Delete app.py

app.py

@@ -1,418 +0,0 @@
-import numpy as np
-import pandas as pd
-import panel as pn
-import xarray as xr
-import param
-
-import hvplot.xarray
-import matplotlib.pyplot as plt
-import cartopy.crs as ccrs
-import cartopy.feature as cfeature
-from datatree import open_datatree
-
-
-def get_range(da):
-    return  ( int(da.min().round() - 1), int(da.max().round() + 1),)
-
-@pn.cache(max_items=32,policy='LRU',per_session=True)
-def load_csv(path='./data/zarr_table.csv'):
-    df = pd.read_csv(path,index_col=None)
-    return df.sort_values(by="year") #inplace=True)
-
-@pn.cache(max_items=4,policy='LRU',per_session=True)
-def load_bathymetry(path='./data/bathy6min.nc'):
-    return xr.open_dataset(path, decode_times=False, use_cftime=True)
-
-@pn.cache(max_items=16,policy='LRU',per_session=True)
-def load_zarr(path='./data/1H_file.zarr'):
-    return open_datatree(path, engine='zarr')
-
-def load_file(tree,selected_file):
-    return tree[selected_file+"/"].ds
-
-def filter_df(sorted_df,selected_file):
-    # include  user_interface_url here
-    dataframe = sorted_df[sorted_df["file_name"] == selected_file].drop(
-                columns=[
-                    "file_name",
-                    "title",
-                    "Conventions",
-                    "featureType",
-                    "date_update",
-                    "ADCP_beam_angle",
-                    "ADCP_ship_angle",
-                    "middle_bin1_depth",
-                    "heading_corr",
-                    "pitch_corr",
-                    "ampli_corr",
-                    "pitch_roll_used",
-                    "date_creation",
-                    "ADCP_type",
-                    "data_type",
-                ]
-            )
-    # include  LOCAL_CDI_ID here
-    dataframe2 = sorted_df[sorted_df["file_name"] == selected_file].drop(
-                columns=[
-                    "file_name",
-                    "date_start",
-                    "date_end",
-                    "ADCP_frequency(kHz)",
-                    "bin_length(meter)",
-                    "year",
-                ]
-            )
-    return dataframe.transpose(), dataframe2.transpose()
-
-def filter_data(ds,longitude_range,latitude_range):
-    return ds.where(
-        (ds.LONGITUDE >= longitude_range[0])
-        & (ds.LONGITUDE <= longitude_range[1])
-        & (ds.LATITUDE >= latitude_range[0])
-        & (ds.LATITUDE <= latitude_range[1]),
-        drop=True,
-    )
-
-
-def quiver_depth_filtered(ax, ds, depth_range, scale_factor, color="blue"):
-    """
-    Plot quiver plot of mean current vectors filtered by depth.
-
-    Parameters:
-        ax (matplotlib.axes.Axes): The matplotlib axes object to plot on.
-        ds (xarray.Dataset): The dataset containing the current data.
-        depth_range (tuple): Tuple containing the minimum and maximum depth values for filtering.
-        scale_factor (float): Scaling factor for the magnitude of the current vectors.
-        color (str, optional): Color of the quiver arrows. Defaults to "blue".
-
-    Returns:
-        matplotlib.quiver.Quiver: The quiver plot object.
-    """
-    # Filter data based on depth range
-    ds = ds.sel( PROFZ=slice(depth_range[1],depth_range[0]))
-
-    # Calculate mean current vectors within the selected depth range
-    u_mean = ds.UCUR.mean(dim="PROFZ", skipna=True)
-    v_mean = ds.VCUR.mean(dim="PROFZ", skipna=True)
-
-    # Extract longitude and latitude coordinates
-    lon = ds.coords["LONGITUDE"].values
-    lat = ds.coords["LATITUDE"].values
-
-    # Plot quiver plot
-    return ax.quiver(
-        lon,
-        lat,
-        u_mean * scale_factor,
-        v_mean * scale_factor,
-        color=color,
-        scale=2,
-        width=0.001,
-        headwidth=3,
-        transform=ccrs.PlateCarree(),
-    )
-
-def bathy_uship_vship_bottom_depth(ds):
-    """
-    Plot maximum values of bathymetry, USHIP, VSHIP, and bottom depth over time.
-
-    Parameters:
-        ds (xarray.Dataset): Dataset containing the required variables.
-
-    Returns:
-        list: List of hvplot objects representing the plots of maximum values of bathymetry,
-              USHIP, VSHIP, and bottom depth over time.
-    """
-    return [
-        ds["BATHY"].max(dim="PROFZ").hvplot(x="TIME", width=400, height=200),
-        ds["USHIP"].max(dim="PROFZ").hvplot(x="TIME", width=400, height=200),
-        ds["VSHIP"].max(dim="PROFZ").hvplot(x="TIME", width=400, height=200),
-        ds["BOTTOM_DEPTH"].max(dim="PROFZ").hvplot(x="TIME", width=400, height=200),
-    ]
-
-
-def corsen_data(ds, sample):
-    """
-    Downsample the dataset `ds` based on the number of vectors specified by `sample`.
-
-    Parameters:
-        ds (xarray.Dataset): Dataset to be downsampled.
-        sample (int): Number of vectors used for downsampling.
-
-    Returns:
-        xarray.Dataset: Downsampled dataset.
-    """
-    coords = ["LATITUDE", "LONGITUDE"]
-    corsen = max(1, ds.TIME.size // sample)
-    return (
-        ds.reset_coords(coords)
-        .coarsen({"TIME": corsen}, boundary="trim")
-        .mean()
-        .set_coords(coords)
-    )
-
-
-
-def vectors_plot(ds, bathy, longitude_range, latitude_range ,
-                 depth_range, depth_2_range, depth_3_range,
-                 scale_factor, sample,
-                 depth_2_checkbox=False, depth_3_checkbox=False, bathy_checkbox=False):
-    """
-    Plot vectors filtered by depth on a map with specified features.
-
-    Parameters:
-        ds (xarray.Dataset): Dataset containing current data.
-        bathy (xarray.Dataset): Dataset containing bathymetry data.
-        longitude_range (tuple): Tuple containing the minimum and maximum longitude values.
-        latitude_range  (tuple): Tuple containing the minimum and maximum latitude values.
-        depth_range (tuple): Tuple containing the minimum and maximum depth values for filtering.
-        depth_2_range (tuple): Tuple containing the minimum and maximum depth values for filtering depth 2.
-        depth_3_range (tuple): Tuple containing the minimum and maximum depth values for filtering depth 3.
-        scale_factor (float): Scaling factor for the magnitude of the current vectors.
-        sample (int): Number of vectors used for downsampling.
-        depth_2_checkbox (bool, optional): Whether to plot vectors for depth 2. Defaults to False.
-        depth_3_checkbox (bool, optional): Whether to plot vectors for depth 3. Defaults to False.
-        bathy_checkbox (bool, optional): Whether to plot bathymetry. Defaults to False.
-
-    Returns:
-        matplotlib.figure.Figure: The generated plot.
-    """
-    # Create subplot with Mercator projection
-    fig, ax = plt.subplots(figsize=(5, 4), subplot_kw={"projection": ccrs.Mercator()})
-
-    # Apply data downsampling
-    ds = corsen_data(ds, sample)
-
-    # Plot vectors filtered by depth
-    quiver_depth_filtered(ax, ds, depth_range, scale_factor, color="blue")
-    if depth_2_checkbox:
-        quiver_depth_filtered(ax, ds, depth_2_range, scale_factor, color="green")
-    if depth_3_checkbox:
-        quiver_depth_filtered(ax, ds, depth_3_range, scale_factor, color="red")
-
-    # Add map features
-    ax.add_feature(cfeature.COASTLINE)
-    ax.add_feature(cfeature.BORDERS, linestyle=":")
-    ax.add_feature(cfeature.LAND, color="lightgray")
-
-    # Plot bathymetry if provided
-    if bathy_checkbox:
-        contour_levels = [-1000]
-        ax.contour(bathy.longitude, bathy.latitude, bathy.z,
-                   levels=contour_levels, colors="black", transform=ccrs.PlateCarree())
-
-    # Set extent and add gridlines
-    ax.set_extent([longitude_range[0], longitude_range[1],
-                   latitude_range[0], latitude_range[1]])
-    ax.gridlines(draw_labels=True)
-
-    # Set labels and close plot
-    plt.ylabel("Latitude", fontsize=15, labelpad=35)
-    plt.xlabel("Longitude", fontsize=15, labelpad=20)
-    #https://panel.holoviz.org/reference/panes/Matplotlib.html#using-the-matplotlib-pyplot-interface
-    plt.close(fig) 
-    return fig
-
-class SADCP_Viewer(param.Parameterized):
-    """
-    A parameterized class for viewing SADCP data.
-    
-    This class provides widgets for selecting data parameters, updating data based on selections,
-    and generating plots to visualize the SADCP data.
-    
-    Available functions:
-        - update_name_options: Update dropdown options and slider ranges based on selected years and file.
-        - update_plots: Update plots based on selected data and parameters.
-    """
-
-    # Load data and initialize widgets
-    df = load_csv()
-    bathy = load_bathymetry()
-    tree=load_zarr()
-    file_names = df["file_name"].tolist()
-    years = sorted(df["year"].unique())
-
-    # Widgets for selecting data parameters
-    year_slider = pn.widgets.IntRangeSlider(name="Year Range", start=df["year"].min(), end=df["year"].max())
-    file_dropdown = pn.widgets.Select(name="File Selector")
-    longitude_slider = pn.widgets.RangeSlider(name="Longitude Range", start=-180, end=180, step=1)
-    latitude_slider = pn.widgets.RangeSlider(name="Latitude Range", start=-90, end=90, step=1)
-    depth_range_slider = pn.widgets.IntRangeSlider(start=100, end=300, value=(100, 300), step=1, name="Depth Range")
-    depth_2_checkbox = pn.widgets.Checkbox(value=False, name="Depth 2 Checkbox")
-    depth_3_checkbox = pn.widgets.Checkbox(value=False, name="Depth 3 Checkbox")
-    depth_2_range_slider = pn.widgets.IntRangeSlider(start=100, end=300, value=(100, 300), step=1, name="Depth 2 Range")
-    depth_3_range_slider = pn.widgets.IntRangeSlider(start=100, end=300, value=(100, 300), step=1, name="Depth 3 Range")
-    num_vectors_slider = pn.widgets.IntSlider(start=40, end=800, step=1, value=100, name="Number of Vectors")
-    scale_factor_slider = pn.widgets.FloatSlider(start=0.1, end=1, step=0.1, value=0.5, name="Scale Factor")
-    bathy_checkbox = pn.widgets.Checkbox(value=False, name="Bathy Checkbox")
-
- 
-    data_table = pn.widgets.Tabulator(width=400, height=200)
-    metadata_table = pn.widgets.Tabulator(width=600, height=800)
-    # Download button is not working : TODO
-    download_button = pn.widgets.Button(name="Download", button_type="primary")
-
-    def __init__(self, **params):
-        """
-        Initialize the SADCP_Viewer class.
-        
-        Parameters:
-            **params: Additional parameters to be passed to the superclass.
-        """
-
-        super(SADCP_Viewer, self).__init__(**params)
-        self.file_dropdown.objects = self.file_names
-        self.file_dropdown.value = (
-            self.file_dropdown.objects[0] if self.file_dropdown.objects else None
-        )
-        self.update_name_options()
-
-    @param.depends("year_slider.value", "file_dropdown.value", watch=True)
-    def update_name_options(self):
-        """
-        Update dropdown options and slider ranges based on selected years and file.
-
-        This function updates the dropdown options and slider ranges based on the selected years
-        and file. It also loads the selected file's data and adjusts slider ranges accordingly.
-
-        """
-        # Extract selected start and end years
-        start_year, end_year = self.year_slider.value
-        
-        # Filter DataFrame based on selected years and sort by year
-        mask = (self.df["year"] >= start_year) & (self.df["year"] <= end_year)
-        sorted_df = self.df[mask].sort_values(by="year")
-        
-        # Get unique file names
-        files = sorted_df["file_name"].unique().tolist()
-        
-        # Update file dropdown options
-        self.file_dropdown.options = files
-        
-        if files:
-            selected_file = self.file_dropdown.value
-            
-            # Set default selected file if not selected or not in options
-            if not selected_file or selected_file not in files:
-                selected_file = files[0]
-                self.file_dropdown.value = selected_file
-            
-            # Update data table and metadata table based on selected file
-            self.data_table.value, self.metadata_table.value = filter_df(sorted_df, selected_file)
-            
-            # Load selected file's data
-            self.ds = load_file(self.tree,selected_file)
-            
-            # Update slider ranges for longitude, latitude, and depth
-            for slider, coord in zip([self.longitude_slider, self.latitude_slider, self.depth_range_slider,
-                          self.depth_2_range_slider, self.depth_3_range_slider],
-                         [self.ds.LONGITUDE, self.ds.LATITUDE, self.ds.PROFZ,self.ds.PROFZ,self.ds.PROFZ]):
-                coord_range = get_range(coord)
-                slider.start, slider.end, slider.value = coord_range[0], coord_range[1], coord_range
-
-
-            # Close dataset to free up resources
-            # self.ds.close()
-
-    @param.depends(
-        "year_slider.value",
-        "file_dropdown.value",
-        "depth_range_slider.value",
-        "depth_2_checkbox.value",
-        "depth_3_checkbox.value",
-        "depth_2_range_slider.value",
-        "depth_3_range_slider.value",
-        "longitude_slider.value",
-        "latitude_slider.value",
-        "num_vectors_slider.value",
-        "scale_factor_slider.value",
-        "bathy_checkbox.value",
-        watch=False,)
-    def update_plots(self):
-        """
-        This function updates the plots based on the selected data and parameters.
-
-        The function filters the data, generates additional plots, and updates the main vector plot based on the selected parameters.
-
-        Returns:
-            pn.Row: A Panel row containing the updated map plot and additional plots.
-
-        """
-        # Filter the data
-        self.ds_filtered = filter_data(self.ds,self.longitude_slider.value,self.latitude_slider.value)
-
-        # Prepare the plots shown in left
-        # Update vector plots
-        vector_plot = vectors_plot(self.ds_filtered, self.bathy, 
-                                   self.longitude_slider.value, self.latitude_slider.value,
-                                   self.depth_range_slider.value, self.depth_2_range_slider.value, self.depth_3_range_slider.value,
-                                   self.scale_factor_slider.value,self.num_vectors_slider.value,
-                                   depth_2_checkbox= self.depth_2_checkbox.value,
-                                   depth_3_checkbox= self.depth_3_checkbox.value,
-                                   bathy_checkbox=self.bathy_checkbox.value,
-                                   )
-
-
-        # Generate plots which will be plotted on the left row.
-        self.plot_left = pn.Column(
-                              # Here adjust the style option later TODO
-                              # https://panel.holoviz.org/how_to/styling/matplotlib.html
-                               pn.pane.Matplotlib(vector_plot, dpi=144),
-                              # Add here the hvplot block of contour TODO
-                                sizing_mode="stretch_both")
-
-        # Generate additional plots which will be plotted on the right row.
-        other_plots = bathy_uship_vship_bottom_depth(self.ds_filtered)
-        self.plot_right = pn.Column(
-            *(pn.pane.HoloViews(plot, width=400, height=200) for plot in other_plots),
-            sizing_mode="stretch_width"
-        )
-
-        # Return a Panel row containing the updated map plot and additional plots
-        return pn.Row(self.plot_left, self.plot_right, sizing_mode="stretch_both")
-
-
-pn.extension("tabulator")
-pn.config.theme = 'dark'
-
-explorer = SADCP_Viewer()
-# Instantiate the SADCP_Viewer class and create a template
-tabs = pn.Tabs(
-    ("Plots", pn.Column(explorer.update_plots)),
-    (
-        "Metadata",
-        pn.Column(
-            explorer.metadata_table, explorer.download_button, height=500, margin=10
-        ),
-    ),
-)
-
-sidebar = [
-    pn.panel('./EuroGO-SHIP_logo_wide_tagline_1.2.png',width=300 ),
-    """This application, developed in the frame of Euro Go Shop, helps to interactively visualise and download ship ADCP data.""",
-    explorer.year_slider,
-    explorer.file_dropdown,
-    explorer.longitude_slider,
-    explorer.latitude_slider,
-    explorer.bathy_checkbox,
-    explorer.depth_range_slider,
-    explorer.depth_2_checkbox,
-    explorer.depth_3_checkbox,
-    explorer.depth_2_range_slider,
-    explorer.depth_3_range_slider,
-    explorer.num_vectors_slider,
-    explorer.scale_factor_slider,
-    explorer.data_table,
-    """You can consult detailed information on this data in the metadata tab shown on the right.
-       To download full dataset, please go to https://cdi.seadatanet.org/search 
-      and search with LOCAL_CDI_ID indicated above.""",
-    #pn.panel('https://upload.wikimedia.org/wikipedia/commons/4/47/PNG_transparency_demonstration_1.png', ),
-#width=10)
-]
-
-template = pn.template.FastListTemplate(
-    title="SADCP data Viewer", logo='https://avatars.githubusercontent.com/u/123177533?s=200&v=4',
-    sidebar=sidebar, main=[tabs]
-    
-)
-template.servable()