Highlight experiments chosen for comparison in the dissertation

softboiler · Nov 7, 2024 · 830f800 · 830f800
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diff --git a/docs/notebooks/get_thermal_data.ipynb b/docs/notebooks/get_thermal_data.ipynb
@@ -39,6 +39,7 @@
     "from boilercv_pipeline.units import U\n",
     "from dev.docs.nbs import get_mode, init\n",
     "from devtools import pprint\n",
+    "from matplotlib.figure import Figure\n",
     "from matplotlib.pyplot import subplots\n",
     "from pandas import DataFrame, Series, concat, read_csv, read_hdf\n",
     "from seaborn import lineplot, scatterplot\n",
@@ -73,6 +74,19 @@
     "\n",
     "contours = FindObjects(context=context).contours\n",
     "\n",
+    "HIGHLIGHTS = [\"2024-07-18T17-07-13\", \"2024-07-18T18-04-05\", \"2024-07-18T18-49-55\"]\n",
+    "\"\"\"Chosen datasets to higlight in the dissertation.\"\"\"\n",
+    "WIDTH_SCALE = 1.257\n",
+    "\"\"\"Width to scale plots by.\"\"\"\n",
+    "HEIGHT_SCALE = 1.000\n",
+    "\"\"\"Width to scale plots by.\"\"\"\n",
+    "\n",
+    "\n",
+    "def scale_figure(fig: Figure, width: float = WIDTH_SCALE, height: float = HEIGHT_SCALE):\n",
+    "    \"\"\"Scale up figure size.\"\"\"\n",
+    "    fig.set_figwidth(width * fig.get_figwidth())\n",
+    "    fig.set_figheight(height * fig.get_figheight())\n",
+    "\n",
     "\n",
     "def fit(df: DataFrame, flux: Col, sample_temps: Iterable[Col]) -> DataFrame:\n",
     "    \"\"\"Fit model function across sample temperatures.\"\"\"\n",
@@ -158,6 +172,20 @@
     "                    ]\n",
     "                )\n",
     "            })\n",
+    "            .assign(**{\n",
+    "                C.highlight(): lambda df: df.index.isin(\n",
+    "                    df.index[\n",
+    "                        df.index.get_indexer(\n",
+    "                            [\n",
+    "                                dt_fromisolike(match)\n",
+    "                                for highlight in HIGHLIGHTS\n",
+    "                                if (match := ISOLIKE.search(highlight))\n",
+    "                            ],\n",
+    "                            method=\"nearest\",\n",
+    "                        )\n",
+    "                    ]\n",
+    "                )\n",
+    "            })\n",
     "            .reset_index()\n",
     "            .ffill()\n",
     "            .assign(**{\n",
@@ -193,6 +221,7 @@
     "        .agg({\n",
     "            **dict.fromkeys([c() for c in C.dests if c != C.index], \"median\"),\n",
     "            C.video(): \"max\",\n",
+    "            C.highlight(): \"max\",\n",
     "        })\n",
     "        .reset_index()\n",
     "    ),\n",
@@ -238,8 +267,29 @@
     "    }).set_index(C.time_elapsed_min())[[C.video()]],\n",
     "    legend=False,\n",
     ")\n",
+    "scatterplot(\n",
+    "    ax=ax,\n",
+    "    s=9 * params.marker_scale,\n",
+    "    markers={C.highlight(): \"d\"},\n",
+    "    palette={C.highlight(): \"green\"},\n",
+    "    data=data.dfs.resampled.assign(**{\n",
+    "        C.highlight(): lambda df: df[df[C.highlight()]][C.superheat()]\n",
+    "    }).set_index(C.time_elapsed_min())[[C.highlight()]],\n",
+    ")\n",
+    "scatterplot(\n",
+    "    ax=ax,\n",
+    "    s=9 * params.marker_scale,\n",
+    "    markers={C.highlight(): \"d\"},\n",
+    "    palette={C.highlight(): \"green\"},\n",
+    "    data=data.dfs.resampled.assign(**{\n",
+    "        C.highlight(): lambda df: df[df[C.highlight()]][C.subcool()]\n",
+    "    }).set_index(C.time_elapsed_min())[[C.highlight()]],\n",
+    "    legend=False,\n",
+    ")\n",
+    "\n",
     "ax.set_ylabel(C.subcool.no_sub())\n",
-    "params.move_legend(ax)"
+    "params.move_legend(ax, ncol=4)\n",
+    "scale_figure(ax.get_figure())"
    ]
   },
   {
@@ -265,6 +315,15 @@
     "        C.video(): lambda df: df[df[C.video()]][C.flux()]\n",
     "    }).set_index(C.subcool())[[C.video()]],\n",
     ")\n",
+    "scatterplot(\n",
+    "    ax=ax,\n",
+    "    s=9 * params.marker_scale,\n",
+    "    markers={C.highlight(): \"d\"},\n",
+    "    palette={C.highlight(): \"green\"},\n",
+    "    data=data.dfs.resampled.assign(**{\n",
+    "        C.highlight(): lambda df: df[df[C.highlight()]][C.flux()]\n",
+    "    }).set_index(C.subcool())[[C.highlight()]],\n",
+    ")\n",
     "ax.get_legend().set_title(C.time_elapsed_min())  # pyright: ignore[reportOptionalMemberAccess]\n",
     "params.move_legend(ax, ncol=4)"
    ]
@@ -296,6 +355,15 @@
     "        C.video(): lambda df: df[df[C.video()]][C.flux()]\n",
     "    }).set_index(C.superheat())[[C.video()]],\n",
     ")\n",
+    "scatterplot(\n",
+    "    ax=ax,\n",
+    "    s=9 * params.marker_scale,\n",
+    "    markers={C.highlight(): \"d\"},\n",
+    "    palette={C.highlight(): \"green\"},\n",
+    "    data=data.dfs.resampled.assign(**{\n",
+    "        C.highlight(): lambda df: df[df[C.highlight()]][C.flux()]\n",
+    "    }).set_index(C.superheat())[[C.highlight()]],\n",
+    ")\n",
     "ax.get_legend().set_title(C.time_elapsed_min())  # pyright: ignore[reportOptionalMemberAccess]\n",
     "params.move_legend(ax, ncol=4)"
    ]

diff --git a/packages/pipeline/boilercv_pipeline/stages/get_thermal_data/__init__.py b/packages/pipeline/boilercv_pipeline/stages/get_thermal_data/__init__.py
@@ -84,6 +84,7 @@ class Cols(columns.Cols):
     flux: Ann[LinkedCol, D.dst] = LinkedCol("q''", "W/cm^2", source=Col("q''", "W/m^2"))
     boiling: Ann[Col, D.dst] = Col("T_sat (C)")
     video: Ann[Col, D.dst] = Col("Video")
+    highlight: Ann[Col, D.dst] = Col("Highlight")
 
 
 @command(