def load_project(data):
"""Method to load a Pastas project.
Parameters
----------
data: dict
Dictionary containing all information to construct the project.
Returns
-------
mls: Pastas.Project class
Pastas Project class object
"""
mls = ps.Project(name=data["name"])
mls.metadata = data["metadata"]
mls.file_info = data["file_info"]
oseries = DataFrame(data["oseries"], columns=data["oseries"].keys()).T
mls.oseries = mls.oseries.append(oseries, sort=False)
stresses = DataFrame(data=data["stresses"],
columns=data["stresses"].keys()).T
mls.stresses = mls.stresses.append(stresses, sort=False)
for ml_name, ml in data["models"].items():
name = str(ml["oseries"]["name"])
ml_name = str(ml_name)
ml["oseries"]["series"] = mls.oseries.loc[name, "series"]
if ml["stressmodels"]:
for ts in ml["stressmodels"].values():
for stress in ts["stress"]:
if 'series' not in stress:
# look up the stress-series in mls.stresses
stress_name = stress["name"]
if stress_name not in mls.stresses.index:
raise (ValueError(
'{} not found in stresses'.format(
stress_name)))
stress["series"] = mls.stresses.loc[
stress_name, "series"]
try:
ml = load_model(ml)
mls.models[ml_name] = ml
except:
try:
mls.del_model(ml_name)
except:
pass
print("model", ml_name, "could not be added")
return mls
def create_pastas_project(oc,
pr=None,
project_name='',
obs_column='stand_m_tov_nap',
kind='oseries',
add_metadata=True,
verbose=False):
"""add observations to a new or existing pastas project
Parameters
----------
oc : observation.ObsCollection
collection of observations
pr : pastas.project, optional
Existing pastas project, if None a new project is created
project_name : str, optional
Name of the pastas project only used if pr is None
obs_column : str, optional
Name of the column in the Obs dataframe to be used
kind : str, optional
The kind of series that is added to the pastas project
add_metadata : boolean, optional
If True metadata from the observations added to the project.
verbose : boolean, optional
Print additional information to the screen (default is False).
Returns
-------
pr : pastas.project
the pastas project with the series from the ObsCollection
"""
if pr is None:
pr = ps.Project(project_name)
for o in oc.obs.values:
if verbose:
print('add to pastas project -> {}'.format(o.name))
if add_metadata:
meta = _get_metadata_from_obs(o, verbose=verbose)
else:
meta = dict()
series = ps.TimeSeries(o[obs_column], name=o.name, metadata=meta)
pr.add_series(series, kind=kind)
return pr
def load_project(data):
"""Method to load a Pastas project.
Parameters
----------
data: dict
Dictionary containing all information to construct the project.
Returns
-------
mls: Pastas.Project class
Pastas Project class object
"""
mls = ps.Project(name=data["name"])
mls.metadata = data["metadata"]
mls.file_info = data["file_info"]
oseries = DataFrame(data["oseries"], columns=data["oseries"].keys()).T
mls.oseries = mls.oseries.append(oseries)
stresses = DataFrame(data=data["stresses"],
columns=data["stresses"].keys()).T
mls.stresses = mls.stresses.append(stresses)
for ml_name, ml in data["models"].items():
name = str(ml["oseries"]["name"])
ml_name = str(ml_name)
ml["oseries"]["series"] = mls.oseries.loc[name, "series"]
if ml["stressmodels"]:
for ts in ml["stressmodels"].values():
for i, stress in enumerate(ts["stress"]):
stress_name = stress["name"]
ts["stress"][i]["series"] = mls.stresses.loc[stress_name,
"series"]
try:
ml = load_model(ml)
mls.models[ml_name] = ml
except:
try:
mls.del_model(ml_name)
except:
pass
print("model", ml_name, "could not be added")
return mls
def test_create_project():
pr = ps.Project(name="test")
return pr
def load_project(fname, **kwargs):
"""
Method to load a Pastas project.
Parameters
----------
fname: str
string with the name of the file to be imported including the file
extension.
kwargs:
extension specific keyword arguments.
Returns
-------
mls: pastas.project.Project
Pastas Project class object
Examples
--------
>>> import pastas as ps
>>> mls = ps.io.load_project("project.pas")
Warnings
--------
All classes and methods dealing with Pastas projects will be moved to a
separate Python package in the near future (mid-2020).
"""
# Dynamic import of the export module
ext = path.splitext(fname)[1]
load_mod = import_module("pastas.io" + ext)
# Get dicts for all data sources
data = load_mod.load(fname, **kwargs)
mls = ps.Project(name=data["name"])
mls.metadata = data["metadata"]
mls.file_info = data["file_info"]
oseries = DataFrame(data["oseries"], columns=data["oseries"].keys()).T
mls.oseries = mls.oseries.append(oseries, sort=False)
stresses = DataFrame(data=data["stresses"],
columns=data["stresses"].keys()).T
mls.stresses = mls.stresses.append(stresses, sort=False)
for ml_name, ml in data["models"].items():
name = str(ml["oseries"]["name"])
ml_name = str(ml_name)
ml["oseries"]["series"] = mls.oseries.loc[name, "series"]
if ml["stressmodels"]:
for ts in ml["stressmodels"].values():
for stress in ts["stress"]:
if 'series' not in stress:
# look up the stress-series in mls.stresses
stress_name = stress["name"]
if stress_name not in mls.stresses.index:
raise (ValueError(
'{} not found in stresses'.format(stress_name))
)
stress["series"] = mls.stresses.loc[stress_name,
"series"]
try:
ml = _load_model(ml)
mls.models[ml_name] = ml
except:
try:
mls.del_model(ml_name)
except:
pass
print("model", ml_name, "could not be added")
logger.info("Pastas project from file {} successfully loaded. This file "
"was created with Pastas {}. Your current version of Pastas "
"is: {}".format(fname, data["file_info"]["pastas_version"],
ps.__version__))
return mls
import pastas as ps
# Create a simple model taken from example.py
obs = pd.read_csv("data/head_nb1.csv",
index_col=0,
parse_dates=True,
squeeze=True)
rain = pd.read_csv("data/rain_nb1.csv",
index_col=0,
parse_dates=True,
squeeze=True)
evap = pd.read_csv("data/evap_nb1.csv",
index_col=0,
parse_dates=True,
squeeze=True)
# Create a Pastas Project
mls = ps.Project(name="test_project")
mls.add_oseries(obs, "GWL", metadata={})
mls.add_stress(rain, name="Prec", kind="prec")
mls.add_stress(evap, name="Evap", kind="evap")
ml = mls.add_model(oseries="GWL")
mls.add_recharge(ml)
mls.solve_models()
mls.to_file("test_project.pas")