more fixes and blacking

Kit Schwarz · Kit Schwarz · commit 83dab9b5dcb9 · 2022-08-03T11:50:12.000-07:00
diff --git a/dscim/__init__.py b/dscim/__init__.py
@@ -201,7 +201,6 @@ def menu_factory(self, menu_key, sector, kwargs=None):
             climate_options.update(**climate_kwargs)
 
         # Set up
-        dir_name = os.path.dirname(os.path.abspath(__file__))
         sector_config = self.param_dict["sectors"].get(sector)
         sector_config.update({k: v for k, v in global_options.items()})
         sector_config.update(
diff --git a/dscim/cli.py b/dscim/cli.py
@@ -78,6 +78,8 @@ def cli(config_file, menu_order, log_level, local, pro=False):
         path_to_scheduler = os.path.join(os.getenv("SCRATCH"), "scheduler.json")
         client = Client(scheduler_file=path_to_scheduler)
 
+    print(logger, client)
+
     if menu_order == "":
         waiter.execute_order()
     else:
@@ -86,12 +88,9 @@ def cli(config_file, menu_order, log_level, local, pro=False):
 
         menus = global_parameters["menu_type"]
         discounts = global_parameters["discounting_type"]
-        course = global_parameters["course"]
         pulse_year = waiter.param_dict["climate"]["pulse_year"]
 
-        for menu, discount, sector, level in itertools.product(
-            menus, discounts, sectors, levels
-        ):
+        for menu, discount, sector in itertools.product(menus, discounts, sectors):
             obj = waiter.menu_factory(
                 menu_key=menu,
                 sector=sector,
diff --git a/dscim/diagnostics/batch_maps.py b/dscim/diagnostics/batch_maps.py
@@ -119,7 +119,7 @@ def batch_maps(
     if gcm == "mean":
         damages = damages.weighted(weights).mean(dim="gcm")
     elif gcm == "ce":
-        damages = c_equivalence(risk.damages, dims=["gcm"])
+        damages = c_equivalence(damages, dims=["gcm"])
     else:
         damages = damages.sel({"gcm": gcm})
 
diff --git a/dscim/diagnostics/crayola_plots.py b/dscim/diagnostics/crayola_plots.py
@@ -180,4 +180,4 @@ def crayola_plots(
                 title = f"{var}_{recipe.discounting_type}_{recipe.NAME}_{recipe.sector}_{ssp}.png"
                 g.savefig(os.path.join(save_plot_path, title))
 
-    return (global_damages, global_consumption)
+    return (global_damages, recipe.global_consumption)
diff --git a/dscim/diagnostics/damage_inc_share_boxplots.py b/dscim/diagnostics/damage_inc_share_boxplots.py
@@ -106,7 +106,7 @@ def income_boxplot(
             }
         )
 
-    bxp = ax.bxp(stats, showmeans=True, meanline=False)
+    ax.bxp(stats, showmeans=True, meanline=False)
     ax.set_ylabel(f"{year} damages as a share of present-day GDP")
     ax.set_xlabel("Decile of present-day GDP per capita")
     ax.spines["top"].set_visible(False)
diff --git a/dscim/diagnostics/fair_step.py b/dscim/diagnostics/fair_step.py
@@ -11,7 +11,6 @@
 logger = logging.getLogger(__name__)
 import matplotlib.pyplot as plt
 import seaborn as sns
-from dscim.utils.dicts import *
 from functools import reduce
 
 sns.set_style("darkgrid")
@@ -42,7 +41,6 @@ def marginal_damages(
         marginal_damages[recipe]["ssp-model"] = (
             marginal_damages[recipe].ssp + "-" + marginal_damages[recipe].model
         )
-        len_ssps = len(marginal_damages[recipe].reset_index().ssp.unique())
         marginal_damages[recipe] = marginal_damages[recipe].sort_values(["ssp-model"])
 
         # to avoid errors when plotting by hue and style
diff --git a/dscim/diagnostics/maps.py b/dscim/diagnostics/maps.py
@@ -28,13 +28,6 @@ def make_map(
     maxmin=True,
 ):
 
-    font = {
-        "family": "serif",
-        "color": "black",
-        "weight": "normal",
-        "size": 14,
-    }
-
     # create figure
     fig, ax = plt.subplots(figsize=figsize, facecolor="white")
 
@@ -147,7 +140,7 @@ def maps(
     elif gcm == "mean":
         damages = damages.weighted(weights).mean(dim="gcm")
     elif gcm == "ce":
-        damages = c_equivalence(risk.damages, dims=["gcm"])
+        damages = c_equivalence(damages, dims=["gcm"])
     else:
         damages = damages.sel({"gcm": gcm})
 
diff --git a/dscim/menu/decorators.py b/dscim/menu/decorators.py
@@ -35,16 +35,14 @@ def save_wrap(self, *args, **kwargs):
                 filename_path = os.path.join(self.save_path, filename)
 
                 if isinstance(save, xr.DataArray):
-                    try:
-                        save = save.to_dataset()
-                    except:
-                        save = save.rename(name).to_dataset()
-                        save.attrs = self.output_attrs
-
-                        # change `None` object to str(None)
-                        for att in save.attrs:
-                            if save.attrs[att] is None:
-                                save.attrs.update({att: "None"})
+
+                    save = save.rename(name).to_dataset()
+                    save.attrs = self.output_attrs
+
+                    # change `None` object to str(None)
+                    for att in save.attrs:
+                        if save.attrs[att] is None:
+                            save.attrs.update({att: "None"})
 
                     self.logger.info(f"Saving {filename_path}.nc4")
                     save.to_netcdf(f"{filename_path}.nc4")
diff --git a/dscim/menu/main_recipe.py b/dscim/menu/main_recipe.py
@@ -278,7 +278,7 @@ def damage_function():
             self.damage_function_coefficients
             try:
                 self.damage_function_fit
-            except:
+            except FileNotFoundError:
                 pass
 
         def scc():
diff --git a/dscim/menu/simple_storage.py b/dscim/menu/simple_storage.py
@@ -81,7 +81,11 @@ def gmst(self):
     @property
     def gmsl(self):
         """Cached GMSL anomalies"""
-        gmsl = xr.open_dataset(self.gmsl_path, engine = "zarr").gmsl.to_dataframe().reset_index()
+        gmsl = (
+            xr.open_dataset(self.gmsl_path, engine="zarr")
+            .gmsl.to_dataframe()
+            .reset_index()
+        )
 
         return gmsl
 
@@ -273,7 +277,7 @@ def __init__(self, path_econ):
     def econ_vars(self):
         """Economic variables"""
         if self.path[-3:] == "arr":
-            raw = xr.open_dataset(self.path, engine = "zarr", consolidated=True)
+            raw = xr.open_dataset(self.path, engine="zarr", consolidated=True)
         else:
             raw = xr.open_dataset(self.path)
         return raw[["gdp", "pop"]]
@@ -400,7 +404,11 @@ def adding_up_damages(self):
                 f"Adding up aggregated damages found at {mean_cc}, {mean_no_cc}. These are being loaded..."
             )
             damages = (
-                (xr.open_dataset(mean_no_cc, engine = "zarr").no_cc - xr.open_dataset(mean_cc, engine = "zarr").cc) * self.pop
+                (
+                    xr.open_dataset(mean_no_cc, engine="zarr").no_cc
+                    - xr.open_dataset(mean_cc, engine="zarr").cc
+                )
+                * self.pop
             ).sum("region")
         else:
             raise NotImplementedError(
@@ -429,4 +437,4 @@ def risk_aversion_damages(self, ce_type):
             raise NotImplementedError(
                 "Risk-aversion CEs not found. Please run CE_calculation.ipynb for `risk_aversion`."
             )
-        return self.cut(xr.open_dataset(file, engine = "zarr"))
+        return self.cut(xr.open_dataset(file, engine="zarr"))
diff --git a/dscim/preprocessing/inputs/energy_inputs.py b/dscim/preprocessing/inputs/energy_inputs.py
@@ -62,4 +62,4 @@ def energy_inputs(
         partial_func = partial(calculate_batch_damages, ec=ec)
         print("Processing batches:")
         print(list(range(i * 5, i * 5 + 5)))
-        r = p_umap(partial_func, list(range(i * 5, i * 5 + 5)))
+        p_umap(partial_func, list(range(i * 5, i * 5 + 5)))
diff --git a/dscim/preprocessing/inputs/labor_inputs.py b/dscim/preprocessing/inputs/labor_inputs.py
@@ -45,4 +45,4 @@ def labor_inputs(
         )
         print("Processing batches:")
         print(list(range(i * 5, i * 5 + 5)))
-        r = p_umap(partial_func, list(range(i * 5, i * 5 + 5)))
+        p_umap(partial_func, list(range(i * 5, i * 5 + 5)))
diff --git a/dscim/preprocessing/misc/check_nans.py b/dscim/preprocessing/misc/check_nans.py
@@ -117,7 +117,7 @@ def check_nc4_nans(path):
         out = path + "   " + str(ds.isnull().sum().data_vars)
         ds.close()
         return out
-    except:
+    except FileNotFoundError:
         print(f"Error checking {path}")
         return f"failed: {path}"
 
@@ -130,7 +130,7 @@ def check_zarr_nans(path):
         out = path + "   " + str(ds.isnull().sum().data_vars)
         ds.close()
         return out
-    except:
+    except FileNotFoundError:
         print(f"Error checking {path}")
         return f"failed: {path}"
 
@@ -141,7 +141,7 @@ def check_csv_nans(path):
         ds = pd.read_csv(path)
         output = path + "   " + str(ds.isnull().sum().sum())
         return output
-    except:
+    except FileNotFoundError:
         print(f"Error checking {path}")
         return f"failed: {path}"
 
diff --git a/dscim/preprocessing/misc/convert_float32.py b/dscim/preprocessing/misc/convert_float32.py
@@ -58,7 +58,7 @@ def process_nc4(f):
     try:
         df.to_netcdf(f_save)
         return f"succeeded: {f}"
-    except:
+    except PermissionError:
         print(f"Error saving {f}")
         return f"failed: {f}"
 
@@ -71,7 +71,7 @@ def process_zarr(f):
     try:
         df.to_zarr(f_save, consolidated=True, mode="w")
         return f"succeeded: {f}"
-    except:
+    except PermissionError:
         print(f"Error saving {f}")
         if os.path.exists(f_save):
             shutil.rmtree(f_save)
diff --git a/dscim/preprocessing/preprocessing.py b/dscim/preprocessing/preprocessing.py
@@ -35,7 +35,7 @@ def ce_from_chunk(
     model = chunk.model.values
 
     gdppc = (
-        xr.open_dataset(socioec, engine = "zarr", chunks=None)
+        xr.open_dataset(socioec, engine="zarr", chunks=None)
         .sel(
             year=year, ssp=ssp, model=model, region=ce_batch_coords["region"], drop=True
         )
@@ -97,11 +97,11 @@ def reduce_damages(
     delta = params["delta"]
     outpath = f"{c['paths']['reduced_damages_library']}/{sector}"
 
-    with xr.open_dataset(damages, engine = "zarr", chunks=None)[histclim] as ds:
-        with xr.open_dataset(socioec, engine = "zarr", chunks=None) as gdppc:
+    with xr.open_dataset(damages, engine="zarr", chunks=None)[histclim] as ds:
+        with xr.open_dataset(socioec, engine="zarr", chunks=None) as gdppc:
 
             assert (
-                xr.open_dataset(damages, engine = "zarr").chunks["batch"][0] == 15
+                xr.open_dataset(damages, engine="zarr").chunks["batch"][0] == 15
             ), "'batch' dim on damages does not have chunksize of 15. Please rechunk."
 
             ce_batch_dims = [i for i in gdppc.dims] + [
@@ -112,15 +112,18 @@ def reduce_damages(
                 i for i in gdppc.region.values if i in ce_batch_coords["region"]
             ]
             ce_shapes = [len(ce_batch_coords[c]) for c in ce_batch_dims]
-            ce_chunks = [xr.open_dataset(damages, engine = "zarr").chunks[c][0] for c in ce_batch_dims]
+            ce_chunks = [
+                xr.open_dataset(damages, engine="zarr").chunks[c][0]
+                for c in ce_batch_dims
+            ]
 
     template = xr.DataArray(
         da.empty(ce_shapes, chunks=ce_chunks),
         dims=ce_batch_dims,
         coords=ce_batch_coords,
     )
 
-    other = xr.open_dataset(damages, engine = "zarr")
+    other = xr.open_dataset(damages, engine="zarr")
 
     out = other.map_blocks(
         ce_from_chunk,
@@ -209,7 +212,9 @@ def sum_AMEL(
 
         for sector in sectors:
             print(f"Opening {sector},{params[sector]['sector_path']}")
-            ds = xr.open_dataset(params[sector]["sector_path"], engine = "zarr", consolidated=True)
+            ds = xr.open_dataset(
+                params[sector]["sector_path"], engine="zarr", consolidated=True
+            )
             ds = ds[params[sector][var]].rename(var)
             ds = xr.where(np.isinf(ds), np.nan, ds)
             datasets.append(ds)
@@ -255,9 +260,13 @@ def subset_USA_reduced_damages(
 ):
 
     if recipe == "adding_up":
-        ds = xr.open_dataset(f"{input_path}/{sector}/{recipe}_{reduction}.zarr", engine = "zarr")
+        ds = xr.open_dataset(
+            f"{input_path}/{sector}/{recipe}_{reduction}.zarr", engine="zarr"
+        )
     elif recipe == "risk_aversion":
-        ds = xr.open_dataset(f"{input_path}/{sector}/{recipe}_{reduction}_eta{eta}.zarr", engine = "zarr")
+        ds = xr.open_dataset(
+            f"{input_path}/{sector}/{recipe}_{reduction}_eta{eta}.zarr", engine="zarr"
+        )
 
     subset = ds.sel(region=[i for i in ds.region.values if "USA" in i])
 
@@ -285,7 +294,7 @@ def subset_USA_ssp_econ(
 
     zarr = xr.open_dataset(
         in_path,
-        engine = "zarr",
+        engine="zarr",
         consolidated=True,
     )
 
@@ -331,16 +340,18 @@ def clip_damages(
     histclim = params["histclim"]
     delta = params["delta"]
 
-    with xr.open_dataset(path, engine = "zarr", chunks=None)[delta] as ds:
-        with xr.open_dataset(econ_path, engine = "zarr", chunks=None) as gdppc:
+    with xr.open_dataset(path, engine="zarr", chunks=None)[delta] as ds:
+        with xr.open_dataset(econ_path, engine="zarr", chunks=None) as gdppc:
 
             ce_batch_dims = [i for i in ds.dims]
             ce_batch_coords = {c: ds[c].values for c in ce_batch_dims}
             ce_batch_coords["region"] = [
                 i for i in ds.region.values if i in gdppc.region.values
             ]
             ce_shapes = [len(ce_batch_coords[c]) for c in ce_batch_dims]
-            ce_chunks = [xr.open_dataset(path, engine = "zarr").chunks[c][0] for c in ce_batch_dims]
+            ce_chunks = [
+                xr.open_dataset(path, engine="zarr").chunks[c][0] for c in ce_batch_dims
+            ]
             print(ce_chunks)
 
     template = xr.DataArray(
@@ -359,7 +370,7 @@ def chunk_func(
         region = damages.region.values
 
         gdppc = (
-            xr.open_dataset(econ_path, engine = "zarr", chunks=None)
+            xr.open_dataset(econ_path, engine="zarr", chunks=None)
             .sel(year=year, ssp=ssp, model=model, region=region, drop=True)
             .gdppc
         )
@@ -391,7 +402,7 @@ def chunk_func(
 
         return damages
 
-    data = xr.open_dataset(path, engine = "zarr")
+    data = xr.open_dataset(path, engine="zarr")
 
     for var in [delta, histclim]:
         out = (
diff --git a/dscim/tests/test_simple_storage.py b/dscim/tests/test_simple_storage.py
@@ -45,8 +45,12 @@ def test_adding_up_damages(stacked_damages):
         stacked_damages.adding_up_damages,
         (
             (
-                xr.open_dataset(f"{stacked_damages.ce_path}/adding_up_no_cc.zarr", engine = "zarr").no_cc
-                - xr.open_dataset(f"{stacked_damages.ce_path}/adding_up_cc.zarr", engine = "zarr").cc
+                xr.open_dataset(
+                    f"{stacked_damages.ce_path}/adding_up_no_cc.zarr", engine="zarr"
+                ).no_cc
+                - xr.open_dataset(
+                    f"{stacked_damages.ce_path}/adding_up_cc.zarr", engine="zarr"
+                ).cc
             )
             * stacked_damages.pop
         ).sum("region"),

Original file line number	Diff line number	Diff line change
`@@ -106,7 +106,7 @@ def income_boxplot(`
`106`	`106`	`}`
`107`	`107`	`)`
`108`	`108`
`109`		`- bxp = ax.bxp(stats, showmeans=True, meanline=False)`
	`109`	`+ ax.bxp(stats, showmeans=True, meanline=False)`
`110`	`110`	`ax.set_ylabel(f"{year} damages as a share of present-day GDP")`
`111`	`111`	`ax.set_xlabel("Decile of present-day GDP per capita")`
`112`	`112`	`ax.spines["top"].set_visible(False)`