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fix typos
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@savente93
savente93 committed Feb 11, 2025
1 parent 30e963b commit 9f08138
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6 changes: 3 additions & 3 deletions hydromt_wflow/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@


def read_csv_results(fn: Union[str, Path], config: Dict, maps: xr.Dataset) -> Dict:
"""Read wflow results csv timeseries and parse to dictionnary.
"""Read wflow results csv timeseries and parse to dictionary.
Parses the wflow csv results file into different ``hydromt.GeoDataArrays``, one per
column (csv section and csv.column sections of the TOML). The xy coordinates are the
Expand All @@ -36,7 +36,7 @@ def read_csv_results(fn: Union[str, Path], config: Dict, maps: xr.Dataset) -> Di
Returns
-------
csv_dict: dict
Dictionnary of hydromt.GeoDataArrays for the different csv.column section \
Dictionary of hydromt.GeoDataArrays for the different csv.column section \
of the config.
"""
# Count items by csv.column
Expand Down Expand Up @@ -204,7 +204,7 @@ def get_config(
fallback value if key(s) not found in config, by default None.
abs_path: bool, optional
If True return the absolute path relative to the model root,
by deafult False.
by default False.
Returns
-------
Expand Down
66 changes: 33 additions & 33 deletions hydromt_wflow/wflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -314,7 +314,7 @@ def setup_rivers(
"""
Set all river parameter maps.
The river mask is defined by all cells with a mimimum upstream area threshold
The river mask is defined by all cells with a minimum upstream area threshold
``river_upa`` [km2].
The river length is defined as the distance from the subgrid outlet pixel to
Expand Down Expand Up @@ -610,7 +610,7 @@ def setup_floodplains(
if self.get_config("model.river_routing") != "local-inertial":
raise ValueError(
"Floodplains (1d or 2d) are currently only supported with \
local intertial river routing"
local inertial river routing"
)

r_list = ["1d", "2d"]
Expand Down Expand Up @@ -760,7 +760,7 @@ def setup_riverwidth(
By default the riverwidth is estimated based on discharge as ``predictor``
and used to set the riverwidth globally based on pre-defined power-law
parameters per climate class. With ``fit`` set to True,
the power-law relationsship paramters are set on-the-fly.
the power-law relationships parameters are set on-the-fly.
With ``fill`` set to True, the estimated river widths are only used
to fill gaps in the observed data. Alternative ``predictor`` values
are precip (accumulated precipitation) and uparea (upstream area).
Expand Down Expand Up @@ -1099,7 +1099,7 @@ def setup_laimaps(
will be derived from the LULC data. These tables can then be re-used later if
you would like to add new LAI maps derived from this mapping table and new
landuse scenarios. We advise to use a larger `buffer` to ensure that LAI values
can be assigned for all landuse classes and based on a lage enough sample of the
can be assigned for all landuse classes and based on a large enough sample of the
LULC data.
Adds model layers:
Expand Down Expand Up @@ -1286,7 +1286,7 @@ def setup_config_output_timeseries(
var_name = "variable"
if self.get_config("netcdf") is None:
self.set_config("netcdf.path", "output_scalar.nc")
# initialise column / varibale section
# initialise column / variable section
if self.get_config(f"{toml_output}.{var_name}") is None:
self.set_config(f"{toml_output}.{var_name}", [])

Expand All @@ -1299,7 +1299,7 @@ def setup_config_output_timeseries(
}
if reducer is not None:
gauge_toml_dict["reducer"] = reducer[o]
# If the gauge column/variable already exists skip writting twice
# If the gauge column/variable already exists skip writing twice
if gauge_toml_dict not in self.config[toml_output][var_name]:
self.config[toml_output][var_name].append(gauge_toml_dict)
else:
Expand Down Expand Up @@ -1513,7 +1513,7 @@ def setup_gauges(
if not np.all(np.isin(gdf_gauges.geometry.type, "Point")):
raise ValueError(f"{gauges_fn} contains other geometries than Point")
elif isfile(gauges_fn):
# try to get epsg number directly, important when writting back data_catalog
# try to get epsg number directly, important when writing back data_catalog
if hasattr(self.crs, "to_epsg"):
code = self.crs.to_epsg()
else:
Expand Down Expand Up @@ -1847,7 +1847,7 @@ def setup_lakes(
self.set_tables(v, name=k)

# if there are lakes, change True in toml
# Lake seetings in the toml to update
# Lake settings in the toml to update
lakes_toml = {
"model.lakes": True,
"state.lateral.river.lake.waterlevel": "waterlevel_lake",
Expand Down Expand Up @@ -1891,14 +1891,14 @@ def setup_reservoirs(
In case the wflow parameters are not directly available they can be computed by
HydroMT based on time series of reservoir surface water area.
These time series can be retreived from either the hydroengine or the gwwapi,
These time series can be retrieved from either the hydroengine or the gwwapi,
based on the Hylak_id the reservoir, found in the GrandD database.
The required variables for computation of the parameters with time series data
are reservoir ID 'waterbody_id', reservoir ID in the HydroLAKES database
'Hylak_id', average volume 'Vol_avg' [m3], average depth 'Depth_avg' [m],
average discharge 'Dis_avg' [m3/s] and dam height 'Dam_height' [m].
To compute parameters without using time series data, the required varibales in
To compute parameters without using time series data, the required variables in
reservoirs_fn are reservoir ID 'waterbody_id', average area 'Area_avg' [m2],
average volume 'Vol_avg' [m3], average depth 'Depth_avg' [m], average discharge
'Dis_avg' [m3/s] and dam height 'Dam_height' [m]
Expand Down Expand Up @@ -2012,7 +2012,7 @@ def setup_reservoirs(
)
intbl_reservoirs = intbl_reservoirs.rename(columns=tbls)

# create a geodf with id of reservoir and gemoetry at outflow location
# create a geodf with id of reservoir and geometry at outflow location
gdf_org_points = gpd.GeoDataFrame(
gdf_org["waterbody_id"],
geometry=gpd.points_from_xy(gdf_org.xout, gdf_org.yout),
Expand All @@ -2021,7 +2021,7 @@ def setup_reservoirs(
gdf_org_points = gdf_org_points.merge(
intbl_reservoirs, on="waterbody_id"
) # merge
# add parameter attributes to polygone gdf:
# add parameter attributes to polygon gdf:
gdf_org = gdf_org.merge(intbl_reservoirs, on="waterbody_id")

# write reservoirs with param values to geoms
Expand Down Expand Up @@ -2062,7 +2062,7 @@ def _setup_waterbodies(self, waterbodies_fn, wb_type, min_area=0.0, **kwargs):
**kwargs,
)
if gdf_org is None:
# Return two times None (similair to main function output), if there is no
# Return two times None (similar to main function output), if there is no
# data found
return None, None

Expand Down Expand Up @@ -2577,13 +2577,13 @@ def setup_glaciers(self, glaciers_fn="rgi", min_area=1):
Generate maps of glacier areas, area fraction and volume fraction.
Also generates tables with temperature threshold, melting factor and snow-to-ice
convertion fraction.
conversion fraction.
The data is generated from features with ``min_area`` [km2] (default is 1 km2)
from a database with glacier geometry, IDs and metadata.
The required variables from glaciers_fn dataset are glacier ID 'simple_id'.
Optionnally glacier area 'AREA' [km2] can be present to filter the glaciers
Optionally glacier area 'AREA' [km2] can be present to filter the glaciers
by size. If not present it will be computed on the fly.
Adds model layers:
Expand Down Expand Up @@ -3211,7 +3211,7 @@ def setup_rootzoneclim(
the rootzone_storage by (thetaS - thetaR).
* **rootzone_storage_{forcing}_{RP}** geom: polygons of rootzone \
storage capacity [mm of water] for each catchment estimated before filling \
the missings with data from downstream catchments.
the missing with data from downstream catchments.
* **rootzone_storage_{forcing}_{RP}** map: rootzone storage capacity \
[mm of water] estimated from hydroclimatic data {forcing: obs, cc_hist or cc_fut} for \
different return periods RP. Only if rootzone_storage is set to True!
Expand All @@ -3238,13 +3238,13 @@ def setup_rootzoneclim(
Chunksize on time dimension for processing data (not for saving to
disk!). The default is 100.
return_period : list, optional
List with one or more values indiciating the return period(s) (in
years) for wich the rootzone storage depth should be calculated. The
List with one or more values indicating the return period(s) (in
years) for which the rootzone storage depth should be calculated. The
default is [2,3,5,10,15,20,25,50,60,100] years.
Imax : float, optional
The maximum interception storage capacity [mm]. The default is 2.0 mm.
start_hydro_year : str, optional
The start month (abreviated to the first three letters of the month,
The start month (abbreviated to the first three letters of the month,
starting with a capital letter) of the hydrological year. The
default is 'Sep'.
start_field_capacity : str, optional
Expand Down Expand Up @@ -3492,7 +3492,7 @@ def setup_1dmodel_connection(
river_upa = self.grid[self._MAPS["rivmsk"]].attrs.get("river_upa", "")
self.logger.warning(
"Not all tributary gauges are on the river network and river "
"discharge canot be saved. You should use a higher threshold "
"discharge cannot be saved. You should use a higher threshold "
f"for the subbasin area than {area_max} to match better the "
f"wflow river in your model {river_upa}."
)
Expand Down Expand Up @@ -3545,7 +3545,7 @@ def setup_allocation_areas(
wflow model rivers and water areas or regions for allocation.
Water regions are generally defined by sub-river-basins within a Country. In
order to mimick reality, it is advisable to avoid cross-Country-border
order to mimic reality, it is advisable to avoid cross-Country-border
abstractions. Whenever information is available, it is strongly recommended to
align the water regions with the actual areas managed by water management
authorities, such as regional water boards.
Expand Down Expand Up @@ -3666,7 +3666,7 @@ def setup_allocation_surfacewaterfrac(
else:
ncfrac = None

# check wether to use the models own allocation areas
# check whether to use the models own allocation areas
if waterareas_fn is None:
self.logger.info("Using wflow model allocation areas.")
if "allocation_areas" not in self.grid:
Expand Down Expand Up @@ -3721,7 +3721,7 @@ def setup_domestic_demand(
original resolution of `domestic_fn` before downsampling with `population_fn`.
For example, the pcr_globwb dataset is at a resolution of 0.0083333333 degrees,
while the original data has a resolution of 0.5 degrees. Use the
`domestic_fn_original_res` parameter to specify the orignal resolution.
`domestic_fn_original_res` parameter to specify the original resolution.
Adds model layer:
Expand Down Expand Up @@ -3924,7 +3924,7 @@ def setup_irrigation(
The function requires data that contains information about the location of the
irrigated areas (``irrigated_area_fn``). This, combined with the wflow landuse
map that contains classes for cropland (``cropland_class) and optionnally for
map that contains classes for cropland (``cropland_class) and optionally for
paddy (rice) (``paddy_class``), determines which locations are considered to be
paddy irrigation, and which locations are considered to be non-paddy irrigation.
Expand Down Expand Up @@ -4146,7 +4146,7 @@ def write(
Parameters
----------
config_fn : str, optional
Name of the config file, realtive to model root. By default None.
Name of the config file, relative to model root. By default None.
grid_fn : str, optional
Name of the grid file, relative to model root/dir_input. By default
'staticmaps.nc'.
Expand All @@ -4157,7 +4157,7 @@ def write(
Name of the forcing file relative to model root/dir_input. By default None
to use the name as defined in the model config file.
states_fn : str, optional
Name of the states file realtive to model root/dir_input. By default None
Name of the states file relative to model root/dir_input. By default None
to use the name as defined in the model config file.
"""
self.logger.info(f"Write model data to {self.root}")
Expand Down Expand Up @@ -4488,8 +4488,8 @@ def read_forcing(self):
root folder.
If several files are used using '*' in ``input.path_forcing``, all corresponding
files are read and merged into one xarray dataset before being splitted to one
xarray dataaray per forcing variable in the hydromt ``forcing`` dictionnary.
files are read and merged into one xarray dataset before being split to one
xarray dataaray per forcing variable in the hydromt ``forcing`` dictionary.
"""
fn_default = "inmaps.nc"
fn = self.get_config(
Expand Down Expand Up @@ -4557,9 +4557,9 @@ def write_forcing(
chunksize: int, optional
Chunksize on time dimension when saving to disk. By default 1.
decimals: int, optional
Round the ouput data to the given number of decimals.
Round the output data to the given number of decimals.
time_units: str, optional
Common time units when writting several netcdf forcing files.
Common time units when writing several netcdf forcing files.
By default "days since 1900-01-01T00:00:00".
"""
Expand Down Expand Up @@ -4595,7 +4595,7 @@ def write_forcing(
else:
fn_out = None

# get deafult filename if file exists
# get default filename if file exists
if fn_out is None or isfile(fn_out):
self.logger.warning(
"Netcdf forcing file from input.path_forcing in the TOML "
Expand Down Expand Up @@ -4703,7 +4703,7 @@ def write_forcing(
# the dask.compute method
forcing_list.append([fn_out, ds])
else:
self.logger.info(f"Writting several forcing with freq {freq_out}")
self.logger.info(f"Writing several forcing with freq {freq_out}")
# For several forcing files add common units attributes to time
encoding["time"] = {"_FillValue": None, "units": time_units}
# Updating path forcing in config
Expand Down Expand Up @@ -4764,7 +4764,7 @@ def write_states(self, fn_out: Union[str, Path] = None):
raise IOError("Model opened in read-only mode")

if self.states:
self.logger.info("Writting states file")
self.logger.info("Writing states file")

# get output filename and if needed update and re-write the config
if fn_out is not None:
Expand Down
14 changes: 7 additions & 7 deletions hydromt_wflow/wflow_sediment.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -133,14 +133,14 @@ def setup_reservoirs(
In case the wflow parameters are not directly available they can be computed by
HydroMT based on time series of reservoir surface water area.
These time series can be retreived from either the hydroengine or the gwwapi,
These time series can be retrieved from either the hydroengine or the gwwapi,
based on the Hylak_id the reservoir, found in the GrandD database.
The required variables for computation of the parameters with time series data
are reservoir ID 'waterbody_id', reservoir ID in the HydroLAKES database
'Hylak_id', average volume 'Vol_avg' [m3], average depth 'Depth_avg' [m],
average discharge 'Dis_avg' [m3/s] and dam height 'Dam_height' [m].
To compute parameters without using time series data, the required varibales in
To compute parameters without using time series data, the required variables in
reservoirs_fn are reservoir ID 'waterbody_id', average area 'Area_avg' [m2],
average volume 'Vol_avg' [m3], average depth 'Depth_avg' [m], average discharge
'Dis_avg' [m3/s] and dam height 'Dam_height' [m] and
Expand Down Expand Up @@ -320,14 +320,14 @@ def setup_lulcmaps(
"""Derive several wflow maps based on landuse-landcover (LULC) data.
Currently, ``lulc_fn`` can be set to the "vito", "globcover", "corine" or
"glmnco", of which lookup tables are constructed to convert lulc classses to
"glmnco", of which lookup tables are constructed to convert lulc classes to
model parameters based on literature. The data is remapped at its original
resolution and then resampled to the model resolution using the average value,
unless noted differently.
The USLE C factor map can be refined for planted forests using the planted
forest data source. The planted forest data source is a polygon layer with
planted forest polygons and optionnally a column with the forest type to
planted forest polygons and optionally a column with the forest type to
identify orchards. The default value for orchards is 0.2188, the default value
for other planted forests is 0.0881.
Expand All @@ -354,7 +354,7 @@ def setup_lulcmaps(
* Optional variable: ["forest_type"]
lulc_vars : dict
List of landuse paramters to prepare.
List of landuse parameters to prepare.
By default ["landuse","Kext","Sl","Swood","USLE_C","PathFrac"]
planted_forest_c : float, optional
Value of USLE C factor for planted forest, by default 0.0881.
Expand Down Expand Up @@ -432,7 +432,7 @@ def setup_lulcmaps_from_vector(
The USLE C factor map can be refined for planted forests using the planted
forest data source. The planted forest data source is a polygon layer with
planted forest polygons and optionnally a column with the forest type to
planted forest polygons and optionally a column with the forest type to
identify orchards. The default value for orchards is 0.2188, the default value
for other planted forests is 0.0881.
Expand Down Expand Up @@ -462,7 +462,7 @@ def setup_lulcmaps_from_vector(
* Optional variable: ["forest_type"]
lulc_vars : dict
List of landuse paramters to prepare.
List of landuse parameters to prepare.
By default ["landuse","Kext","Sl","Swood","USLE_C","PathFrac"]
lulc_res : float, int, optional
Resolution of the intermediate rasterized landuse map. The unit (meter or
Expand Down
2 changes: 1 addition & 1 deletion hydromt_wflow/workflows/basemaps.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -94,7 +94,7 @@ def hydrography(
basins = flwdir.basins(xy=xy).astype(np.int32)
ds.coords["mask"].data = basins != 0
if not np.any(ds.coords["mask"]):
raise ValueError("Delineating subbasins not successfull.")
raise ValueError("Delineating subbasins not successful.")
elif xy is not None:
# NOTE: this mask is passed on from get_basin_geometry method
logger.debug("Mask in dataset assumed to represent subbasins.")
Expand Down
2 changes: 1 addition & 1 deletion hydromt_wflow/workflows/connect.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,7 @@ def wflow_1dmodel_connection(
tributaries.
If `add_tributary` option is on, you can decide to include or exclude the upstream
boundary of the 1d river as an additionnal tributary using the
boundary of the 1d river as an additional tributary using the
`include_river_boundaries` option.
River edges or river nodes are snapped to the closest downstream wflow river
Expand Down
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