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fix #551: up and gain_scale_factor are set by StressModel #573

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merged 5 commits into from
May 4, 2023
Merged

fix #551: up and gain_scale_factor are set by StressModel #573

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4f0c464
fix #551
dbrakenhoff Mar 21, 2023
406e5d6
add trap for up and gain_scale_factor
dbrakenhoff Mar 21, 2023
f89f543
revert the nochange setting for up
dbrakenhoff Mar 21, 2023
2fd896a
update nb
dbrakenhoff Mar 21, 2023
51b148c
update _load_stressmodel and tests
dbrakenhoff Mar 21, 2023
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26 changes: 13 additions & 13 deletions doc/benchmarks/check_response_functions.ipynb
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6 changes: 5 additions & 1 deletion pastas/io/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -142,7 +142,11 @@ def _load_stressmodel(ts, data):

if "rfunc" in ts.keys():
rfunc_class = ts["rfunc"].pop("class") # Determine response class
ts["rfunc"] = getattr(ps.rfunc, rfunc_class)(**ts["rfunc"])
rfunc_up = ts["rfunc"].pop("up", None) # get up value
rfunc_gsf = ts["rfunc"].pop("gain_scale_factor", None) # get gain_scale_factor
rfunc = getattr(ps.rfunc, rfunc_class)(**ts["rfunc"])
rfunc.update_rfunc_settings(up=rfunc_up, gain_scale_factor=rfunc_gsf)
ts["rfunc"] = rfunc

if "recharge" in ts.keys():
recharge_class = ts["recharge"].pop("class")
Expand Down
85 changes: 25 additions & 60 deletions pastas/rfunc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -55,16 +55,25 @@ class RfuncBase:

def __init__(
self,
up: bool = True,
gain_scale_factor: float = 1.0,
cutoff: float = 0.999,
**kwargs,
) -> None:
self.up = up
self.gain_scale_factor = gain_scale_factor
self.cutoff = cutoff
if "up" in kwargs:
raise TypeError(
"keyword argument 'up' is not supported in init. "
"Set with update_rfunc_settings()."
)
if "gain_scale_factor" in kwargs:
raise TypeError(
"keyword argument 'gain_scale_factor' is not supported in "
"init. Set with update_rfunc_settings()."
)
# initialize attributes, these can be set with update_rfunc_settings()
self.up = None
self.gain_scale_factor = 1.0

def _update_rfunc_settings(
def update_rfunc_settings(
self,
up: Optional[bool] = "nochange",
gain_scale_factor: Optional[float] = None,
Expand Down Expand Up @@ -302,14 +311,10 @@ class Gamma(RfuncBase):

def __init__(
self,
up: bool = True,
gain_scale_factor: float = 1.0,
cutoff: float = 0.999,
**kwargs,
) -> None:
RfuncBase.__init__(
self, up=up, gain_scale_factor=gain_scale_factor, cutoff=cutoff, **kwargs
)
RfuncBase.__init__(self, cutoff=cutoff, **kwargs)
self.nparam = 3

def get_init_parameters(self, name: str) -> DataFrame:
Expand Down Expand Up @@ -398,14 +403,10 @@ class Exponential(RfuncBase):

def __init__(
self,
up: bool = True,
gain_scale_factor: float = 1.0,
cutoff: float = 0.999,
**kwargs,
) -> None:
RfuncBase.__init__(
self, up=up, gain_scale_factor=gain_scale_factor, cutoff=cutoff, **kwargs
)
RfuncBase.__init__(self, cutoff=cutoff, **kwargs)
self.nparam = 2

def get_init_parameters(self, name: str) -> DataFrame:
Expand Down Expand Up @@ -503,16 +504,12 @@ class HantushWellModel(RfuncBase):

def __init__(
self,
up: bool = True,
gain_scale_factor: float = 1.0,
cutoff: float = 0.999,
use_numba: bool = False,
quad: bool = False,
**kwargs,
) -> None:
RfuncBase.__init__(
self, up=up, gain_scale_factor=gain_scale_factor, cutoff=cutoff, **kwargs
)
RfuncBase.__init__(self, cutoff=cutoff, **kwargs)
self.distances = None
self.nparam = 3
self.use_numba = use_numba # requires numba_scipy for real speedups
Expand Down Expand Up @@ -795,16 +792,12 @@ class Hantush(RfuncBase):

def __init__(
self,
up: bool = True,
gain_scale_factor: float = 1.0,
cutoff: float = 0.999,
use_numba: bool = False,
quad: bool = False,
**kwargs,
) -> None:
RfuncBase.__init__(
self, up=up, gain_scale_factor=gain_scale_factor, cutoff=cutoff, **kwargs
)
RfuncBase.__init__(self, cutoff=cutoff, **kwargs)
self.nparam = 3
self.use_numba = use_numba
self.quad = quad
Expand Down Expand Up @@ -990,14 +983,10 @@ class Polder(RfuncBase):

def __init__(
self,
up: bool = True,
gain_scale_factor: float = 1.0,
cutoff: float = 0.999,
**kwargs,
) -> None:
RfuncBase.__init__(
self, up=up, gain_scale_factor=gain_scale_factor, cutoff=cutoff, **kwargs
)
RfuncBase.__init__(self, cutoff=cutoff, **kwargs)
self.nparam = 3

def get_init_parameters(self, name) -> DataFrame:
Expand Down Expand Up @@ -1074,14 +1063,10 @@ class One(RfuncBase):

def __init__(
self,
up: bool = True,
gain_scale_factor: float = 1.0,
cutoff: float = 0.999,
**kwargs,
) -> None:
RfuncBase.__init__(
self, up=up, gain_scale_factor=gain_scale_factor, cutoff=cutoff, **kwargs
)
RfuncBase.__init__(self, cutoff=cutoff, **kwargs)
self.nparam = 1

def get_init_parameters(self, name: str) -> DataFrame:
Expand Down Expand Up @@ -1171,15 +1156,11 @@ class FourParam(RfuncBase):

def __init__(
self,
up: bool = True,
gain_scale_factor: float = 1.0,
cutoff: float = 0.999,
quad: bool = False,
**kwargs,
) -> None:
RfuncBase.__init__(
self, up=up, gain_scale_factor=gain_scale_factor, cutoff=cutoff, **kwargs
)
RfuncBase.__init__(self, cutoff=cutoff, **kwargs)
self.nparam = 4
self.quad = quad

Expand Down Expand Up @@ -1385,14 +1366,10 @@ class DoubleExponential(RfuncBase):

def __init__(
self,
up: bool = True,
gain_scale_factor: float = 1.0,
cutoff: float = 0.999,
**kwargs,
) -> None:
RfuncBase.__init__(
self, up=up, gain_scale_factor=gain_scale_factor, cutoff=cutoff, **kwargs
)
RfuncBase.__init__(self, cutoff=cutoff, **kwargs)
self.nparam = 4

def get_init_parameters(self, name: str) -> DataFrame:
Expand Down Expand Up @@ -1489,14 +1466,10 @@ class Edelman(RfuncBase):

def __init__(
self,
up: bool = True,
gain_scale_factor: float = 1.0,
cutoff: float = 0.999,
**kwargs,
) -> None:
RfuncBase.__init__(
self, up=up, gain_scale_factor=gain_scale_factor, cutoff=cutoff, **kwargs
)
RfuncBase.__init__(self, cutoff=cutoff, **kwargs)
self.nparam = 1

def get_init_parameters(self, name: str) -> DataFrame:
Expand Down Expand Up @@ -1578,15 +1551,11 @@ class Kraijenhoff(RfuncBase):

def __init__(
self,
up: bool = True,
gain_scale_factor: float = 1.0,
cutoff: float = 0.999,
n_terms: int = 10,
**kwargs,
) -> None:
RfuncBase.__init__(
self, up=up, gain_scale_factor=gain_scale_factor, cutoff=cutoff, **kwargs
)
RfuncBase.__init__(self, cutoff=cutoff, **kwargs)
self.nparam = 3
self.n_terms = n_terms

Expand Down Expand Up @@ -1723,16 +1692,12 @@ class Spline(RfuncBase):

def __init__(
self,
up: bool = True,
gain_scale_factor: float = 1.0,
cutoff: float = 0.999,
kind: str = "quadratic",
t: Optional[list] = None,
**kwargs,
) -> None:
RfuncBase.__init__(
self, up=up, gain_scale_factor=gain_scale_factor, cutoff=cutoff, **kwargs
)
RfuncBase.__init__(self, cutoff=cutoff, **kwargs)
self.kind = kind
if t is None:
t = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]
Expand Down
6 changes: 3 additions & 3 deletions pastas/stressmodels.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@ def __init__(
self.freq = None

if rfunc is not None:
rfunc._update_rfunc_settings(up=up, gain_scale_factor=gain_scale_factor)
rfunc.update_rfunc_settings(up=up, gain_scale_factor=gain_scale_factor)
self.rfunc = rfunc

self.parameters = DataFrame(columns=["initial", "pmin", "pmax", "vary", "name"])
Expand Down Expand Up @@ -1604,10 +1604,10 @@ def __init__(
tmax=stress.series.index.max(),
)

rfunc1._update_rfunc_settings(up=up)
rfunc1.update_rfunc_settings(up=up)
self.rfunc1 = rfunc1

rfunc2._update_rfunc_settings(up=up)
rfunc2.update_rfunc_settings(up=up)
self.rfunc2 = rfunc2
self.tchange = Timestamp(tchange)

Expand Down
3 changes: 3 additions & 0 deletions tests/test_rfuncs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,10 @@ def test_to_dict_rfuncs(rfunc_name) -> None:
# Create the exact same instance using to_dict
data = rfunc1.to_dict()
rfunc_class = data.pop("class") # Determine response class
rfunc_up = data.pop("up", None)
rfunc_gsf = data.pop("gain_scale_factor", None)
rfunc2 = getattr(ps.rfunc, rfunc_class)(**data)
rfunc2.update_rfunc_settings(up=rfunc_up, gain_scale_factor=rfunc_gsf)

if rfunc_name == "HantushWellModel":
rfunc1.set_distances(100.0)
Expand Down