dynamic properties via JS in dash-cytoscape

lang_main_config.toml

@@ -0,0 +1,56 @@
+# lang_main: Config file
+
+[paths]
+inputs = './inputs/'
+results = './results/test_new2/'
+dataset = './01_2_Rohdaten_neu/Export4.csv'
+#results = './results/Export7/'
+#dataset = './01_03_Rohdaten_202403/Export7_59499_Zeilen.csv'
+#results = './results/Export7_trunc/'
+#dataset = './01_03_Rohdaten_202403/Export7_trunc.csv'
+
+[control]
+preprocessing = true
+preprocessing_skip = false
+token_analysis = false
+token_analysis_skip = false
+graph_postprocessing = false
+graph_postprocessing_skip = false
+time_analysis = false
+time_analysis_skip = false
+
+#[export_filenames]
+#filename_cossim_filter_candidates = 'CosSim-FilterCandidates'
+
+[preprocess]
+filename_cossim_filter_candidates = 'CosSim-FilterCandidates'
+date_cols = [
+    "VorgangsDatum", 
+    "ErledigungsDatum", 
+    "Arbeitsbeginn", 
+    "ErstellungsDatum",
+]
+threshold_amount_characters = 5
+threshold_similarity = 0.8
+
+[graph_postprocessing]
+threshold_edge_weight = 150
+
+[time_analysis.uniqueness]
+threshold_unique_texts = 4
+criterion_feature = 'HObjektText'
+feature_name_obj_id = 'ObjektID'
+
+[time_analysis.model_input]
+input_features = [
+    'VorgangsTypName',
+    'VorgangsArtText',
+    'VorgangsBeschreibung',
+]
+activity_feature = 'VorgangsTypName'
+activity_types = [
+    'Reparaturauftrag (Portal)',
+    'Störungsmeldung',
+]
+threshold_num_acitivities = 1
+threshold_similarity = 0.8

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "lang-main"
-version = "0.1.0"
+version = "0.1.0dev1"
 description = "Several tools to analyse maintenance data with strong focus on language processing"
 authors = [
     {name = "d-opt GmbH, resp. Florian Förster", email = "f.foerster@d-opt.com"},
@@ -40,6 +40,7 @@ trials = [
 line-length = 94
 indent-width = 4
 target-version = "py311"
+src = ["src"]
 
 [tool.ruff.format]
 quote-style = "single"

scripts/dashboard/cyto.py

@@ -3,26 +3,74 @@ import webbrowser
 from pathlib import Path
 from threading import Thread
 from typing import cast
+import copy
 
 import dash_cytoscape as cyto
-import lang_main.io
 from dash import Dash, Input, Output, State, dcc, html
+from dash.exceptions import PreventUpdate
+
+import lang_main.io
 from lang_main.analysis import graphs
 
 target = '../results/test_20240529/Pipe-Token_Analysis_Step-1_build_token_graph.pkl'
 p = Path(target).resolve()
 ret = lang_main.io.load_pickle(p)
 tk_graph = cast(graphs.TokenGraph, ret[0])
-tk_graph_filtered = tk_graph.filter_by_edge_weight(150)
+tk_graph_filtered = tk_graph.filter_by_edge_weight(150, None)
-tk_graph_filtered = tk_graph_filtered.filter_by_node_degree(1)
+tk_graph_filtered = tk_graph_filtered.filter_by_node_degree(1, None)
-cyto_data, weight_data = graphs.convert_graph_to_cytoscape(tk_graph_filtered)
+cyto_data_base, weight_data = graphs.convert_graph_to_cytoscape(tk_graph_filtered)
 
 MIN_WEIGHT = weight_data['min']
 MAX_WEIGHT = weight_data['max']
 
 
 cyto.load_extra_layouts()
-app = Dash(__name__)
+external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
+app = Dash(__name__, external_stylesheets=external_stylesheets)
+
+cose_layout = {
+    'name': 'cose',
+    'nodeOverlap': 20,
+    'refresh': 20,
+    'fit': True,
+    'padding': 30,
+    'randomize': True,
+    'componentSpacing': 40,
+    'nodeRepulsion': 2000,
+    'edgeElasticity': 1000,
+    'idealEdgeLength': 100,
+    'nestingFactor': 1.2,
+    'gravity': 50,
+    'numIter': 2000,
+    'initialTemp': 1000,
+    'coolingFactor': 0.95,
+    'minTemp': 1.0,
+    'nodeDimensionsIncludeLabels': True,
+}
+
+cose_bilkent_layout = {
+    'name': 'cose-bilkent',
+    'nodeDimensionsIncludeLabels': True,
+    'idealEdgeLength': 100,
+    'edgeElasticity': 0.45,
+    'nodeRepulsion': 10000,
+    'nestingFactor': 0.1,
+    'gravity': 0.25,
+    'numIter': 2500,
+    'initialTemp': 1000,
+    'coolingFactor': 0.95,
+    'minTemp': 1.0,
+}
+
+cola_layout = {
+    'name': 'cola',
+    'nodeDimensionsIncludeLabels': True,
+    'nodeSpacing': 30,
+    'edgeLength': 45,
+    'animate': True,
+    'centerGraph': True,
+    'randomize': False,
+}
 
 my_stylesheet = [
     # Group selectors
@@ -45,7 +93,10 @@ my_stylesheet = [
     {
         'selector': 'edge',
         'style': {
-            'width': 2,
+            #'width': f'mapData(weight, {MIN_WEIGHT}, {MAX_WEIGHT}, 1, 10)',
+            # 'width': """function(ele) {
+            #     return ele.data('weight');
+            # """,
             'curve-style': 'bezier',
             'line-color': 'grey',
             'line-style': 'solid',
@@ -59,130 +110,201 @@ my_stylesheet = [
 
 app.layout = html.Div(
     [
-        html.Button('Reset', id='bt-reset'),
+        html.Button('Trigger JS Layout', id='test_js'),
+        html.Button('Trigger JS Weight', id='test_js_weight'),
+        html.Div(id='output'),
+        html.Div(
+            [
+                html.H2('Token Graph', style={'margin': 0}),
+                html.Button(
+                    'Reset Default',
+                    id='bt-reset',
+                    style={
+                        'marginLeft': 'auto',
+                        'width': '300px',
+                    },
+                ),
+            ],
+            style={
+                'display': 'flex',
+                'marginBottom': '1em',
+            },
+        ),
+        html.H3('Layout'),
         dcc.Dropdown(
-            id='layout_choice_internal',
+            id='layout_choice',
             options=[
-                'random',
-                'grid',
-                'circle',
-                'concentric',
-                'breadthfirst',
                 'cose',
+                'cola',
+                'euler',
+                'random',
             ],
             value='cose',
             clearable=False,
         ),
-        dcc.Dropdown(
+        html.Div(
-            id='layout_choice_external',
+            [
-            options=[
+                html.H3('Graph Filter'),
-                'cose-bilkent',
+                dcc.Input(
-                'cola',
+                    id='weight_min',
-                'euler',
+                    type='number',
-                'spread',
+                    min=MIN_WEIGHT,
-                'dagre',
+                    max=MAX_WEIGHT,
-                'klay',
+                    step=1,
+                    placeholder=f'Minimum edge weight: {MIN_WEIGHT} - {MAX_WEIGHT}',
+                    debounce=True,
+                    style={'width': '40%'},
+                ),
+                dcc.Input(
+                    id='weight_max',
+                    type='number',
+                    min=MIN_WEIGHT,
+                    max=MAX_WEIGHT,
+                    step=1,
+                    placeholder=f'Maximum edge weight: {MIN_WEIGHT} - {MAX_WEIGHT}',
+                    debounce=True,
+                    style={'width': '40%'},
+                ),
+                html.H3('Graph'),
+                html.Div(
+                    [
+                        cyto.Cytoscape(
+                            id='cytoscape-graph',
+                            style={'width': '100%', 'height': '600px'},
+                            stylesheet=my_stylesheet,
+                            elements=cyto_data_base,
+                            zoom=1,
+                        ),
+                    ],
+                    style={
+                        'border': '3px solid black',
+                        'borderRadius': '25px',
+                        'marginTop': '1em',
+                        'marginBottom': '2em',
+                        'padding': '7px',
+                    },
+                ),
             ],
-            clearable=False,
+            style={'marginTop': '1em'},
         ),
-        dcc.RangeSlider(
+    ],
-            id='weight_slider',
+    style={'margin': '2em'},
-            min=MIN_WEIGHT,
-            max=MAX_WEIGHT,
-            step=1000,
-        ),
-        cyto.Cytoscape(
-            id='cytoscape-graph',
-            layout={'name': 'cose'},
-            style={'width': '100%', 'height': '600px'},
-            stylesheet=my_stylesheet,
-            elements=cyto_data,
-            zoom=1,
-        ),
-    ]
 )
 
 
 @app.callback(
     Output('cytoscape-graph', 'layout', allow_duplicate=True),
-    Input('layout_choice_internal', 'value'),
+    Input('layout_choice', 'value'),
     prevent_initial_call=True,
 )
 def update_layout_internal(layout_choice):
-    return {'name': layout_choice}
+    # return {'name': layout_choice}
-
+    return cose_layout
-
+    # return cose_bilkent_layout
-@app.callback(
+    # return cola_layout
-    Output('cytoscape-graph', 'layout', allow_duplicate=True),
-    Input('layout_choice_external', 'value'),
-    prevent_initial_call=True,
-)
-def update_layout_external(layout_choice):
-    return {'name': layout_choice}
 
 
 @app.callback(
     Output('cytoscape-graph', 'zoom'),
-    Output('cytoscape-graph', 'elements'),
+    Output('cytoscape-graph', 'elements', allow_duplicate=True),
+    Output('weight_min', 'value'),
+    Output('weight_max', 'value'),
     Input('bt-reset', 'n_clicks'),
     prevent_initial_call=True,
 )
 def reset_layout(n_clicks):
-    return (1, cyto_data)
+    return (1, cyto_data_base, None, None)
 
 
-# @app.callback(
+# update edge weight
-#     Output('cytoscape-graph', 'stylesheet'),
+@app.callback(
-#     Input('weight_slider', 'value'),
+    Output('cytoscape-graph', 'elements', allow_duplicate=True),
-#     State('cytoscape-graph', 'stylesheet'),
+    Input('weight_min', 'value'),
+    Input('weight_max', 'value'),
+    prevent_initial_call=True,
+)
+def update_edge_weight(weight_min, weight_max):
+    if not any([weight_min, weight_max]):
+        return cyto_data_base
+
+    if weight_min is None:
+        weight_min = MIN_WEIGHT
+    if weight_max is None:
+        weight_max = MAX_WEIGHT
+    tk_graph_filtered = tk_graph.filter_by_edge_weight(weight_min, weight_max)
+    tk_graph_filtered = tk_graph_filtered.filter_by_node_degree(1, None)
+    cyto_data, _ = graphs.convert_graph_to_cytoscape(tk_graph_filtered)
+    return cyto_data
+
+
+# app.clientside_callback(
+#     """
+#     function(n_clicks, layout) {
+#         let threshold = 1000;
+#         layout.edgeLength = function(edge) {
+#             let weight = edge.data().weight;
+#             let length;
+#             if (weight > threshold) {
+#                 length = 10;
+#             } else {
+#                 length = 1000 / edge.data().weight;
+#                 length = Math.max(20, length);
+#             }
+#             return length;
+#         };
+#         cy.layout(layout).run();
+#         return layout;
+#     }
+#     """,
+#     Output('cytoscape-graph', 'layout', allow_duplicate=True),
+#     Input('test_js', 'n_clicks'),
+#     State('cytoscape-graph', 'layout'),
 #     prevent_initial_call=True,
 # )
-# def select_weight(range_chosen, stylesheet):
-#     min_weight, max_weight = range_chosen
-#     new_stylesheet = stylesheet.copy()
-#     new_stylesheet.append(
-#         {
-#             'selector': f'[weight >= {min_weight}]',
-#             'style': {'line-color': 'blue', 'line-style': 'dashed'},
-#         }
-#     )
-#     new_stylesheet.append(
-#         {
-#             'selector': f'[weight <= {max_weight}]',
-#             'style': {'line-color': 'blue', 'line-style': 'dashed'},
-#         }
-#     )
-#     return new_stylesheet
 
+app.clientside_callback(
+    """
+    function(n_clicks, layout) {
+        layout.edgeElasticity = function(edge) {
+            return edge.data().weight * 4;
+        };
+        layout.idealEdgeLength = function(edge) {
+            return edge.data().weight * 0.8;
+        };
+        cy.layout(layout).run();
+        return layout;
+    }
+    """,
+    Output('cytoscape-graph', 'layout', allow_duplicate=True),
+    Input('test_js', 'n_clicks'),
+    State('cytoscape-graph', 'layout'),
+    prevent_initial_call=True,
+)
 
-# app.layout = html.Div(
+app.clientside_callback(
-#     [
+    """
-#         cyto.Cytoscape(
+    function(n_clicks, stylesheet) {
-#             id='cytoscape-two-nodes',
+        function edge_weight(ele) {
-#             layout={'name': 'preset'},
+            let threshold = 1000;
-#             style={'width': '100%', 'height': '400px'},
+            let weight = ele.data('weight');
-#             stylesheet=my_stylesheet,
+            if (weight > threshold) {
-#             elements=[
+                weight = 12;
-#                 {
+            } else {
-#                     'data': {
+                weight = weight / threshold * 10;
-#                         'id': 'one',
+                weight = Math.max(1, weight);
-#                         'label': 'Titel 1',
+            }
-#                     },
+            return weight;
-#                     'position': {'x': 75, 'y': 75},
+        }
-#                     'grabbable': False,
+        stylesheet[1].style.width = edge_weight;
-#                     #'locked': True,
+        cy.style(stylesheet).update();
-#                     'classes': 'red',
+        return stylesheet;
-#                 },
+    }
-#                 {
+    """,
-#                     'data': {'id': 'two', 'label': 'Title 2'},
+    Output('cytoscape-graph', 'stylesheet'),
-#                     'position': {'x': 200, 'y': 200},
+    Input('test_js_weight', 'n_clicks'),
-#                     'classes': 'triangle',
+    State('cytoscape-graph', 'stylesheet'),
-#                 },
+    prevent_initial_call=False,
-#                 {'data': {'source': 'one', 'target': 'two', 'weight': 2000}},
+)
-#             ],
-#         )
-#     ]
-# )
 
 
 def _start_webbrowser():

scripts/dashboard/test.py

@@ -0,0 +1,18 @@
+from pathlib import Path
+from typing import cast
+import statistics
+
+import lang_main.io
+from lang_main.analysis import graphs
+
+# target = '../results/test_20240529/Pipe-Token_Analysis_Step-1_build_token_graph.pkl'
+# p = Path(target).resolve()
+# ret = lang_main.io.load_pickle(p)
+# tk_graph = cast(graphs.TokenGraph, ret[0])
+# tk_graph_filtered = tk_graph.filter_by_edge_weight(150, None)
+# tk_graph_filtered = tk_graph_filtered.filter_by_node_degree(1, None)
+# cyto_data_base, weight_data, all_weights = graphs.convert_graph_to_cytoscape(tk_graph_filtered)
+
+
+test = [1, 1, 1, 2, 2, 3, 3, 4, 4, 1000]
+print(statistics.mean(test))

src/lang_main/analysis/graphs.py

@@ -130,7 +130,7 @@ def convert_graph_to_cytoscape(
     cyto_data: list[CytoscapeData] = []
     # iterate over nodes
     nodes = cast(Iterable[NodeTitle], graph.nodes)
-    for i, node in enumerate(nodes):
+    for node in nodes:
         node_data: CytoscapeData = {
             'data': {
                 'id': node,
@@ -151,7 +151,7 @@ def convert_graph_to_cytoscape(
         ],
         graph.edges.data('weight', default=1),  # type: ignore
     )
-    for i, (source, target, weight) in enumerate(edges):
+    for source, target, weight in edges:
         weights.add(weight)
         edge_data: CytoscapeData = {
             'data': {
@@ -288,27 +288,35 @@ class TokenGraph(DiGraph):
 
     def filter_by_edge_weight(
         self,
-        threshold: int,
+        bound_lower: int | None,
+        bound_upper: int | None,
     ) -> Self:
-        """filters all edges which are below the given threshold
+        """filters all edges which are within the provided bounds
 
         Parameters
         ----------
-        threshold : int
+        bound_lower : int | None
-            edges with weights smaller than this value will be removed
+            lower bound for edge weights, edges with weight equal to this value are retained
+        bound_upper : int | None
+            upper bound for edge weights, edges with weight equal to this value are retained
 
         Returns
         -------
         Self
             a copy of the graph with filtered edges
         """
-        # filter edges by weight
         original_graph_edges = copy.deepcopy(self.edges)
         filtered_graph = self.copy()
 
+        if not any([bound_lower, bound_upper]):
+            logger.warning('No bounds provided, returning original graph.')
+            return filtered_graph
+
         for edge in original_graph_edges:
             weight = typing.cast(int, filtered_graph[edge[0]][edge[1]]['weight'])
-            if weight < threshold:
+            if bound_lower is not None and weight < bound_lower:
+                filtered_graph.remove_edge(edge[0], edge[1])
+            if bound_upper is not None and weight > bound_upper:
                 filtered_graph.remove_edge(edge[0], edge[1])
 
         if filtered_graph._undirected is not None:
@@ -320,14 +328,17 @@ class TokenGraph(DiGraph):
 
     def filter_by_node_degree(
         self,
-        threshold: int,
+        bound_lower: int | None,
+        bound_upper: int | None,
     ) -> Self:
-        """filters all nodes which have a degree below the given threshold
+        """filters all nodes which are within the provided bounds by their degree
 
         Parameters
         ----------
-        threshold : int
+        bound_lower : int | None
-            nodes with a degree smaller than this value will be removed
+            lower bound for node degree, nodes with degree equal to this value are retained
+        bound_upper : int | None
+            upper bound for node degree, nodes with degree equal to this value are retained
 
         Returns
         -------
@@ -338,9 +349,15 @@ class TokenGraph(DiGraph):
         original_graph_nodes = copy.deepcopy(self.nodes)
         filtered_graph = self.copy()
 
+        if not any([bound_lower, bound_upper]):
+            logger.warning('No bounds provided, returning original graph.')
+            return filtered_graph
+
         for node in original_graph_nodes:
             degree = filtered_graph.degree[node]  # type: ignore
-            if degree < threshold:
+            if bound_lower is not None and degree < bound_lower:
+                filtered_graph.remove_node(node)
+            if bound_upper is not None and degree > bound_upper:
                 filtered_graph.remove_node(node)
 
         if filtered_graph._undirected is not None:

src/lang_main/types.py

@@ -21,7 +21,7 @@ class STFRDeviceTypes(enum.StrEnum):
     GPU = 'cuda'
 
 
-# ** datatsets
+# ** datasets
 PandasIndex: TypeAlias = int | np.int64
 ObjectID: TypeAlias = int
 Embedding: TypeAlias = SpacyDoc | Tensor