{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# **LT Toolbox Tutorial: 4. Ocean Parcels output with Trajectory Frames**\n", "\n", "Welcome to the Lagrangian Trajectories Toolbox tutorial! \n", "\n", "The LT Toolbox is a Python library dedicated to the post-processing, visualisation and analysis of Lagrangian water parcel trajectories. The toolbox offers users two structures for working with Lagrangian trajectories: Trajectory Arrays (TrajArrays) and Trajectory Frames (TrajFrames). In this tutorial, we continue exploring TrajFrames, which make use of [polars](http://pola.rs) blazingly fast DataFrames to store column variables associated with trajectories (e.g. lat, lon, in-situ temperature etc.).\n", "\n", "In this tutorial, we will learn how to:\n", "\n", "+ **Store** Lagrangian trajectory output from Ocean Parcels (variables are contained in an NCEI compliant 2-dimensional array stored in netCDF file or .zarr store) in an eager TrajFrame using xarray.\n", "\n", "+ **Convert** larger-than-memory Ocean Parcels .zarr output to a multifile .parquet store which can be used as the source for a lazy TrajFrame.\n", "\n", "To learn about the basics of working with TrajFrames, including simple filtering and using datetimes, users should see **LT Toolbox Tutorial: 2. Getting Started with Trajectory Frames**.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting Started\n", "\n", "Let us begin by importing the relevant packages we'll need to get started with the LT Toolbox. \n", "\n", "**Note**: Since lt_toolbox is still undergoing unit testing, the package is not yet available on PyPi, we use pip to install a local development version." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import relevant Python packages:\n", "import xarray as xr\n", "import numpy as np\n", "import polars as pl\n", "\n", "# Following pip installation as shown on the LT Toolbox github:\n", "import lt_toolbox as ltt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Storing Trajectory Data\n", "\n", "To explore the functionality of the LT Toolbox, we will use example output from a Lagrangian particle tracking experiment using the eddy-rich ORCA0083-GO8p7 JRA55 ocean sea-ice hindcast ([Megann et al. 2022](https://dx.doi.org/10.5285/399b0f762a004657a411a9ea7203493a)). Trajectories were advected forwards-in-time from the full-depth northward inflows across the Overturning in the Subpolar North Atlantic (OSNAP) East array.\n", "\n", "We have provided some example trajectory output for the January 1995 initialisation in the typical output format of the [OceanParcels](https://oceanparcels.org) Python package in .nc file format. Users should note that OceanParcels now outputs Lagrangian trajectory data to .zarr stores by default, however we use a .nc file here to make the example data more accessible via GitHub. The underlying data structure conntaining Lagrangian trajectory data is the same as if stored in the .zarr file format.\n", "\n", "Below we load the output .zarr file as a DataSet with xarray, before creating a TrajFrame, traj. Note, that behind the scenes the trajectory data will be transformed from regular 2-dimensional arrays with dimensions {**trajectory**, **obs**} to a condensed tabular format where the attribute variables recorded along each trajectory are stored in lists of variable lengths." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 701MB\n",
       "Dimensions:     (trajectory: 2500, obs: 3895)\n",
       "Coordinates:\n",
       "  * obs         (obs) int32 16kB 1 2 3 4 5 6 7 ... 3890 3891 3892 3893 3894 3895\n",
       "  * trajectory  (trajectory) int64 20kB 1 12 15 21 ... 28512 28549 28956 28990\n",
       "Data variables:\n",
       "    boxface     (trajectory, obs) float64 78MB ...\n",
       "    mask        (trajectory, obs) float64 78MB ...\n",
       "    so          (trajectory, obs) float64 78MB ...\n",
       "    subvol      (trajectory, obs) float64 78MB ...\n",
       "    thetao      (trajectory, obs) float64 78MB ...\n",
       "    time        (trajectory, obs) float64 78MB ...\n",
       "    x           (trajectory, obs) float64 78MB ...\n",
       "    y           (trajectory, obs) float64 78MB ...\n",
       "    z           (trajectory, obs) float64 78MB ...\n",
       "Attributes:\n",
       "    description:  Example Lagrangian trajectory dataset including a example w...\n",
       "    title:        ORCA0083-GO8p7_JRA55_SPNA_1995_example
" ], "text/plain": [ " Size: 701MB\n", "Dimensions: (trajectory: 2500, obs: 3895)\n", "Coordinates:\n", " * obs (obs) int32 16kB 1 2 3 4 5 6 7 ... 3890 3891 3892 3893 3894 3895\n", " * trajectory (trajectory) int64 20kB 1 12 15 21 ... 28512 28549 28956 28990\n", "Data variables:\n", " boxface (trajectory, obs) float64 78MB ...\n", " mask (trajectory, obs) float64 78MB ...\n", " so (trajectory, obs) float64 78MB ...\n", " subvol (trajectory, obs) float64 78MB ...\n", " thetao (trajectory, obs) float64 78MB ...\n", " time (trajectory, obs) float64 78MB ...\n", " x (trajectory, obs) float64 78MB ...\n", " y (trajectory, obs) float64 78MB ...\n", " z (trajectory, obs) float64 78MB ...\n", "Attributes:\n", " description: Example Lagrangian trajectory dataset including a example w...\n", " title: ORCA0083-GO8p7_JRA55_SPNA_1995_example" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Defining filepath to our example ORCA0083-GO8p7 output trajectory file:\n", "traj_filepath = \"./data/ORCA0083-GO8p7_JRA55_SPNA_1995_example.nc\"\n", "\n", "# Open output .nc file as a DataSet.\n", "dataset = xr.open_dataset(traj_filepath)\n", "\n", "# Let's have a look at our original dataset:\n", "dataset" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'x', 'y', 'z']\n", "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a TrajFrame from the DataSet:\n", "traj = ltt.TrajFrame(source=dataset, condense=True)\n", "\n", "# Here we see how condensing a TrajFrame will store all observations in\n", "# lists per trajectory (rows) instead of in a row per observation.\n", "traj" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **What is a TrajFrame?**\n", "\n", "At its simplest the TrajFrame, traj, is a containter for our original xarray DataSet (now transformed to a Polars DataFrame) accessible as an attribute with traj.data.\n", "\n", "The columns of the DataFrame represent variables and each row represents a single Lagrangian trajectory. Yet, in constrast to our original DataFrame, each row-element of our DataFrame is now a list containing the individual values of each variable recorded along the whole trajectory. \n", "\n", "The true value of a TrajFrame comes with the use of the built-in functions specifically designed for post-processing, visualisation and analysis of Lagrangain water parcel trajectories." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exploring our TrajFrame\n", "\n", "By accessing the .data attribute of our TrajFrame above, we can see the original Polars DataFrame containing our Lagrangian trajectories." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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i64list[f64]list[f64]list[f64]list[f64]list[f64]list[f64]list[f64]list[f64]list[f64]
1[1.0, 4.0, … 4.0][1.0, 1.0, … 0.5][35.22, 35.22, … 35.16][237.17, 237.17, … 237.17][4.35, 4.34, … 3.81][0.0, 78090.8, … 7.2643e7][3085.0, 3085.0, … 2840.83][2888.5, 2888.0, … 2793.0][46.5, 46.42, … 48.03]
12[1.0, 4.0, … 4.0][1.0, 1.0, … 0.5][35.25, 35.25, … 35.16][1208.82, 1208.82, … 1208.82][4.02, 4.01, … 2.84][0.0, 116391.69, … 4.1395e7][3085.0, 3085.34, … 2847.51][2888.83, 2888.0, … 2797.0][48.83, 48.14, … 58.28]
15[1.0, 6.0, … 1.0][1.0, 1.0, … 0.0][35.25, 35.25, … 35.17][1833.07, 1833.07, … 1833.07][4.0, 4.09, … 3.56][0.0, 104679.2, … 3.5617e7][3085.0, 3085.98, … 2844.0][2888.12, 2888.07, … 2794.02][49.87, 49.0, … 46.57]
21[1.0, 6.0, … 4.0][1.0, 1.0, … 0.5][35.25, 35.25, … 35.18][1833.07, 1833.07, … 1833.07][4.0, 4.1, … 3.35][0.0, 44020.31, … 3.6198e7][3085.0, 3085.36, … 2842.74][2888.37, 2888.3, … 2794.0][49.37, 49.0, … 52.38]
26[1.0, 6.0, … 4.0][1.0, 1.0, … 0.5][35.25, 35.25, … 35.19][1833.07, 1833.07, … 1833.07][4.0, 4.1, … 3.52][0.0, 14532.79, … 8.1625e7][3085.0, 3085.11, … 2843.97][2888.62, 2888.58, … 2794.0][49.12, 49.0, … 50.7]
…………………………
28473[3.0, 5.0, … 4.0][1.0, 1.0, … 0.5][35.54, 35.54, … 35.17][2195.11, 2195.11, … 2195.11][10.22, 10.2, … 3.21][0.0, 80378.74, … 1.1211e8][3311.75, 3311.52, … 2844.18][2865.0, 2865.4, … 2795.0][37.75, 38.0, … 54.49]
28512[3.0, 6.0, … 1.0][1.0, 1.0, … 0.0][35.53, 35.53, … 35.11][1720.66, 1720.66, … 1720.66][10.27, 9.97, … 4.67][0.0, 41232.2, … 8.9916e7][3312.83, 3312.65, … 2840.0][2864.0, 2864.56, … 2792.97][24.5, 24.0, … 40.77]
28549[3.0, 2.0, … 4.0][1.0, 1.0, … 0.5][35.53, 35.53, … 35.18][2289.29, 2289.29, … 2289.29][10.26, 10.23, … 3.57][0.0, 78200.71, … 7.8223e7][3312.5, 3312.0, … 2844.09][2864.0, 2864.52, … 2795.0][35.5, 35.47, … 47.4]
28956[3.0, 1.0, … 4.0][1.0, 1.0, … 0.5][35.35, 35.33, … 35.18][545.0, 545.0, … 545.0][9.61, 8.72, … 3.69][0.0, 79550.58, … 1.5925e8][3322.5, 3323.0, … 2842.39][2862.0, 2862.47, … 2794.0][11.5, 11.65, … 48.38]
28990[3.0, 1.0, … 4.0][1.0, 1.0, … 0.5][35.32, 35.32, … 35.19][446.65, 446.65, … 446.65][9.54, 8.65, … 3.54][0.0, 122199.1, … 6.5770e7][3323.5, 3324.0, … 2845.52][2862.0, 2862.27, … 2795.0][18.5, 18.56, … 49.83]
" ], "text/plain": [ "shape: (2_500, 10)\n", "┌───────┬────────────┬────────────┬────────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", "│ id ┆ boxface ┆ mask ┆ so ┆ … ┆ time ┆ x ┆ y ┆ z │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ i64 ┆ list[f64] ┆ list[f64] ┆ list[f64] ┆ ┆ list[f64] ┆ list[f64] ┆ list[f64] ┆ list[f64] │\n", "╞═══════╪════════════╪════════════╪════════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", "│ 1 ┆ [1.0, 4.0, ┆ [1.0, 1.0, ┆ [35.22, ┆ … ┆ [0.0, ┆ [3085.0, ┆ [2888.5, ┆ [46.5, │\n", "│ ┆ … 4.0] ┆ … 0.5] ┆ 35.22, … ┆ ┆ 78090.8, ┆ 3085.0, … ┆ 2888.0, … ┆ 46.42, … │\n", "│ ┆ ┆ ┆ 35.16] ┆ ┆ … ┆ 2840.83] ┆ 2793.0] ┆ 48.03] │\n", "│ ┆ ┆ ┆ ┆ ┆ 7.2643e7] ┆ ┆ ┆ │\n", "│ 12 ┆ [1.0, 4.0, ┆ [1.0, 1.0, ┆ [35.25, ┆ … ┆ [0.0, 116 ┆ [3085.0, ┆ [2888.83, ┆ [48.83, │\n", "│ ┆ … 4.0] ┆ … 0.5] ┆ 35.25, … ┆ ┆ 391.69, … ┆ 3085.34, ┆ 2888.0, … ┆ 48.14, … │\n", "│ ┆ ┆ ┆ 35.16] ┆ ┆ 4.1395e7] ┆ … ┆ 2797.0] ┆ 58.28] │\n", "│ ┆ ┆ ┆ ┆ ┆ ┆ 2847.51] ┆ ┆ │\n", "│ 15 ┆ [1.0, 6.0, ┆ [1.0, 1.0, ┆ [35.25, ┆ … ┆ [0.0, ┆ [3085.0, ┆ [2888.12, ┆ [49.87, │\n", "│ ┆ … 1.0] ┆ … 0.0] ┆ 35.25, … ┆ ┆ 104679.2, ┆ 3085.98, ┆ 2888.07, ┆ 49.0, … │\n", "│ ┆ ┆ ┆ 35.17] ┆ ┆ … ┆ … 2844.0] ┆ … ┆ 46.57] │\n", "│ ┆ ┆ ┆ ┆ ┆ 3.5617e7] ┆ ┆ 2794.02] ┆ │\n", "│ 21 ┆ [1.0, 6.0, ┆ [1.0, 1.0, ┆ [35.25, ┆ … ┆ [0.0, ┆ [3085.0, ┆ [2888.37, ┆ [49.37, │\n", "│ ┆ … 4.0] ┆ … 0.5] ┆ 35.25, … ┆ ┆ 44020.31, ┆ 3085.36, ┆ 2888.3, … ┆ 49.0, … │\n", "│ ┆ ┆ ┆ 35.18] ┆ ┆ … ┆ … ┆ 2794.0] ┆ 52.38] │\n", "│ ┆ ┆ ┆ ┆ ┆ 3.6198e7] ┆ 2842.74] ┆ ┆ │\n", "│ 26 ┆ [1.0, 6.0, ┆ [1.0, 1.0, ┆ [35.25, ┆ … ┆ [0.0, ┆ [3085.0, ┆ [2888.62, ┆ [49.12, │\n", "│ ┆ … 4.0] ┆ … 0.5] ┆ 35.25, … ┆ ┆ 14532.79, ┆ 3085.11, ┆ 2888.58, ┆ 49.0, … │\n", "│ ┆ ┆ ┆ 35.19] ┆ ┆ … ┆ … ┆ … 2794.0] ┆ 50.7] │\n", "│ ┆ ┆ ┆ ┆ ┆ 8.1625e7] ┆ 2843.97] ┆ ┆ │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ 28473 ┆ [3.0, 5.0, ┆ [1.0, 1.0, ┆ [35.54, ┆ … ┆ [0.0, ┆ [3311.75, ┆ [2865.0, ┆ [37.75, │\n", "│ ┆ … 4.0] ┆ … 0.5] ┆ 35.54, … ┆ ┆ 80378.74, ┆ 3311.52, ┆ 2865.4, … ┆ 38.0, … │\n", "│ ┆ ┆ ┆ 35.17] ┆ ┆ … ┆ … ┆ 2795.0] ┆ 54.49] │\n", "│ ┆ ┆ ┆ ┆ ┆ 1.1211e8] ┆ 2844.18] ┆ ┆ │\n", "│ 28512 ┆ [3.0, 6.0, ┆ [1.0, 1.0, ┆ [35.53, ┆ … ┆ [0.0, ┆ [3312.83, ┆ [2864.0, ┆ [24.5, │\n", "│ ┆ … 1.0] ┆ … 0.0] ┆ 35.53, … ┆ ┆ 41232.2, ┆ 3312.65, ┆ 2864.56, ┆ 24.0, … │\n", "│ ┆ ┆ ┆ 35.11] ┆ ┆ … ┆ … 2840.0] ┆ … ┆ 40.77] │\n", "│ ┆ ┆ ┆ ┆ ┆ 8.9916e7] ┆ ┆ 2792.97] ┆ │\n", "│ 28549 ┆ [3.0, 2.0, ┆ [1.0, 1.0, ┆ [35.53, ┆ … ┆ [0.0, ┆ [3312.5, ┆ [2864.0, ┆ [35.5, │\n", "│ ┆ … 4.0] ┆ … 0.5] ┆ 35.53, … ┆ ┆ 78200.71, ┆ 3312.0, … ┆ 2864.52, ┆ 35.47, … │\n", "│ ┆ ┆ ┆ 35.18] ┆ ┆ … ┆ 2844.09] ┆ … 2795.0] ┆ 47.4] │\n", "│ ┆ ┆ ┆ ┆ ┆ 7.8223e7] ┆ ┆ ┆ │\n", "│ 28956 ┆ [3.0, 1.0, ┆ [1.0, 1.0, ┆ [35.35, ┆ … ┆ [0.0, ┆ [3322.5, ┆ [2862.0, ┆ [11.5, │\n", "│ ┆ … 4.0] ┆ … 0.5] ┆ 35.33, … ┆ ┆ 79550.58, ┆ 3323.0, … ┆ 2862.47, ┆ 11.65, … │\n", "│ ┆ ┆ ┆ 35.18] ┆ ┆ … ┆ 2842.39] ┆ … 2794.0] ┆ 48.38] │\n", "│ ┆ ┆ ┆ ┆ ┆ 1.5925e8] ┆ ┆ ┆ │\n", "│ 28990 ┆ [3.0, 1.0, ┆ [1.0, 1.0, ┆ [35.32, ┆ … ┆ [0.0, ┆ [3323.5, ┆ [2862.0, ┆ [18.5, │\n", "│ ┆ … 4.0] ┆ … 0.5] ┆ 35.32, … ┆ ┆ 122199.1, ┆ 3324.0, … ┆ 2862.27, ┆ 18.56, … │\n", "│ ┆ ┆ ┆ 35.19] ┆ ┆ … ┆ 2845.52] ┆ … 2795.0] ┆ 49.83] │\n", "│ ┆ ┆ ┆ ┆ ┆ 6.5770e7] ┆ ┆ ┆ │\n", "└───────┴────────────┴────────────┴────────────┴───┴───────────┴───────────┴───────────┴───────────┘" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# To return details of our original condensed DataFrame.\n", "traj.data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (2_500,)
thetao
list[f64]
[4.35, 4.34, … 3.81]
[4.02, 4.01, … 2.84]
[4.0, 4.09, … 3.56]
[4.0, 4.1, … 3.35]
[4.0, 4.1, … 3.52]
…
[10.22, 10.2, … 3.21]
[10.27, 9.97, … 4.67]
[10.26, 10.23, … 3.57]
[9.61, 8.72, … 3.69]
[9.54, 8.65, … 3.54]
" ], "text/plain": [ "shape: (2_500,)\n", "Series: 'thetao' [list[f64]]\n", "[\n", "\t[4.35, 4.34, … 3.81]\n", "\t[4.02, 4.01, … 2.84]\n", "\t[4.0, 4.09, … 3.56]\n", "\t[4.0, 4.1, … 3.35]\n", "\t[4.0, 4.1, … 3.52]\n", "\t…\n", "\t[10.22, 10.2, … 3.21]\n", "\t[10.27, 9.97, … 4.67]\n", "\t[10.26, 10.23, … 3.57]\n", "\t[9.61, 8.72, … 3.69]\n", "\t[9.54, 8.65, … 3.54]\n", "]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# To access the temperature column variable as a Polars Series.\n", "traj.data['thetao']\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Transforming x,y,z variables to lon,lat,depth in a TrajFrame.\n", "\n", "In our example TrajFrame, we have stored the positions of each trajectory as indices referring to the original numerical model grid (eORCA12). However, we can easily transform these model coordinates to geographical coordinates using the **.transform_trajectory_coords()** method which uses bi-linear interpolation to determine each trajectories geographical position." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 125MB\n",
       "Dimensions:  (y: 3606, x: 4322, z: 75)\n",
       "Dimensions without coordinates: y, x, z\n",
       "Data variables:\n",
       "    nav_lat  (y, x) float32 62MB ...\n",
       "    nav_lon  (y, x) float32 62MB ...\n",
       "    nav_lev  (z) float32 300B ...\n",
       "Attributes:\n",
       "    file_name:  mesh_mask.nc\n",
       "    TimeStamp:  30/09/2016 08:33:26 +0000
" ], "text/plain": [ " Size: 125MB\n", "Dimensions: (y: 3606, x: 4322, z: 75)\n", "Dimensions without coordinates: y, x, z\n", "Data variables:\n", " nav_lat (y, x) float32 62MB ...\n", " nav_lon (y, x) float32 62MB ...\n", " nav_lev (z) float32 300B ...\n", "Attributes:\n", " file_name: mesh_mask.nc\n", " TimeStamp: 30/09/2016 08:33:26 +0000" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Importing eORCA12 ocean model grid from a NetCDF file using xarray:\n", "ds_grid = xr.open_dataset('/home/snapdragon/HadGEM3-GC31-MM/Proj_Future_Pathways/src/Software/lt_toolbox/docs/tutorials/data/ORCA0083-GO8p7_JRA55_model_grid.nc')\n", "\n", "ds_grid" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth']\n", "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Transform the TrajFrame to geographical coordinates and store as new TrajFrame object:\n", "traj_geo = traj.transform_trajectory_coords(lon=ds_grid.nav_lon.values,\n", " lat=ds_grid.nav_lat.values,\n", " depth=ds_grid.nav_lev.values,\n", " )\n", "\n", "traj_geo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Transforming time variable to datetime format in a TrajFrame.\n", "\n", "We can transform a time variable defined in terms of time elapsed since water parcel seeding into a datetime using the **.use_datetime()** method. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (2_500,)
time
list[datetime[μs]]
[1995-01-01 00:00:00, 1995-01-01 21:41:30.800, … 1997-04-20 18:43:04.670]
[1995-01-01 00:00:00, 1995-01-02 08:19:51.690, … 1996-04-24 02:29:28.240]
[1995-01-01 00:00:00, 1995-01-02 05:04:39.200, … 1996-02-17 05:40:44.610]
[1995-01-01 00:00:00, 1995-01-01 12:13:40.310, … 1996-02-23 22:52:35.930]
[1995-01-01 00:00:00, 1995-01-01 04:02:12.790, … 1997-08-02 17:39:38.190]
…
[1995-01-01 00:00:00, 1995-01-01 22:19:38.740, … 1998-07-21 14:53:46.360]
[1995-01-01 00:00:00, 1995-01-01 11:27:12.200, … 1997-11-06 16:39:41.550]
[1995-01-01 00:00:00, 1995-01-01 21:43:20.710, … 1997-06-24 08:29:10.470]
[1995-01-01 00:00:00, 1995-01-01 22:05:50.580, … 2000-01-18 03:37:14.510]
[1995-01-01 00:00:00, 1995-01-02 09:56:39.100, … 1997-01-31 05:22:00.820]
" ], "text/plain": [ "shape: (2_500,)\n", "Series: 'time' [list[datetime[μs]]]\n", "[\n", "\t[1995-01-01 00:00:00, 1995-01-01 21:41:30.800, … 1997-04-20 18:43:04.670]\n", "\t[1995-01-01 00:00:00, 1995-01-02 08:19:51.690, … 1996-04-24 02:29:28.240]\n", "\t[1995-01-01 00:00:00, 1995-01-02 05:04:39.200, … 1996-02-17 05:40:44.610]\n", "\t[1995-01-01 00:00:00, 1995-01-01 12:13:40.310, … 1996-02-23 22:52:35.930]\n", "\t[1995-01-01 00:00:00, 1995-01-01 04:02:12.790, … 1997-08-02 17:39:38.190]\n", "\t…\n", "\t[1995-01-01 00:00:00, 1995-01-01 22:19:38.740, … 1998-07-21 14:53:46.360]\n", "\t[1995-01-01 00:00:00, 1995-01-01 11:27:12.200, … 1997-11-06 16:39:41.550]\n", "\t[1995-01-01 00:00:00, 1995-01-01 21:43:20.710, … 1997-06-24 08:29:10.470]\n", "\t[1995-01-01 00:00:00, 1995-01-01 22:05:50.580, … 2000-01-18 03:37:14.510]\n", "\t[1995-01-01 00:00:00, 1995-01-02 09:56:39.100, … 1997-01-31 05:22:00.820]\n", "]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Using datetime64 format for time instead of timedelta64.\n", "# Start date of simulation is 1995-01-01\n", "traj_geo = traj_geo.use_datetime(start_date='1995-01-01', unit='s')\n", "\n", "traj_geo.data['time']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Getting existing features from a TrajFrame.\n", "\n", "The LT Toolbox includes a range of **.get_** methods to extract important features from existing attribute variables in a TrajFrame. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth', 'start_time', 'lon_start', 'lat_start', 'depth_start']\n", "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the times and locations when particle are released. \n", "traj_geo.get_start_time().get_start_loc()\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth', 'end_time', 'lon_end', 'lat_end', 'depth_end']\n", "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the times and locations when particles are terminated.\n", "traj_geo.get_end_time().get_end_loc()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth', 'thetao_max']\n", "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the maximum value of the conservative temperature variable for each trajectory. \n", "traj_geo.get_max('thetao')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth', 'thetao_min']\n", "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the minimum value of the conservative temperature variable for each trajectory. \n", "traj_geo.get_min('thetao')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth', 'thetao_i']\n", "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the value of the temp variable for each trajectory on 1995-06-01. \n", "traj_geo.get_values('thetao', '1995-01-01')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth', 'dt']\n", "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the duration of each trajectory, t_total.\n", "traj_geo.get_duration()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding new attribute variables to our TrajFrame.\n", "\n", "To add a new attribute variable to a TrajFrame use the **.add_variable()** method. \n", "\n", "There are two ways to use the **.add_variable()** method:\n", "\n", "1. Users can either provide a list of values to instantiate a new column variable.\n", "\n", "2. Users can define a new column variable using the polars expression syntax." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth', 'thetao_max', 'temp_max_K']\n", "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Consider now if we wanted to create a new attribute variable, temp_max_K,\n", "# to store the maximum along-path temperature in Kelvin. \n", "\n", "# OPTION 1.\n", "# Define a list of values for the temperature .\n", "traj_add = traj_geo.get_max('thetao')\n", "temp_max_k = (traj_add.data['thetao_max'] + 273.15).to_list()\n", "traj_add.add_variable(name='temp_max_K', values=temp_max_k)\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth', 'thetao_max', 'temp_max_K']\n", "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# OPTION 2. Using Polars expression:\n", "(traj_geo\n", " .get_max('thetao')\n", " .add_variable(name='temp_max_K', expr=pl.col('thetao_max') + 273.15)\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filtering trajectories using a column variable.\n", "\n", "Filtering our trajectories to include only those complete trajectories where a specific criteria is met is integral to Lagrangian analysis. \n", "\n", "The LT Toolbox includes two primary methods **.filter()** and **filter_isin()** to allow users to filter on any attribute variable conatined in their TrajFrame." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2196\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth']\n", "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# OPTION 1: String Query Syntax.\n", "# Filtering all our trajectories to include only those which are advected north of 65 N:\n", "traj_geo.filter(expr='lat > 65', drop=False)\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2196\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth']\n", "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# OPTION 2: Polars Expression Syntax for Lists.\n", "(traj_geo\n", " .filter(\n", " expr=(pl.col('lat').list.eval(pl.element() > pl.lit('65').cast(pl.Float64)).list.any()),\n", " drop=False\n", " ))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 4\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth']\n", "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Filtering all of our trajectories according to the unique IDs\n", "# contained in a given list:\n", "traj_geo.filter_isin('id', [1, 12, 15, 21], drop=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filtering trajectories which intersect a polygon." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 323\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth']\n", "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Defining longitude and latitude coordinates of square polygon:\n", "x_square = [-15, -5, -5, -5, -15]\n", "y_square = [58, 61, 61, 58, 58]\n", "\n", "# Filter trajectories to only include those which pass through the square polygon:\n", "traj_geo.filter_polygon(xy_vars=['lon','lat'], x_poly=x_square, y_poly=y_square, drop=False)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 323\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth']\n", "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Filter trajectories to only include those which pass through the square polygon:\n", "(traj_geo\n", " .filter_polygon(xy_vars=['lon','lat'], x_poly=x_square, y_poly=y_square, drop=False)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Computing diagnostic metrics for trajectories.\n", "\n", "Computation is another important feature of the LT toolbox.\n", "\n", "In particular, there are **.compute** methods available to compute the distance travelled by particles along their trajectories, particle displacements (zonal/meridional/vertical) and Lagrangian velocities (zonal/meridional/vertical).\n", "\n", "Below we show how to combine **filter**, **compute** and **plot** methods to efficiently generate visualisations of output data." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth', 'dist']\n", "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "traj_geo.compute_distance(cum_dist=True, unit='km')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "----- Trajectory DataFrame -----\n", "Trajectories: 2500\n", "Variables: ['id', 'boxface', 'mask', 'so', 'subvol', 'thetao', 'time', 'lon', 'lat', 'depth', 'dist', 'direction', 'speed']\n", "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute velocity in of water parcels in km/h:\n", "traj_geo.compute_distance(cum_dist=False, unit='m').compute_velocity()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting Lagrangian trajectories and properties.\n", "\n", "Now we know how to compute diagnostics along our trajectories, so next we need to learn how to visualise our Lagrangian trajectories interactively." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "customdata": [ [ 28454, -9.347183227539062, 57.09975242614746, 10.23, 35.53, 306.1635620117186 ], [ 28454, -9.364932250976558, 57.15259712219238, 9.91, 35.52, 310.4503524780273 ], [ 28454, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting a timeseries of the conservative temperature variable for a sample of trajectories\n", "# which intersect a given square polygon:\n", "(traj_geo\n", " .filter_polygon(xy_vars=['lon','lat'], x_poly=x_square, y_poly=y_square, drop=False)\n", " .plot_timeseries(var='thetao',\n", " sample_size=10,\n", " width=7E2,\n", " height=7E2,\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Converting larger-than-memory OceanParcels output .zarr stores to multiple-file .parquet stores**\n", "\n", "You probably noticed that, compared with the creation of a TrajFrame from a Polars DataFrame read eagerly from a .parquet file, using an eager xarray DataSet is noticably slower. This is because of the overheads required to transform the Lagrangian trajectory data output from OceanParcels from the {trajectory, obs} 2-D array format into the condensed tabular format used in the TrajFrame.\n", "\n", "Although this may initially appear inconvinent, the perfomance improvement from working with the Polars DataFrame library (written in Rust) coupled with the inefficient nature of 2D arrays for storing what are often unnevely sampled positions and properties along Lagragian trajectories (sparse arrays are often needed) makes this worth pursuing. To help users with larger-than-memory Lagrangian trajectory datasets in .zarr stores, we have added the functionality to write the data contained in a larger-than-memory .zarr store to a multi-file .parquet store which can then be used in the creation of lazy TrajFrames.\n", "\n", "Below we illustrate how to use the conversions module of the LT-Toolbox and deemonstrate the syntax to convert a large .zarr store to a multi-file .parquet store:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from lt_toolbox.conversions import export_zarr_to_mfparquet\n", "\n", "# Exporting a Zarr file to a multi-file .parquet store:\n", "export_zarr_to_mfparquet(zarr_filename='/data/ORCA0083-GO8p7_JRA55_SPNA_1995_example.zarr',\n", " parquet_filedir='/data/',\n", " parquet_filename='ORCA0083-GO8p7_JRA55_SPNA_1995_mf_example.parquet',\n", " npartitions=10,\n", " read_options={'chunk_size': 'auto'},\n", " write_options={'use_pyarrow': True},\n", " )" ] } ], "metadata": { "kernelspec": { "display_name": "jlab_LMOC", "language": "python", "name": "jlab_lmoc" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 }