migration-fy24/main/annual_cycle_zonal_mean/AOD_550/AOD_550-AODVIS-Annual-Cycle_test.nc"
var_a = xc.open_dataset(dev_path)["AODVIS"]
var_b = xc.open_dataset(main_path)["AODVIS"]
"""
Floating point comparison
AssertionError:
Not equal to tolerance rtol=1e-07, atol=0
Mismatched elements: 1808 / 2160 (83.7%)
Max absolute difference: 0.12250582
Max relative difference: 91.14554689
x: array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],...
y: array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],...
"""
np.testing.assert_allclose(var_a, var_b)
# Get the max of all values
# -------------------------
# 0.28664299845695496
print(var_a.max().item())
# 0.2866430557436412
print(var_b.max().item())
# Get the min of all values
# -------------------------
# 0.0
print(var_a.min().item())
# 0.0
print(var_b.min().item())
# Get the sum of all values
# -------------------------
# 224.2569122314453
print(var_a.sum().item())
# 224.25691348856003
print(var_b.sum().item())
# Get the mean of all values
# -------------------------
# 0.10382264107465744
print(var_a.mean().item())
# 0.1038226451335926
print(var_b.mean().item())
# %%
# Get the max absolute diff
# -------------------------
# 0.12250582128763199
print((var_a - var_b).max().item()) |
yeah, the values and metrics look all very close. Based on the plots i saw earlier, months were off. |
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@tomvothecoder I think we can merge after CI/CD test is completed! |
This was referenced Jul 25, 2024
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* Refactor `annual_cycle_zonal_mean` set * Address PR review comments * Add lat lon regression testing * Add debugging scripts * Update `_open_climo_dataset()` to decode times as workaround to misaligned time coords - Update `annual_cycle_zonal_mean_plot.py` to convert time coordinates to month integers * Fix unit tests * Remove old plotter * Add script to debug decode_times=True and ncclimo file * Update plotter time values to month integers * Fix slow `.load()` and multiprocessing issue - Due to incorrectly updating `keep_bnds` logic - Add `_encode_time_coords()` to workaround cftime issue `ValueError: "months since" units only allowed for "360_day" calendar` * Update `_encode_time_coords()` docstring * Add AODVIS debug script * update AODVIS obs datasets; regression test results --------- Co-authored-by: Tom Vo <[email protected]>
tomvothecoder
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Aug 21, 2024
* Refactor `annual_cycle_zonal_mean` set * Address PR review comments * Add lat lon regression testing * Add debugging scripts * Update `_open_climo_dataset()` to decode times as workaround to misaligned time coords - Update `annual_cycle_zonal_mean_plot.py` to convert time coordinates to month integers * Fix unit tests * Remove old plotter * Add script to debug decode_times=True and ncclimo file * Update plotter time values to month integers * Fix slow `.load()` and multiprocessing issue - Due to incorrectly updating `keep_bnds` logic - Add `_encode_time_coords()` to workaround cftime issue `ValueError: "months since" units only allowed for "360_day" calendar` * Update `_encode_time_coords()` docstring * Add AODVIS debug script * update AODVIS obs datasets; regression test results --------- Co-authored-by: Tom Vo <[email protected]>
tomvothecoder
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* Refactor `annual_cycle_zonal_mean` set * Address PR review comments * Add lat lon regression testing * Add debugging scripts * Update `_open_climo_dataset()` to decode times as workaround to misaligned time coords - Update `annual_cycle_zonal_mean_plot.py` to convert time coordinates to month integers * Fix unit tests * Remove old plotter * Add script to debug decode_times=True and ncclimo file * Update plotter time values to month integers * Fix slow `.load()` and multiprocessing issue - Due to incorrectly updating `keep_bnds` logic - Add `_encode_time_coords()` to workaround cftime issue `ValueError: "months since" units only allowed for "360_day" calendar` * Update `_encode_time_coords()` docstring * Add AODVIS debug script * update AODVIS obs datasets; regression test results --------- Co-authored-by: Tom Vo <[email protected]>
tomvothecoder
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Oct 25, 2024
* Refactor `annual_cycle_zonal_mean` set * Address PR review comments * Add lat lon regression testing * Add debugging scripts * Update `_open_climo_dataset()` to decode times as workaround to misaligned time coords - Update `annual_cycle_zonal_mean_plot.py` to convert time coordinates to month integers * Fix unit tests * Remove old plotter * Add script to debug decode_times=True and ncclimo file * Update plotter time values to month integers * Fix slow `.load()` and multiprocessing issue - Due to incorrectly updating `keep_bnds` logic - Add `_encode_time_coords()` to workaround cftime issue `ValueError: "months since" units only allowed for "360_day" calendar` * Update `_encode_time_coords()` docstring * Add AODVIS debug script * update AODVIS obs datasets; regression test results --------- Co-authored-by: Tom Vo <[email protected]>
tomvothecoder
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Oct 29, 2024
* Refactor `annual_cycle_zonal_mean` set * Address PR review comments * Add lat lon regression testing * Add debugging scripts * Update `_open_climo_dataset()` to decode times as workaround to misaligned time coords - Update `annual_cycle_zonal_mean_plot.py` to convert time coordinates to month integers * Fix unit tests * Remove old plotter * Add script to debug decode_times=True and ncclimo file * Update plotter time values to month integers * Fix slow `.load()` and multiprocessing issue - Due to incorrectly updating `keep_bnds` logic - Add `_encode_time_coords()` to workaround cftime issue `ValueError: "months since" units only allowed for "360_day" calendar` * Update `_encode_time_coords()` docstring * Add AODVIS debug script * update AODVIS obs datasets; regression test results --------- Co-authored-by: Tom Vo <[email protected]>
tomvothecoder
added a commit
that referenced
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Oct 29, 2024
* Refactor `annual_cycle_zonal_mean` set * Address PR review comments * Add lat lon regression testing * Add debugging scripts * Update `_open_climo_dataset()` to decode times as workaround to misaligned time coords - Update `annual_cycle_zonal_mean_plot.py` to convert time coordinates to month integers * Fix unit tests * Remove old plotter * Add script to debug decode_times=True and ncclimo file * Update plotter time values to month integers * Fix slow `.load()` and multiprocessing issue - Due to incorrectly updating `keep_bnds` logic - Add `_encode_time_coords()` to workaround cftime issue `ValueError: "months since" units only allowed for "360_day" calendar` * Update `_encode_time_coords()` docstring * Add AODVIS debug script * update AODVIS obs datasets; regression test results --------- Co-authored-by: Tom Vo <[email protected]>
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Description
Refactor
annual_cycle_zonal_meanwith xarray/xcdatDriver is pretty short and has unique
_create_annual_cyclefunctionannual_cycle_zonal_meanset #669Checklist
If applicable: