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fitsignal: Merge uq and uqanalysis keyword arguments #98

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Mar 18, 2021
Merged

fitsignal: Merge uq and uqanalysis keyword arguments #98

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fbf6bb9
fitsignal: merge uq and uqanalysis keywords
luisfabib Mar 18, 2021
ffac445
fitsignal: update uq option docstring
luisfabib Mar 18, 2021
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18 changes: 10 additions & 8 deletions deerlab/fitsignal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -88,6 +88,9 @@ def fitsignal(Vexp, t, r, dd_model='P', bg_model=bg_hom3d, ex_model=ex_4pdeer,
* ``'covariance'`` - Covariance-based uncertainty quantification. Fast, but approximate.
* ``'bootstrap'`` - Bootstrapped uncertainty quantification. Slow, but accurate. By default, 1000 bootstrap
samples are used. Alternatively, a different number can be specified as follows ``uq=['bootstrap',Nsamples]``.
* ``None`` - Disable the uncertainty quantification analysis.

The default is ``'covariance'``.

weights : array_like, optional
Array of weighting coefficients for the individual signals in global fitting,
Expand Down Expand Up @@ -124,10 +127,6 @@ def fitsignal(Vexp, t, r, dd_model='P', bg_model=bg_hom3d, ex_model=ex_4pdeer,

verbose : boolean, optional
Enable/disable printing a table of fit results, by default is disabled

uqanalysis : boolean, optional
Enable/disable the uncertainty quantification analysis, by default it is enabled.



Returns
Expand Down Expand Up @@ -249,15 +248,18 @@ def fitsignal(Vexp, t, r, dd_model='P', bg_model=bg_hom3d, ex_model=ex_4pdeer,

# Default bootstrap samples
bootsamples = 1000
if isinstance(uq, str):
if isinstance(uq, str) or uq==None:
uq = [uq]
if uq[0]!='bootstrap' and uq[0]!='covariance':
raise KeyError("Uncertainty quantification must be either 'covariance' or 'bootstrap'.")

if uq[0]!='bootstrap' and uq[0]!='covariance'and uq[0]!=None:
raise KeyError("Uncertainty quantification must be either 'covariance', 'bootstrap', or None.")
if uq[0]=='bootstrap':
# OVerride default if user has specified bootstraped samples
if len(uq)>1: bootsamples = uq[1]
uq = uq[0]
if uq is None:
uqanalysis = False
else:
uqanalysis = True

# Combine input boundary and start conditions
par0 = [[] if par0_i is None else par0_i for par0_i in [dd_par0,bg_par0,ex_par0]]
Expand Down
46 changes: 23 additions & 23 deletions test/test_fitsignal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ def assert_experiment_model(model):
K = dipolarkernel(t,r,pathways,Bmodel)
V = K@P

fit = fitsignal(V,t,r,'P',bg_exp,model,uqanalysis=False)
fit = fitsignal(V,t,r,'P',bg_exp,model,uq=None)

assert ovl(P,fit.P) > 0.90
# --------------------------------------------------------------------
Expand Down Expand Up @@ -88,7 +88,7 @@ def test_dipevo_function():
P = dd_gauss(r,[4.5, 0.25])
K = dipolarkernel(t,r)
V = K@P
fit = fitsignal(V,t,r,'P',None,None,uqanalysis=False)
fit = fitsignal(V,t,r,'P',None,None,uq=None)
assert ovl(P,fit.P) > 0.90
# ======================================================================

Expand All @@ -101,7 +101,7 @@ def test_form_factor():
P = dd_gauss(r,[4.5, 0.25])
K = dipolarkernel(t,r,0.3)
V = K@P
fit = fitsignal(V,t,r,'P',None,ex_4pdeer,uqanalysis=False)
fit = fitsignal(V,t,r,'P',None,ex_4pdeer,uq=None)
assert ovl(P,fit.P) > 0.90
# ======================================================================

Expand All @@ -117,7 +117,7 @@ def test_full_parametric():
B = bg_exp(t,lam*kappa)
V = dipolarkernel(t,r,lam,B)@P

fit = fitsignal(V,t,r,dd_gauss,bg_exp,ex_4pdeer,uqanalysis=False)
fit = fitsignal(V,t,r,dd_gauss,bg_exp,ex_4pdeer,uq=None)

assert ovl(P,fit.P) > 0.99
# ======================================================================
Expand All @@ -131,7 +131,7 @@ def test_no_foreground():
k = 0.2
B = bg_exp(t,k)

fit = fitsignal(B,t,r,None,bg_exp,None,uqanalysis=False)
fit = fitsignal(B,t,r,None,bg_exp,None,uq=None)

assert abs(k-fit.bgparam) < 1
# ======================================================================
Expand All @@ -149,7 +149,7 @@ def test_start_values():
K = dipolarkernel(t,r,lam,Bmodel)
V = K@P

fit = fitsignal(V,t,r,'P',bg_exp,ex_4pdeer,bg_par0=0.5,ex_par0=0.5,uqanalysis=False)
fit = fitsignal(V,t,r,'P',bg_exp,ex_4pdeer,bg_par0=0.5,ex_par0=0.5,uq=None)

assert ovl(P,fit.P) > 0.95
# ======================================================================
Expand All @@ -167,7 +167,7 @@ def test_boundaries():
K = dipolarkernel(t,r,lam,Bmodel)
V = K@P

fit = fitsignal(V,t,r,'P',bg_exp,ex_4pdeer, uqanalysis=False,
fit = fitsignal(V,t,r,'P',bg_exp,ex_4pdeer, uq=None,
bg_par0=0.4, bg_lb=0.2, bg_ub=0.5,
ex_par0=0.4, ex_lb=0.2, ex_ub=0.5)

Expand All @@ -187,7 +187,7 @@ def test_boundaries_adjust_bg():
K = dipolarkernel(t,r,lam,Bmodel)
V = K@P

fit = fitsignal(V,t,r,'P',bg_hom3d,ex_4pdeer, uqanalysis=False, bg_lb=70, bg_ub=90)
fit = fitsignal(V,t,r,'P',bg_hom3d,ex_4pdeer, uq=None, bg_lb=70, bg_ub=90)

assert ovl(P,fit.P) > 0.95
# ======================================================================
Expand All @@ -205,7 +205,7 @@ def test_boundaries_adjust_ex():
K = dipolarkernel(t,r,lam,Bmodel)
V = K@P

fit = fitsignal(V,t,r,'P',bg_hom3d,ex_4pdeer, uqanalysis=False, ex_lb=0.55, ex_ub=0.65)
fit = fitsignal(V,t,r,'P',bg_hom3d,ex_4pdeer, uq=None, ex_lb=0.55, ex_ub=0.65)

assert ovl(P,fit.P) > 0.95
# ======================================================================
Expand All @@ -223,7 +223,7 @@ def test_boundaries_adjust_dd():
K = dipolarkernel(t,r,lam,Bmodel)
V = K@P

fit = fitsignal(V,t,r,dd_gauss,bg_hom3d,ex_4pdeer, uqanalysis=False, dd_lb=[4,0.3],dd_ub=[6,0.5])
fit = fitsignal(V,t,r,dd_gauss,bg_hom3d,ex_4pdeer, uq=None, dd_lb=[4,0.3],dd_ub=[6,0.5])

assert ovl(P,fit.P) > 0.95
# ======================================================================
Expand All @@ -248,7 +248,7 @@ def test_global_4pdeer():
t2 = np.linspace(0,5,250)
V2 = dipolarkernel(t2,r,pathways,Bmodel)@P

fit = fitsignal([V1,V2],[t1,t2],r,'P',bg_exp,ex_4pdeer,uqanalysis=False)
fit = fitsignal([V1,V2],[t1,t2],r,'P',bg_exp,ex_4pdeer,uq=None)

assert ovl(P,fit.P) > 0.90
# ======================================================================
Expand All @@ -267,7 +267,7 @@ def test_global_full_parametric():
V1 = dipolarkernel(t1,r,lam,B)@P
V2 = dipolarkernel(t2,r,lam,B)@P

fit = fitsignal([V1,V2],[t1,t2],r,dd_gauss,bg_exp,ex_4pdeer,uqanalysis=False)
fit = fitsignal([V1,V2],[t1,t2],r,dd_gauss,bg_exp,ex_4pdeer,uq=None)

assert ovl(P,fit.P) > 0.99
# ======================================================================
Expand All @@ -286,7 +286,7 @@ def test_global_mixed_backgrounds():
V1 = dipolarkernel(t1,r,lam,B)@P
V2 = dipolarkernel(t2,r,lam)@P

fit = fitsignal([V1,V2],[t1,t2],r,dd_gauss,[bg_exp,None],ex_4pdeer,uqanalysis=False)
fit = fitsignal([V1,V2],[t1,t2],r,dd_gauss,[bg_exp,None],ex_4pdeer,uq=None)

assert ovl(P,fit.P) > 0.90
# ======================================================================
Expand All @@ -303,7 +303,7 @@ def test_global_mixed_experiments():
V1 = dipolarkernel(t1,r,lam)@P
V2 = dipolarkernel(t2,r)@P

fit = fitsignal([V1,V2],[t1,t2],r,dd_gauss,None,[ex_4pdeer,None],uqanalysis=False)
fit = fitsignal([V1,V2],[t1,t2],r,dd_gauss,None,[ex_4pdeer,None],uq=None)

assert ovl(P,fit.P) > 0.9
# ======================================================================
Expand Down Expand Up @@ -542,7 +542,7 @@ def test_global_scale_4pdeer():
t2 = np.linspace(0,5,250)
V2 = scales[1]*dipolarkernel(t2,r,pathways,Bmodel)@P

fit = fitsignal([V1,V2],[t1,t2],r,'P',bg_exp,ex_4pdeer,uqanalysis=False)
fit = fitsignal([V1,V2],[t1,t2],r,'P',bg_exp,ex_4pdeer,uq=None)

assert max(abs(np.asarray(scales)/np.asarray(fit.scale) - 1)) < 1e-2
# ======================================================================
Expand All @@ -559,7 +559,7 @@ def test_V_scale_parametric():
scale = 1.54e6
V = scale*(K@P)

fit = fitsignal(V,t,r,dd_gauss,bg_exp,ex_4pdeer,uqanalysis=False)
fit = fitsignal(V,t,r,dd_gauss,bg_exp,ex_4pdeer,uq=None)

assert max(abs(1 - V/fit.V)) < 1e-4
# ======================================================================
Expand All @@ -575,7 +575,7 @@ def test_V_scale():
scale =1.54e6
V = scale*(K@P)

fit = fitsignal(V,t,r,'P',bg_exp,ex_4pdeer,uqanalysis=False)
fit = fitsignal(V,t,r,'P',bg_exp,ex_4pdeer,uq=None)

assert max(abs(1 - V/fit.V)) < 1e-4
# ======================================================================
Expand All @@ -590,7 +590,7 @@ def test_V_scale_regularized():
scale = 1.54e6
V = scale*(K@P)

fit = fitsignal(V,t,r,'P',None,None,uqanalysis=False)
fit = fitsignal(V,t,r,'P',None,None,uq=None)

assert max(abs(1 - V/fit.V)) < 1e-4
# ======================================================================
Expand All @@ -606,7 +606,7 @@ def test_plot():
K = dipolarkernel(t,r,0.4,Bmodel)
V = K@P

fit = fitsignal(V,t,r,'P',bg_exp,ex_4pdeer,uqanalysis=False)
fit = fitsignal(V,t,r,'P',bg_exp,ex_4pdeer,uq=None)

fig = fit.plot(show=False)
assert str(fig.__class__)=="<class 'matplotlib.figure.Figure'>"
Expand All @@ -625,7 +625,7 @@ def test_physical_bg_model():
V = K@P
V = V0*V

fit = dl.fitsignal(V,t,r,'P',dl.bg_hom3d,dl.ex_4pdeer,uqanalysis=False)
fit = dl.fitsignal(V,t,r,'P',dl.bg_hom3d,dl.ex_4pdeer,uq=None)

assert abs(fit.bgparam - 50)<1e-1 and abs(fit.exparam - 0.4)<1e-1
# ======================================================================
Expand All @@ -642,7 +642,7 @@ def test_phenomenological_bg_model():
V = K@P
V = V0*V

fit = dl.fitsignal(V,t,r,'P',dl.bg_exp,dl.ex_4pdeer,uqanalysis=False)
fit = dl.fitsignal(V,t,r,'P',dl.bg_exp,dl.ex_4pdeer,uq=None)

assert abs(fit.bgparam - 0.3)<1e-1 and abs(fit.exparam - 0.4)<1e-1
# ======================================================================
Expand All @@ -660,7 +660,7 @@ def test_Vunmod():
Bscaled = (1-lam)*dl.bg_hom3d(t,50,lam)
V = K@P

fit = dl.fitsignal(V,t,r,'P',dl.bg_exp,dl.ex_4pdeer,uqanalysis=False)
fit = dl.fitsignal(V,t,r,'P',dl.bg_exp,dl.ex_4pdeer,uq=None)

assert max(abs(Bscaled - fit.Vunmod))<1e-4
# ======================================================================
Expand All @@ -677,7 +677,7 @@ def test_cost_value():
K = dipolarkernel(t,r,0.4,Bmodel)
V = K@P

fit = fitsignal(V,t,r,'P',bg_exp,ex_4pdeer,uqanalysis=False)
fit = fitsignal(V,t,r,'P',bg_exp,ex_4pdeer,uq=None)

assert isinstance(fit.cost,float) and np.round(fit.cost/np.sum(fit.residuals**2),5)==1
# ======================================================================
Expand Down