Add functions to get 3D krypton maps #948
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gonzaponte
requested changes
Dec 22, 2025
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| def normalization(krmap, method, x_low, x_high, y_low, y_high): |
Collaborator
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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
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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() |
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Suggested change
| 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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Suggested change
| def create_NaN_map(xy_range, dt_range, xy_nbins, dt_nbins): | |
| def create_empty_map(xy_range, dt_range, xy_nbins, dt_nbins): |
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| 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) |
Collaborator
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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
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| 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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| 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
…n since it wasn't really necessary
…ngle variable (dtbins)
…n that wasn't being used
…new variables to groupby('event')
…e selections in the same mask. Remove unnecessary functions
…want. Rewrite plot_lifetime_fit because LT_fit function no longer exists
…ies/core/fit_functions
…ies/core/fit_functions
…ed some comments in get_median and name of arguments in gaussian_fit and fit_map
…3D_map (bins to nbins_S2e, S2e_range)
…fixed value inside the function updated append_time_evol to these changes
… function as a function of len(df)
…ges done to the functions
…n compute_drift_v
…n (should I remove the limit values tests?). Also added tests for compute_drift_v() function
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