From ade4ad2a01f2c24ea370dd1af839945f77606ed4 Mon Sep 17 00:00:00 2001 From: flicj191 Date: Fri, 6 Sep 2024 11:58:37 +1000 Subject: [PATCH 1/9] add core api tutorial --- _episodes/11-esmvalcoreapi.md | 510 ++++++++++++++++++++++++++++ _includes/links.md | 5 + files/Minimal_example.ipynb | 596 +++++++++++++++++++++++++++++++++ files/example_seaicearea.ipynb | 183 ++++++++++ 4 files changed, 1294 insertions(+) create mode 100644 _episodes/11-esmvalcoreapi.md create mode 100644 files/Minimal_example.ipynb create mode 100644 files/example_seaicearea.ipynb diff --git a/_episodes/11-esmvalcoreapi.md b/_episodes/11-esmvalcoreapi.md new file mode 100644 index 00000000..e948307e --- /dev/null +++ b/_episodes/11-esmvalcoreapi.md @@ -0,0 +1,510 @@ +--- +title: "ESMValCore API in a Jupyter notebook" +teaching: 20 +exercises: 30 +compatibility: ESMValTool, ESMValCore v2.11.0 + +questions: +- "How to find data for ESMValTool in a Jupyter Notebook?" +- "How to use preprocessor functions?" +objectives: +- "Use the Dataset object" +- "Import and use preprocessor functions" +- "View and check the data" +keypoints: +- "API can be used as a helper to develop recipes" +- "Preprocessors can be used in a Jupyter Notebook to check the output" +- "Use `datasets_to_recipe` helper to start making recipes" +--- + +In this episode we will introduce the ESMValCore API in a jupyter notebook. This is reformatted from material from +this [blog post](https://blog.esciencecenter.nl/easy-ipcc-powered-by-esmvalcore-19a0b6366ea7){:target="_blank"} +by Peter Kalverla. There's also material from the [example notebooks][docs-notebooks]{:target="_blank"} and the +[API reference documentation][api-reference]{:target="_blank"}. + +## Start JupyterLab +A [jupyter notebook](https://jupyter.org/){:target="_blank"} is an interactive document where you can run code. +You will need to use a python environment with ESMValTool and ESMValCore installed. + +## Find Datasets with facets +We have seen from running available recipes that ESMValTool is able to find data from facets that were given in +the recipe. We can use this in a Notebook, including filling out the facets for data definition. +To do this we will use the `Dataset` object from the API. Let's look at this example. + +```python +from esmvalcore.dataset import Dataset + +dataset = Dataset( + short_name='tos', + mip='Omon', + project='CMIP6', + exp='historical', + dataset='ACCESS-ESM1-5', + ensemble='r4i1p1f1', + grid='gn', +) +dataset.augment_facets() +print(dataset) +``` +> ## Pro tip: Augmented facets in the output +> When running a recipe there is a `_filled` recipe `yml` file in the output `/run` folder which augments the facets. +> > ## Example recipe output folder +> > ```output +> > esmvaltool_output/flato13ipcc_figure914_20240729_043707/run +> > ├── cmor_log.txt +> > ├── fig09-14 +> > ├── flato13ipcc_figure914_filled.yml +> > ├── flato13ipcc_figure914.yml +> > ├── main_log_debug.txt +> > ├── main_log.txt +> > └── resource_usage.txt +> > ``` +> {: .solution} +{: .callout} + +> ## Search available +> Search from files available locally with wildcard functionality `'*'` to get all the available datasets. +> - How can you search for all available ensembles? +> +> > ## Solution +> > ```python +> > +> > dataset_search = Dataset( +> > short_name='tos', +> > mip='Omon', +> > project='CMIP6', +> > exp='historical', +> > dataset='ACCESS-ESM1-5', +> > ensemble='*', +> > grid='gn', +> > ) +> > ensemble_datasets = list(dataset_search.from_files()) +> > +> > print([ds['ensemble'] for ds in ensemble_datasets]) +> > ``` +> {: .solution} +> There is also the ability to search on ESGF nodes and download. See +> [reference](https://docs.esmvaltool.org/projects/ESMValCore/en/latest/api/esmvalcore.esgf.html){:target="_blank"} +> for more details. Check the configuration settings for this. +>```python +>from esmvalcore.config import CFG +>CFG['search_esgf'] = 'always' +>CFG['download_dir'].mkdir(exist_ok=True) +>``` +{: .challenge} + +> ## Add supplementary variables +> Supplementary variables can be added to the `Dataset` object which can be used for certain +> preprocessors such as area statistics and weighting. +> - Add the area file to this Dataset. +> +> > ## Solution +> > ```python +> > # Discard augmented facets as they will be different for areacello +> > dataset = Dataset(**dataset.minimal_facets) +> > +> > # Add areacello as supplementary dataset +> > dataset.add_supplementary(short_name='areacello', mip='Ofx') +> > +> > # Autocomplete and inspect +> > dataset.augment_facets() +> > print(dataset.summary()) +> > ``` +> {: .solution} +{: .challenge} + + +> ## Loading the data and inspect +> +> ```python +> # Before load, checks location of file +> print(dataset.files) +> +> cube = dataset.load() +> cube +> ``` +> > ## Output +> > ```output +> > sea_surface_temperature / (degC) (time: 1980; cell index along second dimension: 300; cell index along first dimension: 360) +> > Dimension coordinates: +> > time x - - +> > cell index along second dimension - x - +> > cell index along first dimension - - x +> > Auxiliary coordinates: +> > latitude - x x +> > longitude - x x +> > Cell measures: +> > cell_area - x x +> > Cell methods: +> > 0 area: mean where sea +> > 1 time: mean +> > Attributes: +> > Conventions 'CF-1.7 CMIP-6.2' +> > activity_id 'CMIP' +> > branch_method 'standard' +> > branch_time_in_child 0.0 +> > branch_time_in_parent -594980 +> > cmor_version '3.4.0' +> > data_specs_version '01.00.30' +> > experiment 'all-forcing simulation of the recent past' +> > experiment_id 'historical' +> > external_variables 'areacello' +> > forcing_index 1 +> > frequency 'mon' +> > further_info_url 'https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.no ...' +> > grid 'native atmosphere N96 grid (145x192 latxlon)' +> > grid_label 'gn' +> > initialization_index 1 +> > institution 'Commonwealth Scientific and Industrial Research Organisation, Aspendale, ...' +> > institution_id 'CSIRO' +> > license 'CMIP6 model data produced by CSIRO is licensed under a Creative Commons ...' +> > mip_era 'CMIP6' +> > nominal_resolution '250 km' +> > notes "Exp: ESM-historical; Local ID: HI-08; Variable: tos (['sst'])" +> > parent_activity_id 'CMIP' +> > parent_experiment_id 'piControl' +> > parent_mip_era 'CMIP6' +> > parent_source_id 'ACCESS-ESM1-5' +> > parent_time_units 'days since 1850-1-1 00:00:00' +> > parent_variant_label 'r1i1p1f1' +> > physics_index 1 +> > product 'model-output' +> > realization_index 4 +> > realm 'ocean' +> > run_variant 'forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, ...' +> > source 'ACCESS-ESM1.5 (2019): \naerosol: CLASSIC (v1.0)\natmos: HadGAM2 (r1.1, ...' +> > source_id 'ACCESS-ESM1-5' +> > source_type 'AOGCM' +> > sub_experiment 'none' +> > sub_experiment_id 'none' +> > table_id 'Omon' +> > table_info 'Creation Date:(30 April 2019) MD5:40e9ef53d4d2ec9daef980b76f23d39a' +> > title 'ACCESS-ESM1-5 output prepared for CMIP6' +> > variable_id 'tos' +> > variant_label 'r4i1p1f1' +> > version 'v20200529' +> > ``` +> {: .solution} +{: .challenge} + +## Preprocessors +As mentioned in previous lessons, the idea of preprocessors are that they are a set of +functions that can be applied in a centralised, documented and efficient way. There +are a broad range of operations that are commonly done to input data before diagnostics +or metrics are applied and can be done to all the datasets in a recipe consistently. +See the [documentation][recipe-section-preprocessors]{:target="_blank"} to read further. + +> ## Exercise: apply preprocessors using the API +> See [API reference][api-preprocessors]{:target="_blank"} to check the +> arguments for preprocessor functions. For this exercise, find; +> 1. The global mean, +> 2. Then anomalies which we can get monthly, +> 3. Then aggregate annually for plotting and inspect the cube. +> +> > ## Solution +> > ```python +> > from esmvalcore.preprocessor import annual_statistics, anomalies, area_statistics +> > +> > # Set the reference period for anomalies +> > reference_period = { +> > "start_year": 1950, "start_month": 1, "start_day": 1, +> > "end_year": 1979, "end_month": 12, "end_day": 31, +> > } +> > +> > cube = area_statistics(cube, operator='mean') +> > cube = anomalies(cube, reference=reference_period, period='month') +> > cube = annual_statistics(cube, operator='mean') +> > cube.convert_units('degrees_C') +> > cube +> > ``` +> > +> > ```output +> > sea_surface_temperature / (degrees_C) (time: 165) +> > Dimension coordinates: +> > time x +> > Auxiliary coordinates: +> > year x +> > Scalar coordinates: +> > cell index along first dimension 179, bound=(0, 359) +> > cell index along second dimension 149, bound=(0, 299) +> > latitude 6.0 degrees_north, bound=(-78.0, 90.0) degrees_north +> > longitude 179.9867706298828 degrees_east, bound=(0.0, 359.9735412597656) degrees_east +> > Cell methods: +> > 0 area: mean where sea +> > 1 time: mean +> > 2 latitude: longitude: mean +> > 3 year: mean +> > ``` +> {: .solution} +{: .challenge} + + +## Custom code +We have so far solely used ESMValCore, however, you can use your own custom code and +being in a Notebook means you can try straight away. Now, continue with other libraries +and make custom plots such as `xarray`. +```python +import xarray as xr +da = xr.DataArray.from_iris(cube) +da.plot() +print(da) +``` +### Plot data +The output from the preprocessor functions are Iris cubes. +[Iris](https://scitools-iris.readthedocs.io/en/latest/index.html){:target="_blank"} +has wrappers for [matplotlib](https://matplotlib.org/){:target="_blank"} to [plot the processed +cubes](https://scitools-iris.readthedocs.io/en/latest/userguide/plotting_a_cube.html#iris-cube-plotting){:target="_blank"}. +This is useful in a notebook to help develop your recipe with the esmvalcore preprocessors. +```python +from iris import quickplot +quickplot.plot(cube) +``` + +## Build workflow and diagnostic +> ## Exercise - Easy IPCC plot for sea surface temperature +> Let's pull some of these bits together to build a diagnostic. +> - Using the `Dataset` object, make a template which we can use to find multiple +> datasets we want to analyse together for variable `tos`. +> - The datasets being `"CESM2", "MPI-ESM1-2-LR", "ACCESS-ESM1-5"` and +> experiments `'ssp126', 'ssp585'` with historical, iterate to build a list of datasets. +> - Apply the preprocessors to each dataset and plot the result +> +> > ## Solution +> > ```python +> > import cf_units +> > import matplotlib.pyplot as plt +> > from iris import quickplot +> > +> > from esmvalcore.config import CFG +> > from esmvalcore.dataset import Dataset +> > from esmvalcore.preprocessor import annual_statistics, anomalies, area_statistics +> > +> > +> > # Settings for automatic ESGF search +> > CFG['search_esgf'] = 'when_missing' +> > +> > # Declare common dataset facets +> > template = Dataset( +> > short_name='tos', +> > mip='Omon', +> > project='CMIP6', +> > exp= '*', # We'll fill this below +> > dataset='*', # We'll fill this below +> > ensemble='r4i1p1f1', +> > grid='gn', +> > ) +> > +> > # Substitute data sources and experiments +> > datasets = [] +> > for dataset_id in ["CESM2", "MPI-ESM1-2-LR", "ACCESS-ESM1-5"]: +> > for experiment_id in ['ssp126', 'ssp585']: +> > dataset = template.copy(dataset=dataset_id, exp=['historical', experiment_id]) +> > dataset.add_supplementary(short_name='areacello', mip='Ofx', exp='historical') +> > dataset.augment_facets() +> > datasets.append(dataset) +> > +> > # Set the reference period for anomalies +> > reference_period = { +> > "start_year": 1950, "start_month": 1, "start_day": 1, +> > "end_year": 1979, "end_month": 12, "end_day": 31, +> > } +> > +> > # (Down)load, pre-process, and plot the cubes +> > for dataset in datasets: +> > cube = dataset.load() +> > cube = area_statistics(cube, operator='mean') +> > cube = anomalies(cube, reference=reference_period, period='month') # notice 'month' +> > cube = annual_statistics(cube, operator='mean') +> > cube.convert_units('degrees_C') +> > +> > # Make sure all datasets use the same calendar for plotting +> > tcoord = cube.coord('time') +> > tcoord.units = cf_units.Unit(tcoord.units.origin, calendar='gregorian') +> > +> > # Plot +> > quickplot.plot(cube, label=f"{dataset['dataset']} - {dataset['exp']}") +> > +> > # Show the plot +> > plt.legend() +> > plt.show() +> > ``` +> {: .solution} +{: .challenge} + +> ## Pro tip: Convert to recipe +> We can use the helper to start making the recipe. A recipe can be used for reproducibility of an +> analysis. This list the datasets in a recipe format and we would then have to create the preprocessors +> and diagnostic script. +> ```python +> from esmvalcore.dataset import datasets_to_recipe +> import yaml +> +> for dataset in datasets: +> dataset.facets['diagnostic'] = 'easy_ipcc' +> print(yaml.safe_dump(datasets_to_recipe(datasets))) +> ``` +> +> > ## Output +> > ```yaml +> > datasets: +> > - dataset: ACCESS-ESM1-5 +> > exp: +> > - historical +> > - ssp126 +> > - dataset: ACCESS-ESM1-5 +> > exp: +> > - historical +> > - ssp585 +> > - dataset: CESM2 +> > exp: +> > - historical +> > - ssp126 +> > - dataset: CESM2 +> > exp: +> > - historical +> > - ssp585 +> > - dataset: MPI-ESM1-2-LR +> > exp: +> > - historical +> > - ssp126 +> > - dataset: MPI-ESM1-2-LR +> > exp: +> > - historical +> > - ssp585 +> > diagnostics: +> > easy_ipcc: +> > variables: +> > tos: +> > ensemble: r4i1p1f1 +> > grid: gn +> > mip: Omon +> > project: CMIP6 +> > supplementary_variables: +> > - exp: historical +> > mip: Ofx +> > short_name: areacello +> > timerange: 1850/2100 +> > ``` +> {: .solution} +{: .callout} + +> ## Run through Minimal example notebook +> You can download an example notebook file [here](../files/Minimal_example.ipynb). +> This notebook includes: +> - Plot 2D field on a map +> - Hovmoller Diagram +> - Wind speed over Australia +> - Air Potential Temperature (3D data) Transect +> - Australian mean temperature timeseries +{: .challenge} + +> ## Exercise: Sea-ice area +> Use observation data and 2 model datasets to show trends in sea-ice. +> +> +> - Using variable `siconc` which is a fraction percent(0-100) +> - Using datasets: +> - `dataset:'ACCESS-ESM1-5', exp:'historical', ensemble:'r1i1p1f1', timerange:'1960/2010'` +> - `dataset :'ACCESS-OM2', exp:'omip2', ensemble='r1i1p1f1', timerange:'0306/0366'` +> - Using observations: +> - `dataset:'NSIDC-G02202-sh', tier:'3', version:'4', timerange:'1979/2018'` +> +> 1. Extract Southern hemisphere +> 2. Use only valid values (15 -100 %) +> 3. Sum sea ice area which will be the fraction multiplied by cell area and summed +> 4. Plot yearly minimum and maximum value +> +> Solution notebook can be downloaded [here](../files/example_seaicearea.ipynb). +> +> > ## 1. Define datasets: +> > +> > ```python +> > from esmvalcore.dataset import Dataset +> > obs = Dataset( +> > short_name='siconc', mip='SImon', project='OBS6', type='reanaly', +> > dataset='NSIDC-G02202-sh', tier='3', version='4', timerange='1979/2018', +> > ) +> > # Add areacello as supplementary dataset +> > obs.add_supplementary(short_name='areacello', mip='Ofx') +> > +> > model = Dataset( +> > short_name='siconc', mip='SImon', project='CMIP6', activity='CMIP', +> > dataset='ACCESS-ESM1-5', ensemble='r1i1p1f1', grid='gn', exp='historical', +> > timerange='1960/2010', institute = '*', +> > ) +> > +> > om_facets={'dataset' :'ACCESS-OM2', 'exp':'omip2', 'activity':'OMIP', 'timerange':'0306/0366' } +> > +> > model.add_supplementary(short_name='areacello', mip='Ofx') +> > +> > model_om = model.copy(**om_facets) +> > ``` +> {: .solution} +> > ## Tip: Check dataset files can be found +> > The observational dataset used is a Tier 3, so with some licensing restrictions. It is not directly +> > accesible here. Check files can be found for all the datasets: +> > +> > ```python +> > for ds in [model, model_om, obs]: +> > print(ds['dataset'],' : ' ,ds.files) +> > print(ds.supplementaries[0].files) +> > ``` +> > This observation dataset does have a downloader and formatter with ESMValTool. +> > ```bash +> > esmvaltool data download --config_file NSIDC-G02202-sh +> > esmvaltool data format --config_file NSIDC-G02202-sh +> > ``` +> > For this plot we can drop it for now. But you can also try to find and add another dataset. eg: +> > ```python +> > obs_other = Dataset( +> > short_name='siconc', mip='*', project='OBS', type='*', +> > dataset='*', tier='*', timerange='1979/2018' +> > ) +> > obs_other.files +> > ``` +> {: .solution} +> > ## 2. Use esmvalcore API preprocessors on the datasets and plot results +> > +> > ```python +> > import iris +> > import matplotlib.pyplot as plt +> > from iris import quickplot +> > from esmvalcore.preprocessor import ( +> > mask_outside_range, +> > extract_region, +> > area_statistics, +> > annual_statistics +> > ) +> > # om - at index 1 to offset years +> > # drop observations that cannot be found +> > load_data = [model, model_om] #, obs] +> > +> > # function to use for both min and max ['max','min'] +> > +> > def trends_seaicearea(min_max): +> > plt.clf() +> > for i,data in enumerate(load_data): +> > cube = data.load() +> > cube = mask_outside_range(cube, 15, 100) +> > cube = extract_region(cube,0,360,-90,0) +> > cube = area_statistics(cube, 'sum') +> > cube = annual_statistics(cube, min_max) +> > +> > iris.util.promote_aux_coord_to_dim_coord(cube, 'year') +> > cube.convert_units('km2') +> > if i == 1: ## om years 306/366 apply offset +> > cube.coord('year').points = [y + 1652 for y in cube.coord('year').points] +> > label_name = data['dataset'] +> > print(label_name, cube.shape) +> > quickplot.plot(cube, label=label_name) +> > +> > plt.title(f'Trends in Sea-Ice {min_max.title()}ima') +> > plt.ylabel('Sea-Ice Area (km2)') +> > plt.legend() +> > +> > trends_seaicearea('min') +> > ``` +> {: .solution} +{: .challenge} + +{% include links.md %} diff --git a/_includes/links.md b/_includes/links.md index 9d788081..147b7f83 100644 --- a/_includes/links.md +++ b/_includes/links.md @@ -83,3 +83,8 @@ [timeranges]: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/recipe/overview.html#time-ranges [workshop-repo]: {{ site.workshop_repo }} [yaml]: http://yaml.org/ +[api-config]: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/api/esmvalcore.config.html +[experimental-output]: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/api/esmvalcore.experimental.recipe_output.html +[docs-notebooks]: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/example-notebooks.html +[api-reference]: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/api/esmvalcore.html#api +[api-preprocessors]: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/api/esmvalcore.preprocessor.html diff --git a/files/Minimal_example.ipynb b/files/Minimal_example.ipynb new file mode 100644 index 00000000..4d3c0179 --- /dev/null +++ b/files/Minimal_example.ipynb @@ -0,0 +1,596 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cbc075ad-698b-461a-b721-b184be99f82b", + "metadata": {}, + "source": [ + "# ESMValCore Minimal example\n", + "\n", + "The following example illustrate how to leverage ESMValCore, the engine powering the ESMValTool collection of recipes, to quickly load CMIP data and do some analysis on them.\n", + "This is a minimal example, we first look at how we can quickly find and plot a map and a timeseries.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7da924b4-bf37-4171-a17f-df8d2fe03174", + "metadata": {}, + "source": [ + "# Plot a 2D field on a map" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "cf28f9c2-7bbf-48e9-98d9-3cdcf913491c", + "metadata": {}, + "outputs": [], + "source": [ + "from esmvalcore.dataset import Dataset" + ] + }, + { + "cell_type": "markdown", + "id": "31bde22b-1f1c-420b-bc1c-0ede8cb1d0e5", + "metadata": {}, + "source": [ + "## Load data\n", + "\n", + "Here we look for some surface temperature data from an historical model experiment" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "935d47dd-1707-4f0f-9082-b31a34bfc367", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = Dataset(\n", + " short_name='tas',\n", + " project='CMIP6',\n", + " mip=\"Amon\",\n", + " exp=\"historical\",\n", + " ensemble=\"r1i1p1f1\",\n", + " dataset='ACCESS-ESM1-5',\n", + " grid=\"gn\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "08bae8c7-6278-4356-b45b-4b3ea6a63942", + "metadata": {}, + "source": [ + "As you can see ESMValCore has found one file on Gadi that contains the surface temperature output for the time range specified.\n", + "By default, the data is loaded in an Iris Cube, sorry Xarray proponent, ESMValCore is build on Iris, but don't go away to fast..." + ] + }, + { + "cell_type": "markdown", + "id": "fe773b7b-3a86-4089-9c6b-cd7edc0e4d73", + "metadata": {}, + "source": [ + "## Processing" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "c6aedc5f-60ea-4a6f-986f-f57c4e989eff", + "metadata": {}, + "outputs": [], + "source": [ + "from esmvalcore.preprocessor import extract_time\n", + "from esmvalcore.preprocessor import climate_statistics\n", + "from esmvalcore.preprocessor import convert_units" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "28195790-79f9-4245-a743-d1ac633f825d", + "metadata": {}, + "outputs": [], + "source": [ + "cube = dataset.load()\n", + "\n", + "temperature_1990_1991 = extract_time(cube, start_year=1990, start_month=1, start_day=1, end_year=1991, end_month=1, end_day=1) \n", + "temperature_weighted_mean = climate_statistics(temperature_1990_1991, operator=\"mean\")\n", + "temperature_celcius = convert_units(temperature_weighted_mean, units=\"degrees_C\")" + ] + }, + { + "cell_type": "markdown", + "id": "f233b96e-d385-42eb-9836-cb8c0f794785", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "94819dec-e0a0-46b1-9d5a-8c9238a2ed64", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import colormaps\n", + "\n", + "import iris\n", + "import iris.plot as iplt\n", + "import iris.quickplot as qplt\n", + "\n", + "# Load a Cynthia Brewer palette.\n", + "brewer_cmap = colormaps[\"brewer_OrRd_09\"]\n", + "\n", + "# Create a figure\n", + "plt.figure(figsize=(12, 5))\n", + "\n", + "# Plot #1: countourf with axes longitude from -180 to 180\n", + "proj = ccrs.PlateCarree(central_longitude=0.0)\n", + "plt.subplot(121, projection=proj)\n", + "qplt.contourf(temperature_weighted_mean, brewer_cmap.N, cmap=brewer_cmap)\n", + "plt.gca().coastlines()\n", + "\n", + "# Plot #2: contourf with axes longitude from 0 to 360\n", + "proj = ccrs.PlateCarree(central_longitude=-180.0)\n", + "plt.subplot(122, projection=proj)\n", + "qplt.contourf(temperature_weighted_mean, brewer_cmap.N, cmap=brewer_cmap)\n", + "plt.gca().coastlines()\n", + "plt.savefig(\"test.png\",bbox_inches='tight')\n", + "iplt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6f68791f-68a8-444d-a369-4290fdbd568e", + "metadata": {}, + "source": [ + "## Hovmoller Diagram" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6640515b-951e-40d1-aa8f-49c4f46b9966", + "metadata": {}, + "outputs": [], + "source": [ + "cube = extract_time(cube, start_year=1990, start_month=1, start_day=1, end_year=1991, end_month=1, end_day=1) \n", + "cube = cube.extract(iris.Constraint(latitude=lambda v: -5 < v < 5))\n", + "cube = cube.collapsed(\"latitude\", iris.analysis.MEAN)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "ba1adb26-6d2e-45a3-9d9d-d77fba76d2a4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.dates as mdates\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import iris\n", + "import iris.plot as iplt\n", + "import iris.quickplot as qplt\n", + "\n", + "\n", + "# Now that we have our data in a nice way, lets create the plot\n", + "# contour with 20 levels\n", + "qplt.contourf(cube, 20)\n", + "\n", + "# Put a custom label on the y axis\n", + "plt.ylabel(\"Time / months\")\n", + "\n", + "# Stop matplotlib providing clever axes range padding\n", + "plt.axis(\"tight\")\n", + "\n", + "# As we are plotting annual variability, put years as the y ticks\n", + "plt.gca().yaxis.set_major_locator(mdates.MonthLocator())\n", + "\n", + "# And format the ticks to just show the year\n", + "plt.gca().yaxis.set_major_formatter(mdates.DateFormatter(\"%m\"))\n", + "\n", + "iplt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1de80c48-1a96-40d9-8503-b2a47cedb77b", + "metadata": {}, + "source": [ + "## Wind speed above Australia\n", + "\n", + "### Load Datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "a9c01e3f-380f-44b6-b925-07dcbd238016", + "metadata": {}, + "outputs": [], + "source": [ + "ua = Dataset(\n", + " short_name='ua',\n", + " project='CMIP6',\n", + " mip=\"Amon\",\n", + " exp=\"historical\",\n", + " ensemble=\"r1i1p1f1\",\n", + " dataset='ACCESS-ESM1-5',\n", + " timerange=\"19900101/19910101\",\n", + " grid=\"gn\"\n", + ")\n", + "va = Dataset(\n", + " short_name='va',\n", + " project='CMIP6',\n", + " mip=\"Amon\",\n", + " exp=\"historical\",\n", + " ensemble=\"r1i1p1f1\",\n", + " dataset='ACCESS-ESM1-5',\n", + " timerange=\"19900101/19910101\",\n", + " grid=\"gn\"\n", + ")\n", + "\n", + "ua = ua.load()\n", + "va = va.load()" + ] + }, + { + "cell_type": "markdown", + "id": "93a72251-b301-41c1-9e8d-9613fa635123", + "metadata": {}, + "source": [ + "## Process data" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "945667ce-7b5f-4215-96ce-aa0e634f21d4", + "metadata": {}, + "outputs": [], + "source": [ + "from esmvalcore.preprocessor import extract_region\n", + "from esmvalcore.preprocessor import extract_levels\n", + "from esmvalcore.preprocessor import extract_month" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "1b337a1c-50ec-46d9-8197-f3f989a10a9a", + "metadata": {}, + "outputs": [], + "source": [ + "ua_australia = extract_region(ua, start_longitude=105, end_longitude=160, start_latitude=-45, end_latitude=-9) \n", + "ua_australia_10000 = extract_levels(ua_australia, levels=10000, scheme=\"linear\")\n", + "ua_australia_10000_jan = extract_month(ua_australia_10000, month=1)\n", + "\n", + "\n", + "va_australia = extract_region(va, start_longitude=105, end_longitude=160, start_latitude=-45, end_latitude=-9) \n", + "va_australia_10000 = extract_levels(va_australia, levels=10000, scheme=\"linear\")\n", + "va_australia_10000_jan = extract_month(va_australia_10000, month=1)" + ] + }, + { + "cell_type": "markdown", + "id": "1b3b551a-fa3e-4476-a990-4262bf65bb93", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "4421d25a-b50a-4fa6-a86c-11b992ca284b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import cartopy\n", + "\n", + "\n", + "uwind = ua_australia_10000_jan\n", + "vwind = va_australia_10000_jan\n", + "\n", + "# Create a cube containing the wind speed.\n", + "windspeed = (uwind**2 + vwind**2) ** 0.5\n", + "windspeed.rename(\"windspeed\")\n", + "\n", + "# Plot the wind speed as a contour plot.\n", + "qplt.contourf(windspeed, 20)\n", + "\n", + "plt.gca().add_feature(cartopy.feature.COASTLINE)\n", + "\n", + "# Add arrows to show the wind vectors.\n", + "iplt.quiver(uwind, vwind, pivot=\"middle\")\n", + "\n", + "plt.title(\"Wind speed over Australia\")\n", + "qplt.show()\n", + "\n", + "# Normalise the data for uniform arrow size.\n", + "u_norm = uwind / windspeed\n", + "v_norm = vwind / windspeed\n", + "\n", + "# Make a new figure for the normalised plot.\n", + "plt.figure()\n", + "\n", + "qplt.contourf(windspeed, 20)\n", + "plt.gca().add_feature(cartopy.feature.COASTLINE)\n", + "\n", + "iplt.quiver(u_norm, v_norm, pivot=\"middle\")\n", + "\n", + "plt.title(\"Wind speed over Australia\")\n", + "qplt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "55804bee-2211-4a33-bef6-bbe0236de4f9", + "metadata": {}, + "source": [ + "## Air Potential Temperature (3d data) Transect\n", + "### Load Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "c863dab8-c2d0-4b30-ae22-f004f54707b0", + "metadata": {}, + "outputs": [], + "source": [ + "ta = Dataset(\n", + " short_name='ta',\n", + " project='CMIP6',\n", + " mip=\"Amon\",\n", + " exp=\"historical\",\n", + " ensemble=\"r1i1p1f1\",\n", + " dataset='ACCESS-ESM1-5',\n", + " timerange=\"19900101/19910101\",\n", + " grid=\"gn\"\n", + ")\n", + "\n", + "ta = ta.load()" + ] + }, + { + "cell_type": "markdown", + "id": "e896ff47-cab8-4d09-8f45-65ca576942da", + "metadata": {}, + "source": [ + "## Process data" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "799ad51d-e7ad-47ae-a219-108a7c86bfb2", + "metadata": {}, + "outputs": [], + "source": [ + "from esmvalcore.preprocessor import extract_transect" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "800e93fc-b610-48b2-ab1f-3fe0e2eb1470", + "metadata": {}, + "outputs": [], + "source": [ + "temperature_jan = extract_month(ta, month=1)\n", + "temperature_transect = extract_transect(cube=temperature_jan, longitude=15.)" + ] + }, + { + "cell_type": "markdown", + "id": "7de1b51a-0d65-431b-aacb-5ddff8598e26", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "7cf3c5b5-7ae9-4112-99f1-37427cb933c9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "\n", + "qplt.contourf(\n", + " temperature_transect,\n", + " #coords=[\"grid_longitude\", \"model_level_number\"],\n", + " cmap=\"RdBu_r\",\n", + ")\n", + "iplt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cc0c551c-8fcf-47ee-ba3b-e60eb5d8f8c6", + "metadata": {}, + "source": [ + "## Australia mean temperature timeseries\n", + "\n", + "### Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "1a9cbe76-2fc7-4a64-a6d9-35b0311e4f19", + "metadata": {}, + "outputs": [], + "source": [ + "ts = Dataset(\n", + " short_name='ts',\n", + " project='CMIP6',\n", + " mip=\"Amon\",\n", + " exp=\"historical\",\n", + " ensemble=\"r1i1p1f1\",\n", + " dataset='ACCESS-ESM1-5',\n", + " grid=\"gn\"\n", + ")\n", + "\n", + "ts = ts.load()" + ] + }, + { + "cell_type": "markdown", + "id": "07a6d760-6581-4e8c-ab59-c9c4b070aa3f", + "metadata": {}, + "source": [ + "## Process data" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "2bed7870-db08-45e2-8937-6a9776219d9c", + "metadata": {}, + "outputs": [], + "source": [ + "from esmvalcore.preprocessor import extract_region\n", + "from esmvalcore.preprocessor import annual_statistics\n", + "from esmvalcore.preprocessor import area_statistics\n", + "from esmvalcore.preprocessor import convert_units" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "ed5875bc-ea27-41ef-854b-a908f1cc3988", + "metadata": {}, + "outputs": [], + "source": [ + "ts_australia = extract_region(ts, start_longitude=105, end_longitude=160, start_latitude=-45, end_latitude=-9) \n", + "ts_australia = area_statistics(ts_australia, operator=\"mean\")\n", + "ts_australia = annual_statistics(ts_australia, operator=\"mean\")\n", + "ts_australia = convert_units(ts_australia, units=\"degrees_C\")" + ] + }, + { + "cell_type": "markdown", + "id": "a61d9042-6782-43b1-a3c9-3c5a84221caa", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "0974423e-dff0-4b66-a60c-3d87fc2e76b7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "qplt.plot(ts_australia, label=\"E1 scenario\", lw=1.5, color=\"blue\")" + ] + } + ], + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/files/example_seaicearea.ipynb b/files/example_seaicearea.ipynb new file mode 100644 index 00000000..266cc1f9 --- /dev/null +++ b/files/example_seaicearea.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1b16af4f", + "metadata": {}, + "source": [ + "Exercise: example sea ice area" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6a914fa6-cb01-473b-8204-42743edbba72", + "metadata": {}, + "outputs": [], + "source": [ + "import iris\n", + "import matplotlib.pyplot as plt\n", + "from iris import quickplot\n", + "\n", + "from esmvalcore.dataset import Dataset\n", + "from esmvalcore.preprocessor import (\n", + " mask_outside_range,\n", + " extract_region,\n", + " area_statistics,\n", + " annual_statistics\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5de86b5f-547e-4f4b-80ee-4c358e2c19b5", + "metadata": {}, + "outputs": [], + "source": [ + "## define data\n", + "\n", + "obs = Dataset(\n", + " short_name='siconc', mip='SImon', project='OBS6', type='reanaly',\n", + " dataset='NSIDC-G02202-sh', tier='3', version='4', timerange='1979/2018',\n", + ")\n", + "\n", + "# Add areacello as supplementary dataset\n", + "obs.add_supplementary(short_name='areacello', mip='Ofx')\n", + "\n", + "model = Dataset(\n", + " short_name='siconc', mip='SImon', project='CMIP6', activity='CMIP',\n", + " dataset='ACCESS-ESM1-5', ensemble='r1i1p1f1', grid='gn', exp='historical',\n", + " timerange='1960/2010', institute = '*',\n", + ")\n", + "\n", + "om_facets={'dataset' :'ACCESS-OM2', 'exp':'omip2', 'activity':'OMIP', 'timerange':'0306/0366' }\n", + "\n", + "model.add_supplementary(short_name='areacello', mip='Ofx')\n", + "\n", + "model_om = model.copy(**om_facets) \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c1624d6", + "metadata": {}, + "outputs": [], + "source": [ + "## check available files\n", + "\n", + "for ds in [model, model_om, obs]:\n", + " print(ds['dataset'],' : ' ,ds.files)\n", + " print(ds.supplementaries[0].files)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cce37969", + "metadata": {}, + "outputs": [], + "source": [ + "# wildcard searches\n", + "\n", + "obs_other = Dataset(\n", + " short_name='siconc', mip='*', project='OBS', type='*',\n", + " dataset='*', tier='*', timerange='1979/2018'\n", + ")\n", + "obs_other.files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36704edb-cda2-4cd2-a550-f550e1c79d08", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "## loop through data, plot\n", + "\n", + "# om - index 1 to offset years\n", + "load_data = [model, model_om] #, obs] # obs not found\n", + "\n", + "# function to use for both min and max ['max','min'] \n", + "\n", + "def trends_seaicearea(min_max):\n", + " plt.clf()\n", + " for i,data in enumerate(load_data):\n", + " cube = data.load()\n", + " cube = mask_outside_range(cube, 15, 100)\n", + " cube = extract_region(cube,0,360,-90,0)\n", + " cube = area_statistics(cube, 'sum')\n", + " cube = annual_statistics(cube, min_max)\n", + " \n", + " iris.util.promote_aux_coord_to_dim_coord(cube, 'year')\n", + " cube.convert_units('km2')\n", + " if i == 1: ## om years 306/366\n", + " cube.coord('year').points = [y + 1652 for y in cube.coord('year').points]\n", + " label_name = data['dataset']\n", + " print(label_name, cube.shape)\n", + " quickplot.plot(cube, label=label_name)\n", + " \n", + " plt.title(f'Trends in Sea-Ice {min_max.title()}ima')\n", + " plt.ylabel('Sea-Ice Area (km2)')\n", + " plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4a1a3fe-7a34-4cca-bf44-52d4e37c3758", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "trends_seaicearea('min')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea439f55-3e55-44f3-aab7-0403d728ea99", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "trends_seaicearea('max')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3fb790f-3970-418a-96e2-91541b364093", + "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": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From e002f437c819f7a1cd2c277fc2763be383422954 Mon Sep 17 00:00:00 2001 From: flicj191 Date: Fri, 6 Sep 2024 12:06:21 +1000 Subject: [PATCH 2/9] edit sea-ice area exercise --- _episodes/11-esmvalcoreapi.md | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/_episodes/11-esmvalcoreapi.md b/_episodes/11-esmvalcoreapi.md index e948307e..80675b76 100644 --- a/_episodes/11-esmvalcoreapi.md +++ b/_episodes/11-esmvalcoreapi.md @@ -441,20 +441,15 @@ quickplot.plot(cube) > > ``` > {: .solution} > > ## Tip: Check dataset files can be found -> > The observational dataset used is a Tier 3, so with some licensing restrictions. It is not directly -> > accesible here. Check files can be found for all the datasets: +> > The observational dataset used is a Tier 3, so with some licensing restrictions. +> > Check files can be found for all the datasets: > > > > ```python > > for ds in [model, model_om, obs]: > > print(ds['dataset'],' : ' ,ds.files) > > print(ds.supplementaries[0].files) > > ``` -> > This observation dataset does have a downloader and formatter with ESMValTool. -> > ```bash -> > esmvaltool data download --config_file NSIDC-G02202-sh -> > esmvaltool data format --config_file NSIDC-G02202-sh -> > ``` -> > For this plot we can drop it for now. But you can also try to find and add another dataset. eg: +> > You can try to find and add another observational dataset. eg: > > ```python > > obs_other = Dataset( > > short_name='siconc', mip='*', project='OBS', type='*', @@ -475,9 +470,9 @@ quickplot.plot(cube) > > area_statistics, > > annual_statistics > > ) -> > # om - at index 1 to offset years -> > # drop observations that cannot be found -> > load_data = [model, model_om] #, obs] +> > # model_om - at index 1 to offset years +> > +> > load_data = [model, model_om, obs] > > > > # function to use for both min and max ['max','min'] > > From aaade54eedebdb4604919344b9824a0852872ac2 Mon Sep 17 00:00:00 2001 From: flicj191 Date: Fri, 6 Sep 2024 12:24:37 +1000 Subject: [PATCH 3/9] clean up --- _episodes/11-esmvalcoreapi.md | 292 +++++++++++++++++----------------- _includes/links.md | 3 + 2 files changed, 152 insertions(+), 143 deletions(-) diff --git a/_episodes/11-esmvalcoreapi.md b/_episodes/11-esmvalcoreapi.md index 80675b76..b71fe6e2 100644 --- a/_episodes/11-esmvalcoreapi.md +++ b/_episodes/11-esmvalcoreapi.md @@ -17,18 +17,21 @@ keypoints: - "Use `datasets_to_recipe` helper to start making recipes" --- -In this episode we will introduce the ESMValCore API in a jupyter notebook. This is reformatted from material from -this [blog post](https://blog.esciencecenter.nl/easy-ipcc-powered-by-esmvalcore-19a0b6366ea7){:target="_blank"} -by Peter Kalverla. There's also material from the [example notebooks][docs-notebooks]{:target="_blank"} and the +In this episode we will introduce the ESMValCore API in a jupyter notebook. This is reformatted +from material from this [blog post][easy-ipcc-blog]{:target="_blank"} +by Peter Kalverla. There's also material from the +[example notebooks][docs-notebooks]{:target="_blank"} and the [API reference documentation][api-reference]{:target="_blank"}. ## Start JupyterLab -A [jupyter notebook](https://jupyter.org/){:target="_blank"} is an interactive document where you can run code. +A [jupyter notebook](https://jupyter.org/){:target="_blank"} is an interactive document where +you can run code. You will need to use a python environment with ESMValTool and ESMValCore installed. ## Find Datasets with facets -We have seen from running available recipes that ESMValTool is able to find data from facets that were given in -the recipe. We can use this in a Notebook, including filling out the facets for data definition. +We have seen from running available recipes that ESMValTool is able to find data from facets that +were given in the recipe. We can use this in a Notebook, including filling out the facets for +data definition. To do this we will use the `Dataset` object from the API. Let's look at this example. ```python @@ -47,7 +50,8 @@ dataset.augment_facets() print(dataset) ``` > ## Pro tip: Augmented facets in the output -> When running a recipe there is a `_filled` recipe `yml` file in the output `/run` folder which augments the facets. +> When running a recipe there is a `_filled` recipe `yml` file in the output `/run` folder which +> augments the facets. > > ## Example recipe output folder > > ```output > > esmvaltool_output/flato13ipcc_figure914_20240729_043707/run @@ -63,7 +67,8 @@ print(dataset) {: .callout} > ## Search available -> Search from files available locally with wildcard functionality `'*'` to get all the available datasets. +> Search from files available locally with wildcard functionality `'*'` to get all the available +> datasets. > - How can you search for all available ensembles? > > > ## Solution @@ -84,8 +89,8 @@ print(dataset) > > ``` > {: .solution} > There is also the ability to search on ESGF nodes and download. See -> [reference](https://docs.esmvaltool.org/projects/ESMValCore/en/latest/api/esmvalcore.esgf.html){:target="_blank"} -> for more details. Check the configuration settings for this. +> [reference][api-esgf]{:target="_blank"} for more details. +> Check the configuration settings for this. >```python >from esmvalcore.config import CFG >CFG['search_esgf'] = 'always' @@ -253,7 +258,7 @@ print(da) The output from the preprocessor functions are Iris cubes. [Iris](https://scitools-iris.readthedocs.io/en/latest/index.html){:target="_blank"} has wrappers for [matplotlib](https://matplotlib.org/){:target="_blank"} to [plot the processed -cubes](https://scitools-iris.readthedocs.io/en/latest/userguide/plotting_a_cube.html#iris-cube-plotting){:target="_blank"}. +cubes][iris-plot]{:target="_blank"}. This is useful in a notebook to help develop your recipe with the esmvalcore preprocessors. ```python from iris import quickplot @@ -270,70 +275,71 @@ quickplot.plot(cube) > - Apply the preprocessors to each dataset and plot the result > > > ## Solution -> > ```python -> > import cf_units -> > import matplotlib.pyplot as plt -> > from iris import quickplot -> > -> > from esmvalcore.config import CFG -> > from esmvalcore.dataset import Dataset -> > from esmvalcore.preprocessor import annual_statistics, anomalies, area_statistics -> > -> > -> > # Settings for automatic ESGF search -> > CFG['search_esgf'] = 'when_missing' -> > -> > # Declare common dataset facets -> > template = Dataset( -> > short_name='tos', -> > mip='Omon', -> > project='CMIP6', -> > exp= '*', # We'll fill this below -> > dataset='*', # We'll fill this below -> > ensemble='r4i1p1f1', -> > grid='gn', -> > ) -> > -> > # Substitute data sources and experiments -> > datasets = [] -> > for dataset_id in ["CESM2", "MPI-ESM1-2-LR", "ACCESS-ESM1-5"]: -> > for experiment_id in ['ssp126', 'ssp585']: -> > dataset = template.copy(dataset=dataset_id, exp=['historical', experiment_id]) -> > dataset.add_supplementary(short_name='areacello', mip='Ofx', exp='historical') -> > dataset.augment_facets() -> > datasets.append(dataset) -> > -> > # Set the reference period for anomalies -> > reference_period = { -> > "start_year": 1950, "start_month": 1, "start_day": 1, -> > "end_year": 1979, "end_month": 12, "end_day": 31, -> > } -> > -> > # (Down)load, pre-process, and plot the cubes -> > for dataset in datasets: -> > cube = dataset.load() -> > cube = area_statistics(cube, operator='mean') -> > cube = anomalies(cube, reference=reference_period, period='month') # notice 'month' -> > cube = annual_statistics(cube, operator='mean') -> > cube.convert_units('degrees_C') -> > -> > # Make sure all datasets use the same calendar for plotting -> > tcoord = cube.coord('time') -> > tcoord.units = cf_units.Unit(tcoord.units.origin, calendar='gregorian') -> > -> > # Plot -> > quickplot.plot(cube, label=f"{dataset['dataset']} - {dataset['exp']}") -> > -> > # Show the plot -> > plt.legend() -> > plt.show() -> > ``` +> >```python +> >import cf_units +> >import matplotlib.pyplot as plt +> >from iris import quickplot +> > +> >from esmvalcore.config import CFG +> >from esmvalcore.dataset import Dataset +> >from esmvalcore.preprocessor import annual_statistics, anomalies, area_statistics +> > +> > +> ># Settings for automatic ESGF search +> >CFG['search_esgf'] = 'when_missing' +> > +> ># Declare common dataset facets +> >template = Dataset( +> > short_name='tos', +> > mip='Omon', +> > project='CMIP6', +> > exp= '*', # We'll fill this below +> > dataset='*', # We'll fill this below +> > ensemble='r4i1p1f1', +> > grid='gn', +> >) +> > +> ># Substitute data sources and experiments +> >datasets = [] +> >for dataset_id in ["CESM2", "MPI-ESM1-2-LR", "ACCESS-ESM1-5"]: +> > for experiment_id in ['ssp126', 'ssp585']: +> > dataset = template.copy(dataset=dataset_id, exp=['historical', experiment_id]) +> > dataset.add_supplementary(short_name='areacello', mip='Ofx', exp='historical') +> > dataset.augment_facets() +> > datasets.append(dataset) +> > +> ># Set the reference period for anomalies +> >reference_period = { +> > "start_year": 1950, "start_month": 1, "start_day": 1, +> > "end_year": 1979, "end_month": 12, "end_day": 31, +> >} +> > +> ># (Down)load, pre-process, and plot the cubes +> >for dataset in datasets: +> > cube = dataset.load() +> > cube = area_statistics(cube, operator='mean') +> > cube = anomalies(cube, reference=reference_period, period='month') # notice 'month' +> > cube = annual_statistics(cube, operator='mean') +> > cube.convert_units('degrees_C') +> > +> > # Make sure all datasets use the same calendar for plotting +> > tcoord = cube.coord('time') +> > tcoord.units = cf_units.Unit(tcoord.units.origin, calendar='gregorian') +> > +> > # Plot +> > quickplot.plot(cube, label=f"{dataset['dataset']} - {dataset['exp']}") +> > +> ># Show the plot +> >plt.legend() +> >plt.show() +> >``` > {: .solution} {: .challenge} > ## Pro tip: Convert to recipe -> We can use the helper to start making the recipe. A recipe can be used for reproducibility of an -> analysis. This list the datasets in a recipe format and we would then have to create the preprocessors +> We can use the helper to start making the recipe. A recipe can be used for +> reproducibility of an analysis. This list the datasets in a +> recipe format and we would then have to create the preprocessors > and diagnostic script. > ```python > from esmvalcore.dataset import datasets_to_recipe @@ -418,87 +424,87 @@ quickplot.plot(cube) > > > ## 1. Define datasets: > > -> > ```python -> > from esmvalcore.dataset import Dataset -> > obs = Dataset( -> > short_name='siconc', mip='SImon', project='OBS6', type='reanaly', -> > dataset='NSIDC-G02202-sh', tier='3', version='4', timerange='1979/2018', -> > ) -> > # Add areacello as supplementary dataset -> > obs.add_supplementary(short_name='areacello', mip='Ofx') -> > -> > model = Dataset( -> > short_name='siconc', mip='SImon', project='CMIP6', activity='CMIP', -> > dataset='ACCESS-ESM1-5', ensemble='r1i1p1f1', grid='gn', exp='historical', -> > timerange='1960/2010', institute = '*', -> > ) -> > -> > om_facets={'dataset' :'ACCESS-OM2', 'exp':'omip2', 'activity':'OMIP', 'timerange':'0306/0366' } -> > -> > model.add_supplementary(short_name='areacello', mip='Ofx') -> > -> > model_om = model.copy(**om_facets) -> > ``` +> >```python +> >from esmvalcore.dataset import Dataset +> >obs = Dataset( +> > short_name='siconc', mip='SImon', project='OBS6', type='reanaly', +> > dataset='NSIDC-G02202-sh', tier='3', version='4', timerange='1979/2018', +> >) +> ># Add areacello as supplementary dataset +> >obs.add_supplementary(short_name='areacello', mip='Ofx') +> > +> >model = Dataset( +> > short_name='siconc', mip='SImon', project='CMIP6', activity='CMIP', +> > dataset='ACCESS-ESM1-5', ensemble='r1i1p1f1', grid='gn', exp='historical', +> > timerange='1960/2010', institute = '*', +> >) +> > +> >om_facets={'dataset' :'ACCESS-OM2', 'exp':'omip2', 'activity':'OMIP', 'timerange':'0306/0366' } +> > +> >model.add_supplementary(short_name='areacello', mip='Ofx') +> > +> >model_om = model.copy(**om_facets) +> >``` > {: .solution} > > ## Tip: Check dataset files can be found > > The observational dataset used is a Tier 3, so with some licensing restrictions. > > Check files can be found for all the datasets: > > -> > ```python -> > for ds in [model, model_om, obs]: -> > print(ds['dataset'],' : ' ,ds.files) -> > print(ds.supplementaries[0].files) -> > ``` -> > You can try to find and add another observational dataset. eg: -> > ```python -> > obs_other = Dataset( -> > short_name='siconc', mip='*', project='OBS', type='*', -> > dataset='*', tier='*', timerange='1979/2018' -> > ) -> > obs_other.files -> > ``` +> >```python +> >for ds in [model, model_om, obs]: +> > print(ds['dataset'],' : ' ,ds.files) +> > print(ds.supplementaries[0].files) +> >``` +> >You can try to find and add another observational dataset. eg: +> >```python +> >obs_other = Dataset( +> > short_name='siconc', mip='*', project='OBS', type='*', +> > dataset='*', tier='*', timerange='1979/2018' +> >) +> >obs_other.files +> >``` > {: .solution} > > ## 2. Use esmvalcore API preprocessors on the datasets and plot results > > -> > ```python -> > import iris -> > import matplotlib.pyplot as plt -> > from iris import quickplot -> > from esmvalcore.preprocessor import ( -> > mask_outside_range, -> > extract_region, -> > area_statistics, -> > annual_statistics -> > ) -> > # model_om - at index 1 to offset years +> >```python +> >import iris +> >import matplotlib.pyplot as plt +> >from iris import quickplot +> >from esmvalcore.preprocessor import ( +> > mask_outside_range, +> > extract_region, +> > area_statistics, +> > annual_statistics +> >) +> ># model_om - at index 1 to offset years +> +> >load_data = [model, model_om, obs] > > -> > load_data = [model, model_om, obs] -> > -> > # function to use for both min and max ['max','min'] -> > -> > def trends_seaicearea(min_max): -> > plt.clf() -> > for i,data in enumerate(load_data): -> > cube = data.load() -> > cube = mask_outside_range(cube, 15, 100) -> > cube = extract_region(cube,0,360,-90,0) -> > cube = area_statistics(cube, 'sum') -> > cube = annual_statistics(cube, min_max) -> > -> > iris.util.promote_aux_coord_to_dim_coord(cube, 'year') -> > cube.convert_units('km2') -> > if i == 1: ## om years 306/366 apply offset -> > cube.coord('year').points = [y + 1652 for y in cube.coord('year').points] -> > label_name = data['dataset'] -> > print(label_name, cube.shape) -> > quickplot.plot(cube, label=label_name) -> > -> > plt.title(f'Trends in Sea-Ice {min_max.title()}ima') -> > plt.ylabel('Sea-Ice Area (km2)') -> > plt.legend() -> > -> > trends_seaicearea('min') -> > ``` +> ># function to use for both min and max ['max','min'] +> > +> >def trends_seaicearea(min_max): +> > plt.clf() +> > for i,data in enumerate(load_data): +> > cube = data.load() +> > cube = mask_outside_range(cube, 15, 100) +> > cube = extract_region(cube,0,360,-90,0) +> > cube = area_statistics(cube, 'sum') +> > cube = annual_statistics(cube, min_max) +> > +> > iris.util.promote_aux_coord_to_dim_coord(cube, 'year') +> > cube.convert_units('km2') +> > if i == 1: ## om years 306/366 apply offset +> > cube.coord('year').points = [y + 1652 for y in cube.coord('year').points] +> > label_name = data['dataset'] +> > print(label_name, cube.shape) +> > quickplot.plot(cube, label=label_name) +> > +> > plt.title(f'Trends in Sea-Ice {min_max.title()}ima') +> > plt.ylabel('Sea-Ice Area (km2)') +> > plt.legend() +> > +> >trends_seaicearea('min') +> >``` > {: .solution} {: .challenge} diff --git a/_includes/links.md b/_includes/links.md index 147b7f83..649e80f6 100644 --- a/_includes/links.md +++ b/_includes/links.md @@ -84,7 +84,10 @@ [workshop-repo]: {{ site.workshop_repo }} [yaml]: http://yaml.org/ [api-config]: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/api/esmvalcore.config.html +[easy-ipcc-blog]: https://blog.esciencecenter.nl/easy-ipcc-powered-by-esmvalcore-19a0b6366ea7 [experimental-output]: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/api/esmvalcore.experimental.recipe_output.html [docs-notebooks]: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/example-notebooks.html [api-reference]: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/api/esmvalcore.html#api +[api-esgf]: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/api/esmvalcore.esgf.html [api-preprocessors]: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/api/esmvalcore.preprocessor.html +[iris-plot]: https://scitools-iris.readthedocs.io/en/latest/userguide/plotting_a_cube.html#iris-cube-plotting From fddd48469d87a098abbfc38f7b0cd10ade02121f Mon Sep 17 00:00:00 2001 From: flicj191 Date: Fri, 6 Sep 2024 12:35:39 +1000 Subject: [PATCH 4/9] shorten lines --- _episodes/11-esmvalcoreapi.md | 39 ++++++++++++++++++----------------- 1 file changed, 20 insertions(+), 19 deletions(-) diff --git a/_episodes/11-esmvalcoreapi.md b/_episodes/11-esmvalcoreapi.md index b71fe6e2..8c43069a 100644 --- a/_episodes/11-esmvalcoreapi.md +++ b/_episodes/11-esmvalcoreapi.md @@ -130,16 +130,16 @@ print(dataset) > ``` > > ## Output > > ```output -> > sea_surface_temperature / (degC) (time: 1980; cell index along second dimension: 300; cell index along first dimension: 360) +> > sea_surface_temperature / (degC) (time: 1980; cell index al...: 300; cell index al...: 360) > > Dimension coordinates: -> > time x - - -> > cell index along second dimension - x - -> > cell index along first dimension - - x +> > time x - - +> > cell index along second dimension - x - +> > cell index along first dimension - - x > > Auxiliary coordinates: -> > latitude - x x -> > longitude - x x +> > latitude - x x +> > longitude - x x > > Cell measures: -> > cell_area - x x +> > cell_area - x x > > Cell methods: > > 0 area: mean where sea > > 1 time: mean @@ -156,16 +156,16 @@ print(dataset) > > external_variables 'areacello' > > forcing_index 1 > > frequency 'mon' -> > further_info_url 'https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.no ...' +> > further_info_url 'https://furtherinfo.es-doc.org/ ...' > > grid 'native atmosphere N96 grid (145x192 latxlon)' > > grid_label 'gn' > > initialization_index 1 -> > institution 'Commonwealth Scientific and Industrial Research Organisation, Aspendale, ...' +> > institution 'Commonwealth Scientific and Industrial Research ...' > > institution_id 'CSIRO' -> > license 'CMIP6 model data produced by CSIRO is licensed under a Creative Commons ...' +> > license 'CMIP6 model data produced by CSIRO is ...' > > mip_era 'CMIP6' > > nominal_resolution '250 km' -> > notes "Exp: ESM-historical; Local ID: HI-08; Variable: tos (['sst'])" +> > notes "Exp: ESM-historical; Local ID: HI-08; " > > parent_activity_id 'CMIP' > > parent_experiment_id 'piControl' > > parent_mip_era 'CMIP6' @@ -176,14 +176,14 @@ print(dataset) > > product 'model-output' > > realization_index 4 > > realm 'ocean' -> > run_variant 'forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, ...' -> > source 'ACCESS-ESM1.5 (2019): \naerosol: CLASSIC (v1.0)\natmos: HadGAM2 (r1.1, ...' +> > run_variant 'forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, ...' +> > source 'ACCESS-ESM1.5 (2019): \naerosol: CLASSIC (v1.0) ...' > > source_id 'ACCESS-ESM1-5' > > source_type 'AOGCM' > > sub_experiment 'none' > > sub_experiment_id 'none' > > table_id 'Omon' -> > table_info 'Creation Date:(30 April 2019) MD5:40e9ef53d4d2ec9daef980b76f23d39a' +> > table_info 'Creation Date:(30 April 2019) MD5:' > > title 'ACCESS-ESM1-5 output prepared for CMIP6' > > variable_id 'tos' > > variant_label 'r4i1p1f1' @@ -233,7 +233,8 @@ See the [documentation][recipe-section-preprocessors]{:target="_blank"} to read > > cell index along first dimension 179, bound=(0, 359) > > cell index along second dimension 149, bound=(0, 299) > > latitude 6.0 degrees_north, bound=(-78.0, 90.0) degrees_north -> > longitude 179.9867706298828 degrees_east, bound=(0.0, 359.9735412597656) degrees_east +> > longitude 179.9867706298828 degrees_east, +> >bound=(0.0, 359.9735412597656) degrees_east > > Cell methods: > > 0 area: mean where sea > > 1 time: mean @@ -459,11 +460,11 @@ quickplot.plot(cube) > >```python > >obs_other = Dataset( > > short_name='siconc', mip='*', project='OBS', type='*', -> > dataset='*', tier='*', timerange='1979/2018' -> >) +> > dataset='*', tier='*', timerange='1979/2018') +> > > >obs_other.files > >``` -> {: .solution} +>{: .solution} > > ## 2. Use esmvalcore API preprocessors on the datasets and plot results > > > >```python @@ -477,7 +478,7 @@ quickplot.plot(cube) > > annual_statistics > >) > ># model_om - at index 1 to offset years -> +> > > >load_data = [model, model_om, obs] > > > ># function to use for both min and max ['max','min'] From 6f4e6c77873fdc622ab6adabff14df0b3cc74677 Mon Sep 17 00:00:00 2001 From: flicj191 Date: Fri, 6 Sep 2024 12:46:18 +1000 Subject: [PATCH 5/9] clean up checks --- _episodes/11-esmvalcoreapi.md | 19 +++++++++---------- bin/lesson_check.py | 2 +- 2 files changed, 10 insertions(+), 11 deletions(-) diff --git a/_episodes/11-esmvalcoreapi.md b/_episodes/11-esmvalcoreapi.md index 8c43069a..9961d221 100644 --- a/_episodes/11-esmvalcoreapi.md +++ b/_episodes/11-esmvalcoreapi.md @@ -130,16 +130,16 @@ print(dataset) > ``` > > ## Output > > ```output -> > sea_surface_temperature / (degC) (time: 1980; cell index al...: 300; cell index al...: 360) +> > sea_surface_temperature / (degC) (time: 1980; cell index...: 300; cell inde...: 360) > > Dimension coordinates: -> > time x - - -> > cell index along second dimension - x - -> > cell index along first dimension - - x +> > time x - - +> > cell index along second dimension - x - +> > cell index along first dimension - - x > > Auxiliary coordinates: -> > latitude - x x -> > longitude - x x +> > latitude - x x +> > longitude - x x > > Cell measures: -> > cell_area - x x +> > cell_area - x x > > Cell methods: > > 0 area: mean where sea > > 1 time: mean @@ -176,7 +176,7 @@ print(dataset) > > product 'model-output' > > realization_index 4 > > realm 'ocean' -> > run_variant 'forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, ...' +> > run_variant 'forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = ...' > > source 'ACCESS-ESM1.5 (2019): \naerosol: CLASSIC (v1.0) ...' > > source_id 'ACCESS-ESM1-5' > > source_type 'AOGCM' @@ -491,7 +491,6 @@ quickplot.plot(cube) > > cube = extract_region(cube,0,360,-90,0) > > cube = area_statistics(cube, 'sum') > > cube = annual_statistics(cube, min_max) -> > > > iris.util.promote_aux_coord_to_dim_coord(cube, 'year') > > cube.convert_units('km2') > > if i == 1: ## om years 306/366 apply offset @@ -506,7 +505,7 @@ quickplot.plot(cube) > > > >trends_seaicearea('min') > >``` -> {: .solution} +>{: .solution} {: .challenge} {% include links.md %} diff --git a/bin/lesson_check.py b/bin/lesson_check.py index 42771d01..b63a95d6 100644 --- a/bin/lesson_check.py +++ b/bin/lesson_check.py @@ -556,7 +556,7 @@ def __init__(self, args, filename, metadata, metadata_len, text, lines, doc): (re.compile(r'README\.md'), CheckNonJekyll), (re.compile(r'index\.md'), CheckIndex), (re.compile(r'reference\.md'), CheckReference), - (re.compile(os.path.join('_episodes', '*\.md')), CheckEpisode), + (re.compile(os.path.join('_episodes', r'*\.md')), CheckEpisode), (re.compile(r'.*\.md'), CheckGeneric) ] From 875e3a1515a8a09a32cea0ad67b19a8e684dddf7 Mon Sep 17 00:00:00 2001 From: flicj191 Date: Thu, 21 Nov 2024 22:07:26 +1100 Subject: [PATCH 6/9] episode update --- _episodes/11-esmvalcoreapi.md | 41 +++++++++++++++++++++++++++++++---- 1 file changed, 37 insertions(+), 4 deletions(-) diff --git a/_episodes/11-esmvalcoreapi.md b/_episodes/11-esmvalcoreapi.md index 9961d221..6511b0f7 100644 --- a/_episodes/11-esmvalcoreapi.md +++ b/_episodes/11-esmvalcoreapi.md @@ -1,5 +1,5 @@ --- -title: "ESMValCore API in a Jupyter notebook" +title: "What is ESMValCore API and using it in a Jupyter notebook" teaching: 20 exercises: 30 compatibility: ESMValTool, ESMValCore v2.11.0 @@ -17,7 +17,9 @@ keypoints: - "Use `datasets_to_recipe` helper to start making recipes" --- -In this episode we will introduce the ESMValCore API in a jupyter notebook. This is reformatted +In this episode we will introduce the ESMValCore API in a jupyter notebook. ESMValTool acts as a +wrapper and collection of recipes and diagnostics and CMORisers for observations that is built on +top of ESMValCore which has the core functionality. This episode is reformatted from material from this [blog post][easy-ipcc-blog]{:target="_blank"} by Peter Kalverla. There's also material from the [example notebooks][docs-notebooks]{:target="_blank"} and the @@ -25,14 +27,45 @@ by Peter Kalverla. There's also material from the ## Start JupyterLab A [jupyter notebook](https://jupyter.org/){:target="_blank"} is an interactive document where -you can run code. +you can run code. If using a HPC server they may provide a service which can start up an interactive job +with Jupyter running for you which is convenient for this exercise, for example, +[ARE](https://opus.nci.org.au/spaces/Help/pages/162431120/ARE+User+Guide) at NCI's Gadi in Australia or +[Jupyterhub@DKRZ](https://docs.dkrz.de/doc/software%26services/jupyterhub/index.html). You will need to use a python environment with ESMValTool and ESMValCore installed. +## Configuration in the notebook + +We can look at the default user configuration file, by default found in `~/.esmvaltool/config-user.yml` +by calling a `CFG` object as a dictionary structure. This gives us the ability to edit the settings. +The tool can automatically download the climate data files required to run a recipe for you. +You can check your download directory and output directory where your recipe runs will be saved. +This `CFG` object is from the `config` module in the ESMValCore API, for more details see [here][api-config]. + +> Call the `CFG` object in a Jupyter notebook and inspect the values. +> > ## Solution +> > ```python +> > from esmvalcore.config import CFG +> > # call CFG object like this +> > CFG +> > ``` +> {: .solution} +> Check output directory and change +> > ## Solution +> > +> > ```python +> > print(CFG['output_dir']) +> > # edit directory +> > CFG['output_dir'] = '/scratch/$USERNAME/esmvaltool_outputs' +> > ``` +> {: .solution} +{: .challenge} + ## Find Datasets with facets We have seen from running available recipes that ESMValTool is able to find data from facets that were given in the recipe. We can use this in a Notebook, including filling out the facets for data definition. -To do this we will use the `Dataset` object from the API. Let's look at this example. +To do this we will use the `Dataset` object from the API. Let's look at this example which you +can copy to a Jupyter notebook. ```python from esmvalcore.dataset import Dataset From 4078a54b317e5a9083a5681b637896c5181077cb Mon Sep 17 00:00:00 2001 From: flicj191 Date: Tue, 26 Nov 2024 16:15:38 +1100 Subject: [PATCH 7/9] Add to episode addressing cooments --- _episodes/11-esmvalcoreapi.md | 74 +++++++++++++++++++++++---------- fig/preproc_warnings.png | Bin 0 -> 141148 bytes files/example_seaicearea.ipynb | 7 ++-- 3 files changed, 54 insertions(+), 27 deletions(-) create mode 100644 fig/preproc_warnings.png diff --git a/_episodes/11-esmvalcoreapi.md b/_episodes/11-esmvalcoreapi.md index 6511b0f7..3fe44d80 100644 --- a/_episodes/11-esmvalcoreapi.md +++ b/_episodes/11-esmvalcoreapi.md @@ -1,7 +1,7 @@ --- title: "What is ESMValCore API and using it in a Jupyter notebook" teaching: 20 -exercises: 30 +exercises: 40 compatibility: ESMValTool, ESMValCore v2.11.0 questions: @@ -17,20 +17,35 @@ keypoints: - "Use `datasets_to_recipe` helper to start making recipes" --- -In this episode we will introduce the ESMValCore API in a jupyter notebook. ESMValTool acts as a +In this episode we will introduce the ESMValCore API in a jupyter notebook. ESMValTool acts as a wrapper and collection of recipes and diagnostics and CMORisers for observations that is built on -top of ESMValCore which has the core functionality. This episode is reformatted +top of ESMValCore which has the core functionality. +A schematic overview is depicted below. +![image](../fig/esmvaltool_architecture.png) +Using the functionalities in the ESMValCore light blue box in this modular way allows users to +use existing code to explore and build insights in the data and can help them build and write a +recipeand diagnostic script with the previous episodes Writing your own [recipe]({{ page.root + }}{% link _episodes/06-preprocessor.md %}) / [diagnostic script]({{ page.root }}{% link +_episodes/08-diagnostics.md %}) + +This episode is reformatted from material from this [blog post][easy-ipcc-blog]{:target="_blank"} -by Peter Kalverla. There's also material from the +by Peter Kalverla. There's also material from the [example notebooks][docs-notebooks]{:target="_blank"} and the [API reference documentation][api-reference]{:target="_blank"}. ## Start JupyterLab A [jupyter notebook](https://jupyter.org/){:target="_blank"} is an interactive document where -you can run code. If using a HPC server they may provide a service which can start up an interactive job -with Jupyter running for you which is convenient for this exercise, for example, -[ARE](https://opus.nci.org.au/spaces/Help/pages/162431120/ARE+User+Guide) at NCI's Gadi in Australia or -[Jupyterhub@DKRZ](https://docs.dkrz.de/doc/software%26services/jupyterhub/index.html). +you can run code. All python code in this episode was written to be in a python sell of a +Jupyter Notebook. + +If using a HPC server they may provide a service which can start up an interactive job +with Jupyter running for you. This would be convenient for this exercise where you can also +access the data stored at those HPC servers, for example, +- [ARE](https://opus.nci.org.au/spaces/Help/pages/162431120/ARE+User+Guide) +at NCI's Gadi in Australia or +- [Jupyterhub@DKRZ](https://docs.dkrz.de/doc/software%26services/jupyterhub/index.html). + You will need to use a python environment with ESMValTool and ESMValCore installed. ## Configuration in the notebook @@ -61,10 +76,12 @@ This `CFG` object is from the `config` module in the ESMValCore API, for more de {: .challenge} ## Find Datasets with facets -We have seen from running available recipes that ESMValTool is able to find data from facets that -were given in the recipe. We can use this in a Notebook, including filling out the facets for -data definition. -To do this we will use the `Dataset` object from the API. Let's look at this example which you +Facets are key names and their values which help define the dataset. They are used to find the +particular dataset. See **Adding a dataset entry** section in the [Writing your own recipe]({{ +page.root }}{% link _episodes/06-preprocessor.md %}) +episode for examples. +We can use this in a Notebook, including filling out the facets for data definition. +To do this we will use the `Dataset` object from the API. Let's look at this example, which you can copy to a Jupyter notebook. ```python @@ -85,13 +102,17 @@ print(dataset) > ## Pro tip: Augmented facets in the output > When running a recipe there is a `_filled` recipe `yml` file in the output `/run` folder which > augments the facets. +> ``` +> esmvaltool run examples/recipe_python.yml +> ``` > > ## Example recipe output folder > > ```output -> > esmvaltool_output/flato13ipcc_figure914_20240729_043707/run +> > esmvaltool_output/recipe_python_20240729_043707/run > > ├── cmor_log.txt -> > ├── fig09-14 -> > ├── flato13ipcc_figure914_filled.yml -> > ├── flato13ipcc_figure914.yml +> > ├── map +> > ├── timeseries +> > ├── recipe_python_filled.yml +> > ├── recipe_python.yml > > ├── main_log_debug.txt > > ├── main_log.txt > > └── resource_usage.txt @@ -277,6 +298,15 @@ See the [documentation][recipe-section-preprocessors]{:target="_blank"} to read > {: .solution} {: .challenge} +> ## Note: Warnings +> When the notebook cell runs you may get some warnings. These would be similar to what is in the +> *main_log.txt* and *main_log_debug.txt* files in the output of a recipe run. The warnings can come +> from any of the python libraries used to process the data. If they are just warnings the cell can +> still complete and return an output +> > ## Example warnings +> > ![image](../fig/preproc_warnings.png) +> {: .solution} +{: .callout} ## Custom code We have so far solely used ESMValCore, however, you can use your own custom code and @@ -445,7 +475,7 @@ quickplot.plot(cube) > - Using variable `siconc` which is a fraction percent(0-100) > - Using datasets: > - `dataset:'ACCESS-ESM1-5', exp:'historical', ensemble:'r1i1p1f1', timerange:'1960/2010'` -> - `dataset :'ACCESS-OM2', exp:'omip2', ensemble='r1i1p1f1', timerange:'0306/0366'` +> - `dataset :'BCC-CSM2-MR', exp:'piControl', ensemble='r1i1p1f1', timerange:'1960/2010'` > - Using observations: > - `dataset:'NSIDC-G02202-sh', tier:'3', version:'4', timerange:'1979/2018'` > @@ -473,11 +503,11 @@ quickplot.plot(cube) > > timerange='1960/2010', institute = '*', > >) > > -> >om_facets={'dataset' :'ACCESS-OM2', 'exp':'omip2', 'activity':'OMIP', 'timerange':'0306/0366' } +> >pi_facets={'dataset' :'BCC-CSM2-MR', 'exp':'piControl', 'activity':'CMIP'} > > > >model.add_supplementary(short_name='areacello', mip='Ofx') > > -> >model_om = model.copy(**om_facets) +> >model_pi = model.copy(**pi_facets) > >``` > {: .solution} > > ## Tip: Check dataset files can be found @@ -485,7 +515,7 @@ quickplot.plot(cube) > > Check files can be found for all the datasets: > > > >```python -> >for ds in [model, model_om, obs]: +> >for ds in [model, model_pi, obs]: > > print(ds['dataset'],' : ' ,ds.files) > > print(ds.supplementaries[0].files) > >``` @@ -512,7 +542,7 @@ quickplot.plot(cube) > >) > ># model_om - at index 1 to offset years > > -> >load_data = [model, model_om, obs] +> >load_data = [model, model_pi, obs] > > > ># function to use for both min and max ['max','min'] > > @@ -526,8 +556,6 @@ quickplot.plot(cube) > > cube = annual_statistics(cube, min_max) > > iris.util.promote_aux_coord_to_dim_coord(cube, 'year') > > cube.convert_units('km2') -> > if i == 1: ## om years 306/366 apply offset 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zYaYAl3!ne|HFr;k>M(>}V-8Xl7M%g-;siTQX*dXyC4ltD9mj9x4UX%xuyoKI_RVC{ zw}k{6DRaSFPGlNm9Dc|)M^AzIDfH1^0CQXqb965h3WY*p>HaN=*aA28)f}4A8>i0 Date: Tue, 26 Nov 2024 16:20:53 +1100 Subject: [PATCH 8/9] add link to JASMIN --- _episodes/11-esmvalcoreapi.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/_episodes/11-esmvalcoreapi.md b/_episodes/11-esmvalcoreapi.md index 3fe44d80..9811b69a 100644 --- a/_episodes/11-esmvalcoreapi.md +++ b/_episodes/11-esmvalcoreapi.md @@ -36,7 +36,7 @@ by Peter Kalverla. There's also material from the ## Start JupyterLab A [jupyter notebook](https://jupyter.org/){:target="_blank"} is an interactive document where -you can run code. All python code in this episode was written to be in a python sell of a +you can run code. All python code in this episode was written to be in a python cell of a Jupyter Notebook. If using a HPC server they may provide a service which can start up an interactive job @@ -45,6 +45,8 @@ access the data stored at those HPC servers, for example, - [ARE](https://opus.nci.org.au/spaces/Help/pages/162431120/ARE+User+Guide) at NCI's Gadi in Australia or - [Jupyterhub@DKRZ](https://docs.dkrz.de/doc/software%26services/jupyterhub/index.html). +- [JASMIN Notebooks Service] +(https://help.jasmin.ac.uk/docs/interactive-computing/jasmin-notebooks-service/). You will need to use a python environment with ESMValTool and ESMValCore installed. From 97f0a8d208d4b51f2339de96259780d9edeee6c5 Mon Sep 17 00:00:00 2001 From: flicj191 Date: Tue, 26 Nov 2024 16:23:15 +1100 Subject: [PATCH 9/9] reduce line lengths --- _episodes/11-esmvalcoreapi.md | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/_episodes/11-esmvalcoreapi.md b/_episodes/11-esmvalcoreapi.md index 9811b69a..11f630da 100644 --- a/_episodes/11-esmvalcoreapi.md +++ b/_episodes/11-esmvalcoreapi.md @@ -52,11 +52,13 @@ You will need to use a python environment with ESMValTool and ESMValCore install ## Configuration in the notebook -We can look at the default user configuration file, by default found in `~/.esmvaltool/config-user.yml` -by calling a `CFG` object as a dictionary structure. This gives us the ability to edit the settings. +We can look at the default user configuration file, by default found in +`~/.esmvaltool/config-user.yml` by calling a `CFG` object as a dictionary structure. +This gives us the ability to edit the settings. The tool can automatically download the climate data files required to run a recipe for you. You can check your download directory and output directory where your recipe runs will be saved. -This `CFG` object is from the `config` module in the ESMValCore API, for more details see [here][api-config]. +This `CFG` object is from the `config` module in the ESMValCore API, +for more details see [here][api-config]. > Call the `CFG` object in a Jupyter notebook and inspect the values. > > ## Solution @@ -302,9 +304,9 @@ See the [documentation][recipe-section-preprocessors]{:target="_blank"} to read > ## Note: Warnings > When the notebook cell runs you may get some warnings. These would be similar to what is in the -> *main_log.txt* and *main_log_debug.txt* files in the output of a recipe run. The warnings can come -> from any of the python libraries used to process the data. If they are just warnings the cell can -> still complete and return an output +> *main_log.txt* and *main_log_debug.txt* files in the output of a recipe run. +> The warnings can come from any of the python libraries used to process the data. +> If they are just warnings the cell can still complete and return an output > > ## Example warnings > > ![image](../fig/preproc_warnings.png) > {: .solution} @@ -323,7 +325,7 @@ print(da) ### Plot data The output from the preprocessor functions are Iris cubes. [Iris](https://scitools-iris.readthedocs.io/en/latest/index.html){:target="_blank"} -has wrappers for [matplotlib](https://matplotlib.org/){:target="_blank"} to [plot the processed +has wrappers for [matplotlib](https://matplotlib.org/){:target="_blank"} to [plot the processed cubes][iris-plot]{:target="_blank"}. This is useful in a notebook to help develop your recipe with the esmvalcore preprocessors. ```python