Add functions to get 3D krypton maps #948
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| def normalization(krmap, method, x_low, x_high, y_low, y_high): |
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The last 4 parameters need to be optional, since they are only relevant for method == "region". I also think it would be interesting to gather them in a dictionary to simplify the logic.
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| def normalization(krmap, method, x_low, x_high, y_low, y_high): | ||
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| mu_values = krmap.mu |
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since you are doing .dropna() everywhere, you can do it here already
| k, i, j = np.meshgrid(k_vals, i_vals, j_vals, indexing='ij') | ||
| k = k.ravel() | ||
| i = i.ravel() | ||
| j = j.ravel() |
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| k, i, j = np.meshgrid(k_vals, i_vals, j_vals, indexing='ij') | |
| k = k.ravel() | |
| i = i.ravel() | |
| j = j.ravel() | |
| k, i, j = map(np.ravel, | |
| np.meshgrid(k_vals, i_vals, j_vals, indexing='ij')) |
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| """ | ||
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| def create_NaN_map(xy_range, dt_range, xy_nbins, dt_nbins): |
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| def create_NaN_map(xy_range, dt_range, xy_nbins, dt_nbins): | |
| def create_empty_map(xy_range, dt_range, xy_nbins, dt_nbins): |
| xy_bins = np.linspace(xy_range[0], xy_range[1], xy_nbins + 1) | ||
| dt_bins = np.linspace(dt_range[0], dt_range[1], dt_nbins + 1) | ||
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| #shift to bin centers invisible_cities.core.core_functions | ||
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| i_range = np.arange(0, len(xy_bins)-1) | ||
| j_range = np.arange(0, len(xy_bins)-1) | ||
| k_range = np.arange(0, len(dt_bins)-1) |
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xy_bins and dt_bins are only used to compute their length, which are given by the variables xy_nbins and dt_nbins
| range_S2t = (low_S2t, high_S2t) | ||
| sel_S2t = in_range(kdst.S2t, *range_S2t) |
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| range_S2t = (low_S2t, high_S2t) | |
| sel_S2t = in_range(kdst.S2t, *range_S2t) | |
| sel_S2t = in_range(kdst.S2t, low_S2t, high_S2t) |
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| def select_DTrange(kdst, low_DT, high_DT): | ||
| df_DTrange = kdst[(kdst.DT >= low_DT) & (kdst.DT <= high_DT)] |
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| def select_nsipm(kdst, low_nsipm, high_nsipm): | ||
| sel_nsipm = (kdst.Nsipm >= low_nsipm) & (kdst.Nsipm <= high_nsipm) |
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| df, eff_1S1_1S2 = select_1S1_1S2(df) | ||
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| df, eff_S2t = select_S2t(df, low_S2t, high_S2t) |
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This one should go before the 1S1+1S2 selection, I think
| d = {'eff diffusion band': [eff_DT], 'eff X rays': [eff_Xrays], 'eff 1S1 & 1S2': [eff_1S1_1S2], | ||
| 'eff S2 trigger time': [eff_S2t], 'eff Rmax': [eff_Rmax], 'eff range DT': [eff_DTrange], | ||
| 'eff number of SiPMS': [eff_nsipm], 'total efficiency': [total_eff]} | ||
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| df_efficiencies = pd.DataFrame(data = d) |
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You can also have numbers in the dictionary (instead of lists of 1 element) and then add index=[0] when creating the dataframe
…p with two columns for corrected energies
gonzaponte
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gonzaponte
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More comments. We will need to build up a bit more testing and put everything together in a city.
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| def normalization(krmap, method, xy_params = None): | ||
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| mu_values = krmap.mu.dropna() |
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dropna should affect all options of method. Right now, it's only affecting the first two, the other three do not use this variable. You should redefine kr_map so it doesn't contain nan values.:
krmap = krmap.dropna(subset=["mu"]). Then use krmap.mu.something everywhere
| from scipy.interpolate import griddata | ||
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| def normalization(krmap, method, xy_params = None): |
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We will discuss how to do this with symbols
| E_reference_anode = mu_values_anode.mean() | ||
| return E_reference_anode | ||
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| mask_region = (krmap['x'] <= xy_params['x_high']) & (krmap['x'] >= xy_params['x_low']) & (krmap['y'] <= xy_params['y_high']) & (krmap['y'] >= xy_params['y_low']) |
| region_mask = ( | ||
| (map_test.x <= 100) & (map_test.x >= -100) & | ||
| (map_test.y <= 100) & (map_test.y >= -100) | ||
| ) | ||
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| def gaussian_fit(df, ebins, min_events = 10): | ||
| if len(df)< min_events: | ||
| results = get_median(df) |
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| results = get_median(df) | |
| return get_median(df) |
| mask_df = ((df.X >= x1) & (df.X <= x1+2*shape_size) & (df.Y >= y1) & | ||
| (df.Y <= y1+2*shape_size)) |
| def LT_fit(DT, s2e, p0): | ||
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| f = fit.fit(fit.expo, DT, s2e, p0) | ||
| return f.values, (f[2][0], f[2][1]) |
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Probably not worth making a function just with this
| med_fun(empty_dst) | ||
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| def test_gaussianfit_does_fit_right(): |
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| def test_gaussianfit_does_fit_right(): | |
| def test_gaussianfit_empty(): |
| result_med = med_fun(df_test) | ||
| assert result_med['nevents'].iloc[0] == N | ||
| assert fit_function(df_test, bins = 6)['nevents'].iloc[0] == N |
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| result_med = med_fun(df_test) | |
| assert result_med['nevents'].iloc[0] == N | |
| assert fit_function(df_test, bins = 6)['nevents'].iloc[0] == N | |
| result_med = med_fun(df_test) | |
| result_fit = fit_function(df_test, bins = 6) | |
| assert result_med['nevents'].iloc[0] == N | |
| assert result_fit['nevents'].iloc[0] == N |
| def test_merge_maps(): | ||
| Nan_map_test = create_empty_map(xy_range = (-500, 500), dt_range = (0, 1400), xy_nbins = 100, dt_nbins = 10) | ||
| d = {'nevents': np.empty(6, dtype = int), 'mu' : np.empty(6), 'sigma' : np.empty(6), 'mu_error' : np.empty(6), 'sigma_error' : np.empty(6)} | ||
| map_3D_test = pd.DataFrame(data = d, index = range(0, 6)) | ||
| assert merge_maps(Nan_map_test, map_3D_test).shape == Nan_map_test.shape |
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map_3D_test needs to contain the columns k, i, and j.
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