### Setup Your Own Data Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

A guide on how to set up and use your own custom climate data with the climpred library.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('custom_data')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### List Available Example Datasets

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/quick-start.ipynb

Displays the names of all example datasets available for loading within the climpred library.

```python
load_dataset()
```

--------------------------------

### Install herbie-data

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/NWP/Herbie.ipynb

Installs the herbie-data library quietly.

```python
!pip install herbie-data --quiet
```

--------------------------------

### Set up Conda Environment and Install

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/contributing.rst

Commands to create a new conda environment using a provided yml file, activate it, and then perform an editable install of climpred.

```bash
conda env create -f ci/requirements/climpred-dev.yml
conda activate climpred-dev

pip install -e .
```

--------------------------------

### Install Dependencies

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/NWP/NWP_GEFS_6h_forecasts.ipynb

Installs necessary Python packages for data analysis and forecast verification using conda and pip.

```conda
#!conda install intake fsspec intake-xarray intake-thredds -c conda-forge -y
```

```conda
#!conda install eccodes cfgrib -c conda-forge -y
```

```pip
#!pip install climetlab --quiet
```

--------------------------------

### Install climpred-dev Environment

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

This snippet shows how to create a conda development environment for climpred, ensuring all necessary dependencies are installed for running the examples.

```bash
conda env create -f ci/requirements/climpred-dev.yml
```

--------------------------------

### Import Libraries and Load Dataset

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/quick-start.ipynb

Imports necessary libraries such as matplotlib, xarray, and climpred, and loads an example dataset.

```python
%matplotlib inline
import matplotlib.pyplot as plt
import xarray as xr

from climpred import HindcastEnsemble
from climpred.tutorial import load_dataset
import climpred
```

--------------------------------

### Install and Use pre-commit

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/contributing.rst

Instructions for installing pre-commit and its hooks into the climpred repository. pre-commit automates code beautification, maintainability checks, and syntax error detection.

```bash
pip install --user pre-commit
pre-commit install
```

--------------------------------

### Install Dependencies

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/subseasonal/daily-S2S-ECMWF.ipynb

Installs necessary Python packages for data handling and analysis, including intake, fsspec, intake-xarray, and climetlab.

```python
#!conda install intake fsspec intake-xarray -c conda-forge -y

```

```python
#!pip install climetlab

#!pip install climetlab_s2s_ai_challenge

```

--------------------------------

### Install climpred from GitHub (clone)

Source: https://github.com/pangeo-data/climpred/blob/main/README.rst

Installs the bleeding-edge version of climpred by cloning the repository from GitHub, navigating into the directory, and installing locally.

```bash
git clone https://github.com/pangeo-data/climpred.git
cd climpred
python -m pip install . --upgrade
```

--------------------------------

### Load CESM-DP-SST Dataset

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/quick-start.ipynb

Loads the 'CESM-DP-SST' example dataset, which is a sample dataset for climate prediction analysis.

```python
initialized = climpred.tutorial.load_dataset("CESM-DP-SST")
```

--------------------------------

### Install climpred from GitHub (local clone)

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/index.rst

Installs the bleeding-edge version of climpred by cloning the repository and installing locally.

```bash
git clone https://github.com/pangeo-data/climpred.git
cd climpred
pip install . --upgrade
```

--------------------------------

### climpred Documentation: Setup Your Own Raw Model Output

Source: https://github.com/pangeo-data/climpred/blob/main/CHANGELOG.rst

A new demo has been added to guide users on setting up their own raw model output that is compliant with climpred. This includes instructions for using `intake-esm` with climpred for CMORized output.

```python
# Demo: Setup your own raw model output compliant to climpred
# Link: examples/misc/setup_your_own_data.html

# Demo: Using intake-esm with climpred for CMORized output
# Link: examples/misc/setup_your_own_data.html#intake-esm-for-cmorized-output
```

--------------------------------

### climpred Initialization and Setup

Source: https://github.com/pangeo-data/climpred/blob/main/CHANGELOG.rst

This snippet demonstrates the basic setup for using climpred, including importing necessary libraries and configuring matplotlib for plotting. It shows how to initialize the environment for climpred operations.

```python
import climpred
from climpred import HindcastEnsemble
import matplotlib as mpl

mpl.rcdefaults()
mpl.use("Agg")
# cut border when saving (for maps)
mpl.rcParams["savefig.bbox"] = "tight"
```

--------------------------------

### Install climpred from GitHub (direct)

Source: https://github.com/pangeo-data/climpred/blob/main/README.rst

Installs the bleeding-edge version of climpred directly from its GitHub repository using pip.

```bash
pip install git+https://github.com/pangeo-data/climpred.git
```

--------------------------------

### DCPP Dataset Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/initialized-datasets.rst

Demonstrates how to use the DCPP dataset with climpred, referencing intake-esm for data loading and providing links to example notebooks.

```python
with intake-esm <examples/misc/setup_your_own_data.html#intake-esm-for-cmorized-output>
```

--------------------------------

### Environment Setup for climpred with GPU Support

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/misc/climpred_gpu.ipynb

Installs necessary packages for climpred, including GPU-accelerated libraries like tensorflow-gpu, pytorch-gpu, cudatoolkit, and cupy, along with the cupy-xarray integration. This setup is crucial for leveraging GPU capabilities.

```bash
#!conda create -n ML_gpu tensorflow-gpu pytorch-gpu xarray dask matplotlib nb_conda_kernels jupyterlab cudatoolkit cupy esmtools climpred -y
#!pip install git+https://github.com/jacobtomlinson/cupy-xarray.git
```

--------------------------------

### NMME Dataset Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/initialized-datasets.rst

Shows how to work with the NMME dataset for seasonal predictions using climpred, with a link to seasonal examples.

```python
`seasonal SubX <examples.html#monthly-and-seasonal>`_
```

--------------------------------

### Install climpred from GitHub (direct URL)

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/index.rst

Installs the bleeding-edge version of climpred directly from its GitHub repository using pip.

```bash
pip install git+https://github.com/pangeo-data/climpred.git
```

--------------------------------

### Get Initialized and Observation Coordinates

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/quick-start.ipynb

Retrieves and displays the coordinates for both the initialized and observation datasets within the HindcastEnsemble. This helps verify data alignment.

```python
hindcast.get_initialized().coords
hindcast.get_observations().coords
```

--------------------------------

### NWP GEFS 6h Forecasts Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

Demonstrates how to work with GEFS 6-hour forecasts using climpred.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('gefs')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### GEFS Dataset Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/initialized-datasets.rst

Demonstrates the usage of the GEFS dataset for numerical weather prediction with climpred, linking to a specific example for GEFS NWP.

```python
`GEFS NWP <examples/NWP/NWP_GEFS_6h_forecasts.html>`_
```

--------------------------------

### SubX Dataset Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/initialized-datasets.rst

Provides information on using the SubX dataset for subseasonal predictions with climpred, linking to relevant examples.

```python
`subseasonal SubX <examples.html#subseasonal>`_
```

--------------------------------

### climpred Examples and Features

Source: https://github.com/pangeo-data/climpred/blob/main/CHANGELOG.rst

This section details various examples and new features added to the climpred library, including NWP, subseasonal forecasts, GPU acceleration, and handling biweekly aggregates.

```python
Add GEFS example <examples/NWP/NWP_GEFS_6h_forecasts.html>_ for numerical weather
prediction. (:issue:`602`, :pr:`603`) `Aaron Spring`_.
Add subseasonal daily ECMWF example <examples/subseasonal/daily-S2S-ECMWF.html>__
using `climetlab <https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge>`_ to access
hindcasts from ECMWF cloud.  (:issue:`587`, :pr:`603`) `Aaron Spring`_.
Add subseasonal daily S2S example <examples/subseasonal/daily-S2S-IRIDL.html>_
accessing `S2S <http://s2sprediction.net/>`_ output on
`IRIDL <https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/>`_ with a cookie and
working with "on-the-fly" reforecasts with ``hdate`` dimension.
(:issue:`588`, :pr:`593`) `Aaron Spring`_.
Added example climpred on GPU <examples/misc/climpred_gpu.ipynb>_. Running
:py:meth:`.PerfectModelEnsemble.verify` on GPU with `cupy-xarray
<https://github.com/xarray-contrib/cupy-xarray>`_ finishes 10x faster.
(:issue:`592`, :pr:`607`) `Aaron Spring`_.
How to work with biweekly aggregates in ``climpred``, see
`daily ECMWF example <examples/subseasonal/daily-S2S-ECMWF.html#biweekly-aggregates>`__.
(:issue:`625`, :pr:`630`) `Aaron Spring`_.
```

--------------------------------

### Herbie NWP Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

An example showcasing the use of climpred with Herbie, likely for NWP data.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('herbie')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### climpred GPU Usage Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

Illustrates how to utilize GPU acceleration with climpred for faster computations.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('gpu')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### Perfect Model Predictability Demo

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

An example demonstrating perfect model predictability analysis with climpred.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('perfect_model')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### Install climpred with pip

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/index.rst

Installs the latest release of climpred with all optional dependencies using pip.

```bash
pip install climpred[complete]
```

--------------------------------

### Daily S2S ECMWF Subseasonal Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

An example for processing daily S2S forecasts from ECMWF using climpred.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('s2s_ecmwf')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### Weekly SubX Subseasonal Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

Shows how to analyze weekly subseasonal forecasts from the SubX dataset with climpred.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('subx_weekly')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### CESM-DPLE Dataset Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/initialized-datasets.rst

Highlights the use of the CESM-DPLE dataset with climpred, noting its compatibility with standard climpred examples.

```python
many standard climpred `examples <quick-start.html>`_
```

--------------------------------

### Seasonal ENSO SubX Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

Shows how to analyze seasonal ENSO forecasts from the SubX dataset using climpred.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('subx_seasonal')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### Daily SubX Subseasonal Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

Illustrates how to use climpred for daily subseasonal forecasts from the SubX dataset.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('subx')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### Linting Notebooks

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/NWP/NWP_GEFS_6h_forecasts.ipynb

Loads nb_black and lab_black extensions for code linting within a Jupyter Notebook environment.

```python
# linting
%load_ext nb_black
%load_ext lab_black
```

--------------------------------

### Install climpred with conda

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/index.rst

Installs the latest release of climpred from the conda-forge channel.

```bash
conda install -c conda-forge climpred
```

--------------------------------

### Load IPython Extensions

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/quick-start.ipynb

Loads the nb_black and lab_black extensions for enhanced code formatting in IPython environments.

```python
# linting
%load_ext nb_black
%load_ext lab_black
```

--------------------------------

### Load IPython Extensions

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/subseasonal/daily-S2S-ECMWF.ipynb

Loads IPython extensions for code formatting and styling within a Jupyter Notebook environment.

```python
# linting
%load_ext nb_black
%load_ext lab_black

```

--------------------------------

### S2S Dataset Examples

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/initialized-datasets.rst

Illustrates how to access and use the S2S dataset for subseasonal predictions via IRIDL and climetlab, with links to specific examples.

```python
`IRIDL <examples/subseasonal/daily-S2S-IRIDL.html>`_
```

```python
`EWC Cloud/climetlab <examples/subseasonal/daily-S2S-ECMWF.html>`_
```

--------------------------------

### Install climpred with pip

Source: https://github.com/pangeo-data/climpred/blob/main/README.rst

Installs the latest release of the climpred package, including optional dependencies for complete functionality, using pip.

```bash
python -m pip install climpred[complete]
```

--------------------------------

### Daily S2S IRIDL Subseasonal Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

Demonstrates working with daily S2S (Subseasonal to Seasonal) forecasts from IRIDL using climpred.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('s2s_iridl')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### Load Black Formatting Extensions

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/NWP/Herbie.ipynb

Loads nb_black and lab_black extensions for code linting and formatting in notebooks.

```python
# linting
%load_ext nb_black
%load_ext lab_black
```

--------------------------------

### Display Initialized Coordinates

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/quick-start.ipynb

Prints the coordinates of the initialized dataset. This is useful for understanding the structure and dimensions of the forecast data.

```python
initialized.coords
```

--------------------------------

### Git Commit and Push

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/contributing.rst

Standard commands for committing changes locally and pushing them to the remote repository.

```shell
$ git commit -a -m "<commit message>"
$ git push -u
```

--------------------------------

### Load Dataset Function

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/api/climpred.tutorial.load_dataset.rst

The `load_dataset` function from the `climpred.tutorial` module is used to load a dataset. It is part of the tutorial section, suggesting its use for examples and learning.

```python
import climpred.tutorial

dataset = climpred.tutorial.load_dataset()
```

--------------------------------

### Prepare Initial Forecast Data

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/NWP/Herbie.ipynb

Takes the first forecast from the downloaded dataset and selects the 't2m' variable, preparing it as the initial forecast data.

```python
# take the first with multiple members as forecast
init = ds[0][["t2m"]]
```

--------------------------------

### Initialize HindcastEnsemble for Smoothing

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/subseasonal/daily-S2S-ECMWF.ipynb

Initializes a `HindcastEnsemble` with daily lead data, adds observations, and computes the ensemble, preparing it for subsequent smoothing operations.

```python
fct = (
    climpred.HindcastEnsemble(forecast_climetlab.drop_vars("valid_time"))  # daily lead
    .add_observations(obs)
    .compute()
)
```

--------------------------------

### Import Core Libraries

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/misc/setup_your_own_data.ipynb

Imports necessary libraries for data manipulation, plotting, and climpred functionality.

```python
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr

import climpred
```

--------------------------------

### Display First Few Rows of Catalog DataFrame

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/misc/setup_your_own_data.ipynb

Shows the beginning of the DataFrame after searching the catalog, providing a preview of the selected data.

```python
cat.df.head()
```

--------------------------------

### Define Reforecast Dates

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/subseasonal/daily-S2S-ECMWF.ipynb

Generates a time series of dates for reforecasts, starting from January 2nd, 2020, with a weekly frequency.

```python
dates = xr.cftime_range(start="2020-01-02", freq="7D", end="2020-12-31")

```

--------------------------------

### Create Biweekly Observation Aggregates

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/subseasonal/daily-S2S-ECMWF.ipynb

Generates biweekly aggregates for observation data using a rolling 14-day mean. The time index is adjusted to represent the starting date of each 14-day period.

```python
# 14D rolling mean
obs_biweekly = obs.rolling(time=14, center=False).mean()
obs_biweekly = obs_biweekly.isel(time=slice(13, None)).assign_coords(
    time=obs.time.isel(time=slice(None, -13))
)  # time represents first day of the biweekly aggregate
```

--------------------------------

### Manual PyPI and TestPyPI Upload

Source: https://github.com/pangeo-data/climpred/blob/main/HOWTORELEASE.rst

Provides the commands to manually build the package and upload it to TestPyPI and PyPI. This is an alternative to the automated GitHub Actions workflow.

```shell
$ git clean -xfd  # remove any files not checked into git
$ python -m build  # build package
$ python -m twine upload --repository-url https://test.pypi.org/legacy dist/*  # register and push to testpypi
$ python -m twine upload dist/*  # register and push to pypi
```

--------------------------------

### Adjust Lead Dimension

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/subseasonal/daily-subx-example.ipynb

Adjusts the 'lead' dimension in the SubX dataset. SubX uses fractional leads (e.g., 0.5, 1.5), which are converted to integers starting from 0 to represent days since initialization.

```python
fcstds["lead"] = (fcstds["lead"] - 0.5).astype("int")
```

--------------------------------

### Create Biweekly Forecast Aggregates

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/subseasonal/daily-S2S-ECMWF.ipynb

Creates biweekly aggregates for forecast data by calculating the mean over specific lead time ranges (1-14 days, 14-28 days, 28-42 days) and concatenating them. The lead dimension is then updated to represent the start of each biweekly period.

```python
# create 14D averages
forecast_w12 = forecast.sel(lead=range(1, 14)).mean(dim="lead")
forecast_w34 = forecast.sel(lead=range(14, 28)).mean(dim="lead")
forecast_w56 = forecast.sel(lead=range(28, 42)).mean(dim="lead")
forecast_biweekly = xr.concat([forecast_w12, forecast_w34, forecast_w56], dim="lead")
forecast_biweekly["lead"] = [
    1,
    14,
    28,
]  # lead represents first day of biweekly aggregate
forecast_biweekly["lead"].attrs["units"] = "days"
forecast_biweekly.coords
```

--------------------------------

### Tagging a Release

Source: https://github.com/pangeo-data/climpred/blob/main/HOWTORELEASE.rst

This snippet demonstrates how to tag a new release and push it to the upstream repository. It follows semantic versioning conventions.

```shell
$ git tag -a v1.0.0 -m "Version 1.0.0"
$ git push upstream main --tags
```

--------------------------------

### Significance Testing Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

An example showing how to perform significance testing on climate predictions with climpred.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('significance')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### Fork and Clone Repository

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/contributing.rst

Steps to fork the climpred GitHub repository, clone it locally, and set up the upstream remote. It also includes creating a new branch for bug fixes or features.

```bash
git clone git@github.com:YOUR_GITHUB_USERNAME/climpred.git
cd climpred
git remote add upstream git@github.com:pangeo-data/climpred.git

# now, to fix a bug or add feature create your own branch off "main":

git checkout -b your-bugfix-feature-branch-name main
```

--------------------------------

### Verify Dimension Implications Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

This example demonstrates how to verify dimension implications in climate model outputs using climpred.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('verify_dim')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### Tropical Pacific SSTs Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

This example focuses on analyzing tropical Pacific Sea Surface Temperatures (SSTs) using climpred.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('ssts')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### Build Documentation Locally

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/contributing.rst

Commands to update the conda environment and build the documentation locally using make html. The built documentation will be available in the docs/build/ directory.

```bash
$ conda env update -f ci/requirements/climpred-dev.yml
$ cd docs
$ make html
```

--------------------------------

### Load Hindcast Data with Preprocessing

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/misc/setup_your_own_data.ipynb

Loads hindcast data using `load_hindcast`, specifying initializations, members, and a preprocessing function to handle data format.

```python
# lead_offset because yearmean output
%time ds = load_hindcast(inits=range(1961, 1965), members=range(1, 3), preprocess=preprocess_1var, get_path=get_path)
```

--------------------------------

### Diagnose Potential Predictability Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

Demonstrates how to diagnose potential predictability of climate variables using climpred.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('diagnose_predictability')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### Import Preprocessing Utilities

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/misc/setup_your_own_data.ipynb

Imports specific preprocessing functions from climpred for loading hindcast data and setting integer time axes.

```python
from climpred.preprocessing.shared import load_hindcast, set_integer_time_axis
from climpred.preprocessing.mpi import get_path
```

--------------------------------

### Load climpred Tutorial Dataset and Set Up Plotting

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/alignment.ipynb

Loads a sample dataset using climpred's tutorial functions and configures matplotlib for plotting. It also imports necessary libraries for data analysis and handling warnings.

```python
from climpred import HindcastEnsemble
from climpred.tutorial import load_dataset
from esmtools.stats import rm_trend

import matplotlib.pyplot as plt

plt.style.use("fivethirtyeight")
%matplotlib inline

import numpy as np

import warnings
```

--------------------------------

### Initialize intake

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/misc/setup_your_own_data.ipynb

Imports the intake library, which is required for intake-esm to function, even if not directly used in the snippet.

```python
# make to have to install intake-esm installed, which is not included in climpred-dev
import intake # this is enough for intake-esm to work
```

--------------------------------

### Monthly ENSO SubX Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples.rst

Demonstrates climpred usage for monthly ENSO forecasts from the SubX dataset.

```python
import climpred.tutorial
import xarray as xr

# Load example data
ds = climpred.tutorial.load_dataset('subx_monthly')

# Example analysis (replace with actual climpred usage)
print(ds)
```

--------------------------------

### Define Data Query and Preprocessing Arguments

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/misc/setup_your_own_data.ipynb

Sets up a query to select specific CMIP data (experiment, members, years, variable, model) and defines keyword arguments for dataset loading, including chunking and time decoding.

```python
query = dict(experiment_id=[
    'dcppA-hindcast'], table_id='Amon', member_id=['r1i1p1f1', 'r2i1p1f1'], dcpp_init_year=[1970, 1971],
    variable_id='tas', source_id='MPI-ESM1-2-HR')
cdf_kwargs = {'chunks': {'time': 12}, 'decode_times': False}
```

--------------------------------

### Tutorial Function

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/api.rst

Provides a function for loading datasets for tutorial purposes.

```APIDOC
climpred.tutorial:
  load_dataset()
```

--------------------------------

### Install climpred with conda

Source: https://github.com/pangeo-data/climpred/blob/main/README.rst

Installs the latest release of the climpred package from the conda-forge channel using conda.

```bash
conda install -c conda-forge climpred
```

--------------------------------

### climpred New Example Pages

Source: https://github.com/pangeo-data/climpred/blob/main/CHANGELOG.rst

New example pages have been added to demonstrate subseasonal-to-seasonal (S2S) prediction using climpred. These examples cover calculating MJO index skill, ENSO skill as a function of lead time and initial month, and seasonal ENSO skill.

```python
# Example: Calculate the skill of the MJO index as a function of lead time
# Link: examples/subseasonal/daily-subx-example.html

# Example: Calculate ENSO skill as a function of initial month vs. lead time
# Link: examples/monseas/monthly-enso-subx-example.html
```

--------------------------------

### climpred Dimension Requirements

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/setting-up-data.rst

Details the expected dimensions ('init', 'lead', 'member') for xarray Datasets used with climpred. Explains data types, CF convention mappings, and attribute requirements for each dimension.

```python
Dimension: 'init'
Description: Initialization time steps.
Required Type: pandas.DatetimeIndex or xarray.CFTimeIndex.
CF Convention Name: 'forecast_reference_time'.
Notes: If int, assumed annual starting Jan 1st (UserWarning issued).

Dimension: 'lead'
Description: Lead time from initialization.
Required Type: int, float, or pandas.Timedelta (up to 'weeks').
CF Convention Name: 'forecast_period'.
Required Attribute: 'units' (e.g., 'years', 'months', 'days').
Notes: pandas.Timedelta larger than 'weeks' (e.g., 'months', 'seasons', 'years') are not converted.

Dimension: 'member'
Description: Ensemble members.
Required Type: int or str.
CF Convention Name: 'realization'.
Notes: Required for probabilistic metrics.
```

--------------------------------

### Open ESM Datastore Catalog

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/misc/setup_your_own_data.ipynb

Opens an intake-esm datastore catalog from a specified URL, enabling searching and loading of CMIP data.

```python
col_url = "/home/mpim/m300524/intake-esm-datastore/catalogs/mistral-cmip6.json"
col_url = "https://raw.githubusercontent.com/NCAR/intake-esm-datastore/master/catalogs/pangeo-cmip6.json"
col = intake.open_esm_datastore(col_url)
```

--------------------------------

### Adding New Datasets

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/initialized-datasets.rst

Instructions for users to contribute by adding new publicly available initialized datasets to climpred, encouraging Pull Requests.

```python
please add a `Pull Request <contributing.html>`_
```

--------------------------------

### climpred Documentation: Verification Alignment

Source: https://github.com/pangeo-data/climpred/blob/main/CHANGELOG.rst

A new 'Verification Alignment' page has been added to the documentation, explaining how initializations are selected and aligned with verification data within climpred.

```python
# Documentation: Verification Alignment page added.
# Explains initialization selection and alignment with verification data.
# Link: alignment.html
```

--------------------------------

### climpred HindcastEnsemble and PerfectModelEnsemble Usage

Source: https://github.com/pangeo-data/climpred/blob/main/CHANGELOG.rst

Demonstrates how to load datasets, initialize HindcastEnsemble objects, add reference data, and retrieve initialized or reference datasets using climpred.

```python
>>> hind = climpred.tutorial.load_dataset("CESM-DP-SST")
>>> ref = climpred.tutorial.load_dataset("ERSST")
>>> hindcast = climpred.HindcastEnsemble(hind)
>>> hindcast = hindcast.add_reference(ref, "ERSST")
>>> print(hindcast)
<climpred.HindcastEnsemble>
Initialized Ensemble:
    SST      (init, lead, member) float64 ...
ERSST:
    SST      (time) float32 ...
Uninitialized:
    None
>>> print(hindcast.get_initialized())
<xarray.Dataset>
Dimensions:  (init: 64, lead: 10, member: 10)
Coordinates:
* lead     (lead) int32 1 2 3 4 5 6 7 8 9 10
* member   (member) int32 1 2 3 4 5 6 7 8 9 10
* init     (init) float32 1954.0 1955.0 1956.0 1957.0 ... 2015.0 2016.0 2017.0
Data variables:
    SST      (init, lead, member) float64 ...
>>> print(hindcast.get_reference("ERSST"))
<xarray.Dataset>
Dimensions:  (time: 61)
Coordinates:
* time     (time) int64 1955 1956 1957 1958 1959 ... 2011 2012 2013 2014 2015
Data variables:
    SST      (time) float32 ...
```

--------------------------------

### List Available Datasets

Source: https://github.com/pangeo-data/climpred/wiki/Sample-Data

Retrieves and displays a list of all available example datasets that can be loaded by the climpred library. This function helps users understand the data sources available for analysis.

```python
from climpred.loadutils import get_datasets
get_datasets()
>>> 'MPI-PM-DP-1D': decadal prediction ensemble area averages of SST/SSS/AMO.
>>> 'MPI-PM-DP-3D': decadal prediction ensemble lat/lon/time of SST/SSS/AMO.
>>> 'MPI-control-1D': area averages for the control run of SST/SSS.
>>> 'MPI-control-3D': lat/lon/time for the control run of SST/SSS.
>>> 'CESM-DP-SST': decadal prediction ensemble of global mean SSTs.
>>> 'CESM-DP-SSS': decadal prediction ensemble of global mean SSS.
>>> 'CESM-LE': uninitialized ensemble of global mean SSTs.
>>> 'ERSST': observations of global mean SSTs.
>>> 'FOSI-SST': reconstruction of global mean SSTs.
>>> 'FOSI-SSS': reconstruction of global mean SSS.
```

--------------------------------

### HindcastEnsemble Initialization and Verification

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/subseasonal/daily-S2S-ECMWF.ipynb

Demonstrates initializing a HindcastEnsemble with forecast data, adding observations, computing the ensemble, and then verifying it using RMSE with specific comparison and alignment parameters. It also shows plotting the results.

```python
fct = (
    climpred.HindcastEnsemble(
        forecast_climetlab.drop_vars("valid_time").isel(lead_time=range(4))
    )
    .add_observations(obs)
    .compute()
)

metric_kwargs = dict(
    metric="rmse", comparison="e2o", alignment="same_inits", dim="init"
)

skill = fct.verify(**metric_kwargs)
skill[var].plot(col="lead", robust=True)
```

--------------------------------

### Extract and Inspect a Dataset

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/misc/setup_your_own_data.ipynb

Retrieves one dataset from the dictionary and displays its coordinates, showing the effect of preprocessing.

```python
# get first dict value
_, ds = dset_dict.popitem()
ds.coords
```

--------------------------------

### GPU Limitations: Bootstrap Method

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/misc/climpred_gpu.ipynb

Demonstrates the usage of the `bootstrap` method within `PerfectModelEnsemble` on the GPU. This example is provided in the context of discussing potential GPU limitations, although the code itself shows a standard operation.

```python
pm_gpu.bootstrap(metric="pearson_r", comparison="m2m", dim=["init", "member"], iterations=10)[v]
```

--------------------------------

### Load and Prepare Dataset for CPU

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/misc/climpred_gpu.ipynb

Loads a sample dataset ('MPI-PM-DP-3D' and 'MPI-control-3D') using climpred's tutorial functions and prepares it for use with the PerfectModelEnsemble on the CPU. It selects a specific variable ('tos') and adds unit attributes.

```python
import xarray as xr
import numpy as np
from climpred.tutorial import load_dataset
from climpred import PerfectModelEnsemble

v = "tos"
ds3d = load_dataset("MPI-PM-DP-3D")[v]
ds3d.lead.attrs["unit"] = "years"
control3d = load_dataset("MPI-control-3D")[v]

pm_cpu = PerfectModelEnsemble(ds3d)
pm_cpu = pm_cpu.add_control(control3d)
```

--------------------------------

### Configure climetlab/cdsapi Logging

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/NWP/NWP_GEFS_6h_forecasts.ipynb

Configures the logging level for climetlab or cdsapi. This example sets the logger to ERROR level, suppressing informational messages.

```python
# climetlab or cds enable logging.INFO
import logging

logger = logging.getLogger()
logger.setLevel(logging.ERROR)
```

--------------------------------

### Same Verifications Alignment Example

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/alignment.ipynb

An example of using the 'same_verifs' alignment strategy in climpred's verify function. This strategy ensures that the verification dates remain constant across all leads.

```python
skill = hindcast.verify(
    metric="acc", comparison="e2o", dim="init", alignment="same_verifs"
)
```

--------------------------------

### Running ASV Benchmarks

Source: https://github.com/pangeo-data/climpred/blob/main/docs/source/contributing.rst

Execute performance benchmarks using the asv tool. This command runs continuous benchmarking against upstream main and the current HEAD, reporting significant changes.

```shell
$ asv continuous -f 1.1 upstream/main HEAD
```