def test_parameter_controller(): param = get_para() pileup_peak = ['Si_Ka1-Si_Ka1', 'Si_Ka1-Ce_La1'] elemental_lines = ['Ar_K', 'Fe_K', 'Ce_L', 'Pt_M'] + pileup_peak PC = ParamController(param, elemental_lines) set_opt = dict(pos='hi', width='lohi', area='hi', ratio='lo') PC.update_element_prop(['Fe_K', 'Ce_L', pileup_peak[0]], **set_opt) PC.set_strategy('linear') # check boundary value for k, v in six.iteritems(PC.params): if 'Fe' in k: if 'ratio' in k: assert_equal(str(v['bound_type']), set_opt['ratio']) if 'center' in k: assert_equal(str(v['bound_type']), set_opt['pos']) elif 'area' in k: assert_equal(str(v['bound_type']), set_opt['area']) elif 'sigma' in k: assert_equal(str(v['bound_type']), set_opt['width']) elif ('pileup_'+pileup_peak[0].replace('-', '_')) in k: if 'ratio' in k: assert_equal(str(v['bound_type']), set_opt['ratio']) if 'center' in k: assert_equal(str(v['bound_type']), set_opt['pos']) elif 'area' in k: assert_equal(str(v['bound_type']), set_opt['area']) elif 'sigma' in k: assert_equal(str(v['bound_type']), set_opt['width'])
def synthetic_spectrum(): param = get_para() x = np.arange(2000) pileup_peak = ['Si_Ka1-Si_Ka1', 'Si_Ka1-Ce_La1'] elemental_lines = ['Ar_K', 'Fe_K', 'Ce_L', 'Pt_M'] + pileup_peak elist, matv, area_v = construct_linear_model(x, param, elemental_lines, default_area=1e5) return np.sum(matv, 1) + 100 # avoid zero values
def test_escape_peak(): y0 = synthetic_spectrum() ratio = 0.01 param = get_para() xnew, ynew = compute_escape_peak(y0, ratio, param) # ratio should be the same assert_array_almost_equal(np.sum(ynew)/np.sum(y0), ratio, decimal=3)
def test_pre_fit(): y0 = synthetic_spectrum() x0 = np.arange(len(y0)) # the following items should appear item_list = ['Ar_K', 'Fe_K', 'compton', 'elastic'] param = get_para() # fit without weights x, y_total, area_v = linear_spectrum_fitting(x0, y0, param, weights=None) for v in item_list: assert_true(v in y_total) sum1 = np.sum(six.itervalues(y_total)) # r squares as a measurement r1 = 1- np.sum((sum1-y0)**2)/np.sum((y0-np.mean(y0))**2) assert_true(r1 > 0.85) # fit with weights w = 1/np.sqrt(y0) x, y_total, area_v = linear_spectrum_fitting(x0, y0, param, weights=1/np.sqrt(y0)) for v in item_list: assert_true(v in y_total) sum2 = np.sum(six.itervalues(y_total)) # r squares as a measurement r2 = 1- np.sum((sum2-y0)**2)/np.sum((y0-np.mean(y0))**2) assert_true(r2 > 0.85)
def test_set_param(): param = get_para() elemental_lines = ['Ar_K', 'Fe_K', 'Ce_L', 'Pt_M'] MS = ModelSpectrum(param, elemental_lines) MS.assemble_models() # get compton model compton = MS.mod.components[0] input_param = {'bound_type': 'other', 'max': 13.0, 'min': 9.0, 'value': 11.0} _set_parameter_hint('coherent_sct_energy', input_param, compton)
def test_fit(): param = get_para() pileup_peak = ['Si_Ka1-Si_Ka1', 'Si_Ka1-Ce_La1'] elemental_lines = ['Ar_K', 'Fe_K', 'Ce_L', 'Pt_M'] + pileup_peak x0 = np.arange(2000) y0 = synthetic_spectrum() x, y = trim(x0, y0, 100, 1300) MS = ModelSpectrum(param, elemental_lines) MS.assemble_models() result = MS.model_fit(x, y, weights=1/np.sqrt(y), maxfev=200) # check area of each element for k, v in six.iteritems(result.values): if 'area' in k: # error smaller than 1% assert_true((v-1e5)/1e5 < 1e-2) # multiple peak sumed, so value should be larger than one peak area 1e5 sum_Fe = sum_area('Fe_K', result) assert_true(sum_Fe > 1e5) sum_Ce = sum_area('Ce_L', result) assert_true(sum_Ce > 1e5) sum_Pt = sum_area('Pt_M', result) assert_true(sum_Pt > 1e5) # create full list of parameters PC = ParamController(param, elemental_lines) new_params = PC.params # update values update_parameter_dict(new_params, result) for k, v in six.iteritems(new_params): if 'area' in k: assert_equal(v['value'], result.values[k]) MS = ModelSpectrum(new_params, elemental_lines) MS.assemble_models() result = MS.model_fit(x, y, weights=1/np.sqrt(y), maxfev=200) # check area of each element for k, v in six.iteritems(result.values): if 'area' in k: # error smaller than 0.1% assert_true((v-1e5)/1e5 < 1e-3)
def test_pre_fit(): y0 = synthetic_spectrum() x0 = np.arange(len(y0)) # the following items should appear item_list = ['Ar_K', 'Fe_K', 'compton', 'elastic'] param = get_para() # with weight pre fit x, y_total, area_v = linear_spectrum_fitting(x0, y0, param) for v in item_list: assert_true(v in y_total) # no weight pre fit x, y_total, area_v = linear_spectrum_fitting(x0, y0, param, constant_weight=None) for v in item_list: assert_true(v in y_total)
def test_set_param_hint(): param = get_para() elemental_lines = ['Ar_K', 'Fe_K', 'Ce_L', 'Pt_M'] bound_options = ['none', 'lohi', 'fixed', 'lo', 'hi'] MS = ModelSpectrum(param, elemental_lines) MS.assemble_models() # get compton model compton = MS.mod.components[0] for v in bound_options: input_param = {'bound_type': v, 'max': 13.0, 'min': 9.0, 'value': 11.0} _set_parameter_hint('coherent_sct_energy', input_param, compton) p = compton.make_params() if v == 'fixed': assert_equal(p['coherent_sct_energy'].vary, False) else: assert_equal(p['coherent_sct_energy'].vary, True)
def test_param_controller_fail(): param = get_para() PC = ParamController(param, []) assert_raises(ValueError, PC._add_area_param, 'Ar')