abkfenris

abkfenris

Xarray-FMRC

Xarray-FMCR uses Xarray datatrees to provide a standard in-memory and storage representation of Forecast Model Run Collections that can then be access via the various forecast views (best estimate/constant offset/constant time/model run).

def from_model_runs(datasets: dict[str | datetime.datetime | pd.Timestamp, xr.Dataset] | Iterable[xr.dataset]) -> datatree.DataTree:
    """
    From a collection of xarray datasets, assemble a datatree of forecasts

    If datasets contain a single length dimension of `forecast_reference_time`, or an attribute as such, they can be passed in as an `Iterable`, otherwise a dictionary mapping `forecast_reference_time` to datasets can be passed in. Dimensions other than time are expected to match.

abkfenris

#syntax=docker/dockerfile:1.3
FROM mambaorg/micromamba:0.21.2@sha256:27cc1034175f16df27d48a3fff8e3beab87b1c22a9c4e369a05a9125e993404b

ENV PYTHON_USER neracoos
ENV HOME /home/${PYTHON_USER}

RUN mkdir ${HOME}

WORKDIR ${HOME}

abkfenris

import fsspec
import requests
import xarray as xr
import xpublish
from xpublish import rest


recipe_runs_url = "https://api.pangeo-forge.org/recipe_runs/"

abkfenris

import os
from tempfile import TemporaryDirectory

from dagster import (
    resource,
    EventMetadata,
    EventMetadataEntry,
    Field,
    Noneable,
)

abkfenris

pandas
xarray
censusdata

abkfenris

abkfenris

abkfenris

abkfenris