add stacked bar for prediction category counts

Author: tiffanychu90

rt_predictions/chart_utils_for_operators.py

@@ -2,10 +2,10 @@
 Chart and map functions for operator report.
 """
 
+import _color_palette
 import altair as alt
 import pandas as pd
 from great_tables import GT
-from gtfs_curator_utils import _color_palette
 
 
 def basic_percentiles_line_chart(

rt_predictions/operator_report.ipynb

@@ -96,8 +96,8 @@
     "    \"n_stops\", \"num_stop_times\", \"daily_arrivals\", 'n_days_schedule_and_rt'\n",
     "]\n",
     "\n",
-    "vp_cols = ['vp_messages_per_minute', 'pct_vp_trips', 'pct_vp_routes'] #'daily_vp_trips'\n",
-    "tu_cols =['tu_messages_per_minute', 'pct_tu_trips', 'pct_tu_routes'] #'daily_tu_trips',\n",
+    "vp_cols = ['vp_messages_per_minute', 'pct_vp_trips', 'pct_vp_routes'] \n",
+    "tu_cols =['tu_messages_per_minute', 'pct_tu_trips', 'pct_tu_routes']\n",
     "\n",
     "tu_prediction_cols = [\n",
     "    \"bus_catch_likelihood\", \"pct_tu_complete_minutes\", # both are percents\n",
@@ -369,6 +369,41 @@
     "# maybe IQR doesn't make sense to color, it'll just be ranked by day_type"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3950f3e-2e99-4da9-bc2d-78f61ab2806b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pct_category_df = prep_operator_data.reshape_prediction_category_counts_to_long(df)\n",
+    "\n",
+    "category_counts_stacked_bar = alt.Chart(pct_category_df).mark_bar().encode(\n",
+    "    x=alt.X(\n",
+    "        'day_type',\n",
+    "        title = \"\", sort=[\"Weekday\", \"Saturday\", \"Sunday\"]\n",
+    "    ),\n",
+    "    y=alt.Y('pct', title=\"Percent\"),\n",
+    "    color=alt.Color(\n",
+    "        'prediction_category:N',\n",
+    "        title=\"Prediction Category\", \n",
+    "        sort=[\"early\", \"ontime\", \"late\"],\n",
+    "        scale=alt.Scale(range=[\n",
+    "            _color_palette.get_color(\"light_cadmium_yellow\"),\n",
+    "            _color_palette.get_color(\"electric_orange\"),\n",
+    "            _color_palette.get_color(\"aquatic\")\n",
+    "        ])\n",
+    "    ),\n",
+    "    column=alt.Column(\"tu_name\", title = \"\"),\n",
+    "    tooltip=[\"tu_name\", \"day_type\", \"prediction_category\", \"pct\"]\n",
+    ").interactive().properties(\n",
+    "    title = \"Predictions by Category\",\n",
+    "    width=150, height = 200\n",
+    ")\n",
+    "\n",
+    "category_counts_stacked_bar"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3033b010-f091-4a32-8df1-38dc7d2fd3af",