cleaning

app.py

@@ -1,14 +1,6 @@
-import io
-import math
-
-# date=dataset['TIME']
-#from datetime import date, datetime, timedelta
-
-import cartopy.crs as ccrs
-import cartopy.feature as cfeature
 import cftime
 import fsspec
-import hvplot.xarray
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -16,24 +8,20 @@ import panel as pn
 import xarray as xr
 from ipywidgets import (
     Checkbox,
-    FloatRangeSlider,
+#    FloatRangeSlider,
     FloatSlider,
     IntRangeSlider,
     IntSlider,
-    interactive,
+#    interactive,
 )
 
-pn.extension("tabulator")
 
-# df = pd.read_csv("table.csv", index_col=None)
-# df = df.sort_values(by="year")
-# df2 = pd.read_csv("test_table.csv", index_col=None)
-# file_names = df["file_name"].tolist()
-# df = df.sort_values(by="year")
-# years = sorted(df["year"].unique())
+import param
 
+pn.extension("tabulator")
 
 def greg_0h(jourjul):
+    import math
     # Julian days start and end at noon.
     # Julian day 2440000 begins at 00 hours, May 23, 1968.
 
@@ -90,28 +78,6 @@ def fix_time(ds):
     date = xr.DataArray(date, dims="MAXT")
     return ds.assign(TIME=date)
 
-
-import cartopy.crs as ccrs
-import cartopy.feature as cfeature
-import fsspec
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-import panel as pn
-import xarray as xr
-from ipywidgets import (
-    Checkbox,
-    FloatRangeSlider,
-    FloatSlider,
-    IntRangeSlider,
-    IntSlider,
-    interactive,
-)
-
-import panel as pn
-import param
-import xarray as xr
-
 @pn.cache(max_items=32,policy='LRU',per_session=True)
 def load_csv():
     url="https://data-eurogoship.ifremer.fr/data/table.csv"
@@ -129,6 +95,7 @@ def load_bathymetry():
 
 @pn.cache(max_items=16,policy='LRU',per_session=True)
 def load_netCDF(selected_file):
+    import os
     data_dir = "https://data-eurogoship.ifremer.fr/copy_seadatanet/"
     file_path = os.path.join(data_dir, selected_file)
     fs = fsspec.filesystem("https")
@@ -196,6 +163,32 @@ def filter_df(sorted_df,selected_file):
             )
     return dataframe.transpose(), dataframe2.transpose()
 
+def quiver_depth_filterd(ax,ds_filtered, depth1, depth_range_slider, scale_factor_slider, color="blue"):
+    import cartopy.crs as ccrs
+    depth_filtered = ds_filtered.where(
+        (depth1 > depth_range_slider.value[0])
+        & (depth1 <= depth_range_slider.value[1])
+    )
+
+    lon = ds_filtered.coords["LONGITUDE"].values
+    lat = ds_filtered.coords["LATITUDE"].values
+
+    # Moyenne des vecteurs de courant sur la plage de profondeur sélectionnée
+    u_mean = depth_filtered.UCUR.mean(dim="MAXZ", skipna=True)
+    v_mean = depth_filtered.VCUR.mean(dim="MAXZ", skipna=True)
+    
+    return ax.quiver(
+        lon,
+        lat,
+        u_mean * scale_factor_slider.value,
+        v_mean * scale_factor_slider.value,
+        color=color,
+        scale=2,
+        width=0.001,
+        headwidth=3,
+        transform=ccrs.PlateCarree(),
+    )
+
 class SADCP_Viewer(param.Parameterized):
     df = load_csv()
     bathy = load_bathymetry()
@@ -323,7 +316,7 @@ class SADCP_Viewer(param.Parameterized):
     def update_plots(self):
         self.ds_filtered = self.filter_data()
         # vector_plot = self.vectors_plot()
-        other_plots = self.plots()
+        other_plots = self.hvplot_plots()
 
         # self.plot_map = pn.pane.Matplotlib(vector_plot, width=800, height=600, sizing_mode="fixed")
         self.plot = pn.Column(
@@ -348,9 +341,11 @@ class SADCP_Viewer(param.Parameterized):
         )
 
     def vectors_plot(self):
-        import hvplot.xarray
+        import cartopy.crs as ccrs
+        import cartopy.feature as cfeature
 
         self.ds_filtered = self.filter_data()
+
         # return self.ds_filtered['VSHIP'].hvplot(x='TIME',width=400, height=200) #if 'BATHY' in self.ds_filtered else hvplot.show(hvplot.text(0, 0, "No data available", fontsize=12))
 
         fig, ax = plt.subplots(
@@ -364,76 +359,23 @@ class SADCP_Viewer(param.Parameterized):
             .mean()
             .set_coords(coords)
         )
+
         # self.ds_filtered =self.ds_filtered.coarsen(MAXT = corsen,  side = "center", boundary = "trim").mean()[["LONGITUDE", "LONGITUDE", "TIME"]].isel(MAXZ=0)
-        depth_filtered = self.ds_filtered.where(
-            (self.depth1 > self.depth_range_slider.value[0])
-            & (self.depth1 <= self.depth_range_slider.value[1])
-        )
 
-        lon = self.ds_filtered.coords["LONGITUDE"].values
-        lat = self.ds_filtered.coords["LATITUDE"].values
-        # skip = max(1, int(np.sqrt(lon.size) / self.num_vectors_slider.value))
-        # skip = (slice(None, None, skip), slice(None, None, skip))
-
-        # Moyenne des vecteurs de courant sur la plage de profondeur sélectionnée
-        u_mean = depth_filtered.UCUR.mean(dim="MAXZ", skipna=True)
-        v_mean = depth_filtered.VCUR.mean(dim="MAXZ", skipna=True)
-        ax.quiver(
-            lon,
-            lat,
-            u_mean * self.scale_factor_slider.value,
-            v_mean * self.scale_factor_slider.value,
-            color="blue",
-            scale=2,
-            width=0.001,
-            headwidth=3,
-            transform=ccrs.PlateCarree(),
-        )
+        quiver_depth_filterd(ax,self.ds_filtered, self.depth1, self.depth_range_slider,self.scale_factor_slider,color="blue")
 
         if self.depth_2_checkbox.value:
-            depth_filtered = self.ds_filtered.where(
-                (self.depth1 > self.depth2_range_slider.value[0])
-                & (self.depth1 <= self.depth2_range_slider.value[1])
-            )
-            u_mean = depth_filtered.UCUR.mean(dim="MAXZ", skipna=True)
-            v_mean = depth_filtered.VCUR.mean(dim="MAXZ", skipna=True)
-            ax.quiver(
-                lon,
-                lat,
-                u_mean * self.scale_factor_slider.value,
-                v_mean * self.scale_factor_slider.value,
-                color="green",
-                scale=2,
-                width=0.001,
-                headwidth=3,
-                transform=ccrs.PlateCarree(),
-            )
+            quiver_depth_filterd(ax,self.ds_filtered, self.depth1, self.depth2_range_slider,self.scale_factor_slider,color="green")
 
         if self.depth_3_checkbox.value:
-            depth_filtered = self.ds_filtered.where(
-                (self.depth1 > self.depth3_range_slider.value[0])
-                & (self.depth1 <= self.depth3_range_slider.value[1])
-            )
-            u_mean = depth_filtered.UCUR.mean(dim="MAXZ", skipna=True)
-            v_mean = depth_filtered.VCUR.mean(dim="MAXZ", skipna=True)
-            ax.quiver(
-                lon,
-                lat,
-                u_mean * self.scale_factor_slider.value,
-                v_mean * self.scale_factor_slider.value,
-                color="red",
-                scale=2,
-                width=0.001,
-                headwidth=3,
-                transform=ccrs.PlateCarree(),
-            )
+            quiver_depth_filterd(ax,self.ds_filtered, self.depth1, self.depth3_range_slider,self.scale_factor_slider,color="red")
 
         ax.add_feature(cfeature.COASTLINE)
         ax.add_feature(cfeature.BORDERS, linestyle=":")
         ax.add_feature(cfeature.LAND, color="lightgray")
 
         if self.bathy_checkbox.value:
-            contour_levels = [-2000]
+            contour_levels = [-1000]
             ax.contour(
                 self.bathy.longitude,
                 self.bathy.latitude,
@@ -460,7 +402,8 @@ class SADCP_Viewer(param.Parameterized):
         # pn.pane.Matplotlib(fig,width=800, height=600, sizing_mode="fixed", name="Plot")
 
     # @param.depends( 'file_dropdown.value', 'longitude_slider.value', 'latitude_slider.value',  watch=True)
-    def plots(self):
+    def hvplot_plots(self):
+        import hvplot.xarray
         return [
             self.ds_filtered["BATHY"].max(dim="MAXZ").hvplot(x="TIME", width=400, height=200),
             self.ds_filtered["USHIP"].max(dim="MAXZ").hvplot(x="TIME", width=400, height=200),