Merge pull request #10 from iiasa/exampledata

Exampledata

rime/generate_aggregated_inputs.py

@@ -107,8 +107,8 @@
     df = df.timeseries().reset_index()
     df.dropna(how="all", inplace=True)
 
-    df[["SSP", "GMT"]] = df.scenario.str.split("_", expand=True)
-    df["GMT"] = df["GMT"].str.replace("p", ".").astype("float")
+    df[["SSP", "GWL"]] = df.scenario.str.split("_", expand=True)
+    df["GWL"] = df["GWL"].str.replace("p", ".").astype("float")
 
     # df.drop(columns=['model', 'scenario'], inplace=True)
 
@@ -129,7 +129,7 @@
             print("alive bar present")
         # Apply function here
         for vari in small_vars:
-            df_ind = loop_interpolate_gmt(
+            df_ind = loop_interpolate_gwl(
                 df.loc[df.variable.str.startswith(vari)], yr_start, yr_end
             )
             # dfbig = pd.concat([dfbig, df_ind])
@@ -146,7 +146,7 @@
                     "region",
                     "unit",
                     "SSP",
-                    "GMT",
+                    "GWL",
                 ],
                 value_vars=years,
                 var_name="year",
@@ -160,7 +160,7 @@
                 print(indicator)
                 dx = (
                     dfp.loc[dfp.variable == indicator]
-                    .set_index(["gmt", "year", "ssp", "region"])
+                    .set_index(["gwl", "year", "ssp", "region"])
                     .to_xarray()
                 )
                 # dx.attrs['unit'] = dx.assign_coords({'unit':dx.unit.values[0,0,0,0]})

rime/process_config.py

@@ -71,12 +71,12 @@
     fname_input_scenarios = f"emissions_temp_AR6_small.xlsx"
 
     # Directory of map files to read as input
-    # impact_data_dir = f"{wd}\\data\\4_split_files_for_geoserver"
-    impact_data_dir = f"{wd_input}split_files"
+    impact_data_dir = f"{wd}\\data\\4_split_files_for_geoserver"
+    # impact_data_dir = f"{wd_input}split_files"
 
 
 # =============================================================================
-# %% From process-iamc_scenarios_gmt.py
+# %% From process-iamc_scenarios_gwl.py
 # =============================================================================
 
 year_resols = [5]

rime/process_maps.py

@@ -60,20 +60,20 @@
 # Test multiple scenarios, 1 indicator
 files = glob.glob(os.path.join(impact_data_dir, ind, f"*{short}_{ssp}*{ftype}.nc4"))
 mapdata = xr.open_mfdataset(
-    files, preprocess=remove_ssp_from_ds, combine="nested", concat_dim="gmt"
+    files, preprocess=remove_ssp_from_ds, combine="nested", concat_dim="gwl"
 )
 
 mapdata = tidy_mapdata(mapdata)
 
 
 df = dft.filter(model="POLES GE*")
 
-map_out_MS = map_transform_gmt_wrapper(
+map_out_MS = map_transform_gwl_wrapper(
     df,
     mapdata,
     years,
     use_dask=True,
-    gmt_name="gmt",
+    gwl_name="gwl",
     interpolation=interpolation,
 )
 
@@ -123,18 +123,18 @@
             os.path.join(impact_data_dir, ind, f"*{short}_{ssp}*{ftype}.nc4")
         )
         mapdata[short] = xr.open_mfdataset(
-            files, preprocess=remove_ssp_from_ds, combine="nested", concat_dim="gmt"
+            files, preprocess=remove_ssp_from_ds, combine="nested", concat_dim="gwl"
         )[short]
 
 mapdata = tidy_mapdata(mapdata)
 
 
-map_out_MI = map_transform_gmt_wrapper(
+map_out_MI = map_transform_gwl_wrapper(
     dft.filter(model="AIM*", scenario="SSP1-34"),
     mapdata,
     years=years,
     use_dask=False,
-    gmt_name="gmt",
+    gwl_name="gwl",
 )
 
 comp = dict(zlib=True, complevel=5)

rime/process_tabledata.py

@@ -47,8 +47,8 @@
             "CO2 mode: Global mean temperatures will be derived from response \
               to cumulative CO2 emissions."
         )
-    elif mode == "GMT":
-        print("GMT mode: Global mean temperatures provided as input.")
+    elif mode == "GWL":
+        print("GWL mode: Global mean temperatures provided as input.")
 
     if parallel:
         dask.config.set(
@@ -99,11 +99,11 @@
             # SCENARIO DATA PRE-PROCESSING
             # Filter for temperature variable
 
-            if mode == "GMT":
+            if mode == "GWL":
                 dfp = df_scens_in.filter(variable=temp_variable)
             elif mode == "CO2":
                 dfp = prepare_cumulative(df_scens_in, years=years, use_dask=True)
-                ts = dfp.timeseries().apply(co2togmt_simple)
+                ts = dfp.timeseries().apply(co2togwl_simple)
                 ts = pyam.IamDataFrame(ts)
                 ts.rename(
                     {
@@ -113,7 +113,7 @@
                     inplace=True,
                 )
                 # Export data to check error and relationships
-                # ts.append(dfp).to_csv('c://users//byers//downloads//tcre_gmt_output.csv')
+                # ts.append(dfp).to_csv('c://users//byers//downloads//tcre_gwl_output.csv')
                 dfp = ts
                 dfp.meta = df_scens_in.meta.copy()
             dfp = dfp.filter(year=years)
@@ -146,9 +146,9 @@
 
                 # dfx = dft.iloc[0].squeeze()  # FOR DEBUIGGING THE FUNCTION
                 outd = ddf.apply(
-                    table_impacts_gmt, dsi=dsi, axis=1, meta=("result", None)
+                    table_impacts_gwl, dsi=dsi, axis=1, meta=("result", None)
                 )
-                # outdd = client.map(ddf.apply(table_impacts_gmt, dsi=dsi,  axis=1))
+                # outdd = client.map(ddf.apply(table_impacts_gwl, dsi=dsi,  axis=1))
 
                 with ProgressBar():
                     # try:
@@ -157,7 +157,7 @@
                 # except(InvalidIndexError):
                 # print(f'PROBLEM {f}')
             else:
-                df_new = dft.apply(table_impacts_gmt, dsi=dsi, axis=1)
+                df_new = dft.apply(table_impacts_gwl, dsi=dsi, axis=1)
 
             expandedd = pd.concat([df_new[x] for x in df_new.index])
             print(f" Done:  {time.time()-start}")