Merge pull request #993 from PCMDI/lee1043-patch-1_pre-commit

Catch up with pre-commit

pcmdi_metrics/__init__.py

@@ -1,5 +1,5 @@
 # put here the import calls to expose whatever we want to the user
-import logging
+import logging # noqa
 
 LOG_LEVEL = logging.DEBUG
 plog = logging.getLogger("pcmdi_metrics")
@@ -15,6 +15,4 @@
 plog.addHandler(ch)
 plog.setLevel(LOG_LEVEL)
 from . import io # noqa
-#from . import pcmdi # noqa
-#from . import mean_climate # noqa
 from .version import __git_sha1__, __git_tag_describe__, __version__ # noqa

pcmdi_metrics/cloud_feedback/cloud_feedback_driver.py

@@ -61,7 +61,10 @@
  with open(input_files_json) as f:
  ncfiles = json.load(f)
 else:
- print('Warning: input files were not explicitly given. They will be searched from ', path)
+ print(
+ "Warning: input files were not explicitly given. They will be searched from ",
+ path,
+ )
 
 for exp in exps:
  filenames[exp] = dict()
@@ -169,15 +172,33 @@
 output_dict["RESULTS"][model] = OrderedDict()
 output_dict["RESULTS"][model][variant] = OrderedDict()
 output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"] = OrderedDict()
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["high_cloud_altitude"] = assessed_cld_fbk[0]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["tropical_marine_low_cloud"] = assessed_cld_fbk[1]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["tropical_anvil_cloud_area"] = assessed_cld_fbk[2]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["land_cloud_amount"] = assessed_cld_fbk[3]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["middle_latitude_marine_low_cloud_amount"] = assessed_cld_fbk[4]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["high_latitude_low_cloud_optical_depth"] = assessed_cld_fbk[5]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["implied_unassessed"] = assessed_cld_fbk[6]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["sum_of_assessed"] = assessed_cld_fbk[7]
-output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"]["total_cloud_feedback"] = assessed_cld_fbk[8]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+ "high_cloud_altitude"
+] = assessed_cld_fbk[0]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+ "tropical_marine_low_cloud"
+] = assessed_cld_fbk[1]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+ "tropical_anvil_cloud_area"
+] = assessed_cld_fbk[2]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+ "land_cloud_amount"
+] = assessed_cld_fbk[3]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+ "middle_latitude_marine_low_cloud_amount"
+] = assessed_cld_fbk[4]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+ "high_latitude_low_cloud_optical_depth"
+] = assessed_cld_fbk[5]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+ "implied_unassessed"
+] = assessed_cld_fbk[6]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+ "sum_of_assessed"
+] = assessed_cld_fbk[7]
+output_dict["RESULTS"][model][variant]["assessed_cloud_feedback"][
+ "total_cloud_feedback"
+] = assessed_cld_fbk[8]
 output_dict["RESULTS"][model][variant]["clim_cloud_rmse"] = climo_cld_rmse
 output_dict["RESULTS"][model][variant]["cloud_feedback_rmse"] = cld_fbk_rmse
 output_dict["RESULTS"][model][variant]["equilibrium_climate_sensitivity"] = ecs

pcmdi_metrics/cloud_feedback/lib/argparse_functions.py

@@ -4,7 +4,6 @@
 
 
 def AddParserArgument():
-
  P = CDPParser(
  default_args_file=[],
  description="Cloud feedback metrics",

pcmdi_metrics/cloud_feedback/lib/cal_CloudRadKernel_xr.py

@@ -7,13 +7,13 @@
 # to expert-assessed values from Sherwood et al (2020)
 # =============================================
 
+from datetime import date
+
 # IMPORT STUFF:
-import cftime
+import numpy as np
 import xarray as xr
 import xcdat as xc
-import numpy as np
 from global_land_mask import globe
-from datetime import date
 
 # =============================================
 # define necessary information
@@ -190,7 +190,7 @@ def get_CRK_data(filepath):
 ###########################################################################
 def get_kernel_regrid(ctl):
  # Read in data and map kernels to lat/lon
- 
+
  f = xc.open_mfdataset(datadir + "cloud_kernels2.nc", decode_times=False)
  f = f.rename({"mo": "time", "tau_midpt": "tau", "p_midpt": "plev"})
  f["time"] = ctl["time"].copy()
@@ -370,7 +370,7 @@ def bony_sorting_part2(OKwaps, data, OKland, WTS, binedges):
  # Ensure that the area matrix has zeros rather than masked points
  CNTS = CNTS.where(~np.isnan(CNTS), 0.0)
 
- if np.allclose(0.5, np.sum(CNTS.values, 1)) == False:
+ if np.allclose(0.5, np.sum(CNTS.values, 1)) is False:
  print(
  "sum of fractional counts over all wapbins does not equal 0.5 (tropical fraction)"
  )
@@ -385,7 +385,7 @@ def bony_sorting_part2(OKwaps, data, OKland, WTS, binedges):
  v2b = (data * WTS).sum(dim=["lat", "lon"]).transpose("time", "tau", "plev")
 
  # if np.allclose(v1.values,v2a.values)==False or np.allclose(v1.values,v2b.values)==False:
- if np.allclose(v1.values, v2b.values) == False:
+ if np.allclose(v1.values, v2b.values) is False:
  print("Cannot reconstruct tropical average via summing regimes")
 
  return DATA, CNTS # [time,wapbin]
@@ -602,7 +602,7 @@ def obscuration_terms3(c1, c2):
  L_R_bar = L_R_bar.where(L_R_bar >= 0, 0.0)
  F_bar = F_bar.where(F_bar >= 0, 0.0)
 
- rep_L_bar = tile_uneven(L_bar, L12)
+ # rep_L_bar = tile_uneven(L_bar, L12)
  rep_L_R_bar = tile_uneven(L_R_bar, L_R12)
  rep_F_bar = tile_uneven(F_bar, F12b)
 
@@ -873,7 +873,7 @@ def klein_metrics(obs_clisccp, gcm_clisccp, LWkern, SWkern, WTS):
  # take only clouds with tau>3.6
  clisccp_bias_CPM = clisccp_bias[:, 2:, :] # 4 tau bins
  obs_clisccp_CPM = obs_clisccp[:, 2:, :] # 4 tau bins
- gcm_clisccp_CPM = gcm_clisccp[:, 2:, :] # 4 tau bins
+ # gcm_clisccp_CPM = gcm_clisccp[:, 2:, :] # 4 tau bins
  LWkernel_CPM = LWkern[:, 2:, :] # 4 tau bins
  SWkernel_CPM = SWkern[:, 2:, :] # 4 tau bins
  NETkernel_CPM = SWkernel_CPM + LWkernel_CPM

pcmdi_metrics/cloud_feedback/lib/cld_fbks_ecs_assessment_v3.py

@@ -4,8 +4,8 @@
 """
 
 import json
-import string
 import os
+import string
 from datetime import date
 
 import matplotlib as mpl
@@ -17,7 +17,7 @@
 HEIGHT = 0.45
 
 # ######################################################
-# DEFINE COLORS FOR ECS COLORBAR 
+# DEFINE COLORS FOR ECS COLORBAR
 # ######################################################
 cmap = plt.cm.RdBu_r # define the colormap
 # extract all colors from the .jet map
@@ -75,7 +75,6 @@
 
 # ######################################################
 def get_expert_assessed_fbks():
-
  # ######################################################
  # ############# WCRP ASSESSMENT VALUES #################
  # ######################################################
@@ -272,7 +271,6 @@ def get_fbks(cld_fbks, obsc_cld_fbks, cld_errs, ecs_dict):
 
 # ######################################################
 def get_gcm_assessed_fbks(fbk_dict, obsc_fbk_dict):
-
  # dictionary is structured: [region][sfc][sec][name]
 
  assessed = []
@@ -364,7 +362,6 @@ def get_gcm_assessed_fbks(fbk_dict, obsc_fbk_dict):
 
 # ######################################################
 def get_gcm_cld_errs(err_dict, name):
-
  # dictionary is structured: [region][sfc][sec][name]
 
  DATA = []
@@ -403,7 +400,6 @@ def get_gcm_cld_errs(err_dict, name):
 
 # ######################################################
 def get_unassessed_fbks(fbk_dict, obsc_fbk_dict):
-
  # dictionary is structured: [region][sfc][sec][name]
 
  fbk_names = []
@@ -651,7 +647,6 @@ def label_models(ax, models5, models6):
 
 # ######################################################
 def plot_expert():
-
  (expert_cld_fbks, err_expert_cld_fbks, fbk_names) = get_expert_assessed_fbks()
  LN = len(fbk_names)
 
@@ -880,10 +875,15 @@ def static_plot(assessed, ecs, models, fbk_names, gen, fig, gs):
 
 # ######################################################
 def make_all_figs(
- cld_fbks6, obsc_cld_fbks6, cld_errs6, ecs_dict56, newmod, 
- figdir=None, datadir=None,
- debug=False):
-
+ cld_fbks6,
+ obsc_cld_fbks6,
+ cld_errs6,
+ ecs_dict56,
+ newmod,
+ figdir=None,
+ datadir=None,
+ debug=False,
+):
  # Set a unique marker for your new model
  MARK[newmod] = "<"
 
@@ -998,7 +998,9 @@ def make_all_figs(
  # new axis for labeling all models
  ax = plt.subplot(gs[:10, 10:12])
  label_models(ax, models5, models6)
- plt.savefig(os.path.join(figdir, "WCRP_assessed_cld_fbks_amip-p4K.png"), bbox_inches="tight")
+ plt.savefig(
+ os.path.join(figdir, "WCRP_assessed_cld_fbks_amip-p4K.png"), bbox_inches="tight"
+ )
  if debug:
  print("make_all_figs: WCRP_assessed_cld_fbks_amip-p4K.png done")
 
@@ -1030,7 +1032,10 @@ def make_all_figs(
  # new axis for labeling all models
  ax = plt.subplot(gs[:10, 10:12])
  label_models(ax, models5, models6)
- plt.savefig(os.path.join(figdir, "WCRP_unassessed_cld_fbks_amip-p4K.png"), bbox_inches="tight")
+ plt.savefig(
+ os.path.join(figdir, "WCRP_unassessed_cld_fbks_amip-p4K.png"),
+ bbox_inches="tight",
+ )
  if debug:
  print("make_all_figs: WCRP_unassessed_cld_fbks_amip-p4K.png done")
 
@@ -1277,8 +1282,9 @@ def make_all_figs(
  )
  cb.ax.tick_params(labelsize=14)
  ax2.set_ylabel(r"$\mathrm{E_{NET}}$", size=14)
- plt.savefig(os.path.join(
- figdir, "WCRP_assessed_RMSE_v_cldfbk2_amip-p4K.png"), bbox_inches="tight"
+ plt.savefig(
+ os.path.join(figdir, "WCRP_assessed_RMSE_v_cldfbk2_amip-p4K.png"),
+ bbox_inches="tight",
  )
 
  # #####################################################
@@ -1402,7 +1408,10 @@ def make_all_figs(
  plt.title("b", fontsize=16, loc="left")
  plt.ylim(0.04, 0.15)
  plt.xlim(0.60, 1.65)
- plt.savefig(os.path.join(figdir, "WCRP_totcldfbks2_v_E_NET_amip-p4K.png"), bbox_inches="tight")
+ plt.savefig(
+ os.path.join(figdir, "WCRP_totcldfbks2_v_E_NET_amip-p4K.png"),
+ bbox_inches="tight",
+ )
 
  # ######################################################
  # PRINT OUT THE TABLE OF EVERYTHING: