def test_plot_wateruse(preprocessed_iwum_data, preprocessed_swuds_data,
test_data_path):
extent = test_data_path / 'extents/shellmound_bbox.shp'
perioddata = test_data_path / 'shellmound/tables/stress_period_data.csv'
preprocessed_iwum_data['geometry'] = [
Point(x, y)
for x, y in zip(preprocessed_iwum_data.x, preprocessed_iwum_data.y)
]
iwum_data = cull_data_to_active_area(preprocessed_iwum_data,
active_area=extent,
data_crs=5070)
swuds_data = preprocessed_swuds_data.df
swuds_data['geometry'] = [
Point(x, y) for x, y in zip(swuds_data.x, swuds_data.y)
]
swuds_data = cull_data_to_active_area(swuds_data,
active_area=extent,
data_crs=5070)
wel_files = sorted(
glob.glob(str(test_data_path / 'shellmound/external/wel_*.dat')))
wel_files = dict(zip(range(1, len(wel_files) + 1), wel_files))
add_data = {
'IWUM estimates': {
'data': iwum_data
},
'SWUDs data': {
'data': swuds_data
}
}
results = plot_wateruse(wel_files, perioddata, add_data)
j = 2
def apply_footprint(
self,
active_area,
active_area_id_column=None,
active_area_feature_id=None,
):
"""Keep sites in the df pandas dataframe that fall
into the passed bounding shapefile polygon. Requires
that df dataframe has a Point geometry column as assigned
in the reproject method.
Parameters
----------
active_area: str
path to shapefile with footprint for current analysis
active_area_id_column : str, optional
Column in active_area with feature ids.
By default, None, in which case all features are used.
active_area_feature_id : str, optional
ID of feature to use for active area
By default, None, in which case all features are used.
outshp: str
optional path to output shapefile with points within the footprint
"""
self.df = cull_data_to_active_area(self.df,
active_area,
active_area_id_column,
active_area_feature_id,
data_crs=self.dest_crs)
def preprocess_flows(
data,
metadata=None,
flow_data_columns=['flow'],
start_date=None,
active_area=None,
active_area_id_column=None,
active_area_feature_id=None,
source_crs=4269,
dest_crs=5070,
datetime_col='datetime',
site_no_col='site_no',
line_id_col='line_id',
x_coord_col='x',
y_coord_col='y',
name_col='name',
flow_qualifier_column=None,
default_qualifier='measured',
include_sites=None,
include_line_ids=None,
source_volume_units='ft3',
source_time_units='s',
dest_volume_units='m3',
dest_time_units='d',
geographic_groups=None,
geographic_groups_col=None,
max_obsname_len=None,
add_leading_zeros_to_sw_site_nos=False,
column_renames=None,
outfile=None,
):
"""Preprocess stream flow observation data, for example, from NWIS or another data source that
outputs time series in CSV format with site locations and identifiers.
* Data are reprojected from a `source_crs` (Coordinate reference system; assumed to be in geographic coordinates)
to the CRS of the model (`dest_crs`)
* Data are culled to a `start_date` and optionally, a polygon or set of polygons defining the model area
* length and time units are converted to those of the groundwater model.
* Prefixes for observation names (with an optional length limit) that identify the location are generated
* Preliminary observation groups can also be assigned, based on geographic areas defined by polygons
(`geographic_groups` parameter)
Parameters
----------
data : csv file or DataFrame
Time series of stream flow observations.
Columns:
===================== ======================================
site_no site identifier
datetime measurement dates/times
x x-coordinate of site
y y-coordinate of site
flow_data_columns Columns of observed streamflow values
flow_qualifier_column Optional column with qualifiers for flow values
===================== ======================================
Notes:
* x and y columns can alternatively be in the metadata table
* flow_data_columns are denoted in `flow_data_columns`; multiple
columns can be included to process base flow and total flow, or
other statistics in tandem
* For example, `flow_qualifier_column` may have "estimated" or "measured"
flags denoting whether streamflows were derived from measured values
or statistical estimates.
metadata : csv file or DataFrame
Stream flow observation site information.
May include columns:
================= ================================================================================
site_no site identifier
x x-coordinate of site
y y-coordinate of site
name name of site
line_id_col Identifier for a line in a hydrography dataset that the site is associated with.
================= ================================================================================
Notes:
* other columns in metadata will be passed through to the metadata output
flow_data_columns : list of strings
Columns in data with flow values or their statistics.
By default, ['q_cfs']
start_date : str (YYYY-mm-dd)
Simulation start date (cull observations before this date)
active_area : str
Shapefile with polygon to cull observations to. Automatically reprojected
to dest_crs if the shapefile includes a .prj file.
by default, None.
active_area_id_column : str, optional
Column in active_area with feature ids.
By default, None, in which case all features are used.
active_area_feature_id : str, optional
ID of feature to use for active area
By default, None, in which case all features are used.
source_crs : obj
Coordinate reference system of the head observation locations.
A Python int, dict, str, or :class:`pyproj.crs.CRS` instance
passed to :meth:`pyproj.crs.CRS.from_user_input`
Can be any of:
- PROJ string
- Dictionary of PROJ parameters
- PROJ keyword arguments for parameters
- JSON string with PROJ parameters
- CRS WKT string
- An authority string [i.e. 'epsg:4326']
- An EPSG integer code [i.e. 4326]
- A tuple of ("auth_name": "auth_code") [i.e ('epsg', '4326')]
- An object with a `to_wkt` method.
- A :class:`pyproj.crs.CRS` class
By default, epsg:4269
dest_crs : obj
Coordinate reference system of the model. Same input types
as ``source_crs``.
By default, epsg:5070
datetime_col : str, optional
Column name in data with observation date/times,
by default 'datetime'
site_no_col : str, optional
Column name in data and metadata with site identifiers,
by default 'site_no'
line_id_col : str, optional
Column name in data or metadata with identifiers for
hydrography lines associated with observation sites.
by default 'line_id'
x_coord_col : str, optional
Column name in data or metadata with x-coordinates,
by default 'x'
y_coord_col : str, optional
Column name in data or metadata with y-coordinates,
by default 'y'
name_col : str, optional
Column name in data or metadata with observation site names,
by default 'name'
flow_qualifier_column : str, optional
Column name in data with flow observation qualifiers, such
as "measured" or "estimated"
by default 'category'
default_qualifier : str, optional
Default qualifier to populate flow_qualifier_column if it
is None. By default, "measured"
include_sites : list-like, optional
Exclude output to these sites.
by default, None (include all sites)
include_line_ids : list-like, optional
Exclude output to these sites, represented by line identifiers.
by default, None (include all sites)
source_volume_units : str, 'm3', 'cubic meters', 'ft3', etc.
Volume units of the source data. By default, 'ft3'
source_time_units : str, 's', 'seconds', 'days', etc.
Time units of the source data. By default, 's'
dest_volume_units : str, 'm3', 'cubic meters', 'ft3', etc.
Volume units of the output (model). By default, 'm3'
dest_time_units : str, 's', 'seconds', 'days', etc.
Time units of the output (model). By default, 'd'
geographic_groups : file, dict or list-like
Option to group observations by area(s) of interest. Can
be a shapefile, list of shapefiles, or dictionary of shapely polygons.
A 'group' column will be created in the metadata, and observation
sites within each polygon will be assigned the group name
associated with that polygon.
For example::
geographic_groups='../source_data/extents/CompositeHydrographArea.shp'
geographic_groups=['../source_data/extents/CompositeHydrographArea.shp']
geographic_groups={'cha': <shapely Polygon>}
Where 'cha' is an observation group name for observations located within the
the area defined by CompositeHydrographArea.shp. For shapefiles,
group names are provided in a `geographic_groups_col`.
geographic_groups_col : str
Field name in the `geographic_groups` shapefile(s) containing the
observation group names associated with each polygon.
max_obsname_len : int or None
Maximum length for observation name prefix. Default of 13
allows for a PEST obsnme of 20 characters or less with
<prefix>_yyyydd or <prefix>_<per>d<per>
(e.g. <prefix>_2d1 for a difference between stress periods 2 and 1)
If None, observation names will not be truncated. PEST++ does not have
a limit on observation name length.
add_leading_zeros_to_sw_site_nos : bool
Whether or not to pad site numbers using the
:func:~`mapgwm.swflows.format_usgs_sw_site_id` function.
By default, False.
column_renames : dict, optional
Option to rename columns in the data or metadata that are different than those listed above.
For example, if the data file has a 'SITE_NO' column instead of 'SITE_BADGE'::
column_renames={'SITE_NO': 'site_no'}
by default None, in which case the renames listed above will be used.
Note that the renames must be the same as those listed above for
:func:`mapgwm.swflows.preprocess_flows` to work.
outfile : str
Where output file will be written. Metadata are written to a file
with the same name, with an additional "_info" suffix prior to
the file extension.
Returns
-------
data : DataFrame
Preprocessed time series
metadata : DataFrame
Preprocessed metadata
References
----------
`The PEST++ Manual <https://github.com/usgs/pestpp/tree/master/documentation>`
Notes
-----
"""
# outputs
if outfile is not None:
outpath, filename = os.path.split(outfile)
makedirs(outpath)
outname, ext = os.path.splitext(outfile)
out_info_csvfile = outname + '_info.csv'
out_data_csvfile = outfile
out_shapefile = outname + '_info.shp'
# read the source data
if not isinstance(data, pd.DataFrame):
df = pd.read_csv(data, dtype={site_no_col: object})
else:
df = data.copy()
# check the columns
for col in [datetime_col] + flow_data_columns:
assert col in df.columns, "Column {} not found in {}".format(col, data)
assert any({site_no_col, line_id_col}.intersection(df.columns)), \
"Neither {} or {} found in {}. Need to specify a site_no_col or line_id_col".format(site_no_col,
line_id_col, data)
# rename input columns to these names,
# for consistent output
dest_columns = {
datetime_col: 'datetime',
site_no_col: 'site_no',
line_id_col: 'line_id',
x_coord_col: 'x',
y_coord_col: 'y',
name_col: 'name',
flow_qualifier_column: 'category'
}
# update the default column renames
# with any supplied via column_renames parameter
if isinstance(column_renames, collections.Mapping):
dest_columns.update(column_renames)
df.rename(columns=dest_columns, inplace=True)
flow_data_columns = [
c if c not in dest_columns else dest_columns[c]
for c in flow_data_columns
]
# convert site numbers to strings;
# add leading 0s to any USGS sites that should have them
if 'site_no' in df.columns:
df['site_no'] = format_site_ids(df['site_no'],
add_leading_zeros_to_sw_site_nos)
else:
df['site_no'] = df[line_id_col]
# read the source data
if metadata is not None:
if not isinstance(metadata, pd.DataFrame):
md = pd.read_csv(metadata, dtype={site_no_col: object})
else:
md = metadata.copy()
if site_no_col not in md.columns or 'site_no' not in df.columns:
raise IndexError(
'If metadata are supplied, both data and metadata must '
'have a site_no column.')
md.rename(columns=dest_columns, inplace=True)
md['site_no'] = format_site_ids(md['site_no'],
add_leading_zeros_to_sw_site_nos)
md.index = md['site_no']
by_site = df.groupby('site_no')
md['start_dt'] = pd.DataFrame(by_site['datetime'].first())
else:
by_site = df.groupby('site_no')
md = pd.DataFrame(by_site['datetime'].first())
md.columns = ['start_dt']
md['site_no'] = md.index
md['end_dt'] = pd.DataFrame(by_site['datetime'].last())
md['n'] = pd.DataFrame(by_site['datetime'].count())
md.reset_index(inplace=True, drop=True)
# assign metadata if supplied
for col in 'x', 'y', 'line_id', 'name':
if col in df.columns and col not in md.columns:
by_site_no = dict(zip(df['site_no'], df[col]))
md[col] = [by_site_no[sn] for sn in md['site_no']]
if col != 'line_id':
df.drop(col, axis=1, inplace=True)
# index the dataframe to times;
# truncate data before start date
df.index = pd.to_datetime(df['datetime'])
df.index.name = 'datetime'
df = df.loc[start_date:].copy()
# project x, y to model crs
x_pr, y_pr = project((md.x.values, md.y.values), source_crs, dest_crs)
md['x'], md['y'] = x_pr, y_pr
md['geometry'] = [Point(x, y) for x, y in zip(x_pr, y_pr)]
# cull data to that within the model area
if active_area is not None:
df, md = cull_data_to_active_area(df,
active_area,
active_area_id_column,
active_area_feature_id,
data_crs=dest_crs,
metadata=md)
# get the hydrography IDs corresponding to each site
# using the included lookup table
#if 'line_id' not in df.columns:
# assert line_id_lookup is not None, \
# "need to include line_ids in a column, or line_id_lookup dictionary mapping line_ids to site numbers"
# df = df.loc[df['site_no'].isin(line_id_lookup)].copy()
# df['line_id'] = [line_id_lookup[sn] for sn in df['site_no']]
if include_sites is not None:
md = md.loc[md.site_no.isin(include_sites)]
df = df.loc[df.site_no.isin(include_sites)]
if include_line_ids is not None:
md = md.loc[md.line_id.isin(include_line_ids)]
df = df.loc[df.line_id.isin(include_line_ids)]
# convert units
# ensure that flow values are numeric (may be objects if taken directly from NWIS)
unit_conversion = (
convert_volume_units(source_volume_units, dest_volume_units) /
convert_time_units(source_time_units, dest_time_units))
for flow_col in flow_data_columns:
df[flow_col] = pd.to_numeric(df[flow_col],
errors='coerce') * unit_conversion
df.dropna(subset=flow_data_columns, axis=0, inplace=True)
# reformat qualifiers for consistent output
# (lump to dest category columns of either estimated or measured)
# with measured including values derived from baseflow separation or actual measurements)
# output column name for flow qualifier column:
dest_flow_qualifier_column = 'category'
if flow_qualifier_column is not None:
flow_qualifiers = {
'calculated': 'measured', # 'measured',
'base flow separated from measured values':
'measured', # 'measured',
'measured total flow': 'measured',
'estimated gaged': 'estimated',
'estimated ungaged': 'estimated'
}
df[dest_flow_qualifier_column] = df[flow_qualifier_column].replace(
flow_qualifiers)
else:
df['category'] = default_qualifier
# make unique n-character prefixes (site identifiers) for each observation location
# 13 character length allows for prefix_yyyymmm in 20 character observation names
# (BeoPEST limit)
unique_obsnames = set()
obsnames = []
for sn in md['site_no'].tolist():
if max_obsname_len is not None:
name = make_obsname(sn,
unique_names=unique_obsnames,
maxlen=max_obsname_len)
assert name not in unique_obsnames
else:
name = sn
unique_obsnames.add(name)
obsnames.append(name)
md['obsprefix'] = obsnames
# add area of interest information
md['group'] = 'fluxes'
md = assign_geographic_obsgroups(md,
geographic_groups,
geographic_groups_col,
metadata_crs=dest_crs)
# data columns
data_cols = ['site_no', 'line_id', 'datetime'
] + flow_data_columns + ['category']
#if 'line_id' in md.columns and 'line_id' not in df.columns:
# # only map line_ids to data if there are more site numbers
# # implying that no site number maps to more than one line_id
# if len(set(df.site_no)) >= len(set(df.line_id)):
# ids = dict(zip(md['site_no'], md['line_id']))
# df['line_id'] = [ids[sn] for sn in df['site_no']]
data_cols = [c for c in data_cols if c in df.columns]
df = df[data_cols]
md.index = md['site_no']
# save out the results
if outfile is not None:
df2shp(md.drop(['x', 'y'], axis=1), out_shapefile, crs=dest_crs)
print('writing {}'.format(out_info_csvfile))
md.drop('geometry', axis=1).to_csv(out_info_csvfile,
index=False,
float_format='%g')
print('writing {}'.format(out_data_csvfile))
df.to_csv(out_data_csvfile, index=False, float_format='%g')
return df, md
def preprocess_iwum_pumping(ncfile,
start_date=None,
end_date=None,
active_area=None,
active_area_id_column=None,
active_area_feature_id=None,
estimated_production_zone_top=None,
estimated_production_zone_botm=None,
flux_variable='value',
nc_crs=5070,
dest_crs=5070,
nc_length_units='meters',
estimated_production_surface_units='meters',
model_length_units='meters',
outfile=None):
"""Get pumping from the Irrigation Water Use Model (IWUM; Wilson, 2020) output and
assign open interval information, using raster surfaces of the
top and bottom of an estimated production zone.
Parameters
----------
ncfile : file path
NetCDF output from Irrigation Water Use Model
start_date : str
Cull data before this date.
end_date : str
Cull data after this date.
active_area : str
Shapefile with polygon to cull observations to. Automatically reprojected
to dest_crs if the shapefile includes a .prj file.
by default, None.
active_area_id_column : str, optional
Column in active_area with feature ids.
By default, None, in which case all features are used.
active_area_feature_id : str, optional
ID of feature to use for active area
By default, None, in which case all features are used.
estimated_production_zone_top : file path
Raster surface for assigning screen tops
estimated_production_zone_botm : file path
Raster surface for assigning screen bottoms
flux_variable : str
Varible in ncfile for pumping fluxes. Fluxes are assumed to
represent total volumes for each time period.
nc_crs : obj
Coordinate Reference System (CRS) of ncfile.
A Python int, dict, str, or pyproj.crs.CRS instance
passed to the pyproj.crs.from_user_input
See http://pyproj4.github.io/pyproj/stable/api/crs/crs.html#pyproj.crs.CRS.from_user_input.
Can be any of:
- PROJ string
- Dictionary of PROJ parameters
- PROJ keyword arguments for parameters
- JSON string with PROJ parameters
- CRS WKT string
- An authority string [i.e. 'epsg:4326']
- An EPSG integer code [i.e. 4326]
- A tuple of ("auth_name": "auth_code") [i.e ('epsg', '4326')]
- An object with a `to_wkt` method.
- A :class:`pyproj.crs.CRS` class
nc_length_units : str, {'meters', 'ft', etc.}
Length units of pumped volumes in ncfile
estimated_production_surface_units : str, {'meters', 'ft', etc.}
Length units of elevations in estimated production surface rasters.
model_length_units : str, {'meters', 'ft', etc.}
Length units of model.
outfile : csv file for output table
Returns
-------
df : DataFrame
Table of pumping rates in m3/day, location
and open interval information.
Columns:
============== ================================================
site_no index position of pumping rate in ncfile grid
x x-coordinate in `dest_crs`
y y-coordinate in `dest_crs`
start_datetime start date of pumping period
end_datetime end date of pumping period
screen_top screen top elevation, in `model_length_units`
screen_botm screen bottom elevation, in `model_length_units`
q pumping rate, in model units
geometry shapely Point object representing location
============== ================================================
Notes
-----
* Time units are assumed to be days.
* Fluxes are assumed to represent total volumes for each time period
indicated by the differences between successive values along the time axis of ncfile.
"""
ds = xr.open_dataset(ncfile)
time_variable = [k for k in ds.coords.keys() if k.lower() not in {'x', 'y'}][0]
ds_x, ds_y = np.meshgrid(ds['x'], ds['y'])
# original values are in m3, in each 1 mi2 cell
# can leave in m3 if reassigning to 1km grid as point values
length_conversion = convert_volume_units(nc_length_units,
model_length_units) ** 3
unit_suffix = vol_suffix[model_length_units] + 'd'
flux_col = 'q' # 'flux_{}'.format(unit_suffix) # output field name for fluxes
# get top/botm elevations
est_screen_top = None
est_screen_botm = None
if estimated_production_zone_top is not None and \
estimated_production_zone_botm is not None:
surf_unit_conversion = convert_length_units(estimated_production_surface_units,
model_length_units)
est_screen_top = get_values_at_points(estimated_production_zone_top, ds_x, ds_y,
points_crs=nc_crs)
est_screen_top *= surf_unit_conversion
est_screen_botm = get_values_at_points(estimated_production_zone_botm, ds_x, ds_y,
points_crs=nc_crs)
est_screen_botm *= surf_unit_conversion
# in any places where screen top is less than the screen botm,
# set both at the mean
loc = est_screen_top < est_screen_botm
means = np.mean([est_screen_top, est_screen_botm], axis=0)
est_screen_top[loc] = means[loc]
est_screen_botm[loc] = means[loc]
print(f'Reset screen top and bottom to mean elevation at {loc.ravel().sum()} '
f'locations where screen top was < screen bottom')
dfs = []
times = pd.DatetimeIndex(ds[time_variable].loc[start_date:end_date].values)
for n, period_start_date in enumerate(times):
# for each time entry, get the data
kwargs = {time_variable: period_start_date}
arr = ds[flux_variable].sel(**kwargs).values
# make sure pumping sign is negative
# based on assumption that values are mostly abstraction
if arr.sum() > 0:
arr *= -1
# set up a dataframe
data = {'site_no': np.arange(ds_x.size),
'x': ds_x.ravel(),
'y': ds_y.ravel(),
}
if est_screen_top is not None and est_screen_botm is not None:
data.update({'screen_top': est_screen_top.ravel(),
'screen_botm': est_screen_botm.ravel()
}
)
df = pd.DataFrame(data)
df['start_datetime'] = period_start_date
# get the end_date, handling last entry
if n + 1 < len(times):
period_end_date = times[n + 1]
else:
# set end date for last period on previous period length
last_start = dfs[-1]['start_datetime'].values[0]
ndays = (pd.Timestamp(period_start_date) -
pd.Timestamp(last_start)).days
period_end_date = period_start_date + pd.Timedelta(ndays, unit='d')
# convert the time units
ndays = (pd.Timestamp(period_end_date) -
pd.Timestamp(period_start_date)).days
assert ndays > 0, "period_end_date {} is before period_start_date {}"\
.format(period_end_date, period_start_date)
time_conversion = 1 / ndays # original quantities are volumes for the time period
# time indexing in pandas is through last value
period_end_date = pd.Timestamp(period_end_date) - pd.Timedelta(1, unit='d')
df['end_datetime'] = period_end_date
df[flux_col] = arr.ravel() * length_conversion * time_conversion
# only includes fluxes > 0
df = df.loc[df[flux_col] < 0]
dfs.append(df)
df = pd.concat(dfs)
# site number column (that would be unique from other integers from other data sources)
df['site_no'] = [f'iwum_{node}' for node in df.site_no]
# project the data to a destination crs, if provided
# make a separate metadata dataframe with 1 row per location
# to avoid redundant operations
metadata = df.groupby('site_no').first().reset_index()[['site_no', 'x', 'y']]
metadata.index = metadata['site_no']
x_pr, y_pr = project((metadata.x.values, metadata.y.values), nc_crs, dest_crs)
metadata['x'], metadata['y'] = x_pr, y_pr
metadata['geometry'] = [Point(x, y) for x, y in zip(x_pr, y_pr)]
# cull the data to the model area, if provided
if active_area is not None:
df, metadata = cull_data_to_active_area(df, active_area,
active_area_id_column,
active_area_feature_id,
data_crs=dest_crs, metadata=metadata)
# update data with x,y values projected in metadata
x = dict(zip(metadata.site_no, metadata.x))
y = dict(zip(metadata.site_no, metadata.y))
df['x'] = [x[sn] for sn in df.site_no]
df['y'] = [y[sn] for sn in df.site_no]
if outfile is not None:
outfile = Path(outfile)
df.to_csv(outfile, index=False, float_format='%g')
print('wrote {}'.format(outfile))
# Make a plot of iwum output in mgal/day
out_pdf_path = outfile.parent / 'plots'
out_pdf_path.mkdir(exist_ok=True)
plot_iwum_output(ncfile, flux_variable=flux_variable, outpath=out_pdf_path)
return df
def preprocess_te_wateruse(data,
start_date=None,
end_date=None,
active_area=None,
active_area_id_column=None,
active_area_feature_id=None,
estimated_production_zone_top=None,
estimated_production_zone_botm=None,
estimated_production_surface_units='feet',
source_crs=4269,
dest_crs=5070,
interp_method='linear',
data_volume_units='mgal',
model_length_units='meters',
outfile=None):
"""Preprocess water use data from thermoelectric power plants:
* reproject data to a destination CRS `dest_crs`)
* cull data to an area of interest (`active_area`)
* if input data do not have information on the well screen intervals;
sample screen tops and bottoms from raster surfaces bounding
an estimated production zone (e.g. `estimated_production_zone_top`)
* reindex the data to continous monthly values extending from `start_date`
to `end_date`. Typically, these would bracket the time period for which
the pumping should be simulated in a model. For example, the earliest data
may be from 2010, but if the model starts in 2008, it may be appropriate to
begin using the 2010 rates then (``start_date='2008'``). If no start or end
date are given, the first and last years of pumping in `data` are used.
* fill empty months by interpolation via a specified `interp_method`
* backfill any remaining empty months going back to the `start_date`
* write processed data to a CSV file and shapefile of the same name
Parameters
----------
data : DataFrame
Thermoelectric water use data in the following format
(similar to that output by :func:`mapgwm.te_wateruse.read_te_water_use_spreadsheet`):
=============== =======================================================
site_no power plant identifier (plant code)
start_datetime pandas datetime representative of flux (e.g. '2010')
x x-coordinate of withdrawl, in `source_crs`
y y-coordinate of withdrawl, in `source_crs`
q withdrawl flux, in `data_volume_units` per days
=============== =======================================================
start_date : str
Start date for pumping rates. If earlier than the dates in `data`,
pumping rates will be backfilled to this date.
end_date : str
End date for pumping rates. If later than the dates in `data`,
pumping rates will be forward filled to this date.
active_area : str
Shapefile with polygon to cull observations to. Automatically reprojected
to dest_crs if the shapefile includes a .prj file.
by default, None.
active_area_id_column : str, optional
Column in active_area with feature ids.
By default, None, in which case all features are used.
active_area_feature_id : str, optional
ID of feature to use for active area
By default, None, in which case all features are used.
estimated_production_zone_top : file path
Raster surface for assigning screen tops
estimated_production_zone_botm : file path
Raster surface for assigning screen bottoms
estimated_production_surface_units : str, {'meters', 'ft', etc.}
Length units of elevations in estimated production surface rasters.
source_crs : obj
Coordinate reference system of the head observation locations.
A Python int, dict, str, or :class:`pyproj.crs.CRS` instance
passed to :meth:`pyproj.crs.CRS.from_user_input`
Can be any of:
- PROJ string
- Dictionary of PROJ parameters
- PROJ keyword arguments for parameters
- JSON string with PROJ parameters
- CRS WKT string
- An authority string [i.e. 'epsg:4326']
- An EPSG integer code [i.e. 4326]
- A tuple of ("auth_name": "auth_code") [i.e ('epsg', '4326')]
- An object with a `to_wkt` method.
- A :class:`pyproj.crs.CRS` class
By default, epsg:4269
dest_crs : obj
Coordinate reference system of the model. Same input types
as ``source_crs``.
By default, epsg:5070
interp_method : str
Interpolation method to use for filling pumping rates to monthly values.
By default, 'linear'
data_volume_units : str; e.g. 'mgal', 'm3', 'cubic feet', etc.
Volume units of pumping data. All time units are assumed to be in days.
model_length_units : str; e.g. 'feet', 'm', 'meters', etc.
Length units of model.
outfile : str
Path for output file. A shapefile of the same name is also written.
If None, no output file is written. By default, None
Returns
-------
df_monthly : DataFrame
Notes
-----
* time units for TE data and model are assumed to be days
"""
df = data.copy()
# reproject to dest_crs
x, y = project(zip(df['x'], df['y']), source_crs, dest_crs)
df['x'], df['y'] = x, y
df['geometry'] = [Point(x, y) for x, y in zip(x, y)]
# drop wells with no location information (for now)
df.dropna(subset=['x', 'y'], axis=0, inplace=True)
# cull sites to those within the Delta footprint
# cull data to that within the model area
if active_area is not None:
df = cull_data_to_active_area(df,
active_area,
active_area_id_column,
active_area_feature_id,
data_crs=dest_crs)
# get top and bottom of estimated production interval at each well
if estimated_production_zone_top is not None and \
estimated_production_zone_botm is not None:
surf_unit_conversion = convert_length_units(
estimated_production_surface_units, model_length_units)
x, y = df.x.values, df.y.values
est_screen_top = get_values_at_points(estimated_production_zone_top,
x,
y,
points_crs=dest_crs)
est_screen_top *= surf_unit_conversion
est_screen_botm = get_values_at_points(estimated_production_zone_botm,
x,
y,
points_crs=dest_crs)
est_screen_botm *= surf_unit_conversion
df['screen_top'] = est_screen_top
df['screen_botm'] = est_screen_botm
# distribute fluxes to monthly values
# set start and end dates if not already set
if start_date is None:
start_date = df.start_datetime.min()
if end_date is None:
end_date = df.start_datetime.mmax()
groups = df.groupby('site_no')
all_groups = []
for site_no, group in groups:
dfg = group.copy()
# create a continuous monthly time index
# labeled at the month start
all_dates = pd.date_range(start_date, end_date, freq='MS')
dfg.index = dfg['start_datetime']
dfg = dfg.reindex(all_dates)
# interpolate the discharge values;
# back filling to the start date
dfg['q'] = dfg.q.interpolate(method=interp_method).bfill()
dfg['q'] *= convert_volume_units(data_volume_units, model_length_units)
# fill remaining columns
dfg['start_datetime'] = dfg.index
fill_columns = set(dfg.columns).difference({'q', 'start_datetime'})
fill_values = group.iloc[0].to_dict()
for c in fill_columns:
dfg[c] = fill_values[c]
# add 'te' prefix to site number
dfg['site_no'] = f'te_{site_no}'
all_groups.append(dfg)
df_monthly = pd.concat(all_groups)
# assume most values represent abstraction
# if sum is positive, invert so that output values are negative
if df_monthly['q'].sum() > 0:
df_monthly['q'] *= -1
# clean up the columns
cols = [
'site_no', 'start_datetime', 'x', 'y', 'screen_top', 'screen_botm',
'q', 'geometry'
]
cols += list(set(df_monthly.columns).difference(cols))
df_monthly = df_monthly[cols]
# write the output
if outfile is not None:
outfile = Path(outfile)
df_monthly.drop('geometry', axis=1).to_csv(outfile,
index=False,
float_format='%g')
print('wrote {}'.format(outfile))
# write only unique pumping values to shapefile
to_shapefile = df_monthly.groupby(['site_no',
'q']).first().reset_index()
shapefile = outfile.with_suffix('.shp')
df2shp(to_shapefile, shapefile, crs=dest_crs)
return df_monthly
def preprocess_headobs(
data,
metadata,
head_data_columns=['head', 'last_head', 'head_std'],
dem=None,
dem_units='meters',
start_date='1998-04-01',
active_area=None,
active_area_id_column=None,
active_area_feature_id=None,
source_crs=4269,
dest_crs=5070,
data_length_units='meters',
model_length_units='meters',
geographic_groups=None,
geographic_groups_col=None,
max_obsname_len=None,
outfile='../source_data/observations/head_obs/preprocessed_head_obs.csv'
):
"""Preprocess head observation data, for example, groundwater level data output from the
`visGWDB program <https://doi.org/10.5066/P9W004O6>`_.
* Data are reprojected from a `source_crs` (Coordinate reference system; assumed to be in geographic coordinates)
to the CRS of the model (`dest_crs`)
* Data are culled to a `start_date` and optionally, a polygon or set of polygons defining the model area
* length units are converted to those of the groundwater model. Open intervals for the wells are
converted from depths to elevations
* missing open intervals are filled based on well bottom depths (if availabile) and the median open
interval length for the dataset.
* Wells are categorized based on the quality of the open interval information (see the documentation
for :func:`mapgwm.headobs.fill_well_open_intervals`).
* Prefixes for observation names (with an optional length limit) that identify the location are generated
* Preliminary observation groups can also be assigned, based on geographic areas defined by polygons
(`aoi` parameter)
Parameters
----------
data : DataFrame
Head observation data, e.g. as output from :func:`mapgwm.headobs.get_data`.
Columns:
========= ================================================================
site_no site identifier
lat lattitude
lon longitude
datetime measurement dates in pandas datetime format
head average head for the period represented by the datetime
last_head last head measurement for the period represented by the datetime
head_std standard deviation of measured heads within the datetime period
========= ================================================================
Notes:
* lat and lon columns can alternatively be in the metadata table
* `last_head` and `head_std` only need to be included if they are in
`head_data_columns`
metadata : DataFrame
Head observation data, e.g. as output from :func:`mapgwm.headobs.get_data`.
Must have the following columns:
================= ==========================================================================
site_no (index) site identifier
aqfr_cd Local aquifer code
screen_botm Well screen bottom, as a depth below land surface, in feet
screen_top Well screen top, as a depth below land surface, in feet
well_depth Well depth, in feet
well_el Altitude of land surface, in feet
================= ==========================================================================
head_data_columns : list of strings
Columns in data with head values or their statistics.
By default, 'head', 'last_head', 'head_std', which allows both
the average and last head values for the stress period to be considered,
as well as the variability of water levels contributing to an average value.
dem : str, optional
DEM raster of the land surface. Used for estimating missing wellhead elevations.
Any reprojection to dest_crs is handled automatically, assuming
the DEM raster has CRS information embedded (arc-ascii grids do not!)
By default, None.
dem_units : str, {'feet', 'meters', ..}
Units of DEM elevations, by default, 'meters'
start_date : str (YYYY-mm-dd)
Simulation start date (cull observations before this date)
active_area : str
Shapefile with polygon to cull observations to. Automatically reprojected
to dest_crs if the shapefile includes a .prj file.
by default, None.
active_area_id_column : str, optional
Column in active_area with feature ids.
By default, None, in which case all features are used.
active_area_feature_id : str, optional
ID of feature to use for active area
By default, None, in which case all features are used.
source_crs : obj
Coordinate reference system of the head observation locations.
A Python int, dict, str, or :class:`pyproj.crs.CRS` instance
passed to :meth:`pyproj.crs.CRS.from_user_input`
Can be any of:
- PROJ string
- Dictionary of PROJ parameters
- PROJ keyword arguments for parameters
- JSON string with PROJ parameters
- CRS WKT string
- An authority string [i.e. 'epsg:4326']
- An EPSG integer code [i.e. 4326]
- A tuple of ("auth_name": "auth_code") [i.e ('epsg', '4326')]
- An object with a `to_wkt` method.
- A :class:`pyproj.crs.CRS` class
By default, epsg:4269
dest_crs : obj
Coordinate reference system of the model. Same input types
as ``source_crs``.
By default, epsg:5070
data_length_units : str; 'meters', 'feet', etc.
Length units of head observations.
model_length_units : str; 'meters', 'feet', etc.
Length units of model.
geographic_groups : file, dict or list-like
Option to group observations by area(s) of interest. Can
be a shapefile, list of shapefiles, or dictionary of shapely polygons.
A 'group' column will be created in the metadata, and observation
sites within each polygon will be assigned the group name
associated with that polygon.
For example::
geographic_groups='../source_data/extents/CompositeHydrographArea.shp'
geographic_groups=['../source_data/extents/CompositeHydrographArea.shp']
geographic_groups={'cha': <shapely Polygon>}
Where 'cha' is an observation group name for observations located within the
the area defined by CompositeHydrographArea.shp. For shapefiles,
group names are provided in a `geographic_groups_col`.
geographic_groups_col : str
Field name in the `geographic_groups` shapefile(s) containing the
observation group names associated with each polygon.
max_obsname_len : int or None
Maximum length for observation name prefix. Default of 13
allows for a PEST obsnme of 20 characters or less with
<prefix>_yyyydd or <prefix>_<per>d<per>
(e.g. <prefix>_2d1 for a difference between stress periods 2 and 1)
If None, observation names will not be truncated. PEST++ does not have
a limit on observation name length.
outfile : str
Where output file will be written. Metadata are written to a file
with the same name, with an additional "_info" suffix prior to
the file extension.
Returns
-------
df : DataFrame
Preprocessed time series
well_info : DataFrame
Preprocessed metadata
References
----------
`The PEST++ Manual <https://github.com/usgs/pestpp/tree/master/documentation>`
"""
df = data.copy()
# multiplier to convert input length units to model units
unit_conversion = convert_length_units(data_length_units,
model_length_units)
# outputs
out_plot = None
if outfile is not None:
outpath, filename = os.path.split(outfile)
makedirs(outpath)
outname, ext = os.path.splitext(outfile)
out_info_csvfile = outname + '_info.csv'
out_data_csvfile = outfile
out_plot = os.path.join(outpath, 'open_interval_lengths.pdf')
out_shapefile = outname + '_info.shp'
# set the starting and ending dates here
stdate = pd.Timestamp(start_date)
# convert to datetime; drop the timestamps
df['datetime'] = pd.to_datetime(df.datetime).dt.normalize()
# trim to the time range
n_measurements = len(data)
n_sites = len(set(data.site_no))
print(
f'starting with {n_measurements:,d} measurements at {n_sites:,d} unique wells'
)
no_data_in_period = df.datetime < stdate
if np.any(no_data_in_period):
in_period = df.datetime >= stdate
n_sites_before = len(
set(df.loc[no_data_in_period,
'site_no']).difference(set(df.loc[in_period,
'site_no'])))
print((
f'culling {in_period.sum():,d} measurements from {n_sites_before:,d} '
f'sites that are prior to start date of {start_date}'))
df = df.loc[in_period]
# collapse dataset to mean values at each site
groups = df.groupby('site_no')
well_info = groups.mean().copy()
well_info = well_info.join(metadata, rsuffix='_meta')
well_info['start_dt'] = groups.datetime.min()
well_info['end_dt'] = groups.datetime.max()
well_info.drop(labels=['year', 'month'], axis=1, inplace=True)
well_info['site_no'] = well_info.index
well_info['n'] = groups.datetime.count()
# project x, y to model crs
x_pr, y_pr = project((well_info.lon.values, well_info.lat.values),
source_crs, dest_crs)
well_info.drop(['lon', 'lat'], axis=1, inplace=True)
well_info['x'], well_info['y'] = x_pr, y_pr
well_info['geometry'] = [Point(x, y) for x, y in zip(x_pr, y_pr)]
# cull data to that within the model area
if active_area is not None:
df, md = cull_data_to_active_area(df,
active_area,
active_area_id_column,
active_area_feature_id,
data_crs=dest_crs,
metadata=well_info)
# convert length units; convert screen tops and botms to depths
missing_elevations = well_info.well_el.isna()
if dem is not None and np.any(missing_elevations):
well_location_elevations = get_values_at_points(dem,
well_info['x'],
well_info['y'],
points_crs=dest_crs)
well_location_elevations *= convert_length_units(
dem_units, model_length_units)
well_info.loc[missing_elevations,
'well_el'] = well_location_elevations[missing_elevations]
length_columns = ['well_el'
] + head_data_columns + ['screen_top', 'screen_botm']
for col in length_columns:
if col in well_info.columns:
well_info[col] *= unit_conversion
well_info['well_botm'] = well_info['well_el'] - well_info['well_depth']
well_info['screen_top'] = well_info['well_el'] - well_info['screen_top']
well_info['screen_botm'] = well_info['well_el'] - well_info['screen_botm']
# just the data, site numbers, times and aquifer
head_data_columns = head_data_columns + ['head_std']
transient_cols = ['site_no', 'datetime'] + head_data_columns + ['n']
transient_cols = [c for c in transient_cols if c in df.columns]
df = df[transient_cols].copy()
for c in head_data_columns:
if c in df.columns:
df[c] *= unit_conversion
# #### trim down to only well_info with both estimated water levels and standard deviation
# monthly measured levels may not have standard deviation
# (as opposed to monthly statistical estimates)
criteria = pd.notnull(well_info['head'])
#if 'head_std' in df.columns:
# criteria = criteria & pd.notnull(well_info['head_std'])
well_info = well_info[criteria]
# verify that all well_info have a wellhead elevation
assert not np.any(np.isnan(well_info.well_el))
# categorize wells based on quality of open interval information
# estimate missing open intervals where possible
well_info = fill_well_open_intervals(well_info, out_plot=out_plot)
# drop well_info with negative reported open interval
#well_info = well_info.loc[open_interval_length > 0]
# cull data to well_info in well info table
has_metadata = df.site_no.isin(well_info.index)
if np.any(~has_metadata):
warnings.warn('culling {} wells not found in metadata table!'.format(
np.sum(~has_metadata)))
df = df.loc[has_metadata].copy()
# make unique n-character prefixes (site identifiers) for each observation location
# 13 character length allows for prefix_yyyymmm in 20 character observation names
# (BeoPEST limit)
unique_obsnames = set()
obsnames = []
for sn in well_info.index.tolist():
if max_obsname_len is not None:
name = make_obsname(sn,
unique_names=unique_obsnames,
maxlen=max_obsname_len)
assert name not in unique_obsnames
else:
name = sn
unique_obsnames.add(name)
obsnames.append(name)
well_info['obsprefix'] = obsnames
obsprefix = dict(zip(well_info.index, well_info.obsprefix))
df['obsprefix'] = [obsprefix[sn] for sn in df.site_no]
# add area of interest information
well_info['group'] = 'heads'
well_info = assign_geographic_obsgroups(well_info,
geographic_groups,
geographic_groups_col,
metadata_crs=dest_crs)
# save out the results
if outfile is not None:
df2shp(well_info.drop(['x', 'y'], axis=1),
out_shapefile,
index=False,
crs=dest_crs)
print('writing {}'.format(out_info_csvfile))
well_info.drop('geometry', axis=1).to_csv(out_info_csvfile,
index=False,
float_format='%.2f')
print('writing {}'.format(out_data_csvfile))
df.to_csv(out_data_csvfile, index=False, float_format='%.2f')
return df, well_info