{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Timeseries at points" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example. we will generate a GeoDataFrame with airport names and their coordinates. We will use those points to build a timeseries of precipitation at those locations. \n", "\n", "First, we import `emaremes` and `geopandas` to build our GeoDataFrame." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd\n", "from pandas import Timestamp, Timedelta\n", "from shapely.geometry import Point\n", "\n", "import emaremes as mrms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, fetch hourly precipitation data for on the days during Hurricaine Helene:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prefered path to store *new* Gribfiles is data\n" ] } ], "source": [ "mrms.fetch.path_config.set_prefered(\"./data\")\n", "\n", "gribfiles = mrms.fetch.timerange(\n", " Timestamp(\"2024-09-26T12:00:00\"),\n", " Timestamp(\"2024-09-28T00:00:00\"),\n", " frequency=Timedelta(minutes=60),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's create a geodataframe with three airports:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Airport NamegeometryCode
0Asheville Regional AirportPOINT (-82.541 35.436)AVL
1Jacksonville International AirportPOINT (-81.689 30.494)JAX
2Hartsfield-Jackson Atlanta International AirportPOINT (-84.428 33.641)ATL
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" ], "text/plain": [ " Airport Name geometry \\\n", "0 Asheville Regional Airport POINT (-82.541 35.436) \n", "1 Jacksonville International Airport POINT (-81.689 30.494) \n", "2 Hartsfield-Jackson Atlanta International Airport POINT (-84.428 33.641) \n", "\n", " Code \n", "0 AVL \n", "1 JAX \n", "2 ATL " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "airports = {\n", " \"Asheville Regional Airport\": Point(-82.541, 35.436),\n", " \"Jacksonville International Airport\": Point(-81.689, 30.494),\n", " \"Hartsfield-Jackson Atlanta International Airport\": Point(-84.428, 33.641),\n", "}\n", "\n", "# Create a GeoDataFrame\n", "gdf = gpd.GeoDataFrame(airports.keys(), geometry=list(airports.values()), columns=[\"Airport Name\"], crs=\"EPSG:4326\")\n", "\n", "gdf[\"Code\"] = [\"AVL\", \"JAX\", \"ATL\"]\n", "\n", "gdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the `explore` method in geopandas to display a map of the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf.explore(column=\"Airport Name\", categorical=True, zoom_start=7, cmap=\"Dark2\", marker_kwds={\"radius\": 10})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To generate a timeseries in points, we use the `ts.point` module. For a single time step, a single MRMS GRIB file is queried, which returns a tuple with the datetime and the value at the points." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(np.datetime64('2024-09-26T16:00:00.000000000'),\n", " {'AVL': 1.5, 'JAX': 0.0, 'ATL': 4.300000190734863})" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mrms.ts.point.query_single_file(gribfiles[0], gdf.set_index(\"Code\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That is basically the building block to generate a timeseries. Passing the list of files to `ts.point.query_files`, emaremes will parallelize the process of opening each dataset and querying the points. This function will return a pandas dataframe with the data for each point. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AVLJAXATL
timestamp
2024-09-26 12:00:00+00:0029.00.03.600000
2024-09-26 13:00:00+00:004.30.00.500000
2024-09-26 14:00:00+00:001.60.00.400000
2024-09-26 15:00:00+00:000.90.00.600000
2024-09-26 16:00:00+00:001.50.04.300000
2024-09-26 17:00:00+00:001.50.01.900000
2024-09-26 18:00:00+00:001.80.02.600000
2024-09-26 19:00:00+00:004.50.016.600000
2024-09-26 20:00:00+00:000.50.02.900000
2024-09-26 21:00:00+00:002.30.06.500000
2024-09-26 22:00:00+00:001.10.07.900000
2024-09-26 23:00:00+00:001.53.46.300000
2024-09-27 00:00:00+00:001.30.08.500000
2024-09-27 01:00:00+00:003.60.437.500000
2024-09-27 02:00:00+00:000.01.31.700000
2024-09-27 03:00:00+00:008.00.07.500000
2024-09-27 04:00:00+00:001.70.223.000000
2024-09-27 05:00:00+00:004.70.34.300000
2024-09-27 06:00:00+00:002.60.03.400000
2024-09-27 07:00:00+00:003.30.60.000000
2024-09-27 08:00:00+00:004.90.00.700000
2024-09-27 09:00:00+00:009.60.056.099998
2024-09-27 10:00:00+00:0014.00.033.000000
2024-09-27 11:00:00+00:009.00.014.800000
2024-09-27 12:00:00+00:0010.70.04.400000
2024-09-27 13:00:00+00:0012.00.00.000000
2024-09-27 14:00:00+00:000.60.00.000000
2024-09-27 15:00:00+00:002.00.00.000000
2024-09-27 16:00:00+00:000.00.00.000000
2024-09-27 17:00:00+00:000.00.00.000000
2024-09-27 18:00:00+00:000.00.00.000000
2024-09-27 19:00:00+00:000.00.00.000000
2024-09-27 20:00:00+00:000.00.00.000000
2024-09-27 21:00:00+00:000.00.00.000000
2024-09-27 22:00:00+00:000.00.00.000000
2024-09-27 23:00:00+00:000.00.00.000000
2024-09-28 00:00:00+00:000.00.00.000000
\n", "
" ], "text/plain": [ " AVL JAX ATL\n", "timestamp \n", "2024-09-26 12:00:00+00:00 29.0 0.0 3.600000\n", "2024-09-26 13:00:00+00:00 4.3 0.0 0.500000\n", "2024-09-26 14:00:00+00:00 1.6 0.0 0.400000\n", "2024-09-26 15:00:00+00:00 0.9 0.0 0.600000\n", "2024-09-26 16:00:00+00:00 1.5 0.0 4.300000\n", "2024-09-26 17:00:00+00:00 1.5 0.0 1.900000\n", "2024-09-26 18:00:00+00:00 1.8 0.0 2.600000\n", "2024-09-26 19:00:00+00:00 4.5 0.0 16.600000\n", "2024-09-26 20:00:00+00:00 0.5 0.0 2.900000\n", "2024-09-26 21:00:00+00:00 2.3 0.0 6.500000\n", "2024-09-26 22:00:00+00:00 1.1 0.0 7.900000\n", "2024-09-26 23:00:00+00:00 1.5 3.4 6.300000\n", "2024-09-27 00:00:00+00:00 1.3 0.0 8.500000\n", "2024-09-27 01:00:00+00:00 3.6 0.4 37.500000\n", "2024-09-27 02:00:00+00:00 0.0 1.3 1.700000\n", "2024-09-27 03:00:00+00:00 8.0 0.0 7.500000\n", "2024-09-27 04:00:00+00:00 1.7 0.2 23.000000\n", "2024-09-27 05:00:00+00:00 4.7 0.3 4.300000\n", "2024-09-27 06:00:00+00:00 2.6 0.0 3.400000\n", "2024-09-27 07:00:00+00:00 3.3 0.6 0.000000\n", "2024-09-27 08:00:00+00:00 4.9 0.0 0.700000\n", "2024-09-27 09:00:00+00:00 9.6 0.0 56.099998\n", "2024-09-27 10:00:00+00:00 14.0 0.0 33.000000\n", "2024-09-27 11:00:00+00:00 9.0 0.0 14.800000\n", "2024-09-27 12:00:00+00:00 10.7 0.0 4.400000\n", "2024-09-27 13:00:00+00:00 12.0 0.0 0.000000\n", "2024-09-27 14:00:00+00:00 0.6 0.0 0.000000\n", "2024-09-27 15:00:00+00:00 2.0 0.0 0.000000\n", "2024-09-27 16:00:00+00:00 0.0 0.0 0.000000\n", "2024-09-27 17:00:00+00:00 0.0 0.0 0.000000\n", "2024-09-27 18:00:00+00:00 0.0 0.0 0.000000\n", "2024-09-27 19:00:00+00:00 0.0 0.0 0.000000\n", "2024-09-27 20:00:00+00:00 0.0 0.0 0.000000\n", "2024-09-27 21:00:00+00:00 0.0 0.0 0.000000\n", "2024-09-27 22:00:00+00:00 0.0 0.0 0.000000\n", "2024-09-27 23:00:00+00:00 0.0 0.0 0.000000\n", "2024-09-28 00:00:00+00:00 0.0 0.0 0.000000" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = mrms.ts.point.query_files(gribfiles, gdf.set_index(\"Code\"))\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the column names of the retuned dataframe are the index in the geodataframe." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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