Merge pull request #643 from kartoza/dialogs

Implementation for disabling workflows from context menu

geest/core/generate_model.py

@@ -27,10 +27,8 @@ def load_spreadsheet(self):
         self.dataframe = self.dataframe[
             [
                 "Dimension",
-                "Dimension Required",
                 "Default Dimension Analysis Weighting",
                 "Factor",
-                "Factor Required",
                 "Default Factor Dimension Weighting",
                 "Indicator",
                 "Default Indicator Factor Weighting",
@@ -62,8 +60,7 @@ def load_spreadsheet(self):
                 "Use Nighttime Lights",
                 "Use Environmental Hazards",
                 "Use Street Lights",
-                "Analysis Mode",  # New column
-                "Indicator Required",  # New column
+                "Analysis Mode",
             ]
         ]
 
@@ -81,25 +78,20 @@ def parse_to_json(self):
         """
         Parse the dataframe into the hierarchical JSON structure.
         """
-        dimension_map = {}
+        analysis_model = {}
 
         for _, row in self.dataframe.iterrows():
             dimension = row["Dimension"]
             factor = row["Factor"]
 
             # Prepare dimension data
             dimension_id = self.create_id(dimension)
-            dimension_required = (
-                row["Dimension Required"]
-                if not pd.isna(row["Dimension Required"])
-                else ""
-            )
             default_dimension_analysis_weighting = (
                 row["Default Dimension Analysis Weighting"]
                 if not pd.isna(row["Default Dimension Analysis Weighting"])
                 else ""
             )
-            if dimension_id not in dimension_map:
+            if dimension_id not in analysis_model:
                 # Hardcoded descriptions for specific dimensions
                 description = ""
                 if dimension_id == "contextual":
@@ -110,58 +102,60 @@ def parse_to_json(self):
                     description = "The Place-Characterization Dimension refers to the social, environmental, and infrastructural attributes of geographical locations, such as walkability, safety, and vulnerability to natural hazards. Unlike the Accessibility Dimension, these factors do not involve mobility but focus on the inherent characteristics of a place that influence women’s ability to participate in the workforce."
 
             # If the Dimension doesn't exist yet, create it
-            if dimension not in dimension_map:
+            if dimension not in analysis_model:
                 new_dimension = {
                     "id": dimension_id,
                     "name": dimension,
-                    "required": dimension_required,
                     "default_analysis_weighting": default_dimension_analysis_weighting,
+                    # Initialise the weighting to the default value
+                    "analysis_weighting": default_dimension_analysis_weighting,
                     "description": description,
                     "factors": [],
                 }
                 self.result["dimensions"].append(new_dimension)
-                dimension_map[dimension] = new_dimension
+                analysis_model[dimension] = new_dimension
 
             # Prepare factor data
             factor_id = self.create_id(factor)
-            factor_required = (
-                row["Factor Required"] if not pd.isna(row["Factor Required"]) else ""
-            )
             default_factor_dimension_weighting = (
                 row["Default Factor Dimension Weighting"]
                 if not pd.isna(row["Default Factor Dimension Weighting"])
                 else ""
             )
 
             # If the Factor doesn't exist in the current dimension, add it
-            factor_map = {f["name"]: f for f in dimension_map[dimension]["factors"]}
+            factor_map = {f["name"]: f for f in analysis_model[dimension]["factors"]}
             if factor not in factor_map:
                 new_factor = {
                     "id": factor_id,
                     "name": factor,
-                    "required": factor_required,
                     "default_dimension_weighting": default_factor_dimension_weighting,
+                    # Initialise the weighting to the default value
+                    "dimension_weighting": default_factor_dimension_weighting,
                     "indicators": [],
                     "description": (
                         row["Factor Description"]
                         if not pd.isna(row["Factor Description"])
                         else ""
                     ),
                 }
-                dimension_map[dimension]["factors"].append(new_factor)
+                analysis_model[dimension]["factors"].append(new_factor)
                 factor_map[factor] = new_factor
 
-            # Add layer data to the current Factor, including new columns
-            layer_data = {
+            # Add indicator data to the current Factor, including new columns
+            default_factor_weighting = (
+                row["Default Indicator Factor Weighting"]
+                if not pd.isna(row["Default Indicator Factor Weighting"])
+                else ""
+            )
+            indicator_data = {
                 # These are all parsed from the spreadsheet
                 "indicator": row["Indicator"] if not pd.isna(row["Indicator"]) else "",
                 "id": row["ID"] if not pd.isna(row["ID"]) else "",
                 "description": "",
-                "default_indicator_factor_weighting": (
-                    row["Default Indicator Factor Weighting"]
-                    if not pd.isna(row["Default Indicator Factor Weighting"])
-                    else ""
-                ),
+                "default_factor_weighting": default_factor_weighting,
+                # Initialise the weighting to the default value
+                "factor_weighting": default_factor_weighting,
                 "default_index_score": (
                     row["Default Index Score"]
                     if not pd.isna(row["Default Index Score"])
@@ -291,14 +285,9 @@ def parse_to_json(self):
                 "analysis_mode": (
                     row["Analysis Mode"] if not pd.isna(row["Analysis Mode"]) else ""
                 ),  # New column
-                "indicator_required": (
-                    row["Indicator Required"]
-                    if not pd.isna(row["Indicator Required"])
-                    else ""
-                ),  # New column
             }
 
-            factor_map[factor]["indicators"].append(layer_data)
+            factor_map[factor]["indicators"].append(indicator_data)
 
     def get_json(self):
         """

geest/core/generate_schema.py

@@ -15,7 +15,6 @@ def infer_schema(data):
         return {
             "type": "object",
             "properties": properties,
-            "required": required_keys,
         }
     elif isinstance(data, list):
         if len(data) > 0:

geest/core/json_tree_item.py

@@ -123,6 +123,47 @@ def clear(self):
         data["result_file"] = ""
         data["error"] = ""
         data["error_file"] = ""
+        data["execution_start_time"] = ""
+        data["execution_end_time"] = ""
+
+    def disable(self):
+        """
+        Mark the item as disabled, which is essentially just setting its weight to zero.
+        """
+        data = self.attributes()
+        data["analysis_mode"] = "Do Not Use"
+
+        if self.isDimension():
+            data["analysis_weighting"] = 0.0
+        if self.isFactor():
+            data["dimension_weighting"] = 0.0
+        if self.isIndicator():
+            data["factor_weighting"] = 0.0
+
+    def enable(self):
+        """
+        Mark the item as enabled, which is essentially just setting its weight to its default.
+        """
+        data = self.attributes()
+        data["analysis_mode"] = ""
+        if self.isDimension():
+            data["analysis_weighting"] = data["default_analysis_weighting"]
+        if self.isFactor():
+            data["dimension_weighting"] = data["default_dimension_weighting"]
+            if self.parent().getStatus() == "Excluded from analysis":
+                self.parent().attributes()[
+                    "analysis_weighting"
+                ] = self.parent().attribute("default_analysis_weighting")
+        if self.isIndicator():
+            data["factor_weighting"] = data["default_factor_weighting"]
+            if self.parent().getStatus() == "Excluded from analysis":
+                self.parent().attributes()[
+                    "dimension_weighting"
+                ] = self.parent().attribute("default_dimension_weighting")
+                if self.parent().parent().getStatus() == "Excluded from analysis":
+                    self.parent().parent().attributes()["analysis_weighting"] = (
+                        self.parent().parent().attribute("default_analysis_weighting")
+                    )
 
     def getIcon(self):
         """Retrieve the appropriate icon for the item based on its role."""
@@ -163,8 +204,7 @@ def getItemTooltip(self):
     def getStatusIcon(self):
         """Retrieve the appropriate icon for the item based on its role."""
         status = self.getStatus()
-        if self.isAnalysis():
-            return None
+
         if status == "Excluded from analysis":
             return QIcon(resources_path("resources", "icons", "excluded.svg"))
         if status == "Completed successfully":
@@ -199,7 +239,6 @@ def getStatus(self):
             # First check if the item weighting is 0, or its parent factor is zero
             # If so, return "Excluded from analysis"
             if self.isIndicator():
-                # Required flag can be overridden by the factor so we dont check it right now
                 required_by_parent = float(
                     self.parentItem.attributes().get("dimension_weighting", 0.0)
                 )
@@ -211,16 +250,31 @@ def getStatus(self):
                     # log_message(f"Excluded from analysis: {data.get('id')}")
                     return "Excluded from analysis"
             if self.isFactor():
-                if not data.get("required", False):
-                    if not float(data.get("dimension_weighting", 0.0)):
-                        return "Excluded from analysis"
-            if self.isDimension():
-                # if the sum of the factor weightings is 0, return "Excluded from analysis"
+                if not float(data.get("dimension_weighting", 0.0)):
+                    return "Excluded from analysis"
+                # If the sum of the indicator weightings is zero, return "Excluded from analysis"
                 weight_sum = 0
                 for child in self.childItems:
                     weight_sum += float(child.attribute("factor_weighting", 0.0))
                 if not weight_sum:
                     return "Excluded from analysis"
+            if self.isDimension():
+                # If the analysis weighting is zero, return "Excluded from analysis"
+                if not float(data.get("analysis_weighting", 0.0)):
+                    return "Excluded from analysis"
+                # If the sum of the factor weightings is zero, return "Excluded from analysis"
+                weight_sum = 0
+                for child in self.childItems:
+                    weight_sum += float(child.attribute("dimension_weighting", 0.0))
+                if not weight_sum:
+                    return "Excluded from analysis"
+            if self.isAnalysis():
+                # If the sum of the dimension weightings is zero, return "Excluded from analysis"
+                weight_sum = 0
+                for child in self.childItems:
+                    weight_sum += float(child.attribute("analysis_weighting", 0.0))
+                if not weight_sum:
+                    return "Excluded from analysis"
 
             if "Error" in data.get("result", ""):
                 return "Workflow failed"
@@ -237,13 +291,17 @@ def getStatus(self):
             ):
                 return "Not configured (optional)"
             # Item required and not configured
-            if (data.get("analysis_mode", "") == "") and data.get(
-                "indicator_required", False
+            if (
+                self.isIndicator()
+                and (data.get("analysis_mode", "") == "")
+                and data.get("indicator_required", False)
             ):
                 return "Required and not configured"
             # Item not required but not configured
-            if (data.get("analysis_mode", "") == "") and not data.get(
-                "indicator_required", False
+            if (
+                self.isIndicator()
+                and (data.get("analysis_mode", "") == "")
+                and not data.get("indicator_required", False)
             ):
                 return "Not configured (optional)"
             if "Not Run" in data.get("result", "") and not data.get("result_file", ""):
@@ -331,28 +389,13 @@ def getDimensionFactorGuids(self):
         return guids
         # attributes["analysis_mode"] = "dimension_aggregation"
 
-    def getAnalysisAttributes(self):
-        """Return the dict of dimensions under this analysis."""
-        attributes = {}
+    def getAnalysisDimensionGuids(self):
+        """Return the list of factors under this dimension."""
+        guids = []
         if self.isAnalysis():
-            attributes["analysis_name"] = self.attribute("analysis_name", "Not Set")
-            attributes["description"] = self.attribute(
-                "analysis_description", "Not Set"
-            )
-            attributes["working_folder"] = self.attribute("working_folder", "Not Set")
-            attributes["cell_size_m"] = self.attribute("cell_size_m", 100)
-
-            attributes["dimensions"] = [
-                {
-                    "dimension_no": i,
-                    "dimension_id": child.attribute("id", ""),
-                    "dimension_name": child.data(0),
-                    "dimension_weighting": child.data(2),
-                    "result_file": child.attribute(f"result_file", ""),
-                }
-                for i, child in enumerate(self.childItems)
-            ]
-        return attributes
+            guids = [child.guid for i, child in enumerate(self.childItems)]
+        return guids
+        # attributes["analysis_mode"] = "dimension_aggregation"
 
     def getItemByGuid(self, guid):
         """Return the item with the specified guid."""
@@ -412,3 +455,26 @@ def updateFactorWeighting(self, factor_guid, new_weighting):
         except Exception as e:
             # Handle any exceptions and log the error
             log_message(f"Error updating weighting: {e}", level=Qgis.Warning)
+
+    def updateDimensionWeighting(self, dimension_guid, new_weighting):
+        """Update the weighting of a specific dimension by its guid."""
+        try:
+            # Search for the factor by name
+            dimension_item = self.getItemByGuid(dimension_guid)
+            # If found, update the weighting
+            if dimension_item:
+                dimension_item.setData(2, f"{new_weighting:.2f}")
+                # weighting references the level above (i.e. analysis)
+                dimension_item.attributes()["analysis_weighting"] = new_weighting
+
+            else:
+                # Log if the factor name is not found
+                log_message(
+                    f"Factor '{dimension_guid}' not found.",
+                    tag="Geest",
+                    level=Qgis.Warning,
+                )
+
+        except Exception as e:
+            # Handle any exceptions and log the error
+            log_message(f"Error updating weighting: {e}", level=Qgis.Warning)

geest/core/workflow_factory.py

@@ -79,7 +79,6 @@ def create_workflow(
                 return PointPerCellWorkflow(item, cell_size_m, feedback, context)
             elif analysis_mode == "use_polyline_per_cell":
                 return PolylinePerCellWorkflow(item, cell_size_m, feedback, context)
-            # TODO fix inconsistent abbreviation below for Poly
             elif analysis_mode == "use_polygon_per_cell":
                 return PolygonPerCellWorkflow(item, cell_size_m, feedback, context)
             elif analysis_mode == "factor_aggregation":

geest/core/workflows/aggregation_workflow_base.py

@@ -189,10 +189,11 @@ def get_raster_list(self, index) -> list:
             item = self.item.getItemByGuid(guid)
             status = item.getStatus() == "Completed successfully"
             mode = item.attributes().get("analysis_mode", "Do Not Use") == "Do Not Use"
+            excluded = item.getStatus() == "Excluded from analysis"
             id = item.attribute("id").lower()
-            if not status and not mode:
+            if not status and not mode and not excluded:
                 raise ValueError(
-                    f"{id} is not completed successfully and is not set to 'Do Not Use'"
+                    f"{id} is not completed successfully and is not set to 'Do Not Use' or 'Excluded from analysis'"
                 )
 
             if mode:
@@ -202,6 +203,13 @@ def get_raster_list(self, index) -> list:
                     level=Qgis.Info,
                 )
                 continue
+            if excluded:
+                log_message(
+                    f"Skipping {item.attribute('id')} as it is excluded from analysis",
+                    tag="Geest",
+                    level=Qgis.Info,
+                )
+                continue
             if not item.attribute("result_file", ""):
                 log_message(
                     f"Skipping {id} as it has no result file",

geest/core/workflows/analysis_aggregation_workflow.py

@@ -30,8 +30,14 @@ def __init__(
         super().__init__(
             item, cell_size_m, feedback, context
         )  # ⭐️ Item is a reference - whatever you change in this item will directly update the tree
-        self.id = "geest_analysis"
-        self.aggregation_attributes = self.item.getAnalysisAttributes()
-        self.layers = self.aggregation_attributes.get(f"dimensions", [])
+        self.guids = (
+            self.item.getAnalysisDimensionGuids()
+        )  # get a list of the items to aggregate
+        self.id = (
+            self.item.attribute("analysis_name")
+            .lower()
+            .replace(" ", "_")
+            .replace("'", "")
+        )  # should not be needed any more
         self.weight_key = "dimension_weighting"
         self.workflow_name = "analysis_aggregation"

geest/gui/dialogs/__init__.py

@@ -1,3 +1,4 @@
 from .factor_aggregation_dialog import FactorAggregationDialog
 from .indicator_detail_dialog import IndicatorDetailDialog
 from .dimension_aggregation_dialog import DimensionAggregationDialog
+from .analysis_aggregation_dialog import AnalysisAggregationDialog