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geostudio.gswidgets

geojson_to_details 

geojson_to_details(geojson)

This function takes a GeoJSON object as input and returns a string containing the area, perimeter, and bounding box coordinates of the polygon.

Parameters:

Name Type Description Default
geojson dict

A dictionary representing a GeoJSON object with 'geometry' and 'type' keys. The 'geometry' key should contain a dictionary with 'type' set to 'Polygon' and 'coordinates' containing a list of coordinate pairs. The 'type' key should be set to 'Feature'.

 required

Returns:

Name Type Description
str

A string containing the area (in square kilometers), perimeter (in kilometers), and bounding box coordinates (in decimal degrees) of the polygon.
Source code in geostudio/gswidgets.py 
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def geojson_to_details(geojson):
    """
    This function takes a GeoJSON object as input and returns a string containing the area, perimeter, and bounding box coordinates of the polygon.

    Args:
        geojson (dict): A dictionary representing a GeoJSON object with 'geometry' and 'type' keys.
                     The 'geometry' key should contain a dictionary with 'type' set to 'Polygon' and 'coordinates' containing a list of coordinate pairs.
                     The 'type' key should be set to 'Feature'.

    Returns:
        str: A string containing the area (in square kilometers), perimeter (in kilometers), and bounding box coordinates (in decimal degrees) of the polygon.
    """

    print(geojson.get("geometry"))
    # gdf4326 = gpd.GeoDataFrame(index=[0], crs='epsg:4326', geometry=[geojson.get('geometry')])
    gdf4326 = gpd.GeoDataFrame.from_features([geojson], crs="epsg:4326")

    # Get the geometry from `gdf4326`
    pgon = gdf4326.geometry.iloc[0]
    # Extract list of longitude/latitude of polygon's boundary
    lons, lats = pgon.exterior.xy[:][0], pgon.exterior.xy[:][1]

    geod = Geod("+a=6378137 +f=0.0033528106647475126")
    poly_area, poly_perimeter = geod.polygon_area_perimeter(lons, lats)

    bbox_list = [round(pgon.bounds[0], 5), round(pgon.bounds[1], 5), round(pgon.bounds[2], 5), round(pgon.bounds[3], 5)]

    if bbox_list[0] > 180:
        bbox_list[0] = round(bbox_list[0] - 360, 5)
    if bbox_list[2] > 180:
        bbox_list[2] = round(bbox_list[2] - 360, 5)

    # Print the results
    output_details = "Area, (sq.km): {:.1f}".format(abs(poly_area) / 1000000) + "\n"
    output_details = output_details + "Perimeter, (km): {:.2f}".format(poly_perimeter / 1000) + "\n"
    output_details = output_details + "Bounding box coordinates: {}".format(str(bbox_list))

    return output_details

geojson_to_bbox 

geojson_to_bbox(geojson)

Convert a GeoJSON feature to a bounding box in the format [west, south, east, north].

Parameters:

Name Type Description Default
geojson dict

A dictionary representing a GeoJSON feature. It should contain a 'geometry' key with a GeoJSON geometry object.

 required

Returns:

Name Type Description
list

A list of four floating point numbers representing the bounding box in the order [west, south, east, north].

The values are rounded to 5 decimal places. Longitude and latitude values are adjusted to be within the range [-180, 180].
Source code in geostudio/gswidgets.py 
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def geojson_to_bbox(geojson):
    """
    Convert a GeoJSON feature to a bounding box in the format [west, south, east, north].

    Args:
        geojson (dict): A dictionary representing a GeoJSON feature. It should contain a 'geometry' key with a GeoJSON geometry object.

    Returns:
        list: A list of four floating point numbers representing the bounding box in the order [west, south, east, north].
        The values are rounded to 5 decimal places. Longitude and latitude values are adjusted to be within the range [-180, 180].
    """

    gdf4326 = gpd.GeoDataFrame.from_features([geojson], crs="epsg:4326")

    # Get the geometry from `gdf4326`
    pgon = gdf4326.geometry.iloc[0]
    bbox_list = [round(pgon.bounds[0], 5), round(pgon.bounds[1], 5), round(pgon.bounds[2], 5), round(pgon.bounds[3], 5)]

    if bbox_list[0] > 180:
        bbox_list[0] = bbox_list[0] - 360
    if bbox_list[2] > 180:
        bbox_list[2] = bbox_list[2] - 360

    return bbox_list

list_output_files 

list_output_files(url, just_tif=True)

Lists the files present in a remote zip archive.

Parameters:

Name Type Description Default
url str

The URL of the remote zip archive.

 required
just_tif bool

If True, only return files with '.tif' extension. Defaults to True.

 True

Returns:

Type Description

List[str]: A list of filenames present in the zip archive.
Source code in geostudio/gswidgets.py 
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def list_output_files(url, just_tif=True):
    """
    Lists the files present in a remote zip archive.

    Args:
        url (str): The URL of the remote zip archive.
        just_tif (bool, optional): If True, only return files with '.tif' extension. Defaults to True.

    Returns:
        List[str]: A list of filenames present in the zip archive.
    """
    with RemoteZip(url) as zip:
        il = zip.infolist()
    if just_tif == True:
        return [X.filename for X in il if X.filename[-4:] == ".tif"]
    else:
        return [X.filename for X in il]

download_file 

download_file(url, filename, output_path='./')

Downloads a file from a given URL and saves it to a specified output path.

Parameters:

Name Type Description Default
url str

The URL of the file to download.

 required
filename str

The name of the file to save locally.

 required
output_path str

The directory where the file will be saved. Defaults to './'.

 './'

Returns:

Name Type Description
str

The path where the file was saved.
Source code in geostudio/gswidgets.py 
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def download_file(url, filename, output_path="./"):
    """
    Downloads a file from a given URL and saves it to a specified output path.

    Args:
        url (str): The URL of the file to download.
        filename (str): The name of the file to save locally.
        output_path (str, optional): The directory where the file will be saved. Defaults to './'.

    Returns:
        str: The path where the file was saved.
    """
    with RemoteZip(url) as zip:
        a = zip.extract(filename, path=output_path)
    return a

bboxSelector 

bboxSelector()

Creates a user interface for selecting a bounding box on a map.

Returns:

Type Description

ipywidgets.widgets.GridspecLayout: A grid layout containing that can be displayed in a Jupyter notebook or similar environment. It contains a map, a search control, a full screen control, a title, a text area for displaying bounding box details,

a clear button, and a use button. The map has a draw control for selecting a bounding box.
Source code in geostudio/gswidgets.py 
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def bboxSelector():
    """
    Creates a user interface for selecting a bounding box on a map.

    Args:
        None

    Returns:
        ipywidgets.widgets.GridspecLayout: A grid layout containing that can be displayed in a Jupyter notebook or similar environment. It contains a map, a search control, a full screen control, a title, a text area for displaying bounding box details,
        a clear button, and a use button. The map has a draw control for selecting a bounding box.
    """
    bbox = []

    grid = widgets.GridspecLayout(4, 4, height="400px")

    m = Map(center=(50, 354), zoom=5, scroll_wheel_zoom=True)

    draw_control = DrawControl(polyline={}, circle={}, polygon={}, circlemarker={})

    draw_control.rectangle = {"shapeOptions": {"fillColor": "#fca45d", "color": "#fca45d", "fillOpacity": 0.3}}

    m.add(SearchControl(position="topleft", url="https://nominatim.openstreetmap.org/search?format=json&q={s}", zoom=8))

    m.add(FullScreenControl())

    grid[:, 1:4] = m

    title = widgets.HTML(
        value="<h1>Bounding box selector</h1> </p>Use the map on the right to draw a bounding box.  Once you selected it you will see the details of the bounding box below."
    )

    grid[0, 0] = title

    bbox_details = widgets.Textarea(value="Your bbox info will appear here", description="", disabled=False, rows=5)

    grid[1, 0] = bbox_details

    def on_bbox_draw(self, action, geo_json):
        """
        Callback function for the draw control's on_draw event.

        Args:
            self: The draw control object.
            action: The action that triggered the event.
            geo_json: The GeoJSON representation of the drawn shape.

        Returns:
            None
        """
        print(geo_json)
        bbox_details.value = geojson_to_details(geo_json)
        # bbox_details.value = json.dumps(geo_json)

    draw_control.on_draw(on_bbox_draw)

    m.add(draw_control)

    button_layout1 = widgets.Layout(width="auto", height="40px")  # set width and height
    button_layout2 = widgets.Layout(width="auto", height="40px")  # set width and height

    clear_button = widgets.Button(
        description="Clear bboxes",
        button_style="warning",
        display="flex",
        flex_flow="column",
        align_items="stretch",
        layout=button_layout1,
    )

    def on_clear_click(b):
        """
        Callback function for the clear button's on_click event.

        Args:
            b: The button object.

        Returns:
            None
        """
        draw_control.clear()
        bbox_details.value = "Your bbox info will appear here"

    clear_button.on_click(on_clear_click)

    grid[2, 0] = clear_button

    use_button = widgets.Button(
        description="Use this bounding box",
        button_style="success",
        display="flex",
        flex_flow="column",
        align_items="stretch",
        layout=button_layout2,
    )

    def on_use_click(b):
        """
        Callback function for the use button's on_click event.

        Args:
            b: The button object.

        Returns:
            The bounding box coordinates as a list.
        """
        # global bbox
        bbox = geojson_to_bbox(draw_control.last_draw)
        draw_control.last_draw["properties"]["style"]["fillColor"] = "#32a852"
        print(bbox)
        return bbox

    bbox = use_button.on_click(on_use_click)

    # grid[3,0] = use_button

    return grid

time_selector 

time_selector()

Creates a time range selector using ipywidgets.

Returns:

Type Description

ipywidgets.widgets.GridspecLayout: A layout containing two datetime pickers for selecting a start and end time.
Source code in geostudio/gswidgets.py 
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def time_selector():
    """
    Creates a time range selector using ipywidgets.

    Args:
        None

    Returns:
        ipywidgets.widgets.GridspecLayout: A layout containing two datetime pickers for selecting a start and end time.
    """
    start_time_picker = widgets.DatetimePicker(description="Start time:  ", disabled=False)

    end_time_picker = widgets.DatetimePicker(description="End time:  ", disabled=False)

    grid = widgets.GridspecLayout(4, 2, height="200px")

    grid[0, 0:2] = widgets.HTML(
        value="<h1>Time range selector</h1> </p>Use the datetime pickers to pick a time window for inference."
    )
    grid[1, 0] = start_time_picker
    grid[2, 0] = end_time_picker

    return grid

add_geotiff 

add_geotiff(
    filename,
    layer_name="",
    colormap="viridis",
    cmin=0,
    cmax="",
    opacity=1.0,
)

Adds a GeoTIFF file to a Folium map as an overlay.

Parameters:

Name Type Description Default
filename str

The path to the GeoTIFF file.

 required
layer_name str

The name of the layer. Defaults to "".

 ''
colormap str

The colormap to use for the GeoTIFF data. Defaults to "viridis".

 'viridis'
cmin int or float

The minimum value for the colormap. Defaults to 0.

 0
cmax int or float

The maximum value for the colormap. If not provided, it is automatically calculated as the maximum value in the GeoTIFF data. Defaults to "".

 ''
opacity float

The opacity of the overlay. Defaults to 1.0.

 1.0

Returns:

Type Description

folium.raster_layers.ImageOverlay: An ImageOverlay object that can be added to a Folium map.
Source code in geostudio/gswidgets.py 
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def add_geotiff(filename, layer_name="", colormap="viridis", cmin=0, cmax="", opacity=1.0):
    """
    Adds a GeoTIFF file to a Folium map as an overlay.

    Args:
        filename (str): The path to the GeoTIFF file.
        layer_name (str, optional): The name of the layer. Defaults to "".
        colormap (str, optional): The colormap to use for the GeoTIFF data. Defaults to "viridis".
        cmin (int or float, optional): The minimum value for the colormap. Defaults to 0.
        cmax (int or float, optional): The maximum value for the colormap. If not provided, it is automatically calculated as the maximum value in the GeoTIFF data. Defaults to "".
        opacity (float, optional): The opacity of the overlay. Defaults to 1.0.

    Returns:
        folium.raster_layers.ImageOverlay: An ImageOverlay object that can be added to a Folium map.
    """
    with rasterio.open(filename) as src:
        dataArray = src.read(1)
        bounds = src.bounds
        nd = src.nodata

    # TODO remove - never used
    # midLat = (bounds[3] + bounds[1]) / 2
    # midLon = (bounds[2] + bounds[0]) / 2

    if cmax == "":
        cmax = np.max(dataArray)
    dataArrayMasked = np.ma.masked_where(dataArray == nd, dataArray)
    imc = colorize(dataArrayMasked, cmax, cmin=cmin, cmap=colormap)

    return folium.raster_layers.ImageOverlay(
        imc,
        [[bounds[1], bounds[0]], [bounds[3], bounds[2]]],
        name=layer_name,
        opacity=opacity,
    )

colorize 

colorize(array, cmax, cmin=0, cmap='rainbow')

Converts a 2D numpy array of values into an RGBA array given a colour map and range.

Parameters:

Name Type Description Default
array ndarray
required
cmax float

Max value for colour range

 required
cmin float

Min value for colour range

 0
cmap string

Colour map to use (from matplotlib colourmaps)

 'rainbow'

Returns:

Name Type Description
rgba_array ndarray

3D RGBA array which can be plotted.
Source code in geostudio/gswidgets.py 
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def colorize(array, cmax, cmin=0, cmap="rainbow"):
    """Converts a 2D numpy array of values into an RGBA array given a colour map and range.

    Args:
        array (ndarray):
        cmax (float): Max value for colour range
        cmin (float): Min value for colour range
        cmap (string): Colour map to use (from matplotlib colourmaps)

    Returns:
            rgba_array (ndarray): 3D RGBA array which can be plotted.
    """
    normed_data = (array - cmin) / (array.max() - cmin)
    cm = plt.cm.get_cmap(cmap)
    return cm(normed_data)

available_models_ui 

available_models_ui(client)

A UI for browsing and selecting available models.

Parameters:

Name Type Description Default
client object

:py:class:geostudio.backends.ginference.client.Client

 required

Returns:

Type Description

widgets.VBox: A Jupyter widget containing a header, text input for filtering, checkbox for active models, interactive dropdown for selecting a model, and the model selection table.
Source code in geostudio/gswidgets.py 
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def available_models_ui(client):
    """
    A UI for browsing and selecting available models.

    Args:
        client (object): :py:class:`geostudio.backends.ginference.client.Client`

    Returns:
        widgets.VBox: A Jupyter widget containing a header, text input for filtering, checkbox for active models, interactive dropdown for selecting a model, and the model selection table.
    """
    # workflows = self.available_workflows()
    models_df = client.list_models(output="df")
    models_trim = models_df[["name", "description", "created_at", "created_by", "active"]].sort_values(by=["name"])

    # all_tags = []
    # [all_tags.extend(X) for X in wf_trim.tags]
    # diff_tags = list(np.unique(np.array(all_tags)))

    def view(name="", active=True):
        """
        Displays a filtered list of models based on the provided name and active status.

        Args:
            name (str, optional): The name of the model(s) to filter by. If not provided, all models are displayed.
            active (bool, optional): Whether to filter models by their active status. If True, only active models are displayed.

        Returns:
            None
        """
        if name != "":
            models_trim_filtered = models_trim[[name in X for X in models_trim.name]]
        else:
            models_trim_filtered = models_trim

        if active == True:
            models_trim_filtered = models_trim_filtered[models_trim_filtered.active == True]
            #     ["true" in X for X in models_trim_filtered.active]
            # ]
        else:
            models_trim_filtered = models_trim_filtered

        model_names = list(models_trim_filtered.name)

        if len(model_names) > 0:
            wf_dd.options = model_names
        else:
            wf_dd.value = "None found"
            wf_dd.options = ["None found"]

        return display(HTML(models_trim_filtered.to_html(index=False)))

    keyText = widgets.Text(
        value="",
        placeholder="Type something to filter results",
        description="Model name:",
        disabled=False,
        layout=widgets.Layout(height="auto", width="400px"),
    )

    wf_dd = widgets.Dropdown(
        options=list(models_trim.name),
        value=list(models_trim.name)[0],
        description="Select Model:",
        disabled=False,
        layout=widgets.Layout(height="auto", width="600px"),
    )

    def on_change(change):
        if change["type"] == "change" and change["name"] == "value":
            model_name = change["new"]

    wf_dd.observe(on_change)

    activeCheck = widgets.Checkbox(value=True, description="Active only?", disabled=False)

    # hdr = widgets.Button(
    #     description="GeoDN Modeling workflow catalogue",
    #     disabled=True,
    #     button_style="info",  # 'success', 'info', 'warning', 'danger' or ''
    #     layout=widgets.Layout(height="auto", width="800px"),
    # )
    hdr = widgets.HTML(value="<h1>Inference model selector</h1> </p>Explore which models are deployed for inference.")

    models_filter = widgets.interactive(
        view,
        name=keyText,
        # tags=tagsSelect,
        active=activeCheck,
        layout=widgets.Layout(height="auto", width="800px"),
    )

    # hdr = widgets.Button(
    #     description="Inference model selector",
    #     disabled=True,
    #     button_style="info",  # 'success', 'info', 'warning', 'danger' or ''
    #     layout=widgets.Layout(height="auto", width="800px"),
    # )

    models_table = widgets.VBox(
        [
            hdr,
            widgets.HBox([widgets.VBox([keyText, activeCheck])]),
            models_filter.children[2],
            wf_dd,
        ],
        layout=widgets.Layout(margin="20px 20px 20px 20px", padding="5px 5px 5px 5px"),
    )

    return models_table

fileDownloader 

fileDownloader(client, id, just_tifs=True)

Downloads the output files of an inference task.

Parameters:

Name Type Description Default
client (

py:class:geostudio.backends.ginference.client.Client): An object representing the client to interact with the inference service.

 required
id str

The unique identifier of the inference task.

 required

Returns:

Type Description

None
Source code in geostudio/gswidgets.py 
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def fileDownloader(client, id, just_tifs=True):
    """
    Downloads the output files of an inference task.

    Args:
        client (:py:class:`geostudio.backends.ginference.client.Client`): An object representing the client to interact with the inference service.
        id (str): The unique identifier of the inference task.

    Returns:
        None
    """

    r = client.get_inference_task(id)
    fl = list_output_files(url=r["output_url"], just_tif=just_tifs)

    sm = widgets.SelectMultiple(
        options=fl,
        value=[],
        rows=10,
        description="Files:",
        disabled=False,
        layout={"width": "1000px"},
    )

    db = widgets.Button(
        description="Download",
        disabled=False,
        button_style="",  # 'success', 'info', 'warning', 'danger' or ''
        tooltip="Click me to download the selected",
        icon="check",
        layout=widgets.Layout(height="auto", width="800px"),
    )

    dlp = widgets.Text(value="./", description="Dl path:", disabled=False)

    hdr = widgets.HTML(
        value="<h1>Inference output downloader</h1> </p>Select the files and the download path and hit download."
    )

    output = widgets.Output()

    display(hdr, sm, dlp, db, output)

    def on_button_clicked(db):
        """This function is triggered when a button is clicked."""
        with output:
            for X in list(sm.value):
                print("Downloading...", end="\r")
                a = download_file(r["output_url"], X)
                print(a)

    db.on_click(on_button_clicked)

fileDownloaderTasks 

fileDownloaderTasks(client, task_id, just_tifs=True)

Downloads the output files of an inference task.

Parameters:

Name Type Description Default
client (

py:class:geostudio.backends.v2.ginference.client.Client): An object representing the client to interact with the inference service.

 required
task_id str

The unique identifier of the inference task id.

 required

Returns:

Type Description

None
Source code in geostudio/gswidgets.py 
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def fileDownloaderTasks(client, task_id, just_tifs=True):
    """
    Downloads the output files of an inference task.

    Args:
        client (:py:class:`geostudio.backends.v2.ginference.client.Client`): An object representing the client to interact with the inference service.
        task_id (str): The unique identifier of the inference task id.

    Returns:
        None
    """

    r = client.get_task_output_url(task_id)
    fl = list_output_files(url=r["output_url"], just_tif=just_tifs)

    sm = widgets.SelectMultiple(
        options=fl,
        value=[],
        rows=10,
        description="Files:",
        disabled=False,
        layout={"width": "1000px"},
    )

    db = widgets.Button(
        description="Download",
        disabled=False,
        button_style="",  # 'success', 'info', 'warning', 'danger' or ''
        tooltip="Click me to download the selected",
        icon="check",
        layout=widgets.Layout(height="auto", width="800px"),
    )

    dlp = widgets.Text(value="./", description="Dl path:", disabled=False)

    hdr = widgets.HTML(
        value="<h1>Inference Task output downloader</h1> </p>Select the files and the download path and hit download."
    )

    output = widgets.Output()

    display(hdr, sm, dlp, db, output)

    def on_button_clicked(db):
        """This function is triggered when a button is clicked."""
        with output:
            for X in list(sm.value):
                print("Downloading...", end="\r")
                a = download_file(r["output_url"], X)
                print(a)

    db.on_click(on_button_clicked)

geotiff2img 

geotiff2img(filename, band=1, cmax='')

Converts a GeoTIFF file to a base64 encoded PNG image URL.

Parameters:

Name Type Description Default
filename str

The path to the GeoTIFF file.

 required
band int

The band number to use for the image. Default is 1.

 1
cmax str or float

The maximum value for color scaling. If not provided, it will be automatically calculated.

 ''

Returns:

Name Type Description
tuple

A tuple containing the base64 encoded PNG image URL and the image bounds.
Source code in geostudio/gswidgets.py 
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def geotiff2img(filename, band=1, cmax=""):
    """
    Converts a GeoTIFF file to a base64 encoded PNG image URL.

    Args:
        filename (str): The path to the GeoTIFF file.
        band (int, optional): The band number to use for the image. Default is 1.
        cmax (str or float, optional): The maximum value for color scaling. If not provided, it will be automatically calculated.

    Returns:
        tuple: A tuple containing the base64 encoded PNG image URL and the image bounds.
    """

    if "_rgb.tif" not in filename:
        # In the case of a non-RGB tagged image convert to an RGB png based on a color map from a single selected band
        with rasterio.open(filename) as src:
            dataArray = src.read(band)
            bounds = src.bounds
            nd = src.nodata

        if cmax == "":
            cmax = np.max(dataArray)
        dataArrayMasked = np.ma.masked_where(dataArray == nd, dataArray)
        imc = colorize(dataArrayMasked, cmax, cmin=0, cmap="viridis")

        img = 255 * imc
        img = img.astype(np.uint8)
        im = PIL.Image.fromarray(img, mode="RGBA")

    elif "_rgb.tif" in filename:
        # In the case of an RGB tagged image convert to an RGB png
        with rasterio.open(filename) as src:
            rBand = src.read(1)
            gBand = src.read(2)
            bBand = src.read(3)
            bounds = src.bounds
            nd = src.nodata

        opacity_layer = np.ma.masked_where(rBand == nd, 255 * np.ones(rBand.shape))
        img = np.stack([rBand, gBand, bBand, opacity_layer], axis=-1)

        img = 2 * img
        img = img.astype(np.uint8)
        im = PIL.Image.fromarray(img, mode="RGBA")

    f = BytesIO()
    im.save(f, "png")

    data = b64encode(f.getvalue())
    data = data.decode("ascii")
    imgurl = "data:image/png;base64," + data

    return imgurl, bounds

inferenceViewer 

inferenceViewer(client, id)

Creates a Jupyter widget for visualizing inference task outputs.

Parameters:

Name Type Description Default
client (

py:class:geostudio.backends.ginference.client.Client): An object representing the client for interacting with the inference service.

 required
id str

The unique identifier for the inference task.

 required

Returns:

Name Type Description
object

A Jupyter widget containing a map with image overlays of the inference outputs.
Source code in geostudio/gswidgets.py 
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def inferenceViewer(client, id):
    """
    Creates a Jupyter widget for visualizing inference task outputs.

    Args:
        client (:py:class:`geostudio.backends.ginference.client.Client`): An object representing the client for interacting with the inference service.
        id (str): The unique identifier for the inference task.

    Returns:
        object: A Jupyter widget containing a map with image overlays of the inference outputs.
    """

    r = client.get_inference_task(id)
    fl = list_output_files(r["output_url"])

    fl_options = [(X.split("/")[-1].replace(r["event_id"] + "_", ""), X) for X in fl]

    sm = widgets.SelectMultiple(
        options=fl_options,
        value=[],
        rows=10,
        description="",
        disabled=False,
        layout={"width": "800px"},
    )

    db = widgets.Button(
        description="Update layers",
        disabled=False,
        button_style="",  # 'success', 'info', 'warning', 'danger' or ''
        tooltip="Click me to update the layers on the map",
        icon="check",
        layout=widgets.Layout(height="auto", width="100px"),
    )

    output = widgets.Output()

    left = widgets.VBox([sm, db, output])

    header = widgets.HTML(value="<h1>Inference output viewer</h1>")
    footer = widgets.HTML(value="Thanks for using the viewer, any questions please ask....")

    map = Map(center=(52.0, 0.0), zoom=8, scroll_wheel_zoom=True, world_copy_jump=False)
    map.add(FullScreenControl())
    control = LayersControl(position="topright")
    map.add(control)

    def on_button_clicked(db):
        """
        Adds image overlays to a map based on a list of URLs.

        Args:
            db (object): The database object containing necessary information for the operation.

        Returns:
            None
        """
        layer_files = []

        # map.clear_layers()

        with output:
            output.clear_output()
            for X in list(sm.value):
                print("Downloading...", end="\r")
                a = download_file(r["output_url"], X)
                print(a)
                layer_files = layer_files + [a]
                imgurl, bounds = geotiff2img(X)
                imgBounds = ((bounds.bottom, bounds.left), (bounds.top, bounds.right))

                map.add(
                    ImageOverlay(
                        name=a.split("/")[-1].replace(r["event_id"] + "_", ""),
                        url=imgurl,
                        bounds=imgBounds,
                        opacity=0.9,
                    )
                )
                # map.add(io)
                # map.fit_bounds([[bounds.bottom, bounds.left],[bounds.top, bounds.right]])
                map.center = (bounds.bottom, bounds.left)
                print(">>> added to map")

    db.on_click(on_button_clicked)

    return widgets.VBox(
        [
            header,
            # widgets.HBox([widgets.VBox([sm, db]), output]),
            sm,
            db,
            map,
            output,
            # footer
        ]
    )

inferenceTaskViewer 

inferenceTaskViewer(client, task_id)

Creates a Jupyter widget for visualizing inference task outputs.

Parameters:

Name Type Description Default
client (

py:class:geostudio.backends.v2.ginference.client.Client): An object representing the client for interacting with the inference service.

 required
task_id str

The unique identifier for the inference task.

 required

Returns:

Name Type Description
object

A Jupyter widget containing a map with image overlays of the inference outputs.
Source code in geostudio/gswidgets.py 
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def inferenceTaskViewer(client, task_id):
    """
    Creates a Jupyter widget for visualizing inference task outputs.

    Args:
        client (:py:class:`geostudio.backends.v2.ginference.client.Client`): An object representing the client for interacting with the inference service.
        task_id (str): The unique identifier for the inference task.

    Returns:
        object: A Jupyter widget containing a map with image overlays of the inference outputs.
    """

    r = client.get_task_output_url(task_id)
    fl = list_output_files(r["output_url"])

    fl_options = [(X.split("/")[-1].replace(r["task_id"] + "_", ""), X) for X in fl]

    sm = widgets.SelectMultiple(
        options=fl_options,
        value=[],
        rows=10,
        description="",
        disabled=False,
        layout={"width": "800px"},
    )

    db = widgets.Button(
        description="Update layers",
        disabled=False,
        button_style="",  # 'success', 'info', 'warning', 'danger' or ''
        tooltip="Click me to update the layers on the map",
        icon="check",
        layout=widgets.Layout(height="auto", width="100px"),
    )

    output = widgets.Output()

    left = widgets.VBox([sm, db, output])

    header = widgets.HTML(value="<h1>Inference output viewer</h1>")
    footer = widgets.HTML(value="Thanks for using the viewer, any questions please ask....")

    map = Map(center=(52.0, 0.0), zoom=8, scroll_wheel_zoom=True, world_copy_jump=False)
    map.add(FullScreenControl())
    control = LayersControl(position="topright")
    map.add(control)

    def on_button_clicked(db):
        """
        Adds image overlays to a map based on a list of URLs.

        Args:
            db (object): The database object containing necessary information for the operation.

        Returns:
            None
        """
        layer_files = []

        # map.clear_layers()

        with output:
            output.clear_output()
            for X in list(sm.value):
                print("Downloading...", end="\r")
                a = download_file(r["output_url"], X)
                print(a)
                layer_files = layer_files + [a]
                imgurl, bounds = geotiff2img(X)
                imgBounds = ((bounds.bottom, bounds.left), (bounds.top, bounds.right))

                map.add(
                    ImageOverlay(
                        name=a.split("/")[-1].replace(r["task_id"] + "_", ""),
                        url=imgurl,
                        bounds=imgBounds,
                        opacity=0.9,
                    )
                )
                # map.add(io)
                # map.fit_bounds([[bounds.bottom, bounds.left],[bounds.top, bounds.right]])
                map.center = (bounds.bottom, bounds.left)
                print(">>> added to map")

    db.on_click(on_button_clicked)

    return widgets.VBox(
        [
            header,
            # widgets.HBox([widgets.VBox([sm, db]), output]),
            sm,
            db,
            map,
            output,
            # footer
        ]
    )

color_inference_tasks_by_status 

color_inference_tasks_by_status(val)

Takes a scalar and returns a string with the css property 'color: red' for negative strings, black otherwise.

Source code in geostudio/gswidgets.py 
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def color_inference_tasks_by_status(val):
    """
    Takes a scalar and returns a string with
    the css property `'color: red'` for negative
    strings, black otherwise.
    """

    if val == "READY":
        color = "#ffd966"
    elif val == "WAITING":
        color = "#f6b26b"
    elif val == "RUNNING":
        color = "#d9ead3"
    elif val == "FINISHED":
        color = "#8fce00"
    elif val == "FAILED":
        color = "#ff0000"
    elif val == "STOP":
        color = "#b00020"
    else:
        color = "black"

    return "color: %s" % color

plot_tune_metrics 

plot_tune_metrics(
    client, tune_id: str, run_name: str = "Train"
)

Plots training and validation metrics for a given tuning experiment in a 2x2 subplot grid.

Parameters:

Name Type Description Default
tune_id str

The unique identifier of the tuning experiment.

 required

Returns:

Type Description

None
Source code in geostudio/gswidgets.py 
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def plot_tune_metrics(client, tune_id: str, run_name: str = "Train"):
    """
    Plots training and validation metrics for a given tuning experiment in a 2x2 subplot grid.

    Parameters:
        tune_id (str): The unique identifier of the tuning experiment.

    Returns:
        None
    """
    mlflow_urls = client.get_mlflow_metrics(tune_id)
    if mlflow_urls:
        print(mlflow_urls)
    else:
        return f"Tune {tune_id}, has not started to generate metrics. Try to rerun this cell after a few moments!"

    mdf = client.get_tune_metrics_df(tune_id, run_name)
    r = client.get_tune(tune_id)
    status = r["status"]

    mdf_columns = mdf.columns.tolist()
    if not mdf_columns:
        return f"Tune {tune_id}, has not started to generate metrics. Try to rerun this cell after a few moments!"
    mdf_columns.remove("epoch")
    mdf_columns_len = len(mdf_columns)

    nrows = math.ceil(mdf_columns_len / 2)
    ncols = 2
    fig, axes = plt.subplots(nrows=nrows, ncols=ncols, sharex=True, sharey=False, figsize=(10, mdf_columns_len))
    fig.tight_layout()
    step_num = max(mdf.epoch)
    fig.suptitle(f"{tune_id} - {status} - Step number: {step_num}")
    fig.subplots_adjust(top=0.88)

    axes = axes.flatten()

    for i, column in enumerate(mdf_columns):
        axes[i].plot(mdf["epoch"], mdf[column], "b.-")
        axes[i].set_title(column)
        axes[i].grid(True)

crop_image_bytes 

crop_image_bytes(img_bytes)

Crops the white space from the training image provided as raw bytes and return PNG bytes.

Parameters

img_bytes : bytes Raw image bytes (any format supported by PIL.Image.open).

Returns

bytes PNG-encoded bytes of the cropped image. The function uses a fixed crop box (left=0, upper=350, right=image_width, lower=650) so the returned image contains the horizontal strip between y=350 and y=650 from the original image.

Notes
  • The result is always encoded as PNG.
  • If the crop box extends beyond the source image bounds, PIL.Image.crop behavior applies.
Source code in geostudio/gswidgets.py 
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def crop_image_bytes(img_bytes):
    """
    Crops the white space from the training image provided as raw bytes and return PNG bytes.

    Parameters
    ----------
    img_bytes : bytes
        Raw image bytes (any format supported by PIL.Image.open).

    Returns
    -------
    bytes
        PNG-encoded bytes of the cropped image. The function uses a fixed crop box
        (left=0, upper=350, right=image_width, lower=650) so the returned image contains
        the horizontal strip between y=350 and y=650 from the original image.

    Notes
    -----
    - The result is always encoded as PNG.
    - If the crop box extends beyond the source image bounds, PIL.Image.crop behavior applies.
    """

    imageFile = Image.open(io.BytesIO(img_bytes))
    w, h = imageFile.size
    croppedImageFile = imageFile.crop((0, 350, w, 650))
    imgBytes = io.BytesIO()
    croppedImageFile.save(imgBytes, format="PNG")
    return imgBytes.getvalue()

save_training_image 

save_training_image(
    image_number, epoch, img_dict, cropped=True
)

Save a training sample image from img_dict to a PNG file.

Parameters

image_number : int The sample index/number to save (matches the 'image_number' key in img_dict). epoch : int The epoch number to save (matches the 'epoch' key in img_dict). img_dict : list[dict] List of artefact records as returned by get_tuning_artefacts. Each item must contain: - 'filename' : str - 'image' : bytes - 'epoch' : int - 'image_number' : int cropped : bool, optional If True (default) crop the image bytes using crop_image_bytes before saving.

Raises

ValueError If no matching image is found in img_dict.

Source code in geostudio/gswidgets.py 
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def save_training_image(image_number, epoch, img_dict, cropped=True):
    """
    Save a training sample image from img_dict to a PNG file.

    Parameters
    ----------
    image_number : int
        The sample index/number to save (matches the 'image_number' key in img_dict).
    epoch : int
        The epoch number to save (matches the 'epoch' key in img_dict).
    img_dict : list[dict]
        List of artefact records as returned by get_tuning_artefacts. Each item must contain:
          - 'filename' : str
          - 'image' : bytes
          - 'epoch' : int
          - 'image_number' : int
    cropped : bool, optional
        If True (default) crop the image bytes using crop_image_bytes before saving.

    Raises
    ------
    ValueError
        If no matching image is found in img_dict.
    """
    img_bytes = [X for X in img_dict if (X["epoch"] == epoch) & (X["image_number"] == image_number)][0]["image"]
    with open(f"training_image_epoch_{epoch}_number_{image_number}.png", "wb") as f:
        if cropped:
            f.write(crop_image_bytes(img_bytes))
        else:
            f.write(img_bytes)

browse_training_images 

browse_training_images(img_dict: object, tune_id: str)

Create an interactive Jupyter widget viewer to browse fine-tuning sample images.

Parameters

img_dict : list[dict] List of artefact records. Each item must be a dict with at least the keys: - 'filename' : str - 'image' : bytes (raw image bytes as returned by get_tuning_artefacts) - 'epoch' : int - 'image_number' : int tune_id : str Identifier shown in the viewer header.

Notes
  • Depends on crop_image_bytes(img_bytes) to produce the PNG bytes shown in the widget.
  • Expects img_dict to contain at least one image; raises ValueError otherwise.
  • Uses ipywidgets and functools to wire button callbacks.
  • To use: viewer = browse_training_images(img_dict, tune_id); display(viewer)
Source code in geostudio/gswidgets.py 
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def browse_training_images(img_dict: object, tune_id: str):
    """
    Create an interactive Jupyter widget viewer to browse fine-tuning sample images.

    Parameters
    ----------
    img_dict : list[dict]
        List of artefact records. Each item must be a dict with at least the keys:
          - 'filename' : str
          - 'image' : bytes  (raw image bytes as returned by get_tuning_artefacts)
          - 'epoch' : int
          - 'image_number' : int
    tune_id : str
        Identifier shown in the viewer header.

    Notes
    -----
    - Depends on crop_image_bytes(img_bytes) to produce the PNG bytes shown in the widget.
    - Expects img_dict to contain at least one image; raises ValueError otherwise.
    - Uses ipywidgets and functools to wire button callbacks.
    - To use: viewer = browse_training_images(img_dict, tune_id); display(viewer)
    """

    if not img_dict:
        raise ValueError("img_dict is empty - must contain at least one image record")

    epochs = sorted(list(set([X["epoch"] for X in img_dict])))
    image_numbers = sorted(list(set([X["image_number"] for X in img_dict])))

    header = widgets.HTML(value=f"<h2>Fine-tuning samples - {tune_id}</h2>")

    image_widget = widgets.Image(
        value=crop_image_bytes(
            [X for X in img_dict if (X["epoch"] == epochs[0]) & (X["image_number"] == image_numbers[0])][0]["image"]
        ),
        format="png",
        width=800,
        height=400,
    )

    # Create buttons for navigation
    back_epoch_button = widgets.Button(description="< Back")
    forward_epoch_button = widgets.Button(description="Next >")
    back_image_button = widgets.Button(description="< Back")
    forward_image_button = widgets.Button(description="Next >")

    # Use a widget to hold the current image index
    epoch_index_w = widgets.IntText(value=0, visible=False)
    image_number_index_w = widgets.IntText(value=0, visible=False)

    epoch_text = widgets.Text(value=str(epochs[epoch_index_w.value]), description="Epoch:", disabled=True)
    image_text = widgets.Text(
        value=str(image_numbers[image_number_index_w.value]), description="Sample:", disabled=True
    )

    # Arrange the widgets in a horizontal box
    viewer_container = widgets.VBox(
        [
            header,
            widgets.HBox([epoch_text, back_epoch_button, forward_epoch_button]),
            widgets.HBox([image_text, back_image_button, forward_image_button]),
            image_widget,
        ]
    )

    # Create a function to handle button clicks
    def on_epoch_button_click(b, epochs=[], image_numbers=[]):
        current_index = epoch_index_w.value
        max_index = len(epochs) - 1

        if b.description == "Next >" and current_index < max_index:
            epoch_index_w.value += 1
        elif b.description == "< Back" and current_index > 0:
            epoch_index_w.value -= 1

        epoch_text.value = str(epochs[epoch_index_w.value])

        # Update the displayed image
        image_widget.value = crop_image_bytes(
            [
                X
                for X in img_dict
                if (X["epoch"] == epochs[epoch_index_w.value])
                & (X["image_number"] == image_numbers[image_number_index_w.value])
            ][0]["image"]
        )

    # Attach the click event to the buttons
    back_epoch_button.on_click(functools.partial(on_epoch_button_click, epochs=epochs, image_numbers=image_numbers))
    forward_epoch_button.on_click(functools.partial(on_epoch_button_click, epochs=epochs, image_numbers=image_numbers))

    def on_image_button_click(b, epochs=[], image_numbers=[]):
        current_index = image_number_index_w.value
        max_index = len(image_numbers) - 1

        if b.description == "Next >" and current_index < max_index:
            image_number_index_w.value += 1
        elif b.description == "< Back" and current_index > 0:
            image_number_index_w.value -= 1

        image_text.value = str(image_numbers[image_number_index_w.value])

        # Update the displayed image
        image_widget.value = crop_image_bytes(
            [
                X
                for X in img_dict
                if (X["epoch"] == epochs[epoch_index_w.value])
                & (X["image_number"] == image_numbers[image_number_index_w.value])
            ][0]["image"]
        )

    # Attach the click event to the buttons
    back_image_button.on_click(functools.partial(on_image_button_click, epochs=epochs, image_numbers=image_numbers))
    forward_image_button.on_click(functools.partial(on_image_button_click, epochs=epochs, image_numbers=image_numbers))

    # Display the interactive viewer
    return viewer_container