start plot dashboards

rime/bivariate_scores.py → rime/plot_dashboards.py

@@ -22,15 +22,18 @@
 import itertools as it
 from scipy.interpolate import interp1d
 
+from rime.process_config import *
+from rime.rime_functions import *
 
-input_dir = "C:\\Users\\werning\\IIASA\\ECE.prog - Research Theme - NEXUS\\Hotspots_Explorer_2p0\\Data\\1_mmm_stats"
-score_dir = "C:\\Users\\werning\\IIASA\ECE.prog - Research Theme - NEXUS\\Hotspots_Explorer_2p0\\Data\\3_scores"
-diff_dir = "C:\\Users\\werning\\IIASA\ECE.prog - Research Theme - NEXUS\\Hotspots_Explorer_2p0\\Data\\2_differences"
-std_dir = "C:\\Users\\werning\\IIASA\\ECE.prog - Research Theme - NEXUS\\Hotspots_Explorer_2p0\\Data\\score_revision\\hist_standard_deviations_V2\\"
-plot_dir = "C:\\Users\\werning\\IIASA\ECE.prog - Research Theme - NEXUS\\Hotspots_Explorer_2p0\\Data\\score_revision\\bivariate"
-yaml_path = "H:\\git\\climate_impacts_processing\\hotspots.yml"
-landmask_path = "H:\\git\\climate_impacts_processing\\landareamaskmap0.nc"
-kg_class_path = "C:\\Users\\werning\\IIASA\\ECE.prog - Research Theme - NEXUS\\Hotspots_Explorer_2p0\\Data\\kg_class.nc"
+
+input_dir = "C:\\Users\\{user}\\IIASA\\ECE.prog - Research Theme - NEXUS\\Hotspots_Explorer_2p0\\Data\\1_mmm_stats"
+score_dir = "C:\\Users\\{user}\\IIASA\ECE.prog - Research Theme - NEXUS\\Hotspots_Explorer_2p0\\Data\\3_scores"
+diff_dir = "C:\\Users\\{user}\\IIASA\ECE.prog - Research Theme - NEXUS\\Hotspots_Explorer_2p0\\Data\\2_differences"
+std_dir = "C:\\Users\\{user}\\IIASA\\ECE.prog - Research Theme - NEXUS\\Hotspots_Explorer_2p0\\Data\\score_revision\\hist_standard_deviations_V2\\"
+# plot_dir = "C:\\Users\\{user}\\IIASA\ECE.prog - Research Theme - NEXUS\\Hotspots_Explorer_2p0\\Data\\score_revision\\bivariate"
+# yaml_path = "H:\\git\\climate_impacts_processing\\hotspots.yml"
+# landmask_path = "H:\\git\\climate_impacts_processing\\landareamaskmap0.nc"
+# kg_class_path = "H:\\git\\climate_impacts_processing\\kg_class.nc"
 
 # Set interpolation & load landarea mask and parameters
 land_mask = hfp.load_landmask(landmask_path)
@@ -61,16 +64,6 @@
 # %% Functions
 # -----------------------------------------------------------------------------
 
-# def bin_data(data, bins):
-
-#     print(bins)
-
-#     no_impact = xr.where(data < bins[1], bins[0], data)
-#     low_impact = xr.where((no_impact < bins[2]) & (no_impact >= bins[1]), 1, no_impact)
-#     medium_impact = xr.where((low_impact < bins[3]) & (low_impact >= bins[2]), 2, low_impact)
-#     high_impact = xr.where(medium_impact >= bins[3], 3, medium_impact)
-
-#     return high_impact
 
 
 def bin_data(data, bins):

rime/process_config.py

@@ -13,6 +13,15 @@
 # =============================================================================
 import os
 
+# Run and environment settings
+user = "byers"
+env = "pc"
+# env = 'server'
+# env = 'ebro3'
+
+git_path = f"C:\\users\\{user}\\Github\\"
+
+
 
 # From generate_aggregated_inputs.py
 
@@ -23,11 +32,7 @@
 yr_start = 2010
 yr_end = 2100
 
-# Run and environment settings
-user = "byers"
-env = "pc"
-# env = 'server'
-# env = 'ebro3'
+
 
 num_workers = 24
 parallel = True  # Uses Dask in processing the IAMC scenarios
@@ -40,7 +45,10 @@
 # %% Working directories
 # =============================================================================
 
-yaml_path = "C:\\users\\byers\\Github\\climate_impacts_processing\\hotspots.yml"
+
+yaml_path = git_path+"climate_impacts_processing\\hotspots.yml"
+landmask_path = git_path+"climate_impacts_processing\\landareamaskmap0.nc"
+kg_class_path = git_path+"climate_impacts_processing\\kg_class.nc"
 
 if env == "pc":
     # Main working directory
@@ -112,7 +120,7 @@
 # impact data settings
 
 indicators = ["cdd", "precip"]
-ftype = "score"
+ftype = "score" #score
 interpolation = 0.01
 
 

rime/process_maps.py

@@ -4,6 +4,7 @@
 Created on Thu Apr  6 11:36:24 2023
 
 @author: werning, byers
+Execute this script ideally in the top level of the RIME directory
 
 To Do:
     

rime/rime_functions.py

@@ -419,6 +419,26 @@ def map_transform_gmt_multi_dask(
 
 
 def calculate_cumulative_CO2(ts, first_year, year, variable):
+    """
+    
+
+    Parameters
+    ----------
+    ts : TYPE
+        DESCRIPTION.
+    first_year : TYPE
+        DESCRIPTION.
+    year : TYPE
+        DESCRIPTION.
+    variable : TYPE
+        DESCRIPTION.
+
+    Returns
+    -------
+    dfo : TYPE
+        DESCRIPTION.
+
+    """
     dfo = (
         ts.apply(
             pyam.cumulative, raw=False, axis=1, first_year=first_year, last_year=year