bkplot.py

import hvplot.xarray
import xarray as xr
import holoviews as hv
from bokeh.layouts import column
from bokeh.models import Select
from bokeh.io import curdoc
import gc
# hv.extension('bokeh')

 
def safe_check(var):
    try:
        ds[var].values
        return var
    except Exception as e:
        # Handle the exception (e.g., log it, return False, etc.)
        print(f"Error processing {var}: {e}")
        return False

                   
def load_data(url):
    try:
        del ds
        gc.collect()
    except UnboundLocalError:
        pass
    ds = None
    try:
        # attempt to load the dataset via xarray with decode_times=True
        ds = xr.open_dataset(str(url).strip())
    except ValueError as e:
        # attempt to load the dataset via xarray with decode_times=False
        ds = xr.open_dataset(str(url).strip(), decode_times=False)
        print(e)
    except OSError as e:
        print(e)
    if ds and not ds.coords:
        del ds
        gc.collect()
        # the following hack is used when the dataset is served as a tabledap via erdap
        erdapp_uglyness = list(dict(xr.open_dataset(url).dims).keys())[0]
        renamed_vars = {i:i.replace(erdapp_uglyness+".", "") for i in list(xr.open_dataset(url).variables.keys())}
        new_nc_url = url+'?'+'time,'+','.join(list(xr.open_dataset(url).variables)).replace(f"{erdapp_uglyness}.", "").replace(f"time,", "")
        ds = xr.open_dataset(new_nc_url)
        ds = ds.set_coords(f"{erdapp_uglyness}.time")
        ds = ds.swap_dims(s=f"time")
        ds = ds.set_xindex(f"{erdapp_uglyness}.time")
        ds = ds.rename_vars(renamed_vars)
    return ds


def update_plot(attr, old, new, var=None, title=None):
    print('update plot')
    if var is None:
        var = new
        title = new
    if 'featureType' in ds.attrs:
        featureType = ds.attrs['featureType'].lower()
    elif 'cdm_data_type' in ds[var].attrs:
        featureType = ds[var].attrs['cdm_data_type'].lower()
    else:
        featureType = None
    if featureType == 'timeseries':
        axis_arguments = {'grid':True, 'title': title, 'responsive': False}
        plot_widget =  ds[var].where(ds[var] != 9.96921e36).hvplot.line(**axis_arguments)
        #return plot_widget
    if featureType != "timeseries":
        axis_arguments = {'x': ds[var], 'grid':True, 'title': title, 'widget_location': 'bottom', 'responsive': False}
        try:
            plot_widget =  ds[var].where(ds[var] != 9.96921e36).hvplot.line(**axis_arguments)
        except TypeError:
            axis_arguments = {'grid':True, 'title': title, 'widget_location': 'bottom', 'responsive': False}
            plot_widget =  ds[var].where(ds[var] != 9.96921e36).hvplot.line(**axis_arguments)
        except ValueError:
            axis_arguments = {'x': var, 'grid':True, 'title': title, 'widget_location': 'bottom', 'responsive': False}
            plot_widget =  ds[var].where(ds[var] != 9.96921e36).hvplot.line(**axis_arguments)
        #return plot_widget
    try:
        container.children[1] = hv.render(plot_widget)
    except NameError:
        return plot_widget
    

    
ds = load_data(curdoc().session_context.request.arguments['url'][0].decode('utf-8'))

if ds:
    plottable_vars = [j for j in ds if len([value for value in list(ds[j].coords) if value in list(ds.dims)]) >= 1]
    plottable_vars = [i for i in plottable_vars if safe_check(i)]
    print("plottable_vars:", plottable_vars )
    mapping_var_names = {}
    for i in plottable_vars:
        if int(len(list(ds[i].coords)) != 0):
            try:
                title = f"{ds[i].attrs['long_name']} [{i}]"
            except KeyError:
                title = f"{i}"
            mapping_var_names[i] = title

select = Select(title="Select a variable:", value=plottable_vars[0], options=plottable_vars)
select.on_change('value', update_plot)
container = column(select, hv.render(update_plot(attr=None, old=None, new=None, var=select.value, title=select.value)))

curdoc().add_root(container)