TEM calculation and plotting updates

lib/adf_web.py

@@ -56,7 +56,7 @@ class _WebData:
     needed by the website generator.
     """
 
-    def __init__(self, web_data, web_name, case_name,
+    def __init__(self, web_data, web_name, case_name,ext="Mean",
                  category = None,
                  season = None,
                  non_season = False,
@@ -74,6 +74,7 @@ def __init__(self, web_data, web_name, case_name,
         self.season     = season
         self.non_season = non_season
         self.plot_type  = plot_type
+        self.ext        = ext
         self.data_frame = data_frame
         self.html_file  = html_file
         self.asset_path = asset_path
@@ -299,7 +300,7 @@ def _write_run_info_to_log(self, config_file, active_env):
 
         self.debug_log(log_msg)
 
-    def add_website_data(self, web_data, web_name, case_name,
+    def add_website_data(self, web_data, web_name, case_name,ext="Mean",
                          category = None,
                          season = None,
                          non_season = False,
@@ -413,6 +414,7 @@ def add_website_data(self, web_data, web_name, case_name,
                             season = season,
                             non_season = non_season,
                             plot_type = plot_type,
+                            ext = ext,
                             data_frame = data_frame,
                             html_file = html_file,
                             asset_path = asset_path,
@@ -659,7 +661,6 @@ def jinja_enumerate(arg):
                 #Initialize Ordered Dictionary for season:
                 mean_html_info[ptype][category][var][season] = web_data.html_file.name
 
-
                 #Initialize Ordered Dictionary for non season kwarg:
                 if ptype not in non_seasons:
                     non_seasons[ptype] = OrderedDict()
@@ -733,7 +734,6 @@ def jinja_enumerate(arg):
 
                 #Check if the mean plot type page exists for this case (or for multi-case):
                 mean_table_file = table_pages_dir / "mean_tables.html"
-
                 #Construct mean_table.html
                 mean_table_tmpl = jinenv.get_template('template_mean_tables.html')
                 #Reuse the rend_kwarg_dict
@@ -742,7 +742,6 @@ def jinja_enumerate(arg):
                 with open(mean_table_file, 'w', encoding='utf-8') as ofil:
                     ofil.write(mean_table_rndr)
                 #End with
-
             #End if (tables)
 
             else: #Plot image
@@ -761,20 +760,21 @@ def jinja_enumerate(arg):
                     plot_types = plot_type_html
                 #End if
 
-                rend_kwarg_dict = {"title": main_title,
-                                   "var_title": web_data.name,
-                                   "season_title": web_data.season,
-                                   "case_name": web_data.case,
-                                   "case_yrs": case_yrs,
-                                   "base_name": data_name,
-                                   "baseline_yrs": baseline_yrs,
-                                   "plottype_title": web_data.plot_type,
-                                   "imgs": img_data,
-                                   "mydata": mean_html_info[web_data.plot_type],
-                                   "plot_types": plot_types,
-                                   "seasons": seasons,
-                                   "non_seasons": non_seasons[web_data.plot_type]}
 
+                rend_kwarg_dict = {"title": main_title,
+                                       "var_title": web_data.name,
+                                       "ext": web_data.ext,
+                                       "season_title": web_data.season,
+                                       "case_name": web_data.case,
+                                       "case_yrs": case_yrs,
+                                       "base_name": data_name,
+                                       "baseline_yrs": baseline_yrs,
+                                       "plottype_title": web_data.plot_type,
+                                       "imgs": img_data,
+                                       "mydata": mean_html_info[web_data.plot_type],
+                                       "plot_types": plot_types,
+                                       "seasons": seasons,
+                                       "non_seasons": non_seasons[web_data.plot_type]}
                 tmpl = jinenv.get_template('template.html')  #Set template
                 rndr = tmpl.render(rend_kwarg_dict) #The template rendered
 
@@ -831,6 +831,7 @@ def jinja_enumerate(arg):
             if web_data.case == 'multi-case':
                 plot_types = multi_plot_type_html
             else:
+                case1 = web_data.case
                 plot_types = plot_type_html
             plot_types = plot_type_html
             #End if

lib/plotting_functions.py

@@ -37,6 +37,7 @@
 import numpy as np
 import xarray as xr
 import matplotlib as mpl
+import matplotlib.cm as cm
 import cartopy.crs as ccrs
 #nice formatting for tick labels
 from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
@@ -70,7 +71,6 @@
             "SON": [9, 10, 11]
             }
 
-
 #################
 #HELPER FUNCTIONS
 #################
@@ -829,7 +829,6 @@ def plot_map_and_save(wks, case_nickname, base_nickname,
                     borderpad=0,
                     )
     fig.colorbar(img[1], cax=cb_mean_ax, **cp_info['colorbar_opt'])
-
 
     cb_pct_ax = inset_axes(ax3,
                     width="5%",  # width = 5% of parent_bbox width
@@ -1451,7 +1450,7 @@ def square_contour_difference(fld1, fld2, **kwargs):
     else:
         dnorm = mpl.colors.TwoSlopeNorm(vmin=dmin, vcenter=0, vmax=dmax)
         cmap = mpl.cm.RdBu_r
-        
+
     img3 = ax3.contourf(xx, yy, diff.transpose(), cmap=cmap, norm=dnorm)
     if (coord1 == 'month') and (fld1.shape[0] ==12):
         ax3.set_xticks(np.arange(1,13))

lib/website_templates/template_mean_diag.html

@@ -53,7 +53,7 @@ <h3 class="block">{{ category }}</h3>
                   {% for i,season in enumerate(ptype_seas.keys()) %}
                       {% if i==0 %}<!-- Grab first available season to make as default -->
                         <div class="grid-item-diag">
-                          <a href="../html_img/plot_page_{{ var_name }}_{{ season }}_{{ plottype_title }}_Mean.html"> {{var_name}} </a><br>
+                          <a href="../html_img/plot_page_{{ var_name }}_{{ season }}_{{ plottype_title }}_{{ ext }}.html"> {{var_name}} </a><br>
                         </div><!--grid-item2-->
                       {% endif %}
                   {% endfor %}<!-- ptype_seas.keys() -->
@@ -62,7 +62,7 @@ <h3 class="block">{{ category }}</h3>
                   {% for season in ptype_seas.keys() %}
                     {% if season == list(ptype_seas.keys())[0] %}
                       <div class="grid-item-diag">
-                        <a href="../html_img/plot_page_{{ var_name }}_{{ season }}_{{ plottype_title }}_Mean.html"> {{var_name}} </a><br>
+                        <a href="../html_img/plot_page_{{ var_name }}_{{ season }}_{{ plottype_title }}_{{ ext }}.html"> {{var_name}} </a><br>
                       </div><!--grid-item2-->
                     {% endif %}
                   {% endfor %}<!-- ptype_seas.keys() -->

scripts/averaging/create_TEM_files.py

@@ -33,10 +33,10 @@ def create_TEM_files(adf):
     res = adf.variable_defaults # will be dict of variable-specific plot preferences
 
     if "qbo" in adf.plotting_scripts:
-        var_list = ['uzm','epfy','epfz','vtem','wtem',
-                    'psitem','utendepfd','utendvtem','utendwtem']
+        var_list = ["UZM","THZM","EPFY","EPFZ","VTEM","WTEM",
+                    "PSITEM","UTENDEPFD","UTENDVTEM","UTENDWTEM"]
     else:
-        var_list = ['uzm','epfy','epfz','vtem','wtem','psitem','utendepfd']
+        var_list = ["UZM","THZM","EPFY","EPFZ","VTEM","WTEM","PSITEM","UTENDEPFD"]
 
     tem_locs = []
 
@@ -62,9 +62,9 @@ def create_TEM_files(adf):
         #End for
 
     #Set default to h4
-    hist_nums = adf.get_cam_info("tem_hist_str")
+    hist_nums = adf.get_cam_info("tem_hist_str")[0]
     if hist_nums is None:
-        hist_nums = ["h4"]*len(case_names)
+        hist_nums = ["h4a"]*len(case_names)
 
     #Get test case(s) tem over-write boolean and force to list if not by default
     overwrite_tem_cases = adf.get_cam_info("overwrite_tem")
@@ -154,7 +154,7 @@ def create_TEM_files(adf):
             #Set default to h4
             hist_num = adf.get_baseline_info("tem_hist_str")
             if hist_num is None:
-                hist_num = "h4"
+                hist_num = "h4a"
 
             #Extract baseline years (which may be empty strings if using Obs):
             syear_baseline = adf.climo_yrs["syear_baseline"]
@@ -200,8 +200,9 @@ def create_TEM_files(adf):
         #End if
 
         #Check if history files actually exist. If not then kill script:
-        hist_str = f"*{hist_nums[case_idx]}"
-        if not list(starting_location.glob(hist_str+'.*.nc')):
+
+        hist_str = f"{hist_nums[case_idx]}"
+        if not list(starting_location.glob("*"+hist_str+'.*.nc')):
             emsg = f"No CAM history {hist_str} files found in '{starting_location}'."
             emsg += " Script is ending here."
             adf.end_diag_fail(emsg)
@@ -237,7 +238,7 @@ def create_TEM_files(adf):
 
                 #Grab all leading zeros for climo year just in case
                 yr = f"{str(yr).zfill(4)}"
-                hist_files.append(glob(f"{starting_location}/{hist_str}.{yr}*.nc"))
+                hist_files.append(glob(f"{starting_location}/*{hist_str}.{yr}*.nc"))
 
             #Flatten list of lists to 1d list
             hist_files = sorted(list(chain.from_iterable(hist_files)))
@@ -423,6 +424,9 @@ def calc_tem(ds):
     vzm.attrs['long_name'] = 'Zonal-Mean meridional wind'
     vzm.attrs['units'] = 'm/s'
 
+    thzm.attrs['long_name'] = 'Zonal-Mean potential temperature'
+    thzm.attrs['units'] = 'K'
+
     epfy.attrs['long_name'] = 'northward component of E-P flux'
     epfy.attrs['units'] = 'm3/s2'
 
@@ -455,19 +459,28 @@ def calc_tem(ds):
     utendvtem.values = np.float32(utendvtem.values)
     utendwtem.values = np.float32(utendwtem.values)
 
+    #Average time dimension over time bounds, if bounds exist:
+    if 'time_bnds' in ds:
+        time_bounds_name = 'time_bnds'
+    elif 'time_bounds' in ds:
+        time_bounds_name = 'time_bounds'
+
     dstem = xr.Dataset(data_vars=dict(date = ds.date,
                                       datesec = ds.datesec,
-                                      time_bnds = ds.time_bnds,
-                                      uzm = uzm,
-                                      vzm = vzm,
-                                      epfy = epfy,
-                                      epfz = epfz,
-                                      vtem = vtem,
-                                      wtem = wtem,
-                                      psitem = psitem,
-                                      utendepfd = utendepfd,
-                                      utendvtem = utendvtem,
-                                      utendwtem = utendwtem
+                                      time_bnds = time_bounds_name,
+                                      hybm=ds.hybm,
+                                      hyam=ds.hyam,
+                                      UZM = uzm,
+                                      VZM = vzm,
+                                      THZM = thzm,
+                                      EPFY = epfy,
+                                      EPFZ = epfz,
+                                      VTEM = vtem,
+                                      WTEM = wtem,
+                                      PSITEM = psitem,
+                                      UTENDEPFD = utendepfd,
+                                      UTENDVTEM = utendvtem,
+                                      UTENDWTEM = utendwtem
                                       ))
 
     return dstem

scripts/plotting/global_latlon_vect_map.py

@@ -58,7 +58,12 @@ def global_latlon_vect_map(adfobj):
     #
     # Use ADF api to get all necessary information
     #
+    #Variable list
     var_list = adfobj.diag_var_list
+    #Remove unneccasry vairbale from plotting
+    if "PMID" in var_list:
+        var_list.remove("PMID")
+
     model_rgrid_loc = adfobj.get_basic_info("cam_regrid_loc", required=True)
 
     #Special ADF variable which contains the output path for

scripts/plotting/meridional_mean.py

@@ -24,7 +24,12 @@ def meridional_mean(adfobj):
 
     #Extract needed quantities from ADF object:
     #-----------------------------------------
+    #Variable list
     var_list = adfobj.diag_var_list
+    #Remove unneccasry vairbale from plotting
+    if "PMID" in var_list:
+        var_list.remove("PMID")
+
     model_rgrid_loc = adfobj.get_basic_info("cam_regrid_loc", required=True)
 
     #Special ADF variable which contains the output paths for