def prj(request):
if request.param == "arctic":
connstr = "mongodb://localhost:27017/"
name = "test_project"
arc = arctic.Arctic(connstr)
if name in [lib.split(".")[0] for lib in arc.list_libraries()]:
connector = pst.ArcticConnector(name, connstr)
prj = pst.PastaStore(name, connector)
else:
connector = pst.ArcticConnector(name, connstr)
prj = initialize_project(connector)
elif request.param == "pystore":
name = "test_project"
path = "./tests/data/pystore"
pystore.set_path(path)
if name in pystore.list_stores():
connector = pst.PystoreConnector(name, path)
prj = pst.PastaStore(name, connector)
else:
connector = pst.PystoreConnector(name, path)
prj = initialize_project(connector)
elif request.param == "dict":
name = "test_project"
connector = pst.DictConnector(name)
prj = initialize_project(connector)
prj.type = request.param # added here for defining test dependencies
yield prj
def initialize_project(conn):
prj = pst.PastaStore("test_project", conn)
# oseries 1
o = pd.read_csv("./tests/data/obs.csv", index_col=0, parse_dates=True)
o.index.name = "oseries1"
prj.conn.add_oseries(o, "oseries1", metadata={"x": 100000, "y": 400000})
# oseries 2
o = pd.read_csv("./tests/data/head_nb1.csv", index_col=0, parse_dates=True)
o.index.name = "oseries2"
prj.conn.add_oseries(o, "oseries2", metadata={"x": 100300, "y": 400400})
# prec 1
s = pd.read_csv("./tests/data/rain.csv", index_col=0, parse_dates=True)
prj.conn.add_stress(s,
"prec1",
kind="prec",
metadata={
"x": 100000,
"y": 400000
})
# prec 2
s = pd.read_csv("./tests/data/rain_nb1.csv", index_col=0, parse_dates=True)
prj.conn.add_stress(s,
"prec2",
kind="prec",
metadata={
"x": 100300,
"y": 400400
})
# evap 1
s = pd.read_csv("./tests/data/evap.csv", index_col=0, parse_dates=True)
prj.conn.add_stress(s,
"evap1",
kind="evap",
metadata={
"x": 100000,
"y": 400000
})
# evap 2
s = pd.read_csv("./tests/data/evap_nb1.csv", index_col=0, parse_dates=True)
prj.conn.add_stress(s,
"evap2",
kind="evap",
metadata={
"x": 100300,
"y": 400400
})
return prj
def create_pastastore(oc,
pstore,
pstore_name='',
conn=pst.DictConnector("my_conn"),
add_metadata=True,
obs_column='stand_m_tov_nap',
kind='oseries',
verbose=False):
"""add observations to a new or existing pastastore
Parameters
----------
oc : observation.ObsCollection
collection of observations
pstore : pastastore.PastasProject, optional
Existing pastastore, if None a new project is created
pstore_name : str, optional
Name of the pastastore only used if pstore is None
conn : pastastore.connectors
connector for database
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
-------
pstore : pastastore.PastasProject
the pastas project with the series from the ObsCollection
"""
if pstore is None:
pstore = pst.PastaStore(pstore_name, connector=conn)
for o in oc.obs.values:
if verbose:
print('add to pastastore -> {}'.format(o.name))
if add_metadata:
meta = _get_metadata_from_obs(o, verbose=verbose)
else:
meta = dict()
if kind == 'oseries':
pstore.conn.add_oseries(o[obs_column], o.name, metadata=meta)
else:
pstore.conn.add_stress(o[obs_column], o.name, kind, metadata=meta)
return pstore
def build_model(conn):
store = pst.PastaStore("test", conn)
# oseries nb1
if "oseries_nb1" not in store.oseries.index:
o = pd.read_csv("./tests/data/head_nb1.csv",
index_col=0,
parse_dates=True)
store.add_oseries(o,
"oseries_nb1",
metadata={
"x": 100300,
"y": 400400
})
# prec nb1
if "prec_nb1" not in store.stresses.index:
s = pd.read_csv("./tests/data/rain_nb1.csv",
index_col=0,
parse_dates=True)
store.add_stress(s,
"prec_nb1",
kind="prec",
metadata={
"x": 100300,
"y": 400400
})
# evap nb1
if "evap_nb1" not in store.stresses.index:
s = pd.read_csv("./tests/data/evap_nb1.csv",
index_col=0,
parse_dates=True)
store.add_stress(s,
"evap_nb1",
kind="evap",
metadata={
"x": 100300,
"y": 400400
})
ml = store.create_model("oseries_nb1", add_recharge=True)
return ml
def example_pastastore(conn="DictConnector"):
"""Example dataset loaded into PastaStore.
Parameters
----------
conn : str or Connector, optional
name of Connector type, by default "DictConnector", which
initializes a default Connector. If an Connector instance is passed,
use that Connector.
Returns
-------
pstore : pastastore.PastaStore
PastaStore containing example dataset
"""
# check it test dataset is available
datadir = os.path.join(os.path.dirname(__file__), "../tests/data")
if not os.path.exists(datadir):
raise FileNotFoundError("Test datasets not available! "
"Clone repository from GitHub.")
# initialize default connector if conn is str
if not isinstance(conn, BaseConnector):
conn = _default_connector(conn)
# initialize PastaStore
pstore = pst.PastaStore("example", conn)
# add data
# oseries 1
o = pd.read_csv(os.path.join(datadir, "obs.csv"),
index_col=0,
parse_dates=True)
pstore.add_oseries(o, "oseries1", metadata={"x": 165000, "y": 424000})
# oseries 2
o = pd.read_csv(os.path.join(datadir, "head_nb1.csv"),
index_col=0,
parse_dates=True)
pstore.add_oseries(o, "oseries2", metadata={"x": 164000, "y": 423000})
# oseries 3
o = pd.read_csv(os.path.join(datadir, "gw_obs.csv"),
index_col=0,
parse_dates=True)
pstore.add_oseries(o, "oseries3", metadata={"x": 165554, "y": 422685})
# prec 1
s = pd.read_csv(os.path.join(datadir, "rain.csv"),
index_col=0,
parse_dates=True)
pstore.add_stress(s,
"prec1",
kind="prec",
metadata={
"x": 165050,
"y": 424050
})
# prec 2
s = pd.read_csv(os.path.join(datadir, "rain_nb1.csv"),
index_col=0,
parse_dates=True)
pstore.add_stress(s,
"prec2",
kind="prec",
metadata={
"x": 164010,
"y": 423000
})
# evap 1
s = pd.read_csv(os.path.join(datadir, "evap.csv"),
index_col=0,
parse_dates=True)
pstore.add_stress(s,
"evap1",
kind="evap",
metadata={
"x": 164500,
"y": 424000
})
# evap 2
s = pd.read_csv(os.path.join(datadir, "evap_nb1.csv"),
index_col=0,
parse_dates=True)
pstore.add_stress(s,
"evap2",
kind="evap",
metadata={
"x": 164000,
"y": 423030
})
# well 1
s = pd.read_csv(os.path.join(datadir, "well.csv"),
index_col=0,
parse_dates=True)
pstore.add_stress(s,
"well1",
kind="well",
metadata={
"x": 164691,
"y": 423579
})
# river notebook data (nb5)
oseries = pd.read_csv(os.path.join(datadir, "nb5_head.csv"),
parse_dates=True,
index_col=0).squeeze("columns")
pstore.add_oseries(oseries,
"head_nb5",
metadata={
"x": 200_000,
"y": 450_000.
})
rain = pd.read_csv(os.path.join(datadir, "nb5_prec.csv"),
parse_dates=True,
index_col=0).squeeze("columns")
pstore.add_stress(rain,
"prec_nb5",
kind="prec",
metadata={
"x": 200_000,
"y": 450_000.
})
evap = pd.read_csv(os.path.join(datadir, "nb5_evap.csv"),
parse_dates=True,
index_col=0).squeeze("columns")
pstore.add_stress(evap,
"evap_nb5",
kind="evap",
metadata={
"x": 200_000,
"y": 450_000.
})
waterlevel = pd.read_csv(os.path.join(datadir, "nb5_riv.csv"),
parse_dates=True,
index_col=0).squeeze("columns")
pstore.add_stress(waterlevel,
"riv_nb5",
kind="riv",
metadata={
"x": 200_000,
"y": 450_000.
})
# multiwell notebook data
fname = os.path.join(datadir, 'MenyanthesTest.men')
meny = ps.read.MenyData(fname)
oseries = meny.H['Obsevation well']['values'].dropna()
ometa = {
"x": meny.H["Obsevation well"]['xcoord'],
"y": meny.H["Obsevation well"]['ycoord']
}
pstore.add_oseries(oseries, "head_mw", metadata=ometa)
prec = meny.IN['Precipitation']['values']
prec.index = prec.index.round("D")
prec.name = "prec"
pmeta = {
"x": meny.IN['Precipitation']['xcoord'],
"y": meny.IN['Precipitation']['ycoord']
}
pstore.add_stress(prec, "prec_mw", kind="prec", metadata=pmeta)
evap = meny.IN['Evaporation']['values']
evap.index = evap.index.round("D")
evap.name = "evap"
emeta = {
"x": meny.IN['Evaporation']['xcoord'],
"y": meny.IN['Evaporation']['ycoord']
}
pstore.add_stress(evap, "evap_mw", kind="evap", metadata=emeta)
extraction_names = ['Extraction 2', 'Extraction 3']
for extr in extraction_names:
wmeta = {"x": meny.IN[extr]["xcoord"], "y": meny.IN[extr]["ycoord"]}
# replace spaces in names for Pastas
name = extr.replace(" ", "_").lower()
ts = meny.IN[extr]["values"]
pstore.add_stress(ts, name, kind="well", metadata=wmeta)
return pstore
def initialize_project(conn):
pstore = pst.PastaStore("test_project", conn)
# oseries 1
o = pd.read_csv("./tests/data/obs.csv", index_col=0, parse_dates=True)
pstore.add_oseries(o, "oseries1", metadata={"x": 165000, "y": 424000})
# oseries 2
o = pd.read_csv("./tests/data/head_nb1.csv", index_col=0, parse_dates=True)
pstore.add_oseries(o, "oseries2", metadata={"x": 164000, "y": 423000})
# oseries 3
o = pd.read_csv("./tests/data/gw_obs.csv", index_col=0, parse_dates=True)
pstore.add_oseries(o, "oseries3", metadata={"x": 165554, "y": 422685})
# prec 1
s = pd.read_csv("./tests/data/rain.csv", index_col=0, parse_dates=True)
pstore.add_stress(s,
"prec1",
kind="prec",
metadata={
"x": 165050,
"y": 424050
})
# prec 2
s = pd.read_csv("./tests/data/rain_nb1.csv", index_col=0, parse_dates=True)
pstore.add_stress(s,
"prec2",
kind="prec",
metadata={
"x": 164010,
"y": 423000
})
# evap 1
s = pd.read_csv("./tests/data/evap.csv", index_col=0, parse_dates=True)
pstore.add_stress(s,
"evap1",
kind="evap",
metadata={
"x": 164500,
"y": 424000
})
# evap 2
s = pd.read_csv("./tests/data/evap_nb1.csv", index_col=0, parse_dates=True)
pstore.add_stress(s,
"evap2",
kind="evap",
metadata={
"x": 164000,
"y": 423030
})
# well 1
s = pd.read_csv("./tests/data/well.csv", index_col=0, parse_dates=True)
pstore.add_stress(s,
"well1",
kind="well",
metadata={
"x": 164691,
"y": 423579
})
return pstore