def test_get_min_val(capsys): # Some parameters fI0 = fdesign.j0_1(5) fI1 = fdesign.j1_1(5) rdef = (1, 1, 2) error = 0.01 # 1. "Normal" case r = np.logspace(0, 2, 10) fC = fdesign.j0_1(5) fC.rhs = fC.rhs(r) out = fdesign._get_min_val((0.05, -1.0), 201, [fI0, ], [fC, ], r, rdef, error, np.real, 'amp', 0, 0, []) assert_allclose(out, 2.386523e-05, rtol=1e-5) # 2. "Normal" case j0 and j1; J0 is better than J1 fC1 = fdesign.j1_1(5) fC1.rhs = fC1.rhs(r) out = fdesign._get_min_val((0.05, -1.0), 201, [fI1, fI0], [fC1, fC], r, rdef, error, np.real, 'amp', 0, 0, []) assert_allclose(out, 2.386523e-05, rtol=1e-5) # 3. f2 fC2 = fdesign.empy_hankel('j2', 950, 1000, 1, 1) fC2.rhs = fC2.rhs(r) out = fdesign._get_min_val((0.05, -1.0), 201, [fI0, fI1], [fC2, ], r, rdef, error, np.real, 'amp', 0, 0, []) assert_allclose(out, 6.831394e-08, rtol=1e-5) # 4. No solution below error out = fdesign._get_min_val((0.05, -10.0), 201, [fI0, ], [fC, ], r, rdef, error, np.real, 'amp', 0, 0, []) assert_allclose(out, np.inf) # 5. All nan's; with max r out = fdesign._get_min_val((0.05, 10.0), 201, [fI0, ], [fC, ], r, rdef, error, np.real, 'r', 0, 0, []) assert_allclose(out, np.inf) # 6. r too small, with verbosity log = {'cnt1': 1, 'cnt2': 9, 'totnr': 10, 'time': default_timer(), 'warn-r': 0} r = np.logspace(0, 1.1, 10) fC = fdesign.j0_1(5) fC.rhs = fC.rhs(r) out, _ = capsys.readouterr() # empty fdesign._get_min_val((0.058, -1.26), 201, [fI0, ], [fC, ], r, rdef, error, np.real, 'amp', 3, 0, log) out, _ = capsys.readouterr() assert "* WARNING :: all data have error < "+str(error)+";" in out assert "brute fct calls : 10" in out
def test_design(self, capsys): # Check it doesn't fail, message is correct, and input doesn't matter # Same test as first in test_design fI = (fdesign.j0_1(5), fdesign.j1_1(5)) dat1 = DATA['case1'][()] _, _ = fdesign.design(fI=fI, verb=1, plot=2, **dat1[0]) out, _ = capsys.readouterr() assert "* WARNING :: `matplotlib` is not installed, no " in out
def test_call_qc_transform_pairs2(self): # plot_transform_pair J2 r = np.logspace(1, 2, 50) fI = (fdesign.j0_1(5), fdesign.j1_1(5)) fC = fdesign.empy_hankel('j2', 950, 1000, 1, 1) fC.rhs = fC.rhs(r) fdesign._call_qc_transform_pairs(101, (0.06, 0.07, 0.01), (-1, 1, 0.3), fI, [fC, ], r, (0, 0, 2), np.imag) return plt.gcf()
def test_call_qc_transform_pairs1(self): # plot_transform_pair "normal" case r = np.logspace(1, 2, 50) fI = (fdesign.j0_1(5), fdesign.j1_1(5)) fC = (fdesign.j0_3(5), fdesign.j1_3(5)) fC[0].rhs = fC[0].rhs(r) fC[1].rhs = fC[1].rhs(r) fdesign._call_qc_transform_pairs(101, (0.06, 0.07, 0.01), (-1, 1, 0.3), fI, fC, r, (0, 0, 2), np.real) return plt.gcf()
def test_design(): # 1. General case with various spacing and shifts fI = (fdesign.j0_1(5), fdesign.j1_1(5)) dat1 = DATA['case1'][()] _, out1 = fdesign.design(fI=fI, verb=0, plot=0, **dat1[0]) # First value is always the same. # Second value jumps btw -2.722222 and -0.777778, so we don't check it. assert_allclose(out1[0][0], dat1[2][0][0]) assert_allclose(out1[1], dat1[2][1], rtol=1e-3) assert_allclose(out1[2], dat1[2][2]) # np.linalg(.qr) can have roundoff errors which are not deterministic, # which can yield different results for badly conditioned matrices. This # only affects the edge-cases, not the best result we are looking for. # However, we have to limit the following comparison; we check that at # least 50% are within a relative error of 0.1%. rate = np.sum(np.abs((out1[3] - dat1[2][3]) / dat1[2][3]) < 1e-3) assert rate > out1[3].size / 2 # 2. Specific model with only one spacing/shift dat2 = DATA['case2'][()] _, out2 = fdesign.design(fI=fI, verb=0, plot=0, **dat2[0]) assert_allclose(out2[0], dat2[2][0]) assert_allclose(out2[1], dat2[2][1], rtol=1e-3) assert_allclose(out2[2], dat2[2][2]) assert_allclose(out2[3], dat2[2][3], rtol=1e-3) # 3. Same, with only one transform dat2b = DATA['case3'][()] _, out2b = fdesign.design(fI=fI[0], verb=0, plot=0, **dat2b[0]) assert_allclose(out2b[0], dat2b[2][0]) assert_allclose(out2b[1], dat2b[2][1], rtol=1e-3) assert_allclose(out2b[2], dat2b[2][2]) assert_allclose(out2b[3], dat2b[2][3], rtol=1e-3) # 4.a Maximize r dat4 = DATA['case4'][()] dat4[0]['save'] = True dat4[0]['name'] = 'tmpfilter' _, out4 = fdesign.design(fI=fI, verb=0, plot=0, **dat4[0]) assert_allclose(out4[0], dat4[2][0]) assert_allclose(out4[1], dat4[2][1], rtol=1e-3) assert_allclose(out4[2], dat4[2][2]) assert_allclose(out4[3], dat4[2][3], rtol=1e-3) # Clean-up # Should be replaced eventually by tmpdir os.remove('./filters/tmpfilter_base.txt') os.remove('./filters/tmpfilter_j0.txt') os.remove('./filters/tmpfilter_j1.txt') os.remove('./filters/tmpfilter_full.txt') # 4.b Without full output and all the other default inputs dat4[0]['full_output'] = False del dat4[0]['name'] dat4[0]['finish'] = 'Wrong input' del dat4[0]['r'] dat4[0]['reim'] = np.imag # Set once to imag fdesign.design(fI=fI, verb=2, plot=0, **dat4[0]) # Clean-up # Should be replaced eventually by tmpdir os.remove('./filters/dlf_201_base.txt') os.remove('./filters/dlf_201_j0.txt') os.remove('./filters/dlf_201_j1.txt') # 5. j2 for fI with pytest.raises(ValueError, match="is only implemented for fC"): fI2 = fdesign.empy_hankel('j2', 0, 50, 100, 1) fdesign.design(fI=fI2, verb=0, plot=0, **dat4[0])
r"""Create data for test_fdesign.""" import numpy as np from copy import deepcopy as dc from empymod.scripts import fdesign # Cannot pickle/shelve this; could dill it. For the moment, we just provide # it separately here and in the tests. fI = (fdesign.j0_1(5), fdesign.j1_1(5)) # Define main model inp1 = {'spacing': (0.04, 0.1, 10), 'shift': (-3, -0.5, 10), 'n': 201, 'cvar': 'amp', 'save': False, 'full_output': True, 'r': np.logspace(0, 3, 10), 'r_def': (1, 1, 2), 'name': 'test', 'finish': None, } # 1. General case with various spacing and shifts filt1, out1 = fdesign.design(verb=0, plot=0, fI=fI, **inp1) case1 = (inp1, filt1, out1) # 2. Specific model with only one spacing/shift inp2 = dc(inp1) inp2['spacing'] = 0.0641 inp2['shift'] = -1.2847 filt2, out2 = fdesign.design(verb=0, plot=0, fI=fI, **inp2)