From 572cead3c0e4e121de014cfa2115e829457d62c8 Mon Sep 17 00:00:00 2001 From: Niclas Rieger Date: Tue, 24 Oct 2023 16:09:13 +0200 Subject: [PATCH 01/12] build: update env --- poetry.lock | 1241 +++++++++++++++++++++++++++--------------------- pyproject.toml | 20 +- 2 files changed, 699 insertions(+), 562 deletions(-) diff --git a/poetry.lock b/poetry.lock index 4b83264..d77de2c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -45,15 +45,18 @@ tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pyte [[package]] name = "babel" -version = "2.12.1" +version = "2.13.0" description = "Internationalization utilities" optional = true python-versions = ">=3.7" files = [ - {file = "Babel-2.12.1-py3-none-any.whl", hash = "sha256:b4246fb7677d3b98f501a39d43396d3cafdc8eadb045f4a31be01863f655c610"}, - {file = "Babel-2.12.1.tar.gz", hash = "sha256:cc2d99999cd01d44420ae725a21c9e3711b3aadc7976d6147f622d8581963455"}, + {file = "Babel-2.13.0-py3-none-any.whl", hash = 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(>=3.5)", "sphinx-lint"] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "7b7b136975a0e65a82eb4ad0de78ee240bd719a40f902a1d60c7ce52467491f2" +content-hash = "a95a480b1227d7937b8afc4d687b3bba88ec3bef4e4ea487ff96f5b2e91a64f1" diff --git a/pyproject.toml b/pyproject.toml index 1485659..2c10291 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,18 +24,20 @@ numba = "^0.57" typing-extensions = "^4.8.0" [tool.poetry.group.dev.dependencies] -flake8 = "^4.0.1" -pytest = "^7.0.1" -coverage = "^6.3.1" -black = "~23.7.0" +flake8 = {version = "^4.0", optional = true} +pytest = {version = "^7.0", optional = true} +coverage = {version = "^6.3", optional = true} +black = {version = "~23.7.0", optional = true} [tool.poetry.group.docs.dependencies] rpy2 = {version = ">=3.5", optional = true} -sphinx-gallery = {version = "^0", optional = true} -sphinx-design = {version = "^0", optional = true} -sphinx-copybutton = {version = "^0", optional = true} -nbsphinx = {version = "^0", optional = true} -pydata-sphinx-theme = {version = "^0", optional = true} +sphinx-gallery = {version = "^0.14", optional = true} +sphinx-design = {version = "^0.5", optional = true} +sphinx-copybutton = {version = "^0.5", optional = true} +nbsphinx = {version = "^0.9", optional = true} +pydata-sphinx-theme = {version = "^0.14", optional = true} +sphinx = {version = "^7.2", optional = true} +nbconvert = {version = "^7.9", optional = true} [build-system] requires = ["setuptools", "poetry-core>=1.0.0"] From ed59dd9a6b66ae878c1eb596a2fa9fb9b0e137ca Mon Sep 17 00:00:00 2001 From: Niclas Rieger Date: Fri, 27 Oct 2023 08:41:46 +0200 Subject: [PATCH 02/12] fix(Bootstrapper): correct scaling of PCs --- xeofs/validation/bootstrapper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/xeofs/validation/bootstrapper.py b/xeofs/validation/bootstrapper.py index 8006793..a5c2111 100644 --- a/xeofs/validation/bootstrapper.py +++ b/xeofs/validation/bootstrapper.py @@ -91,7 +91,7 @@ def fit(self, model: EOF): expvar = bst_model.data["explained_variance"] totvar = bst_model.data["total_variance"] components = bst_model.data["components"] - scores = bst_model.transform(input_data) + scores = bst_model.transform(input_data, normalized=False) bst_expvar.append(expvar) bst_total_variance.append(totvar) bst_components.append(components) From 70f10b29e3ac22b49bb3de0f00992761c1e6a059 Mon Sep 17 00:00:00 2001 From: Niclas Rieger Date: Sat, 28 Oct 2023 15:32:53 +0200 Subject: [PATCH 03/12] docs: add CoC --- CODE_OF_CONDUCT.md | 134 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 134 insertions(+) create mode 100644 CODE_OF_CONDUCT.md diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..0f7f9fa --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,134 @@ + +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall + community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or advances of + any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email address, + without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +[niclasrieger@gmail.com](mailto:niclasrieger@gmail.com). +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series of +actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the +community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.1, available at +[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at +[https://www.contributor-covenant.org/translations][translations]. + +[homepage]: https://www.contributor-covenant.org +[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations + From 3482fa465da66a8e540d10e3a13ef3b8c1461e70 Mon Sep 17 00:00:00 2001 From: Niclas Rieger Date: Sat, 28 Oct 2023 15:42:39 +0200 Subject: [PATCH 04/12] fix: parameter to show dask progress bar By default, dask progress bar is turned of but can be activated by setting verbose=True in EOF and Complex EOF models --- xeofs/models/_base_model.py | 13 ++++++++++++- xeofs/models/decomposer.py | 23 ++++++++++++++++++----- xeofs/models/eof.py | 16 ++++++++++++++-- 3 files changed, 44 insertions(+), 8 deletions(-) diff --git a/xeofs/models/_base_model.py b/xeofs/models/_base_model.py index bbf0639..5188cf0 100644 --- a/xeofs/models/_base_model.py +++ b/xeofs/models/_base_model.py @@ -49,6 +49,10 @@ class _BaseModel(ABC): Whether to compute the decomposition immediately. This is recommended if the SVD result for the first ``n_modes`` can be accommodated in memory, as it boosts computational efficiency compared to deferring the computation. + verbose: bool, default=False + Whether to show a progress bar when computing the decomposition. + random_state: Optional[int], default=None + Seed for the random number generator. solver: {"auto", "full", "randomized"}, default="auto" Solver to use for the SVD computation. solver_kwargs: dict, default={} @@ -65,6 +69,7 @@ def __init__( sample_name="sample", feature_name="feature", compute=True, + verbose=False, random_state=None, solver="auto", solver_kwargs={}, @@ -82,12 +87,18 @@ def __init__( "sample_name": sample_name, "feature_name": feature_name, "random_state": random_state, + "verbose": verbose, "compute": compute, "solver": solver, } self._solver_kwargs = solver_kwargs self._solver_kwargs.update( - {"solver": solver, "random_state": random_state, "compute": compute} + { + "solver": solver, + "random_state": random_state, + "compute": compute, + "verbose": verbose, + } ) # Define analysis-relevant meta data diff --git a/xeofs/models/decomposer.py b/xeofs/models/decomposer.py index 2416308..9716372 100644 --- a/xeofs/models/decomposer.py +++ b/xeofs/models/decomposer.py @@ -32,6 +32,8 @@ class Decomposer: and optionally truncated afterwards. random_state : Optional[int], default=None Seed for the random number generator. + verbose: bool, default=False + Whether to show a progress bar when computing the decomposition. **kwargs Additional keyword arguments passed to the SVD solver. """ @@ -43,11 +45,13 @@ def __init__( compute: bool = True, solver: str = "auto", random_state: Optional[int] = None, + verbose: bool = False, **kwargs, ): self.n_modes = n_modes self.flip_signs = flip_signs self.compute = compute + self.verbose = verbose self.solver = solver self.random_state = random_state self.solver_kwargs = kwargs @@ -229,9 +233,18 @@ def _compute_svd_result(self, U, s, VT): VT : DataArray Right singular vectors. """ - if self.compute: - with ProgressBar(): - U = U.compute() - s = s.compute() - VT = VT.compute() + match self.compute: + case False: + pass + case True: + match self.verbose: + case True: + with ProgressBar(): + U = U.compute() + s = s.compute() + VT = VT.compute() + case False: + U = U.compute() + s = s.compute() + VT = VT.compute() return U, s, VT diff --git a/xeofs/models/eof.py b/xeofs/models/eof.py index 367b8cc..b6d0cc2 100644 --- a/xeofs/models/eof.py +++ b/xeofs/models/eof.py @@ -34,6 +34,10 @@ class EOF(_BaseModel): Whether to compute the decomposition immediately. This is recommended if the SVD result for the first ``n_modes`` can be accommodated in memory, as it boosts computational efficiency compared to deferring the computation. + verbose: bool, default=False + Whether to show a progress bar when computing the decomposition. + random_state : Optional[int], default=None + Seed for the random number generator. solver: {"auto", "full", "randomized"}, default="auto" Solver to use for the SVD computation. solver_kwargs: dict, default={} @@ -56,6 +60,7 @@ def __init__( sample_name: str = "sample", feature_name: str = "feature", compute: bool = True, + verbose: bool = False, random_state: Optional[int] = None, solver: str = "auto", solver_kwargs: Dict = {}, @@ -69,6 +74,7 @@ def __init__( sample_name=sample_name, feature_name=feature_name, compute=compute, + verbose=verbose, random_state=random_state, solver=solver, solver_kwargs=solver_kwargs, @@ -275,6 +281,10 @@ class ComplexEOF(EOF): Whether to compute the decomposition immediately. This is recommended if the SVD result for the first ``n_modes`` can be accommodated in memory, as it boosts computational efficiency compared to deferring the computation. + verbose: bool, default=False + Whether to show a progress bar when computing the decomposition. + random_state : Optional[int], default=None + Seed for the random number generator. solver: {"auto", "full", "randomized"}, default="auto" Solver to use for the SVD computation. solver_kwargs: dict, default={} @@ -286,8 +296,8 @@ class ComplexEOF(EOF): ---------- .. [1] Rasmusson, E. M., Arkin, P. A., Chen, W.-Y. & Jalickee, J. B. Biennial variations in surface temperature over the United States as revealed by singular decomposition. Monthly Weather Review 109, 587–598 (1981). .. [2] Barnett, T. P. Interaction of the Monsoon and Pacific Trade Wind System at Interannual Time Scales Part I: The Equatorial Zone. Monthly Weather Review 111, 756–773 (1983). - .. [3] Horel, J., 1984. Complex Principal Component Analysis: Theory and Examples. J. Climate Appl. Meteor. 23, 1660–1673. https://doi.org/10.1175/1520-0450(1984)023<1660:CPCATA>2.0.CO;2 - .. [4] Hannachi, A., Jolliffe, I., Stephenson, D., 2007. Empirical orthogonal functions and related techniques in atmospheric science: A review. International Journal of Climatology 27, 1119–1152. https://doi.org/10.1002/joc.1499 + .. [3] Horel, J. Complex Principal Component Analysis: Theory and Examples. J. Climate Appl. Meteor. 23, 1660–1673 (1984). + .. [4] Hannachi, A., Jolliffe, I. & Stephenson, D. Empirical orthogonal functions and related techniques in atmospheric science: A review. International Journal of Climatology 27, 1119–1152 (2007). Examples -------- @@ -307,6 +317,7 @@ def __init__( sample_name: str = "sample", feature_name: str = "feature", compute: bool = True, + verbose: bool = False, random_state: Optional[int] = None, solver: str = "auto", solver_kwargs: Dict = {}, @@ -319,6 +330,7 @@ def __init__( sample_name=sample_name, feature_name=feature_name, compute=compute, + verbose=verbose, random_state=random_state, solver=solver, solver_kwargs=solver_kwargs, From 3219562d225f5c5cf6bb1719bbd2c15689c4ea11 Mon Sep 17 00:00:00 2001 From: Niclas Rieger Date: Sun, 29 Oct 2023 12:19:34 +0100 Subject: [PATCH 05/12] build: add pre-commit as dev dependency --- poetry.lock | 122 ++++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 1 + 2 files changed, 122 insertions(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index d77de2c..b6d6c9b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -213,6 +213,17 @@ files = [ [package.dependencies] pycparser = "*" +[[package]] +name = "cfgv" +version = "3.4.0" +description = "Validate configuration and produce human readable error messages." +optional = true +python-versions = ">=3.8" +files = [ + {file = "cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9"}, + {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"}, +] + [[package]] name = "cftime" version = "1.6.2" @@ -516,6 +527,17 @@ files = [ {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"}, ] +[[package]] +name = "distlib" +version = "0.3.7" +description = "Distribution utilities" +optional = true +python-versions = "*" +files = [ + {file = "distlib-0.3.7-py2.py3-none-any.whl", hash = "sha256:2e24928bc811348f0feb63014e97aaae3037f2cf48712d51ae61df7fd6075057"}, + {file = "distlib-0.3.7.tar.gz", hash = "sha256:9dafe54b34a028eafd95039d5e5d4851a13734540f1331060d31c9916e7147a8"}, +] + [[package]] name = "docutils" version = "0.20.1" @@ -555,6 +577,22 @@ files = [ [package.extras] devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"] +[[package]] +name = "filelock" +version = "3.13.0" +description = "A platform independent file lock." +optional = true +python-versions = ">=3.8" +files = [ + {file = "filelock-3.13.0-py3-none-any.whl", hash = "sha256:a552f4fde758f4eab33191e9548f671970f8b06d436d31388c9aa1e5861a710f"}, + {file = "filelock-3.13.0.tar.gz", hash = "sha256:63c6052c82a1a24c873a549fbd39a26982e8f35a3016da231ead11a5be9dad44"}, +] + +[package.extras] +docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"] +testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"] +typing = ["typing-extensions (>=4.8)"] + [[package]] name = "flake8" version = "4.0.1" @@ -606,6 +644,20 @@ smb = ["smbprotocol"] ssh = ["paramiko"] tqdm = ["tqdm"] +[[package]] +name = "identify" +version = "2.5.31" +description = "File identification library for Python" +optional = true +python-versions = ">=3.8" +files = [ + {file = "identify-2.5.31-py2.py3-none-any.whl", hash = "sha256:90199cb9e7bd3c5407a9b7e81b4abec4bb9d249991c79439ec8af740afc6293d"}, + {file = "identify-2.5.31.tar.gz", hash = "sha256:7736b3c7a28233637e3c36550646fc6389bedd74ae84cb788200cc8e2dd60b75"}, +] + +[package.extras] +license = ["ukkonen"] + [[package]] name = "idna" version = "3.4" @@ -1050,6 +1102,20 @@ certifi = "*" cftime = "*" numpy = "*" +[[package]] +name = "nodeenv" +version = "1.8.0" +description = "Node.js virtual environment builder" +optional = true +python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*" +files = [ + {file = "nodeenv-1.8.0-py2.py3-none-any.whl", hash = "sha256:df865724bb3c3adc86b3876fa209771517b0cfe596beff01a92700e0e8be4cec"}, + {file = "nodeenv-1.8.0.tar.gz", hash = "sha256:d51e0c37e64fbf47d017feac3145cdbb58836d7eee8c6f6d3b6880c5456227d2"}, +] + +[package.dependencies] +setuptools = "*" + [[package]] name = "numba" version = "0.57.1" @@ -1369,6 +1435,24 @@ progress = ["tqdm (>=4.41.0,<5.0.0)"] sftp = ["paramiko (>=2.7.0)"] xxhash = ["xxhash (>=1.4.3)"] +[[package]] +name = "pre-commit" +version = "3.5.0" +description = "A framework for managing and maintaining multi-language pre-commit hooks." +optional = true +python-versions = ">=3.8" +files = [ + {file = "pre_commit-3.5.0-py2.py3-none-any.whl", hash = "sha256:841dc9aef25daba9a0238cd27984041fa0467b4199fc4852e27950664919f660"}, + {file = "pre_commit-3.5.0.tar.gz", hash = "sha256:5804465c675b659b0862f07907f96295d490822a450c4c40e747d0b1c6ebcb32"}, +] + +[package.dependencies] +cfgv = ">=2.0.0" +identify = ">=1.0.0" +nodeenv = ">=0.11.1" +pyyaml = ">=5.1" +virtualenv = ">=20.10.0" + [[package]] name = "pycodestyle" version = "2.8.0" @@ -1923,6 +2007,22 @@ dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"] doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"] test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] +[[package]] +name = "setuptools" +version = "68.2.2" +description = "Easily download, build, install, upgrade, and uninstall Python packages" +optional = true +python-versions = ">=3.8" +files = [ + {file = "setuptools-68.2.2-py3-none-any.whl", hash = "sha256:b454a35605876da60632df1a60f736524eb73cc47bbc9f3f1ef1b644de74fd2a"}, + {file = "setuptools-68.2.2.tar.gz", hash = "sha256:4ac1475276d2f1c48684874089fefcd83bd7162ddaafb81fac866ba0db282a87"}, +] + +[package.extras] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"] +testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] +testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.1)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] + [[package]] name = "six" version = "1.16.0" @@ -2356,6 +2456,26 @@ secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17. socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] zstd = ["zstandard (>=0.18.0)"] +[[package]] +name = "virtualenv" +version = "20.24.6" +description = "Virtual Python Environment builder" +optional = true +python-versions = ">=3.7" +files = [ + {file = "virtualenv-20.24.6-py3-none-any.whl", hash = "sha256:520d056652454c5098a00c0f073611ccbea4c79089331f60bf9d7ba247bb7381"}, + {file = "virtualenv-20.24.6.tar.gz", hash = "sha256:02ece4f56fbf939dbbc33c0715159951d6bf14aaf5457b092e4548e1382455af"}, +] + +[package.dependencies] +distlib = ">=0.3.7,<1" +filelock = ">=3.12.2,<4" +platformdirs = ">=3.9.1,<4" + +[package.extras] +docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] +test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"] + [[package]] name = "webencodings" version = "0.5.1" @@ -2408,4 +2528,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "a95a480b1227d7937b8afc4d687b3bba88ec3bef4e4ea487ff96f5b2e91a64f1" +content-hash = "2f1e99dd97eb2e4202d205c98e0d77ee3bd1ede18a30d6f434547bf67aa298eb" diff --git a/pyproject.toml b/pyproject.toml index 2c10291..55c8d8a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,6 +28,7 @@ flake8 = {version = "^4.0", optional = true} pytest = {version = "^7.0", optional = true} coverage = {version = "^6.3", optional = true} black = {version = "~23.7.0", optional = true} +pre-commit = {version = "^3.3", optional = true} [tool.poetry.group.docs.dependencies] rpy2 = {version = ">=3.5", optional = true} From 47e13ea6d0972f67b3e362473380ec802598e409 Mon Sep 17 00:00:00 2001 From: Niclas Rieger Date: Sun, 29 Oct 2023 12:22:15 +0100 Subject: [PATCH 06/12] docs: complete 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100644 examples/1single/weighted_eof.jpg delete mode 100644 examples/2mca/mca.jpg delete mode 100644 examples/2mca/rotated_mca.jpg rename examples/{2mca => 2multi}/README.rst (52%) create mode 100644 examples/2multi/mca.jpg create mode 100644 examples/2multi/plot_cca.py rename {docs/auto_examples/2mca => examples/2multi}/plot_mca.py (75%) rename {docs/auto_examples/2mca => examples/2multi}/plot_rotated_mca.py (76%) create mode 100644 examples/2multi/rotated_mca.jpg diff --git a/docs/_autosummary/xeofs.models.CCA.rst b/docs/_autosummary/xeofs.models.CCA.rst new file mode 100644 index 0000000..3238c99 --- /dev/null +++ b/docs/_autosummary/xeofs.models.CCA.rst @@ -0,0 +1,39 @@ +xeofs.models.CCA +================ + +.. currentmodule:: xeofs.models + +.. autoclass:: CCA + :members: + :show-inheritance: + :inherited-members: + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~CCA.__init__ + ~CCA.components + ~CCA.explained_covariance + ~CCA.explained_covariance_ratio + ~CCA.explained_variance + ~CCA.explained_variance_ratio + ~CCA.fit + ~CCA.get_metadata_routing + ~CCA.get_params + ~CCA.scores + ~CCA.set_fit_request + ~CCA.set_params + ~CCA.set_transform_request + ~CCA.transform + ~CCA.weights + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/xeofs.models.ExtendedEOF.rst b/docs/_autosummary/xeofs.models.ExtendedEOF.rst new file mode 100644 index 0000000..39d6b9d --- /dev/null +++ b/docs/_autosummary/xeofs.models.ExtendedEOF.rst @@ -0,0 +1,36 @@ +xeofs.models.ExtendedEOF +======================== + +.. currentmodule:: xeofs.models + +.. autoclass:: ExtendedEOF + :members: + :show-inheritance: + :inherited-members: + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~ExtendedEOF.__init__ + ~ExtendedEOF.components + ~ExtendedEOF.compute + ~ExtendedEOF.explained_variance + ~ExtendedEOF.explained_variance_ratio + ~ExtendedEOF.fit + ~ExtendedEOF.fit_transform + ~ExtendedEOF.get_params + ~ExtendedEOF.inverse_transform + ~ExtendedEOF.scores + ~ExtendedEOF.singular_values + ~ExtendedEOF.transform + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/xeofs.models.GWPCA.rst b/docs/_autosummary/xeofs.models.GWPCA.rst new file mode 100644 index 0000000..af8f45a --- /dev/null +++ b/docs/_autosummary/xeofs.models.GWPCA.rst @@ -0,0 +1,36 @@ +xeofs.models.GWPCA +================== + +.. currentmodule:: xeofs.models + +.. autoclass:: GWPCA + :members: + :show-inheritance: + :inherited-members: + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~GWPCA.__init__ + ~GWPCA.components + ~GWPCA.compute + ~GWPCA.explained_variance + ~GWPCA.explained_variance_ratio + ~GWPCA.fit + ~GWPCA.fit_transform + ~GWPCA.get_params + ~GWPCA.inverse_transform + ~GWPCA.largest_locally_weighted_components + ~GWPCA.scores + ~GWPCA.transform + + + + + + \ No newline at end of file diff --git a/docs/api.rst b/docs/api.rst index 036cb3f..4d566e9 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -1,17 +1,60 @@ -:sd_hide_title: +.. _api: + +====================== +API Reference +====================== + +Methods are categorized based on their analytical focus: + +1. **Single-Set Analysis**: Methods that examine relationships among variables within a single dataset, or when multiple datasets are combined and analyzed as one. +2. **Multi-Set Analysis**: Methods that investigate relationships or patterns between variables across two or more distinct datasets. + + +.. grid:: 2 + + .. grid-item-card:: Single-Set Analysis + :link: api_1_intra + :link-type: doc + + + Unveil relationships between variables within a single dataset. + + .. grid-item-card:: Multi-Set Analysis + :link: api_2_inter + :link-type: doc + + Uncover relationships between two or more datasets. + + +.. grid:: 2 + + .. grid-item-card:: Model Evaluation + :link: api_3_eval + :link-type: doc + + + Assess the quality of your model. + + .. grid-item-card:: Utilities + :link: api_4_utils + :link-type: doc + + Support functions. + + +.. warning:: + + The package is under development, and its API may change. -================== -API -================== .. toctree:: :maxdepth: 3 :hidden: - api_core_methods - api_rotation - api_advanced_techniques - api_validation + api_1_intra + api_2_inter + api_3_eval + api_4_utils diff --git a/docs/api_rotation.rst b/docs/api_1_intra.rst similarity index 50% rename from docs/api_rotation.rst rename to docs/api_1_intra.rst index 5a66882..f78f553 100644 --- a/docs/api_rotation.rst +++ b/docs/api_1_intra.rst @@ -1,13 +1,14 @@ -Rotation -================== +Single-Set Analysis +=================== .. autosummary:: :toctree: _autosummary :template: custom-class-template.rst :recursive: + xeofs.models.EOF xeofs.models.EOFRotator - xeofs.models.MCARotator + xeofs.models.ComplexEOF xeofs.models.ComplexEOFRotator - xeofs.models.ComplexMCARotator - xeofs.models.RotatorFactory - + xeofs.models.ExtendedEOF + xeofs.models.OPA + xeofs.models.GWPCA diff --git a/docs/api_advanced_techniques.rst b/docs/api_2_inter.rst similarity index 68% rename from docs/api_advanced_techniques.rst rename to docs/api_2_inter.rst index 52287af..c01eb44 100644 --- a/docs/api_advanced_techniques.rst +++ b/docs/api_2_inter.rst @@ -1,11 +1,11 @@ -Advanced techniques +Multi-Set Analysis ================== .. autosummary:: :toctree: _autosummary :template: custom-class-template.rst :recursive: - xeofs.models.ComplexEOF + xeofs.models.MCA xeofs.models.ComplexMCA - xeofs.models.ComplexEOFRotator xeofs.models.ComplexMCARotator + xeofs.models.CCA diff --git a/docs/api_validation.rst b/docs/api_3_eval.rst similarity index 90% rename from docs/api_validation.rst rename to docs/api_3_eval.rst index 175c1fd..6d87d1d 100644 --- a/docs/api_validation.rst +++ b/docs/api_3_eval.rst @@ -1,4 +1,4 @@ -Validation +Model Evaluation ================== .. autosummary:: :toctree: _autosummary diff --git a/docs/api_core_methods.rst b/docs/api_4_utils.rst similarity index 61% rename from docs/api_core_methods.rst rename to docs/api_4_utils.rst index 8b8486d..5b18ab1 100644 --- a/docs/api_core_methods.rst +++ b/docs/api_4_utils.rst @@ -1,10 +1,9 @@ -Core methods +Utilities ================== .. autosummary:: :toctree: _autosummary :template: custom-class-template.rst :recursive: - xeofs.models.EOF - xeofs.models.OPA - xeofs.models.MCA + xeofs.models.RotatorFactory + diff --git a/docs/auto_examples/1eof/images/sphx_glr_plot_eeof_001.png b/docs/auto_examples/1eof/images/sphx_glr_plot_eeof_001.png deleted file mode 100644 index a6f4eb745cfc0f44ad79a483077ce5752c1288f4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 18216 zcmeHvWmuJMx9vlSf`S2vpnwXBvmm(Z|Rwl=nRx@~KO$=$ZMxo2&E&+N`|Mh9 zt)2N5!`$q^z$e_|$8qk4NlNzdg~9FVl{bk=B)p*RMZ0M&&(ji(%q>HNui|bI&hL zeN*=ESU$Fw1uJ|n*zcC`89dM%lH8}B5nO9xYiBQ#!)SQeS`-`yVY7bzS-W>PDuV43;78a#|W!4-9 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@@ -1176,23 +1176,27 @@ for the chemical elements. fill: currentColor; }
<xarray.DataArray 'components' (mode: 5, element: 10, station: 768)>
-    0.1813 -0.3584 0.1243 0.2 -0.3812 ... -0.1229 0.2865 -0.4732 -0.4197 -0.4249
+    0.1813 -0.3584 0.1243 0.2 -0.3812 ... 0.1229 -0.2865 0.4732 0.4197 -0.4249
     Coordinates:
-      * element  (element) object 'SiO2_T' 'TiO2_T' 'Al2O3_T' ... 'K2O_T' 'P2O5_T'
       * mode     (mode) int64 1 2 3 4 5
+      * element  (element) object 'SiO2_T' 'TiO2_T' 'Al2O3_T' ... 'K2O_T' 'P2O5_T'
       * station  (station) object MultiIndex
       * x        (station) float64 -6.197e+05 2.147e+05 ... -2.82e+05 -1.273e+05
       * y        (station) float64 6.805e+06 7.746e+06 ... 5.796e+06 6.523e+06
-    Attributes:
-        model:        GWPCA
-        n_modes:      5
-        center:       True
-        standardize:  True
-        use_coslat:   False
-        solver:       auto
-        software:     xeofs
-        version:      1.0.3
-        date:         2023-10-21 13:16:29
  • model :
    GWPCA
    software :
    xeofs
    version :
    1.2.0
    date :
    2023-10-25 19:16:03
    n_modes :
    5
    center :
    True
    standardize :
    True
    use_coslat :
    False
    sample_name :
    sample
    feature_name :
    feature
    random_state :
    None
    compute :
    True
    solver :
    auto


  • @@ -1656,14 +1660,14 @@ dominate the local PCAs. * mode (mode) int64 1 2 3 4 5 * station (station) object MultiIndex * x (station) float64 -6.197e+05 2.147e+05 ... -2.82e+05 -1.273e+05 - * y (station) float64 6.805e+06 7.746e+06 ... 5.796e+06 6.523e+06


  • @@ -1735,9 +1739,9 @@ For demonstation, we'll concentrate on the first mode: -.. image-sg:: /auto_examples/1eof/images/sphx_glr_plot_gwpca_001.png +.. image-sg:: /auto_examples/1single/images/sphx_glr_plot_gwpca_001.png :alt: Largest locally weighted element - :srcset: /auto_examples/1eof/images/sphx_glr_plot_gwpca_001.png + :srcset: /auto_examples/1single/images/sphx_glr_plot_gwpca_001.png :class: sphx-glr-single-img @@ -1772,9 +1776,9 @@ roughly 40% to 70%. -.. image-sg:: /auto_examples/1eof/images/sphx_glr_plot_gwpca_002.png +.. image-sg:: /auto_examples/1single/images/sphx_glr_plot_gwpca_002.png :alt: Fraction of locally explained variance - :srcset: /auto_examples/1eof/images/sphx_glr_plot_gwpca_002.png + :srcset: /auto_examples/1single/images/sphx_glr_plot_gwpca_002.png :class: sphx-glr-single-img @@ -1784,10 +1788,10 @@ roughly 40% to 70%. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 33.235 seconds) + **Total running time of the script:** (0 minutes 28.277 seconds) -.. _sphx_glr_download_auto_examples_1eof_plot_gwpca.py: +.. _sphx_glr_download_auto_examples_1single_plot_gwpca.py: .. only:: html diff --git a/docs/auto_examples/1single/plot_gwpca_codeobj.pickle b/docs/auto_examples/1single/plot_gwpca_codeobj.pickle new file mode 100644 index 0000000000000000000000000000000000000000..7bd751392a2a8eea6ad9325bf8b40fd42b52a706 GIT binary patch literal 27949 zcmdsAX^b346;5LB;jF#uJ-j|+C4dNyV1S^YAkK|Vzyv!cMu`F}L+?!O?o4K-*|^ z)m5*mUcD|qYv^TnTRB1f7mroKMnhCOwXoS7ao~-fa}0 zI=|IEP+P4v$@>7!Tc~FBA{(_!gtYiH7dV%A4;K*8c)C%3_K@lo=P%Tu^O-s~Q=JPz zTS+{_70qSd#}!DV-p{~n-QTPVXw4-{o6bDI-a~pK z?SWvZRi>?^L&)kL$n4&~L{OJDy+^-zFtrRO>A74a2C~HkD6N<&|({C2YzDn|Ej6TA$($ zC6X%!>Xf9F=a!Ztu@tB|%hbJ{>MjSQoRq)l$;NG{dE$v74FkF}H6()eUlE;J#WZ7< znh|7$qz$1L5w{@nLKFR612}mgXtls0nyM$rQlb%~9f!RLS=|pqN zPig;)!w-g7PDj1WgMzowJFfME6BAx;Ah##4AzK<5-N?{i?i_20 zYB_ARWel*_*A<%|^G4w>P;#`H*|70h>5ndzz^Buk0OtQM{ga z8;Gg>9T5c!4K^xlcD>I9$ZNaa08Q@=o1LHrOZEPi-Qa}e_1&1DL_AiN3x!Ci4U*n1 z!OCVl>4WKm<60tE$awd=$*{r#Absv;qwE%`lq?PsOY$A=7aZYS;+Gt8MCn%z$d05< zjWq$vEnX0O-G_=xztsm!^xtOS)G`cw5%H|3-f&CiI^bLGEL`|^+@VDF?FMQ*T>+q2 z+pxW(ABYd39~*#KKKEm7IaBA)T=Be7e(4G%P4XK9vpa9f?<@NEzC2v~AAJdk5q~zY z+buIa>M!VzfAgT>^8e|9CCdL}z-ALikWKJ8vW?lje$t62oZw2sWY>p0uqeAe%3+ga zf=AJo;Zz@UgR((YALp=*dV(*mY_czk*E7Qr6WIh`T-8<=Ag^r)2W`d)zPP$moRGY} zQxPTN*>0!SfSBfT7`DaU3+4{4)S?LDDA`;;voEZ4<@#S#4?q6gZc{~A*I)wO8nup{ zS&Bjx_-dCHlSgu`S@4Vo2Xd)58Pq^n--DWU&@>mjUScKW-Ut!#h&U46A7Z~~mx_|8 zfT%Ra659ncY`;GTxwU#M_}0!G%KPU$#@O+qur0X=+NG| z{DNw!DZ<5e362T`3^he8-_yob^7ESEyd6jx`grMvlo;7`PsC=>@BZ#(`PvUi^@V0` zqxO`_GT>cSHcQqg56a5ztOtWKp@~&av1{&}y@j@|(_x_c5+X%B3B5!13*9APGuFUv zu}RM2D)_5@hhC5u=5-4I_ww;syoJ*Ad+UXe$-B)og=1XyOIr^Y;-lmR5%?xgL08coVWf=M>rSyY)2eX`aBNVn)Lv(B}(Mx zuRrTo`0#MuSNVX6_!o1WiZZF5VC+W|`GuR&`Za!J+=ewjII-dt968OmUB$iS)vkCh z`L(V_aNV0Wc;>@b|!9T1)6 zP@Tsp119KEfDTF4-{VWd^}pYjfLQQBj-9n^8T*gd4|~vX`5*Pb66K%ZFcsxeohI8f z+Ju`s#!tJmaNVDEhZ5PJN0f;531l@2)ePFki8DL>!)M^%?}QUbgO6MEHPogjPrD3a z%`F_k&moQaa^}%*c#&|?-}FKg?ce4=>w%#G)T^wB=;du%}=K z{)sOaH|6KP1jLYEacp)(X~?$)RetLR<>7!oa9GblrBQQEPyUl13vaT&_`yko{hcFo zPb^)`_}5@;+?*3mV$nD^QN*luoWXWukk@t^2jRhH-wN;Hh~;&j&T-^; 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DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "auto_examples/1eof/plot_mreof.py" +.. "auto_examples/1single/plot_mreof.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html @@ -10,12 +10,12 @@ .. note:: :class: sphx-glr-download-link-note - :ref:`Go to the end ` + :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_1eof_plot_mreof.py: +.. _sphx_glr_auto_examples_1single_plot_mreof.py: Varimax-rotated Multivariate EOF analysis @@ -134,9 +134,9 @@ Plot mode 1 -.. image-sg:: /auto_examples/1eof/images/sphx_glr_plot_mreof_001.png +.. image-sg:: /auto_examples/1single/images/sphx_glr_plot_mreof_001.png :alt: Mode 5, Subset 1, Subset 2, Subset 3, Subset 4 - :srcset: /auto_examples/1eof/images/sphx_glr_plot_mreof_001.png + :srcset: /auto_examples/1single/images/sphx_glr_plot_mreof_001.png :class: sphx-glr-single-img @@ -146,10 +146,10 @@ Plot mode 1 .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 6.262 seconds) + **Total running time of the script:** (0 minutes 5.799 seconds) -.. _sphx_glr_download_auto_examples_1eof_plot_mreof.py: +.. _sphx_glr_download_auto_examples_1single_plot_mreof.py: .. only:: html diff --git a/docs/auto_examples/1single/plot_mreof_codeobj.pickle b/docs/auto_examples/1single/plot_mreof_codeobj.pickle new file mode 100644 index 0000000000000000000000000000000000000000..653a735fbe79c433645cabed76b08b7bd29fe4cf GIT binary patch literal 19592 zcmd5^e~4UH6;867-DGF8$?hhbWNoRmG^8jKQez8&K(`yWgr=r!tQ94WZ)aY1-pkCq z@x3>3R~uU@g+?C*b%Ii@ii)BYgle%UiWMt@(9(YjLeW1&EEc6=1wjzceZS7RXYQN1 zcV=taKT7i6{l4>^`|dsW+;i{bk)7ABoZS}x7nQ1BqhVJ=$7==MsC3S+*}h#n?Nr0= z7rVEuc8^AfHvDqf2|eGj8fC9-w<KOwuv?#sb~!<%+OUG4do?P-pY4ry!>Kx9_i6`N>yy#6WURhB z+Ph)-zO|XF>DEh%XVX{fd#-luXfo)mfy&!cnLxPvGzec36kT9Dkg`=h-!2Q6VsgP* z^d@0W#K~MG!UYWV#d>kuiKMM)c4OMij>g-*=c3Zm{S7O$mqC$j3$3N9<%eE-vt0E9 zp-}!iwZ)N>DN>j_lAGIgkMGpZx9uvL#7AInzG;Q+h8H%RwemVN4MbD0tA0NHxajeu zUnH6-q!IRy)oIJ=OvBwUn1qvRavh^RRLt-ll3_`rp;38ky$8Eb9uGw$Xe-9nvfkKQ z$v^hk`_8SP(D-lA+xs?b?_yAfS!p+dGW=P5-)Z`=EA?82V@yKm*E^UDm)5b(>Q2Inj|1I$?d;^ZZ)U%g~zz@*dK`a(xf$ zfQZ0HrS=E&FTLc0%%&YW)vOa2XeV0vYtfO9X_Iidk82}|=Fdr>!>DqkRU4g}{r*m~y?Gvz z!29g5?%|}%^0hd_^4cPEsejLify@4Z5t<19p#)qw-)XlY4Ta7&n1avfa&Xnp>H>-2 z=OoO5d;*?p%cEEd8I^FeZPzMkSN}|tg3JAdCXT3mK84uP#HCMI{(1l_TQd8`Yho(_ z5g4}uFbaN?wzr}kaa*yEfK_nh%uGSUQo)yUrBkqMuX>>cI~!bw=|d1LCmoO^Rs0mu zKUrcl12Y4XKr<+&B(K(Ko|C0x*C&npi=>6+Tym$qHEG=6`w754`-jwwr*UseZ&}9= z{ATG^pyUT4zFqHp&fUI!+cs(9_Dkv9ao*Y_t8*vy&@8aHQ+hCrEAAeSFiP*-eVTAC zbY2rjlrC_{aCx44*o24czQF`c#NWhmBP4w8&1PiWhg;3y#EYXGX_oo9@6yF{$z@$2 z5qq3thDib46J{)2_?>2OBL5^u4v`DGr%Xw>{<};GhzH9YJ5*BWt{Bj8`DYBUMEO|` z8zwt+&*`&p-K+XgBKrYE+0kBWqtX`p-mnrnA(VY<^OLbRQHo8B>7^pX)rw?@Tujq+p?D)5}K_chn4dTI_xny-ENg_a(};HWfrMgPTZYP}F#hYn;Vm zF_Aj-W`oFAGjlK8uj(m67rxE`Q&2}H+S78f`%P?TNAnk*^)eh?iQ|``V%6MEke#%_ z4tZh4S?|Dk6>k8;jZReHfL0`c1c6Yv^*m}zK2`ya91ps`=9s+-<-@MUDlWxhHef|pv9Pc568B=i zyc*+2=yfGsjui<<@`iqhLRy7a?kn1&m^Z)Yz}akoL*|68&{>XS`&B(KGxXS^mX~5!QYM6<>$c$BK!+3OFuDJ9e-N(WF@QQ^BT7KE;|S zq&SqehfA9wi@mZ-4a$mSrB~2$@$P;NEvWn;Z#^m2_*8PXz%ODWJKApH$eB!+2Qg&S zu^+BKDAx)aiYNBlISCSi+xC_@jH5ruYx(Nwccr1{_w^NNvQEt-1yY61{shV zIn9lXEw7$8>&py5Y47E<`sgjX{!+ zkz^uC#>I!F$$`=|P_xAT|j<6PY4eCwv{) z@9+&v_B080d6K=+FUd|{f)I7!)Cs6kvW$fJcS)1%N4RqZI67}W!!elBqM_c*G}|Eb zCYN*}P;vDl235RNENn9Yuq*`z`y^f7w zA%`pgYM5;nLH4d>)y?z`#19&Ev)o~DSyRQtM#h^cDCzI{#ClrK992OP8&mNgu3~Ri z;K_Q2^#QIt|J8vqbKA%A8UUtK4Qq|wMV7OePQGU!I|RDb*OuKo zHQ`+4NlhG4dWu8lg=6H-{Q#+V>EgN8WnCZF+arFPrdi z4>nD}MEpY>w~$ZX{2sqjyL6G?eb|78OMlb=OO!u`Fgu#dPQAnr*Swbal?oOs4v@ny z@*Y+yd9yO$@;o`DDQ4vRlzvBH#VojS`2u>GF45vdO!mTbQz(w$7=?*!1swH0#q}N_ z$7Hf>x$}oREh}c{9=YGvSBCC< zgrP`tP5n(|0U66Ze}u}CL6lypiT9CPqN5KX)ACcUWiH)K_R)d4_s=!a%;+x>20vsM zKiGxuk#vRfMEwP9Z)LyIKaQVJKII=RO_#ZNYK@aF4AvKP9LT^3U96yNrH*FT|iNhiJ?_O5@`7RAQ$-#E}@ z@A`%wn3?=1qTt}&C*d!fMe0%YZ^T*A80>kk#}wK=p4XEGOrddXDK7tRGn#zr=A|m~64sq9ID(W3dChDQwIyB@2%$Bz8#yM4 zJF=m|xZ}Q42d*k8-J%0zlj8`-kmxcVR9>#aXHT=U`f+jAf_9C5uSzC# zhT|D$ztlg&;1s?Weap+` + :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_1eof_plot_multivariate-eof.py: +.. _sphx_glr_auto_examples_1single_plot_multivariate-eof.py: Multivariate EOF analysis @@ -118,9 +118,9 @@ Plot mode 1 -.. image-sg:: /auto_examples/1eof/images/sphx_glr_plot_multivariate-eof_001.png +.. image-sg:: /auto_examples/1single/images/sphx_glr_plot_multivariate-eof_001.png :alt: Mode 5, Subset 1, Subset 2, Subset 3, Subset 4 - 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:srcset: /auto_examples/1eof/images/sphx_glr_plot_rotated_eof_001.png + :srcset: /auto_examples/1single/images/sphx_glr_plot_rotated_eof_001.png :class: sphx-glr-single-img @@ -157,28 +157,25 @@ were mere artifacts due to the orthogonality. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 17.020 seconds) + **Total running time of the script:** (0 minutes 17.268 seconds) -.. _sphx_glr_download_auto_examples_1eof_plot_rotated_eof.py: +.. _sphx_glr_download_auto_examples_1single_plot_rotated_eof.py: +.. only:: html -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - + .. container:: sphx-glr-footer sphx-glr-footer-example - .. container:: sphx-glr-download sphx-glr-download-python - :download:`Download Python source code: plot_rotated_eof.py ` + .. container:: sphx-glr-download sphx-glr-download-python + :download:`Download Python source code: plot_rotated_eof.py ` - .. container:: sphx-glr-download sphx-glr-download-jupyter + .. container:: sphx-glr-download sphx-glr-download-jupyter - :download:`Download Jupyter notebook: plot_rotated_eof.ipynb ` + :download:`Download Jupyter notebook: plot_rotated_eof.ipynb ` .. only:: html diff --git a/docs/auto_examples/1single/plot_rotated_eof_codeobj.pickle b/docs/auto_examples/1single/plot_rotated_eof_codeobj.pickle new file mode 100644 index 0000000000000000000000000000000000000000..62d37d0cf3587e73798a944160e2b2c3bdf87e0d GIT binary patch literal 13294 zcmds7U5F(|72e&Mo!OZ?zq{EXk(GcTY)ELct9elgOeSs+iCJdVmB`T6+*>n!J3Y6r z-F?>?FcJvL4)%jT*9XC$mxzCFDu{tVf}jS3fR90+M16}WLS6(t)z#hS)S2o#b?=yD zbe`^XpYNQns!p9c=Ttv;{+oY#|C!=1Z?)qnlI^S;_tIhBdMfG2M0OtUwzJ_kh7W8G zujcpeCtfzl;-o7gFYe2p-w~NeWj5RzUVRn>rh8&f4)Y5;gI=L$n9uCRok66YZ4bIp z)>Wz(^fNyV;v^de^ZA8t>bE12ro+R$1;6EfKkBx-+3;`xT7%hq!7vug<;(jbNyI_T zW*D?A&lV1Y#lwNjXVP|@$P`#=b<3$N2<(RX1i&FjJwNw;&WFWX2B9|6Bhdm`&caTayAyWd+BLS6Ms3ac)4^xw3SMFl;h$?3I!-nCLjI?yPVm-EsJpukx zt&Y>GlDVpI$F8acgRAw?DakmRwe#Tw&pOh=fjnm*mcUin2r93Az7&Kb=$X%pWS1O49d)jP zkKwrpUVgWf7x}qDw)D^wM$}FXG<(3?>|ft^ zq=o+VLjy6wzkX7$+M>7yOD0k{l6ys_Q@YN@CuF?7FH`ywyzWw!#q9MXZ&$_wM2Z94 z`kcnL28DGR1PWEW{<$%P1`th(eqjKXEu1f7w%{qgo?o&KUOX1N%&>~$OUI(v9(maS z+~X^b>NI6@dz7a-R+YtHxhb%b{k0pEF@DuRt*$2-B)*L9DxI1_KvDgj1Ckkgt%S&Y z9#2#}X6eIm&sLJ)Ff}}yA32trp}61v5YT+4O1qpw8&O~3#-UVUkjq~UE-bg2IC2r1 zOeJRb`BFBs$;=&bY9X8gNoY9Q>8V8OM>%#loz|7=cjE>>lGWO^@hyBL1|F-S-qzs*I4Yeqs=Z5?I|W5zkHN>G%Am;c1DxY3 zDJ7aFd5qeY(6?*mqX<9b#t4V-CDe+REqXfEd=q*)*5@=2rCM0>8Qw-Hb#gH{fQU`^*#Su!V@fVL8-vo1_w>yYLGP&_aCDiyU-1z*Y1{shV`7JjxwXu0? zyt%9pRPAeA?L8ZVG>i8(;My=t25o4Mmv)x=2hej5)lFG-TQ-l6;0B8>PMMZ&PRQY;k`GX z>p8^g<7B3q%Q<5%!B^p06@1nDRL`)?7sbP;sxh+0N9WKOxsPMUYm8iRf!kUP_qza@ z&%TFaCTNVj*I9>+^;Ksyqy8WVj?) z{1%su9(on5pYMd9L5Rp#RCvz1yFunh;vgOn-)j!ipG6JY z(1M`>Vmdwc+(LEX|EgYB1N>j}|IhIWp}zRe`u`U=HH)K~A?2;}|K}aJp#OiFOWXYa z5QSvE47q|QIvqb9Y!~T4?MD}H6d9TElDTP>XP6m9^_1pp4a!(_|2i_$n`BLMC@w-N z$%%Q4P{_D^i*vbH-f)iK0blRI9jK}XzU_!+M!$zJ9h%Y%^KW}-dI2HuaiRLY0z!eF zJ!{Y8M^qj^BdnJlAHgIaQ!|hn%Yf06?x%%4B6-EXEt0MWAJ%V$>MUkZCw<(on{sJGVKm1j+FXh1b}wTLbie*T6ebSN;zXSBGI{0B-x?WZq^PEz>4iTXsooc2-= zKH>J;aW9kmWGb2yrGKFUO}#}Gz`V_-Qf$Wwd@$V#|Bb>rUg~;Z1MTs0daACp;Y&Di z4VQ;r9kActSp^yJ6RW z*~z` + :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_1eof_plot_weighted-eof.py: +.. _sphx_glr_auto_examples_1single_plot_weighted-eof.py: Weighted EOF analysis @@ -133,9 +133,9 @@ Create figure showing the first mode for all 4 cases -.. image-sg:: /auto_examples/1eof/images/sphx_glr_plot_weighted-eof_001.png +.. image-sg:: /auto_examples/1single/images/sphx_glr_plot_weighted-eof_001.png :alt: (1) Covariances, (2) Covariances + coslat, (3) Correlation, (4) Correlation + coslat - 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(``plot_complex_eof.py``) | 00:15.825 | 0.0 MB | ++-----------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_auto_examples_1single_plot_eeof.py` (``plot_eeof.py``) | 00:00.000 | 0.0 MB | ++-----------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_auto_examples_1single_plot_eof-smode.py` (``plot_eof-smode.py``) | 00:00.000 | 0.0 MB | ++-----------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_auto_examples_1single_plot_eof-tmode.py` (``plot_eof-tmode.py``) | 00:00.000 | 0.0 MB | ++-----------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_auto_examples_1single_plot_gwpca.py` (``plot_gwpca.py``) | 00:00.000 | 0.0 MB | ++-----------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_auto_examples_1single_plot_mreof.py` (``plot_mreof.py``) | 00:00.000 | 0.0 MB | ++-----------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_auto_examples_1single_plot_multivariate-eof.py` (``plot_multivariate-eof.py``) | 00:00.000 | 0.0 MB | ++-----------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_auto_examples_1single_plot_rotated_eof.py` (``plot_rotated_eof.py``) | 00:00.000 | 0.0 MB | ++-----------------------------------------------------------------------------------------------+-----------+--------+ +| :ref:`sphx_glr_auto_examples_1single_plot_weighted-eof.py` (``plot_weighted-eof.py``) | 00:00.000 | 0.0 MB | 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    + + +.. raw:: html + +
    + +.. only:: html + + .. image:: /auto_examples/2multi/images/thumb/sphx_glr_plot_cca_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_2multi_plot_cca.py` + +.. raw:: html + +
    Canonical Correlation Analysis
    +
    + + +.. raw:: html + +
    + +.. only:: html + + .. image:: /auto_examples/2multi/images/thumb/sphx_glr_plot_mca_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_2multi_plot_mca.py` + +.. raw:: html + +
    Maximum Covariance Analysis
    +
    + + +.. raw:: html + +
    + +.. only:: html + + .. image:: /auto_examples/2multi/images/thumb/sphx_glr_plot_rotated_mca_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_2multi_plot_rotated_mca.py` + +.. raw:: html + +
    Rotated Maximum Covariance Analysis
    +
    + + +.. raw:: html + +
    + + +.. toctree:: + :hidden: + + /auto_examples/2multi/plot_cca + /auto_examples/2multi/plot_mca + /auto_examples/2multi/plot_rotated_mca + diff --git a/docs/auto_examples/2multi/plot_cca.ipynb b/docs/auto_examples/2multi/plot_cca.ipynb new file mode 100644 index 0000000..056f191 --- /dev/null +++ b/docs/auto_examples/2multi/plot_cca.ipynb @@ -0,0 +1,133 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Canonical Correlation Analysis\n\nIn this example, we're going to perform a Canonical Correlation Analysis (CCA) \non three datasets using the ERSSTv5 monthly sea surface temperature (SST) data \nfrom 1970 to 2022. We divide this data into three areas: the Indian Ocean, \nthe Pacific Ocean, and the Atlantic Ocean. Our goal is to perform CCA on these \nregions.\n\nFirst, we'll import the necessary modules.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import xarray as xr\nimport xeofs as xe\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.gridspec import GridSpec\nimport cartopy.crs as ccrs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we load the data and compute the SST anomalies. This removes the\nmonthly climatologies, so the seasonal cycle doesn't impact our CCA.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sst = xr.tutorial.load_dataset(\"ersstv5\").sst\nsst = sst.groupby(\"time.month\") - sst.groupby(\"time.month\").mean(\"time\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we define the three regions of interest and store them in a list.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "indian = sst.sel(lon=slice(35, 115), lat=slice(30, -30))\npacific = sst.sel(lon=slice(130, 290), lat=slice(30, -30))\natlantic = sst.sel(lon=slice(320, 360), lat=slice(70, 10))\n\ndata_list = [indian, pacific, atlantic]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now perform CCA. Since we are dealing with a high-dimensional feature space, we first\nperform PCA to reduce the dimensionality (this is kind of a regularized CCA) by setting\n``pca=True``. By setting the ``variance_fraction`` keyword argument, we specify that we\nwant to keep the number of PCA modes that explain 90% of the variance in each of the\nthree data sets.\n\nAn important parameter is ``init_pca_modes``. It specifies the number\nof PCA modes that are initially compute before truncating them to account for 90 %. If this\nnumber is small enough, randomized PCAs will be performed instead of the full SVD decomposition\nwhich is much faster. We can also specify ``init_pca_modes`` as a float (0 < x <= 1),\nin which case the number of PCA modes is given by the fraction of the data matrix's rank\nThe default is set to 0.75 which will ensure that randomized PCAs are performed.\n\nGiven the nature of SST data, we might lower it to something like 0.3, since\nwe expect that most of the variance in the data will be explained by a small\nnumber of PC modes.\n\nNote that if our initial PCA modes don't hit the 90% variance target, ``xeofs``\nwill give a warning.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model = xe.models.CCA(\n n_modes=2,\n use_coslat=True,\n pca=True,\n variance_fraction=0.9,\n init_pca_modes=0.30,\n)\nmodel.fit(data_list, dim=\"time\")\ncomponents = model.components()\nscores = model.scores()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the canonical loadings (components) of the first mode.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mode = 1\n\ncentral_longitudes = [\n indian.lon.median().item(),\n pacific.lon.median().item(),\n pacific.lon.median().item(),\n]\nprojections = [ccrs.PlateCarree(central_longitude=lon) for lon in central_longitudes]\n\nfig = plt.figure(figsize=(12, 2.5))\ngs = GridSpec(1, 4, figure=fig, width_ratios=[2, 4, 1, 0.2])\naxes = [fig.add_subplot(gs[0, i], projection=projections[i]) for i in range(3)]\ncax = fig.add_subplot(1, 4, 4)\nkwargs = dict(transform=ccrs.PlateCarree(), vmin=-1, vmax=1, cmap=\"RdBu_r\", cbar_ax=cax)\ncomponents[0].sel(mode=mode).plot(ax=axes[0], **kwargs)\ncomponents[1].sel(mode=mode).plot(ax=axes[1], **kwargs)\nim = components[2].sel(mode=mode).plot(ax=axes[2], **kwargs)\nfig.colorbar(im, cax=cax, orientation=\"vertical\")\nfor ax in axes:\n ax.coastlines()\n ax.set_title(\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And lastly, we'll check out the canonical variates (scores) of the first mode.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 4))\nscores[0].sel(mode=mode).plot(ax=ax, label=\"Indian Ocean\")\nscores[1].sel(mode=mode).plot(ax=ax, label=\"Central Pacific\")\nscores[2].sel(mode=mode).plot(ax=ax, label=\"North Atlantic\")\nax.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/auto_examples/2multi/plot_cca.py b/docs/auto_examples/2multi/plot_cca.py new file mode 100644 index 0000000..b84cf0d --- /dev/null +++ b/docs/auto_examples/2multi/plot_cca.py @@ -0,0 +1,102 @@ +""" +Canonical Correlation Analysis +============================== + +In this example, we're going to perform a Canonical Correlation Analysis (CCA) +on three datasets using the ERSSTv5 monthly sea surface temperature (SST) data +from 1970 to 2022. We divide this data into three areas: the Indian Ocean, +the Pacific Ocean, and the Atlantic Ocean. Our goal is to perform CCA on these +regions. + +First, we'll import the necessary modules. +""" + +import xarray as xr +import xeofs as xe + +import matplotlib.pyplot as plt +from matplotlib.gridspec import GridSpec +import cartopy.crs as ccrs + +# %% +# Next, we load the data and compute the SST anomalies. This removes the +# monthly climatologies, so the seasonal cycle doesn't impact our CCA. + +sst = xr.tutorial.load_dataset("ersstv5").sst +sst = sst.groupby("time.month") - sst.groupby("time.month").mean("time") + + +# %% +# Now, we define the three regions of interest and store them in a list. + +indian = sst.sel(lon=slice(35, 115), lat=slice(30, -30)) +pacific = sst.sel(lon=slice(130, 290), lat=slice(30, -30)) +atlantic = sst.sel(lon=slice(320, 360), lat=slice(70, 10)) + +data_list = [indian, pacific, atlantic] + +# %% +# We now perform CCA. Since we are dealing with a high-dimensional feature space, we first +# perform PCA to reduce the dimensionality (this is kind of a regularized CCA) by setting +# ``pca=True``. By setting the ``variance_fraction`` keyword argument, we specify that we +# want to keep the number of PCA modes that explain 90% of the variance in each of the +# three data sets. +# +# An important parameter is ``init_pca_modes``. It specifies the number +# of PCA modes that are initially compute before truncating them to account for 90 %. If this +# number is small enough, randomized PCAs will be performed instead of the full SVD decomposition +# which is much faster. We can also specify ``init_pca_modes`` as a float (0 < x <= 1), +# in which case the number of PCA modes is given by the fraction of the data matrix's rank +# The default is set to 0.75 which will ensure that randomized PCAs are performed. +# +# Given the nature of SST data, we might lower it to something like 0.3, since +# we expect that most of the variance in the data will be explained by a small +# number of PC modes. +# +# Note that if our initial PCA modes don't hit the 90% variance target, ``xeofs`` +# will give a warning. + +model = xe.models.CCA( + n_modes=2, + use_coslat=True, + pca=True, + variance_fraction=0.9, + init_pca_modes=0.30, +) +model.fit(data_list, dim="time") +components = model.components() +scores = model.scores() + +# %% +# Let's look at the canonical loadings (components) of the first mode. + +mode = 1 + +central_longitudes = [ + indian.lon.median().item(), + pacific.lon.median().item(), + pacific.lon.median().item(), +] +projections = [ccrs.PlateCarree(central_longitude=lon) for lon in central_longitudes] + +fig = plt.figure(figsize=(12, 2.5)) +gs = GridSpec(1, 4, figure=fig, width_ratios=[2, 4, 1, 0.2]) +axes = [fig.add_subplot(gs[0, i], projection=projections[i]) for i in range(3)] +cax = fig.add_subplot(1, 4, 4) +kwargs = dict(transform=ccrs.PlateCarree(), vmin=-1, vmax=1, cmap="RdBu_r", cbar_ax=cax) +components[0].sel(mode=mode).plot(ax=axes[0], **kwargs) +components[1].sel(mode=mode).plot(ax=axes[1], **kwargs) +im = components[2].sel(mode=mode).plot(ax=axes[2], **kwargs) +fig.colorbar(im, cax=cax, orientation="vertical") +for ax in axes: + ax.coastlines() + ax.set_title("") + +# %% +# And lastly, we'll check out the canonical variates (scores) of the first mode. + +fig, ax = plt.subplots(figsize=(12, 4)) +scores[0].sel(mode=mode).plot(ax=ax, label="Indian Ocean") +scores[1].sel(mode=mode).plot(ax=ax, label="Central Pacific") +scores[2].sel(mode=mode).plot(ax=ax, label="North Atlantic") +ax.legend() diff --git a/docs/auto_examples/2multi/plot_cca.py.md5 b/docs/auto_examples/2multi/plot_cca.py.md5 new file mode 100644 index 0000000..009b5fb --- /dev/null +++ b/docs/auto_examples/2multi/plot_cca.py.md5 @@ -0,0 +1 @@ +ae159e7d6eb88d368d77effb49f30a2e \ No newline at end of file diff --git a/docs/auto_examples/2multi/plot_cca.rst b/docs/auto_examples/2multi/plot_cca.rst new file mode 100644 index 0000000..50c078d --- /dev/null +++ b/docs/auto_examples/2multi/plot_cca.rst @@ -0,0 +1,242 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples/2multi/plot_cca.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_2multi_plot_cca.py: + + +Canonical Correlation Analysis +============================== + +In this example, we're going to perform a Canonical Correlation Analysis (CCA) +on three datasets using the ERSSTv5 monthly sea surface temperature (SST) data +from 1970 to 2022. We divide this data into three areas: the Indian Ocean, +the Pacific Ocean, and the Atlantic Ocean. Our goal is to perform CCA on these +regions. + +First, we'll import the necessary modules. + +.. GENERATED FROM PYTHON SOURCE LINES 13-21 + +.. code-block:: default + + + import xarray as xr + import xeofs as xe + + import matplotlib.pyplot as plt + from matplotlib.gridspec import GridSpec + import cartopy.crs as ccrs + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 22-24 + +Next, we load the data and compute the SST anomalies. This removes the +monthly climatologies, so the seasonal cycle doesn't impact our CCA. + +.. GENERATED FROM PYTHON SOURCE LINES 24-29 + +.. code-block:: default + + + sst = xr.tutorial.load_dataset("ersstv5").sst + sst = sst.groupby("time.month") - sst.groupby("time.month").mean("time") + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 30-31 + +Now, we define the three regions of interest and store them in a list. + +.. GENERATED FROM PYTHON SOURCE LINES 31-38 + +.. code-block:: default + + + indian = sst.sel(lon=slice(35, 115), lat=slice(30, -30)) + pacific = sst.sel(lon=slice(130, 290), lat=slice(30, -30)) + atlantic = sst.sel(lon=slice(320, 360), lat=slice(70, 10)) + + data_list = [indian, pacific, atlantic] + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 39-58 + +We now perform CCA. Since we are dealing with a high-dimensional feature space, we first +perform PCA to reduce the dimensionality (this is kind of a regularized CCA) by setting +``pca=True``. By setting the ``variance_fraction`` keyword argument, we specify that we +want to keep the number of PCA modes that explain 90% of the variance in each of the +three data sets. + +An important parameter is ``init_pca_modes``. It specifies the number +of PCA modes that are initially compute before truncating them to account for 90 %. If this +number is small enough, randomized PCAs will be performed instead of the full SVD decomposition +which is much faster. We can also specify ``init_pca_modes`` as a float (0 < x <= 1), +in which case the number of PCA modes is given by the fraction of the data matrix's rank +The default is set to 0.75 which will ensure that randomized PCAs are performed. + +Given the nature of SST data, we might lower it to something like 0.3, since +we expect that most of the variance in the data will be explained by a small +number of PC modes. + +Note that if our initial PCA modes don't hit the 90% variance target, ``xeofs`` +will give a warning. + +.. GENERATED FROM PYTHON SOURCE LINES 58-70 + +.. code-block:: default + + + model = xe.models.CCA( + n_modes=2, + use_coslat=True, + pca=True, + variance_fraction=0.9, + init_pca_modes=0.30, + ) + model.fit(data_list, dim="time") + components = model.components() + scores = model.scores() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 71-72 + +Let's look at the canonical loadings (components) of the first mode. + +.. GENERATED FROM PYTHON SOURCE LINES 72-95 + +.. code-block:: default + + + mode = 1 + + central_longitudes = [ + indian.lon.median().item(), + pacific.lon.median().item(), + pacific.lon.median().item(), + ] + projections = [ccrs.PlateCarree(central_longitude=lon) for lon in central_longitudes] + + fig = plt.figure(figsize=(12, 2.5)) + gs = GridSpec(1, 4, figure=fig, width_ratios=[2, 4, 1, 0.2]) + axes = [fig.add_subplot(gs[0, i], projection=projections[i]) for i in range(3)] + cax = fig.add_subplot(1, 4, 4) + kwargs = dict(transform=ccrs.PlateCarree(), vmin=-1, vmax=1, cmap="RdBu_r", cbar_ax=cax) + components[0].sel(mode=mode).plot(ax=axes[0], **kwargs) + components[1].sel(mode=mode).plot(ax=axes[1], **kwargs) + im = components[2].sel(mode=mode).plot(ax=axes[2], **kwargs) + fig.colorbar(im, cax=cax, orientation="vertical") + for ax in axes: + ax.coastlines() + ax.set_title("") + + + + +.. image-sg:: /auto_examples/2multi/images/sphx_glr_plot_cca_001.png + :alt: plot cca + :srcset: /auto_examples/2multi/images/sphx_glr_plot_cca_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 96-97 + +And lastly, we'll check out the canonical variates (scores) of the first mode. + +.. GENERATED FROM PYTHON SOURCE LINES 97-103 + +.. code-block:: default + + + fig, ax = plt.subplots(figsize=(12, 4)) + scores[0].sel(mode=mode).plot(ax=ax, label="Indian Ocean") + scores[1].sel(mode=mode).plot(ax=ax, label="Central Pacific") + scores[2].sel(mode=mode).plot(ax=ax, label="North Atlantic") + ax.legend() + + + +.. image-sg:: /auto_examples/2multi/images/sphx_glr_plot_cca_002.png + :alt: mode = 1 + :srcset: /auto_examples/2multi/images/sphx_glr_plot_cca_002.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 13.071 seconds) + + +.. _sphx_glr_download_auto_examples_2multi_plot_cca.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + + + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_cca.py ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_cca.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/auto_examples/2multi/plot_cca_codeobj.pickle b/docs/auto_examples/2multi/plot_cca_codeobj.pickle new file mode 100644 index 0000000000000000000000000000000000000000..7d21e7e90a821f4824b61c346f640d992b654314 GIT binary patch literal 16155 zcmd5@eTXDS6+dtHcK3F6Z@+H$c9T0#g~*9I{vk*}{8;z&IN{8?OCo_ghuWUnneOYE zo^0n#_fWL5V~HfrN+{5X3;>AP@)yf`LF#R208ZA`t~eU-ehLdebvg z)h8tP$8pzPzu)`4>UveLUe#PZa&7(-hvGj`#qwO2TcPc>gI-j*&98Hx*U#Ek*n6n= zzOCMwXx_5?pt|X@kZ&;G=e&2RcjgMb7;m!{2Mm4{O|-mv*AB}iQs97!xg5Ld61^&(VI<9TmVeg;|cAAr_iRRI0GUe_yE6s)G?ETGSS<98$ z#=dK}>%MISe`CxjR3KQ z_bL{~Hnz6Sx-dgv&JQY_$2Yn`=(RRr+=YJE3OzqHa;&r8fe$CfO+0qsQ%%c9Jv|Zy zQHEe5F)2pat%}LavjUkp1`npJJyA5YZT_;-+)#`wu{L^4ABe!<(G&|^)(&keja-PR zqi0#j)_tGti|@L;2Z7BXR6U

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For example, the p-values obtained from the two-sided t-test @@ -46,30 +46,26 @@ pvals_hom[0] -#%% +# %% # Create a mask to identifiy where p-values are below 0.05 hom_mask = [values < 0.05 for values in pvals_hom] het_mask = [values < 0.05 for values in pvals_het] -#%% +# %% # Plot some relevant quantities of mode 2. lonlats = [ np.meshgrid(pvals_hom[0].lon.values, pvals_hom[0].lat.values), - np.meshgrid(pvals_hom[1].lon.values, pvals_hom[1].lat.values) + np.meshgrid(pvals_hom[1].lon.values, pvals_hom[1].lat.values), ] proj = [ Orthographic(central_latitude=30, central_longitude=-120), - Orthographic(central_latitude=30, central_longitude=-60) + Orthographic(central_latitude=30, central_longitude=-60), ] -kwargs1 = { - 'cmap' : 'BrBG', 'vmin' : -.05, 'vmax': .05, 'transform': PlateCarree() -} -kwargs2 = { - 'cmap' : 'RdBu', 'vmin' : -1, 'vmax': 1, 'transform': PlateCarree() -} +kwargs1 = {"cmap": "BrBG", "vmin": -0.05, "vmax": 0.05, "transform": PlateCarree()} +kwargs2 = {"cmap": "RdBu", "vmin": -1, "vmax": 1, "transform": PlateCarree()} mode = 2 @@ -86,24 +82,32 @@ for i, a in enumerate(ax2): hom_pats[i].sel(mode=mode).plot(ax=a, **kwargs2) a.scatter( - lonlats[i][0], lonlats[i][1], hom_mask[i].sel(mode=mode).values * .5, - color='k', alpha=.5, transform=PlateCarree() + lonlats[i][0], + lonlats[i][1], + hom_mask[i].sel(mode=mode).values * 0.5, + color="k", + alpha=0.5, + transform=PlateCarree(), ) for i, a in enumerate(ax3): het_pats[i].sel(mode=mode).plot(ax=a, **kwargs2) a.scatter( - lonlats[i][0], lonlats[i][1], het_mask[i].sel(mode=mode).values * .5, - color='k', alpha=.5, transform=PlateCarree() + lonlats[i][0], + lonlats[i][1], + het_mask[i].sel(mode=mode).values * 0.5, + color="k", + alpha=0.5, + transform=PlateCarree(), ) for i, a in enumerate(ax4): scores[i].sel(mode=mode).plot(ax=a) - a.set_xlabel('') + a.set_xlabel("") for a in np.ravel([ax1, ax2, ax3]): - a.coastlines(color='.5') + a.coastlines(color=".5") a.add_feature(LAND) plt.tight_layout() -plt.savefig('mca.jpg') +plt.savefig("mca.jpg") diff --git a/docs/auto_examples/2mca/plot_mca.py.md5 b/docs/auto_examples/2multi/plot_mca.py.md5 similarity index 100% rename from docs/auto_examples/2mca/plot_mca.py.md5 rename to docs/auto_examples/2multi/plot_mca.py.md5 diff --git a/docs/auto_examples/2mca/plot_mca.rst b/docs/auto_examples/2multi/plot_mca.rst similarity index 59% rename from docs/auto_examples/2mca/plot_mca.rst rename to docs/auto_examples/2multi/plot_mca.rst index b8934bc..a138793 100644 --- a/docs/auto_examples/2mca/plot_mca.rst +++ b/docs/auto_examples/2multi/plot_mca.rst @@ -2,7 +2,7 @@ .. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "auto_examples/2mca/plot_mca.py" +.. "auto_examples/2multi/plot_mca.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html @@ -10,12 +10,12 @@ .. note:: :class: sphx-glr-download-link-note - :ref:`Go to the end ` + :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_2mca_plot_mca.py: +.. _sphx_glr_auto_examples_2multi_plot_mca.py: Maximum Covariance Analysis @@ -82,6 +82,12 @@ Perform MCA +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + @@ -492,105 +498,78 @@ for the homogeneous patterns of ``X`` are: stroke: currentColor; fill: currentColor; } -

    <xarray.DataArray 'pvalues' (mode: 20, lat: 25, lon: 26)>
    -    array([[[0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    -             0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
    -            [0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    -             0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
    -            [0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    -             0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
    -            ...,
    -            [0.00000000e+000, 0.00000000e+000, 3.61675717e-317, ...,
    -             7.98578688e-039, 2.30265328e-094, 2.21636508e-303],
    -            [0.00000000e+000, 1.10521052e-316, 1.20913062e-261, ...,
    -             4.10956354e-131, 1.44021963e-128, 2.12663257e-154],
    -            [7.31912400e-298, 4.90446231e-268, 8.34068732e-265, ...,
    -             1.08663393e-108, 1.03811248e-164, 0.00000000e+000]],
    -
    -           [[1.51981041e-003, 1.51451638e-003, 1.42686956e-003, ...,
    -             4.15630876e-003, 1.08530307e-002, 3.82734916e-002],
    -            [3.23966412e-014, 1.08441128e-013, 1.89745649e-013, ...,
    -             2.46091910e-002, 4.03766463e-002, 9.24328603e-002],
    -            [9.63407018e-022, 1.29733008e-018, 1.60662353e-017, ...,
    -             2.41514331e-001, 3.29854905e-001, 5.51406500e-001],
    -    ...
    -            [5.09236781e-003, 1.98352340e-007, 1.34969548e-011, ...,
    -             1.58214300e-007, 9.29302667e-006, 9.02537697e-001],
    -            [5.39064666e-002, 1.98926739e-004, 3.79438101e-007, ...,
    -             1.82153703e-005, 6.37958521e-001, 2.32301899e-001],
    -            [1.51404932e-002, 1.36629419e-004, 1.67583602e-004, ...,
    -             1.25110766e-041, 1.65586720e-031, 1.34206313e-001]],
    -
    -           [[3.43753004e-002, 6.48016671e-002, 1.22411131e-001, ...,
    -             8.67961330e-002, 1.68835662e-001, 2.89690199e-001],
    -            [7.98697796e-007, 2.85782833e-006, 1.07495997e-005, ...,
    -             8.17826134e-001, 9.27578102e-001, 9.60654654e-001],
    -            [4.03319590e-019, 1.71002077e-018, 2.01847273e-017, ...,
    -             6.54015121e-001, 6.06587120e-001, 5.74060529e-001],
    -            ...,
    -            [4.94557351e-001, 3.67606324e-001, 1.12711681e-001, ...,
    -             1.02202704e-007, 7.09368589e-005, 3.77117795e-004],
    -            [3.51796573e-001, 2.03865930e-001, 1.77962276e-001, ...,
    -             1.67780289e-003, 1.75525951e-004, 3.62660983e-006],
    -            [3.52331479e-001, 1.68751652e-002, 1.89118411e-002, ...,
    -             7.85268523e-001, 2.77801713e-004, 2.09208191e-009]]])
    +    
    <xarray.DataArray 'pvalues_of_left_homogeneous_patterns' (mode: 20, lat: 25,
    +                                                              lon: 26)>
    +    7.294e-298 4.922e-268 8.324e-265 3.639e-251 ... 0.04257 0.08684 0.1689 0.2898
         Coordinates:
    -      * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
    +      * lat      (lat) float32 15.0 17.5 20.0 22.5 25.0 ... 65.0 67.5 70.0 72.5 75.0
           * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 255.0 257.5 260.0 262.5
    -      * mode     (mode) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
  • mode
    (mode)
    int64
    1 2 3 4 5 6 7 ... 15 16 17 18 19 20
    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
    +           19, 20])
    • mode
      PandasIndex
      PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], dtype='int64', name='mode'))
    • lat
      PandasIndex
      PandasIndex(Index([15.0, 17.5, 20.0, 22.5, 25.0, 27.5, 30.0, 32.5, 35.0, 37.5, 40.0, 42.5,
      +           45.0, 47.5, 50.0, 52.5, 55.0, 57.5, 60.0, 62.5, 65.0, 67.5, 70.0, 72.5,
      +           75.0],
      +          dtype='float32', name='lat'))
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
      +           225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,
      +           250.0, 252.5, 255.0, 257.5, 260.0, 262.5],
      +          dtype='float32', name='lon'))
  • model :
    MCA
    software :
    xeofs
    version :
    1.2.0
    date :
    2023-10-25 19:14:23
    n_modes :
    20
    center :
    True
    standardize :
    False
    use_coslat :
    True
    n_pca_modes :
    None
    compute :
    True
    sample_name :
    sample
    feature_name :
    feature
    solver :
    auto
    random_state :
    None


  • @@ -678,9 +657,9 @@ Plot some relevant quantities of mode 2. -.. image-sg:: /auto_examples/2mca/images/sphx_glr_plot_mca_001.png +.. image-sg:: /auto_examples/2multi/images/sphx_glr_plot_mca_001.png :alt: mode = 2, mode = 2, mode = 2, mode = 2, mode = 2, mode = 2, mode = 2, mode = 2 - :srcset: /auto_examples/2mca/images/sphx_glr_plot_mca_001.png + :srcset: /auto_examples/2multi/images/sphx_glr_plot_mca_001.png :class: sphx-glr-single-img @@ -690,10 +669,10 @@ Plot some relevant quantities of mode 2. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 4.241 seconds) + **Total running time of the script:** (0 minutes 5.242 seconds) -.. _sphx_glr_download_auto_examples_2mca_plot_mca.py: +.. _sphx_glr_download_auto_examples_2multi_plot_mca.py: .. only:: html diff --git a/docs/auto_examples/2multi/plot_mca_codeobj.pickle b/docs/auto_examples/2multi/plot_mca_codeobj.pickle new file mode 100644 index 0000000000000000000000000000000000000000..fc05016e17b90104cec253ea25f381f5acbbbac2 GIT binary patch literal 12758 zcmcIq-HRMq6;HNjrf0f4Gn1KQb~1)l#E&dOVRje4Ky=dCEP*7mPDCF>iaXVJx~fv$ zRa8~S9xx~i>IMrzY!MYf))$w3*#|*z5dmMMfDB_~*n|N-0-E&V@byxLd z7nV#{o!>dXbMLw5o_lV6efj&pzj`MAFKQdE<8ULi++i?@+6R7*`@DDEGQ!FCCQlzu zu0*RQ4|R`)!Q{)62k?7o$Oe28Er%n|fqz#9Zf_+1SUnn9PG|`=o#dGwn64jA%vQ8& z1-jv|AecOi+VD3YdyZvT;pE|nMJs{f`aD%{HZ#rU8d7Pt%`LO@&^#vqOP=rA2^$b! za;yN15a}m#>D3o}t9RGq1|Y700|1ea0Jc3~q35`vV;yOI5DkQ=*)DnBd0B8MP}7oX z0Byd{#FCwi!!;Ig!F6$?Q>uvJ4=aXG<7qtFJZjwT?#rQze}My=W9}XY8cZ~I;2(pD zA?x6W6NN~IBO;YB%duK9s-IAFZs=k-yS^I)w*?O|uuowMntlWVx*;U=n5tkoR|L;o zkqzpD`I))2qzl&+*~b30uAtpuM*>dwg(II!%G)n+cYn+SokHkco} z-ibEw9Iek?1~P(r@pDYPOk*2q6x>b}7s^o06eSQ}RS=s+v@gmestL-Po)R~|HXpn} zz{2KB^I;@7zM&v4>Vl?7GjxQfsft`9Y`$DkLlNI^SHu$1-%(&&`+f*}B4vCYk54Qi z*nYPPl$e9mo^B4SraM5zLHX|e!xX}z&Ve7AZr^8~X`ww?eGDO-2b|%@p{xGJIeH3; zqwW)r+7E!R0m=w|UhQd4Ztbk^Y!GoO16Gk&-^OtS& zkz#x*D&|af8qsUsedYu@#C5)ZrKK*!i?V^fnTPb!t^GUKQBuAmvNUs^ha7C)NK-bT zH?kC(rE!?9>1RqZF42TTkfkIj*{ZH|t03eGc`|Q~>XB6?R z{Ub=1%n0L>E+@T(YB4D81_O5pnaf_P2qzTnWH+=Y?|zfxHNW&M%LNW@p8 zpuL-L7~umx41;(@9`fGE;PY5tt*M4`f37ATdGK)xy?k?MIHMlFBTmS7p$&GMLn(_E z>;?`V*AoSq{i)hos0*L2tpPdlSqgsg?#T0?F9(xQXZd#P=%F6mr~^jiZ&KV&zIYEj z67@>z(Zdsu_w;lMZq-mjsUOt9BIbt@#-nvnUBOo4xkC~CvZ7iS6%~51KowP>g4Pqo z?V_Y|DuCxjgsn>_(mIP4Oe**JMN)<48?F#%o1=c($2Iv%Pc4dE7ZsE0U0NmCj+BM& zXpf0TSK7Q%yFRBYk5pYWEJu<=GBv6=io~&$1iEbe9MDZ5g?%~ufs|*_Su2-rSi`9{ zyZsUt-W~f|ID*ru#T?CXSx@glT@&z74$x{@JJSA^)ReI6C>q3xCQYuH2}rI5&$7-$ z`$cG2@-8&(IO>-vPeJFLGa+jBQzSZq*^`(%65i3fCyq7j`If<)MOzLtlwjjOo;H36 z9S%0^j~wPNZoE_pHvS4V{w_3F>{qFgtWQvV_A&(K{yJ4#+94_EUnngK+`}kUq99J! zX0hL()HDAInC1-<%rcWFq-M(IFWdGbYM-u-IWv>?S{lbzU*?9D8&+R5w_#c0Rwc;9 zPuqo0akm61P#c!^A7j3$)t2XkTBxfzP zQihSG!_pIZ-vKVl5@*l=6gsmeF)Z2?@fcctGt?b+;*Kh9m+MkPGSVGI16e~^6wZ## z4%1+>*2fMz!i}G;?2ZY&JXOS&D5Syr9_nHV4#)9R6tfXuGpWp{uWr&?rfB*~2IrR| zUDF6CO7KNXeDO9<3rSZ`Q!g&aYp8jyq_I%-9&^V7j>S)^^s|F?}KPgc>+JJl=--QLEqc}z9iDTH#Fcmjh~cN4(aIvWAT$<2UCqRI4b(<5w3^Kl=Q%j{5>Y_lpu z>C`<6X^O*f>PU7f9!en|Es2k9@;NO$IR;Wbt{I-q#fy)nV=NzEJ6DLC2N<*l+nW(z zYRSmi$oIDN&8arFB5ib6`=+FnL`V?`Z)p!Hpp$HAlJIn@ueP5}DJn?&b2hUn6=bzf zDEaefC!6I#x{cXCsHUKRx&5PRP{Q~p6t$l1R+4V;@Dag~===;f)FLWx4U8h#zwaN@r)HnOz zHF!$z{zFkMQL#vmvZCc3DaWH-F?I0W&x|i%cH=K#ibNKe)|txaK+KxtnTq6~EH&~* zMH1;Bpi^GxJa{%g0PN<%%BiPzYhhK=*K<_IZu|vI$;@Tpuqjb+RRW*D$ONKZue_T# UJC`JhWSUgd=w|1#l;k7r|9k;LdH?_b literal 0 HcmV?d00001 diff --git a/docs/auto_examples/2mca/plot_rotated_mca.ipynb b/docs/auto_examples/2multi/plot_rotated_mca.ipynb similarity index 100% rename from docs/auto_examples/2mca/plot_rotated_mca.ipynb rename to docs/auto_examples/2multi/plot_rotated_mca.ipynb diff --git a/examples/2mca/plot_rotated_mca.py b/docs/auto_examples/2multi/plot_rotated_mca.py similarity index 76% rename from examples/2mca/plot_rotated_mca.py rename to docs/auto_examples/2multi/plot_rotated_mca.py index 42ecc5d..59d337b 100644 --- a/examples/2mca/plot_rotated_mca.py +++ b/docs/auto_examples/2multi/plot_rotated_mca.py @@ -16,26 +16,26 @@ from xeofs.models import MCA, MCARotator -#%% +# %% # Create 2 different DataArrays -t2m = xr.tutorial.load_dataset('air_temperature')['air'] +t2m = xr.tutorial.load_dataset("air_temperature")["air"] da1 = t2m.isel(lon=slice(0, 26)) da2 = t2m.isel(lon=slice(27, None)) -#%% +# %% # Perform MCA mca = MCA(n_modes=20, standardize=False, use_coslat=True) -mca.fit(da1, da2, dim='time') +mca.fit(da1, da2, dim="time") -#%% +# %% # Apply Varimax-rotation to MCA solution rot = MCARotator(n_modes=10) rot.fit(mca) -#%% +# %% # Get rotated singular vectors, projections (PCs), homogeneous and heterogeneous # patterns: @@ -44,7 +44,7 @@ hom_pats, pvals_hom = rot.homogeneous_patterns() het_pats, pvals_het = rot.heterogeneous_patterns() -#%% +# %% # When two fields are expected, the output of the above methods is a list of # length 2, with the first and second entry containing the relevant object for # ``X`` and ``Y``. For example, the p-values obtained from the two-sided t-test @@ -52,30 +52,26 @@ pvals_hom[0] -#%% +# %% # Create a mask to identifiy where p-values are below 0.05 hom_mask = [values < 0.05 for values in pvals_hom] het_mask = [values < 0.05 for values in pvals_het] -#%% +# %% # Plot some relevant quantities of mode 2. lonlats = [ np.meshgrid(pvals_hom[0].lon.values, pvals_hom[0].lat.values), - np.meshgrid(pvals_hom[1].lon.values, pvals_hom[1].lat.values) + np.meshgrid(pvals_hom[1].lon.values, pvals_hom[1].lat.values), ] proj = [ Orthographic(central_latitude=30, central_longitude=-120), - Orthographic(central_latitude=30, central_longitude=-60) + Orthographic(central_latitude=30, central_longitude=-60), ] -kwargs1 = { - 'cmap' : 'BrBG', 'vmin' : -.05, 'vmax': .05, 'transform': PlateCarree() -} -kwargs2 = { - 'cmap' : 'RdBu', 'vmin' : -1, 'vmax': 1, 'transform': PlateCarree() -} +kwargs1 = {"cmap": "BrBG", "vmin": -0.05, "vmax": 0.05, "transform": PlateCarree()} +kwargs2 = {"cmap": "RdBu", "vmin": -1, "vmax": 1, "transform": PlateCarree()} mode = 2 @@ -92,24 +88,32 @@ for i, a in enumerate(ax2): hom_pats[i].sel(mode=mode).plot(ax=a, **kwargs2) a.scatter( - lonlats[i][0], lonlats[i][1], hom_mask[i].sel(mode=mode).values * .5, - color='k', alpha=.5, transform=PlateCarree() + lonlats[i][0], + lonlats[i][1], + hom_mask[i].sel(mode=mode).values * 0.5, + color="k", + alpha=0.5, + transform=PlateCarree(), ) for i, a in enumerate(ax3): het_pats[i].sel(mode=mode).plot(ax=a, **kwargs2) a.scatter( - lonlats[i][0], lonlats[i][1], het_mask[i].sel(mode=mode).values * .5, - color='k', alpha=.5, transform=PlateCarree() + lonlats[i][0], + lonlats[i][1], + het_mask[i].sel(mode=mode).values * 0.5, + color="k", + alpha=0.5, + transform=PlateCarree(), ) for i, a in enumerate(ax4): scores[i].sel(mode=mode).plot(ax=a) - a.set_xlabel('') + a.set_xlabel("") for a in np.ravel([ax1, ax2, ax3]): - a.coastlines(color='.5') + a.coastlines(color=".5") a.add_feature(LAND) plt.tight_layout() -plt.savefig('rotated_mca.jpg') +plt.savefig("rotated_mca.jpg") diff --git a/docs/auto_examples/2mca/plot_rotated_mca.py.md5 b/docs/auto_examples/2multi/plot_rotated_mca.py.md5 similarity index 100% rename from docs/auto_examples/2mca/plot_rotated_mca.py.md5 rename to docs/auto_examples/2multi/plot_rotated_mca.py.md5 diff --git a/docs/auto_examples/2mca/plot_rotated_mca.rst b/docs/auto_examples/2multi/plot_rotated_mca.rst similarity index 59% rename from docs/auto_examples/2mca/plot_rotated_mca.rst rename to docs/auto_examples/2multi/plot_rotated_mca.rst index 0bfc965..dab214a 100644 --- a/docs/auto_examples/2mca/plot_rotated_mca.rst +++ b/docs/auto_examples/2multi/plot_rotated_mca.rst @@ -2,7 +2,7 @@ .. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "auto_examples/2mca/plot_rotated_mca.py" +.. "auto_examples/2multi/plot_rotated_mca.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html @@ -10,12 +10,12 @@ .. note:: :class: sphx-glr-download-link-note - :ref:`Go to the end ` + :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_2mca_plot_rotated_mca.py: +.. _sphx_glr_auto_examples_2multi_plot_rotated_mca.py: Rotated Maximum Covariance Analysis @@ -82,6 +82,12 @@ Perform MCA +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + @@ -511,102 +517,73 @@ for the homogeneous patterns of ``X`` are: stroke: currentColor; fill: currentColor; } -
    <xarray.DataArray 'pvalues' (mode: 10, lat: 25, lon: 26)>
    -    array([[[0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    -             0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
    -            [0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    -             0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
    -            [0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    -             0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
    -            ...,
    -            [0.00000000e+000, 0.00000000e+000, 5.71183469e-315, ...,
    -             3.19445735e-038, 3.22483501e-093, 3.35133107e-301],
    -            [0.00000000e+000, 1.74073643e-314, 1.47740148e-259, ...,
    -             1.52114978e-129, 4.89767775e-127, 1.23416833e-152],
    -            [1.08303160e-295, 6.19667456e-266, 1.04372372e-262, ...,
    -             2.28296022e-107, 7.36340147e-163, 0.00000000e+000]],
    -
    -           [[1.48420079e-001, 1.48420079e-001, 1.41692384e-001, ...,
    -             3.26778228e-001, 5.18645669e-001, 8.68577902e-001],
    -            [7.61414446e-012, 2.53765164e-011, 4.38329769e-011, ...,
    -             7.75764527e-001, 8.77782357e-001, 9.78839632e-001],
    -            [2.60120133e-019, 3.34709440e-016, 4.04905719e-015, ...,
    -             9.98481418e-001, 9.99666320e-001, 9.99995654e-001],
    -    ...
    -            [1.00000000e+000, 9.99972360e-001, 4.27732780e-002, ...,
    -             1.60308926e-034, 7.22688919e-032, 3.43068347e-014],
    -            [1.00000000e+000, 9.99937055e-001, 7.33509565e-001, ...,
    -             1.75537328e-008, 1.30029823e-023, 4.13149368e-024],
    -            [8.30900507e-001, 4.79459188e-001, 8.10448246e-001, ...,
    -             1.82285738e-003, 1.00000000e+000, 2.21215877e-008]],
    -
    -           [[1.00000000e+000, 1.00000000e+000, 1.00000000e+000, ...,
    -             9.94508099e-001, 9.99944145e-001, 9.99999922e-001],
    -            [9.98614281e-001, 9.99534019e-001, 9.99882864e-001, ...,
    -             6.93478082e-001, 9.81134366e-001, 9.99983456e-001],
    -            [7.19670598e-009, 1.52745556e-009, 8.45317840e-010, ...,
    -             3.02005464e-001, 8.81667078e-001, 9.99871671e-001],
    -            ...,
    -            [8.17647083e-002, 1.46889596e-001, 1.23796769e-001, ...,
    -             1.08025932e-002, 9.97958950e-001, 9.99999998e-001],
    -            [4.21488073e-002, 3.57874032e-003, 9.38013602e-006, ...,
    -             5.16267064e-001, 4.12408196e-001, 9.99999998e-001],
    -            [1.87253318e-001, 2.72339755e-002, 2.53455741e-004, ...,
    -             9.50324215e-001, 1.00000000e+000, 9.99999968e-001]]])
    +    
    <xarray.DataArray 'pvalues_of_left_homogeneous_patterns' (mode: 10, lat: 25,
    +                                                              lon: 26)>
    +    5.918e-81 4.09e-64 6.234e-58 5.974e-49 ... 0.001974 0.005479 0.01494 0.03604
         Coordinates:
    -      * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
    +      * lat      (lat) float32 15.0 17.5 20.0 22.5 25.0 ... 65.0 67.5 70.0 72.5 75.0
           * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 255.0 257.5 260.0 262.5
    -      * mode     (mode) int64 1 2 3 4 5 6 7 8 9 10
  • mode
    (mode)
    int64
    1 2 3 4 5 6 7 8 9 10
    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])
    • mode
      PandasIndex
      PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='int64', name='mode'))
    • lat
      PandasIndex
      PandasIndex(Index([15.0, 17.5, 20.0, 22.5, 25.0, 27.5, 30.0, 32.5, 35.0, 37.5, 40.0, 42.5,
      +           45.0, 47.5, 50.0, 52.5, 55.0, 57.5, 60.0, 62.5, 65.0, 67.5, 70.0, 72.5,
      +           75.0],
      +          dtype='float32', name='lat'))
    • lon
      PandasIndex
      PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
      +           225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,
      +           250.0, 252.5, 255.0, 257.5, 260.0, 262.5],
      +          dtype='float32', name='lon'))
  • model :
    Rotated MCA
    n_modes :
    10
    power :
    1
    max_iter :
    1000
    rtol :
    1e-08
    squared_loadings :
    False
    software :
    xeofs
    version :
    1.2.0
    date :
    2023-10-25 19:14:30


  • @@ -694,9 +671,9 @@ Plot some relevant quantities of mode 2. -.. image-sg:: /auto_examples/2mca/images/sphx_glr_plot_rotated_mca_001.png +.. image-sg:: /auto_examples/2multi/images/sphx_glr_plot_rotated_mca_001.png :alt: mode = 2, mode = 2, mode = 2, mode = 2, mode = 2, mode = 2, mode = 2, mode = 2 - :srcset: /auto_examples/2mca/images/sphx_glr_plot_rotated_mca_001.png + :srcset: /auto_examples/2multi/images/sphx_glr_plot_rotated_mca_001.png :class: sphx-glr-single-img @@ -706,10 +683,10 @@ Plot some relevant quantities of mode 2. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 3.490 seconds) + **Total running time of the script:** (0 minutes 4.406 seconds) -.. _sphx_glr_download_auto_examples_2mca_plot_rotated_mca.py: +.. _sphx_glr_download_auto_examples_2multi_plot_rotated_mca.py: .. only:: html diff --git a/docs/auto_examples/2multi/plot_rotated_mca_codeobj.pickle b/docs/auto_examples/2multi/plot_rotated_mca_codeobj.pickle new file mode 100644 index 0000000000000000000000000000000000000000..4774caabfc2a2ac1165f39de1f863f83fc1e2ba8 GIT binary patch literal 15039 zcmd5@eTW@b6;JZAAKACr&A00!mOxq~>VwqKKl~?bHchRX4>r_VY12zx$||-J#XgC%x(k= zCA%}{ch2wJIrp4%KlalzUwQ6*d*c71w&6OCG(yWA2IHuG#qUX9_AXdPIDUHk&W-Wu zXx@}z*ArndzCL~ue&0V7138Xn!jb2|zq13kH&Q>$Z;mV{w3M2~q4 zbPY!Y!T46xhM(n@=U9dnj&F@bwCcNI2aHJH?aQHbM}aa2zk;gk zd{)8RDQ+I3o+vjf+^bO6#IFif=+NDoL-%ygi$JdXZV+5jA7_elZ`YjbZUU}rC}21K ztZJ}M?^HMU)O^=YHKC&&O~vZk`SmHzwZx{OJUEkrWHcKXt}oNc1%aNg~FkoUWo7UdfGKu()ngb44$MXJewlqxZ~2&OXw|^|xy1;Vn1FA0%NXqXm_5@-&rZo5LjAr0+oGP783c zwt57U5?-tU2k7%!Krg5agS2af9qAVsya`VRMS{<01W&LaBxLVMED~p?nBtC^U#m+J z{rZLmSx}h-2R;kX+o_O@<{$8_-Zf7em>lpCMA*@R2tCIQ9c#1G2hl)@LU&jEoX$B- z%>t@X_4vEm1X;{DsHR2Dlw##|j83RxhJUCT)+jbK8h=q4%{lJS$-DK^MEsLep3XsOn9wTUh@ zP%mjismsnLvP$gboajN(5xrjbh7{q*mq_{O1JYgFl7aSC1+5|c(Dk-E1JCL7r7J*2 zQ7?W@iI-_?6OD@7E1C;ssJRrS5U*;8Lq)WY-!+vj(zoO^2DzhhPC>-)Km6&2`Z{$-K>K}10(4ANxdwRCUDzKSRjEne|M)9w4h zGcD3X>yKeVN$JR$d=nmFlFFf=|2J9QBc9y(twYkxOu~Pp-&5RcC5$q+C0Vq|X zA$Fo*HZcj-ZJ$%m{3~FZ!zjy4o{^d=o4;(^PjdScK~nMI)78oW@1}5qE<}31QYhX4 zwLceIjA0_$|Dmo{GsAbAe5Q52C7Nj1d=Z%> zNjYKD&>qb1@ZLek3h2UXDPMggFFeDeEWcF~N0@$xLyoG^eb@@kb=UQKK^4_!>*6`r@6`no zvOnOMl{^&Ie4sp#p=IQZPtd^?`Fl0M&oxrSssE@En(+TI2d*Oje&3gU5g#$;9=w%$ z&=o{PIq`f$ZQPBYHpCN0e!-!~*22(VGYknwSJsv_%v~sn@L$zdz*)ag8%g;7hJ#ig zg!R50sI#GXMsCR7$dFa6f7?(E=l)_tJn`W79Qw8ghlVri$;)a_=^C6iUy{($kO{j? 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    + +.. only:: html + + .. image:: /auto_examples/1single/images/thumb/sphx_glr_plot_complex_eof_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_1single_plot_complex_eof.py` + +.. raw:: html + +
    Complex/Hilbert EOF analysis
    +
    + + .. raw:: html
    .. only:: html - .. image:: /auto_examples/1eof/images/thumb/sphx_glr_plot_eof-smode_thumb.png + .. image:: /auto_examples/1single/images/thumb/sphx_glr_plot_eof-smode_thumb.png :alt: - :ref:`sphx_glr_auto_examples_1eof_plot_eof-smode.py` + :ref:`sphx_glr_auto_examples_1single_plot_eof-smode.py` .. raw:: html @@ -84,10 +100,10 @@ Examples .. only:: html - .. image:: /auto_examples/1eof/images/thumb/sphx_glr_plot_multivariate-eof_thumb.png + .. image:: /auto_examples/1single/images/thumb/sphx_glr_plot_multivariate-eof_thumb.png :alt: - :ref:`sphx_glr_auto_examples_1eof_plot_multivariate-eof.py` + :ref:`sphx_glr_auto_examples_1single_plot_multivariate-eof.py` .. raw:: html @@ -101,10 +117,10 @@ Examples .. only:: html - .. image:: /auto_examples/1eof/images/thumb/sphx_glr_plot_mreof_thumb.png + .. image:: /auto_examples/1single/images/thumb/sphx_glr_plot_mreof_thumb.png :alt: - :ref:`sphx_glr_auto_examples_1eof_plot_mreof.py` + :ref:`sphx_glr_auto_examples_1single_plot_mreof.py` .. raw:: html @@ -118,10 +134,10 @@ Examples .. only:: html - .. image:: /auto_examples/1eof/images/thumb/sphx_glr_plot_rotated_eof_thumb.png + .. image:: /auto_examples/1single/images/thumb/sphx_glr_plot_rotated_eof_thumb.png :alt: - :ref:`sphx_glr_auto_examples_1eof_plot_rotated_eof.py` + :ref:`sphx_glr_auto_examples_1single_plot_rotated_eof.py` .. raw:: html @@ -135,10 +151,10 @@ Examples .. only:: html - .. image:: /auto_examples/1eof/images/thumb/sphx_glr_plot_weighted-eof_thumb.png + .. image:: /auto_examples/1single/images/thumb/sphx_glr_plot_weighted-eof_thumb.png :alt: - :ref:`sphx_glr_auto_examples_1eof_plot_weighted-eof.py` + :ref:`sphx_glr_auto_examples_1single_plot_weighted-eof.py` .. raw:: html @@ -152,10 +168,10 @@ Examples .. only:: html - .. image:: /auto_examples/1eof/images/thumb/sphx_glr_plot_gwpca_thumb.png + .. image:: /auto_examples/1single/images/thumb/sphx_glr_plot_gwpca_thumb.png :alt: - :ref:`sphx_glr_auto_examples_1eof_plot_gwpca.py` + :ref:`sphx_glr_auto_examples_1single_plot_gwpca.py` .. raw:: html @@ -167,7 +183,7 @@ Examples
    -2 | MCA +2 | Multi-Set Analysis ======================== @@ -177,16 +193,33 @@ Examples
    " ], "text/plain": [ - "\n", - "array([0.7968352 , 0.02702056, 0.02226538, 0.0163209 , 0.01500371,\n", - " 0.01136572, 0.01082397, 0.00727871, 0.00684999, 0.00615871],\n", - " dtype=float32)\n", + "\n", + "array([0.79683532, 0.0270206 ])\n", "Coordinates:\n", - " * mode (mode) int64 1 2 3 4 5 6 7 8 9 10\n", - "Attributes:\n", - " model: EOF analysis\n", - " n_modes: 10\n", - " standardize: False\n", - " use_coslat: False\n", - " use_weights: False\n", - " software: xeofs\n", - " version: 1.0.2\n", - " date: 2023-07-30 12:56:09" + " * mode (mode) int64 1 2\n", + "Attributes: (13)" ] }, - "execution_count": 5, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -913,16 +953,457 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, let's retrieve the components, the eigenvectors of the covariance matrix. In climate science, they are often just called the *EOFs*." + "We can next examine the spatial patterns, which are the eigenvectors of the covariance matrix, often referred to as EOFs or principal components." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> **_NOTE:_** The `xeofs` library aims to adhere to the convention where the primary patterns obtained from dimensionality reduction (which typically exclude the sample dimension) are termed components (akin to principal components). When data is projected onto these patterns, for instance using the `transform` method, the outcome is termed `scores` (similar to principal component scores). However, this terminology is more of a guideline than a strict rule." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
    +       "Dimensions:  (lat: 25, lon: 53, mode: 2)\n",
    +       "Coordinates:\n",
    +       "  * lat      (lat) float32 15.0 17.5 20.0 22.5 25.0 ... 65.0 67.5 70.0 72.5 75.0\n",
    +       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
    +       "  * mode     (mode) int64 1 2\n",
    +       "Data variables:\n",
    +       "    air      (mode, lat, lon) float64 0.0022 0.002131 ... 0.02168 0.0221
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, lon: 53, mode: 2)\n", + "Coordinates:\n", + " * lat (lat) float32 15.0 17.5 20.0 22.5 25.0 ... 65.0 67.5 70.0 72.5 75.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * mode (mode) int64 1 2\n", + "Data variables:\n", + " air (mode, lat, lon) float64 0.0022 0.002131 ... 0.02168 0.0221" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "components = model.components()" + "components = model.components()\n", + "components" ] }, { @@ -930,29 +1411,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It's important to note that since we utilized `xr.Dataset` as our data input, the components will also be represented as an `xr.Dataset`.\n", - "\n", - "To better understand them, we can visualize these components using the built-in plot method of `xarray`:" + "You'll observe that the result is an `xr.Dataset`, mirroring the format of our original input data. To visualize the components, we can use typical methods for `xarray` objects. Now, let's inspect the first component." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> **_NOTE:_** `xeofs` is designed to match the data type of its input. For instance, if you provide an `xr.DataArray` as input, the components will also be of type `xr.DataArray`. Similarly, if the input is an `xr.Dataset`, the components will mirror that as an `xr.Dataset`. The same principle applies if the input is a `list`; the output components will be presented in a `list` format. This consistent behavior is maintained across all `xeofs` methods." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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    " ] @@ -962,7 +1448,7 @@ } ], "source": [ - "components['air'].sel(mode=1).plot()" + "components[\"air\"].sel(mode=1).plot()" ] }, { @@ -970,27 +1456,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In a similar fashion, we can also retrieve and examine the scores, i.e. the projection of the original data onto the eigenvectors. In climate science they are often referred to as Principal Components (PCs). The PCs are always in form of a `xr.DataArray`." + "We can also examine the principal component scores, which represent the corresponding time series." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> **_NOTE:_** When comparing the scores from `xeofs` to outputs from other PCA implementations like `sklearn` or `eofs`, you might spot discrepancies in the absolute values. This arises because `xeofs` typically returns scores normalized by the L2 norm. However, if you prefer unnormalized scores, simply set `normalized=False` when using the `scores` or `transform` method.\"" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 8, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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DIBfFZInIBljaiEh6f528JXUIBl1IvWf8IHJKTJaIbCA1m9P0iciwQ1czpA6BLMRkichKB6/ewZDF+6QOQ6fDSfxwJnIWwf5cL06umCwRWen73VelDkGv501Ye46IHKNsmSOSHyZLRFZy5uFKd3MKpQ6BiP6z6ZRzj6ki/ZgsEREROcjPB69h8Nd7cDfH+CLa5DyYLBFZiTPhiMhUr/50FDsvpGPe1gtSh0JmYLJEZKV7LIRHJKniEoHt525LHYZZvv73stQhkBm43AmRlbaeSZU6BCK39u3Oy/jwj9NSh0EujD1LRC6u/8JdUodAZFc/7EuSOgRycUyWiCx0OzsfQgYDlg5ezcCsv85KHQaRzRWXCDT5YCMu3r4vdSgWmfLbSTz6+b9ISs+ROhQygskSkQX+OpmCh6dtxuRfjksdiknmbeNgUnI9vxy6JuvyGEt2XcHx65l4a608PkfcGZMlIguM+v4gAODH/ckSR2K663dzUVIicCtLfWmW1Ow8bDl9CyUlzt9LRqRKrj1Kmu5zkojT4wBvIjMIIfDwtM1Sh2GReVvPI6+wBGsOX8cXQ5qjW4NI5BcVo8W0LQCAmf0fwoCHoyWOksh0RcUlUodgEz5e7LdwdvwJEZlh0fZLSLsnz2JyHgoF1hy+DgCY/99jubpvb1Du33aWs/pIXlxl+r2vl6fUIZARTJaIzPDxhjNSh2Cx5XsfzBjyUCi09nt78uOASArentrXIzkXfjoSuaG8wmIUa4xR8uIHNpEkNp9mr66zY7JEZCJXGgB9JiUb+UXFatt82LNEJJmCItcYf+Wq+OlIZKI5W85LHYJN5RSoJ0ueHuxZIpJKiQxqtrkzJktEJprrYslSvsadrI5hTETkIEyWnBuTJSKZmzOwiUXnXbp9z7aBEDmQI6rnO3LgteYYQnIuTJaITLD++E2pQ9DycqdaeLxpFfRpEmXR+UMW71N7zRtbkpMiOycXF6b1gJ+346b0M1dybkyWiEwwZvkhqUNQ8/vYR/BqYl18NrAJFDZ6fsbPapITe/bERFf0h5enB/o3q2q376FpxV4uBuzMmCwRyVCjqiE2f88LqXwsR/JQUiIwY/1pm73f8Edi1V53jYsEALzRox4WDm6Go+8l4uP+jdSOSawfYbPvD8i7hps7YLJEJDMjND7YAWBAvPV3wPsu37H6PYgc4cXlB/Hd7qs2eS8fTw+M61xbbVtMaAAAwM/bEz0aVUaIvzeebB6N1xLrKI9599H6Nvn+JA9cG47IiEInWn9qco96eLZ1jNb2yiH+jg+GSAJCCPx18pbN3q9jvTCtbU+3qKa1zcNDgTEdauHkjSzUDg+02fcneWDPEpER3+50nvWnRrWvCX8f7UGnw9po9zYRuaJOn2636ftN6l5PrWzGjkkd9S794+GhwMJnmmNiYl2bjRUkeWDPEpER09c7fixB1Qr+uJaRa/LxIQHedoyGyDkIIXA57b5N37NmWCCEEGhfJwwFRSWoWsG0XlqmSu6FPUtETujXl9pobWtbO9Tu39cRtWuILKVZSNVWFAoFvhvWAitGtmSPEenEZInICVUI8MEXQ5qrbZs/uJndvy9rvZAzy8ottOv7m5MoBfrxwYw7YbJEZCf+ZhS0W/fyI2qvFQqgW4NIXPmoFy5M64HLM3oi2M/wozbNGT2WOHUjCzsvpKHIiQa1E5V55ccjUoegpHk9TupeV6JIyBGYLBEZ8KcVlbsPvdMV3wyN19qua+p/wyoh6BIXrnyteofr5elh0h3vsDYxlgWq4tF5/2Lw13vx5Y5LVr8Xka3tvpQudQhqqpR/ML7p+YRY7Hqjk1Xvx1pnzkt2ydKCBQsQGxsLPz8/NG/eHDt27DB4/Pbt29G8eXP4+fmhRo0aWLRokdr+r776Cm3btkWFChVQoUIFdOnSBfv27dPzbuRuXrSwcreHAvD38USnehFYPqKl2r4K5Xx0nvPVs/Ho1iACQxNiLPqeChsOOV194JrN3ovImfh6PfizN7p9Tave653epbWWRraNhb+PJ6LK++PE+91w8v1uFr3fltO2K4lAtiWrZGnlypUYP3483nrrLRw+fBht27ZFjx49kJSku0z85cuX0bNnT7Rt2xaHDx/Gm2++iXHjxuHnn39WHvP333/j6aefxrZt27B7925Uq1YNiYmJuH79uqOaRS7IQ6UnqE2tBwOz60UG4XmNHqDfx5Y+glMoFPhiSDymPNbAsm9qw3GpXg5cQJTIFBn3C8w+Z1S7Gni924PHY/UrB+P4lAeJTGID66pwd28YiaPvJuKtXg8KVAb6eqGcrxd6P1QZANC0WnmT3y/En7NanZWskqXZs2dj+PDhGDFiBOLi4jBnzhxER0dj4cKFOo9ftGgRqlWrhjlz5iAuLg4jRozAsGHDMGvWLOUxy5cvx5gxY9CkSRPUq1cPX331FUpKSrBlyxZHNYtckOZTs6l9GmBwy2r485W2CPDxwpF3uyr3RYT42uV7WoMroJOz+cOCR+LPJsTgpY61MLhlaZHJ17vVhY+XBx5vWgXx1SugcdXyVselr2zHnIFNsGlCO3zc/yGT3+uNX45bHQ/Zh2yG8xcUFODgwYN444031LYnJiZi165dOs/ZvXs3EhMT1bZ169YNixcvRmFhIby9tX/Jc3JyUFhYiIoVK+qNJT8/H/n5+crXWVlZ5jSFZCIpPcficzUfiQ3RUXVbyQnzkou376OouAReeorzEclBwX+lBj7s2xCvd6uL8gGlj8A/G9jE7t/by9MDtSOCzD6vsLhEb1FMko5sfiJpaWkoLi5GRIR6t2lERARSUlJ0npOSkqLz+KKiIqSlpek854033kCVKlXQpUsXvbHMmDEDISEhyn/R0dFmtobkYPu5VLu9t2oVbntMQd5p5UBTAMi08zRtInNYck9RvWLpGm8KhUKZKDm7WX+dlToE0kE2yVIZzVlBQgiDM4V0Ha9rOwDMnDkTP/zwA3755Rf4+fnpfc/JkycjMzNT+S85OdmcJpAbEEY+2n29PLH2pTb4+cUEBPjYJlnyUbkb9bHBnSl7lUjOVoxsCQ8P+Y29++IfzkR1RrJ5DBcaGgpPT0+tXqTU1FSt3qMykZGROo/38vJCpUqV1LbPmjUL06dPx+bNm/HQQ4afMfv6+sLX1zbjTMh5fb/H8lXNC4uN3wc3iS5v8fvr4uftibd7xaGguARhQdb/frKaNzmT73dfMfnYM1O7w8+MOmfO5mr6fVSvVE7qMEiFbG4dfXx80Lx5c2zatElt+6ZNm5CQkKDznNatW2sdv3HjRsTHx6uNV/rkk08wdepUbNiwAfHx2nVxyD2duyW/micj2tbAmA61bPJed3P4GI6kkXwnB2sPX1dONDh+LdOs69HZEqWFZlbfT7tn/sw/si/Z9CwBwMSJEzFkyBDEx8ejdevW+PLLL5GUlITRo0cDKH08dv36dSxduhQAMHr0aMybNw8TJ07EyJEjsXv3bixevBg//PCD8j1nzpyJd955BytWrEBMTIyyJyowMBCBgYGObySRk3hm8V78+3/Wj30iMtWeS+n4fOt57LxQWnwyt7AYTzavikfn/StxZNbp0aiy1CGQlWSVLA0cOBDp6en44IMPcPPmTTRs2BDr169H9erVAQA3b95Uq7kUGxuL9evXY8KECZg/fz6ioqIwd+5c9O/fX3nMggULUFBQgCeeeELte7333nuYMmWKQ9pFriOmUgCupOegmRm1VZzVtYxcqUMgN/PUl3vUXv9y6BqCuAYbOQHZ/RaOGTMGY8aM0blvyZIlWtvat2+PQ4f0V2G+cuWKjSIjd7d0WAvEVQ7GqgPJeDK+qtThwMfTAwVc441kbP+VDOy/kiF1GDbRvUEkNpzUPXObnJ9sxiwRObt2dcIQFuSLlzrWQniQ/tmUjnLCwiUXiOSsc71w4wdJwNebf27ljD89Ihfl4+WB74e3sOo9cguKbRQNkWG2mn35+aCmNnkfW2tXO0z5dc0wwzPdeN05HyZLRC7sEZV16SzR7pNtNoqEyLDt525b/R4JNSvZrG6ZrT3etAo+G9gYi55phgHxhgsZP7N4r959LOkhDSZLRDqk38s3fpAMKBQKBFsxQPZ2tmv8fyDnd+qm9ctGqS6a62w8PBR4vGlVdG9YGY2qhBg9PqegCEMW78UylXpvE1YeQY//7VAu40KOw2SJSEVRcQmEEC61oKWhCvdErqRptQpSh2CShFqhmD+oGdaPa6v3mEV/X8SO82l4e+0J5BUWIyk9B2sOX8eZlGzsu3zHgdESIMPZcET2sv74TYxZrn/mpCEvdqhp42hs5+P+jTB6mWXtInIUd3u61Oshw7WX5m69oPy6z7ydOHsrW/n6mcV7se21DogNZZVvR2HPEtF/LE2UAKBWmPMWMO3esDJ+H/uITd6rpERg4d8X1R4NENkCx+Lop5oolXnvt5MSROK+2LNEZAN5Rc49e6VRVeNjJEzx3m8nlWvmda0fgYhg6UskEL33aH2rJzNIpV+zKvjl0HWzzytiDTWHYs8SkQ34eTnXWlTmeKFdDZOPVV1c+Pvd7F0i27GmY+n5NrGoHRFku2Ac6LHGURadt+tiuo0jIUOYLBEBSMnMM+v4n0a3hpfHg4HTj1r4gSeFKuX91V7HVTbtj8y9/CK11/O2XdBzJJH53PUhnDUTMPjo0nGYLJHb23f5DlrN2GLy8W/3isPDMRXh5fngQ87Hy/kvpaXDWqBRlRB89Wy82nZjn7dlZRQ4Tons6WjyXalDkIQ1c1VLmCs5jPN/whPZ2cwNZ8w6vrC49BMqSqOHxtm1qxOG319+BPWjgrF0WAu17YZMWHUUQgh89Kd5/5+ITHUmJQtbzqRadO4bPerZOBr52HuZj+IchckSuT1zb87KCsJ9OSQej9QKxapRrW0flJ21qxOGC9N64PiURIQG+ho89p9zt/H1jss69yXfybFHeORmhn273+Jzi2XevaLaQ22uQV/pr/RNtsVkidzewavmrWpeUFw6861WeCCWjWiJFrEV7RGW3Xl5eiDIz9vocQE+nvhYT+9b25nbkJ1XaOvQyI0UFpfghpljBjXPl7OWsZWkDoFMwGSJ3JolAyR9POU7802f+pWD9e7LKShGkYG79+nrT9sjJHITZ1O0awiZo6hY3j1Lnh6ssC8HTJbIrRlKAvR5/pEY2wciscGtqll8rrk9c0Sq8q2sUSb3niVrcUacYzBZIrdmyXiHYBMeXcmNwoo5OYUyv7Mnaf1sQUFGVQE+7l1befu521KH4BaYLJFbM7dnaeur7e0UibSsWWuXK6CTpYQQWLE3yar3GN421kbRyNPpm9Y9xiTTMFkit2bOkgENqwSjhhOvAWcNa0ZNuPtjELLchhMpFp8bHuSLI+92RaCve/csWXOjQ6Zz798ycnvm9Cx5erjuvUWcgQHexjBZIkv9aUWytO21Dijn5okSOY7rfvoTmeB6Rq7JxxaXuG5S0Di6PBY/F4+NE9qZfa7My9yQTDFRKsWOJcdgskRurd/CXSYfK/cpysZ0jotAHQsWIy3hbByyEH9zSn3cv5HF5/IxnGMwWSK3Zs5sOLlXCjbV/3U3b/mI7Lwit/l/Q86hpUwLweoz8GHLS3eQYzBZIjLRo42jpA7BIeJjKph9zt5LXKOKzGdpjaCVMlxiyF6sKftBpuNDXyITTO3bEE89HC11GA5hyUevlyfvu8h8+Sw7YTU+hnMMfsKRW8kvKsZnm87haPJdlJjx6GhIq+rwdpOEwJIP37R7+bYPhFzeplO3pA6ByCTu8elP9J+v/rmE/205jz7zd+KP4zelDsdJmZ8tjVl+yA5xEBE5ByZL5FaOJN9Vfn3x9j3pAnFi7NYnR9h9kePcVHVvEGnReZfS7ts4EtKFyRK5lc2nU5Vff771goSROC/mSuQIT3+1R+oQnMr8wc3Qv1lVs8+zdrkYMg2TJXJbnO6um4JdS2Rnls6CA4AZ/SyvSeTMPD0UeL1bXanDID2YLBGRmlrhrrn+HTmP7eduW3zu0y1ctyZRoB8nqDsrJktEpCbQ1wuH3+kqdRjkwradSTV+kBty90WBnRmTJSIjxnWqJXUIDlehnI/UIZAL+273ValDcFrVKgZIHQLpwGSJyIgJXetIHYIsFRSV4J9zt5FbUCx1KOQiBsSbPwCayBaYLBEZwQHPptEcMD99/Wk8+80+jPvxsEQRkTPKziu0+Nx3ete3YSTOiR83zonJEhHpNKp9DbOOX7ZH/dHKkl1XALBKM6mbuu6U2eeEBvpiZv+HEOTnbYeInEtEkJ/efdtf74CGVYIdGA2VYbJEbsOa6cruyMPMW9w/jj2oiM5Hb6TPLguKUR54uwsGuMnajJ8OaKx3X/VK5RBfvaIDo6EyTJbIbRxKuit1CLJi7tOAYP8HM3kOXL1j22DIZVzLyJU6BKcWrWeA929j2wAAXu9WV6t36cAV9ett9cFr+OXQNfsE6KaYLJHb6L9wl9QhyIq+jiUfPQsK56j0JmXlFqnt03xER+5JdbkhU73SubbtA5Ghh6qWBwCU8/XCy53U/5/M3HBW+XXynRy89tNRTFx1FAVFJY4M0aUxWSIyoPdDlaUOQTIKPX1LZ6Z2x9S+DbW277qYjuISgeISgVUHktX2vb32hF1iJOeXlJ6DVQeSkVtQjL7zd5p9fp8mUXaISt40RxTkFz9Iinp//q/y66KSEg4/sBFWwCK3UGLh0iZ+3p42jkQ+dPUsPd0iGh4eCgxpVR1ZuYX45K+zavtzC4vx474kqyo0k2tp98k2AMCCbaavxfjLmAQ0qhKC7LwiVGTNLy3BGpW+j6r02GXmPpht+Mexm5jx5xnMe7opEmqFOio8l8SeJXILWyysGOzO68fpKpnwfJtY5de6BoALIbD++E2t7URX0nNMOq5eZBCaVasAb08Pt02UTrzfDV89G49FzzQDACTUrKS2v7XGawCYueGM1rbXVx/DnfsFGPX9QfsE6kaYLJFbyMgpsOg8d06WhibEaC2/UCciSPm1rp6nkhIgO69IeweAS7fv2TQ+cn63svLMPufr5+LtEIm8BPp6oWv9CHRvWBm73uiEpcNaqO3XdSOz4O+LyMzRXcPK05PFm6wlu2RpwYIFiI2NhZ+fH5o3b44dO3YYPH779u1o3rw5/Pz8UKNGDSxatEht/8mTJ9G/f3/ExMRAoVBgzpw5doyepGJp0lPsxs/7K5bzwZ43O+vdr+vjd/SygzifqjspeuPn4zaKjOTiwz9Om31O1Qpc7kNVVHl/eOmZVKHp47+0e5cAwN+NhxPYiqySpZUrV2L8+PF46623cPjwYbRt2xY9evRAUlKSzuMvX76Mnj17om3btjh8+DDefPNNjBs3Dj///LPymJycHNSoUQMfffQRIiMjHdUUcrCiYstmhdRT6UlxRx4Gbkh19SztvqS/hk52vu4eJ3JdGffN69Fd+UIrO0XiHlbs1f23kP1K1pNVsjR79mwMHz4cI0aMQFxcHObMmYPo6GgsXLhQ5/GLFi1CtWrVMGfOHMTFxWHEiBEYNmwYZs2apTzm4YcfxieffIKnnnoKvr6+jmoKOViRBT1L3p4KjGxnXhVrV+NpIFvSN1tOn0Bf3t26G3MT5JY1tMfikPV4o2I92SRLBQUFOHjwIBITE9W2JyYmYtcu3fVzdu/erXV8t27dcODAARQWWr4+UX5+PrKystT+kXMz5zHchC51cGFaD5yf1tOtZ8MBgK+X/vabu4bV/isZXPrEzRy1oK4SmaZmWDmTj83OK0Jqtvnjx+gB2SRLaWlpKC4uRkREhNr2iIgIpKSk6DwnJSVF5/FFRUVIS0uzOJYZM2YgJCRE+S862j3K8MtZYbFpydL5aT3wSpfaJo8RcAeNqoTo3N6nSRWz32vk0gNWLaRKRKWWPN/C+EEqWkzbYqdI3IPs/iJozgIQQhhcFV7X8bq2m2Py5MnIzMxU/ktOTjZ+Ekkmr7AYP+7X/SxfE5/taxv+SGm5gE71wtW2hwVZ9th60faLVsdEzs/cSRWDW1azUySuKaq8v9QhuBXZFKUMDQ2Fp6enVi9SamqqVu9RmcjISJ3He3l5oVIly5+N+/r6cnyTjHy26RyumljjxdAYHXfVt2kVNKoagmo61qxqVycM/5hZgFK1aB65rkIzJ1VoLuFBhvGjyrFk07Pk4+OD5s2bY9OmTWrbN23ahISEBJ3ntG7dWuv4jRs3Ij4+Ht7e3naLlZzLptOmjZNpXDXEqh5HV1YzLBDeOh5N/m9gE7Pfy9yB4SRPx65lmnW8r5ds/hw5BX5WOZasfjsnTpyIr7/+Gt988w1Onz6NCRMmICkpCaNHjwZQ+njs2WefVR4/evRoXL16FRMnTsTp06fxzTffYPHixXjttdeUxxQUFODIkSM4cuQICgoKcP36dRw5cgQXLphemp+cm6mlkno2ct914CxlyQB4fsa7hwFf7Dbr+ApuWq3bkc6kcDKSpWTzGA4ABg4ciPT0dHzwwQe4efMmGjZsiPXr16N69eoAgJs3b6rVXIqNjcX69esxYcIEzJ8/H1FRUZg7dy769++vPObGjRto2rSp8vWsWbMwa9YstG/fHn///bfD2kb2Y+rYibKxOWQ6SxKfy2n3bR8IyVqDqGCpQ3ALs/46i6+fe1jqMGRJVskSAIwZMwZjxozRuW/JkiVa29q3b49Dhw7pfb+YmBiuyuziTE2WOAPOfJaM8dpx3vKZqOSa3HhVIYfydfNSKNbgXwdyeUyG7ceTz9TIBkqYLTlEFidXWIzJErk8d17fzd6YK5EtFJVYthwRmYe9upZjskQujzet9sMZOaSLuT1FvEbJ2TFZIpdnymO4xPq6a3WRfaw6wEKurmzjKd2rKujDniXLNI4ub/Y5HJZgGSZL5PLS7hlf+dzdF8y1hiUf2JNWH7N9IOQ0rt81bx2yYhOXIyJ1tcMDzT6HvXiWYbJELi2/qNik48ytNkwPeLOUMGkoMvN68uDvkMP8euS61CHIEpMlcmmmLq1RxDtbi3l5mv+Hzot/HF1akZndF58/3dT4QaQl3IL1GSeuOmqHSFwfkyVyaQVFpt3hmrvoJz3wUsdaevf9+EIrPNe6utZ2XUunkOsw5+bjlc610bRaBTtG47rGGLj2yLb4iUUuzdRkydebl4Kl2tYOwwd9Gmht9/H0QKsalfBWr/pa+ziJznX9fTYVn20+Z/Lx4cFclNxSgb6yqystW/wLQS6t0MQ73NY1Ktk5EtdWrWKA1rYGVUqXsPDRsUBqCWfkuKTM3EIM/Xa/WeewV5fkgMkSuTRTB26zXpB1PHT8//uo30N6j88rLEFeoWmD70k+Mu4bn3mqqZYFM7rogZc78VGcIzBZIiKr6UqW6kYGGTxn5wVWE3YlPx1IxsAvd5t9XkLNUDtE4z5eTayLRxtHmXUOay2Zj8kSuTRTPhO8LZjNReqiyvsZ3L/11fZa23QlWCRfr68+hltZ+VKH4ZbMnU3IZU/Mx2SJXJopY2N+fKG1AyJxbTXCDD9K0bWfuRKRfbzT+8GkCl1jBlOzmdSai8kSubS3154wekzz6py27AiayRF7llwHH+s4lziVR+DbXuugtb+cj6cDo3ENTJbIZc3dch7Hr2dKHYbbeLe3dokAVZqpEZMl12HqrFOyn/7Nqiq/9lQp+urvrZ0YrT+RgtwCTrAwB5Mlckl/Hr+J2ZtMr/VC1hv2SCyGtYnVu19zxiGLeLuOzadvSR2C2+taP1z5dXxMRbSIqYgB8VV11mL6/egNPDxtM5d5MgMrWpHLuZp+Hy8uP6R3/xdDmqNZtQr459xtdGsY6cDIXN+k7nWRkVOALnERWvve7BmHqetOKV8fvZaJhFqcCeUKxhi43gwJDWRBSlvp1iAS4zrVQpNq5eHpocCq0Q/GYnp5KLSWoLmXX4T0ewWIDDE8OYNKsWeJXM4NIyueFxSVICzIF/2b677rIsv5eXvis4FN0Ouhylr7nk+IUXv98YYzDoqKnNUvLyZIHYLLUCgUmJhYF53qad+o6CoaC6g/riPDmCyRy5m46ojB/fkmLoFCtsWV5UnVB30aoFol3X/Eybaq6kmWWEnfdEyWyOXczDTcs5RfxIGNUqlnpFAluYeRbWMxuKX2AstkHx/3b6Rzu+ajOdKPyRK5nSbR5aUOwW31bVpF6hDICXSqF8FHQA5UOcRf5/a31xzHJ3+dQYqRG0xiskRuqEFUiNQhuC32+hMACPAXwRlsO3sb87ddxKjvD0gditNjskQu5eLte1KHQAZojpHgYrruqUFl3rA4k6PXWI/OGIuSpUOHDuH48ePK17/++iv69u2LN998EwUF5q86TWQrH/x+yvhB5DQ+33pe6hDIwQ690xUhAd5Sh0FkFouSpVGjRuHcudKCf5cuXcJTTz2FgIAA/PTTT5g0aZJNAyQyBzv3nVuJxoDSvZfuSBQJSaViOR+pQyAdftiXJHUITs2iZOncuXNo0qQJAOCnn35Cu3btsGLFCixZsgQ///yzLeMjMgvXqHJugX7qda244ol7GdKKM+Cc1eRfjhs/yI1ZlCwJIVBSUlqrZvPmzejZsycAIDo6GmlpabaLjohcytMtqqm91lwCheTnzn3Th174cwFXybxjZO1GMsyiZCk+Ph4ffvghvv/+e2zfvh29evUCAFy+fBkREdrVQ4mIgNIK36o4e1yebtzNxb7LpY9QD13NMPm8+OoV7BUSGVHen+PErGHRWg9z5szB4MGDsXbtWrz11luoVasWAGD16tVISGD5epLO+VuGZ8ON61TLQZGQKdLucUKIHCV8tBUAsPalNiYd36tRZfRpEoWu9XkzLRUPzn23ikXJ0kMPPaQ2G67MJ598Ak9PdrOSdFKyDBdXm9C1joMiIVNcSL2HP47d1LmWHDkn1YHAh5My9K47purTAY21ehXJsbo3qIwJOCp1GLJlca559+5dfP3115g8eTLu3Cntjj116hRSU1NtFhyRrXGMjPN5aYVlK9aTNN777aTya29P0/6EMFGSHseLWceinqVjx46hc+fOKF++PK5cuYKRI0eiYsWKWLNmDa5evYqlS5faOk4io4q5zhGRXRUVl6BAZSFqH08Pzmgkt2BRz9LEiRPx/PPP4/z58/Dz81Nu79GjB/755x+bBUdkjsLiEoP7NWdiEZF5Tt/MVnu9fO9VfPnPJYPnfP1svD1DIhu6nHZf6hCclkXJ0v79+zFq1Cit7VWqVEFKSorVQRGZ449jN7Hp1C2jK2hrFkQkIvMUa9QxO3otE3uMFBYt52vRAwyyg871wg3uX/T3RQdFIj8W/Rb7+fkhKytLa/vZs2cRFhZmdVBEpsq4X6Ac8/J8mxiDxxrreSIiwyx51F3A685pzH26KRq895fUYciSRT1Lffr0wQcffIDCwkIApYNmk5KS8MYbb6B///42DZDIkKy8QuXX3+68YvDYQvYsEVlFcyFkU6iOcSJpGevl4/gz/SxKlmbNmoXbt28jPDwcubm5aN++PWrVqoWgoCBMmzbN1jES6WVO9eBCfmgTWcWiniVed7LB2cL6WfQYLjg4GP/++y+2bt2KQ4cOoaSkBM2aNUOXLl1sHR+RQY8v2GXysXwM5xxWj26NJxbtljoMsgDH/bk25kr6mZ0sFRUVwc/PD0eOHEGnTp3QqVMne8RFZHN9mlaROgQCUCs8UGubEIJ3tTLwvy3nzT6nS33Dg4rJsXZM6oitZ1LV6mWRcWY/hvPy8kL16tVRXFxsj3iI7OZRVol2Ch46FoTbdpbFbOVg72XDM9908fViMURnEl0xAG1qVdK5b/WBaw6ORj4sGrP09ttvq1XuJpKCuY/V2HPhHDx1/Bwu3WZ9F2enOpmC5E3fOP2C4hKk3ct3bDAyYdGYpblz5+LChQuIiopC9erVUa5cObX9hw5x+QKyv+92XZE6BLKAp46eJS7F4PzmbDL/ERw5J0Mjz4Yt2Y/fxj7isFjkwqJkqW/fvjYOw3QLFizAJ598gps3b6JBgwaYM2cO2rZtq/f47du3Y+LEiTh58iSioqIwadIkjB49Wu2Yn3/+Ge+88w4uXryImjVrYtq0aXj88cft3RSy0vlb90w+NjzI146RkDk8dPQs8VGN87uVbXiRapIPQyUgjl3LdGAk8mFRsvTee+/ZOg6TrFy5EuPHj8eCBQvQpk0bfPHFF+jRowdOnTqFatW0l7K4fPkyevbsiZEjR2LZsmXYuXMnxowZg7CwMGU9qN27d2PgwIGYOnUqHn/8caxZswYDBgzAv//+i5YtWzq6iWSGw8kZJh/73bAWdoyEzKGrZ0kBIK+wGDfu5qJGmPYAcCKyHQvKZbk9i8YslTl48CCWLVuG5cuX4/Dhw7aKSa/Zs2dj+PDhGDFiBOLi4jBnzhxER0dj4cKFOo9ftGgRqlWrhjlz5iAuLg4jRozAsGHDMGvWLOUxc+bMQdeuXTF58mTUq1cPkydPRufOnTFnzhy7t4esc86MniVvT45XchaeHgr46FitfuCXe9Dp0+3459xtCaIich+VAn0M7meJCG0WJUupqano1KkTHn74YYwbNw5jx45F8+bN0blzZ9y+bZ8PuoKCAhw8eBCJiYlq2xMTE7Frl+5aO7t379Y6vlu3bjhw4ICy+ri+Y/S9JzkHc4pRlmKy5EzOTeuh9loAOJp8FwCw4O8Ljg+IyI2EB/mpvW5arbza69+P3XBgNPJgUbL08ssvIysrCydPnsSdO3eQkZGBEydOICsrC+PGjbN1jACAtLQ0FBcXIyIiQm17RESE3sV7U1JSdB5fVFSEtLQ0g8cYWhA4Pz8fWVlZav/IscYsPyh1CGRD+UUPSpEYW5iV5KNaxQCpQyATaPYknUnJligS52VRsrRhwwYsXLgQcXFxym3169fH/Pnz8eeff9osOF00p38bK2an63jN7ea+54wZMxASEqL8Fx0dbXL8ZL2CohKz/6BasqYVOc5ba05IHQLZwfIRHPfprFTHDhZrfD4u/PsiTlznQG9VFiVLJSUl8Pb21tru7e2NkhL7LCkRGhoKT09PrR6f1NRUrZ6hMpGRkTqP9/LyQqVKlQweo+89AWDy5MnIzMxU/ktOTrakSWShAV+Yv1RGUTGTJSJHi2bPktOqGfag5I+uz8fXfjrqyHCcnkXJUqdOnfDKK6/gxo0HzzWvX7+OCRMmoHPnzjYLTpWPjw+aN2+OTZs2qW3ftGkTEhISdJ7TunVrreM3btyI+Ph4ZbKn7xh97wkAvr6+CA4OVvtHjnPkv7Et5rBkAVCyr4Hx7JGVkz+O3ZQ6BLKhL4fEo3O9cPz8YoLOz8czKdnIK+RKHWUsSpbmzZuH7OxsxMTEoGbNmqhVqxZiY2ORnZ2Nzz//3NYxKk2cOBFff/01vvnmG5w+fRoTJkxAUlKSsm7S5MmT8eyzzyqPHz16NK5evYqJEyfi9OnT+Oabb7B48WK89tprymNeeeUVbNy4ER9//DHOnDmDjz/+GJs3b8b48ePt1g5yvEI79XiS5TrF6V8zTPCxqSy93KkWBsRXlToMMkFMaDksHvowmlevoHcB3dvZrOZdxqI6S9HR0Th06BA2bdqEM2fOQAiB+vXro0uXLraOT83AgQORnp6ODz74ADdv3kTDhg2xfv16VK9eHQBw8+ZNJCUlKY+PjY3F+vXrMWHCBMyfPx9RUVGYO3eussYSACQkJODHH3/E22+/jXfeeQc1a9bEypUrWWPJSZ26YdlgevYsyUtxiYAXyz3IToi/N+JjKmIV1xiTFU8P3f0mXCHqAYuSpTJdu3ZF165dbRWLScaMGYMxY8bo3LdkyRKtbe3btze6/MoTTzyBJ554whbhkZ1NXnPcovNqhJYzfhA5jaISARb1lh8hWKRDjrx0FIoFeJOpyqLHcOPGjcPcuXO1ts+bN4+Pr8i+LHw8UymQy53ICT+k5UlAsFyADOkrUlnIiTFKFiVLP//8M9q0aaO1PSEhAatXr7Y6KHJfmbmFmPXXWZy/lY2CohLcyy+SOiSSQEERx5jJkRClY2G+Hfowfhur/TeCnNPUPg11budNywMWJUvp6ekICQnR2h4cHKws9khkifd/O4l52y6g62f/oMMn29Dwvb+QnVeo3M9L13UYelzz4R+nHRYH2U7Z39aO9cLxUNXyksZCptNX4mHv5XQHR+K8LEqWatWqhQ0bNmht//PPP1GjRg2rgyL3dVilLMCNzNJVzo9bWRytVY2KVp1P9tGmVqjefT8f4gBhORK8nZGt+YOaaW1799eTEkTinCwa4D1x4kSMHTsWt2/fRqdOnQAAW7ZswaxZs/C///3PpgES6Vp01VSvdK6NIa2r2zAaspVyvlbNLyEnxIoP8hXgwxkVhlj0aTVs2DDk5+dj2rRpmDp1KoDSafqLFi1Sq3NEZC5d9XW8VZIlcz+MJ3StY21IRETk5iy6Zc/NzcVzzz2Ha9eu4datWzh27BjGjh1rcIkQIkup1tsxp5t/8XPx9giHyO2YWiT0mZbsxZUrPkI1zKJkqU+fPli6dCmA0vXgunTpgtmzZ6Nv375YuHChTQMk96JrqqqXnoJphnSJC0fnOCbvRLZgao9uSID2mqFErsCiZOnQoUNo27YtAGD16tWIiIjA1atXsXTpUp31l4hMUVwicP1urtb2P449WIPwxHXTKngXccqr7HHasvO4k1MgdQhkZxxvZphFyVJOTg6CgoIAlC46269fP3h4eKBVq1a4evWqTQMk9/Hrkes6t8/degGAeX887+YUGj+IJBdoYJD366u56rmzGPHdAalDIDsLD/KTOgSnZnHpgLVr1yI5ORl//fUXEhMTAQCpqakIDg62aYDkPiauMvzHsbDY9EKFb/eKszYccoCVo1rp3ffLId3JMzmWEAJHVEp6kGtqVDUED8dUkDoMp2VRsvTuu+/itddeQ0xMDFq2bInWrVsDKO1latq0qU0DJCrz/u+m1/yIj2FtJTloEKVd3FaVakFSksbfZ29LHQI5yE+jE9C/WVWpw3BKFiVLTzzxBJKSknDgwAG14pSdO3fGZ599ZrPgiFT9sC9Z6hDIwaavPyN1CG7v9r18k477qF8jO0dCjhDoy3pLulhcFS4yMhKRkZFq21q0aGF1QEREZQ4nZUgdApmob9MqUodANvBSp1r4bveDscdCCCgUhhYncg+Wl0YmsqG8wmKD+29nm3Z3S67F04Mf0lJb9PdFqUMgB9Ic6P3Pea73CjBZIoloFrn7Zudlg8fPWM+FVV3d2pe0V6lnsuR4uQXF+OPYTWT9N17sUtp9k85j54Nr2nQqReoQnAKTJXK4rLxC1Hn7T8R/uBm5BaU9SsZ6jtLvs86LqzrxfjfsmdwZTaLL440e9dT2sfvf8d5acxwvrTiEl5YfMrlyNwAowJ+VK+IajqWYLJHDPTRlIwqLBdLu5WPKb6Uz3Ix9JpvTw/BY4yhrwiMHC/T1QmRIade/Zi0tL/YsOdwvh0tLNuw4n4bYyetNPo8/KtexYkTLBy9YrBIAkyWS2Fo9hSg1bT2TavJ7Pt6MA03lqkhjuRtP9izJBnsBXUdokK/UITgdJkvkcgqLTC9eSc7FU+MTyYJlAYnIShwrqI0fReRydC3GS/LgrZEtXUnLkSgS9/TdrisWn8s/sK5D9fE3P01LMVkih7qfX6Rz+xIrPqQ1CV7esqWZLKVk5SG/yHBZCbKd934zvUq+Ki6T4Vo8VB6pfvnPJey/ckfCaJwDkyVyKM0lS+wxzKFr/Qjbvyk5RJ8m2oPz8/lY1elxxXrXovm5/OSi3dIE4kSYLJFDrTpwze7fw9eL5frlqlKgLxpHl1fbJpgrOaV+KhMpmCuRq2OyRA6z+qD9EyWSv+Wq05YBFJUwW3JGft6eaBAVDEA9cSJyRaw2RQ7z2k9H7fK+gb5eeKJ5VSzZdYUrZruAQI0ieJq1l8g5CAGsGtUap29moVk1jllyJSH+3lKH4HTYs0SSyiu0Ta/BW73isGpUa8zgyucuoW3tUOXXhSUC4388jMfm/YvUrDwJoyJVjauGoJyvF+JjKsKDM+FcSpCfdrI0+ZfjSL/nvmt0Mlkih9h1Uf9ijBk2WMrE29MDLWIrwseLv9KuprhYYO2RGzh2LRMtpm/Brgtc2FNq4zrXxpPx0VKHQXZUPkA9YfphXxLeXntComikx78s5BC/HNJfqfvlHw47MBKSgxqh5ZRfa45ZGvT1XkeHQxqebhHNukouTrOMBwCcvpklQSTOgckSSe5fK3sKzFnsk+RhYmJd5dcsMuoYJRwbRip0LTV0JT0HJSUCS3ZextR1p9zqs5fJEhE5nRB/b4QGlq5P9dtR09YPJOv8uD/Z5GPd6G+k2/L30V2CJSuvEFN+P4XF/17G8euZDo5KOkyWiMgplS25MH/bRa197AWxvfXHb5p8LP/vu77Pn26qc7vqgslrDrvPjQyTJXII1lgicxkaE1PI2ktEdtWwSojO7aqP3r7decVB0UiPyRIROaUbmbl697H2ku2Zs/SQO41VIXVnU7KlDkESTJZI9j7o01DqEMgODP09LmKyJKmy8WTkfgZ+uUfqECTBCt4kW7Gh5bB0WAtEVwyQOhRysCLOkJPM/3WvBz9vrr9I7oXJEsnW1lfbqw02JPfB9eJsb8d500p4vNihpp0jIXI+fAxHdrf3UrrN3/OhqiFMlNwYxywR2d/4LrWNHuMuY5iYLJHd2eMZty+XNXFre+yQgBORuvFd6hg9ZunuK/YPxAnwLw4Ryc6ElUelDsGlnElx32UsyDq6lkVxRRyzRLLEmctEtvHZpnP435bzUodBMuUuvfzu0UpyOcyVXN+jjaOkDsHlbT1zi4kSGdSvWRWD+5ksERFJqA+TJbs6ePUOhi05IHUY5OSmP97I4P4AX/d4QMVkiWRp4MPRUodAdubpydmO9tR/4W6pQyAZMNZz5Okms5JlkyxlZGRgyJAhCAkJQUhICIYMGYK7d+8aPEcIgSlTpiAqKgr+/v7o0KEDTp48qXbMl19+iQ4dOiA4OBgKhcLoe5JzeLJ5ValDIDvzMrA2HMAlN4gcwViJlmI3uQ5lkywNGjQIR44cwYYNG7BhwwYcOXIEQ4YMMXjOzJkzMXv2bMybNw/79+9HZGQkunbtiuzsB3UhcnJy0L17d7z55pv2bgLZEGssuT5DC+kCwK6LLB9A5Aij2tXQu89dap7J4mHj6dOnsWHDBuzZswctW7YEAHz11Vdo3bo1zp49i7p162qdI4TAnDlz8NZbb6Ffv34AgO+++w4RERFYsWIFRo0aBQAYP348AODvv/92SFuIyDReHur3cl3rR2DTqVvK15fS7qNNrVBHh0Xkdib3jEOrGpXw/JL9WvvcZekhWfQs7d69GyEhIcpECQBatWqFkJAQ7Nq1S+c5ly9fRkpKChITE5XbfH190b59e73nmCo/Px9ZWVlq/8i2Zg9oLHUIJDHNnqWXO9VSe33jbq4jwyFyayV6HrcVu8nSQ7JIllJSUhAeHq61PTw8HCkpKXrPAYCIiAi17REREXrPMdWMGTOUY6dCQkIQHc3BxrbWr1lVbHm1vdRhkIQ0k6WHqpZH4+jyytcL/77o4IiI3JeHnsfiRW7yGE7SZGnKlClQKBQG/x04UDq1VdcYFSGE0bErmvtNOceYyZMnIzMzU/kvOTnZqvcj3cKCfKUOgZzMS1zE1SbyCoulDoFkxttDd7qQX+QePUuSjlkaO3YsnnrqKYPHxMTE4NixY7h165bWvtu3b2v1HJWJjIwEUNrDVLlyZeX21NRUveeYytfXF76+/ENuihIL7jra1uY4FFLv9n+jRz0AHNhvK4v/vWzReaPbM1l1V0F+6ulC9UoBuJqeg8X/XsY7vetLFJXjSJoshYaGIjTU+B/G1q1bIzMzE/v27UOLFi0AAHv37kVmZiYSEhJ0nhMbG4vIyEhs2rQJTZs2BQAUFBRg+/bt+Pjjj23XCDJo58U0s8/5dujDdoiE5EZ1iMSwNrEA3Gfmjb1dTb9v0Xk1wsrZOBKSi4eqhqi9vpqeo/z6xPVMNKwSonmKS5HFmKW4uDh0794dI0eOxJ49e7Bnzx6MHDkSvXv3VpsJV69ePaxZswZA6R3o+PHjMX36dKxZswYnTpzA0KFDERAQgEGDBinPSUlJwZEjR3DhwgUAwPHjx3HkyBHcuXPHsY10Udl5RWaf4/Xfwoz6+hAqlfOxIiKSC9U6St7/FajUN8iUHMOSnmJyDYZ6dUd9f9CBkUhDFqUDAGD58uUYN26ccnbbY489hnnz5qkdc/bsWWRmZipfT5o0Cbm5uRgzZgwyMjLQsmVLbNy4EUFBQcpjFi1ahPfff1/5ul27dgCAb7/9FkOHDrVji8gYfRfn5083dXAkJAXVP8tlvwtMlmxDofdWxDB3KUBI5rnuBjNTZZMsVaxYEcuWLTN4jGZFX4VCgSlTpmDKlCl6zzG2n6RTzsdT53Z3mX3h7uqEB2lt449eWoFusg4YkSb+5pNdmXsjOm/Qg14jhUKBFzvU1JoiHhHsZ4vQyMmFBHhj/1td4Of9YLRAeX9vCSNyHYeTM8w+p0+TKPRqVNn4geSyFArzP9NdhSzGLJH85BUWW7R2V++H1FeaH9+lttYxdSO1exzINYUF+SLI70GCxJmStnHu1j2zjm9XJwz/e6qpcjwhuSfVj/T6lYM19rl2FsXffLK5G3dzUe+dDRi9zPpBf75enqhWMcAGUZErYOkAaYQFslQKAe3rhAEAaoRqz4rcd9m1J0UxWSKrnb6ZhXE/HMaVtNLpyD/uLy3S+dfJW5iw6ojV7z+hq3bvErmvOQObKL929btZZzG5Zz2pQyAn8NnAJni9W10sH9kSlQLVZyXfzS2UKCrHYLJEVuszfyd+O3oDw3Qsslhgg+quls7cIdfUoW6Y8msO9neMUPYsEYCK5XzwUsdaqBzij+mPN1LbV1bew1UxWSKrlSVEl9IsK3RnDJ+8kCrVNeOuZ7j+lGUiZxTtZsMjmCyRw3HNN7KGt8og4/d+OylhJM4vt6AYM/48jSU7LVveBAB6P8QZcKRb2RgmwPVnybF0ANncumM3DO5fMaIlbmXl45nFe9W2j+tUS+fxqoN6vxjS3PoASdZUe5aS7uQYONK9XUm7jw6z/la+rh0RhDa1zJ9NOKkbxyuRbi1iK2L7udsAgJwC116cmT1LZJWzKdla2y7dNv447hEzpoB3rBuGQF8vtK5RCd0aRJoVH7keL5VkydXHSViqpESoJUoAsOdSOgBgxd4ks96rWiX3etxCplOdYPHyD4cljMT+mCyRVQ4lmV/cTh99vbhBft449E5XrBjZ0mbfi+RLtaeRg/91++Xwda1tn28tXf/yzTXHjZ7/ere6CPLzwvfDW9g8NnIdmqU8Mu4XmHyu3GayMlkiq2j+qTp1I8voOZZcIj5eHqyxQ1r0/UrczMzFou0XcTfH9A9vV7L51C2rzh/SujqOvpuItrXDjB9MbuuxxupFhJtO3YS8QuOP495acxydZ29HToH5C61LhckSWUXzj1XPuTukCYTckr4E+qkv9+CjP89g0upjDo5Iemn38rHhZIrOfflFpo8r8fDgzQkZpmtG3K2sPKPnLd+bhEu372PdsZv2CMsumCyRVSx5DFJdzxgImfXKkhNoVaOizu1X00sHfv9z/rYjw3EKz3+rXe+szKcbz5n0HgHeuhexJjLG04wkW06P4pgskXXMzJXOTO0OXy9+EJN1RrWrAUB9sLcuHm746Pb49Uy9+9boGMukqUp5f64BRxYz55qb9sdpO0ZiW7wiyKH8eMdKNlB292qsgre7pUrGxouY8v8jyI8VZchyxUauyV0X0pRfZ+UV4UyK8XGuzoDJElnFln+MhEVDv8kdlSVLxj6Y3a1naf1x+YwBIdf01Y5LBvcP+lq9vt4nG87aMxybYbJEVrHlDLXnWsfY7L3ItZUlQUt3X0WJoYTJvXIlo2sxpmbnOygScldLd1816/iCYuvXD3UEJkvkNMKD/aQOgWRCtUfpWkYuJv9yDL3m7tCa7ZWdJ5+pyc5CRmNuyQXsOJ+GH/aZVyhVCkyWyCq2unH/5ImHbPRO5A5UH9kqFMAP+5Jx8kYW/jmXpnXsFTst8OyqOtYLlzoEkpGB8dFWv8fkX4wXSpUakyWyyJ/Hb6LX3B24mm6bP0SJXMaEzKCv515X8m7OVGa5s0Wn0PgutW3wLuQuJnStY9P3yyssRqETPprjtAcymxACLy4/BAA4aULFblO42ThcslJxyYMPU9XHRp4eCuy/ckft2MzcQlh/7+s+OGOVzFGhnLfN3iuvsBiN39+IiuV8sHtyZ5u9ry2wZ4nM9oeFM27GdVa/Y+3XrIrya+ZKZA7VG0/VcUoKBfDdritqx/ZbsMtBUUmP1xE5mq+XJ55oXtUm73Xp9n3kF5XgZmae4YkbEmCyRGbbf/mO8YN0qByiPoD79W51bREOuaESle6k7PwHg7i9PDy0llCQy2wbW3CuPy/kLj7s21DttaE13xpXDdG5Pa+wWO0JQ2GJc123TJbIbN+ZOTW0TJHGHy3OuiFLqSZLb685ofzaw80/0d799YTxg4hszM/bE6+oPDn4Yrv+WkvVK5XTuX3ZHvW/K4XFzvUHws0/WsiRNH/5y/k8GDLnzeUVyAyqpQNO3Xwwbm7U9welCMdpONsfGHIfbWuHKr82VM/rt6M3dG6/c79A7bXmzbXUOMCbjFp37AY++P0UFj7TDHGVgy1+H83HcCEB3vjfU03g5eHBQaVklhI93ZKsq0QkDdX1BL09dY+eO38rW+/5ft6eatd12r0CBPl5O81sVt7Ok1FjVxxGanY+nv92P9742fJ6GN10lAfo06QKej1U2ZrwyA0ZW+bEGZ2+mYVnv9mHY9fuSh2KXuM61ZI6BJIp1UWt9SU4d3MLlV/7eKmnH37eHihS6RntMns7nv5yj42jtByTJTJZVl6R3i5UU3g4yR0CyZ+T9dCbZPDXe/HPudt4bN5OqUPRqUZYOUxM5KQLsozqUAovPZ/1qtuFRu+wv7cnijQGde+7YtlkIntgskREsqPvMZwzUx2ToVkLyhZ2XdSuXm6OZcNb2igSckdenqo9S7pTCy+V7cMeiVXbV1AskFeofReUnVeotU0KTJYIJSUC1zJypA6DyGRd4iLMOv5+vnONZXp28T6bv6e1S0ZElfe3USTkjrxVEiEfPWOWVG9yKpXzwQd9GihfT113CoO/3qt1zpTfTtkwSssxWSJM/uU4Hvl4G346kKy1T7OrlMgZ9Gxk3vI4X+3QP5XZETQL7OUWFus50vFe71YXa8YkSB0GyZxqjaQA3wdzx2ZuOIMOn2zD3ZwCtcdsRSUCbWuHGX3fbWdTbRqnpZgsEVb+lyTN2Xxea99fJ1McHQ6RUQoz18dJycyzUyTA7ex8TFx1BAev6n+05oixF5be13StH4Gm1SrYNhhyO0UqNwT+KrObF/x9EVfSc/DdrqtqpS2KigViQ3XXXFLl4SRrYTFZIqXrd3O1tl1J5+M5kr8Qf9utX6Xp7bXH8cuh6+i/cLfeY3IL7N+TJCys310zLNDGkZA7ql4xQPm1rsk8RSUlarPdTF0s11lK8DlJGOQs7uYUGD+ISGYCfOxXUu7i7fsqX98z+bzMXOkHrgb7eTlNHRuSNw8PhfLxuK513UqEUFvCxN/HtNp6nuxZImegOSZJ12wEImfUJS7c5GM/23zObnGo/mH4Xs9SQLp6fab9YduBq062lBa5obJHZrpmqwoBtZ6l51rHmPaeTpLMM1lyc5tPqw+e05zxyfHd5KwGxEebdfzeS+l2iaNY5SIx5yb45I0s4weZaMpvJ3U+RidypLJkSVfR2OPXM5VLmDwcUwHl/hsEPqm74dpeztLzyWTJzR1JzlB7zbWlSC5M+RAtH/BgrNJAO1UDvqoyrk/fYFQFtLcbi3/B3xfQcdbfuG1gnS0AOJSUgSW7rhgPlMjOyv56nNJxI7DjfBoK/0uiVOstGbvpcZZ1Q50jCpKMZs/R0G9sX/8FAB6pFWr8ICIzmNI9fzfHvuOCNOs3mXMXbOzYmRvO4nLafczfdsHgcTPWnzb5e2oay+VNyIb+PH4TAPDL4es699/4r/dT9XdfX7VvU/c7CpMlUnM+1fQBquZY8Ewzu7wvuS9jU4odUTuoSKMnVl9IusYsHb+WadL3KDAya2j/lQyD+w0Z2baGxecSaVItH6BrkPdHf54BAPx74UG1eWM3Dc2rO0dZCyZLMpOZU4iNJ1NQUGSb0Zz2fOhWTWUqaYC3aTMfiEyVfk//46nXu9V1SO0gzYGs5tSEKSoRNruOLWVuvSoiU9V4cz1OXDd+Q+ClZ2mUMqyzRBYZvHgPXvj+IOZt1S4gaQ+W1m4BgPmDHvQm8UOZbE11rTUA8FZZYmFE21jNw+2iWMcMiN+O3kDyndJxTBn3C7Dm8DW9s0z3XXaehUKJrBWv0QvU+/N/jZ7jpWdplDJX0u8b3O8oTJZk5sT10oFza47ofiZsrjv3tOsq2ar+i2p+xFSJbK1bA/UlT1QnJ/h6lfZk/vlKW7vGoDnr5+eD1zDuh8NoO3MbAOC5b/dhwsqjmLjqiM7zn1msvRaWphV7k6yOk8gRPny8odnnGBvAveN8GnIKpF/bkcmSm1upYz24Rdsv2vz7sGOJbC26YgCC/R4Um9RVpbtuRJDy69BAX5vHoFmFOFVj5tqx/8YlsX4ZuYPy/j52eV9nKOAqm2QpIyMDQ4YMQUhICEJCQjBkyBDcvXvX4DlCCEyZMgVRUVHw9/dHhw4dcPLkSeX+O3fu4OWXX0bdunUREBCAatWqYdy4ccjMNG3gpZTsWf/IViu0q/UsMVsiO5jerxGiQvywfERLLB3WAvHVK+AXlYHdqjPm9K2EbomTNzJxNf2+wSTo1VVHbfb99DFWVoDIkUydDbromeZqrxtWCVZ+/cWQ5pqHO8W4JdkkS4MGDcKRI0ewYcMGbNiwAUeOHMGQIUMMnjNz5kzMnj0b8+bNw/79+xEZGYmuXbsiOzsbAHDjxg3cuHEDs2bNwvHjx7FkyRJs2LABw4cPd0STrGLPAnS2+sWsHR6E8CBf1IsMMn4wkQV6PxSFXZM7o02tUDSOLo/VLyagmZ6B3ZEhfjb5nqnZeeg191+0/+RvvLjsoN7jfj50zaT3O5J81+gxb689jh3nb2ttH7Nc//d/snlVg+/5ejfDxQCJzGVqslS/crDa6+Yq12y3BpH431NN0KhKiHKbriKXjma/BZNs6PTp09iwYQP27NmDli1bAgC++uortG7dGmfPnkXdutoXvRACc+bMwVtvvYV+/foBAL777jtERERgxYoVGDVqFBo2bIiff/5ZeU7NmjUxbdo0PPPMMygqKoKXl/P+7xECyCsshp8dZpmp/sJb2oNVIcAbPl4e2PlGJ6dZ24fcU58mUfj1yA0cSrprk/e7kvagCKUtSm1sPnULTaLLq23LK1RfeHfZniQs25OEKx/1Um4rKCoxWDagefUK2Hv5DpLu6F4M29uGPW1EgOnruHlq/O692q0ugv290fuhKABAnyZV0DK2ElrN2AIAGPrtPvw0OsGuC2IbI4uepd27dyMkJESZKAFAq1atEBISgl27duk85/Lly0hJSUFiYqJym6+vL9q3b6/3HADIzMxEcHCwwUQpPz8fWVlZav+kcM9Gj8s0qSZL/55PM3Ckfo81Lv2l9/b0cJq1fcg9xcdUVH598KrlNYnK2PrXWdfd+JzNhme7Hr+WiTpv/2nwGAHgxxdaWRMakVk0kyB9NAtNBvt549XEuqir8hQiMsQP5f5bbPfcrXtGi7PamyySpZSUFISHay+aGR4ejpSUFL3nAEBERITa9oiICL3npKenY+rUqRg1apTBeGbMmKEcOxUSEoLoaPPWqLIVez3HVX3f3SaupzX98UZ2iYXIWqp3u5duW98TZOvxd7oqFG8/p/3ITdWHJi7CG1XeH3smd0a3BhHGDyayksk9SybecajeaN/N0Z657UiSJktTpkyBQqEw+O/AgQMAdH9ACSGMfnBp7td3TlZWFnr16oX69evjvffeM/iekydPRmZmpvJfcrL2jDJbOHcrG9vOpOJyWmmdiSKNmTf2eo5ryZ1zp3qmrwBP5Eiqv8+2SHRsfY+i625cGHj+fTenAHvNqM8UGeKHAB/tnnIukk22ZqS+pJKpS5io3rh7mvrmdiLpoJyxY8fiqaeeMnhMTEwMjh07hlu3bmntu337tlbPUZnIyNIaLCkpKahcubJye2pqqtY52dnZ6N69OwIDA7FmzRp4ext+Lurr6wtfX9tPQ9b0zb+X8eP+ZIxuXxNBfl5ag7o/23zOLj06C/6+iEnd65l1Dp+0kbNSvTu1xe+prXt0zV37aui3+83+HoaSLyJbMVaNu4ypPUv5RQ/G7km9RpykyVJoaChCQ40vsNq6dWtkZmZi3759aNGiBQBg7969yMzMREKC7vWfYmNjERkZiU2bNqFp06YAgIKCAmzfvh0ff/yx8risrCx069YNvr6++O233+DnZ5sZM7bg41X6i6ev7tGKvUlO8/iLpQHIWeUWPPjAdYZf094PVca6YzeVr3UlX2dSsvWeb8rsOUB9uSEiR7B1PqNamsOcRartQRZjluLi4tC9e3eMHDkSe/bswZ49ezBy5Ej07t1bbSZcvXr1sGbNGgClf7zHjx+P6dOnY82aNThx4gSGDh2KgIAADBo0CEBpj1JiYiLu37+PxYsXIysrCykpKUhJSUFxcbHOWBzJx0hlU2vpWujQUuxZImd1QGVQty16hax9/D2jn/oNjmYFY83H7ZZ4t3d9JNSspHytK2L2NZGtGbppfrtXHLw9FYivXgGBvub300idLDnv3HgNy5cvx7hx45Sz2x577DHMmzdP7ZizZ8+qFZScNGkScnNzMWbMGGRkZKBly5bYuHEjgoJKR9wfPHgQe/eWLjdQq1Yttfe6fPkyYmJi7Ngi47y97JssrTZSB8acrnspp3QSGTKuUy38fvQGANt84FqbLAX5qV8r5TT+cBTYIFl6vk2M2h+upx6uhl+P3FA7hk/myJ5Ue1CHPxKLEW1rYPgjpWs2WvIkQurHcLLoWQKAihUrYtmyZcqp+suWLUP58uXVjhFCYOjQocrXCoUCU6ZMwc2bN5GXl4ft27ejYcMHa9d06NABQgid/6ROlAD79yxNWn3M4H5jH6bt64Qpv/b0UODUB92Ur1vEVtJ1CpHD1VZZ8mT3RdNmdxpSVGLbpUt8NW6KDF13GfdNmxGk+ceodU1ej+QYT7eohtBAH3zQ58Hf2rLfxrKJW5b46aBpRV7tRTbJkjvysXPPkjElRrKlUe1rKL9WKBQI8PHCjkkdseiZZujZKNLAmUTSWG6DRWmt6VnqEqc9IUVz3StD7z5t/WmLv7cmJlBkDzP6NcLeN7ugYrkH68TZotbeHRNvFOyFyZIT07zjdDRDfxN6PVQZCmhfANEVA9C9YWUO+CaXVaTjwlg4uJlJ577Ro3SM5Q8jHxSLfHvtCbVjDCVjq210d732pTZaVcOJbEXq8UX2wGTJiTnyGe3UPg3UXu+7fAc/7NN/F/7pk43tHRKRU9I1MaJKBX+TzvX/r96RoV4dR0zzZ6JEjqQ6I1WuZDPA2x3puoNVpbrQoLluZqrXbKqqMc14wBe7DSZr9liTjkhqRcUlKBGGH4Hrui59vUy7HvSNQ7yQeg+1wgMBOMeioUS2FBNazur3kHotQ/YsObH8IsMDSQ0lU3svpePDdae0FuQscz1DPVmKrqBdk8VYssYZcORKhBDo9Ol2tJy+GYUGZqTpSmaC/Ey779SXhHWZvR1p9/IBGH78bYqhCTEG97eIrWhwP5Gt/D72EUzsWgfPtKomdShWY8+SE9OX6JQp1jMrZ/Ivx5WP0IL8vPFKl9pax2h+Hpfd1ZqjflQwXulcG1HlnaeQJ5ExeYXFOntGi0oEku7kAACS7uSgZpjua0LXTYTqYFZDDI1DvJJ2H6GBvkYnVhij7zHe2I61MG/bBbzZM86q9ycyVaOqIWhU1fInILOebIwPfj+JrLwiyXtc2bPkxCzpWSopEWpjjT7bfE7nuUeS7loVW5kJXetg4MPyv2sg9zF7U+k1kZKZh2PX7uLYtbsQQpj8YazrJsXUx9KGyoGUzYmwNlnSV6fptW51cfbD7hyvRLLxRPOq2PZaBwClPa5SLtvDniUnFlc5yOD+S7fvY97W87h+Nxc9G1VG29phKDbxl8mWU5CJ5GTTqVt4s2ccWs3Yotz22cDGSKxvWrmLouLSa6xj3TBM6l4P5XQsUqtL3Yggg1Ooy2aQWnsHnV+o/ybL1LFVRM5Cdb25ZXuuYkjrGEniYM+SE+vTuIrRY2ZtPIcf9iVjyOJ9AIB7eUV2iSWBNVnIRehKV34+eF2ZBAHAR3+e0Xt+WTLj6aFAXOVgVKtUOt4vIlh7ce0hraorvw7XsV+V53/JkrU1L/OK5D/ziKiM6tq8UpakYbLkxCwp5DVi6QGtbS2mbTY6/smYj/s/pPx69gCWDSB5Wfxc/IMXOi4rDw+FWmXuTadu6X2vIpVkSVW72mFax07t+6CKsb4xUMoY/vtD8N5vJwweZ8yTzaOtOp/Imaj2LElZqJnJkgs5knwXB1UWDS2Tmp2Peu9ssOq9fb0f/Kr0bFTZqvcicrTa4eqPtDVrJXkqtB9/6VvQtmxMkeqHOAB46RmP9PvYRzD8kVhM6FpHbfu0xxuqvVYoSsdkbDt7W08rTNOxXrhV5xM5E9XLTMpCzUyWXEjf+TtNOu5o8l211xO6lH6IG6pjER7kh/FdauONHvVYY4lkR/OOtFDjWZenh0JrwsQKPUVZf9yXDADIyFFffkFf+YBGVUPwTu/6WqU2ej8UpfZaoQC2nknV0wIi96R6U8JkiczStFp5i89NzcrDzL/Ux2ME+5d+yH/Yt6GuU5TGd6mD0e1rWvy9iaRSPuBBonIvrwiFxeqJkYdCodWzdOJ6ps73OnUzCwCwS2NR3gHxVc2KSdeSELp6honcmeplUjnEtEr5dolDsu9MFnvv0QbGD9Ih+U4OWkzfgp0X1D/ky34XWQKAXJVqb2hqdr7W4rXnU+9pFaL09DDv47FWeBAOvN0Fwx+JBWA8edKskJ9fVGJwEV0id6RQKPBCuxp4onlVPGRFzSZrsXSADNW2oIAkAPx9VncXPxe9JXfQv1lV/HyodCHaIYv3qu27nHZf6zGcgZJIeoUG+uL/utdD53rhaB5TweCxmj1Lr/10FN0amFa+gMidOEMhVfYsyUSV8g+6H8v52jbHZa5E7sBPZZLCpdv3tfYnfvaP2mtPIxeGvuV+fLw8kFAr1GhNI833v3T7PiSsuUdEBjBZcnLfPv8wRraNxaj2Nax+L32fw5qzeohckbnFHlcduIbMnEKt7aGBpUubzHziIa195tBVGkTfEkZEJC3+lXRyHeuG461e9bUSmvAgwwXudCkq1v3H4tHGLAVArs/YwtCacguL8dKKQwBKE6231x7HmsPXEOxX2qNUIcC09eDM8f2eq2qvDS2Poirmv8KYUs4WInJlvLJkol2dUAAPqgT/+EIrs9/jg3WndG4P8tP9OIHIleirm2TIvxfSAADrj9/Esj1JmLDyqLLsgJeBUhuWytNYquTlTrVMOm9U+5r431NNlOtoEZFtcYC3TFStEIB9b3VW3tXWMFIN2JaC9dSPIZITzXIB5ki7l//gfYpK38fbAY+v29YJw6ebdC+GrcrLQ4E+TYwvj0RElmHPkoyEB/lJUhDy5U61Hf49iWxNX9FIU6g+wSsrMeDtZf+ZEU2iy2Pdy48YnQHLGa1E9sVkScbCLBi3ZAnB6i/kAl5NrGvReUIICJVpaun3Syt3O2piRMMqIUbHR93Pt88C2kRUismSjD3dwvZFJNvUqqS1jdOZyRVYenNx4GqGcj04VYaWB3K06IrSVTYmcgdMlmRs+COxqBFWzqbvqbngKKC/5ACRO7hzvwC/H72ptV3fwrlS6FiXi+cS2ZPzXO1kthB/b2x9tYNN39PXW/tXwtTpy0SuyMtDgeM61omzRc/Szy8moF2dMKvfh2OWiOyLfwVdQPPqhpdVMIevjsTo0cZROo4kkr/3HzO+zqK+HiRbzIZrXr0CxncxPoGC4waJpMVkyUXNGdgEv4xJMHpci9iKaq8Ht6qudYyjBpITOVrfptrT7TVvPrx1VNoGbFdnydiyKgDHDRJJjcmSCxAan6Q9Gkaib9MqaFatAk5/0B1ta4fqPXfRM83VXkcE+9klRiJnpLm+25LnH8anTzZW26ZrWRKgdA04W9BcUNdUvRqVVt4fmhBjkziISD9WG3QBqqlSbGg5fKyyZpW/j+G6TIE2XpSXSM461A3XWg/udna+zmONLZRrKg9TepZ0bBvVvgbee7Q+e36JHIA9Sy5AtWPpieZVlVW+de1XVSs80GZ3x0Ry8G7v+lrbTrzfDcMficXGCe0AAAqNS2L8yiN2jUlfz9KiZ5oZPO+hquURHuzHwd1EDsC/lC5A8zGcpur/LbKpKao8a7OQe1HthWlYJRhAae/qO73ro05EadkMzTFExWYuwGsuXclShQBvdG/IBa6JnAWTJReg+lGuq9LvpG71zHq/dS8/YmVERM5JNTEZ21H3IrWmjCGK0XMDYm1MD7apfzQbuyEiIvtisuQCVD9H+zbVnuYfEuCttQ0ASvTcMdcysg4VkVyp5iWNo8vrOcZ4shTgY7uxfrpmw2nmT0yViKTFZMkFqNZgMedD/M2ecTq3Wzo7h8jZqSZC3nrqJ5ny+2/La0RXuSbNtd4CjEzUICL7YrLkAj7o0xAA8GrXOiaf46EA6kcF69xnSt0XIjlS7aHRV1TSlDxIXzkBS+hKvJ6Mj1Z7Pa1vI9SJeNDjG84ZcEQOxWTJBTSrVgHnPuyBlzsbrwRcZucbnfTu8/BQYO7TTQEAo9rVsDo+Imeh+ujZ20t3wqNQKPB/3Q2P8ysuKbFZTLpuTt7oof79Y0LLYeOE9vj7tQ54snlV/PBCK5t9fyIyjkV2XIQ5JQBqhJVD5RDDM+EeaxyF9rXDEOzPXxFyHcUqA/y8DCxX8mKHmvh4wxm9+yuWs13Pjq6eJT9v3Y/dYkLL4RONoplEZH/sWXJDy4a3NOm4kABv1nAhl6I6TsmShXC/GRqPVjUq4sP/Hn3bgikDyolIWuw2cEOsr0TuqlK5B6U1zL0R6Ne0CjrVi0CnehG2DouInBx7ltzEttc6SB0CkeSaVauAxPoRFq2npq8Eh61xrTci58OeJTcRG1oO21/vgHJcC47cmIeHAl8+G2/RuQVFthvUrSrIj9ckkbNjz5IbqV6pHEIDOeWYyBSNq4aovS4stk+y5OXpgVMfdLPLexORbTBZIiLS4d1HG6i9Liy2Xx1tW1YEJyLbk02ylJGRgSFDhiAkJAQhISEYMmQI7t69a/AcIQSmTJmCqKgo+Pv7o0OHDjh58qTaMaNGjULNmjXh7++PsLAw9OnTB2fO6J8yTETuoZyv+vT97g0jHfJ9OTmOyPnIJlkaNGgQjhw5gg0bNmDDhg04cuQIhgwZYvCcmTNnYvbs2Zg3bx7279+PyMhIdO3aFdnZ2cpjmjdvjm+//RanT5/GX3/9BSEEEhMTUVxcbO8mEZET01xyJLE+Z8ERuStZ9P2ePn0aGzZswJ49e9CyZWmNoK+++gqtW7fG2bNnUbduXa1zhBCYM2cO3nrrLfTr1w8A8N133yEiIgIrVqzAqFGjAAAvvPCC8pyYmBh8+OGHaNy4Ma5cuYKaNWs6oHVE5IwaVy2v9po1x4jclyx6lnbv3o2QkBBlogQArVq1QkhICHbt2qXznMuXLyMlJQWJiYnKbb6+vmjfvr3ec+7fv49vv/0WsbGxiI6O1nkMAOTn5yMrK0vtHxG5Fi9PD9SvrHv9RHtSgEkZkbORRbKUkpKC8PBwre3h4eFISUnRew4ARESod51HRERonbNgwQIEBgYiMDAQGzZswKZNm+Dj4wN9ZsyYoRw7FRISYjCxIiL5MmcZISJyXZJ+EkyZMgUKhcLgvwMHDgDQ3QUuhDDaNa65X9c5gwcPxuHDh7F9+3bUrl0bAwYMQF5ent73nDx5MjIzM5X/kpOTTW0yEclIbGg5h32vR2qFAgAGPsybLyJnI+mYpbFjx+Kpp54yeExMTAyOHTuGW7duae27ffu2Vs9RmcjI0pkrKSkpqFy5snJ7amqq1jllPUS1a9dGq1atUKFCBaxZswZPP/20zvf29fWFry/rFRG5und610d+UTEGPlzN7t9r6bAWyMorRPkA/b3aRCQNSZOl0NBQhIaGGj2udevWyMzMxL59+9CiRQsAwN69e5GZmYmEhASd58TGxiIyMhKbNm1C06ZNAQAFBQXYvn07Pv74Y4PfTwiB/Px8M1tDRK6mYjkfLBjc3CHfy8NDwUSJyEnJ4oF8XFwcunfvjpEjR2LPnj3Ys2cPRo4cid69e6vNhKtXrx7WrFkDoPTx2/jx4zF9+nSsWbMGJ06cwNChQxEQEIBBgwYBAC5duoQZM2bg4MGDSEpKwu7duzFgwAD4+/ujZ8+ekrSViIiInIssSgcAwPLlyzFu3Djl7LbHHnsM8+bNUzvm7NmzyMzMVL6eNGkScnNzMWbMGGRkZKBly5bYuHEjgoKCAAB+fn7YsWMH5syZg4yMDERERKBdu3bYtWuXzgHlRERE5H4UQgj71fB3E1lZWQgJCUFmZiaCgx0/1ZiIiIjMZ+rfb1k8hiMiIiKSCpMlIiIiIgOYLBEREREZwGSJiIiIyAAmS0REREQGMFkiIiIiMoDJEhEREZEBTJaIiIiIDGCyRERERGQAkyUiIiIiA5gsERERERkgm4V0nVnZ8npZWVkSR0JERESmKvu7bWyZXCZLNpCdnQ0AiI6OljgSIiIiMld2djZCQkL07lcIY+kUGVVSUoIbN24gKCgICoXCZu+blZWF6OhoJCcnG1wNWa7YPvljG+WP7ZM/ttFyQghkZ2cjKioKHh76RyaxZ8kGPDw8ULVqVbu9f3BwsMteAADb5wrYRvlj++SPbbSMoR6lMhzgTURERGQAkyUiIiIiA5gsOTFfX1+899578PX1lToUu2D75I9tlD+2T/7YRvvjAG8iIiIiA9izRERERGQAkyUiIiIiA5gsERERERnAZImIiIjIACZLZHeuOofgwIEDyMvLkzoMIpPwOiSyHJMlidy5cwdpaWkASpdLcSU3b97Ek08+iZUrVwJwvfZdunQJffr0QYsWLbBq1Sqpw7GL5ORkrF69GocOHUJhYSEA1/tj68rXIMDr0BXwOnQeTJYk8NZbb6FevXr48ssvAcDgejRytHjxYvz888+YM2cOcnJy4Onp6dQXgamEEBgzZgxq164NhUKBkJAQBAYGSh2WzU2ePBl16tTBp59+ioSEBLz44ou4dOkSFAqFy3xQu/o1CPA6lDteh87FeSNzQXfv3sXw4cOxefNmVKtWDXv27MH+/fsBuNbdwq5duzBw4ED4+vpi5syZUodjE2vXrkW5cuVw8OBB7Nq1C2vXrkVcXBz+/PNPAK7z89u7dy9+/fVXrF69Gtu2bcPXX3+N8+fPY8iQIQBg04WipeAu1yDA61DOeB06HyZLdqb6g/f390f16tUxefJkfPrpp7h+/TrWrFmDwsJCWd4taMZbVFQEAKhcuTIGDhyIhIQErFq1CqdPn4aHh4es23f79m0sW7YMe/fuRcuWLZGbm4uaNWvizp07yMnJkf2HV5m1a9eiuLgYvXr1gp+fH5555hl89NFHOHbsGD777DMAzvthpo8rX4MAr0Neh/Ig++tQkN3k5OSIvLw85euSkhJx9+5d5etXX31VtGnTRvzxxx/K/XKhq21lGjVqJE6ePCn27dsnOnbsKMaNGyfy8/PFiRMnpAjVIprtKy4uVn5dVFQkhBBi/Pjx4qGHHtLaLxdlPzPV2GfPni0aN24s7t+/r3bclClTRIUKFdT+n8iBK1+DQvA6FILXoRy4wnXIniU7mTx5Mh555BH07t0bc+fORVZWFhQKBYKDg5XjBsaNGwchBNauXYu0tDTnzag16GtbSUkJrl+/jnLlyiEmJgYPP/wwHn30UaxYsQJ+fn7YunUrCgoKpA7fKM32ZWdnw8PDQ/lzK7t77dKlC65cuYKkpCSnftauy+zZszF9+nQA6uMEgoOD4eXlhS1btii3KRQKPPfccwgICJDVXa0rX4MAr0Neh7wOHUlev1kyUFBQgCeffBK//fYbJk2ahKioKHzxxRcYNGgQgNJf+LILvlq1ahgwYAAOHTqEdevWKfc72y9JGWNt8/DwQHBwMLy9vaFQKLBmzRp8+OGHKCwsRKNGjfDyyy/Dx8dHdu17+umnATz4MCv7b3FxMSpVqoTk5GTJYjbX/v370bFjR7z22mv45ZdfsHv3bgBQzrR58sknUVBQgA0bNiA1NVV5XuXKldG1a1ecO3cOxcXFTv24w5WvQYDXIa9DXoeSkKQ/y4WdOnVK1K5dW2zcuFG57d9//xX+/v5i5syZWl2ueXl5omfPnmLAgAHi2LFjYtmyZeLDDz+UJHZjjLVNCCG2bNkiKleuLBo2bCjKly8vZs2aJb744gvRpEkTMX/+fCGE83aVm/uzS09PFz4+PmLdunVq253Z1KlTxRNPPCG+/fZbkZiYKEaMGKHcV1BQIIQQYv78+aJOnTriyy+/VDu3TZs2Yvjw4Q6N1xKufA0KweuQ1yGvQykwWbKxgwcPCoVCIdLT04UQD569zpgxQ1SoUEGcO3dOeWzZL8natWtFjRo1RKVKlYSPj4+YNWuW4wM3gaG2lS9fXly6dEkUFhaK+vXrixdeeEFcvnxZCCHEjRs3xIABA0S7du2c+lm7OT87IYS4e/euaNeunXj11VcdHqu5ytpy9epVsWvXLiFEabtatmwpVq1aJYQQorCwUHn8oEGDRJMmTcQXX3whMjIyxMGDB0WzZs3Ejz/+6PjgzeTK16AQvA55HfI6lAKTJRs7fPiwaNCggfj888+FEA9+QQoKCkRsbKzygi4bnHjhwgXx7LPPCoVCIV588UVx7949aQI3gaG2xcTEiPHjxwshhLh165bWAL2TJ0869Qe0EKb/7Mo+zIqKikTt2rXF6NGjlXeDcnLx4kXRt29f0bdvX3Hnzh0hhBD5+fnKfe+++67w9PQUzZs3F/7+/mL48OGyaKcrX4NC8DrkdcjrUApMlmzszp07om/fvmLgwIHixo0bQogHF/Wnn34qoqKi1LqJX3/9dVG1alVx7NgxSeI1h7G2Va5cWasL3BlnNehjzs+u7AJfunSpOHv2rDQBW6Hs57J48WLRsmVLMXv2bJ3HnThxQqxbt06cPn3akeFZxZWvQSF4HfI6lAdXuw45wNsMqampuH37tnImSXFxsXJfWW2TChUq4NFHH8WZM2eUJfi9vLwAACEhIahQoQKSk5OVswA++ugjJCcno1GjRo5sihZbtK1ixYpagyydZQCiLX92AODp6QkAGDJkCOrUqeOwdhhiShvLlO174oknUL9+faxbtw7nz58HABw6dAhA6dIDDRo0QK9evVCvXj1HNMGoCxcuYNOmTTr3yf0aBGzTPme+Dm358wOc8zo0pY1l5Hodnjx5EpMmTcK5c+e09rnCdagLkyUTFBYWYvTo0WjXrh0effRRPPbYY8jPz4enp6dy9oKXlxfy8vLw448/YtiwYWjSpAlWrlyJbdu2Kd/n2rVrCAsLQ/Xq1bVmdEjFHm1zJq7ePsD0NhYWFuK7775Tvi4pKUFwcDCefPJJlJSU4P3330fnzp0RHx+PjIwMyX83NR07dgx16tTBoEGDcPXqVeX2sg9buV6DZWzdPmfj6u0DTGujnK/DgoICPP/882jUqBHy8vIQExOj3Cf+m7km9+tQL6m7tpzdTz/9JGrWrCnat28vtm7dKr788ktRo0YNMWbMGLXj/ve//4mKFSuKPn36CCGEOHr0qBg8eLDw8fERL774onjhhRdEUFCQWLhwoRDCObrFXbltQrh++4Qwv439+/dXjosoc/XqVVGzZk2hUCjEU089JVJSUhzZBJPt379fdO/eXURGRmq1Twh5/xyFYPvk3j4hTG+jHK/DxYsXi6CgIJGQkKD1qEz1Z+EKP0ddmCwZ8dJLL4l33nlHbYbCc889JyZOnKh8/fnnn4uYmBixfPlytWewJSUlYvr06WLkyJGiZ8+eYufOnQ6N3RhXbpsQrt8+Icxvo+YH05YtW0RgYKBo0qSJOHDggMPitsQXX3whnn76abFlyxbh5eUl9u7dq9w3b948Wf8chWD75N4+IUxvoxyvw4SEBBEXFycyMjKEEKWz3davXy/Onj0rcnNzhRDy/zw1hMmSHmUDB2/evCmSkpKU269cuSKaNWsmZs2apfyBFxYWao3cd+Zs2ZXbJoTrt08I69tYJi0tTaxYscL+AdvAkiVLxP/93/8JIYRo3bq16NmzpxDiQV2anJwctePl8HNUxfbJu31CmN/GMs58HZbdiO3atUvUqFFDvP/+++Kxxx4TNWrUEA0aNBARERHiySefVB4rx89TUzBZUmFsXZq5c+cKhUIhHnnkEdG+fXtRoUIF8e677yqzamfmym0TwvXbJ4Tt2+iMH2KG2jhu3DgxduxYIYQQly9fFh4eHqJ79+6iZcuW4tSpUw6N01Jsn7zbJ4Tt2yiH67Dsv8OGDRN+fn5i6NCh4siRI+LYsWPi999/F35+fmLKlCmSxesITJaEEOvWrRNVqlQRCoVCeTeu6xd4yZIl4p9//lHuW758ufD39xdXrlxxaLzmcOW2CeH67ROCbSz771NPPSU2b94shBDi66+/Fv7+/sLb21usXr1amqDNwPbJu31CuHcby3qyb9++Ld5++21x/fp1tfM+/fRTUalSJVnUf7KU2ydLO3bsEN27dxdjx44VPXr0EPHx8VrH6Mv8T58+LTw9PdXKuTsTV26bEK7fPiHYRiEeVPd97rnnxJAhQ8TDDz8swsLCxNSpU0X58uXFp59+KkXYJmP75N0+IdhGIR58zty/f1/r3B9++EFUqFBBHD9+3CGxSsFtk6WyH/y5c+fE7NmzxaVLl8SBAwdEQECA+Prrr4UQxtcYmjFjhkhMTNT7HFoqrtw2IVy/fUKwjZptzMnJEY8//rioVKmSeOmll8S1a9eEEEJ89NFHQqFQKJf0cCZsn7zbJwTbaOpnzYsvvij69etn91il5HbJ0sGDB8Xdu3fVtpV1MRYWFopXX31VhIWF6V0S4OrVq+LChQtixIgRIioqSixZskQI4RzPnV25bUK4fvuEYBt1tbFs3759+8TJkyfVzsvLyxMzZ850qsVT2T55t08IttGUz5rLly+LCxcuiOHDh4tq1aqJtWvXCiGc67PGltwmWVq9erWoWrWqqFmzpqhWrZp49913xc2bN4UQpT/csh/wpUuXRHR0tHLdGtUf/Llz58TEiRNF1apVRceOHZ2mvL4rt00I12+fEGyjoTaWfYA7O7ZP3u0Tgm009bPmzJkz4qWXXhLh4eGiQ4cOTvdZYw9ukSzt379f1KtXT8yZM0ccPXpULFiwQISFhYkXX3xRuSJy2S97SUmJWLBggfDy8hKXLl0SQpTeGeTn54uSkhKxbds2p6oR4cptE8L12ycE22hKG/Pz85VjJZzxzpXtk3f7hGAbzfmsKSoqEn/99Zf4559/JGuLo7l0slT2C7tw4UJRtWpVkZmZqdw3b9480apVKzF16lSt89LT00VCQoLo06ePOHjwoOjatav4/vvvneoCcOW2CeH67ROCbTSnjYmJiU7ZRrZP3u0Tgm10lc8ae3PpZKnMpEmTRKdOndRG8d+7d0+89NJLIiEhQZw4cUIIod6V+u233wqFQiE8PDxE7969dc4AcAau3DYhXL99QrCNprbRWQeqC8H2yb19QrCNrvJZYy8ulSxt3LhRvPzyy2LOnDlqZeZ//fVX4efnJy5evCiEePCLsHHjRtGmTRsxe/Zs5bH5+fli/vz5wsPDQ7Rv3175yyM1V26bEK7fPiHYRldoI9sn7/YJwTa6ShsdzSWSpRs3bojevXuL8PBwMXjwYNGoUSMREhKi/CXJzc0V9erVEy+88IIQQn0aZNu2bdUWPExJSRGvvPKK+O677xzbCD1cuW1CuH77hGAbhZB/G9k+ebdPCLZRCNdoo1Rknyzdv39fPPfcc2LgwIHKQWhCCPHwww+LoUOHCiFKs+elS5cKDw8PrcGvgwcPFh07dnRozKZy5bYJ4frtE4JtdIU2sn3ybp8QbKOrtFFKHpC5gIAA+Pr6YujQoYiNjUVRUREAoHfv3jh9+jQAwNPTEwMGDECfPn0wYsQIbN++HUIIpKSk4Pz58xg8eLCUTdDLldsGuH77ALbRFdrI9sm7fQDb6CptlJRkaZoNqa5HUzZK/5lnnhEjR45U25abmys6dOggwsPDRWJiooiKihKtWrVSW7Xd2bhy24Rw/fYJwTaqbpNrG9k+ebdPCLZRdZuc2ygVhRBCSJ2w2UO7du0wbNgwDB06FEIIlJSUwNPTE7du3cKxY8ewf/9+xMTEYNCgQVKHajZXbhvg+u0D2EZXaCPbJ+/2AWyjq7TRISRK0uzq4sWLIiIiQhw4cEC5LT8/X8KIbMeV2yaE67dPCLbRFbB98sc2kjlkP2ZJlfivk+zff/9FYGAgmjdvDgB4//338corryA1NVXK8Kziym0DXL99ANvoCm1k++TdPoBtdJU2OpqX1AHYkkKhAADs27cP/fv3x6ZNm/DCCy8gJycH33//PcLDwyWO0HKu3DbA9dsHsI2u0Ea2T97tA9hGV2mjw0nWp2Unubm5olatWkKhUAhfX1/x0UcfSR2Szbhy24Rw/fYJwTa6ArZP/thGMpdLDvDu2rUrateujdmzZ8PPz0/qcGzKldsGuH77ALbRFbB98sc2kjlcMlkqLi6Gp6en1GHYhSu3DXD99gFsoytg++SPbSRzuGSyRERERGQrLjUbjoiIiMjWmCwRERERGcBkiYiIiMgAJktEREREBjBZIiIiIjKAyRIRERGRAUyWiMjtTZkyBU2aNJE6DCJyUqyzREQurWydLH2ee+45zJs3D/n5+ahUqZKDoiIiOWGyREQuLSUlRfn1ypUr8e677+Ls2bPKbf7+/ggJCZEiNCKSCT6GIyKXFhkZqfwXEhIChUKhtU3zMdzQoUPRt29fTJ8+HREREShfvjzef/99FBUV4fXXX0fFihVRtWpVfPPNN2rf6/r16xg4cCAqVKiASpUqoU+fPrhy5YpjG0xENsdkiYhIh61bt+LGjRv4559/MHv2bEyZMgW9e/dGhQoVsHfvXowePRqjR49GcnIyACAnJwcdO3ZEYGAg/vnnH/z7778IDAxE9+7dUVBQIHFriMgaTJaIiHSoWLEi5s6di7p162LYsGGoW7cucnJy8Oabb6J27dqYPHkyfHx8sHPnTgDAjz/+CA8PD3z99ddo1KgR4uLi8O233yIpKQl///23tI0hIqt4SR0AEZEzatCgATw8HtxPRkREoGHDhsrXnp6eqFSpElJTUwEABw8exIULFxAUFKT2Pnl5ebh48aJjgiYiu2CyRESkg7e3t9prhUKhc1tJSQkAoKSkBM2bN8fy5cu13issLMx+gRKR3TFZIiKygWbNmmHlypUIDw9HcHCw1OEQkQ1xzBIRkQ0MHjwYoaGh6NOnD3bs2IHLly9j+/bteOWVV3Dt2jWpwyMiKzBZIiKygYCAAPzzzz+oVq0a+vXrh7i4OAwbNgy5ubnsaSKSORalJCIiIjKAPUtEREREBjBZIiIiIjKAyRIRERGRAUyWiIiIiAxgskRERERkAJMlIiIiIgOYLBEREREZwGSJiIiIyAAmS0REREQGMFkiIiIiMoDJEhEREZEBTJaIiIiIDPh/wrQ8gdAMeg4AAAAASUVORK5CYII=", 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DIBfFZInIBljaiEh6f528JXUIBl1IvWf8IHJKTJaIbCA1m9P0iciwQ1czpA6BLMRkichKB6/ewZDF+6QOQ6fDSfxwJnIWwf5cL06umCwRWen73VelDkGv501Ye46IHKNsmSOSHyZLRFZy5uFKd3MKpQ6BiP6z6ZRzj6ki/ZgsEREROcjPB69h8Nd7cDfH+CLa5DyYLBFZiTPhiMhUr/50FDsvpGPe1gtSh0JmYLJEZKV7LIRHJKniEoHt525LHYZZvv73stQhkBm43AmRlbaeSZU6BCK39u3Oy/jwj9NSh0EujD1LRC6u/8JdUodAZFc/7EuSOgRycUyWiCx0OzsfQgYDlg5ezcCsv85KHQaRzRWXCDT5YCMu3r4vdSgWmfLbSTz6+b9ISs+ROhQygskSkQX+OpmCh6dtxuRfjksdiknmbeNgUnI9vxy6JuvyGEt2XcHx65l4a608PkfcGZMlIguM+v4gAODH/ckSR2K663dzUVIicCtLfWmW1Ow8bDl9CyUlzt9LRqRKrj1Kmu5zkojT4wBvIjMIIfDwtM1Sh2GReVvPI6+wBGsOX8cXQ5qjW4NI5BcVo8W0LQCAmf0fwoCHoyWOksh0RcUlUodgEz5e7LdwdvwJEZlh0fZLSLsnz2JyHgoF1hy+DgCY/99jubpvb1Du33aWs/pIXlxl+r2vl6fUIZARTJaIzPDxhjNSh2Cx5XsfzBjyUCi09nt78uOASArentrXIzkXfjoSuaG8wmIUa4xR8uIHNpEkNp9mr66zY7JEZCJXGgB9JiUb+UXFatt82LNEJJmCItcYf+Wq+OlIZKI5W85LHYJN5RSoJ0ueHuxZIpJKiQxqtrkzJktEJprrYslSvsadrI5hTETkIEyWnBuTJSKZmzOwiUXnXbp9z7aBEDmQI6rnO3LgteYYQnIuTJaITLD++E2pQ9DycqdaeLxpFfRpEmXR+UMW71N7zRtbkpMiOycXF6b1gJ+346b0M1dybkyWiEwwZvkhqUNQ8/vYR/BqYl18NrAJFDZ6fsbPapITe/bERFf0h5enB/o3q2q376FpxV4uBuzMmCwRyVCjqiE2f88LqXwsR/JQUiIwY/1pm73f8Edi1V53jYsEALzRox4WDm6Go+8l4uP+jdSOSawfYbPvD8i7hps7YLJEJDMjND7YAWBAvPV3wPsu37H6PYgc4cXlB/Hd7qs2eS8fTw+M61xbbVtMaAAAwM/bEz0aVUaIvzeebB6N1xLrKI9599H6Nvn+JA9cG47IiEInWn9qco96eLZ1jNb2yiH+jg+GSAJCCPx18pbN3q9jvTCtbU+3qKa1zcNDgTEdauHkjSzUDg+02fcneWDPEpER3+50nvWnRrWvCX8f7UGnw9po9zYRuaJOn2636ftN6l5PrWzGjkkd9S794+GhwMJnmmNiYl2bjRUkeWDPEpER09c7fixB1Qr+uJaRa/LxIQHedoyGyDkIIXA57b5N37NmWCCEEGhfJwwFRSWoWsG0XlqmSu6FPUtETujXl9pobWtbO9Tu39cRtWuILKVZSNVWFAoFvhvWAitGtmSPEenEZInICVUI8MEXQ5qrbZs/uJndvy9rvZAzy8ottOv7m5MoBfrxwYw7YbJEZCf+ZhS0W/fyI2qvFQqgW4NIXPmoFy5M64HLM3oi2M/wozbNGT2WOHUjCzsvpKHIiQa1E5V55ccjUoegpHk9TupeV6JIyBGYLBEZ8KcVlbsPvdMV3wyN19qua+p/wyoh6BIXrnyteofr5elh0h3vsDYxlgWq4tF5/2Lw13vx5Y5LVr8Xka3tvpQudQhqqpR/ML7p+YRY7Hqjk1Xvx1pnzkt2ydKCBQsQGxsLPz8/NG/eHDt27DB4/Pbt29G8eXP4+fmhRo0aWLRokdr+r776Cm3btkWFChVQoUIFdOnSBfv27dPzbuRuXrSwcreHAvD38USnehFYPqKl2r4K5Xx0nvPVs/Ho1iACQxNiLPqeChsOOV194JrN3ovImfh6PfizN7p9Tave653epbWWRraNhb+PJ6LK++PE+91w8v1uFr3fltO2K4lAtiWrZGnlypUYP3483nrrLRw+fBht27ZFjx49kJSku0z85cuX0bNnT7Rt2xaHDx/Gm2++iXHjxuHnn39WHvP333/j6aefxrZt27B7925Uq1YNiYmJuH79uqOaRS7IQ6UnqE2tBwOz60UG4XmNHqDfx5Y+glMoFPhiSDymPNbAsm9qw3GpXg5cQJTIFBn3C8w+Z1S7Gni924PHY/UrB+P4lAeJTGID66pwd28YiaPvJuKtXg8KVAb6eqGcrxd6P1QZANC0WnmT3y/En7NanZWskqXZs2dj+PDhGDFiBOLi4jBnzhxER0dj4cKFOo9ftGgRqlWrhjlz5iAuLg4jRozAsGHDMGvWLOUxy5cvx5gxY9CkSRPUq1cPX331FUpKSrBlyxZHNYtckOZTs6l9GmBwy2r485W2CPDxwpF3uyr3RYT42uV7WoMroJOz+cOCR+LPJsTgpY61MLhlaZHJ17vVhY+XBx5vWgXx1SugcdXyVselr2zHnIFNsGlCO3zc/yGT3+uNX45bHQ/Zh2yG8xcUFODgwYN444031LYnJiZi165dOs/ZvXs3EhMT1bZ169YNixcvRmFhIby9tX/Jc3JyUFhYiIoVK+qNJT8/H/n5+crXWVlZ5jSFZCIpPcficzUfiQ3RUXVbyQnzkou376OouAReeorzEclBwX+lBj7s2xCvd6uL8gGlj8A/G9jE7t/by9MDtSOCzD6vsLhEb1FMko5sfiJpaWkoLi5GRIR6t2lERARSUlJ0npOSkqLz+KKiIqSlpek854033kCVKlXQpUsXvbHMmDEDISEhyn/R0dFmtobkYPu5VLu9t2oVbntMQd5p5UBTAMi08zRtInNYck9RvWLpGm8KhUKZKDm7WX+dlToE0kE2yVIZzVlBQgiDM4V0Ha9rOwDMnDkTP/zwA3755Rf4+fnpfc/JkycjMzNT+S85OdmcJpAbEEY+2n29PLH2pTb4+cUEBPjYJlnyUbkb9bHBnSl7lUjOVoxsCQ8P+Y29++IfzkR1RrJ5DBcaGgpPT0+tXqTU1FSt3qMykZGROo/38vJCpUqV1LbPmjUL06dPx+bNm/HQQ4afMfv6+sLX1zbjTMh5fb/H8lXNC4uN3wc3iS5v8fvr4uftibd7xaGguARhQdb/frKaNzmT73dfMfnYM1O7w8+MOmfO5mr6fVSvVE7qMEiFbG4dfXx80Lx5c2zatElt+6ZNm5CQkKDznNatW2sdv3HjRsTHx6uNV/rkk08wdepUbNiwAfHx2nVxyD2duyW/micj2tbAmA61bPJed3P4GI6kkXwnB2sPX1dONDh+LdOs69HZEqWFZlbfT7tn/sw/si/Z9CwBwMSJEzFkyBDEx8ejdevW+PLLL5GUlITRo0cDKH08dv36dSxduhQAMHr0aMybNw8TJ07EyJEjsXv3bixevBg//PCD8j1nzpyJd955BytWrEBMTIyyJyowMBCBgYGObySRk3hm8V78+3/Wj30iMtWeS+n4fOt57LxQWnwyt7AYTzavikfn/StxZNbp0aiy1CGQlWSVLA0cOBDp6en44IMPcPPmTTRs2BDr169H9erVAQA3b95Uq7kUGxuL9evXY8KECZg/fz6ioqIwd+5c9O/fX3nMggULUFBQgCeeeELte7333nuYMmWKQ9pFriOmUgCupOegmRm1VZzVtYxcqUMgN/PUl3vUXv9y6BqCuAYbOQHZ/RaOGTMGY8aM0blvyZIlWtvat2+PQ4f0V2G+cuWKjSIjd7d0WAvEVQ7GqgPJeDK+qtThwMfTAwVc441kbP+VDOy/kiF1GDbRvUEkNpzUPXObnJ9sxiwRObt2dcIQFuSLlzrWQniQ/tmUjnLCwiUXiOSsc71w4wdJwNebf27ljD89Ihfl4+WB74e3sOo9cguKbRQNkWG2mn35+aCmNnkfW2tXO0z5dc0wwzPdeN05HyZLRC7sEZV16SzR7pNtNoqEyLDt525b/R4JNSvZrG6ZrT3etAo+G9gYi55phgHxhgsZP7N4r959LOkhDSZLRDqk38s3fpAMKBQKBFsxQPZ2tmv8fyDnd+qm9ctGqS6a62w8PBR4vGlVdG9YGY2qhBg9PqegCEMW78UylXpvE1YeQY//7VAu40KOw2SJSEVRcQmEEC61oKWhCvdErqRptQpSh2CShFqhmD+oGdaPa6v3mEV/X8SO82l4e+0J5BUWIyk9B2sOX8eZlGzsu3zHgdESIMPZcET2sv74TYxZrn/mpCEvdqhp42hs5+P+jTB6mWXtInIUd3u61Oshw7WX5m69oPy6z7ydOHsrW/n6mcV7se21DogNZZVvR2HPEtF/LE2UAKBWmPMWMO3esDJ+H/uITd6rpERg4d8X1R4NENkCx+Lop5oolXnvt5MSROK+2LNEZAN5Rc49e6VRVeNjJEzx3m8nlWvmda0fgYhg6UskEL33aH2rJzNIpV+zKvjl0HWzzytiDTWHYs8SkQ34eTnXWlTmeKFdDZOPVV1c+Pvd7F0i27GmY+n5NrGoHRFku2Ac6LHGURadt+tiuo0jIUOYLBEBSMnMM+v4n0a3hpfHg4HTj1r4gSeFKuX91V7HVTbtj8y9/CK11/O2XdBzJJH53PUhnDUTMPjo0nGYLJHb23f5DlrN2GLy8W/3isPDMRXh5fngQ87Hy/kvpaXDWqBRlRB89Wy82nZjn7dlZRQ4Tons6WjyXalDkIQ1c1VLmCs5jPN/whPZ2cwNZ8w6vrC49BMqSqOHxtm1qxOG319+BPWjgrF0WAu17YZMWHUUQgh89Kd5/5+ITHUmJQtbzqRadO4bPerZOBr52HuZj+IchckSuT1zb87KCsJ9OSQej9QKxapRrW0flJ21qxOGC9N64PiURIQG+ho89p9zt/H1jss69yXfybFHeORmhn273+Jzi2XevaLaQ22uQV/pr/RNtsVkidzewavmrWpeUFw6861WeCCWjWiJFrEV7RGW3Xl5eiDIz9vocQE+nvhYT+9b25nbkJ1XaOvQyI0UFpfghpljBjXPl7OWsZWkDoFMwGSJ3JolAyR9POU7802f+pWD9e7LKShGkYG79+nrT9sjJHITZ1O0awiZo6hY3j1Lnh6ssC8HTJbIrRlKAvR5/pEY2wciscGtqll8rrk9c0Sq8q2sUSb3niVrcUacYzBZIrdmyXiHYBMeXcmNwoo5OYUyv7Mnaf1sQUFGVQE+7l1befu521KH4BaYLJFbM7dnaeur7e0UibSsWWuXK6CTpYQQWLE3yar3GN421kbRyNPpm9Y9xiTTMFkit2bOkgENqwSjhhOvAWcNa0ZNuPtjELLchhMpFp8bHuSLI+92RaCve/csWXOjQ6Zz798ycnvm9Cx5erjuvUWcgQHexjBZIkv9aUWytO21Dijn5okSOY7rfvoTmeB6Rq7JxxaXuG5S0Di6PBY/F4+NE9qZfa7My9yQTDFRKsWOJcdgskRurd/CXSYfK/cpysZ0jotAHQsWIy3hbByyEH9zSn3cv5HF5/IxnGMwWSK3Zs5sOLlXCjbV/3U3b/mI7Lwit/l/Q86hpUwLweoz8GHLS3eQYzBZIjLRo42jpA7BIeJjKph9zt5LXKOKzGdpjaCVMlxiyF6sKftBpuNDXyITTO3bEE89HC11GA5hyUevlyfvu8h8+Sw7YTU+hnMMfsKRW8kvKsZnm87haPJdlJjx6GhIq+rwdpOEwJIP37R7+bYPhFzeplO3pA6ByCTu8elP9J+v/rmE/205jz7zd+KP4zelDsdJmZ8tjVl+yA5xEBE5ByZL5FaOJN9Vfn3x9j3pAnFi7NYnR9h9kePcVHVvEGnReZfS7ts4EtKFyRK5lc2nU5Vff771goSROC/mSuQIT3+1R+oQnMr8wc3Qv1lVs8+zdrkYMg2TJXJbnO6um4JdS2Rnls6CA4AZ/SyvSeTMPD0UeL1bXanDID2YLBGRmlrhrrn+HTmP7eduW3zu0y1ctyZRoB8nqDsrJktEpCbQ1wuH3+kqdRjkwradSTV+kBty90WBnRmTJSIjxnWqJXUIDlehnI/UIZAL+273ValDcFrVKgZIHQLpwGSJyIgJXetIHYIsFRSV4J9zt5FbUCx1KOQiBsSbPwCayBaYLBEZwQHPptEcMD99/Wk8+80+jPvxsEQRkTPKziu0+Nx3ete3YSTOiR83zonJEhHpNKp9DbOOX7ZH/dHKkl1XALBKM6mbuu6U2eeEBvpiZv+HEOTnbYeInEtEkJ/efdtf74CGVYIdGA2VYbJEbsOa6cruyMPMW9w/jj2oiM5Hb6TPLguKUR54uwsGuMnajJ8OaKx3X/VK5RBfvaIDo6EyTJbIbRxKuit1CLJi7tOAYP8HM3kOXL1j22DIZVzLyJU6BKcWrWeA929j2wAAXu9WV6t36cAV9ett9cFr+OXQNfsE6KaYLJHb6L9wl9QhyIq+jiUfPQsK56j0JmXlFqnt03xER+5JdbkhU73SubbtA5Ghh6qWBwCU8/XCy53U/5/M3HBW+XXynRy89tNRTFx1FAVFJY4M0aUxWSIyoPdDlaUOQTIKPX1LZ6Z2x9S+DbW277qYjuISgeISgVUHktX2vb32hF1iJOeXlJ6DVQeSkVtQjL7zd5p9fp8mUXaISt40RxTkFz9Iinp//q/y66KSEg4/sBFWwCK3UGLh0iZ+3p42jkQ+dPUsPd0iGh4eCgxpVR1ZuYX45K+zavtzC4vx474kqyo0k2tp98k2AMCCbaavxfjLmAQ0qhKC7LwiVGTNLy3BGpW+j6r02GXmPpht+Mexm5jx5xnMe7opEmqFOio8l8SeJXILWyysGOzO68fpKpnwfJtY5de6BoALIbD++E2t7URX0nNMOq5eZBCaVasAb08Pt02UTrzfDV89G49FzzQDACTUrKS2v7XGawCYueGM1rbXVx/DnfsFGPX9QfsE6kaYLJFbyMgpsOg8d06WhibEaC2/UCciSPm1rp6nkhIgO69IeweAS7fv2TQ+cn63svLMPufr5+LtEIm8BPp6oWv9CHRvWBm73uiEpcNaqO3XdSOz4O+LyMzRXcPK05PFm6wlu2RpwYIFiI2NhZ+fH5o3b44dO3YYPH779u1o3rw5/Pz8UKNGDSxatEht/8mTJ9G/f3/ExMRAoVBgzpw5doyepGJp0lPsxs/7K5bzwZ43O+vdr+vjd/SygzifqjspeuPn4zaKjOTiwz9Om31O1Qpc7kNVVHl/eOmZVKHp47+0e5cAwN+NhxPYiqySpZUrV2L8+PF46623cPjwYbRt2xY9evRAUlKSzuMvX76Mnj17om3btjh8+DDefPNNjBs3Dj///LPymJycHNSoUQMfffQRIiMjHdUUcrCiYstmhdRT6UlxRx4Gbkh19SztvqS/hk52vu4eJ3JdGffN69Fd+UIrO0XiHlbs1f23kP1K1pNVsjR79mwMHz4cI0aMQFxcHObMmYPo6GgsXLhQ5/GLFi1CtWrVMGfOHMTFxWHEiBEYNmwYZs2apTzm4YcfxieffIKnnnoKvr6+jmoKOViRBT1L3p4KjGxnXhVrV+NpIFvSN1tOn0Bf3t26G3MT5JY1tMfikPV4o2I92SRLBQUFOHjwIBITE9W2JyYmYtcu3fVzdu/erXV8t27dcODAARQWWr4+UX5+PrKystT+kXMz5zHchC51cGFaD5yf1tOtZ8MBgK+X/vabu4bV/isZXPrEzRy1oK4SmaZmWDmTj83OK0Jqtvnjx+gB2SRLaWlpKC4uRkREhNr2iIgIpKSk6DwnJSVF5/FFRUVIS0uzOJYZM2YgJCRE+S862j3K8MtZYbFpydL5aT3wSpfaJo8RcAeNqoTo3N6nSRWz32vk0gNWLaRKRKWWPN/C+EEqWkzbYqdI3IPs/iJozgIQQhhcFV7X8bq2m2Py5MnIzMxU/ktOTjZ+Ekkmr7AYP+7X/SxfE5/taxv+SGm5gE71wtW2hwVZ9th60faLVsdEzs/cSRWDW1azUySuKaq8v9QhuBXZFKUMDQ2Fp6enVi9SamqqVu9RmcjISJ3He3l5oVIly5+N+/r6cnyTjHy26RyumljjxdAYHXfVt2kVNKoagmo61qxqVycM/5hZgFK1aB65rkIzJ1VoLuFBhvGjyrFk07Pk4+OD5s2bY9OmTWrbN23ahISEBJ3ntG7dWuv4jRs3Ij4+Ht7e3naLlZzLptOmjZNpXDXEqh5HV1YzLBDeOh5N/m9gE7Pfy9yB4SRPx65lmnW8r5ds/hw5BX5WOZasfjsnTpyIr7/+Gt988w1Onz6NCRMmICkpCaNHjwZQ+njs2WefVR4/evRoXL16FRMnTsTp06fxzTffYPHixXjttdeUxxQUFODIkSM4cuQICgoKcP36dRw5cgQXLphemp+cm6mlkno2ct914CxlyQB4fsa7hwFf7Dbr+ApuWq3bkc6kcDKSpWTzGA4ABg4ciPT0dHzwwQe4efMmGjZsiPXr16N69eoAgJs3b6rVXIqNjcX69esxYcIEzJ8/H1FRUZg7dy769++vPObGjRto2rSp8vWsWbMwa9YstG/fHn///bfD2kb2Y+rYibKxOWQ6SxKfy2n3bR8IyVqDqGCpQ3ALs/46i6+fe1jqMGRJVskSAIwZMwZjxozRuW/JkiVa29q3b49Dhw7pfb+YmBiuyuziTE2WOAPOfJaM8dpx3vKZqOSa3HhVIYfydfNSKNbgXwdyeUyG7ceTz9TIBkqYLTlEFidXWIzJErk8d17fzd6YK5EtFJVYthwRmYe9upZjskQujzet9sMZOaSLuT1FvEbJ2TFZIpdnymO4xPq6a3WRfaw6wEKurmzjKd2rKujDniXLNI4ub/Y5HJZgGSZL5PLS7hlf+dzdF8y1hiUf2JNWH7N9IOQ0rt81bx2yYhOXIyJ1tcMDzT6HvXiWYbJELi2/qNik48ytNkwPeLOUMGkoMvN68uDvkMP8euS61CHIEpMlcmmmLq1RxDtbi3l5mv+Hzot/HF1akZndF58/3dT4QaQl3IL1GSeuOmqHSFwfkyVyaQVFpt3hmrvoJz3wUsdaevf9+EIrPNe6utZ2XUunkOsw5+bjlc610bRaBTtG47rGGLj2yLb4iUUuzdRkydebl4Kl2tYOwwd9Gmht9/H0QKsalfBWr/pa+ziJznX9fTYVn20+Z/Lx4cFclNxSgb6yqystW/wLQS6t0MQ73NY1Ktk5EtdWrWKA1rYGVUqXsPDRsUBqCWfkuKTM3EIM/Xa/WeewV5fkgMkSuTRTB26zXpB1PHT8//uo30N6j88rLEFeoWmD70k+Mu4bn3mqqZYFM7rogZc78VGcIzBZIiKr6UqW6kYGGTxn5wVWE3YlPx1IxsAvd5t9XkLNUDtE4z5eTayLRxtHmXUOay2Zj8kSuTRTPhO8LZjNReqiyvsZ3L/11fZa23QlWCRfr68+hltZ+VKH4ZbMnU3IZU/Mx2SJXJopY2N+fKG1AyJxbTXCDD9K0bWfuRKRfbzT+8GkCl1jBlOzmdSai8kSubS3154wekzz6py27AiayRF7llwHH+s4lziVR+DbXuugtb+cj6cDo3ENTJbIZc3dch7Hr2dKHYbbeLe3dokAVZqpEZMl12HqrFOyn/7Nqiq/9lQp+urvrZ0YrT+RgtwCTrAwB5Mlckl/Hr+J2ZtMr/VC1hv2SCyGtYnVu19zxiGLeLuOzadvSR2C2+taP1z5dXxMRbSIqYgB8VV11mL6/egNPDxtM5d5MgMrWpHLuZp+Hy8uP6R3/xdDmqNZtQr459xtdGsY6cDIXN+k7nWRkVOALnERWvve7BmHqetOKV8fvZaJhFqcCeUKxhi43gwJDWRBSlvp1iAS4zrVQpNq5eHpocCq0Q/GYnp5KLSWoLmXX4T0ewWIDDE8OYNKsWeJXM4NIyueFxSVICzIF/2b677rIsv5eXvis4FN0Ouhylr7nk+IUXv98YYzDoqKnNUvLyZIHYLLUCgUmJhYF53qad+o6CoaC6g/riPDmCyRy5m46ojB/fkmLoFCtsWV5UnVB30aoFol3X/Eybaq6kmWWEnfdEyWyOXczDTcs5RfxIGNUqlnpFAluYeRbWMxuKX2AstkHx/3b6Rzu+ajOdKPyRK5nSbR5aUOwW31bVpF6hDICXSqF8FHQA5UOcRf5/a31xzHJ3+dQYqRG0xiskRuqEFUiNQhuC32+hMACPAXwRlsO3sb87ddxKjvD0gditNjskQu5eLte1KHQAZojpHgYrruqUFl3rA4k6PXWI/OGIuSpUOHDuH48ePK17/++iv69u2LN998EwUF5q86TWQrH/x+yvhB5DQ+33pe6hDIwQ690xUhAd5Sh0FkFouSpVGjRuHcudKCf5cuXcJTTz2FgIAA/PTTT5g0aZJNAyQyBzv3nVuJxoDSvZfuSBQJSaViOR+pQyAdftiXJHUITs2iZOncuXNo0qQJAOCnn35Cu3btsGLFCixZsgQ///yzLeMjMgvXqHJugX7qda244ol7GdKKM+Cc1eRfjhs/yI1ZlCwJIVBSUlqrZvPmzejZsycAIDo6GmlpabaLjohcytMtqqm91lwCheTnzn3Th174cwFXybxjZO1GMsyiZCk+Ph4ffvghvv/+e2zfvh29evUCAFy+fBkREdrVQ4mIgNIK36o4e1yebtzNxb7LpY9QD13NMPm8+OoV7BUSGVHen+PErGHRWg9z5szB4MGDsXbtWrz11luoVasWAGD16tVISGD5epLO+VuGZ8ON61TLQZGQKdLucUKIHCV8tBUAsPalNiYd36tRZfRpEoWu9XkzLRUPzn23ikXJ0kMPPaQ2G67MJ598Ak9PdrOSdFKyDBdXm9C1joMiIVNcSL2HP47d1LmWHDkn1YHAh5My9K47purTAY21ehXJsbo3qIwJOCp1GLJlca559+5dfP3115g8eTLu3Cntjj116hRSU1NtFhyRrXGMjPN5aYVlK9aTNN777aTya29P0/6EMFGSHseLWceinqVjx46hc+fOKF++PK5cuYKRI0eiYsWKWLNmDa5evYqlS5faOk4io4q5zhGRXRUVl6BAZSFqH08Pzmgkt2BRz9LEiRPx/PPP4/z58/Dz81Nu79GjB/755x+bBUdkjsLiEoP7NWdiEZF5Tt/MVnu9fO9VfPnPJYPnfP1svD1DIhu6nHZf6hCclkXJ0v79+zFq1Cit7VWqVEFKSorVQRGZ449jN7Hp1C2jK2hrFkQkIvMUa9QxO3otE3uMFBYt52vRAwyyg871wg3uX/T3RQdFIj8W/Rb7+fkhKytLa/vZs2cRFhZmdVBEpsq4X6Ac8/J8mxiDxxrreSIiwyx51F3A685pzH26KRq895fUYciSRT1Lffr0wQcffIDCwkIApYNmk5KS8MYbb6B///42DZDIkKy8QuXX3+68YvDYQvYsEVlFcyFkU6iOcSJpGevl4/gz/SxKlmbNmoXbt28jPDwcubm5aN++PWrVqoWgoCBMmzbN1jES6WVO9eBCfmgTWcWiniVed7LB2cL6WfQYLjg4GP/++y+2bt2KQ4cOoaSkBM2aNUOXLl1sHR+RQY8v2GXysXwM5xxWj26NJxbtljoMsgDH/bk25kr6mZ0sFRUVwc/PD0eOHEGnTp3QqVMne8RFZHN9mlaROgQCUCs8UGubEIJ3tTLwvy3nzT6nS33Dg4rJsXZM6oitZ1LV6mWRcWY/hvPy8kL16tVRXFxsj3iI7OZRVol2Ch46FoTbdpbFbOVg72XDM9908fViMURnEl0xAG1qVdK5b/WBaw6ORj4sGrP09ttvq1XuJpKCuY/V2HPhHDx1/Bwu3WZ9F2enOpmC5E3fOP2C4hKk3ct3bDAyYdGYpblz5+LChQuIiopC9erVUa5cObX9hw5x+QKyv+92XZE6BLKAp46eJS7F4PzmbDL/ERw5J0Mjz4Yt2Y/fxj7isFjkwqJkqW/fvjYOw3QLFizAJ598gps3b6JBgwaYM2cO2rZtq/f47du3Y+LEiTh58iSioqIwadIkjB49Wu2Yn3/+Ge+88w4uXryImjVrYtq0aXj88cft3RSy0vlb90w+NjzI146RkDk8dPQs8VGN87uVbXiRapIPQyUgjl3LdGAk8mFRsvTee+/ZOg6TrFy5EuPHj8eCBQvQpk0bfPHFF+jRowdOnTqFatW0l7K4fPkyevbsiZEjR2LZsmXYuXMnxowZg7CwMGU9qN27d2PgwIGYOnUqHn/8caxZswYDBgzAv//+i5YtWzq6iWSGw8kZJh/73bAWdoyEzKGrZ0kBIK+wGDfu5qJGmPYAcCKyHQvKZbk9i8YslTl48CCWLVuG5cuX4/Dhw7aKSa/Zs2dj+PDhGDFiBOLi4jBnzhxER0dj4cKFOo9ftGgRqlWrhjlz5iAuLg4jRozAsGHDMGvWLOUxc+bMQdeuXTF58mTUq1cPkydPRufOnTFnzhy7t4esc86MniVvT45XchaeHgr46FitfuCXe9Dp0+3459xtCaIich+VAn0M7meJCG0WJUupqano1KkTHn74YYwbNw5jx45F8+bN0blzZ9y+bZ8PuoKCAhw8eBCJiYlq2xMTE7Frl+5aO7t379Y6vlu3bjhw4ICy+ri+Y/S9JzkHc4pRlmKy5EzOTeuh9loAOJp8FwCw4O8Ljg+IyI2EB/mpvW5arbza69+P3XBgNPJgUbL08ssvIysrCydPnsSdO3eQkZGBEydOICsrC+PGjbN1jACAtLQ0FBcXIyIiQm17RESE3sV7U1JSdB5fVFSEtLQ0g8cYWhA4Pz8fWVlZav/IscYsPyh1CGRD+UUPSpEYW5iV5KNaxQCpQyATaPYknUnJligS52VRsrRhwwYsXLgQcXFxym3169fH/Pnz8eeff9osOF00p38bK2an63jN7ea+54wZMxASEqL8Fx0dbXL8ZL2CohKz/6BasqYVOc5ba05IHQLZwfIRHPfprFTHDhZrfD4u/PsiTlznQG9VFiVLJSUl8Pb21tru7e2NkhL7LCkRGhoKT09PrR6f1NRUrZ6hMpGRkTqP9/LyQqVKlQweo+89AWDy5MnIzMxU/ktOTrakSWShAV+Yv1RGUTGTJSJHi2bPktOqGfag5I+uz8fXfjrqyHCcnkXJUqdOnfDKK6/gxo0HzzWvX7+OCRMmoHPnzjYLTpWPjw+aN2+OTZs2qW3ftGkTEhISdJ7TunVrreM3btyI+Ph4ZbKn7xh97wkAvr6+CA4OVvtHjnPkv7Et5rBkAVCyr4Hx7JGVkz+O3ZQ6BLKhL4fEo3O9cPz8YoLOz8czKdnIK+RKHWUsSpbmzZuH7OxsxMTEoGbNmqhVqxZiY2ORnZ2Nzz//3NYxKk2cOBFff/01vvnmG5w+fRoTJkxAUlKSsm7S5MmT8eyzzyqPHz16NK5evYqJEyfi9OnT+Oabb7B48WK89tprymNeeeUVbNy4ER9//DHOnDmDjz/+GJs3b8b48ePt1g5yvEI79XiS5TrF6V8zTPCxqSy93KkWBsRXlToMMkFMaDksHvowmlevoHcB3dvZrOZdxqI6S9HR0Th06BA2bdqEM2fOQAiB+vXro0uXLraOT83AgQORnp6ODz74ADdv3kTDhg2xfv16VK9eHQBw8+ZNJCUlKY+PjY3F+vXrMWHCBMyfPx9RUVGYO3eussYSACQkJODHH3/E22+/jXfeeQc1a9bEypUrWWPJSZ26YdlgevYsyUtxiYAXyz3IToi/N+JjKmIV1xiTFU8P3f0mXCHqAYuSpTJdu3ZF165dbRWLScaMGYMxY8bo3LdkyRKtbe3btze6/MoTTzyBJ554whbhkZ1NXnPcovNqhJYzfhA5jaISARb1lh8hWKRDjrx0FIoFeJOpyqLHcOPGjcPcuXO1ts+bN4+Pr8i+LHw8UymQy53ICT+k5UlAsFyADOkrUlnIiTFKFiVLP//8M9q0aaO1PSEhAatXr7Y6KHJfmbmFmPXXWZy/lY2CohLcyy+SOiSSQEERx5jJkRClY2G+Hfowfhur/TeCnNPUPg11budNywMWJUvp6ekICQnR2h4cHKws9khkifd/O4l52y6g62f/oMMn29Dwvb+QnVeo3M9L13UYelzz4R+nHRYH2U7Z39aO9cLxUNXyksZCptNX4mHv5XQHR+K8LEqWatWqhQ0bNmht//PPP1GjRg2rgyL3dVilLMCNzNJVzo9bWRytVY2KVp1P9tGmVqjefT8f4gBhORK8nZGt+YOaaW1799eTEkTinCwa4D1x4kSMHTsWt2/fRqdOnQAAW7ZswaxZs/C///3PpgES6Vp01VSvdK6NIa2r2zAaspVyvlbNLyEnxIoP8hXgwxkVhlj0aTVs2DDk5+dj2rRpmDp1KoDSafqLFi1Sq3NEZC5d9XW8VZIlcz+MJ3StY21IRETk5iy6Zc/NzcVzzz2Ha9eu4datWzh27BjGjh1rcIkQIkup1tsxp5t/8XPx9giHyO2YWiT0mZbsxZUrPkI1zKJkqU+fPli6dCmA0vXgunTpgtmzZ6Nv375YuHChTQMk96JrqqqXnoJphnSJC0fnOCbvRLZgao9uSID2mqFErsCiZOnQoUNo27YtAGD16tWIiIjA1atXsXTpUp31l4hMUVwicP1urtb2P449WIPwxHXTKngXccqr7HHasvO4k1MgdQhkZxxvZphFyVJOTg6CgoIAlC46269fP3h4eKBVq1a4evWqTQMk9/Hrkes6t8/degGAeX887+YUGj+IJBdoYJD366u56rmzGPHdAalDIDsLD/KTOgSnZnHpgLVr1yI5ORl//fUXEhMTAQCpqakIDg62aYDkPiauMvzHsbDY9EKFb/eKszYccoCVo1rp3ffLId3JMzmWEAJHVEp6kGtqVDUED8dUkDoMp2VRsvTuu+/itddeQ0xMDFq2bInWrVsDKO1latq0qU0DJCrz/u+m1/yIj2FtJTloEKVd3FaVakFSksbfZ29LHQI5yE+jE9C/WVWpw3BKFiVLTzzxBJKSknDgwAG14pSdO3fGZ599ZrPgiFT9sC9Z6hDIwaavPyN1CG7v9r18k477qF8jO0dCjhDoy3pLulhcFS4yMhKRkZFq21q0aGF1QEREZQ4nZUgdApmob9MqUodANvBSp1r4bveDscdCCCgUhhYncg+Wl0YmsqG8wmKD+29nm3Z3S67F04Mf0lJb9PdFqUMgB9Ic6P3Pea73CjBZIoloFrn7Zudlg8fPWM+FVV3d2pe0V6lnsuR4uQXF+OPYTWT9N17sUtp9k85j54Nr2nQqReoQnAKTJXK4rLxC1Hn7T8R/uBm5BaU9SsZ6jtLvs86LqzrxfjfsmdwZTaLL440e9dT2sfvf8d5acxwvrTiEl5YfMrlyNwAowJ+VK+IajqWYLJHDPTRlIwqLBdLu5WPKb6Uz3Ix9JpvTw/BY4yhrwiMHC/T1QmRIade/Zi0tL/YsOdwvh0tLNuw4n4bYyetNPo8/KtexYkTLBy9YrBIAkyWS2Fo9hSg1bT2TavJ7Pt6MA03lqkhjuRtP9izJBnsBXUdokK/UITgdJkvkcgqLTC9eSc7FU+MTyYJlAYnIShwrqI0fReRydC3GS/LgrZEtXUnLkSgS9/TdrisWn8s/sK5D9fE3P01LMVkih7qfX6Rz+xIrPqQ1CV7esqWZLKVk5SG/yHBZCbKd934zvUq+Ki6T4Vo8VB6pfvnPJey/ckfCaJwDkyVyKM0lS+wxzKFr/Qjbvyk5RJ8m2oPz8/lY1elxxXrXovm5/OSi3dIE4kSYLJFDrTpwze7fw9eL5frlqlKgLxpHl1fbJpgrOaV+KhMpmCuRq2OyRA6z+qD9EyWSv+Wq05YBFJUwW3JGft6eaBAVDEA9cSJyRaw2RQ7z2k9H7fK+gb5eeKJ5VSzZdYUrZruAQI0ieJq1l8g5CAGsGtUap29moVk1jllyJSH+3lKH4HTYs0SSyiu0Ta/BW73isGpUa0zv19Am70fSals7VPl1YYnA+B8P47F5/yI1K0/CqEhV46ohKOfrhfiYivDgTDiXEuSnnSxN/uU40u+57xqdTJbIIXZd1L8YY4YNljLx9vRAi9iKHK/kgoqLBdYeuYFj1zLRYvoW7LrAhT2lNq5zbTwZHy11GGRH5QPUE6Yf9iXh7bUnJIpGekyWyCF+OaS/UvfLPxx2YCQkBzVCyym/1hyzNOjrvY4OhzQ83SKadZVcnGYZDwA4fTNLgkicA5Mlkty/VvYUmLPYJ8nDxMS6yq9ZZNQxSjg2jFToWmroSnoOSkoEluy8jKnrTrnVZy+TJSJyOiH+3ggNLF2f6rejpq0fSNb5cX+yyce60d9It+Xvo3tIQ1ZeIab8fgqL/72M49czHRyVdJgsEZFTKltyYf62i1r72Atie+uP3zT5WP7fd32fP91U53bVBZPXHHafGxkmS+QQrLFE5jI0JqaQtZeI7KphlRCd21UfvX2784qDopEekyUicko3MnP17mPtJdszZ+khdxqrQurOpmRLHYIkmCyR7H3Qh7WVXJGhv8dFTJYkVTaejNzPwC/3SB2CJFjBm2QrNrQclg5rgeiKAVKHQg5WxBlykvm/7vXg5816ZuRemCyRbG19tb3aYENyH1wvzvZ2nDethMeLHWraORIi58PHcGR3ey+l2/w9H6oawkTJjXHMEpH9je9S2+gx7jKGickS2Z09nnH7evFX153tsUMCTkTqxnepY/SYpbuv2D8QJ8C/OEQkOxNWHpU6BJdyJsV9l7Eg6+haFsUVccwSyRJnLhPZxmebzuF/W85LHQbJlLv08rtHK8nlMFdyfY82jpI6BJe39cwtJkpkUL9mVQzuZ7JERCShPkyW7Org1TsYtuSA1GGQk5v+eCOD+wN83eMBFZMlkqWBD0dLHQLZmacnZzvaU/+Fu6UOgWTAWM+Rp5vMSpZNspSRkYEhQ4YgJCQEISEhGDJkCO7evWvwHCEEpkyZgqioKPj7+6NDhw44efKk2jFffvklOnTogODgYCgUCqPvSc7hyeZVpQ6B7MzLwNpwAJfcIHIEYyVait3kOpRNsjRo0CAcOXIEGzZswIYNG3DkyBEMGTLE4DkzZ87E7NmzMW/ePOzfvx+RkZHo2rUrsrMf1IXIyclB9+7d8eabb9q7CWRDrLHk+gwtpAsAuy6yfACRI4xqV0PvPnepeSaLh42nT5/Ghg0bsGfPHrRs2RIA8NVXX6F169Y4e/Ys6tatq3WOEAJz5szBW2+9hX79+gEAvvvuO0RERGDFihUYNWoUAGD8+PEAgL///tshbSEi03h5qN/Lda0fgU2nbilfX0q7jza1Qh0dFpHbmdwzDq1qVMLzS/Zr7XOXpYdk0bO0e/duhISEKBMlAGjVqhVCQkKwa9cunedcvnwZKSkpSExMVG7z9fVF+/bt9Z5jqvz8fGRlZan9I9uaPaCx1CGQxDR7ll7uVEvt9Y27uY4Mh8itleh53FbsJksPySJZSklJQXh4uNb28PBwpKSk6D0HACIiItS2R0RE6D3HVDNmzFCOnQoJCUF0NAcb21q/ZlWx5dX2UodBEtJMlh6qWh6No8srXy/8+6KDIyJyXx56HosXucljOEmTpSlTpkChUBj8d+BA6dRWXWNUhBBGx65o7jflHGMmT56MzMxM5b/k5GSr3o90CwvylToEcjIvcRFXm8grLJY6BJIZbw/d6UJ+kXv0LEk6Zmns2LF46qmnDB4TExODY8eO4datW1r7bt++rdVzVCYyMhJAaQ9T5cqVldtTU1P1nmMqX19f+PryD7kpSiy462hbm+NQSL3b/40e9QBwYL+tLP73skXnjW7PZNVdBfmppwvVKwXganoOFv97Ge/0ri9RVI4jabIUGhqK0FDjfxhbt26NzMxM7Nu3Dy1atAAA7N27F5mZmUhISNB5TmxsLCIjI7Fp0yY0bdoUAFBQUIDt27fj448/tl0jyKCdF9PMPufboQ/bIRKSG9UhEsPaxAJwn5k39nY1/b5F59UIK2fjSEguHqoaovb6anqO8usT1zPRsEqI5ikuRRZjluLi4tC9e3eMHDkSe/bswZ49ezBy5Ej07t1bbSZcvXr1sGbNGgCld6Djx4/H9OnTsWbNGpw4cQJDhw5FQEAABg0apDwnJSUFR44cwYULFwAAx48fx5EjR3Dnzh3HNtJFZecVmX2O138LM+rrQ6hUzseKiEguVOsoef9XoFLfIFNyDEt6isk1GOrVHfX9QQdGIg1ZlA4AgOXLl2PcuHHK2W2PPfYY5s2bp3bM2bNnkZmZqXw9adIk5ObmYsyYMcjIyEDLli2xceNGBAUFKY9ZtGgR3n//feXrdu3aAQC+/fZbDB061I4tImP0XZyfP93UwZGQFFT/LJf9LjBZsg2F3lsRw9ylACGZ57obzEyVTbJUsWJFLFu2zOAxmhV9FQoFpkyZgilTpug9x9h+kk45H0+d291l9oW7qxMepLWNP3ppBbrJOmBEmvibT3Zl7o3ovEEPeo0UCgVe7FBTa4p4RLCfLUIjJxcS4I39b3WBn/eD0QLl/b0ljMh1HE7OMPucPk2i0KtRZeMHkstSKMz/THcVshizRPKTV1hs0dpdvR9SX2l+fJfaWsfUjdTucSDXFBbkiyC/BwkSZ0raxrlb98w6vl2dMPzvqabK8YTknlQ/0utXDtbY59pZFH/zyeZu3M1FvXc2YPQy6wf9+Xp5olrFABtERa6ApQOkERbIUikEtK8TBgCoEao9K3LfZdeeFMVkiax2+mYWxv1wGFfSSqcj/7i/tEjnXydvYcKqI1a//4Su2r1L5L7mDGyi/NrV72adxeSe9aQOgZzAZwOb4PVudbF8ZEtUClSflXw3t1CiqByDyRJZrc/8nfjt6A0M07HIYoENqrtaOnOHXFOHumHKrznY3zFC2bNEACqW88FLHWuhcog/pj/eSG1fWXkPV8VkiaxWlhBdSrOs0J0xfPJCqlTXjLue4fpTlomcUbSbDY9gskQOxzXfyBreKoOM3/vtpISROL/cgmLM+PM0luy0bHkTAOj9EGfAkW5lY5gA158lx9IBZHPrjt0wuH/FiJa4lZWPZxbvVds+rlMtncerDur9Ykhz6wMkWVPtWUq6k2PgSPd2Je0+Osz6W/m6dkQQ2tQyfzbhpG4cr0S6tYitiO3nbgMAcgpce3Fm9iyRVc6mZGttu3Tb+OO4R8yYAt6xbhgCfb3QukYldGsQaVZ85Hq8VJIlVx8nYamSEqGWKAHAnkvpAIAVe5PMeq9qldzrcQuZTnWCxcs/HJYwEvtjskRWOZRkfnE7ffT14gb5eePQO12xYmRLm30vki/VnkYO/tftl8PXtbZ9vrV0/cs31xw3ev7r3eoiyM8L3w9vYfPYyHVolvLIuF9g8rlym8nKZImsovmn6tSNLKPnWHKJ+Hh5sMYOadH3K3EzMxeLtl/E3RzTP7xdyeZTt6w6f0jr6jj6biLa1g4zfjC5rccaqxcRbjp1E/IKjT+Oe2vNcXSevR05BeYvtC4VJktkFc0/Vj3n7pAmEHJL+hLop77cg4/+PINJq485OCLppd3Lx4aTKTr35ReZPq7Ew4M3J2SYrhlxt7LyjJ63fG8SLt2+j3XHbtojLLtgskRWseQxSHU9YyBk1itLTqBVjYo6t19NLx34/c/5244Mxyk8/612vbMyn248Z9J7BHjrXsSayBhPM5JsOT2KY7JE1jEzVzoztTt8vfhBTNYZ1a4GAPXB3rp4uOGj2+PXM/XuW6NjLJOmKuX9uQYcWcyca27aH6ftGIlt8Yogh/LjHSvZQNndq7EK3u6WKhkbL2LK/48gP1aUIcsVG7kmd11IU36dlVeEMynGx7k6AyZLZBVb/jESFg39JndUliwZ+2B2t56l9cflMwaEXNNXOy4Z3D/oa/X6ep9sOGvPcGyGyRJZxZYz1J5rHWOz9yLXVpYELd19FSWGEib3ypWMrsWYmp3voEjIXS3dfdWs4wuKrV8/1BGYLJHTCA/2kzoEkgnVHqVrGbmY/Msx9Jq7Q2u2V3aefKYmOwsZjbklF7DjfBp+2GdeoVQpMFkiq9jqxv2TJx6y0TuRO1B9ZKtQAD/sS8bJG1n451ya1rFX7LTAs6vqWC9c6hBIRgbGR1v9HpN/MV4oVWpMlsgifx6/iV5zd+Bqum3+ECVyGRMyg76ee13JuzlTmeXOFp1C47vUtsG7kLuY0LWOTd8vr7AYhU74aI7THshsQgi8uPwQAOCkCRW7TeFm43DJSsUlDz5MVR8beXoosP/KHbVjM3MLYf29r/vgjFUyR4Vy3jZ7r7zCYjR+fyMqlvPB7smdbfa+tsCeJTLbHxbOuBnXWf2OtV+zKsqvmSuROVRvPFXHKSkUwHe7rqgd22/BLgdFJT1eR+Rovl6eeKJ5VZu816Xb95FfVIKbmXmGJ25IgMkSmW3/5TvGD9Khcoj6AO7Xu9W1RTjkhkpUupOy8x8M4vby8NBaQkEus21swbn+vJC7+LBvQ7XXhtZ8a1w1ROf2vMJitScMhSXOdd0yWSKzfWfm1NAyRRp/tDjrhiylmiy9veaE8msPN/9Ee/fXE8YPIrIxP29PvKLy5OCL7fprLVWvVE7n9mV71P+uFBY71x8IN/9oIUfS/OUv5/NgyJw3l1cgM6iWDjh188G4uVHfH5QiHKfhbH9gyH20rR2q/NpQPa/fjt7Quf3O/QK115o311LjAG8yat2xG/jg91NY+EwzxFUOtvh9NB/DhQR4439PNYGXhwcHlZJZSvR0S7KuEpE0VNcT9PbUPXru/K1svef7eXuqXddp9woQ5OftNLNZeTtPRo1dcRip2fl4/tv9eONny+thdNNRHqBPkyro9VBla8IjN2RsmRNndPpmFp79Zh+OXbsrdSh6jetUS+oQSKZUF7XWl+DczS1Ufu3jpZ5++Hl7oEilZ7TL7O14+ss9No7SckyWyGRZeUV6u1BN4eEkdwgkf07WQ2+SwV/vxT/nbuOxeTulDkWnGmHlMDGRky7IMqpDKbz0fNarbhcavcP+3p4o0hjUve+KZZOJ7IHJEhHJjr7HcM5MdUyGZi0oW9h1Ubt6uTmWDW9po0jIHXl5qvYs6U4tvFS2D3skVm1fQbFAXqH2XVB2XqHWNikwWSKUlAhcy8iROgwik3WJizDr+Pv5zjWW6dnF+2z+ntYuGRFV3t9GkZA78lZJhHz0jFlSvcmpVM4HH/RpoHw9dd0pDP56r9Y5U347ZcMoLcdkiTD5l+N45ONt+OlAstY+za5SImfQs5F5y+N8tUP/VGZH0Cywl1tYrOdIx3u9W12sGZMgdRgkc6o1kgJ8H8wdm7nhDDp8sg13cwrUHrMVlQi0rR1m9H23nU21aZyWYrJEWPlfkjRn83mtfX+dTHF0OERGKcxcHyclM89OkQC3s/MxcdURHLyq/9GaI8ZeWHpf07V+BJpWq2DbYMjtFKncEPirzG5e8PdFXEnPwXe7rqqVtigqFogN1V1zSZWHk6yFxWSJlK7fzdXadiWdj+dI/kL8bbd+laa31x7HL4euo//C3XqPyS2wf0+SsLB+d82wQBtHQu6oesUA5de6JvMUlZSozXYzdbFcZynB5yRhkLO4m1Ng/CAimQnwsV9JuYu376t8fc/k8zJzpR+4Guzn5TR1bEjePDwUysfjutZ1KxFCbQkTfx/Taut5smeJnIHmmCRdsxGInFGXuHCTj/1s8zm7xaH6h+F7PUsB6er1mfaHbQeuOtlSWuSGyh6Z6ZqtKgTUepaeax1j2ns6STLPZMnNbT6tPnhOc8Ynx3eTsxoQH23W8XsvpdsljmKVi8Scm+CTN7KMH2SiKb+d1PkYnciRypIlXUVjj1/PVC5h8nBMBZT7bxD4pO6Ga3s5S88nkyU3dyQ5Q+0115YiuTDlQ7R8wIOxSgPtVA34qsq4Pn2DURXQ3m4s/gV/X0DHWX/jtoF1tgDgUFIGluy6YjxQIjsr++txSseNwI7zaSj8L4lSrbdk7KbHWdYNdY4oSDKaPUdDv7F9/RcAeKRWqPGDiMxgSvf83Rz7jgvSrN9kzl2wsWNnbjiLy2n3MX/bBYPHzVh/2uTvqWkslzchG/rz+E0AwC+Hr+vcf+O/3k/V33191b5N3e8oTJZIzflU0weommPBM83s8r7kvoxNKXZE7aAijZ5YfSHpGrN0/FqmSd+jwMisof1XMgzuN2Rk2xoWn0ukSbV8gK5B3h/9eQYA8O+FB9Xmjd00NK/uHGUtmCzJTGZOITaeTEFBkW1Gc9rzoVs1lamkAd6mzXwgMlX6Pf2Pp17vVtchtYM0B7KaUxOmqETY7Dq2lLn1qohMVePN9Thx3fgNgZeepVHKsM4SWWTw4j144fuDmLdVu4CkPVhauwUA5g960JvED2WyNdW11gDAW2WJhRFtYzUPt4tiHTMgfjt6A8l3SscxZdwvwNrD1/XOMt132XkWCiWyVrxGL1Dvz/81eo6XnqVRylxJv29wv6MwWZKZE9dLB86tOaL7mbC57tzTrqtkq/ovqvkRUyWytW4N1Jc8UZ2c4OtV2pP55ytt7RqD5qyfnw9ew7gfDqPtzG0AgOe+3YfxK49g4qojOs9/ZrH2WliaVuxNsjpOIkf48PGGZp9jbAD3jvNpyCmQfm1HJktubqWO9eAWbb9o8+/DjiWyteiKAQj2e1BsUleV7roRQcqvQwN9bR6DZhXiVI2Za8f+G5fE+mXkDsr7+9jlfZ2hgKtskqWMjAwMGTIEISEhCAkJwZAhQ3D37l2D5wghMGXKFERFRcHf3x8dOnTAyZMnlfvv3LmDl19+GXXr1kVAQACqVauGcePGITPTtIGXUrJn/SNbrdCu1rPEbInsYHq/RogK8cPyES2xdFgLxFevgF9UBnarzpjTtxK6JU7eyMTV9PsGk6BXVx212ffTx1hZASJHMnU26KJnmqu9blglWPn1F0Oaax7uFOOWZJMsDRo0CEeOHMGGDRuwYcMGHDlyBEOGDDF4zsyZMzF79mzMmzcP+/fvR2RkJLp27Yrs7GwAwI0bN3Djxg3MmjULx48fx5IlS7BhwwYMHz7cEU2yij0L0NnqF7N2eBDCg3xRLzLI+MFEFuj9UBR2Te6MNrVC0Ti6PFa/mIBmegZ2R4b42eR7pmbnodfcf9H+k7/x4rKDeo/7+dA1k97vSPJdo8e8vfY4dpy/rbV9zHL93//J5lUNvufr3QwXAyQyl6nJUv3KwWqvm6tcs90aROJ/TzVBoyohym26ilw6mv0WTLKh06dPY8OGDdizZw9atmwJAPjqq6/QunVrnD17FnXral/0QgjMmTMHb731Fvr16wcA+O677xAREYEVK1Zg1KhRaNiwIX7++WflOTVr1sS0adPwzDPPoKioCF5ezvu/Rwggr7AYfnaYZab6C29pD1aFAG/4eHlg5xudnGZtH3JPfZpE4dcjN3Ao6a5N3u9K2oMilLYotbH51C00iS6vti2vUH3h3WV7krBsTxKufNRLua2gqMRg2YDm1Stg7+U7SLqjezFsbxv2tBEBpq/j5qnxu/dqt7oI9vdG74eiAAB9mlRBy9hKaDVjCwBg6Lf78NPoBLsuiG2MLHqWdu/ejZCQEGWiBACtWrVCSEgIdu3apfOcy5cvIyUlBYmJicptvr6+aN++vd5zACAzMxPBwcEGE6X8/HxkZWWp/ZPCPRs9LtOkmiz9ez7NwJH6Pda49Jfe29PDadb2IfcUH1NR+fXBq5bXJCpj619nXXfjczYbnu16/Fom6rz9p8FjBIAfX2hlTWhEZtFMgvTRLDQZ7OeNVxProq7KU4jIED+U+2+x3XO37hktzmpvskiWUlJSEB6uvWhmeHg4UlJS9J4DABEREWrbIyIi9J6Tnp6OqVOnYtSoUQbjmTFjhnLsVEhICKKjzVujylbs9RxX9X13m7ie1vTHG9klFiJrqd7tXrptfU+Qrcff6apQvP2c9iM3VR+auAhvVHl/7JncGd0aRBg/mMhKJvcsmXjHoXqjfTdHe+a2I0maLE2ZMgUKhcLgvwMHDgDQ/QElhDD6waW5X985WVlZ6NWrF+rXr4/33nvP4HtOnjwZmZmZyn/Jydozymzh3K1sbDuTistppXUmijRm3tjrOa4ld86d6pm+AjyRI6n+Ptsi0bH1PYquu3Fh4Pn33ZwC7DWjPlNkiB8CfLR7yrlINtmakfqSSqYuYaJ64+5p6pvbiaSDcsaOHYunnnrK4DExMTE4duwYbt26pbXv9u3bWj1HZSIjS2uwpKSkoHLlysrtqampWudkZ2eje/fuCAwMxJo1a+Dtbfi5qK+vL3x9bT8NWdM3/17Gj/uTMbp9TQT5eWkN6v5s8zm79Ogs+PsiJnWvZ9Y5fNJGzkr17tQWv6e27tE1d+2rod/uN/t7GEq+iGzFWDXuMqb2LOUXPRi7J/UacZImS6GhoQgNNb7AauvWrZGZmYl9+/ahRYsWAIC9e/ciMzMTCQm613+KjY1FZGQkNm3ahKZNmwIACgoKsH37dnz88cfK47KystCtWzf4+vrit99+g5+fbWbM2IKPV+kvnr66Ryv2JjnN4y+WBiBnlVvw4APXGX5Nez9UGeuO3VS+1pV8nUnJ1nu+KbPnAPXlhogcwdb5jGppDnMWqbYHWYxZiouLQ/fu3TFy5Ejs2bMHe/bswciRI9G7d2+1mXD16tXDmjVrAJT+8R4/fjymT5+ONWvW4MSJExg6dCgCAgIwaNAgAKU9SomJibh//z4WL16MrKwspKSkICUlBcXFxTpjcSQfI5VNraVroUNLsWeJnNUBlUHdtugVsvbx94x+6jc4mhWMNR+3W+Ld3vWRULOS8rWuiNnXRLZm6Kb57V5x8PZUIL56BQT6mt9PI3Wy5Lxz4zUsX74c48aNU85ue+yxxzBv3jy1Y86ePatWUHLSpEnIzc3FmDFjkJGRgZYtW2Ljxo0ICiodcX/w4EHs3Vu63ECtWrXU3uvy5cuIiYmxY4uM8/ayb7K02kgdGHO67qWc0klkyLhOtfD70RsAbPOBa22yFOSnfq2U0/jDUWCDZOn5NjFqf7ieergafj1yQ+0YPpkje1LtQR3+SCxGtK2B4Y+UrtloyZMIqR/DyaJnCQAqVqyIZcuWKafqL1u2DOXLl1c7RgiBoUOHKl8rFApMmTIFN2/eRF5eHrZv346GDR+sXdOhQwcIIXT+kzpRAuzfszRp9TGD+419mLavE6b82tNDgVMfdFO+bhFbSdcpRA5XW2XJk90XTZvdaUhRiW2XLvHVuCkydN1l3DdtRpDmH6PWNXk9kmM83aIaQgN98EGfB39ry34byyZuWeKng6YVebUX2SRL7sjHzj1LxpQYyZZGta+h/FqhUCDAxws7JnXEomeaoWejSANnEkljuQ0WpbWmZ6lLnPaEFM11rwy9+7T1py3+3pqYQJE9zOjXCHvf7IKK5R6sE2eLWnt3TLxRsBcmS05M847T0Qz9Tej1UGUooH0BRFcMQPeGlTngm1xWkY4LY+HgZiad+0aP0jGWP4x8UCzy7bUn1I4xlIytttHd9dqX2mhVDSeyFanHF9kDkyUn5shntFP7NFB7ve/yHfywT/9d+KdPNrZ3SEROSdfEiCoV/E061/+/ekeGenUcMc2fiRI5kuqMVLmSzQBvd6TrDlaV6kKD5rqZqV6zqarGNOMBX+w2mKzZY006IqkVFZegRBh+BK7ruvT1Mu160DcO8ULqPdQKDwTgHIuGEtlSTGg5q99D6rUM2bPkxPKLDA8kNZRM7b2Ujg/XndJakLPM9Qz1ZCm6gnZNFmPJGmfAkSsRQqDTp9vRcvpmFBqYkaYrmQnyM+2+U18S1mX2dqTdywdg+PG3KYYmxBjc3yK2osH9RLby+9hHMLFrHTzTqprUoViNPUtOTF+iU6ZYz6ycyb8cVz5CC/Lzxitdamsdo/l5XHZXa476UcF4pXNtRJV3nkKeRMbkFRbr7BktKhFIupMDAEi6k4OaYbqvCV03EaqDWQ0xNA7xStp9hAb6Gp1YYYy+x3hjO9bCvG0X8GbPOKven8hUjaqGoFFVy5+AzHqyMT74/SSy8ook73Flz5ITs6RnqaREqI01+mzzOZ3nHkm6a1VsZSZ0rYOBD8v/roHcx+xNpddESmYejl27i2PX7kIIYfKHsa6bFFMfSxsqB1I2J8LaZElfnabXutXF2Q+7c7wSycYTzati22sdAJT2uEq5bA97lpxYXOUgg/sv3b6PeVvP4/rdXPRsVBlta4eh2MRfJltOQSaSk02nbuHNnnFoNWOLcttnAxsjsb5p5S6KikuvsY51wzCpez2U07FIrS51IgINTqEum0Fq7R10fqH+myxTx1YROQvV9eaW7bmKIa1jJImDPUtOrE/jKkaPmbXxHH7Yl4whi/cBAO7lFdkllgTWZCEXoStd+fngdWUSBAAf/XlG7/llyYynhwJxlYNRrVLpeL+IYO3FtYe0qq78OiLY8ONqz/+SJWtrXuYVyX/mEVEZ1bV5pSxJw2TJiVlSyGvE0gNa21pM22x0/JMxH/d/SPn17AEsG0Dysvi5+AcvdFxWHh4Ktcrcm07d0vteRSrJkqp2tcO0jp3a90EVY31joJQx/PeH4L3fThg8zpgnm0dbdT6RM1HtWZKyUDOTJRdyJPkuDqosGlomNTsf9d7ZYNV7+3o/+FXp2aiyVe9F5Gi1w9UfaWvWSvJUaD/+0regbdmYItUPcQDw0jMe6fexj2D4I7GY0LWO2vYPVRIpoHTMkhAC287e1tMK03SsF27V+UTORPUyk7JQM5MlF9J3/k6TjjuafFft9YQupR/ihupYhAf5YXyX2nijRz3WWCLZ0bwjLdR41uXpodCaMLFCT1HWH/clAwAyctSXX9BXPqBR1RC807u+VqmNRxtHqb1WKICtZ1L1tIDIPanelDBZIrM0rVbe4nNTs/Iw8y/18RjB/qUf8pp3uprGd6mD0e1rWvy9iaRSPuBBonIvrwiFxeqJkYdCodWzdOJ6ps73OnUzCwCwS2NR3gHxVc2KSdeSELp6honcmeplUjnEtEr5dolDsu9MFnvv0QbGD9Ih+U4OWkzfgp0X1D/ky34XWQKAXJVqb2hqdr7W4rXnU+9pFaL09DDv47FWeBAOvN0Fwx+JBWA8edKskJ9fVGJwEV0id6RQKPBCuxp4onlVPGRFzSZrsXSADNW2oIAkAPx9VncXPxe9JXfQv1lV/HyodCHaIYv3qu27nHZf6zGcgZJIeoUG+uL/utdD53rhaB5TweCxmj1Lr/10FN0amFa+gMidOEMhVfYsyUSV8g+6H8v52jbHZa5E7sBPZZLCpdv3tfYnfvaP2mtPIxeGvuV+fLw8kFAr1GhNI833v3T7PiSsuUdEBjBZcnLfPv8wRraNxaj2Nax+L32fw5qzeohckbnFHlcduIbMnEKt7aGBpUubzHziIa195tBVGkTfEkZEJC3+lXRyHeuG461e9bUSmvAg7QJ4xhQV6/5j8WhjlgIg12dsYWhNuYXFeGnFIQClidbba49jzeFrCPYr7VGqEGDaenDm+H7PVbXXhpZHURXzX2FMKWcLEbkyXlky0a5OKIAHVYJ/fKGV2e/xwbpTOrcH+el+nEDkSvTVTTLk3wtpAID1x29i2Z4kTFh5VFl2wMtAqQ1L5WksVfJyp1omnTeqfU3876kmynW0iMi2OMBbJqpWCMC+tzor72prGKkGbEvBeurHEMmJZrkAc6Tdy3/wPkWl7+PtgMfXbeuE4dNNuhfDVuXloUCfJsaXRyIiy7BnSUbCg/wkKQj5cqfaDv+eRLamr2ikKVSf4JWVGPD2sv/MiCbR5bHu5UeMzoDljFYi+2KyJGNhFoxbsoRg9RdyAa8m1rXoPCEEhMo0tfT7pZW7HTUxomGVEKPjo+7n22cBbSIqxWRJxp5uYfsikm1qVdLaxunM5Aosvbk4cDVDuR6cKkPLAzladEXpKhsTuQMmSzI2/JFY1AgrZ9P31FxwFNBfcoDIHdy5X4Dfj97U2q5v4VwpdKzLxXOJ7Ml5rnYyW4i/N7a+2sGm7+nrrf0rYer0ZSJX5OWhwHEd68TZomfp5xcT0K5OmNXvwzFLRPbFv4IuoHl1w8sqmMNXR2KkuTo6kat4/zHj6yzq60GyxWy45tUrYHwX4xMoOG6QSFpMllzU/55qgl/GJBg9rkVsRbXXg1tV1zrGUQPJiRytb1Pt6faaNx/eOiptA7ars2RsWRWA4waJpMZkyQUIjU/SHg0j0adJFTSrVgGnP+iOtrVD9Z676Jnmaq8jgv3sEiORM9Jc323J8w/j0ycbq23TtSwJULoGnC1oLqhrql6NSivvD02IsUkcRKQfqw26ANVUKTa0HD5WWbPK38dwXaZAGy/KSyRnHeqGa60Hdzs7X+exxhbKNZWHKT1LOraNal8D7z1anz2/RA7AniUXoNqx9ETzqsoq37r2q6oVHmizu2MiOXi3d32tbSfe74bhj8Ri44R2AACFxiUxfuURu8akr2dp0TPNDJ73UNXyCA/24+BuIgfgX0oXoPkYTlP1/xbZ1BRVnrVZyL2o9sI0rBIMoLR39Z3e9VEnorRshuYYomIzF+A1l65kqUKAN7o35ALXRM6CyZILUP0o11Xpd1K3ema937qXH7EyIiLnpJqYjO2oe5FaU8YQxei5AbE2pgfb1D+ajd0QEZF9MVlyAaqfo32bak/zDwnw1toGACV67phrGVmHikiuVPOSxtHl9RxjPFkK8LHdWD9ds+E08yemSkTSYrLkAlRrsJjzIf5mzzid2y2dnUPk7FQTIW899ZNM+f235TWiq1yT5lpvAUYmahCRfTFZcgEf9GkIAHi1ax2Tz/FQAPWjgnXuM6XuC5EcqfbQ6CsqaUoepK+cgCV0JV5PxkervZ7WtxHqRDzo8Q3nDDgih2Ky5AKaVauAcx/2wMudjVcCLrPzjU5693l4KDD36aYAgFHtalgdH5GzUH307O2lO+FRKBT4v+6Gx/kVl5TYLCZdNydv9FD//jGh5bBxQnv8/VoHPNm8Kn54oZXNvj8RGcciOy7CnBIANcLKoXKI4ZlwjzWOQvvaYQj2568IuY5ilQF+XgaWK3mxQ018vOGM3v0Vy9muZ0dXz5Kft+7HbjGh5fCJRtFMIrI/9iy5oWXDW5p0XEiAN2u4kEtRHadkyUK43wyNR6saFfHhf4++bcGUAeVEJC12G7gh1lcid1Wp3IPSGubeCPRrWgWd6kWgU70IW4dFRE6OPUtuYttrHaQOgUhyzapVQGL9CIvWU9NXgsPWuNYbkfNhz5KbiA0th+2vd0A5rgVHbszDQ4Evn4236NyCItsN6lYV5MdrksjZsWfJjVSvVA6hgZxyTGSKxlVD1F4XFtsnWfLy9MCpD7rZ5b2JyDaYLBER6fDuow3UXhcW26+Oti0rghOR7ckmWcrIyMCQIUMQEhKCkJAQDBkyBHfv3jV4jhACU6ZMQVRUFPz9/dGhQwecPHlS7ZhRo0ahZs2a8Pf3R1hYGPr06YMzZ/RPGSYi91DOV336frcGkQ75vpwcR+R8ZJMsDRo0CEeOHMGGDRuwYcMGHDlyBEOGDDF4zsyZMzF79mzMmzcP+/fvR2RkJLp27Yrs7GzlMc2bN8e3336L06dP46+//oIQAomJiSguLrZ3k4jIiWkuOdKtAWfBEbkrWfT9nj59Ghs2bMCePXvQsmVpjaCvvvoKrVu3xtmzZ1G3bl2tc4QQmDNnDt566y3069cPAPDdd98hIiICK1aswKhRowAAL7zwgvKcmJgYfPjhh2jcuDGuXLmCmjVrOqB1ROSMGlctr/aaNceI3JcsepZ2796NkJAQZaIEAK1atUJISAh27dql85zLly8jJSUFiYmJym2+vr5o37693nPu37+Pb7/9FrGxsYiOjtZ5DADk5+cjKytL7R8RuRYvTw/Ur6x7/UR7UoBJGZGzkUWylJKSgvDwcK3t4eHhSElJ0XsOAEREqHedR0REaJ2zYMECBAYGIjAwEBs2bMCmTZvg4+MDfWbMmKEcOxUSEmIwsSIi+TJnGSEicl2SfhJMmTIFCoXC4L8DBw4A0N0FLoQw2jWuuV/XOYMHD8bhw4exfft21K5dGwMGDEBeXp7e95w8eTIyMzOV/5KTk01tMhHJSGxoOYd9r0dqhQIABj7Mmy8iZyPpmKWxY8fiqaeeMnhMTEwMjh07hlu3bmntu337tlbPUZnIyNKZKykpKahcubJye2pqqtY5ZT1EtWvXRqtWrVChQgWsWbMGTz/9tM739vX1ha8v6xURubp3etdHflExBj5cze7fa+mwFsjKK0T5AP292kQkDUmTpdDQUISGhho9rnXr1sjMzMS+ffvQokULAMDevXuRmZmJhIQEnefExsYiMjISmzZtQtOmTQEABQUF2L59Oz7++GOD308Igfz8fDNbQ0SupmI5HywY3Nwh38vDQ8FEichJyeKBfFxcHLp3746RI0diz5492LNnD0aOHInevXurzYSrV68e1qxZA6D08dv48eMxffp0rFmzBidOnMDQoUMREBCAQYMGAQAuXbqEGTNm4ODBg0hKSsLu3bsxYMAA+Pv7o2fPnpK0lYiIiJyLLEoHAMDy5csxbtw45ey2xx57DPPmzVM75uzZs8jMzFS+njRpEnJzczFmzBhkZGSgZcuW2LhxI4KCggAAfn5+2LFjB+bMmYOMjAxERESgXbt22LVrl84B5UREROR+FEII+9XwdxNZWVkICQlBZmYmgoMdP9WYiIiIzGfq329ZPIYjIiIikgqTJSIiIiIDmCwRERERGcBkiYiIiMgAJktEREREBjBZIiIiIjKAyRIRERGRAUyWiIiIiAxgskRERERkAJMlIiIiIgOYLBEREREZIJuFdJ1Z2fJ6WVlZEkdCREREpir7u21smVwmSzaQnZ0NAIiOjpY4EiIiIjJXdnY2QkJC9O5XCGPpFBlVUlKCGzduICgoCAqFwmbvm5WVhejoaCQnJxtcDVmu2D75Yxvlj+2TP7bRckIIZGdnIyoqCh4e+kcmsWfJBjw8PFC1alW7vX9wcLDLXgAA2+cK2Eb5Y/vkj220jKEepTIc4E1ERERkAJMlIiIiIgOYLDkxX19fvPfee/D19ZU6FLtg++SPbZQ/tk/+2Eb74wBvIiIiIgPYs0RERERkAJMlIiIiIgOYLBEREREZwGSJiIiIyAAmS2R3rjqH4MCBA8jLy5M6DCKT8DokshyTJYncuXMHaWlpAEqXS3ElN2/exJNPPomVK1cCcL32Xbp0CX369EGLFi2watUqqcOxi+TkZKxevRqHDh1CYWEhANf7Y+vK1yDA69AV8Dp0HkyWJPDWW2+hXr16+PLLLwHA4Ho0crR48WL8/PPPmDNnDnJycuDp6enUF4GphBAYM2YMateuDYVCgZCQEAQGBkodls1NnjwZderUwaeffoqEhAS8+OKLuHTpEhQKhct8ULv6NQjwOpQ7XofOxXkjc0F3797F8OHDsXnzZlSrVg179uzB/v37AbjW3cKuXbswcOBA+Pr6YubMmVKHYxNr165FuXLlcPDgQezatQtr165FXFwc/vzzTwCu8/Pbu3cvfv31V6xevRrbtm3D119/jfPnz2PIkCEAYNOFoqXgLtcgwOtQzngdOh8mS3am+oP39/dH9erVMXnyZHz66ae4fv061qxZg8LCQlneLWjGW1RUBACoXLkyBg4ciISEBKxatQqnT5+Gh4eHrNt3+/ZtLFu2DHv37kXLli2Rm5uLmjVr4s6dO8jJyZH9h1eZtWvXori4GL169YKfnx+eeeYZfPTRRzh27Bg+++wzAM77YaaPK1+DAK9DXofyIPvrUJDd5OTkiLy8POXrkpIScffuXeXrV199VbRp00b88ccfyv1yoattZRo1aiROnjwp9u3bJzp27CjGjRsn8vPzxYkTJ6QI1SKa7SsuLlZ+XVRUJIQQYvz48eKhhx7S2i8XZT8z1dhnz54tGjduLO7fv6923JQpU0SFChXU/p/IgStfg0LwOhSC16EcuMJ1yJ4lO5k8eTIeeeQR9O7dG3PnzkVWVhYUCgWCg4OV4wbGjRsHIQTWrl2LtLQ0582oNehrW0lJCa5fv45y5cohJiYGDz/8MB599FGsWLECfn5+2Lp1KwoKCqQO3yjN9mVnZ8PDw0P5cyu7e+3SpQuuXLmCpKQkp37Wrsvs2bMxffp0AOrjBIKDg+Hl5YUtW7YotykUCjz33HMICAiQ1V2tK1+DAK9DXoe8Dh1JXr9ZMlBQUIAnn3wSv/32GyZNmoSoqCh88cUXGDRoEIDSX/iyC75atWoYMGAADh06hHXr1in3O9svSRljbfPw8EBwcDC8vb2hUCiwZs0afPjhhygsLESjRo3w8ssvw8fHR3bte/rppwE8+DAr+29xcTEqVaqE5ORkyWI21/79+9GxY0e89tpr+OWXX7B7924AUM60efLJJ1FQUIANGzYgNTVVeV7lypXRtWtXnDt3DsXFxU79uMOVr0GA1yGvQ16HkpCkP8uFnTp1StSuXVts3LhRue3ff/8V/v7+YubMmVpdrnl5eaJnz55iwIAB4tixY2LZsmXiww8/lCR2Y4y1TQghtmzZIipXriwaNmwoypcvL2bNmiW++OIL0aRJEzF//nwhhPN2lZv7s0tPTxc+Pj5i3bp1atud2dSpU8UTTzwhvv32W5GYmChGjBih3FdQUCCEEGL+/PmiTp064ssvv1Q7t02bNmL48OEOjdcSrnwNCsHrkNchr0MpMFmysYMHDwqFQiHS09OFEA+evc6YMUNUqFBBnDt3Tnls2S/J2rVrRY0aNUSlSpWEj4+PmDVrluMDN4GhtpUvX15cunRJFBYWivr164sXXnhBXL58WQghxI0bN8SAAQNEu3btnPpZuzk/OyGEuHv3rmjXrp149dVXHR6rucracvXqVbFr1y4hRGm7WrZsKVatWiWEEKKwsFB5/KBBg0STJk3EF198ITIyMsTBgwdFs2bNxI8//uj44M3kytegELwOeR3yOpQCkyUbO3z4sGjQoIH4/PPPhRAPfkEKCgpEbGys8oIuG5x44cIF8eyzzwqFQiFefPFFce/ePWkCN4GhtsXExIjx48cLIYS4deuW1gC9kydPOvUHtBCm/+zKPsyKiopE7dq1xejRo5V3g3Jy8eJF0bdvX9G3b19x584dIYQQ+fn5yn3vvvuu8PT0FM2bNxf+/v5i+PDhsminK1+DQvA65HXI61AKTJZs7M6dO6Jv375i4MCB4saNG0KIBxf1p59+KqKiotS6iV9//XVRtWpVcezYMUniNYextlWuXFmrC9wZZzXoY87PruwCX7p0qTh79qw0AVuh7OeyePFi0bJlSzF79mydx504cUKsW7dOnD592pHhWcWVr0EheB3yOpQHV7sOOcDbDKmpqbh9+7ZyJklxcbFyX1ltkwoVKuDRRx/FmTNnlCX4vby8AAAhISGoUKECkpOTlbMAPvroIyQnJ6NRo0aObIoWW7StYsWKWoMsnWUAoi1/dgDg6ekJABgyZAjq1KnjsHYYYkoby5Tte+KJJ1C/fn2sW7cO58+fBwAcOnQIQOnSAw0aNECvXr1Qr149RzTBqAsXLmDTpk0698n9GgRs0z5nvg5t+fMDnPM6NKWNZeR6HZ48eRKTJk3CuXPntPa5wnWoC5MlExQWFmL06NFo164dHn30UTz22GPIz8+Hp6encvaCl5cX8vLy8OOPP2LYsGFo0qQJVq5ciW3btinf59q1awgLC0P16tW1ZnRIxR5tcyau3j7A9DYWFhbiu+++U74uKSlBcHAwnnzySZSUlOD9999H586dER8fj4yMDMl/NzUdO3YMderUwaBBg3D16lXl9rIPW7leg2Vs3T5n4+rtA0xro5yvw4KCAjz//PNo1KgR8vLyEBMTo9wn/pu5JvfrUC+pu7ac3U8//SRq1qwp2rdvL7Zu3Sq+/PJLUaNGDTFmzBi14/73v/+JihUrij59+gghhDh69KgYPHiw8PHxES+++KJ44YUXRFBQkFi4cKEQwjm6xV25bUK4fvuEML+N/fv3V46LKHP16lVRs2ZNoVAoxFNPPSVSUlIc2QST7d+/X3Tv3l1ERkZqtU8Ief8chWD75N4+IUxvoxyvw8WLF4ugoCCRkJCg9ahM9WfhCj9HXZgsGfHSSy+Jd955R22GwnPPPScmTpyofP3555+LmJgYsXz5crVnsCUlJWL69Oli5MiRomfPnmLnzp0Ojd0YV26bEK7fPiHMb6PmB9OWLVtEYGCgaNKkiThw4IDD4rbEF198IZ5++mmxZcsW4eXlJfbu3avcN2/ePFn/HIVg++TePiFMb6Mcr8OEhAQRFxcnMjIyhBCls93Wr18vzp49K3Jzc4UQ8v88NYTJkh5lAwdv3rwpkpKSlNuvXLkimjVrJmbNmqX8gRcWFmqN3HfmbNmV2yaE67dPCOvbWCYtLU2sWLHC/gHbwJIlS8T//d//CSGEaN26tejZs6cQ4kFdmpycHLXj5fBzVMX2ybt9QpjfxjLOfB2W3Yjt2rVL1KhRQ7z//vviscceEzVq1BANGjQQERER4sknn1QeK8fPU1MwWVJhbF2auXPnCoVCIR555BHRvn17UaFCBfHuu+8qs2pn5sptE8L12yeE7dvojB9ihto4btw4MXbsWCGEEJcvXxYeHh6ie/fuomXLluLUqVMOjdNSbJ+82yeE7dsoh+uw7L/Dhg0Tfn5+YujQoeLIkSPi2LFj4vfffxd+fn5iypQpksXrCEyWhBDr1q0TVapUEQqFQnk3rusXeMmSJeKff/5R7lu+fLnw9/cXV65ccWi85nDltgnh+u0Tgm0s++9TTz0lNm/eLIQQ4uuvvxb+/v7C29tbrF69WpqgzcD2ybt9Qrh3G8t6sm/fvi3efvttcf36dbXzPv30U1GpUiVZ1H+ylNsnSzt27BDdu3cXY8eOFT169BDx8fFax+jL/E+fPi08PT3Vyrk7E1dumxCu3z4h2EYhHlT3fe6558SQIUPEww8/LMLCwsTUqVNF+fLlxaeffip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(>=0.17.0)", "pytest-xdist (>=2.2.0)"] -xml = ["lxml (>=4.8.0)"] - [[package]] name = "pandas" version = "2.1.1" @@ -2527,5 +2436,5 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" -python-versions = "^3.10" -content-hash = "2f1e99dd97eb2e4202d205c98e0d77ee3bd1ede18a30d6f434547bf67aa298eb" +python-versions = ">=3.10,<3.12" +content-hash = "0436df2d92185c9809dceefcb24249016e6d53da4bab75b64bb83d0e901ff899" diff --git a/pyproject.toml b/pyproject.toml index 55c8d8a..7037b88 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,7 @@ repository = "https://github.com/nicrie/xeofs" documentation = "https://xeofs.readthedocs.io/en/latest/" [tool.poetry.dependencies] -python = "^3.10" +python = ">=3.10,<3.12" numpy = "~1.24" pandas = ">=1.4.1" xarray = ">=0.21.1" From c181972253d9e6bc3ddcfd945f14bc1f06d7ef5f Mon Sep 17 00:00:00 2001 From: Niclas Rieger Date: Sun, 29 Oct 2023 13:21:02 +0100 Subject: [PATCH 08/12] docs: build with Python 3.11 --- docs/environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/environment.yml b/docs/environment.yml index a850368..210140e 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -3,7 +3,7 @@ channels: - conda-forge - nodefaults # https://github.com/readthedocs/readthedocs.org/issues/9527 dependencies: - - python>3.10 + - python==3.11 - numpy - pandas - xarray From 977a89e99b74d7629a6478356ffc458ea1c0359f Mon Sep 17 00:00:00 2001 From: Niclas Rieger Date: Sun, 29 Oct 2023 14:10:20 +0100 Subject: [PATCH 09/12] docs: minor tweaks in documentation --- README.rst | 52 +++---- .../images/sphx_glr_plot_mreof_001.png | Bin 110047 -> 110073 bytes .../images/sphx_glr_plot_rotated_eof_001.png | Bin 392280 -> 392269 bytes .../thumb/sphx_glr_plot_mreof_thumb.png | Bin 57145 -> 57147 bytes .../thumb/sphx_glr_plot_rotated_eof_thumb.png | Bin 116273 -> 116263 bytes docs/auto_examples/1single/plot_mreof.ipynb | 6 +- docs/auto_examples/1single/plot_mreof.py.md5 | 2 +- docs/auto_examples/1single/plot_mreof.rst | 34 ++--- .../1single/plot_mreof_codeobj.pickle | Bin 19592 -> 19598 bytes .../1single/plot_rotated_eof.ipynb | 6 +- .../1single/plot_rotated_eof.py.md5 | 2 +- .../1single/plot_rotated_eof.rst | 34 +++-- .../1single/plot_rotated_eof_codeobj.pickle | Bin 13294 -> 13285 bytes .../1single/plot_weighted-eof.ipynb | 6 +- .../1single/plot_weighted-eof.py.md5 | 2 +- .../1single/plot_weighted-eof.rst | 40 ++--- .../1single/plot_weighted-eof_codeobj.pickle | Bin 13598 -> 13598 bytes .../1single/sg_execution_times.rst | 16 +- docs/auto_examples/2multi/plot_mca.ipynb | 6 +- docs/auto_examples/2multi/plot_mca.py.md5 | 2 +- docs/auto_examples/2multi/plot_mca.rst | 138 ++++++++++++------ .../2multi/plot_mca_codeobj.pickle | Bin 12758 -> 12749 bytes .../2multi/plot_rotated_mca.ipynb | 6 +- .../2multi/plot_rotated_mca.py.md5 | 2 +- .../auto_examples/2multi/plot_rotated_mca.rst | 100 +++++++++---- .../2multi/plot_rotated_mca_codeobj.pickle | Bin 15039 -> 15036 bytes .../2multi/sg_execution_times.rst | 8 +- docs/auto_examples/auto_examples_jupyter.zip | Bin 69207 -> 69570 bytes docs/auto_examples/auto_examples_python.zip | Bin 42035 -> 42208 bytes docs/index.rst | 12 +- docs/overview.rst | 5 +- docs/overview_2_features.rst | 8 +- examples/1single/mreof-analysis.jpg | Bin 49628 -> 49638 bytes examples/1single/rotated_eof.jpg | Bin 269185 -> 269177 bytes 34 files changed, 290 insertions(+), 197 deletions(-) diff --git a/README.rst b/README.rst index 592a853..026db44 100644 --- a/README.rst +++ b/README.rst @@ -58,22 +58,21 @@ Overview --------------------- -``xeofs`` is a Python toolbox designed for methods like **Empirical Orthogonal Function (EOF) analysis**, also known as Principal Component Analysis (PCA), -and **related variants**. The package stands out due to its capacity -to handle multi-dimensional Earth observation data. +``xeofs`` is a dedicated Python package for dimensionality reduction in the realm of climate science, +offering methods like PCA, known as EOF analysis within the field, and related variants. +Seamlessly integrated with ``xarray`` and ``Dask``, it's tailored for easy handling and scalable +computation on large, multi-dimensional datasets, making advanced climate data analysis both accessible and efficient. -Here are the key strengths of ``xeofs``: +- **Multi-Dimensional**: Designed for ``xarray`` objects, it applies dimensionality reduction to multi-dimensional data while maintaining data labels. +- **Dask-Integrated**: Supports large datasets via ``Dask`` xarray objects +- **Extensive Methods**: Offers various dimensionality reduction techniques +- **Adaptable Output**: Provides output corresponding to the type of input, whether single or list of ``xr.DataArray`` or ``xr.Dataset`` +- **Missing Values**: Handles ``NaN`` values within the data +- **Bootstrapping**: Comes with a user-friendly interface for model evaluation using bootstrapping +- **Efficient**: Ensures computational efficiency, particularly with large datasets through randomized SVD +- **Modular**: Allows users to implement and incorporate new dimensionality reduction methods -- **Multi-dimensional Analysis**: Execute labeled EOF analysis with the extensive features of ``xarray``. -- **Scalability**: Handle large datasets effectively with ``dask``. -- **Speed**: Enjoy quick EOF analysis using ``scipy``'s randomized SVD. -- **Variety of Methods**: Perform diverse variants of EOF analysis, including **complex and rotated EOF analysis**, along with related techniques such as **Maximum Covariance Analysis (MCA)**. -- **Model Validation**: Validate models through bootstrapping. -- **Modular Code Structure**: Incorporate new EOF variants with ease due to the package's modular structure. -- **Flexible Data Formats**: Accepts a variety of ``xarray`` input types (``DataArray``, ``Dataset``, list of ``DataArray``). -Compared to similar packages like eofs_, pyEOF_, and xMCA_, ``xeofs`` is more comprehensive and flexible, providing unique capabilities like handling fully multidimensional dimensions -(both samples and features) and a simple interface for bootstrapping. This makes ``xeofs`` a powerful one-stop-shop for most of your EOF analysis needs. .. _pyEOF: https://github.com/zhonghua-zheng/pyEOF .. _xMCA: https://github.com/Yefee/xMCA @@ -157,12 +156,13 @@ For a more comprehensive overview and usage examples, visit the documentation_. Contributing ------------ -Contributions are highly welcomed and appreciated. If you're interested in improving ``xeofs`` or fixing issues, please open a Github issue_. +Contributions are highly welcomed and appreciated. If you're interested in improving ``xeofs`` or fixing issues, please read our +`Contributing Guide`_. License ------- -This project is licensed under the terms of the MIT license. +This project is licensed under the terms of the MIT_ license. Contact ------- @@ -172,31 +172,31 @@ For questions or support, please open a Github issue_. .. _issue: https://github.com/nicrie/xeofs/issues +.. _`Contributing Guide`: https://xeofs.readthedocs.io/en/latest/overview_3_contributing.html .. _documentation: https://xeofs.readthedocs.io/en/latest/ .. _examples: https://xeofs.readthedocs.io/en/latest/auto_examples/index.html - +.. _MIT: https://github.com/nicrie/xeofs/blob/main/LICENSE Credits ---------------------- -I want to acknowledge - -- Andrew Dawson_, for his foundational Python package for EOF analysis. -- Yefee_, whose work provided useful references for implementing MCA in ``xeofs``. -- James Chapman_, creator of a Python package for Canonical Correlation Analysis. -- Diego Bueso_, for his open-source ROCK-PCA implementation in Matlab. -- The developers of NumPy_, pandas_, and xarray_ for their indispensable tools for scientific computations in Python. +- Randomized PCA: scikit-learn_ +- EOF analysis: Python package eofs_ by Andrew Dawson +- MCA: Python package xMCA_ by Yefee +- CCA: Python package CCA-Zoo_ by James Chapman +- ROCK-PCA: Matlab implementation by Diego Bueso_ .. _NumPy: https://www.numpy.org .. _pandas: https://pandas.pydata.org .. _xarray: https://xarray.pydata.org -.. _Chapman: https://github.com/jameschapman19/cca_zoo +.. _scikit-learn: https://scikit-learn.org/stable/ +.. _CCA-Zoo: https://github.com/jameschapman19/cca_zoo .. _Bueso: https://github.com/DiegoBueso/ROCK-PCA -.. _Dawson: https://github.com/ajdawson/eofs -.. _Yefee: https://github.com/Yefee/xMCA +.. _eofs: https://github.com/ajdawson/eofs +.. _xMCA: https://github.com/Yefee/xMCA How to cite? diff --git a/docs/auto_examples/1single/images/sphx_glr_plot_mreof_001.png b/docs/auto_examples/1single/images/sphx_glr_plot_mreof_001.png index c1dc8d7b0a20922bd9cf38f8cfea89240e9b2841..135ec6399140559547280c5c76fff3f574597d2e 100644 GIT binary patch delta 71558 zcmW(+V_aPiAHUhQYuUDzYiTXJ<+W^|Y%Ckg%lK#8wq46y#&dh#-52-8`JD5^SHI0w 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zM-~$kz`Cq{&k#{aG?=JMK%!=l=mvjaj09YaMu7#QV!|Jq7!r*}BdCy9O*^a@Hp9x3B!7_hhs198my!brB$`~qtZcHKZh-p?Z*S!Y#4Z`6TxpqjHK{Y z*-^^IaN1sjlzk6n6S!f21|K;lDa&HqrBZeVr)r$ISH2$4)dVSfmz!QUdykoqiF(G> z0Mlort|vCB%#e{H`=cr|vT4k@I#3$akr*}_Tgk^r8oh>@!Wpu9zxmUAB^f%9#t&s% zvDo6H>?&S2PLr>-Hr!ll!-8l7Wp{DMd8&l{QcxPVPEqzdZdvQi`eb+vW4&3~s*&2?k6?ev+h(PAiHRX7 zU8l-EUU)T_Y{6<-p~^tPtkGE{MG+@(;QrjY4Z-LcorCFi6fvRHhYRpLj+SEo(IpPUPna z3u_hmR}qnI>ZA_{t~G5(Wy@=_L^)>(ou{P~bDC7wHL#3+)a3?MTwB zfNioWgyxF;l5&zF(E1rUs|ijQ1kVXU`GN=w={}c~*(HgqIf*X{64xk-S6w3CYZ7-0 z5iJVxL3at*MtdcI59_lM=Dj + @@ -500,7 +500,47 @@ for the homogeneous patterns of ``X`` are: }

    <xarray.DataArray 'pvalues_of_left_homogeneous_patterns' (mode: 20, lat: 25,
                                                                   lon: 26)>
    -    7.294e-298 4.922e-268 8.324e-265 3.639e-251 ... 0.04257 0.08684 0.1689 0.2898
    +    array([[[7.29374897e-298, 4.92157472e-268, 8.32382290e-265, ...,
    +             1.08681083e-108, 1.03639765e-164, 0.00000000e+000],
    +            [0.00000000e+000, 1.10991037e-316, 1.20740718e-261, ...,
    +             4.10518390e-131, 1.44361817e-128, 2.12644639e-154],
    +            [0.00000000e+000, 0.00000000e+000, 3.62777187e-317, ...,
    +             7.98234852e-039, 2.30423459e-094, 2.21447015e-303],
    +            ...,
    +            [0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    +             0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
    +            [0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    +             0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
    +            [0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    +             0.00000000e+000, 0.00000000e+000, 0.00000000e+000]],
    +
    +           [[3.53356464e-053, 2.16458187e-064, 1.82082352e-072, ...,
    +             2.73963215e-061, 1.91557069e-001, 1.78821911e-051],
    +            [1.09082456e-048, 9.63033598e-054, 5.03588837e-061, ...,
    +             5.32308100e-062, 3.17748175e-139, 4.93155255e-125],
    +            [2.28500864e-026, 6.67730087e-023, 1.31657526e-042, ...,
    +             3.45643640e-301, 5.93676048e-211, 6.80890371e-056],
    +    ...
    +            [6.68028323e-015, 6.36799636e-016, 1.40424635e-015, ...,
    +             7.79519418e-001, 5.47948179e-001, 3.94369444e-001],
    +            [1.60502590e-010, 2.56625354e-010, 1.28102262e-009, ...,
    +             2.29172525e-001, 1.46246437e-001, 1.06877108e-001],
    +            [1.32442878e-009, 3.66374656e-009, 1.85914222e-008, ...,
    +             1.67860641e-001, 1.50368398e-001, 1.54500266e-001]],
    +
    +           [[3.52336556e-001, 1.68719893e-002, 1.89032329e-002, ...,
    +             7.85325588e-001, 2.77656062e-004, 2.09278514e-009],
    +            [3.51835129e-001, 2.03858184e-001, 1.77920685e-001, ...,
    +             1.67769352e-003, 1.75517410e-004, 3.62760486e-006],
    +            [4.94662972e-001, 3.67637823e-001, 1.12698398e-001, ...,
    +             1.02161213e-007, 7.09256483e-005, 3.77172316e-004],
    +            ...,
    +            [4.02700721e-019, 1.70640543e-018, 2.01380734e-017, ...,
    +             6.53881051e-001, 6.06515118e-001, 5.74031976e-001],
    +            [7.98658654e-007, 2.85676775e-006, 1.07423123e-005, ...,
    +             8.18013734e-001, 9.27700927e-001, 9.60593627e-001],
    +            [3.43962237e-002, 6.48273766e-002, 1.22432687e-001, ...,
    +             8.68261063e-002, 1.68867366e-001, 2.89714726e-001]]])
         Coordinates:
           * lat      (lat) float32 15.0 17.5 20.0 22.5 25.0 ... 65.0 67.5 70.0 72.5 75.0
           * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 255.0 257.5 260.0 262.5
    @@ -509,7 +549,7 @@ for the homogeneous patterns of ``X`` are:
             model:         MCA
             software:      xeofs
             version:       1.2.0
    -        date:          2023-10-25 19:14:23
    +        date:          2023-10-29 13:29:43
             n_modes:       20
             center:        True
             ...            ...
    @@ -518,7 +558,7 @@ for the homogeneous patterns of ``X`` are:
             sample_name:   sample
             feature_name:  feature
             solver:        auto
    -        random_state:  None
  • model :
    MCA
    software :
    xeofs
    version :
    1.2.0
    date :
    2023-10-29 13:29:43
    n_modes :
    20
    center :
    True
    standardize :
    False
    use_coslat :
    True
    n_pca_modes :
    None
    compute :
    True
    sample_name :
    sample
    feature_name :
    feature
    solver :
    auto
    random_state :
    None


  • @@ -598,25 +638,21 @@ Create a mask to identifiy where p-values are below 0.05 Plot some relevant quantities of mode 2. -.. GENERATED FROM PYTHON SOURCE LINES 58-110 +.. GENERATED FROM PYTHON SOURCE LINES 58-114 .. code-block:: default lonlats = [ np.meshgrid(pvals_hom[0].lon.values, pvals_hom[0].lat.values), - np.meshgrid(pvals_hom[1].lon.values, pvals_hom[1].lat.values) + np.meshgrid(pvals_hom[1].lon.values, pvals_hom[1].lat.values), ] proj = [ Orthographic(central_latitude=30, central_longitude=-120), - Orthographic(central_latitude=30, central_longitude=-60) + Orthographic(central_latitude=30, central_longitude=-60), ] - kwargs1 = { - 'cmap' : 'BrBG', 'vmin' : -.05, 'vmax': .05, 'transform': PlateCarree() - } - kwargs2 = { - 'cmap' : 'RdBu', 'vmin' : -1, 'vmax': 1, 'transform': PlateCarree() - } + kwargs1 = {"cmap": "BrBG", "vmin": -0.05, "vmax": 0.05, "transform": PlateCarree()} + kwargs2 = {"cmap": "RdBu", "vmin": -1, "vmax": 1, "transform": PlateCarree()} mode = 2 @@ -633,27 +669,35 @@ Plot some relevant quantities of mode 2. for i, a in enumerate(ax2): hom_pats[i].sel(mode=mode).plot(ax=a, **kwargs2) a.scatter( - lonlats[i][0], lonlats[i][1], hom_mask[i].sel(mode=mode).values * .5, - color='k', alpha=.5, transform=PlateCarree() + lonlats[i][0], + lonlats[i][1], + hom_mask[i].sel(mode=mode).values * 0.5, + color="k", + alpha=0.5, + transform=PlateCarree(), ) for i, a in enumerate(ax3): het_pats[i].sel(mode=mode).plot(ax=a, **kwargs2) a.scatter( - lonlats[i][0], lonlats[i][1], het_mask[i].sel(mode=mode).values * .5, - color='k', alpha=.5, transform=PlateCarree() + lonlats[i][0], + lonlats[i][1], + het_mask[i].sel(mode=mode).values * 0.5, + color="k", + alpha=0.5, + transform=PlateCarree(), ) for i, a in enumerate(ax4): scores[i].sel(mode=mode).plot(ax=a) - a.set_xlabel('') + a.set_xlabel("") for a in np.ravel([ax1, ax2, ax3]): - a.coastlines(color='.5') + a.coastlines(color=".5") a.add_feature(LAND) plt.tight_layout() - plt.savefig('mca.jpg') + plt.savefig("mca.jpg") @@ -669,7 +713,7 @@ Plot some relevant quantities of mode 2. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 5.242 seconds) + 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t2m.isel(lon=slice(0, 26))\nda2 = t2m.isel(lon=slice(27, None))" + "t2m = xr.tutorial.load_dataset(\"air_temperature\")[\"air\"]\nda1 = t2m.isel(lon=slice(0, 26))\nda2 = t2m.isel(lon=slice(27, None))" ] }, { @@ -51,7 +51,7 @@ }, "outputs": [], "source": [ - "mca = MCA(n_modes=20, standardize=False, use_coslat=True)\nmca.fit(da1, da2, dim='time')" + "mca = MCA(n_modes=20, standardize=False, use_coslat=True)\nmca.fit(da1, da2, dim=\"time\")" ] }, { @@ -141,7 +141,7 @@ }, "outputs": [], "source": [ - "lonlats = [\n np.meshgrid(pvals_hom[0].lon.values, pvals_hom[0].lat.values),\n np.meshgrid(pvals_hom[1].lon.values, pvals_hom[1].lat.values)\n]\nproj = [\n Orthographic(central_latitude=30, central_longitude=-120),\n Orthographic(central_latitude=30, central_longitude=-60)\n]\nkwargs1 = {\n 'cmap' : 'BrBG', 'vmin' : -.05, 'vmax': .05, 'transform': PlateCarree()\n}\nkwargs2 = {\n 'cmap' : 'RdBu', 'vmin' : -1, 'vmax': 1, 'transform': PlateCarree()\n}\n\nmode = 2\n\nfig = plt.figure(figsize=(7, 14))\ngs = GridSpec(5, 2)\nax1 = [fig.add_subplot(gs[0, i], projection=proj[i]) for i in range(2)]\nax2 = [fig.add_subplot(gs[1, i], projection=proj[i]) for i in range(2)]\nax3 = [fig.add_subplot(gs[2, i], projection=proj[i]) for i in range(2)]\nax4 = [fig.add_subplot(gs[3, i]) for i in range(2)]\n\nfor i, a in enumerate(ax1):\n singular_vectors[i].sel(mode=mode).plot(ax=a, **kwargs1)\n\nfor i, a in enumerate(ax2):\n hom_pats[i].sel(mode=mode).plot(ax=a, **kwargs2)\n a.scatter(\n lonlats[i][0], lonlats[i][1], hom_mask[i].sel(mode=mode).values * .5,\n color='k', alpha=.5, transform=PlateCarree()\n )\nfor i, a in enumerate(ax3):\n het_pats[i].sel(mode=mode).plot(ax=a, **kwargs2)\n a.scatter(\n lonlats[i][0], lonlats[i][1], het_mask[i].sel(mode=mode).values * .5,\n color='k', alpha=.5, transform=PlateCarree()\n )\n\nfor i, a in enumerate(ax4):\n scores[i].sel(mode=mode).plot(ax=a)\n a.set_xlabel('')\n\n\nfor a in np.ravel([ax1, ax2, ax3]):\n a.coastlines(color='.5')\n a.add_feature(LAND)\n\nplt.tight_layout()\nplt.savefig('rotated_mca.jpg')" + "lonlats = [\n np.meshgrid(pvals_hom[0].lon.values, pvals_hom[0].lat.values),\n np.meshgrid(pvals_hom[1].lon.values, pvals_hom[1].lat.values),\n]\nproj = [\n Orthographic(central_latitude=30, central_longitude=-120),\n Orthographic(central_latitude=30, central_longitude=-60),\n]\nkwargs1 = {\"cmap\": \"BrBG\", \"vmin\": -0.05, \"vmax\": 0.05, \"transform\": PlateCarree()}\nkwargs2 = {\"cmap\": \"RdBu\", \"vmin\": -1, \"vmax\": 1, \"transform\": PlateCarree()}\n\nmode = 2\n\nfig = plt.figure(figsize=(7, 14))\ngs = GridSpec(5, 2)\nax1 = [fig.add_subplot(gs[0, i], projection=proj[i]) for i in range(2)]\nax2 = [fig.add_subplot(gs[1, i], projection=proj[i]) for i in range(2)]\nax3 = [fig.add_subplot(gs[2, i], projection=proj[i]) for i in range(2)]\nax4 = [fig.add_subplot(gs[3, i]) for i in range(2)]\n\nfor i, a in enumerate(ax1):\n singular_vectors[i].sel(mode=mode).plot(ax=a, **kwargs1)\n\nfor i, a in enumerate(ax2):\n hom_pats[i].sel(mode=mode).plot(ax=a, **kwargs2)\n a.scatter(\n lonlats[i][0],\n lonlats[i][1],\n hom_mask[i].sel(mode=mode).values * 0.5,\n color=\"k\",\n alpha=0.5,\n transform=PlateCarree(),\n )\nfor i, a in enumerate(ax3):\n het_pats[i].sel(mode=mode).plot(ax=a, **kwargs2)\n a.scatter(\n lonlats[i][0],\n lonlats[i][1],\n het_mask[i].sel(mode=mode).values * 0.5,\n color=\"k\",\n alpha=0.5,\n transform=PlateCarree(),\n )\n\nfor i, a in enumerate(ax4):\n scores[i].sel(mode=mode).plot(ax=a)\n a.set_xlabel(\"\")\n\n\nfor a in np.ravel([ax1, ax2, ax3]):\n a.coastlines(color=\".5\")\n a.add_feature(LAND)\n\nplt.tight_layout()\nplt.savefig(\"rotated_mca.jpg\")" ] } ], diff --git a/docs/auto_examples/2multi/plot_rotated_mca.py.md5 b/docs/auto_examples/2multi/plot_rotated_mca.py.md5 index 3799a09..ead7bc4 100644 --- a/docs/auto_examples/2multi/plot_rotated_mca.py.md5 +++ b/docs/auto_examples/2multi/plot_rotated_mca.py.md5 @@ -1 +1 @@ -1263b6d3dae8967e7d9dcefb72cc0e61 \ No newline at end of file +03131f27bffceae9db1f188ff8f11a23 \ No newline at end of file diff --git a/docs/auto_examples/2multi/plot_rotated_mca.rst b/docs/auto_examples/2multi/plot_rotated_mca.rst index dab214a..f283e8c 100644 --- a/docs/auto_examples/2multi/plot_rotated_mca.rst +++ b/docs/auto_examples/2multi/plot_rotated_mca.rst @@ -55,7 +55,7 @@ Create 2 different DataArrays .. code-block:: default - t2m = xr.tutorial.load_dataset('air_temperature')['air'] + t2m = xr.tutorial.load_dataset("air_temperature")["air"] da1 = t2m.isel(lon=slice(0, 26)) da2 = t2m.isel(lon=slice(27, None)) @@ -76,7 +76,7 @@ Perform MCA mca = MCA(n_modes=20, standardize=False, use_coslat=True) - mca.fit(da1, da2, dim='time') + mca.fit(da1, da2, dim="time") @@ -87,7 +87,7 @@ Perform MCA .. code-block:: none - + @@ -519,7 +519,47 @@ for the homogeneous patterns of ``X`` are: }
    <xarray.DataArray 'pvalues_of_left_homogeneous_patterns' (mode: 10, lat: 25,
                                                                   lon: 26)>
    -    5.918e-81 4.09e-64 6.234e-58 5.974e-49 ... 0.001974 0.005479 0.01494 0.03604
    +    array([[[5.91798633e-081, 4.09037030e-064, 6.23425561e-058, ...,
    +             4.45216591e-046, 7.99246088e-107, 6.95533368e-234],
    +            [9.61231974e-109, 4.30909052e-079, 2.41956104e-054, ...,
    +             1.13316213e-079, 1.17033263e-086, 1.58776474e-105],
    +            [3.65945612e-169, 1.03636735e-150, 5.85938600e-088, ...,
    +             2.66091078e-037, 1.39445455e-071, 1.89947470e-183],
    +            ...,
    +            [0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    +             0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
    +            [0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    +             0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
    +            [0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    +             0.00000000e+000, 0.00000000e+000, 0.00000000e+000]],
    +
    +           [[0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    +             2.15049395e-115, 1.25690777e-037, 3.79422747e-010],
    +            [0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
    +             3.68434950e-006, 4.54442588e-001, 7.47361402e-001],
    +            [5.82970051e-304, 1.04446785e-286, 1.67982320e-322, ...,
    +             3.12415723e-031, 2.97910617e-009, 3.83983102e-013],
    +    ...
    +            [3.81951811e-033, 1.24766309e-035, 9.83759118e-037, ...,
    +             1.08208863e-007, 2.09241618e-007, 9.75026228e-007],
    +            [7.63507975e-018, 1.84998265e-018, 1.85005250e-018, ...,
    +             1.47665784e-002, 2.23166837e-002, 3.74986857e-002],
    +            [5.23820545e-013, 5.59326448e-014, 1.46539526e-014, ...,
    +             7.79688798e-001, 7.97072146e-001, 8.38057312e-001]],
    +
    +           [[1.33009336e-001, 3.88106643e-002, 2.39386496e-003, ...,
    +             9.33561796e-001, 6.41357062e-001, 5.12874473e-001],
    +            [2.47971905e-002, 1.49401803e-002, 1.01803200e-003, ...,
    +             9.48611093e-001, 3.16366009e-001, 6.29374852e-002],
    +            [1.00284803e-002, 8.50825810e-002, 1.51720312e-001, ...,
    +             2.62222025e-001, 1.27842313e-002, 1.43419078e-004],
    +            ...,
    +            [6.95159651e-010, 1.10849674e-010, 2.79734189e-011, ...,
    +             2.20338645e-004, 2.58483821e-003, 2.44608586e-002],
    +            [2.08872724e-002, 1.93770600e-002, 2.08168263e-002, ...,
    +             3.74564462e-004, 2.56337651e-003, 1.63483691e-002],
    +            [9.06082861e-001, 8.64987130e-001, 8.39176635e-001, ...,
    +             5.47872690e-003, 1.49439193e-002, 3.60352909e-002]]])
         Coordinates:
           * lat      (lat) float32 15.0 17.5 20.0 22.5 25.0 ... 65.0 67.5 70.0 72.5 75.0
           * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 255.0 257.5 260.0 262.5
    @@ -533,7 +573,7 @@ for the homogeneous patterns of ``X`` are:
             squared_loadings:  False
             software:          xeofs
             version:           1.2.0
    -        date:              2023-10-25 19:14:30
  • model :
    Rotated MCA
    n_modes :
    10
    power :
    1
    max_iter :
    1000
    rtol :
    1e-08
    squared_loadings :
    False
    software :
    xeofs
    version :
    1.2.0
    date :
    2023-10-29 13:29:48


  • @@ -612,25 +652,21 @@ Create a mask to identifiy where p-values are below 0.05 Plot some relevant quantities of mode 2. -.. GENERATED FROM PYTHON SOURCE LINES 64-116 +.. GENERATED FROM PYTHON SOURCE LINES 64-120 .. code-block:: default lonlats = [ np.meshgrid(pvals_hom[0].lon.values, pvals_hom[0].lat.values), - np.meshgrid(pvals_hom[1].lon.values, pvals_hom[1].lat.values) + np.meshgrid(pvals_hom[1].lon.values, pvals_hom[1].lat.values), ] proj = [ Orthographic(central_latitude=30, central_longitude=-120), - Orthographic(central_latitude=30, central_longitude=-60) + Orthographic(central_latitude=30, central_longitude=-60), ] - kwargs1 = { - 'cmap' : 'BrBG', 'vmin' : -.05, 'vmax': .05, 'transform': PlateCarree() - } - kwargs2 = { - 'cmap' : 'RdBu', 'vmin' : -1, 'vmax': 1, 'transform': PlateCarree() - } + kwargs1 = {"cmap": "BrBG", "vmin": -0.05, "vmax": 0.05, "transform": PlateCarree()} + kwargs2 = {"cmap": "RdBu", "vmin": -1, "vmax": 1, "transform": PlateCarree()} mode = 2 @@ -647,27 +683,35 @@ Plot some relevant quantities of mode 2. for i, a in enumerate(ax2): hom_pats[i].sel(mode=mode).plot(ax=a, **kwargs2) a.scatter( - lonlats[i][0], lonlats[i][1], hom_mask[i].sel(mode=mode).values * .5, - color='k', alpha=.5, transform=PlateCarree() + lonlats[i][0], + lonlats[i][1], + hom_mask[i].sel(mode=mode).values * 0.5, + color="k", + alpha=0.5, + transform=PlateCarree(), ) for i, a in enumerate(ax3): het_pats[i].sel(mode=mode).plot(ax=a, **kwargs2) a.scatter( - lonlats[i][0], lonlats[i][1], het_mask[i].sel(mode=mode).values * .5, - color='k', alpha=.5, transform=PlateCarree() + lonlats[i][0], + lonlats[i][1], + het_mask[i].sel(mode=mode).values * 0.5, + color="k", + alpha=0.5, + transform=PlateCarree(), ) for i, a in enumerate(ax4): scores[i].sel(mode=mode).plot(ax=a) - a.set_xlabel('') + a.set_xlabel("") for a in np.ravel([ax1, ax2, ax3]): - a.coastlines(color='.5') + a.coastlines(color=".5") a.add_feature(LAND) plt.tight_layout() - plt.savefig('rotated_mca.jpg') + plt.savefig("rotated_mca.jpg") @@ -683,7 +727,7 @@ Plot some relevant quantities of mode 2. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 4.406 seconds) + **Total running time of the script:** (0 minutes 3.714 seconds) .. _sphx_glr_download_auto_examples_2multi_plot_rotated_mca.py: diff --git a/docs/auto_examples/2multi/plot_rotated_mca_codeobj.pickle b/docs/auto_examples/2multi/plot_rotated_mca_codeobj.pickle index 4774caabfc2a2ac1165f39de1f863f83fc1e2ba8..6b946dbbd8faa356adb05a3abd04ead45a2a9c07 100644 GIT binary patch literal 15036 zcmd5@U5w;L5x&db{@l*q?)_)8A_PJRi9nVvL`caC%B#-Cfny?V1-BU-|iy2h#tNj^%qEw_?X1Mw6s-HSF<__s%(1Jh?r2bZ7EN za)cSI*E9Dxi$@`!JUe;hOYrp2kPY}GIdPHu-7$|ekz{G$_eP$0ZLu)+gMDKVct)T5 z3`8f%TK3wEY(I>vgjSr$v^*9?lUqp#{?5mN=U7fWxitd&_Hwyt`!IN` z%U-WTE$1kKIH@6yPWcB$P7PQbcz*0TyM}PeFjLn6d~ZE?gMfw2t$Nt1f(gX^8sd~G zXr5&e*QTPB>MlqL4ex8Hp^Wbb8e$3Qhc(#oZWudJOc@_jjv6qRAR*X3+5}3>eI&>5 zWO-mQV-J>3wtiv#EMMO4rcYpuj*hv%7a6cFxd;EV7#K{}@WY8_9b<`DW87k@UU>F| zru&2`W^p_8qv*2WBRReo(=kjjiKZo_%#^lZTq~kQLWLdbkNt^8^prc>n*4J2?6!1a zf$dtt;S0G0PmV^;us`xxXx@Y*d2)Lc#r|L$a(x_*#CYdM)`R^395P^t#2+zCtLhP~eKbmyN;Jeg 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b/docs/auto_examples/2multi/sg_execution_times.rst index baa8f52..726e6a3 100644 --- a/docs/auto_examples/2multi/sg_execution_times.rst +++ b/docs/auto_examples/2multi/sg_execution_times.rst @@ -6,12 +6,12 @@ Computation times ================= -**00:22.719** total execution time for **auto_examples_2multi** files: +**00:08.063** total execution time for **auto_examples_2multi** files: +------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_2multi_plot_cca.py` (``plot_cca.py``) | 00:13.071 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_2multi_plot_mca.py` (``plot_mca.py``) | 00:04.349 | 0.0 MB | +------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_2multi_plot_mca.py` (``plot_mca.py``) | 00:05.242 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_2multi_plot_rotated_mca.py` (``plot_rotated_mca.py``) | 00:03.714 | 0.0 MB | 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+141,10 @@ In order to get started with ``xeofs``, follow these simple steps: rot_scores = rotator.scores() # Rotated expansion coefficients (temporal patterns) -To further explore the capabilities of ``xeofs``, check the available documentation_ and examples_. -For a full list of currently available methods, see the methods_ section. +To further explore the capabilities of ``xeofs``, check out the available documentation_ and examples_. +For a full list of currently available methods, see the `Reference API`_. -.. _methods: https://xeofs.readthedocs.io/en/latest/models.html +.. _`Reference API`: https://xeofs.readthedocs.io/en/latest/api.html From be8bffdd746a3a8683a42cfebccac107ff16db60 Mon Sep 17 00:00:00 2001 From: Niclas Rieger Date: Sun, 29 Oct 2023 17:10:55 +0100 Subject: [PATCH 11/12] ci: install dev dependencies --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index ad6a984..7bd81ca 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -38,7 +38,7 @@ jobs: run: poetry --version - name: Install Project Dependencies - run: python -m poetry install + run: python -m poetry install --with dev - name: Lint Codebase with flake8 run: | From 52070d34d9fe67e71a8efed28058823d3de80b43 Mon Sep 17 00:00:00 2001 From: Niclas Rieger Date: Sun, 29 Oct 2023 18:13:41 +0100 Subject: [PATCH 12/12] build: declare dev docs as optional groups --- poetry.lock | 1038 ++++++++++++++++++++++++++++++++++++++---------- pyproject.toml | 33 +- 2 files changed, 852 insertions(+), 219 deletions(-) diff --git a/poetry.lock b/poetry.lock index 1ee4c43..0732ab3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,10 +1,10 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. [[package]] name = "accessible-pygments" version = "0.0.4" description = "A collection of accessible pygments styles" -optional = true +optional = false python-versions = "*" files = [ {file = "accessible-pygments-0.0.4.tar.gz", hash = "sha256:e7b57a9b15958e9601c7e9eb07a440c813283545a20973f2574a5f453d0e953e"}, @@ -18,7 +18,7 @@ pygments = ">=1.5" name = "alabaster" version = "0.7.13" description = "A configurable sidebar-enabled Sphinx theme" -optional = true +optional = false python-versions = ">=3.6" files = [ {file = "alabaster-0.7.13-py3-none-any.whl", hash = "sha256:1ee19aca801bbabb5ba3f5f258e4422dfa86f82f3e9cefb0859b283cdd7f62a3"}, @@ -29,7 +29,7 @@ files = [ name = "attrs" version = "23.1.0" description = "Classes Without Boilerplate" -optional = true +optional = false python-versions = ">=3.7" files = [ {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"}, @@ -47,7 +47,7 @@ tests-no-zope = ["cloudpickle", 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    +.. raw:: html + +
    + +.. only:: html + + .. image:: /auto_examples/2multi/images/thumb/sphx_glr_plot_cca_thumb.png + :alt: + + :ref:`sphx_glr_auto_examples_2multi_plot_cca.py` + +.. raw:: html + +
    Canonical Correlation Analysis
    +
    + + .. raw:: html
    .. only:: html - .. image:: /auto_examples/2mca/images/thumb/sphx_glr_plot_mca_thumb.png + .. image:: /auto_examples/2multi/images/thumb/sphx_glr_plot_mca_thumb.png :alt: - :ref:`sphx_glr_auto_examples_2mca_plot_mca.py` + :ref:`sphx_glr_auto_examples_2multi_plot_mca.py` .. raw:: html @@ -200,10 +233,10 @@ Examples .. only:: html - .. image:: /auto_examples/2mca/images/thumb/sphx_glr_plot_rotated_mca_thumb.png + .. image:: /auto_examples/2multi/images/thumb/sphx_glr_plot_rotated_mca_thumb.png :alt: - :ref:`sphx_glr_auto_examples_2mca_plot_rotated_mca.py` + :ref:`sphx_glr_auto_examples_2multi_plot_rotated_mca.py` .. raw:: html @@ -251,8 +284,8 @@ Examples :includehidden: - /auto_examples/1eof/index.rst - /auto_examples/2mca/index.rst + /auto_examples/1single/index.rst + /auto_examples/2multi/index.rst /auto_examples/3validation/index.rst diff --git a/docs/img/timings_dark.png b/docs/img/timings_dark.png new file mode 100644 index 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a/docs/index.rst b/docs/index.rst index db30ad1..4bf42d7 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -2,7 +2,7 @@ .. image:: logos/xeofs_logo.png :align: center - :width: 800 + :width: 1000 :alt: logo of xeofs ############################################################# @@ -25,7 +25,7 @@ thereby enhancing their application in the broader scientific community. :link-type: doc - Get an idea of what `xeofs` is all about. + Get an idea of what ``xeofs`` is all about. .. grid-item-card:: Quickstart :link: quickstart @@ -36,11 +36,11 @@ thereby enhancing their application in the broader scientific community. .. grid:: 2 - .. grid-item-card:: Methods - :link: models + .. grid-item-card:: API Reference + :link: api :link-type: doc - Check what methods are available. + Check out what methods are available. .. grid-item-card:: Examples :link: auto_examples/index @@ -50,8 +50,7 @@ thereby enhancing their application in the broader scientific community. -.. admonition:: Note - :class: info +.. info:: Work in progress. @@ -63,6 +62,5 @@ thereby enhancing their application in the broader scientific community. overview installation quickstart - models - auto_examples/index api + auto_examples/index diff --git a/docs/model_cpca.rst b/docs/model_cpca.rst deleted file mode 100644 index f821d37..0000000 --- a/docs/model_cpca.rst +++ /dev/null @@ -1,8 +0,0 @@ -Complex EOF analysis --------------------- - -.. admonition:: Note - :class: info - - Coming soon. - diff --git a/docs/model_rpca.rst b/docs/model_rpca.rst deleted file mode 100644 index 0bd35df..0000000 --- a/docs/model_rpca.rst +++ /dev/null @@ -1,9 +0,0 @@ -Rotated EOF analysis --------------------- - - -.. admonition:: Note - :class: info - - Coming soon. - diff --git a/docs/models.rst b/docs/models.rst deleted file mode 100644 index 95644b7..0000000 --- a/docs/models.rst +++ /dev/null @@ -1,82 +0,0 @@ -.. _methods: - -====================== -Methods -====================== - - -Core Models ------------------ - -The supported models in ``xeofs`` currently include: - -.. grid:: 2 - - .. grid-item-card:: EOF Analysis - :link: model_pca - :link-type: doc - - - Unveils dominant patterns of variabililty in your data. - - .. grid-item-card:: Maximum Covariance Analysis (MCA) - :link: api_core_methods - :link-type: doc - - Uncovers co-varying patterns between two sets of data. - - -.. grid:: 2 - - .. grid-item-card:: Optimal Persistence Analysis (OPA) - :link: api_core_methods - :link-type: doc - - - Identifies the optimal persistence patterns (OPP) with the largest decorrelation time. - - - -Enhanced Interpretability ---------------------------- - -.. grid:: 2 - - .. grid-item-card:: Varimax Rotation - :link: api_rotation - :link-type: doc - - Breaks orthogonal constrain for easier interpretation. - - .. grid-item-card:: Promax Rotation - :link: api_rotation - :link-type: doc - - Allow oblique rotation to identify correlated patterns. - - -Advanced Techniques --------------------- - -.. grid:: 2 - - .. grid-item-card:: Complex/Hilbert Extensions - :link: api_advanced_techniques - :link-type: doc - - Identify oscillatory patterns in your data. - - - -Model Validation --------------------- - -.. grid:: 2 - - .. grid-item-card:: Bootstrapper - :link: api_validation - :link-type: doc - - Bootstrap your model to assess its robustness. - - diff --git a/docs/overview.rst b/docs/overview.rst index 6e9f6c9..cc8c25b 100644 --- a/docs/overview.rst +++ b/docs/overview.rst @@ -1,161 +1,40 @@ -:sd_hide_title: - ========= Overview_ ========= -Why xeofs? -================== +If you are new to ``xeofs``, that's the place to start. + +.. grid:: 3 + + .. grid-item-card:: Why xeofs? + :link: overview_1_why + :link-type: doc + + Understand the motivation behind ``xeofs``. + + .. grid-item-card:: Key Features + :link: overview_2_features + :link-type: doc + + What can ``xeofs`` do for you? + + + .. grid-item-card:: Contributing + :link: overview_3_contributing + :link-type: doc + + Learn how you can make ``xeofs`` better. + + + -Empirical orthogonal function (EOF) analysis, also known as principal component analysis (PCA), -and related variants are typically based on the -decomposition of a 2D matrix. +.. toctree:: + :maxdepth: 3 + :hidden: -However, Earth observation data is often multi-dimensional, -characterized by spatial (longitude, latitude, height etc.), temporal (time, steps, lead times -in forecasts etc.) and other (variable, sensor etc.) dimensions. ``xarray`` is an excellent -tool in Python for handling such multi-dimensional datasets. + overview_1_why + overview_2_features + overview_3_contributing -By using ``xarray`` for EOF -analysis and similar techniques, it's possible to speed up the analysis by automatically -taking care of dimension labels. While numerous Python packages exist for EOF analysis -in ``xarray``, none fulfilled all personal needs, leading to the creation of ``xeofs``. - - -Supported Models -================ - -As a toolbox, ``xeofs`` aims at harboring a battery of methods related to decomposition. Currently -available methods can be found in the :doc:`models` model section. - - -Key Features -================== - -There are numerous advantages of using ``xeofs``, including: - -- **Multi-dimensional Analysis**: Perform labeled EOF analysis using the robust functionality of ``xarray``. -- **Scalability**: Efficiently process large datasets with the help of ``dask``. -- **Speed**: Benefit from the quick execution of EOF analysis using ``scipy``'s randomized SVD. -- **Model Validation**: Validate your models through bootstrapping. -- **Modular Code Structure**: Easily incorporate new EOF variants with the package's modular design. - - -Flexible Data Formats -==================================== - -``xeofs`` is designed to work seamlessly with a variety of data structures, thereby accommodating a wide range of use cases and applications. -Specifically, it accepts three types of input: - -- ``xr.DataArray`` -- ``xr.Dataset`` -- a list of ``xr.DataArray`` - -This flexibility enables you to fully leverage the powerful data structures provided by ``xarray``, making your analyses more streamlined and unconstrained. - - -Handling Missing Values -==================================== - -``xeofs`` provides intelligent handling of missing values (``NaN``) in your data. - -While isolated ``NaNs``, i.e., sporadic missing values within your data grid, -are not considered valid input, ``xeofs`` is designed to handle full-dimensional ``NaNs`` gracefully. - -A full-dimensional ``NaN`` occurs when an entire grid point -(for instance, a longitude-latitude point) contains ``NaNs`` across all time steps. A typical scenario would be an ocean cell in land-only data. -In such instances, ``xeofs`` treats the full-dimensional ``NaNs`` appropriately without disrupting your EOF analysis. - -Comparison With Other Packages -==================================== - -``xeofs`` joins a collection of Python packages designed for EOF analysis, each with their unique sets of features. `eofs`_, developed by Andrew Dawson, supports fundamental functionalities such as Dask compatibility for handling large datasets and multivariate EOF analysis. - -Yet, the demand for EOF analysis extends to a wider range of functionalities, making room for alternative tools. Complex EOF analysis, Maximum Covariance Analysis (MCA), and rotated EOF analysis, for instance, are not covered by `eofs`_. For MCA, `xMCA`_ serves as a reliable option. `pyEOF`_, on the other hand, caters to the need for Varimax-rotated EOF analysis, although it's strictly confined to 2D (pandas) input data, necessitating additional processing. At present, none of the existing packages accommodate complex EOF analysis or any combinations of such methods, such as complex rotated EOF analysis or rotated MCA. - -In view of these varied requirements, ``xeofs`` was designed with the intention of offering a comprehensive platform, incorporating the above-mentioned methods, and more, in a single package. Furthermore, ``xeofs`` is designed to be flexible, accommodating different xarray objects as input and allowing easy incorporation of new methods. Notably, it supports fully multidimensional dimensions (both samples and features), providing unique capacity compared to other packages. For example, if your data has dimensions (year, month, lon, lat) and you wish to perform EOF analysis on the time (year, month) dimensions, ``xeofs`` can handle this, enabling EOF analysis in T-mode, not just S-mode. - -Additionally, ``xeofs`` offers a simple interface for bootstrapping, useful for model validation. A full comparison of the different packages is provided in the table below. - -.. list-table:: - :header-rows: 1 - - * - - - **xeofs** - - **eofs** - - **pyEOF** - - **xMCA** - * - **Supported methods** - - - - - - - - - * - EOF analysis - - ✅ - - ✅ - - ✅ - - ❌ - * - MCA - - ✅ - - ❌ - - ❌ - - ✅ - * - Complex - - ✅ - - ❌ - - ❌ - - ❌ - * - Rotation - - ✅ - - ❌ - - ✅ - - ❌ - * - Multivariate - - ✅ - - ✅ - - ❌ - - ❌ - * - **Validation** - - - - - - - - - * - Bootstrapping - - ✅ - - ❌ - - ❌ - - ❌ - * - **Miscellaneous** - - - - - - - - - * - xarray interface - - ✅ - - ✅ - - ❌ - - ✅ - * - Multidimensional - - ✅ - - Only 1D sample dim - - 2D input only - - Only 1D sample dim - * - Dask support - - ✅ - - ✅ - - ❌ - - ❌ - * - Algorithm\ :sup:`1`\ - - Randomized SVD - - Full decomposition - - Randomized or full SVD - - Full decomposition - -\ :sup:`1`\ **Note on the algorithm:** The computational complexity of full SVD decomposition for a m x n matrix is O(min(mn², m²n)). However, randomized SVD, which only finds the first k singular values, significantly reduces this complexity to O(m n log(k)). This makes randomized SVD, as used by ``xeofs``, more efficient for large datasets. For more details, see the `sklearn docs on PCA`_. - -.. _pyEOF: https://github.com/zhonghua-zheng/pyEOF -.. _xMCA: https://github.com/Yefee/xMCA -.. _eofs: https://github.com/ajdawson/eofs -.. _`sklearn docs on PCA`: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html diff --git a/docs/overview_1_why.rst b/docs/overview_1_why.rst new file mode 100644 index 0000000..40d1b13 --- /dev/null +++ b/docs/overview_1_why.rst @@ -0,0 +1,30 @@ +Why xeofs? +========== + +Dimensionality reduction and pattern recognition techniques often rest on a foundational idea: +data can be represented as a compilation of observations, referred to as *samples*, +of certain variables, or *features*. This representation effectively forms a 2D matrix. +A paramount example of this is Principal Component Analysis (PCA). In climate science, PCA is +more commonly known as Empirical Orthogonal Functions (EOF) analysis, which relies on the +Singular Value Decomposition (SVD) of a 2D matrix. + +When examining Earth observations, we often encounter two distinctive characteristics of the data: + +1. **Multi-dimensional Nature** : The data usually spans multiple dimensions, encompassing spatial coordinates (like longitude, latitude, height, etc.), time metrics (such as time, steps, or forecast lead times), and other categorizations (like variable types, sensors, etc.). +2. **Large Volume** : Such datasets are typically vast, frequently surpassing the memory capabilities of a single computer. + +For handling multi-dimensional data in Python, the ``xarray`` library stands out. It can manage +large datasets proficiently, especially when integrated with ``dask``. + +However, the challenge arises when applying dimensionality reduction techniques. A conventional +workflow necessitates preprocessing the data into a 2D matrix format using ``xarray``, which is +subsequently input into packages like ``scikit-learn`` or ``dask``. This transformation often +results in the loss of dimension labels. Consequently, users are compelled to manually track +dimensions—a process that's cumbersome, prone to errors, and time-intensive. While this might +be manageable for extensive projects, it becomes a burdensome overhead for smaller ventures. + +``xeofs`` is engineered to address the challenges stated above. It offers +a collection of widely-used dimensionality reduction techniques, with a spotlight on methods +prevalent in climate sciences. In doing so, it complements robust libraries like +``scikit-learn``. What sets ``xeofs`` apart is its seamless compatibility with +both ``xarray`` and ``dask``, ensuring a streamlined user experience. \ No newline at end of file diff --git a/docs/overview_2_features.rst b/docs/overview_2_features.rst new file mode 100644 index 0000000..9a54415 --- /dev/null +++ b/docs/overview_2_features.rst @@ -0,0 +1,345 @@ +Key Features +================== + + +There are numerous advantages of using ``xeofs``, including: + +- **Multi-Dimensional**: Designed for ``xarray`` objects, it applies dimensionality reduction to multi-dimensional data while maintaining data labels. +- **Dask-Integrated**: Supports large datasets via ``Dask`` xarray objects +- **Extensive Methods**: Offers various dimensionality reduction techniques +- **Adaptable Output**: Provides output corresponding to the type of input, whether single or list of ``xr.DataArray`` or ``xr.Dataset`` +- **Missing Values**: Handles ``NaN`` values within the data +- **Bootstrapping**: Comes with a user-friendly interface for model evaluation using bootstrapping +- **Efficient**: Ensures computational efficiency, particularly with large datasets through randomized SVD +- **Modular**: Allows users to implement and incorporate new dimensionality reduction methods + + + +Working with Labeled & Multi-Dimensional Data +--------------------------------------------- + +``xeofs`` is specifically designed for ``xarray`` objects. This design choice enables you +to apply dimensionality reduction techniques to multi-dimensional data, while still maintaining +data labels. The only requirement for the user is to specify the *sample* dimensions. By +default, ``xeofs`` assumes any other dimensions present in the data as *feature* dimensions. +For instance, + +.. code-block:: python + + model.fit(data, dim="time") + +will assume that the ``data`` object has a *sample* dimension named ``time``, while all other +dimensions are *feature* dimensions to be expressed as latent variables. + +.. note:: + + In this context, *sample* refers to the dimension representing the number of observations. + In contrast, *feature* dimensions denote the variables. The main goal of + dimensionality reduction techniques is to minimize the number of *feature* dimensions + without altering the *sample* dimensions. + +.. note:: + + Multiple *sample* dimensions can be specified as long as at least one *feature* dimension remains. + +Support for Dask +---------------- + +If you handle large datasets that exceed memory capacity, you can pass a ``Dask`` ``xarray`` object to ``xeofs``. +Contrary to expectations, ``xeofs`` computes the matrix decomposition results, assuming the output can fit +into memory. This behavior is the default because deferring computations usually causes substantial +increase in computational time since any derivative of the decomposition will have to recompute the entire SVD decomposition. + +.. code-block:: python + + model = xe.models.EOF() + model.fit(data, dim="time") # <- data is a Dask object + + model.components() # Returns a non-Dask object + +However, if you wish to postpone computation, you can achieve this by setting ``compute=False`` during model initialization: + +.. code-block:: python + + model = xe.models.EOF(compute=False) + model.fit(data, dim="time") + + model.components() # Returns a Dask object + + model.compute() # Computes all deferred objects + + model.components() # Now, it's no longer a Dask object + +.. note:: + + Importantly, ``xeofs`` never computes the input data directly. + +Available Methods +----------------- + +As a multifaceted toolbox, ``xeofs`` aims to host a variety of dimensionality reduction +and related decomposition techniques. +Refer to the :doc:`api` section to discover the methods currently available. + +.. note:: + + Please note that ``xeofs`` is in its developmental phase. If there's a specific method + you'd like to see included, we encourage you to open an issue on `GitHub`_. + +Input Data Compatibility +------------------------ + +``xeofs`` is tailored to function harmoniously with `xarray` objects. Currently, it supports: + +- Single instances of ``xr.DataArray`` or ``xr.Dataset`` +- Lists comprising ``xr.DataArray`` or ``xr.Dataset`` + +An intelligent feature of ``xeofs`` is its ability to deliver the appropriate output based on +the input type. For instance, executing PCA on a singular ``xr.DataArray`` will yield a single +``xr.DataArray`` for the PC components. Conversely, if a list of ``xr.DataArray`` is inputted, +``xeofs`` will return a list of ``xr.DataArray`` as PC components. + +.. warning:: + + A mixed list containing both ``xr.DataArray`` and ``xr.Dataset`` objects is not currently supported. + +Handling Missing Values +----------------------- + +Conventional SVD algorithms aren't typically configured to manage missing values. To address this, +``xeofs`` will take of missing values (``NaN``) within your data. There are two primary types of missing values: + +1. **Full-dimensional**: ``NaNs`` spanning all samples for a specific feature or vice versa. +2. **Isolated**: Occasional or sporadic ``NaNs`` within the dataset. + +Consider a 3D dataset with dimensions (time, lon, lat). A full-dimensional ``NaN`` might represent a +grid point (lon, lat) exhibiting ``NaNs`` across all time steps. Conversely, an isolated +``NaN`` might indicate a grid point (lon, lat) displaying ``NaNs`` for only certain time steps. + +``xeofs`` is adept at handling full-dimensional ``NaNs``. However, it cannot manage isolated ``NaNs``. In situations where isolated ``NaNs`` are detected, ``xeofs`` will raise an error. + +Model Evaluation +---------------- + +``xeofs`` is dedicated to providing a user-friendly interface for model evaluations using bootstrapping. Currently, only bootstrapping for PCA/EOF analysis is supported +(for a practical example, see :doc:`auto_examples/3validation/index`). + +Achieving Computational Efficiency +---------------------------------- + +Regardless of whether you're dealing with in-memory or out-of-memory data, ``xeofs`` ensures computational efficiency. +This is achieved using randomized SVD, a swift method for large matrix decomposition. For an in-depth understanding, +you can refer to the `sklearn documentation on PCA`_. + +To illustrate, a comparison between the computational times of ``xeofs`` (randomized SVD) and ``eofs`` (full SVD) +for a 3D dataset with dimensions (time, lon, lat) with a varying number features. For comparison, the number of samples is kept fixed to 1000. +It reveals that ``xeofs`` generally outperforms for datasets with over ~500 features both with and without ``Dask``. +For datasets with fewer than ~500 features, ``eofs`` tends to be quicker, probably because the computational overhead of ``xeofs`` is too large for small datasets. + +.. image:: img/timings_dark.png + :height: 400px + :width: 800px + :alt: Comparison of computational times between xeofs and eofs for data sets of varying sizes + :align: center + + + +Implement Your Own Model +------------------------- + +The ``xeofs`` package has been designed with modularity in mind, allowing you to seamlessly incorporate new methods. +For instance, if you'd like to introduce a new dimensionality reduction technique named ``MyModel``, +you can achieve this by inheriting the ``_BaseModel`` class and implementing its ``_fit_algorithm()`` method. + +Here's a detailed walkthrough on how to incorporate a new model: + +1. Inherit the BaseModel +^^^^^^^^^^^^^^^^^^^^^^^^ + +Your new model should inherit from the `_BaseModel` class. This abstract base class enables +the transformation of any ND ``xarray`` object into a 2D ``xarray.DataArray`` with dimensions +(sample, feature) and back. Additionally, it grants access to handy parameters like +``n_modes``, ``standardize``, and ``use_coslat``. + +.. code-block:: python + + from xeofs.models._base_model import _BaseModel + from xeofs.models.decomposer import Decomposer + + class MyModel(_BaseModel): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + +2. Define the Fit Algorithm +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Your chosen method's entire operation should be encapsulated within the +``_fit_algorithm()``. This function is triggered by ``fit()`` and handles the model fitting. +By this stage, ``xeofs`` has already processed essential preprocessing steps, ranging from +centering and weighting to stacking and handling ``NaN`` values. + +Here's a basic PCA example to illustrate the process: + +.. code-block:: python + + def _fit_algorithm(self, data): + # NOTE: The `data` here is a 2D xarray.DataArray with dimensions (sample, feature). + + # We'll illustrate with a simplified PCA. + # The goal is to perform an SVD on the `data` matrix. + decomposer = Decomposer(n_modes=self.n_modes) + decomposer.fit(data) + + # Extract the necessary components from the decomposer. + scores = decomposer.U_ + components = decomposer.V_ + singular_values = decomposer.s_ + + # Store the data for later access using the internal DataContainer class. + self.data.add(name="my_singular_values", data=singular_values) + self.data.add(name="my_components", data=components) + self.data.add(name="my_scores", data=scores) + + # (Optional) Attach model parameters as attributes to your data. + self.data.set_attrs(self.attrs) + +3. Access the Results +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +After fitting your model, results can be retrieved by creating a method for each data +piece. The internal ``Preprocessor`` class can assist with this task, +ensuring that the retrieved data retains the correct format. + +Depending on their dimensions, data types are categorized into four groups: + +1. (sample, feature, ...) +2. (sample, ...) +3. (feature, ...) +4. (...) + +The `Preprocessor` class offers methods corresponding to the first three data groups: + +- ``inverse_transform_data`` for (sample, feature, ...) +- ``inverse_transform_scores`` for (sample, ...) +- ``inverse_transform_components`` for (feature, ...) + +For group (4), data can be accessed directly since there's no need for back transformation. + +.. code-block:: python + + def my_singular_values(self): + return self.data.get("my_singular_values") + + def my_components(self): + return self.preprocessor.inverse_transform_components( + self.data.get("my_components") + ) + + def my_scores(self): + return self.preprocessor.inverse_transform_scores(self.data.get("my_scores")) + + +4. Optional: Implement Transform and Inverse Transform Methods +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +While it's required to implement the ``transform`` and ``inverse_transform`` methods for a complete model, +we'll merely indicate their absence for this example. + +.. code-block:: python + + def _transform_algorithm(self, data): + raise NotImplementedError("This model does not support transform.") + + def _inverse_transform_algorithm(self, data): + raise NotImplementedError("This model does not support inverse transform.") + + +5. Execute the Model +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +With all parts in place, you can now initialize and use the new model: + +.. code-block:: python + + model = MyModel(n_modes=3) + model.fit(t2m, dim="time") + model.my_components() + + +Comparison With Other Packages +============================== + +``xeofs`` stands among a suite of Python packages dedicated to dimensionality reduction. +Its development has been influenced by other notable packages, each boasting unique and robust features. +For instance, `eofs`_, crafted by Andrew Dawson, is renowned for its compatibility with ``Dask`` and ``xarray``, +offering an intuitive EOF analysis interface with a 1D sample dimension. `xMCA`_ is another cherished +tool, presenting an interface for Maximum Covariance Analysis in ``xarray``. In contrast, `pyEOF`_ is +tailored for Varimax-rotated EOF analysis but is restricted to 2D (``pandas``) input data. While all these +tools are useful in their specific realms, they possess limitations. ``xeofs`` aspires to present a more general +toolkit for dimensionality reduction techniques. + + +.. list-table:: + :header-rows: 1 + + * - + - **xeofs** + - **eofs** + - **pyEOF** + - **xMCA** + * - xarray Interface + - ✅ + - ✅ + - ❌ + - ✅ + * - Dask Support + - ✅ + - ✅ + - ❌ + - ❌ + * - Multi-Dimensional + - ✅ + - Only 1D sample dim + - 2D input only + - Only 1D sample dim + * - Missing Values + - ✅ + - ✅ + - ❌ + - ✅ + * - Support for ``xr.Dataset`` + - ✅ + - ❌ + - ❌ + - ❌ + * - Algorithm\ :sup:`1`\ + - Randomized SVD + - Full SVD + - Randomized SVD + - Full SVD + * - Extensible Code Structure + - ✅ + - ❌ + - ❌ + - ❌ + * - **Validation** + - + - + - + - + * - Bootstrapping + - ✅ + - ❌ + - ❌ + - ❌ + +\ :sup:`1`\ **Note on the algorithm:** The computational burden of a full SVD decomposition for an m x n matrix is O(min(mn², m²n)). However, the randomized SVD, which identifies only the initial k singular values, notably curtails this complexity to O(m n log(k)), making the randomized SVD, as utilized by ``xeofs``, more suitable for expansive datasets. For an in-depth exploration, refer to the `sklearn docs on PCA`_. + + +.. _pyEOF: https://github.com/zhonghua-zheng/pyEOF +.. _xMCA: https://github.com/Yefee/xMCA +.. _eofs: https://github.com/ajdawson/eofs +.. _sklearn docs on PCA: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html +.. _`GitHub`: https://github.com/nicrie/xeofs/issues + diff --git a/docs/overview_3_contributing.rst b/docs/overview_3_contributing.rst new file mode 100644 index 0000000..b1adaa4 --- /dev/null +++ b/docs/overview_3_contributing.rst @@ -0,0 +1,141 @@ +Contributing Guide +================== + +Introduction +------------ +Looking to fix a bug, suggest an improvement, or even add a new feature? You're in the right place! We welcome all contributions, regardless of size or complexity. + +This guide will walk you through the process step-by-step. Here's what we'll cover: + +1. Creating a local copy of the repository +2. Setting up your development environment +3. Updating your local copy +4. Creating a new branch +5. Making your changes +6. Testing your changes +7. Submitting a pull request + + +.. note:: + + Steps 1 and 2 only need to be done once, but starting from Step 3, you should repeat them for every new contribution. + +For this guide, we assume you have a basic understanding of version control, Git, and GitHub. For a more in-depth look, consider the `xarray Contributing Guide`_ and its references. + + + +1. Create a local copy of the repository +---------------------------------------- + +To work on the code, start by creating your own fork. Visit the xeofs project page and click the 'Fork' button at the top. This action copies the code to your GitHub account. + +Next, clone your fork to your computer: + +.. code-block:: bash + + git clone https://github.com/your-user-name/xeofs.git + cd xeofs + git remote add upstream https://github.com/nicrie/xeofs.git + +This command sequence creates a local directory named *xeofs* and links your version to the primary xeofs project. + +2. Set up your development environment +-------------------------------------- + +Using the commands below, prepare your environment: + +.. code-block:: bash + + conda create -n xeofs python=3.10 poetry + conda activate xeofs + poetry install --with dev,docs + +This will install all necessary dependencies, including those for development and documentation. If you're only updating the code (without modifying online documentation), you can skip the docs dependency: + +.. code-block:: bash + + poetry install --with dev + +On the other hand, if you're just updating documentation: + +.. code-block:: bash + + poetry install --with docs + +Additionally, install the pre-commit hooks: + +.. code-block:: bash + + pre-commit install + +If you've completed Steps 1 and 2, you can move directly to Step 4. + +3. Update your local copy +------------------------- + +Before diving into your contribution, ensure your local main branch is updated: + +.. code-block:: bash + + git checkout main + git fetch upstream + git merge upstream/main + +This syncs your local main branch with the latest from the primary `xeofs` repository. + +4. Create a new branch +---------------------- + +For your new contribution, initiate a separate branch. Ensure your branch name reflects the essence of your contribution: + +.. code-block:: bash + + git checkout -b my-new-feature + +5. Make your changes +-------------------- + +After making your updates, remember to commit them: + +.. code-block:: bash + + git add . + git commit -m "concise commit message" + + +.. note:: + We use the `conventional commit`_ format for commit messages in ``xeofs``. + This format helps us automatically release new versions. Key points to note: + + - Use **fix:** prefix for **bug fixes**. This will trigger a patch release. + - Use **feat:** prefix for **new features**. This will initiate a minor release. + + + +6. Test your changes +-------------------- + +It's essential to test any modifications to ensure compatibility with the existing code. Run the following test from the repository's root directory: + +.. code-block:: bash + + pytest + +If you introduce a new feature or function, please also add corresponding tests in the `tests` directory. + +7. Submit a pull request +------------------------ + +Once satisfied with your changes, push them to your GitHub fork: + +.. code-block:: bash + + git push origin my-new-feature + + +Then, on your GitHub fork page, select "Compare & pull request" to initiate the pull request. + + +.. _convential commit: https://www.conventionalcommits.org/en/v1.0.0/ +.. _xarray Contributing Guide: https://docs.xarray.dev/en/stable/contributing.html#version-control-git-and-github + diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb index cb46bb6..607276a 100644 --- a/docs/quickstart.ipynb +++ b/docs/quickstart.ipynb @@ -13,12 +13,51 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We begin with a straightforward example: decomposing the temperature over North America using tutorial data provided by ``xarray``." + "We begin with a straightforward example: PCA (EOF analysis) of a 3D `xarray.Dataset`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import the package\n", + "We start by importing the ``xarray`` and ``xeofs`` package." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import xarray as xr\n", + "import xeofs as xe\n", + "\n", + "xr.set_options(display_expand_attrs=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the data\n", + "Next, we fetch the data from the ``xarray`` tutorial repository. The data is a 3D dataset of 6 hourly surface air temperature over North America between 2013 and 2014. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -395,31 +434,26 @@ " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", "Data variables:\n", " air (time, lat, lon) float32 ...\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...

    " ], "text/plain": [ @@ -442,24 +476,15 @@ " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", "Data variables:\n", " air (time, lat, lon) float32 ...\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + "Attributes: (5)" ] }, - "execution_count": 2, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import xarray as xr\n", - "import xeofs as xe\n", - "\n", - "\n", "t2m = xr.tutorial.open_dataset('air_temperature')\n", "t2m" ] @@ -469,32 +494,69 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Our first step is to use standard EOF analysis in S-mode, as this is the most commonly employed variant of EOF analysis. If you're unfamiliar with the distinction between S-mode and T-mode, the article by [Compagnucci & Richmann (2007)](https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.1574) provides an excecllent summary.\n", + "## Fit the model\n", "\n", - "To conduct EOF analysis, we first establish an `EOF` instance by determining the model parameters. For this example, we are interested in the first 15 modes using a non-standardized EOF analysis (based on the covariance matrix) without any weighting. Subsequently, we fit the data, specifying the sample dimensions." + "In order to apply PCA to the data, we first have to create an `EOF` object." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ - "model = xe.models.EOF(n_modes=10)\n", + "model = xe.models.EOF()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now fit the model to the data. If you've worked with `sklearn` before, this process will seem familiar. However, there's an important difference: while `sklearn` `fit` method typically assumes 2D input data shaped as (`sample` x `feature`), our scenario is less straightforward. For any model, including PCA, we must specify the sample dimension. With this information, `xeofs` will interpret all other dimensions as feature dimensions.\n", + "\n", + "In climate science, it's common to maximize variance along the time dimension when applying PCA. Yet, this isn't the sole approach. For instance, [Compagnucci & Richmann (2007)](https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.1574) discuss alternative applications.\n", + "\n", + "`xeofs` offers flexibility in this aspect. You can designate multiple sample dimensions, provided at least one feature dimension remains. For our purposes, we'll set `time` as our sample dimension and then fit the model to our data." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "model.fit(t2m, dim='time')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspect the results" + ] + }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "After completing these steps, we can examine the fraction of variance explained by each mode:" + "Now that the model has been fitted, we can examine the result. For example, one typically starts by looking at the explained variance (ratio). " ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -863,43 +925,21 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    <xarray.DataArray 'explained_variance_ratio' (mode: 10)>\n",
    -       "array([0.7968352 , 0.02702056, 0.02226538, 0.0163209 , 0.01500371,\n",
    -       "       0.01136572, 0.01082397, 0.00727871, 0.00684999, 0.00615871],\n",
    -       "      dtype=float32)\n",
    +       "
    <xarray.DataArray 'explained_variance_ratio' (mode: 2)>\n",
    +       "array([0.79683532, 0.0270206 ])\n",
            "Coordinates:\n",
    -       "  * mode     (mode) int64 1 2 3 4 5 6 7 8 9 10\n",
    -       "Attributes:\n",
    -       "    model:        EOF analysis\n",
    -       "    n_modes:      10\n",
    -       "    standardize:  False\n",
    -       "    use_coslat:   False\n",
    -       "    use_weights:  False\n",
    -       "    software:     xeofs\n",
    -       "    version:      1.0.2\n",
    -       "    date:         2023-07-30 12:56:09
    " + " * mode (mode) int64 1 2\n", + "Attributes: (13)

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