Mean clim patch

pcmdi_metrics/graphics/parallel_coordinate_plot/parallel_coordinate_plot_lib.py

@@ -37,8 +37,11 @@ def parallel_coordinate_plot(
  comparing_models=None,
  fill_between_lines=False,
  fill_between_lines_colors=("green", "red"),
- median_centered=False,
- median_line=False,
+ vertical_center=None,
+ vertical_center_line=False,
+ vertical_center_line_label=None,
+ ymax=None,
+ ymin=None,
 ):
  """
  Parameters
@@ -72,8 +75,11 @@ def parallel_coordinate_plot(
  - `comparing_models`: tuple or list containing two strings for models to compare with colors filled between the two lines.
  - `fill_between_lines`: bool, default=False, fill color between lines for models in comparing_models
  - `fill_between_lines_colors`: tuple or list containing two strings for colors filled between the two lines. Default=('green', 'red')
- - `median_centered`: bool, default=False, adjust range of vertical axis to set center of vertical axis as median
- - `median_line`: bool, default=False, show median as line
+ - `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
 
  Return
  ------
@@ -82,9 +88,10 @@ def parallel_coordinate_plot(
 
  Author: Jiwoo Lee @ LLNL (2021. 7)
  Update history: 
+ 2021-07 Plotting code created. Inspired by https://stackoverflow.com/questions/8230638/parallel-coordinates-plot-in-matplotlib
  2022-09 violin plots added
  2023-03 median centered option added
- Inspired by https://stackoverflow.com/questions/8230638/parallel-coordinates-plot-in-matplotlib
+ 2023-04 vertical center option diversified (median, mean, or given number)
  """
  params = {
  "legend.fontsize": "large",
@@ -99,14 +106,16 @@ def parallel_coordinate_plot(
  _quick_qc(data, model_names, metric_names, model_names2=model_names2)
 
  # Transform data for plotting
- zs, zs_meds, N, ymins, ymaxs, df_stacked, df2_stacked = _data_transform(
+ zs, zs_middle, N, ymins, ymaxs, df_stacked, df2_stacked = _data_transform(
  data,
  metric_names,
  model_names,
  model_names2=model_names2,
  group1_name=group1_name,
  group2_name=group2_name,
- median_centered=median_centered,
+ vertical_center=vertical_center,
+ ymax=ymax,
+ ymin=ymin,
  )
 
  # Prepare plot
@@ -226,8 +235,12 @@ def parallel_coordinate_plot(
  clip_on=False,
  )
 
- if median_line: 
- ax.plot(range(N), zs_meds, "-", c="k", label="median", lw=1)
+ if vertical_center_line:
+ if vertical_center_line_label is None:
+ vertical_center_line_label = str(vertical_center)
+ elif vertical_center_line_label == "off":
+ vertical_center_line_label = None 
+ ax.plot(range(N), zs_middle, "-", c="k", label=vertical_center_line_label, lw=1)
 
  # Fill between lines
  if fill_between_lines and (comparing_models is not None):
@@ -309,30 +322,54 @@ def _data_transform(
  model_names2=None,
  group1_name="group1",
  group2_name="group2",
- median_centered=False,
+ vertical_center=None,
+ ymax=None,
+ ymin=None,
 ):
  # Data to plot
  ys = data # stacked y-axis values
  N = ys.shape[1] # number of vertical axis (i.e., =len(metric_names))
- ymins = np.nanmin(ys, axis=0) # minimum (ignore nan value)
- ymaxs = np.nanmax(ys, axis=0) # maximum (ignore nan value)
+ if ymax is None:
+ ymaxs = np.nanmax(ys, axis=0) # maximum (ignore nan value)
+ else:
+ ymaxs = np.repeat(ymax, N)
+
+ if ymin is None:
+ ymins = np.nanmin(ys, axis=0) # minimum (ignore nan value)
+ else:
+ ymins = np.repeat(ymin, N)
+
  ymeds = np.nanmedian(ys, axis=0) # median
- if median_centered:
+ ymean = np.nanmean(ys, axis=0) # mean
+
+ if vertical_center is not None:
+ if vertical_center == "median":
+ ymids = ymeds
+ elif vertical_center == "mean":
+ ymids = ymean
+ else:
+ ymids = np.repeat(vertical_center, N)
  for i in range(0, N):
- max_distance_from_median = max(abs(ymaxs[i] - ymeds[i]), abs(ymeds[i] - ymins[i]))
- ymaxs[i] = ymeds[i] + max_distance_from_median
- ymins[i] = ymeds[i] - max_distance_from_median
+ 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
+
  dys = ymaxs - ymins
- ymins -= dys * 0.05 # add 5% padding below and above
- ymaxs += dys * 0.05
+ if ymin is None:
+ ymins -= dys * 0.05 # add 5% padding below and above
+ if ymax is None:
+ ymaxs += dys * 0.05
  dys = ymaxs - ymins
 
  # Transform all data to be compatible with the main axis
  zs = np.zeros_like(ys)
  zs[:, 0] = ys[:, 0]
  zs[:, 1:] = (ys[:, 1:] - ymins[1:]) / dys[1:] * dys[0] + ymins[0]
 
- zs_meds = (ymeds[:] - ymins[:]) / dys[:] * dys[0] + ymins[0]
+ if vertical_center is not None:
+ zs_middle = (ymids[:] - ymins[:]) / dys[:] * dys[0] + ymins[0]
+ else:
+ zs_middle = (ymaxs[:] - ymins[:]) / 2 / dys[:] * dys[0] + ymins[0]
 
  if model_names2 is not None:
  print("Models in the second group:", model_names2)
@@ -355,7 +392,7 @@ def _data_transform(
  group2_name=group2_name,
  )
 
- return zs, zs_meds, N, ymins, ymaxs, df_stacked, df2_stacked
+ return zs, zs_middle, N, ymins, ymaxs, df_stacked, df2_stacked
 
 
 def _to_pd_dataframe(