diff --git a/docs/userguide/about/install.md b/docs/userguide/about/install.md index 443c8fe7b..c46d68280 100644 --- a/docs/userguide/about/install.md +++ b/docs/userguide/about/install.md @@ -521,8 +521,8 @@ uv add earth2studio --extra corrdiff :::: ::::: :::::{tab-item} Cyclone Trackers -Notes: Additional dependencies for all cyclone tracking models. Only Python 3.12 and -below support. +Notes: Additional dependencies for cyclone tracking models `TCTrackerVitart` and `TCTrackerWuDuan`. +Only Python 3.12 and below support. ::::{tab-set} :::{tab-item} uv @@ -538,6 +538,16 @@ uv pip install earth2studio --extra cyclone pip install earth2studio[cyclone] ``` +`TempestExtremes` is not provided as a Python library and must be installed +separately by the user. Installation instructions can be found on the +[TempestExtremes GitHub page](https://github.com/ClimateGlobalChange/tempestextremes?tab=readme-ov-file#installation-via-cmake-recommended). + +By default, `TempestExtremes` binaries are installed to the bin folder of the +`TempestExtremes` repository. Users can specify paths to executables by passing +the detect and stitch commands to the initialisation of the `TempestExtremes` +class in earth2studio. Examples for both commands are provided in the docstring +of this class. + ::: :::: ::::: diff --git a/earth2studio/models/px/fcn3.py b/earth2studio/models/px/fcn3.py index b4196e473..b5cdd518f 100644 --- a/earth2studio/models/px/fcn3.py +++ b/earth2studio/models/px/fcn3.py @@ -21,6 +21,7 @@ import numpy as np import torch from loguru import logger +from packaging.version import Version from earth2studio.models.auto import AutoModelMixin, Package from earth2studio.models.batch import batch_coords, batch_func @@ -52,6 +53,15 @@ load_model_package = None _cuda_extension_available = False +if Version(torch_harmonics.__version__) >= Version("0.8.1"): + from torch_harmonics.disco import cuda_kernels_is_available + + _cuda_extension_available = cuda_kernels_is_available() +else: + from importlib.util import find_spec + + _cuda_extension_available = find_spec("disco_cuda_extension") is not None + VARIABLES = [ "u10m", diff --git a/recipes/tc_tracking/.gitignore b/recipes/tc_tracking/.gitignore new file mode 100644 index 000000000..0eb96be1b --- /dev/null +++ b/recipes/tc_tracking/.gitignore @@ -0,0 +1,15 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info +outputs*/ +*.zarr +*.nc +*.gif +.python-version + +# Virtual environments +.venv diff --git a/recipes/tc_tracking/Dockerfile b/recipes/tc_tracking/Dockerfile new file mode 100644 index 000000000..e99e9dd53 --- /dev/null +++ b/recipes/tc_tracking/Dockerfile @@ -0,0 +1,35 @@ +FROM nvcr.io/nvidia/physicsnemo/physicsnemo:25.11 + +# update repo info +RUN apt update -y && \ + apt install -y libibmad5 unixodbc && \ + apt install -y netcdf-bin libnetcdf-dev + +# upgrade cmake +RUN apt remove cmake -y && \ + pip install cmake --upgrade + +# Install uv +RUN wget -qO- https://astral.sh/uv/install.sh | sh +ENV PATH="/root/.local/bin:$PATH" +ENV CC=/usr/bin/gcc +ENV CXX=/usr/bin/g++ + +# install TempestExtremes +WORKDIR / +RUN git clone https://github.com/ClimateGlobalChange/tempestextremes.git && \ + mkdir -p /tempestextremes/build +WORKDIR /tempestextremes/build +RUN cmake .. && \ + make -j && \ + cp ./bin/DetectNodes /usr/local/bin && \ + cp ./bin/StitchNodes /usr/local/bin + +# copy source into the container. +RUN mkdir -p /tc_tracking_src +COPY . /tc_tracking_src +WORKDIR /tc_tracking_src + +ENV FORCE_CUDA_EXTENSION=1 +ENV TORCH_CUDA_ARCH_LIST="8.0 8.6 9.0 10.0 12.0, 13.0+PTX" +RUN uv pip install --system --break-system-packages --no-cache-dir . diff --git a/recipes/tc_tracking/PR_SPLIT_PLAN.md b/recipes/tc_tracking/PR_SPLIT_PLAN.md new file mode 100644 index 000000000..aeac33bc6 --- /dev/null +++ b/recipes/tc_tracking/PR_SPLIT_PLAN.md @@ -0,0 +1,116 @@ +# Splitting the TC Tracking Recipe into Incremental PRs + +Breakdown into 4 PRs, ordered from foundational to supplementary. Each PR is self-contained and reviewable on its own. + +--- + +## PR 1 -- Core infrastructure + `generate_ensemble` mode + +The bulk of the code. Introduces the full pipeline for running AI weather model ensembles and tracking tropical cyclones with TempestExtremes. + +**Files to include:** + +- `tc_hunt.py` -- entry point, but only dispatching `generate_ensemble` (no import of `baseline_extraction`, no `reproduce_members` dispatch) +- `src/__init__.py` +- `src/tempest_extremes.py` -- the full TempestExtremes + async wrapper (~1250 lines, core of the recipe) +- `src/utils.py` -- shared helpers +- `src/data/utils.py` -- `DataSourceManager`, `load_heights` +- `src/data/file_output.py` -- output setup for Zarr/NetCDF +- `src/modes/generate_ensembles.py` -- **full file including `reproduce_members`**. Since `reproduce_members` and `generate_ensemble` share `initialise`, `load_model`, `run_inference`, `distribute_runs`, and `configure_runs`, splitting the file would be artificial. The function simply sits unused until PR 2 wires it up. +- `cfg/helene.yaml`, `cfg/hato.yaml` -- example configs for tracking +- `pyproject.toml` -- **without `tropycal`** (only needed by baseline extraction in PR 3) +- `Dockerfile`, `set_envs.sh`, `.gitignore` +- `README.md` -- documenting only the `generate_ensemble` mode +- `test/test_tc_hunt.sh`, `test/cfg/baseline_helene.yaml`, `test/README.md`, `test/.gitignore` -- basic test for the generate mode + +**Notes:** + +- This is the largest PR but it is all one coherent feature: "run ensemble forecasts and track cyclones". +- `tropycal`, `moviepy`, and plotting-only dependencies can be dropped from `pyproject.toml` for this PR to keep the dependency surface small. +- The `testsource.py` debug script is not part of the recipe proper; leave it out (it is an untracked file anyway). + +--- + +## PR 2 -- Reproduction mode + +A very small, easy-to-review PR. Wires up the `reproduce_members` function that already exists in `generate_ensembles.py`. + +**Changes:** + +- `tc_hunt.py` -- add `reproduce_members` import and dispatch case (~3 lines changed) +- `cfg/reproduce_helene.yaml` -- example config for reproducing specific ensemble members +- `test/cfg/reproduce_helene.yaml` -- test config +- `README.md` -- add documentation for the `reproduce_members` mode + +This PR is intentionally tiny. The only new logic is the dispatch wiring and configs; the implementation already landed in PR 1 as part of `generate_ensembles.py`. + +--- + +## PR 3 -- Baseline extraction from reanalysis + +Adds the `extract_baseline` mode, which fetches ERA5 reanalysis data, runs TempestExtremes on it, and matches the detected tracks against IBTrACS ground truth. + +**Files to include:** + +- `src/modes/baseline_extraction.py` -- the full extraction pipeline (~208 lines) +- `tc_hunt.py` -- add `extract_baseline` import and dispatch case +- `cfg/extract_era5.yaml` -- config for Helene + Hato extraction +- `aux_data/ibtracs.HATO_HELENE.list.v04r01.csv` -- IBTrACS subset +- `aux_data/reference_track_hato_2017_west_pacific.csv` -- reference track +- `aux_data/reference_track_helene_2024_north_atlantic.csv` -- reference track +- `test/test_historic_tc_extraction.sh`, `test/cfg/extract_era5.yaml` -- extraction test +- `pyproject.toml` -- add `tropycal>=1.4` dependency +- `README.md` -- add documentation for `extract_baseline` + +**Notes:** + +- This is the only PR that adds `tropycal` as a dependency (used for IBTrACS access). +- The `aux_data/` CSV files are small reference datasets, fine to commit. + +--- + +## PR 4 -- Plotting and analysis tools + +Adds the visualisation and analysis tooling. Entirely optional for the core pipeline to work; can be merged last or even deferred. + +**Files to include:** + +- `plotting/analyse_n_plot.py` +- `plotting/data_handling.py` +- `plotting/plotting_helpers.py` +- `plotting/plot_tracks_n_fields.ipynb` +- `plotting/tracks_slayground.ipynb` +- `plotting/README.md` +- `plotting/.gitignore` +- `pyproject.toml` -- ensure `cartopy`, `matplotlib`, `moviepy` are present (likely already there from PR 1, but verify) + +--- + +## Dependency flow between PRs + +```mermaid +graph LR + PR1["PR 1: Core + generate_ensemble"] + PR2["PR 2: reproduce_members"] + PR3["PR 3: extract_baseline"] + PR4["PR 4: Plotting"] + PR1 --> PR2 + PR1 --> PR3 + PR1 --> PR4 + PR3 --> PR4 +``` + +PR 2, PR 3, and PR 4 all depend on PR 1. PR 3 and PR 4 are independent of PR 2. PR 4 may reference outputs from PR 3 (reference tracks), so ordering PR 3 before PR 4 is ideal but not strictly required. + +--- + +## Implementation approach + +For each PR, we create a branch off main and stage only the relevant files. Since all files are new (no modifications to existing e2s files), this is straightforward -- each PR is a subset of the current `recipes/tc_tracking/` directory. The main work is: + +1. For PR 1: temporarily strip `tc_hunt.py` to only handle `generate_ensemble`, and trim `pyproject.toml` dependencies. +2. For PR 2: minimal diff -- add 3 lines to `tc_hunt.py` + config files. +3. For PR 3: add `baseline_extraction.py` + wiring + configs + aux data + tropycal dep. +4. For PR 4: add `plotting/` directory. + +Each subsequent PR is a clean additive diff on top of the previous one. diff --git a/recipes/tc_tracking/README.md b/recipes/tc_tracking/README.md new file mode 100644 index 000000000..f057ff33c --- /dev/null +++ b/recipes/tc_tracking/README.md @@ -0,0 +1,390 @@ +# Cyclone Tracking + +![Helene Prediction](helene_pred.gif) + +> [!Note] +> This recipe serves as an example of how to integrate custom downstream models into a forecasting pipeline. While TempestExtremes is used here for tropical cyclone tracking, the patterns demonstrated—such as asynchronous CPU/GPU execution, in-memory file handling, and diagnostic model integration—can be adapted for other downstream analysis tools. + +This pipeline produces ensemble prediction and tracks tropical cyclones (TCs) within the prediction. Here, we demonstrate how to integrate downstream analysis tools like [TempestExtremes](https://github.com/ClimateGlobalChange/tempestextremes) into a prediction workflow while avoiding the need to store large atmospheric field data to disk, which can grow prohibitively large and significantly impact inference speed. + +Although TempestExtremes is not GPU-accelerated, its execution time on CPU is typically shorter than the time required for the GPU to produce the next prediction. We therefore implement an asynchronous mode where the CPU executes TempestExtremes on completed time steps while the GPU continues producing subsequent predictions, resulting in virtually no computational overhead from the tracking process. + +## Table of Contents + +1. [Setting up the Environment](#1-setting-up-the-environment) + - [1.1 Container (Recommended)](#11-container-recommended) + - [1.2 UV Environment](#12-uv-environment) +2. [Configure and Execute Runs](#2-configure-and-execute-runs) + - [2.1 Generate Ensemble](#21-generate-ensemble) + - [2.2 Reproduce Individual Ensemble Members](#22-reproduce-individual-ensemble-members) + - [2.3 Extract Reference Tracks from ERA5](#23-extract-reference-tracks-from-era5-using-ibtracs-as-ground-truth) +3. [Visualisation](#3-visualisation) +4. [TempestExtremes Integration](#4-tempestextremes-integration) +5. [Example Workflow](#5-example-workflow) + - [5.1 Extract Baseline](#51-extract-baseline-optional) + - [5.2 Produce Ensemble Forecasts](#52-produce-ensemble-forecasts) + - [5.3 Analyse Tracks](#53-analyse-tracks) + - [5.4 Reproduce Interesting Members](#54-reproduce-interesting-members-to-extract-fields) + + +## 1. Setting up the Environment + +There are two ways to set up the environment: either directly using a uv environment, or using uv to install dependencies inside a container. Since flash-attention, torch-harmonics and TempestExtremes all require compilation, **the container approach is strongly recommended**. + +### 1.1 Container (Recommended) + +A Dockerfile is provided in the top-level directory of the repository. [Building an image from this Dockerfile](https://docs.docker.com/get-started/docker-concepts/building-images/build-tag-and-publish-an-image/#build-an-image) is highly recommended as it avoids the need to compile flash-attention (which otherwise can take hours) and installs TempestExtremes. Note that compiling torch-harmonics and therefore building the container might still take around 20min. + +### 1.2 UV Environment + +> [!Note] +> When installing through uv, note that compiling `flash-attn` (required for AIFS-ENS) can take several hours. See the [troubleshooting notes](https://nvidia.github.io/earth2studio/userguide/support/troubleshooting.html#flash-attention-has-long-build-time-for-aifs-models) for more information. Consider using the container instead, where `flash-attn` is pre-installed in the base image. +> +> Additionally, you will need to install TempestExtremes yourself and point the configuration files to the TempestExtremes executables. + +The project contains a `pyproject.toml` and a `uv.lock` file for setting up the environment. To install with locked dependencies: + +```bash +uv sync --frozen +``` + +This creates a uv environment in the `.venv` directory. The `--frozen` flag ensures the exact versions specified in the lock file are used. Optionally, activate the virtual environment: + +```bash +source .venv/bin/activate +``` + +Additionally, you need to [install TempestExtremes](https://github.com/ClimateGlobalChange/tempestextremes?tab=readme-ov-file#installation-via-cmake-recommended) and either add the executables for `DetectNodes` and `StitchNodes` to the `PATH` or point the associated commands in the configs to these executables. + +#### Potential Issues with torch-harmonics + +If the `--frozen` flag cannot be used (e.g. when updating dependencies to their latest versions), torch-harmonics may fail during the dependency resolution phase. During this phase, uv must build torch-harmonics to resolve dependencies before installation, but the build can fail if other dependencies are not yet installed. Additionally, a Git LFS error occasionally appears. To address these issues, split the installation into a two-step process and skip Git LFS: + +```bash +GIT_LFS_SKIP_SMUDGE=1 uv sync --no-install-package torch-harmonics +GIT_LFS_SKIP_SMUDGE=1 uv sync +``` + + +## 2. Configure and Execute Runs + +Runs are configured through YAML files located in `tc_tracking/cfg`. To execute the pipeline: + +```bash +python tc_hunt.py --config-name=config.yaml +``` + +The script can also be executed in distributed settings using Slurm, MPI, or torchrun. For example: + +```bash +torchrun --nproc-per-node=2 tc_hunt.py --config-name=config.yaml +``` + +The pipeline has three operational modes: + +- **`generate_ensemble`**: Generate an ensemble prediction and extract tropical cyclones. +- **`reproduce_members`**: Reproduce individual ensemble members to store atmospheric fields of interesting tracks. Note: Currently only works with FCN3, as AIFS-ENS does not expose a method to set the model's internal random state. +- **`extract_baseline`**: Extract tropical cyclone tracks from historical reanalysis data (e.g. ERA5) for validation purposes. + +In the following we will explain how to configure the yaml files for those three modes. You can find example configs in `./cfg`. + +### 2.1 Generate Ensemble + +Here we explain how to configure a yaml file to produce an ensemble prediction with either FCN3 or AIFS-ENS and track tropical cyclones within the predictions. A full example can be found at `cfg/helene.yaml`. + +**Basics** + +```yaml +project: 'helene' # project name used for output names +mode: 'generate_ensemble' # choose mode +model: 'aifs' # 'fcn3' or 'aifs' +random_seed: 7777 # optional: random seed (DOES NOT directly control model-internal random state) +``` + +**Ensemble Configuration** + +```yaml +ics: ["2024-09-24 00:00:00", "2024-09-25 18:00:00"] # list of initial conditions +n_steps: 28 # number of forecast steps +ensemble_size: 16 # number of ensemble members FOR EACH initial condition +batch_size: 2 # batch size for inference +``` + +**GPU Memory Requirements**: For a batch size of 1, AIFS-ENS requires approximately 50GB of GPU memory and scales nearly linearly with increasing batch size. FCN3 requires over 60GB for batch size 1, but memory usage increases only marginally with larger batch sizes, making it more memory-efficient for batched inference. + +**IO Configuration** + +```yaml +data_source: # data source for initial conditions + _target_: earth2studio.data.NCAR_ERA5 # choose any of the online or on-prem sources + +store_type: "netcdf" # 'none', 'netcdf', 'zarr' # file type for field data output; 'none' produces no field output +store_dir: "./outputs_${project}" # directory to which all outputs are written +out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m'] # field variables to write to file +``` + +The data source can be any of the Earth-2 Studio data sources or a custom data source implementation. + +**Storage Considerations**: Exercise caution when storing field variables, as storage requirements grow rapidly (proportional to number of ICs × ensemble size × forecast length). Additionally, writing large volumes of data to disk can significantly slow down inference. For FCN3 workflows, consider storing only track data initially and using the [reproducibility feature](#22-reproduce-individual-ensemble-members) to regenerate fields for ensemble members with interesting tracks. + +**Cyclone Tracking with TempestExtremes** (optional) + +```yaml +cyclone_tracking: + asynchronous: True # let GPU produce next prediction while CPU executes TempestExtremes + vars: ['msl', 'u10m', 'v10m', 'z300', 'z500'] # variables required by TempestExtremes + detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,1.0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0' + stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10' + # detect node and stitch node commands from TempestExtremes + orography_path: './orography.nc' # path to orography file (required only if height is relevant to TempestExtremes) + keep_raw_data: False # whether to keep raw field data used for tracking + use_ram: True # write in-memory files to avoid disk I/O + task_timeout_seconds: 120 # timeout for tracking tasks + print_te_output: False # print TempestExtremes output to terminal +``` + +For more information on [asynchronous mode](#async), see the dedicated section below. + +**Debugging TempestExtremes Commands**: To debug `detect_cmd` and `stitch_cmd`, set `print_te_output` to `True` to see TempestExtremes logs. However, iterating through full inference runs for debugging can be cumbersome. For more efficient debugging, set `keep_raw_data` to `True` and `use_ram` to `False` to write field snapshots as NetCDF files to `${store_dir}/cyclone_tracks_te/raw_data`. You can then debug the TempestExtremes commands directly from the command line using these files. Once the commands work correctly, copy them back to the configuration file. When running TempestExtremes from the command line, you need to specify file paths explicitly (see [TempestExtremes documentation](https://climate.ucdavis.edu/tempestextremes.php)): use `--in_data_list` and `--out_file_list` for `DetectNodes`, and `--in` and `--out` for `StitchNodes`. + +**Stability Check** (optional) + +FCN3 can perform stable rollouts on subseasonal timescales (weeks to months), but occasionally experiences numerical instabilities on seasonal timescales. A simple stability check has been implemented to detect and handle these cases: + +**Key Points**: +- **Applicable to**: FCN3 for (sub)seasonal forecasts. The long-term behaviour of AIFS-ENS is still under investigation. +- **Detection mechanism**: Instabilities manifest in global averages of field variables, which typically build up over ~10 days before diverging rapidly. +- **Implementation**: Provide a list of variables to monitor and a threshold for each. Global averages are computed at initialisation and at each time step. If any variable's global average exceeds its threshold, the simulation is interrupted and restarted with a new random seed. +- **Limitation**: This is a basic implementation. More advanced detection methods that don't require 10 extra simulation days, and better load-balancing when many ensemble members become unstable, would be beneficial improvements. + +```yaml +stability_check: + variables: ['t1000', 'u250', 'v850'] + thresholds: [10, 10, 10] +``` + +**Hydra Configuration** (optional) + +```yaml +hydra: + job: + name: ${project} + run: + dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S} +``` + +This propagates the project name and output directory to Hydra's logging system. You will most likely never need to modify this section. + + +### 2.2 Reproduce Individual Ensemble Members + +A common workflow is to run ensemble forecasts without storing atmospheric fields, since track data is approximately five orders of magnitude smaller than the associated field data. This enables sending results of hundreds of hypothetical cyclone seasons as an email attachment. However, for ensemble members with particularly interesting tracks, examining the atmospheric fields can help understand the storm's evolution. + +FCN3 provides access to its internal random state, enabling exact reproduction of individual ensemble members. In theory, AIFS-ENS should have this capability as well, but the internal random state is not exposed through the Anemoi models interface, preventing reproducibility of AIFS-ENS runs. + +Note that [reproducibility is challenging](https://arxiv.org/abs/1605.04339) and only works reliably when using the same environment on the same system to produce the predictions. + +To reproduce a run, you need: +- Configuration file from the original run (ideally) +- Ensemble member ID and random seed for all members to be reproduced (both provided in the track CSV filename) +- Batch size (also provided in the track CSV filename) + +**Configuration Changes** + +The following describes what to change in the configuration file to reproduce ensemble members. You can compare `cfg/helene.yaml` and `cfg/reproduce_helene.yaml` as reference examples. In the original configuration, consider changing the project name to avoid overwriting files, and set the mode to: + +```yaml +mode: 'reproduce_members' +``` + +In the [**ensemble configuration section**](#config_ens), remove the `ics` variable and replace it with a list of ensemble members to reproduce. For each ensemble member, provide the initial condition, member ID, and random seed: + +```yaml +reproduce_members: [ + ['2024-09-24 00:00:00', 5, 2148531792], + ['2024-09-24 00:00:00', 8, 1342799488], + ['2024-09-24 12:00:00', 4, 2528887706], + ['2024-09-24 12:00:00', 5, 2528887706], + ['2024-09-24 12:00:00', 13, 1968734508], + ] +``` + +The example above would reproduce members 5 and 8 from the ensemble starting at midnight, and members 4, 5, and 13 from the ensemble starting at midday. + +**Batch Reproduction Behaviour**: Note that the internal random state can only be set for a complete batch. While individual members within a batch will produce independent predictions, they share the same random seed. This means a full batch must always be reproduced. For example, with a batch size of 2 in the example above, the run would reproduce members 4, 5, 8, and 9 from the midnight ensemble, and members 4, 5, 12, and 13 from the midday ensemble. + + +### 2.3 Extract Reference Tracks from ERA5 Using IBTrACS as Ground Truth + +[IBTrACS](https://www.ncei.noaa.gov/products/international-best-track-archive) (International Best Track Archive for Climate Stewardship) is considered the gold standard for tropical cyclone observation data. However, it represents point observations that a global model at quarter-degree resolution cannot reproduce accurately. Therefore, a meaningful validation of model predictions must be performed against ERA5. To do so, we first have to extract tropical cyclone tracks directly from reanalysis data (such as ERA5) and compare the predictions against these reference tracks. This pipeline demonstrates how to extract reference tracks for named storms, using IBTrACS to identify the temporal windows and approximate locations of storms of interest. + +**Basics** + +```yaml +project: 'reference_tracks' +mode: 'extract_baseline' +``` + +**Cases** + +Specify which storms to extract by providing their name, year, and basin for each case: + +```yaml +cases: + hato: + year: 2017 + basin: west_pacific + helene: + year: 2024 + basin: north_atlantic +``` + +The script queries the IBTrACS database to determine when each storm was active, retrieves the corresponding atmospheric data from the specified data source, and applies TempestExtremes to extract tropical cyclone tracks. Since multiple storms may be active globally at any given time, a second matching step compares all extracted tracks against the IBTrACS ground truth to identify the correct track for each named storm. + +**IO Configuration** + +**IBTrACS Data**: +- Download the IBTrACS data in CSV format using: + ```bash + wget https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r01/access/csv/ibtracs.ALL.list.v04r01.csv + ``` + Note: This file is over 300MB and may take several minutes to download. +- For testing with only Helene and Hato, you can use the subset provided at `./test/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv`. + +**Reanalysis Data**: +- For extracting multiple storms, we recommend using an on-premise dataset to avoid lengthy download times. +- The data source can be configured using the standard approach (as shown in the ensemble generation section). +- Alternatively, different data sources can be specified for different year ranges, which is particularly useful when datasets are partitioned into training/testing/out-of-sample periods. +- The example below shows how to configure different ERA5 sources for different time periods: + +```yaml +store_dir: "./outputs_${project}" +ibtracs_source_data: "/path/to/ibtracs.ALL.list.v04r01.csv" + +data_source: + era5_train: + years: "1980-2016" + source: + _target_: earth2studio.data.NCAR_ERA5 + + era5_oos: + years: "2017-2022" + source: + _target_: earth2studio.data.CDS +``` + + +## 3. Visualisation + +Two Jupyter notebooks are provided in `./plotting` for analysing and visualising tropical cyclone tracking results: + +- **`tracks_slayground.ipynb`**: Ensemble track analysis including spaghetti plots (trajectory visualisation), absolute and relative intensity metrics (wind speed, MSLP), comparisons against ERA5 reference tracks and IBTrACS observations, extreme value statistics, and error moment analysis over lead time. + +- **`plot_tracks_n_fields.ipynb`**: Create animated visualisations of storm tracks overlaid on atmospheric field data. + +## 4. TempestExtremes Integration + +This pipeline uses TempestExtremes to demonstrate how to integrate custom downstream analysis tools into a prediction workflow. TempestExtremes requires file-based input, does not run on GPU, and cannot be installed as a Python library—making it a useful reference case, as tools with fewer constraints should be simpler to integrate. The current implementation avoids writing atmospheric field data to disk and reading it back, which would otherwise slow down inference significantly and become prohibitive given the large data volumes involved. + +**General Integration Approach** + +TempestExtremes is implemented as a diagnostic model class but without returning any data, since results are written directly to disk. The diagnostic model class spawns a subprocess to execute TempestExtremes command-line tools. Track files are produced directly by TempestExtremes and written to the specified output directory. + +Since TempestExtremes requires file-based input, we must provide atmospheric data through files. To avoid the overhead of writing and reading large volumes of data to/from disk, we write temporary files to `/dev/shm` (shared memory filesystem), effectively creating in-memory files that TempestExtremes can access while avoiding physical disk I/O. + +**Asynchronous Mode** + +On many platforms, executing TempestExtremes on the CPU is faster than producing the prediction on the GPU. We therefore implemented an asynchronous mode where a CPU thread executes TempestExtremes on completed time steps while the GPU continues producing the next prediction, enabling tracking with virtually no overhead. + +**Implementation Details**: +- In asynchronous mode, each ensemble member in a batch is assigned its own processing thread, allowing parallel tracking across the batch. +- If TempestExtremes execution becomes slower than GPU prediction (which might occur with AIFS-ENS on newer GPUs like B200), consider increasing the batch size. GPU prediction time scales approximately linearly with batch size, while the time for a single CPU thread to process one member remains constant. +- If you encounter issues, first verify whether they also occur with asynchronous mode disabled before debugging further. + + +## 5. Example Workflow + +This section walks through an example workflow demonstrating the pipeline's core functionality: generating an ensemble prediction, reproducing individual members, visualising results, and comparing predicted tracks against reference tracks extracted from ERA5 data. + +### 5.1 Extract Baseline (Optional) + +This step extracts reference tracks from ERA5 reanalysis for comparison with forecast predictions. Note that downloading the ERA5 field data for Hato and Helene is required and can take some time. If it takes too long, you can skip this step and use the pre-computed reference tracks provided in `./test/aux_data` instead. + +First, download the IBTrACS data, then extract the baseline tracks: + +```bash +python tc_hunt.py --config-name=extract_era5.yaml +``` + +This should produce a folder called `outputs_reference_tracks/` containing two reference track files: +- `reference_track_hato_2017_west_pacific.csv` +- `reference_track_helene_2024_north_atlantic.csv` + +### 5.2 Produce Ensemble Forecasts + +Run the forecast loop for Helene using FCN3 and for Hato using AIFS-ENS: + +```bash +python tc_hunt.py --config-name=helene.yaml +python tc_hunt.py --config-name=hato.yaml +``` + +This should produce two output folders: `outputs_helene` and `outputs_hato`, each containing tracked tropical cyclone trajectories. + +### 5.3 Analyse Tracks + +Visualise the results using the notebook `plotting/tracks_slayground.ipynb`. + +**For Helene**, configure the first cell of the notebook with: +```python +case = 'helene_2024_north_atlantic' +pred_track_dir = '/path/to/outputs_helene/cyclone_tracks_te' +tru_track_dir = '/path/to/outputs_reference_tracks' +# If you skipped baseline extraction, use: +# tru_track_dir = '/path/to/test/aux_data' +``` + +**For Hato**, use: +```python +case = 'hato_2017_west_pacific' +pred_track_dir = '/path/to/outputs_hato/cyclone_tracks_te' +tru_track_dir = '/path/to/outputs_reference_tracks' +# If you skipped baseline extraction, use: +# tru_track_dir = '/path/to/test/aux_data' +``` + +### 5.4 Reproduce Interesting Members to Extract Fields + +Suppose that after conducting the above analysis you want to take a closer look at ensemble members 0, 8, 9, and 12 of the Helene prediction. Since the forecast was produced with FCN3, we can reproduce these members and store the atmospheric fields for detailed analysis. + +**Reproduce with Field Storage**: + +1. In `cfg/reproduce_helene.yaml`, verify that the random seeds in the `reproduce_members` variable match the seeds from the track filenames in `outputs_helene/cyclone_tracks_te`. If they do not, update the seeds in the config accordingly. +2. Double-check that the batch size, ensemble size, and data source are identical to the original run. +3. Execute the pipeline: + ```bash + python tc_hunt.py --config-name=reproduce_helene.yaml + ``` +4. This should produce a folder `outputs_reproduce_helene` containing both track files and a NetCDF file named `reproduce_helene_2024-09-24T00.00.00_mems0000-0013.nc` with the atmospheric fields. + +**Visualise Tracks and Fields**: + +Use the notebook `plotting/plot_tracks_n_fields.ipynb` to create animated visualisations: + +1. Configure the file paths: + ```python + field_data = '/path/to/outputs_reproduce_helene/helene_2024-09-24T00.00.00_mems0000-0015.nc' + track_dir = '/path/to/outputs_reproduce_helene/cyclone_tracks_te' + ``` + +2. Select visualisation parameters: + ```python + variable = 'wind_speed' # or 'msl', etc. + ensemble_member = 8 # choose from the reproduced subset + region = 'gulf_of_mexico' + ``` + +3. Run the notebook to generate an interactive animation where you can step through time. + +**Verification**: To verify that reproducibility worked correctly, point `track_dir` to the original Helene run (`outputs_helene/cyclone_tracks_te`) and confirm that the tracks are identical. + diff --git a/recipes/tc_tracking/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv b/recipes/tc_tracking/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv new file mode 100644 index 000000000..235107e4f --- /dev/null +++ b/recipes/tc_tracking/aux_data/ibtracs.HATO_HELENE.list.v04r01.csv @@ -0,0 +1,92 @@ 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+0,2024,9,28,6,271.75,37.25,99192.81,10.94264,271.9,37.4,99200,30,LO +0,2024,9,28,12,272.0,36.5,99795.0,6.417272,272.4,36.6,99700,25,LO diff --git a/recipes/tc_tracking/cfg/extract_era5.yaml b/recipes/tc_tracking/cfg/extract_era5.yaml new file mode 100644 index 000000000..352564991 --- /dev/null +++ b/recipes/tc_tracking/cfg/extract_era5.yaml @@ -0,0 +1,115 @@ +project: 'reference_tracks' +mode: 'extract_baseline' + +cases: + # erin: + # year: 2025 + # basin: north_atlantic + + # harvey: + # year: 2017 + # basin: north_atlantic + + # irma: + # year: 2017 + # basin: north_atlantic + + # maria: + # year: 2017 + # basin: north_atlantic + + # ophelia: + # year: 2017 + # basin: north_atlantic + + # michael: + # year: 2018 + # basin: north_atlantic + + # dorian: + # year: 2019 + # basin: north_atlantic + + # lorenzo: + # year: 2019 + # basin: north_atlantic + + # iota: + # year: 2020 + # basin: north_atlantic + + # ian: + # year: 2022 + # basin: north_atlantic + + # lee: + # year: 2023 + # basin: north_atlantic + + # beryl: + # year: 2024 + # basin: north_atlantic + + # milton: + # year: 2024 + # basin: north_atlantic + + helene: + year: 2024 + basin: north_atlantic + + # lan: + # year: 2017 + # basin: west_pacific + + hato: + year: 2017 + basin: west_pacific + + # mawar: + # year: 2023 + # basin: west_pacific + + # yagi: + # year: 2024 + # basin: west_pacific + + # amphan: + # year: 2020 + # basin: north_indian + + # debbie: + # year: 2017 + # basin: southern_pacific + +# CAUTION: ibtracs file for hato and helene only. obtain full data at https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r01/access/csv/ibtracs.ALL.list.v04r01.csv +ibtracs_source_data: "./aux_data/ibtracs.HATO_HELENE.list.v04r01.csv" + +data_source: + era5_train: + years: "1980-2017" + source: + _target_: earth2studio.data.NCAR_ERA5 + + era5_oos: + years: "2018-2026" + source: + _target_: earth2studio.data.CDS + +store_dir: "./outputs_${project}" + +cyclone_tracking: + vars: ['msl', 'u10m', 'v10m', 'z300', 'z500'] + detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0' + stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10' + orography_path: './aux_data/orography.nc' + keep_raw_data: False + use_ram: True + print_te_output: False + +# hydra logs +hydra: + job: + name: ${project} + run: + dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S} diff --git a/recipes/tc_tracking/cfg/hato.yaml b/recipes/tc_tracking/cfg/hato.yaml new file mode 100644 index 000000000..9167a7544 --- /dev/null +++ b/recipes/tc_tracking/cfg/hato.yaml @@ -0,0 +1,37 @@ +project: 'hato' +mode: 'generate_ensemble' +model: 'aifs' # 'fcn3' 'aifs' + +ics: ["2017-08-21 12:00:00"] +n_steps: 20 +ensemble_size: 16 +batch_size: 2 + +data_source: + _target_: earth2studio.data.CDS + +store_type: "netcdf" +store_dir: "./outputs_${project}" +out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m'] + +cyclone_tracking: + asynchronous: True + vars: ['msl', 'u10m', 'v10m', 'z300', 'z500'] + detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0' + stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10' + orography_path: './aux_data/orography.nc' + keep_raw_data: False + use_ram: True + task_timeout_seconds: 120 + print_te_output: False + +stability_check: + variables: ['t1000', 'u250', 'v850'] + thresholds: [10, 10, 10] + +# hydra logs +hydra: + job: + name: ${project} + run: + dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S} diff --git a/recipes/tc_tracking/cfg/helene.yaml b/recipes/tc_tracking/cfg/helene.yaml new file mode 100644 index 000000000..5143f8f4e --- /dev/null +++ b/recipes/tc_tracking/cfg/helene.yaml @@ -0,0 +1,37 @@ +project: 'helene' +mode: 'generate_ensemble' +model: 'fcn3' # 'fcn3' 'aifs' + +ics: ["2024-09-24 00:00:00"] +n_steps: 28 +ensemble_size: 16 +batch_size: 2 + +data_source: + _target_: earth2studio.data.NCAR_ERA5 + +store_type: "netcdf" +store_dir: "./outputs_${project}" +out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m'] + +cyclone_tracking: + asynchronous: True + vars: ['msl', 'u10m', 'v10m', 'z300', 'z500'] + detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0' + stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10' + orography_path: './aux_data/orography.nc' + keep_raw_data: False + use_ram: True + task_timeout_seconds: 120 + print_te_output: False + +stability_check: + variables: ['t1000', 'u250', 'v850'] + thresholds: [10, 10, 10] + +# hydra logs +hydra: + job: + name: ${project} + run: + dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S} diff --git a/recipes/tc_tracking/cfg/reproduce_helene.yaml b/recipes/tc_tracking/cfg/reproduce_helene.yaml new file mode 100644 index 000000000..9fc750022 --- /dev/null +++ b/recipes/tc_tracking/cfg/reproduce_helene.yaml @@ -0,0 +1,39 @@ +project: 'reproduce_helene' +mode: 'reproduce_members' +model: 'fcn3' + +reproduce_members: [ + ['2024-09-24 00:00:00', 0, 2148531792], + ['2024-09-24 00:00:00', 8, 1342799488], + ['2024-09-24 00:00:00', 9, 1342799488], + ['2024-09-24 00:00:00', 12, 1968734508], + ] + +n_steps: 28 +ensemble_size: 16 +batch_size: 2 + +data_source: + _target_: earth2studio.data.NCAR_ERA5 + +store_type: "netcdf" +store_dir: "./outputs_${project}" +out_vars: ['msl', 'z300', 'z500', 'u10m', 'v10m'] + +cyclone_tracking: + asynchronous: True + vars: ['msl', 'u10m', 'v10m', 'z300', 'z500'] + detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0' + stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10' + orography_path: './aux_data/orography.nc' + keep_raw_data: False + use_ram: True + task_timeout_seconds: 120 + print_te_output: False + +# hydra logs +hydra: + job: + name: ${project} + run: + dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S} diff --git a/recipes/tc_tracking/helene_pred.gif b/recipes/tc_tracking/helene_pred.gif new file mode 100644 index 000000000..c82fe7b2f Binary files /dev/null and b/recipes/tc_tracking/helene_pred.gif differ diff --git a/recipes/tc_tracking/plotting/.gitignore b/recipes/tc_tracking/plotting/.gitignore new file mode 100644 index 000000000..e34b598e6 --- /dev/null +++ b/recipes/tc_tracking/plotting/.gitignore @@ -0,0 +1 @@ +plots/ diff --git a/recipes/tc_tracking/plotting/README.md b/recipes/tc_tracking/plotting/README.md new file mode 100644 index 000000000..86550a357 --- /dev/null +++ b/recipes/tc_tracking/plotting/README.md @@ -0,0 +1,11 @@ +# Analysing and Plotting TC Tracks + +## Notebooks + +- **`plot_tracks_n_fields.ipynb`** – Plotting tracks and fields for individual ensemble members +- **`tracks_slayground.ipynb`** – Analysing and plotting complete tracks from a full ensemble run for a case study on a given storm + +## Additional Information + +- This directory includes Python scripts with methods for plotting and analysing results, which are called from the notebooks +- Each notebook specifies at the beginning what data is required and how to produce it using the TC tracking pipeline diff --git a/recipes/tc_tracking/plotting/analyse_n_plot.py b/recipes/tc_tracking/plotting/analyse_n_plot.py new file mode 100644 index 000000000..d8e06daed --- /dev/null +++ b/recipes/tc_tracking/plotting/analyse_n_plot.py @@ -0,0 +1,320 @@ +import os +from collections import OrderedDict + +import numpy as np +from data_handling import ( + compute_averages_of_errors_over_lead_time, + extract_tracks, + extract_tracks_from_file, + get_ensemble_averages, + match_tracks, +) +from plotting_helpers import ( + plot_ensemble_mean_metrics, + plot_errors_over_lead_time, + plot_extreme_extremes_histograms, + plot_ib_era5, + plot_over_time, + plot_relative_over_time, + plot_spaghetti, +) +from tqdm import tqdm + + +def load_tracks(case, pred_track_dir, tru_track_dir, out_dir): + tru_track = extract_tracks_from_file( + os.path.join(tru_track_dir, f"reference_track_{case}.csv") + ) + tru_track["dist"] = np.zeros(len(tru_track)) + + pred_tracks = extract_tracks(in_dir=os.path.join(pred_track_dir)) + n_members = len(pred_tracks) + + pred_tracks = match_tracks(pred_tracks, tru_track) + + if out_dir: + out_dir = os.path.join(out_dir, case) + os.makedirs(out_dir, exist_ok=True) + + ens_mean = get_ensemble_averages(pred_tracks=pred_tracks, tru_track=tru_track) + + return tru_track, pred_tracks, ens_mean, n_members, out_dir + + +def analyse_individual_storms(cases, pred_track_dir, tru_track_dir, out_path): + if isinstance(cases, str): + cases = [cases] + + for case in tqdm(cases): + tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks( + case=case, + pred_track_dir=pred_track_dir, + tru_track_dir=tru_track_dir, + out_dir=out_path, + ) + + # spaghetti plot + plot_spaghetti( + true_track=tru_track, + pred_tracks=pred_tracks, + ensemble_mean=ens_mean["mean"], + case=case, + n_members=n_members, + out_dir=out_dir, + ) + + # plot intensities over time + plot_over_time( + pred_tracks=pred_tracks, + tru_track=tru_track, + ensemble_mean=ens_mean, + case=case, + n_members=n_members, + out_dir=out_dir, + ) + + # plot relative intensities over time + plot_relative_over_time( + pred_tracks=pred_tracks, + tru_track=tru_track, + ensemble_mean=ens_mean, + case=case, + n_members=n_members, + out_dir=out_dir, + ) + + # plot comparison of intensities between ibtracs and era5 over time + plot_ib_era5( + tru_track=tru_track, case=case, vars=["msl", "wind_speed"], out_dir=out_dir + ) + + # plot histogram of extreme values of intensities over full track + plot_extreme_extremes_histograms( + pred_tracks=pred_tracks, + tru_track=tru_track, + ensemble_mean=ens_mean, + case=case, + out_dir=out_dir, + ) + + # plot error metrics over lead time + err_dict, _ = compute_averages_of_errors_over_lead_time( + pred_tracks=pred_tracks, + tru_track=tru_track, + vars=["wind_speed", "msl", "dist"], + ) + + plot_errors_over_lead_time( + err_dict=err_dict, + case=case, + ic=pred_tracks[0]["ic"], + n_members=n_members, + n_tracks=len(pred_tracks), + out_dir=out_dir, + ) + + return + + +def stack_metrics(err_dict): + var_errs = [] + for var in err_dict.keys(): + metrics = [] + for metric in err_dict[var].keys(): + metrics.append(err_dict[var][metric]) + + metrics = np.stack(metrics, axis=0) + var_errs.append(metrics) + + return np.stack(var_errs, axis=0) + + +def stack_cases(storm_metrics, max_len): + for ii in range(len(storm_metrics["case"])): + # pad storm_metrics['data'][ii].shape with nan to max_len + storm_metrics["data"][ii] = np.pad( + storm_metrics["data"][ii], + pad_width=( + (0, 0), + (0, 0), + (0, max_len - storm_metrics["data"][ii].shape[-1]), + ), + mode="constant", + constant_values=np.nan, + ) + + storm_metrics["data"] = np.stack(storm_metrics["data"], axis=0) + + # check if shapes ok + should_shape = ( + len(storm_metrics["case"]), + len(storm_metrics["var"]), + len(storm_metrics["metric"]), + max_len, + ) + assert ( + storm_metrics["data"].shape == should_shape + ), f'shapes not matching when stacking cases: {storm_metrics["data"].shape=} {should_shape=}' + + return storm_metrics + + +def extract_weights(storm_metrics, max_len): + + # extract weights + ens_idx = storm_metrics["metric"].index("n_members") + weights = storm_metrics["data"][:, 0, ens_idx, :] + weights = np.nan_to_num(weights, nan=0).astype(int) + + # remove 'n_members' from metrics + storm_metrics["metric"].remove("n_members") + storm_metrics["data"] = np.delete(storm_metrics["data"], ens_idx, axis=-2) + + # add weights to storm_metrics OrderedDict + storm_metrics["weights"] = weights + + # check if shapes ok + should_shape = ( + len(storm_metrics["case"]), + len(storm_metrics["var"]), + len(storm_metrics["metric"]), + max_len, + ) + assert ( + storm_metrics["data"].shape == should_shape + ), f'shapes not matching when stacking cases: {storm_metrics["data"].shape=} {should_shape=}' + + return storm_metrics + + +def get_individual_storm_metrics( + cases, pred_track_dir, tru_track_dir, out_path, vars=["wind_speed", "msl", "dist"] +): + storm_metrics = OrderedDict( + {"case": [], "var": None, "metric": None, "lead time": None, "data": []} + ) + max_len, ensemble_averages, extremes = 0, {}, {} + for case in tqdm(cases, desc="loading storm data"): + tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks( + case=case, + pred_track_dir=pred_track_dir, + tru_track_dir=tru_track_dir, + out_dir=out_path, + ) + # record ensemble mean + ensemble_averages[case] = ens_mean + + # compute averages of error metrics over lead time for storm + err_dict, _max_len = compute_averages_of_errors_over_lead_time( + pred_tracks=pred_tracks, tru_track=tru_track, vars=vars + ) + + extremes[case] = {} + for var in vars: + extremes[case][var] = {} + for ext, npfun in zip(["min", "max"], [np.nanmin, np.nanmax]): + extremes[case][var][ext + "_pred"] = err_dict[var].pop(ext) + extremes[case][var][ext + "_tru"] = npfun(tru_track[var]) + + max_len = max(max_len, _max_len) + storm_metrics["case"].append(case) + storm_metrics["data"].append(stack_metrics(err_dict)) + + storm_metrics["var"] = list(err_dict.keys()) + storm_metrics["metric"] = list( + err_dict[list(err_dict.keys())[0]].keys() + ) # TODO remove n_members + storm_metrics["lead time"] = np.arange(max_len) * np.timedelta64(6, "h") + + return storm_metrics, max_len, ensemble_averages, extremes + + +def reduce_over_all_storms(storm_metrics): + + ensemble_metrics = {} + for var in storm_metrics["var"]: + ensemble_metrics[var] = {} + var_idx = storm_metrics["var"].index(var) + for metric in storm_metrics["metric"]: + met_idx = storm_metrics["metric"].index(metric) + ensemble_metrics[var][metric] = np.nanmean( + storm_metrics["data"][:, var_idx, met_idx, :], axis=0 + ) + + ensemble_metrics["n_members"] = np.sum(storm_metrics["weights"], axis=0) + + return ensemble_metrics + + +def analyse_ensemble_of_storms(cases, pred_track_dir, tru_track_dir, out_path): + + storm_metrics, max_len, ens_means = get_individual_storm_metrics( + cases, pred_track_dir, tru_track_dir, out_path + ) + + storm_metrics = stack_cases(storm_metrics, max_len) + + storm_metrics = extract_weights(storm_metrics, max_len) + + ensemble_metrics = reduce_over_all_storms(storm_metrics) + + plot_ensemble_mean_metrics(ens_means, out_path) + + return ensemble_metrics + + +def analyse_n_plot_tracks(): + cases = [ + "amphan_2020_north_indian", # 00 + "beryl_2024_north_atlantic", # 01 + "debbie_2017_southern_pacific", # 02 + "dorian_2019_north_atlantic", # 03 + "harvey_2017_north_atlantic", # 04 + "hato_2017_west_pacific", # 05 + "helene_2024_north_atlantic", # 06 + "ian_2022_north_atlantic", # 07 + "iota_2020_north_atlantic", # 08 + "irma_2017_north_atlantic", # 09 + "lan_2017_west_pacific", # 10 + "lee_2023_north_atlantic", # 11 + "lorenzo_2019_north_atlantic", # 12 + "maria_2017_north_atlantic", # 13 + "mawar_2023_west_pacific", # 14 + "michael_2018_north_atlantic", # 15 + "milton_2024_north_atlantic", # 16 + "ophelia_2017_north_atlantic", # 17 + "yagi_2024_west_pacific", # 18 + ] + + # case_selection = list(range(len(cases))) + case_selection = [6, 13] + individual_storms = False + ensemble_of_storms = True + + pred_track_dir = f"/path/to/predictions/cyclone_tracks_te" + tru_track_dir = ( + f"/path/to/reference_tracks" + ) + out_dir = f"./plots" + + if individual_storms: + analyse_individual_storms( + cases=[cases[ii] for ii in case_selection], + pred_track_dir=pred_track_dir, + tru_track_dir=tru_track_dir, + out_path=out_dir, + ) + + if ensemble_of_storms: + analyse_ensemble_of_storms( + cases=[cases[ii] for ii in case_selection], + pred_track_dir=pred_track_dir, + tru_track_dir=tru_track_dir, + out_path=out_dir, + ) + + return + + +if __name__ == "__main__": + analyse_n_plot_tracks() diff --git a/recipes/tc_tracking/plotting/data_handling.py b/recipes/tc_tracking/plotting/data_handling.py new file mode 100644 index 000000000..80cf08d79 --- /dev/null +++ b/recipes/tc_tracking/plotting/data_handling.py @@ -0,0 +1,417 @@ +import glob + +import numpy as np +import pandas as pd + + +def great_circle_distance(lat1, lon1, lat2, lon2, radius=6371000): + lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2]) + dlon = lon2 - lon1 + dlat = lat2 - lat1 + + aa = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2 + cc = 2 * np.arctan2(np.sqrt(aa), np.sqrt(1 - aa)) + + return radius * cc + + +def merge_tracks_by_time(track, tru_track): + merged_track = pd.merge( + track, tru_track, on="time", how="left", suffixes=("", "_tru") + ) + + # remove columns which are later than last time of tru_track + merged_track = merged_track[merged_track["time"] <= tru_track["time"].max()] + + return merged_track + + +def add_track_distance(track, tru_track): + + merged_track = merge_tracks_by_time(track, tru_track)[ + ["time", "lat", "lon", "lat_tru", "lon_tru"] + ] + + # compute great circle distance between each point on the track and the true track + dist = great_circle_distance( + merged_track["lat"], + merged_track["lon"], + merged_track["lat_tru"], + merged_track["lon_tru"], + ) + + # add to merged_track + merged_track["dist"] = dist + + # add dist to track + track = pd.merge( + track, merged_track[["time", "dist"]], on="time", how="left", suffixes=("", "") + ) + + return track + + +def match_tracks(pred_tracks, true_track): + # iterate over tracks and see if track exists in simulation + matched_tracks = [] + min_dist, max_dist = 25371000, -1.0 + + for _pred_track_dict in pred_tracks: + _pred_tracks = _pred_track_dict["tracks"] + + if len(_pred_tracks) == 0: + continue + + n_tracks = _pred_tracks["track_id"].iloc[-1] + 1 + + for ii in range(n_tracks): + track = _pred_tracks.loc[_pred_tracks["track_id"] == ii].copy() + + lat_pred = track["lat"].iloc[0] + lon_pred = track["lon"].iloc[0] + + if not (true_track["time"] == track["time"].iloc[0]).any(): + continue + + lat_true = true_track.loc[true_track["time"] == track["time"].iloc[0]][ + "lat_ib" + ].item() + lon_true = true_track.loc[true_track["time"] == track["time"].iloc[0]][ + "lon_ib" + ].item() + dist = great_circle_distance(lat_pred, lon_pred, lat_true, lon_true) + + if dist <= 300000: + min_dist, max_dist = min(min_dist, dist), max(max_dist, dist) + + # add dist to track + track = add_track_distance(track, true_track) + + matched_tracks.append( + { + "ic": _pred_track_dict["ic"], + "member": _pred_track_dict["member"], + "first_match": track["time"].iloc[0], + "initial_dist": dist, + "tracks": track, + } + ) + break + + line = f"matched {len(matched_tracks)} out of {len(pred_tracks)} tracks, " + line += f"with distances ranging from {min_dist/1000:.1f} to {max_dist/1000:.1f} km" + print(line) + + return matched_tracks + + +def extract_tracks_from_file(csv_file): + tracks = pd.read_csv(csv_file, sep=",") + tracks.columns = tracks.columns.str.strip() + + times = [ + pd.to_datetime( + f'{tracks["year"].iloc[jj]}-{int(tracks.iloc[jj]["month"]):02d}-{int(tracks.iloc[jj]["day"]):02d} {int(tracks.iloc[jj]["hour"]):02d}:00:00' + ) + for jj in range(len(tracks)) + ] + + tracks.drop(columns=["year", "month", "day", "hour"], inplace=True) + # if i j in tracks, drop them + if "i" in tracks.columns: + tracks.drop(columns=["i", "j"], inplace=True) + + tracks.insert(0, "time", times) + + return tracks + + +def extract_tracks(in_dir): + tracks = [] + files = glob.glob(f"{in_dir}/*.csv") + files.sort() + + for csv_file in files: + # read csv, strip whitespace from column names + _tracks = extract_tracks_from_file(csv_file) + + mem = int(csv_file.split("_mem_")[-1].split("_seed_")[0]) + ic = pd.to_datetime(csv_file.split("_mem_")[0][-19:]) + + tracks.append({"ic": ic, "member": mem, "tracks": _tracks}) + + return tracks + + +def merge_tracks_by_lead_time(track_dict, tru_track): + + merged_track = merge_tracks_by_time(track_dict["tracks"], tru_track) + + # clip leading and trailing rows in which only predicted or only true track are present + t_max = max(track_dict["tracks"]["time"].max(), tru_track["time"].max()) + t_min = min(track_dict["tracks"]["time"].min(), tru_track["time"].min()) + merged_track = merged_track[merged_track["time"] >= t_min] + merged_track = merged_track[merged_track["time"] <= t_max] + + return merged_track + + +# function computing MAE +def compute_mae(tru_vars, pred_vars): + return np.nanmean(np.abs(tru_vars - pred_vars), axis=0) + + +# function computing mse +def compute_mse(tru_vars, pred_vars): + return np.nanmean((tru_vars - pred_vars) ** 2, axis=0) + + +# function computing RMSE +def compute_rmse(tru_vars, pred_vars): + return np.sqrt(compute_mse(tru_vars, pred_vars)) + + +# function computing variance +def compute_variance(arr): + return np.nanvar(arr, axis=0) + + +def compute_std(arr): + return np.sqrt(compute_variance(arr)) + + +def remove_trailing_nans(merged_track, var): + either_nans = np.logical_or( + merged_track[var + "_tru"].isna(), merged_track[var].isna() + ) + cut_off = np.where(~either_nans)[0][-1] + + return merged_track.iloc[: cut_off + 1] + + +def rebase_by_lead_time(pred_tracks, tru_track, vars): + err_dict = {} + for var in vars: + err_dict[var] = {"pred": [], "tru": []} + + max_len = 0 + ii = 0 + for track in pred_tracks: + ii += 1 + merged_track = merge_tracks_by_lead_time(track, tru_track) + if merged_track is None: + continue + + # remove trailing rows in which both true and predicted track are nan + merged_track = remove_trailing_nans(merged_track, "msl") + + max_len = max(max_len, len(merged_track)) + + for var in err_dict.keys(): + err_dict[var]["pred"].append(merged_track[var]) + err_dict[var]["tru"].append(merged_track[var + "_tru"]) + + return err_dict, max_len + + +def compute_error_metrics(err_dict, max_len): + for var in err_dict.keys(): + pred_vars = err_dict[var]["pred"] + tru_vars = err_dict[var]["tru"] + + # pad with nans to make all arrays the same length + counts = np.zeros(max_len, dtype=int) + for ii in range(len(pred_vars)): + counts[: len(pred_vars[ii])] += 1 + + pred_vars[ii] = np.pad( + pred_vars[ii], + (0, max_len - len(pred_vars[ii])), + mode="constant", + constant_values=np.nan, + ) + tru_vars[ii] = np.pad( + tru_vars[ii], + (0, max_len - len(tru_vars[ii])), + mode="constant", + constant_values=np.nan, + ) + + # merge to single array + pred_vars, tru_vars = np.array(pred_vars), np.array(tru_vars) + + err_dict[var] = { + "mae": compute_mae(tru_vars, pred_vars), + "mse": compute_mse(tru_vars, pred_vars), + "variance": compute_variance(pred_vars), + "max": np.nanmax(pred_vars, axis=-1), + "min": np.nanmin(pred_vars, axis=-1), + "n_members": counts, + } + + return err_dict + + +def compute_averages_of_errors_over_lead_time(pred_tracks, tru_track, vars): + + err_dict, max_len = rebase_by_lead_time(pred_tracks, tru_track, vars) + + err_dict = compute_error_metrics(err_dict, max_len) + + return err_dict, max_len + + +def lat_lon_to_xyz(lat, lon, radius=6371000): + """ + Converts longitude and latitude coordinates to 3D Cartesian (XYZ) coordinates using numpy. + This function can handle single values or numpy arrays as input. + + Args: + lon (float or np.ndarray): Longitude(s) in degrees (range [0, 360)). + lat (float or np.ndarray): Latitude(s) in degrees (range [-90, 90]). + radius (float, optional): The radius of the sphere (e.g., Earth's radius). + Defaults to 1.0 for a unit sphere. + + Returns: + np.ndarray: A numpy array containing the X, Y, and Z coordinates. + - If inputs are scalars, returns a (3,) array. + - If inputs are arrays of length N, returns an (N, 3) array. + """ + # Convert degrees to radians using numpy's vectorized function + lat_rad, lon_rad = np.radians(lat), np.radians(lon) + + # Apply the conversion formulas using numpy's trigonometric functions + xx = radius * np.cos(lat_rad) * np.cos(lon_rad) + yy = radius * np.cos(lat_rad) * np.sin(lon_rad) + zz = radius * np.sin(lat_rad) + + # Stack the coordinates into a single numpy array + return xx, yy, zz + + +def xyz_to_lat_lon(xx, yy, zz): + """ + Converts 3D Cartesian (XYZ) coordinates back to longitude and latitude using numpy. + This function can handle single values or numpy arrays as input. + + Args: + x (float or np.ndarray): X coordinate(s). + y (float or np.ndarray): Y coordinate(s). + z (float or np.ndarray): Z coordinate(s). + + Returns: + tuple: A tuple containing longitude and latitude (in degrees). + - Longitude is in the range [0, 360). + - Latitude is in the range [-90, 90]. + """ + # Calculate the radius. This is needed to normalize the vector for latitude calculation. + radius = np.sqrt(xx**2 + yy**2 + zz**2) + + # Latitude can be calculated using arcsin. + # We add a small epsilon to the denominator to avoid division by zero if the input is (0,0,0). + lat_rad = np.arcsin(zz / (radius + 1e-9)) + + # Longitude is calculated using arctan2(y, x) for quadrant-correct results. + lon_rad = np.arctan2(yy, xx) + + # Convert radians to degrees and adjust longitude to [0, 360) + lat = np.degrees(lat_rad) + lon = (np.degrees(lon_rad) + 360) % 360 + + return lat, lon + + +def cartesian_to_spherical_track(stats, tru_track, frame_of_reference): + + mean_lat, mean_lon = xyz_to_lat_lon( + stats["mean"]["x"], stats["mean"]["y"], stats["mean"]["z"] + ) + + # delete cartesian mean/variance + for var in ["x", "y", "z"]: + for metric in ["mean", "variance"]: + del stats[metric][var] + + # add spherical mean + stats["mean"]["lat"] = mean_lat + stats["mean"]["lon"] = mean_lon + + # compute distance + tru_cont = pd.merge(frame_of_reference, tru_track, on="time", how="left") + + # compute great circle distance between each point on the track and the true track + dist = great_circle_distance( + tru_cont["lat"], tru_cont["lon"], stats["mean"]["lat"], stats["mean"]["lon"] + ) + + # add to merged_track + stats["mean"]["dist"] = dist.to_numpy(dtype=float) + + for var in ["msl", "wind_speed"]: + stats["mean"][var + "_err_of_mean"] = stats["mean"][var] - tru_cont[var] + + return stats + + +def get_ensemble_averages( + pred_tracks, tru_track, vars: list[str] = ["msl", "wind_speed", "x", "y", "z"] +): + + stats = { + "time": None, + "n_members": None, + "mean": {var: [] for var in vars}, + "variance": {var: [] for var in vars}, + } + + # loop over all tracks, match with + this_is_the_last_time = pred_tracks[0]["ic"] + + # iterate over all tracks to get set of all times present + for track in pred_tracks: + this_is_the_last_time = max( + this_is_the_last_time, track["tracks"]["time"].values[-1] + ) + + all_times = np.arange( + pred_tracks[0]["ic"], this_is_the_last_time, np.timedelta64(6, "h") + ) + stats["time"] = all_times + + # start data frame single column called time which holds all_times + frame_of_reference = pd.DataFrame( + data=all_times, index=np.arange(len(all_times)), columns=["time"] + ) + + for track in pred_tracks: + # convert lat/lon to xyz + xx, yy, zz = lat_lon_to_xyz(track["tracks"]["lat"], track["tracks"]["lon"]) + track["tracks"]["x"] = xx + track["tracks"]["y"] = yy + track["tracks"]["z"] = zz + + contextualised = pd.merge( + frame_of_reference, track["tracks"], on="time", how="left" + ) + + for var in vars: + stats["mean"][var].append(contextualised[var]) + + for var in vars: + # get number of ensemble members contributing to each time step + if stats["n_members"] is None: + stats["n_members"] = np.count_nonzero(~np.isnan(stats["mean"][var]), axis=0) + else: + assert np.all( + stats["n_members"] + == np.count_nonzero(~np.isnan(stats["mean"][var]), axis=0) + ), f"n_members is not the same for all variables but should be...." + + help = np.stack(stats["mean"][var]) + stats["variance"][var] = np.nanvar(help, axis=0) + stats["mean"][var] = np.nanmean(help, axis=0) + + # convert xyz back to lat/lon + stats = cartesian_to_spherical_track(stats, tru_track, frame_of_reference) + + return stats diff --git a/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb new file mode 100644 index 000000000..885531ed7 --- /dev/null +++ b/recipes/tc_tracking/plotting/plot_tracks_n_fields.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot Evolution of Storm Tracks and Atmospheric Fields\n", + "\n", + "This notebook demonstrates how to visualize tropical cyclone (TC) tracks overlaid on atmospheric field data. Use this to validate tracking algorithm outputs or create publication-ready animations.\n", + "\n", + "### Workflow Overview\n", + "1. Configure paths and plotting parameters\n", + "2. Load and subset field data from NetCDF\n", + "3. Load and align track data from CSV files\n", + "4. Generate animated visualization\n", + "5. (Optional) Save animation to file\n", + "\n", + "### Data Prerequesits\n", + "- NetCDF file containing atmospheric field data\n", + "- Track CSV files generated by the TC tracking algorithm\n", + "Both files can be produced by configuring a tc_hunt run with `store_type: \"netcdf\"` and cyclone tracking switched on, as e.g. done in `cfg/reproduce_helene.yaml`.\n", + "\n", + "### Configuration\n", + "\n", + "**Path Settings**\n", + "- `field_data` - path to NetCDF file (e.g. `/path/to/outputs_reproduce_helene/helene_2024-09-24T00.00.00_mems0000-0013.nc`)\n", + "- `track_dir` - folder containing track CSVs (e.g. `/path/to/outputs_reproduce_helene/cyclone_tracks_te`)\n", + "\n", + "**Plotting Settings**\n", + "- `variable` - field variable to plot (e.g., `'u10m'`, `'mslp'`). Use `'wind_speed'` to compute 10m wind magnitude from `u10m` and `v10m`\n", + "- `ensemble_member` - which ensemble member to visualize\n", + "- `region` - geographic region (`'global'`, `'north_atlantic'`, `'gulf_of_mexico'`)\n", + "\n", + "**Animation Settings**\n", + "- `max_frames` - limit number of frames (set high to include full forecast)\n", + "- `scale` - spatial coarsening factor (1 = full resolution, 2 = half, etc.)\n", + "- `fps` - frames per second for animation playback\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "field_data = '/path/to/outputs_reproduce_helene/reproduce_helene_2024-09-24T00.00.00_mems0000-0013.nc'\n", + "track_dir = '/path/to/outputs_reproduce_helene/cyclone_tracks_te'\n", + "\n", + "variable = 'wind_speed'\n", + "ensemble_member = 3\n", + "region = 'gulf_of_mexico'\n", + "\n", + "max_frames = 99 # maximum number of frames to plot\n", + "scale = 1\n", + "fps = 4\n", + "\n", + "if region == 'global':\n", + " lat_min, lat_max = -90, 90\n", + " lon_min, lon_max = 0, 359.75\n", + "elif region == 'west_pacific':\n", + " lat_min, lat_max = 9, 60\n", + " lon_min, lon_max = 100, 180\n", + "elif region == 'north_atlantic':\n", + " lat_min, lat_max = 9, 65\n", + " lon_min, lon_max = 250, 360\n", + "elif region == 'gulf_of_mexico':\n", + " lat_min, lat_max = 9, 45\n", + " lon_min, lon_max = 250, 310\n", + "elif region == 'north_indian':\n", + " lat_min, lat_max = 9, 40\n", + " lon_min, lon_max = 50, 100\n", + "elif region == 'southern_pacific':\n", + " lat_min, lat_max = -40, 5\n", + " lon_min, lon_max = 140, 240\n", + "else:\n", + " raise ValueError(f'region {region} not yet implemented. Feel free to add it by providing lat/lon coords of the bounding box')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1: Load Field Data\n", + "\n", + "- Read field data from NetCDF file\n", + "- Sub-select region and variable; coarsen data if `scale > 1` for faster iteration\n", + "- Compute min/max values for consistent colormap across all timesteps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import matplotlib.animation as animation\n", + "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n", + "import xarray as xr\n", + "\n", + "\n", + "base_name = field_data.split('/')[-1].split('.')[0]\n", + "out_dir = f'outputs_{base_name}'\n", + "\n", + "ds = xr.open_dataset(field_data)\n", + "\n", + "countries = cfeature.NaturalEarthFeature(\n", + " category=\"cultural\",\n", + " name=\"admin_0_countries\",\n", + " scale=\"110m\",\n", + " facecolor=\"none\",\n", + " edgecolor=\"black\",\n", + ")\n", + "\n", + "# subselect lat/lon box, coarsen data if scale factor > 1\n", + "sub_ds = ds.sel(lat=list(np.arange(lat_min, lat_max, scale*.25)),\n", + " lon=list(np.arange(lon_min, lon_max, scale*.25)))\n", + "\n", + "# extract variable and obtain global min/max values\n", + "if variable == 'wind_speed':\n", + " sub_ds = np.sqrt(np.square(sub_ds.u10m) + np.square(sub_ds.v10m))\n", + "else:\n", + " sub_ds = sub_ds[variable] # np.sqrt(np.square(reg_ds.u10m) + np.square(reg_ds.v10m))\n", + "\n", + "display(ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2: Load Track Data\n", + "\n", + "This step loads and processes the cyclone track data to align with field timestamps.\n", + "\n", + "**Processing steps:**\n", + "1. **Select file** - filter by `ensemble_member` from filenames ignoring the random seed\n", + "2. **Separate tracks** - split into individual DataFrames for each track\n", + "3. **Align timestamps** - reindex each track to match field data timesteps\n", + "\n", + "**Why reindexing matters:**\n", + "Tracks may not have positions at every timestep (e.g., storm genesis/dissipation). Reindexing fills gaps with NaN, enabling frame-by-frame plotting without index errors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from data_handling import extract_tracks_from_file\n", + "\n", + "track_dir = os.path.abspath(track_dir)\n", + "\n", + "# extract time steps of field data\n", + "time_stamps = ds.time.values + ds.lead_time.values\n", + "\n", + "# select track file of ensemble member\n", + "track_file = [f for f in os.listdir(track_dir) if f.endswith('.csv') and\n", + " int(f.split('_mem_')[-1].split('_seed_')[0]) == ensemble_member][0]\n", + "track_file = os.path.join(track_dir, track_file)\n", + "\n", + "# extract tracks from prediction\n", + "tracks = extract_tracks_from_file(track_file)\n", + "\n", + "# separate individual tracks in prediction\n", + "n_tracks = tracks[\"track_id\"].iloc[-1] + 1\n", + "tracks = [tracks.loc[tracks[\"track_id\"] == ii].copy() for ii in range(n_tracks)]\n", + "\n", + "# align track data with simulation time steps\n", + "for ii in range(n_tracks):\n", + " # extract the lines of tracks for which track['time'] is in time_stamps\n", + " tracks[ii] = tracks[ii][tracks[ii]['time'].isin(time_stamps)]\n", + "\n", + " # fill the 'time' column with the time_stamps values\n", + " tracks[ii] = tracks[ii].set_index('time').reindex(time_stamps).reset_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3: Create Animation\n", + "\n", + "Creates an interactive animation where you can click through timesteps. Each frame shows the field data with tracks drawn progressively up to that point." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "colour_map = 'plasma'\n", + "projection=ccrs.PlateCarree()\n", + "\n", + "min_val = float(np.min(sub_ds[ensemble_member, ...]))\n", + "max_val = float(np.max(sub_ds[ensemble_member, ...]))\n", + "\n", + "# suppress line warnings stemming from potential NANs in track data\n", + "warnings.filterwarnings(\"ignore\", message=\"invalid value encountered in linestrings\")\n", + "\n", + "# get index of ensemble member\n", + "ensemble_idx = np.argwhere(ds.ensemble.values == ensemble_member)[0,0]\n", + "\n", + "# define plots\n", + "def make_figure():\n", + " fig = plt.figure(figsize=(11,7))\n", + " ax = fig.add_subplot(1, 1, 1, projection=projection)\n", + "\n", + " lon_formatter = LongitudeFormatter(zero_direction_label=False)\n", + " lat_formatter = LatitudeFormatter()\n", + " ax.xaxis.set_major_formatter(lon_formatter)\n", + " ax.yaxis.set_major_formatter(lat_formatter)\n", + "\n", + " return fig, ax\n", + "\n", + "\n", + "# make animation\n", + "%matplotlib inline\n", + "plt.rcParams[\"animation.html\"] = \"jshtml\"\n", + "fig, ax = make_figure()\n", + "\n", + "def make_frame(frame):\n", + " print(f'\\rprocessing frame {frame+1} of {min(max_frames, sub_ds.shape[2])}', end='')\n", + "\n", + " # Clear previous plot objects\n", + " for artist in ax.get_children():\n", + " if hasattr(artist, 'get_array'): # This targets pcolormesh objects\n", + " artist.remove()\n", + "\n", + "\n", + " plot_ds = sub_ds[ensemble_idx, 0, max(frame,0), :, :]\n", + " pc = ax.pcolormesh(sub_ds.lon, sub_ds.lat, plot_ds, transform=projection,\n", + " cmap=colour_map,\n", + " vmin=min_val, vmax=max_val,\n", + " )\n", + "\n", + " ax.add_feature(cfeature.COASTLINE,lw=.5)\n", + " ax.add_feature(cfeature.RIVERS,lw=.5)\n", + " for track in tracks:\n", + " ax.plot(track[\"lon\"][:frame+1], track[\"lat\"][:frame+1],\n", + " transform=ccrs.PlateCarree(),\n", + " color=\"lime\", linewidth=1., alpha=1)\n", + "\n", + " # Enforce the plotting region extent\n", + " ax.set_extent([lon_min, lon_max, lat_min, lat_max], crs=ccrs.PlateCarree())\n", + "\n", + " if frame==-1:\n", + " cbar = fig.colorbar(pc, extend='both', shrink=0.8, ax=ax)\n", + "\n", + " header =f'{base_name} {variable} {'lead time'} {frame*6}h'\n", + " ax.set_title(header, fontsize=14)\n", + "\n", + " return pc\n", + "\n", + "def animate(frame):\n", + " return make_frame(frame)\n", + "\n", + "def first_frame():\n", + " return make_frame(-1)\n", + "\n", + "ani = animation.FuncAnimation(fig,\n", + " animate,\n", + " min(max_frames, sub_ds.shape[2]),\n", + " init_func=first_frame,\n", + " blit=False,\n", + " repeat=False,\n", + " interval=1000/fps)\n", + "plt.close('all')\n", + "ani" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4 (Optional): Save Animation\n", + "\n", + "Uncomment and run the cell below to save the animation as GIF." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.close('all')\n", + "\n", + "# Recreate figure and animation\n", + "cbar = None\n", + "fig = plt.figure(figsize=(12, 8))\n", + "ax = fig.add_subplot(1, 1, 1, projection=projection)\n", + "\n", + "ani = animation.FuncAnimation(fig, animate, min(max_frames, sub_ds.shape[2]),\n", + " init_func=first_frame, blit=False,\n", + " repeat=False, interval=1000/fps)\n", + "\n", + "ani.save('tracks_n_fields_ani.gif', fps=fps, savefig_kwargs={'bbox_inches': 'tight', 'pad_inches': 0.1})\n", + "plt.close(fig)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/recipes/tc_tracking/plotting/plotting_helpers.py b/recipes/tc_tracking/plotting/plotting_helpers.py new file mode 100644 index 000000000..5d698785f --- /dev/null +++ b/recipes/tc_tracking/plotting/plotting_helpers.py @@ -0,0 +1,538 @@ +import os + +import cartopy.crs as ccrs +import cartopy.feature as cfeature +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from data_handling import merge_tracks_by_time + + +def add_some_gap(lat_min, lat_max, lon_min, lon_max): + gap_fac = 0.1 + lat_gap = (lat_max - lat_min) * gap_fac + lon_gap = (lon_max - lon_min) * gap_fac + + lat_min, lat_max = lat_min - lat_gap, lat_max + lat_gap + lon_min, lon_max = lon_min - lon_gap, lon_max + lon_gap + + if lat_gap / lon_gap > 2: + d_lon = 0.5 * (lat_max - lat_min) + med_lon = 0.5 * (lon_min + lon_max) + lon_min, lon_max = med_lon - d_lon / 2, med_lon + d_lon / 2 + + elif lon_gap / lat_gap > 2: + d_lat = 0.5 * (lon_max - lon_min) + med_lat = 0.5 * (lat_min + lat_max) + lat_min, lat_max = med_lat - d_lat / 2, med_lat + d_lat / 2 + + return lat_min, lat_max, lon_min, lon_max + + +def get_central_coords(track): + lat_cen = track["lat"].median() + lon_cen = track["lon"].median() + + return lat_cen, lon_cen + + +def plot_spaghetti( + true_track, + pred_tracks, + ensemble_mean, + case, + n_members, + out_dir: str | None = None, + alpha: float = 0.2, + line_width: float = 2, + ic=None, +): + plt.close("all") + + lat_cen, lon_cen = get_central_coords(true_track) + + # Create figure and axis + fig = plt.figure(figsize=(22, 10)) + sup_title = f'{(case.split("_")[0]).upper()} ' + sup_title += f'\n initialised on {pred_tracks[0]["ic"]}' + sup_title += f"\n {len(pred_tracks)} tracks in {n_members} ensemble members" + fig.suptitle(sup_title, fontsize=16) + + projection = ccrs.LambertAzimuthalEqualArea( + central_longitude=lon_cen, central_latitude=lat_cen + ) + ax = fig.add_subplot(1, 1, 1, projection=projection) + + ax.add_feature(cfeature.COASTLINE, lw=0.5) + ax.add_feature(cfeature.RIVERS, lw=0.5) + if case != "debbie_2017_southern_pacific": # cartopy issues with small islands + ax.add_feature(cfeature.OCEAN, facecolor="#b0c4de") + ax.add_feature(cfeature.LAND, facecolor="#C4B9A3") + + lat_min, lat_max, lon_min, lon_max = 90, -90, 360, -0.1 + + # Plot the line in white + for _track in pred_tracks: + track = _track["tracks"] + if ic is not None and _track["ic"] not in ic: + continue + + lat_min, lat_max = min(lat_min, track["lat"].min()), max( + lat_max, track["lat"].max() + ) + lon_min, lon_max = min(lon_min, track["lon"].min()), max( + lon_max, track["lon"].max() + ) + + ax.plot( + track["lon"], + track["lat"], + transform=ccrs.PlateCarree(), + color="black", + linewidth=line_width, + alpha=alpha, + ) + + ax.plot( + true_track["lon"], + true_track["lat"], + transform=ccrs.PlateCarree(), + color="red", + linewidth=line_width, + alpha=1.0, + ) + + ax.plot( + ensemble_mean["lon"], + ensemble_mean["lat"], + transform=ccrs.PlateCarree(), + color="lime", + linewidth=line_width, + alpha=1.0, + ) + + lat_min, lat_max = min(lat_min, true_track["lat"].min()), max( + lat_max, true_track["lat"].max() + ) + lon_min, lon_max = min(lon_min, true_track["lon"].min()), max( + lon_max, true_track["lon"].max() + ) + + lat_min, lat_max, lon_min, lon_max = add_some_gap( + lat_min, lat_max, lon_min, lon_max + ) + + # Remove extra space around the plot + plt.tight_layout() + + ax.set_extent([lon_min, lon_max, lat_min, lat_max], crs=ccrs.PlateCarree()) + ax.gridlines(draw_labels=False, dms=False, x_inline=False, y_inline=False) + + if out_dir: + fig.savefig(os.path.join(out_dir, f"{case}_tracks.png")) + + return + + +def normalised_intensities(track, tru_track, var): + + merged_track = merge_tracks_by_time(track, tru_track) + + if var == "msl": + merged_track[var] = (merged_track[var] - merged_track[var + "_tru"]) / ( + 101325 - merged_track[var + "_tru"] + ) + else: + merged_track[var] = ( + merged_track[var] - merged_track[var + "_tru"] + ) / merged_track[var + "_tru"] + + return merged_track + + +def plot_relative_over_time( + pred_tracks, + tru_track, + ensemble_mean, + case, + n_members, + ics=None, + out_dir: str | None = None, +): + + fig, _ax = plt.subplots(2, 1, figsize=(11, 11), sharex=True) + sup_title = f'{(case.split("_")[0]).upper()} ' + sup_title += f'\n initialised on {pred_tracks[0]["ic"]}' + sup_title += f"\n {len(pred_tracks)} tracks in {n_members} ensemble members" + fig.suptitle(sup_title, fontsize=16) + + vars = ["msl", "wind_speed"] + labels = ["(msl - msl_ref)/(101325Pa - msl_ref)", "max_wind/max_wind_ref - 1"] + + ic, end = pred_tracks[0]["ic"], tru_track["time"].max() + rel_steps = int(((end - ic) / np.timedelta64(6, "h") + 1) * 0.75) + + for ii in range(_ax.shape[0]): + vmin, vmax = 1000, -1000 + for _track in pred_tracks: + + track = _track["tracks"] + if ics is not None and _track["ic"] not in ics: + continue + + track = normalised_intensities(track, tru_track, vars[ii]) + + vmin, vmax = min(vmin, track[vars[ii]][:rel_steps].min()), max( + vmax, track[vars[ii]][:rel_steps].max() + ) + + ax = _ax[ii] + ax.plot(track["time"], track[vars[ii]], color="black", alpha=0.1) + + _ax[ii].set_ylabel(labels[ii]) + _ax[ii].grid(True) + _ax[ii].set_ylim(vmin, vmax) + + ax.plot( + tru_track["time"], + [0 for _ in range(len(tru_track))], + color="orangered", + linewidth=2.5, + label="era5 comparison", + ) + + mean = pd.DataFrame( + {"time": ensemble_mean["time"], vars[ii]: ensemble_mean["mean"][vars[ii]]} + ) + _track = normalised_intensities(mean, tru_track, vars[ii]) + ax.plot( + _track["time"], + _track[vars[ii]], + color="lime", + linewidth=2.5, + label="ensemble mean", + linestyle="--", + ) + + ax.legend() + + # Set x-axis label to indicate hours + _ax[-1].set_xlabel("time [UTC]") + + plt.xlim( + track["time"].min() - np.timedelta64(6, "h"), + tru_track["time"].max() + np.timedelta64(6, "h"), + ) + + if out_dir: + plt.savefig(os.path.join(out_dir, f"{case}_rel_intensities.png")) + + return + + +def plot_over_time( + pred_tracks, + tru_track, + ensemble_mean, + case, + n_members, + vars=["msl", "wind_speed", "dist"], + labels=["msl [hPa]", "maximum instantaneous wind speed [m/s]", "distance [km]"], + ics=None, + out_dir: str | None = None, +): + + fig, _ax = plt.subplots(len(vars), 1, figsize=(11, 15), sharex=True) + sup_title = f'{(case.split("_")[0]).upper()} ' + sup_title += f'\n initialised on {pred_tracks[0]["ic"]}' + sup_title += f"\n {len(pred_tracks)} tracks in {n_members} ensemble members" + fig.suptitle(sup_title, fontsize=16) + + t_min, t_max = np.datetime64("2120-05-16 12:00:00"), np.datetime64( + "1820-05-16 12:00:00" + ) + + for ii in range(_ax.shape[0]): + for _track in pred_tracks: + track = _track["tracks"] + if ics is not None and _track["ic"] not in ics: + continue + + if vars[ii] == "dist": + yy = track[vars[ii]] / 1000 + elif vars[ii] == "msl": + yy = track[vars[ii]] / 100 + else: + yy = track[vars[ii]] + + _ax[ii].plot(track["time"], yy, color="black", alpha=0.1) + + t_min, t_max = min(t_min, track["time"].min()), max( + t_max, track["time"].max() + ) + + _ax[ii].set_xlim(t_min - np.timedelta64(6, "h"), t_max + np.timedelta64(6, "h")) + _ax[ii].set_ylabel(labels[ii]) + _ax[ii].grid(True) + + # if vars[ii] != 'dist': + yy = tru_track[vars[ii]] / 100 if vars[ii] == "msl" else tru_track[vars[ii]] + _ax[ii].plot( + tru_track["time"], + yy, + color="orangered", + linewidth=2.5, + label="era5 comparison", + ) + + if vars[ii] == "dist": + yy = ensemble_mean["mean"][vars[ii]] / 1000 + elif vars[ii] == "msl": + yy = ensemble_mean["mean"][vars[ii]] / 100 + else: + yy = ensemble_mean["mean"][vars[ii]] + _ax[ii].plot( + ensemble_mean["time"], + yy, + color="lime", + linewidth=2.5, + label="ensemble mean", + linestyle="--", + ) + _ax[ii].legend() + + # Set x-axis label to indicate hours + _ax[-1].set_xlabel("time [UTC]") + + if out_dir: + plt.savefig(os.path.join(out_dir, f"{case}_abs_intensities.png")) + + return + + +def plot_ib_era5( + tru_track, case, vars=["msl", "wind_speed"], out_dir: str | None = None +): + plt.close("all") + + fig, ax1 = plt.subplots(1, 1, figsize=(8, 5)) + sup_title = f'{(case.split("_")[0]).upper()} ' + fig.suptitle(sup_title, fontsize=16) + + ax2 = ax1.twinx() + + if "msl" in vars: + p_norm = 101325 + ax1.plot( + tru_track["time"], + (p_norm - tru_track["msl"]) / (p_norm - tru_track["msl_ib"]), + "black", + ) + ax1.set_ylabel("(1013hPa-msl_era5)/(1013hPa-msl_ib)", color="black") + + if "wind_speed" in vars: + ax2.plot( + tru_track["time"], + tru_track["wind_speed"] / tru_track["wind_speed_ib"], + "orangered", + ) + ax2.set_ylabel("wind_speed_era5/wind_speed_ib", color="orangered") + + fig.tight_layout() + if out_dir: + plt.savefig(os.path.join(out_dir, f"{case}_ib_era5_wind_speed.png")) + + return + + +def root_metrics(err_dict): + for var in err_dict.keys(): + mse = err_dict[var].pop("mse") + err_dict[var]["rmse"] = np.sqrt(mse) + variance = err_dict[var].pop("variance") + err_dict[var]["standard_deviation"] = np.sqrt(variance) + n_mems_lt = err_dict[var].pop("n_members") + + return err_dict, n_mems_lt + + +def plot_errors_over_lead_time( + err_dict, + case, + ic, + n_members, + n_tracks, + norm_dict={"msl": 100, "wind_speed": 1, "dist": 1000}, + unit_dict={"msl": "hPa", "wind_speed": "m/s", "dist": "km"}, + out_dir: str | None = None, +): + + if "mse" in err_dict[list(err_dict.keys())[0]].keys(): + err_dict, n_mems_lt = root_metrics(err_dict) + + vars = list(err_dict.keys()) + metrics = list(err_dict[vars[0]].keys()) + + # remove min, max as not over lead time but per member + for extreme in ["min", "max"]: + if extreme in metrics: + metrics.remove(extreme) + + print(metrics) + + lead_time = np.arange(err_dict[vars[0]][metrics[0]].shape[0]) * np.timedelta64( + 6, "h" + ) + + fig, ax = plt.subplots( + len(vars), + len(metrics), + figsize=((len(metrics) + 1) * 2, (len(vars) + 1) * 2), + sharex=True, + ) + + for ivar, var in enumerate(err_dict.keys()): + for imet, metric in enumerate(metrics): + + ax[ivar, imet].plot(lead_time, err_dict[var][metric] / norm_dict[var]) + + if ivar == 0: + ax[ivar, imet].set_title(metric, fontsize=12, weight="bold") + + if imet == 0: + ax[ivar, imet].set_ylabel( + f"{var} [{unit_dict[var]}]", fontsize=12, weight="bold" + ) + + if ivar == len(vars) - 1: + ax[ivar, imet].set_xlabel("lead time [h]", fontsize=12) + + sup_title = f'{(case.split("_")[0]).upper()} ' + sup_title += f"\n initialised on {ic}" + sup_title += f"\n {n_tracks} tracks in {n_members} ensemble members" + fig.suptitle(sup_title, fontsize=16) + + fig.tight_layout() + if out_dir: + plt.savefig(os.path.join(out_dir, f"{case}_error_metrics_over_lead_time.png")) + + return + + +def extract_reference_extremes(tru_track, pred_tracks, ens_mean, vars): + + extreme_dict = {} + for var in vars: + extreme_dict[var] = {"pred": np.zeros(len(pred_tracks))} + for ii, track in enumerate(pred_tracks): + if var in ["wind_speed"]: + extreme_dict[var]["pred"][ii] = np.nanmax(track["tracks"][var]) + extreme_dict[var]["tru"] = np.nanmax(tru_track[var]) + extreme_dict[var]["ens_mean"] = np.nanmax(ens_mean["mean"][var]) + elif var in ["msl"]: + extreme_dict[var]["pred"][ii] = np.nanmin(track["tracks"][var]) + extreme_dict[var]["tru"] = np.nanmin(tru_track[var]) + extreme_dict[var]["ens_mean"] = np.nanmin(ens_mean["mean"][var]) + + return extreme_dict + + +def add_stats_box(ax, pred_var, tru_var, var, reduction, unit): + + # add text box below plot with number of members exceeding the threshold + n_exceed_spd = len(pred_var[pred_var > tru_var]) + n_total = len(pred_var) + + # Create table-like format with aligned colons for wind speed + comp = "exceeding" if var in ["wind_speed"] else "below" + stats = [ + ("era5 reference:", f"{tru_var:.1f} {unit}"), + ( + f"members {comp} ref:", + f"{n_exceed_spd} of {n_total} ({(n_exceed_spd/n_total)*100:.1f}%)", + ), + (f"max {reduction} {var}:", f"{pred_var.max():.1f} {unit}"), + (f"min {reduction} {var}:", f"{pred_var.min():.1f} {unit}"), + (f"avg {reduction} {var}:", f"{pred_var.mean():.1f} {unit}"), + (f"std {reduction} {var}:", f"{pred_var.std():.1f} {unit}"), + ] + + # Format as table with aligned colons + max_label_width = max(len(label) for label, _ in stats) + text = "\n".join([f"{label:<{max_label_width}} {value}" for label, value in stats]) + + ax.text( + 0.01, + -0.25, + text, + transform=ax.transAxes, + ha="left", + va="top", + bbox=dict(boxstyle="round,pad=0.3", facecolor="lightgray", alpha=0.8), + fontfamily="monospace", + ) + + return + + +def plot_extreme_extremes_histograms( + pred_tracks, + tru_track, + ensemble_mean, + case, + vars=["wind_speed", "msl"], + out_dir: str | None = None, + nbins: int = 12, +): + + extreme_dict = extract_reference_extremes( + tru_track, pred_tracks, ensemble_mean, vars + ) + + # plot histogram of spd and msl + fig, ax = plt.subplots(1, len(vars), figsize=(3 * (len(vars) + 1), 6), sharey=True) + fig.suptitle( + f'{(case.split("_")[0]).upper()} initialised on {pred_tracks[0]["ic"]}', + fontsize=16, + ) + ax[0].set_ylabel("count") + + for ii, var in enumerate(vars): + + reduction = "max" if var in ["wind_speed"] else "min" + unit = "m/s" if var in ["wind_speed"] else "hPa" + scale = 100 if var in ["msl"] else 1 + + pred_var = extreme_dict[var]["pred"] / scale + tru_var = extreme_dict[var]["tru"] / scale + mean_var = extreme_dict[var]["ens_mean"] / scale + + # plot histo and vertical line at tru_var + ax[ii].hist(pred_var, bins=nbins) + ax[ii].axvline( + tru_var, color="orangered", linestyle="--", label="era5 reference" + ) + ax[ii].axvline(mean_var, color="lime", linestyle="--", label="ensemble mean") + + ax[ii].set_title(f"{reduction} {var} (x, t)") + ax[ii].set_xlabel(f"{var} [{unit}]") + ax[ii].legend() + + add_stats_box(ax[ii], pred_var, tru_var, var, reduction, unit) + + fig.tight_layout() + if out_dir: + fig.savefig(os.path.join(out_dir, f"{case}_histograms.png")) + + plt.show() + + +def plot_ensemble_mean_metrics(ens_means, out_path): + + errs = [] + for _, means in ens_means.items(): + errs.append(means) + + errs = np.stack(errs) + + return diff --git a/recipes/tc_tracking/plotting/tracks_slayground.ipynb b/recipes/tc_tracking/plotting/tracks_slayground.ipynb new file mode 100644 index 000000000..77af85e15 --- /dev/null +++ b/recipes/tc_tracking/plotting/tracks_slayground.ipynb @@ -0,0 +1,255 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyse Tropical Cyclone Track Ensembles\n", + "\n", + "This notebook demonstrates how to analyse and validate ensemble tropical cyclone (TC) track predictions. Use this to compare forecast tracks against observations ([IBTrACS](https://www.ncei.noaa.gov/products/international-best-track-archive)) and reanalysis-based reference tracks (ERA5). We explain the workflow using the example of [Hurricane Helene](https://en.wikipedia.org/wiki/Hurricane_Helene), but it can be easily configured to investigate other storms.\n", + "\n", + "### Workflow Overview\n", + "1. Configure case and paths for predicted ensemble and reference track\n", + "2. Plot track trajectories (spaghetti plot)\n", + "3. Plot absolute intensities over time (wind speed, MSLP)\n", + "4. Plot relative intensities over time (normalised by reference)\n", + "5. Compare ERA5 reference against IBTrACS observations\n", + "6. Analyse extreme value statistics (histograms)\n", + "7. Compute error moments over lead time\n", + "\n", + "### Data Prerequisites\n", + "- Ensemble of predicted TC tracks (CSV files from tracking algorithm)\n", + "- Reference track from ERA5 or IBTrACS observations (CSV file)\n", + "\n", + "Both can be produced by configuring a `tc_hunt.py` run with cyclone tracking enabled. The ensemble of predicted tracks can, for example, be produced with the config `cfg/helene.yaml`. Reference tracks can be extracted using `cfg/extract_era5.yaml`.\n", + "\n", + "### Analysis Notes\n", + "- Track positions are typically well-represented by the model\n", + "- Intensity metrics (wind speed, pressure) show larger biases due to the coarse (~0.25°) resolution, which cannot fully resolve TC structure\n", + "- IBTrACS provides observed best track data; ERA5 represents the best achievable reference for reanalysis-driven forecasts\n", + "\n", + "### Configuration\n", + "- `case` - named storm to analyse. Can be expanded with additional named storms by following the pattern `{name}_{YYYY}_{basin}`\n", + "- `pred_track_dir` - folder containing predicted track CSVs (if using data produced with `cfg/helene.yaml`, this would be `/path/to/outputs_helene/cyclone_tracks_te`)\n", + "- `tru_track_dir` - folder containing reference track CSV file (if using data produced with `cfg/extract_era5.yaml`, this would be `/path/to/outputs_reference_tracks/`)\n", + "- `out_dir` - path for storing plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from analyse_n_plot import load_tracks\n", + "\n", + "# case = 'amphan_2020_north_indian'\n", + "# case = 'beryl_2024_north_atlantic'\n", + "# case = 'debbie_2017_southern_pacific'\n", + "# case = 'dorian_2019_north_atlantic'\n", + "# case = 'harvey_2017_north_atlantic'\n", + "case = 'hato_2017_west_pacific'\n", + "# case = 'helene_2024_north_atlantic'\n", + "# case = 'ian_2022_north_atlantic'\n", + "# case = 'iota_2020_north_atlantic'\n", + "# case = 'irma_2017_north_atlantic'\n", + "# case = 'lan_2017_west_pacific'\n", + "# case = 'lee_2023_north_atlantic'\n", + "# case = 'lorenzo_2019_north_atlantic'\n", + "# case = 'maria_2017_north_atlantic'\n", + "# case = 'mawar_2023_west_pacific'\n", + "# case = 'michael_2018_north_atlantic'\n", + "# case = 'milton_2024_north_atlantic'\n", + "# case = 'ophelia_2017_north_atlantic'\n", + "# case = 'yagi_2024_west_pacific'\n", + "# case = 'erin_2025_north_atlantic'\n", + "\n", + "pred_track_dir = '/path/to/outputs_hato/cyclone_tracks_te'\n", + "tru_track_dir = '/path/to/outputs_reference_tracks'\n", + "out_dir = './plots'\n", + "\n", + "tru_track, pred_tracks, ens_mean, n_members, out_dir = load_tracks(case=case,\n", + " pred_track_dir=pred_track_dir,\n", + " tru_track_dir=tru_track_dir,\n", + " out_dir=out_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spaghetti Plot\n", + "\n", + "- **Ensemble members** are shown in grey\n", + "- **Ensemble mean** is displayed in green\n", + "- **ERA5 reference** is shown in red" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from plotting_helpers import plot_spaghetti\n", + "\n", + "plot_spaghetti(true_track=tru_track,\n", + " pred_tracks=pred_tracks,\n", + " ensemble_mean=ens_mean['mean'],\n", + " case=case,\n", + " n_members=n_members,\n", + " out_dir=out_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot Absolute Intensities and Track Distance Over Time\n", + "\n", + "This section examines the temporal evolution of cyclone intensities and the distance from the reference track. Intensities are represented by minimum sea level pressure and maximum wind speed. The reference track should mostly fall within the ensemble spread for intensity predictions. However, some storms exhibit phenomena such as rapid intensification that cannot be adequately captured by models on quarter-degree resolution. In such cases, the reference intensity may lie outside the ensemble spread, indicating model limitations in resolving fine-scale processes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from plotting_helpers import plot_over_time\n", + "\n", + "plot_over_time(pred_tracks=pred_tracks,\n", + " tru_track=tru_track,\n", + " ensemble_mean=ens_mean,\n", + " case=case,\n", + " n_members=n_members,\n", + " out_dir=out_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot Relative Intensities Over Time\n", + "\n", + "This cell shows the same intensity metrics as in the previous cell, this time normalised by the reference. Note that for the pressure field we normalise the deviation from normal pressure. This normalisation helps to identify systematic biases in the ensemble predictions and highlights periods where the model over- or underestimates cyclone intensity relative to observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from plotting_helpers import plot_relative_over_time\n", + "\n", + "plot_relative_over_time(pred_tracks=pred_tracks,\n", + " tru_track=tru_track,\n", + " ensemble_mean=ens_mean,\n", + " case=case,\n", + " n_members=n_members,\n", + " out_dir=out_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot ERA5 Against IBTrACS Variables\n", + "\n", + "This cell compares the intensities reached in the ERA5 reanalysis data against those obtained from IBTrACS observations. The deviation between both datasets is usually larger the more intense the storm becomes. Note that there are two separate y-axes for the different intensity metrics (pressure and wind speed)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from plotting_helpers import plot_ib_era5\n", + "\n", + "plot_ib_era5(tru_track=tru_track,\n", + " case=case,\n", + " vars=['msl', 'wind_speed'],\n", + " out_dir=out_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extreme Values Over Lifetime of Storm\n", + "\n", + "This cell computes the maximum intensity reached along each track throughout the storm's lifetime and displays the distribution across ensemble members as histograms. For comparison, the extreme values from the reference track are shown as vertical lines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from plotting_helpers import plot_extreme_extremes_histograms\n", + "\n", + "plot_extreme_extremes_histograms(pred_tracks=pred_tracks,\n", + " tru_track=tru_track,\n", + " ensemble_mean=ens_mean,\n", + " case=case,\n", + " out_dir=out_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Statistics\n", + "\n", + "This cell computes error metrics as a function of lead time across all ensemble members. The following statistics are calculated: mean absolute error, root mean square error, and standard deviation for wind speed, pressure intensity, and track distance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from data_handling import compute_averages_of_errors_over_lead_time\n", + "from plotting_helpers import plot_errors_over_lead_time\n", + "\n", + "vars = ['wind_speed', 'msl', 'dist']\n", + "\n", + "err_dict, _ = compute_averages_of_errors_over_lead_time(pred_tracks=pred_tracks,\n", + " tru_track=tru_track,\n", + " vars=vars)\n", + "\n", + "plot_errors_over_lead_time(err_dict=err_dict,\n", + " case=case,\n", + " ic=pred_tracks[0]['ic'],\n", + " n_members=n_members,\n", + " n_tracks=len(pred_tracks),\n", + " out_dir=out_dir)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/recipes/tc_tracking/pyproject.toml b/recipes/tc_tracking/pyproject.toml new file mode 100644 index 000000000..8e9af4441 --- /dev/null +++ b/recipes/tc_tracking/pyproject.toml @@ -0,0 +1,35 @@ +[project] +name = "tc_hunt" +version = "0.1.0" +description = "no, i won't" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "cartopy>=0.24.1", + "earth2studio[data,aifsens,fcn3]>=0.11.0", + "hydra-core>=1.3.2", + "importlib-metadata>=8.7.0", + "ipykernel>=6.30.1", + "matplotlib>=3.10.3", + "more-itertools>=10.7.0", + "moviepy>=2.2.1", + "ninja>=1.13.0", + "omegaconf>=2.3.0", + "pytest", + "ruamel-yaml>=0.18.14", + "scipy>=1.16.0", + "setuptools>=80.9.0", + "tqdm>=4.67.1", + "tropycal>=1.4", + "zarrdump>=0.6.0", +] + +[tool.uv.sources] +torch-harmonics = { git = "https://github.com/NVIDIA/torch-harmonics.git" } +earth2studio = { git = "https://github.com/NVIDIA/earth2studio.git" } + +[tool.uv] +no-build-isolation-package = ["torch-harmonics"] + +[tool.setuptools.packages.find] +where = ["src"] diff --git a/recipes/tc_tracking/set_envs.sh b/recipes/tc_tracking/set_envs.sh new file mode 100644 index 000000000..e324a0ddc --- /dev/null +++ b/recipes/tc_tracking/set_envs.sh @@ -0,0 +1,3 @@ +export LOGURU_LEVEL=INFO +export PYTHONWARNINGS=ignore +export HYDRA_FULL_ERROR=1 diff --git a/recipes/tc_tracking/src/__init__.py b/recipes/tc_tracking/src/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/recipes/tc_tracking/src/data/__init__.py b/recipes/tc_tracking/src/data/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/recipes/tc_tracking/src/data/file_output.py b/recipes/tc_tracking/src/data/file_output.py new file mode 100644 index 000000000..1d171df61 --- /dev/null +++ b/recipes/tc_tracking/src/data/file_output.py @@ -0,0 +1,169 @@ +import copy +import os +import shutil +from collections import OrderedDict + +import numpy as np +from torch import from_numpy + +from earth2studio.io import NetCDF4Backend, ZarrBackend +from earth2studio.utils.coords import map_coords, split_coords + + +def initialise_output_coords( + cfg, + model, + ics, + out_vars, +) -> dict: + """Initialize output coordinates + + Parameters + ---------- + cfg : DictConfig + Hydra config object + lon_coords: np.ndarray[np.float64] + a 1d array containing the longitude values of the grid. ordered ascending + lat_coords: np.ndarray[np.float64] + a 1d array containing the latitude values of the grid. ordered descending + + Returns + ------- + output_coords: dict + output coordinates + """ + out_coords = OrderedDict( + { + k: v + for k, v in model.output_coords(model.input_coords()).items() + if (k != "batch") and (v.shape != 0) + } + ) + + out_coords["time"] = ics + out_coords["lead_time"] = np.asarray( + [out_coords["lead_time"] * i for i in range(cfg.n_steps + 1)] + ).flatten() + out_coords["variable"] = np.array(out_vars) + + out_coords.move_to_end("lead_time", last=False) + out_coords.move_to_end("time", last=False) + + return out_coords + + +def add_arrays_to_store(store, out_coords, mems, add_arrays, ic=None, array_kwargs={}): + oco = copy.deepcopy(out_coords) + oco["ensemble"] = mems + oco.move_to_end("ensemble", last=False) + out_vars = oco.pop("variable") + + if ic is not None: + oco["time"] = np.array([ic]) + + if add_arrays: + store.add_array(coords=oco, array_name=out_vars, **array_kwargs) + + return + + +def setup_output(cfg, model, ics, add_arrays): + + if 'out_vars' in cfg: + out_vars = list(dict.fromkeys(cfg.out_vars)) + else: + out_vars = [] + if not cfg.store_type == 'none': + raise ValueError("out_vars must be specified if store_type is not none.") + out_coords = initialise_output_coords(cfg, model, ics, out_vars) + chunks = { + "ensemble": 1, + "time": 1, + "lead_time": 1, + "variable": 1, + } + array_kwargs = {} + + # TODO: let proc zero create dir and add barrier + os.makedirs(cfg.store_dir, exist_ok=True) + file_name = os.path.join(cfg.store_dir, cfg.project) + + if cfg.store_type == "netcdf" or cfg.store_type == "none": + store = None + + elif cfg.store_type == "zarr": + # TODO: wrap in ordered_execution function + if not file_name.endswith(".zarr"): + file_name += ".zarr" + + if os.path.exists(file_name) and add_arrays: + shutil.rmtree(file_name) + + store = ZarrBackend( + file_name=file_name, + chunks=chunks, + backend_kwargs={"overwrite": False}, + ) + + add_arrays_to_store( + store=store, + out_coords=out_coords, + mems=np.asarray(list(range(cfg.ensemble_size))).flatten(), + add_arrays=add_arrays, + array_kwargs=array_kwargs, + ) + + else: + raise ValueError(f"Invalid store type: {cfg.store_type}") + + return store, out_coords + + +def initialise_netcdf_output(cfg, out_coords, ic, ic_mems): + mems = np.concatenate([mem for iic, mem, _ in ic_mems if iic == ic]).flatten() + seeds = np.concatenate( + [ + np.array([seed] * len(mem), dtype=int) + for iic, mem, seed in ic_mems + if iic == ic + ] + ).flatten() + + # setup filename + file_name = os.path.join(cfg.store_dir, cfg.project) + if file_name.endswith(".nc"): + file_name = file_name.replace(".nc", "") + file_name = ( + file_name + + f"_{np.datetime_as_string(ic, unit='s')}_mems{mems[0]:04d}-{mems[-1]:04d}.nc" + ) + file_name = file_name.replace(":", ".") + + # create store + chunks = { + "ensemble": 1, + "time": 1, + "lead_time": 1, + "variable": 1, + } + store = NetCDF4Backend( + file_name=file_name, + backend_kwargs={"mode": "w", "diskless": False, "chunks": chunks}, + ) + + # add random seed to store + store.add_array(coords={"ensemble": mems}, array_name="random_seed") + store.write(from_numpy(seeds), {"ensemble": mems}, "random_seed") + + # add arrays to the store + add_arrays_to_store( + store=store, out_coords=out_coords, mems=mems, ic=ic, add_arrays=True + ) + + return store + + +def write_to_store(store, xx, coords, out_coords): + if store is not None: + xx_sub, coords_sub = map_coords(xx, coords, out_coords) + store.write(*split_coords(xx_sub, coords_sub, dim="variable")) diff --git a/recipes/tc_tracking/src/data/utils.py b/recipes/tc_tracking/src/data/utils.py new file mode 100644 index 000000000..197982e0d --- /dev/null +++ b/recipes/tc_tracking/src/data/utils.py @@ -0,0 +1,72 @@ +from collections import OrderedDict + +import hydra +import numpy as np +import torch +import xarray as xr + + +def load_heights(oro_path): + oro = xr.load_dataset(oro_path) + + coords = OrderedDict( + {"variable": np.array(["height"]), "lat": oro.latitude.values, "lon": oro.longitude.values} + ) + geop = torch.Tensor(oro["Z"].to_numpy()) / 9.80665 # divide by gravity to get height + + return geop, coords + + +class DataSourceManager: + # TODO: catch case in which there's only single data source + + def __init__(self, cfg): + self.cfg = cfg + + # build dict of data sources + self.data_sources = {} + self.data_source_mapping = {} + self.single_source = False + for name, source in self.cfg.data_source.items(): + # catch classic case of single source + if name == "_target_": + self.single_source = True + self.data_source = hydra.utils.instantiate(self.cfg.data_source) + return + + # instantiate data source + self.data_sources[name] = hydra.utils.instantiate(source.source) + + # extract valid years for source and build mapping + years = source.years + if isinstance(years, str): + if not "-" in years: + years = int(years) + + if isinstance(years, int): + years = [years] + else: + years = list( + range(int(years.split("-")[0]), int(years.split("-")[1]) + 1) + ) + + for year in years: + self.data_source_mapping[year] = name + + return + + def select_data_source(self, time_stamps): + if self.single_source: + return self.data_source + + year = np.unique(time_stamps.astype("datetime64[Y]").astype(int) + 1970) + + if len(year) > 1: + raise ValueError(f"track spans multiple years, which is not yet supported") + else: + year = year[0] + + if year not in self.data_source_mapping: + raise ValueError(f"no data source provided for year {year}") + + return self.data_sources[self.data_source_mapping[year]] diff --git a/recipes/tc_tracking/src/modes/__init__.py b/recipes/tc_tracking/src/modes/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/recipes/tc_tracking/src/modes/baseline_extraction.py b/recipes/tc_tracking/src/modes/baseline_extraction.py new file mode 100644 index 000000000..363fb58b9 --- /dev/null +++ b/recipes/tc_tracking/src/modes/baseline_extraction.py @@ -0,0 +1,207 @@ +import os + +import numpy as np +import pandas as pd +import tropycal.tracks as tropytracks +from physicsnemo.distributed import DistributedManager + +from earth2studio.data import fetch_data +from src.tempest_extremes import TempestExtremes +from src.data.utils import DataSourceManager, load_heights +from src.utils import great_circle_distance + + +def extract_from_historic_data(cfg, + ic, + n_steps, + time_step: np.timedelta64 = np.timedelta64(6, 'h'), + vars: list[str] = ['msl', 'z300', 'z500', 'u10m', 'v10m'], + data_source_mngr: DataSourceManager = None): + + times = np.arange(np.timedelta64(0, 'h'), n_steps*time_step, time_step) + data_source = data_source_mngr.select_data_source(ic+times) + + # fetch data on which TC tracking is executed and add ensemble dim, so that it works with the diagnostic + xx, coords = fetch_data(data_source, + time=[ic], + lead_time=times, + variable=vars, + device='cpu') + xx = xx.unsqueeze(0) + coords["ensemble"] = np.array([0]) + coords.move_to_end("ensemble", last=False) + + heights, height_coords = ( + load_heights(cfg.cyclone_tracking.orography_path) + if "orography_path" in cfg.cyclone_tracking + else (None, None) + ) + + # set up TC tracking, add downloaded data and execute tracking algorithm + cyclone_tracking = TempestExtremes(detect_cmd=cfg.cyclone_tracking.detect_cmd, + stitch_cmd=cfg.cyclone_tracking.stitch_cmd, + input_vars=cfg.cyclone_tracking.vars, + batch_size=1, + n_steps=n_steps-1, # assumes prediction steps + time_step=time_step, + lats=coords['lat'], + lons=coords['lon'], + static_vars=heights, + static_coords=height_coords, + store_dir=cfg.store_dir, + keep_raw_data=cfg.cyclone_tracking.keep_raw_data, + print_te_output=cfg.cyclone_tracking.print_te_output, + use_ram=cfg.cyclone_tracking.use_ram) + cyclone_tracking.record_state(xx, coords) + cyclone_tracking() + + # load track_file into dataFrame + hist_tracks = pd.read_csv(cyclone_tracking.track_files[0]) + hist_tracks.columns = hist_tracks.columns.str.strip() + os.remove(cyclone_tracking.track_files[0]) + + return hist_tracks + + +def extract_from_ibtracs(cfg, ibtracs, case, time_step): + print(f'extracting storm {case} ({cfg.cases[case].year}, {cfg.cases[case].basin}) from ibtracs') + storm = ibtracs.get_storm((case, cfg.cases[case].year)) + storm.time = storm.time.astype(np.datetime64) + + # convert to pd.DataFrame, add day year month hour for easier merging later on + ib_storm = pd.DataFrame({ + 'time': storm.time, + 'year': storm.time.astype('datetime64[Y]').astype(int) + 1970, + 'month': storm.time.astype('datetime64[M]').astype(int) % 12 + 1, + 'day': np.array([int(pd.Timestamp(tt).day) for tt in storm.time]), + 'hour': storm.time.astype('datetime64[h]').astype(int) % 24, + 'lat': storm.lat, + 'lon': storm.lon, + 'wind_speed': storm.vmax, + 'mslp': storm.mslp, + 'type': storm.type, + }) + + # get time window to extract from historic data + ic = storm.time[0] + + # Ensure ic is at 00h, 06h, 12h, or 18h. if not, set to previous 6-hourly time step + ic_datetime = pd.to_datetime(ic) + hour = ic_datetime.hour + if hour % 6 != 0: + # Round down to the previous 6-hourly time step + hours_to_subtract = hour % 6 + ic = ic - np.timedelta64(hours_to_subtract, 'h') + ic = ic.astype('datetime64[h]') + + # calculate number of steps to extract from historic data + n_steps = (storm.time[-1] - ic) // time_step + if (storm.time[-1] - ic) % time_step != 0: + n_steps += 1 + + # select ib_storm times that are in query_times + ib_storm = ib_storm[ib_storm['time'].isin(np.arange(ic, ic + n_steps*time_step, time_step))] + + return ib_storm, ic, n_steps + + +def match_tracks(ib_storm, hist_tracks, case): + # add time as np.datetime64 + times = np.array([pd.to_datetime(f'{hist_tracks["year"].iloc[jj]}-{int(hist_tracks.iloc[jj]["month"]):02d}-{int(hist_tracks.iloc[jj]["day"]):02d} {int(hist_tracks.iloc[jj]["hour"]):02d}:00:00') for jj in range(len(hist_tracks))]) + hist_tracks.insert(0, 'time', times) + + n_tracks = hist_tracks['track_id'].nunique() + + # go over tracks and see if they match ibtracs + matched_track = None + for time in ib_storm['time']: + lat_ib = ib_storm.loc[ib_storm['time'] == time]['lat'].item() + lon_ib = ib_storm.loc[ib_storm['time'] == time]['lon'].item() + + for track_id in range(n_tracks): + track = hist_tracks[hist_tracks['track_id'] == track_id] + # check if time is in track + # if time in track['time']: + if (track['time'] == time).any(): + lat_track = track.loc[track['time'] == time]['lat'].item() + lon_track = track.loc[track['time'] == time]['lon'].item() + dist = great_circle_distance(lat_ib, lon_ib, lat_track, lon_track) + if dist < 300000: + matched_track = track_id + print(f'matched track {matched_track} for storm {case} with distance {dist/1000:.2f}km') + break + + if matched_track is not None: + break + + return hist_tracks[hist_tracks['track_id'] == matched_track].reset_index(drop=True) + + +def add_ibtracs_data(matched_track, ib_storm): + # add ib_storm data to matched_track, only at times in matched_track + matched_track = pd.merge(matched_track, ib_storm, on='time', how='right', suffixes=('', '_ib')).reset_index(drop=True) + matched_track.rename(columns={'mslp': 'msl_ib'}, inplace=True) + + # Preserve integer data types for datetime components + # Convert back to integers, handling any NaN values appropriately + datetime_cols = ['year', 'month', 'day', 'hour'] + for col in datetime_cols: + if col in matched_track.columns: + # Use 'Int64' (nullable integer) to handle NaN values if they exist + matched_track[col] = matched_track[col].astype('Int64') + + # set track_id to 0, keep as dummy value to have data format consistent with simulation output + matched_track['track_id'] = 0 + + # if values at lon_ib are negative, set to 360 + lon_ib + matched_track.loc[matched_track['lon_ib'] < 0, 'lon_ib'] = 360 + matched_track.loc[matched_track['lon_ib'] < 0, 'lon_ib'] + + return matched_track + + +def write_track_to_csv(matched_track, case, store_dir, basin): + # remove columns i, j, time + matched_track.drop(columns=['i', 'j', 'time'], inplace=True) + + # re-order columns + matched_track = matched_track[['track_id', 'year_ib', 'month_ib', 'day_ib', 'hour_ib', 'lon', 'lat', 'msl', 'wind_speed', 'lon_ib', 'lat_ib', 'msl_ib', 'wind_speed_ib', 'type']] + + # rename columns + matched_track.rename(columns={'year_ib': 'year', 'month_ib': 'month', 'day_ib': 'day', 'hour_ib': 'hour'}, inplace=True) + + # multiply msl_ib by 100 to get Pa + matched_track['msl_ib'] = matched_track['msl_ib'] * 100 + + # write to csv + csv_name = f'reference_track_{case}_{matched_track['year'].iloc[0]}_{basin}.csv' + matched_track.to_csv(os.path.join(store_dir, csv_name), index=False) + + return + + +def extract_baseline(cfg, + time_step: np.timedelta64 = np.timedelta64(6, 'h'), + vars:list[str] = ['msl', 'z300', 'z500', 'u10m', 'v10m'],): + + DistributedManager.initialize() + + ibtracs = tropytracks.TrackDataset(basin='all', + source='ibtracs', + ibtracs_mode='jtwc_neumann', + ibtracs_url=os.path.abspath(cfg.ibtracs_source_data)) + + data_source_mngr = DataSourceManager(cfg) + + for case in cfg.cases: + + ib_storm, ic, n_steps = extract_from_ibtracs(cfg, ibtracs, case, time_step) + + hist_tracks = extract_from_historic_data(cfg=cfg, ic=ic, n_steps=n_steps, vars=vars, data_source_mngr=data_source_mngr) + + matched_track = match_tracks(ib_storm, hist_tracks, case) + + matched_track = add_ibtracs_data(matched_track, ib_storm) + + write_track_to_csv(matched_track, case, cfg.store_dir, cfg.cases[case].basin) + + return diff --git a/recipes/tc_tracking/src/modes/generate_ensembles.py b/recipes/tc_tracking/src/modes/generate_ensembles.py new file mode 100644 index 000000000..9d22161be --- /dev/null +++ b/recipes/tc_tracking/src/modes/generate_ensembles.py @@ -0,0 +1,330 @@ +import os +import random +from copy import deepcopy + +import numpy as np +import torch +from omegaconf import OmegaConf +from physicsnemo.distributed import DistributedManager +from tqdm import tqdm +from zarr import consolidate_metadata + +from earth2studio.data import fetch_data +from earth2studio.models.auto import Package +from earth2studio.perturbation import SphericalGaussian +from earth2studio.utils.coords import map_coords +from src.tempest_extremes import AsyncTempestExtremes, TempestExtremes +from src.data.file_output import initialise_netcdf_output, setup_output, write_to_store +from src.data.utils import DataSourceManager, load_heights +from src.utils import ( + InstabilityDetection, + get_set_of_random_seeds, + remove_duplicates, + run_with_rank_ordered_execution, + set_initial_times, +) + + +def initialise(cfg): + os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" + + DistributedManager.initialize() + + # set seed + if "random_seed" in cfg: + torch.manual_seed(cfg.random_seed) + np.random.seed(cfg.random_seed) + random.seed(cfg.random_seed) + torch.cuda.manual_seed(cfg.random_seed) + + return + + +def load_model(cfg): + model = "fcn3" + if "model" in cfg: + model = cfg.model + + # select model + if model[:4] == "aifs": + from earth2studio.models.px import AIFSENS + + model = AIFSENS + elif model == "fcn3": + from earth2studio.models.px import FCN3 + + model = FCN3 + elif model == "sfno": + from earth2studio.models.px import SFNO + + model = SFNO + else: + raise ValueError(f"model {model} not supported") + + # load weights + pkg = ( + Package(cfg.model_package) + if "model_package" in cfg + else model.load_default_package() + ) + + # load full model + model = model.load_model(pkg).to(DistributedManager().device) + + return model + + +def run_inference(model, cfg, store, out_coords, ic_mems): + dist = DistributedManager() + + # data_source = hydra.utils.instantiate(cfg.data_source) + data_source_mngr = DataSourceManager(cfg) + + # iterate over initial conditions + ic_prev = None + + cyclone_tracking = None + if "cyclone_tracking" in cfg: + oco = model.output_coords(model.input_coords()) + + heights, height_coords = ( + load_heights(cfg.cyclone_tracking.orography_path) + if "orography_path" in cfg.cyclone_tracking + else (None, None) + ) + + tracker = ( + AsyncTempestExtremes + if cfg.cyclone_tracking.asynchronous + else TempestExtremes + ) + cyclone_tracking = tracker( + detect_cmd=cfg.cyclone_tracking.detect_cmd, + stitch_cmd=cfg.cyclone_tracking.stitch_cmd, + input_vars=cfg.cyclone_tracking.vars, + batch_size=cfg.batch_size, + n_steps=cfg.n_steps, + time_step=oco["lead_time"][0], + lats=oco["lat"], + lons=oco["lon"], + static_vars=heights, + static_coords=height_coords, + store_dir=cfg.store_dir, + keep_raw_data=cfg.cyclone_tracking.keep_raw_data, + print_te_output=cfg.cyclone_tracking.print_te_output, + use_ram=cfg.cyclone_tracking.use_ram, + timeout=cfg.cyclone_tracking.task_timeout_seconds, + ) + + stability_check = None + if "stability_check" in cfg: + stability_check = InstabilityDetection( + vars=np.array(cfg.stability_check.variables), + thresholds=np.array(cfg.stability_check.thresholds), + ) + + for ic, mems, seed in ic_mems: + mini_batch_size = len(mems) + + data_source = data_source_mngr.select_data_source(ic) + + # if new IC, fetch data, create iterator + if ic != ic_prev: + if cfg.store_type == "netcdf": + store = initialise_netcdf_output(cfg, out_coords, ic, ic_mems) + x0, coords0 = fetch_data( + data_source, + time=[np.datetime64(ic)], + lead_time=model.input_coords()["lead_time"], + variable=model.input_coords()["variable"], + device=dist.device, + ) + ic_prev = ic + + coords = {"ensemble": np.array(mems)} | coords0.copy() + xx = x0.unsqueeze(0).repeat(mini_batch_size, *([1] * x0.ndim)) + + if stability_check: + stability_check.reset(deepcopy(coords)) + # print(stability_check.input_coords) + # exit() + + # set random state or apply perturbation + if (not "model" in cfg) or (cfg.model == "fcn3"): + model.set_rng(seed=seed) + elif cfg.model[:4] == 'aifs': # no need for perturbation, but also cannot set internal noise state + pass + else: + sg = SphericalGaussian(noise_amplitude=0.0005) + xx, coords = sg(xx, coords) + + iterator = model.create_iterator(xx, coords) + + # roll out the model and record data as desired + for _, (xx, coords) in tqdm( + zip(range(cfg.n_steps + 1), iterator), total=cfg.n_steps + 1 + ): + write_to_store(store, xx, coords, out_coords) + if cyclone_tracking: + cyclone_tracking.record_state(xx, coords) + + if stability_check: + yy, coy = map_coords(xx, coords, stability_check.input_coords) + stab, _ = stability_check(yy, coy) + if not stab.all(): + ic_mems.append((ic, mems, seed + 1)) + print( + f"CAUTION: one of members {mems} became unstable. will re-create with new seed." + ) + break + + if cyclone_tracking and (not stability_check or stab.all()): + cyclone_tracking( + out_file_names=[ # TODO add seed only for FCN3 members, as the seed in AIFS is not exposed. also check for netcdf output + f"tracks_{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}_seed_{seed}_bs_{cfg.batch_size}.csv" + for mem in mems + ] + ) + + # Wait for all async cyclone tracking tasks to complete before continuing + if cyclone_tracking and isinstance(cyclone_tracking, AsyncTempestExtremes): + cyclone_tracking.cleanup() + + # Consolidate metadata in zarr files + if dist.rank == 0 and cfg.store_type == "zarr": + # TODO add barrier such that rank 0 finishes last + consolidate_metadata(store.store) + + return store + + +def distribute_runs(ic_mems): + dist = DistributedManager() + + # get the number of initial conditions + ic_mems_per_rank = len(ic_mems) // dist.world_size + if len(ic_mems) % dist.world_size != 0: + ic_mems_per_rank += 1 + + # get the initial conditions for this rank + ic_mems = ic_mems[dist.rank * ic_mems_per_rank : (dist.rank + 1) * ic_mems_per_rank] + + if len(ic_mems) == 0: + print(f"nothing to do for rank {dist.rank}, exiting") + ic_mems = None + + return ic_mems + + +def configure_runs(cfg): + ic_mems = [] + + ics = set_initial_times(cfg) + + seeds = get_set_of_random_seeds( + n_ics=len(ics), + ensemble_size=cfg.ensemble_size, + batch_size=cfg.batch_size, + seed=cfg.random_seed if "random_seed" in cfg else None, + ) + + ii = 0 + for ic in ics: + for mem in range(0, cfg.ensemble_size, cfg.batch_size): + mems = np.arange(mem, min(mem + cfg.batch_size, cfg.ensemble_size)) + ic_mems.append((ic, mems, int(seeds[ii]))) + ii += 1 + + if not DistributedManager().distributed: + return ic_mems, ics + + ic_mems = distribute_runs(ic_mems) + + return ic_mems, ics + + +def set_reproduction_configs(cfg): + ic_mems = OmegaConf.to_container(cfg.reproduce_members) + + ics = [] + for ii in range(len(ic_mems)): + # time to numpy object + ic_mems[ii][0] = np.datetime64(ic_mems[ii][0]) + ics.append(ic_mems[ii][0]) + + # get full batches that include members which shall be reproduced + batch_id = ic_mems[ii][1] // cfg.batch_size + ic_mems[ii][1] = np.arange( + batch_id * cfg.batch_size, + min((batch_id + 1) * cfg.batch_size, cfg.ensemble_size), + ) + + # remove duplicates + ic_mems = remove_duplicates(ic_mems) + ics = list(set(ics)) + + if not DistributedManager().distributed: + return ic_mems, ics + + ic_mems = distribute_runs(ic_mems) + + return ic_mems, ics + + +def generate_ensemble(cfg): + + initialise(cfg) + + ic_mems, ics = configure_runs(cfg) + + model = load_model(cfg) + + # store, out_coords = setup_output(cfg, model, add_arrays=DistributedManager().rank == 0) + store, out_coords = ( + run_with_rank_ordered_execution( # TODO: wrap only zarr store in that loop + setup_output, + cfg=cfg, + model=model, + ics=ics, + add_arrays=DistributedManager().rank == 0, + ) + ) + + if ic_mems is None: + DistributedManager().cleanup() + exit() + + store = run_inference(model, cfg, store, out_coords, ic_mems) + + return + + +def reproduce_members(cfg): + if cfg.store_type == "zarr": + raise ValueError("Zarr output not supported for reproducing ensemble members") + if cfg.model != 'fcn3': + raise ValueError('Currently, reproducibility works for FCN3 only') + + initialise(cfg) + + ic_mems, ics = set_reproduction_configs(cfg) + + model = load_model(cfg) + + store, out_coords = ( + run_with_rank_ordered_execution( # TODO: wrap only zarr store in that loop + setup_output, + cfg=cfg, + model=model, + ics=ics, + add_arrays=DistributedManager().rank == 0, + ) + ) + + if ic_mems is None: + DistributedManager().cleanup() + exit() + + store = run_inference(model, cfg, store, out_coords, ic_mems) + + return diff --git a/recipes/tc_tracking/src/tempest_extremes.py b/recipes/tc_tracking/src/tempest_extremes.py new file mode 100644 index 000000000..e2aabd551 --- /dev/null +++ b/recipes/tc_tracking/src/tempest_extremes.py @@ -0,0 +1,1250 @@ +import atexit +import copy +import os +import shutil +import threading +import time +from collections import OrderedDict +from concurrent.futures import Future, ThreadPoolExecutor +from subprocess import run +from types import TracebackType +from typing import Any + +import numpy as np +import torch +from physicsnemo.distributed import DistributedManager +from rich.console import Console +from rich.table import Table +from rich.traceback import Traceback + +from earth2studio.io import KVBackend +from earth2studio.utils.coords import ( + CoordSystem, + cat_coords, + map_coords, + split_coords, + tile_coords, +) + + +class TempestExtremes: + """TempestExtremes cyclone tracking diagnostic class. + + This class provides functionality for detecting and tracking tropical cyclones + using the TempestExtremes (TE) software package directly on model outputs. + To be compatible with TE, model outputs are buffered as a netcdf file with the + coordinates (time, lat, lon). Optionally, this netcdf file can be stored in RAM to + avoid writing large amount of data to disk. + Since TE natively only provides a command-line interface, a subprocess is spawned + calling TE. To provide users with as much flexibility as possible, the command to + execute TE has to be specified by the user and passed to the class during + initialisation. Any arguments regarding input and output files will be ignored + and automatically populated with the files holding the model output. + Additionally, this approach allows the user to point the class to TE executables + at any location of the system. + TE is purely CPU-based, but for most applications still faster than producing the + atmospheric data on the GPU. AsyncTempestExtremes provides an implementation in + which a CPU thread applies TE to the generated data, while the GPU continues + producing the next prediction. + + The result will be a TE-generated track file, stored in a user-defined location + + NOTE: + - currently works on batch size 1 only (currently worked on) + - length of time direction in input coords must be 1 (add test) + + Tip: To iterate on the TE command without having to always run a model, set + keep_raw_data to True and work with TE directly on the netcdf file that the + class generates. + + Parameters + ---------- + detect_cmd : str + TempestExtremes DetectNodes command with arguments + Note that --in_data_list and --out_file_list will be ignored if provided + example: "/path/to/DetectNodes --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0" + stitch_cmd : str + TempestExtremes StitchNodes command with arguments + Note that --in and --out will be ignored if provided + example: "/path/to/StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10" + input_vars : + List of variables which are required for the tracking algorithm + n_steps : + Number of time steps + time_step : + Time step interval + lats : + Latitude coordinates + lons : + Longitude coordinates + static_vars : torch.Tensor, optional + A tensor holding static data like orography, by default None + static_coords : dict, optional + Coordinates of the static data, by default None + store_dir : str, optional + Path for storing output files, by default None. + if use_ram==False, intermediate files will also be written to that path + keep_raw_data : bool, optional + Whether to keep raw data files, by default False + print_te_output : bool, optional + Whether to print TempestExtremes command line output, by default False + use_ram : bool, optional + Whether to use RAM (/dev/shm) for temporary storage, by default True + **kwargs + Additional keyword arguments for robustness + """ + + def __init__( + self, + detect_cmd: str, + stitch_cmd: str, + input_vars: list[str], + batch_size: int, + n_steps: int, + time_step: np.ndarray | list[np.timedelta64] | np.timedelta64, + lats: np.ndarray | torch.Tensor, + lons: np.ndarray | torch.Tensor, + store_dir: str, + static_vars: torch.Tensor | None = None, + static_coords: dict | None = None, + keep_raw_data: bool = False, + print_te_output: bool = False, + use_ram: bool = True, + **kwargs: Any, + ): # leave kwargs to make call robust to switching between this and async version + + self.rank = DistributedManager().rank + self.print_te_output = print_te_output + self.keep_raw = keep_raw_data + self.static_vars = static_vars + self.static_coords = static_coords + self.time_step = time_step + self.input_vars = input_vars + self.batch_size = batch_size + self.n_steps = n_steps + self.lats = lats + self.lons = lons + + if isinstance(self.time_step, np.ndarray) or isinstance(self.time_step, list): + if not len(self.time_step) == 1: + raise ValueError( + "TempestExtremes connector currently only accepts multiple ensemble members from a single ICs." + ) + self.time_step = self.time_step[0] + if (static_vars is None) != (static_coords is None): + raise ValueError( + "provide both values and coords for static fields or don't." + + " currently, only one of them is provided" + ) + + self.format_tempestextremes_commands(detect_cmd, stitch_cmd) + + self.check_tempest_extremes_availability() + + self.initialise_store_coords() + + self.initialise_te_kvstore() + + self.setup_output_directories(use_ram, store_dir) + + return + + def format_tempestextremes_commands(self, detect_cmd: str, stitch_cmd: str) -> None: + """Format TempestExtremes commands by removing input/output file arguments. + These arguments are populated dynamically by the method. + + Parameters + ---------- + detect_cmd : str + TempestExtremes detect command + stitch_cmd : str + TempestExtremes stitch command + """ + self.detect_cmd = detect_cmd.split(" ") + self.stitch_cmd = stitch_cmd.split(" ") + + self.detect_cmd = self.remove_arguments( + self.detect_cmd, ["--in_data_list", "--out_file_list"] + ) + self.stitch_cmd = self.remove_arguments(self.stitch_cmd, ["--in", "--out"]) + + return + + def check_tempest_extremes_availability(self) -> None: + """Check if TempestExtremes executables are available on the system. + + Verifies that both detect and stitch commands can be executed by attempting + to run them. If either command fails, raises an OptionalDependencyFailure. + + Raises + ------ + OptionalDependencyFailure + If TempestExtremes executables are not available or cannot be executed + """ + for cmd in (self.detect_cmd[0], self.stitch_cmd[0]): + try: + run(cmd, capture_output=True) # noqa: S603 + except Exception as e: + self.dependency_failure(e.__traceback__, e) + + return + + def dependency_failure( + self, error_traceback: TracebackType | None, error_value: BaseException + ) -> None: + """Display a formatted error message for TempestExtremes dependency failures. + + This method creates and prints information about TempestExtremes dependency + errors and raises a RuntimeError to halt execution. + + Parameters + ---------- + error_traceback : TracebackType | None + The traceback object from the exception (typically from exception.__traceback__) + error_value : BaseException + The exception object that was raised + + Raises + ------ + RuntimeError + Always raised after displaying the error message to halt execution + """ + doc_url = "https://nvidia.github.io/earth2studio/userguide/about/install.html#optional-dependencies" + + console = Console() + table = Table(show_header=False, show_lines=True) + table.add_row( + "[blue]Earth2Studio Extra Dependency Error\n" + + "This error typically indicates an extra dependency group is needed.\n" + + "Don't panic, this is usually an easy fix.[/blue]" + ) + table.add_row( + "[yellow]The TempestExtremes class is marked needing optional dependencies.\n\n" + + "unlike other dependencies, TempestExtremes has to be installed to the system by the user.\n\n" + + "For more information, visit the install documentation: \n" + + f"{doc_url}[/yellow]" + ) + if error_value: + table.add_row( + Traceback( + Traceback.extract( + type(error_value), + error_value, + error_traceback, + show_locals=False, + ) + ) + ) + console.print(table) + + raise RuntimeError( + "TempestExtremes dependency not available. " + f"Please install TempestExtremes. See {doc_url}" + ) + + @staticmethod + def remove_arguments(cmd: list[str], args: list[str]) -> list[str]: + """Remove specified arguments from command list. + + Parameters + ---------- + cmd : List[str] + Command as list of strings + args : List[str] + Arguments to remove + + Returns + ------- + List[str] + Command with specified arguments removed + """ + for arg in args: + if arg in cmd: + remove = [cmd.index(arg)] + if len(cmd) > remove[0] + 1: + if not cmd[remove[0] + 1].startswith("--"): + remove.append(remove[0] + 1) + cmd = [_arg for ii, _arg in enumerate(cmd) if ii not in remove] + + return cmd + + def setup_output_directories(self, use_ram: bool, store_dir: str) -> None: + """Set up output directories for final data storage and for storing + auxiliary files. + + Parameters + ---------- + use_ram : bool + Whether to use RAM for temporary storage + store_dir : Optional[str] + Base storage directory path + """ + self.store_dir = os.path.abspath(os.path.join(store_dir, "cyclone_tracks_te")) + + if self.rank == 0: + os.makedirs(self.store_dir, exist_ok=True) # output for track data + if self.keep_raw: + os.makedirs( + os.path.join(self.store_dir, "raw_data"), exist_ok=True + ) # output for raw data + + if use_ram: + ram_path = "/dev/shm" # noqa: S108 + if not os.path.exists(ram_path): + raise ValueError( + "/dev/shm not available. Set use_ram=False and use store_dir to specify a different location." # noqa: S108 + ) + self.ram_dir = os.path.join(ram_path, "cyclone_tracking") + if DistributedManager().local_rank == 0: + os.makedirs( + self.ram_dir, exist_ok=True + ) # RAM location for processing fields + else: + self.ram_dir = os.path.join(self.store_dir, "raw_data") + if self.rank == 0: + os.makedirs( + self.ram_dir, exist_ok=True + ) # disk location for processing fields + + return + + def initialise_store_coords(self) -> None: + """Initialize coordinate system for data storage. + + Creates the internal coordinate system for storing to the internal + KV io backend, with optionally adding static variables at each time step. + """ + # extract time step in case it's passed as list or array + _input_vars = self.input_vars + if self.static_coords: + _input_vars = np.array( + list(self.input_vars) + list(self.static_coords["variable"]) + ) + + self._store_coords = OrderedDict( + { + "lead_time": np.asarray( + [self.time_step * i for i in range(self.n_steps + 1)] + ).flatten(), + "variable": np.array(_input_vars), + "lat": np.array(self.lats), + "lon": np.array(self.lons), + } + ) + + return + + @property + def input_coords(self) -> OrderedDict: + """Returns the coords of the data to be passed to record_state. + + Returns + ------- + OrderedDict + Coordinate system for input data + """ + return OrderedDict( + { + "time": np.empty(0), + "lead_time": np.asarray( + [self.time_step * i for i in range(self.n_steps + 1)] + ).flatten(), + "variable": np.array(self.input_vars), + "lat": np.array(self.lats), + "lon": np.array(self.lons), + } + ) + + def initialise_te_kvstore(self) -> None: + """Initialize the KV store that stores model output in TE compatible format.""" + # create store + self.store = KVBackend() + + # add arrays to the store + oco = copy.deepcopy(self._store_coords) + out_vars = oco.pop("variable") + + oco["time"] = np.array([None]) + oco["ensemble"] = np.array([None] * self.batch_size) + oco.move_to_end("time", last=False) + oco.move_to_end("ensemble", last=False) + + self.store.add_array(coords=oco, array_name=out_vars) + + return + + def dump_raw_data(self) -> tuple[list[str], np.ndarray]: + """Dump raw data from store to NetCDF file. + + Morphs the store to TempestExtremes-compatible [time, lat, lon] structure + and writes to a (optionally in-RAM) NetCDF file, which can be ingested by TE. + + Returns + ------- + str + Path to the created NetCDF file + """ + if len(self.store.coords["time"]) > 1: + raise ValueError( + "Currently, TempestExtremes interface only works for single IC per evaluation cycle." + ) + + ic = self.store.coords["time"][0] + mems = self.store.coords["ensemble"] + + # morph store to TempestExtremes-compatible [time, lat, lon] structure + lead_times = self.store.coords.pop("lead_time") + self.store.coords["time"] = [ic + lt for lt in lead_times] + + for var in self.store.root.keys(): + self.store.root[var] = self.store.root[var].squeeze(1) + self.store.dims[var] = ["ensemble", "time", "lat", "lon"] + + # for each member, write te compatible nc file + raw_files = [] + + # in case last batch has less ensemble members than batch size, expand coords of store so to_xarray() does not fail + if len(mems) < self.batch_size: + self.store.coords["ensemble"] = list(self.store.coords["ensemble"]) + [ + None + ] * (self.batch_size - len(mems)) + + for mem in mems: + raw_dat = f"{np.datetime_as_string(ic, unit='s')}_mem_{mem:04d}.nc" + raw_dat = os.path.join(self.ram_dir, raw_dat) + raw_files.append(raw_dat) + kw_args = {"path": raw_dat, "format": "NETCDF4", "mode": "w"} + + _store = self.store.to_xarray().sel(ensemble=mem).drop_vars("ensemble") + _store.to_netcdf(**kw_args) + + # new clean store + self.initialise_te_kvstore() + + return raw_files, mems + + def setup_files( + self, out_file_names: list[str] | None = None + ) -> tuple[list[str], list[str], list[str], list[str]]: + """Set up input and output files for TE processing. + + Creates necessary file lists and paths for TE detect and stitch operations. + + Returns + ------- + tuple[list[str], list[str], list[str], list[str]] + Tuple containing (input_file_list, output_file_list, node_file, track_file) + """ + # write store to in-memory file + raw_files, mems = self.dump_raw_data() + + # make sure if file names are passed that there's one per member + if out_file_names is not None: + if len(out_file_names) != len(mems): + raise ValueError( + f"{len(out_file_names)} passed for {len(mems)} ensemble members" + ) + + ins, outs, node_files, track_files = [], [], [], [] + for ii, (mem, raw_dat) in enumerate(zip(mems, raw_files)): + # in_files to file + _ins = os.path.join( + self.ram_dir, f"input_files_{self.rank:04d}_mem_{mem:05d}.txt" + ) + with open(_ins, "w") as fl: + fl.write(raw_dat + "\n") + + _outs = os.path.join( + self.ram_dir, f"output_files_{self.rank:04d}_mem_{mem:05d}.txt" + ) + base_name = os.path.basename(raw_dat) + base_name = base_name.rsplit(".", 1)[0] + node_file = os.path.join( + self.ram_dir, base_name + f"_mem_{mem:05d}_tc_centres.txt" + ) + with open(_outs, "w") as fl: + fl.write(node_file + "\n") + + track_file = ( + out_file_names[ii] if out_file_names else "tracks_" + base_name + ".csv" + ) + track_file = os.path.join(self.store_dir, track_file) + + ins.append(_ins) + outs.append(_outs) + node_files.append(node_file) + track_files.append(track_file) + + return ins, outs, node_files, track_files + + def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None: + """Record state data to the internal store. + + Updates store with current time and ensemble information, + concatenates static data if available, and writes to store. + + Parameters + ---------- + xx : torch.Tensor + Input tensor data + coords : CoordSystem + Coordinate system for the input data + """ + # update store mem and time + for dim in ["time", "ensemble"]: + if not np.array_equal(self.store.coords[dim], coords[dim]): + self.store.coords[dim] = coords[dim] + + # concatenate static data + if self.static_vars is not None: + self.static_vars = self.static_vars.to(xx.device) + if len(xx.shape) > len(self.static_vars.shape): + _static_vars, _static_coords = tile_coords( + self.static_vars, self.static_coords, coords + ) + else: + _static_vars, _static_coords = self.static_vars, self.static_coords + xx, coords = cat_coords((xx, _static_vars), (coords, _static_coords)) + + # select output data and write to store + xx_sub, coords_sub = map_coords(xx, coords, self._store_coords) + self.store.write(*split_coords(xx_sub, coords_sub, dim="variable")) + + return + + @staticmethod + def run_te(command: list[str], print_output: bool) -> None: + """Run TempestExtremes command and handle output. + + Parameters + ---------- + command : list[str] + Command to execute as list of strings + print_output : bool + Whether to print command output to terminal + + Raises + ------ + ChildProcessError + If TempestExtremes command fails (detected by "EXCEPTION" in output) + """ + # detect nodes + out = run(command, capture_output=True) # noqa: S603 + + # Print output to terminal if requested + if print_output: + print(out.stdout.decode("utf-8")) + + # unfortunately, TE does not fail with proper returncode + if "EXCEPTION" in out.stdout.decode("utf-8"): + print(out.stdout.decode("utf-8")) + raise ChildProcessError( + f"\nERROR: {command[0]} failed, see output above from TempestExtremes for details." + ) + + return + + def track_cyclones(self, out_file_names: list[str] | None = None) -> None: + """Execute cyclone tracking using TempestExtremes. + + Runs the complete tracking pipeline including node detection + and stitching operations. + + Raises + ------ + ChildProcessError + If TempestExtremes detect or stitch operations fail + """ + then = time.time() + + # set up TE helper files + insies, outsies, node_files, self.track_files = self.setup_files(out_file_names) + + for ins, outs, node_file, track_file in zip( + insies, outsies, node_files, self.track_files + ): + # detect nodes + self.run_te( + command=self.detect_cmd + + ["--in_data_list", ins, "--out_file_list", outs], + print_output=self.print_te_output, + ) + + # stitch them together + self.run_te( + command=self.stitch_cmd + ["--in", node_file, "--out", track_file], + print_output=self.print_te_output, + ) + + # remove helper files + self.tidy_up(insies, outsies) + + print(f"took {(time.time() - then):.1f}s to track cyclones") + + return + + def __call__(self, out_file_names: list[str] | None = None) -> None: + """Make the class callable to execute cyclone tracking. + + Returns + ------- + None + Calls track_cyclones method + """ + return self.track_cyclones(out_file_names) + + def tidy_up(self, insies: list[str], outsies: list[str]) -> None: + """Clean up temporary files after processing. + + Handles raw data files based on keep_raw_data setting and + removes TempestExtremes helper files. + + Parameters + ---------- + ins : str + Path to input file list + outs : str + Path to output file list + """ + for ins, outs in zip(insies, outsies): + with open(ins) as fl: + raw_dat = fl.read().strip() + + if self.keep_raw: # move field data to disk + out_path = os.path.join(self.store_dir, "raw_data") + raw_dest = os.path.join(out_path, os.path.basename(raw_dat)) + if raw_dat != raw_dest: # nothing to do if use_ram=False + if os.path.exists(raw_dest): + os.remove(raw_dest) + shutil.move(raw_dat, out_path) + else: # delete field data + os.remove(raw_dat) + + # delete TE helper files + files = [ins, outs] + with open(outs) as fl: + _files = [line.strip() for line in fl] + files.extend(_files) + + for file in files: + os.remove(file) + + return + + +# Global thread pool for background TempestExtremes processing +_tempest_executor: ThreadPoolExecutor | None = None +_tempest_background_tasks: list[Future] = [] +_tempest_executor_lock: threading.Lock = threading.Lock() + + +def get_tempest_executor() -> ThreadPoolExecutor: + """Get or create the thread pool executor for TempestExtremes processing.""" + global _tempest_executor + with _tempest_executor_lock: + if _tempest_executor is None: + # Reduced worker count for long-running tasks to avoid resource exhaustion + max_workers = min(4, os.cpu_count() or 1) + _tempest_executor = ThreadPoolExecutor( + max_workers=max_workers, thread_name_prefix="tempest_extremes" + ) + # Ensure cleanup on exit + atexit.register(cleanup_tempest_executor) + return _tempest_executor + + +def cleanup_tempest_executor(timeout_per_task: int = 360) -> None: + """Clean up the thread pool executor. + + Parameters + ---------- + timeout_per_task : int, optional + Timeout in seconds for each task, by default 360 + + Raises + ------ + ChildProcessError + If one or more background TempestExtremes tasks failed + """ + global _tempest_executor, _tempest_background_tasks + with _tempest_executor_lock: + if _tempest_executor is not None: + # Wait for all background tasks to complete + child_process_errors = [] + for i, future in enumerate(_tempest_background_tasks): + try: + future.result( + timeout=timeout_per_task + ) # Configurable timeout per task + except ChildProcessError as e: + print( + f"Background TempestExtremes task {i+1} failed with ChildProcessError: {e}" + ) + child_process_errors.append(e) + except Exception as e: + print(f"Background TempestExtremes task {i+1} failed: {e}") + _tempest_executor.shutdown(wait=True) + _tempest_executor = None + _tempest_background_tasks.clear() + + # Re-raise ChildProcessError if any occurred + if child_process_errors: + raise ChildProcessError( + f"One or more background TempestExtremes tasks failed: {child_process_errors}" + ) + + +class AsyncTempestExtremes(TempestExtremes): + """Asynchronous version of TempestExtremes that runs cyclone tracking in background threads. + + This class extends TempestExtremes to provide asynchronous cyclone tracking capabilities + using background thread pools. It allows for non-blocking cyclone tracking operations + while maintaining data integrity through synchronization mechanisms. + + Follows the parallelization paradigm from async_tracking_utils.py. + + Parameters + ---------- + *args + Positional arguments passed to parent TempestExtremes class + **kwargs + Keyword arguments passed to parent TempestExtremes class. + Special parameters: + - timeout : int, optional + Timeout in seconds for operations, by default 60 + - max_workers : int, optional + Maximum number of worker threads for parallel ensemble member processing. + If None (default), uses min(n_members, cpu_count). If provided, caps + the number of workers to this value, by default None + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + # Extract async-specific parameters with default values + self.timeout = kwargs.pop("timeout", 60) + self.max_workers = kwargs.pop("max_workers", None) + + # Initialize the parent class + super().__init__(*args, **kwargs) + + # Track background tasks for this instance + self._instance_tasks: list[Future] = [] + self._instance_lock = threading.Lock() + self._has_failed = False # Track if any previous task failed + self._cleanup_done = False # Track if cleanup has been performed + + def record_state(self, xx: torch.Tensor, coords: CoordSystem) -> None: + """Record state data, checking for any previous task failures. + + Parameters + ---------- + xx : torch.Tensor + Input tensor data + coords : CoordSystem + Coordinate system for the input data + + Raises + ------ + ChildProcessError + If any previous cyclone tracking task failed + """ + # Check if any previous task failed and raise error immediately + self._check_for_failures() + + # Call parent's record_state method + super().record_state(xx, coords) + + def _check_for_failures(self) -> None: + """Check if any previous tasks failed with ChildProcessError and raise immediately. + + This method checks the status of all background tasks and raises an exception + if any have failed, ensuring early failure detection. + + Raises + ------ + ChildProcessError + If any previous cyclone tracking task failed + Exception + If any task failed with a different exception type + """ + if self._has_failed: + raise ChildProcessError( + "Previous cyclone tracking task failed - stopping execution" + ) + + # Check completed tasks for failures + with self._instance_lock: + for future in self._instance_tasks: + if future.done(): + try: + future.result() # This will raise if the task failed + except ChildProcessError as e: + self._has_failed = True + raise e # Re-raise immediately + except Exception as e: + self._has_failed = True + raise e # Re-raise other exceptions too + + def track_cyclones_async(self, out_file_names: list[str] | None = None) -> Future: + """Submit cyclone tracking to background thread pool. + + This method submits cyclone tracking operations to a background thread pool + for asynchronous execution, allowing the main thread to continue processing. + + IMPORTANT: The netCDF file writing (dump_raw_data) is done SYNCHRONOUSLY + in the main thread before submitting, because HDF5/netCDF4 is not thread-safe. + Only the CPU-intensive TempestExtremes detect/stitch runs in the background. + + Returns + ------- + Future + A Future object that can be used to check status or get results + + Raises + ------ + ChildProcessError + If any previous cyclone tracking task failed + """ + # Check if any previous task failed and raise error immediately + self._check_for_failures() + + global _tempest_background_tasks + + # Clean up completed tasks to avoid memory buildup + with _tempest_executor_lock: + _tempest_background_tasks = [ + f for f in _tempest_background_tasks if not f.done() + ] + + with self._instance_lock: + self._instance_tasks = [f for f in self._instance_tasks if not f.done()] + + # Check if we have too many running tasks + with _tempest_executor_lock: + running_tasks = len(_tempest_background_tasks) + if running_tasks >= 3: # Conservative limit for long-running tasks + print( + f"Warning: {running_tasks} TempestExtremes tasks already running. Consider waiting for some to complete." + ) + + # CRITICAL: Run setup_files() (which includes dump_raw_data with netCDF writes) + # SYNCHRONOUSLY in the main thread. HDF5/netCDF4 is not thread-safe, and + # concurrent writes from multiple threads cause segmentation faults. + insies, outsies, node_files, track_files = self.setup_files(out_file_names) + self.track_files = track_files + + # Submit only the TE processing (detect/stitch) to the background thread + executor = get_tempest_executor() + future = executor.submit( + self._run_te_and_cleanup, insies, outsies, node_files, track_files + ) + + # Add to both global and instance task lists + with _tempest_executor_lock: + _tempest_background_tasks.append(future) + + with self._instance_lock: + self._instance_tasks.append(future) + + return future + + def get_task_status(self) -> dict[str, int]: + """Get status of background tasks for this instance. + + Returns + ------- + Dict[str, int] + Dictionary containing task status counts with keys: + - 'running': Number of currently running tasks + - 'pending': Number of pending tasks + - 'completed': Number of completed tasks + - 'failed': Number of failed tasks + - 'total': Total number of tasks + """ + with self._instance_lock: + running = sum(1 for f in self._instance_tasks if f.running()) + pending = sum( + 1 for f in self._instance_tasks if not f.done() and not f.running() + ) + completed = sum(1 for f in self._instance_tasks if f.done()) + failed = sum( + 1 + for f in self._instance_tasks + if f.done() and f.exception() is not None + ) + + return { + "running": running, + "pending": pending, + "completed": completed, + "failed": failed, + "total": len(self._instance_tasks), + } + + def get_task_results( + self, raise_on_error: bool = True + ) -> dict[str, Any] | tuple[list[Any], list[tuple[str, Exception]]]: + """Get results from all completed tasks. + + Parameters + ---------- + raise_on_error : bool, optional + If True, raises ChildProcessError if any task failed with that error. + If False, returns results and exceptions separately, by default True + + Returns + ------- + Union[Dict[str, Any], tuple[list[Any], list[tuple[str, Exception]]]] + If raise_on_error=True: dict with successful results + If raise_on_error=False: tuple of (results, exceptions) + + Raises + ------ + ChildProcessError + If raise_on_error=True and any task failed with ChildProcessError + Exception + If raise_on_error=True and any task failed with other exceptions + """ + with self._instance_lock: + tasks_to_check = list(self._instance_tasks) + + results = [] + exceptions: list[tuple[str, Exception]] = [] + + for i, future in enumerate(tasks_to_check): + if future.done(): + try: + result = future.result() + results.append(result) + except ChildProcessError as e: + if raise_on_error: + raise # Re-raise ChildProcessError immediately + exceptions.append(("ChildProcessError", e)) + except Exception as e: + if raise_on_error: + raise # Re-raise any other exceptions + exceptions.append(("Exception", e)) + + if raise_on_error: + return {"results": results, "task_count": len(tasks_to_check)} + else: + return results, exceptions + + def wait_for_completion(self, timeout_per_task: int = 360) -> None: + """Wait for all background tasks associated with this instance to complete. + + This method blocks until all background tasks have finished execution. + + Parameters + ---------- + timeout_per_task : int, optional + Timeout in seconds for each task, by default 360 + + Raises + ------ + ChildProcessError + If any background task failed with ChildProcessError + Exception + If any background task failed with other exceptions + """ + with self._instance_lock: + tasks_to_wait = list(self._instance_tasks) + + if tasks_to_wait: + print( + f"Waiting for {len(tasks_to_wait)} TempestExtremes tasks to complete..." + ) + + for i, future in enumerate(tasks_to_wait): + try: + print(f"Waiting for task {i+1}/{len(tasks_to_wait)} to complete...") + future.result(timeout=timeout_per_task) + except ChildProcessError as e: + print(f"Task {i+1} failed with ChildProcessError: {e}") + raise # Re-raise ChildProcessError to propagate it + except Exception as e: + print(f"Task {i+1} failed: {e}") + raise # Re-raise any other exceptions as well + else: + print("No background tasks to wait for.") + + def _process_single_member( + self, ins: str, outs: str, node_file: str, track_file: str + ) -> None: + """Process a single ensemble member (detect + stitch). + + This method is designed to run in a separate thread for parallel processing + of ensemble members. + + Parameters + ---------- + ins : str + Path to input file list for this member + outs : str + Path to output file list for this member + node_file : str + Path to node file for this member + track_file : str + Path to track file for this member + + Raises + ------ + ChildProcessError + If TempestExtremes detect or stitch operations fail + """ + then = time.time() + + # detect nodes + self.run_te( + command=self.detect_cmd + ["--in_data_list", ins, "--out_file_list", outs], + print_output=self.print_te_output, + ) + + # stitch them together + self.run_te( + command=self.stitch_cmd + ["--in", node_file, "--out", track_file], + print_output=self.print_te_output, + ) + + if self.print_te_output: + print(f"took {(time.time() - then):.1f}s to track cyclones for one member") + + def _run_te_and_cleanup( + self, + insies: list[str], + outsies: list[str], + node_files: list[str], + track_files: list[str], + ) -> None: + """Run TempestExtremes processing and cleanup (no netCDF writing). + + This method is designed to run in a background thread after the netCDF + files have already been written by the main thread. + + Parameters + ---------- + insies : list[str] + List of input file list paths + outsies : list[str] + List of output file list paths + node_files : list[str] + List of node file paths + track_files : list[str] + List of track file paths + + Raises + ------ + ChildProcessError + If TempestExtremes detect or stitch operations fail for any member + """ + then = time.time() + + # Create a dedicated local thread pool for member processing + n_members = len(insies) + max_workers = min(n_members, os.cpu_count() or 1) + if self.max_workers is not None: + max_workers = min(self.max_workers, max_workers) + + with ThreadPoolExecutor( + max_workers=max_workers, thread_name_prefix="te_members" + ) as executor: + # Submit one task per member to process in parallel + member_futures = [] + + for ins, outs, node_file, track_file in zip( + insies, outsies, node_files, track_files + ): + future = executor.submit( + self._process_single_member, ins, outs, node_file, track_file + ) + member_futures.append(future) + + # Wait for all members to complete + exceptions = [] + for i, future in enumerate(member_futures): + try: + future.result() + except Exception as e: + print(f"Member {i+1} processing failed: {e}") + exceptions.append((i, e)) + + # Raise if any failed + if exceptions: + error_msg = ( + f"Processing failed for {len(exceptions)} member(s): {exceptions}" + ) + raise ChildProcessError(error_msg) + + # Clean up after all are done + self.tidy_up(insies, outsies) + + print( + f"took {(time.time() - then):.1f}s to track cyclones for all {len(member_futures)} members" + ) + + def track_cyclones(self, out_file_names: list[str] | None = None) -> None: + """Execute cyclone tracking with parallel processing per ensemble member. + + This override of the parent method dumps data synchronously (within this thread), + then spawns separate threads for each ensemble member's processing. This allows + multiple ensemble members to be processed in parallel while avoiding race + conditions during data dumping. + + Uses a dedicated local thread pool for member processing to avoid conflicts + with the global executor during shutdown. + + Parameters + ---------- + out_file_names : list of str, optional + Custom output file names for each ensemble member + + Raises + ------ + ChildProcessError + If TempestExtremes detect or stitch operations fail for any member + """ + then = time.time() + + # Dump data and setup files (synchronous within this thread to avoid race conditions) + insies, outsies, node_files, self.track_files = self.setup_files(out_file_names) + + # Create a dedicated local thread pool for member processing + # This avoids conflicts with the global executor during shutdown + n_members = len(insies) + max_workers = min(n_members, os.cpu_count() or 1) + if self.max_workers is not None: + max_workers = min(self.max_workers, max_workers) + + with ThreadPoolExecutor( + max_workers=max_workers, thread_name_prefix="te_members" + ) as executor: + # Submit one task per member to process in parallel + member_futures = [] + + for ins, outs, node_file, track_file in zip( + insies, outsies, node_files, self.track_files + ): + future = executor.submit( + self._process_single_member, ins, outs, node_file, track_file + ) + member_futures.append(future) + + # Wait for all members to complete + exceptions = [] + for i, future in enumerate(member_futures): + try: + future.result() # This will raise if the task failed + except Exception as e: + print(f"Member {i+1} processing failed: {e}") + exceptions.append((i, e)) + + # Raise if any failed + if exceptions: + error_msg = ( + f"Processing failed for {len(exceptions)} member(s): {exceptions}" + ) + raise ChildProcessError(error_msg) + + # Clean up after all are done + self.tidy_up(insies, outsies) + + print( + f"took {(time.time() - then):.1f}s to track cyclones for all {len(member_futures)} members" + ) + + def cleanup(self, timeout_per_task: int = 360) -> None: + """Explicitly clean up and wait for all background tasks to complete. + + This method should be called before the object is destroyed or the program exits + to ensure all cyclone tracking tasks complete successfully. + + Parameters + ---------- + timeout_per_task : int, optional + Timeout in seconds for each task, by default 360 + + Raises + ------ + ChildProcessError + If any background task failed + Exception + If any task failed with other exceptions + """ + if self._cleanup_done: + return + + try: + # Wait for all instance tasks to complete + if hasattr(self, "_instance_tasks") and hasattr(self, "_instance_lock"): + with self._instance_lock: + tasks_to_wait = list(self._instance_tasks) + + if tasks_to_wait: + print( + f"AsyncTempestExtremes: waiting for {len(tasks_to_wait)} background tasks to complete..." + ) + + for i, future in enumerate(tasks_to_wait): + try: + print(f" Waiting for task {i+1}/{len(tasks_to_wait)}...") + future.result(timeout=timeout_per_task) + print( + f" Task {i+1}/{len(tasks_to_wait)} completed successfully" + ) + except ChildProcessError as e: + print( + f" Task {i+1}/{len(tasks_to_wait)} failed with ChildProcessError: {e}" + ) + raise # Re-raise to propagate the error + except Exception as e: + print(f" Task {i+1}/{len(tasks_to_wait)} failed: {e}") + raise # Re-raise to propagate the error + + print( + f"All {len(tasks_to_wait)} background tasks completed successfully" + ) + + self._cleanup_done = True + + except Exception as _: + self._cleanup_done = True # Mark as done even on failure to avoid retry + raise + + def __del__(self) -> None: + """Destructor that ensures all processes have finished before the class gets destroyed. + + This method performs cleanup operations when the object is being destroyed, + ensuring all background tasks complete and resources are properly released. + """ + try: + # Use getattr to handle case where initialization failed before _cleanup_done was set + if not getattr(self, "_cleanup_done", True): + self.cleanup(timeout_per_task=30) # Shorter timeout in destructor + except Exception as e: + print( + "\033[95mError in AsyncTempestExtremes destructor: had no time to cleanup.\033[0m" + ) + print( + "\033[95mplease call self.cleanup() from script before class goes out of scope.\033[0m" + ) + print(f"\033[95mThis is resulting in the following error: {e}\033[0m") + # Note: In destructor, we log but don't re-raise to avoid issues during interpreter shutdown + + def __call__(self, out_file_names: list[str] | None = None) -> Future[None]: # type: ignore[override] + """Override call method to use async version by default. + + This method makes the class callable and uses the asynchronous + tracking method by default. + + Returns + ------- + Future[None] + A Future object for the submitted cyclone tracking task + + Raises + ------ + ChildProcessError + If any previous cyclone tracking task failed + """ + # Check if any previous task failed and raise error immediately + self._check_for_failures() + return self.track_cyclones_async(out_file_names) diff --git a/recipes/tc_tracking/src/utils.py b/recipes/tc_tracking/src/utils.py new file mode 100644 index 000000000..fbab30d1b --- /dev/null +++ b/recipes/tc_tracking/src/utils.py @@ -0,0 +1,226 @@ +from collections import OrderedDict +from collections.abc import Callable +from math import ceil +from typing import Any + +import numpy as np +import torch +from omegaconf import DictConfig +from physicsnemo.distributed import DistributedManager + +from earth2studio.utils.time import to_time_array + + +def set_initial_times(cfg: DictConfig) -> list[np.datetime64]: + """Build list of IC times. + + Parameters + ---------- + cfg : DictConfig + Hydra config object + + Returns + ------- + list[np.datetime64] + List of initial condition times. + """ + # list of ICs + if "ics" in cfg: + if "ic_block_start" in cfg: + raise ValueError( + "either provide a list of start times or define a block, not both" + ) + ics = to_time_array(sorted(cfg.ics)) + + # block of ICs + else: + ics = to_time_array([cfg.ic_block_start, cfg.ic_block_end]) + ics = np.arange( + ics[0], + ics[1] + np.timedelta64(cfg.ic_block_step, "h"), + np.timedelta64(cfg.ic_block_step, "h"), + ) + + return ics + + +def remove_duplicates(data_list): + """ + Remove duplicates while preserving numpy dtype distinctions. + Arrays with same values but different dtypes are considered different. + """ + + def to_hashable(item): + if isinstance(item, np.ndarray): + # Include dtype and shape information + return (tuple(item.tolist()), str(item.dtype), item.shape) + elif isinstance(item, np.datetime64): + return str(item) + return item + + seen = set() + result = [] + + for sublist in data_list: + hashable_key = tuple(to_hashable(item) for item in sublist) + if hashable_key not in seen: + seen.add(hashable_key) + result.append(sublist) + + return result + + +def get_set_of_random_seeds(n_ics, ensemble_size, batch_size, seed): + n_batches = ceil(ensemble_size / batch_size) + rng = np.random.default_rng(seed=seed) + + seeds = np.array([]) + ii = 0 + while len(np.unique(seeds)) != n_batches * n_ics: + ii += 1 + if ii > 1000: + raise RecursionError( + f"failed to generate unique set of {n_batches*n_ics} random seeds after 1000 iterations. giving up :(" + ) + seeds = rng.integers(low=0, high=2**32, size=n_batches * n_ics, dtype=np.uint32) + + return seeds + + +def run_with_rank_ordered_execution( + func: Callable, *args: Any, first_rank: int = 0, **kwargs: Any +) -> Any: + """Executes `func(*args, **kwargs)` safely in a distributed setting: + - First on the specified `rank` + - Then, after synchronization, on the other ranks + + Args: + func (Callable): Function to execute + args (tuple, optional): Positional arguments for the function. Defaults to (). + first_rank (int, optional): Rank to run the function first. Defaults to 0. + kwargs (dict, optional): Keyword arguments for the function. Defaults to None. + + Returns: + The return value of func(*args, **kwargs) + """ + if kwargs is None: + kwargs = {} + + dist = DistributedManager() + current_rank = dist.rank + + if current_rank == first_rank: + result = func(*args, **kwargs) + else: + result = None + + # Synchronize all processes after the first rank runs the function + # Skip the barrier if single-process (no distributed process group) + if dist.distributed: + torch.distributed.barrier() + + if current_rank != first_rank: + result = func(*args, **kwargs) + + if dist.distributed: + torch.distributed.barrier() + + return result + + +def squeeze_coords(xx, coords, squeeze_dim): + if isinstance(squeeze_dim, str): + squeeze_dim = [squeeze_dim] + + for dim in squeeze_dim: + idx = list(coords.keys()).index(dim) + + assert coords[dim].shape == ( + 1, + ), f"can only squeeze dims of length 1, coords[{dim}] has shape {coords[dim].shape}" + assert ( + xx.shape[idx] == 1 + ), f"can only squeeze dims of length 1, xx[{dim}] has shape {xx.shape[idx]}" + + xx = xx.squeeze(idx) + coords.pop(dim) + + return xx, coords + + +def great_circle_distance(lat1, lon1, lat2, lon2): + lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2]) + dlon = lon2 - lon1 + dlat = lat2 - lat1 + + aa = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2 + cc = 2 * np.arctan2(np.sqrt(aa), np.sqrt(1 - aa)) + + return 6371000 * cc + + +class InstabilityDetection: + def __init__( + self, + vars: list[str], + thresholds: list[float], + input_coords: OrderedDict | None = None, + ) -> None: + + self.vars = vars + self.thresh = torch.Tensor(thresholds) + self.reset(input_coords) + + if not len(vars) == len(thresholds): + raise ValueError("please provide exactly one threshold per variable") + + def reset(self, coords: OrderedDict = None): + self.baseline = None + self._input_coords = None + self._output_coords = None + + if coords: + self.update_coords(coords) + + def update_coords(self, coords): + # scrub time and lead_time dimensions if exist + coords.pop("time", None) + coords.pop("lead_time", None) + self._output_coords = OrderedDict({"ensemble": coords.pop("ensemble", None)}) + + self._input_coords = coords + self._input_coords["variable"] = self.vars + + @property + def input_coords(self): + return self._input_coords + + @property + def output_coords(self): + return self._output_coords + + def __call__(self, xx: torch.Tensor, coords: OrderedDict): + + var_dim = list(coords).index("variable") + batch_dim = list(coords).index("ensemble") if "ensemble" in coords else None + if "ensemble" in coords: + batch_dim = list(coords).index("ensemble") + + if self.baseline is None: + self.baseline = xx.mean( + dim=tuple( + [ii for ii in range(len(coords)) if not ii in (var_dim, batch_dim)] + ) + ) + comp = self.baseline + self.update_coords(coords) + else: + comp = xx.mean( + dim=tuple( + [ii for ii in range(len(coords)) if not ii in (var_dim, batch_dim)] + ) + ) + + return (torch.abs(comp - self.baseline) < self.thresh.to(xx.device)).all( + dim=-1 + ), self._output_coords diff --git a/recipes/tc_tracking/tc_hunt.py b/recipes/tc_tracking/tc_hunt.py new file mode 100644 index 000000000..257ed4893 --- /dev/null +++ b/recipes/tc_tracking/tc_hunt.py @@ -0,0 +1,39 @@ +import os + +# Set MKL/OMP threading environment variables BEFORE any other imports +# This prevents MKL initialization race conditions that cause +# divide-by-zero crashes when running FCN3 with NCCL/UCX +os.environ.setdefault("MKL_NUM_THREADS", "1") +os.environ.setdefault("OMP_NUM_THREADS", "1") +os.environ.setdefault("MKL_DYNAMIC", "FALSE") + +import hydra +from omegaconf import DictConfig +from src.modes.baseline_extraction import extract_baseline +from src.modes.generate_ensembles import generate_ensemble, reproduce_members + + +@hydra.main(version_base=None, config_path="cfg", config_name="none") +def main(cfg: DictConfig) -> None: + + if cfg.mode == "extract_baseline": + extract_baseline(cfg) + + elif cfg.mode == "generate_ensemble": + generate_ensemble(cfg) + + elif cfg.mode == "reproduce_members": + reproduce_members(cfg) + + else: + raise ValueError( + f'invalid mode: {cfg.mode}, choose from "extract_baseline" or "generate_ensemble"' + ) + + print(f"finished **yaaayyyy**") + + return + + +if __name__ == "__main__": + main() diff --git a/recipes/tc_tracking/test/.gitignore b/recipes/tc_tracking/test/.gitignore new file mode 100644 index 000000000..c6db2a46f --- /dev/null +++ b/recipes/tc_tracking/test/.gitignore @@ -0,0 +1 @@ +outputs_* diff --git a/recipes/tc_tracking/test/README.md b/recipes/tc_tracking/test/README.md new file mode 100644 index 000000000..136100324 --- /dev/null +++ b/recipes/tc_tracking/test/README.md @@ -0,0 +1,88 @@ +# Tests + +## Test 1: Reproducibility of Helene Ensemble Members + +> [!Note] +> The test can be done manually following the steps below. Alternatively, +> `test_tc_hunt.sh` provides an automated way for running the full test. +> If, at the end you see a message containing `all good, yay (:`, the test +> finished successfully. + +This case will test whether the pipeline can generate an ensemble forecast +using FCN3 and track tropical cyclones using TempestExtremes. +Furthermore, it is tested if those exact tracks can be reproduced. +Note that reproducing with AIFS-ENS won't work as the model does not provide +access to its internal random state. + +First, let us produce a five member ensemble of a 90h prediction of Hurricane Helene +```bash +cd earth2studio/recipes/tc_tracking/test +python ../tc_hunt.py --config-path=$(pwd) --config-name=baseline_helene.yaml +``` + +You can find the track files in `outputs_baseline_helene/cyclone_tracks_te`. +The directory should contain five csv files with naming pattern according to +`tracks_2024-09-29T00:00:00_mem_000X_seed_YYYYYYYYY_bs_2.csv` +This pattern provides information about initial condition, ensemble member ID +and batch size. + +Next, check if the random seeds for members three and four provided in `reproduce_helene.yaml` +are identical to the seeds in the csv file names. If not, replace the seeds for both members +in the yaml file with the seeds from the csv file names. + +Having made sure that the configurations for reproducing the ensemble members +is correct, let us now reproduce members three and four of the previous run: +```bash +python ../tc_hunt.py --config-path=$(pwd)/cfg --config-name=reproduce_helene.yaml +``` +Note that the baseline run was produced with batch size two. For reproducibility, +we need to apply the same batch size in the reproduction run and reproduce the full +batch that contained the ensemble member in question. For the present case, this means +that member two will also be reproduced, as it is in the same batch as member three. + +### Expected Result + +There should now be three track files in `outputs_reproduce_helene/cyclone_tracks_te`, +with file names identical to the associated files in `outputs_baseline_helene/cyclone_tracks_te`. +Whether the content of the files (i.e., the generated tracks) is also identical can be tested through +a simple `diff` call: + +```bash +diff outputs_baseline_helene/cyclone_tracks_te/tracks_2024-09-29T00:00:00_mem_0002_seed_4045540270_bs_2.csv \ + outputs_reproduce_helene/cyclone_tracks_te/tracks_2024-09-29T00:00:00_mem_0002_seed_4045540270_bs_2.csv +``` + +Repeat the diff call for all reproduced ensemble members, the return should always be empty. + + +## Test 2: Extracting individual storms from historic data + +> [!Note] +> The test can be done manually following the steps below. Alternatively, +> `test_historic_tc_extraction.sh` provides an automated way for running +> the full test. If, at the end you see a message containing `all good, yay (:`, +> the test finished successfully. + +This mode lets users extract storms from historic data sets. The user can choose +a storm, the script will look for that storm in the IBTrACS data base, obtain data +around the storm's life time from a data source (on-prem or online) and then extract +that storm using TempestExtremes. + +For the current test, let us extract Typhoon Hato (2017) and Hurricane Helene (2024) +from ERA5: +```bash +cd earth2studio/recipes/tc_tracking/test +python ../tc_hunt.py --config-path=$(pwd)/cfg --config-name=extract_era5.yaml +``` + +### Expected Result + +The run should produce two reference tracks in `outputs_reference_tracks/`. +Now, let us compare the extracted tracks with the baseline, in both cases +the files should be identical: +```bash +diff outputs_reference_tracks/reference_track_hato_2017_west_pacific.csv \ + aux_data/reference_track_hato_2017_west_pacific.csv +diff outputs_reference_tracks/reference_track_helene_2024_north_atlantic.csv \ + aux_data/reference_track_helene_2024_north_atlantic.csv +``` diff --git a/recipes/tc_tracking/test/cfg/baseline_helene.yaml b/recipes/tc_tracking/test/cfg/baseline_helene.yaml new file mode 100644 index 000000000..247c55286 --- /dev/null +++ b/recipes/tc_tracking/test/cfg/baseline_helene.yaml @@ -0,0 +1,38 @@ +project: 'baseline_helene' +mode: 'generate_ensemble' +model: 'fcn3' # 'fcn3' 'sfno' 'aifs' +random_seed: 7777 + +ics: ["2024-09-24 00:00:00"] #, "2024-09-24 06:00:00"] #, "2024-09-24 12:00:00"] + +n_steps: 15 +ensemble_size: 5 +batch_size: 2 + +data_source: + _target_: earth2studio.data.CDS + +store_type: "none" +store_dir: ./outputs_${project} + +cyclone_tracking: + asynchronous: True + vars: ['msl', 'u10m', 'v10m', 'z300', 'z500'] + detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0' + stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10' + orography_path: '../aux_data/orography.nc' + keep_raw_data: False + use_ram: True + task_timeout_seconds: 120 + print_te_output: False + +stability_check: + variables: ['t1000', 'u250', 'v850'] + thresholds: [10, 10, 10] + +# hydra logs +hydra: + job: + name: ${project} + run: + dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S} \ No newline at end of file diff --git a/recipes/tc_tracking/test/cfg/extract_era5.yaml b/recipes/tc_tracking/test/cfg/extract_era5.yaml new file mode 100644 index 000000000..b9dc83c31 --- /dev/null +++ b/recipes/tc_tracking/test/cfg/extract_era5.yaml @@ -0,0 +1,38 @@ +project: 'reference_tracks' +mode: 'extract_baseline' + +cases: + helene: + year: 2024 + basin: north_atlantic + + hato: + year: 2017 + basin: west_pacific + +ibtracs_source_data: "../aux_data/ibtracs.HATO_HELENE.list.v04r01.csv" + +data_source: + ncar_era5: + years: "2017-2024" + source: + _target_: earth2studio.data.NCAR_ERA5 + +orography_path: '../orography.nc' +store_dir: "./outputs_${project}" + +cyclone_tracking: + vars: ['msl', 'u10m', 'v10m', 'z300', 'z500'] + detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0' + stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10' + orography_path: '../aux_data/orography.nc' + keep_raw_data: False + use_ram: True + print_te_output: False + +# hydra logs +hydra: + job: + name: ${project} + run: + dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S} diff --git a/recipes/tc_tracking/test/cfg/reproduce_helene.yaml b/recipes/tc_tracking/test/cfg/reproduce_helene.yaml new file mode 100644 index 000000000..5f6d4a0cf --- /dev/null +++ b/recipes/tc_tracking/test/cfg/reproduce_helene.yaml @@ -0,0 +1,37 @@ +project: 'reproduce_helene' +mode: 'reproduce_members' +model: 'fcn3' + +reproduce_members: [ + ['2024-09-24 00:00:00', 3, 4045540270], + ['2024-09-24 00:00:00', 4, 2135091646], + ] + +n_steps: 15 +ensemble_size: 5 +batch_size: 2 +orography_path: '../orography.nc' + +data_source: + _target_: earth2studio.data.CDS + +store_type: "none" +store_dir: "./outputs_${project}" + +cyclone_tracking: + asynchronous: True + vars: ['msl', 'u10m', 'v10m', 'z300', 'z500'] + detect_cmd: 'DetectNodes --verbosity 0 --mergedist 6 --closedcontourcmd _DIFF(z300,z500),-58.8,6.5,0;msl,200.,5.5,0 --searchbymin msl --outputcmd msl,min,0;_VECMAG(u10m,v10m),max,5;height,min,0' + stitch_cmd: 'StitchNodes --in_fmt lon,lat,msl,wind_speed,height --range 8.0 --mintime 54h --maxgap 4 --out_file_format csv --threshold wind_speed,>=,10.,10;lat,<=,50.,10;lat,>=,-50.,10;height,<=,150.,10' + orography_path: '../aux_data/orography.nc' + keep_raw_data: False + use_ram: True + task_timeout_seconds: 120 + print_te_output: False + +# hydra logs +hydra: + job: + name: ${project} + run: + dir: ${store_dir}/${project}_logs/${now:%Y-%m-%d_%H-%M-%S} \ No newline at end of file diff --git a/recipes/tc_tracking/test/test_historic_tc_extraction.sh b/recipes/tc_tracking/test/test_historic_tc_extraction.sh new file mode 100644 index 000000000..a8265f408 --- /dev/null +++ b/recipes/tc_tracking/test/test_historic_tc_extraction.sh @@ -0,0 +1,36 @@ +#!/bin/bash +set -e + + +# Run extraction +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echo " >>> running baseline. might take a couple of minutes to download data..." +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echo "" +python ../tc_hunt.py --config-path=$(pwd)/cfg --config-name=extract_era5.yaml + + +# Compare reference track files between ground truth and produced output +reference_tracks=( + "reference_track_hato_2017_west_pacific.csv" + "reference_track_helene_2024_north_atlantic.csv" +) +for f in "${reference_tracks[@]}"; do + if ! diff -q "../aux_data/$f" "outputs_reference_tracks/$f" > /dev/null 2>&1; then + echo "" + echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo " >>> oh no!!! ERROR: $f differs between baseline and reproduce" + echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "" + diff "../aux_data/$f" "outputs_reference_tracks/$f" + exit 1 + fi +done + + +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echo " >>> all good, yay (: reference tracks have been reproduced successfully" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echo "" diff --git a/recipes/tc_tracking/test/test_tc_hunt.sh b/recipes/tc_tracking/test/test_tc_hunt.sh new file mode 100644 index 000000000..c75b7aa49 --- /dev/null +++ b/recipes/tc_tracking/test/test_tc_hunt.sh @@ -0,0 +1,57 @@ +#!/bin/bash +set -e + +baseline_dir="outputs_baseline_helene/cyclone_tracks_te" +reproduce_dir="outputs_reproduce_helene/cyclone_tracks_te" + +# Run baseline +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echo " >>> running baseline. might take a couple of minutes..." +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echo "" +python ../tc_hunt.py --config-path=$(pwd)/cfg --config-name=baseline_helene.yaml + +# Get track files for members 2, 3, 4 +mapfile -t track_files < <(ls "$baseline_dir"/tracks_*.csv | sort | grep -E 'mem_000[234]' | xargs -n1 basename) + +# Extract seeds for members 3 and 4 from filenames +seed_mem3=$(printf '%s\n' "${track_files[@]}" | grep 'mem_0003' | grep -oP 'seed_\K\d+') +seed_mem4=$(printf '%s\n' "${track_files[@]}" | grep 'mem_0004' | grep -oP 'seed_\K\d+') + +# Check if seeds in reproduce_helene.yaml match, update if not +yaml_seed3=$(grep -oP "\['2024-09-24 00:00:00', 3, \K\d+" cfg/reproduce_helene.yaml) +yaml_seed4=$(grep -oP "\['2024-09-24 00:00:00', 4, \K\d+" cfg/reproduce_helene.yaml) + +if [ "$seed_mem3" != "$yaml_seed3" ] || [ "$seed_mem4" != "$yaml_seed4" ]; then + echo "WARNING: Updating seeds in cfg/reproduce_helene.yaml (mem3: $yaml_seed3->$seed_mem3, mem4: $yaml_seed4->$seed_mem4)" + sed -i "s/\['2024-09-24 00:00:00', 3, [0-9]*\]/['2024-09-24 00:00:00', 3, $seed_mem3]/" cfg/reproduce_helene.yaml + sed -i "s/\['2024-09-24 00:00:00', 4, [0-9]*\]/['2024-09-24 00:00:00', 4, $seed_mem4]/" cfg/reproduce_helene.yaml +fi + +# Run reproduce +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echo " >>> reproducing members 2, 3 and 4. might take a couple of minutes..." +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echo "" +python ../tc_hunt.py --config-path=$(pwd)/cfg --config-name=reproduce_helene.yaml + +# Compare track files for members 3 and 4, error if any differ +for f in "${track_files[@]}"; do + [[ $f =~ mem_000[34] ]] || continue + if ! diff -q "$baseline_dir/$f" "$reproduce_dir/$f" > /dev/null 2>&1; then + echo "" + echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo " >>> oh no!!! 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a/test/models/dx/test_tempest_extremes.py b/test/models/dx/test_tempest_extremes.py new file mode 100644 index 000000000..d10d9e5f0 --- /dev/null +++ b/test/models/dx/test_tempest_extremes.py @@ -0,0 +1,648 @@ +# test_tempest_extremes.py + +import os +import tempfile +import time +from collections import OrderedDict +from unittest.mock import patch + +import numpy as np +import pytest +import torch +from physicsnemo.distributed import DistributedManager + +from earth2studio.models.dx import AsyncTempestExtremes, TempestExtremes + + +@pytest.fixture(scope="session", autouse=True) +def initialize_distributed_manager(): + """Initialize DistributedManager once for all tests.""" + # Initialize if not already initialized + if not DistributedManager.is_initialized(): + DistributedManager.initialize() + yield + # Note: We don't cleanup torch.distributed as it can't be reinitialized in the same process + + +# ============================================================================ +# 1. INITIALIZATION AND SETUP TESTS +# ============================================================================ + + +def test_initialization_with_static_vars(): + """Test initialization with static variables""" + with tempfile.TemporaryDirectory() as tmpdir: + static_vars = torch.randn(2, 721, 1440) + static_coords = OrderedDict( + { + "variable": np.array(["z", "lsm"]), + "lat": np.linspace(90, -90, 721), + "lon": np.linspace(0, 360, 1440), + } + ) + + te = TempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m", "v10m", "msl"], + batch_size=1, + n_steps=5, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + static_vars=static_vars, + static_coords=static_coords, + use_ram=False, + ) + + assert te.static_vars is not None + assert te.static_coords is not None + + +def test_initialization_missing_static_coords_raises_error(): + """Test that providing static_vars without static_coords raises error""" + with tempfile.TemporaryDirectory() as tmpdir: + with pytest.raises(ValueError, match="provide both values and coords"): + TempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m"], + batch_size=1, + n_steps=5, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + static_vars=torch.randn(1, 721, 1440), + static_coords=None, + use_ram=False, + ) + + +# ============================================================================ +# 2. COMMAND FORMATTING TESTS +# ============================================================================ + + +def test_format_tempestextremes_commands(): + """Test command formatting removes input/output arguments""" + with tempfile.TemporaryDirectory() as tmpdir: + detect_cmd = "DetectNodes --in_data_list old.txt --verbosity 0" + stitch_cmd = "StitchNodes --in old.txt --out old_tracks.txt" + + te = TempestExtremes( + detect_cmd=detect_cmd, + stitch_cmd=stitch_cmd, + input_vars=["u10m"], + batch_size=1, + n_steps=5, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + use_ram=False, + ) + + assert "--in_data_list" not in te.detect_cmd + assert "--out_file_list" not in te.detect_cmd + assert "--in" not in te.stitch_cmd + assert "--out" not in te.stitch_cmd + assert "--verbosity" in te.detect_cmd + + +# ============================================================================ +# 3. COORDINATE SYSTEM TESTS +# ============================================================================ + + +def test_input_coords(): + """Test that input_coords returns correct coordinate system""" + with tempfile.TemporaryDirectory() as tmpdir: + input_vars = ["u10m", "v10m", "msl"] + lats = np.linspace(90, -90, 721) + lons = np.linspace(0, 360, 1440) + + te = TempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=input_vars, + batch_size=1, + n_steps=10, + time_step=np.timedelta64(6, "h"), + lats=lats, + lons=lons, + store_dir=tmpdir, + use_ram=False, + ) + + coords = te.input_coords + + assert "time" in coords + assert "lead_time" in coords + assert "variable" in coords + assert "lat" in coords + assert "lon" in coords + assert len(coords["lead_time"]) == 11 # n_steps + 1 + assert np.array_equal(coords["variable"], input_vars) + + +def test_store_coords_with_static_vars(): + """Test that store coords include static variables""" + with tempfile.TemporaryDirectory() as tmpdir: + static_vars = torch.randn(1, 721, 1440) + static_coords = OrderedDict( + { + "variable": np.array(["z"]), + "lat": np.linspace(90, -90, 721), + "lon": np.linspace(0, 360, 1440), + } + ) + + te = TempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m", "v10m"], + batch_size=1, + n_steps=5, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + static_vars=static_vars, + static_coords=static_coords, + use_ram=False, + ) + + # Check that static vars are in store coords + assert "z" in te._store_coords["variable"] + + +# ============================================================================ +# 4. DATA RECORDING TESTS +# ============================================================================ + + +def test_record_state_basic(): + """Test that record_state correctly stores data""" + with tempfile.TemporaryDirectory() as tmpdir: + te = TempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m", "v10m", "msl"], + batch_size=1, + n_steps=2, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + use_ram=False, + ) + + # Create test data + time = np.datetime64("2024-01-01T00:00:00") + lead_time = np.timedelta64(0, "h") + x = torch.randn(1, 1, 1, 3, 721, 1440) + coords = OrderedDict( + { + "ensemble": np.array([0]), + "time": np.array([time]), + "lead_time": np.array([lead_time]), + "variable": np.array(["u10m", "v10m", "msl"]), + "lat": np.linspace(90, -90, 721), + "lon": np.linspace(0, 360, 1440), + } + ) + + # Record state + te.record_state(x, coords) + + # Check that data was written to store + assert len(te.store.coords["time"]) == 1 + assert len(te.store.coords["ensemble"]) == 1 + + +@patch( + "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability" +) +def test_record_state_with_static_vars(mock_check): + """Test record_state correctly concatenates static variables""" + mock_check.return_value = None + + with tempfile.TemporaryDirectory() as tmpdir: + static_vars = torch.ones(2, 721, 1440) * 0.5 + static_coords = OrderedDict( + { + "variable": np.array(["z", "lsm"]), + "lat": np.linspace(90, -90, 721), + "lon": np.linspace(0, 360, 1440), + } + ) + + te = TempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m", "v10m"], + batch_size=1, + n_steps=2, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + static_vars=static_vars, + static_coords=static_coords, + use_ram=False, + ) + + # Create test data (without static vars) + time = np.datetime64("2024-01-01T00:00:00") + x = torch.randn(1, 1, 1, 2, 721, 1440) + coords = OrderedDict( + { + "ensemble": np.array([0]), + "time": np.array([time]), + "lead_time": np.array([np.timedelta64(0, "h")]), + "variable": np.array(["u10m", "v10m"]), + "lat": np.linspace(90, -90, 721), + "lon": np.linspace(0, 360, 1440), + } + ) + + te.record_state(x, coords) + + # Verify static vars were concatenated + assert "z" in te._store_coords["variable"] + assert "lsm" in te._store_coords["variable"] + + +# ============================================================================ +# 5. DATA DUMPING TESTS +# ============================================================================ + + +@patch( + "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability" +) +def test_dump_raw_data_creates_netcdf(mock_check): + """Test that dump_raw_data creates NetCDF files with correct structure""" + mock_check.return_value = None + + with tempfile.TemporaryDirectory() as tmpdir: + te = TempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m", "v10m", "msl"], + batch_size=2, + n_steps=3, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + use_ram=False, + ) + + # Record some data + time = np.datetime64("2024-01-01T00:00:00") + for lt in range(4): # n_steps + 1 + x = torch.randn(2, 1, 1, 3, 721, 1440) + coords = OrderedDict( + { + "ensemble": np.array([0, 1]), + "time": np.array([time]), + "lead_time": np.array([np.timedelta64(lt * 6, "h")]), + "variable": np.array(["u10m", "v10m", "msl"]), + "lat": np.linspace(90, -90, 721), + "lon": np.linspace(0, 360, 1440), + } + ) + te.record_state(x, coords) + + # Dump data + raw_files, mems = te.dump_raw_data() + + # Check that files were created + assert len(raw_files) == 2 # batch_size + assert len(mems) == 2 + for f in raw_files: + assert os.path.exists(f) + assert f.endswith(".nc") + + +# ============================================================================ +# 6. FILE SETUP TESTS +# ============================================================================ + + +@patch( + "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability" +) +@patch("earth2studio.models.dx.tempest_extremes.TempestExtremes.dump_raw_data") +def test_setup_files(mock_dump, mock_check): + """Test that setup_files creates correct file structure""" + mock_check.return_value = None + + with tempfile.TemporaryDirectory() as tmpdir: + mock_dump.return_value = ( + [ + os.path.join(tmpdir, "data_mem_0000.nc"), + os.path.join(tmpdir, "data_mem_0001.nc"), + ], + np.array([0, 1]), + ) + te = TempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m"], + batch_size=2, + n_steps=5, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + use_ram=False, + ) + + ins, outs, node_files, track_files = te.setup_files() + + # Check that file lists were created + assert len(ins) == 2 + assert len(outs) == 2 + assert len(node_files) == 2 + assert len(track_files) == 2 + + # Check that input/output lists exist and contain expected content + for i, in_file in enumerate(ins): + assert os.path.exists(in_file) + with open(in_file) as f: + content = f.read() + assert "mem_000" in content + + +# ============================================================================ +# 7. ASYNC FUNCTIONALITY TESTS +# ============================================================================ + + +@patch( + "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability" +) +def test_async_initialization(mock_check): + """Test AsyncTempestExtremes initialization""" + mock_check.return_value = None + + with tempfile.TemporaryDirectory() as tmpdir: + ate = AsyncTempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m"], + batch_size=1, + n_steps=5, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + timeout=120, + max_workers=2, + use_ram=False, + ) + + assert ate.timeout == 120 + assert ate.max_workers == 2 + assert hasattr(ate, "_instance_tasks") + assert hasattr(ate, "_dump_in_progress") + + +@patch( + "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability" +) +def test_async_task_status(mock_check): + """Test getting task status from AsyncTempestExtremes""" + mock_check.return_value = None + + with tempfile.TemporaryDirectory() as tmpdir: + ate = AsyncTempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m"], + batch_size=1, + n_steps=5, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + use_ram=False, + ) + + status = ate.get_task_status() + + assert "running" in status + assert "pending" in status + assert "completed" in status + assert "failed" in status + assert "total" in status + assert status["total"] == 0 # No tasks submitted yet + + +@patch( + "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability" +) +def test_async_record_state_waits_for_dump(mock_check): + """Test that async record_state waits for ongoing dumps""" + mock_check.return_value = None + + with tempfile.TemporaryDirectory() as tmpdir: + ate = AsyncTempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m"], + batch_size=1, + n_steps=5, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + use_ram=False, + ) + + # Simulate dump in progress + ate._dump_in_progress.clear() + + # Start recording in a thread (would block) + import threading + + record_started = threading.Event() + record_completed = threading.Event() + + def record(): + record_started.set() + time = np.datetime64("2024-01-01T00:00:00") + x = torch.randn(1, 1, 1, 1, 721, 1440) + coords = OrderedDict( + { + "ensemble": np.array([0]), + "time": np.array([time]), + "lead_time": np.array([np.timedelta64(0, "h")]), + "variable": np.array(["u10m"]), + "lat": np.linspace(90, -90, 721), + "lon": np.linspace(0, 360, 1440), + } + ) + ate.record_state(x, coords) + record_completed.set() + + thread = threading.Thread(target=record) + thread.start() + + # Wait a bit and verify record hasn't completed + record_started.wait(timeout=1) + time.sleep(0.1) + assert not record_completed.is_set() + + # Signal dump complete + ate._dump_in_progress.set() + + # Now record should complete + thread.join(timeout=2) + assert record_completed.is_set() + + +# ============================================================================ +# 8. ERROR HANDLING TESTS +# ============================================================================ + + +@patch( + "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability" +) +@patch("earth2studio.models.dx.tempest_extremes.TempestExtremes.dump_raw_data") +def test_setup_files_mismatched_file_names_raises_error(mock_dump, mock_check): + """Test that providing wrong number of output file names raises error""" + mock_check.return_value = None + + with tempfile.TemporaryDirectory() as tmpdir: + mock_dump.return_value = ( + [os.path.join(tmpdir, "data1.nc"), os.path.join(tmpdir, "data2.nc")], + np.array([0, 1]), + ) + te = TempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m"], + batch_size=2, + n_steps=5, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + use_ram=False, + ) + + with pytest.raises(ValueError, match="passed for.*ensemble members"): + te.setup_files(out_file_names=["only_one_file.csv"]) + + +@patch( + "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability" +) +def test_async_check_for_failures_raises(mock_check): + """Test that async tracker raises error if previous task failed""" + mock_check.return_value = None + + with tempfile.TemporaryDirectory() as tmpdir: + ate = AsyncTempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m"], + batch_size=1, + n_steps=5, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + use_ram=False, + ) + + # Manually set failure flag + ate._has_failed = True + + # Should raise on next operation + with pytest.raises(ChildProcessError, match="Previous cyclone tracking"): + ate._check_for_failures() + + +# ============================================================================ +# 9. CLEANUP TESTS +# ============================================================================ + + +@patch( + "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability" +) +@patch("earth2studio.models.dx.tempest_extremes.run") +def test_tidy_up_keeps_raw_data(mock_run, mock_check): + """Test that tidy_up keeps raw data when keep_raw=True""" + mock_check.return_value = None + + with tempfile.TemporaryDirectory() as tmpdir: + te = TempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m"], + batch_size=1, + n_steps=5, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + keep_raw_data=True, + use_ram=False, + ) + + # Create dummy files + raw_file = os.path.join(tmpdir, "raw_data", "test_data.nc") + os.makedirs(os.path.dirname(raw_file), exist_ok=True) + with open(raw_file, "w") as f: + f.write("dummy") + + in_list = os.path.join(tmpdir, "raw_data", "input_list.txt") + with open(in_list, "w") as f: + f.write(raw_file) + + out_list = os.path.join(tmpdir, "raw_data", "output_list.txt") + node_file = os.path.join(tmpdir, "raw_data", "nodes.txt") + with open(out_list, "w") as f: + f.write(node_file) + with open(node_file, "w") as f: + f.write("dummy") + + # Run cleanup + te.tidy_up([in_list], [out_list]) + + # Raw data should still exist (moved to raw_data subdir) + assert os.path.exists(os.path.join(te.store_dir, "raw_data")) + + +@patch( + "earth2studio.models.dx.tempest_extremes.TempestExtremes.check_tempest_extremes_availability" +) +def test_async_cleanup(mock_check): + """Test that async cleanup waits for all tasks""" + mock_check.return_value = None + + with tempfile.TemporaryDirectory() as tmpdir: + ate = AsyncTempestExtremes( + detect_cmd="DetectNodes", + stitch_cmd="StitchNodes", + input_vars=["u10m"], + batch_size=1, + n_steps=5, + time_step=np.timedelta64(6, "h"), + lats=np.linspace(90, -90, 721), + lons=np.linspace(0, 360, 1440), + store_dir=tmpdir, + use_ram=False, + ) + + # Cleanup should not raise when no tasks exist + ate.cleanup() + assert ate._cleanup_done diff --git a/test/models/px/test_dxwrapper.py b/test/models/px/test_dxwrapper.py index 5152e93ab..479097249 100644 --- a/test/models/px/test_dxwrapper.py +++ b/test/models/px/test_dxwrapper.py @@ -284,7 +284,9 @@ def test_dxwrapper_iter(device, times, number_of_samples): ([np.datetime64("2025-08-21T00:00:00")], 1), ], ) -def test_dxwrapper_run(device, times, number_of_samples): +@pytest.mark.parametrize("keep_px_output", [False, True]) +def test_fcn3_tc_tracker(device, times, keep_px_output): + # Spoof models model = PhooCorrDiff() in_center = torch.zeros(12, 1, 1)