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Merge pull request #96 from efiring/order_constit
Order constit
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,77 @@ | ||
| import numpy as np | ||
| import pytest | ||
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| from utide import reconstruct, solve | ||
| from utide._ut_constants import constit_index_dict, ut_constants | ||
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| ts = 735604 | ||
| duration = 35 | ||
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| time = np.linspace(ts, ts + duration, 842) | ||
| tref = (time[-1] + time[0]) / 2 | ||
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| const = ut_constants.const | ||
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| amps = [1.0, 0.5, 0.6, 0.1] | ||
| names = ["M2", "S2", "K1", "O1"] | ||
| cpds = [24 * const.freq[constit_index_dict[name]] for name in names] | ||
| sinusoids = [] | ||
| for amp, cpd in zip(amps, cpds): | ||
| arg = 2 * np.pi * (time - tref) * cpd | ||
| sinusoids.append(amp * np.cos(arg)) | ||
| tide = np.hstack(tuple(sinusoids)).sum(axis=0) | ||
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| np.random.seed(1234) | ||
| noise = 1e-3 * np.random.randn(len(time)) | ||
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| time_series = tide + noise | ||
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| opts0 = { | ||
| "constit": ["K1", "M2", "O1", "S2"], | ||
| "order_constit": "frequency", | ||
| "phase": "raw", | ||
| "nodal": False, | ||
| "trend": False, | ||
| "method": "ols", | ||
| "conf_int": "MC", | ||
| "Rayleigh_min": 0.95, | ||
| } | ||
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| @pytest.mark.parametrize("conf_int", ["none", "linear", "MC"]) | ||
| def test_order(conf_int): | ||
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| orders = [None, "PE", "frequency", opts0["constit"]] | ||
| if conf_int != "none": | ||
| orders.append("SNR") | ||
| elevs = [] | ||
| ts_elevs = [] | ||
| vels = [] | ||
| ts_vels = [] | ||
| for order in orders: | ||
| opts = opts0.copy() | ||
| opts["order_constit"] = order | ||
| opts["conf_int"] = conf_int | ||
| elevs.append(solve(time, time_series, lat=45, **opts)) | ||
| vels.append(solve(time, time_series, time_series, lat=45, **opts)) | ||
| ts_elevs.append(reconstruct(time, elevs[-1], min_SNR=0)) | ||
| ts_vels.append(reconstruct(time, vels[-1], min_SNR=0)) | ||
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| # Are the reconstructions all the same? | ||
| for i in range(1, len(elevs)): | ||
| assert (ts_elevs[i].h == ts_elevs[0].h).all() | ||
| assert (ts_vels[i].u == ts_vels[0].u).all() | ||
| assert (ts_vels[i].v == ts_vels[0].v).all() | ||
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| # Is None equivalent to "PE"? (Just a spot check.) | ||
| assert (elevs[0].name == elevs[1].name).all() | ||
| assert (elevs[0].A == elevs[1].A).all() | ||
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| def test_invalid_snr(): | ||
| opts = opts0.copy() | ||
| opts["conf_int"] = "none" | ||
| opts["order_constit"] = "SNR" | ||
| with pytest.raises(ValueError): | ||
| solve(time, time_series, lat=45, **opts) |
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,58 +1,44 @@ | ||
| import numpy as np | ||
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| def ut_diagn(coef, opt): | ||
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| if opt["RunTimeDisp"]: | ||
| print("diagnostics ... ", end="") | ||
| coef["diagn"] = {} | ||
| def _PE(coef): | ||
| """ | ||
| Return the energy percentage for each constituent. | ||
| """ | ||
| if "Lsmaj" in coef: | ||
| E = coef["Lsmaj"] ** 2 + coef["Lsmin"] ** 2 | ||
| PE = (100 / np.sum(E)) * E | ||
| else: | ||
| PE = 100 * coef["A"] ** 2 / np.sum(coef["A"] ** 2) | ||
| return PE | ||
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| if opt["twodim"]: | ||
| PE = np.sum(coef["Lsmaj"] ** 2 + coef["Lsmin"] ** 2) | ||
| PE = 100 * (coef["Lsmaj"] ** 2 + coef["Lsmin"] ** 2) / PE | ||
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| def _SNR(coef): | ||
| """ | ||
| Return the signal-to-noise ratio for each constituent. | ||
| """ | ||
| if "Lsmaj" in coef: | ||
| SNR = (coef["Lsmaj"] ** 2 + coef["Lsmin"] ** 2) / ( | ||
| (coef["Lsmaj_ci"] / 1.96) ** 2 + (coef["Lsmin_ci"] / 1.96) ** 2 | ||
| ) | ||
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| else: | ||
| PE = 100 * coef["A"] ** 2 / np.sum(coef["A"] ** 2) | ||
| SNR = (coef["A"] ** 2) / (coef["A_ci"] / 1.96) ** 2 | ||
| return SNR | ||
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| def ut_diagn(coef): | ||
| """ | ||
| Add to coef the names, PE, and SNR, *always* sorted by energy. | ||
| To be eliminated... | ||
| """ | ||
| coef["diagn"] = {} | ||
| PE = _PE(coef) | ||
| SNR = _SNR(coef) | ||
| indPE = PE.argsort()[::-1] | ||
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| coef["diagn"]["name"] = coef["name"][indPE] | ||
| coef["diagn"]["PE"] = PE[indPE] | ||
| coef["diagn"]["SNR"] = SNR[indPE] | ||
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| return coef, indPE | ||
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| # [~,indPE] = sort(PE,'descend'); | ||
| # coef.diagn.name = coef.name(indPE); | ||
| # coef.diagn.PE = PE(indPE); | ||
| # coef.diagn.SNR = SNR; % used in ut_diagntable; ordered by PE there | ||
| # if opt.twodim | ||
| # [coef.diagn,usnrc,vsnrc] = ut_diagntable(coef,cnstit,... | ||
| # t,u,v,xmod,m,B,W,varMSM,Gall,Hall,elor,varcov_mCw,indPE); | ||
| # else | ||
| # [coef.diagn,usnrc,~] = ut_diagntable(coef,cnstit,... | ||
| # t,u,[],xmod,m,B,W,varMSM,Gall,Hall,elor,varcov_mCw,indPE); | ||
| # end | ||
| # if opt.diagnplots | ||
| # tmp = nan*ones(size(uin)); | ||
| # tmp(uvgd) = usnrc; | ||
| # usnrc = tmp; | ||
| # tmp = nan*ones(size(uin)); | ||
| # tmp(uvgd) = e; | ||
| # e = tmp; | ||
| # if opt.twodim | ||
| # tmp = nan*ones(size(uin)); | ||
| # tmp(uvgd) = vsnrc; | ||
| # vsnrc = tmp; | ||
| # ut_diagnfigs(coef,indPE,tin,uin,vin,usnrc,vsnrc,e); | ||
| # else | ||
| # ut_diagnfigs(coef,indPE,tin,uin,[],usnrc,[],e); | ||
| # end | ||
| # end | ||
| # end | ||
| return coef |