Added some examples in the notebook.

Author: jonbrenas

malariagen_data/anoph/gplt_params.py

@@ -1,7 +1,7 @@
 """Parameters for genome plotting functions. N.B., genome plots are always
 plotted with bokeh."""
 
-from typing import Literal, Mapping, Optional, Union, Final
+from typing import Literal, Mapping, Optional, Union, Final, Sequence
 
 import bokeh.models
 from typing_extensions import Annotated, TypeAlias
@@ -118,3 +118,5 @@
 ]
 
 contig_colors_default: Final[contig_colors] = list(bokeh.palettes.d3["Category20b"][5])
+
+colors: TypeAlias = Annotated[Sequence[str], "List of colors."]

malariagen_data/anoph/h12.py

@@ -587,7 +587,7 @@ def plot_h12_gwss_multi_overlay_track(
             )
 
         # Determine X axis range.
-        x, _ = res[list(cohort_queries.keys())[0]]
+        x, _, _ = res[list(cohort_queries.keys())[0]]
         x_min = x[0]
         x_max = x[-1]
         if x_range is None:
@@ -622,7 +622,7 @@ def plot_h12_gwss_multi_overlay_track(
         )
 
         # Plot H12.
-        for i, (cohort_label, (x, h12)) in enumerate(res.items()):
+        for i, (cohort_label, (x, h12, contig)) in enumerate(res.items()):
             fig.scatter(
                 x=x,
                 y=h12,

notebooks/plot_h12_h1x.ipynb

@@ -75,7 +75,8 @@
     "coh2 = \"ML-2_Kati_gamb_2014\"\n",
     "coh1_query = f\"cohort_admin2_year == '{coh1}'\"\n",
     "coh2_query = f\"cohort_admin2_year == '{coh2}'\"\n",
-    "contig = \"2L\""
+    "contig = \"2L\"\n",
+    "contigs = \"2RL\""
    ]
   },
   {
@@ -114,6 +115,23 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4470d24c-8cf1-4d22-b774-0121b4560e27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ag3.plot_h12_gwss(\n",
+    "    contig=contigs,\n",
+    "    analysis=\"gamb_colu\",\n",
+    "    window_size=2000,\n",
+    "    sample_query=coh1_query,\n",
+    "    sample_sets=\"3.0\",\n",
+    "    cohort_size=20,\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -173,6 +191,25 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4b7a8d2-95d0-48dc-a32f-3bc96aacfb9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ag3.plot_h1x_gwss(\n",
+    "    contig=contigs,\n",
+    "    window_size=2000,\n",
+    "    cohort1_query=coh1_query,\n",
+    "    cohort2_query=coh2_query,\n",
+    "    sample_sets=\"3.0\",\n",
+    "    analysis=\"gamb_colu\",\n",
+    "    cohort_size=20,\n",
+    "    contig_colors=[\"red\", \"green\"]\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -261,6 +298,22 @@
     "contig = \"2RL\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1aaa0573-723c-43b1-baea-750172c4dabc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffc7dc06-6bdb-42d2-a1fb-878612d10dd1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -364,14 +417,6 @@
     "    cohort_size=20,\n",
     ")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "67e3bfcc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -382,7 +427,7 @@
    "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/workbench-notebooks:m125"
   },
   "kernelspec": {
-   "display_name": "malariagen-data-python",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -396,7 +441,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.15"
+   "version": "3.10.11"
   },
   "vscode": {
    "interpreter": {