#18 fixed branch information

.gitignore

@@ -165,4 +165,6 @@ cython_debug/
 .DS_Store
 
 # development environment
-bw_webapp_dev/
+bw_webapp_dev/
+
+.vscode

app/index.py

@@ -80,7 +80,7 @@ def nodes_dict_to_dataframe(nodes: dict) -> pd.DataFrame:
         A dataframe with human-readable descriptions and emissions values of the nodes in the graph traversal.
     """
     list_of_row_dicts = []
-    for i in range(0, len(nodes)-1):
+    for i in range(0, len(nodes)):
         current_node: Node = nodes[i]
         scope_1: bool = False
         if current_node.unique_id == 0:
@@ -122,7 +122,7 @@ def edges_dict_to_dataframe(edges: dict) -> pd.DataFrame:
 
 def trace_branch(df: pd.DataFrame, start_node: int) -> list:
     """
-    Given a dataframe of graph edges and a starting node, returns the branch of nodes that lead to the starting node.
+    Given a dataframe of graph edges and a "starting node" (producer_unique_id), returns the branch of nodes that lead to the starting node.
 
     For example:
 
@@ -142,7 +142,7 @@ def trace_branch(df: pd.DataFrame, start_node: int) -> list:
     df : pd.DataFrame
         Dataframe of graph edges. Must contain integer-type columns 'consumer_unique_id' and 'producer_unique_id'.
     start_node : int
-        The integer indicating the starting node to trace back from.
+        The integer indicating the producer_unique_id starting node to trace back from.
 
     Returns
     -------
@@ -162,7 +162,7 @@ def trace_branch(df: pd.DataFrame, start_node: int) -> list:
     return branch
 
 
-def add_branch_information_to_dataframe(df: pd.DataFrame) -> pd.DataFrame:
+def add_branch_information_to_edges_dataframe(df: pd.DataFrame) -> pd.DataFrame:
     """
     Adds 'branch' information to terminal nodes in a dataframe of graph edges.
 
@@ -471,7 +471,7 @@ def perform_graph_traversal(self, event):
         if self.df_graph_traversal_edges.empty:
             return
         else:
-            self.df_graph_traversal_edges = add_branch_information_to_dataframe(self.df_graph_traversal_edges)
+            self.df_graph_traversal_edges = add_branch_information_to_edges_dataframe(self.df_graph_traversal_edges)
             self.df_graph_traversal_nodes = pd.merge(
                 self.df_graph_traversal_nodes,
                 self.df_graph_traversal_edges,

dev/trace_branch.py

@@ -0,0 +1,206 @@
+# %%
+import bw_graph_tools as bgt
+import bw2io as bi
+import bw2data as bd
+import bw2calc as bc
+import pandas as pd
+
+from bw2data.backends.proxies import Activity
+from bw_graph_tools.graph_traversal import Node
+
+if 'USEEIO-1.1' not in bd.projects:
+    bi.install_project(project_key="USEEIO-1.1", overwrite_existing=True)
+    bd.projects.set_current("USEEIO-1.1")
+else:
+    bd.projects.set_current("USEEIO-1.1")
+
+useeio = bd.Database("USEEIO-1.1")
+
+cars_activity = bd.utils.get_node(
+    database = 'USEEIO-1.1',
+    name = 'Automobiles; at manufacturer',
+    type = 'product',
+    location = 'United States'
+)
+
+method_gcc = bd.Method(('Impact Potential', 'GCC'))
+lca = bc.LCA( 
+    demand={cars_activity: 1}, 
+    method = method_gcc.name # attention here: it's not the Method object, just its name!!!
+) 
+lca.lci() 
+lca.lcia()
+
+graph_traversal: dict = bgt.NewNodeEachVisitGraphTraversal.calculate(lca, cutoff=0.005)
+
+
+def nodes_dict_to_dataframe(nodes: dict) -> pd.DataFrame:
+    """
+    Returns a dataframe with human-readable descriptions and emissions values of the nodes in the graph traversal.
+
+    Parameters
+    ----------
+    nodes : dict
+        A dictionary of nodes in the graph traversal.
+        Can be created by selecting the 'nodes' key from the dictionary
+        returned by the function `bw_graph_tools.NewNodeEachVisitGraphTraversal.calculate()`.
+
+    Returns
+    -------
+    pd.DataFrame
+        A dataframe with human-readable descriptions and emissions values of the nodes in the graph traversal.
+    """
+    list_of_row_dicts = []
+    for i in range(0, len(nodes)-1):
+        current_node: Node = nodes[i]
+        scope_1: bool = False
+        if current_node.unique_id == 0:
+            scope_1 = True
+        else:
+            pass
+        list_of_row_dicts.append(
+            {
+                'UID': current_node.unique_id,
+                'Scope 1?': scope_1,
+                'Name': bd.get_node(id=current_node.activity_datapackage_id)['name'],
+                'Cumulative': current_node.cumulative_score,
+                'Direct': current_node.direct_emissions_score,
+                'Depth': current_node.depth,
+                'activity_datapackage_id': current_node.activity_datapackage_id,
+            }
+        )
+    return pd.DataFrame(list_of_row_dicts)
+
+
+def edges_dict_to_dataframe(edges: dict) -> pd.DataFrame:
+    """
+    To be added...
+    """
+    if len(edges) < 2:
+        return pd.DataFrame()
+    else:
+        list_of_row_dicts = []
+        for i in range(0, len(edges)):
+            current_edge: Edge = edges[i]
+            list_of_row_dicts.append(
+                {
+                    'consumer_unique_id': current_edge.consumer_unique_id,
+                    'producer_unique_id': current_edge.producer_unique_id
+                }
+            )
+        return pd.DataFrame(list_of_row_dicts).drop(0)
+
+
+def trace_branch(df: pd.DataFrame, start_node: int) -> list:
+    """
+    Given a dataframe of graph edges and a starting node, returns the branch of nodes that lead to the starting node.
+
+    For example:
+
+    | consumer_unique_id | producer_unique_id |
+    |--------------------|--------------------|
+    | 0                  | 1                  | # 1 is terminal producer node
+    | 0                  | 2                  |
+    | 0                  | 3                  |
+    | 2                  | 4                  | # 4 is terminal producer node
+    | 3                  | 5                  |
+    | 5                  | 6                  | # 6 is terminal producer node
+
+    For start_node = 6, the function returns [0, 3, 5, 6]
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Dataframe of graph edges. Must contain integer-type columns 'consumer_unique_id' and 'producer_unique_id'.
+    start_node : int
+        The integer indicating the starting node to trace back from.
+
+    Returns
+    -------
+    list
+        A list of integers indicating the branch of nodes that lead to the starting node.
+    """
+
+    branch: list = [start_node]
+
+    while True:
+        previous_node: int = df[df['producer_unique_id'] == start_node]['consumer_unique_id']
+        if previous_node.empty:
+            break
+        start_node: int = previous_node.values[0]
+        branch.insert(0, start_node)
+
+    return branch
+
+
+def add_branch_information_to_dataframe(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Adds 'branch' information to terminal nodes in a dataframe of graph edges.
+
+    For example:
+
+    | consumer_unique_id | producer_unique_id |
+    |--------------------|--------------------|
+    | 0                  | 1                  | # 1 is terminal producer node
+    | 0                  | 2                  |
+    | 0                  | 3                  |
+    | 2                  | 4                  | # 4 is terminal producer node
+    | 3                  | 5                  |
+    | 5                  | 6                  | # 6 is terminal producer node
+
+    | consumer_unique_id | producer_unique_id | branch       |
+    |--------------------|--------------------|--------------|
+    | 0                  | 1                  | [0, 1]       |
+    | 0                  | 2                  | [0, 2]       |
+    | 0                  | 3                  | [0, 3]       |
+    | 2                  | 4                  | [0, 2, 4]    |
+    | 3                  | 5                  | [0, 3, 5]    |
+    | 5                  | 6                  | [0, 3, 5, 6] |
+
+    Parameters
+    ----------
+    df_edges : pd.DataFrame
+        A dataframe of graph edges.
+        Must contain integer-type columns 'consumer_unique_id' and 'producer_unique_id'.
+
+    Returns
+    -------
+    pd.DataFrame
+        A dataframe of graph nodes with a column 'branch' that contains the branch of nodes that lead to the terminal producer node.
+    """
+    # initialize empty list to store branches
+    branches: list = []
+
+    for _, row in df.iterrows():
+        branch: list = trace_branch(df, int(row['producer_unique_id']))
+        branches.append({
+            'producer_unique_id': int(row['producer_unique_id']),
+            'Branch': branch
+        })
+
+    return pd.DataFrame(branches)
+
+
+def merge_enriched_edge_dataframe_into_nodes_dataframe(df_left, df_right):
+    """
+    Merges the enriched edges DataFrame with the nodes DataFrame
+    to add branch information.
+
+    Note that the last row is dropped, since 
+
+    | UID | ... |
+    |-----|-----|
+    | 0   | ... |
+    | 1   | ... |
+    | 2   | ... |
+
+
+
+    """
+    self.df_graph_traversal_nodes = pd.merge(
+        df_nodes,
+        df_edges_enriched,
+        left_on='UID',
+        right_on='producer_unique_id',
+        how='left'
+    ).iloc[:-1] # the