Merge pull request #6 from danhooke/ploomber_v0

Ploomber v0

README.md

@@ -1,2 +1,10 @@
 # CSE_dashboards
-Exploratory dashboards for gridded data from https://www.climate-solutions-explorer.eu/ (Werning et al. 2023)[https://zenodo.org/record/8134869].
+Exploratory dashboards for gridded data from Werning et al. (2023).
+
+ <a href="https://zenodo.org/doi/10.5281/zenodo.7971429" target="_blank"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.7971429.svg" alt="DOI"/></a>
+
+Using *Panel, Xarray, hvPlot, Bokeh and Ploomber*.
+
+View the 👉 [**deployment**](https://cold-voice-5221.ploomberapp.io/app) 👈 (using [Ploomber](https://ploomber.io/)).
+
+<img src="./images/preview1.png" alt="preview of the dashboard" width="100%">

app.py

@@ -70,36 +70,36 @@
 precip_inds = ["cdd", "pr_r10", "pr_r20", "pr_r95p", "pr_r99p", "sdii"]
 
 names = dict(
-    dri_dis="Drought severity (discharge)",
-    dri_qtot="Drought severity (runoff)",
-    iavar_dis="Interannual variability (discharge)",
-    iavar_qtot="Interannual variability (runoff)",
-    seas_dis="Seasonality (discharge)",
-    seas_qtot="Seasonality (runoff)",
-    wsi="Water stress index",
+    hw_97_3="Heat wave events",  # (97th percentile, 3 days)
+    dri_dis="Drought severity",  # (discharge)
+    # dri_qtot="Drought severity (runoff)",
+    # iavar_dis="Interannual variability (discharge)",
+    # iavar_qtot="Interannual variability (runoff)",
+    # seas_dis="Seasonality (discharge)",
+    # seas_qtot="Seasonality (runoff)",
+    wsi="Water Stress Index",
     cdd="Consecutive dry days",
     pr_r10="Heavy precipitation days",
-    pr_r20="Very heavy precipitation days",
-    pr_r95p="Wet days",
-    pr_r99p="Very wet days",
-    sdii="Precipitation intensity index",
-    hw_95_10="Heat wave events (95th percentile, 10 days)",
-    hw_95_3="Heat wave events (95th percentile, 3 days)",
-    hw_95_5="Heat wave events (95th percentile, 5 days)",
-    hw_95_7="Heat wave events (95th percentile, 7 days)",
-    hw_97_10="Heat wave events (97th percentile, 10 days)",
-    hw_97_3="Heat wave events (97th percentile, 3 days)",
-    hw_97_5="Heat wave events (97th percentile, 5 days)",
-    hw_97_7="Heat wave events (97th percentile, 7 days)",
-    hw_99_10="Heat wave events (99th percentile, 10 days)",
-    hw_99_3="Heat wave events (99th percentile, 3 days)",
-    hw_99_5="Heat wave events (99th percentile, 5 days)",
-    hw_99_7="Heat wave events (99th percentile, 7 days)",
+    # pr_r20="Very heavy precipitation days",
+    # pr_r95p="Wet days",
+    # pr_r99p="Very wet days",
+    # sdii="Precipitation intensity index",
+    # hw_95_10="Heat wave events (95th percentile, 10 days)",
+    # hw_95_3="Heat wave events (95th percentile, 3 days)",
+    # hw_95_5="Heat wave events (95th percentile, 5 days)",
+    # hw_95_7="Heat wave events (95th percentile, 7 days)",
+    # hw_97_10="Heat wave events (97th percentile, 10 days)",
+    # hw_97_5="Heat wave events (97th percentile, 5 days)",
+    # hw_97_7="Heat wave events (97th percentile, 7 days)",
+    # hw_99_10="Heat wave events (99th percentile, 10 days)",
+    # hw_99_3="Heat wave events (99th percentile, 3 days)",
+    # hw_99_5="Heat wave events (99th percentile, 5 days)",
+    # hw_99_7="Heat wave events (99th percentile, 7 days)",
     tr20="Tropical nights",
-    sdd_c_24p0="Cooling degree days (24°C)",
+    sdd_c_24p0="Cooling degree days",  # (24°C)
     # sdd_c="Cooling degree days (26°C)",
-    sdd_c_18p3="Cooling degree days (18.3°C)",
-    sdd_c_20p0="Cooling degree days (20°C)",
+    # sdd_c_18p3="Cooling degree days (18.3°C)",
+    # sdd_c_20p0="Cooling degree days (20°C)",
 )
 
 
@@ -145,6 +145,32 @@ def make_ds(
     return ds
 
 
+def make_ds_local(l_name):
+    ind = match_name(l_name)
+    vars = ["abs", "diff", "score"]
+    fp = "data/"
+    ds = xr.merge(
+        [
+            xr.concat(
+                [
+                    xr.open_dataarray(f).rename(f"{var}")
+                    for f in glob.glob(fp + rf"\*{ind}*{var}*")
+                ],
+                dim="threshold",
+            )
+            for var in vars
+        ]
+    )
+    ds = ds.rename({"diff": "difference", "lon": "Longitude", "lat": "Latitude"})
+    return ds
+
+
+def make_ds_combined(l_name):
+    ind = match_name(l_name)
+    ds = xr.open_dataset(f"data/{ind}_combined.nc4")
+    return ds
+
+
 info = dfs.get_info()
 
 
@@ -182,7 +208,7 @@ def get_ind_text(ind):
 
 
 def make_score_map_test(ind, t):
-    ds = make_ds(ind)
+    ds = make_ds_combined(ind)
     ind_info = get_plot_info(ind)
     score_map = (
         ds["score"]

indicator_info.yml

@@ -288,7 +288,7 @@ indicators:
             diff_max: 140
             diff_cmap: 'Reds'
             score_range: [7676, 1638, 561, 0]
-            description: 'Maximum number of consecutive days per year with a minimum surface air temperature above 20 °C.'
+            description: 'Maximum number of consecutive days per year with a minimum surface air temperature above 20C.'
     # yields_cereals:
     #     SimU_yields:
     #         short_name: 'yld_crls'
@@ -365,6 +365,6 @@ indicators:
             diff: relative
             diff_min: -1910
             diff_max: 1910
-            diff_cmap: 'PuRd'
+            diff_cmap: 'coolwarm'
             score_range: [0.4, 0.3, 0.2, 0.1]
             description: 'Water stress index compares water demands to available water supply. High water stress means that a high proportion of the available water is being used.'