Merge branch 'main' into lee1043-patch-1

pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py

@@ -106,7 +106,7 @@ def portrait_plot(
  # ----------------
  # Prepare plotting
  # ----------------
- data, num_divide = prepare_data(data, xaxis_labels, yaxis_labels, debug)
+ data, num_divide = prepare_data(data, xaxis_labels, yaxis_labels, debug=debug)
 
  if num_divide not in [1, 2, 4]:
  sys.exit("Error: Number of (stacked) array is not 1, 2, or 4.")
@@ -117,7 +117,7 @@ def portrait_plot(
  num_divide_annotate = num_divide
  else:
  annotate_data, num_divide_annotate = prepare_data(
- annotate_data, xaxis_labels, yaxis_labels, debug
+ annotate_data, xaxis_labels, yaxis_labels, debug=debug
  )
  if num_divide_annotate != num_divide:
  sys.exit("Error: annotate_data does not have same size as data")
@@ -154,8 +154,8 @@ def portrait_plot(
  if num_divide == 1:
  ax, im = heatmap(
  data,
- yaxis_labels,
  xaxis_labels,
+ yaxis_labels,
  ax=ax,
  invert_yaxis=invert_yaxis,
  cmap=cmap,
@@ -335,7 +335,7 @@ def portrait_plot(
 # ======================================================================
 # Prepare data
 # ----------------------------------------------------------------------
-def prepare_data(data, xaxis_labels, yaxis_labels, debug):
+def prepare_data(data, xaxis_labels, yaxis_labels, debug=False):
  # In case data was given as list of arrays, convert it to numpy (stacked) array
  if type(data) == list:
  if debug:
@@ -362,7 +362,7 @@ def prepare_data(data, xaxis_labels, yaxis_labels, debug):
  sys.exit("Error: Number of elements in yaxis_label mismatchs to the data")
 
  if type(data) == np.ndarray:
- data = np.squeeze(data)
+ # data = np.squeeze(data)
  if len(data.shape) == 2:
  num_divide = 1
  elif len(data.shape) == 3:
@@ -386,17 +386,17 @@ def prepare_data(data, xaxis_labels, yaxis_labels, debug):
 # Portrait plot 1: heatmap-style (no triangle)
 # (Inspired from: https://matplotlib.org/devdocs/gallery/images_contours_and_fields/image_annotated_heatmap.html)
 # ----------------------------------------------------------------------
-def heatmap(data, row_labels, col_labels, ax=None, invert_yaxis=False, **kwargs):
+def heatmap(data, xaxis_labels, yaxis_labels, ax=None, invert_yaxis=False, **kwargs):
  """
  Create a heatmap from a numpy array and two lists of labels.
 
  Parameters
  ----------
  data
  A 2D numpy array of shape (M, N).
- row_labels
+ yaxis_labels
  A list or array of length M with the labels for the rows.
- col_labels
+ xaxis_labels
  A list or array of length N with the labels for the columns.
  ax
  A `matplotlib.axes.Axes` instance to which the heatmap is plotted. If
@@ -419,8 +419,8 @@ def heatmap(data, row_labels, col_labels, ax=None, invert_yaxis=False, **kwargs)
  # Show all ticks and label them with the respective list entries.
  ax.set_xticks(np.arange(data.shape[1]) + 0.5, minor=False)
  ax.set_yticks(np.arange(data.shape[0]) + 0.5, minor=False)
- ax.set_xticklabels(col_labels)
- ax.set_yticklabels(row_labels)
+ ax.set_xticklabels(xaxis_labels)
+ ax.set_yticklabels(yaxis_labels)
  ax.tick_params(which="minor", bottom=False, left=False)
 
  return ax, im

pcmdi_metrics/graphics/share/read_json_mean_clim.py

@@ -4,6 +4,8 @@
 import numpy as np
 import pandas as pd
 
+from pcmdi_metrics.variability_mode.lib import sort_human
+
 
 def read_mean_clim_json_files(
  json_list, regions=None, stats=None, mip=None, debug=False
@@ -121,7 +123,7 @@ def extract_stat(var, results_dict_var):
 
 def extract_region_stat(var, results_dict_var):
  model_list = sorted(list(results_dict_var["RESULTS"].keys()))
- run_list = sorted(
+ run_list = sort_human(
  list(results_dict_var["RESULTS"][model_list[0]]["default"].keys())
  )
  if "source" in run_list:
@@ -144,18 +146,20 @@ def extract_data(results_dict, var_list, region, stat, season, mip, debug=False)
  Return a pandas dataframe for metric numbers at given region/stat/season.
  Rows: models, Columns: variables (i.e., 2d array)
  """
- if "rlut" in list(results_dict["rlut"]["RESULTS"].keys()):
- model_list = sorted(list(results_dict["rlut"]["RESULTS"].keys()))
+ if "rlut" in list(results_dict.keys()):
+ if "rlut" in list(results_dict["rlut"]["RESULTS"].keys()):
+ model_list = sorted(list(results_dict["rlut"]["RESULTS"].keys()))
  else:
  model_list = sorted(list(results_dict[var_list[0]]["RESULTS"].keys()))
 
  data_list = []
  for model in model_list:
- if "rlut" in list(results_dict["rlut"]["RESULTS"].keys()):
- run_list = list(results_dict["rlut"]["RESULTS"][model]["default"].keys())
+ if "rlut" in list(results_dict.keys()):
+ if "rlut" in list(results_dict["rlut"]["RESULTS"].keys()):
+ run_list = sort_human(list(results_dict["rlut"]["RESULTS"][model]["default"].keys()))
  else:
- run_list = list(
- results_dict[var_list[0]]["RESULTS"][model]["default"].keys()
+ run_list = sort_human(list(
+ results_dict[var_list[0]]["RESULTS"][model]["default"].keys())
  )
 
  if debug: