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meta.yaml
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meta.yaml
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{% set name = "pcmdi_metrics" %}
{% set version = "3.5" %}
package:
name: {{ name|lower }}
version: {{ version }}
source:
url: https://github.com/PCMDI/pcmdi_metrics/archive/refs/tags/v{{ version }}.tar.gz
sha256: c7c1f5c152dab173e758a1bf3650968bd8378ac7b8e012d9516b2732e4625e60
build:
number: 0
script: {{ PYTHON }} -m pip install . -vv
noarch: python
entry_points:
- compositeDiurnalStatistics.py = pcmdi_metrics.diurnal.scripts.compositeDiurnalStatistics:main
- computeStdOfDailyMeans.py = pcmdi_metrics.diurnal.scripts.computeStdOfDailyMeans:main
- fourierDiurnalAllGrid.py = pcmdi_metrics.diurnal.scripts.fourierDiurnalAllGrid:main
- fourierDiurnalGridpoints.py = pcmdi_metrics.diurnal.scripts.fourierDiurnalGridpoints:main
- savg_fourier.py = pcmdi_metrics.diurnal.scripts.savg_fourier:main
- std_of_dailymeans.py = pcmdi_metrics.diurnal.scripts.std_of_dailymeans:main
- std_of_hourlyvalues.py = pcmdi_metrics.diurnal.scripts.std_of_hourlyvalues:main
- std_of_meandiurnalcycle.py = pcmdi_metrics.diurnal.scripts.std_of_meandiurnalcycle:main
requirements:
host:
- python >=3.6
- pip
run:
- python >=3.6
- cdms2
- genutil
- cdutil
- cdp
- matplotlib-base
- numpy =1.23.5
- cartopy
- eofs
- jupyterlab
- nb_conda
- nb_conda_kernels
- nc-time-axis
- notebook <7.0.0
- numdifftools
- enso_metrics
- seaborn
- netcdf4
- regionmask
- rasterio
- shapely
- xmltodict
- xcdat >=0.6.1
test:
imports:
- pcmdi_metrics
commands:
- mean_climate_driver.py --help
- mpindex_compute.py --help
- driver_monsoon_sperber.py --help
- std_of_dailymeans.py --help
- variability_modes_driver.py --help
- mjo_metrics_driver.py --help
- enso_driver.py --help
- variability_across_timescales_PS_driver.py --help
- precip_distribution_driver.py --help
- extremes_driver.py --help
- sea_ice_driver.py --help
about:
home: http://github.com/PCMDI/pcmdi_metrics
license: BSD-3-Clause
license_family: BSD
license_file: LICENSE
summary: The PCMDI metrics package is used to objectively compare results from climate models with observations using well-established statistical tests. Results are produced in the context of all model simulations contributed to CMIP5 and earlier CMIP phases. Among other purposes, this enables modeling groups to evaluate
changes during the development cycle in the context of the structural error distribution of the multi-model ensemble. Currently, the comparisons are focused on large- to global-scale annual cycle performance metrics.
description: model metrics tools
doc_url: https://pcmdi.github.io/pcmdi_metrics/
dev_url: https://github.com/PCMDI/pcmdi_metrics
extra:
recipe-maintainers:
- acordonez
- lee1043
- gleckler1