MJO xcdat conversion

docs/metrics_sea_ice.rst

@@ -28,7 +28,7 @@ or as a combination of an input parameter file and the command line, e.g.: ::
 
 Outputs
 =======
-The driver produces a JSON file containing mean square error metrics for all input models and realizations relative to the reference data set. It also produces a bar chart displaying these metrics.
+The driver produces two JSON files. The first contains mean square error metrics for all input models and realizations relative to the reference data set. The second contains sea ice climatology and area data. The driver also produces a bar chart displaying these metrics.
 
 Sectors
 ########
@@ -66,6 +66,7 @@ A `demo parameter file`_ is provided in the sea ice code.
 * **obs_area_template**: File path of grid area data. If unavailalbe, skip and use "obs_cell_area".
 * **obs_area_var**: Name of reference area variable, if available. If unavailable, skip and use "obs_cell_area".
 * **obs_cell_area**: For equal area grids, the area of a single grid cell in units of km :sup:`2` . Only required if obs area file is not available.
+* **pole**: Set the maximum latitude for the Central Arctic and Arctic regions to exclude ice over the pole. Default is 90.1 to include all ice.
 
 Reference
 =========

pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py

@@ -49,11 +49,16 @@ def parallel_coordinate_plot(
  arrow_between_lines=False,
  arrow_between_lines_colors=("red", "green"),
  arrow_alpha=1,
+ arrow_width=0.05,
+ arrow_linewidth=0,
+ arrow_head_width=0.15,
+ arrow_head_length=0.15,
  vertical_center=None,
  vertical_center_line=False,
  vertical_center_line_label=None,
  ymax=None,
  ymin=None,
+ debug=False,
 ):
  """
  Parameters
@@ -99,11 +104,15 @@ def parallel_coordinate_plot(
  - `arrow_between_lines`: bool, default=False, place arrows between two lines for models in comparing_models
  - `arrow_between_lines_colors`: tuple or list containing two strings of colors for arrow between the two lines. Default=('red', 'green')
  - `arrow_alpha`: float, default=1, transparency of arrow (faction between 0 to 1)
+ - `arrow_width`: float, default is 0.05, width of arrow
+ - `arrow_linewidth`: float, default is 0, width of arrow edge line
+ - `arrow_head_width`: float, default is 0.15, widht of arrow head
+ - `arrow_head_length`: float, default is 0.15, length of arrow head
  - `vertical_center`: string ("median", "mean")/float/integer, default=None, adjust range of vertical axis to set center of vertical axis as median, mean, or given number
  - `vertical_center_line`: bool, default=False, show median as line
  - `vertical_center_line_label`: str, default=None, label in legend for the horizontal vertical center line. If not given, it will be automatically assigned. It can be turned off by "off"
- - `ymax`: int or float, default=None, specify value of vertical axis top
- - `ymin`: int or float, default=None, specify value of vertical axis bottom
+ - `ymax`: int or float or string ('percentile'), default=None, specify value of vertical axis top. If percentile, 95th percentile or extended for top
+ - `ymin`: int or float or string ('percentile'), default=None, specify value of vertical axis bottom. If percentile, 5th percentile or extended for bottom
 
  Return
  ------
@@ -117,6 +126,7 @@ def parallel_coordinate_plot(
  2023-03 median centered option added
  2023-04 vertical center option diversified (median, mean, or given number)
  2024-03 parameter added for violin plot label
+ 2024-04 parameters added for arrow and option added for ymax/ymin setting
  """
  params = {
  "legend.fontsize": "large",
@@ -143,6 +153,10 @@ def parallel_coordinate_plot(
  ymin=ymin,
  )
 
+ if debug:
+ print("ymins:", ymins)
+ print("ymaxs:", ymaxs)
+
  # Prepare plot
  if N > 20:
  if xtick_labelsize is None:
@@ -317,8 +331,8 @@ def parallel_coordinate_plot(
  alpha=0.5,
  )
 
+ # Add vertical arrows
  if arrow_between_lines:
- # Add vertical arrows
  for xi, yi1, yi2 in zip(x, y1, y2):
  if yi2 > yi1:
  arrow_color = arrow_between_lines_colors[0]
@@ -335,8 +349,11 @@ def parallel_coordinate_plot(
  color=arrow_color,
  length_includes_head=True,
  alpha=arrow_alpha,
- width=0.05,
- head_width=0.15,
+ width=arrow_width,
+ linewidth=arrow_linewidth,
+ head_width=arrow_head_width,
+ head_length=arrow_head_length,
+ zorder=999,
  )
 
  ax.set_xlim(-0.5, N - 0.5)
@@ -421,15 +438,28 @@ def _data_transform(
  # Data to plot
  ys = data # stacked y-axis values
  N = ys.shape[1] # number of vertical axis (i.e., =len(metric_names))
+
  if ymax is None:
  ymaxs = np.nanmax(ys, axis=0) # maximum (ignore nan value)
  else:
- ymaxs = np.repeat(ymax, N)
+ try:
+ if isinstance(ymax, str) and ymax == "percentile":
+ ymaxs = np.nanpercentile(ys, 95, axis=0)
+ else:
+ ymaxs = np.repeat(ymax, N)
+ except ValueError:
+ print(f"Invalid input for ymax: {ymax}")
 
  if ymin is None:
  ymins = np.nanmin(ys, axis=0) # minimum (ignore nan value)
  else:
- ymins = np.repeat(ymin, N)
+ try:
+ if isinstance(ymin, str) and ymin == "percentile":
+ ymins = np.nanpercentile(ys, 5, axis=0)
+ else:
+ ymins = np.repeat(ymin, N)
+ except ValueError:
+ print(f"Invalid input for ymin: {ymin}")
 
  ymeds = np.nanmedian(ys, axis=0) # median
  ymean = np.nanmean(ys, axis=0) # mean
@@ -439,14 +469,17 @@ def _data_transform(
  ymids = ymeds
  elif vertical_center == "mean":
  ymids = ymean
- else:
+ elif isinstance(vertical_center, float) or isinstance(vertical_center, int):
  ymids = np.repeat(vertical_center, N)
+ else:
+ raise ValueError(f"vertical center {vertical_center} unknown.")
+
  for i in range(0, N):
- max_distance_from_middle = max(
+ distance_from_middle = max(
  abs(ymaxs[i] - ymids[i]), abs(ymids[i] - ymins[i])
  )
- ymaxs[i] = ymids[i] + max_distance_from_middle
- ymins[i] = ymids[i] - max_distance_from_middle
+ ymaxs[i] = ymids[i] + distance_from_middle
+ ymins[i] = ymids[i] - distance_from_middle
 
  dys = ymaxs - ymins
  if ymin is None:

pcmdi_metrics/sea_ice/README.md

@@ -28,7 +28,7 @@ sea_ice_driver.py -p parameter_file.py --msyear 1991 --meyear 2020
 
 ## Outputs
 
-The driver produces a JSON file containing mean square error metrics for all input models and realizations relative to the reference data set. It also produces a bar chart displaying these metrics.
+The driver produces two JSON files. The first contains mean square error metrics for all input models and realizations relative to the reference data set. The second contains sea ice climatology and area data. The driver also produces a bar chart displaying these metrics.
 
 ### Sectors
 
@@ -62,6 +62,7 @@ A [demo parameter file](https://github.com/PCMDI/pcmdi_metrics/blob/405_sic_ao/p
 * **obs_area_template**: File path of grid area data. If unavailalbe, skip and use "obs_cell_area".
 * **obs_area_var**: Name of reference area variable, if available. If unavailable, skip and use "obs_cell_area".
 * **obs_cell_area**: For equal area grids, the area of a single grid cell in units of km2. Only required if obs area file is not available.
+* **pole**: Set the maximum latitude for the Central Arctic and Arctic regions to exclude ice over the pole. Default is 90.1 to include all ice.
 
 
 ## Reference

pcmdi_metrics/sea_ice/lib/sea_ice_lib.py

@@ -61,18 +61,18 @@ def write(self):
 # ------------------------------------
 # Define region coverage in functions
 # ------------------------------------
-def central_arctic(ds, ds_var, xvar, yvar):
+def central_arctic(ds, ds_var, xvar, yvar, pole):
  if (ds[xvar] > 180).any(): # 0 to 360
  data_ca1 = ds[ds_var].where(
  (
  (ds[yvar] > 80)
- & (ds[yvar] <= 87.2)
+ & (ds[yvar] <= pole)
  & ((ds[xvar] > 240) | (ds[xvar] <= 90))
  ),
  0,
  )
  data_ca2 = ds[ds_var].where(
- ((ds[yvar] > 65) & (ds[yvar] < 87.2))
+ ((ds[yvar] > 65) & (ds[yvar] < pole))
  & ((ds[xvar] > 90) & (ds[xvar] <= 240)),
  0,
  )
@@ -81,14 +81,14 @@ def central_arctic(ds, ds_var, xvar, yvar):
  data_ca1 = ds[ds_var].where(
  (
  (ds[yvar] > 80)
- & (ds[yvar] <= 87.2)
+ & (ds[yvar] <= pole)
  & (ds[xvar] > -120)
  & (ds[xvar] <= 90)
  ),
  0,
  )
  data_ca2 = ds[ds_var].where(
- ((ds[yvar] > 65) & (ds[yvar] < 87.2))
+ ((ds[yvar] > 65) & (ds[yvar] < pole))
  & ((ds[xvar] > 90) | (ds[xvar] <= -120)),
  0,
  )
@@ -180,8 +180,8 @@ def indian_ocean(ds, ds_var, xvar, yvar):
  return data_io
 
 
-def arctic(ds, ds_var, xvar, yvar):
- data_arctic = ds[ds_var].where(ds[yvar] > 0, 0)
+def arctic(ds, ds_var, xvar, yvar, pole):
+ data_arctic = ds[ds_var].where((ds[yvar] > 0) & (ds[yvar] < pole), 0)
  return data_arctic
 
 
@@ -190,13 +190,13 @@ def antarctic(ds, ds_var, xvar, yvar):
  return data_antarctic
 
 
-def choose_region(region, ds, ds_var, xvar, yvar):
+def choose_region(region, ds, ds_var, xvar, yvar, pole):
  if region == "arctic":
- return arctic(ds, ds_var, xvar, yvar)
+ return arctic(ds, ds_var, xvar, yvar, pole)
  elif region == "na":
  return north_atlantic(ds, ds_var, xvar, yvar)
  elif region == "ca":
- return central_arctic(ds, ds_var, xvar, yvar)
+ return central_arctic(ds, ds_var, xvar, yvar, pole)
  elif region == "np":
  return north_pacific(ds, ds_var, xvar, yvar)
  elif region == "antarctic":
@@ -236,14 +236,14 @@ def get_clim(total_extent, ds, ds_var):
  return clim
 
 
-def process_by_region(ds, ds_var, ds_area):
+def process_by_region(ds, ds_var, ds_area, pole):
  regions_list = ["arctic", "antarctic", "ca", "na", "np", "sa", "sp", "io"]
  clims = {}
  means = {}
  for region in regions_list:
  xvar = find_lon(ds)
  yvar = find_lat(ds)
- data = choose_region(region, ds, ds_var, xvar, yvar)
+ data = choose_region(region, ds, ds_var, xvar, yvar, pole)
  total_extent, te_mean = get_total_extent(data, ds_area)
  clim = get_clim(total_extent, ds, ds_var)
  clims[region] = clim

pcmdi_metrics/sea_ice/lib/sea_ice_parser.py

@@ -223,4 +223,11 @@ def create_sea_ice_parser():
  default=True,
  help="Option for generate individual plots for models: True (default) / False",
  )
+
+ parser.add_argument(
+ "--pole",
+ type=float,
+ default=90.1,
+ help="Set to a latitude value to exclude sea ice at North pole. Must be > 80.",
+ )
  return parser

pcmdi_metrics/sea_ice/sea_ice_driver.py

@@ -48,6 +48,7 @@
  osyear = parameter.osyear
  oeyear = parameter.oeyear
  plot = parameter.plot
+ pole = parameter.pole
 
  print("Model list:", model_list)
  model_list.sort()
@@ -106,7 +107,7 @@
  mask = create_land_sea_mask(obs, lon_key=xvar, lat_key=yvar)
  obs[obs_var] = obs[obs_var].where(mask < 1)
  # Get regions
- clims, means = lib.process_by_region(obs, obs_var, area_val)
+ clims, means = lib.process_by_region(obs, obs_var, area_val, pole)
 
  arctic_clims = {
  "arctic": clims["arctic"],
@@ -149,7 +150,7 @@
  # Remove land areas (including lakes)
  mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat")
  obs[obs_var] = obs[obs_var].where(mask < 1)
- clims, means = lib.process_by_region(obs, obs_var, area_val)
+ clims, means = lib.process_by_region(obs, obs_var, area_val, pole)
  antarctic_clims = {
  "antarctic": clims["antarctic"],
  "io": clims["io"],
@@ -358,7 +359,7 @@
  ds[var] = ds[var].where(mask < 1)
 
  # Get regions
- clims, means = lib.process_by_region(ds, var, area[area_var].data)
+ clims, means = lib.process_by_region(ds, var, area[area_var].data, pole)
 
  ds.close()
  # Running sum of all realizations