lair.air.stilt#

Stochastic Time-Inverted Lagrangian Transport (STILT) Model.

Functions

`Lin2021`(dCH4, footprints[, weight, filter_sims])	Estimate basin emissions using Lin et al. 2021 method.
`extract_simulation_id`(simulation)	Extract simulation id from simulation name
`fix_sim_links`(old_out_dir, new_out_dir)	Fix sim symlinks if stilt wd was changed
`init_project`(project[, repo, branch])	Initialize STILT project

Classes

`Footprints`(footprint_dir[, subset, engine, ...])	Footprints class for STILT simulations.
`Receptors`(loc, times)	Receptors class for STILT simulations.
`STILT`(project[, directory, symlink_dir])	STILT class for running STILT simulations.

lair.air.stilt.init_project(project, repo='https://github.com/jmineau/stilt', branch='main')[source]#

Initialize STILT project

Python implementation of Rscript -e “uataq::stilt_init(‘project’)”

Parameters:

projectstr: Name/path of STILT project.
repostr, optional: URL of STILT project repo. The default is jmineau/stilt.
branchstr, optional: Branch of STILT project repo. The default is main.

lair.air.stilt.extract_simulation_id(simulation)[source]#

Extract simulation id from simulation name

Parameters:

simulationstr: Name of simulation.

Returns:

sim_iddict: Dictionary with keys: time, lati, long, zagl.

lair.air.stilt.fix_sim_links(old_out_dir, new_out_dir)[source]#

Fix sim symlinks if stilt wd was changed

Parameters:

old_out_dirstr: old out directory where symlinks currently point to and shouldnt.
new_out_dirstr: new out directory where symlinks should point to.

Returns:

None.

class lair.air.stilt.Receptors(loc, times)[source]#

Receptors class for STILT simulations.

__init__(loc, times)[source]#

generate(loc, times: Series)[source]#: Generate receptor dataframe when given a location and list of datetimes

update(receptors)[source]#: Update data with more receptors from a different Receptors instance

class lair.air.stilt.Footprints(footprint_dir: str, subset: list | bool = False, engine: str = 'rasterio', cache: str | bool = False, reload_cache: bool = False)[source]#

Footprints class for STILT simulations.

Attributes

footprint_dir	(str) Directory containing footprint files.
files	(list[str]) List of footprint files.
cache	(bool \| str) Whether to cache the footprints. If True, path to cache file.
foots	(xarray.Dataset) Footprints dataset.

__init__(footprint_dir: str, subset: list | bool = False, engine: str = 'rasterio', cache: str | bool = False, reload_cache: bool = False)[source]#

read(footprint_dir: str, subset: bool | list, engine: str)[source]#

Read footprints from footprint_dir.

Parameters:

footprint_dirstr: Directory containing footprint.
subsetlist | bool: Bounds to subset the footprints. If False, no subsetting.
enginestr: Engine to use to read the footprints.

Returns:

footsxarray.Dataset: Footprints dataset.

sub(subset: list[float])[source]#

Subset the footprints.

Parameters:

subsetlist[float]: Bounds to subset the footprints.

Returns:

xr.Dataset: The subsetted footprints.

get_receptors_locs()[source]#

Get the locations of the receptors.

Returns:

list[dict]: list of dictionaries containing the locations of the receptors.

property area: DataArray#

Calculate the area of the footprints grid.

Returns:

xr.DataArray: Area of the footprints grid.

apply_weights() → Dataset[source]#

Apply cosine weights to the footprints.

Returns:

xr.Dataset: Footprints with cosine weights applied.

static get_files(footprint_dir) → list[str][source]#

Get footprint files in footprint dir.

Parameters:

footprint_dirstr: Directory containing footprint files.

Returns:

list[str]: List of footprint files.

static plot(foot: DataArray, ax: plt.Axes | None = None, crs: ccrs.CRS | None = None, label_deci: int = 1, tiler=None, tiler_zoom: int = 9, bounds=None, x_buff: float = 0.1, y_buff: float = 0.1, labelsize: int | None = None, more_lon_ticks: int = 0, more_lat_ticks: int = 0) → plt.Axes[source]#

Plot footprints on cartopy axes.

Parameters:

footxr.DataArray: Footprints to plot.
axplt.Axes, optional: Cartopy axes, by default None
crsccrs.CRS , optional: Cartopy CRS, by default None
label_deciint, optional: Log 10 decimals, by default 1
tilercartopy.io.img_tiles.GoogleTiles,: Tiler to use for background map, by default None
tiler_zoomint, optional: Zoom level of tiler, by default 9
boundslist[float], optional: Bounds to plot, by default None
x_bufffloat, optional: x buffer, by default 0.1
y_bufffloat, optional: y buffer, by default 0.1
labelsizeint | None, optional: Label size, by default None
more_lon_ticksint, optional: Number of additional longitude ticks, by default 0
more_lat_ticksint, optional: Number of additional latitude ticks, by default 0

Returns:

plt.Axes: Cartopy axes.

class lair.air.stilt.STILT(project: str, directory: str | None = None, symlink_dir: str | None = None)[source]#

STILT class for running STILT simulations.

I don’t think this is ready…

Attributes

project	(str) Name of STILT project.
stilt_wd	(str) Working directory of STILT project.
receptors	(Receptors) Receptors instance.

Methods

generate_receptors(loc, times)	Generate receptors.
get_sims(id_dir)	Get simulations.
get_missing_sims(receptors, id_dir)	Get missing simulations.
read_footprints(footprint_dir, subset, engine, cache, reload_cache)	Read footprints.
get_foots(footprint_dir, subset, engine, cache, reload_cache)	Get footprints.

__init__(project: str, directory: str | None = None, symlink_dir: str | None = None)[source]#

Initialize STILT project.

Parameters:

projectstr: Name of STILT project.
directorystr, optional: Directory to initialize project in, by default None
symlink_dirstr, optional: Directory to create symlink to STILT project, by default None

generate_receptors(loc, times)[source]#: Generate receptors - not ready

get_sims(id_dir: str | None = None) → DataFrame[source]#

Get simulations from STILT output directory.

Parameters:

id_dirstr, optional: Directory containing simulations, by default None

Returns:

pd.DataFrame: Simulations.

get_missing_sims(receptors: DataFrame, id_dir=None)[source]#

Get missing simulations from STILT output directory.

Parameters:

receptorspd.DataFrame: Receptors used to generate simulations.
id_dirstr, optional: Directory containing simulations, by default None

Returns:

pd.DataFrame: Missing simulations.

read_footprints(footprint_dir: str | None = None, subset: bool | list = False, engine: str = 'rasterio', cache: bool | str = False, reload_cache: bool = False) → Footprints[source]#

_summary_

Parameters:

footprint_dirstr, optional: Directory containing footprint files, by default None
subsetbool | list, optional: Bounds to subset the footprints, by default False
enginestr, optional: Engine to use to read the footprints, by default ‘rasterio’
cachebool | str, optional: Whether to cache the footprints. If True, path to cache file, by default False
reload_cachebool, optional: Whether to reload the cache, by default False

Returns:

Footprints: Footprints instance.

get_foots(footprint_dir: str | None = None, subset: bool | list = False, engine: str = 'rasterio', cache: bool | str = False, reload_cache: bool = False) → DataArray[source]#

Get footprints from STILT output directory.

Parameters:

footprint_dirstr | None, optional: Directory containing footprint files, by default None.
subsetbool | list, optional: Bounds to subset the footprints, by default False.
enginestr, optional: Engine to use to read the footprints, by default ‘rasterio’.
cachebool | str, optional: Whether to cache the footprints. If True, path to cache file, by default False.
reload_cachebool, optional: Whether to reload the cache, by default False.
Returns
——-
xr.DataArray: Footprints.

lair.air.stilt.Lin2021(dCH4: Series, footprints: Footprints, weight: bool = False, filter_sims: bool = False) → DataFrame[source]#

Estimate basin emissions using Lin et al. 2021 method.

Following this logic, we estimate the Basin-averaged CH4 emissions Φ by dividing the CH4 enhancement measured at HPL by the total footprint integrating over all gridcells i within the Uinta Basin (defined as between 39.9N to 40.5N and 110.6W to 109W), where Φ at daily timescales was determined by dividing the ∆CH4 averaged over the afternoon (13:00–16:00 MST) by the total footprint averaged over the same hours.

Parameters:

dCH4pd.Series: Time series of CH4 enhancements.
footprintsFootprints: Footprints instance.
weightbool, optional: Whether to weight the footprints by area, by default False
filter_simsbool, optional: Whether to filter simulations, by default False.

Returns:

pd.DataFrame: Estimated emissions