{
"cells": [
{
"cell_type": "markdown",
"id": "c70ce35d-6112-4c12-9387-9c788c84a8e9",
"metadata": {},
"source": [
"## OctTree!\n",
"\n",
"Testing the time to look-up nearby records with the PyCOADS OctTree implementation."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c0956916-f50a-444d-a5b6-f06d3fb9b44d",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import gzip\n",
"os.environ[\"POLARS_MAX_THREADS\"] = \"4\"\n",
"\n",
"from datetime import datetime, timedelta\n",
"from random import choice\n",
"from string import ascii_letters, digits\n",
"import random\n",
"import inspect\n",
"\n",
"import polars as pl\n",
"import numpy as np\n",
"\n",
"from GeoSpatialTools.octtree import OctTree, SpaceTimeRecord as Record, SpaceTimeRectangle as Rectangle"
]
},
{
"cell_type": "raw",
"id": "99295bad-0db3-444b-8d38-acc7875cc0f0",
"metadata": {},
"source": [
"## Generate Data\n",
"\n",
"16,000 rows of data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d8f1e5e1-513c-4bdf-a9f9-cef9562a7cb7",
"metadata": {},
"outputs": [],
"source": [
"def generate_uid(n: int) -> str:\n",
" chars = ascii_letters + digits\n",
" return \"\".join(random.choice(chars) for _ in range(n))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "986d9cc5-e610-449a-9ee7-e281b7558ca9",
"metadata": {},
"outputs": [],
"source": [
"N = 16_000\n",
"lons = pl.int_range(-180, 180, eager=True)\n",
"lats = pl.int_range(-90, 90, eager=True)\n",
"dates = pl.datetime_range(datetime(1900, 1, 1, 0), datetime(1900, 1, 31, 23), interval=\"1h\", eager=True)\n",
"\n",
"lons_use = lons.sample(N, with_replacement=True).alias(\"lon\")\n",
"lats_use = lats.sample(N, with_replacement=True).alias(\"lat\")\n",
"dates_use = dates.sample(N, with_replacement=True).alias(\"datetime\")\n",
"uids = pl.Series(\"uid\", [generate_uid(8) for _ in range(N)])\n",
"\n",
"df = pl.DataFrame([lons_use, lats_use, dates_use, uids]).unique()"
]
},
{
"cell_type": "markdown",
"id": "237096f1-093e-49f0-9a9a-2bec5231726f",
"metadata": {},
"source": [
"## Add extra rows\n",
"\n",
"For testing larger datasets. Uncomment to use."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0b8fd425-8a90-4f76-91b7-60df48aa98e4",
"metadata": {},
"outputs": [],
"source": [
"# _df = df.clone()\n",
"# for i in range(100):\n",
"# df2 = pl.DataFrame([\n",
"# _df[\"lon\"].shuffle(),\n",
"# _df[\"lat\"].shuffle(),\n",
"# _df[\"datetime\"].shuffle(),\n",
"# _df[\"uid\"].shuffle(),\n",
"# ]).with_columns(pl.concat_str([pl.col(\"uid\"), pl.lit(f\"{i:03d}\")]).alias(\"uid\"))\n",
"# df = df.vstack(df2)\n",
"# df.shape\n",
"# df"
]
},
{
"cell_type": "markdown",
"id": "c7bd16e0-96a6-426b-b00a-7c3b8a2aaddd",
"metadata": {},
"source": [
"## Intialise the OctTree Object"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "af06a976-ff52-49e0-a886-91bcbe540ffe",
"metadata": {},
"outputs": [],
"source": [
"otree = OctTree(Rectangle(-180, 180, -90, 90, datetime(1900, 1, 1, 0), datetime(1900, 1, 31, 23)), capacity = 10, max_depth = 25)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2ba99b37-787c-4862-8075-a7596208c60e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 106 ms, sys: 3.98 ms, total: 110 ms\n",
"Wall time: 109 ms\n"
]
}
],
"source": [
"%%time\n",
"for r in df.rows():\n",
" otree.insert(Record(*r))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "59d38446-f7d2-4eec-bba3-c39bd7279623",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OctTree:\n",
"- boundary: SpaceTimeRectangle(west=-180, east=180, south=-90, north=90, start=datetime.datetime(1900, 1, 1, 0, 0), end=datetime.datetime(1900, 1, 31, 23, 0))\n",
"- capacity: 10\n",
"- depth: 0\n",
"- max_depth: 25\n",
"- contents:\n",
"- number of elements: 10\n",
" * SpaceTimeRecord(x = 92, y = 15, datetime = 1900-01-17 08:00:00, uid = HRF401hH)\n",
" * SpaceTimeRecord(x = -35, y = 37, datetime = 1900-01-04 08:00:00, uid = CXZaSOdh)\n",
" * SpaceTimeRecord(x = 84, y = -7, datetime = 1900-01-07 16:00:00, uid = 2aEjxGwG)\n",
" * SpaceTimeRecord(x = 68, y = 73, datetime = 1900-01-18 17:00:00, uid = Ah7lanWB)\n",
" * SpaceTimeRecord(x = -179, y = 40, datetime = 1900-01-01 11:00:00, uid = HGxSJzf4)\n",
" * SpaceTimeRecord(x = -73, y = 23, datetime = 1900-01-09 12:00:00, uid = qHQ8opO9)\n",
" * SpaceTimeRecord(x = 117, y = -23, datetime = 1900-01-31 06:00:00, uid = ctvs56Fq)\n",
" * SpaceTimeRecord(x = 109, y = 55, datetime = 1900-01-13 14:00:00, uid = C2xXIglD)\n",
" * SpaceTimeRecord(x = 104, y = -10, datetime = 1900-01-06 16:00:00, uid = WEpQKIOV)\n",
" * SpaceTimeRecord(x = 45, y = -71, datetime = 1900-01-29 00:00:00, uid = 7r1UeXRi)\n",
"- with children:\n",
" OctTree:\n",
" - boundary: SpaceTimeRectangle(west=-180, east=0.0, south=0.0, north=90, start=datetime.datetime(1900, 1, 1, 0, 0), end=datetime.datetime(1900, 1, 16, 11, 30))\n",
" - capacity: 10\n",
" - depth: 1\n",
" - max_depth: 25\n",
" - contents:\n",
" - number of elements: 10\n",
" * SpaceTimeRecord(x = -84, y = 38, datetime = 1900-01-15 10:00:00, uid = 63mpq3Kx)\n",
" * SpaceTimeRecord(x = -78, y = 60, datetime = 1900-01-10 01:00:00, uid = vZ8HLu5t)\n",
" * SpaceTimeRecord(x = -89, y = 24, datetime = 1900-01-12 17:00:00, uid = gn2o9tYQ)\n",
" * SpaceTimeRecord(x = -149, y = 7, datetime = 1900-01-08 11:00:00, uid = 2ODnGJO6)\n",
" * SpaceTimeRecord(x = -37, y = 54, datetime = 1900-01-12 13:00:00, uid = 11cApOwm)\n",
" * SpaceTimeRecord(x = -34, y = 88, datetime = 1900-01-03 05:00:00, uid = 8SN6zPWh)\n",
" * SpaceTimeRecord(x = -36, y = 13, datetime = 1900-01-14 13:00:00, uid = ijfjmp8E)\n",
" * SpaceTimeRecord(x = -168, y = 62, datetime = 1900-01-03 09:00:00, uid = Cc4m1azR)\n",
" * SpaceTimeRecord(x = -76, y = 67, datetime = 1900-01-06 04:00:00, uid = 4WeWpZUz)\n",
" * SpaceTimeRecord(x = -156, y = 39, datetime = 1900-01-13 10:00:00, uid = dZXAMaXq)\n",
" - with children:\n",
" OctTree:\n",
" - boundary: SpaceTimeRectangle(west=-180, east=-90.0, south=45.0, north=90, start=datetime.datetime(1900, 1, 1, 0, 0), end=datetime.datetime(1900, 1, 8, 17, 45))\n",
" - capacity: 10\n",
" - depth: 2\n",
" - max_depth: 25\n",
" - contents:\n",
" - number of elements: 10\n",
" * SpaceTimeRecord(x = -141, y = 79, datetime = 1900-01-03 05:00:00, uid = mN1Mg7Vn)\n",
" * SpaceTimeRecord(x = -172, y = 80, datetime = 1900-01-01 14:00:00, uid = NBBZ3bCW)\n",
" * SpaceTimeRecord(x = -93, y = 53, datetime = 1900-01-06 07:00:00, uid = jX8HZPJT)\n",
" * SpaceTimeRecord(x = -168, y = 82, datetime = 1900-01-03 08:00:00, uid = dlxpN1Ew)\n",
" * SpaceTimeRecord(x = -111, y = 83, datetime = 1900-01-02 12:00:00, uid = GXLopHH0)\n",
" * SpaceTimeRecord(x = -178, y = 61, datetime = 1900-01-02 00:00:00, uid = 0ut6CLe5)\n",
" * SpaceTimeRecord(x = -148, y = 74, datetime = 1900-01-07 23:00:00, uid = xUySW1tx)\n",
" * SpaceTimeRecord(x = -174, y = 63, datetime = 1900-01-06 22:00:00, uid = 8sI94Lt6)\n",
" * SpaceTimeRecord(x = -114, y = 84, datetime = 1900-01-05 15:00:00, uid = OoY9mEkQ)\n",
" * SpaceTimeRecord(x = -102, y = 82, datetime = 1900-01-02 15:00:00, uid = bd4sLang)\n",
" - with children:\n",
" OctTree:\n",
" - boundary: SpaceTimeRectangle(west=-180, east=-135.0, south=67.5, north=90, start=datetime.datetime(1900, 1, 1, 0, 0), end=datetime.datetime(1900, 1, 4, 20, 52, 30))\n",
" - capacity: 10\n",
" - depth: 3\n",
" - max_depth: 25\n",
" - contents:\n",
" - number of elements: 10\n",
" * SpaceTimeRecord(x = -148, y = 79, datetime = 1900-01-03 21:00:00, uid = kNWm70rm)\n",
" * SpaceTimeRecord(x = -157, y = 80, datetime = 1900-01-03 05:00:00, uid = 471X27tA)\n",
" * SpaceTimeRecord(x = -152, y = 85, datetime = 1900-01-03 01:00:00, uid = cjTyQn7E)\n",
" * SpaceTimeRecord(x = -154, y = 88, datetime = 1900-01-03 15:00:00, uid = JTnjCJZN)\n",
" * SpaceTimeRecord(x = -139, y = 83, datetime = 1900-01-01 21:00:00, uid = kZ28j8I5)\n",
" * SpaceTimeRecord(x = -161, y = 73, datetime = 1900-01-03 02:00:00, uid = wsHJBLLC)\n",
" * SpaceTimeRecord(x = -140, y = 71, datetime = 1900-01-02 07:00:00, uid = 4bTg1N2k)\n",
" * SpaceTimeRecord(x = -141, y = 74, datetime = 1900-01-04 09:00:00, uid = I6M8kuue)\n",
" * SpaceTimeRecord(x = -144, y = 72, datetime = 1900-01-04 17:00:00, uid = 0fPvYOC9)\n",
" * SpaceTimeRecord(x = -157, y = 78, datetime = 1900-01-03 16:00:00, uid = yAL3OeaK)\n",
" - with children:\n",
" OctTree:\n",
" - boundary: SpaceTimeRectangle(west=-180, east=-157.5, south=78.75, north=90, start=datetime.datetime(1900, 1, 1, 0, 0), end=datetime.datetime(1900, 1, 2, 22, 26, 15))\n",
" - capacity: 10\n",
" - depth: 4\n",
" - max_depth: 25\n",
" - contents:\n",
" - number of elements: 4\n",
" * SpaceTimeRecord(x = -180, y = 88, datetime = 1900-01-02 12:00:00, uid = CXeAd3y4)\n",
" * SpaceTimeRecord(x = -180, y = 87, datetime = 1900-01-01 16:00:00, uid = TB2xKFgK)\n",
" * SpaceTimeRecord(x = -171, y = 79, datetime = 1900-01-02 04:00:00, uid = pIU8qvxT)\n",
" * SpaceTimeRecord(x = -168, y = 85, datetime = 1900-01-01 22:00:00, uid = 7zL4gz8K)\n",
" OctTree:\n",
" - boundary: SpaceTimeRectangle(west=-157.5, east=-135.0, south=78.75, north=90, start=datetime.datetime(1900, 1, 1, 0, 0), end=datetime.datetime(1900, 1, 2, 22, 26, 15))\n",
" - capacity: 10\n",
" - depth: 4\n",
" - max_depth: 25\n",
" - contents:\n",
" - number of elements: 2\n",
" * SpaceTimeRecord(x = -149, y = 82, datetime = 1900-01-01 20:00:00, uid = xTYMs6Xp)\n",
" * SpaceTimeRecord(x = -154, y = 84, datetime = 1900-01-02 21:00:00, uid = JSEaGBsn)\n",
" OctTree:\n",
" - boundary: SpaceTimeRectangle(west=-180, east=-157.5, south=67.5, north=78.75, start=datetime.datetime(1900, 1, 1, 0, 0), end=datetime.datetime(1900, 1, 2, 22, 26, 15))\n",
" - capacity: 10\n",
" - depth: 4\n",
" - max_depth: 25\n",
" - contents:\n",
" - number of elements: 3\n",
" * SpaceTimeRecord(x = -173, y = 75, datetime = 1900-01-01 06:00:00, uid = M4N3amQ3)\n"
]
}
],
"source": [
"s = str(otree)\n",
"print(\"\\n\".join(s.split(\"\\n\")[:100]))"
]
},
{
"cell_type": "markdown",
"id": "6b02c2ea-6566-47c2-97e0-43d8b18e0713",
"metadata": {},
"source": [
"## Time Execution\n",
"\n",
"Testing the identification of nearby points against the original full search"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "094b588c-e938-4838-9719-1defdfff74fa",
"metadata": {},
"outputs": [],
"source": [
"dts = pl.datetime_range(datetime(1900, 1, 1), datetime(1900, 2, 1), interval=\"1h\", eager=True, closed=\"left\")\n",
"N = dts.len()\n",
"lons = 180 - 360 * np.random.rand(N)\n",
"lats = 90 - 180 * np.random.rand(N)\n",
"test_df = pl.DataFrame({\"lon\": lons, \"lat\": lats, \"datetime\": dts})\n",
"test_recs = [Record(*r) for r in test_df.rows()]\n",
"dt = timedelta(days = 1)\n",
"dist = 350"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "66a48b86-d449-45d2-9837-2b3e07f5563d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"207 μs ± 6.25 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"otree.nearby_points(random.choice(test_recs), dist=dist, t_dist=dt)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "972d4a16-39fd-4f80-8592-1c5d5cabf5be",
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"def check_cols(\n",
" df: pl.DataFrame | pl.LazyFrame,\n",
" cols: list[str],\n",
" var_name: str = \"dataframe\",\n",
") -> None:\n",
" \"\"\"\n",
" Check that a dataframe contains a list of columns. Raises an error if not.\n",
"\n",
" Parameters\n",
" ----------\n",
" df : polars Frame\n",
" Dataframe to check\n",
" cols : list[str]\n",
" Requried columns\n",
" var_name : str\n",
" Name of the Frame - used for displaying in any error.\n",
" \"\"\"\n",
" calling_func = inspect.stack()[1][3]\n",
" if isinstance(df, pl.DataFrame):\n",
" have_cols = df.columns\n",
" elif isinstance(df, pl.LazyFrame):\n",
" have_cols = df.collect_schema().names()\n",
" else:\n",
" raise TypeError(\"Input Frame is not a polars Frame\")\n",
"\n",
" cols_in_frame = intersect(cols, have_cols)\n",
" missing = [c for c in cols if c not in cols_in_frame]\n",
"\n",
" if len(missing) > 0:\n",
" err_str = f\"({calling_func}) - {var_name} missing required columns. \"\n",
" err_str += f'Require: {\", \".join(cols)}. '\n",
" err_str += f'Missing: {\", \".join(missing)}.'\n",
" logging.error(err_str)\n",
" raise ValueError(err_str)\n",
"\n",
" return\n",
"\n",
"\n",
"def haversine_df(\n",
" df: pl.DataFrame | pl.LazyFrame,\n",
" date_var: str = \"datetime\",\n",
" R: float = 6371,\n",
" reverse: bool = False,\n",
" out_colname: str = \"dist\",\n",
" lon_col: str = \"lon\",\n",
" lat_col: str = \"lat\",\n",
" lon2_col: str | None = None,\n",
" lat2_col: str | None = None,\n",
" sorted: bool = False,\n",
" rev_prefix: str = \"rev_\",\n",
") -> pl.DataFrame | pl.LazyFrame:\n",
" \"\"\"\n",
" Compute haversine distance on earth surface between lon-lat positions.\n",
"\n",
" If only 'lon_col' and 'lat_col' are specified then this computes the\n",
" distance between consecutive points. If a second set of positions is\n",
" included via the optional 'lon2_col' and 'lat2_col' arguments then the\n",
" distances between the columns are computed.\n",
"\n",
" Parameters\n",
" ----------\n",
" df : polars.DataFrame\n",
" The data, containing required columns:\n",
" * lon_col\n",
" * lat_col\n",
" * date_var\n",
" date_var : str\n",
" Name of the datetime column on which to sort the positions\n",
" R : float\n",
" Radius of earth in km\n",
" reverse : bool\n",
" Compute distances in reverse\n",
" out_colname : str\n",
" Name of the output column to store distances. Prefixed with 'rev_' if\n",
" reverse is True\n",
" lon_col : str\n",
" Name of the longitude column\n",
" lat_col : str\n",
" Name of the latitude column\n",
" lon2_col : str\n",
" Name of the 2nd longitude column if present\n",
" lat2_col : str\n",
" Name of the 2nd latitude column if present\n",
" sorted : bool\n",
" Compute distances assuming that the frame is already sorted\n",
" rev_prefix : str\n",
" Prefix to use for colnames if reverse is True\n",
"\n",
" Returns\n",
" -------\n",
" polars.DataFrame\n",
" With additional column specifying distances between consecutive points\n",
" in the same units as 'R'. With colname defined by 'out_colname'.\n",
" \"\"\"\n",
" required_cols = [lon_col, lat_col]\n",
"\n",
" if lon2_col is not None and lat2_col is not None:\n",
" required_cols += [lon2_col, lat2_col]\n",
" check_cols(df, required_cols, \"df\")\n",
" return (\n",
" df.with_columns(\n",
" [\n",
" pl.col(lat_col).radians().alias(\"_lat0\"),\n",
" pl.col(lat2_col).radians().alias(\"_lat1\"),\n",
" (pl.col(lon_col) - pl.col(lon2_col))\n",
" .radians()\n",
" .alias(\"_dlon\"),\n",
" (pl.col(lat_col) - pl.col(lat2_col))\n",
" .radians()\n",
" .alias(\"_dlat\"),\n",
" ]\n",
" )\n",
" .with_columns(\n",
" (\n",
" (pl.col(\"_dlat\") / 2).sin().pow(2)\n",
" + pl.col(\"_lat0\").cos()\n",
" * pl.col(\"_lat1\").cos()\n",
" * (pl.col(\"_dlon\") / 2).sin().pow(2)\n",
" ).alias(\"_a\")\n",
" )\n",
" .with_columns(\n",
" (2 * R * (pl.col(\"_a\").sqrt().arcsin()))\n",
" .round(2)\n",
" .alias(out_colname)\n",
" )\n",
" .drop([\"_lat0\", \"_lat1\", \"_dlon\", \"_dlat\", \"_a\"])\n",
" )\n",
"\n",
" if lon2_col is not None or lat2_col is not None:\n",
" logging.warning(\n",
" \"(haversine_df) 2nd position incorrectly specified. \"\n",
" + \"Calculating consecutive distances.\"\n",
" )\n",
"\n",
" required_cols += [date_var]\n",
" check_cols(df, required_cols, \"df\")\n",
" if reverse:\n",
" out_colname = rev_prefix + out_colname\n",
" if not sorted:\n",
" df = df.sort(date_var, descending=reverse)\n",
" return (\n",
" df.with_columns(\n",
" [\n",
" pl.col(lat_col).radians().alias(\"_lat0\"),\n",
" pl.col(lat_col).shift(n=-1).radians().alias(\"_lat1\"),\n",
" (pl.col(lon_col).shift(n=-1) - pl.col(lon_col))\n",
" .radians()\n",
" .alias(\"_dlon\"),\n",
" (pl.col(lat_col).shift(n=-1) - pl.col(lat_col))\n",
" .radians()\n",
" .alias(\"_dlat\"),\n",
" ]\n",
" )\n",
" .with_columns(\n",
" (\n",
" (pl.col(\"_dlat\") / 2).sin().pow(2)\n",
" + pl.col(\"_lat0\").cos()\n",
" * pl.col(\"_lat1\").cos()\n",
" * (pl.col(\"_dlon\") / 2).sin().pow(2)\n",
" ).alias(\"_a\")\n",
" )\n",
" .with_columns(\n",
" (2 * R * (pl.col(\"_a\").sqrt().arcsin()))\n",
" .round(2)\n",
" .fill_null(strategy=\"forward\")\n",
" .alias(out_colname)\n",
" )\n",
" .drop([\"_lat0\", \"_lat1\", \"_dlon\", \"_dlat\", \"_a\"])\n",
" )\n",
"\n",
"def intersect(a, b) -> set:\n",
" return set(a) & set(b)\n",
"\n",
"def nearby_ships(\n",
" lon: float,\n",
" lat: float,\n",
" pool: pl.DataFrame,\n",
" max_dist: float,\n",
" lon_col: str = \"lon\",\n",
" lat_col: str = \"lat\",\n",
" dt: datetime | None = None,\n",
" date_col: str | None = None,\n",
" dt_gap: timedelta | None = None,\n",
" filter_datetime: bool = False,\n",
") -> pl.DataFrame:\n",
" \"\"\"\n",
" Find observations nearby to a position in space (and optionally time).\n",
"\n",
" Get a frame of all records that are within a maximum distance of the\n",
" provided point.\n",
"\n",
" If filter_datetime is True, then only records from the same datetime will\n",
" be returned. If a specific filter is desired this should be performed\n",
" before calling this function and set filter_datetime to False.\n",
"\n",
" Parameters\n",
" ----------\n",
" lon : float\n",
" The longitude of the position.\n",
" lat : float\n",
" The latitude of the position.\n",
" pool : polars.DataFrame\n",
" The pool of records to search. Can be pre-filtered and filter_datetime\n",
" set to False.\n",
" max_dist : float\n",
" Will return records that have distance to the point <= this value.\n",
" lon_col : str\n",
" Name of the longitude column in the pool DataFrame\n",
" lat_col : str\n",
" Name of the latitude column in the pool DataFrame\n",
" dt : datetime | None\n",
" Datetime of the record. Must be set if filter_datetime is True.\n",
" date_col : str | None\n",
" Name of the datetime column in the pool. Must be set if filter_datetime\n",
" is True.\n",
" dt_gap : timedelta | None\n",
" Allowed time-gap for records. Records that fall between\n",
" dt - dt_gap and dt + dt_gap will be returned. If not set then only\n",
" records at dt will be returned. Applies if filter_datetime is True.\n",
" filter_datetime : bool\n",
" Only return records at the same datetime record as the input value. If\n",
" assessing multiple points with different datetimes, hence calling this\n",
" function frequently it will be more efficient to partition the pool\n",
" first, then set this value to False and only input the subset of data.\n",
"\n",
" Returns\n",
" -------\n",
" polars.DataFrame\n",
" Containing only records from the pool within max_dist of the input\n",
" point, optionally at the same datetime if filter_datetime is True.\n",
" \"\"\"\n",
" required_cols = [lon_col, lat_col]\n",
" check_cols(pool, required_cols, \"pool\")\n",
"\n",
" if filter_datetime:\n",
" if not dt or not date_col:\n",
" raise ValueError(\n",
" \"'dt' and 'date_col' must be provided if 'filter_datetime' \"\n",
" + \"is True\"\n",
" )\n",
" if date_col not in pool.columns:\n",
" raise ValueError(f\"'date_col' value {date_col} not found in pool.\")\n",
" if not dt_gap:\n",
" pool = pool.filter(pl.col(date_col).eq(dt))\n",
" else:\n",
" pool = pool.filter(\n",
" pl.col(date_col).is_between(\n",
" dt - dt_gap, dt + dt_gap, closed=\"both\"\n",
" )\n",
" )\n",
"\n",
" return (\n",
" pool.with_columns(\n",
" [pl.lit(lon).alias(\"_lon\"), pl.lit(lat).alias(\"_lat\")]\n",
" )\n",
" .pipe(\n",
" haversine_df,\n",
" lon_col=lon_col,\n",
" lat_col=lat_col,\n",
" out_colname=\"_dist\",\n",
" lon2_col=\"_lon\",\n",
" lat2_col=\"_lat\",\n",
" )\n",
" .filter(pl.col(\"_dist\").le(max_dist))\n",
" .drop([\"_dist\", \"_lon\", \"_lat\"])\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "8b9279ed-6f89-4423-8833-acd0b365eb7b",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5.36 ms ± 164 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"rec = random.choice(test_recs)\n",
"nearby_ships(lon=rec.lon, lat=rec.lat, dt=rec.datetime, max_dist=dist, dt_gap=dt, date_col=\"datetime\", pool=df, filter_datetime=True)"
]
},
{
"cell_type": "markdown",
"id": "d148f129-9d8c-4c46-8f01-3e9c1e93e81a",
"metadata": {},
"source": [
"## Verify\n",
"\n",
"Check that records are the same"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "11f3d73a-fbe5-4f27-88d8-d0d687bd0eac",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.52 s, sys: 253 ms, total: 2.78 s\n",
"Wall time: 2.66 s\n"
]
}
],
"source": [
"%%time\n",
"dist = 250\n",
"for _ in range(250):\n",
" rec = Record(*random.choice(df.rows()))\n",
" orig = nearby_ships(lon=rec.lon, lat=rec.lat, dt=rec.datetime, max_dist=dist, dt_gap=dt, date_col=\"datetime\", pool=df, filter_datetime=True)\n",
" tree = otree.nearby_points(rec, dist=dist, t_dist=dt)\n",
" if orig.height > 0:\n",
" if not tree:\n",
" print(rec)\n",
" print(\"NO TREE!\")\n",
" print(f\"{orig = }\")\n",
" else:\n",
" tree = pl.from_records([(r.lon, r.lat, r.datetime, r.uid) for r in tree], orient=\"row\").rename({\"column_0\": \"lon\", \"column_1\": \"lat\", \"column_2\": \"datetime\", \"column_3\": \"uid\"})\n",
" if tree.height != orig.height:\n",
" print(\"Tree and Orig Heights Do Not Match\")\n",
" print(f\"{orig = }\")\n",
" print(f\"{tree = }\")\n",
" else:\n",
" # tree = tree.with_columns(pl.col(\"uid\").str.slice(0, 6))\n",
" if not tree.sort(\"uid\").equals(orig.sort(\"uid\")):\n",
" print(\"Tree and Orig Do Not Match\")\n",
" print(f\"{orig = }\")\n",
" print(f\"{tree = }\")"
]
},
{
"cell_type": "markdown",
"id": "1223529e-bfae-4b83-aba7-505d05e588d3",
"metadata": {},
"source": [
"## Check -180/180 boundary"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "4c392292-2d9f-4301-afb5-019fde069a1e",
"metadata": {},
"outputs": [],
"source": [
"out = otree.nearby_points(Record(179.5, -43.1, datetime(1900, 1, 14, 13)), dist=200, t_dist=timedelta(days=3))\n",
"for o in out:\n",
" print(o)"
]
}
],
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"language": "python",
"name": "geospatialtools"
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"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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