Miranda #

Python utilities for climate data collection, conversion, and management

Documentation:
Free Software:

Features#

Data collection functions for climate and forecast data hosted at:

ECMWF (ERA5, ERA5-Land, TIGGE)
ECCC (Canada) (Monthly Climate Summaries, ECCC GEOAPI - In development)
NCAR (CORDEX-NA on AWS)

Data conversion for Climate and Forecasting (CF) Variable and Metadata compliance:

ECMWF (ERA5, ERA5-Land, TIGGE - In Development)
ECCC (Canada) (Flat File Observations, Monthly Climate Summaries, Adjusted and Homogenized Climate Data, ECCC GEOAPI - In Development)
MELCC (Québec)
Hydro-Québec (In Development)

Database structuring and facets validation:

Simulations:
- WCRP (CMIP5, CMIP6, CMIP5-CORDEX, CORDEX-ADJUST, ISIMIP, etc.)
Station-Observations:
- MELCC (Québec) (Needs mdbtools installed)
- ECCC (Canada) (In Development)
- Hydro-Québec (In Development)
Gridded-Observations:
- NRCAN (Canada) (Future)
- MELCC (Future)
Reanalyses:
- ECMWF (ERA5, ERA5-Land, TIGGE)
- NASA (DayMET, AgMerra/AgCFSR, MERRA2) - In Development
- NCEP (CFSR/CFSv2) - In Development
- WFDEI-GEM-CaPa (University of Saskatchewan) - In Development

Installation#

miranda can be installed from PyPI:

$ pip install miranda

Some functionalities require complex-to-install dependencies. In order to gain access to them, we strongly suggest using Anaconda to manage your environment:

$ conda env create -f environment.yml
$ conda activate miranda
$ pip install miranda[full]

For more information about Anaconda/Miniconda/conda-forge:

Miniconda: https://docs.conda.io/en/latest/miniconda.html
conda-forge: https://conda-forge.org/#about

miranda also relies on PyESSV for its climate data controlled vocabulary. This library is optional for users who do not require validation checks, but enabling this feature requires additional installation steps:

$ mkdir -p ~/.esdoc
$ git clone git@github.com:ES-DOC/pyessv-archive.git ~/.esdoc/pyessv-archive

Contributing#

See the contributing documentation: https://miranda.readthedocs.io/en/latest/contributing.html

Contents:#

Installation#

At the command line, enter:

$ pip install miranda

This is the preferred method to install miranda, as it will always install the most recent stable release.

To make use of remote operations (miranda.remote) and some dataset downloading functions (miranda.ncar miranda.ecmwf), additional libraries are needed. They can can be installed with the following:

$ pip install miranda[remote]

For better RAM usage when converting datasets, some additional/optional GIS libraries can be installed as well:

$ pip install miranda[gis]

If you don’t have pip installed, this Python installation guide can guide you through the process.

From sources#

The sources for miranda can be downloaded from the Github repo.

You can either clone the public repository:

$ git clone git://github.com/Ouranosinc/miranda

Or download the tarball:

$ curl -OL https://github.com/Ouranosinc/miranda/tarball/main

Once you have a copy of the source, you can install it with:

$ python setup.py install

Alternatively, you can also install a local copy via pip:

$ pip install .

Creating a Conda environment#

To create a conda development environment including all miranda dependencies, enter the following command from within your cloned repo:

$ conda create -n my_miranda_env python=3.8 --file=environment.yml
$ conda activate my_miranda_env
$ pip install -e .[dev]

Usage#

To use Miranda in a project:

import miranda

Contributing#

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Types of Contributions#

Report Bugs#

Report bugs at https://github.com/Ouranosinc/miranda/issues.

If you are reporting a bug, please include:

Your operating system name and version.
Any details about your local setup that might be helpful in troubleshooting.
Detailed steps to reproduce the bug.

Fix Bugs#

Look through the GitHub issues for bugs. Anything tagged with “bug” is open to whoever wants to implement it.

Implement Features#

Look through the GitHub issues for features. Anything tagged with “feature” is open to whoever wants to implement it.

Write Documentation#

Miranda could always use more documentation, whether as part of the official Miranda docs, in docstrings, or even on the web in blog posts, articles, and such.

Submit Feedback#

The best way to send feedback is to file an issue at https://github.com/Ouranosinc/miranda/issues.

If you are proposing a feature:

Explain in detail how it would work.
Keep the scope as narrow as possible, to make it easier to implement.
Remember that this is a volunteer-driven project, and that contributions are welcome :)

Get Started!#

Ready to contribute? Here’s how to set up miranda for local development.

Fork the miranda repo on GitHub.

Clone your fork locally:

$ git clone git@github.com:your_name_here/miranda.git

Create a branch for local development:

$ git checkout -b name-of-your-bugfix-or-feature

Now you can make your changes locally.

Begin by installing a development build of your branch:

# To install miranda with its development environment dependencies
$ pip install -e .[dev]
# To install miranda with GIS libraries
$ pip install -e .[gis]
# To install miranda with its documentation dependencies
$ pip install -e .[docs]
# To install miranda with its remote API dependencies
$ pip install -e .[remote]

When you’re done making changes, check that your changes pass style and unit tests, including testing other Python versions with tox:
```
$ tox
```

To get tox, just pip install it.

Commit your changes and push your branch to GitHub:

$ git add .
$ git commit -m "Your detailed description of your changes."
$ git push origin name-of-your-bugfix-or-feature

Submit a pull request through the GitHub website.

Pull Request Guidelines#

Before you submit a pull request, check that it meets these guidelines:

The pull request should include tests.
If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
The pull request should work for Python 3.7, 3.8, 3.9, 3.10, and 3.11. Check https://github.com/Ouranosinc/miranda/actions for active pull request builds or run the tox command and make sure that the tests pass for all supported Python versions.

Tips#

To run a subset of tests:

$ pytest test/test_miranda.py

Credits#

History#

v0.5.0 (2023-06-19)#

Contributors to this version: Juliette Lavoie (:user: juliettelavoie), Trevor James Smith (@Zeitsperre).

New features#

Added support for collecting and converting ptype ECMWF ERA5 variable.
A new "_frequency": true toggle for returning the output frequency of converted data.
Added a new JSON template for NEX-GDDP-CMIP6 datasets.
miranda is now PEP 517 and PEP 621 compliant, using the flit backend.

Internal changes#

Various fixes to existing docstrings.
Time frequency checks are more resilient when converting Monthly time-step data.
Masking and regridding of datasets when running convert_dataset is now optional or automatic.
Updated templates to newest API.
Created a gis recipe for exclusively installing GIS libraries.
Removed many unneeded dependencies, cleaned up Makefile.
All public-facing functions now contain at least a minimal docstring for documentation generation.

v0.4.0 (2023-03-30)#

Contributors to this version: Trevor James Smith (@Zeitsperre), Pascal Bourgault (@aulemahal), Travis Logan (@tlogan2000).

New features#

Improvements have been made to the development documentation; Project URLs, ReadTheDocs theming, and other quality of life changes.
Conversion JSON definitions now support pre-processing to render dimensions and variable names consistent before running corrections/conversions.
New datasets with CF-like attributes conversion supported:
- RDRS (ECCC)
- GRNCH (ETS)
Preliminary miranda.io module for organizing output-writing functionality.
New miranda.io.fetch_chunk_config function for “rechunking” datasets according to project presets.
New mirands.io.utils.name_output_file for generating names from Dataset facets or from a dictionary.
New mirands.gis.subset_domain for clipping dataset to a preconfigured region.

Bug fixes#

Many data-related utilities now have more accurate static typing.
Converted dataset global attributes are now synchronized for consistency.
ECMWF-based datasets now implement more consistent conversion factors and metadata.
miranda.storage.file_size now handles dictionaries of Pathlib objects.

Internal changes#

Pre-commit version updates.
Improvements have been made to the development documentation; Project URLs, ReadTheDocs theming, installation methods, and other quality of life changes.
Schema and folder structure updates:
- gridded-obs -> reconstruction
- bias-adjust-project is used when present and not just when level==”biasadjusted”
CI now using tox>=4.0 and ubuntu-latest virtual machine images.

v0.3.0 (2022-11-24)#

Contributors to this version: Trevor James Smith (@Zeitsperre), Pascal Bourgault (@aulemahal), David Huard (@huard), Travis Logan (@tlogan2000), Gabriel Rondeau-Genesse (@RondeauG), and Sébastien Biner (@sbiner).

Announcements#

First public release on PyPI.

New features#

Dataset conversion tools (miranda.convert) use a JSON-definition file to dynamically populate metadata, run data quality checks, and convert units to CF-compliant standard. Supported datasets are:
- ERA5/ERA5-Land (complete)
- MELCC (stations) (beta)
- ECCC (stations) (alpha)
- NASA DayMet (WIP)
- NASA AgMerra/AgCFSR (WIP)
- Hydro Québec (stations) (WIP)
- DEH (stations) (WIP)
- WFDEI-GEM-CAPA (WIP)
Module (miranda.eccc) for ECCC station data and ECCC Adjusted and Homogenized Canadian Climate Data (AHCCD) conversion (WIP).
Module (miranda.ncar) for fetching interpolated CORDEX-NAM (22i/44i) from NCAR AWS data storage.
Module (miranda.ecmwf) for fetching ECMWF ERA5/-Land (single-levels, pressure-levels, monthly-means) datasets via CDSAPI.
Module (miranda.gis) for setting specific subsetting domains used when converting gridded datasets.
Modules (miranda.archive and miranda.remote) for performing data archiving actions locally and remotely (powered by fabric and paramiko) (WIP).
Module (miranda.decode) for ingesting and parsing dataset metadata based on filename and dataset attributes. Supported datasets are:
- miranda converted datasets
- CMIP6
- CMIP5
- CMIP5-CORDEX
- ISIMIP-FT
- CanDCS-U6 (PCIC)
Module (miranda.structure) for create constructing file-tree databases based on YAML-defined metadata schemas (WIP).
Modules (miranda.cv and miranda.validators) for validating metadata using ESGF controlled vocabularies (taken from pyessv-archive) and schema definitions (powered by schema), respectively (WIP).

License#

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miranda#

miranda package#

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Bases: object

Database management class.

archive()[source]#: Not yet implemented.

group_by(*, common_path: Path | str | None = None, subdirectories: bool = True, dates: bool = True, size: int = 10737418240)[source]#

Grouping meta-function.

Notes

Not yet implemented.

items()[source]#: Show items.

keys()[source]#: Show keys.

target(target: Path | str)[source]#: Target directory or server address.

transfer()[source]#: Not yet implemented.

values()[source]#: Show values.

class miranda.FileMeta(path: str, size: int = -1)[source]#

Bases: object

File path and size.

django = {'path': ['CharField', 'max_length=512'], 'size': ['IntegerField', 'null=True', 'blank=True']}#

class miranda.StorageState(base_path, capacity=-1, used_space=-1, free_space=-1)[source]#

Bases: object

Information regarding the storage capacity of a disk.

Subpackages#

miranda.archive package#

Archive module.

miranda.archive.group_by_deciphered_date(files: generator | list[str | pathlib.Path]) → dict[str, list[pathlib.Path]][source]#

Find a common date and groups files based on year and month.

Parameters:: files (GeneratorType or list of str or pathlib.Path)
Returns:: dict[str, list[pathlib.Path]]

miranda.archive.group_by_length(files: generator | list[str | pathlib.Path], size: int = 10, sort: bool = False) → list[list[pathlib.Path]][source]#

Group files by an arbitrary number of file entries.

Parameters:

files (GeneratorType or list of str or pathlib.Path)
size (int)
sort (bool)

Returns:

list[list[pathlib.Path]]

miranda.archive.group_by_size(files: generator | list[str | pathlib.Path], size: int = 10737418240) → list[list[pathlib.Path]][source]#

Group files up until a desired size and save it as a grouping within a list.

Parameters:

files (GeneratorType or list of str or pathlib.Path)
size (int)

Returns:

list[list[pathlib.Path]]

miranda.archive.group_by_subdirectories(files: generator | list[str | pathlib.Path], within: Path | str | None = None) → dict[str, list[pathlib.Path]][source]#

Group files based on the parent folder that they are located within.

Parameters:

files (GeneratorType or list of str or pathlib.Path)
within (str or pathlib.Path)

Returns:

dict[str, list[pathlib.Path]]

miranda.archive.select_by_date_modified(source: str | Path, year: int | None, month: int | None, day: int | None, *, suffixes: str = 'nc', date: datetime) → list[pathlib.Path][source]#

Select files by the date on which they were last modified.

Parameters:

source (str or Path)
year (int)
month (int)
day (int)
suffixes (str)
date (datetime.date)

Returns:

list of Path

Submodules#

miranda.archive._groupings module#

miranda.archive._groupings.group_by_deciphered_date(files: generator | list[str | pathlib.Path]) → dict[str, list[pathlib.Path]][source]#

Find a common date and groups files based on year and month.

Parameters:: files (GeneratorType or list of str or pathlib.Path)
Returns:: dict[str, list[pathlib.Path]]

miranda.archive._groupings.group_by_length(files: generator | list[str | pathlib.Path], size: int = 10, sort: bool = False) → list[list[pathlib.Path]][source]#

Group files by an arbitrary number of file entries.

Parameters:

files (GeneratorType or list of str or pathlib.Path)
size (int)
sort (bool)

Returns:

list[list[pathlib.Path]]

miranda.archive._groupings.group_by_size(files: generator | list[str | pathlib.Path], size: int = 10737418240) → list[list[pathlib.Path]][source]#

Group files up until a desired size and save it as a grouping within a list.

Parameters:

files (GeneratorType or list of str or pathlib.Path)
size (int)

Returns:

list[list[pathlib.Path]]

miranda.archive._groupings.group_by_subdirectories(files: generator | list[str | pathlib.Path], within: Path | str | None = None) → dict[str, list[pathlib.Path]][source]#

Group files based on the parent folder that they are located within.

Parameters:

files (GeneratorType or list of str or pathlib.Path)
within (str or pathlib.Path)

Returns:

dict[str, list[pathlib.Path]]

miranda.archive._selection module#

miranda.archive._selection.select_by_date_modified(source: str | Path, year: int | None, month: int | None, day: int | None, *, suffixes: str = 'nc', date: datetime) → list[pathlib.Path][source]#

Select files by the date on which they were last modified.

Parameters:

source (str or Path)
year (int)
month (int)
day (int)
suffixes (str)
date (datetime.date)

Returns:

list of Path

miranda.convert package#

Data Conversion module.

miranda.convert.aggregate(ds: Dataset, freq: str = 'day') → dict[str, xarray.core.dataset.Dataset][source]#

Parameters:

ds (xarray.Dataset)
freq (str)

Returns:

dict[str, xarray.Dataset]

miranda.convert.aggregations_possible(ds: Dataset, freq: str = 'day') → dict[str, set[str]][source]#

Determine which aggregations are possible based on variables within a dataset.

Parameters:

ds (xarray.Dataset)
freq (str)

Returns:

dict[str, set[str]]

Convert an existing Xarray-compatible dataset to another format with variable corrections applied.

Parameters:

input_files (str or os.PathLike or Sequence[str or os.PathLike] or Iterator[os.PathLike] or xr.Dataset) – Files or objects to be converted. If sent a list or GeneratorType, will open with xarray.open_mfdataset() and concatenate files.
project ({“cordex”, “cmip5”, “cmip6”, “ets-grnch”, “isimip-ft”, “pcic-candcs-u6”, “converted”}) – Project name for decoding/handling purposes.
domain ({“global”, “nam”, “can”, “qc”, “mtl”}, optional) – Domain to perform subsetting for. Default: None.
mask (Optional[Union[xr.Dataset, xr.DataArray]]) – DataArray or single data_variable dataset containing mask.
mask_cutoff (float or bool) – If land_sea_mask supplied, the threshold above which to mask with land_sea_mask. Default: False.
regrid (bool) – Performing regridding with xesmf. Default: False.
add_version_hashes (bool) – If True, version name and sha256sum of source file(s) will be added as a field among the global attributes.
preprocess (callable or str, optional) – Preprocessing functions to perform over each Dataset. Default: “auto” - Run preprocessing fixes based on supplied fields from metadata definition. Callable - Runs function over Dataset (single) or supplied to preprocess (multifile dataset).
**xr_kwargs – Arguments passed directly to xarray.

Returns:

xr.Dataset or xr.DataArray

miranda.convert.dataset_corrections(ds: Dataset, project: str) → Dataset[source]#: Convert variables to CF-compliant format

miranda.convert.dims_conversion(d: Dataset, p: str, m: dict) → Dataset[source]#

Rename dimensions to CF to their equivalents.

Parameters:

d (xarray.Dataset) – Dataset with dimensions to be updated.
p (str) – Dataset project name.
m (dict) – Metadata definition dictionary for project and variable(s).

Returns:

xarray.Dataset

miranda.convert.gather_agcfsr(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather agCFSR source data.

Parameters:: path (str or os.PathLike)
Returns:: dict[str, list[pathlib.Path]]

miranda.convert.gather_agmerra(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather agMERRA source data.

Parameters:: path (str or os.PathLike)
Returns:: dict[str, list[pathlib.Path]]

miranda.convert.gather_ecmwf(project: str, path: str | PathLike, back_extension: bool = False, monthly_means: bool = False) → dict[str, list[pathlib.Path]][source]#

Parameters:

project ({“era5-single-levels”, “era5-pressure-levels”, “era5-land”})
path (str or os.PathLike)
back_extension (bool)
monthly_means (bool)

Returns:

dict[str, list[pathlib.Path]]

miranda.convert.gather_emdna(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather raw EMDNA files for preprocessing.

Put all files with the same member together.

Parameters:: path (str or os.PathLike)
Returns:: dict[str, list[pathlib.Path]]

miranda.convert.gather_grnch(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather raw ETS-GRNCH files for preprocessing.

Parameters:: path (str or os.PathLike)
Returns:: dict(str, dict(str, list[Path])) or None

miranda.convert.gather_nex(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather raw NEX files for preprocessing.

Put all files that should be contained in one dataset in one entry of the dictionary.

Parameters:: path (str or os.PathLike)
Returns:: dict[str, list[pathlib.Path]]

miranda.convert.gather_nrcan_gridded_obs(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather NRCan Gridded Observations source data.

Parameters:: path (str or os.PathLike)
Returns:: dict(str, list[pathlib.Path])

miranda.convert.gather_raw_rdrs_by_years(path: str | PathLike) → dict[str, dict[str, list[pathlib.Path]]][source]#

Gather raw RDRS files for preprocessing.

Parameters:: path (str or os.PathLike)
Returns:: dict[str, dict[str, list[pathlib.Path]]

miranda.convert.gather_rdrs(name: str, path: str | PathLike, suffix: str, key: str) → dict[str, dict[str, list[pathlib.Path]]][source]#

Gather RDRS processed source data.

Parameters:

name (str)
path (str or os.PathLike)
suffix (str)
key ({“raw”, “cf”}) – Indicating which variable name dictionary to search for.

Returns:

dict[str, list[pathlib.Path]]

miranda.convert.gather_sc_earth(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather SC-Earth source data

Parameters:: path (str or os.PathLike)
Returns:: dict[str, list[pathlib.Path]]

miranda.convert.gather_wfdei_gem_capa(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather WFDEI-GEM-CaPa source data.

Parameters:: path (str or os.PathLike)
Returns:: dict[str, list[pathlib.Path]]

miranda.convert.load_json_data_mappings(project: str) → dict[str, Any][source]#

Load JSON mappings for supported dataset conversions.

Parameters:: project (str)
Returns:: dict[str, Any]

miranda.convert.metadata_conversion(d: Dataset, p: str, m: dict) → Dataset[source]#

Update xarray dataset and data_vars with project-specific metadata fields.

Parameters:

d (xarray.Dataset) – Dataset with metadata to be updated.
p (str) – Dataset project name.
m (dict) – Metadata definition dictionary for project and variable(s).

Returns:

xarray.Dataset

miranda.convert.threshold_mask(ds: Dataset | DataArray, *, mask: Dataset | DataArray, mask_cutoff: float | bool = False) → Dataset | DataArray[source]#

Land-Sea mask operations.

Parameters:

ds (xr.Dataset or str or os.PathLike)
mask (xr.Dataset or xr.DataArray)
mask_cutoff (float or bool)

Returns:

xr.Dataset or xr.DataArray

miranda.convert.variable_conversion(d: Dataset, p: str, m: dict) → Dataset[source]#

Add variable metadata and remove nonstandard entries.

Parameters:

d (xarray.Dataset) – Dataset with variable(s) to be updated.
p (str) – Dataset project name.
m (dict) – Metadata definition dictionary for project and variable(s).

Returns:

xarray.Dataset

Submodules#

miranda.convert._aggregation module#

Aggregation module.

miranda.convert._aggregation.aggregate(ds: Dataset, freq: str = 'day') → dict[str, xarray.core.dataset.Dataset][source]#

Parameters:

ds (xarray.Dataset)
freq (str)

Returns:

dict[str, xarray.Dataset]

miranda.convert._aggregation.aggregations_possible(ds: Dataset, freq: str = 'day') → dict[str, set[str]][source]#

Determine which aggregations are possible based on variables within a dataset.

Parameters:

ds (xarray.Dataset)
freq (str)

Returns:

dict[str, set[str]]

miranda.convert._data_corrections module#

Convert an existing Xarray-compatible dataset to another format with variable corrections applied.

Parameters:

input_files (str or os.PathLike or Sequence[str or os.PathLike] or Iterator[os.PathLike] or xr.Dataset) – Files or objects to be converted. If sent a list or GeneratorType, will open with xarray.open_mfdataset() and concatenate files.
project ({“cordex”, “cmip5”, “cmip6”, “ets-grnch”, “isimip-ft”, “pcic-candcs-u6”, “converted”}) – Project name for decoding/handling purposes.
domain ({“global”, “nam”, “can”, “qc”, “mtl”}, optional) – Domain to perform subsetting for. Default: None.
mask (Optional[Union[xr.Dataset, xr.DataArray]]) – DataArray or single data_variable dataset containing mask.
mask_cutoff (float or bool) – If land_sea_mask supplied, the threshold above which to mask with land_sea_mask. Default: False.
regrid (bool) – Performing regridding with xesmf. Default: False.
add_version_hashes (bool) – If True, version name and sha256sum of source file(s) will be added as a field among the global attributes.
preprocess (callable or str, optional) – Preprocessing functions to perform over each Dataset. Default: “auto” - Run preprocessing fixes based on supplied fields from metadata definition. Callable - Runs function over Dataset (single) or supplied to preprocess (multifile dataset).
**xr_kwargs – Arguments passed directly to xarray.

Returns:

xr.Dataset or xr.DataArray

miranda.convert._data_corrections.dataset_corrections(ds: Dataset, project: str) → Dataset[source]#: Convert variables to CF-compliant format

miranda.convert._data_corrections.dims_conversion(d: Dataset, p: str, m: dict) → Dataset[source]#

Rename dimensions to CF to their equivalents.

Parameters:

d (xarray.Dataset) – Dataset with dimensions to be updated.
p (str) – Dataset project name.
m (dict) – Metadata definition dictionary for project and variable(s).

Returns:

xarray.Dataset

miranda.convert._data_corrections.load_json_data_mappings(project: str) → dict[str, Any][source]#

Load JSON mappings for supported dataset conversions.

Parameters:: project (str)
Returns:: dict[str, Any]

miranda.convert._data_corrections.metadata_conversion(d: Dataset, p: str, m: dict) → Dataset[source]#

Update xarray dataset and data_vars with project-specific metadata fields.

Parameters:

d (xarray.Dataset) – Dataset with metadata to be updated.
p (str) – Dataset project name.
m (dict) – Metadata definition dictionary for project and variable(s).

Returns:

xarray.Dataset

miranda.convert._data_corrections.threshold_mask(ds: Dataset | DataArray, *, mask: Dataset | DataArray, mask_cutoff: float | bool = False) → Dataset | DataArray[source]#

Land-Sea mask operations.

Parameters:

ds (xr.Dataset or str or os.PathLike)
mask (xr.Dataset or xr.DataArray)
mask_cutoff (float or bool)

Returns:

xr.Dataset or xr.DataArray

miranda.convert._data_corrections.variable_conversion(d: Dataset, p: str, m: dict) → Dataset[source]#

Add variable metadata and remove nonstandard entries.

Parameters:

d (xarray.Dataset) – Dataset with variable(s) to be updated.
p (str) – Dataset project name.
m (dict) – Metadata definition dictionary for project and variable(s).

Returns:

xarray.Dataset

miranda.convert._data_definitions module#

miranda.convert._data_definitions.gather_agcfsr(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather agCFSR source data.

Parameters:: path (str or os.PathLike)
Returns:: dict[str, list[pathlib.Path]]

miranda.convert._data_definitions.gather_agmerra(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather agMERRA source data.

Parameters:: path (str or os.PathLike)
Returns:: dict[str, list[pathlib.Path]]

miranda.convert._data_definitions.gather_ecmwf(project: str, path: str | PathLike, back_extension: bool = False, monthly_means: bool = False) → dict[str, list[pathlib.Path]][source]#

Parameters:

project ({“era5-single-levels”, “era5-pressure-levels”, “era5-land”})
path (str or os.PathLike)
back_extension (bool)
monthly_means (bool)

Returns:

dict[str, list[pathlib.Path]]

miranda.convert._data_definitions.gather_emdna(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather raw EMDNA files for preprocessing.

Put all files with the same member together.

Parameters:: path (str or os.PathLike)
Returns:: dict[str, list[pathlib.Path]]

miranda.convert._data_definitions.gather_grnch(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather raw ETS-GRNCH files for preprocessing.

Parameters:: path (str or os.PathLike)
Returns:: dict(str, dict(str, list[Path])) or None

miranda.convert._data_definitions.gather_nex(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather raw NEX files for preprocessing.

Put all files that should be contained in one dataset in one entry of the dictionary.

Parameters:: path (str or os.PathLike)
Returns:: dict[str, list[pathlib.Path]]

miranda.convert._data_definitions.gather_nrcan_gridded_obs(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather NRCan Gridded Observations source data.

Parameters:: path (str or os.PathLike)
Returns:: dict(str, list[pathlib.Path])

miranda.convert._data_definitions.gather_raw_rdrs_by_years(path: str | PathLike) → dict[str, dict[str, list[pathlib.Path]]][source]#

Gather raw RDRS files for preprocessing.

Parameters:: path (str or os.PathLike)
Returns:: dict[str, dict[str, list[pathlib.Path]]

miranda.convert._data_definitions.gather_rdrs(name: str, path: str | PathLike, suffix: str, key: str) → dict[str, dict[str, list[pathlib.Path]]][source]#

Gather RDRS processed source data.

Parameters:

name (str)
path (str or os.PathLike)
suffix (str)
key ({“raw”, “cf”}) – Indicating which variable name dictionary to search for.

Returns:

dict[str, list[pathlib.Path]]

miranda.convert._data_definitions.gather_sc_earth(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather SC-Earth source data

Parameters:: path (str or os.PathLike)
Returns:: dict[str, list[pathlib.Path]]

miranda.convert._data_definitions.gather_wfdei_gem_capa(path: str | PathLike) → dict[str, list[pathlib.Path]][source]#

Gather WFDEI-GEM-CaPa source data.

Parameters:: path (str or os.PathLike)
Returns:: dict[str, list[pathlib.Path]]

miranda.convert._reconstruction module#

miranda.convert._reconstruction.reanalysis_processing(data: dict[str, list[str | os.PathLike]], output_folder: str | PathLike, variables: Sequence[str], aggregate: str | bool = False, domains: str | list[str] = '_DEFAULT', start: str | None = None, end: str | None = None, target_chunks: dict | None = None, output_format: str = 'netcdf', overwrite: bool = False, engine: str = 'h5netcdf', n_workers: int = 4, **dask_kwargs) → None[source]#

Parameters:

data (dict[str, list[str]])
output_folder (str or os.PathLike)
variables (Sequence[str])
aggregate ({“day”, None})
domains ({“QC”, “CAN”, “AMNO”, “NAM”, “GLOBAL”})
start (str, optional)
end (str, optional)
target_chunks (dict, optional)
output_format ({“netcdf”, “zarr”})
overwrite (bool)
engine ({“netcdf4”, “h5netcdf”})
n_workers (int)

Returns:

None

miranda.convert.deh module#

DEH Hydrograph Conversion module.

miranda.convert.deh.open_txt(path: str | Path, cf_table: dict | None = {'flag': {'comment': 'See DEH technical information for details.', 'long_name': 'data flag'}, 'q': {'long_name': 'River discharge', 'units': 'm3 s-1'}}) → Dataset[source]#: Extract daily HQ meteorological data and convert to xr.DataArray with CF-Convention attributes.

miranda.convert.eccc module#

Environment and Climate Change Canada Data Conversion module.

miranda.convert.eccc.convert_canswe(file: str | Path, output: str | Path)[source]#: Convert the CanSWE netCDF files to production-ready CF-compliant netCDFs.

miranda.convert.eccc_rdrs module#

Environment and Climate Change Canada RDRS conversion tools.

miranda.convert.eccc_rdrs.convert_rdrs(project: str, input_folder: str | PathLike, output_folder: str | PathLike, output_format: str = 'zarr', working_folder: str | PathLike | None = None, overwrite: bool = False, **dask_kwargs) → None[source]#

Parameters:

project (str)
input_folder (str or os.PathLike)
output_folder (str or os.PathLike)
output_format ({“netcdf”, “zarr”})
working_folder (str or os.PathLike, optional)
overwrite (bool)
**dask_kwargs

Returns:

None

miranda.convert.eccc_rdrs.rdrs_to_daily(project: str, input_folder: str | PathLike, output_folder: str | PathLike, working_folder: str | PathLike | None = None, overwrite: bool = False, output_format: str = 'zarr', year_start: int | None = None, year_end: int | None = None, process_variables: list[str] | None = None, **dask_kwargs) → None[source]#

Write out RDRS files to daily-timestep files.

Parameters:

project (str)
input_folder (str or os.PathLike)
output_folder (str or os.PathLike)
working_folder (str or os.PathLike)
overwrite (bool)
output_format ({“netcdf”, “zarr”})
year_start (int, optional)
year_end (int, optional)
process_variables (list of str, optional)
**dask_kwargs

Returns:

None

miranda.convert.ecmwf module#

ECMWF TIGGE Conversion module.

miranda.convert.ecmwf.tigge_convert(source: PathLike | None = None, target: PathLike | None = None, processes: int = 8) → None[source]#

Convert grib2 file to netCDF format.

Parameters:

source (os.PathLike, optional)
target (os.PathLike, optional)
processes (int)

Returns:

None

miranda.convert.hq module#

Hydro Quebec Weather Station Data Conversion module.

miranda.convert.hq.open_csv(path: str | Path, cf_table: dict | None = {'hurs': {'cell_methods': 'time: point', 'comment': 'The relative humidity with respect to liquid water for T> 0 C, and with respect to ice for T<0 C.', 'frequency': '1h', 'long_name': 'Near-Surface Relative Humidity', 'out_name': 'hurs', 'standard_name': 'relative_humidity', 'type': 'real', 'units': '%'}, 'prlp': {'cell_methods': 'time: mean', 'comment': 'At surface; includes precipitation of all forms of water in the liquid phase.', 'frequency': 'day', 'long_name': 'Rainfall Flux', 'out_name': 'prlp', 'standard_name': 'rainfall_flux', 'type': 'real', 'units': 'kg m-2 s-1'}, 'prsn': {'cell_methods': 'time: mean', 'comment': 'At surface; includes precipitation of all forms of water in the solid phase.', 'frequency': 'day', 'long_name': 'Snowfall Flux', 'out_name': 'prsn', 'standard_name': 'snowfall_flux', 'type': 'real', 'units': 'kg m-2 s-1'}, 'sfcWind': {'cell_methods': 'time: point', 'comment': 'Near-surface (usually, 10 meters) wind speed.', 'frequency': '1h', 'long_name': 'Near-Surface Wind Speed', 'out_name': 'sfcWind', 'standard_name': 'wind_speed', 'type': 'real', 'units': 'm s-1'}, 'sfcWindAz': {'cell_methods': 'time: point', 'comment': 'Near-surface (usually, 10 meters) direction from which wind originates.', 'frequency': '1h', 'long_name': 'Near-Surface Wind Direction', 'out_name': 'sfcWindAz', 'standard_name': 'wind_direction', 'type': 'real', 'units': 'degree'}, 'snd': {'cell_methods': 'time: point', 'comment': 'The thickness of snow.', 'frequency': '1h', 'long_name': 'Snow Depth', 'out_name': 'snd', 'standard_name': 'surface_snow_thickness', 'type': 'real', 'units': 'm'}, 'tasmax_1h': {'cell_methods': 'time: maximum', 'comment': 'Maximum near-surface (usually, 2 meter) air temperature.', 'frequency': '1h', 'long_name': 'Hourly Maximum Near-Surface Air Temperature', 'out_name': 'tasmax', 'standard_name': 'air_temperature', 'type': 'real', 'units': 'K'}, 'tasmax_day': {'cell_methods': 'time: maximum', 'comment': 'Maximum near-surface (usually, 2 meter) air temperature.', 'frequency': 'day', 'long_name': 'Daily Maximum Near-Surface Air Temperature', 'out_name': 'tasmax', 'standard_name': 'air_temperature', 'type': 'real', 'units': 'K'}, 'tasmin_1h': {'cell_methods': 'time: minimum', 'comment': 'Minimum near-surface (usually, 2 meter) air temperature.', 'frequency': '1h', 'long_name': 'Hourly Minimum Near-Surface Air Temperature', 'out_name': 'tasmin', 'standard_name': 'air_temperature', 'type': 'real', 'units': 'K'}, 'tasmin_day': {'cell_methods': 'time: minimum', 'comment': 'Minimum near-surface (usually, 2 meter) air temperature.', 'frequency': 'day', 'long_name': 'Daily Minimum Near-Surface Air Temperature', 'out_name': 'tasmin', 'standard_name': 'air_temperature', 'type': 'real', 'units': 'K'}}) → DataArray[source]#: Extract daily HQ meteo data and convert to xr.DataArray with CF-Convention attributes.

miranda.convert.melcc module#

MELCC (Québec) Weather Stations data conversion module.

miranda.convert.melcc.concat(files: Sequence[str | Path], output_folder: str | Path, overwrite: bool = True) → Path[source]#

Concatenate converted weather station files.

Parameters:

files (sequence of str or Path)
output_folder (str or Path)
overwrite (bool)

Returns:

Path

miranda.convert.melcc.convert_mdb(database: str | Path, stations: Dataset, definitions: Dataset, output: str | Path, overwrite: bool = True) → dict[tuple[str, str], pathlib.Path][source]#

Convert microsoft databases of MELCC observation data to xarray objects.

Parameters:

database (str or Path)
stations
definitions
output
overwrite

Returns:

dict[tuple[str, str], Path]

miranda.convert.melcc.convert_melcc_obs(metafile: str | Path, folder: str | Path, output: Path | str | None = None, overwrite: bool = True) → dict[tuple[str, str], pathlib.Path][source]#

Convert MELCC observation data to xarray data objects, returning paths.

Parameters:

metafile (str or Path)
folder (str or Path)
output (str or Path, optional)
overwrite (bool)

Returns:

dict[str, Path]

miranda.convert.melcc.convert_snow_table(file: str | Path, output: str | Path)[source]#

Convert snow data given through an Excel file.

This private data is not included in the MDB files.

Parameters:

file (path) – The excel file with sheets: “Stations”, “Périodes standards” and “Données”
output (path) – Folder where to put the netCDF files (one for each of snd, sd and snw).

miranda.convert.melcc.list_tables(db_file)[source]#: List the tables of an MDB file.

miranda.convert.melcc.parse_var_code(vcode: str) → dict[str, Any][source]#

Parse variable code to generate metadata

Parameters:: vcode (str)
Returns:: dict[str, Any]

miranda.convert.melcc.read_definitions(dbfile: str)[source]#

Read variable definition file using mdbtools.

Parameters:: dbfile (str)
Returns:: pandas.DataFrame

miranda.convert.melcc.read_stations(db_file: str | PathLike) → DataFrame[source]#

Read station file using mdbtools.

Parameters:: db_file (str or os.PathLike)
Returns:: pandas.DataFrame

miranda.convert.melcc.read_table(db_file: str | PathLike, tab: str | PathLike) → Dataset[source]#

Read a MySQL table into an xarray object.

Parameters:

db_file (str or os.PathLike)
tab (str or os.PathLike)

Returns:

xarray.Dataset

miranda.convert.utils module#

Conversion Utilities submodule.

miranda.convert.utils.date_parser(date: str, *, end_of_period: bool = False, output_type: str = 'str', strftime_format: str = '%Y-%m-%d') → str | Timestamp | NaTType[source]#

Parses datetime objects from a string representation of a date or both a start and end date.

Parameters:

date (str) – Date to be converted.
end_of_period (bool) – If True, the date will be the end of month or year depending on what’s most appropriate.
output_type ({“datetime”, “str”}) – Desired returned object type.
strftime_format (str) – If output_type==’str’, this sets the strftime format.

Returns:

pd.Timestamp or str or pd.NaT – Parsed date.

Notes

Adapted from code written by Gabriel Rondeau-Genesse (@RondeauG)

miranda.convert.utils.find_version_hash(file: PathLike | str) → dict[source]#

Check for an existing version hash file and, if one cannot be found, generate one from file.

Parameters:: file (str or os.PathLike)
Returns:: dict

miranda.decode package#

class miranda.decode.Decoder(project: str | None)[source]#

Bases: object

Decode facets from file or list of files.

Parameters:

files (str or Path or list of str or Path or generator)
chunks (int, optional) – The chunk size used when processing files. Not to be confused with xarray chunks for dimensions.
raise_error (bool)

static decode_ahccd_obs(self, file: PathLike | str) → dict[source]#

classmethod decode_cmip5(file: PathLike | str) → dict[source]#

classmethod decode_cmip6(file: PathLike | str) → dict[source]#

classmethod decode_converted(file: PathLike | str) → dict[source]#

classmethod decode_cordex(file: PathLike | str) → dict[source]#

static decode_eccc_obs(self, file: PathLike | str) → dict[source]#

classmethod decode_espo_g6_e5l(file: PathLike | str) → dict[source]#

classmethod decode_espo_g6_r2(file: PathLike | str) → dict[source]#

classmethod decode_isimip_ft(file: PathLike | str) → dict[source]#

static decode_melcc_obs(self, file: PathLike | str) → dict[source]#

classmethod decode_nex_gddp_cmip6(file: PathLike | str) → dict[source]#

classmethod decode_pcic_candcs_u6(file: PathLike | str) → dict[source]#

facets_table()[source]#

file_facets() → dict[os.PathLike, dict][source]#

guess = False#

project = None#

exception miranda.decode.DecoderError[source]#: Bases: Exception

miranda.decode.guess_project(file: PathLike | str) → str[source]#

Guess the name of the project

Parameters:: file (str or os.PathLike)
Returns:: str

Submodules#

miranda.decode._decoder module#

class miranda.decode._decoder.Decoder(project: str | None)[source]#

Bases: object

Decode facets from file or list of files.

Parameters:

files (str or Path or list of str or Path or generator)
chunks (int, optional) – The chunk size used when processing files. Not to be confused with xarray chunks for dimensions.
raise_error (bool)

static decode_ahccd_obs(self, file: PathLike | str) → dict[source]#

classmethod decode_cmip5(file: PathLike | str) → dict[source]#

classmethod decode_cmip6(file: PathLike | str) → dict[source]#

classmethod decode_converted(file: PathLike | str) → dict[source]#

classmethod decode_cordex(file: PathLike | str) → dict[source]#

static decode_eccc_obs(self, file: PathLike | str) → dict[source]#

classmethod decode_espo_g6_e5l(file: PathLike | str) → dict[source]#

classmethod decode_espo_g6_r2(file: PathLike | str) → dict[source]#

classmethod decode_isimip_ft(file: PathLike | str) → dict[source]#

static decode_melcc_obs(self, file: PathLike | str) → dict[source]#

classmethod decode_nex_gddp_cmip6(file: PathLike | str) → dict[source]#

classmethod decode_pcic_candcs_u6(file: PathLike | str) → dict[source]#

facets_table()[source]#

file_facets() → dict[os.PathLike, dict][source]#

guess = False#

project = None#

miranda.decode._decoder.guess_project(file: PathLike | str) → str[source]#

Guess the name of the project

Parameters:: file (str or os.PathLike)
Returns:: str

miranda.decode._time module#

exception miranda.decode._time.DecoderError[source]#: Bases: Exception

miranda.eccc package#

Environment and Climate Change Canada specialized conversion module.

Parameters:

source_files (str or Path)
output_folder (str or Path)
variables (str or int or list of str or int, optional)
time_step ({“hourly”, “daily”})
include_flags (bool)
groupings (int) – The number of files in each group used for converting to multi-file Datasets.
mf_dataset_freq (str, optional) – Resampling frequency for creating output multi-file Datasets. E.g. ‘YS’: 1 year per file, ‘5YS’: 5 years per file.
temp_directory (str or Path, optional) – Use another temporary directory location in case default location is not spacious enough.
n_workers (int)

Returns:

None

miranda.eccc.convert_ahccd(data_source: str | Path, output_dir: str | Path, variable: str, generation: int | None = None) → None[source]#

Convert Adjusted and Homogenized Canadian Climate Dataset files.

Parameters:

data_source (str or Path)
output_dir (str or Path)
variable (str)
generation (int, optional)

Returns:

None

miranda.eccc.convert_ahccd_fwf_files(ff: Path | str, metadata: DataFrame, variable: str, generation: int | None = None, cols_specs: list[tuple[int, int]] | None = None, attrs: dict | None = None) → Dataset[source]#

Convert AHCCD fixed-width files.

Parameters:

ff (str or Path)
metadata (pandas.DataFrame)
variable (str)
generation
cols_specs
attrs

Returns:

xarray.Dataset

Parameters:

source_files (str or Path)
output_folder (str or Path)
variables (str or List[str])
mode ({“hourly”, “daily”})
n_workers (int)

Returns:

None

miranda.eccc.daily_summaries_to_netcdf(station: dict, path_output: Path | str) → None[source]#

Convert daily climate summaries to NetCDF files.

Uses xarray to transform the ‘station’ from find_and_extract_dly into a CF-Convention netCDF file

Parameters:

station (dict) – dict created by using find_and_extract_dly
path_output (str or Path) – Output path.

Returns:

None

miranda.eccc.extract_daily_summaries(path_station: Path | str, rm_flags: bool = False, file_suffix: str = '.csv') → dict[source]#

Extract daily climate summaries from ECCC CSV files.

Parameters:

path_station (str or Path) – PathLike or str to the station’s folder containing the csv files.
rm_flags (bool) – Removes the ‘Flag’ and ‘Quality’ columns of the ECCC files.
file_suffix (str) – File suffixes used by the tabular data. Default: “.csv”.

Returns:

dict – dict containing the station metadata, as well as the data stored within a pandas Dataframe.

Parameters:

source_files (str, Path)
output_folder (str, Path)
variables (str or int or list of str or int, optional)
station_metadata (str or Path, optional)
overwrite (bool)
n_workers (int)

Returns:

None

Submodules#

miranda.eccc._homogenized module#

Adjusted and Homogenized Canadian Clime Data module.

miranda.eccc._homogenized.convert_ahccd(data_source: str | Path, output_dir: str | Path, variable: str, generation: int | None = None) → None[source]#

Convert Adjusted and Homogenized Canadian Climate Dataset files.

Parameters:

data_source (str or Path)
output_dir (str or Path)
variable (str)
generation (int, optional)

Returns:

None

miranda.eccc._homogenized.convert_ahccd_fwf_files(ff: Path | str, metadata: DataFrame, variable: str, generation: int | None = None, cols_specs: list[tuple[int, int]] | None = None, attrs: dict | None = None) → Dataset[source]#

Convert AHCCD fixed-width files.

Parameters:

ff (str or Path)
metadata (pandas.DataFrame)
variable (str)
generation
cols_specs
attrs

Returns:

xarray.Dataset

miranda.eccc._raw module#

Parameters:

source_files (str or Path)
output_folder (str or Path)
variables (str or int or list of str or int, optional)
time_step ({“hourly”, “daily”})
include_flags (bool)
groupings (int) – The number of files in each group used for converting to multi-file Datasets.
mf_dataset_freq (str, optional) – Resampling frequency for creating output multi-file Datasets. E.g. ‘YS’: 1 year per file, ‘5YS’: 5 years per file.
temp_directory (str or Path, optional) – Use another temporary directory location in case default location is not spacious enough.
n_workers (int)

Returns:

None

Parameters:

source_files (str or Path)
output_folder (str or Path)
variables (str or List[str])
mode ({“hourly”, “daily”})
n_workers (int)

Returns:

None

Parameters:

source_files (str, Path)
output_folder (str, Path)
variables (str or int or list of str or int, optional)
station_metadata (str or Path, optional)
overwrite (bool)
n_workers (int)

Returns:

None

miranda.eccc._summaries module#

miranda.eccc._summaries.daily_summaries_to_netcdf(station: dict, path_output: Path | str) → None[source]#

Convert daily climate summaries to NetCDF files.

Uses xarray to transform the ‘station’ from find_and_extract_dly into a CF-Convention netCDF file

Parameters:

station (dict) – dict created by using find_and_extract_dly
path_output (str or Path) – Output path.

Returns:

None

miranda.eccc._summaries.extract_daily_summaries(path_station: Path | str, rm_flags: bool = False, file_suffix: str = '.csv') → dict[source]#

Extract daily climate summaries from ECCC CSV files.

Parameters:

path_station (str or Path) – PathLike or str to the station’s folder containing the csv files.
rm_flags (bool) – Removes the ‘Flag’ and ‘Quality’ columns of the ECCC files.
file_suffix (str) – File suffixes used by the tabular data. Default: “.csv”.

Returns:

dict – dict containing the station metadata, as well as the data stored within a pandas Dataframe.

miranda.eccc._support_rvt module#

class miranda.eccc._support_rvt.Path(*args, **kwargs)[source]#

Bases: PurePath

PurePath subclass that can make system calls.

Path represents a filesystem path but unlike PurePath, also offers methods to do system calls on path objects. Depending on your system, instantiating a Path will return either a PosixPath or a WindowsPath object. You can also instantiate a PosixPath or WindowsPath directly, but cannot instantiate a WindowsPath on a POSIX system or vice versa.

absolute()[source]#

Return an absolute version of this path. This function works even if the path doesn’t point to anything.

No normalization is done, i.e. all ‘.’ and ‘..’ will be kept along. Use resolve() to get the canonical path to a file.

chmod(mode, *, follow_symlinks=True)[source]#: Change the permissions of the path, like os.chmod().

classmethod cwd()[source]#: Return a new path pointing to the current working directory (as returned by os.getcwd()).

exists()[source]#: Whether this path exists.

expanduser()[source]#: Return a new path with expanded ~ and ~user constructs (as returned by os.path.expanduser)

glob(pattern)[source]#: Iterate over this subtree and yield all existing files (of any kind, including directories) matching the given relative pattern.

group()[source]#: Return the group name of the file gid.

hardlink_to(target)[source]#

Make this path a hard link pointing to the same file as target.

Note the order of arguments (self, target) is the reverse of os.link’s.

classmethod home()[source]#: Return a new path pointing to the user’s home directory (as returned by os.path.expanduser(‘~’)).

is_block_device()[source]#: Whether this path is a block device.

is_char_device()[source]#: Whether this path is a character device.

is_dir()[source]#: Whether this path is a directory.

is_fifo()[source]#: Whether this path is a FIFO.

is_file()[source]#: Whether this path is a regular file (also True for symlinks pointing to regular files).

is_mount()[source]#: Check if this path is a POSIX mount point

is_socket()[source]#: Whether this path is a socket.

is_symlink()[source]#: Whether this path is a symbolic link.

iterdir()[source]#: Iterate over the files in this directory. Does not yield any result for the special paths ‘.’ and ‘..’.

lchmod(mode)[source]#: Like chmod(), except if the path points to a symlink, the symlink’s permissions are changed, rather than its target’s.

link_to(target)[source]#

Make the target path a hard link pointing to this path.

Note this function does not make this path a hard link to target, despite the implication of the function and argument names. The order of arguments (target, link) is the reverse of Path.symlink_to, but matches that of os.link.

Deprecated since Python 3.10 and scheduled for removal in Python 3.12. Use hardlink_to() instead.

lstat()[source]#: Like stat(), except if the path points to a symlink, the symlink’s status information is returned, rather than its target’s.

mkdir(mode=511, parents=False, exist_ok=False)[source]#: Create a new directory at this given path.

open(mode='r', buffering=-1, encoding=None, errors=None, newline=None)[source]#: Open the file pointed by this path and return a file object, as the built-in open() function does.

owner()[source]#: Return the login name of the file owner.

read_bytes()[source]#: Open the file in bytes mode, read it, and close the file.

read_text(encoding=None, errors=None)[source]#: Open the file in text mode, read it, and close the file.

readlink()[source]#: Return the path to which the symbolic link points.

rename(target)[source]#

Rename this path to the target path.

The target path may be absolute or relative. Relative paths are interpreted relative to the current working directory, not the directory of the Path object.

Returns the new Path instance pointing to the target path.

replace(target)[source]#

Rename this path to the target path, overwriting if that path exists.

The target path may be absolute or relative. Relative paths are interpreted relative to the current working directory, not the directory of the Path object.

Returns the new Path instance pointing to the target path.

resolve(strict=False)[source]#: Make the path absolute, resolving all symlinks on the way and also normalizing it (for example turning slashes into backslashes under Windows).

rglob(pattern)[source]#: Recursively yield all existing files (of any kind, including directories) matching the given relative pattern, anywhere in this subtree.

rmdir()[source]#: Remove this directory. The directory must be empty.

samefile(other_path)[source]#: Return whether other_path is the same or not as this file (as returned by os.path.samefile()).

stat(*, follow_symlinks=True)[source]#: Return the result of the stat() system call on this path, like os.stat() does.

symlink_to(target, target_is_directory=False)[source]#: Make this path a symlink pointing to the target path. Note the order of arguments (link, target) is the reverse of os.symlink.

touch(mode=438, exist_ok=True)[source]#: Create this file with the given access mode, if it doesn’t exist.

unlink(missing_ok=False)[source]#: Remove this file or link. If the path is a directory, use rmdir() instead.

write_bytes(data)[source]#: Open the file in bytes mode, write to it, and close the file.

write_text(data, encoding=None, errors=None, newline=None)[source]#: Open the file in text mode, write to it, and close the file.

Collect ECCC station data from the Environment and Climate Change Canada API.

Parameters:

timestep (str)
start_date (datetime.datetime or str, optional)
end_date (datetime.datetime or str, optional)
climate_id (str, optional)

Returns:

pandas.DataFrame

miranda.eccc._utils module#

miranda.eccc._utils.cf_ahccd_metadata(code: str, gen: int)[source]#

Parameters:

code ({“dx”, “dn”, “dm”, “dt”, “ds”, “dr”})
gen ({1, 2, 3})

Returns:

dict[str, int or str or float], dict, list[tuple[int, int]], int

miranda.eccc._utils.cf_station_metadata(variable_code: int | str) → Mapping[str, int | float | str][source]#

Parameters:: variable_code (int or str)
Returns:: dict

miranda.ecmwf package#

ECMWF Download module.

miranda.ecmwf.rename_era5_files(path: PathLike | str) → None[source]#

Rename badly named ERA5 files.

Notes

Requires that the proper ERA5 project name is in the filename, separated by underscores. Assumes that the data

Parameters:: path (os.PathLike or str) – Path to a folder containing netcdf files
Returns:: None

Request ERA5/ERA5-Land from Copernicus Data Store in NetCDF4 format.

Parameters:

projects (str or List[str]) – Allowed keys: {“era5-land”, “era5-land-monthly-means”, “era5-single-levels”, “era5-single-levels-monthly-means”, “era5-single-levels-preliminary-back-extension”, “era5-single-levels-monthly-means-preliminary-back-extension”, “era5-pressure-levels”, “era5-pressure-levels-monthly-means”, “era5-pressure-levels-preliminary-back-extension”, “era5-pressure-levels-monthly-means-preliminary-back-extension”}
variables (str or Sequence[str]) – Variable codes requested. If None, will attempt all hardcoded variables supported by miranda converter.
domain ({“GLOBAL”, “AMNO”, “NAM”, “CAN”, “QC”, “MTL”}) – Geographic domain requested. Default: “AMNO” (North America).
pressure_levels (List[int], optional) – If set and project requested has pressure levels, will download specific pressure levels.
separate_pressure_levels (bool) – Whether to separate files for each pressure level. Default: True
output_folder (str or os.PathLike, optional) – Folder to send files to. If None, will create a “downloaded” folder in current working directory.
year_start (int, optional) – Starting year for data download. If None, will download from first available year for project.
year_end (int, optional) – End year for data download. If None, will download files for current year and two months prior to present day.
dry_run (bool) – Do not send request. For debugging purposes.
processes (int) – The number of simultaneous download requests. Default: 10.
url (str, optional) – URL for Copernicus Data Store API (if not already using .cdsapirc)
key (str, optional) – Personal access key for Copernicus Data Store (if not already using .cdsapirc)

Returns:

None

miranda.ecmwf.request_tigge(variables: list[str], providers: list[str] | None = None, *, forecast_type: str = 'pf', times: list[str] | None = None, dates: list[str] | None = None, date_start: str | None = None, date_end: str | None = None, output_folder: PathLike | None = None, processes: int = 4) → None[source]#

Request tigge data from ECMWF in grib format.

Parameters:

variables (list of str)
providers (ist of str, optional)
forecast_type ({“pf”, “cf”})
times (list of str, optional)
dates (list of str. optional)
date_start (str, optional)
date_end (str, optional)
output_folder (os.PathLike, optional)
processes (int)

Returns:

None

Submodules#

miranda.ecmwf._era5 module#

miranda.ecmwf._era5.rename_era5_files(path: PathLike | str) → None[source]#

Rename badly named ERA5 files.

Notes

Requires that the proper ERA5 project name is in the filename, separated by underscores. Assumes that the data

Parameters:: path (os.PathLike or str) – Path to a folder containing netcdf files
Returns:: None

Request ERA5/ERA5-Land from Copernicus Data Store in NetCDF4 format.

Parameters:

projects (str or List[str]) – Allowed keys: {“era5-land”, “era5-land-monthly-means”, “era5-single-levels”, “era5-single-levels-monthly-means”, “era5-single-levels-preliminary-back-extension”, “era5-single-levels-monthly-means-preliminary-back-extension”, “era5-pressure-levels”, “era5-pressure-levels-monthly-means”, “era5-pressure-levels-preliminary-back-extension”, “era5-pressure-levels-monthly-means-preliminary-back-extension”}
variables (str or Sequence[str]) – Variable codes requested. If None, will attempt all hardcoded variables supported by miranda converter.
domain ({“GLOBAL”, “AMNO”, “NAM”, “CAN”, “QC”, “MTL”}) – Geographic domain requested. Default: “AMNO” (North America).
pressure_levels (List[int], optional) – If set and project requested has pressure levels, will download specific pressure levels.
separate_pressure_levels (bool) – Whether to separate files for each pressure level. Default: True
output_folder (str or os.PathLike, optional) – Folder to send files to. If None, will create a “downloaded” folder in current working directory.
year_start (int, optional) – Starting year for data download. If None, will download from first available year for project.
year_end (int, optional) – End year for data download. If None, will download files for current year and two months prior to present day.
dry_run (bool) – Do not send request. For debugging purposes.
processes (int) – The number of simultaneous download requests. Default: 10.
url (str, optional) – URL for Copernicus Data Store API (if not already using .cdsapirc)
key (str, optional) – Personal access key for Copernicus Data Store (if not already using .cdsapirc)

Returns:

None

miranda.ecmwf._tigge module#

miranda.ecmwf._tigge.request_tigge(variables: list[str], providers: list[str] | None = None, *, forecast_type: str = 'pf', times: list[str] | None = None, dates: list[str] | None = None, date_start: str | None = None, date_end: str | None = None, output_folder: PathLike | None = None, processes: int = 4) → None[source]#

Request tigge data from ECMWF in grib format.

Parameters:

variables (list of str)
providers (ist of str, optional)
forecast_type ({“pf”, “cf”})
times (list of str, optional)
dates (list of str. optional)
date_start (str, optional)
date_end (str, optional)
output_folder (os.PathLike, optional)
processes (int)

Returns:

None

miranda.gis package#

GIS Operations module.

miranda.gis.add_ar6_regions(ds: Dataset) → Dataset[source]#

Add the IPCC AR6 Regions to dataset.

Parameters:: ds (xarray.Dataset)
Returns:: xarray.Dataset

miranda.gis.subset_domain(ds: Dataset | DataArray, domain: str, **kwargs) → Dataset | DataArray[source]#

Subset an xarray object according to a specific domain.

Notes

Requires installation of GIS libraries.

Parameters:

ds (xarray.Dataset or xarray.DataArray)
domain (str)
**kwargs

Returns:

xarray.Dataset or xarray.DataArray

miranda.gis.subsetting_domains(domain: str) → list[source]#

Provides the bounding box coordinates for specific domains.

Parameters:: domain ({“global”, “nam”, “can”, “qc”, “mtl”})
Returns:: np.array – North, West, South, and East coordinates

Submodules#

miranda.gis._domains module#

miranda.gis._domains.add_ar6_regions(ds: Dataset) → Dataset[source]#

Add the IPCC AR6 Regions to dataset.

Parameters:: ds (xarray.Dataset)
Returns:: xarray.Dataset

miranda.gis._domains.subset_domain(ds: Dataset | DataArray, domain: str, **kwargs) → Dataset | DataArray[source]#

Subset an xarray object according to a specific domain.

Notes

Requires installation of GIS libraries.

Parameters:

ds (xarray.Dataset or xarray.DataArray)
domain (str)
**kwargs

Returns:

xarray.Dataset or xarray.DataArray

miranda.gis._domains.subsetting_domains(domain: str) → list[source]#

Provides the bounding box coordinates for specific domains.

Parameters:: domain ({“global”, “nam”, “can”, “qc”, “mtl”})
Returns:: np.array – North, West, South, and East coordinates

miranda.io package#

IO Utilities module.

miranda.io.concat_rechunk_zarr(freq: str, input_folder: str | PathLike, output_folder: str | PathLike, overwrite: bool = False, **dask_kwargs) → None[source]#

Concatenate and rechunk zarr files.

Parameters:

freq (str)
input_folder (str or os.PathLike)
output_folder (str or os.PathLike)
overwrite (bool)
**dask_kwargs

Returns:

None

Discover data.

Parameters:

input_files (str, pathlib.Path, list of str or Path, or GeneratorType) – Path or string to a file, a folder, or a generator of paths.
suffix (str) – File-ending suffix to search for. Default: “nc”.
recurse (bool) – Whether to recurse through folders or not. Default: True.

Returns:

list of pathlib.Path or GeneratorType of pathlib.Path

Warning

Recursion through “.zarr” files is explicitly disabled. Recursive globs and generators will not be expanded/sorted.

miranda.io.fetch_chunk_config(priority: str, freq: str, dims: Sequence[str] | dict[str, int] | Frozen | tuple[collections.abc.Hashable], default_config: dict = {'files': {'1hr': {'default': {'lat': 250, 'lon': 250, 'time': 168}, 'rotated': {'rlat': 250, 'rlon': 250, 'time': 168}}, 'day': {'default': {'lat': 125, 'lon': 125, 'time': '1 year'}, 'rotated': {'rlat': 125, 'rlon': 125, 'time': '1 year'}}, 'month': {'default': {'lat': 500, 'lon': 500, 'time': 120}, 'rotated': {'rlat': 500, 'rlon': 500, 'time': 120}}}, 'time': {'1hr': {'default': {'lat': 50, 'lon': 50, 'time': 1440}, 'rotated': {'rlat': 50, 'rlon': 50, 'time': 1440}}, 'day': {'default': {'lat': 50, 'lon': 50, 'time': '4 years'}, 'rotated': {'rlat': 50, 'rlon': 50, 'time': '4 years'}}, 'month': {'default': {'lat': 250, 'lon': 250, 'time': 240}, 'rotated': {'rlat': 250, 'rlon': 250, 'time': 240}}}}) → dict[str, int][source]#

Parameters:

priority ({“time”, “files”}) – Specifies whether the chunking regime should prioritize file granularity (“files”) or time series (“time”).
freq ({“1hr”, “day”, “month”}) – The time frequency of the input data.
dims (sequence of str) – The dimension names that will be used for chunking.
default_config (dict) – The dictionary to use for determining the chunking configuration.

Returns:

dict[str, int]

Find all available filepaths at a given source.

Parameters:

source (str, Path, GeneratorType, or list[str or Path])
recursive (bool)
file_suffixes (str or list of str, optional)

Returns:

list of pathlib.Path

miranda.io.merge_rechunk_zarrs(input_folder: str | PathLike, output_folder: str | PathLike, project: str | None = None, target_chunks: dict[str, int] | None = None, variables: Sequence[str] | None = None, freq: str | None = None, suffix: str = 'zarr', overwrite: bool = False) → None[source]#

Merge and rechunk zarr files.

Parameters:

input_folder (str or os.PathLike)
output_folder (str or os.PathLike)
project (str, optional)
target_chunks (dict[str, int], optional)
variables (Sequence of str, optional)
freq (str, optional)
suffix ({“nc”, “zarr”})
overwrite (bool)

Returns:

None

miranda.io.prepare_chunks_for_ds(ds: Dataset, chunks: dict[str, str | int]) → dict[str, int][source]#

Prepare the chunks to be used to write Dataset.

This includes translating the time chunks, making sure chunks are not too small, and removing -1.

Parameters:

ds (xr.Dataset) – Dataset that we want to write with the chunks.
chunks (dict) – Desired chunks in human-readable format (with “4 years” and -1).

Returns:

dict – Chunks in a format that is ready to be used to write to disk.

miranda.io.rechunk_files(input_folder: str | PathLike, output_folder: str | PathLike, project: str | None = None, time_step: str | None = None, chunking_priority: str = 'auto', target_chunks: dict[str, int] | None = None, variables: Sequence[str] | None = None, suffix: str = 'nc', output_format: str = 'netcdf', overwrite: bool = False) → None[source]#

Rechunks dataset for better loading/reading performance.

Warning

Globbing assumes that target datasets to be rechunked have been saved in NetCDF format. File naming requires the following order of facets: {variable}_{time_step}_{institute}_{project}_reanalysis_*.nc. Chunking dimensions are assumed to be CF-Compliant (lat, lon, rlat, rlon, time).

Parameters:

input_folder (str or os.PathLike) – Folder to be examined. Performs globbing.
output_folder (str or os.PathLike) – Target folder.
project (str, optional) – Supported projects. Used for determining chunk dictionary. Superseded if target_chunks is set.
time_step ({“1hr”, “day”}, optional) – Time step of the input data. Parsed from dataset attrs if not set. Superseded if target_chunks is set.
chunking_priority ({“time”, “files”, “auto”}) – The chunking regime to use. Default: “auto”.
target_chunks (dict, optional) – Must include “time”, optionally “lat” and “lon”, depending on dataset structure.
variables (Sequence[str], optional) – If no variables set, will attempt to process all variables supported based on project name.
suffix ({“nc”, “zarr”}) – Suffix used to identify data files. Default: “nc”.
output_format ({“netcdf”, “zarr”}) – Default: “zarr”.
overwrite (bool) – Will overwrite files. For zarr, existing folders will be removed before writing.

Returns:

None

miranda.io.translate_time_chunk(chunks: dict, calendar: str, timesize: int) → dict[source]#

Translate chunk specification for time into a number.

Notes

-1 translates to timesize ‘Nyear’ translates to N times the number of days in a year of calendar calendar.

miranda.io.write_dataset(ds: DataArray | Dataset, output_path: str | PathLike, output_format: str, chunks: dict | None = None, overwrite: bool = False, compute: bool = True) → dict[str, pathlib.Path][source]#

Write xarray object to NetCDf or Zarr with appropriate chunking regime.

Parameters:

ds (xr.DataArray or xr.Dataset) – Dataset or DatArray.
output_path (str or os.PathLike) – Output folder path.
output_format ({“netcdf”, “zarr”}) – Output data container type.
chunks (dict, optional) – Chunking layout to be written to new files. If None, chunking will be left to the relevant backend engine.
overwrite (bool) – Whether to remove existing files or fail if files already exist.
compute (bool) – If True, files will be converted with each call to file conversion. If False, will return a dask.Delayed object that can be computed later. Default: True.

Returns:

dict[str, Path]

miranda.io.write_dataset_dict(dataset_dict: dict[str, xarray.core.dataset.Dataset | None], output_folder: str | PathLike, temp_folder: str | PathLike, *, output_format: str = 'zarr', overwrite: bool = False, chunks: dict[str, int], **dask_kwargs)[source]#

Write dataset from Miranda-formatted dataset.

Parameters:

dataset_dict (dict[str, xr.Dataset or None])
output_folder (str or os.PathLike)
temp_folder (str or os.PathLike)
output_format ({“netcdf”, “zarr”})
overwrite (bool)
chunks (dict[str, int])
**dask_kwargs

Returns:

None

Submodules#

miranda.io._input module#

Discover data.

Parameters:

input_files (str, pathlib.Path, list of str or Path, or GeneratorType) – Path or string to a file, a folder, or a generator of paths.
suffix (str) – File-ending suffix to search for. Default: “nc”.
recurse (bool) – Whether to recurse through folders or not. Default: True.

Returns:

list of pathlib.Path or GeneratorType of pathlib.Path

Warning

Recursion through “.zarr” files is explicitly disabled. Recursive globs and generators will not be expanded/sorted.

Find all available filepaths at a given source.

Parameters:

source (str, Path, GeneratorType, or list[str or Path])
recursive (bool)
file_suffixes (str or list of str, optional)

Returns:

list of pathlib.Path

miranda.io._output module#

IO Output Operations module.

miranda.io._output.concat_rechunk_zarr(freq: str, input_folder: str | PathLike, output_folder: str | PathLike, overwrite: bool = False, **dask_kwargs) → None[source]#

Concatenate and rechunk zarr files.

Parameters:

freq (str)
input_folder (str or os.PathLike)
output_folder (str or os.PathLike)
overwrite (bool)
**dask_kwargs

Returns:

None

miranda.io._output.merge_rechunk_zarrs(input_folder: str | PathLike, output_folder: str | PathLike, project: str | None = None, target_chunks: dict[str, int] | None = None, variables: Sequence[str] | None = None, freq: str | None = None, suffix: str = 'zarr', overwrite: bool = False) → None[source]#

Merge and rechunk zarr files.

Parameters:

input_folder (str or os.PathLike)
output_folder (str or os.PathLike)
project (str, optional)
target_chunks (dict[str, int], optional)
variables (Sequence of str, optional)
freq (str, optional)
suffix ({“nc”, “zarr”})
overwrite (bool)

Returns:

None

miranda.io._output.write_dataset(ds: DataArray | Dataset, output_path: str | PathLike, output_format: str, chunks: dict | None = None, overwrite: bool = False, compute: bool = True) → dict[str, pathlib.Path][source]#

Write xarray object to NetCDf or Zarr with appropriate chunking regime.

Parameters:

ds (xr.DataArray or xr.Dataset) – Dataset or DatArray.
output_path (str or os.PathLike) – Output folder path.
output_format ({“netcdf”, “zarr”}) – Output data container type.
chunks (dict, optional) – Chunking layout to be written to new files. If None, chunking will be left to the relevant backend engine.
overwrite (bool) – Whether to remove existing files or fail if files already exist.
compute (bool) – If True, files will be converted with each call to file conversion. If False, will return a dask.Delayed object that can be computed later. Default: True.

Returns:

dict[str, Path]

miranda.io._output.write_dataset_dict(dataset_dict: dict[str, xarray.core.dataset.Dataset | None], output_folder: str | PathLike, temp_folder: str | PathLike, *, output_format: str = 'zarr', overwrite: bool = False, chunks: dict[str, int], **dask_kwargs)[source]#

Write dataset from Miranda-formatted dataset.

Parameters:

dataset_dict (dict[str, xr.Dataset or None])
output_folder (str or os.PathLike)
temp_folder (str or os.PathLike)
output_format ({“netcdf”, “zarr”})
overwrite (bool)
chunks (dict[str, int])
**dask_kwargs

Returns:

None

miranda.io._rechunk module#

miranda.io._rechunk.fetch_chunk_config(priority: str, freq: str, dims: Sequence[str] | dict[str, int] | Frozen | tuple[collections.abc.Hashable], default_config: dict = {'files': {'1hr': {'default': {'lat': 250, 'lon': 250, 'time': 168}, 'rotated': {'rlat': 250, 'rlon': 250, 'time': 168}}, 'day': {'default': {'lat': 125, 'lon': 125, 'time': '1 year'}, 'rotated': {'rlat': 125, 'rlon': 125, 'time': '1 year'}}, 'month': {'default': {'lat': 500, 'lon': 500, 'time': 120}, 'rotated': {'rlat': 500, 'rlon': 500, 'time': 120}}}, 'time': {'1hr': {'default': {'lat': 50, 'lon': 50, 'time': 1440}, 'rotated': {'rlat': 50, 'rlon': 50, 'time': 1440}}, 'day': {'default': {'lat': 50, 'lon': 50, 'time': '4 years'}, 'rotated': {'rlat': 50, 'rlon': 50, 'time': '4 years'}}, 'month': {'default': {'lat': 250, 'lon': 250, 'time': 240}, 'rotated': {'rlat': 250, 'rlon': 250, 'time': 240}}}}) → dict[str, int][source]#

Parameters:

priority ({“time”, “files”}) – Specifies whether the chunking regime should prioritize file granularity (“files”) or time series (“time”).
freq ({“1hr”, “day”, “month”}) – The time frequency of the input data.
dims (sequence of str) – The dimension names that will be used for chunking.
default_config (dict) – The dictionary to use for determining the chunking configuration.

Returns:

dict[str, int]

miranda.io._rechunk.prepare_chunks_for_ds(ds: Dataset, chunks: dict[str, str | int]) → dict[str, int][source]#

Prepare the chunks to be used to write Dataset.

This includes translating the time chunks, making sure chunks are not too small, and removing -1.

Parameters:

ds (xr.Dataset) – Dataset that we want to write with the chunks.
chunks (dict) – Desired chunks in human-readable format (with “4 years” and -1).

Returns:

dict – Chunks in a format that is ready to be used to write to disk.

miranda.io._rechunk.rechunk_files(input_folder: str | PathLike, output_folder: str | PathLike, project: str | None = None, time_step: str | None = None, chunking_priority: str = 'auto', target_chunks: dict[str, int] | None = None, variables: Sequence[str] | None = None, suffix: str = 'nc', output_format: str = 'netcdf', overwrite: bool = False) → None[source]#

Rechunks dataset for better loading/reading performance.

Warning

Parameters:

input_folder (str or os.PathLike) – Folder to be examined. Performs globbing.
output_folder (str or os.PathLike) – Target folder.
project (str, optional) – Supported projects. Used for determining chunk dictionary. Superseded if target_chunks is set.
time_step ({“1hr”, “day”}, optional) – Time step of the input data. Parsed from dataset attrs if not set. Superseded if target_chunks is set.
chunking_priority ({“time”, “files”, “auto”}) – The chunking regime to use. Default: “auto”.
target_chunks (dict, optional) – Must include “time”, optionally “lat” and “lon”, depending on dataset structure.
variables (Sequence[str], optional) – If no variables set, will attempt to process all variables supported based on project name.
suffix ({“nc”, “zarr”}) – Suffix used to identify data files. Default: “nc”.
output_format ({“netcdf”, “zarr”}) – Default: “zarr”.
overwrite (bool) – Will overwrite files. For zarr, existing folders will be removed before writing.

Returns:

None

miranda.io._rechunk.translate_time_chunk(chunks: dict, calendar: str, timesize: int) → dict[source]#

Translate chunk specification for time into a number.

Notes

-1 translates to timesize ‘Nyear’ translates to N times the number of days in a year of calendar calendar.

miranda.io.utils module#

IO Utilities module.

miranda.io.utils.creation_date(path_to_file: str | PathLike) → float | date[source]#

Return the date that a file was created, falling back to when it was last modified if unable to determine.

See https://stackoverflow.com/a/39501288/1709587 for explanation.

Parameters:: path_to_file (str or os.PathLike)
Returns:: float or date

miranda.io.utils.delayed_write(ds: Dataset, outfile: str | PathLike, output_format: str, overwrite: bool, target_chunks: dict | None = None) → delayed[source]#

Stage a Dataset writing job using dask.delayed objects.

Parameters:

ds (xr.Dataset)
outfile (str or os.PathLike)
target_chunks (dict)
output_format ({“netcdf”, “zarr”})
overwrite (bool)

Returns:

dask.delayed.delayed

miranda.io.utils.get_chunks_on_disk(file: PathLike | str) → dict[source]#

Determine the chunks on disk for a given NetCDF or Zarr file.

Parameters:: file (str or os.PathLike) – File to be examined. Supports NetCDF and Zarr.
Returns:: dict

miranda.io.utils.get_global_attrs(file_or_dataset: str | PathLike | Dataset) → dict[str, str | int][source]#: Collect global attributes from NetCDF, Zarr, or Dataset object.

miranda.io.utils.get_time_attrs(file_or_dataset: str | os.PathLike | xr.Dataset)[source]#: Determine attributes related to time dimensions.

miranda.io.utils.name_output_file(ds_or_dict: Dataset | dict[str, str], output_format: str) → str[source]#

Name an output file based on facets within a Dataset or a dictionary.

Parameters:

ds_or_dict (xr.Dataset or dict) – A miranda-converted Dataset or a dictionary containing the appropriate facets.
output_format ({“netcdf”, “zarr”}) – Output filetype to be used for generating filename suffix.

Returns:

str

Notes

If using a dictionary, the following keys must be set: * “variable”, “frequency”, “institution”, “time_start”, “time_end”.

miranda.io.utils.sort_variables(files: list[pathlib.Path], variables: Sequence[str]) → dict[str, list[pathlib.Path]][source]#

Sort all variables within supplied files for treatment.

Parameters:

files (list of Path)
variables (sequence of str)

Returns:

dict[str, list[Path]]

miranda.ncar package#

NCAR Download module

miranda.ncar.cordex_aws_calendar_correction(ds) → Dataset | None[source]#

AWS-stored CORDEX datasets are all on the same standard calendar, this converts the data back to the original calendar, removing added NaNs.

Credit: Pascal Bourgault (@aulemahal)

miranda.ncar.cordex_aws_download(target_folder: str | Path, *, search: dict[str, str | list[str]], correct_times: bool = False, domain: str | None = None)[source]#: Download CORDEX interpolated grid for North America from Amazon S3.

Submodules#

miranda.ncar._aws_cordex module#

miranda.ncar._aws_cordex.cordex_aws_calendar_correction(ds) → Dataset | None[source]#

AWS-stored CORDEX datasets are all on the same standard calendar, this converts the data back to the original calendar, removing added NaNs.

Credit: Pascal Bourgault (@aulemahal)

miranda.ncar._aws_cordex.cordex_aws_download(target_folder: str | Path, *, search: dict[str, str | list[str]], correct_times: bool = False, domain: str | None = None)[source]#: Download CORDEX interpolated grid for North America from Amazon S3.

miranda.remote package#

Remote Operations module.

class miranda.remote.Connection(username: str | Path | None = None, host: str | Path | None = None, protocol: str = 'sftp', *args, **kwargs)[source]#

Bases: object

Connection contextualise class.

connect(**kwargs)[source]#: Connect to a remote server with credential prompts.

update(**kwargs)[source]#: Update connection keyword arguments.

Warning

Credentials are not encrypted.

miranda.remote.archive_database(source: Path | str | list, common_path: Path | str, destination: Path | str, file_suffixes: str = '.nc', server: str | None = None, username: str | None = None, project_name: str | None = None, overwrite: bool = False, compression: bool = False, recursive: bool = False, use_grouping: bool = True, use_subdirectories: bool = True, dry_run: bool = False) → None[source]#

Given a source, destination, and dependent on file size limit, create tarfile archives and transfer: files to another server for backup purposes

Create an archive from source files and transfer to another location (remote or local).

Parameters:

source_files (list of str or os.PathLike)
destination (str or os.PathLike)
transport (SCPClient or SFTPClient or fabric.Connection or miranda.remote.Connection, optional)
delete (bool)
compression (bool)
recursive (bool)

Returns:

None

miranda.remote.create_remote_directory(directory: str | os.PathLike, transport: SSHClient | fabric.Connection | miranda.remote.Connection) → None[source]#

Call “mkdir -p” function to create a folder structure over SFTP/SSH and wait for confirmation before continuing.

Parameters:

directory (str or os.PathLike)
transport (SSHClient or fabric.Connection or miranda.remote.Connection)

Returns:

None

Remove a selection of files based on a given date of last modification.

Parameters:

source (str or Path)
year (int, optional)
month (int, optional)
day (int, optional)
pattern (str, optional)
server (str or Path, optional)
user (str, optional)
password (str, optional)
date_object (date, optional)

Returns:

None

Delete according to variable name.

Given target location(s), a list of variables and a server address, perform a glob search and delete file names starting with the variables identified

Parameters:

target (str, Path, list of str or Path, or GeneratorType])
variables (list of str)
server (str or Path, optional)
user (str, optional)
password (str, optional)
file_suffix (str, optional)
delete (bool)

Returns:

None

Parameters:

source (str or Path)
target (str or Path)
server (str or Path, optional)
user (str)
password (str)
pattern (str)
delete_target_duplicates (bool)

Returns:

None

miranda.remote.file_emptier(*, file_list: list[str | pathlib.Path] | generator) → None[source]#

Open and overwrite a list of file paths in order to delete data while preserving the file name.

Parameters:: file_list (list of str or Path, or GeneratorType) – List of files to be overwritten
Returns:: None

Transfer file from one location (remote or local) to another.

Parameters:

source_file (str or os.PathLike)
destination_file (str or os.PathLike)
transport (SCPClient or SFTPClient or fabric.Connection or miranda.remote.Connection, optional)

Returns:

bool

Submodules#

miranda.remote.archiver module#

Archive Module.

miranda.remote.archiver.archive_database(source: Path | str | list, common_path: Path | str, destination: Path | str, file_suffixes: str = '.nc', server: str | None = None, username: str | None = None, project_name: str | None = None, overwrite: bool = False, compression: bool = False, recursive: bool = False, use_grouping: bool = True, use_subdirectories: bool = True, dry_run: bool = False) → None[source]#

Given a source, destination, and dependent on file size limit, create tarfile archives and transfer: files to another server for backup purposes

miranda.remote.connect module#

Remote Connection Operations module.

class miranda.remote.connect.Connection(username: str | Path | None = None, host: str | Path | None = None, protocol: str = 'sftp', *args, **kwargs)[source]#

Bases: object

Connection contextualise class.

connect(**kwargs)[source]#: Connect to a remote server with credential prompts.

update(**kwargs)[source]#: Update connection keyword arguments.

Warning

Credentials are not encrypted.

miranda.remote.ops module#

Remote Operations module.

miranda.remote.ops.create_archive(source_files: list[str | os.PathLike], destination: str | os.PathLike, transport: SCPClient | SFTPClient | fabric.Connection | miranda.remote.Connection | None = None, delete: bool = True, compression: bool = False, recursive: bool = True) → None[source]#

Create an archive from source files and transfer to another location (remote or local).

Parameters:

source_files (list of str or os.PathLike)
destination (str or os.PathLike)
transport (SCPClient or SFTPClient or fabric.Connection or miranda.remote.Connection, optional)
delete (bool)
compression (bool)
recursive (bool)

Returns:

None

miranda.remote.ops.create_remote_directory(directory: str | os.PathLike, transport: SSHClient | fabric.Connection | miranda.remote.Connection) → None[source]#

Call “mkdir -p” function to create a folder structure over SFTP/SSH and wait for confirmation before continuing.

Parameters:

directory (str or os.PathLike)
transport (SSHClient or fabric.Connection or miranda.remote.Connection)

Returns:

None

Transfer file from one location (remote or local) to another.

Parameters:

source_file (str or os.PathLike)
destination_file (str or os.PathLike)
transport (SCPClient or SFTPClient or fabric.Connection or miranda.remote.Connection, optional)

Returns:

bool

miranda.remote.remove module#

Remote File Removal Operations module.

Remove a selection of files based on a given date of last modification.

Parameters:

source (str or Path)
year (int, optional)
month (int, optional)
day (int, optional)
pattern (str, optional)
server (str or Path, optional)
user (str, optional)
password (str, optional)
date_object (date, optional)

Returns:

None

Delete according to variable name.

Given target location(s), a list of variables and a server address, perform a glob search and delete file names starting with the variables identified

Parameters:

target (str, Path, list of str or Path, or GeneratorType])
variables (list of str)
server (str or Path, optional)
user (str, optional)
password (str, optional)
file_suffix (str, optional)
delete (bool)

Returns:

None

Parameters:

source (str or Path)
target (str or Path)
server (str or Path, optional)
user (str)
password (str)
pattern (str)
delete_target_duplicates (bool)

Returns:

None

miranda.remote.remove.file_emptier(*, file_list: list[str | pathlib.Path] | generator) → None[source]#

Open and overwrite a list of file paths in order to delete data while preserving the file name.

Parameters:: file_list (list of str or Path, or GeneratorType) – List of files to be overwritten
Returns:: None

miranda.structure package#

Data Structure module.

Build a filepath based on a valid data schema.

Parameters:

facets (dict) – Facets for a given dataset.
output_folder (str or os.PathLike) – Parent folder on which to extend the filetree structure.
schema (str or os.PathLike, optional) – Path to YAML schematic of database structure. If None, will use Ouranos schema.
top_folder (str) – Top-level of supplied schema, used for validation purposes. Default: “datasets”.
validate (bool) – Run facets-validation checks over given file. Default: True.

Returns:

Path or None

Create version hashes based on files or a facets dictionary.

Parameters:

input_files (str, os.PathLike, list of str or os.PathLike, or GeneratorType)
facet_dict (dict, optional)
verify_hash (bool)

Returns:

None

miranda.structure.structure_datasets(input_files: str | PathLike | list[str | os.PathLike] | generator, output_folder: str | PathLike, *, project: str | None = None, guess: bool = True, dry_run: bool = False, method: str = 'copy', make_dirs: bool = False, set_version_hashes: bool = False, verify_hashes: bool = False, suffix: str = 'nc') → dict[pathlib.Path, pathlib.Path][source]#

Parameters:

input_files (str, Path, list of str or Path, or GeneratorType) – Files to be sorted.
output_folder (str or Path) – The desired location for the folder-tree.
project ({“cordex”, “cmip5”, “cmip6”, “isimip-ft”, “pcic-candcs-u6”, “converted”}, optional) – Project used to parse the facets of all supplied datasets. If not supplied, will attempt parsing with all available data categories for each file (slow) unless guess is True.
guess (bool) – If project not supplied, suggest to decoder that activity is the same for all input_files. Default: True.
dry_run (bool) – Prints changes that would have been made without performing them. Default: False.
method ({“move”, “copy”}) – Method to transfer files to intended location. Default: “move”.
make_dirs (bool) – Make folder tree if it does not already exist. Default: False.
set_version_hashes (bool) – Make an accompanying file with version in filename and sha256sum in contents. Default: False.
verify_hashes (bool) – Ensure that any existing she256sum files correspond with companion file. Raise on error. Default: False.
suffix ({“nc”, “zarr”}) – If “zarr”, will perform a ‘glob’ with provided pattern. Otherwise, will perform an ‘rglob’ (recursive) operation.

Returns:

dict[Path, Path]

Submodules#

miranda.structure._structure module#

Build a filepath based on a valid data schema.

Parameters:

facets (dict) – Facets for a given dataset.
output_folder (str or os.PathLike) – Parent folder on which to extend the filetree structure.
schema (str or os.PathLike, optional) – Path to YAML schematic of database structure. If None, will use Ouranos schema.
top_folder (str) – Top-level of supplied schema, used for validation purposes. Default: “datasets”.
validate (bool) – Run facets-validation checks over given file. Default: True.

Returns:

Path or None

Create version hashes based on files or a facets dictionary.

Parameters:

input_files (str, os.PathLike, list of str or os.PathLike, or GeneratorType)
facet_dict (dict, optional)
verify_hash (bool)

Returns:

None

miranda.structure._structure.structure_datasets(input_files: str | PathLike | list[str | os.PathLike] | generator, output_folder: str | PathLike, *, project: str | None = None, guess: bool = True, dry_run: bool = False, method: str = 'copy', make_dirs: bool = False, set_version_hashes: bool = False, verify_hashes: bool = False, suffix: str = 'nc') → dict[pathlib.Path, pathlib.Path][source]#

Parameters:

input_files (str, Path, list of str or Path, or GeneratorType) – Files to be sorted.
output_folder (str or Path) – The desired location for the folder-tree.
project ({“cordex”, “cmip5”, “cmip6”, “isimip-ft”, “pcic-candcs-u6”, “converted”}, optional) – Project used to parse the facets of all supplied datasets. If not supplied, will attempt parsing with all available data categories for each file (slow) unless guess is True.
guess (bool) – If project not supplied, suggest to decoder that activity is the same for all input_files. Default: True.
dry_run (bool) – Prints changes that would have been made without performing them. Default: False.
method ({“move”, “copy”}) – Method to transfer files to intended location. Default: “move”.
make_dirs (bool) – Make folder tree if it does not already exist. Default: False.
set_version_hashes (bool) – Make an accompanying file with version in filename and sha256sum in contents. Default: False.
verify_hashes (bool) – Ensure that any existing she256sum files correspond with companion file. Raise on error. Default: False.
suffix ({“nc”, “zarr”}) – If “zarr”, will perform a ‘glob’ with provided pattern. Otherwise, will perform an ‘rglob’ (recursive) operation.

Returns:

dict[Path, Path]

Submodules#

miranda.cv module#

Controlled Vocabulary module.

miranda.data module#

Database Management module.

Bases: object

Database management class.

archive()[source]#: Not yet implemented.

group_by(*, common_path: Path | str | None = None, subdirectories: bool = True, dates: bool = True, size: int = 10737418240)[source]#

Grouping meta-function.

Notes

Not yet implemented.

items()[source]#: Show items.

keys()[source]#: Show keys.

target(target: Path | str)[source]#: Target directory or server address.

transfer()[source]#: Not yet implemented.

values()[source]#: Show values.

miranda.scripting module#

Scripting Helpers module.

miranda.storage module#

Disk space management#

Classes:

DiskSpaceError - the exception raised on failure.

FileMeta - file and its size.

StorageState - storage capacity and availability of a medium.

Functions:

total_size() - get total size of a list of files.

size_division() - divide files based on number and size restrictions.

exception miranda.storage.DiskSpaceError[source]#

Bases: Exception

DiskSpaceError Exception.

class miranda.storage.FileMeta(path: str, size: int = -1)[source]#

Bases: object

File path and size.

django = {'path': ['CharField', 'max_length=512'], 'size': ['IntegerField', 'null=True', 'blank=True']}#

class miranda.storage.StorageState(base_path, capacity=-1, used_space=-1, free_space=-1)[source]#

Bases: object

Information regarding the storage capacity of a disk.

Return size of object in bytes.

Parameters:: file_path_or_bytes_or_dict (Path or str or int, list of str or Path, GeneratorType, or dict[str, Path or list of Path])
Returns:: int

Report file size in a human-readable format.

This function will parse the contents of a list or generator of files and return the size in bytes of a file or a list of files in pretty formatted text.

Parameters:

file_path_or_bytes_or_dict (Path or str or int, list of str or Path, GeneratorType, or dict[str, Path or list of Path])
use_binary (bool)
significant_digits (int)

miranda.storage.size_division(files_to_divide: list | FileMeta | Path, size_limit: int = 0, file_limit: int = 0, check_name_repetition: bool = False, preserve_order: bool = False) → list[list][source]#

Divide files according to size and number limits.

Parameters:

files_to_divide (list of str or Path, FileMeta, Path) – Files to be sorted.
size_limit (int) – Size limit of divisions in bytes. Default: 0 (no limit).
file_limit (int) – Number of files limit of divisions. Default: 0 (no limit).
check_name_repetition (bool) – Flag to prevent file name repetitions. Default: False.
preserve_order (bool) – Flag to force files to be restored in the order they are given. Default: False.

Returns:

list[list] – list of divisions (each division is a list of FileMeta objects).

miranda.storage.size_evaluation(file_list: list[str | miranda.storage.FileMeta | pathlib.Path]) → int[source]#

Total size of files.

Parameters:: file_list (list of str or Path or FileMeta)
Returns:: int – total size of files in bytes.

miranda.units module#

Special Time Units-Handling submodule.

miranda.units.get_time_frequency(d: Dataset, expected_period: str | None = None, minimum_continuous_period: str = '1M') → tuple[list[int | str], str][source]#

Try to understand the Dataset frequency.

If it can’t be inferred with xarray.infer_freq() it tries to: - look for a “freq” attrs in the global or time variable attributes. - infer monthly frequency if all time steps are between 27 and 32 days

In the event that an expected_period is supplied, special handling will be called allowing for determining data that may be internally discontinuous (e.g. discontinuous overall, but continuous for minimum_continuous_period). This is provided for instances where input data in a multifile dataset is sparse.

Parameters:

d (xr.Dataset) – An xarray.Dataset.
expected_period (str) – An xarray-compatible time period (e.g. “1H”, “1D”, “7D”, “1M”, “1A”). The time period expected of the input dataset. The “1M” period is specially-handled.
minimum_continuous_period (str) – An xarray-compatible time period (e.g. “1H”, “1D”, “7D”, “1M”, “1A”). The minimum expected granular period that data should have continuous values for. The “1M” period is specially-handled.

Returns:

offset (list of int or str) – The offset a list of (multiplier, base)
offset_meaning (str) – The offset meaning (single word)

miranda.units.parse_offset(freq: str) → Sequence[str][source]#

Parse an offset string.

Parse a frequency offset and, if needed, convert to cftime-compatible components.

Parameters:

freq (str) – Frequency offset.

Returns:

multiplier (int) – Multiplier of the base frequency. “[n]W” is always replaced with “[7n]D”, as xarray doesn’t support “W” for cftime indexes.
offset_base (str) – Base frequency. “Y” is always replaced with “A”.
is_start_anchored (bool) – Whether coordinates of this frequency should correspond to the beginning of the period (True) or its end (False). Can only be False when base is A, Q or M; in other words, xclim assumes frequencies finer than monthly are all start-anchored.
anchor (str or None) – Anchor date for bases A or Q. As xarray doesn’t support “W”, neither does xclim (anchor information is lost when given).

miranda.utils module#

Miscellaneous Helper Utilities module.

class miranda.utils.HiddenPrints[source]#

Bases: object

Special context manager for hiding print statements.

Notes

Solution from https://stackoverflow.com/a/45669280/7322852 Credit to Alexander C (https://stackoverflow.com/users/2039471/alexander-c) CC-BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)-

miranda.utils.chunk_iterables(iterable: Sequence, chunk_size: int) → Iterable[source]#

Generate lists of chunk_size elements from iterable.

Notes

Adapted from eidord (2012) https://stackoverflow.com/a/12797249/7322852 (https://creativecommons.org/licenses/by-sa/4.0/)

Extract archives (tar/zip) to a working directory.

Parameters:

resources (str or Path or list of bytes or str or Path) – list of archive files (if netCDF files are in list, they are passed and returned as well in the return).
output_dir (str or Path, optional) – string or Path to a working location (default: temporary folder).

Returns:

list – List of original or of extracted files

miranda.utils.list_paths_with_elements(base_paths: str | list[str], elements: list[str]) → list[dict][source]#

List a given path structure.

Parameters:

base_paths (list of str) – List of paths from which to start the search.
elements (list of str) – Ordered list of the expected elements.

Returns:

list of dict – The keys are ‘path’ and each of the members of the given elements, the path is the absolute path.

Notes

Suppose you have the following structure: /base_path/{color}/{shape} The resulting list would look like:

[{'path':/base_path/red/square, 'color':'red', 'shape':'square'},
{'path':/base_path/red/circle, 'color':'red', 'shape':'circle'},
{'path':/base_path/blue/triangle, 'color':'blue', 'shape':'triangle'},
...]

Obviously, ‘path’ should not be in the input list of elements.

miranda.utils.publish_release_notes(style: str = 'md', file: PathLike | StringIO | TextIO | None = None) → str | None[source]#

Format release history in Markdown or ReStructuredText.

Parameters:

style ({“rst”, “md”}) – Use ReStructuredText formatting or Markdown. Default: Markdown.
file ({os.PathLike, StringIO, TextIO}, optional) – If provided, prints to the given file-like object. Otherwise, returns a string.

Returns:

str, optional

Notes

This function is solely for development purposes.

miranda.utils.single_item_list(iterable: Iterable) → bool[source]#

Ascertain whether a list has exactly one entry.

See: https://stackoverflow.com/a/16801605/7322852

Parameters:: iterable (Iterable)
Returns:: bool

miranda.utils.working_directory(directory: str | Path) → None[source]#

Change the working directory within a context object.

This function momentarily changes the working directory within the context and reverts to the file working directory when the code block it is acting upon exits

Parameters:: directory (str or pathlib.Path)
Returns:: None

miranda.validators module#

Data Validation module.

miranda.validators.url_validate(target: str) → Match[str] | None[source]#

Validate whether a supplied URL is reliably written.

Parameters:: target (str)

References

https://stackoverflow.com/a/7160778/7322852

Indices and tables#

Feedback#

If you have any suggestions or questions about Miranda feel free to email me at smith.trevorj@ouranos.ca.

If you encounter any errors or problems with Miranda, please let me know! Open an Issue at the GitHub https://github.com/Ouranosinc/miranda main repository.

Miranda #

Features#

Installation#

Contributing#

Contents:#

Installation#

From sources#

Creating a Conda environment#

Usage#

Contributing#

Types of Contributions#

Report Bugs#

Fix Bugs#

Implement Features#

Write Documentation#

Submit Feedback#

Get Started!#

Pull Request Guidelines#

Tips#

Credits#

Development Lead#

Co-Developers#

Contributors#

History#

v0.5.0 (2023-06-19)#

New features#

Internal changes#

v0.4.0 (2023-03-30)#

New features#

Bug fixes#

Internal changes#

v0.3.0 (2022-11-24)#

Announcements#

New features#

License#

miranda#

miranda package#

Subpackages#

miranda.archive package#

Submodules#

miranda.archive._groupings module#

miranda.archive._selection module#

miranda.convert package#

Submodules#

miranda.convert._aggregation module#

miranda.convert._data_corrections module#

miranda.convert._data_definitions module#

miranda.convert._reconstruction module#

miranda.convert.deh module#

miranda.convert.eccc module#

miranda.convert.eccc_rdrs module#

miranda.convert.ecmwf module#

miranda.convert.hq module#

miranda.convert.melcc module#

miranda.convert.utils module#

miranda.decode package#

Submodules#

miranda.decode._decoder module#

miranda.decode._time module#

miranda.eccc package#

Submodules#

miranda.eccc._homogenized module#

miranda.eccc._raw module#

miranda.eccc._summaries module#

miranda.eccc._support_rvt module#

miranda.eccc._utils module#

miranda.ecmwf package#

Submodules#

miranda.ecmwf._era5 module#

miranda.ecmwf._tigge module#

miranda.gis package#

Submodules#

miranda.gis._domains module#

miranda.io package#

Submodules#

miranda.io._input module#

miranda.io._output module#

miranda.io._rechunk module#

miranda.io.utils module#

miranda.ncar package#