From 05db99adfe10da9226e0ff74f75553e562f38e16 Mon Sep 17 00:00:00 2001 From: amandaha8 Date: Tue, 1 Aug 2023 21:52:37 +0000 Subject: [PATCH] integrate it with b2 draft --- rt_segment_speeds/19_meters_threshold.ipynb | 4168 +++++++++---------- 1 file changed, 1964 insertions(+), 2204 deletions(-) diff --git a/rt_segment_speeds/19_meters_threshold.ipynb b/rt_segment_speeds/19_meters_threshold.ipynb index 145c36c2d..3e1f74676 100644 --- a/rt_segment_speeds/19_meters_threshold.ipynb +++ b/rt_segment_speeds/19_meters_threshold.ipynb @@ -12,7 +12,7 @@ "text": [ "/opt/conda/lib/python3.9/site-packages/geopandas/_compat.py:123: UserWarning: The Shapely GEOS version (3.11.1-CAPI-1.17.1) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n", " warnings.warn(\n", - "/tmp/ipykernel_453/355420618.py:4: UserWarning: Shapely 2.0 is installed, but because PyGEOS is also installed, GeoPandas will still use PyGEOS by default for now. To force to use and test Shapely 2.0, you have to set the environment variable USE_PYGEOS=0. You can do this before starting the Python process, or in your code before importing geopandas:\n", + "/tmp/ipykernel_891/3696898552.py:4: UserWarning: Shapely 2.0 is installed, but because PyGEOS is also installed, GeoPandas will still use PyGEOS by default for now. To force to use and test Shapely 2.0, you have to set the environment variable USE_PYGEOS=0. You can do this before starting the Python process, or in your code before importing geopandas:\n", "\n", "import os\n", "os.environ['USE_PYGEOS'] = '0'\n", @@ -37,7 +37,7 @@ " SEGMENT_GCS, \n", " analysis_date,\n", ")\n", - "from scripts import (A1_sjoin_vp_segments, A2_valid_vehicle_positions)\n", + "from scripts import (A1_sjoin_vp_segments, A2_valid_vehicle_positions,B2_avg_speeds_by_segment)\n", "from shared_utils import calitp_color_palette as cp, rt_utils, geography_utils\n", "import _threshold_utils as threshold_utils\n", "import _rt_scheduled_utils as rt_scheduled_utils\n", @@ -48,16 +48,6 @@ { "cell_type": "code", "execution_count": 2, - "id": "f07df469-d682-44ee-8ae3-83446c000d21", - "metadata": {}, - "outputs": [], - "source": [ - "DICT = helpers.get_parameters(CONFIG_PATH, \"stop_segments\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, "id": "6305e1fd-3383-4960-baa2-db169e86033d", "metadata": {}, "outputs": [], @@ -89,46 +79,66 @@ "id": "857ab4ad-1dde-4c65-bd88-99696ab9f478", "metadata": {}, "source": [ - "### Files" + "### Open Files\n", + "* Add `import_speeds_segs`,`calculate_segment_length`,`merge_segments_speeds` into `B2`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f07df469-d682-44ee-8ae3-83446c000d21", + "metadata": {}, + "outputs": [], + "source": [ + "# dict_inputs = helpers.get_parameters(CONFIG_PATH, \"stop_segments\")" ] }, { "cell_type": "code", "execution_count": 4, - "id": "b153add6-b4f9-4b42-94da-5709629e8fbb", + "id": "ab54a5c4-d9e5-49c3-a70d-cb0f0034a99c", "metadata": {}, "outputs": [], "source": [ - "STG5_FILE = DICT['stage5']\n", - "# avg_speeds = gpd.read_parquet(f\"{SEGMENT_GCS}{STG5_FILE}_{analysis_date}.parquet\")\n", - "# avg_speeds = avg_speeds.drop(columns=[\"district\", \"district_name\"])" + "# dict_inputs" ] }, { "cell_type": "code", "execution_count": 5, + "id": "ec7d196d-3dd3-445b-bd8a-9daf48fa5a88", + "metadata": {}, + "outputs": [], + "source": [ + "dictionary = helpers.get_parameters(CONFIG_PATH, \"stop_segments\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "id": "62548e77-825c-4ed3-9f90-05a07e0fab82", "metadata": {}, "outputs": [], "source": [ - "def import_speeds_segs(analysis_date:str):\n", - " FILE = DICT['stage4']\n", - " df = pd.read_parquet(f\"{SEGMENT_GCS}{FILE}_{analysis_date}\")\n", + "def import_speeds_segs(analysis_date:str, max_speed_cutoff: int, dict_inputs:dict):\n", + " FILE = dict_inputs['stage4']\n", + " df = pd.read_parquet(f\"{SEGMENT_GCS}{FILE}_{analysis_date}\", \n", + " filters = [[(\"speed_mph\", \"<=\", max_speed_cutoff)]])\n", " \n", " return df" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "c90ac266-38ca-4e94-ab25-68ee40829605", "metadata": {}, "outputs": [], "source": [ - "def import_segments(analysis_date:str) -> gpd.GeoDataFrame:\n", - " # Load in ALL segments, flag them.\n", - " FILE = STOP_SEG_DICT['segments_file']\n", - " gdf = gpd.read_parquet(f\"{SEGMENT_GCS}{FILE}_{analysis_date}.parquet\").to_crs(PROJECT_CRS)\n", + "def calculate_segment_length(analysis_date:str, dict_inputs:dict) -> gpd.GeoDataFrame:\n", + " # Load in ALL segments, find the length\n", + " FILE = dict_inputs['segments_file']\n", + " gdf = gpd.read_parquet(f\"{SEGMENT_GCS}{FILE}_{analysis_date}.parquet\")\n", " \n", " gdf = gdf.assign(\n", " meters_length=(gdf.geometry.length)\n", @@ -140,15 +150,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "961e15dc-1c07-411f-92be-785721b7d67e", "metadata": {}, "outputs": [], "source": [ - "def merge_segments_speeds(analysis_date:str):\n", - " speeds = import_speeds_segs(analysis_date)\n", - " segments = import_segments(analysis_date)\n", - " merge1 = pd.merge(segments, speeds, on = ['shape_array_key','gtfs_dataset_key','stop_sequence'], how = \"inner\")\n", + "def merge_segments_speeds(analysis_date:str, max_speed_cutoff:int, dict_inputs:dict):\n", + " speeds = import_speeds_segs(analysis_date,max_speed_cutoff, dict_inputs)\n", + " segments = calculate_segment_length(analysis_date, dict_inputs)\n", + " merge1 = pd.merge(segments, speeds, on = ['shape_array_key','gtfs_dataset_key','stop_sequence','schedule_gtfs_dataset_key'], how = \"inner\")\n", " \n", " merge1['percent'] = merge1.meters_elapsed/merge1.meters_length * 100\n", " \n", @@ -159,30 +169,17 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "9f02d167-8ec0-441f-b502-5ac7644e4f3a", - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [], - "source": [ - "merge1 = merge_segments_speeds(analysis_date)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "0e727efb-8136-4cdc-b9a5-c9bb17bf74cd", "metadata": {}, "outputs": [], "source": [ - "# test1 = row_cat(merge1)" + "merge1= merge_segments_speeds(analysis_date,70, dictionary)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "id": "d7737449-92b0-417e-ad76-fceb20e9403e", "metadata": {}, "outputs": [], @@ -228,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "id": "02da2cef-c921-4d79-856b-a797f715b7ed", "metadata": {}, "outputs": [], @@ -238,8 +235,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "18ec9a14-2015-41b9-8620-6679f9c33e5b", + "execution_count": 101, + "id": "20aff75d-f059-4af5-aa71-aaed42d55674", "metadata": {}, "outputs": [ { @@ -263,151 +260,151 @@ " \n", " \n", " \n", - " percentile\n", - " n_rows\n", - " mean_speed_mph\n", - " n_kept_routes\n", - " n_kept_trips\n", - " n_kept_operators\n", - " percentage_kept_rows\n", - " percentage_kept_trips\n", - " percentage_kept_routes\n", + " Percentile\n", + " N Rows\n", + " Mean Speed Mph\n", + " N Kept Routes\n", + " N Kept Trips\n", + " N Kept Operators\n", + " Percentage Kept Rows\n", + " Percentage Kept Trips\n", + " Percentage Kept Routes\n", " \n", " \n", " \n", " \n", " 0\n", - " Min. of 0% of seg length covered\n", - " 2528980\n", - " 10.79\n", - " 4088\n", - " 66255\n", - " 75\n", - " 100.00\n", - " 100.00\n", - " 100.00\n", + " Min. of 13% of seg length covered\n", + " 2002522\n", + " 11.90\n", + " 4079\n", + " 65931\n", + " 74\n", + " 90.00\n", + " 99.60\n", + " 99.80\n", " \n", " \n", " 0\n", - " Min. of 10% of seg length covered\n", - " 2023184\n", - " 11.86\n", - " 4080\n", - " 65952\n", - " 75\n", + " Min. of 43% of seg length covered\n", + " 1780020\n", + " 12.40\n", + " 4065\n", + " 65696\n", + " 74\n", " 80.00\n", - " 99.54\n", - " 99.80\n", + " 99.20\n", + " 99.50\n", " \n", " \n", " 0\n", - " Min. of 44% of seg length covered\n", - " 1770286\n", - " 12.38\n", - " 4065\n", - " 65685\n", - " 75\n", + " Min. of 60% of seg length covered\n", + " 1557517\n", + " 12.20\n", + " 4055\n", + " 65420\n", + " 74\n", " 70.00\n", - " 99.14\n", - " 99.44\n", + " 98.80\n", + " 99.20\n", " \n", " \n", " 0\n", - " Min. of 63% of seg length covered\n", - " 1517388\n", - " 12.19\n", - " 4054\n", - " 65367\n", - " 75\n", + " Min. of 73% of seg length covered\n", + " 1335015\n", + " 11.90\n", + " 4041\n", + " 65010\n", + " 74\n", " 60.00\n", - " 98.66\n", - " 99.17\n", + " 98.20\n", + " 98.90\n", " \n", " \n", " 0\n", - " Min. of 76% of seg length covered\n", - " 1264490\n", - " 11.86\n", - " 4035\n", - " 64807\n", - " 75\n", + " Min. of 83% of seg length covered\n", + " 1112513\n", + " 11.60\n", + " 4026\n", + " 64250\n", + " 74\n", " 50.00\n", - " 97.81\n", - " 98.70\n", + " 97.00\n", + " 98.50\n", " \n", " \n", " 0\n", - " Min. of 86% of seg length covered\n", - " 1011592\n", - " 11.48\n", - " 4005\n", - " 63650\n", - " 75\n", + " Min. of 90% of seg length covered\n", + " 890010\n", + " 11.20\n", + " 3977\n", + " 62802\n", + " 74\n", " 40.00\n", - " 96.07\n", - " 97.97\n", + " 94.80\n", + " 97.30\n", " \n", " \n", " 0\n", - " Min. of 93% of seg length covered\n", - " 758694\n", - " 11.01\n", - " 3939\n", - " 61725\n", - " 75\n", + " Min. of 95% of seg length covered\n", + " 667508\n", + " 10.80\n", + " 3902\n", + " 60798\n", + " 74\n", " 30.00\n", - " 93.16\n", - " 96.36\n", + " 91.80\n", + " 95.50\n", " \n", " \n", " 0\n", - " Min. of 97% of seg length covered\n", - " 505796\n", - " 10.27\n", - " 3799\n", - " 58662\n", - " 75\n", + " Min. of 98% of seg length covered\n", + " 445005\n", + " 10.10\n", + " 3735\n", + " 57281\n", + " 74\n", " 20.00\n", - " 88.54\n", - " 92.93\n", + " 86.50\n", + " 91.40\n", " \n", " \n", "\n", "" ], "text/plain": [ - " percentile n_rows mean_speed_mph n_kept_routes \\\n", - "0 Min. of 0% of seg length covered 2528980 10.79 4088 \n", - "0 Min. of 10% of seg length covered 2023184 11.86 4080 \n", - "0 Min. of 44% of seg length covered 1770286 12.38 4065 \n", - "0 Min. of 63% of seg length covered 1517388 12.19 4054 \n", - "0 Min. of 76% of seg length covered 1264490 11.86 4035 \n", - "0 Min. of 86% of seg length covered 1011592 11.48 4005 \n", - "0 Min. of 93% of seg length covered 758694 11.01 3939 \n", - "0 Min. of 97% of seg length covered 505796 10.27 3799 \n", + " Percentile N Rows Mean Speed Mph N Kept Routes \\\n", + "0 Min. of 13% of seg length covered 2002522 11.90 4079 \n", + "0 Min. of 43% of seg length covered 1780020 12.40 4065 \n", + "0 Min. of 60% of seg length covered 1557517 12.20 4055 \n", + "0 Min. of 73% of seg length covered 1335015 11.90 4041 \n", + "0 Min. of 83% of seg length covered 1112513 11.60 4026 \n", + "0 Min. of 90% of seg length covered 890010 11.20 3977 \n", + "0 Min. of 95% of seg length covered 667508 10.80 3902 \n", + "0 Min. of 98% of seg length covered 445005 10.10 3735 \n", "\n", - " n_kept_trips n_kept_operators percentage_kept_rows \\\n", - "0 66255 75 100.00 \n", - "0 65952 75 80.00 \n", - "0 65685 75 70.00 \n", - "0 65367 75 60.00 \n", - "0 64807 75 50.00 \n", - "0 63650 75 40.00 \n", - "0 61725 75 30.00 \n", - "0 58662 75 20.00 \n", + " N Kept Trips N Kept Operators Percentage Kept Rows \\\n", + "0 65931 74 90.00 \n", + "0 65696 74 80.00 \n", + "0 65420 74 70.00 \n", + "0 65010 74 60.00 \n", + "0 64250 74 50.00 \n", + "0 62802 74 40.00 \n", + "0 60798 74 30.00 \n", + "0 57281 74 20.00 \n", "\n", - " percentage_kept_trips percentage_kept_routes \n", - "0 100.00 100.00 \n", - "0 99.54 99.80 \n", - "0 99.14 99.44 \n", - "0 98.66 99.17 \n", - "0 97.81 98.70 \n", - "0 96.07 97.97 \n", - "0 93.16 96.36 \n", - "0 88.54 92.93 " + " Percentage Kept Trips Percentage Kept Routes \n", + "0 99.60 99.80 \n", + "0 99.20 99.50 \n", + "0 98.80 99.20 \n", + "0 98.20 98.90 \n", + "0 97.00 98.50 \n", + "0 94.80 97.30 \n", + "0 91.80 95.50 \n", + "0 86.50 91.40 " ] }, - "execution_count": 12, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -416,141 +413,10 @@ "test" ] }, - { - "cell_type": "markdown", - "id": "1483859e-bd07-4d5f-b86f-0f9d7bebe121", - "metadata": { - "tags": [] - }, - "source": [ - "## Visualizing Speed\n", - "* https://nbviewer.org/github/cal-itp/data-analyses/blob/filter-speeds-avgs/rt_segment_speeds/18_speed_distribution.ipynb\n", - "* https://analysis.calitp.org/rt/district_07-los-angeles/9__speedmaps__district_07-los-angeles__itp_id_300.html" - ] - }, - { - "cell_type": "markdown", - "id": "938e5d67-7fd0-4d62-af9d-0f26d08f12ed", - "metadata": {}, - "source": [ - "### % of rows kept" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "561eb328-18d5-4b2a-bd1e-4777b23b765c", - "metadata": {}, - "outputs": [], - "source": [ - "test = threshold_utils.pre_clean(test)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "65d0b237-544f-4245-9de0-f2308ae8c8f2", - "metadata": {}, - "outputs": [], - "source": [ - "# Main chart\n", - "def bar_chart(df, x_column: str, y_column:str, title:str):\n", - " chart = (alt.Chart(df)\n", - " .mark_bar()\n", - " .encode(x=x_column, y= y_column, \n", - " color=alt.Color(y_column, \n", - " scale=alt.Scale(range=cp.CALITP_CATEGORY_BRIGHT_COLORS),\n", - " legend=None),\n", - " tooltip = df.columns.tolist())\n", - " .properties(title = title)\n", - " )\n", - " chart = threshold_utils.chart_size((chart), 400,300)\n", - " return chart\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "4c8a453f-e38f-4a37-9a3e-06f8069f7dca", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bar_chart(test, 'Percentage Kept Rows','Percentile', 'Rows Kept After % Segment Cutoff')" - ] - }, { "cell_type": "code", - "execution_count": 16, - "id": "ee0be75d-8329-43e6-ac78-12721e1148e8", + "execution_count": 105, + "id": "74891de3-f7b1-4942-a38f-2503779e0d23", "metadata": {}, "outputs": [ { @@ -574,176 +440,300 @@ " \n", " \n", " \n", - " Percentile\n", - " N Rows\n", - " Mean Speed Mph\n", - " N Kept Routes\n", - " N Kept Trips\n", - " N Kept Operators\n", - " Percentage Kept Rows\n", - " Percentage Kept Trips\n", - " Percentage Kept Routes\n", + " percentile\n", + " n_rows\n", + " mean_speed_mph\n", + " n_kept_routes\n", + " n_kept_trips\n", + " n_kept_operators\n", + " percentage_kept_rows\n", + " percentage_kept_trips\n", + " percentage_kept_routes\n", " \n", " \n", " \n", " \n", " 0\n", - " Min. of 0% of seg length covered\n", - " 2528980\n", - " 10.80\n", - " 4088\n", - " 66255\n", - " 75\n", - " 100.00\n", - " 100.00\n", - " 100.00\n", - " \n", - " \n", - " 0\n", - " Min. of 10% of seg length covered\n", - " 2023184\n", - " 11.90\n", - " 4080\n", - " 65952\n", - " 75\n", - " 80.00\n", - " 99.50\n", - " 99.80\n", + " Min. of 30% of seg length covered\n", + " 1891271\n", + " 12.26\n", + " 4070\n", + " 65795\n", + " 74\n", + " 85.00\n", + " 99.36\n", + " 99.58\n", " \n", " \n", " 0\n", - " Min. of 44% of seg length covered\n", - " 1770286\n", - " 12.40\n", - " 4065\n", - " 65685\n", - " 75\n", + " Min. of 60% of seg length covered\n", + " 1557517\n", + " 12.22\n", + " 4055\n", + " 65420\n", + " 74\n", " 70.00\n", - " 99.10\n", - " 99.40\n", - " \n", - " \n", - " 0\n", - " Min. of 63% of seg length covered\n", - " 1517388\n", - " 12.20\n", - " 4054\n", - " 65367\n", - " 75\n", - " 60.00\n", - " 98.70\n", - " 99.20\n", + " 98.79\n", + " 99.22\n", " \n", " \n", " 0\n", - " Min. of 76% of seg length covered\n", - " 1264490\n", - " 11.90\n", - " 4035\n", - " 64807\n", - " 75\n", - " 50.00\n", - " 97.80\n", - " 98.70\n", + " Min. of 78% of seg length covered\n", + " 1223764\n", + " 11.79\n", + " 4033\n", + " 64695\n", + " 74\n", + " 55.00\n", + " 97.69\n", + " 98.68\n", " \n", " \n", " 0\n", - " Min. of 86% of seg length covered\n", - " 1011592\n", - " 11.50\n", - " 4005\n", - " 63650\n", - " 75\n", + " Min. of 90% of seg length covered\n", + " 890010\n", + " 11.25\n", + " 3977\n", + " 62802\n", + " 74\n", " 40.00\n", - " 96.10\n", - " 98.00\n", - " \n", - " \n", - " 0\n", - " Min. of 93% of seg length covered\n", - " 758694\n", - " 11.00\n", - " 3939\n", - " 61725\n", - " 75\n", - " 30.00\n", - " 93.20\n", - " 96.40\n", + " 94.84\n", + " 97.31\n", " \n", " \n", " 0\n", " Min. of 97% of seg length covered\n", - " 505796\n", - " 10.30\n", - " 3799\n", - " 58662\n", - " 75\n", - " 20.00\n", - " 88.50\n", - " 92.90\n", + " 556257\n", + " 10.45\n", + " 3840\n", + " 59431\n", + " 74\n", + " 25.00\n", + " 89.75\n", + " 93.96\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Percentile N Rows Mean Speed Mph N Kept Routes \\\n", - "0 Min. of 0% of seg length covered 2528980 10.80 4088 \n", - "0 Min. of 10% of seg length covered 2023184 11.90 4080 \n", - "0 Min. of 44% of seg length covered 1770286 12.40 4065 \n", - "0 Min. of 63% of seg length covered 1517388 12.20 4054 \n", - "0 Min. of 76% of seg length covered 1264490 11.90 4035 \n", - "0 Min. of 86% of seg length covered 1011592 11.50 4005 \n", - "0 Min. of 93% of seg length covered 758694 11.00 3939 \n", - "0 Min. of 97% of seg length covered 505796 10.30 3799 \n", + " percentile n_rows mean_speed_mph n_kept_routes \\\n", + "0 Min. of 30% of seg length covered 1891271 12.26 4070 \n", + "0 Min. of 60% of seg length covered 1557517 12.22 4055 \n", + "0 Min. of 78% of seg length covered 1223764 11.79 4033 \n", + "0 Min. of 90% of seg length covered 890010 11.25 3977 \n", + "0 Min. of 97% of seg length covered 556257 10.45 3840 \n", "\n", - " N Kept Trips N Kept Operators Percentage Kept Rows \\\n", - "0 66255 75 100.00 \n", - "0 65952 75 80.00 \n", - "0 65685 75 70.00 \n", - "0 65367 75 60.00 \n", - "0 64807 75 50.00 \n", - "0 63650 75 40.00 \n", - "0 61725 75 30.00 \n", - "0 58662 75 20.00 \n", + " n_kept_trips n_kept_operators percentage_kept_rows \\\n", + "0 65795 74 85.00 \n", + "0 65420 74 70.00 \n", + "0 64695 74 55.00 \n", + "0 62802 74 40.00 \n", + "0 59431 74 25.00 \n", "\n", - " Percentage Kept Trips Percentage Kept Routes \n", - "0 100.00 100.00 \n", - "0 99.50 99.80 \n", - "0 99.10 99.40 \n", - "0 98.70 99.20 \n", - "0 97.80 98.70 \n", - "0 96.10 98.00 \n", - "0 93.20 96.40 \n", - "0 88.50 92.90 " + " percentage_kept_trips percentage_kept_routes \n", + "0 99.36 99.58 \n", + "0 98.79 99.22 \n", + "0 97.69 98.68 \n", + "0 94.84 97.31 \n", + "0 89.75 93.96 " ] }, - "execution_count": 16, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "test" + "valid_trips_by_cutoff(merge1, [.15,0.30,.45,0.6,0.75])" + ] + }, + { + "cell_type": "markdown", + "id": "9daf292e-e8d8-4813-86c9-7a09fc035c4a", + "metadata": { + "tags": [] + }, + "source": [ + "## Sample segments and routes for Big Blue Bus" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "edbaac05-d03a-42d4-abba-4a4483fa3005", + "metadata": {}, + "outputs": [], + "source": [ + "test_operator = \"Big Blue Bus VehiclePositions\"\n", + "test_org = \"City of Santa Monica\"\n", + "test_key = \"6c2d7daaf979779fa2089c6395baf98b\"" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9c7826f6-27ed-498f-97b8-ee0f4c2a992a", + "metadata": {}, + "outputs": [], + "source": [ + "pub_df = pd.read_parquet(\n", + " f\"{SEGMENT_GCS}export/avg_speeds_stop_segments_{analysis_date}_tabular.parquet\", \n", + " filters = [[(\"agency\", \"==\", test_org)]]\n", + ")" ] }, { "cell_type": "code", "execution_count": 17, - "id": "bbc14bae-327a-4269-bf2f-1e0089379f28", + "id": "288a9881-aa75-4331-9c4c-e01c3954fafe", + "metadata": {}, + "outputs": [], + "source": [ + "# Dark orange\n", + "shape_id1 = \"26375\"\n", + "stop_seq1 = 7\n", + "shape_array1 = pub_df[pub_df.shape_id==shape_id1].shape_array_key.iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "05465822-ff34-47cb-ac43-2d11e12e3494", + "metadata": {}, + "outputs": [], + "source": [ + "# Light yellow \n", + "shape_id2 = \"26342\"\n", + "stop_seq2 = 23\n", + "shape_array2 = pub_df[pub_df.shape_id==shape_id2].shape_array_key.iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d1315d79-276d-4810-9593-07e8770f67ba", + "metadata": {}, + "outputs": [], + "source": [ + "# Dark Red\n", + "shape_id3 = \"26393\"\n", + "stop_seq3 = 32\n", + "shape_array3 = pub_df[pub_df.shape_id==shape_id3].shape_array_key.iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d163d014-0e76-41ef-813f-ff2617077249", + "metadata": {}, + "outputs": [], + "source": [ + "# Light orange\n", + "shape_id4 = \"26372\"\n", + "stop_seq4 = 14\n", + "shape_array4 = pub_df[pub_df.shape_id==shape_id4].shape_array_key.iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "d946f239-b242-4a3e-95f6-580690e245b7", + "metadata": {}, + "outputs": [], + "source": [ + "# Green\n", + "shape_id5 = \"26400\"\n", + "stop_seq5= 8\n", + "shape_array5 = pub_df[pub_df.shape_id==shape_id5].shape_array_key.iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ec9da438-07d4-45ef-85c1-fc44d6eaadbf", + "metadata": {}, + "outputs": [], + "source": [ + "def one_route_map(avg_speeds:gpd.GeoDataFrame, shape_array_key:str):\n", + " display(avg_speeds[avg_speeds.shape_array_key==shape_array_key].explore(\n", + " \"p50_mph\", \n", + " tiles = \"CartoDB Positron\",\n", + " cmap = rt_utils.ZERO_THIRTY_COLORSCALE,\n", + " style_kwds = {'weight':5}))\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "1483859e-bd07-4d5f-b86f-0f9d7bebe121", + "metadata": { + "tags": [] + }, + "source": [ + "## Visualizing Speed\n", + "* https://nbviewer.org/github/cal-itp/data-analyses/blob/filter-speeds-avgs/rt_segment_speeds/18_speed_distribution.ipynb\n", + "* https://analysis.calitp.org/rt/district_07-los-angeles/9__speedmaps__district_07-los-angeles__itp_id_300.html" + ] + }, + { + "cell_type": "markdown", + "id": "938e5d67-7fd0-4d62-af9d-0f26d08f12ed", + "metadata": {}, + "source": [ + "### % of rows kept" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "561eb328-18d5-4b2a-bd1e-4777b23b765c", + "metadata": {}, + "outputs": [], + "source": [ + "test = threshold_utils.pre_clean(test)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "65d0b237-544f-4245-9de0-f2308ae8c8f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Main chart\n", + "def bar_chart(df, x_column: str, y_column:str, title:str):\n", + " chart = (alt.Chart(df)\n", + " .mark_bar()\n", + " .encode(x=x_column, y= y_column, \n", + " color=alt.Color(y_column, \n", + " scale=alt.Scale(range=cp.CALITP_CATEGORY_BRIGHT_COLORS),\n", + " legend=None),\n", + " tooltip = df.columns.tolist())\n", + " .properties(title = title)\n", + " )\n", + " chart = threshold_utils.chart_size((chart), 400,300)\n", + " return chart\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "4c8a453f-e38f-4a37-9a3e-06f8069f7dca", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - 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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 17, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "bar_chart(test, 'Percentage Kept Trips','Percentile', 'Trips Kept After % Segment Cutoff')" + "bar_chart(test, 'Percentage Kept Rows','Percentile', 'Rows Kept After % Segment Cutoff')" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "6e3b93d2-572d-4e56-82f7-d0627b106d38", + "execution_count": 99, + "id": "bbc14bae-327a-4269-bf2f-1e0089379f28", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 18, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "bar_chart(test, 'Percentage Kept Routes','Percentile', 'Routes Kept After % Segment Cutoff')" + "bar_chart(test, 'Percentage Kept Trips','Percentile', '% of Trips Kept After Segment Cutoff')" ] }, { - "cell_type": "markdown", - "id": "cc37a935-ab39-4e6d-b00b-84d71ee7b654", + "cell_type": "code", + "execution_count": 100, + "id": "6e3b93d2-572d-4e56-82f7-d0627b106d38", "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### % of rows kept & average speeds" + "bar_chart(test, 'Percentage Kept Routes','Percentile', '% of Routes Kept After Segment Cutoff')" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 28, "id": "5982d881-f62d-4de8-84a3-2bfed4b1ed1f", "metadata": {}, "outputs": [ @@ -901,13 +961,13 @@ "data": { "text/html": [ "\n", - "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 19, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -975,318 +1035,694 @@ "metadata": {}, "source": [ "### Only keep speeds that meet a certain threshold\n", - "* Put it in `speeds_with_segment_geom` in `B2`\n" + "* Put it in `speeds_with_segment_geom` in `B2`" ] }, { "cell_type": "code", - "execution_count": 59, - "id": "9260bdb8-1f48-4d6a-9412-5b7cfb26bf19", + "execution_count": 69, + "id": "ee843630-501d-4a46-82a4-8005cb0f4fe4", "metadata": {}, "outputs": [], "source": [ - "percent = merge1.percent.quantile(0.30).astype(float)" + "def speeds_length_filter(analysis_date:str, threshold: float, max_speed_cut_off: int, dict_inputs:dict):\n", + " \n", + " df = merge_segments_speeds(analysis_date, max_speed_cut_off,dict_inputs)\n", + " \n", + " percent = df.percent.quantile(threshold).astype(float)\n", + " \n", + " df = (df[(df.percent >= percent)])\n", + " \n", + " # ADD PERCENT TO THIS\n", + " columns_to_keep = ['shape_array_key', 'stop_sequence', 'gtfs_dataset_key',\n", + " 'gtfs_dataset_name', 'trip_id', 'min_time', 'min_dist', 'max_time',\n", + " 'max_dist', 'meters_elapsed', 'sec_elapsed', 'speed_mph',\n", + " 'trip_instance_key', 'schedule_gtfs_dataset_key','percent']\n", + " \n", + " df = df[columns_to_keep]\n", + " \n", + " return df " ] }, { "cell_type": "code", - "execution_count": 60, - "id": "f4f13924-361c-4e45-b2d6-24041dcb8f4a", + "execution_count": 70, + "id": "30bc569f-6e19-4369-b6a9-165e6c77b4b5", "metadata": {}, "outputs": [], "source": [ - "percentile_df = (merge1[(merge1.percent >= percent)])" + "percentile_df = speeds_length_filter(analysis_date, 0.2, 70, dictionary)" ] }, { "cell_type": "code", - "execution_count": 61, - "id": "7243b6b1-1595-4960-92f9-fa0b1438002b", + "execution_count": 71, + "id": "eeb0be03-dd95-464b-8110-1007996d5429", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1770286, 21)" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "percentile_df.shape" + "def speeds_with_segment_geom(\n", + " analysis_date: str, \n", + " threshold:float, \n", + " dict_inputs:dict,\n", + " max_speed_cutoff: int = 70\n", + " ) -> gpd.GeoDataFrame: \n", + " \"\"\"\n", + " Import the segment-trip table. \n", + " Average the speed_mph across all trips present in the segment.\n", + " \"\"\"\n", + " SEGMENT_IDENTIFIER_COLS = dict_inputs['segment_identifier_cols']\n", + " SEGMENT_FILE = dict_inputs[\"segments_file\"]\n", + " \n", + " df = speeds_length_filter(analysis_date, threshold, max_speed_cutoff, dict_inputs)\n", + " \n", + " time_of_day_df = sched_rt_utils.get_trip_time_buckets(analysis_date)\n", + " \n", + " df2 = pd.merge(\n", + " df, \n", + " time_of_day_df, \n", + " on = [\"gtfs_dataset_key\", \"trip_id\"], \n", + " how = \"inner\"\n", + " )\n", + " \n", + " all_day = B2_avg_speeds_by_segment.calculate_avg_speeds(\n", + " df2, \n", + " SEGMENT_IDENTIFIER_COLS\n", + " )\n", + " \n", + " peak = B2_avg_speeds_by_segment.calculate_avg_speeds(\n", + " df2[df2.time_of_day.isin([\"AM Peak\", \"PM Peak\"])], \n", + " SEGMENT_IDENTIFIER_COLS\n", + " )\n", + " \n", + " stats = pd.concat([\n", + " all_day.assign(time_of_day = \"all_day\"),\n", + " peak.assign(time_of_day = \"peak\")\n", + " ], axis=0)\n", + " \n", + " \n", + " # Merge in segment geometry\n", + " segments = helpers.import_segments(\n", + " SEGMENT_GCS,\n", + " f\"{SEGMENT_FILE}_{analysis_date}\",\n", + " columns = SEGMENT_IDENTIFIER_COLS + [\n", + " \"gtfs_dataset_key\", \n", + " \"stop_id\",\n", + " \"loop_or_inlining\",\n", + " \"geometry\", \n", + " \"district\", \"district_name\"\n", + " ]\n", + " ).to_crs(geography_utils.WGS84)\n", + " \n", + " gdf = pd.merge(\n", + " segments,\n", + " stats,\n", + " on = SEGMENT_IDENTIFIER_COLS,\n", + " how = \"inner\"\n", + " )\n", + " \n", + " return gdf" ] }, { "cell_type": "markdown", - "id": "9daf292e-e8d8-4813-86c9-7a09fc035c4a", - "metadata": { - "tags": [] - }, - "source": [ - "### Sample segments and routes for Big Blue Bus" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "edbaac05-d03a-42d4-abba-4a4483fa3005", + "id": "04682811-eda1-424f-9a3d-4609f37f5a80", "metadata": {}, - "outputs": [], "source": [ - "test_operator = \"Big Blue Bus VehiclePositions\"\n", - "test_org = \"City of Santa Monica\"\n", - "test_key = \"6c2d7daaf979779fa2089c6395baf98b\"" + "### Check out speeds" ] }, { "cell_type": "code", - "execution_count": 63, - "id": "9c7826f6-27ed-498f-97b8-ee0f4c2a992a", + "execution_count": 72, + "id": "5355132e-988c-40f0-8648-afaf5fe9b026", "metadata": {}, "outputs": [], "source": [ - "pub_df = pd.read_parquet(\n", - " f\"{SEGMENT_GCS}export/avg_speeds_stop_segments_{analysis_date}_tabular.parquet\", \n", - " filters = [[(\"agency\", \"==\", test_org)]]\n", - ")" + "avg_test = speeds_with_segment_geom(analysis_date, 0.2, dictionary)" ] }, { "cell_type": "code", - "execution_count": 64, - "id": "288a9881-aa75-4331-9c4c-e01c3954fafe", + "execution_count": 73, + "id": "781ca8a6-50dd-4257-9919-a9c9ddc1019f", "metadata": {}, "outputs": [], "source": [ - "# Dark orange\n", - "shape_id1 = \"26375\"\n", - "stop_seq1 = 7\n", - "shape_array1 = pub_df[pub_df.shape_id==shape_id1].shape_array_key.iloc[0]" + "STG5_FILE = dictionary['stage5']\n", + "og_avg = gpd.read_parquet(f\"{SEGMENT_GCS}{STG5_FILE}_{analysis_date}.parquet\")\n", + "og_avg = og_avg.drop(columns=[\"district\", \"district_name\"])" ] }, { "cell_type": "code", - "execution_count": 65, - "id": "05465822-ff34-47cb-ac43-2d11e12e3494", + "execution_count": 74, + "id": "19bd5cbe-0bc6-4dfd-b6fb-37a307dfb9db", "metadata": {}, "outputs": [], "source": [ - "# Light yellow \n", - "shape_id2 = \"26342\"\n", - "stop_seq2 = 23\n", - "shape_array2 = pub_df[pub_df.shape_id==shape_id2].shape_array_key.iloc[0]" + "def compare_average(avg_test:pd.DataFrame,\n", + " og_avg:pd.DataFrame, \n", + " shape_array_key:str,\n", + " stop_sequence:int):\n", + " print('Original')\n", + " display(og_avg[(og_avg.shape_array_key == shape_array_key) &(og_avg.stop_sequence == stop_sequence)].drop(columns = ['geometry']))\n", + " \n", + " print('New')\n", + " display(avg_test[(avg_test.shape_array_key == shape_array_key) & (avg_test.stop_sequence == stop_sequence)].drop(columns = ['geometry'])) " ] }, { "cell_type": "code", - "execution_count": 66, - "id": "d1315d79-276d-4810-9593-07e8770f67ba", + "execution_count": 75, + "id": "16f98b58-75ae-4a5c-a947-c6a4a58b7644", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original\n" + ] + }, + { + "data": { + "text/html": [ + "
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shape_array_keystop_sequencegtfs_dataset_keystop_idloop_or_inliningp50_mphn_tripsp20_mphp80_mphtime_of_day
13375294e02a46331c8b449aedb4469f49764a326c2d7daaf979779fa2089c6395baf98b164801.90230.919.85all_day
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shape_array_keystop_sequencegtfs_dataset_keystop_idloop_or_inliningdistrictdistrict_namep50_mphn_tripsp20_mphp80_mphtime_of_day
15216894e02a46331c8b449aedb4469f49764a326c2d7daaf979779fa2089c6395baf98b164807District 7 - Los Angeles1.27160.887.27all_day
15216994e02a46331c8b449aedb4469f49764a326c2d7daaf979779fa2089c6395baf98b164807District 7 - Los Angeles1.2790.867.09peak
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" + ], + "text/plain": [ + " shape_array_key stop_sequence \\\n", + "152168 94e02a46331c8b449aedb4469f49764a 32 \n", + "152169 94e02a46331c8b449aedb4469f49764a 32 \n", + "\n", + " gtfs_dataset_key stop_id loop_or_inlining district \\\n", + "152168 6c2d7daaf979779fa2089c6395baf98b 1648 0 7 \n", + "152169 6c2d7daaf979779fa2089c6395baf98b 1648 0 7 \n", + "\n", + " district_name p50_mph n_trips p20_mph p80_mph \\\n", + "152168 District 7 - Los Angeles 1.27 16 0.88 7.27 \n", + "152169 District 7 - Los Angeles 1.27 9 0.86 7.09 \n", + "\n", + " time_of_day \n", + "152168 all_day \n", + "152169 peak " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Light orange\n", - "shape_id4 = \"26372\"\n", - "stop_seq4 = 14\n", - "shape_array4 = pub_df[pub_df.shape_id==shape_id4].shape_array_key.iloc[0]" + "compare_average(avg_test, og_avg, shape_array3, stop_seq3)" ] }, { - "cell_type": "code", - "execution_count": 68, - "id": "d946f239-b242-4a3e-95f6-580690e245b7", + "cell_type": "markdown", + "id": "a37b765b-1d23-4e69-8959-474289814ac7", "metadata": {}, - "outputs": [], "source": [ - "# Green\n", - "shape_id5 = \"26400\"\n", - "stop_seq5= 8\n", - "shape_array5 = pub_df[pub_df.shape_id==shape_id5].shape_array_key.iloc[0]" + "* This is missing, not sure why?" ] }, { "cell_type": "code", - "execution_count": 69, - "id": "ec9da438-07d4-45ef-85c1-fc44d6eaadbf", - "metadata": {}, - "outputs": [], - "source": [ - "def one_route_map(avg_speeds:gpd.GeoDataFrame, shape_array_key:str):\n", - " display(avg_speeds[avg_speeds.shape_array_key==shape_array_key].explore(\n", - " \"p50_mph\", \n", - " tiles = \"CartoDB Positron\",\n", - " cmap = rt_utils.ZERO_THIRTY_COLORSCALE,\n", - " style_kwds = {'weight':5}))\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "7cff986a-6bb0-4f0f-942c-093cf8df1e04", - "metadata": {}, - "source": [ - "### Speeds for 1 Sequence" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "2eaca342-b677-4a7f-b6cb-f70aefd517b1", - "metadata": {}, - "outputs": [], - "source": [ - "def speeds_one_trip_seq(shape_array_key:str, stop_sequence:int):\n", - " STG4_FILE = DICT['stage4']\n", - " df = pd.read_parquet(f\"{SEGMENT_GCS}{STG4_FILE}_{analysis_date}\", \n", - " filters = [[(\"shape_array_key\", \"==\", shape_array_key),\n", - " (\"stop_sequence\", \"==\", stop_sequence)]]) \n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "d398a5dd-3578-4db1-8e6c-616812de7747", - "metadata": {}, - "outputs": [], - "source": [ - "def avg_speeds_one_trip_seq(shape_array_key:str, stop_sequence:int):\n", - " STG5_FILE = DICT['stage5']\n", - " df = pd.read_parquet(f\"{SEGMENT_GCS}{STG5_FILE}_{analysis_date}.parquet\", \n", - " filters = [[(\"shape_array_key\", \"==\", shape_array_key),\n", - " (\"stop_sequence\", \"==\", stop_sequence),\n", - " (\"time_of_day\", \"==\", \"all_day\")]])\n", - " df = df.drop(columns=[\"district\", \"district_name\", \"geometry\"])\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "f7dce6cc-07f6-492b-a747-b927535f1a91", - "metadata": {}, - "outputs": [], - "source": [ - "def merge_avg_all_speeds(shape_array_key:str, stop_sequence:int):\n", - " speeds = speeds_one_trip_seq(shape_array_key, stop_sequence)\n", - " avg = avg_speeds_one_trip_seq(shape_array_key, stop_sequence)\n", - " m1 = pd.merge(speeds,avg, on =['shape_array_key','stop_sequence','gtfs_dataset_key'], how = \"inner\")\n", - " return m1" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "3651e675-043e-41c2-a1f3-e703d6c86727", - "metadata": {}, - "outputs": [], - "source": [ - "def display_speeds(shape_array_key:str, stop_sequence:int):\n", - " m1 = merge_avg_all_speeds(shape_array_key,stop_sequence)\n", - " \n", - " # Fill any nan values\n", - " m1= m1.fillna(0)\n", - " \n", - " # Create chart title \n", - " chart_title = f\"Speed Distribution for Seq {m1.stop_sequence.iloc[0]}/Shape {m1.shape_array_key.iloc[0]}\"\n", - " \n", - " # Main chart\n", - " chart = (alt.Chart(m1)\n", - " .mark_bar()\n", - " .encode(x='trip_id', y='speed_mph')\n", - " .properties(title = chart_title))\n", - " \n", - " # Rule\n", - " rule1 = alt.Chart(m1).mark_rule(color='red', strokeDash=[10, 7]).encode(y='p20_mph')\n", - " rule2 = alt.Chart(m1).mark_rule(color='blue', strokeDash=[10, 7]).encode(y='p80_mph')\n", - " rule3 = alt.Chart(m1).mark_rule(color='green', strokeDash=[10, 7]).encode(y='p50_mph')\n", - " chart = threshold_utils.chart_size((chart+rule1+rule2+rule3), 600,300)\n", - " chart = chart.interactive()\n", - " display(chart)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "3ac82a7e-5b28-4265-ae53-6e06f20b50ba", + "execution_count": 77, + "id": "13a5f252-5bf6-44b4-b1e0-baec918fa61f", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original\n" + ] + }, { "data": { "text/html": [ + "
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shape_array_keystop_sequencegtfs_dataset_keystop_idloop_or_inliningp50_mphn_tripsp20_mphp80_mphtime_of_day
7518350d9dd7ba11f08a8c86130778d6cabc4146c2d7daaf979779fa2089c6395baf98b916011.34111.3411.34all_day
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${err}
`;\n", - " throw err;\n", + " gtfs_dataset_key stop_id loop_or_inlining p50_mph \\\n", + "75183 6c2d7daaf979779fa2089c6395baf98b 916 0 11.34 \n", + "\n", + " n_trips p20_mph p80_mph time_of_day \n", + "75183 1 11.34 11.34 all_day " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "New\n" + ] + }, + { + "data": { + "text/html": [ + "
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shape_array_keystop_sequencegtfs_dataset_keystop_idloop_or_inliningdistrictdistrict_namep50_mphn_tripsp20_mphp80_mphtime_of_day
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" ], "text/plain": [ - "alt.LayerChart(...)" + "Empty DataFrame\n", + "Columns: [shape_array_key, stop_sequence, gtfs_dataset_key, stop_id, loop_or_inlining, district, district_name, p50_mph, n_trips, p20_mph, p80_mph, time_of_day]\n", + "Index: []" ] }, "metadata": {}, @@ -1294,76 +1730,197 @@ } ], "source": [ - "display_speeds(shape_array2, stop_seq2)" + "\n", + "compare_average(avg_test, og_avg, shape_array4, stop_seq4)" ] }, { "cell_type": "code", - "execution_count": 75, - "id": "a1b6b30c-ec03-42ca-97c9-fdc6da81b898", + "execution_count": 78, + "id": "abf0ee1b-fb56-46e9-848d-adb596b84743", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original\n" + ] + }, { "data": { "text/html": [ - "\n", - "
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shape_array_keystop_sequencegtfs_dataset_keystop_idloop_or_inliningdistrictdistrict_namep50_mphn_tripsp20_mphp80_mphtime_of_day
1512235a788bd9c9aa5c5465875689a626baa976c2d7daaf979779fa2089c6395baf98b89407District 7 - Los Angeles8.9418.948.94all_day
1512245a788bd9c9aa5c5465875689a626baa976c2d7daaf979779fa2089c6395baf98b89407District 7 - Los Angeles8.9418.948.94peak
\n", + "" ], "text/plain": [ - "alt.LayerChart(...)" + " shape_array_key stop_sequence \\\n", + "151223 5a788bd9c9aa5c5465875689a626baa9 7 \n", + "151224 5a788bd9c9aa5c5465875689a626baa9 7 \n", + "\n", + " gtfs_dataset_key stop_id loop_or_inlining district \\\n", + "151223 6c2d7daaf979779fa2089c6395baf98b 894 0 7 \n", + "151224 6c2d7daaf979779fa2089c6395baf98b 894 0 7 \n", + "\n", + " district_name p50_mph n_trips p20_mph p80_mph \\\n", + "151223 District 7 - Los Angeles 8.94 1 8.94 8.94 \n", + "151224 District 7 - Los Angeles 8.94 1 8.94 8.94 \n", + "\n", + " time_of_day \n", + "151223 all_day \n", + "151224 peak " ] }, "metadata": {}, @@ -1371,277 +1928,367 @@ } ], "source": [ - "display_speeds(shape_array3, stop_seq3)" - ] - }, - { - "cell_type": "markdown", - "id": "06e27a6d-3806-47d9-9382-41eea46b9d54", - "metadata": {}, - "source": [ - "### Speeds for 1 Shape" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "fa68336b-a4d5-4b79-94ec-9251888357d6", - "metadata": {}, - "outputs": [], - "source": [ - "def box_whisker(df:pd.DataFrame, route:str):\n", - " \n", - " one_shape = df[df.shape_array_key == route]\n", - " \n", - " chart_title = f\"Speed Distribution for Seq {one_shape.gtfs_dataset_name.iloc[0]}/Shape {one_shape.shape_array_key.iloc[0]}\"\n", - " \n", - " one_shape = threshold_utils.pre_clean(one_shape)\n", - " \n", - " chart = (alt.Chart(one_shape)\n", - " .mark_boxplot(extent='min-max').encode(\n", - " x='Stop Sequence:O',\n", - " y='Speed Mph:Q', color=alt.Color('Shape Array Key',\n", - " scale=alt.Scale(range=cp.CALITP_CATEGORY_BRIGHT_COLORS),\n", - " legend=None))\n", - " .properties(title = chart_title)\n", - " )\n", - " \n", - " chart = threshold_utils.chart_size((chart), 800,300)\n", - " \n", - " return chart" + "compare_average(avg_test, og_avg, shape_array1, stop_seq1)" ] }, { "cell_type": "code", - "execution_count": 77, - "id": "29d713b0-e413-48cc-8dbe-65bccfdfc91d", + "execution_count": 79, + "id": "64a82a45-2df9-49e6-b1e1-9aaaef90e818", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original\n" + ] + }, { "data": { "text/html": [ + "
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shape_array_keystop_sequencegtfs_dataset_keystop_idloop_or_inliningp50_mphn_tripsp20_mphp80_mphtime_of_day
2971821a802700dadbc2e249b595773a5036386c2d7daaf979779fa2089c6395baf98b579022.24620.7625.12all_day
2971921a802700dadbc2e249b595773a5036386c2d7daaf979779fa2089c6395baf98b579022.94418.2227.10peak
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shape_array_keystop_sequencegtfs_dataset_keystop_idloop_or_inliningdistrictdistrict_namep50_mphn_tripsp20_mphp80_mphtime_of_day
15074221a802700dadbc2e249b595773a5036386c2d7daaf979779fa2089c6395baf98b57907District 7 - Los Angeles22.75520.2626.11all_day
15074321a802700dadbc2e249b595773a5036386c2d7daaf979779fa2089c6395baf98b57907District 7 - Los Angeles25.12318.6928.09peak
\n", + "
" ], "text/plain": [ - "alt.Chart(...)" + " shape_array_key stop_sequence \\\n", + "150742 21a802700dadbc2e249b595773a50363 8 \n", + "150743 21a802700dadbc2e249b595773a50363 8 \n", + "\n", + " gtfs_dataset_key stop_id loop_or_inlining district \\\n", + "150742 6c2d7daaf979779fa2089c6395baf98b 579 0 7 \n", + "150743 6c2d7daaf979779fa2089c6395baf98b 579 0 7 \n", + "\n", + " district_name p50_mph n_trips p20_mph p80_mph \\\n", + "150742 District 7 - Los Angeles 22.75 5 20.26 26.11 \n", + "150743 District 7 - Los Angeles 25.12 3 18.69 28.09 \n", + "\n", + " time_of_day \n", + "150742 all_day \n", + "150743 peak " ] }, - "execution_count": 77, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "box_whisker(merge1, shape_array5)" + "compare_average(avg_test, og_avg, shape_array5, stop_seq5)" + ] + }, + { + "cell_type": "markdown", + "id": "7cff986a-6bb0-4f0f-942c-093cf8df1e04", + "metadata": { + "tags": [] + }, + "source": [ + "### Speeds for 1 Sequence" ] }, { "cell_type": "code", - "execution_count": 78, - "id": "31c98032-924a-437f-9114-1006766e6822", + "execution_count": 80, + "id": "2eaca342-b677-4a7f-b6cb-f70aefd517b1", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "box_whisker(percentile_df, shape_array5)" + "def speeds_one_trip_seq(shape_array_key:str, stop_sequence:int):\n", + " STG4_FILE = DICT['stage4']\n", + " df = pd.read_parquet(f\"{SEGMENT_GCS}{STG4_FILE}_{analysis_date}\", \n", + " filters = [[(\"shape_array_key\", \"==\", shape_array_key),\n", + " (\"stop_sequence\", \"==\", stop_sequence)]]) \n", + " return df" ] }, { "cell_type": "code", - "execution_count": 79, - "id": "c27380bd-3239-4da0-90b3-cdf53cba26dd", + "execution_count": 81, + "id": "d398a5dd-3578-4db1-8e6c-616812de7747", "metadata": {}, "outputs": [], "source": [ - "def dotplot_trip_time_rt_coverage(df:pd.DataFrame, route:str):\n", - " \"\"\"\n", - " Create a dotplot showing trips\n", - " by its duration and % of RT coverage.\n", - " \"\"\"\n", + "def avg_speeds_one_trip_seq(shape_array_key:str, stop_sequence:int):\n", + " STG5_FILE = DICT['stage5']\n", + " df = pd.read_parquet(f\"{SEGMENT_GCS}{STG5_FILE}_{analysis_date}.parquet\", \n", + " filters = [[(\"shape_array_key\", \"==\", shape_array_key),\n", + " (\"stop_sequence\", \"==\", stop_sequence),\n", + " (\"time_of_day\", \"==\", \"all_day\")]])\n", + " df = df.drop(columns=[\"district\", \"district_name\", \"geometry\"])\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "f7dce6cc-07f6-492b-a747-b927535f1a91", + "metadata": {}, + "outputs": [], + "source": [ + "def merge_avg_all_speeds(shape_array_key:str, stop_sequence:int):\n", + " speeds = speeds_one_trip_seq(shape_array_key, stop_sequence)\n", + " avg = avg_speeds_one_trip_seq(shape_array_key, stop_sequence)\n", + " m1 = pd.merge(speeds,avg, on =['shape_array_key','stop_sequence','gtfs_dataset_key'], how = \"inner\")\n", + " return m1" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "3651e675-043e-41c2-a1f3-e703d6c86727", + "metadata": {}, + "outputs": [], + "source": [ + "def display_speeds(shape_array_key:str, stop_sequence:int):\n", + " m1 = merge_avg_all_speeds(shape_array_key,stop_sequence)\n", + " \n", + " # Fill any nan values\n", + " m1= m1.fillna(0)\n", + " \n", + " # Create chart title \n", + " chart_title = f\"Speed Distribution for Seq {m1.stop_sequence.iloc[0]}/Shape {m1.shape_array_key.iloc[0]}\"\n", + " \n", + " # Main chart\n", + " chart = (alt.Chart(m1)\n", + " .mark_bar()\n", + " .encode(x='trip_id', y='speed_mph')\n", + " .properties(title = chart_title))\n", + " \n", + " # Rule\n", + " rule1 = alt.Chart(m1).mark_rule(color='red', strokeDash=[10, 7]).encode(y='p20_mph')\n", + " rule2 = alt.Chart(m1).mark_rule(color='blue', strokeDash=[10, 7]).encode(y='p80_mph')\n", + " rule3 = alt.Chart(m1).mark_rule(color='green', strokeDash=[10, 7]).encode(y='p50_mph')\n", + " chart = threshold_utils.chart_size((chart+rule1+rule2+rule3), 600,300)\n", + " chart = chart.interactive()\n", + " display(chart)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "3ac82a7e-5b28-4265-ae53-6e06f20b50ba", + "metadata": {}, + "outputs": [], + "source": [ + "# display_speeds(shape_array2, stop_seq2)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "a1b6b30c-ec03-42ca-97c9-fdc6da81b898", + "metadata": {}, + "outputs": [], + "source": [ + "# display_speeds(shape_array3, stop_seq3)" + ] + }, + { + "cell_type": "markdown", + "id": "06e27a6d-3806-47d9-9382-41eea46b9d54", + "metadata": {}, + "source": [ + "### Speeds for 1 Shape" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "fa68336b-a4d5-4b79-94ec-9251888357d6", + "metadata": {}, + "outputs": [], + "source": [ + "def box_whisker(df:pd.DataFrame, route:str):\n", + " \n", " one_shape = df[df.shape_array_key == route]\n", + " \n", " chart_title = f\"Speed Distribution for Seq {one_shape.gtfs_dataset_name.iloc[0]}/Shape {one_shape.shape_array_key.iloc[0]}\"\n", + " \n", " one_shape = threshold_utils.pre_clean(one_shape)\n", " \n", - " dot_chart = (\n", - " alt.Chart(one_shape, width=0.5)\n", - " .mark_circle(size=50)\n", - " .encode(\n", - " x=alt.X(\n", - " \"jitter:Q\",\n", - " title=None,\n", - " axis=alt.Axis(values=[0], ticks=False, grid=False, labels=False),\n", - " scale=alt.Scale(),\n", - " ),\n", - " y=alt.Y(\"Speed Mph:Q\", axis=alt.Axis(labelAngle=90)),\n", - " color=alt.Color(\n", - " \"Stop Sequence:N\",\n", - " scale=alt.Scale(range=cp.CALITP_CATEGORY_BRIGHT_COLORS),\n", - " legend=None,\n", - " ),\n", - " tooltip=['Stop Sequence','Speed Mph','Percent'],\n", - " column=alt.Column(\n", - " \"Stop Sequence:N\",\n", - " header=alt.Header(\n", - " labelAngle=45,\n", - " titleOrient=\"top\",\n", - " labelOrient=\"bottom\",\n", - " labelAlign=\"right\",\n", - " labelPadding=2,\n", - " ),\n", - " ),\n", - " )\n", - " .transform_calculate(\n", - " # Generate Gaussian jitter with a Box-Muller transform\n", - " jitter=\"sqrt(-2*log(random()))*cos(2*PI*random())\"\n", - " )\n", - " .configure_facet(spacing=0)\n", - " .configure_view(stroke=None)\n", - " .properties(title=chart_title)\n", - " )\n", + " chart = (alt.Chart(one_shape)\n", + " .mark_boxplot(extent='min-max').encode(\n", + " x='Stop Sequence:O',\n", + " y='Speed Mph:Q', color=alt.Color('Shape Array Key',\n", + " scale=alt.Scale(range=cp.CALITP_CATEGORY_BRIGHT_COLORS),\n", + " legend=None))\n", + " .properties(title = chart_title)\n", + " )\n", " \n", - " dot_chart = threshold_utils.chart_size(dot_chart, 25, 150).interactive()\n", + " chart = threshold_utils.chart_size((chart), 800,300)\n", " \n", - " return dot_chart" + " return chart" ] }, { "cell_type": "code", - "execution_count": 82, - "id": "5a41a359-1377-40fa-93b3-59301bdbfac4", + "execution_count": 87, + "id": "29d713b0-e413-48cc-8dbe-65bccfdfc91d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 82, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dotplot_trip_time_rt_coverage(merge1, shape_array1)" + "box_whisker(merge1, shape_array5)" ] }, { "cell_type": "code", - "execution_count": 83, - "id": "16303ead-f09c-4c2f-921c-74d0c2409491", + "execution_count": 88, + "id": "31c98032-924a-437f-9114-1006766e6822", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 83, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dotplot_trip_time_rt_coverage(percentile_df, shape_array1)" + "box_whisker(percentile_df, shape_array5)" ] }, { "cell_type": "code", - "execution_count": 84, - "id": "06311f15-7be8-464e-a9df-df6a145fb4b7", + "execution_count": 103, + "id": "b051e113-d8b5-4c5b-b1f3-16666a5889c4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 84, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dotplot_trip_time_rt_coverage(merge1, shape_array3)" + "box_whisker(merge1, shape_array4)" ] }, { "cell_type": "code", - "execution_count": 85, - "id": "9086ed54-e4dc-42e4-813c-a62c5d583bf5", + "execution_count": 102, + "id": "3f7f10e7-5108-4583-9aff-3a4f8da56cba", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 85, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dotplot_trip_time_rt_coverage(percentile_df, shape_array3)" + "box_whisker(percentile_df, shape_array4)" ] }, { "cell_type": "code", - "execution_count": 86, - "id": "a66d7e9b-67ee-464d-8db7-2a6109d95e4e", + "execution_count": 89, + "id": "c27380bd-3239-4da0-90b3-cdf53cba26dd", "metadata": {}, "outputs": [], "source": [ - "def preview(df:pd.DataFrame, shape_array_key:str, stop_sequence: str):\n", - " df = df[(df.shape_array_key ==shape_array_key) & (df.stop_sequence == stop_sequence)]\n", + "def dotplot_trip_time_rt_coverage(df:pd.DataFrame, route:str):\n", + " \"\"\"\n", + " Create a dotplot showing trips\n", + " by its duration and % of RT coverage.\n", + " \"\"\"\n", + " one_shape = df[df.shape_array_key == route]\n", + " chart_title = f\"Speed Distribution for Seq {one_shape.gtfs_dataset_name.iloc[0]}/Shape {one_shape.shape_array_key.iloc[0]}\"\n", + " one_shape = threshold_utils.pre_clean(one_shape)\n", " \n", - " display(df[['stop_sequence','sec_elapsed','meters_elapsed','percent', 'speed_mph']])" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "54fbb847-0035-47bb-9b4a-605e39d304b6", - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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stop_sequencesec_elapsedmeters_elapsedpercentspeed_mph
202111021213.00524.54100.000.97
20211132148.00431.3182.236.52
20211142900.00472.2990.041.17
20211152869.00485.2792.511.25
20211162798.00479.0091.321.34
202111721227.00521.0999.340.95
20211182183.00393.7175.064.81
202111921458.00280.3453.450.43
202112021355.00372.1970.960.61
202112121176.00506.4596.550.96
20211222751.00273.0052.050.81
20211232825.00473.0090.171.28
20211242178.00384.1073.234.83
20211262840.00289.2555.140.77
20211272587.00454.4086.631.73
20211282725.00470.9789.791.45
202112921139.00430.8782.140.85
202113021184.00438.7183.640.83
202113121444.00458.0687.330.71
202113221053.00482.2691.941.02
20211332827.00358.6768.380.97
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" - ], - "text/plain": [ - " stop_sequence sec_elapsed meters_elapsed percent speed_mph\n", - "2021110 2 1213.00 524.54 100.00 0.97\n", - "2021113 2 148.00 431.31 82.23 6.52\n", - "2021114 2 900.00 472.29 90.04 1.17\n", - "2021115 2 869.00 485.27 92.51 1.25\n", - "2021116 2 798.00 479.00 91.32 1.34\n", - "2021117 2 1227.00 521.09 99.34 0.95\n", - "2021118 2 183.00 393.71 75.06 4.81\n", - "2021119 2 1458.00 280.34 53.45 0.43\n", - "2021120 2 1355.00 372.19 70.96 0.61\n", - "2021121 2 1176.00 506.45 96.55 0.96\n", - "2021122 2 751.00 273.00 52.05 0.81\n", - "2021123 2 825.00 473.00 90.17 1.28\n", - "2021124 2 178.00 384.10 73.23 4.83\n", - "2021126 2 840.00 289.25 55.14 0.77\n", - "2021127 2 587.00 454.40 86.63 1.73\n", - "2021128 2 725.00 470.97 89.79 1.45\n", - "2021129 2 1139.00 430.87 82.14 0.85\n", - "2021130 2 1184.00 438.71 83.64 0.83\n", - "2021131 2 1444.00 458.06 87.33 0.71\n", - "2021132 2 1053.00 482.26 91.94 1.02\n", - "2021133 2 827.00 358.67 68.38 0.97" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "preview(percentile_df, shape_array3, 2)" + " dot_chart = (\n", + " alt.Chart(one_shape, width=0.5)\n", + " .mark_circle(size=50)\n", + " .encode(\n", + " x=alt.X(\n", + " \"jitter:Q\",\n", + " title=None,\n", + " axis=alt.Axis(values=[0], ticks=False, grid=False, labels=False),\n", + " scale=alt.Scale(),\n", + " ),\n", + " y=alt.Y(\"Speed Mph:Q\", axis=alt.Axis(labelAngle=90)),\n", + " color=alt.Color(\n", + " \"Stop Sequence:N\",\n", + " scale=alt.Scale(range=cp.CALITP_CATEGORY_BRIGHT_COLORS),\n", + " legend=None,\n", + " ),\n", + " tooltip=['Stop Sequence','Speed Mph','Percent'],\n", + " column=alt.Column(\n", + " \"Stop Sequence:N\",\n", + " header=alt.Header(\n", + " labelAngle=45,\n", + " titleOrient=\"top\",\n", + " labelOrient=\"bottom\",\n", + " labelAlign=\"right\",\n", + " labelPadding=2,\n", + " ),\n", + " ),\n", + " )\n", + " .transform_calculate(\n", + " # Generate Gaussian jitter with a Box-Muller transform\n", + " jitter=\"sqrt(-2*log(random()))*cos(2*PI*random())\"\n", + " )\n", + " .configure_facet(spacing=0)\n", + " .configure_view(stroke=None)\n", + " .properties(title=chart_title)\n", + " )\n", + " \n", + " dot_chart = threshold_utils.chart_size(dot_chart, 25, 150).interactive()\n", + " \n", + " return dot_chart" ] }, { "cell_type": "code", - "execution_count": 88, - "id": "fcd76d56-cc61-4f7d-9707-440607bdaebb", - "metadata": { - "tags": [] - }, + "execution_count": 90, + "id": "5a41a359-1377-40fa-93b3-59301bdbfac4", + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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stop_sequencesec_elapsedmeters_elapsedpercentspeed_mph
20037074994.00383.7865.529.13
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" + "\n", + " if(typeof define === \"function\" && define.amd) {\n", + " requirejs.config({paths});\n", + " require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n", + " } else {\n", + " maybeLoadScript(\"vega\", \"5\")\n", + " .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n", + " .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n", + " .catch(showError)\n", + " .then(() => displayChart(vegaEmbed));\n", + " }\n", + " })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300, \"stroke\": null}, \"facet\": {\"spacing\": 0}}, \"data\": {\"name\": \"data-94999f131954d398bbb7228a02708cd8\"}, \"mark\": {\"type\": \"circle\", \"size\": 50}, \"encoding\": {\"color\": {\"field\": \"Stop Sequence\", \"legend\": null, \"scale\": {\"range\": [\"#2EA8CE\", \"#EB9F3C\", \"#F4D837\", \"#51BF9D\", \"#8CBCCB\", \"#9487C0\"]}, \"type\": \"nominal\"}, \"column\": {\"field\": \"Stop Sequence\", \"header\": {\"labelAlign\": 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Time\": \"2023-07-12T12:00:37\", \"Min Dist\": 298.3, \"Max Time\": \"2023-07-12T12:16:10\", \"Max Dist\": 578.8, \"Meters Elapsed\": 280.5, \"Sec Elapsed\": 933.0, \"Speed Mph\": 0.7, \"Trip Instance Key\": \"45997c52152e45b2abf28c156229ae06\", \"Schedule Gtfs Dataset Key\": \"dbbe8ee4864a2715a40749605395d584\", \"Percent\": 48.1}, {\"Shape Array Key\": \"94e02a46331c8b449aedb4469f49764a\", \"Stop Sequence\": 32, \"Gtfs Dataset Key\": \"6c2d7daaf979779fa2089c6395baf98b\", \"Gtfs Dataset Name\": \"Big Blue Bus VehiclePositions\", \"Trip Id\": \"905080\", \"Min Time\": \"2023-07-12T12:47:23\", \"Min Dist\": 0.0, \"Max Time\": \"2023-07-12T12:48:13\", \"Max Dist\": 312.3, \"Meters Elapsed\": 312.3, \"Sec Elapsed\": 50.0, \"Speed Mph\": 14.0, \"Trip Instance Key\": \"a94c4c41871ed299ee93b09b46f719eb\", \"Schedule Gtfs Dataset Key\": \"dbbe8ee4864a2715a40749605395d584\", \"Percent\": 53.5}, {\"Shape Array Key\": \"94e02a46331c8b449aedb4469f49764a\", \"Stop Sequence\": 32, \"Gtfs Dataset Key\": \"6c2d7daaf979779fa2089c6395baf98b\", \"Gtfs Dataset Name\": \"Big Blue Bus VehiclePositions\", \"Trip Id\": \"905071\", \"Min Time\": \"2023-07-12T16:56:57\", \"Min Dist\": 0.0, \"Max Time\": \"2023-07-12T17:13:59\", \"Max Dist\": 578.0, \"Meters Elapsed\": 578.0, \"Sec Elapsed\": 1022.0, \"Speed Mph\": 1.3, \"Trip Instance Key\": \"2b4063db24e36f823387d6f7438dc8d0\", \"Schedule Gtfs Dataset Key\": \"dbbe8ee4864a2715a40749605395d584\", \"Percent\": 99.0}]}}, {\"mode\": \"vega-lite\"});\n", + "" ], "text/plain": [ - " stop_sequence sec_elapsed meters_elapsed percent speed_mph\n", - "2003344 24 11.00 176.26 92.05 35.85\n", - "2003352 24 138.00 130.55 68.18 2.12" + "alt.Chart(...)" ] }, + "execution_count": 93, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "preview(percentile_df, shape_array5, 24)" + "dotplot_trip_time_rt_coverage(percentile_df, shape_array3)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "a66d7e9b-67ee-464d-8db7-2a6109d95e4e", + "metadata": {}, + "outputs": [], + "source": [ + "def preview(df:pd.DataFrame, shape_array_key:str, stop_sequence: str):\n", + " df = df[(df.shape_array_key ==shape_array_key) & (df.stop_sequence == stop_sequence)]\n", + " \n", + " display(df[['stop_sequence','sec_elapsed','meters_elapsed','percent', 'speed_mph']])" ] }, { @@ -3107,7 +3024,7 @@ " \n", " \n", " \n", - " 2003228\n", + " 1796745\n", " 14\n", " 44.00\n", " 372.68\n", @@ -3120,7 +3037,7 @@ ], "text/plain": [ " stop_sequence sec_elapsed meters_elapsed percent speed_mph\n", - "2003228 14 44.00 372.68 67.31 18.95" + "1796745 14 44.00 372.68 67.31 18.95" ] }, "metadata": {}, @@ -3136,7 +3053,6 @@ "execution_count": 96, "id": "4c1c3646-004a-4b8b-9d5a-ca88e643c69d", "metadata": { - "scrolled": true, "tags": [] }, "outputs": [ @@ -3170,23 +3086,7 @@ " \n", " \n", " \n", - " 2003223\n", - " 14\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003224\n", - " 14\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003225\n", + " 1796744\n", " 14\n", " 26.00\n", " 0.00\n", @@ -3194,23 +3094,7 @@ " 0.00\n", " \n", " \n", - " 2003226\n", - " 14\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003227\n", - " 14\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003228\n", + " 1796745\n", " 14\n", " 44.00\n", " 372.68\n", @@ -3218,55 +3102,7 @@ " 18.95\n", " \n", " \n", - " 2003229\n", - " 14\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003230\n", - " 14\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003231\n", - " 14\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003232\n", - " 14\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003233\n", - " 14\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003234\n", - " 14\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003235\n", + " 1796746\n", " 14\n", " 89.00\n", " 200.33\n", @@ -3274,60 +3110,23 @@ " 5.04\n", " \n", " \n", - " 2003236\n", + " 1796747\n", " 14\n", " 24.00\n", " 207.60\n", " 37.49\n", " 19.35\n", " \n", - " \n", - " 2003237\n", - " 14\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003238\n", - " 14\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003239\n", - " 14\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", " \n", "\n", "" ], "text/plain": [ " stop_sequence sec_elapsed meters_elapsed percent speed_mph\n", - "2003223 14 0.00 0.00 0.00 NaN\n", - "2003224 14 0.00 0.00 0.00 NaN\n", - "2003225 14 26.00 0.00 0.00 0.00\n", - "2003226 14 0.00 0.00 0.00 NaN\n", - "2003227 14 0.00 0.00 0.00 NaN\n", - "2003228 14 44.00 372.68 67.31 18.95\n", - "2003229 14 0.00 0.00 0.00 NaN\n", - "2003230 14 0.00 0.00 0.00 NaN\n", - "2003231 14 0.00 0.00 0.00 NaN\n", - "2003232 14 0.00 0.00 0.00 NaN\n", - "2003233 14 0.00 0.00 0.00 NaN\n", - "2003234 14 0.00 0.00 0.00 NaN\n", - "2003235 14 89.00 200.33 36.18 5.04\n", - "2003236 14 24.00 207.60 37.49 19.35\n", - "2003237 14 0.00 0.00 0.00 NaN\n", - "2003238 14 0.00 0.00 0.00 NaN\n", - "2003239 14 0.00 0.00 0.00 NaN" + "1796744 14 26.00 0.00 0.00 0.00\n", + "1796745 14 44.00 372.68 67.31 18.95\n", + "1796746 14 89.00 200.33 36.18 5.04\n", + "1796747 14 24.00 207.60 37.49 19.35" ] }, "metadata": {}, @@ -3343,7 +3142,6 @@ "execution_count": 97, "id": "e1e27b55-f678-43f4-8ceb-2cdcdb4448e6", "metadata": { - "scrolled": true, "tags": [] }, "outputs": [ @@ -3377,7 +3175,7 @@ " \n", " \n", " \n", - " 2003343\n", + " 1796777\n", " 24\n", " 124.00\n", " 42.99\n", @@ -3385,7 +3183,7 @@ " 0.78\n", " \n", " \n", - " 2003344\n", + " 1796778\n", " 24\n", " 11.00\n", " 176.26\n", @@ -3393,31 +3191,7 @@ " 35.85\n", " \n", " \n", - " 2003345\n", - " 24\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003346\n", - " 24\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003347\n", - " 24\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003348\n", + " 1796779\n", " 24\n", " 123.00\n", " 3.86\n", @@ -3425,31 +3199,7 @@ " 0.07\n", " \n", " \n", - " 2003349\n", - " 24\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003350\n", - " 24\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003351\n", - " 24\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003352\n", + " 1796780\n", " 24\n", " 138.00\n", " 130.55\n", @@ -3457,60 +3207,84 @@ " 2.12\n", " \n", " \n", - " 2003353\n", - " 24\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003354\n", + " 1796781\n", " 24\n", " 26.00\n", " 0.50\n", " 0.26\n", " 0.04\n", " \n", - " \n", - " 2003355\n", - " 24\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003356\n", - " 24\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", - " \n", - " \n", - " 2003357\n", - " 24\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " NaN\n", + " \n", + "\n", + "" + ], + "text/plain": [ + " stop_sequence sec_elapsed meters_elapsed percent speed_mph\n", + "1796777 24 124.00 42.99 22.45 0.78\n", + "1796778 24 11.00 176.26 92.05 35.85\n", + "1796779 24 123.00 3.86 2.02 0.07\n", + "1796780 24 138.00 130.55 68.18 2.12\n", + "1796781 24 26.00 0.50 0.26 0.04" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "preview(merge1, shape_array5, 24)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "a6016a87-a6cb-4394-a978-95874cf88d60", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stop_sequencesec_elapsedmeters_elapsedpercentspeed_mph
20033581796778240.000.000.00NaN11.00176.2692.0535.85
20033591796780240.000.000.00NaN138.00130.5568.182.12
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