diff --git a/.github/workflows/linting.yaml b/.github/workflows/linting.yaml index aeb7c0a1..a745d8ce 100644 --- a/.github/workflows/linting.yaml +++ b/.github/workflows/linting.yaml @@ -30,6 +30,9 @@ jobs: - run: pip install mypy + - name: Install relevant Python type stub libraries + run: pip install types-requests + - name: mypy run: mypy --strict . diff --git a/MLproject b/MLproject index d3eee54e..2b277086 100755 --- a/MLproject +++ b/MLproject @@ -1,20 +1,8 @@ -name: cmip2_6 +# vim: ft=yaml -# conda_env: conda.yaml +name: gz21_ocean_momentum entry_points: - main: - parameters: - ntimes : {type: float, default: 10000} - CO2: {type: float, default: 0} - lat_min : float - lat_max : float - long_min : float - long_max : float - factor: {type: float, default: 0} - chunk_size: {type: string, default: 50} - global: {type: str, default: 0} - command: "python src/gz21_ocean_momentum/cmip26.py {lat_min} {lat_max} {long_min} {long_max} --CO2 {CO2} --ntimes {ntimes} --factor {factor} --chunk_size {chunk_size} --global_ {global}" train: parameters: @@ -35,9 +23,11 @@ entry_points: submodel : {type: string, default : transform3} features_transform_cls_name : {type : string, default : None} targets_transform_cls_name : {type : string, default : None} + subdomains_file: {type: string, default: resources/cli-configs/train-subdomains-paper.yaml} command: "python src/gz21_ocean_momentum/trainScript.py --run-id {run_id} --forcing-data-path {forcing_data_path} + --subdomains-file {subdomains_file} --batchsize {batchsize} --learning_rate {learning_rate} --n_epochs {n_epochs} --train_split {train_split} --test_split {test_split} --time_indices {time_indices} diff --git a/README.md b/README.md index e8cc9015..c42f7f2d 100644 --- a/README.md +++ b/README.md @@ -1,51 +1,75 @@ -# Stochastic-Deep Learning Parameterization of Ocean Momentum Forcing +# GZ21: stochastic deep learning parameterization of ocean momentum forcing [gz21-paper-code-zenodo]: https://zenodo.org/record/5076046#.ZF4ulezMLy8 [gz21-paper-agupubs]: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021MS002534 - -This repository provides a subgrid model of ocean momentum forcing, based on a -convolutional neural network (CNN) trained on high-resolution surface velocity -data from CM2.6. This model can then be coupled into larger GCMs, e.g., at -coarser granularity to provide high-fidelity parameterization of ocean momentum -forcing. The parameterization output by the CNN consists of a Gaussian -distribution specified by 2 parameters (mean and standard deviation), which -allows for stochastic implementations in online models. - -The model is based on the paper [Arthur P. Guillaumin, Laure Zanna (2021). -Stochastic-deep learning parameterization of ocean momentum -forcing][gz21-paper-agupubs]. The exact version of the code used to produce said -paper can be found on [Zenodo][gz21-paper-code-zenodo]. The present repository -provides a version of this model which is designed for others to reproduce, -replicate, and reuse. - -_This repository is currently work-in-progress following a process of -refreshing the code and making it available for easy reuse by others._ - -## Architecture -The model is written in Python, using PyTorch for the CNN. We provide 3 separate -"stages", which are run using different commands and arguments: - -* data processing: downloads part of CM2.6 dataset and processes -* model training: train model on processed data -* model testing: tests the trained model on an unseen region - -For more details on each of the stages, see the [`docs`](docs/) directory. - -## Usage -### Dependencies +[cm26-ds]: https://catalog.pangeo.io/browse/master/ocean/GFDL_CM2_6/ + +This repository trains a PyTorch convolutional neural network (CNN) to predict +subgrid ocean momentum forcing from ocean surface velocity, intended for +coupling with larger GCMs to provide a performant, high-fidelity +parameterization in coarse-resolution climate models. + +Command-line scripts for preparing training data, training up a model, testing +model performance, and using the model to make predictions (inference mode) are +provided. + +For further detail and discussion, please see +[Arthur P. Guillaumin, Laure Zanna (2021). Stochastic-deep learning +parameterization of ocean momentum forcing][gz21-paper-agupubs] which originally +introduced this work. Documentation in this repository will refer back to +sections from the paper e.g. *Guillaumin (2021) 2.1* to provide context and +further reading. (A snapshot of the code used in the paper can be found on +[Zenodo][gz21-paper-code-zenodo].) + +This repository also aims to enable reproducing the 2021 paper. The Jupyter +notebooks at [`resources/jupyter-notebooks`][resources/jupyter-notebooks] +generate some figures shown in the paper. + +## Overview +Model training and usage is separated into a handful of steps. Steps are +executed via a command-line interface (CLI) Python script, and will save some +data to disk to then be loaded in the next step. + +In the "data" step, we generate training data using +[simulation data from the CM2.6 climate model][cm26-ds] +(which we refer to as the CM2.6 dataset, or just CM2.6). +We calculate the subgrid forcing needed for coarse-resolution models using the +high-resolution ocean velocity data in the CM2.6 dataset, then coarsen. This +coarsened, with-forcings dataset is saved to disk. You may generate training +data using either the "control" CM2.6 simulation, or the "1-percent annual CO2 +increase" one. *(See Guillaumin (2021) 2.1.)* + +In the "training" step, we train a NN to predict the true forcing from the +coarse velocity data generated above. This forcing term tends to have a large +amount of uncertainty. Rather than a single value, we predict both the mean and +standard deviation of a Gaussian probability distribution for the forcing. This +allows for stochastic implementations in online models. *(See Guillaumin (2021) +2.3 for a more in-depth explanation and how to interpret the NN output.)* + +In the "testing" step, we test a trained model on an unseen region of data (the +subset not used in the previous training step). + +We also provide a basic script for predicting forcings on a prepared dataset. + +### Repository layout +* `src`: source code (library and CLI scripts) +* `tests`: pytest tests +* `docs`: detailed project documentation, implementation notes +* `resources`: CLI configs, Jupyter notebooks +* `flake.nix`, `flake.lock`: helper files for building on Nix (ignore) + +## Installation Python 3.9 or newer is required. We primarily test on Python 3.11. -#### Python To avoid any conflicts with local packages, we recommend using a virtual environment. In the root directory: - virtualenv venv - source venv/bin/activate + python -m venv venv + +or using [virtualenv](https://virtualenv.pypa.io/en/latest/): -See [virtualenv docs](https://virtualenv.pypa.io/en/latest/) for more details. + virtualenv venv -Alternatively, if you are using python to manage virtual environments using the -`venv` module, then the first line above can be replaced by `python -m venv -venv` (where the second `venv` is the virtual environment name). +Then load with `source venv/bin/activate`. With `pip` installed, run the following in the root directory: @@ -57,201 +81,155 @@ With `pip` installed, run the following in the root directory: that the Poetry build is not actively supported-- if it fails, check that the dependencies are up-to-date with the setuptools `pyproject.toml`.)* -#### System -Some graphing code uses cartopy, which requires [GEOS](https://libgeos.org/). To -install on Ubuntu: +Note that if you are running Python 3.9 or older, you may also need to install +the [GEOS](https://libgeos.org/) library, due to `cartopy` requiring it. (Newer +versions moved away from the C dependency.) - sudo apt install libgeos-dev +## Usage +Execute these commands from the repository root. -On macOS, via Homebrew: +See [`docs`](docs/) directory for more details. - brew install geos +For command-line option explanation, run the appropriate step with `--help` e.g. +`python src/gz21_ocean_momentum/cli/data.py --help`. -On Windows, consider using MSYS2 to install the library in a Linux-esque manner. -The [mingw-w64-x86_64-geos](https://packages.msys2.org/package/mingw-w64-x86_64-geos) -package should be appropriate. If this doesn't work or isn't suitable, cartopy -or GEOS might have more ideas in their documentation. +Most CLI scripts support reading in options from a YAML file using a +`--config-file` flag. In general, a flag `--name value` will be converted to a +top-level `name: value` line. Examples are provided in +[`resources/cli-configs`](resources/cli-configs/). CLI options override file +options, so you may provide partial configuration in a file and fill out the +rest (e.g. file paths) on the command line. -### Running unit tests +### Unit tests There are a handful of unit tests using pytest, in the [`tests`](tests/) -directory. These assert some operations and methods used in the stages. They may +directory. These assert some operations and methods used in the steps. They may be run in the regular method: pytest -### Running stages -Execute these commands from the repository root. - -See [`docs`](docs/) directory for more details. - -MLflow parameters: - -* `experiment-name`: "tag" to use for MLflow experiment. Used to share artifacts - between stages, i.e. you should run the training stage with a name you used to - run the data processing stage. -* `exp_id`: TODO: one way MLflow distinguishes runs. May need to set to share - artifacts between stages...? -* `run_id`: TODO: one way MLflow distinguishes runs. May need to set to share - artifacts between stages...? - -For old MLflow versions (TODO: which?), replace the `--env-manager=local` flag -with `--no-conda` +### Training data generation +[`cli/data.py`](src/gz21_ocean_momentum/cli/data.py) calculates coarse +surface velocities and diagnosed forcings from the CM2.6 dataset and saves them +to disk. This is used as training data for the neural net. -In order to make sure that data in- and output locations are well-defined, the -environment variable `MLFLOW_TRACKING_URI` must be set to the intended data location: +**You must configure GCP credentials in order to download the CM2.6 dataset.** +See [`docs/data.md`](docs/data.md) for more details. - export MLFLOW_TRACKING_URI="/path/to/data/dir" +Example invocation: -in Linux, or -``` -%env MLFLOW_TRACKING_URI /path/to/data/dir -``` + python src/gz21_ocean_momentum/cli/data.py \ + --lat-min -80 --lat-max 80 --long-min -280 --long-max 80 \ + --factor 4 --ntimes 100 --co2-increase --out-dir forcings -in a Jupyter Notebook, or +Alternatively, you may write (all or part of) these options into a YAML file: +```yaml +lat-min: -80 +lat-max: 80 +long-min: -280 +long-max: 80 +ntimes: 100 +factor: 4 +co2-increase: true ``` -import os -os.environ['MLFLOW_TRACKING_URI'] = '/path/to/data/dir' -``` -in Python. - -#### Data processing -The [`cmip26.py`](src/gz21_ocean_momentum/cmip26.py) script runs the data -processing stage. It generates coarse surface velocities and diagnosed forcings -from the CM2.6 dataset and saves them to disk. You may configure certain -parameters such as bounds (lat/lon) and CO2 level. -**You must configure GCP credentials to download the CM2.6 dataset used.** -See [`docs/data.md`](docs/data.md) for more details. +and use this file in an invocation with the `--config-file` option: -Relevant parameters: + python src/gz21_ocean_momentum/cli/data.py \ + --config-file resources/cli-configs/data-paper.yaml --out-dir forcings -* `factor`: the factor definining the low-resolution grid of the generated data - with respect to the high-resolution grid. -* `CO2`: 0 for control, 1 for 1% increase per year dataset. -* `global`: TODO "make data cyclic along longitude". Set to 0; currently fails when set to 1. -* `ntimes`: the number of days to process, knowing that the data set is at a - time resolution of one per day. If not specified, uses the complete dataset. -* `lat_min`, `lat_max`, `lon_min`, `lon_max`: the spatial domain to process. +Some preprocessed data is hosted on HuggingFace at +[datasets/M2LInES/gz21-forcing-cm26](https://huggingface.co/datasets/M2LInES/gz21-forcing-cm26). -Direct call (without MLflow) example: +### Model training +[cli-train]: src/gz21_ocean_momentum/cli/train.py - python src/gz21_ocean_momentum/cmip26.py -85 85 -280 80 --factor 4 --ntimes 10 +The [`cli/train.py`][cli-train] script trains the model using data generated +previously. You may configure various training parameters through command-line +arguments, such as number of training epochs, loss function, etc. -MLflow call example: +Example invocation: ``` -mlflow run . --experiment-name --env-manager=local \ --P lat_min=-80 -P lat_max=80 -P long_min=-280 -P long_max=80 \ --P factor=4 \ --P CO2=1 -P global=0 \ --P ntimes=100 \ --P chunk_size=1 +python src/gz21_ocean_momentum/cli/train.py \ +--lat-min -80 --lat-max 80 --long-min -280 --long-max 80 \ +--factor 4 --ntimes 100 --co2-increase --out-dir forcings \ +--train-split-end 0.8 --test-split-start 0.85 \ +--subdomains-file resources/cli-configs/train-subdomains-paper.yaml \ +--forcing-data-path ``` -Some preprocessed data is hosted on HuggingFace at -[datasets/M2LInES/gfdl-cmip26-gz21-ocean-forcing](https://huggingface.co/datasets/M2LInES/gfdl-cmip26-gz21-ocean-forcing). - -#### Training -The [`trainScript.py`](src/gz21_ocean_momentum/trainScript.py) script runs the -model training stage. You may configure various training parameters through -command-line arguments, such as number of training epochs, loss functions, and -training data. (You will want to select the output from a data processing stage -for the latter.) - -MLflow call example: - -``` -mlflow run . --experiment-name -e train --env-manager=local \ --P run_id= \ --P learning_rate=0/5e-4/15/5e-5/30/5e-6 -P n_epochs=200 -P weight_decay=0.00 -P train_split=0.8 \ --P test_split=0.85 -P model_module_name=models.models1 -P model_cls_name=FullyCNN -P batchsize=4 \ --P transformation_cls_name=SoftPlusTransform -P submodel=transform3 \ --P loss_cls_name=HeteroskedasticGaussianLossV2 +You may place options into a YAML file and load with the `--config-file` option. + +Notable parameters: + +* `--subdomains-file`: path to YAML file storing a list of subdomains to select + from the forcing data, which are then used for training. (Note that at + runtime, domains are be truncated to the size of the smallest domain in terms + of number of points.) +* `--train-split-end`: use `0->N` percent of the dataset for training +* `--test-split-start`: use `N->100` percent of the dataset for testing + +The `--subdomains-file` format is a YAML list of bounding boxes, each defined +using four labelled floats: + +```yaml +- lat-min: 35 + lat-max: 50 + long-min: -50 + long-max: -20 +- lat-min: -40 + lat-max: -25 + long-min: -180 + long-max: -162 +# - ... ``` -Relevant parameters: - -* `exp_id`: id of the experiment containing the run that generated the forcing - data. -* `run_id`: id of the run that generated the forcing data that will be used for - training. -* `loss_cls_name`: name of the class that defines the loss. This class should be - defined in train/losses.py in order for the script to find it. Currently, the - main available options are: - * `HeteroskedasticGaussianLossV2`: this corresponds to the loss used in the - 2021 paper - * `BimodalGaussianLoss`: a Gaussian loss defined using two Gaussian modes -* `model_module_name`: name of the module that contains the class defining the - NN used -* `model_cls_name`: name of the class defining the NN used, should be defined in - the module specified by `model_module_name` -* `train_split`: use `0->N` percent of the dataset for training -* `test_split`: use `N->100` percent of the dataset for testing - -Another important way to modify the way the script runs consists in modifying -the domains used for training. These are defined in -[`training_subdomains.yaml`](training_subdomains.yaml) in terms of their -coordinates. Note that at run time domains will be truncated to the size of the -smallest domain in terms of number of points. - -*Note:* Ensure that the spatial subdomains defined in `training_subdomains.yaml` -are contained in the domain of the forcing data you use. If they aren't, you may -get a Python error along the lines of: +`lat-min` must be smaller than `lat-max`, likewise for `long-min`. + +*Note:* Ensure that the subdomains you use are contained in the domain of the +forcing data you use. If they aren't, you may get a confusing Python error along +the lines of: ``` RuntimeError: Calculated padded input size per channel: . Kernel size: (5 x 5). Kernel size can't be greater than actual input size ``` -#### Inference -The [`inference/main.py`](src/gz21_ocean_momentum/inference/main.py) script runs the -model testing stage. This consists of running a trained model on a dataset. -The model's output are then stored as an artefact. This step -should ideally be run with a GPU device available, to achieve a better speed. - -In this step it is particularly important to set the environment variable `MLFLOW_TRACKING_URI` -in order for the data to be found and stored in a sensible place. - -One can run the inference step by interactively -running the following in the project root directory: +### Predicting using the trained model +[cli-infer]: src/gz21_ocean_momentum/cli/infer.py - python3 -m gz21_ocean_momentum.inference.main --n_splits=40 +The [`cli/infer.py`][cli-infer] script allows loading a trained model and a set +of (low resolution) velocity data, and predicts forcings. -with `n_splits` being the number of subsets which the dataset is split -into for the processing, before being put back together for the final output. -This is done in order to avoid memory issues for large datasets. -Other useful arguments for this call would be -- `to_experiment`: the name of the mlflow experiment used for this run (default is "test"). -- `batch_size`: the batch size used in running the neural network on the data. +Example invocation: +``` +python src/gz21_ocean_momentum/cli/infer.py \ +--model-state-dict-file model.pth \ +--input-data-dir \ +--device cuda:0 +``` -After the script has started running, it will first require -the user to select an experiment and a run corresponding to a -training step run previously. -The user will then be required to select an experiment and a run -corresponding to a data step previously run. +We have tested with data from the forcing generation step -- the forcings are +not used, it is to obtain low-resolution velocities. -The inference step should then start. +See Guillaumin (2021) for detail on how to use model output. ### Jupyter Notebooks -The [examples/jupyter-notebooks](examples/jupyter-notebooks/) folder stores +The [resources/jupyter-notebooks](resources/jupyter-notebooks/) folder stores notebooks developed during early project development, some of which were used to -generate figures used in the 2021 paper. See the readme in the folder for +generate figures used in the 2021 paper. See the readme in the above folder for details. -### Dev Branch -The `dev` branch contains ongoing refactoring work which removes the necessity to use -mlflow. Currently, the code has been refactored into a clearer structure and easier -use through a command line interface for the data step, and the training step -is in progress. Further work is needed for the inference step, and to adapt the Jupyter -notebooks. - -## Data on Huggingface -There is GZ21 Ocean Momentum data available on [Huggingface](https://huggingface.co/): -- [the output of the data step](https://huggingface.co/datasets/M2LInES/gfdl-cmip26-gz21-ocean-forcing) -and -- [the trained model](https://huggingface.co/M2LInES/gz21-ocean-momentum) +## Data on HuggingFace +There is GZ21 Ocean Momentum data available on [HuggingFace](https://huggingface.co/) + +* [the output of the data step][datasets/M2LInES/gz21-forcing-cm26] and +* [the trained model](https://huggingface.co/M2LInES/gz21-ocean-momentum). + +As of 2023-12-08, these are currently low-resolution: forcings generated +for few time points (100 vs. 4000 available), and a model trained on that data. ## Contributing We are not currently accepting contributions outside of the M2LInES and ICCS diff --git a/docs/2021-paper-reproduction.md b/docs/2021-paper-reproduction.md new file mode 100644 index 00000000..3dec6820 --- /dev/null +++ b/docs/2021-paper-reproduction.md @@ -0,0 +1,45 @@ +# Reproducing results from the Guillaumin (2021) paper +See README.md at repo root for further details. Here, we will provide just +commands and commentary. + +Options such as `--out-dir` will be omitted. (The script will prompt you for +missing options. You can display help by adding `--help` to your invocation.) + +## 1. Training data generation +``` +python src/gz21_ocean_momentum/cli/data.py \ +--config-file resources/cli-configs/data-paper.yaml +``` + +Unclear whether you may need `--ntimes 4000`. + +## 2. Model training +Not tested due to issues with training. + +Model hyperparameters adapted from Table A1. + +``` +python src/gz21_ocean_momentum/cli/train.py \ +--config-file resources/cli-configs/train-paper.yaml \ +--subdomains-file resources/cli-configs/train-subdomains-paper.yaml \ +--train-split-end 0.8 --test-split-start 0.85 +``` + +Add `--in-train-data-dir `. + +## 3. Inference +The CLI inference script has no configuration other than model to predict on, +and input low-resolution data to predict forcings of: + + python src/gz21_ocean_momentum/cli/infer.py + +Currently will not reproduce the same predictions as used in the paper. See +https://github.com/m2lines/gz21_ocean_momentum/pull/97 for further details. + +For `--model-state-dict-file`, you may use a pretrained model instead of running +the training described above. A low-resolution one is provided here: +https://huggingface.co/M2LInES/gz21-ocean-momentum/blob/main/low-resolution/files/trained_model.pth + +Similarly, instead of generating forcings as above, you may use pre-generated +data for `--input-data-dir`. Low-resolution (~100 timepoints) CM2.6 data: +https://huggingface.co/datasets/M2LInES/gz21-forcing-cm26/tree/main/forcing diff --git a/docs/admin.md b/docs/admin.md new file mode 100644 index 00000000..1248171c --- /dev/null +++ b/docs/admin.md @@ -0,0 +1,16 @@ +# Project & repository admin notes +Last updated: 2023-12-05 + +## Hugging Face data +We store some training data and trained models on Hugging Face. They're on +separate repositories under the [M2LInES](https://huggingface.co/M2LInES) +organization. You need org permissions to edit these repositories -- check with +M2LInES folks. + +## PyPI library +*We don't publish a PyPI package.* Much of the work is in place, however. + +## `flake.nix`, `flake.lock` +These are files to assist building using the Nix package manager. You may ignore +them if you don't use them. They remain useful for certain users, so please do +not delete without consideration. diff --git a/docs/common-errors.md b/docs/common-errors.md index 42188a03..594514af 100644 --- a/docs/common-errors.md +++ b/docs/common-errors.md @@ -1,4 +1,4 @@ -# Common errors +# Troubleshooting common errors ## `User project specified in the request is invalid.` If when running the data processing step, you see an error message like this: diff --git a/docs/data.md b/docs/data.md index 1317a0a3..4b793b06 100644 --- a/docs/data.md +++ b/docs/data.md @@ -1,7 +1,12 @@ -# Notes on the data processing stage +# GZ21: Forcing generation +See Guillaumin (2021) 2.2, 2.3. + ## Notes on the CM2.6 dataset ### Requester Pays -We use the CM2.6 dataset hosted on the Pangeo Cloud Datastore. Though public, it +[cm26-pangeo-ds]: https://catalog.pangeo.io/browse/master/ocean/GFDL_CM2_6/ + +We use the [CM2.6 dataset][cm26-pangeo-ds] hosted on the Pangeo Cloud Datastore, +which is simulation output from the CM2.6 climate model. Though public, the data is *not* freely available, due to the data being in a Requester Pays bucket on Google Cloud Platform (GCP). Reading data from the bucket requires you to have Google Cloud access credentials configured with billing access; bandwidth diff --git a/examples/jupyter-notebooks/README.md b/examples/jupyter-notebooks/README.md deleted file mode 100644 index bd8d5678..00000000 --- a/examples/jupyter-notebooks/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# Jupyter notebooks -[gz21-paper-code-zenodo]: https://zenodo.org/record/5076046#.ZF4ulezMLy8 -[gz21-paper-agupubs]: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021MS002534 -These Jupyter notebooks were created & used during original development of the -code and associated paper: [Arthur P. Guillaumin, Laure Zanna (2021). -Stochastic-deep learning parameterization of ocean momentum -forcing][gz21-paper-agupubs]. The exact version of the code used to produce said -paper can be found on [Zenodo][gz21-paper-code-zenodo]. - -## 2021 paper figures -There are several notebooks which were used to generate the figures in the 2021 paper. - -The data for `generate-paper-figure-1.ipynb` can be generated by running - -``` -mlflow run . --experiment-name --env-manager=local \ --P lat_min=-80 -P lat_max=80 -P long_min=-280 -P long_max=80 \ --P factor=4 \ --P CO2=1 -P global=0 \ --P ntimes=4000 \ --P chunk_size=1 -``` -. The notebook generates figure 1b. - -For `generate-paper-figure-6.ipynb`, which generates figure 6b, -the same call has to be run with again with `CO2=1`. -The notebook is then asking for the data set with `CO2=0` first and the one with `CO2=1` second. - -`test-global-control.ipynb` generates figures 4, 5 and 7, as well as D4 and D5. For this, the inference step with -the trained neural network has to be run both on the data with `CO2=0` and with `CO2=1`, and then the notebook needs to -be run once with each set. The paper figures referring to _piControl_ are those with `CO2=0` (the control simulation -with pre-industrial CO2 levels), and the figures referring to _1pctCO2_ are those with `CO2=1` (a 1% increase per -year in CO2 levels for the first 70 years, after which they remain constant). -The notebook needs to be handed the experiment and run ID of the inference run, which is linked to the data and training -runs through `params.data_run_id` (run ID of data run) and `params.model_run_id` (run ID of training run), -respectively. \ No newline at end of file diff --git a/examples/jupyter-notebooks/generate-paper-figure-1.ipynb b/examples/jupyter-notebooks/generate-paper-figure-1.ipynb deleted file mode 100644 index 7e223da9..00000000 --- a/examples/jupyter-notebooks/generate-paper-figure-1.ipynb +++ /dev/null @@ -1,368 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Code to generate Figure 1 " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%cd ../../src/gz21_ocean_momentum\n", - "import os\n", - "from utils import select_experiment, select_run\n", - "from analysis.utils import plot_dataset, GlobalPlotter, plot_training_subdomains\n", - "from data.utils import load_training_datasets\n", - "import mlflow\n", - "from mlflow.tracking import MlflowClient\n", - "import xarray as xr\n", - "from dask.diagnostics import ProgressBar\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "import cmocean\n", - "cmap_solar = cmocean.cm.solar\n", - "cmap_balance = cmocean.cm.balance\n", - "\n", - "mlruns_path=os.path.join(os.getcwd(), '../../mlruns')\n", - "%env MLFLOW_TRACKING_URI $mlruns_path\n", - "\n", - "plt.rcParams[\"figure.figsize\"] = (4, 4 / 1.618)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Select an experiment by experiment ID, and get the associated run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "exp_id, exp_name = select_experiment()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "experiment_id = mlflow.get_experiment_by_name(exp_name).experiment_id\n", - "cols = ['params.CO2', ]\n", - "run = select_run(cols=cols, experiment_ids=(experiment_id,))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get the data set of the selected run " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ml_client = MlflowClient()\n", - "data_fname = ml_client.download_artifacts(run.run_id, 'forcing')\n", - "data = xr.open_zarr(data_fname)\n", - "#data = data.rename(dict(xu_ocean='longitude', yu_ocean='latitude'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define a plotter which plots the dataset as a map, and the areas for the training, and execute the plotting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from data.pangeo_catalog import get_patch\n", - "\n", - "#run = select_run(experiment_ids=('497746281881301089'))\n", - "run_id = run.run_id\n", - "\n", - "from cartopy.crs import PlateCarree\n", - "from data.pangeo_catalog import get_patch, get_whole_data\n", - "from scipy.ndimage import gaussian_filter\n", - "from matplotlib import colors\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from importlib import reload\n", - "reload(plt)\n", - "\n", - "%matplotlib widget\n", - "#%matplotlib inline #this option does not work with jupyterlab\n", - "\n", - "CATALOG_URL = 'https://raw.githubusercontent.com/pangeo-data/pangeo-datastore\\\n", - "/master/intake-catalogs/master.yaml'\n", - "\n", - "\n", - "class GlobalPlotter:\n", - " \"\"\"General class to make plots for global data. Handles masking of\n", - " continental data + showing a band near coastlines.\"\"\"\n", - "\n", - " def __init__(self, margin: int = 10, cbar: bool = True, ice: bool = True):\n", - " self.mask = self._get_global_u_mask()\n", - " self.margin = margin\n", - " self.cbar = cbar\n", - " self.ticks = dict(x=None, y=None)\n", - " self.ice = ice\n", - "\n", - " @property\n", - " def mask(self):\n", - " return self._mask\n", - "\n", - " @mask.setter\n", - " def mask(self, value):\n", - " self._mask = value\n", - "\n", - " @property\n", - " def borders(self):\n", - " return self._borders\n", - "\n", - " @borders.setter\n", - " def borders(self, value):\n", - " self._borders = value\n", - "\n", - " @property\n", - " def margin(self):\n", - " return self._margin\n", - "\n", - " @margin.setter\n", - " def margin(self, margin):\n", - " self._margin = margin\n", - " self.borders = self._get_continent_borders(self.mask, self.margin)\n", - "\n", - " @property\n", - " def x_ticks(self):\n", - " return self.ticks['x']\n", - "\n", - " @x_ticks.setter\n", - " def x_ticks(self, value):\n", - " self.ticks['x'] = value\n", - "\n", - " @property\n", - " def y_ticks(self):\n", - " return self.ticks['y']\n", - "\n", - " @y_ticks.setter\n", - " def y_ticks(self, value):\n", - " self.ticks['y'] = value\n", - "\n", - " def plot(self, u: xr.DataArray = None, projection_cls=PlateCarree,\n", - " lon: float = -100.0, lat: float = None, ax=None, animated=False,\n", - " borders_color='grey', borders_alpha=1., **plot_func_kw):\n", - " \"\"\"\n", - " Plots the passed velocity component on a map, using the specified\n", - " projection. Uses the instance's mask to set as nan some values.\n", - "\n", - " Parameters\n", - " ----------\n", - " u : xr.DataArray\n", - " Velocity array. The default is None.\n", - " projection : Projection\n", - " Projection used for the 2D plot.\n", - " lon : float, optional\n", - " Central longitude. The default is -100.0.\n", - " lat : float, optional\n", - " Central latitude. The default is None.\n", - "\n", - " Returns\n", - " -------\n", - " None.\n", - "\n", - " \"\"\"\n", - "\n", - " fig = plt.figure()\n", - " projection = projection_cls(lon)\n", - " if ax is None:\n", - " ax = plt.axes(projection=projection)\n", - " mesh_x, mesh_y = np.meshgrid(u['xu_ocean'], u['yu_ocean'])\n", - " if u is not None:\n", - " extra = self.mask.isel(xu_ocean=slice(0, 10))\n", - " extra['xu_ocean'] = extra['xu_ocean'] + 360\n", - " mask = xr.concat((self.mask, extra), dim='xu_ocean')\n", - " mask = mask.interp({k: u.coords[k] for k in ('xu_ocean',\n", - " 'yu_ocean')})\n", - " u = u * mask\n", - " im = ax.pcolormesh(mesh_x, mesh_y, u.values,\n", - " transform=PlateCarree(),\n", - " animated=animated, **plot_func_kw)\n", - " if self.x_ticks is not None:\n", - " ax.set_xticks(self.x_ticks)\n", - " if self.y_ticks is not None:\n", - " ax.set_yticks(self.y_ticks)\n", - " ax.set_global()\n", - " ax.coastlines()\n", - " # \"Gray-out\" near continental locations\n", - " if self.margin > 0:\n", - " extra = self.borders.isel(longitude=slice(0, 10))\n", - " extra['xu_ocean'] = extra['xu_ocean'] + 360\n", - " borders = xr.concat((self.borders, extra), dim='xu_ocean')\n", - " borders = borders.interp({k: u.coords[k]\n", - " for k in ('xu_ocean', 'yu_ocean')})\n", - " borders_cmap = colors.ListedColormap([borders_color, ])\n", - " ax.pcolormesh(mesh_x, mesh_y, borders, animated=animated,\n", - " transform=PlateCarree(), alpha=borders_alpha,\n", - " cmap=borders_cmap)\n", - " # Add locations of ice\n", - " if self.ice:\n", - " ice = self._get_ice_border()\n", - " ice = xr.where(ice, 1., 0.)\n", - " ice = ice.interp({k: u.coords[k] for k in ('xu_ocean',\n", - " 'yu_ocean')})\n", - " ice = xr.where(ice != 0, 1., 0.)\n", - " ice = abs(ice.diff(dim='xu_ocean')) + abs(ice.diff(dim='yu_ocean'))\n", - " ice = xr.where(ice != 0., 1, np.nan)\n", - " ice_cmap = colors.ListedColormap(['black', ])\n", - " ax.pcolormesh(mesh_x, mesh_y, ice, animated=animated,\n", - " transform=PlateCarree(), alpha=0.5,\n", - " cmap=ice_cmap)\n", - " if u is not None and self.cbar:\n", - " cbar = plt.colorbar(im, shrink=0.6)\n", - " cbar.set_label('m/s')\n", - " return ax\n", - "\n", - " @staticmethod\n", - " def _get_global_u_mask(factor: int = 4, base_mask: xr.DataArray = None):\n", - " \"\"\"\n", - " Return the global mask of the low-resolution surface velocities for\n", - " plots. While the coarse-grained velocities might be defined on\n", - " continental points due to the coarse-graining procedures, these are\n", - " not shown as we do not use them -- the mask for the forcing is even\n", - " more restrictive, as it removes any point within some margin of the\n", - " velocities mask.\n", - "\n", - " Parameters\n", - " ----------\n", - " factor : int, optional\n", - " Coarse-graining factor. The default is 4.\n", - "\n", - " base_mask: xr.DataArray, optional\n", - " # TODO\n", - " Not implemented for now.\n", - "\n", - " Returns\n", - " -------\n", - " None.\n", - "\n", - " \"\"\"\n", - " if base_mask is not None:\n", - " mask = base_mask\n", - " else:\n", - " _, grid_info = get_whole_data(CATALOG_URL, 0)\n", - " mask = grid_info['wet']\n", - " mask = mask.coarsen(dict(xt_ocean=factor, yt_ocean=factor))\n", - " mask_ = mask.max()\n", - " mask_ = mask_.where(mask_ > 0.1)\n", - " mask_ = mask_.rename(dict(xt_ocean='xu_ocean', yt_ocean='yu_ocean'))\n", - " return mask_.compute()\n", - "\n", - " @staticmethod\n", - " def _get_ice_border():\n", - " \"\"\"Return an xarray.DataArray that indicates the locations of ice\n", - " in the oceans. \"\"\"\n", - " temperature, _ = get_patch(CATALOG_URL, 1, None, 0,\n", - " 'surface_temp')\n", - " temperature = temperature.rename(dict(xt_ocean='xu_ocean',\n", - " yt_ocean='yu_ocean'))\n", - " temperature = temperature['surface_temp'].isel(time=0)\n", - " ice = xr.where(temperature <= 0., True, False)\n", - " return ice\n", - "\n", - " @staticmethod\n", - " def _get_continent_borders(base_mask: xr.DataArray, margin: int):\n", - " \"\"\"\n", - " Returns a boolean xarray DataArray corresponding to a mask of the\n", - " continents' coasts, which we do not process.\n", - " Hence margin should be set according to the model.\n", - "\n", - " Parameters\n", - " ----------\n", - " mask : xr.DataArray\n", - " Mask taking value 1 where coarse velocities are defined and used\n", - " as input and nan elsewhere.\n", - " margin : int\n", - " Margin imposed by the model used, i.e. number of points lost on\n", - " one side of a square.\n", - "\n", - " Returns\n", - " -------\n", - " mask : xr.DataArray\n", - " Boolean DataArray taking value True for continents.\n", - "\n", - " \"\"\"\n", - " assert margin >= 0, 'The margin parameter should be a non-negative' \\\n", - " ' integer'\n", - " assert base_mask.ndim <= 2, 'Velocity array should have two'\\\n", - " ' dims'\n", - " # Small trick using the guassian filter function\n", - " mask = xr.apply_ufunc(lambda x: gaussian_filter(x, 1., truncate=margin),\n", - " base_mask)\n", - " mask = np.logical_and(np.isnan(mask), ~np.isnan(base_mask))\n", - " mask = mask.where(mask)\n", - " return mask.compute()\n", - "\n", - "plotter = GlobalPlotter(cbar=True, margin=0)\n", - "plotter.x_ticks = np.arange(-150., 151., 50)\n", - "plotter.y_ticks = np.arange(-80., 81., 20)\n", - "plot_training_subdomains(plotter, bg_variable=data['usurf'].isel(time=0), facecolor='green', edgecolor='black', linewidth=2, fill=False, vmin=-0.5, vmax=0.5, lon=0., cmap=cmap_balance)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('figure1b.jpg', dpi=250)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/jupyter-notebooks/test_global.ipynb b/examples/jupyter-notebooks/test_global.ipynb deleted file mode 100644 index 91455134..00000000 --- a/examples/jupyter-notebooks/test_global.ipynb +++ /dev/null @@ -1,136 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import mlflow\n", - "from mlflow.tracking import client\n", - "import xarray as xr\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import os,sys\n", - "sys.path.insert(1, os.path.join(os.getcwd() , '../../src/gz21_ocean_momentum'))\n", - "from utils import select_experiment, select_run\n", - "from analysis.utils import plot_dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_exp_name = select_experiment()\n", - "test_exp = mlflow.get_experiment_by_name(test_exp_name)\n", - "test_exp_id = test_exp.experiment_id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = select_run(experiment_ids=test_exp_id)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client_ = client.MlflowClient()\n", - "data_file_name = client_.download_artifacts(run['params.data_run_id'], 'forcing')\n", - "print(data_file_name)\n", - "data = xr.open_zarr(data_file)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pred_file_name = client_.download_artifacts(run.run_id, 'test_output_0')\n", - "pred = xr.open_zarr(pred_file_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "merged = xr.merge((data, pred))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "merged" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_dataset(merged.isel(time=10), vmin=-1e-7, vmax=1e-7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/jupyter-notebooks/test_global_control.ipynb b/examples/jupyter-notebooks/test_global_control.ipynb deleted file mode 100644 index ff60f675..00000000 --- a/examples/jupyter-notebooks/test_global_control.ipynb +++ /dev/null @@ -1,1288 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test on Global scale " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To produce the paper figures, this notebook has to be run twice: once with CO2=1 settings and once with CO2=0." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib notebook #This option does not work in Jupyterlab\n", - "%matplotlib widget \n", - "\n", - "%cd ../../src/gz21_ocean_momentum\n", - "\n", - "import os\n", - "mlruns_path=os.path.join(os.getcwd(), '../../mlruns/')\n", - "%env MLFLOW_TRACKING_URI $mlruns_path\n", - "\n", - "# See https://github.com/m2lines/gz21_ocean_momentum/blob/main/docs/data.md for an explanation \n", - "# The environment variable does not need setting if you place the credentials file at ~/.config/gcloud/application_default_credentials.json .\n", - "%env GOOGLE_APPLICATION_CREDENTIALS /home/marion/.config/gcloud/application_default_credentials.json" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import mlflow\n", - "from mlflow.tracking import client\n", - "import xarray as xr\n", - "import numpy as np\n", - "import dask.array as da\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "from gz21_ocean_momentum.utils import select_experiment, select_run\n", - "from gz21_ocean_momentum.analysis.utils import (plot_dataset, GlobalPlotter, anomalies,\n", - " download_data_pred, plot_time_series, apply_complete_mask)\n", - "from gz21_ocean_momentum.data.pangeo_catalog import get_whole_data\n", - "from gz21_ocean_momentum.data.xrtransforms import SeasonalStdizer, TargetedTransform, ScalingTransform\n", - "from dask.diagnostics import ProgressBar\n", - "from models.submodels import transform3\n", - "\n", - "import cartopy.crs as ccrs\n", - "import cmocean\n", - "cmap = cmocean.cm.balance\n", - "cmap_balance = cmocean.cm.balance\n", - "cmap_balance_r=cmocean.cm.balance_r\n", - "cmap_amp = cmocean.cm.amp\n", - "cmap_amp_r = cmocean.cm.amp_r\n", - "\n", - "plt.rcParams[\"figure.figsize\"] = (4, 4 / 1.618)\n", - "\n", - "#uv_plotter.x_ticks = np.arange(-150., 151., 50)\n", - "#uv_plotter.y_ticks = np.arange(-80., 81., 20)\n", - "#uv_plotter.margin = 10\n", - "\n", - "client_ = client.MlflowClient()\n", - "\n", - "from importlib import reload \n", - "import gz21_ocean_momentum.analysis as analysis\n", - "GlobalPlotter = analysis.utils.GlobalPlotter\n", - "uv_plotter = GlobalPlotter() \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams[\"figure.figsize\"] = (4, 4 / 1.618)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "old_plot = GlobalPlotter.plot\n", - "\n", - "def new_plot_func(self, name: str, *args, **kargs):\n", - " data = xr.Dataset({name: args[0]})\n", - " old_plot(self, *args, **kargs)\n", - "\n", - "#GlobalPlotter.plot = new_plot_func" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This downloads some information about the grid, used later on" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "CATALOG_URL = 'https://raw.githubusercontent.com/pangeo-data/pangeo-datastore\\\n", - "/master/intake-catalogs/master.yaml'\n", - "data = get_whole_data(CATALOG_URL, 0)\n", - "grid_info = data[1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Select the experiment of the inference run.\")\n", - "\n", - "exp_id, test_exp_name = select_experiment()\n", - "cols=['status', 'start_time', 'params.CO2', 'params.factor',\n", - " 'params.submodel', 'params.loss_cls_name']\n", - "# In the following merge parameter, the first strings in the tupels need to be the experiment names for the data run and the training run, respectively.\n", - "# If these are in the same experiment they can either be duplicated, or you can set merge=[].\n", - "# This is used to show the available runs.\n", - "merge=[('data', 'params.data_run_id', 'run_id'),\n", - " ('train', 'params.model_run_id', 'run_id')]\n", - "run = select_run(experiment_ids=exp_id, cols=cols, merge=merge)\n", - "data, pred = download_data_pred(run['params.data_run_id'], run.run_id)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.core.display import HTML\n", - "html = '

Menu

'\n", - "html += 'MSE and R²  '\n", - "html += ' Correlation  '\n", - "html += 'Variance of forcing  '\n", - "html += 'Comparison of distributions  '\n", - "html += 'QQ-plot  '\n", - "html += 'Bias analysis  '\n", - "html += 'Time series plots'\n", - "HTML(html)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "uv_plotter.plot(data['S_x'].isel(time=70), lon=0., projection_cls = ccrs.PlateCarree,\n", - " colorbar_label='m/s', cmap=cmocean.cm.delta, vmin=-1, vmax=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below is the plot shown in Figure 5 of the paper" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_time_series(data, pred, longitude: float, latitude: float, time: slice,\n", - " std: bool = True, true: bool = True):\n", - " plt.figure()\n", - " xs = np.arange(time.start, time.stop, time.step)\n", - " truth = data['S_x'].sel(longitude=longitude, latitude=latitude,\n", - " method='nearest').isel(time=time)\n", - " pred_mean = pred['S_x'].sel(longitude=longitude, latitude=latitude,\n", - " method='nearest').isel(time=time)\n", - " pred_std = pred['S_xscale'].sel(longitude=longitude, latitude=latitude,\n", - " method='nearest').isel(time=time)\n", - " if true:\n", - " plt.plot(xs, truth, 'darkblue')\n", - " plt.plot(xs, pred_mean, 'darkorange')\n", - " if std:\n", - " plt.plot(xs, pred_mean + 1.96 * pred_std, 'g--', linewidth=1)\n", - " plt.plot(xs, pred_mean - 1.96 * pred_std, 'g--', linewidth=1)\n", - " plt.ylabel(r'$1e^{-7}m/s^2$')\n", - " _ = plt.xlabel('days')\n", - " \n", - "time_slice=slice(0, 300)\n", - "plt.rcParams[\"figure.figsize\"] = (4 * 2, 4 * 2 / 1.618)\n", - "\n", - "plot_time_series(data, pred, longitude=-60, latitude=30, time=time_slice, std=True, true=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('timeseries3.jpg', dpi=400)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('timeseriespredcontrol.jpg', dpi=400)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## MSE and R²" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we compute the seasonal (monthly) means of the data. This will be used later in some of the metrics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "forcing_vars = ['S_x', 'S_y']\n", - "errors = pred[forcing_vars] - data[forcing_vars]\n", - "errors_cycle = anomalies(data[forcing_vars])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, mse is the time-mean MSE of the mean component of our predicted forcing, mse_month is the variance of the residuals of the data after removing monthly variation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mse = (errors**2).mean(dim='time')\n", - "mse_cycle = (errors_cycle**2).mean(dim='time')\n", - "amplitudes = (data[forcing_vars]**2).mean(dim='time')\n", - "\n", - "with ProgressBar():\n", - " mse = mse.compute()\n", - " mse_cycle = mse_cycle.compute()\n", - " amplitudes = amplitudes.compute()\n", - "mse['total'] = mse['S_x'] + mse['S_y']\n", - "mse_cycle['total'] = mse_cycle['S_x'] + mse_cycle['S_y']\n", - "amplitudes['total'] = amplitudes['S_x'] + amplitudes['S_y']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### MSE plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below is Figure 4a of the paper" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams[\"figure.figsize\"] = (4*2, 4 * 2 / 1.618)\n", - "x = uv_plotter.plot(mse['total'], lon=0., cmap=cmocean.cm.dense,\n", - " colorbar_label=r'$1e^{-14}m^2/s^4$', norm=matplotlib.colors.LogNorm(vmin=0.01, vmax=10))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('msecontrol.jpg', dpi=400)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('mse1pct.jpg', dpi=400)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### R² plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below is Figure 4b of the paper" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib\n", - "rsquared = 1 - mse / amplitudes\n", - "rsquared_cycles = 1 - mse / mse_cycle\n", - "mse_ratio_2 = 1 - mse_cycle / amplitudes\n", - "uv_plotter.plot(rsquared_cycles['total'], cmap=cmocean.cm.delta, lon=0., norm=matplotlib.colors.LogNorm(vmin=0.5, vmax=1))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('r2_control_month.jpg', dpi=400)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('r2_1pctC02_month.jpg', dpi=400)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Scalar R²" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We limit the range to latitudes -60 to 60, and we apply a mask that discards points near continents according to the mask used in the plotter (the points shown in gray on the maps in the paper). This is why we define these quantities \"to_scalar\", in order to not account for points near continents in the computation of the scalar R²." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "latitudes = slice(-60, 60)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mse_to_scalar = apply_complete_mask(mse, pred, uv_plotter)\n", - "mse_cycle_to_scalar = apply_complete_mask(mse_cycle, pred, uv_plotter)\n", - "amplitudes_to_scalar = apply_complete_mask(amplitudes, pred, uv_plotter)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with ProgressBar():\n", - " mse_scalar = mse_to_scalar.sel(latitude=latitudes).sum().compute()\n", - " mse_cycle_scalar = mse_cycle_to_scalar.sel(latitude=latitudes).sum().compute()\n", - " amplitudes_scalar = amplitudes_to_scalar.sel(latitude=latitudes).sum().compute()\n", - " rsquared_scalar_cycle = 1 - mse_scalar / mse_cycle_scalar\n", - " rsquared_scalar = 1 - mse_scalar / amplitudes_scalar\n", - "print(rsquared_scalar)\n", - "print(rsquared_scalar_cycle)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Correlation between true forcing and mean component of the prediction " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "forcing_vars = ['S_x', 'S_y']\n", - "data_anomaly = anomalies(data[forcing_vars])\n", - "pred_anomaly = anomalies(pred[forcing_vars])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "std_data = data_anomaly.std(dim='time')\n", - "std_pred = pred_anomaly.std(dim='time')\n", - "corr = ((data_anomaly * pred_anomaly).mean(dim='time') - data_anomaly.mean(dim='time') * pred_anomaly.mean(dim='time')) / (std_data * std_pred)\n", - "# corr_s_y = xr.corr(data.S_y, pred.S_y, dim='time')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with ProgressBar():\n", - " corr = corr.compute()\n", - " # corr_s_y = corr_s_y.compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "uv_plotter.plot(corr['S_x'], vmin=0.7, vmax=1., lon=0., cmap=cmocean.cm.balance_r)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('corr_X_1pct.jpg', dpi=400)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Variance of norm of subgrid momentum forcing " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "norm_S = np.sqrt(data['S_x']**2 + data['S_y']**2)\n", - "norm_Spred = np.sqrt(pred['S_x']**2 + pred['S_y']**2)\n", - "var_norm_S = norm_S.var(dim='time')\n", - "var_norm_Spred = norm_Spred.var(dim='time')\n", - "with ProgressBar():\n", - " var_norm_S = var_norm_S.compute()\n", - " var_norm_Spred = var_norm_Spred.compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "uv_plotter.plot(var_norm_S, cmap=cmocean.cm.dense, lon=0., colorbar_label=r'$1e^{-14}m^2s^{-4}$', norm=matplotlib.colors.LogNorm(vmin=0.01, vmax=10,))\n", - "uv_plotter.plot(var_norm_Spred, cmap=cmocean.cm.dense, lon=0., colorbar_label=r'$1e^{-14}m^2s^{-4}$', norm=matplotlib.colors.LogNorm(vmin=0.01, vmax=10,))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('variance_forcing_control.jpg', dpi=400)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('variance_forcing_control_pred.jpg', dpi=400)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('variance_forcing_control_pred.jpg', dpi=400)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compare distributions of true and stochastic simulated forcing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "forcing_vars = ['S_x', 'S_y']\n", - "scale_vars = ['S_xscale', 'S_yscale']\n", - "\n", - "pred_ = apply_complete_mask(pred, pred, uv_plotter)\n", - "pred_scale = pred_[scale_vars].rename(dict(S_xscale='S_x', S_yscale='S_y'))\n", - "pred_ = pred_[forcing_vars]\n", - "data_ = apply_complete_mask(data[forcing_vars], pred, uv_plotter)\n", - "\n", - "# Subsample the data\n", - "time_slice = slice(None, None, 1)\n", - "lon_slice = slice(None, None, 2)\n", - "lat_slice = slice(-60, 60, 2)\n", - "pred_ = pred_.sel(longitude=lon_slice, latitude=lat_slice).isel(time=time_slice)\n", - "pred_scale = pred_scale.sel(longitude=lon_slice, latitude=lat_slice).isel(time=time_slice)\n", - "data_ = data_.sel(longitude=lon_slice, latitude=lat_slice).isel(time=time_slice)\n", - "\n", - "# Standardized residuals\n", - "residuals = (data_ - pred_) / pred_scale" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert np.all(np.isnan(data_['S_x']) == np.isnan(pred_['S_x'])), \"Not the same number of points!\"\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we do a stochastic simulation of the forcing given the parameters of the Gaussian distribution at each location and each time point" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "shape = tuple(pred_.dims.values())\n", - "epsilons = dict(x=np.random.randn(*shape), y=np.random.randn(*shape))\n", - "epsilons = xr.Dataset(dict(S_x=(pred_.dims, epsilons['x']), S_y=(pred_.dims, epsilons['y'])))\n", - "pred_stochastic = pred_ + pred_scale * epsilons" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pred_" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bins = np.arange(-20, 21, 1)\n", - "\n", - "# assert np.all(np.isnan(data_['S_x']) == np.isnan(pred_stochastic['S_x'])), \"Not the same number of points!\"\n", - "\n", - "plt.figure()\n", - "plt.subplot(121)\n", - "with ProgressBar():\n", - " plt.hist(np.ravel(data_['S_x']), bins=bins, density=True, log=True, alpha=0.5, color='purple')\n", - " plt.hist(np.ravel(pred_stochastic['S_x']), bins=bins, density=True, log=True, alpha=0.5, color='green')\n", - "plt.title('Zonal component')\n", - "plt.xlabel(r'$1e^{-7}m/s^2$')\n", - "plt.ylabel('log density')\n", - "plt.subplot(122)\n", - "with ProgressBar():\n", - " plt.hist(np.ravel(data_['S_y']), bins=bins, density=True, log=True, alpha=0.5, color='purple')\n", - " plt.hist(np.ravel(pred_stochastic['S_y']), bins=bins, density=True, log=True, alpha=0.5, color='green')\n", - "plt.title('Meridional component')\n", - "plt.xlabel(r'$1e^{-7}m/s^2$')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('forcing_dist_control.jpg', dpi=400)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure()\n", - "bins=np.arange(-6, 6, 0.025)\n", - "from scipy.stats import norm\n", - "with ProgressBar():\n", - " plt.subplot(121)\n", - " plt.hist(np.ravel(residuals['S_x'].compute()), bins=bins, density=True, color='orange')\n", - " plt.plot(bins, (norm.pdf(bins)), 'r')\n", - " plt.title('Meridional component')\n", - " plt.subplot(122)\n", - " plt.hist(np.ravel(residuals['S_y'].compute()), bins=bins, density=True, color='orange')\n", - " plt.plot(bins, (norm.pdf(bins)), 'r')\n", - " plt.title('Zonal component')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('normalized_residuals_ditribution.jpg', dpi=300)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### QQ plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "quantiles = np.exp(np.linspace(-5, 5, 100)) / (1 + np.exp(np.linspace(-5, 5, 100)))\n", - "quantiles = np.linspace(0.01, 0.99, 99)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "quantiles" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with ProgressBar():\n", - " quantiles_x = np.nanquantile(residuals['S_x'].compute(), quantiles)\n", - " quantiles_y = np.nanquantile(residuals['S_y'].compute(), quantiles)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import norm, cauchy, t\n", - "quantiles_norm = norm.ppf(quantiles)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure()\n", - "reference = quantiles_norm\n", - "plt.subplot(121)\n", - "plt.plot(reference, quantiles_x, 'x')\n", - "plt.plot(reference, reference, 'g')\n", - "plt.axis([None, None, -5, 5])\n", - "plt.title('Zonal component')\n", - "plt.subplot(122)\n", - "plt.plot(reference, quantiles_y, 'x')\n", - "plt.plot(reference, reference, 'g')\n", - "plt.axis([None, None, -5, 5])\n", - "plt.title('Meridional component')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('normalized_residuals_qq.jpg')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (IGNORE THIS) Another way to do it" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import norm\n", - "lon = slice(None, None, 1)\n", - "lat= slice(-40, 40, 1)\n", - "time_slice = slice(None, 1000, 1)\n", - "\n", - "true = apply_complete_mask(data['S_x'])\n", - "pred_mean = apply_complete_mask(pred['S_x'])\n", - "pred_std = apply_complete_mask(pred['S_xscale'])\n", - "\n", - "def my_transform(x , mean, precision):\n", - " cdf = lambda x: norm.cdf((x - mean) * precision) \n", - " return cdf(x)\n", - "\n", - "v = xr.apply_ufunc(my_transform, true, pred_mean, 1 / pred_std,\n", - " dask='parallelized', output_dtypes=[np.float64, ])\n", - "residuals = (true - pred_mean) / pred_std\n", - "residuals = residuals.sel(longitude=lon, latitude=lat).isel(time=time_slice)\n", - "v = v.sel(longitude=lon, latitude=lat).isel(time=time_slice)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with ProgressBar():\n", - " q2 = np.nanquantile(residuals, quantiles)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "norm_quantiles = norm.ppf(quantiles)\n", - "plt.figure()\n", - "plt.plot(norm_quantiles, q2)\n", - "plt.plot(norm_quantiles, norm_quantiles)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "q = np.nanquantile(v, quantiles)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure()\n", - "plt.plot(quantiles, q, 'x')\n", - "plt.plot(quantiles, quantiles)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## (IGNORE THIS) Likelihood plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import norm\n", - "lon = slice(None, None, 1)\n", - "lat= slice(-80, 80, 1)\n", - "time_slice = slice(None, None, 1)\n", - "\n", - "true = data['S_x'].isel(time=time_slice)\n", - "pred_mean = pred['S_x'].isel(time=time_slice)\n", - "pred_std = pred['S_xscale'].isel(time=time_slice)\n", - "\n", - "residuals = (true - pred_mean) / pred_std\n", - "log_lkh = xr.apply_ufunc(lambda x: np.log(norm.pdf(x)), residuals, dask='parallelized', output_dtypes=[np.float64,])\n", - "with ProgressBar():\n", - " log_lkh = log_lkh.compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "true" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "uv_plotter.margin=10\n", - "uv_plotter.plot(-log_lkh.mean(dim='time'), vmin=0, vmax=2.5)\n", - "apply_complete_mask(-log_lkh, pred, uv_plotter).mean()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lat= slice(-40, 40, 1)\n", - "\n", - "with ProgressBar():\n", - " lkh_mean = lkh.sel(latitude=lat).isel(time=time_slice).mean().compute()\n", - "lkh_mean" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bias analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "forcing_vars = ['S_x', 'S_y']\n", - "errors = pred[forcing_vars] - data[forcing_vars]\n", - "map_errors = errors.mean(dim='time')\n", - "with ProgressBar():\n", - " map_errors = map_errors.compute()\n", - " absolute = (abs(data[forcing_vars])).mean(dim='time').compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "relative_bias = (map_errors / absolute).compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with ProgressBar():\n", - " uv_plotter.plot(abs(relative_bias['S_x']), cmap=cmocean.cm.delta, lon=0., vmin=0.01, vmax=1, norm=matplotlib.colors.LogNorm())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('relative_bias_control.jpg', dpi=400)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time series plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams[\"figure.figsize\"] = (4*2, 4*2 / 1.618)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "points = [(-60, 30), (-104, -20), (-129, 29)]\n", - "\n", - "plot_time_series(data, pred, *points[1], slice(0, 300), std=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.savefig('timeseries_quescient.jpg', dpi=400)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# (IGNORE THIS) Comparison of quantiles" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pred" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from analysis.base import QuantileCompare\n", - "\n", - "with ProgressBar():\n", - " qq = QuantileCompare()\n", - " qq.quantiles = [0.5, 0.25, 0.5, 0.75, 0.95]\n", - " qq.data = ((pred['S_x']-data['S_x']) / pred['S_xscale']).isel(time=slice(None, None, 1)).compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with ProgressBar():\n", - " q_0_75 = qq.data_quantiles[0.75]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import norm\n", - "norm.ppf(0.75)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import cartopy.crs as ccrs\n", - "import cmocean\n", - "cmap = cmocean.cm.balance\n", - "uv_plotter.plot(np.abs(q_0_75 - 0.6745) < 0.05, vmin=0, vmax=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "uv_plotter.plot(data['S_x'].isel(time=0), cmap=cmap_balance, vmin=-2, vmax=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot((data['S_x'].sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800))).data)\n", - "plt.plot((pred['S_xpred'].sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800))).data)\n", - "plt.plot((pred['S_xpred'].sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800)) + 1.96 * pred['S_xscale'].sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800))).data, '--')\n", - "plt.plot((pred['S_xpred'].sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800)) - 1.96 * pred['S_xscale'].sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800))).data, '--')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "r = qq.data.sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800)).compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.quantile(r, [0.25, 0.5, 0.75])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.hist(np.ravel(q_0_25.data), bins=np.arange(-2, 2, 0.1))\n", - "plt.title('Histogram of 0.25 quantiles of normalized residuals')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.quantile(r.data, [0.25, 0.5, 0.75])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.max(q_0_5).compute()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Snapshot of the forcing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import cartopy.crs as ccrs\n", - "cmap = cmocean.cm.balance\n", - "s_x = v\n", - "\n", - "ax = plt.axes(projection=ccrs.PlateCarree(-100.))\n", - "mesh_x, mesh_y = np.meshgrid(s_x['longitude'], s_x['latitude'])\n", - "mesh_x = mesh_x + 360\n", - "ax.pcolormesh(mesh_x, mesh_y, s_x.values, vmin=-4, vmax=4, transform = ccrs.PlateCarree(), cmap=cmap, alpha=1)\n", - "mesh_x, mesh_y = np.meshgrid(borders['longitude'], borders['latitude'])\n", - "mesh_x = mesh_x + 360\n", - "ax.pcolormesh(mesh_x, mesh_y, borders * 1., transform=ccrs.PlateCarree(), alpha=0.1)\n", - "ax.set_global()\n", - "ax.coastlines()\n", - "ax.set_xticks(np.arange(-180, 181, 20))\n", - "ax.set_yticks(np.arange(-80,81, 20))\n", - "#ax.set_extent([-20, 20, -20, 20])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib notebook" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import animation\n", - "cmap = cmocean.cm.amp\n", - "import cartopy.crs as ccrs\n", - "\n", - "fig = plt.figure()\n", - "\n", - "try:\n", - " del video\n", - "except:\n", - " pass\n", - "\n", - "uv_plotter.x_ticks = None\n", - "uv_plotter.y_ticks = None\n", - "\n", - "def animate(i):\n", - " print(i)\n", - " v = pred['S_xscale'].isel(time=i)\n", - " uv_plotter.plot(v, projection_cls = ccrs.Orthographic, lon=(i/5)%360, cmap=cmap, vmin=0, vmax=2, animated=True)\n", - " \n", - "ani = animation.FuncAnimation(fig, animate, frames = 500, interval = 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib\n", - "matplotlib.rcParams['animation.embed_limit'] = 100" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ani.save('forcing_pred_mean.mp4', fps=60, dpi=300)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import HTML\n", - "video = ani.to_html5_video()\n", - "HTML(video)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "e = (merged['S_xpred'] - merged['S_x']) / merged['S_xscale']\n", - "d = (e**2).mean(dim='time').compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "d_ = abs(d-1)\n", - "d_ = d_.interp(mask_.coords)\n", - "d_ = xr.where(borders, -1000, d_)\n", - "d_ = xr.where(mask__, d_, np.nan)\n", - "d_ = d_.interp(latitude = np.arange(-80, 80, 0.1), longitude = np.arange(-279.9, 80.1, 0.1))\n", - "d_['longitude'] = d_['longitude'] + 100.\n", - "\n", - "ax = plt.axes(projection=ccrs.PlateCarree())\n", - "d_.plot.imshow(x='longitude', y='latitude', ax=ax, vmin=0, vmax=2, cmap=cmap,\n", - " transform = ccrs.PlateCarree(-100.))\n", - "ax.set_global()\n", - "ax.coastlines()\n", - "x_ticks = plt.xticks(np.arange(-180, 181, 20))\n", - "y_ticks = plt.yticks(np.arange(-80, 81, 20))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/flake.lock b/flake.lock index e253de0a..f2bcbe4b 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "systems": "systems" }, "locked": { - "lastModified": 1694529238, - "narHash": "sha256-zsNZZGTGnMOf9YpHKJqMSsa0dXbfmxeoJ7xHlrt+xmY=", + "lastModified": 1701680307, + "narHash": "sha256-kAuep2h5ajznlPMD9rnQyffWG8EM/C73lejGofXvdM8=", "owner": "numtide", "repo": "flake-utils", - "rev": "ff7b65b44d01cf9ba6a71320833626af21126384", + "rev": "4022d587cbbfd70fe950c1e2083a02621806a725", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1699725108, - "narHash": "sha256-NTiPW4jRC+9puakU4Vi8WpFEirhp92kTOSThuZke+FA=", + "lastModified": 1701693815, + "narHash": "sha256-7BkrXykVWfkn6+c1EhFA3ko4MLi3gVG0p9G96PNnKTM=", "owner": "nixos", "repo": "nixpkgs", - "rev": "911ad1e67f458b6bcf0278fa85e33bb9924fed7e", + "rev": "09ec6a0881e1a36c29d67497693a67a16f4da573", "type": "github" }, "original": { diff --git a/pyproject.toml b/pyproject.toml index 04d80550..0cfebaef 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,24 +17,25 @@ classifiers = [ ] dependencies = [ - # general, likely used all over - "scipy", "xarray", + "torch>=1.13.1", "dask", "mlflow-skinny", + "scipy", # data analysis, graphing + "matplotlib>=3.7", # analysis, examples + "cartopy>=0.21", # analysis (plotting data) + # data download "intake", + "intake-xarray", + "gcsfs", # required for downloading CM2.6 via intake (dataset stored on GCP) "requests", "aiohttp", - "intake-xarray", - "gcsfs", - "matplotlib>=3.7", # analysis, examples - "cartopy>=0.21", # analysis (plotting data) - - "torch>=1.13.1", - "progressbar2>=4.2.0" # inference/utils (but imported by trainScript) + # common CLI packages + "progressbar2>=4.2.0", + "configargparse>=1.7", ] authors = [ diff --git a/resources/cli-configs/README.md b/resources/cli-configs/README.md new file mode 100644 index 00000000..0cc5502b --- /dev/null +++ b/resources/cli-configs/README.md @@ -0,0 +1,4 @@ +# Example run configurations +## General tips +* If the data step (forcing generation) is taking too long, lower `ntimes`. On a + consumer machine, for testing, 100 is good enough. (4000 will take ages.) diff --git a/resources/cli-configs/data-paper.yaml b/resources/cli-configs/data-paper.yaml new file mode 100644 index 00000000..c73ee3fb --- /dev/null +++ b/resources/cli-configs/data-paper.yaml @@ -0,0 +1,9 @@ +# Shared configuration showing up in the 2021 paper. +# You may pass --ntimes and --co2-increase on the CLI. + +lat-min: -80 +lat-max: 80 +long-min: -280 +long-max: 80 + +factor: 4 diff --git a/resources/cli-configs/train-paper.yaml b/resources/cli-configs/train-paper.yaml new file mode 100644 index 00000000..6b7dd58c --- /dev/null +++ b/resources/cli-configs/train-paper.yaml @@ -0,0 +1,6 @@ +# Adapted from Guillaumin (2021), Table A1. +batch-size: 4 +epochs: 100 +initial-learning-rate: 5.0e-4 +decay-factor: 0.1 +decay-at-epoch-milestones: [10, 20] diff --git a/resources/cli-configs/train-subdomains-paper.yaml b/resources/cli-configs/train-subdomains-paper.yaml new file mode 100644 index 00000000..7e1991a1 --- /dev/null +++ b/resources/cli-configs/train-subdomains-paper.yaml @@ -0,0 +1,16 @@ +- lat-min: 35 + lat-max: 50 + long-min: -50 + long-max: -20 +- lat-min: -40 + lat-max: -25 + long-min: -180 + long-max: -162 +- lat-min: -20 + lat-max: -5 + long-min: -110 + long-max: -92 +- lat-min: -0 + lat-max: 15 + long-min: -48 + long-max: -30 diff --git a/resources/cli-configs/train-wip.yaml b/resources/cli-configs/train-wip.yaml new file mode 100644 index 00000000..9933de71 --- /dev/null +++ b/resources/cli-configs/train-wip.yaml @@ -0,0 +1,9 @@ +batch-size: 4 + +epochs: 20 +initial-learning-rate: 5.0e-4 +decay-factor: 0.0 +decay-at-epoch-milestones: [15, 30] # TODO is 0 implicit? confirm + +train-split-end: 0.80 +test-split-start: 0.85 diff --git a/resources/jupyter-notebooks/.gitignore b/resources/jupyter-notebooks/.gitignore new file mode 100644 index 00000000..5cf9afe9 --- /dev/null +++ b/resources/jupyter-notebooks/.gitignore @@ -0,0 +1,2 @@ +# ignore generated images +*.jpg diff --git a/resources/jupyter-notebooks/README.md b/resources/jupyter-notebooks/README.md new file mode 100644 index 00000000..731d0764 --- /dev/null +++ b/resources/jupyter-notebooks/README.md @@ -0,0 +1,31 @@ +# Jupyter notebooks +[gz21-paper-code-zenodo]: https://zenodo.org/record/5076046#.ZF4ulezMLy8 +[gz21-paper-agupubs]: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021MS002534 + +These Jupyter notebooks were created & used during original development of the +code and associated paper: [Arthur P. Guillaumin, Laure Zanna (2021). +Stochastic-deep learning parameterization of ocean momentum +forcing][gz21-paper-agupubs]. The exact version of the code used to produce said +paper can be found on [Zenodo][gz21-paper-code-zenodo]. + +## Usage +Follow the installation instructions in the main project readme. + +Run `jupyter notebook` from project/repository root. + +You will also need to install Jupyter Notebook: `pip install notebook` + +Individual notebooks may have further dependencies. These should be displayed at +the top of the notebook. The following packages are particularly common (`pip +install `): + + * `ipympl` + * `cmocean` + +## 2021 paper figures +There are several notebooks which were used to generate the figures in the 2021 +paper. These are stored in [`paper/`](paper/). See the readme in that folder for +further details. + +## Other notebooks +The other notebooks stored here may need updating in order to run. diff --git a/examples/jupyter-notebooks/offline_test_SWM.ipynb b/resources/jupyter-notebooks/offline_test_SWM.ipynb similarity index 100% rename from examples/jupyter-notebooks/offline_test_SWM.ipynb rename to resources/jupyter-notebooks/offline_test_SWM.ipynb diff --git a/examples/jupyter-notebooks/other-shallow-water-model.ipynb b/resources/jupyter-notebooks/other-shallow-water-model.ipynb similarity index 100% rename from examples/jupyter-notebooks/other-shallow-water-model.ipynb rename to resources/jupyter-notebooks/other-shallow-water-model.ipynb diff --git a/resources/jupyter-notebooks/paper/README.md b/resources/jupyter-notebooks/paper/README.md new file mode 100644 index 00000000..dfec9929 --- /dev/null +++ b/resources/jupyter-notebooks/paper/README.md @@ -0,0 +1,28 @@ +# Jupyter notebooks for generating figures used in Guillaumin (2021) +Ensure to read the readme in the folder above this one first. + +## Figure 1 +`generate-paper-figure-1.ipynb` generates figure 1b. The forcings it uses can be +generated by running the data step with the following configuration: + +``` +python src/gz21_ocean_momentum/cli/data.py \ +--config-file resources/cli-configs/data-paper.yaml \ +--ntimes 4000 +``` + +## Figure 6 +`generate-paper-figure-6.ipynb`, which generates figure 6b, requires the above +forcing data, plus another set of forcings generated using the 1% annual CO2 +increase CM2.6 dataset. Use the same command as above, with `--co2-increase`. + +## Figures 4, 5, 7 +`test-global-fig-4-5-7.ipynb` generates figures 4, 5 and 7, as well as D4 and +D5. For this, the inference step with the trained neural network has to be run +both on the data with and without `--co2-increase`, and then the notebook needs +to be run once with each set. *(The neural net may be trained only once, on data +without `--co2-increase`.)* The paper figures referring to _piControl_ are those +without `--co2-increase` (the control simulation with pre-industrial CO2 +levels), and the figures referring to _1pctCO2_ are those with `--co2-increase` +(a 1% increase per year in CO2 levels for the first 70 years, after which they +remain constant). diff --git a/resources/jupyter-notebooks/paper/generate-paper-figure-1.ipynb b/resources/jupyter-notebooks/paper/generate-paper-figure-1.ipynb new file mode 100644 index 00000000..ba11aadd --- /dev/null +++ b/resources/jupyter-notebooks/paper/generate-paper-figure-1.ipynb @@ -0,0 +1,386 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Code to generate Figure 1\n", + "## Dependencies\n", + "* `ipympl` (`pip install ipympl`)\n", + "* `cmocean` (`pip install cmocean`)\n", + "\n", + "## Steps\n", + "### Locate forcing data\n", + "Ensure that this path points to some existing forcings, which are generated with whatever configuration is expected." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "forcings_path = \"~/sh/gz21/gz21/tmp/generated/forcings/paper-n100\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set subdomains\n", + "We use the subdomains used in the 2021 paper." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To load the net from the paper, use the function load_paper_net().\n" + ] + } + ], + "source": [ + "from gz21_ocean_momentum.common.bounding_box import BoundingBox\n", + "\n", + "bboxes = [BoundingBox( 35, 50, -50, -20),\n", + " BoundingBox(-40, -25, -180, -162),\n", + " BoundingBox(-20, -5, -110, -92),\n", + " BoundingBox( -0, 15, -48, -30)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a plotter which plots the dataset as a map, and the areas for the training, and execute the plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "056637ab52c5467c8542cd4c64a5f00c", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\\begin{table}[]\n", + "\\begin{tabular}{lcccc}\n", + "subdomain & latitude range & longitude range & training & validation \\\\\n", + "0 & 35\\degree, 50\\degree & -50\\degree, -20\\degree & X & X \\\\\n", + "1 & -40\\degree, -25\\degree & -180\\degree, -162\\degree & X & X \\\\\n", + "2 & -20\\degree, -5\\degree & -110\\degree, -92\\degree & X & X \\\\\n", + "3 & 0\\degree, 15\\degree & -48\\degree, -30\\degree & X & X \\\\\n", + "\n", + "\\end{tabular}\n", + "\\end{table}\n", + "\n" + ] + } + ], + "source": [ + "from gz21_ocean_momentum.data.pangeo_catalog import get_patch, get_whole_data\n", + "import gz21_ocean_momentum.analysis.utils as analysisutils\n", + "\n", + "import xarray as xr\n", + "from dask.diagnostics import ProgressBar\n", + "import numpy as np\n", + "import cmocean\n", + "from cartopy.crs import PlateCarree\n", + "from scipy.ndimage import gaussian_filter\n", + "from matplotlib import colors\n", + "import matplotlib.pyplot as plt\n", + "\n", + "#from importlib import reload\n", + "#reload(plt)\n", + "\n", + "data = xr.open_zarr(forcings_path)\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (4, 4 / 1.618)\n", + "\n", + "%matplotlib widget\n", + "#%matplotlib inline #this option does not work with jupyterlab\n", + "\n", + "CATALOG_URL = 'https://raw.githubusercontent.com/pangeo-data/pangeo-datastore\\\n", + "/master/intake-catalogs/master.yaml'\n", + "\n", + "\n", + "class GlobalPlotter:\n", + " \"\"\"General class to make plots for global data. Handles masking of\n", + " continental data + showing a band near coastlines.\"\"\"\n", + "\n", + " def __init__(self, margin: int = 10, cbar: bool = True, ice: bool = True):\n", + " self.mask = self._get_global_u_mask()\n", + " self.margin = margin\n", + " self.cbar = cbar\n", + " self.ticks = dict(x=None, y=None)\n", + " self.ice = ice\n", + "\n", + " @property\n", + " def mask(self):\n", + " return self._mask\n", + "\n", + " @mask.setter\n", + " def mask(self, value):\n", + " self._mask = value\n", + "\n", + " @property\n", + " def borders(self):\n", + " return self._borders\n", + "\n", + " @borders.setter\n", + " def borders(self, value):\n", + " self._borders = value\n", + "\n", + " @property\n", + " def margin(self):\n", + " return self._margin\n", + "\n", + " @margin.setter\n", + " def margin(self, margin):\n", + " self._margin = margin\n", + " self.borders = self._get_continent_borders(self.mask, self.margin)\n", + "\n", + " @property\n", + " def x_ticks(self):\n", + " return self.ticks['x']\n", + "\n", + " @x_ticks.setter\n", + " def x_ticks(self, value):\n", + " self.ticks['x'] = value\n", + "\n", + " @property\n", + " def y_ticks(self):\n", + " return self.ticks['y']\n", + "\n", + " @y_ticks.setter\n", + " def y_ticks(self, value):\n", + " self.ticks['y'] = value\n", + "\n", + " def plot(self, u: xr.DataArray = None, projection_cls=PlateCarree,\n", + " lon: float = -100.0, lat: float = None, ax=None, animated=False,\n", + " borders_color='grey', borders_alpha=1., **plot_func_kw):\n", + " \"\"\"\n", + " Plots the passed velocity component on a map, using the specified\n", + " projection. Uses the instance's mask to set as nan some values.\n", + "\n", + " Parameters\n", + " ----------\n", + " u : xr.DataArray\n", + " Velocity array. The default is None.\n", + " projection : Projection\n", + " Projection used for the 2D plot.\n", + " lon : float, optional\n", + " Central longitude. The default is -100.0.\n", + " lat : float, optional\n", + " Central latitude. The default is None.\n", + "\n", + " Returns\n", + " -------\n", + " None.\n", + "\n", + " \"\"\"\n", + "\n", + " fig = plt.figure()\n", + " projection = projection_cls(lon)\n", + " if ax is None:\n", + " ax = plt.axes(projection=projection)\n", + " mesh_x, mesh_y = np.meshgrid(u['xu_ocean'], u['yu_ocean'])\n", + " if u is not None:\n", + " extra = self.mask.isel(xu_ocean=slice(0, 10))\n", + " extra['xu_ocean'] = extra['xu_ocean'] + 360\n", + " mask = xr.concat((self.mask, extra), dim='xu_ocean')\n", + " mask = mask.interp({k: u.coords[k] for k in ('xu_ocean',\n", + " 'yu_ocean')})\n", + " u = u * mask\n", + " im = ax.pcolormesh(mesh_x, mesh_y, u.values,\n", + " transform=PlateCarree(),\n", + " animated=animated, **plot_func_kw)\n", + " if self.x_ticks is not None:\n", + " ax.set_xticks(self.x_ticks)\n", + " if self.y_ticks is not None:\n", + " ax.set_yticks(self.y_ticks)\n", + " ax.set_global()\n", + " ax.coastlines()\n", + " # \"Gray-out\" near continental locations\n", + " if self.margin > 0:\n", + " extra = self.borders.isel(longitude=slice(0, 10))\n", + " extra['xu_ocean'] = extra['xu_ocean'] + 360\n", + " borders = xr.concat((self.borders, extra), dim='xu_ocean')\n", + " borders = borders.interp({k: u.coords[k]\n", + " for k in ('xu_ocean', 'yu_ocean')})\n", + " borders_cmap = colors.ListedColormap([borders_color, ])\n", + " ax.pcolormesh(mesh_x, mesh_y, borders, animated=animated,\n", + " transform=PlateCarree(), alpha=borders_alpha,\n", + " cmap=borders_cmap)\n", + " # Add locations of ice\n", + " if self.ice:\n", + " ice = self._get_ice_border()\n", + " ice = xr.where(ice, 1., 0.)\n", + " ice = ice.interp({k: u.coords[k] for k in ('xu_ocean',\n", + " 'yu_ocean')})\n", + " ice = xr.where(ice != 0, 1., 0.)\n", + " ice = abs(ice.diff(dim='xu_ocean')) + abs(ice.diff(dim='yu_ocean'))\n", + " ice = xr.where(ice != 0., 1, np.nan)\n", + " ice_cmap = colors.ListedColormap(['black', ])\n", + " ax.pcolormesh(mesh_x, mesh_y, ice, animated=animated,\n", + " transform=PlateCarree(), alpha=0.5,\n", + " cmap=ice_cmap)\n", + " if u is not None and self.cbar:\n", + " cbar = plt.colorbar(im, shrink=0.6)\n", + " cbar.set_label('m/s')\n", + " return ax\n", + "\n", + " @staticmethod\n", + " def _get_global_u_mask(factor: int = 4, base_mask: xr.DataArray = None):\n", + " \"\"\"\n", + " Return the global mask of the low-resolution surface velocities for\n", + " plots. While the coarse-grained velocities might be defined on\n", + " continental points due to the coarse-graining procedures, these are\n", + " not shown as we do not use them -- the mask for the forcing is even\n", + " more restrictive, as it removes any point within some margin of the\n", + " velocities mask.\n", + "\n", + " Parameters\n", + " ----------\n", + " factor : int, optional\n", + " Coarse-graining factor. The default is 4.\n", + "\n", + " base_mask: xr.DataArray, optional\n", + " # TODO\n", + " Not implemented for now.\n", + "\n", + " Returns\n", + " -------\n", + " None.\n", + "\n", + " \"\"\"\n", + " if base_mask is not None:\n", + " mask = base_mask\n", + " else:\n", + " _, grid_info = get_whole_data(CATALOG_URL, 0)\n", + " mask = grid_info['wet']\n", + " mask = mask.coarsen(dict(xt_ocean=factor, yt_ocean=factor))\n", + " mask_ = mask.max()\n", + " mask_ = mask_.where(mask_ > 0.1)\n", + " mask_ = mask_.rename(dict(xt_ocean='xu_ocean', yt_ocean='yu_ocean'))\n", + " return mask_.compute()\n", + "\n", + " @staticmethod\n", + " def _get_ice_border():\n", + " \"\"\"Return an xarray.DataArray that indicates the locations of ice\n", + " in the oceans. \"\"\"\n", + " temperature, _ = get_patch(CATALOG_URL, 1, None, 0,\n", + " 'surface_temp')\n", + " temperature = temperature.rename(dict(xt_ocean='xu_ocean',\n", + " yt_ocean='yu_ocean'))\n", + " temperature = temperature['surface_temp'].isel(time=0)\n", + " ice = xr.where(temperature <= 0., True, False)\n", + " return ice\n", + "\n", + " @staticmethod\n", + " def _get_continent_borders(base_mask: xr.DataArray, margin: int):\n", + " \"\"\"\n", + " Returns a boolean xarray DataArray corresponding to a mask of the\n", + " continents' coasts, which we do not process.\n", + " Hence margin should be set according to the model.\n", + "\n", + " Parameters\n", + " ----------\n", + " mask : xr.DataArray\n", + " Mask taking value 1 where coarse velocities are defined and used\n", + " as input and nan elsewhere.\n", + " margin : int\n", + " Margin imposed by the model used, i.e. number of points lost on\n", + " one side of a square.\n", + "\n", + " Returns\n", + " -------\n", + " mask : xr.DataArray\n", + " Boolean DataArray taking value True for continents.\n", + "\n", + " \"\"\"\n", + " assert margin >= 0, 'The margin parameter should be a non-negative' \\\n", + " ' integer'\n", + " assert base_mask.ndim <= 2, 'Velocity array should have two'\\\n", + " ' dims'\n", + " # Small trick using the guassian filter function\n", + " mask = xr.apply_ufunc(lambda x: gaussian_filter(x, 1., truncate=margin),\n", + " base_mask)\n", + " mask = np.logical_and(np.isnan(mask), ~np.isnan(base_mask))\n", + " mask = mask.where(mask)\n", + " return mask.compute()\n", + "\n", + "plotter = GlobalPlotter(cbar=True, margin=0)\n", + "plotter.x_ticks = np.arange(-150., 151., 50)\n", + "plotter.y_ticks = np.arange(-80., 81., 20)\n", + "\n", + "analysisutils.plot_training_subdomains(plotter, bboxes, bg_variable=data['usurf'].isel(time=0), \\\n", + " facecolor='green', edgecolor='black', linewidth=2, \\\n", + " fill=False, vmin=-0.5, vmax=0.5, lon=0., cmap=cmocean.cm.balance)\n", + "print(analysisutils.training_subdomains_latex(bboxes))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "plt.savefig('figure1b.jpg', dpi=250)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/jupyter-notebooks/generate-paper-figure-6.ipynb b/resources/jupyter-notebooks/paper/generate-paper-figure-6.ipynb similarity index 61% rename from examples/jupyter-notebooks/generate-paper-figure-6.ipynb rename to resources/jupyter-notebooks/paper/generate-paper-figure-6.ipynb index c094c212..3206e616 100644 --- a/examples/jupyter-notebooks/generate-paper-figure-6.ipynb +++ b/resources/jupyter-notebooks/paper/generate-paper-figure-6.ipynb @@ -9,63 +9,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To load the net from the paper, use the function load_paper_net().\n" + ] + } + ], "source": [ - "%cd ../../src/gz21_ocean_momentum\n", - "import os\n", - "from utils import select_experiment, select_run\n", - "from analysis.utils import plot_dataset, GlobalPlotter\n", - "import mlflow\n", - "from mlflow.tracking import MlflowClient\n", + "from gz21_ocean_momentum.analysis.utils import plot_dataset, GlobalPlotter\n", "import xarray as xr\n", "from dask.diagnostics import ProgressBar\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "from numpy.linalg import norm\n", - "\n", - "import cmocean\n", - "cmap_solar = cmocean.cm.solar\n", - "cmap_balance = cmocean.cm.balance\n", - "\n", - "mlruns_path=os.path.join(os.getcwd(), '../../mlruns')\n", - "%env MLFLOW_TRACKING_URI $mlruns_path\n", - "\n", - "plt.rcParams[\"figure.figsize\"] = (4, 4 / 1.618)" + "import cmocean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Script parameters" + "## Steps\n", + "### Locate forcing data\n", + "* `forcings_ctrl_path` should point to forcing data generated using the control CM2.6 dataset.\n", + "* `forcings_1pct_path` should point to forcing data generated using the annual 1% CO2 increase CM2.6 dataset.\n", + "\n", + "See the Jupyter notebook README and the example CLI configs for help selecting/generating these." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "run_control_id = None\n", - "run_1pct_id = None\n", - "var_name = 'vsurf'\n", - "cmap = cmocean.cm.amp\n" + "forcings_ctrl_path = \"~/sh/gz21/gz21/tmp/generated/forcings/paper-fig-1-ctrl-n100\"\n", + "forcings_1pct_path = \"~/sh/gz21/gz21/tmp/generated/forcings/paper-fig-1-ctrl-n100\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Various parameters" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "def select_run_id():\n", - " exp_id, exp_name = select_experiment()\n", - " #experiment_id = mlflow.get_experiment_by_name(exp_name).experiment_id\n", - " cols = ['params.CO2', 'params.factor']\n", - " run = select_run(cols=cols, experiment_ids=(exp_id,))\n", - " return run.run_id" + "plt.rcParams[\"figure.figsize\"] = (4, 4 / 1.618)\n", + "var_name = 'vsurf'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The rest" ] }, { @@ -74,21 +81,11 @@ "metadata": {}, "outputs": [], "source": [ - "ml_client = MlflowClient()\n", - "if not run_control_id:\n", - " print(\"Please select run with CO2=0.\")\n", - " run_control_id = select_run_id()\n", - "if not run_1pct_id:\n", - " print(\"Please select run with CO2=1.\")\n", - " run_1pct_id = select_run_id()\n", + "data_control = xr.open_zarr(forcings_ctrl_path)\n", + "data_1pct = xr.open_zarr(forcings_1pct_path)\n", "\n", - "run_control = mlflow.get_run(run_control_id)\n", - "run_1pct = mlflow.get_run(run_1pct_id)\n", - "\n", - "data_control = xr.open_zarr(ml_client.download_artifacts(run_control_id, 'forcing'))\n", - "data_1pct = xr.open_zarr(ml_client.download_artifacts(run_1pct_id, 'forcing'))\n", "data_control = data_control.rename(dict(xu_ocean='longitude', yu_ocean='latitude'))\n", - "data_1pct = data_1pct.rename(dict(xu_ocean='longitude', yu_ocean='latitude'))\n", + "data_1pct = data_1pct.rename(dict(xu_ocean='longitude', yu_ocean='latitude'))\n", "\n", "# Rescale the forcing\n", "for var in ('S_x', 'S_y'):\n", @@ -134,21 +131,12 @@ "source": [ "#%matplotlib notebook #this option does not work with jupyterlab\n", "%matplotlib widget\n", - "from cartopy.crs import PlateCarree\n", - "from data.pangeo_catalog import get_patch, get_whole_data\n", - "from scipy.ndimage import gaussian_filter\n", - "from matplotlib import colors\n", - "\n", - "\n", - "CATALOG_URL = 'https://raw.githubusercontent.com/pangeo-data/pangeo-datastore\\\n", - "/master/intake-catalogs/master.yaml'\n", - "\n", "\n", "plotter = GlobalPlotter(cbar=True, margin=4)\n", "plotter.x_ticks = np.arange(-150., 151., 50)\n", "plotter.y_ticks = np.arange(-80., 81., 20)\n", "\n", - "ax=plotter.plot(diff, vmin=-0.05, vmax=0.05, cmap=cmocean.cm.delta, lon=0., colorbar_label='m/s')\n" + "ax=plotter.plot(diff, vmin=-0.05, vmax=0.05, cmap=cmocean.cm.delta, lon=0., colorbar_label='m/s')" ] }, { @@ -163,7 +151,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "uv_plotter = plotter\n", @@ -177,13 +167,6 @@ " return array\n", "apply_complete_mask(r_diff).sel(latitude=slice(-60, 60)).mean().compute()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -202,7 +185,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/resources/jupyter-notebooks/paper/test-global-fig-4-5-7.ipynb b/resources/jupyter-notebooks/paper/test-global-fig-4-5-7.ipynb new file mode 100644 index 00000000..a5448c1b --- /dev/null +++ b/resources/jupyter-notebooks/paper/test-global-fig-4-5-7.ipynb @@ -0,0 +1,4334 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test on Global scale (Code to generate figures 4, 5, 7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dependencies\n", + "* `ipympl` (`pip install ipympl`)\n", + "* `cmocean` (`pip install cmocean`)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that this notebook is intended to be run with both the \"control\" CM2.6 simulation run dataset, and the 1% annual CO2 increase one.　To produce the paper figures:\n", + "\n", + "* run the data step to generate two sets of training data, one with `--co2-increase` (1ptCO2) and one without (control)\n", + "* train your model with the control dataset\n", + "* for each set of training data, predict with your trained model. The predictions should override the forcings we calculated (clumsy, but it's fine).\n", + "* load the two training data and prediction pairs in this notebook, making edits to the configuration part below as required" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## To-dos\n", + "* Clean up grid data downloading -- should be a utility in our library for this." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Descriptive name for the CM2.6 data set being used in this notebook execution.\n", + "# This is used to create file names for exporting figures.\n", + "# For example, you may use `control` and `1pct`.\n", + "cm26_sim_run = \"ctrl\"\n", + "\n", + "# path to zarr (folder) holding the computed forcings output from a data step invocation\n", + "# (these may be downloaded from Hugging Face: see the project root readme)\n", + "forcings_computed_path = \"~/sh/gz21/gz21/tmp/generated/forcings/paper-n100\"\n", + "\n", + "# path to zarr (folder) holding the predicted forcings output from an inference step invocation\n", + "forcings_predicted_path = \"~/sh/gz21/gz21/tmp/generated/predictions/1701268939\"\n", + "\n", + "# Slice 0-x from time dimension from both computing and predicted forcings.\n", + "# Ensure that both of your datasets are long enough!\n", + "time_slice_to = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#%matplotlib notebook #This option does not work in Jupyterlab\n", + "%matplotlib widget\n", + "\n", + "# See https://github.com/m2lines/gz21_ocean_momentum/blob/main/docs/data.md for an explanation\n", + "# The environment variable does not need setting if you place the credentials file at ~/.config/gcloud/application_default_credentials.json .\n", + "\n", + "# Or you can set it by uncommenting the below with your path to the credentials file\n", + "# %env GOOGLE_APPLICATION_CREDENTIALS my_path/application_default_credentials.json" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To load the net from the paper, use the function load_paper_net().\n" + ] + } + ], + "source": [ + "import xarray as xr\n", + "import numpy as np\n", + "import dask.array as da\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from gz21_ocean_momentum.utils import select_experiment, select_run\n", + "from gz21_ocean_momentum.analysis.utils import (plot_dataset, GlobalPlotter, anomalies,\n", + " download_data_pred, plot_time_series, apply_complete_mask)\n", + "from gz21_ocean_momentum.data.pangeo_catalog import get_whole_data\n", + "from gz21_ocean_momentum.data.xrtransforms import SeasonalStdizer, TargetedTransform, ScalingTransform\n", + "from dask.diagnostics import ProgressBar\n", + "from models.submodels import transform3\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cmocean" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup the plotter\n", + "plt.rcParams[\"figure.figsize\"] = (4, 4 / 1.618)\n", + "\n", + "import gz21_ocean_momentum.analysis as analysis\n", + "GlobalPlotter = analysis.utils.GlobalPlotter\n", + "uv_plotter = GlobalPlotter()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "old_plot = GlobalPlotter.plot\n", + "\n", + "def new_plot_func(self, name: str, *args, **kargs):\n", + " data = xr.Dataset({name: args[0]})\n", + " old_plot(self, *args, **kargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This downloads some information about the grid, used later on" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "CATALOG_URL = 'https://raw.githubusercontent.com/pangeo-data/pangeo-datastore\\\n", + "/master/intake-catalogs/master.yaml'\n", + "data = get_whole_data(CATALOG_URL, 0)\n", + "grid_info = data[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "data = xr.open_zarr(forcings_computed_path)\n", + "\n", + "# various transforms\n", + "data = data.rename(xu_ocean=\"longitude\", yu_ocean=\"latitude\")\n", + "data[\"S_x\"] = data[\"S_x\"] * 1e7\n", + "data[\"S_y\"] = data[\"S_y\"] * 1e7\n", + "\n", + "pred = xr.open_zarr(forcings_predicted_path)\n", + "\n", + "# various transforms\n", + "if \"S_xpred\" in pred.keys():\n", + " # \"for compatibility with old version\"\n", + " pred = pred.rename(S_xpred=\"S_x\", S_ypred=\"S_y\")\n", + "pred[\"S_xscale\"] = 1 / pred[\"S_xscale\"]\n", + "pred[\"S_yscale\"] = 1 / pred[\"S_yscale\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

Menu

MSE and R²   Correlation  Variance of forcing  Comparison of distributions  QQ-plot  Bias analysis  Time series plots" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# generate a nice little in-line menu for navigating through the rest of this notebook\n", + "from IPython.core.display import HTML\n", + "html = '

Menu

'\n", + "html += 'MSE and R²  '\n", + "html += ' Correlation  '\n", + "html += 'Variance of forcing  '\n", + "html += 'Comparison of distributions  '\n", + "html += 'QQ-plot  '\n", + "html += 'Bias analysis  '\n", + "html += 'Time series plots'\n", + "HTML(html)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:    (time: 100, latitude: 609, longitude: 900)\n",
+       "Coordinates:\n",
+       "  * time       (time) object 0181-01-01 12:00:00 ... 0181-04-10 12:00:00\n",
+       "  * longitude  (longitude) float64 -279.7 -279.3 -278.9 ... 79.05 79.45 79.85\n",
+       "  * latitude   (latitude) float64 -79.93 -79.76 -79.59 ... 79.55 79.71 79.88\n",
+       "Data variables:\n",
+       "    S_x        (time, latitude, longitude) float64 dask.array<chunksize=(1, 609, 900), meta=np.ndarray>\n",
+       "    S_y        (time, latitude, longitude) float64 dask.array<chunksize=(1, 609, 900), meta=np.ndarray>\n",
+       "    usurf      (time, latitude, longitude) float64 dask.array<chunksize=(1, 609, 900), meta=np.ndarray>\n",
+       "    vsurf      (time, latitude, longitude) float64 dask.array<chunksize=(1, 609, 900), meta=np.ndarray>
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 100, latitude: 609, longitude: 900)\n", + "Coordinates:\n", + " * time (time) object 0181-01-01 12:00:00 ... 0181-04-10 12:00:00\n", + " * longitude (longitude) float64 -279.7 -279.3 -278.9 ... 79.05 79.45 79.85\n", + " * latitude (latitude) float64 -79.93 -79.76 -79.59 ... 79.55 79.71 79.88\n", + "Data variables:\n", + " S_x (time, latitude, longitude) float64 dask.array\n", + " S_y (time, latitude, longitude) float64 dask.array\n", + " usurf (time, latitude, longitude) float64 dask.array\n", + " vsurf (time, latitude, longitude) float64 dask.array" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9d1a9b6371b1446db16376a006bb0a26", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "uv_plotter.plot(data['S_x'].isel(time=70), lon=0., projection_cls = ccrs.PlateCarree,\n", + " colorbar_label='m/s', cmap=cmocean.cm.delta, vmin=-1, vmax=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is the plot shown in Figure 5 of the paper" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "591966b5f8f941ed9c06b86c72ca6bbb", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_time_series(data, pred, longitude: float, latitude: float, time: slice,\n", + " std: bool = True, true: bool = True):\n", + " plt.figure()\n", + " xs = np.arange(time.start, time.stop, time.step)\n", + " truth = data['S_x'].sel(longitude=longitude, latitude=latitude,\n", + " method='nearest').isel(time=time)\n", + " pred_mean = pred['S_x'].sel(longitude=longitude, latitude=latitude,\n", + " method='nearest').isel(time=time)\n", + " pred_std = pred['S_xscale'].sel(longitude=longitude, latitude=latitude,\n", + " method='nearest').isel(time=time)\n", + " if true:\n", + " plt.plot(xs, truth, 'darkblue')\n", + " plt.plot(xs, pred_mean, 'darkorange')\n", + " if std:\n", + " plt.plot(xs, pred_mean + 1.96 * pred_std, 'g--', linewidth=1)\n", + " plt.plot(xs, pred_mean - 1.96 * pred_std, 'g--', linewidth=1)\n", + " plt.ylabel(r'$1e^{-7}m/s^2$')\n", + " _ = plt.xlabel('days')\n", + " \n", + "time_slice=slice(0, time_slice_to)\n", + "plt.rcParams[\"figure.figsize\"] = (4 * 2, 4 * 2 / 1.618)\n", + "\n", + "plot_time_series(data, pred, longitude=-60, latitude=30, time=time_slice, std=True, true=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "plt.savefig(f\"timeseries-cm26-{cm26_sim_run}.jpg\", dpi=400)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MSE and R²" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we compute the seasonal (monthly) means of the data. This will be used later in some of the metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "forcing_vars = ['S_x', 'S_y']\n", + "errors = pred[forcing_vars] - data[forcing_vars]\n", + "errors_cycle = anomalies(data[forcing_vars])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, mse is the time-mean MSE of the mean component of our predicted forcing, mse_month is the variance of the residuals of the data after removing monthly variation." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[########################################] | 100% Completed | 7.10 sms\n", + "[########################################] | 100% Completed | 7.75 sms\n", + "[########################################] | 100% Completed | 3.91 sms\n" + ] + } + ], + "source": [ + "mse = (errors**2).mean(dim='time')\n", + "mse_cycle = (errors_cycle**2).mean(dim='time')\n", + "amplitudes = (data[forcing_vars]**2).mean(dim='time')\n", + "\n", + "with ProgressBar():\n", + " mse = mse.compute()\n", + " mse_cycle = mse_cycle.compute()\n", + " amplitudes = amplitudes.compute()\n", + "mse['total'] = mse['S_x'] + mse['S_y']\n", + "mse_cycle['total'] = mse_cycle['S_x'] + mse_cycle['S_y']\n", + "amplitudes['total'] = amplitudes['S_x'] + amplitudes['S_y']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MSE plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is Figure 4a of the paper" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d620f5394c5645989b492f8d2c20c35e", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (4*2, 4 * 2 / 1.618)\n", + "x = uv_plotter.plot(mse['total'], lon=0., cmap=cmocean.cm.dense,\n", + " colorbar_label=r'$1e^{-14}m^2/s^4$', norm=matplotlib.colors.LogNorm(vmin=0.01, vmax=10))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "plt.savefig(f\"mse-cm26-{cm26_sim_run}.jpg\", dpi=400)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### R² plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is Figure 4b of the paper" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d258fd430efd41a98c5295c47da41295", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib\n", + "rsquared = 1 - mse / amplitudes\n", + "rsquared_cycles = 1 - mse / mse_cycle\n", + "mse_ratio_2 = 1 - mse_cycle / amplitudes\n", + "uv_plotter.plot(rsquared_cycles['total'], cmap=cmocean.cm.delta, lon=0., norm=matplotlib.colors.LogNorm(vmin=0.5, vmax=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "plt.savefig(f\"r2-month-cm26-{cm26_sim_run}.jpg\", dpi=400)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scalar R²" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We limit the range to latitudes -60 to 60, and we apply a mask that discards points near continents according to the mask used in the plotter (the points shown in gray on the maps in the paper). This is why we define these quantities \"to_scalar\", in order to not account for points near continents in the computation of the scalar R²." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "latitudes = slice(-60, 60)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "mse_to_scalar = apply_complete_mask(mse, pred, uv_plotter)\n", + "mse_cycle_to_scalar = apply_complete_mask(mse_cycle, pred, uv_plotter)\n", + "amplitudes_to_scalar = apply_complete_mask(amplitudes, pred, uv_plotter)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dimensions: ()\n", + "Data variables:\n", + " S_x float64 0.8546\n", + " S_y float64 0.8717\n", + " total float64 0.8634\n", + "\n", + "Dimensions: ()\n", + "Data variables:\n", + " S_x float64 0.7717\n", + " S_y float64 0.7863\n", + " total float64 0.779\n" + ] + } + ], + "source": [ + "with ProgressBar():\n", + " mse_scalar = mse_to_scalar.sel(latitude=latitudes).sum().compute()\n", + " mse_cycle_scalar = mse_cycle_to_scalar.sel(latitude=latitudes).sum().compute()\n", + " amplitudes_scalar = amplitudes_to_scalar.sel(latitude=latitudes).sum().compute()\n", + " rsquared_scalar_cycle = 1 - mse_scalar / mse_cycle_scalar\n", + " rsquared_scalar = 1 - mse_scalar / amplitudes_scalar\n", + "print(rsquared_scalar)\n", + "print(rsquared_scalar_cycle)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Correlation between true forcing and mean component of the prediction " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "forcing_vars = ['S_x', 'S_y']\n", + "data_anomaly = anomalies(data[forcing_vars])\n", + "pred_anomaly = anomalies(pred[forcing_vars])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "std_data = data_anomaly.std(dim='time')\n", + "std_pred = pred_anomaly.std(dim='time')\n", + "corr = ((data_anomaly * pred_anomaly).mean(dim='time') - data_anomaly.mean(dim='time') * pred_anomaly.mean(dim='time')) / (std_data * std_pred)\n", + "# corr_s_y = xr.corr(data.S_y, pred.S_y, dim='time')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[######################## ] | 62% Completed | 20.75 ss" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/raehik/proj/work/2020-ukc-camfort-iccs/iccs/proj/gz21/gz21/venv/lib/python3.11/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n", + " x = np.divide(x1, x2, out)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[########################################] | 100% Completed | 32.95 s\n" + ] + } + ], + "source": [ + "with ProgressBar():\n", + " corr = corr.compute()\n", + " # corr_s_y = corr_s_y.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "36f65d8408d3417a9dd6ad2bed5e2cf8", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "uv_plotter.plot(corr['S_x'], vmin=0.7, vmax=1., lon=0., cmap=cmocean.cm.balance_r)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "plt.savefig(f\"corr-x-cm26-{cm26_sim_run}.jpg\", dpi=400)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Variance of norm of subgrid momentum forcing " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[########################################] | 100% Completed | 6.39 sms\n", + "[ ] | 0% Completed | 630.87 us" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/raehik/proj/work/2020-ukc-camfort-iccs/iccs/proj/gz21/gz21/venv/lib/python3.11/site-packages/dask/array/numpy_compat.py:51: RuntimeWarning: invalid value encountered in divide\n", + " x = np.divide(x1, x2, out)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[########################################] | 100% Completed | 7.79 ss\n" + ] + } + ], + "source": [ + "norm_S = np.sqrt(data['S_x']**2 + data['S_y']**2)\n", + "norm_Spred = np.sqrt(pred['S_x']**2 + pred['S_y']**2)\n", + "var_norm_S = norm_S.var(dim='time')\n", + "var_norm_Spred = norm_Spred.var(dim='time')\n", + "with ProgressBar():\n", + " var_norm_S = var_norm_S.compute()\n", + " var_norm_Spred = var_norm_Spred.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "24640fe0e3cc4f86a29daf031a6f9f3b", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "uv_plotter.plot(var_norm_S, cmap=cmocean.cm.dense, lon=0., colorbar_label=r'$1e^{-14}m^2s^{-4}$', norm=matplotlib.colors.LogNorm(vmin=0.01, vmax=10,))\n", + "plt.savefig(f\"variance-forcing-control-cm26-data-{cm26_sim_run}.jpg\", dpi=400)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8303d87d7065423bba2868354f4a5474", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "uv_plotter.plot(var_norm_Spred, cmap=cmocean.cm.dense, lon=0., colorbar_label=r'$1e^{-14}m^2s^{-4}$', norm=matplotlib.colors.LogNorm(vmin=0.01, vmax=10,))\n", + "plt.savefig(f\"variance-forcing-control-cm26-pred-{cm26_sim_run}.jpg\", dpi=400)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare distributions of true and stochastic simulated forcing" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "forcing_vars = ['S_x', 'S_y']\n", + "scale_vars = ['S_xscale', 'S_yscale']\n", + "\n", + "pred_ = apply_complete_mask(pred, pred, uv_plotter)\n", + "pred_scale = pred_[scale_vars].rename(dict(S_xscale='S_x', S_yscale='S_y'))\n", + "pred_ = pred_[forcing_vars]\n", + "data_ = apply_complete_mask(data[forcing_vars], pred, uv_plotter)\n", + "\n", + "# Subsample the data\n", + "time_slice = slice(None, None, 1)\n", + "lon_slice = slice(None, None, 2)\n", + "lat_slice = slice(-60, 60, 2)\n", + "pred_ = pred_.sel(longitude=lon_slice, latitude=lat_slice).isel(time=time_slice)\n", + "pred_scale = pred_scale.sel(longitude=lon_slice, latitude=lat_slice).isel(time=time_slice)\n", + "data_ = data_.sel(longitude=lon_slice, latitude=lat_slice).isel(time=time_slice)\n", + "\n", + "# Standardized residuals\n", + "residuals = (data_ - pred_) / pred_scale" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "assert np.all(np.isnan(data_['S_x']) == np.isnan(pred_['S_x'])), \"Not the same number of points!\"\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we do a stochastic simulation of the forcing given the parameters of the Gaussian distribution at each location and each time point" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "shape = tuple(pred_.dims.values())\n", + "epsilons = dict(x=np.random.randn(*shape), y=np.random.randn(*shape))\n", + "epsilons = xr.Dataset(dict(S_x=(pred_.dims, epsilons['x']), S_y=(pred_.dims, epsilons['y'])))\n", + "pred_stochastic = pred_ + pred_scale * epsilons" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:    (time: 100, latitude: 189, longitude: 450)\n",
+       "Coordinates:\n",
+       "  * time       (time) object 0181-01-01 12:00:00 ... 0181-04-10 12:00:00\n",
+       "  * longitude  (longitude) float64 -279.7 -278.9 -278.1 ... 77.85 78.65 79.45\n",
+       "  * latitude   (latitude) float64 -59.9 -59.49 -59.08 ... 59.03 59.44 59.85\n",
+       "Data variables:\n",
+       "    S_x        (time, latitude, longitude) float64 dask.array<chunksize=(1, 189, 450), meta=np.ndarray>\n",
+       "    S_y        (time, latitude, longitude) float64 dask.array<chunksize=(1, 189, 450), meta=np.ndarray>
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 100, latitude: 189, longitude: 450)\n", + "Coordinates:\n", + " * time (time) object 0181-01-01 12:00:00 ... 0181-04-10 12:00:00\n", + " * longitude (longitude) float64 -279.7 -278.9 -278.1 ... 77.85 78.65 79.45\n", + " * latitude (latitude) float64 -59.9 -59.49 -59.08 ... 59.03 59.44 59.85\n", + "Data variables:\n", + " S_x (time, latitude, longitude) float64 dask.array\n", + " S_y (time, latitude, longitude) float64 dask.array" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:    (time: 100, latitude: 189, longitude: 440)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 -59.9 -59.49 -59.08 ... 59.03 59.44 59.85\n",
+       "  * longitude  (longitude) float64 -275.7 -274.9 -274.1 ... 73.85 74.65 75.45\n",
+       "  * time       (time) object 0181-01-01 12:00:00 ... 0181-04-10 12:00:00\n",
+       "Data variables:\n",
+       "    S_x        (time, latitude, longitude) float64 dask.array<chunksize=(32, 189, 440), meta=np.ndarray>\n",
+       "    S_y        (time, latitude, longitude) float64 dask.array<chunksize=(32, 189, 440), meta=np.ndarray>
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 100, latitude: 189, longitude: 440)\n", + "Coordinates:\n", + " * latitude (latitude) float64 -59.9 -59.49 -59.08 ... 59.03 59.44 59.85\n", + " * longitude (longitude) float64 -275.7 -274.9 -274.1 ... 73.85 74.65 75.45\n", + " * time (time) object 0181-01-01 12:00:00 ... 0181-04-10 12:00:00\n", + "Data variables:\n", + " S_x (time, latitude, longitude) float64 dask.array\n", + " S_y (time, latitude, longitude) float64 dask.array" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred_" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[########################################] | 100% Completed | 527.31 ms\n", + "[########################################] | 100% Completed | 4.56 sms\n", + "[########################################] | 100% Completed | 644.09 ms\n", + "[########################################] | 100% Completed | 2.39 sms\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 0, '$1e^{-7}m/s^2$')" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e471a333fb314eea935a0c5c4c1b309c", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bins = np.arange(-20, 21, 1)\n", + "\n", + "# assert np.all(np.isnan(data_['S_x']) == np.isnan(pred_stochastic['S_x'])), \"Not the same number of points!\"\n", + "\n", + "plt.figure()\n", + "plt.subplot(121)\n", + "with ProgressBar():\n", + " plt.hist(np.ravel(data_['S_x']), bins=bins, density=True, log=True, alpha=0.5, color='purple')\n", + " plt.hist(np.ravel(pred_stochastic['S_x']), bins=bins, density=True, log=True, alpha=0.5, color='green')\n", + "plt.title('Zonal component')\n", + "plt.xlabel(r'$1e^{-7}m/s^2$')\n", + "plt.ylabel('log density')\n", + "plt.subplot(122)\n", + "with ProgressBar():\n", + " plt.hist(np.ravel(data_['S_y']), bins=bins, density=True, log=True, alpha=0.5, color='purple')\n", + " plt.hist(np.ravel(pred_stochastic['S_y']), bins=bins, density=True, log=True, alpha=0.5, color='green')\n", + "plt.title('Meridional component')\n", + "plt.xlabel(r'$1e^{-7}m/s^2$')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "plt.savefig(f\"forcing-dist-cm26-{cm26_sim_run}.jpg\", dpi=400)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[########################################] | 100% Completed | 2.83 ss\n", + "[########################################] | 100% Completed | 2.82 ss\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c38d10e79aef49aca45f02e8bdcd9db8", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "bins=np.arange(-6, 6, 0.025)\n", + "from scipy.stats import norm\n", + "with ProgressBar():\n", + " plt.subplot(121)\n", + " plt.hist(np.ravel(residuals['S_x'].compute()), bins=bins, density=True, color='orange')\n", + " plt.plot(bins, (norm.pdf(bins)), 'r')\n", + " plt.title('Meridional component')\n", + " plt.subplot(122)\n", + " plt.hist(np.ravel(residuals['S_y'].compute()), bins=bins, density=True, color='orange')\n", + " plt.plot(bins, (norm.pdf(bins)), 'r')\n", + " plt.title('Zonal component')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "plt.savefig('normalized_residuals_ditribution.jpg', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### QQ plot" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "quantiles = np.exp(np.linspace(-5, 5, 100)) / (1 + np.exp(np.linspace(-5, 5, 100)))\n", + "quantiles = np.linspace(0.01, 0.99, 99)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 , 0.11,\n", + " 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21, 0.22,\n", + " 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32, 0.33,\n", + " 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43, 0.44,\n", + " 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54, 0.55,\n", + " 0.56, 0.57, 0.58, 0.59, 0.6 , 0.61, 0.62, 0.63, 0.64, 0.65, 0.66,\n", + " 0.67, 0.68, 0.69, 0.7 , 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77,\n", + " 0.78, 0.79, 0.8 , 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88,\n", + " 0.89, 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quantiles" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[########################################] | 100% Completed | 2.80 ss\n", + "[########################################] | 100% Completed | 2.41 ss\n" + ] + } + ], + "source": [ + "with ProgressBar():\n", + " quantiles_x = np.nanquantile(residuals['S_x'].compute(), quantiles)\n", + " quantiles_y = np.nanquantile(residuals['S_y'].compute(), quantiles)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import norm, cauchy, t\n", + "quantiles_norm = norm.ppf(quantiles)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Meridional component')" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ab06987a2eb741f99a0d204865cee804", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "reference = quantiles_norm\n", + "plt.subplot(121)\n", + "plt.plot(reference, quantiles_x, 'x')\n", + "plt.plot(reference, reference, 'g')\n", + "plt.axis([None, None, -5, 5])\n", + "plt.title('Zonal component')\n", + "plt.subplot(122)\n", + "plt.plot(reference, quantiles_y, 'x')\n", + "plt.plot(reference, reference, 'g')\n", + "plt.axis([None, None, -5, 5])\n", + "plt.title('Meridional component')" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "plt.savefig(f\"normalized-residuals-qq-cm26-{cm26_sim_run}.jpg\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (IGNORE THIS) Another way to do it" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "apply_complete_mask() missing 2 required positional arguments: 'pred' and 'uv_plotter'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[45], line 6\u001b[0m\n\u001b[1;32m 3\u001b[0m lat\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mslice\u001b[39m(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m40\u001b[39m, \u001b[38;5;241m40\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 4\u001b[0m time_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mslice\u001b[39m(\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m1000\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m true \u001b[38;5;241m=\u001b[39m \u001b[43mapply_complete_mask\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mS_x\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m pred_mean \u001b[38;5;241m=\u001b[39m apply_complete_mask(pred[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mS_x\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 8\u001b[0m pred_std \u001b[38;5;241m=\u001b[39m apply_complete_mask(pred[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mS_xscale\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", + "\u001b[0;31mTypeError\u001b[0m: apply_complete_mask() missing 2 required positional arguments: 'pred' and 'uv_plotter'" + ] + } + ], + "source": [ + "from scipy.stats import norm\n", + "lon = slice(None, None, 1)\n", + "lat= slice(-40, 40, 1)\n", + "time_slice = slice(None, 1000, 1)\n", + "\n", + "true = apply_complete_mask(data['S_x'])\n", + "pred_mean = apply_complete_mask(pred['S_x'])\n", + "pred_std = apply_complete_mask(pred['S_xscale'])\n", + "\n", + "def my_transform(x , mean, precision):\n", + " cdf = lambda x: norm.cdf((x - mean) * precision)\n", + " return cdf(x)\n", + "\n", + "v = xr.apply_ufunc(my_transform, true, pred_mean, 1 / pred_std,\n", + " dask='parallelized', output_dtypes=[np.float64, ])\n", + "residuals = (true - pred_mean) / pred_std\n", + "residuals = residuals.sel(longitude=lon, latitude=lat).isel(time=time_slice)\n", + "v = v.sel(longitude=lon, latitude=lat).isel(time=time_slice)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with ProgressBar():\n", + " q2 = np.nanquantile(residuals, quantiles)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "norm_quantiles = norm.ppf(quantiles)\n", + "plt.figure()\n", + "plt.plot(norm_quantiles, q2)\n", + "plt.plot(norm_quantiles, norm_quantiles)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "q = np.nanquantile(v, quantiles)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(quantiles, q, 'x')\n", + "plt.plot(quantiles, quantiles)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## (IGNORE THIS) Likelihood plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import norm\n", + "lon = slice(None, None, 1)\n", + "lat= slice(-80, 80, 1)\n", + "time_slice = slice(None, None, 1)\n", + "\n", + "true = data['S_x'].isel(time=time_slice)\n", + "pred_mean = pred['S_x'].isel(time=time_slice)\n", + "pred_std = pred['S_xscale'].isel(time=time_slice)\n", + "\n", + "residuals = (true - pred_mean) / pred_std\n", + "log_lkh = xr.apply_ufunc(lambda x: np.log(norm.pdf(x)), residuals, dask='parallelized', output_dtypes=[np.float64,])\n", + "with ProgressBar():\n", + " log_lkh = log_lkh.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "true" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uv_plotter.margin=10\n", + "uv_plotter.plot(-log_lkh.mean(dim='time'), vmin=0, vmax=2.5)\n", + "apply_complete_mask(-log_lkh, pred, uv_plotter).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lat= slice(-40, 40, 1)\n", + "\n", + "with ProgressBar():\n", + " lkh_mean = lkh.sel(latitude=lat).isel(time=time_slice).mean().compute()\n", + "lkh_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bias analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "forcing_vars = ['S_x', 'S_y']\n", + "errors = pred[forcing_vars] - data[forcing_vars]\n", + "map_errors = errors.mean(dim='time')\n", + "with ProgressBar():\n", + " map_errors = map_errors.compute()\n", + " absolute = (abs(data[forcing_vars])).mean(dim='time').compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "relative_bias = (map_errors / absolute).compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with ProgressBar():\n", + " uv_plotter.plot(abs(relative_bias['S_x']), cmap=cmocean.cm.delta, lon=0., vmin=0.01, vmax=1, norm=matplotlib.colors.LogNorm())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.savefig(f\"relative-bias-cm26-{cm26_sim_run}.jpg\", dpi=400)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Time series plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (4*2, 4*2 / 1.618)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "points = [(-60, 30), (-104, -20), (-129, 29)]\n", + "\n", + "plot_time_series(data, pred, *points[1], slice(0, 300), std=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.savefig(f\"timeseries-quescient-cm26-{cm26_sim_run}.jpg\", dpi=400)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# (IGNORE THIS) Comparison of quantiles" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pred" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from analysis.base import QuantileCompare\n", + "\n", + "with ProgressBar():\n", + " qq = QuantileCompare()\n", + " qq.quantiles = [0.5, 0.25, 0.5, 0.75, 0.95]\n", + " qq.data = ((pred['S_x']-data['S_x']) / pred['S_xscale']).isel(time=slice(None, None, 1)).compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with ProgressBar():\n", + " q_0_75 = qq.data_quantiles[0.75]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import norm\n", + "norm.ppf(0.75)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy.crs as ccrs\n", + "import cmocean\n", + "cmap = cmocean.cm.balance\n", + "uv_plotter.plot(np.abs(q_0_75 - 0.6745) < 0.05, vmin=0, vmax=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uv_plotter.plot(data['S_x'].isel(time=0), cmap=cmap_balance, vmin=-2, vmax=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot((data['S_x'].sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800))).data)\n", + "plt.plot((pred['S_xpred'].sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800))).data)\n", + "plt.plot((pred['S_xpred'].sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800)) + 1.96 * pred['S_xscale'].sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800))).data, '--')\n", + "plt.plot((pred['S_xpred'].sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800)) - 1.96 * pred['S_xscale'].sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800))).data, '--')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "r = qq.data.sel(longitude=-140, latitude=30, method='nearest').isel(time=slice(0, 800)).compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.quantile(r, [0.25, 0.5, 0.75])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.hist(np.ravel(q_0_25.data), bins=np.arange(-2, 2, 0.1))\n", + "plt.title('Histogram of 0.25 quantiles of normalized residuals')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.quantile(r.data, [0.25, 0.5, 0.75])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.max(q_0_5).compute()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Snapshot of the forcing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy.crs as ccrs\n", + "cmap = cmocean.cm.balance\n", + "s_x = v\n", + "\n", + "ax = plt.axes(projection=ccrs.PlateCarree(-100.))\n", + "mesh_x, mesh_y = np.meshgrid(s_x['longitude'], s_x['latitude'])\n", + "mesh_x = mesh_x + 360\n", + "ax.pcolormesh(mesh_x, mesh_y, s_x.values, vmin=-4, vmax=4, transform = ccrs.PlateCarree(), cmap=cmap, alpha=1)\n", + "mesh_x, mesh_y = np.meshgrid(borders['longitude'], borders['latitude'])\n", + "mesh_x = mesh_x + 360\n", + "ax.pcolormesh(mesh_x, mesh_y, borders * 1., transform=ccrs.PlateCarree(), alpha=0.1)\n", + "ax.set_global()\n", + "ax.coastlines()\n", + "ax.set_xticks(np.arange(-180, 181, 20))\n", + "ax.set_yticks(np.arange(-80,81, 20))\n", + "#ax.set_extent([-20, 20, -20, 20])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib notebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import animation\n", + "cmap = cmocean.cm.amp\n", + "import cartopy.crs as ccrs\n", + "\n", + "fig = plt.figure()\n", + "\n", + "try:\n", + " del video\n", + "except:\n", + " pass\n", + "\n", + "uv_plotter.x_ticks = None\n", + "uv_plotter.y_ticks = None\n", + "\n", + "def animate(i):\n", + " print(i)\n", + " v = pred['S_xscale'].isel(time=i)\n", + " uv_plotter.plot(v, projection_cls = ccrs.Orthographic, lon=(i/5)%360, cmap=cmap, vmin=0, vmax=2, animated=True)\n", + " \n", + "ani = animation.FuncAnimation(fig, animate, frames = 500, interval = 50)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "matplotlib.rcParams['animation.embed_limit'] = 100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ani.save('forcing_pred_mean.mp4', fps=60, dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import HTML\n", + "video = ani.to_html5_video()\n", + "HTML(video)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "e = (merged['S_xpred'] - merged['S_x']) / merged['S_xscale']\n", + "d = (e**2).mean(dim='time').compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "d_ = abs(d-1)\n", + "d_ = d_.interp(mask_.coords)\n", + "d_ = xr.where(borders, -1000, d_)\n", + "d_ = xr.where(mask__, d_, np.nan)\n", + "d_ = d_.interp(latitude = np.arange(-80, 80, 0.1), longitude = np.arange(-279.9, 80.1, 0.1))\n", + "d_['longitude'] = d_['longitude'] + 100.\n", + "\n", + "ax = plt.axes(projection=ccrs.PlateCarree())\n", + "d_.plot.imshow(x='longitude', y='latitude', ax=ax, vmin=0, vmax=2, cmap=cmap,\n", + " transform = ccrs.PlateCarree(-100.))\n", + "ax.set_global()\n", + "ax.coastlines()\n", + "x_ticks = plt.xticks(np.arange(-180, 181, 20))\n", + "y_ticks = plt.yticks(np.arange(-80, 81, 20))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/jupyter-notebooks/train_results.ipynb b/resources/jupyter-notebooks/train_results.ipynb similarity index 100% rename from examples/jupyter-notebooks/train_results.ipynb rename to resources/jupyter-notebooks/train_results.ipynb diff --git a/resources/slurm-jobs/README.md b/resources/slurm-jobs/README.md new file mode 100644 index 00000000..f56b1170 --- /dev/null +++ b/resources/slurm-jobs/README.md @@ -0,0 +1,2 @@ +# Example Slurm jobs +Run on CSD3. diff --git a/resources/slurm-jobs/data.sh b/resources/slurm-jobs/data.sh new file mode 100755 index 00000000..0e2229d0 --- /dev/null +++ b/resources/slurm-jobs/data.sh @@ -0,0 +1,125 @@ +#!/bin/bash +#! +#! Example SLURM job script for Peta4-IceLake (Ice Lake CPUs, HDR200 IB) +#! Last updated: Sat Jul 31 15:39:45 BST 2021 +#! + +#!############################################################# +#!#### Modify the options in this section as appropriate ###### +#!############################################################# + +#! sbatch directives begin here ############################### +#! Name of the job: +#SBATCH -J cpujob +#! Which project should be charged: +#SBATCH -A ICCS-SL3-CPU +#SBATCH -p icelake +#! How many whole nodes should be allocated? +#SBATCH --nodes=1 +#! How many (MPI) tasks will there be in total? (<= nodes*76) +#! The Ice Lake (icelake) nodes have 76 CPUs (cores) each and +#! 3380 MiB of memory per CPU. +#SBATCH --ntasks=4 +##SBATCH --mem=16G +#SBATCH --time=00:02:00 +#! What types of email messages do you wish to receive? +#SBATCH --mail-type=NONE +#! Uncomment this to prevent the job from being requeued (e.g. if +#! interrupted by node failure or system downtime): +##SBATCH --no-requeue + +#! sbatch directives end here (put any additional directives above this line) + +#! Notes: +#! Charging is determined by cpu number*walltime. +#! The --ntasks value refers to the number of tasks to be launched by SLURM only. This +#! usually equates to the number of MPI tasks launched. Reduce this from nodes*76 if +#! demanded by memory requirements, or if OMP_NUM_THREADS>1. +#! Each task is allocated 1 CPU by default, and each CPU is allocated 3380 MiB +#! of memory. If this is insufficient, also specify +#! --cpus-per-task and/or --mem (the latter specifies MiB per node). + +#! Number of nodes and tasks per node allocated by SLURM (do not change): +numnodes=$SLURM_JOB_NUM_NODES +numtasks=$SLURM_NTASKS +mpi_tasks_per_node=$(echo "$SLURM_TASKS_PER_NODE" | sed -e 's/^\([0-9][0-9]*\).*$/\1/') +#! ############################################################ +#! Modify the settings below to specify the application's environment, location +#! and launch method: + +workdir=~/sh/gz21 + +#! Optionally modify the environment seen by the application +#! (note that SLURM reproduces the environment at submission irrespective of ~/.bashrc): +. /etc/profile.d/modules.sh # Leave this line (enables the module command) +module purge # Removes all modules still loaded +module load rhel8/default-icl # REQUIRED - loads the basic environment +module load python/3.11.0-icl +source $workdir/venv/bin/activate + +#! Insert additional module load commands after this line if needed: + +#! Full path to application executable: +application=python + +#! Run options for the application: +options="src/gz21_ocean_momentum/new/data/cli.py --config-file ~/sh/slurm/jobs/gz21/data-config.yaml" + +#! Are you using OpenMP (NB this is unrelated to OpenMPI)? If so increase this +#! safe value to no more than 76: +export OMP_NUM_THREADS=1 + +#! Number of MPI tasks to be started by the application per node and in total (do not change): +np=$[${numnodes}*${mpi_tasks_per_node}] + +#! The following variables define a sensible pinning strategy for Intel MPI tasks - +#! this should be suitable for both pure MPI and hybrid MPI/OpenMP jobs: +export I_MPI_PIN_DOMAIN=omp:compact # Domains are $OMP_NUM_THREADS cores in size +export I_MPI_PIN_ORDER=scatter # Adjacent domains have minimal sharing of caches/sockets +#! Notes: +#! 1. These variables influence Intel MPI only. +#! 2. Domains are non-overlapping sets of cores which map 1-1 to MPI tasks. +#! 3. I_MPI_PIN_PROCESSOR_LIST is ignored if I_MPI_PIN_DOMAIN is set. +#! 4. If MPI tasks perform better when sharing caches/sockets, try I_MPI_PIN_ORDER=compact. + + +#! Uncomment one choice for CMD below (add mpirun/mpiexec options if necessary): + +#! Choose this for a MPI code (possibly using OpenMP) using Intel MPI. +#CMD="mpirun -ppn $mpi_tasks_per_node -np $np $application $options" + +#! Choose this for a pure shared-memory OpenMP parallel program on a single node: +#! (OMP_NUM_THREADS threads will be created): +CMD="$application $options" + +#! Choose this for a MPI code (possibly using OpenMP) using OpenMPI: +#CMD="mpirun -npernode $mpi_tasks_per_node -np $np $application $options" + + +############################################################### +### You should not have to change anything below this line #### +############################################################### + +cd $workdir +echo -e "Changed directory to `pwd`.\n" + +JOBID=$SLURM_JOB_ID + +echo -e "JobID: $JOBID\n======" +echo "Time: `date`" +echo "Running on master node: `hostname`" +echo "Current directory: `pwd`" + +if [ "$SLURM_JOB_NODELIST" ]; then + #! Create a machine file: + export NODEFILE=`generate_pbs_nodefile` + cat $NODEFILE | uniq > machine.file.$JOBID + echo -e "\nNodes allocated:\n================" + echo `cat machine.file.$JOBID | sed -e 's/\..*$//g'` +fi + +echo -e "\nnumtasks=$numtasks, numnodes=$numnodes, mpi_tasks_per_node=$mpi_tasks_per_node (OMP_NUM_THREADS=$OMP_NUM_THREADS)" + +echo -e "\nExecuting command:\n==================\n$CMD\n" + +eval $CMD diff --git a/resources/slurm-jobs/train.sh b/resources/slurm-jobs/train.sh new file mode 100755 index 00000000..d38e3f9c --- /dev/null +++ b/resources/slurm-jobs/train.sh @@ -0,0 +1,117 @@ +#!/bin/bash +#! +#! Example SLURM job script for Wilkes3 (AMD EPYC 7763, ConnectX-6, A100) +#! Last updated: Fri 30 Jul 11:07:58 BST 2021 +#! + +#!############################################################# +#!#### Modify the options in this section as appropriate ###### +#!############################################################# + +#! sbatch directives begin here ############################### +#! Name of the job: +#SBATCH -J gpujob +#! Which project should be charged (NB Wilkes2 projects end in '-GPU'): +#SBATCH -A ICCS-SL3-GPU +#! How many whole nodes should be allocated? +#SBATCH --nodes=1 +#! How many (MPI) tasks will there be in total? +#! Note probably this should not exceed the total number of GPUs in use. +#SBATCH --ntasks=1 +#! Specify the number of GPUs per node (between 1 and 4; must be 4 if nodes>1). +#! Note that the job submission script will enforce no more than 32 cpus per GPU. +#SBATCH --gres=gpu:1 +#! How much wallclock time will be required? +#SBATCH --time=01:00:00 +#! What types of email messages do you wish to receive? +#SBATCH --mail-type=ALL +#! Uncomment this to prevent the job from being requeued (e.g. if +#! interrupted by node failure or system downtime): +##SBATCH --no-requeue + +#! Do not change: +#SBATCH -p ampere + +#! sbatch directives end here (put any additional directives above this line) + +#! Notes: +#! Charging is determined by GPU number*walltime. + +#! Number of nodes and tasks per node allocated by SLURM (do not change): +numnodes=$SLURM_JOB_NUM_NODES +numtasks=$SLURM_NTASKS +mpi_tasks_per_node=$(echo "$SLURM_TASKS_PER_NODE" | sed -e 's/^\([0-9][0-9]*\).*$/\1/') +#! ############################################################ +#! Modify the settings below to specify the application's environment, location +#! and launch method: + +workdir=~/sh/gz21 + +#! Optionally modify the environment seen by the application +#! (note that SLURM reproduces the environment at submission irrespective of ~/.bashrc): +. /etc/profile.d/modules.sh # Leave this line (enables the module command) +module purge # Removes all modules still loaded +module load rhel8/default-icl # REQUIRED - loads the basic environment +module load python/3.11.0-icl +source $workdir/venv/bin/activate + +#! Insert additional module load commands after this line if needed: + +#! Full path to application executable: +application="mlflow" + +#! Run options for the application: +options="run . --experiment-name raehik -e train --env-manager=local \ +-P forcing_data_path=/rds/user/bhgo2/hpc-work/generated/gz21/forcing/default-ish \ +-P learning_rate=0/5e-4/10/5e-5/20/5e-6 -P n_epochs=10000 -P weight_decay=0.00 -P train_split=0.8 \ +-P test_split=0.85 -P model_module_name=models.models1 -P model_cls_name=FullyCNN -P batchsize=4 \ +-P transformation_cls_name=SoftPlusTransform -P submodel=transform3 \ +-P loss_cls_name=HeteroskedasticGaussianLossV2 \ +" + +#! Work directory (i.e. where the job will run): +#workdir="$SLURM_SUBMIT_DIR" # The value of SLURM_SUBMIT_DIR sets workdir to the directory + # in which sbatch is run. + +#! Are you using OpenMP (NB this is unrelated to OpenMPI)? If so increase this +#! safe value to no more than 128: +export OMP_NUM_THREADS=1 + +#! Number of MPI tasks to be started by the application per node and in total (do not change): +np=$[${numnodes}*${mpi_tasks_per_node}] + +#! Choose this for a pure shared-memory OpenMP parallel program on a single node: +#! (OMP_NUM_THREADS threads will be created): +CMD="$application $options" + +#! Choose this for a MPI code using OpenMPI: +#CMD="mpirun -npernode $mpi_tasks_per_node -np $np $application $options" + + +############################################################### +### You should not have to change anything below this line #### +############################################################### + +cd $workdir +echo -e "Changed directory to `pwd`.\n" + +JOBID=$SLURM_JOB_ID + +echo -e "JobID: $JOBID\n======" +echo "Time: `date`" +echo "Running on master node: `hostname`" +echo "Current directory: `pwd`" + +if [ "$SLURM_JOB_NODELIST" ]; then + #! Create a machine file: + export NODEFILE=`generate_pbs_nodefile` + cat $NODEFILE | uniq > machine.file.$JOBID + echo -e "\nNodes allocated:\n================" + echo `cat machine.file.$JOBID | sed -e 's/\..*$//g'` +fi + +echo -e "\nnumtasks=$numtasks, numnodes=$numnodes, mpi_tasks_per_node=$mpi_tasks_per_node (OMP_NUM_THREADS=$OMP_NUM_THREADS)" + +echo -e "\nExecuting command:\n==================\n$CMD\n" + +eval $CMD diff --git a/src/gz21_ocean_momentum/analysis/analysis.py b/src/gz21_ocean_momentum/analysis/analysis.py index 6b18dfa4..deab15a5 100644 --- a/src/gz21_ocean_momentum/analysis/analysis.py +++ b/src/gz21_ocean_momentum/analysis/analysis.py @@ -12,13 +12,9 @@ only one figure. """ import numpy as np -import matplotlib.pyplot as plt +import matplotlib.pyplot as plt # type: ignore from os.path import join -data_location = "/data/ag7531/" -figures_directory = "figures" - - def allow_hold_on(f): """Decorator that allows to specify a hold_on parameter that makes the plotting use the current figure instead of creating a new one.""" @@ -84,7 +80,7 @@ def plot_pred_vs_true(self): plt.title("Prediction errors for point {}, {}".format(*self.point)) plt.show() - def save_fig(self): + def save_fig(self, figures_directory: str, data_location: str): if not self._fig: self.plot_pred_vs_true() plt.savefig(join(data_location, figures_directory, self.name)) diff --git a/src/gz21_ocean_momentum/analysis/latex_table.txt b/src/gz21_ocean_momentum/analysis/latex_table.txt deleted file mode 100644 index 032785d0..00000000 --- a/src/gz21_ocean_momentum/analysis/latex_table.txt +++ /dev/null @@ -1,8 +0,0 @@ -\begin{table}[] -\begin{tabular}{lcccc} -subdomain & latitude range & longitude range & training & validation \\ - -{} & {} & {} & X & X \\ - -\end{tabular} -\end{table} diff --git a/src/gz21_ocean_momentum/analysis/multiscale_analysis.py b/src/gz21_ocean_momentum/analysis/multiscale_analysis.py index 481d0b17..4db1ffeb 100644 --- a/src/gz21_ocean_momentum/analysis/multiscale_analysis.py +++ b/src/gz21_ocean_momentum/analysis/multiscale_analysis.py @@ -7,9 +7,10 @@ to the script inference/multiscale.py. """ -from analysis.loadmlflow import LoadMLFlow -from analysis.utils import select_run, view_predictions, DisplayMode -import mlflow +from gz21_ocean_momentum.analysis.loadmlflow import LoadMLFlow +from gz21_ocean_momentum.utils import select_run +from gz21_ocean_momentum.analysis.utils import view_predictions, DisplayMode +import mlflow # type: ignore # We'll run this locally mlflow.set_tracking_uri("file:///d:\\Data sets\\NYU\\mlruns") diff --git a/src/gz21_ocean_momentum/analysis/utils.py b/src/gz21_ocean_momentum/analysis/utils.py index b6bd7c23..47cfcb10 100755 --- a/src/gz21_ocean_momentum/analysis/utils.py +++ b/src/gz21_ocean_momentum/analysis/utils.py @@ -4,6 +4,11 @@ @author: Arthur """ + +from gz21_ocean_momentum.analysis.analysis import TimeSeriesForPoint +from gz21_ocean_momentum.data.pangeo_catalog import get_patch, get_whole_data +from gz21_ocean_momentum.common.bounding_box import BoundingBox + import numpy as np import mlflow from mlflow.tracking import client @@ -12,22 +17,17 @@ import matplotlib.animation as animation from matplotlib.patches import Rectangle import pandas as pd -from gz21_ocean_momentum.analysis.analysis import TimeSeriesForPoint import xarray as xr from typing import Optional from scipy.ndimage import gaussian_filter -from gz21_ocean_momentum.data.pangeo_catalog import get_patch, get_whole_data from cartopy.crs import PlateCarree import yaml -from gz21_ocean_momentum.data.utils import load_training_datasets - from enum import Enum CATALOG_URL = "https://raw.githubusercontent.com/pangeo-data/pangeo-datastore\ /master/intake-catalogs/master.yaml" - def correlation_map(truth: np.ndarray, pred: np.ndarray): """ Return the correlation map. @@ -588,9 +588,9 @@ def apply_complete_mask(array, pred, uv_plotter): array = array.sel(latitude=slice(pred["latitude"][0], pred["latitude"][-1])) return array - def plot_training_subdomains( global_plotter: GlobalPlotter, + bboxes: list[BoundingBox], alpha=0.5, bg_variable=None, facecolor="blue", @@ -601,10 +601,7 @@ def plot_training_subdomains( **plot_kwd_args, ): """ - Plots the training subdomains used for a training run. Retrieves - those subdomains from the training_subdomains.yaml file. Additionally, provide the - latex code of a table with the latitudes and longitudes of each - subdomain. + Plots the training subdomains used for a training run. Parameters ---------- @@ -619,58 +616,56 @@ def plot_training_subdomains( Returns ------- - None. + Plotted map. """ - # retrieve the latex code for the table from file - with open("analysis/latex_table.txt") as f: - lines = f.readlines() - latex_start = "".join(lines[:3]) - latex_line = lines[4] - latex_end = "".join(lines[6:]) - latex_lines = [] - subdomain_names = "ABCDE" # Plot the map ax = global_plotter.plot(bg_variable, *plot_args, **plot_kwd_args) - # Recover the coordinates of the rectangular subdomain - with open("../../training_subdomains.yaml", encoding="utf-8") as config_file: - subdomains = yaml.full_load(config_file) - for i in range(len(subdomains)): - lat_min = dict(subdomains[i][1])["lat_min"] - lat_max = dict(subdomains[i][1])["lat_max"] - lon_min = dict(subdomains[i][1])["lon_min"] - lon_max = dict(subdomains[i][1])["lon_max"] - - lat_min, lat_max = float(lat_min), float(lat_max) - lon_min, lon_max = float(lon_min), float(lon_max) - x, y = lon_min, lat_min - width, height = lon_max - lon_min, lat_max - lat_min - ax.add_patch( - Rectangle( - (x, y), - width, - height, - facecolor=facecolor, - edgecolor=edgecolor, - linewidth=linewidth, - fill=fill, - alpha=alpha, - ) - ) - # Add the table line - lat_range = str(lat_min) + "\\degree, " + str(lat_max) + "\\degree" - lon_range = str(lon_min) + "\\degree, " + str(lon_max) + "\\degree" - latex_lines.append( - latex_line.format(subdomain_names[i], lat_range, lon_range) + for bbox in bboxes: + ax.add_patch( + Rectangle( + (bbox.long_min, bbox.lat_min), + bbox.long_max - bbox.long_min, + bbox.lat_max - bbox.lat_min, + facecolor=facecolor, + edgecolor=edgecolor, + linewidth=linewidth, + fill=fill, + alpha=alpha, ) + ) - latex_lines = "".join(latex_lines) - latex = "".join((latex_start, latex_lines, latex_end)) - print(latex) plt.show() return ax +def training_subdomains_latex(bboxes: list[BoundingBox]) -> str: + """ + Return the LaTeX code of a table with the latitudes and longitudes of each + subdomain. + + Potentially useful in visualizations, Jupyter notebooks. + """ + text_lines = [] + for i in range(len(bboxes)): + bbox = bboxes[i] + text_bbox_range_lat = f"{bbox.lat_min}\\degree, {bbox.lat_max}\\degree" + text_bbox_range_long = f"{bbox.long_min}\\degree, {bbox.long_max}\\degree" + text_line = f"{i} & {text_bbox_range_lat} & {text_bbox_range_long} & X & X \\\\\n" + text_lines.append(text_line) + + text_pre = """ +\\begin{table}[] +\\begin{tabular}{lcccc} +subdomain & latitude range & longitude range & training & validation \\\\ +""" + text_post = """ +\\end{tabular} +\\end{table} +""" + + text = text_pre + "".join(text_lines) + text_post + return text def anomalies(dataset: xr.Dataset, dim: str = "time.month"): """Returns a dataset of the anomalies.""" diff --git a/src/gz21_ocean_momentum/cli/data.py b/src/gz21_ocean_momentum/cli/data.py new file mode 100755 index 00000000..3fdfdcb6 --- /dev/null +++ b/src/gz21_ocean_momentum/cli/data.py @@ -0,0 +1,107 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import gz21_ocean_momentum.lib.data as lib +import gz21_ocean_momentum.common.cli as cli +from gz21_ocean_momentum.common.bounding_box import BoundingBox +import gz21_ocean_momentum.common.bounding_box as bounding_box + +import configargparse + +import dask.diagnostics +import logging + +import xarray as xr +import dask.multiprocessing + +# Description of this module +_cli_desc = "GZ21 data step: download CM2.6 dataset, apply coarse graining \ +and generate forcings. Saves result to disk in zarr format." + +# up to date as of 2023-09-01 +DEF_CATALOG_URI = "https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/d684158e92fb3f3ad3b34e7dc5bba52b22a3ba80/intake-catalogs/ocean.yaml" + +p = configargparse.ArgParser(description=_cli_desc) +p.add("--config-file", is_config_file=True, help="config file path") +p.add("--out-dir", type=str, required=True, help="folder to save generated forcings to (in zarr format)" ) +p.add("--lat-min", type=float, required=True, help="bounding box minimum latitude") +p.add("--lat-max", type=float, required=True, help="bounding box maximum latitude") +p.add("--long-min", type=float, required=True, help="bounding box minimum longitude") +p.add("--long-max", type=float, required=True, help="bounding box maximum longitude") +p.add("--cyclize", action="store_true", help="global data; make cyclic along longitude") +p.add("--ntimes", type=int, help="number of time points to process, starting from the first. Note that the CM2.6 dataset is daily, so this would be number of days. If unset, uses whole dataset.") +p.add("--co2-increase", action="store_true", help="use 1%% annual CO2 increase CM2.6 dataset. By default, uses control (no increase)") +p.add("--factor", type=int, required=True, help="resolution degradation factor") +p.add("--pangeo-catalog-uri", type=str, default=DEF_CATALOG_URI, help="URI to Pangeo ocean dataset intake catalog file") +p.add("--verbose", action="store_true", help="be more verbose (displays progress, debug messages)") +p.add("--dask-workers", type=int, help="num_workers for Dask computations, higher is more parallel & memory hungry") + +options = p.parse_args() + +# set up logging immediately after parsing CLI options (need to check verbosity) +# (would like to simplify this, maybe with `basicConfig(force=True)`) +if options.verbose: + logging.basicConfig(level=logging.DEBUG) + dask.diagnostics.ProgressBar().register() + logger = logging.getLogger(__name__) + logger.debug("verbose mode; displaying all debug messages, progress bars)") +else: + logging.basicConfig(level=logging.INFO) + logger = logging.getLogger(__name__) + +if options.dask_workers is not None: + dask.config.set(num_workers=1) + +cli.fail_if_path_is_nonempty_dir( + 1, f"--out-dir \"{options.out_dir}\" invalid", options.out_dir) + +# store bounding box in a struct-like +bbox = BoundingBox( + options.lat_min, options.lat_max, + options.long_min, options.long_max) +if not bounding_box.validate_nonempty(bbox): + cli.fail(2, f"provided bounding box describes an empty region: {bbox}") + +logger.info("retrieving CM2.6 dataset via Pangeo Cloud Datastore...") +surface_fields, grid = lib.retrieve_cm2_6(options.pangeo_catalog_uri, options.co2_increase) + +logger.debug("dropping irrelevant data variables...") +surface_fields = surface_fields[["usurf", "vsurf"]] + +logger.info("selecting input data bounding box...") +surface_fields = bounding_box.bound_dataset("yu_ocean", "xu_ocean", surface_fields, bbox) +grid = bounding_box.bound_dataset("yu_ocean", "xu_ocean", grid, bbox) + +if options.ntimes is not None: + logger.info(f"slicing {options.ntimes} time points...") + surface_fields = surface_fields.isel(time=slice(0, options.ntimes)) + +logger.debug("placing grid dataset into local memory...") +grid = grid.compute() + +if options.cyclize: + logger.info("making dataset cyclic along longitude...") + logger.info("WARNING: may be nonfunctional or have poor performance") + surface_fields = lib.cyclize( + surface_fields, "xu_ocean", options.factor) + grid = lib.cyclize( + grid, "xu_ocean", options.factor) + + logger.debug("rechunking along cyclized dimension...") + surface_fields = surface_fields.chunk({"xu_ocean": -1}) + grid = grid.chunk({"xu_ocean": -1}) + +# we may compute by running the function normally, but Dask may schedule poorly, +# so be explicit with `map_blocks` +# (dask.array.map_blocks may also work, `meta=` instead of `template=`) +logger.info("computing forcings...") +shape = lib.compute_forcings_and_coarsen_cm2_6_shape(surface_fields, options.factor) +f = lambda x: lib.compute_forcings_and_coarsen_cm2_6(x, grid, options.factor) +forcings = xr.map_blocks(f, surface_fields, template=shape) + +logger.info("selecting forcing bounding box...") +forcings = bounding_box.bound_dataset("yu_ocean", "xu_ocean", forcings, bbox) + +# to_zarr below now finally incurs the data processing computation +logger.info(f"writing forcings zarr to directory: {options.out_dir}") +forcings.to_zarr(options.out_dir) diff --git a/src/gz21_ocean_momentum/cli/infer.py b/src/gz21_ocean_momentum/cli/infer.py new file mode 100755 index 00000000..5ca78744 --- /dev/null +++ b/src/gz21_ocean_momentum/cli/infer.py @@ -0,0 +1,104 @@ +import configargparse + +import gz21_ocean_momentum.common.cli as cli +import logging +from dask.diagnostics import ProgressBar +from gz21_ocean_momentum.utils import TaskInfo + +import gz21_ocean_momentum.lib.model as lib +from gz21_ocean_momentum.data.datasets import ( + #DatasetPartitioner, + DatasetTransformer, + DatasetWithTransform, + ComposeTransforms, +) + +import xarray as xr +import torch +from torch.utils.data import DataLoader + +import gz21_ocean_momentum.models.models1 as model +import gz21_ocean_momentum.models.submodels as submodels +import gz21_ocean_momentum.models.transforms as transforms +import gz21_ocean_momentum.train.losses as loss_funcs +from gz21_ocean_momentum.inference.utils import predict_lazy_cm2_6 + +# Description of this module +_cli_desc = """ +Use a trained GZ21 neural net to predict forcing for input ocean velocity data. + +This script is intended as example of how use the GZ21 neural net, generating +data for analyzing and visualizing model behaviour, and for general tinkering. + +Designed to ingest coarsened CM2.6 data: looks for data variables at certain +names (`xu_ocean`, ...) with certain units. If these do not match up, the neural +net will not operate properly. + +More specifically, this script is designed to ingest coarsened CM2.6 data as +output from the GZ21 data step. This also computes forcings, which are ignored. +(Ideally, we would provide a short script to simply coarsen some data, without +computing the associated forcings.) + +Note that the neural net has two outputs per grid point. See project +documentation (specifically `README.md` in the project repository), and the +associated paper Guillaumin (2021) for suggestions on how to integrate these +into your GCM of choice. +""" + +submodel = submodels.transform3 + +p = configargparse.ArgParser(description=_cli_desc) +p.add("--config-file", is_config_file=True, help="config file path") +p.add("--input-data-dir", type=str, required=True, help="path to input ocean velocity data, in zarr format (folder)") +p.add("--model-state-dict-file", type=str, required=True, help="model state dict file (*.pth)") +p.add("--out-dir", type=str, required=True, help="folder to save forcing predictions dataset to (in zarr format)") +p.add("--device", type=str, default="cuda", help="neural net device (e.g. cuda, cuda:0, cpu)") + +options = p.parse_args() + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +cli.fail_if_path_is_nonempty_dir( + 1, f"--out-dir \"{options.out_dir}\" invalid", options.out_dir) + +# --- + +logger.info("loading input (coarse) ocean momentum data...") +ds_computed_xr = xr.open_zarr(options.input_data_dir) + +with ProgressBar(), TaskInfo("Applying transforms to dataset"): + ds_computed_xr = submodel.fit_transform(ds_computed_xr) + +# wrap xarray into PyTorch-compatible data +dataset = lib.gz21_train_data_subdomain_xr_to_torch(ds_computed_xr) +loader = DataLoader(dataset) + +criterion = loss_funcs.HeteroskedasticGaussianLossV2(dataset.n_targets) +net = model.FullyCNN(dataset.n_features, criterion.n_required_channels) + +# load final net transformation +# (this is correct, assuming any transformation state if present is stored in +# the model state dict) +transformation = transforms.SoftPlusTransform() +transformation.indices = criterion.precision_indices +net.final_transformation = transformation + +net.load_state_dict(torch.load(options.model_state_dict_file)) + +dataset.add_transforms_from_model(net) + +with TaskInfo(f"moving neural network to requested device: {options.device}"): + net.to(options.device) + +with ProgressBar(), TaskInfo("Predict & save prediction dataset"): + out = predict_lazy_cm2_6(net, + criterion.n_required_channels, + criterion.channel_names, + [dataset], [loader], options.device) + ProgressBar().register() + logger.info(f"chunk predictions to time=32 ...") + out = out.chunk(dict(time=32)) + print(f"Size of output data is {out.nbytes/1e9} GB") + logger.info(f"writing re-chunked predictions zarr to directory: {options.out_dir}") + out.to_zarr(options.out_dir) diff --git a/src/gz21_ocean_momentum/inference/main.py b/src/gz21_ocean_momentum/cli/test.py similarity index 82% rename from src/gz21_ocean_momentum/inference/main.py rename to src/gz21_ocean_momentum/cli/test.py index 25e37b5a..9626364a 100755 --- a/src/gz21_ocean_momentum/inference/main.py +++ b/src/gz21_ocean_momentum/cli/test.py @@ -22,7 +22,6 @@ import torch import numpy as np -import mlflow from torch.utils.data import DataLoader import xarray as xr from gz21_ocean_momentum.utils import select_run, select_experiment, TaskInfo @@ -55,10 +54,32 @@ from gz21_ocean_momentum.data.xrtransforms import SeasonalStdizer from gz21_ocean_momentum.models import submodels +# module description (displayed in CLI via --help) +_cli_desc = """ +Predict forcings for input ocean velocity with specific settings, and optional +fine-tuning. +""" + +p = configargparse.ArgParser(description=_cli_desc) +p.add("--config-file", is_config_file=True, help="config file path") +p.add("--epochs", type=int, default=0) + +p.add("--in-train-data-dir", type=str, required=True, help="training data in zarr format, containing ocean velocities and forcings") +p.add("--subdomains-file", type=str, required=True, help="YAML file describing subdomains to split input data into (see readme for format)") +p.add("--batch-size", type=int, required=True, help="PyTorch DataLoader batch size") +p.add("--epochs", type=int, required=True, help="number of epochs to train for") +p.add("--out-model", type=str, required=True, help="save trained model to this path") +p.add("--initial-learning-rate", type=float, required=True, help="initial learning rate for optimization algorithm") +p.add("--decay-factor", type=float, required=True, help="learning rate decay factor, applied each time an epoch milestone is reached") +p.add("--decay-at-epoch-milestones", type=int, action="append", required=True, help="milestones to decay at. May specify multiple times. Must be strictly increasing with no duplicates") +p.add("--device", type=str, default="cuda:0", help="neural net device (e.g. cuda:0, cpu)") +p.add("--weight-decay", type=float, default=0.0, help="Weight decay parameter for Adam loss function. Deprecated, default 0.") +p.add("--train-split-end", type=float, required=True, help="0>=x>=1. Use 0->x of input dataset for training") +p.add("--test-split-start", type=float, required=True, help="0>=x>=1. Use x->end of input dataset for testing. Must be greater than --train-split-start") +p.add("--printevery", type=int, default=20) +options = p.parse_args() # Parse arguments -parser = argparse.ArgumentParser() -parser.add_argument("--n_epochs", type=int, default=0) parser.add_argument("--lr_ratio", type=float, default=1) parser.add_argument("--train_mode", type=str, default="all") parser.add_argument("--n_test_times", type=int, default=None) @@ -67,7 +88,7 @@ parser.add_argument("--n_splits", type=int, default=1) script_params = parser.parse_args() -n_epochs = script_params.n_epochs +n_epochs = options.epochs lr_ratio = script_params.lr_ratio to_experiment = script_params.to_experiment n_test_times = script_params.n_test_times @@ -281,5 +302,4 @@ mlflow.log_artifact(file_path) print(f"Size of output data is {out.nbytes/1e9} GB") -mlflow.end_run() print("Done") diff --git a/src/gz21_ocean_momentum/cli/train.py b/src/gz21_ocean_momentum/cli/train.py new file mode 100755 index 00000000..1fd24a48 --- /dev/null +++ b/src/gz21_ocean_momentum/cli/train.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + + +import gz21_ocean_momentum.common.cli as cli +import gz21_ocean_momentum.common.assorted as common +import gz21_ocean_momentum.common.bounding_box as bounding_box +import gz21_ocean_momentum.lib.model as lib +import gz21_ocean_momentum.models.submodels as submodels +import gz21_ocean_momentum.models.transforms as transforms +import gz21_ocean_momentum.models.models1 as model +import gz21_ocean_momentum.train.losses as loss +from gz21_ocean_momentum.train.base import Trainer +from gz21_ocean_momentum.inference.metrics import MSEMetric, MaxMetric +from gz21_ocean_momentum.data.datasets import Subset_, ConcatDataset_ + +import configargparse + +import os + +import xarray as xr +import numpy as np + +import torch +from torch.utils.data import DataLoader, ConcatDataset +from torch import optim +from torch.optim.lr_scheduler import MultiStepLR + +# Description of this module +_cli_desc = """ +Train a Pytorch neural net to predict subgrid ocean momentum forcing from +ocean surface velocity. + +Uses data generated by the GZ21 data step script. +""" + +p = configargparse.ArgParser(description=_cli_desc) +p.add("--config-file", is_config_file=True, help="config file path") +p.add("--in-train-data-dir", type=str, required=True, help="training data in zarr format, containing ocean velocities and forcings") +p.add("--subdomains-file", type=str, required=True, help="YAML file describing subdomains to split input data into (see readme for format)") +p.add("--batch-size", type=int, required=True, help="PyTorch DataLoader batch size") +p.add("--epochs", type=int, required=True, help="number of epochs to train for") +p.add("--out-model", type=str, required=True, help="save trained model to this path") +p.add("--initial-learning-rate", type=float, required=True, help="initial learning rate for optimization algorithm") +p.add("--decay-factor", type=float, required=True, help="learning rate decay factor, applied each time an epoch milestone is reached") +p.add("--decay-at-epoch-milestones", type=int, action="append", required=True, help="milestones to decay at. May specify multiple times. Must be strictly increasing with no duplicates") +p.add("--device", type=str, default="cuda:0", help="neural net device (e.g. cuda:0, cpu)") +p.add("--weight-decay", type=float, default=0.0, help="Weight decay parameter for Adam loss function. Deprecated, default 0.") +p.add("--train-split-end", type=float, required=True, help="0>=x>=1. Use 0->x of input dataset for training") +p.add("--test-split-start", type=float, required=True, help="0>=x>=1. Use x->end of input dataset for testing. Must be greater than --train-split-start") +p.add("--printevery", type=int, default=20) +options = p.parse_args() + +if not common.list_is_strictly_increasing(options.decay_at_epoch_milestones): + cli.fail(2, "epoch milestones list is not strictly increasing") + +torch.autograd.set_detect_anomaly(True) + +# dataset prep: load data, select subdomains via provided bounding boxes +ds_xr = xr.open_zarr(options.in_train_data_dir) +bboxes = bounding_box.load_bounding_boxes_yaml(options.subdomains_file) +sds_xr = [ bounding_box.bound_dataset("yu_ocean", "xu_ocean", ds_xr, bbox) for bbox in bboxes ] + +# dataset prep: transform, wrap into PyTorch dataset +def _transform_and_to_torch(ds_xr): + """Attach a transformation to an xarray dataset, then convert to PyTorch.""" + # must deepcopy due to transformation implementation! + ds_xr = copy.deepcopy(submodels.transform3).fit_transform(ds_xr) + #ds_xr = ds_xr.compute() # should we force compute underlying xarray? + ds_torch = lib.gz21_train_data_subdomain_xr_to_torch(ds_xr) + return ds_torch +datasets = [ _transform_and_to_torch(sd_xr) for sd_xr in sds_xr ] + +train_dataloader, test_dataloader = lib.prep_train_test_dataloaders( + datasets, + options.train_split_end, options.test_split_start, + options.batch_size) + +# set up neural network +criterion = loss.HeteroskedasticGaussianLossV2(datasets[0].n_targets) +net = model.FullyCNN(datasets[0].n_features, criterion.n_required_channels) +transformation = transforms.SoftPlusTransform() +transformation.indices = criterion.precision_indices +net.final_transformation = transformation + +# add automatic feature & target transforms to datasets using model +# e.g. reshape targets to match model output shape +for dataset in datasets: + dataset.add_transforms_from_model(net) + +# ------------------- +# TRAINING OF NETWORK +# ------------------- +# Adam optimizer +# To GPU +net.to(options.device) + +# Optimizer and learning rate scheduler +optimizer = optim.Adam( + list(net.parameters()), + lr=options.initial_learning_rate, weight_decay=options.weight_decay) +lr_scheduler = MultiStepLR( + optimizer, options.decay_at_epoch_milestones, + gamma=options.decay_factor) + +trainer = Trainer(net, options.device) +trainer.criterion = criterion +trainer.print_loss_every = options.printevery + +# metrics saved independently of the training criterion. +metrics = {"R2": MSEMetric(), "Inf Norm": MaxMetric()} +for metric_name, metric in metrics.items(): + metric.inv_transform = lambda x: test_dataset.inverse_transform_target(x) + trainer.register_metric(metric_name, metric) + +for i_epoch in range(options.epochs): + print(f"Epoch number {i_epoch}.") + # 2023-12-08 raehik: old note: remove clipping? + train_loss = trainer.train_for_one_epoch( + train_dataloader, optimizer, lr_scheduler, clip=1.0 + ) + test = trainer.test(test_dataloader) + if test == "EARLY_STOPPING": + print(test) + break + test_loss, metrics_results = test + print(f"Train loss for this epoch is {train_loss}") + print(f"Test loss for this epoch is {test_loss}") + + for metric_name, metric_value in metrics_results.items(): + print(f"Test {metric_name} for this epoch is {metric_value}") + +#net.cpu() +torch.save(net.state_dict(), options.out_model) diff --git a/src/gz21_ocean_momentum/cmip26.py b/src/gz21_ocean_momentum/cmip26.py deleted file mode 100755 index 4c3d3421..00000000 --- a/src/gz21_ocean_momentum/cmip26.py +++ /dev/null @@ -1,154 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Compute subgrid forcing on requested dataset. - -Script to compute the subgrid forcing for a given region, using -data from cmip2.6 on one of the pangeo data catalogs. -Command line parameters include region specification. -Reads data from the CM2.6 and apply coarse graining. -Stores the resulting dataset into an MLFLOW -experiment within a specific run. -""" -import os -import logging -import tempfile -import argparse - -import xarray as xr -from dask.diagnostics import ProgressBar -import mlflow - -from data.utils import cyclize_dataset -from data.coarse import eddy_forcing -from data.pangeo_catalog import get_patch -import logging -import tempfile - -# obtain logging config from LOGGING_LEVEL environment variable -# e.g. `LOGGING_LEVEL=20 python cmip26.py ...` -# common numeric values: https://docs.python.org/3/library/logging.html#levels -logging_level = os.environ.get("LOGGING_LEVEL") -if logging_level is not None: - logging.basicConfig(level=int(logging_level)) -logger = logging.getLogger(__name__) - -# Script parameters -CATALOG_URL = ( - "https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/" - "master/intake-catalogs/master.yaml" -) - -DESCRIPTION = "Read data from the CM2.6 and \ - apply coarse graining. Stores the resulting dataset into an MLFLOW \ - experiment within a specific run." - -data_location = tempfile.mkdtemp() -logger.info(f"working dir: {data_location}") - -# Parse the command-line parameters -parser = argparse.ArgumentParser(description=DESCRIPTION) -parser.add_argument( - "bounds", - type=float, - nargs=4, - help="min lat, max_lat,\ - min_long, max_long", -) -parser.add_argument( - "--global_", - type=int, - help="True if global data. In this\ - case the data is made cyclic along longitude", - default=False, -) -parser.add_argument( - "--ntimes", - type=int, - default=10000, - help="number of days,\ - starting from first day.", -) -parser.add_argument( - "--CO2", - type=int, - default=0, - choices=[0, 1], - help="CO2\ - level, O (control) or 1 (1 percent CO2 increase)", -) -parser.add_argument( - "--factor", type=int, default=0, help="Factor of degrading. Should be integer > 1." -) -parser.add_argument( - "--chunk_size", type=str, default="50", help="Chunk size along the time dimension" -) -params = parser.parse_args() - - -# Retrieve the patch of data specified in the command-line args -patch_data, grid_data = get_patch( - CATALOG_URL, params.ntimes, params.bounds, params.CO2, "usurf", "vsurf" -) - -logger.debug(patch_data) -logger.debug(grid_data) - -# If global data, we make the dataset cyclic along longitude -if params.global_ == 1: - logger.info("Cyclic data... Making the dataset cyclic along longitude...") - patch_data = cyclize_dataset(patch_data, "xu_ocean", params.factor) - grid_data = cyclize_dataset(grid_data, "xu_ocean", params.factor) - # Rechunk along the cyclized dimension - patch_data = patch_data.chunk({"xu_ocean": -1}) - grid_data = grid_data.chunk({"xu_ocean": -1}) - -logger.debug("Getting grid data locally") -# grid data is saved locally, no need for dask -grid_data = grid_data.compute() - -logger.debug("Mapping blocks") -# Calculate eddy-forcing dataset for that particular patch -debug_mode = os.environ.get("DEBUG_MODE") -if params.factor != 0 and not debug_mode: - scale_m = params.factor - forcing = eddy_forcing( - patch_data, grid_data, scale=scale_m, method="mean", scale_mode="factor" - ) -elif not debug_mode: - scale_m = params.scale * 1e3 - forcing = eddy_forcing(patch_data, grid_data, scale=scale_m, method="mean") -else: - logger.info("!!!Debug mode!!!") - forcing = patch_data - -# Progress bar -ProgressBar().register() - -# Specify input vs output type for each variable of the dataset. Might -# be used later on for training or inference. -if not debug_mode: - forcing["S_x"].attrs["type"] = "output" - forcing["S_y"].attrs["type"] = "output" - forcing["usurf"].attrs["type"] = "input" - forcing["vsurf"].attrs["type"] = "input" - -# Crop according to bounds -bounds = params.bounds -forcing = forcing.sel( - xu_ocean=slice(bounds[2], bounds[3]), yu_ocean=slice(bounds[0], bounds[1]) -) - -for var in forcing: - forcing[var].encoding = {} -forcing = forcing.chunk(dict(time=1)) - -logger.info("Preparing forcing data") -logger.debug(forcing) -# export data -forcing.to_zarr(os.path.join(data_location, "forcing"), mode="w") - -# Log as an artifact the forcing data -logger.info("Logging processed dataset as an artifact...") -mlflow.log_artifact(os.path.join(data_location, "forcing")) -logger.info("Completed...") diff --git a/src/gz21_ocean_momentum/common/assorted.py b/src/gz21_ocean_momentum/common/assorted.py new file mode 100644 index 00000000..14e1af34 --- /dev/null +++ b/src/gz21_ocean_momentum/common/assorted.py @@ -0,0 +1,25 @@ +def list_is_strictly_increasing(xs: list) -> bool: + """ + Is this list monotonically increasing? Does not permit repeated elements. + List elements must be orderable. + + Asserts that a list is in the correct format to be consumed by the + `milestones` parameter in `torch.optim.MultiStepLR(optimizer: list, ...)`. + """ + return all(xl int: + """ + Obtain the index into the given list-like to the given percent. + No interpolation is performed: we choose the leftmost closest index i.e. the + result is floored. + + e.g. `at_idx_pct(0.5, [0,1,2]) == 1` + + Must be able to `len(a)`. + + Invariant: `0<=pct<=1`. + + Returns a valid index into `a`. + """ + return int(pct * len(a)) diff --git a/src/gz21_ocean_momentum/common/bounding_box.py b/src/gz21_ocean_momentum/common/bounding_box.py new file mode 100644 index 00000000..a587485d --- /dev/null +++ b/src/gz21_ocean_momentum/common/bounding_box.py @@ -0,0 +1,55 @@ +import xarray as xr + +from dataclasses import dataclass +from typing import Optional +from typing import Tuple +from typing import List + +import yaml + +@dataclass +class BoundingBox(): + """A rectangle defined by two latitudes and two longitudes. + + Initialization order is `lat_min, lat_max, long_min, long_max`. + """ + lat_min: float + lat_max: float + long_min: float + long_max: float + +#@staticmethod +def validate_nonempty(bbox: BoundingBox) -> bool: + """Validate that a bounding box represents a non-empty region.""" + return bbox.lat_max > bbox.lat_min and bbox.long_max > bbox.long_min + +def bound_dataset( + dim_lat: str, dim_long: str, + data: xr.Dataset, bbox: BoundingBox + ): + """Bound an xarray `Dataset` to the given `BoundingBox` using the given + dimension names as spatial axes to bound along. + + The spatial dimensions should be `float`s. Argument order is latitude (y) + followed by longitude (x). + + Pure function -- does not alter the input dataset or bounding box. + """ + return data.sel({ + dim_lat: slice(bbox.lat_min, bbox.lat_max), + dim_long: slice(bbox.long_min, bbox.long_max)}) + +def load_bounding_boxes_yaml(path: str) -> list[BoundingBox]: + """Load a YAML file of bounding boxes. + + The YAML value must be a list where each element contains `float` fields + `lat-min`, `lat-max`, `long-min` and `long-max`. + """ + with open(path, "r") as f: + data = yaml.safe_load(f) + bboxes = [] + for el in data: + bboxes.append(BoundingBox( + el["lat-min"], el["lat-max"], + el["long-min"], el["long-max"])) + return bboxes diff --git a/src/gz21_ocean_momentum/common/cli.py b/src/gz21_ocean_momentum/common/cli.py new file mode 100644 index 00000000..761317ce --- /dev/null +++ b/src/gz21_ocean_momentum/common/cli.py @@ -0,0 +1,50 @@ +import os +import sys + +from typing import Optional + +def path_is_nonexist_or_empty_dir(path) -> bool: + """Is the given path either nonexistent or an empty directory?""" + if os.path.exists(path): + # path exists + if os.path.isdir(path): + # path is directory: check contents + with os.scandir(path) as it: + if any(it): + # path is non-empty directory: fail + return False + else: + # path is empty directory: all good + return True + else: + # path is non-directory: fail + return False + else: + # path does not exist: all good + return True + +def fail_if_path_is_nonempty_dir(err_code: int, msg_pre: str, path): + msg = f"{msg_pre}: path should not exist, or be empty directory" + if os.path.exists(path): + # path exists + if os.path.isdir(path): + # path is directory: check contents + with os.scandir(path) as it: + if any(it): + # path is non-empty directory: fail + fail(err_code, msg, "path is non-empty directory") + # else path is empty directory: all good, do nothing + else: + # path is non-directory: fail + fail(err_code, msg, "path is not a directory") + # else path does not exist: all good, do nothing + +def fail(err_code: int, msg: str, hint: Optional[str] = None): + """Exit the program with the given message and error code. + + Also prints a hint (extra message) afterwards if provided. + """ + print(f"ERROR: {msg}") + if hint is not None: + print(f"hint: {hint}") + sys.exit(err_code) diff --git a/src/gz21_ocean_momentum/data/coarse.py b/src/gz21_ocean_momentum/data/coarse.py deleted file mode 100755 index 6b55c493..00000000 --- a/src/gz21_ocean_momentum/data/coarse.py +++ /dev/null @@ -1,208 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -"""Routines for coarsening a dataset.""" - -import logging -import xarray as xr -from scipy.ndimage import gaussian_filter -import numpy as np - - -def advections(u_v_field: xr.Dataset, grid_data: xr.Dataset): - """ - Compute advection terms corresponding to the passed velocity field. - - Parameters - ---------- - u_v_field : xarray Dataset - Velocity field, must contains variables "usurf" and "vsurf" - grid_data : xarray Dataset - grid data, must contain variables "dxu" and "dyu" - - Returns - ------- - result : xarray Dataset - Advection components, under variable names "adv_x" and "adv_y" - """ - dxu = grid_data["dxu"] - dyu = grid_data["dyu"] - gradient_x = u_v_field.diff(dim="xu_ocean") / dxu - gradient_y = u_v_field.diff(dim="yu_ocean") / dyu - # Interpolate back the gradients - interp_coords = { - "xu_ocean": u_v_field.coords["xu_ocean"], - "yu_ocean": u_v_field.coords["yu_ocean"], - } - gradient_x = gradient_x.interp(interp_coords) - gradient_y = gradient_y.interp(interp_coords) - u, v = u_v_field["usurf"], u_v_field["vsurf"] - adv_x = u * gradient_x["usurf"] + v * gradient_y["usurf"] - adv_y = u * gradient_x["vsurf"] + v * gradient_y["vsurf"] - result = xr.Dataset({"adv_x": adv_x, "adv_y": adv_y}) - # TODO check if we can simply prevent the previous operation from adding - # chunks - # result = result.chunk(dict(xu_ocean=-1, yu_ocean=-1)) - return result - - -def spatial_filter(data: np.ndarray, sigma: float): - """ - Apply a gaussian filter to spatial data. - - Apply scipy gaussian filter to along all dimensions except first one, which - corresponds to time. - - Parameters - ---------- - data : ndarray - Data to filter. - sigma : float - Unitless scale of the filter. - - Returns - ------- - result : ndarray - Filtered data - """ - result = np.zeros_like(data) - for t in range(data.shape[0]): - data_t = data[t, ...] - result_t = gaussian_filter(data_t, sigma, mode="constant") - result[t, ...] = result_t - return result - - -def spatial_filter_dataset(dataset: xr.Dataset, grid_info: xr.Dataset, sigma: float): - """ - Apply spatial filtering to the dataset across the spatial dimensions. - - Parameters - ---------- - dataset : xarray Dataset - Dataset to filter. First dimension must be time, followed by spatial dimensions - grid_info : xarray Dataset - grid data, must include variables "dxu" and "dyu" - sigma : float - Scale of the filtering, same unit as those of the grid (often, meters) - - Returns - ------- - filt_dataset : xarray Dataset - Filtered dataset - """ - area_u = grid_info["dxu"] * grid_info["dyu"] / 1e8 - dataset = dataset * area_u - # Normalisation term, so that if the quantity we filter is constant - # over the domain, the filtered quantity is constant with the same value - norm = xr.apply_ufunc( - lambda x: gaussian_filter(x, sigma, mode="constant"), - area_u, - dask="parallelized", - output_dtypes=[ - float, - ], - ) - filtered = xr.apply_ufunc( - lambda x: spatial_filter(x, sigma), - dataset, - dask="parallelized", - output_dtypes=[ - float, - ], - ) - return filtered / norm - - -def eddy_forcing( - u_v_dataset: xr.Dataset, - grid_data: xr.Dataset, - scale: int, - method: str = "mean", - nan_or_zero: str = "zero", - scale_mode: str = "factor", - debug_mode=False, -) -> xr.Dataset: - """ - Compute the sub-grid forcing terms. - - Parameters - ---------- - u_v_dataset : xarray Dataset - High-resolution velocity field. - grid_data : xarray Dataset - High-resolution grid details. - scale : float - Scale, in meters, or factor, if scale_mode is set to 'factor' - method : str, optional - Coarse-graining method. The default is 'mean'. - nan_or_zero: str, optional - String set to either 'nan' or 'zero'. Determines whether we keep the - nan values in the initial surface velocities array or whether we - replace them by zeros before applying the procedure. - In the second case, remaining zeros after applying the procedure will - be replaced by nans for consistency. - The default is 'zero'. - scale_mode: str, optional - DEPRECIATED, should always be left as 'factor' - - Returns - ------- - forcing : xarray Dataset - Dataset containing the low-resolution velocity field and forcing. - """ # Replace nan values with zeros. - if nan_or_zero == "zero": - u_v_dataset = u_v_dataset.fillna(0.0) - if scale_mode == "factor": - print("Using factor mode") - scale_x = scale - scale_y = scale - # Interpolate temperature - # interp_coords = dict(xt_ocean=u_v_dataset.coords['xu_ocean'], - # yt_ocean=u_v_dataset.coords['yu_ocean']) - # u_v_dataset['temp'] = u_v_dataset['surface_temperature'].interp( - # interp_coords) - - scale_filter = (scale_x / 2, scale_y / 2) - # High res advection terms - adv = advections(u_v_dataset, grid_data) - # Filtered advections - filtered_adv = spatial_filter_dataset(adv, grid_data, scale_filter) - # Filtered u,v field and temperature - u_v_filtered = spatial_filter_dataset(u_v_dataset, grid_data, scale_filter) - # Advection term from filtered velocity field - adv_filtered = advections(u_v_filtered, grid_data) - # Forcing - forcing = adv_filtered - filtered_adv - forcing = forcing.rename({"adv_x": "S_x", "adv_y": "S_y"}) - # Merge filtered u,v, temperature and forcing terms - forcing = forcing.merge(u_v_filtered) - logging.debug(forcing) - # Coarsen - print("scale factor: ", scale) - forcing_coarse = forcing.coarsen( - {"xu_ocean": int(scale_x), "yu_ocean": int(scale_y)}, boundary="trim" - ) - if method == "mean": - forcing_coarse = forcing_coarse.mean() - else: - raise ValueError("Passed coarse-graining method not implemented.") - if nan_or_zero == "zero": - # Replace zeros with nans for consistency - forcing_coarse = forcing_coarse.where(forcing_coarse["usurf"] != 0) - if not debug_mode: - return forcing_coarse - u_v_dataset = u_v_dataset.merge(adv) - filtered_adv = filtered_adv.rename({"adv_x": "f_adv_x", "adv_y": "f_adv_y"}) - adv_filtered = adv_filtered.rename({"adv_x": "adv_f_x", "adv_y": "adv_f_y"}) - u_v_filtered = u_v_filtered.rename({"usurf": "f_usurf", "vsurf": "f_vsurf"}) - u_v_dataset = xr.merge( - ( - u_v_dataset, - u_v_filtered, - adv, - filtered_adv, - adv_filtered, - forcing[["S_x", "S_y"]], - ) - ) - return u_v_dataset, forcing_coarse diff --git a/src/gz21_ocean_momentum/data/datasets.py b/src/gz21_ocean_momentum/data/datasets.py index 35612db8..bfd07690 100644 --- a/src/gz21_ocean_momentum/data/datasets.py +++ b/src/gz21_ocean_momentum/data/datasets.py @@ -12,6 +12,7 @@ import numpy as np import torch +import torch.utils.data as torch from torch.utils.data import Dataset, ConcatDataset, Subset import xarray as xr @@ -206,6 +207,7 @@ def fit(self, x: Dataset): """ # TODO Arthur check this features, targets = x[:] + print("TODO HI this is almost certainly used") self.transforms["features"].fit(features) self.transforms["targets"].fit(targets) return self @@ -428,7 +430,6 @@ def transform(self, x): return np.concatenate((left, x, right), axis=self.axis) def transform_coordinate(self, coords, dim): - print(f"{dim}, {self.dim_name}") if dim == self.dim_name: left = coords[-self.nb_points :] - self.length right = coords[: self.nb_points] + self.length @@ -712,7 +713,6 @@ def __getattr__(self, attr_name): return getattr(self.xr_dataset, attr_name) raise AttributeError() - class DatasetWithTransform: def __init__(self, dataset, transform: DatasetTransformer): self.dataset = dataset @@ -895,18 +895,17 @@ def get_partition(self, dataset): class ConcatDataset_(ConcatDataset): """Extends the Pytorch Concat Dataset in two ways: - - enforces (by default) the concatenated dataset to have the same - shapes + - enforces the concatenated dataset to have the same shapes - passes on attributes (from the first dataset, assuming they are equal accross concatenated datasets) + + TODO input datasets need to have .height, .width """ - def __init__(self, datasets, enforce_same_dims=True): + def __init__(self, datasets): super(ConcatDataset_, self).__init__(datasets) - self.enforce_same_dims = enforce_same_dims - if enforce_same_dims: - heights = [dataset.height for dataset in self.datasets] - widths = [dataset.width for dataset in self.datasets] + heights = [dataset.height for dataset in self.datasets] + widths = [dataset.width for dataset in self.datasets] self.height = min(heights) self.width = min(widths) for dataset in self.datasets: diff --git a/src/gz21_ocean_momentum/data/setup.py b/src/gz21_ocean_momentum/data/setup.py deleted file mode 100644 index 827ad982..00000000 --- a/src/gz21_ocean_momentum/data/setup.py +++ /dev/null @@ -1,18 +0,0 @@ -# TODO Don't think there should be a setup.py here... Remove/refactor? -# -*- coding: utf-8 -*- -""" -Created on Thu Nov 21 22:11:09 2019 - -@author: Arthur -""" -import setuptools -from distutils.core import setup -from Cython.Build import cythonize -import numpy - - -setup( - name="utils", - ext_modules=cythonize("_utils.pyx"), - include_dirs=[numpy.get_include()], -) diff --git a/src/gz21_ocean_momentum/data/utils.py b/src/gz21_ocean_momentum/data/utils.py deleted file mode 100755 index da5917a8..00000000 --- a/src/gz21_ocean_momentum/data/utils.py +++ /dev/null @@ -1,115 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -"""Utilities for handling data.""" -import mlflow -import xarray as xr -import yaml - - -def load_data_from_run(run_id): - """ - Load data from a previous run from mlflow files. - - Parameters - ---------- - run_id : str - unique mlflow identifier for run to load - - Returns - ------- - xr_dataset : xr.dataset - xarray dataset populated with data for requested run - """ - mlflow_client = mlflow.tracking.MlflowClient() - data_file = mlflow_client.download_artifacts(run_id, "forcing") - xr_dataset = xr.open_zarr(data_file) - return xr_dataset - - -def load_data_from_runs(run_ids): - """ - Load data from previous runs from mlflow files. - - Parameters - ---------- - run_id : list of str - list of unique mlflow identifiers for runs to load - - Returns - ------- - xr_datasets : list of xr.dataset - list of xarray datasets populated with data for requested runs - """ - xr_datasets = [] - for run_id in run_ids: - xr_datasets.append(load_data_from_run(run_id)) - return xr_datasets - - -def load_training_datasets(ds: xr.Dataset, config_fname: str): - """ - Load training data from a previous run from mlflow files. - - Parameters - ---------- - run_id : str - unique mlflow identifier for run to load - - Returns - ------- - results : list of ??? - description? - """ - results = [] - with open(config_fname, encoding="utf-8") as config_file: - try: - # AB TODO check that safe_load() is OK rather than load() - # TODO 2023-05-12 raehik: `full_load()` used in another changeset. - # safe_load gives errors. - #subdomains = yaml.safe_load(config_file) - subdomains = yaml.full_load(config_file) - except FileNotFoundError as e: - raise type(e)("Configuration file of subdomains not found") - for subdomain in subdomains: - coords = subdomain[1] - lats = slice(coords["lat_min"], coords["lat_max"]) - lons = slice(coords["lon_min"], coords["lon_max"]) - results.append(ds.sel(xu_ocean=lons, yu_ocean=lats)) - return results - - -def cyclize_dataset(ds: xr.Dataset, coord_name: str, nb_points: int): - """ - Generate a cyclic dataset from non-cyclic input. - - Return a cyclic dataset, with nb_points added on each end, along - the coordinate specified by coord_name. - - Parameters - ---------- - ds : xr.Dataset - Dataset to process. - coord_name : str - Name of the coordinate along which the data is made cyclic. - nb_points : int - Number of points added on each end. - - Returns - ------- - New extended dataset. - """ - # TODO make this flexible - cycle_length = 360.0 - left = ds.roll({coord_name: nb_points}, roll_coords=True) - right = ds.roll({coord_name: nb_points}, roll_coords=True) - right = right.isel({coord_name: slice(0, 2 * nb_points)}) - left[coord_name] = xr.concat( - (left[coord_name][:nb_points] - cycle_length, left[coord_name][nb_points:]), - coord_name, - ) - right[coord_name] = xr.concat( - (right[coord_name][:nb_points], right[coord_name][nb_points:] + cycle_length), - coord_name, - ) - new_ds = xr.concat((left, right), coord_name) - return new_ds diff --git a/src/gz21_ocean_momentum/inference/utils.py b/src/gz21_ocean_momentum/inference/utils.py index 6bd83aac..f598d093 100755 --- a/src/gz21_ocean_momentum/inference/utils.py +++ b/src/gz21_ocean_momentum/inference/utils.py @@ -1,5 +1,5 @@ #!/usr/bin/env python3 -# TODO 2023-05-12 raehik: in `inference/`, but also used by `trainScript.py` +# TODO 2023-05-12 raehik: in `inference/`, but also used by `cli/train.py` # -*- coding: utf-8 -*- """ Created on Tue Jun 9 17:58:33 2020 @@ -105,8 +105,10 @@ def _dataset_from_channels(array, channels_names: list, dims, coords): return xr.Dataset(data) -def create_large_test_dataset( - net, criterion, test_datasets, test_loaders, device, save_input: bool = False +def predict_lazy_cm2_6( + net: torch.nn.Module, + n_required_channels, channel_names, + test_datasets, test_loaders, device, save_input: bool = False ): """ Return an xarray dataset with the predictions carried out on the @@ -120,8 +122,13 @@ def create_large_test_dataset( ---------- net : torch.nn.Module Neural net used to make predictions + n_required_channels: int + number of channels, used for output data shape + channel_names: list[string] + channel names, used for output datasets test_datasets : list - List of PytTorch datasets containing the input data. + List of PyTorch datasets containing the input data. + Actual data is not used: we extract metadata such as height/width. test_loaders : list List of Pytorch DataLoaders corresponding to the datasets device : torch.device @@ -133,7 +140,6 @@ def create_large_test_dataset( ------- xarray.Dataset Dataset of predictions. - """ inputs = [] outputs = [] @@ -143,7 +149,7 @@ def create_large_test_dataset( temp = delayed_apply(net, loader, device) shape = ( len(test_dataset), - criterion.n_required_channels, + n_required_channels, test_dataset.output_height, test_dataset.output_width, ) @@ -157,17 +163,15 @@ def create_large_test_dataset( coords_s = test_dataset.output_coords coords_s["latitude"] = coords_s.pop("yu_ocean") coords_s["longitude"] = coords_s.pop("xu_ocean") - var_names = criterion.channel_names - output_dataset = _dataset_from_channels(output, var_names, new_dims, coords_s) + output_dataset = _dataset_from_channels(output, channel_names, new_dims, coords_s) outputs.append(output_dataset) # same for input if save_input: coords_uv = test_dataset.input_coords coords_uv["latitude"] = coords_uv.pop("yu_ocean") coords_uv["longitude"] = coords_uv.pop("xu_ocean") - var_names = ["usurf", "vsurf"] input_dataset = _dataset_from_channels( - input_, var_names, new_dims, coords_uv + input_, ["usurf", "vsurf"], new_dims, coords_uv ) inputs.append(input_dataset) if save_input: diff --git a/src/gz21_ocean_momentum/lib/data.py b/src/gz21_ocean_momentum/lib/data.py new file mode 100644 index 00000000..cda159a7 --- /dev/null +++ b/src/gz21_ocean_momentum/lib/data.py @@ -0,0 +1,299 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""Data step API: CM2.6 downloading, forcing generation and coarsening.""" + +import xarray as xr +import intake +from scipy.ndimage import gaussian_filter +import numpy as np + +from typing import Optional +from typing import Tuple + +import logging + +logger = logging.getLogger(__name__) + +def retrieve_cm2_6(catalog_uri: str, co2_increase: bool) -> xr.Dataset: + """ + Retrieve CM2.6 velocity and grid data (lazily, as Dask arrays) + via the given Pangeo ocean intake catalog URI. + + Returns a tuple of `(velocities, grid)`. + + Will download if given an `http://` URI. Will use local files such as + `/home/user/catalog.yaml` directly. + """ + catalog = intake.open_catalog(catalog_uri) + surface_fields = retrieve_cm2_6_velocities(catalog, co2_increase) + grid = retrieve_cm2_6_grid(catalog) + return surface_fields, grid + +def retrieve_cm2_6_grid(catalog: str) -> xr.Dataset: + """ + Retrieve CM2.6 grid data (lazily, as a Dask array) + via the given Pangeo ocean intake catalog. + """ + grid = catalog.GFDL_CM2_6.GFDL_CM2_6_grid + grid = grid.to_dask() + + # transform non-primary coords into vars + grid = grid.reset_coords()[["dxu", "dyu", "wet"]] + + return grid + +def retrieve_cm2_6_velocities(catalog: str, co2_increase: bool) -> xr.Dataset: + """ + Retrieve CM2.6 velocity data (lazily, as a Dask array) + via the given Pangeo ocean intake catalog. + """ + if co2_increase: + logger.info("using 1% annual CO2 increase dataset") + surface_fields = catalog.GFDL_CM2_6.GFDL_CM2_6_one_percent_ocean_surface + else: + logger.info("using control dataset -> no annual CO2 increase") + surface_fields = catalog.GFDL_CM2_6.GFDL_CM2_6_control_ocean_surface + surface_fields = surface_fields.to_dask() + return surface_fields + +def cyclize(dim_name: str, ds: xr.Dataset, nb_points: int) -> xr.Dataset: + """ + Generate a cyclic dataset from non-cyclic input. + + Return a cyclic dataset, with `nb_points` added on each end, along + the dimension specified by `dim_name`. + + Parameters + ---------- + dim_name: str + Name of the dimension along which the data is made cyclic. + ds : xr.Dataset + Dataset to process. + nb_points : int + Number of points added on each end. + + Returns + ------- + New extended dataset. + """ + # 2023-09-20 raehik: old note from original import: "make this flexible" + cycle_length = 360.0 + left = ds.roll({dim_name: nb_points}, roll_coords=True) + right = left.isel({dim_name: slice(0, 2 * nb_points)}) + left[dim_name] = xr.concat( + (left[dim_name][:nb_points] - cycle_length, left[dim_name][nb_points:]), + dim_name, + ) + right[dim_name] = xr.concat( + (right[dim_name][:nb_points], right[dim_name][nb_points:] + cycle_length), + dim_name, + ) + return xr.concat((left, right), dim_name) + + +def compute_forcings_and_coarsen_cm2_6_shape( + u_v_dataset: xr.Dataset, + scale: int, +) -> xr.Dataset: + """ + Template for output array of `compute_forcings_and_coarsen_cm2_6`. Required + for `xarray.map_blocks`, as it can only infer shape for simple functions. + + Should be used on Dask arrays. + """ + t = u_v_dataset.coarsen({"xu_ocean": scale, "yu_ocean": scale}, boundary="trim").mean() + + # clumsy way to create vars `S_x`, `S_y` of same coords as `usurf`, `vsurf` + t2 = t.copy() + t2 = t2.rename({"usurf": "S_x", "vsurf": "S_y"}) + t = xr.merge((t, t2)) + + return t + + +def compute_forcings_and_coarsen_cm2_6( + u_v_dataset: xr.Dataset, + grid_data: xr.Dataset, + scale: int, + nan_or_zero: str = "zero", +) -> xr.Dataset: + """ + Coarsen and compute subgrid forcings for the given ocean surface velocities. + Takes in high-resolution data, outputs low-resolution with associated + subgrid forcings. + + Designed for CM2.6 simulation data. + + Rough outline: + + * apply a Gaussian filter + * compute subgrid forcing from filtered data, save in filtered dataset + * coarsen this amended filtered dataset + + See Guillaumin (2021) 2.2 for further details. + + Parameters + ---------- + u_v_dataset : xarray Dataset + High-resolution velocity field in "usurf" and "vsurf". + grid_data : xarray Dataset + High-resolution grid details. + scale : int + gaussian filtering & coarsening factor + nan_or_zero: str, optional + String set to either 'nan' or 'zero'. Determines whether we keep the + nan values in the initial surface velocities array or whether we + replace them by zeros before applying the procedure. + In the second case, remaining zeros after applying the procedure will + be replaced by NaNs for consistency. + The default is 'zero'. + + Returns + ------- + forcing : xarray Dataset + Dataset containing the low-resolution velocity field in "usurf" and + "vsurf", and forcing in data variables "S_x" and "S_y". + """ + # Replace nan values with zeros. + if nan_or_zero == "zero": + u_v_dataset = u_v_dataset.fillna(0.0) + + # High res advection terms + adv = _advections(u_v_dataset, grid_data) + # Filtered advections + filtered_adv = _spatial_filter_dataset(adv, grid_data, float(scale)/2) + + # Filtered u,v field and temperature + u_v_filtered = _spatial_filter_dataset(u_v_dataset, grid_data, float(scale)/2) + # Advection term from filtered velocity field + adv_filtered = _advections(u_v_filtered, grid_data) + + # Forcing + ds_forcing = adv_filtered - filtered_adv + ds_forcing = ds_forcing.rename({"adv_x": "S_x", "adv_y": "S_y"}) + # Merge filtered u,v, temperature and forcing terms + ds_merged = ds_forcing.merge(u_v_filtered) + logger.debug("uncoarsened forcings follow below:") + logger.debug(ds_merged) + + # Coarsen + ds_merged_coarse = ds_merged.coarsen( + {"xu_ocean": scale, "yu_ocean": scale}, boundary="trim" + ).mean() + + if nan_or_zero == "zero": + # Replace zeros with nans for consistency + ds_merged_coarse = ds_merged_coarse.where(ds_merged_coarse["usurf"] != 0) + + # Specify input vs output type for each variable of the dataset. Might + # be used later on for training or testing. + ds_merged_coarse["S_x"].attrs["type"] = "output" + ds_merged_coarse["S_y"].attrs["type"] = "output" + ds_merged_coarse["usurf"].attrs["type"] = "input" + ds_merged_coarse["vsurf"].attrs["type"] = "input" + + return ds_merged_coarse + + +def _advections(u_v_field: xr.Dataset, grid_data: xr.Dataset) -> xr.Dataset: + """ + Compute advection terms corresponding to the passed velocity field. + + Parameters + ---------- + u_v_field : xarray Dataset + Velocity field, must contains variables "usurf" and "vsurf", coordinates + "xu_ocean" and "yu_ocean" + grid_data : xarray Dataset + grid data, must contain variables "dxu" and "dyu" + + Returns + ------- + result : xarray Dataset + Advection components, under variable names "adv_x" and "adv_y" + """ + dxu = grid_data["dxu"] + dyu = grid_data["dyu"] + gradient_x = u_v_field.diff(dim="xu_ocean") / dxu + gradient_y = u_v_field.diff(dim="yu_ocean") / dyu + # Interpolate back the gradients + interp_coords = { + "xu_ocean": u_v_field.coords["xu_ocean"], + "yu_ocean": u_v_field.coords["yu_ocean"], + } + gradient_x = gradient_x.interp(interp_coords) + gradient_y = gradient_y.interp(interp_coords) + u, v = u_v_field["usurf"], u_v_field["vsurf"] + adv_x = u * gradient_x["usurf"] + v * gradient_y["usurf"] + adv_y = u * gradient_x["vsurf"] + v * gradient_y["vsurf"] + result = xr.Dataset({"adv_x": adv_x, "adv_y": adv_y}) + # can these ops add chunks to our underlying Dask array? (should not) + return result + +def _spatial_filter_dataset( + dataset: xr.Dataset, grid_data: xr.Dataset, sigma: float + ) -> xr.Dataset: + """ + Apply spatial filtering to the dataset across the spatial dimensions. + + Parameters + ---------- + dataset : xarray Dataset + Dataset to filter. First dimension must be time, followed by spatial dimensions + grid_data: xarray Dataset + grid data, must include variables "dxu" and "dyu" + sigma : float + Scale of the filtering, same unit as those of the grid (often, meters) + + Returns + ------- + filt_dataset : xarray Dataset + Filtered dataset + """ + area_u = grid_data["dxu"] * grid_data["dyu"] / 1e8 + + # Normalisation term, so that if the quantity we filter is constant + # over the domain, the filtered quantity is constant with the same value + norm = xr.apply_ufunc( + lambda x: gaussian_filter(x, sigma, mode="constant"), + area_u, + dask="parallelized", + output_dtypes=[ + float, + ], + ) + filtered = xr.apply_ufunc( + lambda x: _spatial_filter(x, sigma), + dataset * area_u, + dask="parallelized", + output_dtypes=[ + float, + ], + ) + return filtered / norm + +def _spatial_filter(data: np.ndarray, sigma: float) -> np.ndarray: + """ + Apply a Gaussian filter to spatial data. + + Apply scipy Gaussian filter to along all dimensions except first one, which + corresponds to time. + + Parameters + ---------- + data : ndarray + Data to filter. + sigma : float + Unitless scale of the filter. + + Returns + ------- + result : ndarray + Filtered data + """ + result = np.zeros_like(data) + for t in range(data.shape[0]): + data_t = data[t, ...] + result_t = gaussian_filter(data_t, sigma, mode="constant") + result[t, ...] = result_t + return result diff --git a/src/gz21_ocean_momentum/lib/model.py b/src/gz21_ocean_momentum/lib/model.py new file mode 100644 index 00000000..75c74dd5 --- /dev/null +++ b/src/gz21_ocean_momentum/lib/model.py @@ -0,0 +1,93 @@ +# Common functions relating to neural net model, training data. + +import xarray as xr +import numpy as np +import torch.utils.data as torch + +from gz21_ocean_momentum.common.assorted import at_idx_pct + +from gz21_ocean_momentum.data.datasets import ( + DatasetWithTransform, + DatasetTransformer, + RawDataFromXrDataset, + ConcatDataset_, + Subset_, + ComposeTransforms, +) + +def cm26_xarray_to_torch(ds_xr: xr.Dataset) -> torch.Dataset: + """ + Obtain a PyTorch `Dataset` view over an xarray dataset, specifically for + CM2.6 ocean velocity data annotated with forcings in `S_x` and `S_y`. + """ + ds_torch = RawDataFromXrDataset(ds_xr) + ds_torch.index = "time" + ds_torch.add_input("usurf") + ds_torch.add_input("vsurf") + ds_torch.add_output("S_x") + ds_torch.add_output("S_y") + return ds_torch + +def gz21_train_data_subdomain_xr_to_torch(ds_xr: xr.Dataset) -> torch.Dataset: + """ + Convert GZ21 training data (coarsened CM2.6 data with diagnosed forcings) + into a PyTorch dataset. + + Intended to take in a single spatial subdomain of the "main" dataset. + Apply submodel transforms first. + Perform dataset splits after. + """ + ds_torch = cm26_xarray_to_torch(ds_xr) + + # prep empty transform, filled in later by custom torch DataLoaders + features_transform = ComposeTransforms() + targets_transform = ComposeTransforms() + transform = DatasetTransformer(features_transform, targets_transform) + ds_torch_with_transform = DatasetWithTransform(ds_torch, transform) + + return ds_torch_with_transform + +def prep_train_test_dataloaders( + dss: list, + pct_train_end: float, + pct_test_start: float, + batch_size: int): + """ + Split a list of PyTorch datasets into two dataloaders: one for training, + one for testing. + + Parameters + ---------- + pct_train_end: float + Training data will be from 0->x of the dataset. 0<=x<=1 + + pct_test_start: float + Test data will be from x->end of the dataset. pct_train_end<=x<=1 + + Returns + ------- + Two PyTorch DataLoaders: train, test. + """ + # split dataset according to requested lengths + train_range = lambda x: np.arange(0, at_idx_pct(pct_train_end, x)) + test_range = lambda x: np.arange(at_idx_pct(pct_test_start, x), len(x)) + + train_datasets = [ Subset_(x, train_range(x)) for x in dss ] + test_datasets = [ Subset_(x, test_range(x)) for x in dss ] + + # Concatenate datasets. This adds shape transforms to ensure that all + # regions produce fields of the same shape, hence should be called after + # saving the transformation so that when we're going to test on another + # region this does not occur. + train_dataset = ConcatDataset_(train_datasets) + test_dataset = ConcatDataset_(test_datasets) + + # Dataloaders + train_dataloader = torch.DataLoader( + train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4 + ) + test_dataloader = torch.DataLoader( + test_dataset, batch_size=batch_size, shuffle=False, drop_last=True + ) + + return train_dataloader, test_dataloader diff --git a/src/gz21_ocean_momentum/models/submodels.py b/src/gz21_ocean_momentum/models/submodels.py index b2b9f0d5..5bdf3c1b 100755 --- a/src/gz21_ocean_momentum/models/submodels.py +++ b/src/gz21_ocean_momentum/models/submodels.py @@ -13,7 +13,12 @@ TargetedTransform, ) +# v CM2.6 specific v + +# velocities (usurf, vsurf) are metres/s velocity_vars = ["usurf", "vsurf"] + +# forcing unitless -- common scale is ? forcing_vars = ["S_x", "S_y"] velocity_scaler = TargetedTransform(ScalingTransform(10.0), velocity_vars) diff --git a/src/gz21_ocean_momentum/train/base.py b/src/gz21_ocean_momentum/train/base.py index 5bc3cb67..46beb891 100755 --- a/src/gz21_ocean_momentum/train/base.py +++ b/src/gz21_ocean_momentum/train/base.py @@ -10,7 +10,7 @@ from torch.nn.utils import clip_grad_norm_ from torch.utils.data import DataLoader -from .utils import print_every, RunningAverage +from gz21_ocean_momentum.train.utils import print_every, RunningAverage class Trainer: @@ -103,32 +103,35 @@ def train_for_one_epoch( forward-backward pass. clip : float, - Value used to clipp gradients. Default is None, in which case - no clipping of gradients. + Value used to clip gradients. Default is None, in which case no + clipping of gradients. Returns ------- float The average train loss for this epoch. + + Effect: backpropagates loss, editing neural network. """ self.net.train() self._locked = True running_loss = RunningAverage() running_loss_ = RunningAverage() - for i_batch, batch in enumerate(dataloader): + for i, (feature, target) in enumerate(dataloader): # Zero the gradients self.net.zero_grad() # Move batch to the GPU (if possible) - X = batch[0].to(self._device, dtype=torch.float) - Y = batch[1].to(self._device, dtype=torch.float) - Y_hat = self.net(X) + feature = feature.to(self._device, dtype=torch.float) + target = target.to(self._device, dtype=torch.float) + # predict with input + predict = self.net(feature) # Compute loss - loss = self.criterion(Y_hat, Y) - running_loss.update(loss.item(), X.size(0)) - running_loss_.update(loss.item(), X.size(0)) + loss = self.criterion(predict, target) + running_loss.update(loss.item(), feature.size(0)) + running_loss_.update(loss.item(), feature.size(0)) # Print current loss loss_text = "Loss value {}".format(running_loss_.average) - if print_every(loss_text, self.print_loss_every, i_batch): + if print_every(loss_text, self.print_loss_every, i): # Every time we print we reset the running average running_loss_.reset() # Backpropagate diff --git a/src/gz21_ocean_momentum/train/utils.py b/src/gz21_ocean_momentum/train/utils.py index 70f5cece..54002edd 100755 --- a/src/gz21_ocean_momentum/train/utils.py +++ b/src/gz21_ocean_momentum/train/utils.py @@ -41,15 +41,15 @@ def print_every(to_print: str, every: int, n_iter: int) -> bool: class RunningAverage: """Class for online computing of a running average""" - def __init__(self): + def __init__(self) -> None: self.n_items = 0 self.average = 0.0 @property - def value(self): + def value(self) -> float: return self.average - def update(self, value: float, weight: float = 1) -> float: + def update(self, value: float, weight: int = 1) -> float: """Adds some value to be used in the running average. Parameters @@ -78,16 +78,16 @@ def update(self, value: float, weight: float = 1) -> float: self.average = temp / self.n_items return self.average - def reset(self): + def reset(self) -> None: """Resets the running average to zero as well as its number of items""" self.n_items = 0 self.average = 0.0 - def __str__(self): + def __str__(self) -> str: return str(self.average) -def learning_rates_from_string(rates_string: str) -> dict: +def learning_rates_from_string(rates_string: str) -> dict[int, float]: temp = rates_string.split("/") if len(temp) == 1: return {0: float(rates_string)} @@ -99,9 +99,9 @@ def learning_rates_from_string(rates_string: str) -> dict: return rates -def run_ids_from_string(run_ids_str: str) -> list: +def run_ids_from_string(run_ids_str: str) -> list[str]: return run_ids_str.split("/") -def list_from_string(string: str) -> list: +def list_from_string(string: str) -> list[str]: return string.split("/") diff --git a/src/gz21_ocean_momentum/trainScript.py b/src/gz21_ocean_momentum/trainScript.py deleted file mode 100755 index 391e50da..00000000 --- a/src/gz21_ocean_momentum/trainScript.py +++ /dev/null @@ -1,472 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -"""Script that performs training of a model on data.""" -import os -import os.path -import copy -import argparse -import importlib -import pickle -import tempfile -from dask.diagnostics import ProgressBar -import numpy as np -import mlflow -import xarray as xr - -import torch -from torch.utils.data import DataLoader -from torch import optim -from torch.optim.lr_scheduler import MultiStepLR - -# These imports are used to create the training datasets -from data.datasets import ( - DatasetWithTransform, - DatasetTransformer, - RawDataFromXrDataset, - ConcatDataset_, - Subset_, - ComposeTransforms, -) -from data.utils import load_training_datasets, load_data_from_run - -# Some utils functions -from train.utils import ( - DEVICE_TYPE, - learning_rates_from_string, -) -from train.base import Trainer -from inference.utils import create_test_dataset -from inference.metrics import MSEMetric, MaxMetric -import train.losses -from models import transforms, submodels - - -from utils import TaskInfo - -from typing import Any - -torch.autograd.set_detect_anomaly(True) - - -def _check_dir(dir_path): - """ - Create directory if it does not already exist. - - Parameters - ---------- - dir_path : str - string of directory to check/make - """ - if not os.path.exists(dir_path): - os.mkdir(dir_path) - - -def negative_int(value: str): - """ - Convert string input to negative integer. - - Parameters - ---------- - value : str - string to convert - - Returns - ------- - : int - negative integer of input string - """ - return -int(value) - - -def check_str_is_None(string_in: str): - """ - Return None if string is "none". - - Parameters - ---------- - string_in : str - string to check - - Returns - ------- - string_in or None : str or None - returns None if string_in is none, else returns string_in - """ - return None if string_in.lower() == "none" else string_in - - -# -------------------- -# READ IN DATA FOR RUN -# -------------------- -description = ( - "Trains a model on a chosen dataset from the store." - "Allows to set training parameters via the CLI." - "Use one of either --run-id or --forcing-data-path." -) -parser = argparse.ArgumentParser(description=description) - -parser.add_argument( - "--run-id", - type=str, - help="MLflow run ID of data step containing forcing data to use", -) - -# access input forcing data via absolute filepath -parser.add_argument( - "--forcing-data-path", type=str, help="Filepath of the forcing data" -) - -parser.add_argument("--batchsize", type=int, default=8) -parser.add_argument("--n_epochs", type=int, default=100) -parser.add_argument( - "--learning_rate", type=learning_rates_from_string, default="0/1e-3" -) -parser.add_argument("--train_split", type=float, default=0.8, help="Between 0 and 1") -parser.add_argument( - "--test_split", - type=float, - default=0.8, - help="Between 0 and 1, greater than train_split.", -) -parser.add_argument("--time_indices", type=negative_int, nargs="*") -parser.add_argument("--printevery", type=int, default=20) -parser.add_argument( - "--weight_decay", - type=float, - default=0.05, - help="Depreciated. Controls the weight decay on the linear " "layer", -) -parser.add_argument( - "--model_module_name", - type=str, - default="models.fully_conv_net", - help="Name of the module containing the nn model", -) -parser.add_argument( - "--model_cls_name", - type=str, - default="FullyCNN", - help="Name of the class defining the nn model", -) -parser.add_argument( - "--loss_cls_name", - type=str, - default="HeteroskedasticGaussianLossV2", - help="Name of the loss function used for training.", -) -parser.add_argument( - "--transformation_cls_name", - type=str, - default="SquareTransform", - help="Name of the transformation applied to outputs " - "required to be positive. Should be defined in " - "models.transforms.", -) -parser.add_argument("--submodel", type=str, default="transform1") -parser.add_argument( - "--features_transform_cls_name", type=str, default="None", help="Depreciated" -) -parser.add_argument( - "--targets_transform_cls_name", type=str, default="None", help="Depreciated" -) -params = parser.parse_args() - - -def argparse_get_mlflow_artifact_path_or_direct_or_fail( - mlflow_artifact_name: str, params: dict[str, Any] -) -> str: - """Obtain a filepath either from an MLflow run ID and artifact name, or a - direct path if provided. - - params must have keys run_id and forcing_data_path. - - Only one of run_id and path should be non-None. - - Note that the filepath is not checked for validity (but for run_id, MLflow - probably will assert that it exists). - - Effectful: errors result in immediate program exit. - """ - if params.run_id is not None and params.run_id != "None": - if params.forcing_data_path is not None and params.forcing_data_path != "None": - # got run ID and direct path: bad - raise TypeError( - "overlapping options provided (--forcing-data-path and --exp-id)" - ) - - # got only run ID: obtain path via MLflow - mlflow.log_param("source.run-id", params.run_id) - mlflow_client = mlflow.tracking.MlflowClient() - return mlflow_client.download_artifacts(params.run_id, mlflow_artifact_name) - - if params.forcing_data_path is not None and params.forcing_data_path != "None": - # got only direct path: use - return params.forcing_data_path - - # if we get here, neither options were provided - raise TypeError("require one of --run-id or --forcing-data-path") - - -forcings_path = argparse_get_mlflow_artifact_path_or_direct_or_fail("forcing", params) - -# -------------------------- -# SET UP TRAINING PARAMETERS -# -------------------------- -# Note that we use two indices for the train/test split. This is because we -# want to avoid the time correlation to play in our favour during test. -batch_size = params.batchsize -learning_rates = params.learning_rate -weight_decay = params.weight_decay -n_epochs = params.n_epochs -train_split = params.train_split -test_split = params.test_split -model_module_name = params.model_module_name -model_cls_name = params.model_cls_name -loss_cls_name = params.loss_cls_name -transformation_cls_name = params.transformation_cls_name -# Transforms applied to the features and targets -temp = params.features_transform_cls_name -features_transform_cls_name = check_str_is_None(temp) -temp = params.targets_transform_cls_name -targets_transform_cls_name = check_str_is_None(temp) -# Submodel (for instance monthly means) -submodel = params.submodel - - -# -------------------------- -# SET UP INPUT PARAMETERS -# -------------------------- -# Parameters specific to the input data -# past specifies the indices from the past that are used for prediction -indices = params.time_indices - -# Other parameters -print_loss_every = params.printevery -MODEL_NAME = "trained_model.pth" - -# Directories where temporary data will be saved -data_location = tempfile.mkdtemp() -print("Created temporary dir at ", data_location) - -FIGURES_DIRECTORY = "figures" -MODELS_DIRECTORY = "models" -MODEL_OUTPUT_DIR = "model_output" - -for directory in [FIGURES_DIRECTORY, MODELS_DIRECTORY, MODEL_OUTPUT_DIR]: - _check_dir(os.path.join(data_location, directory)) - -# Device selection. If available we use the GPU. -# TODO Allow CLI argument to select the GPU -device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") -device_type = DEVICE_TYPE.GPU if torch.cuda.is_available() else DEVICE_TYPE.CPU -print("Selected device type: ", device_type.value) - - -# ------------------ -# LOAD TRAINING DATA -# ------------------ -# Extract the run ids for the datasets to use in training -global_ds = xr.open_zarr(forcings_path) -# Load data from the store, according to experiment id and run id -xr_datasets = load_training_datasets(global_ds, "training_subdomains.yaml") -# Split into train and test datasets -datasets, train_datasets, test_datasets = [], [], [] - - -for xr_dataset in xr_datasets: - # TODO this is a temporary fix to implement seasonal patterns - submodel_transform = copy.deepcopy(getattr(submodels, submodel)) - print(submodel_transform) - xr_dataset = submodel_transform.fit_transform(xr_dataset) - with ProgressBar(), TaskInfo("Computing dataset"): - # Below line only for speeding up debugging - # xr_dataset = xr_dataset.isel(time=slice(0, 1000)) - xr_dataset = xr_dataset.compute() - print(xr_dataset) - dataset = RawDataFromXrDataset(xr_dataset) - dataset.index = "time" - dataset.add_input("usurf") - dataset.add_input("vsurf") - dataset.add_output("S_x") - dataset.add_output("S_y") - # TODO temporary addition, should be made more general - if submodel == "transform2": - dataset.add_output("S_x_d") - dataset.add_output("S_y_d") - if submodel == "transform4": - dataset.add_input("s_x_formula") - dataset.add_input("s_y_formula") - train_index = int(train_split * len(dataset)) - test_index = int(test_split * len(dataset)) - features_transform = ComposeTransforms() - targets_transform = ComposeTransforms() - transform = DatasetTransformer(features_transform, targets_transform) - dataset = DatasetWithTransform(dataset, transform) - # dataset = MultipleTimeIndices(dataset) - # dataset.time_indices = [0, ] - train_dataset = Subset_(dataset, np.arange(train_index)) - test_dataset = Subset_(dataset, np.arange(test_index, len(dataset))) - train_datasets.append(train_dataset) - test_datasets.append(test_dataset) - datasets.append(dataset) - -# Concatenate datasets. This adds shape transforms to ensure that all regions -# produce fields of the same shape, hence should be called after saving -# the transformation so that when we're going to test on another region -# this does not occur. -train_dataset = ConcatDataset_(train_datasets) -test_dataset = ConcatDataset_(test_datasets) - -# Dataloaders -train_dataloader = DataLoader( - train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4 -) -test_dataloader = DataLoader( - test_dataset, batch_size=batch_size, shuffle=False, drop_last=True -) - -print(f"Size of training data: {len(train_dataset)}") -print(f"Size of validation data : {len(test_dataset)}") - - -# ------------------- -# LOAD NEURAL NETWORK -# ------------------- -# Load the loss class required in the script parameters -n_target_channels = datasets[0].n_targets -criterion = getattr(train.losses, loss_cls_name)(n_target_channels) - -# Recover the model's class, based on the corresponding CLI parameters -try: - models_module = importlib.import_module(model_module_name) - model_cls = getattr(models_module, model_cls_name) -except ModuleNotFoundError as e: - raise type(e)("Could not find the specified module for : " + str(e)) -except AttributeError as e: - raise type(e)("Could not find the specified model class: " + str(e)) -net = model_cls(datasets[0].n_features, criterion.n_required_channels) -try: - transformation_cls = getattr(transforms, transformation_cls_name) - transformation = transformation_cls() - transformation.indices = criterion.precision_indices - net.final_transformation = transformation -except AttributeError as e: - raise type(e)("Could not find the specified transformation class: " + str(e)) - -print("--------------------") -print(net) -print("--------------------") -print("***") - - -# Log the text representation of the net into a txt artifact -with open( - os.path.join(data_location, MODELS_DIRECTORY, "nn_architecture.txt"), - "w", - encoding="utf-8", -) as f: - print("Writing neural net architecture into txt file.") - f.write(str(net)) - -# Add transforms required by the model. -for dataset in datasets: - dataset.add_transforms_from_model(net) - - -# ------------------- -# TRAINING OF NETWORK -# ------------------- -# Adam optimizer -# To GPU -net.to(device) - -# Optimizer and learning rate scheduler -params = list(net.parameters()) -optimizer = optim.Adam(params, lr=learning_rates[0], weight_decay=weight_decay) -lr_scheduler = MultiStepLR(optimizer, list(learning_rates.keys())[1:], gamma=0.1) - -trainer = Trainer(net, device) -trainer.criterion = criterion -trainer.print_loss_every = print_loss_every - -# metrics saved independently of the training criterion. -metrics = {"R2": MSEMetric(), "Inf Norm": MaxMetric()} -for metric_name, metric in metrics.items(): - metric.inv_transform = lambda x: test_dataset.inverse_transform_target(x) - trainer.register_metric(metric_name, metric) - -for i_epoch in range(n_epochs): - print(f"Epoch number {i_epoch}.") - # TODO remove clipping? - train_loss = trainer.train_for_one_epoch( - train_dataloader, optimizer, lr_scheduler, clip=1.0 - ) - test = trainer.test(test_dataloader) - if test == "EARLY_STOPPING": - print(test) - break - test_loss, metrics_results = test - # Log the training loss - print("Train loss for this epoch is ", train_loss) - print("Test loss for this epoch is ", test_loss) - - for metric_name, metric_value in metrics_results.items(): - print(f"Test {metric_name} for this epoch is {metric_value}") - mlflow.log_metric("train loss", train_loss, i_epoch) - mlflow.log_metric("test loss", test_loss, i_epoch) - mlflow.log_metrics(metrics_results) -# Update the logged number of actual training epochs -mlflow.log_param("n_epochs_actual", i_epoch + 1) - - -# ------------------------------ -# SAVE THE TRAINED MODEL TO DISK -# ------------------------------ -net.cpu() -full_path = os.path.join(data_location, MODELS_DIRECTORY, MODEL_NAME) -torch.save(net.state_dict(), full_path) -net.to(device=device) - -# Save other parts of the model -# TODO this should not be necessary -print("Saving other parts of the model") -full_path = os.path.join(data_location, MODELS_DIRECTORY, "transformation") -with open(full_path, "wb") as f: - pickle.dump(transformation, f) - -with TaskInfo("Saving trained model"): - mlflow.log_artifact(os.path.join(data_location, MODELS_DIRECTORY)) - - -# ---------- -# DEBUT TEST -# ---------- -for i_dataset, dataset, test_dataset, xr_dataset in zip( - range(len(datasets)), datasets, test_datasets, xr_datasets -): - test_dataloader = DataLoader( - test_dataset, batch_size=batch_size, shuffle=False, drop_last=True - ) - output_dataset = create_test_dataset( - net, - criterion.n_required_channels, - xr_dataset, - test_dataset, - test_dataloader, - test_index, - device, - ) - - # Save model output on the test dataset - output_dataset.to_zarr( - os.path.join(data_location, MODEL_OUTPUT_DIR, f"test_output{i_dataset}") - ) - - -# ----------------------- -# LOG ARTIFACTS IN MLFLOW -# ----------------------- -print("Logging artifacts...") -mlflow.log_artifact(os.path.join(data_location, FIGURES_DIRECTORY)) -mlflow.log_artifact(os.path.join(data_location, MODEL_OUTPUT_DIR)) -print("Done...") diff --git a/tests/data/test_coarse.py b/tests/lib/test_data.py similarity index 95% rename from tests/data/test_coarse.py rename to tests/lib/test_data.py index 335f34c6..8fd5e6c3 100755 --- a/tests/data/test_coarse.py +++ b/tests/lib/test_data.py @@ -1,14 +1,13 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -"""Unit tests for the data/coarse.py module""" +"""Unit tests for the data step.""" import pytest import xarray as xr import numpy as np from numpy import ma import matplotlib.pyplot as plt -from gz21_ocean_momentum.data.coarse import spatial_filter_dataset, spatial_filter, eddy_forcing - +import gz21_ocean_momentum.lib.data as lib class TestEddyForcing: "Class to test eddy forcing routines." @@ -18,7 +17,7 @@ def test_spatial_filter(self): Check that number of dimensions stay the same through spatial_filter(). """ a = np.random.randn(10, 4, 4) - filtered_a = spatial_filter(a, 5) + filtered_a = lib._spatial_filter(a, 5) assert a.ndim == filtered_a.ndim def test_spatial_filter_of_constant(self): @@ -49,7 +48,7 @@ def test_spatial_filter_of_constant(self): } ) - filtered_data = spatial_filter_dataset(data, grid_info, (5, 5)) + filtered_data = lib._spatial_filter_dataset(data, grid_info, (5, 5)) assert data["a"].values == pytest.approx(filtered_data["a"].values) @@ -88,7 +87,7 @@ def test_eddy_forcing_chunks(self): ), } ) - forcing = eddy_forcing(data, grid_info, 4) + forcing = lib.compute_forcings_and_coarsen_cm2_6(data, grid_info, 4) usurf_0, usurf_1 = forcing.usurf.isel(time=0), forcing.usurf.isel(time=1) # remove nan values at the boundaries from the test usurf_0 = usurf_0.data[~np.isnan(usurf_0)] @@ -144,9 +143,7 @@ def test_eddy_forcing_chunking(self): ) # new forcing: apply - forcing_new = eddy_forcing( - data, grid_info, scale=scale_m, - method='mean', scale_mode='factor') + forcing_new = lib.compute_forcings_and_coarsen_cm2_6(data, grid_info, scale=scale_m) # new forcing: post-chunk for var in forcing_new: @@ -172,7 +169,7 @@ def f(block): - S_x and S_y, the two components of the diagnosed subgrid momentum forcing """ - return eddy_forcing(block, grid_info, scale=scale_m) + return lib.compute_forcings_and_coarsen_cm2_6(block, grid_info, scale=scale_m) # old forcing: apply template = data.coarsen( diff --git a/training_subdomains.yaml b/training_subdomains.yaml deleted file mode 100644 index d653be1c..00000000 --- a/training_subdomains.yaml +++ /dev/null @@ -1,26 +0,0 @@ -#Configuration file for the subdomains used in the training and validation phase -!!python/tuple -- !!python/tuple - - training - - lat_min: 35. - lat_max: 50. - lon_min: -50. - lon_max: -20. -- !!python/tuple - - training - - lat_min: -40. - lat_max: -25. - lon_min: -180. - lon_max: -162. -- !!python/tuple - - training - - lat_min: -20. - lat_max: -5. - lon_min: -110. - lon_max: -92. -- !!python/tuple - - training - - lat_min: -0. - lat_max: 15. - lon_min: -48 - lon_max: -30