fix: Remove unnecessary computations

src/metaDMG/fit/bayesian.py

@@ -97,32 +97,33 @@ def add_D_information(
     beta = (1 - Dx) * phi
 
     # 1000x faster approximation for sp_betabinom(N, alpha, beta)
-    pdf_approx = sp_betabinom(N, alpha.mean(), beta.mean())
+    # pdf_approx = sp_betabinom(N, alpha.mean(), beta.mean())
 
     fit_result[f"{prefix}damage"] = mu.item()
     fit_result[f"{prefix}damage_std"] = std.item()
     fit_result[f"{prefix}significance"] = mu.item() / std.item()
-    fit_result[f"{prefix}damage_median"] = np.median(A)
+
+    # fit_result[f"{prefix}damage_median"] = np.median(A)
 
     # fit_result[f"{prefix}prob_lt_1p_damage"] = pdf_beta.cdf(0.01).mean()
     # # fit_result[f"{prefix}prob_lt_1p_damage_betabinom"] = pdf.cdf(0.01 * N).mean()
     # fit_result[f"{prefix}prob_zero_damage"] = pdf.cdf(0).mean()
 
-    for n_sigma in [1, 2, 3]:
-        conf = get_n_sigma_probability(n_sigma)
-        fit_result[f"{prefix}damage_CI_{n_sigma}_sigma_low"] = (
-            pdf_approx.ppf((1 - conf) / 2.0).mean() / N
-        )
-        fit_result[f"{prefix}damage_CI_{n_sigma}_sigma_high"] = (
-            pdf_approx.ppf((1 + conf) / 2.0).mean() / N
-        )
-
-    fit_result[f"{prefix}damage_CI_95_low"] = (
-        pdf_approx.ppf((1 - 0.95) / 2.0).mean() / N
-    )
-    fit_result[f"{prefix}damage_CI_95_high"] = (
-        pdf_approx.ppf((1 + 0.95) / 2.0).mean() / N
-    )
+    # for n_sigma in [1, 2, 3]:
+    #     conf = get_n_sigma_probability(n_sigma)
+    #     fit_result[f"{prefix}damage_CI_{n_sigma}_sigma_low"] = (
+    #         pdf_approx.ppf((1 - conf) / 2.0).mean() / N
+    #     )
+    #     fit_result[f"{prefix}damage_CI_{n_sigma}_sigma_high"] = (
+    #         pdf_approx.ppf((1 + conf) / 2.0).mean() / N
+    #     )
+
+    # fit_result[f"{prefix}damage_CI_95_low"] = (
+    #     pdf_approx.ppf((1 - 0.95) / 2.0).mean() / N
+    # )
+    # fit_result[f"{prefix}damage_CI_95_high"] = (
+    #     pdf_approx.ppf((1 + 0.95) / 2.0).mean() / N
+    # )
 
     return fit_result
 
@@ -158,9 +159,9 @@ def add_summary_of_variable(
 
     fit_result[f"{s}"] = np.mean(values, axis=axis)
     fit_result[f"{s}_std"] = np.std(values, axis=axis)
-    fit_result[f"{s}_median"] = np.median(values, axis=axis)
-    fit_result[f"{s}_CI_1_sigma_low"] = np.quantile(values, q_low, axis=axis)
-    fit_result[f"{s}_CI_1_sigma_high"] = np.quantile(values, q_high, axis=axis)
+    # fit_result[f"{s}_median"] = np.median(values, axis=axis)
+    # fit_result[f"{s}_CI_1_sigma_low"] = np.quantile(values, q_low, axis=axis)
+    # fit_result[f"{s}_CI_1_sigma_high"] = np.quantile(values, q_high, axis=axis)
 
 
 def compute_rho_Ac(mcmc):

src/metaDMG/viz/app.py

@@ -263,39 +263,39 @@ def update_sidebar_right_datatable_results(click_data):
             bayesian_list = []
             if viz_results.Bayesian:
 
-                s_D = f", [{ds['damage_CI_1_sigma_low']:.2%}, {ds['damage_CI_1_sigma_high']:.2%}]"
+                # s_D = f", [{ds['damage_CI_1_sigma_low']:.2%}, {ds['damage_CI_1_sigma_high']:.2%}]"
 
-                conf_q_low = ds["q_CI_1_sigma_low"]
-                conf_q_high = ds["q_CI_1_sigma_high"]
-                s_q = f", [{conf_q_low:.3f}, {conf_q_high:.3f}]"
+                # conf_q_low = ds["q_CI_1_sigma_low"]
+                # conf_q_high = ds["q_CI_1_sigma_high"]
+                # s_q = f", [{conf_q_low:.3f}, {conf_q_high:.3f}]"
 
-                conf_phi_low = viz_utils.human_format(ds["phi_CI_1_sigma_low"])
-                conf_phi_high = viz_utils.human_format(ds["phi_CI_1_sigma_high"])
-                s_phi = f", [{conf_phi_low}, {conf_phi_high}]"
+                # conf_phi_low = viz_utils.human_format(ds["phi_CI_1_sigma_low"])
+                # conf_phi_high = viz_utils.human_format(ds["phi_CI_1_sigma_high"])
+                # s_phi = f", [{conf_phi_low}, {conf_phi_high}]"
 
-                conf_A_low = ds["A_CI_1_sigma_low"]
-                conf_A_high = ds["A_CI_1_sigma_high"]
-                s_A = f", [{conf_A_low:.3f}, {conf_A_high:.3f}]"
+                # conf_A_low = ds["A_CI_1_sigma_low"]
+                # conf_A_high = ds["A_CI_1_sigma_high"]
+                # s_A = f", [{conf_A_low:.3f}, {conf_A_high:.3f}]"
 
-                conf_c_low = ds["c_CI_1_sigma_low"]
-                conf_c_high = ds["c_CI_1_sigma_high"]
-                s_c = f", [{conf_c_low:.3f}, {conf_c_high:.3f}]"
+                # conf_c_low = ds["c_CI_1_sigma_low"]
+                # conf_c_high = ds["c_CI_1_sigma_high"]
+                # s_c = f", [{conf_c_low:.3f}, {conf_c_high:.3f}]"
 
                 bayesian_list = [
                     "Fit results:",
                     html.Br(),
-                    f"D: {ds['damage']:.2%} " f"± {ds['damage_std']:.2%}" + s_D,
+                    f"D: {ds['damage']:.2%} " f"± {ds['damage_std']:.2%}",  # + s_D,
                     html.Br(),
                     f"significance: {ds['significance']:.2f}",
                     html.Br(),
-                    f"q: {ds['q']:.3f} " f"± {ds['q_std']:.3f}" + s_q,
+                    f"q: {ds['q']:.3f} " f"± {ds['q_std']:.3f}",  # + s_q,
                     html.Br(),
                     f"phi: {viz_utils.human_format(ds['phi'])} "
-                    f"± {viz_utils.human_format(ds['phi_std'])}" + s_phi,
+                    f"± {viz_utils.human_format(ds['phi_std'])}",  # + s_phi,
                     html.Br(),
-                    f"A: {ds['A']:.3f} " f"± {ds['A_std']:.3f}" + s_A,
+                    f"A: {ds['A']:.3f} " f"± {ds['A_std']:.3f}",  # + s_A,
                     html.Br(),
-                    f"c: {ds['c']:.3f} " f"± {ds['c_std']:.3f}" + s_c,
+                    f"c: {ds['c']:.3f} " f"± {ds['c_std']:.3f}",  # + s_c,
                     html.Br(),
                     f"rho Ac: {ds['rho_Ac']:.3f}",
                     html.Br(),

src/metaDMG/viz/figures.py

@@ -12,11 +12,14 @@
 from matplotlib import ticker
 from matplotlib.backends.backend_pdf import PdfPages
 from matplotlib.ticker import EngFormatter
-from tqdm import tqdm
+from tqdm.rich import tqdm
 
 from metaDMG.viz import viz_utils
 
 
+# from tqdm import tqdm
+
+
 #%%
 
 plt.rcParams["text.usetex"] = True
@@ -255,7 +258,8 @@ def plot_group(
         # add damage as single errorbar
         if D_info is not None:
 
-            D, D_low, D_high = D_info
+            # D, D_low, D_high = D_info
+            D, D_std = D_info
 
             # fit with errorbars
             fig.add_trace(
@@ -264,9 +268,10 @@ def plot_group(
                     y=[D],
                     error_y=dict(
                         type="data",
-                        symmetric=False,
-                        array=[D_high - D],
-                        arrayminus=[D - D_low],
+                        array=[D_std],
+                        # symmetric=False,
+                        # array=[D_high - D],
+                        # arrayminus=[D - D_low],
                         visible=True,
                         color="black",
                     ),

src/metaDMG/viz/results.py

@@ -105,14 +105,14 @@ def correct_for_non_LCA(df):
 
 def add_MAP_measures(df):
     df["MAP_rho_Ac_abs"] = np.abs(df["MAP_rho_Ac"])
-    df["MAP_damage_CI_low"] = df["MAP_damage"] - df["MAP_damage_std"]
-    df["MAP_damage_CI_high"] = df["MAP_damage"] + df["MAP_damage_std"]
+    # df["MAP_damage_CI_low"] = df["MAP_damage"] - df["MAP_damage_std"]
+    # df["MAP_damage_CI_high"] = df["MAP_damage"] + df["MAP_damage_std"]
 
 
 def add_bayesian_measures(df):
     df["rho_Ac_abs"] = np.abs(df["rho_Ac"])
-    df["damage_CI_low"] = df["damage_CI_1_sigma_low"]
-    df["damage_CI_high"] = df["damage_CI_1_sigma_high"]
+    # df["damage_CI_low"] = df["damage_CI_1_sigma_low"]
+    # df["damage_CI_high"] = df["damage_CI_1_sigma_high"]
 
 
 #%%
@@ -502,13 +502,14 @@ def get_D(self, sample, tax_id):
             prefix = "MAP_"
 
         D = ds[f"{prefix}damage"].iloc[0]
+        D_sigma = ds[f"{prefix}damage_std"].iloc[0]
 
-        s1 = f"{prefix}damage_CI_low"
-        s2 = f"{prefix}damage_CI_high"
-        D_low = ds[s1].iloc[0]
-        D_high = ds[s2].iloc[0]
+        # s1 = f"{prefix}damage_CI_low"
+        # s2 = f"{prefix}damage_CI_high"
+        # D_low = ds[s1].iloc[0]
+        # D_high = ds[s2].iloc[0]
 
-        return D, D_low, D_high
+        return D, D_sigma
 
     def _ds_to_fit_text(self, ds):
 

src/metaDMG/viz/viz_utils.py

@@ -267,8 +267,8 @@ def get_range_slider_keywords(viz_results, column="N_reads", N_steps=100):
         "q",
         "A",
         "c",
-        "damage_CI_low",
-        "damage_CI_high",
+        # "damage_CI_low",
+        # "damage_CI_high",
     ]:
         range_min = 0.0
         range_max = 1.0
@@ -633,10 +633,10 @@ def get_d_columns_latex(viz_results):
         "significance": r"$\text{significance}$",
         "MAP_significance": r"$\text{significance} \,\, \text{(MAP)}$",
         #
-        "damage_CI_low": r"$D \,\, \text{CI. low}$",
-        "damage_CI_high": r"$D \,\, \text{CI. high}$",
-        "MAP_damage_CI_low": r"$D \,\, \text{CI. low} \,\, \text{(MAP)}$",
-        "MAP_damage_CI_high": r"$D \,\, \text{CI. high} \,\, \text{(MAP)}$",
+        # "damage_CI_low": r"$D \,\, \text{CI. low}$",
+        # "damage_CI_high": r"$D \,\, \text{CI. high}$",
+        # "MAP_damage_CI_low": r"$D \,\, \text{CI. low} \,\, \text{(MAP)}$",
+        # "MAP_damage_CI_high": r"$D \,\, \text{CI. high} \,\, \text{(MAP)}$",
         #
         "q": r"$q$",
         "MAP_q": r"$q \text{(MAP)}$",