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Update testing for release #475
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grantfirl
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NCAR:main
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dustinswales:feature/testing_for_release
May 23, 2024
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52edf2a
Update SCM RTs for CCPP v7 release
dustinswales 5fb29d4
Added plotting step to SCM RT Baseline comparison in CI
dustinswales b018124
Omission from previous commit
dustinswales fbd9cc9
Update CI script to updload plots as Github artifact
dustinswales be613eb
Update CI script
dustinswales 31c68d7
Update CI script
dustinswales 7d77c36
Update CI script
dustinswales fa511d3
Merge branch 'add_RRFS_suite' of https://github.com/grantfirl/ccpp-sc…
dustinswales 01846e7
Merge branch 'HR3_CCPPV7' of https://github.com/mzhangw/ccpp-scm into…
dustinswales 230fb35
Update CI script
dustinswales bf5ab1d
Update CI script
dustinswales a302d5a
Update CI script
dustinswales d7cb558
Merge branch 'main' of https://github.com/NCAR/ccpp-scm into feature/…
dustinswales 824dfa6
Update CI script
dustinswales 659f1f0
Update CI script
dustinswales e4ebc56
Update CI script
dustinswales 266895f
Update CI script
dustinswales 34e9298
Update CI script
dustinswales 7699051
Update CI script
dustinswales d7772b3
Update CI script
dustinswales b6fb86e
Update CI script
dustinswales cf36f61
Update CI script
dustinswales bd8ff34
Add script to fetch aerosol data to workflow
dustinswales e5a3413
Bug fix in supported suites
dustinswales c68a82e
Merge branch 'main' of https://github.com/NCAR/ccpp-scm into feature/…
dustinswales 1a48be5
Updated CI. generalized plotting
dustinswales 220a3f6
Merge branch 'main' of https://github.com/NCAR/ccpp-scm into feature/…
dustinswales bc615f0
Fix to CI script
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,177 @@ | ||
#!/usr/bin/env python | ||
|
||
############################################################################## | ||
# | ||
# This script compares SCM RT output to baselines. | ||
# | ||
############################################################################## | ||
from netCDF4 import Dataset | ||
import numpy as np | ||
import matplotlib.pyplot as plt | ||
# | ||
def plot_results(file_BL, file_RT, plot_RT_only): | ||
# List of SCM output fields to plot (This is everything) | ||
vars2plot = ["time_inst","time_diag","time_swrad","time_lwrad","time_rad","pres", \ | ||
"pres_i","sigma","sigma_i","pres_s","qv","T","u","v","ql","qi","qc", \ | ||
"qv_force_tend","T_force_tend","u_force_tend","v_force_tend","w_ls", \ | ||
"u_g","v_g","dT_dt_rad_forc","h_advec_thil","h_advec_qt", \ | ||
"v_advec_thil","v_advec_qt","T_s","lhf","shf","tprcp_inst", \ | ||
"tprcp_rate_inst","t2m","q2m","ustar","tsfc","tau_u","tau_v","upd_mf", \ | ||
"dwn_mf","det_mf","sfc_up_lw_land","sfc_up_lw_ice","sfc_up_lw_water", \ | ||
"sfc_up_sw_dir_nir","sfc_up_sw_dif_nir","sfc_up_sw_dir_vis", \ | ||
"sfc_up_sw_dif_vis","sfc_dwn_sw_dir_nir","sfc_dwn_sw_dif_nir", \ | ||
"sfc_dwn_sw_dir_vis","sfc_dwn_sw_dif_vis","mp_prcp_inst", \ | ||
"dcnv_prcp_inst","scnv_prcp_inst","pwat","dT_dt_lwrad","dT_dt_swrad", \ | ||
"dT_dt_pbl","dT_dt_deepconv","dT_dt_shalconv","dT_dt_micro", \ | ||
"dT_dt_ogwd","dT_dt_cgwd","dT_dt_phys","dT_dt_nonphys","dq_dt_pbl", \ | ||
"dq_dt_deepconv","dq_dt_shalconv","dq_dt_micro","dq_dt_phys", \ | ||
"dq_dt_nonphys","doz_dt_pbl","doz_dt_prodloss","doz_dt_oz","doz_dt_T", \ | ||
"doz_dt_ovhd","doz_dt_phys","doz_dt_nonphys","du_dt_pbl","du_dt_ogwd", \ | ||
"du_dt_deepconv","du_dt_cgwd","du_dt_shalconv","du_dt_phys", \ | ||
"du_dt_nonphys","dv_dt_pbl","dv_dt_ogwd","dv_dt_deepconv","dv_dt_cgwd", \ | ||
"dv_dt_shalconv","dv_dt_phys","dv_dt_nonphys","sfc_dwn_sw","sfc_up_sw", \ | ||
"sfc_net_sw","sfc_dwn_lw","gflux","u10m","v10m","hpbl","tprcp_accum", \ | ||
"ice_accum","snow_accum","graupel_accum","conv_prcp_accum", \ | ||
"tprcp_rate_accum","ice_rate_accum","snow_rate_accum", \ | ||
"graupel_rate_accum","conv_prcp_rate_accum","max_cloud_fraction", \ | ||
"toa_total_albedo","vert_int_lwp_mp","vert_int_iwp_mp", \ | ||
"vert_int_lwp_cf","vert_int_iwp_cf"] | ||
# Use subset of available SCM output for plots. | ||
vars2plot = ["qv","T","u","v","ql","qi","qc","sfc_dwn_sw","sfc_up_sw", \ | ||
"sfc_net_sw","sfc_dwn_lw", "u10m","v10m","hpbl","gflux", \ | ||
"qv_force_tend","T_force_tend","u_force_tend","v_force_tend","w_ls", \ | ||
"u_g","v_g","dT_dt_rad_forc","h_advec_thil","h_advec_qt", \ | ||
"v_advec_thil","v_advec_qt","T_s","lhf","shf","tprcp_inst", \ | ||
"tprcp_rate_inst","t2m","q2m","ustar","tsfc","tau_u","tau_v"] | ||
|
||
# Open SCM datasets | ||
SCM_BL = Dataset(file_BL) | ||
SCM_RT = Dataset(file_RT) | ||
|
||
plot_diff = True | ||
if plot_RT_only: | ||
plot_diff = False | ||
# end if | ||
|
||
plot_files = [] | ||
for var in SCM_BL.variables.keys(): | ||
if (var in vars2plot): | ||
# Handle temporal axis. | ||
# There are 4 different dimensions in the SCM output, identified by the suffix "_dim". | ||
# Here the suffix is stripped and used to identify the temporal dimenesion (index 0 in netcdf file) | ||
timeD = SCM_BL[var].dimensions[0] | ||
timeD = timeD[0:len(timeD)-4] | ||
x1 = SCM_BL[timeD][:].squeeze()/3600. #seconds -> hours | ||
x2 = SCM_RT[timeD][:].squeeze()/3600. #seconds - >hours | ||
# Is this a 2D (time, x) variable? (Really 1D since x=1 in SCM) | ||
is2D = False | ||
if (len(SCM_BL[var].dimensions)==2): | ||
is2D = True | ||
# endif | ||
# one/two-dimensional variables | ||
if (len(SCM_BL[var].shape) != 3): | ||
if (is2D): | ||
y1 = SCM_BL[var][:,0].squeeze() | ||
y2 = SCM_RT[var][:,0].squeeze() | ||
else: | ||
y1 = SCM_BL[var][:] | ||
y2 = SCM_RT[var][:] | ||
# endif | ||
|
||
# Make figure | ||
if (np.size(x1) > 1): | ||
fig = plt.figure(figsize=(13,10)) | ||
# Baselines and SCM RTs on same plot | ||
if plot_diff: | ||
plt.subplot(2,1,1) | ||
# end if | ||
plt.title(SCM_BL[var].description) | ||
if plot_RT_only: | ||
plt.plot(x1, y1, color='blue') | ||
else: | ||
plt.plot(x1, y1, color='blue') | ||
plt.plot(x2, y2, color='black') | ||
# end if | ||
plt.ylabel('('+SCM_BL[var].units+')') | ||
plt.xlabel('(hours)') | ||
|
||
# Difference (Baseline-SCMRT) | ||
if plot_diff: | ||
plt.subplot(2,1,2) | ||
plt.title("Difference (blue - black)") | ||
plt.plot(x1, y1 - y2, color='red') | ||
plt.plot(x1, np.zeros(len(y1)), color='grey',linestyle='dashed') | ||
plt.ylabel('('+SCM_RT[var].units+')') | ||
plt.xlabel('(hours)') | ||
# | ||
fileOUT = 'scm.' + var +'.png' | ||
plt.savefig(fileOUT) | ||
# | ||
plot_files.append(fileOUT) | ||
# three-dimensional variables | ||
elif len(SCM_BL[var].shape) == 3: | ||
z1 = np.transpose(SCM_BL[var][:,:,0]).squeeze() | ||
z2 = np.transpose(SCM_RT[var][:,:,0]).squeeze() | ||
|
||
# vertical axis | ||
y1 = SCM_BL["pres"][0,:].squeeze()*0.01 | ||
y2 = SCM_RT["pres"][0,:].squeeze()*0.01 | ||
nlev = SCM_BL[var][:,:,0].shape[1] | ||
# Layer (nlev) quantities are the default, so check for case where we have an | ||
# interface (nlev+1) variable to plot. | ||
if (SCM_BL[var][:,:,0].shape[1] > len(y1)): | ||
y1 = SCM_BL["pres_i"][0,:].squeeze()*0.01 | ||
y2 = SCM_RT["pres_i"][0,:].squeeze()*0.01 | ||
# endif | ||
|
||
# Comppute differences and determine valid plot range(s). | ||
dz = z1-z2 | ||
|
||
# Finally, make figure. | ||
if (np.size(x1) > 1): | ||
fig = plt.figure(figsize=(13,10)) | ||
if plot_RT_only: | ||
plt.contourf(x2, y2, z2, 20, cmap='YlGnBu') | ||
plt.ylim(1000,200) | ||
plt.ylabel('(Pa)') | ||
plt.xlabel('(hours)') | ||
cbr = plt.colorbar() | ||
cbr.set_label('('+SCM_RT[var].units+')') | ||
else: | ||
# Baselines | ||
plt.subplot(3,1,1) | ||
plt.title(SCM_BL[var].description, fontsize=12) | ||
plt.contourf(x1, y1, z1, 20, cmap='YlGnBu') | ||
plt.ylim(1000,200) | ||
plt.ylabel('(Pa)') | ||
cbr = plt.colorbar() | ||
cbr.set_label('('+SCM_RT[var].units+')') | ||
# SCM RTs | ||
plt.subplot(3,1,2) | ||
plt.contourf(x2, y2, z2, 20, cmap='YlGnBu') | ||
plt.ylim(1000,200) | ||
plt.ylabel('(Pa)') | ||
plt.xlabel('(hours)') | ||
cbr = plt.colorbar() | ||
cbr.set_label('('+SCM_RT[var].units+')') | ||
# Only plot differences if they exist. | ||
if np.count_nonzero(dz) > 0: | ||
plt.subplot(3,1,3) | ||
plt.title("Difference (top - middle)", fontsize=8) | ||
plt.contourf(x2, y2, dz, 20, cmap='bwr') | ||
plt.ylim(1000,200) | ||
plt.ylabel('(Pa)') | ||
plt.xlabel('(hours)') | ||
cbr = plt.colorbar() | ||
cbr.set_label('('+SCM_RT[var].units+')') | ||
# end if (no differences exist) | ||
fileOUT = 'scm.' + var +'.png' | ||
plt.savefig(fileOUT) | ||
# | ||
plot_files.append(fileOUT) | ||
# end if (Have enought pts to plot?) | ||
# end if (fields exist?) | ||
# end if (field requested?) | ||
# end for (fields in file) | ||
|
||
return(plot_files) |
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This is going to (potentially) slow down CI a lot since the files are big. Any way to stash this data for future use?
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I was worried that this was going to bring this whole thing crashing down, but to my surprise it is faster to download this data on the GitHub servers than on Hera. It only took ~2min., but that's more than the ~15sec before.
There may be slick ways to stash this, I just don't know any.