changed source requirements to the rdd package, and fixed some additional edge cases (#7)

Co-authored-by: amca2892 <amca2892@gmail.com>

Author: AlejandroMC28

pages/01_Create_RDD_Count_Table.py

@@ -9,7 +9,7 @@
 if SRC not in sys.path:
     sys.path.insert(0, SRC)
 
-from src.RDDcounts import RDDCounts  # noqa: E402
+from rdd import RDDCounts  # noqa: E402
 from src.state_helpers import set_group  # noqa: E402
 
 
@@ -194,7 +194,7 @@ def load_demo_file(filename):
                 # GNPS1 requires group selection from the network data
                 with st.spinner("📊 Fetching GNPS1 data to display available groups..."):
                     try:
-                        from src.utils import get_gnps_task_data
+                        from rdd.utils import get_gnps_task_data
 
                         temp_gnps_df = get_gnps_task_data(gnps_task_id, gnps2=False)
                         if "DefaultGroups" in temp_gnps_df.columns:

pages/02_Visualizations.py

@@ -6,7 +6,7 @@
 if SRC not in sys.path:
     sys.path.insert(0, SRC)
 
-from src.visualization import Visualizer, PlotlyBackend, MatplotlibBackend  # noqa: E402
+from rdd.visualization import Visualizer, PlotlyBackend, MatplotlibBackend  # noqa: E402
 
 if "rdd" not in st.session_state:
     st.warning("First create an RDDCounts object.")

pages/03_PCA_Analysis.py

@@ -6,8 +6,8 @@
 if SRC not in sys.path:
     sys.path.insert(0, SRC)
 
-from src.analysis import perform_pca_RDD_counts  # noqa: E402
-from src.visualization import Visualizer, PlotlyBackend, MatplotlibBackend  # noqa: E402
+from rdd.analysis import perform_pca_RDD_counts  # noqa: E402
+from rdd.visualization import Visualizer, PlotlyBackend, MatplotlibBackend  # noqa: E402
 
 if "rdd" not in st.session_state:
     st.warning("First create an RDDCounts object.")

pages/04_Sankey_Diagram.py

@@ -6,7 +6,7 @@
 if SRC not in sys.path:
     sys.path.insert(0, SRC)
 
-from src.visualization import Visualizer, PlotlyBackend  # noqa: E402
+from rdd.visualization import Visualizer, PlotlyBackend  # noqa: E402
 
 st.header("Sankey Diagram")
 
@@ -18,55 +18,158 @@
 rdd = st.session_state["rdd"]
 viz = Visualizer(PlotlyBackend())  # Sankey supported only in Plotly
 
+# ── guard: need at least 2 ontology levels for Sankey ─────────────────
+if rdd.levels < 2:
+    st.warning(
+        "⚠️ Sankey diagrams require at least 2 ontology levels to visualize hierarchical flows."
+    )
+    st.info(
+        f"Current RDD object has {rdd.levels} level(s). Please create an RDD count table with levels ≥ 2."
+    )
+    st.stop()
+
 # ── user controls ──────────────────────────────────────────────────────
 sample_choice = st.selectbox(
     "Filter by sample filename (optional)",
     ["<all samples>"] + sorted(rdd.counts["filename"].unique()),
 )
 
-max_level = st.number_input(
-    "Maximum hierarchy level", 1, rdd.levels, rdd.levels, step=1
-)
+max_level = st.number_input("Maximum hierarchy level", 1, rdd.levels, rdd.levels, step=1)
+
 
 def load_demo_file(filename):
     import io
+
     ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
     path = os.path.join(ROOT, "data", filename)
     with open(path, "rb") as f:
         file_obj = io.BytesIO(f.read())
         file_obj.name = filename
         return file_obj
 
+
 color_map_up = None
 if st.session_state.get("use_demo"):
-    # Automatically use demo color map if in demo mode
+    # Automatically use demo color map if in demo mode - clean it to 2 columns
     try:
-        color_map_up = load_demo_file("sample_type_hierarchy.csv")
+        import pandas as pd
+        import io
+
+        demo_file = load_demo_file("sample_type_hierarchy.csv")
+        color_df = pd.read_csv(demo_file, sep=";")
+
+        # Keep only descriptor and color_code columns
+        if "descriptor" in color_df.columns and "color_code" in color_df.columns:
+            color_df = color_df[["descriptor", "color_code"]]
+
+        # Convert back to BytesIO
+        csv_str = color_df.to_csv(index=False, sep=";")
+        color_map_up = io.BytesIO(csv_str.encode())
+        color_map_up.name = "sample_type_hierarchy.csv"
         st.info("Demo color map loaded automatically.")
-    except Exception:
-        st.warning("Demo color map not found.")
+    except Exception as e:
+        st.warning(f"Demo color map not found: {e}")
 else:
-    color_map_up = st.file_uploader(
-        "Colour-mapping CSV (`descriptor;color_code`, optional)", type=("csv", "tsv", "txt")
+    color_option = st.radio(
+        "Color mapping option",
+        ["Use foodomics color mapping", "Upload custom file", "Use grayscale"],
+        horizontal=True,
     )
 
+    if color_option == "Use foodomics color mapping":
+        # Use the default foodomics color mapping - clean to 2 columns
+        try:
+            import pandas as pd
+            import io
+
+            demo_file = load_demo_file("sample_type_hierarchy.csv")
+            color_df = pd.read_csv(demo_file, sep=";")
+
+            # Keep only descriptor and color_code columns
+            if "descriptor" in color_df.columns and "color_code" in color_df.columns:
+                color_df = color_df[["descriptor", "color_code"]]
+
+            # Convert back to BytesIO
+            csv_str = color_df.to_csv(index=False, sep=";")
+            color_map_up = io.BytesIO(csv_str.encode())
+            color_map_up.name = "sample_type_hierarchy.csv"
+            st.info("✓ Using foodomics reference color mapping")
+        except Exception as e:
+            st.error(f"Could not load foodomics color mapping: {e}")
+    elif color_option == "Upload custom file":
+        color_map_up = st.file_uploader(
+            "Colour-mapping file (CSV/TSV with 2 columns: descriptor and color_code)",
+            type=("csv", "tsv", "txt"),
+        )
+    else:
+        # Generate grayscale mapping automatically
+        import io
+        import pandas as pd
+
+        # Get all unique reference types from level 0
+        unique_types = rdd.counts[rdd.counts["level"] == 0]["reference_type"].unique()
+        n_types = len(unique_types)
+
+        # Generate grayscale colors
+        grayscale_colors = [
+            f"#{int(255 - (i * 200 / max(n_types-1, 1))):02x}" * 3 for i in range(n_types)
+        ]
+
+        # Create mapping dataframe
+        color_df = pd.DataFrame({"descriptor": unique_types, "color_code": grayscale_colors})
+
+        # Convert to BytesIO object (with header to match expected format)
+        csv_str = color_df.to_csv(index=False, sep=";", header=True)
+        color_map_up = io.BytesIO(csv_str.encode())
+        color_map_up.name = "grayscale_mapping.csv"
+        st.info(f"✓ Generated grayscale mapping for {n_types} reference types")
+
 dark_mode = st.checkbox("Dark mode")
 
 # ── draw button ────────────────────────────────────────────────────────
 if st.button("Draw Sankey"):
-    # persist colour map only if provided
-    
-    colour_path = None
-    if color_map_up:
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmp:
-            tmp.write(color_map_up.getbuffer())
+    if not color_map_up:
+        st.error("⚠️ Please select a color mapping option.")
+        st.stop()
+
+    # Read uploaded file and convert to semicolon-separated format for backend
+    import pandas as pd
+    import io
+
+    try:
+        # Try to read with common separators
+        color_map_up.seek(0)
+        content = color_map_up.read().decode("utf-8")
+
+        # Detect separator and read
+        if ";" in content.split("\n")[0]:
+            sep = ";"
+        elif "\t" in content.split("\n")[0]:
+            sep = "\t"
+        else:
+            sep = ","
+
+        color_map_up.seek(0)
+        color_df = pd.read_csv(color_map_up, sep=sep)
+
+        # Ensure we have the expected columns
+        if len(color_df.columns) >= 2:
+            color_df = color_df.iloc[:, :2]  # Take first two columns
+            color_df.columns = ["descriptor", "color_code"]
+
+        # Save as semicolon-separated for backend
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode="w") as tmp:
+            color_df.to_csv(tmp, sep=";", index=False)
             colour_path = tmp.name
+    except Exception as e:
+        st.error(f"Error reading color mapping file: {e}")
+        st.stop()
 
     fig = viz.plot_sankey(
         rdd,
-        color_mapping_file=colour_path,  # may be None
+        color_mapping_file=colour_path,
         max_hierarchy_level=max_level or None,
         filename_filter=None if sample_choice == "<all samples>" else sample_choice,
         dark_mode=dark_mode,
     )
-    st.plotly_chart(fig, use_container_width=True)
+    st.plotly_chart(fig, use_container_width=True)

requirements.txt

@@ -12,6 +12,7 @@ scikit-bio>=0.7.1   # CLR transformation
 
 # ───────── GNPS data access ─────────
 gnpsdata @ git+https://github.com/Wang-Bioinformatics-Lab/GNPSDataPackage.git@f4ca8d9b7fab87823179b122b7e4a0a0b62e9d65
+gnps-rdd @ git+https://github.com/bittremieuxlab/gnps-rdd.git@review/manuscript-revisions
 
 # ───────── optional / indirect ─────────
 # pyarrow is auto-installed by pandas ≥2 for fast parquet/feather,