def save_timeseries(df, outfile, standard_name, **kw):
"""http://cfconventions.org/Data/cf-convetions/cf-conventions-1.6/build
/cf-conventions.html#idp5577536"""
cube = as_cube(df, calendars={1: cf_units.CALENDAR_GREGORIAN})
cube.coord("index").rename("time")
# Cast all station names to strings and renamed it.
columns = cube.coord('columns').points.astype(str).tolist()
cube.coord('columns').points = columns
cube.coord("columns").rename("station name")
cube.rename(standard_name)
cube.coord("station name").var_name = 'station'
longitude = kw.get("longitude")
latitude = kw.get("latitude")
if longitude is not None:
longitude = iris.coords.AuxCoord(np.float_(longitude),
var_name="lon",
standard_name="longitude",
long_name="station longitude",
units=cf_units.Unit("degrees"))
cube.add_aux_coord(longitude, data_dims=1)
if latitude is not None:
latitude = iris.coords.AuxCoord(np.float_(latitude),
var_name="lat",
standard_name="latitude",
long_name="station latitude",
units=cf_units.Unit("degrees"))
cube.add_aux_coord(latitude, data_dims=1)
cube.units = kw.get('units')
station_attr = kw.get("station_attr")
if station_attr is not None:
cube.coord("station name").attributes.update(station_attr)
cube_attr = kw.get("cube_attr")
if cube_attr is not None:
cube.attributes.update(cube_attr)
iris.save(cube, outfile)
def save_timeseries(df, outfile, standard_name, **kw):
"""http://cfconventions.org/Data/cf-convetions/cf-conventions-1.6/build
/cf-conventions.html#idp5577536"""
cube = as_cube(df, calendars={1: iris.unit.CALENDAR_GREGORIAN})
cube.coord("index").rename("time")
cube.coord("columns").rename("station name")
cube.rename(standard_name)
longitude = kw.get("longitude")
latitude = kw.get("latitude")
if longitude is not None:
longitude = iris.coords.AuxCoord(longitude,
var_name="lon",
standard_name="longitude",
long_name="station longitude",
units=iris.unit.Unit("degrees"))
cube.add_aux_coord(longitude, data_dims=1)
if latitude is not None:
latitude = iris.coords.AuxCoord(latitude,
var_name="lat",
standard_name="latitude",
long_name="station latitude",
units=iris.unit.Unit("degrees"))
cube.add_aux_coord(latitude, data_dims=1)
# Work around iris to get String instead of np.array object.
string_list = cube.coord("station name").points.tolist()
cube.coord("station name").points = string_list
cube.coord("station name").var_name = 'station'
station_attr = kw.get("station_attr")
if station_attr is not None:
cube.coord("station name").attributes.update(station_attr)
cube_attr = kw.get("cube_attr")
if cube_attr is not None:
cube.attributes.update(cube_attr)
iris.save(cube, outfile)
def save_timeseries(df, outfile, standard_name, **kw):
"""http://cfconventions.org/Data/cf-convetions/cf-conventions-1.6/build
/cf-conventions.html#idp5577536"""
cube = as_cube(df, calendars={1: iris.unit.CALENDAR_GREGORIAN})
cube.coord("index").rename("time")
cube.coord("columns").rename("station name")
cube.rename(standard_name)
longitude = kw.get("longitude")
latitude = kw.get("latitude")
if longitude is not None:
longitude = iris.coords.AuxCoord(longitude,
var_name="lon",
standard_name="longitude",
long_name="station longitude",
units=iris.unit.Unit("degrees"))
cube.add_aux_coord(longitude, data_dims=1)
if latitude is not None:
latitude = iris.coords.AuxCoord(latitude,
var_name="lat",
standard_name="latitude",
long_name="station latitude",
units=iris.unit.Unit("degrees"))
cube.add_aux_coord(latitude, data_dims=1)
# Work around iris to get String instead of np.array object.
string_list = cube.coord("station name").points.tolist()
cube.coord("station name").points = string_list
cube.coord("station name").var_name = 'station'
station_attr = kw.get("station_attr")
if station_attr is not None:
cube.coord("station name").attributes.update(station_attr)
cube_attr = kw.get("cube_attr")
if cube_attr is not None:
cube.attributes.update(cube_attr)
iris.save(cube, outfile)
df['low'] = np.convolve(wt, df['u'], mode='same')
df['high'] = df['u'] - df['low']
# <codecell>
import pandas as pd
roll_l = pd.rolling_mean(df['u'], window=40, center=True, freq='1H')
roll_h = df['u'] - roll_l
# <codecell>
import iris
from iris.pandas import as_cube
cube = as_cube(df['u'])
low = cube.rolling_window('index',
iris.analysis.SUM,
len(wt),
weights=wt)
t = low.coord('index')
t = t.units.num2date(t.points)
pad = np.zeros(window_size) * np.NaN
low = np.r_[pad, low.data, pad]
high = df['u'] - low
# <codecell>
