/
output.py
123 lines (107 loc) · 5.19 KB
/
output.py
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from typing import List, Tuple, MutableMapping, Any, Optional
import datetime
import netCDF4
import numpy as np
from .. import shared
class NetCDF(shared.Plugin):
"""Plugin that saves the forecast and analysis states to NetCDF."""
def __init__(self, path: str, sync_interval: int = 1):
self.nc = netCDF4.Dataset(path, "w")
self.variables: List[Tuple[netCDF4.Variable, Any, bool]] = []
self.itime = 0
self.sync_interval = sync_interval
self.reftime: Optional[datetime.datetime] = None
self.nctime: Optional[netCDF4.Variable] = None
def initialize(self, variables: MutableMapping[str, Any], ensemble_size: int):
self.nc.createDimension("da_step", 2)
self.nc.createDimension("member", ensemble_size)
for name, metadata in variables.items():
for n, l in metadata["dimensions"].items():
if n not in self.nc.dimensions:
self.nc.createDimension(n, l)
dimnames = list(metadata["dimensions"].keys())[::-1]
time_dependent = "time" in metadata["dimensions"]
if time_dependent:
dimnames.insert(1, "da_step")
dimnames.insert(2, "member")
ncvar = self.nc.createVariable(name, float, dimnames)
ncvar.units = metadata["units"]
ncvar.long_name = metadata["long_name"]
self.variables.append((ncvar, metadata, time_dependent))
def before_analysis(self, time: datetime.datetime, *args, **kwargs):
if self.nctime is None:
self.reftime = time
self.nctime = self.nc.createVariable("time", float, ("time",))
self.nctime.units = "seconds since %s" % self.reftime.strftime(
"%Y-%m-%d %H:%M:%S"
)
self.nctime[self.itime] = (time - self.reftime).total_seconds()
for ncvar, metadata, time_dependent in self.variables:
if time_dependent:
ncvar[self.itime, 0, ...] = metadata["data"]
elif self.itime == 0:
ncvar[...] = metadata["data"][0, ...]
def after_analysis(self, *args, **kwargs):
for ncvar, metadata, time_dependent in self.variables:
if time_dependent:
ncvar[self.itime, 1, ...] = metadata["data"]
self.itime += 1
if self.itime % self.sync_interval == 0:
self.nc.sync()
def finalize(self):
self.nc.close()
class NetCDFStats(shared.Plugin):
"""Plugin that saves the ensemble mean and sd of forecast and analysis
states to NetCDF."""
def __init__(self, path: str, sync_interval: int = 1):
self.nc = netCDF4.Dataset(path, "w")
self.variables: List[Tuple[netCDF4.Variable, Optional[netCDF4.Variable], Any]] = []
self.itime = 0
self.sync_interval = sync_interval
self.reftime: Optional[datetime.datetime] = None
self.nctime: Optional[netCDF4.Variable] = None
def initialize(self, variables: MutableMapping[str, Any], ensemble_size: int):
self.nc.createDimension("da_step", 2)
for name, metadata in variables.items():
for n, l in metadata["dimensions"].items():
if n not in self.nc.dimensions:
self.nc.createDimension(n, l)
dimnames = list(metadata["dimensions"].keys())[::-1]
if "time" in metadata["dimensions"]:
dimnames.insert(1, "da_step")
ncmean = self.nc.createVariable(name + '_mean', float, dimnames)
ncmean.units = metadata["units"]
ncmean.long_name = 'mean ' + metadata["long_name"]
ncsd = self.nc.createVariable(name + '_sd', float, dimnames)
ncsd.units = metadata["units"]
ncsd.long_name = 'sd ' + metadata["long_name"]
else:
ncmean = self.nc.createVariable(name, float, dimnames)
ncmean.units = metadata["units"]
ncmean.long_name = metadata["long_name"]
ncsd = None
self.variables.append((ncmean, ncsd, metadata))
def before_analysis(self, time: datetime.datetime, *args, **kwargs):
if self.nctime is None:
self.reftime = time
self.nctime = self.nc.createVariable("time", float, ("time",))
self.nctime.units = "seconds since %s" % self.reftime.strftime(
"%Y-%m-%d %H:%M:%S"
)
self.nctime[self.itime] = (time - self.reftime).total_seconds()
for ncmean, ncsd, metadata in self.variables:
if ncsd is not None:
ncmean[self.itime, 0, ...] = metadata["data"].mean(axis=0)
ncsd[self.itime, 0, ...] = np.std(metadata["data"], axis=0)
elif self.itime == 0:
ncmean[...] = metadata["data"][0, ...]
def after_analysis(self, *args, **kwargs):
for ncmean, ncsd, metadata in self.variables:
if ncsd is not None:
ncmean[self.itime, 1, ...] = metadata["data"].mean(axis=0)
ncsd[self.itime, 1, ...] = np.std(metadata["data"], axis=0)
self.itime += 1
if self.itime % self.sync_interval == 0:
self.nc.sync()
def finalize(self):
self.nc.close()