From 5217200c7bba70e35821a11b6765ce68906d4096 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Tue, 14 Oct 2025 13:10:43 +0200
Subject: [PATCH 01/39] cleanup unused files

---
 .../synthetic_benchmark/run_dextramixer.py    | 76 -------------------
 .../run_dextramixerkmeans.py                  | 76 -------------------
 2 files changed, 152 deletions(-)
 delete mode 100644 experiments/synthetic_benchmark/run_dextramixer.py
 delete mode 100644 experiments/synthetic_benchmark/run_dextramixerkmeans.py

diff --git a/experiments/synthetic_benchmark/run_dextramixer.py b/experiments/synthetic_benchmark/run_dextramixer.py
deleted file mode 100644
index 83f947c..0000000
--- a/experiments/synthetic_benchmark/run_dextramixer.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import itertools
-import sys
-import os
-
-sys.path.append("../../")
-
-import itertools as itr
-import numpyro
-import pandas as pd
-
-from dextrademixer.model import DextraDemixer
-import muon as mu
-
-
-def main(f_in, f_out_csv):
-    numpyro.set_host_device_count(4)
-    base_path = os.path.basename(f_out_csv)
-
-    mdata = mu.read(f_in)
-    true_binder = mdata.mod["airr"].obs["is_binder"]
-    N = len(true_binder)
-
-    d = {"model": [], "thresh": [], "p": [], "assignment": [], "true_binder": []}
-    for m, neg_ctrl, ir_clone, ir_cov, svi in itr.product(["I", "H", "C"],
-                                                     [None, "neg_control"],
-                                                     [None, "clone_id"],
-                                                     [None, "clone_cov"],
-                                                     [True]):
-
-        if "_cov1_" not in f_in and ir_cov is not None and ir_clone is not None:
-            continue
-
-        if ir_cov is not None and ir_clone is None:
-            continue
-
-        if m == "C" and ir_clone is None:
-            continue
-
-        model_config = f"dextramixer_svi_{int(svi)}_mode_{m}_negctrl_{int(neg_ctrl is not None)}_clone_{int(ir_clone is not None)}_cov_{int(ir_cov is not None)}"
-
-        print(model_config)
-
-        mixer = DextraDemixer(model_type="mixturemodel", mode=m)
-        mixer.preprocess_model_data(mdata, "pmhc1",
-                                    neg_ctrl_key=neg_ctrl,
-                                    ir_clone_key=ir_clone,
-                                    ir_cov_key=ir_cov)
-        if svi:
-            trace = mixer.fit_svi()
-        else:
-            trace = mixer.fit()
-
-        # trace.to_netcdf(filename=os.path.join(base_path, "dextramier_"+model_config+"{}.ncdf"))
-
-        p_thr, assignment_thr = mixer.predict_posterior_class(threshold=0.5)
-        p_fdr, assignment_fdr = mixer.predict_posterior_class(target_fdr=0.05)
-        d["model"].extend([model_config] * N)
-        d["thresh"].extend(["thresh"] * N)
-        d["p"].extend(p_thr)
-        d["assignment"].extend(assignment_thr)
-        d["true_binder"].extend(true_binder)
-
-        d["model"].extend([model_config] * N)
-        d["thresh"].extend(["fdr"] * N)
-        d["p"].extend(p_fdr)
-        d["assignment"].extend(assignment_fdr)
-        d["true_binder"].extend(true_binder)
-
-    df = pd.DataFrame.from_dict(d)
-    df.to_csv(f_out_csv)
-
-
-if __name__ == "__main__":
-    f_in = sys.argv[1]
-    f_out_csv = sys.argv[2]
-    main(f_in, f_out_csv)
diff --git a/experiments/synthetic_benchmark/run_dextramixerkmeans.py b/experiments/synthetic_benchmark/run_dextramixerkmeans.py
deleted file mode 100644
index c32aebc..0000000
--- a/experiments/synthetic_benchmark/run_dextramixerkmeans.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import itertools
-import sys
-import os
-
-sys.path.append("../../")
-
-import itertools as itr
-import numpyro
-import pandas as pd
-
-from dextrademixer.model import DextraDemixer
-import muon as mu
-
-
-def main(f_in, f_out_csv):
-    numpyro.set_host_device_count(4)
-    base_path = os.path.basename(f_out_csv)
-
-    mdata = mu.read(f_in)
-    true_binder = mdata.mod["airr"].obs["is_binder"]
-    N = len(true_binder)
-
-    d = {"model": [], "thresh": [], "p": [], "assignment": [], "true_binder": []}
-    for m, neg_ctrl, ir_clone, ir_cov, svi in itr.product(["I", "H", "C"],
-                                                     [None, "neg_control"],
-                                                     [None, "clone_id"],
-                                                     [None, "clone_cov"],
-                                                     [True]):
-
-        if "_cov1_" not in f_in and ir_cov is not None and ir_clone is not None:
-            continue
-
-        if ir_cov is not None and ir_clone is None:
-            continue
-
-        if m == "C" and ir_clone is None:
-            continue
-
-        model_config = f"dextramixerkmeans_svi_{int(svi)}_mode_{m}_negctrl_{int(neg_ctrl is not None)}_clone_{int(ir_clone is not None)}_cov_{int(ir_cov is not None)}"
-
-        print(model_config)
-
-        mixer = DextraDemixer(model_type="mixturemodelkmeans", mode=m)
-        mixer.preprocess_model_data(mdata, "pmhc1",
-                                    neg_ctrl_key=neg_ctrl,
-                                    ir_clone_key=ir_clone,
-                                    ir_cov_key=ir_cov)
-        if svi:
-            trace = mixer.fit_svi()
-        else:
-            trace = mixer.fit()
-
-        # trace.to_netcdf(filename=os.path.join(base_path, "dextramier_"+model_config+"{}.ncdf"))
-
-        p_thr, assignment_thr = mixer.predict_posterior_class(threshold=0.5)
-        p_fdr, assignment_fdr = mixer.predict_posterior_class(target_fdr=0.05)
-        d["model"].extend([model_config] * N)
-        d["thresh"].extend(["thresh"] * N)
-        d["p"].extend(p_thr)
-        d["assignment"].extend(assignment_thr)
-        d["true_binder"].extend(true_binder)
-
-        d["model"].extend([model_config] * N)
-        d["thresh"].extend(["fdr"] * N)
-        d["p"].extend(p_fdr)
-        d["assignment"].extend(assignment_fdr)
-        d["true_binder"].extend(true_binder)
-
-    df = pd.DataFrame.from_dict(d)
-    df.to_csv(f_out_csv)
-
-
-if __name__ == "__main__":
-    f_in = sys.argv[1]
-    f_out_csv = sys.argv[2]
-    main(f_in, f_out_csv)

From 642b983c2a68b07bb0fb3bf23a0d5d790882ac21 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Tue, 14 Oct 2025 13:24:35 +0200
Subject: [PATCH 02/39] feature: Use AutoNormal guide instead of
 AutoMultivariateNormal guide due to computation and memory problems.

---
 dextrademixer/model/Dextrademixer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dextrademixer/model/Dextrademixer.py b/dextrademixer/model/Dextrademixer.py
index facf550..1e9603b 100644
--- a/dextrademixer/model/Dextrademixer.py
+++ b/dextrademixer/model/Dextrademixer.py
@@ -194,7 +194,7 @@ def fit(self, sampler_config: Dict[str, Union[int, float]] = None, rng_key: int
 
         return self.__make_arvis()
 
-    def fit_svi(self, guide=npy.infer.autoguide.AutoMultivariateNormal, svi_config: Dict[str, Union[int, float]] = None,
+    def fit_svi(self, guide=npy.infer.autoguide.AutoNormal, svi_config: Dict[str, Union[int, float]] = None,
                 nof_inits: int = 100, use_minimal_loss: bool = True, rng_key: int = 998777,
                 return_loss: bool = False) \
             -> az.InferenceData:

From a3d72f90fcc68f85e9bba888eb8e67051c7320fe Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Tue, 14 Oct 2025 13:36:02 +0200
Subject: [PATCH 03/39] refactor: renaming variables of posterior bfdr
 thresholding

---
 dextrademixer/model/Dextrademixer.py | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/dextrademixer/model/Dextrademixer.py b/dextrademixer/model/Dextrademixer.py
index 1e9603b..426e50a 100644
--- a/dextrademixer/model/Dextrademixer.py
+++ b/dextrademixer/model/Dextrademixer.py
@@ -429,28 +429,29 @@ def _predict_posterior_class_dist(p_samples,  target_fdr, cred_intvl, nof_thresh
         candidate_thresh = jnp.linspace(0.0, 1.0, nof_thresh+2)[1:-1]
 
         # Expand shapes: (n_draws, n) vs (T,)
-        # -> mask has shape (T, n_draws, n)
-        mask = p_samples[None, :, :] >= candidate_thresh[:, None, None]
+        # -> disc_per_thr_draw: shape (T, n_draws, n)
+        # contains the binder assignment/discoveries for each candidate threshold and draw
+        disc_per_thr_draw = p_samples[None, :, :] >= candidate_thresh[:, None, None]
 
         # number of discoveries per draw: (T, n_draws)
-        n_disc = mask.sum(axis=2)
+        n_disc = disc_per_thr_draw.sum(axis=2)
 
         # local fdr = 1 - p
         lfdr = 1.0 - p_samples  # (n_draws, n)
 
-        # numerator = sum of lfdr among discoveries: (T, n_draws)
-        num = jnp.einsum("tns,ns->tn", mask, lfdr)
+        # sum of lfdr among discoveries: (T, n_draws), sum of local FDR per threshold and draw
+        sum_lfdr_per_thr_draw = jnp.einsum("tns,ns->tn", disc_per_thr_draw, lfdr)
 
-        # avoid div-by-zero: set fdr_draws=0 where n_disc=0
-        fdr_draws = jnp.where(n_disc > 0, num / n_disc, 0.0)
+        # global FDR per threshold and draw, avoid div-by-zero: set gfdr_per_thr_draw=0 where n_disc=0: (T, n_draws)
+        gfdr_per_thr_draw = jnp.where(n_disc > 0, sum_lfdr_per_thr_draw / n_disc, 0.0)
 
         # posterior probability that FDR ≤ target: (T,)
         # keep thresholds that pass cred_level
-        valid = (fdr_draws <= target_fdr).mean(axis=1) >= cred_intvl
+        valid = (gfdr_per_thr_draw <= target_fdr).mean(axis=1) >= cred_intvl
 
         if valid.any():
             n_discoveries = n_disc.mean(axis=1)
-            # select threshold that passes credibility interval on FDR with the largest number of discoveries
+            # select largest threshold that passes credibility interval on FDR
             threshold_idx = jnp.argmax(jnp.where(valid, n_discoveries, -1))
             threshold = candidate_thresh[threshold_idx]
         else:

From a3878f07fbc3772db46a4f904513acdb19187038 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Tue, 14 Oct 2025 13:41:02 +0200
Subject: [PATCH 04/39] figures: small improvements on figures: 1. shorter
 titles, 2. make save and show figure optional, 3. legend frameon off and add
 titles, 4. changed indentation to spaces

---
 dextrademixer/model/Dextrademixer.py | 182 ++++++++++++++++++---------
 1 file changed, 123 insertions(+), 59 deletions(-)

diff --git a/dextrademixer/model/Dextrademixer.py b/dextrademixer/model/Dextrademixer.py
index 426e50a..71f6ebc 100644
--- a/dextrademixer/model/Dextrademixer.py
+++ b/dextrademixer/model/Dextrademixer.py
@@ -460,7 +460,54 @@ def _predict_posterior_class_dist(p_samples,  target_fdr, cred_intvl, nof_thresh
         assignment = (p_mean >= threshold).astype(jnp.int32)
         return p_mean, assignment, threshold
 
-    def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
+    def get_posterior_samples(self, num_samples: int = 1000, seed: int = 42) -> Dict[str, Array]:
+        """
+        Returns posterior samples of model parameters after fitting the model
+
+        Args:
+            num_samples: number of posterior samples to draw
+            seed: random seed to initialize numpyros RNG-Key store
+
+        Returns:
+            A dictionary with posterior samples of model parameters
+        """
+        if self.trace is None and self.svi_result is None:
+            raise RuntimeError("Model has not been fit yet. Please call `fit` or `fit_svi` first.")
+
+        if self.is_svi:  # svi
+            predictive = npy.infer.Predictive(self.guide, params=self.svi_result.params, num_samples=num_samples)
+            posterior_samples = predictive(jax.random.PRNGKey(seed), data=None)
+        else:  # mcmc inference
+            posterior_samples = self.sampler.get_samples(num_samples)
+
+        if self.is_svi: # svi
+            predictive = npy.infer.Predictive(self.guide, params=self.svi_result.params, num_samples=1000)
+            posterior_samples = predictive(jax.random.PRNGKey(seed), data=None)
+        else: # mcmc inference
+            posterior_samples = self.sampler.get_samples()
+
+        # Extract mean from posterior samples
+        q = posterior_samples["delta_q"].mean(0).cumsum(0)
+        w = posterior_samples["w"].mean(0)
+
+        # extract alpha, which depends on the mode and alpha_model
+        if self.alpha_model == 'kmeans':
+            # shape will be defined by mode, mode=='C': (C, 1), mode=='I': (1, 2)
+            alpha = posterior_samples["alpha"].mean(0)
+        else:
+            overdispersion = posterior_samples["overdispersion"].mean(0) + 1
+            if self.mode == "C":
+                # alpha.shape = (C, 1)
+                q_weighted = (w * q).mean(1)
+                alpha = q_weighted ** 2 / (q_weighted * (overdispersion) - q_weighted)
+            elif self.mode == "I":
+                # alpha.shape = (1, 2)
+                alpha = q ** 2 / (q * (overdispersion) - q)
+
+        return {"q": q, "w": w, "alpha": alpha, "posterior_samples": posterior_samples}
+
+    def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', additional_text=None,
+                     save_dir='figs/', show=False):
 
         if self.trace is None and self.svi_result is None:
             raise RuntimeError("Model has not been fit yet. Please call `fit` or `fit_svi` first.")
@@ -469,71 +516,78 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
 
         # Plot data colored in predicted class assignment
         plt.subplot(3, 4, 1)
-        sns.histplot(x=self.model.data["x"], hue=assignment,
-                     discrete=True, element="step", alpha=0.7)
+        ax = sns.histplot(x=self.model.data["x"], hue=assignment,
+                          discrete=True, element="step", alpha=0.7)
+        leg = ax.get_legend()
+        leg.set_title("Pred class")
+        leg.set_frame_on(False)
+
         sns.despine()
         plt.title("Predicted class assignment")
 
         plt.subplot(3, 4, 5)
-        sns.histplot(x=self.model.data["x"], hue=assignment,
-                     discrete=True, element="step", alpha=0.7)
+        ax = sns.histplot(x=self.model.data["x"], hue=assignment,
+                          discrete=True, element="step", alpha=0.7)
+        leg = ax.get_legend()
+        leg.set_title("Pred class")
+        leg.set_frame_on(False)
         sns.despine()
         plt.yscale("log")
         plt.title("Predicted class assignment log-scale")
 
         plt.subplot(3, 4, 9)
-        sns.scatterplot(x=self.model.data["x"], y=p_pred, hue=assignment,
-                        markers={0: ".", 1: "X"})
+        ax = sns.scatterplot(x=self.model.data["x"], y=p_pred, hue=assignment,
+                             markers={0: ".", 1: "X"})
+        leg = ax.get_legend()
+        leg.set_title("Pred class")
+        leg.set_frame_on(False)
         sns.despine()
+        plt.xlabel("UMI count")
         plt.ylabel("Posterior probability")
-        plt.title("Predicted probability and label of UMI count")
+        plt.title("Pred prob and pred label")
 
         # Plot data colored in true class assignment
         plt.subplot(3, 4, 2)
-        sns.histplot(x=self.model.data["x"], hue=y_true,
-                     discrete=True, element="step", alpha=0.7)
+        ax = sns.histplot(x=self.model.data["x"], hue=y_true,
+                          discrete=True, element="step", alpha=0.7)
+        leg = ax.get_legend()
+        leg.set_title("True class")
+        leg.set_frame_on(False)
         sns.despine()
         plt.title("True class assignment")
 
         plt.subplot(3, 4, 6)
-        sns.histplot(x=self.model.data["x"], hue=y_true,
-                     discrete=True, element="step", alpha=0.7)
+        ax = sns.histplot(x=self.model.data["x"], hue=y_true,
+                          discrete=True, element="step", alpha=0.7)
+        leg = ax.get_legend()
+        leg.set_title("True class")
+        leg.set_frame_on(False)
         sns.despine()
         plt.yscale("log")
         plt.title("True class assignment log-scale")
 
         plt.subplot(3, 4, 10)
-        sns.scatterplot(x=self.model.data["x"], y=p_pred, hue=y_true)
+        ax = sns.scatterplot(x=self.model.data["x"], y=p_pred, hue=y_true)
+        leg = ax.get_legend()
+        leg.set_title("True class")
+        leg.set_frame_on(False)
         sns.despine()
+        plt.xlabel("UMI count")
         plt.ylabel("Posterior probability")
-        plt.title("Predicted probability of UMI count with true label")
-
-        # Plot posterior distribution of Negative Binomial
-        if self.is_svi: # svi
-            predictive = npy.infer.Predictive(self.guide, params=self.svi_result.params, num_samples=1000)
-            posterior_samples = predictive(jax.random.PRNGKey(seed), data=None)
-        else: # mcmc inference
-            posterior_samples = self.sampler.get_samples()
-
-        # Extract mean from posterior samples
-        q = posterior_samples["delta_q"].mean(0).cumsum(0)
-        w = posterior_samples["w"].mean(0)
+        plt.title("Pred prob and true label")
+
+        if additional_text is not None:
+            plt.subplot(3, 4, 11)
+            plt.text(0.01, 0.95, additional_text, fontsize=10, ha='left', va='top')
+            plt.axis('off')
+
+        # # Plot posterior distribution of Negative Binomial
+        posterior_samples = self.get_posterior_samples(num_samples=1000, seed=seed)
+        q = posterior_samples["q"]
+        w = posterior_samples["w"]
+        alpha = posterior_samples["alpha"]
         x = np.arange(0, self.model.data["x"].max())
 
-        # extract alpha, which depends on the mode and alpha_model
-        if self.alpha_model == 'kmeans':
-            # shape will be defined by mode, mode=='C': (C, 1), mode=='I': (1, 2)
-            alpha = posterior_samples["alpha"].mean(0)
-        else:
-            overdispersion = posterior_samples["overdispersion"].mean(0) + 1
-            if self.mode == "C":
-                # alpha.shape = (C, 1)
-                q_weighted = (w * q).mean(1)
-                alpha = q_weighted**2 / (q_weighted * (overdispersion) - q_weighted)
-            elif self.mode == "I":
-                # alpha.shape = (1, 2)
-                alpha = q**2 / (q * (overdispersion) - q)
-
         if self.mode == "C":
             # alpha_weighted is the mean of alpha weighted by w for each cell with shape (2, )
             # This reflects better the contribution of each alpha on average for the binder and non-binder NB component
@@ -547,15 +601,15 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
             # Individual Negative Binomial
             plt.subplot(3, 4, 8)
             ax1 = sns.lineplot(x=x, y=prob0.mean(0), c=sns.color_palette('tab10')[0],
-							   label=f"q={q[0]:.2f} alpha={alpha_weighted[0]:.2f}")
+                               label=f"q={q[0]:.2f} alpha={alpha_weighted[0]:.2f}")
             plt.fill_between(x, np.quantile(prob0, 0.05, axis=0), np.quantile(prob0, 0.95, axis=0), alpha=0.3,
-							 label='5%-95% percentile')
+                             label='5%-95% percentile')
             ax2 = ax1.twinx()
             sns.lineplot(x=x, y=prob1.mean(0), ax=ax2, c=sns.color_palette('tab10')[1],
-						 label=f"q={q[1]:.2f} alpha={alpha_weighted[1]:.2f}")
+                         label=f"q={q[1]:.2f} alpha={alpha_weighted[1]:.2f}")
             plt.fill_between(x, np.quantile(prob1, 0.05, axis=0), np.quantile(prob1, 0.95, axis=0), alpha=0.3,
-							 label='5%-95% percentile')
-            plt.legend()
+                             label='5%-95% percentile')
+            plt.legend(frameon=False)
             sns.despine()
             plt.title("Posterior NB without mixing weights")
             plt.ylabel("Probability")
@@ -568,16 +622,16 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
 
             plt.subplot(3, 4, 4)
             sns.lineplot(x=x, y=prob0_mix.mean(0), c=sns.color_palette('tab10')[0],
-						 label=f"q={q[0]:.2f} alpha={alpha_weighted[0]:.2f}", color=sns.color_palette('tab10')[0])
+                         label=f"q={q[0]:.2f} alpha={alpha_weighted[0]:.2f}", color=sns.color_palette('tab10')[0])
             sns.lineplot(x=x, y=prob1_mix.mean(0), c=sns.color_palette('tab10')[1],
-						 label=f"q={q[1]:.2f} alpha={alpha_weighted[1]:.2f}", color=sns.color_palette('tab10')[0])
+                         label=f"q={q[1]:.2f} alpha={alpha_weighted[1]:.2f}", color=sns.color_palette('tab10')[0])
             sns.lineplot(x=x, y=prob0_mix.mean(0)+prob1_mix.mean(0), c="k", linestyle=":",
-						 label=f"mixture w={w_mean[0]:.4f}, {w_mean[1]:.4f}")
+                         label=f"mixture w={w_mean[0]:.4f}, {w_mean[1]:.4f}")
             plt.fill_between(x, np.quantile(prob0_mix, 0.05, axis=0), np.quantile(prob0_mix, 0.95, axis=0),
-							 alpha=0.3, label='5%-95% percentile')
+                             alpha=0.3, label='5%-95% percentile')
             plt.fill_between(x, np.quantile(prob1_mix, 0.05, axis=0), np.quantile(prob1_mix, 0.95, axis=0),
-							 alpha=0.3, label='5%-95% percentile')
-            plt.legend()
+                             alpha=0.3, label='5%-95% percentile')
+            plt.legend(frameon=False)
             sns.despine()
             plt.title("Posterior Mixture NB")
             plt.ylabel("Probability")
@@ -590,10 +644,10 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
             # Individual Negative Binomial
             plt.subplot(3, 4, 8)
             ax1 = sns.lineplot(x=np.arange(0, self.model.data["x"].max()), y=prob0,
-							   label=f"q={q[0]:.2f} alpha={alpha[0]:.2f}", color=sns.color_palette('tab10')[0])
+                               label=f"q={q[0]:.2f} alpha={alpha[0]:.2f}", color=sns.color_palette('tab10')[0])
             ax2 = ax1.twinx()
             sns.lineplot(x=np.arange(0, self.model.data["x"].max()), y=prob1, ax=ax2,
-						 label=f"q={q[1]:.2f} alpha={alpha[1]:.2f}", color=sns.color_palette('tab10')[1])
+                         label=f"q={q[1]:.2f} alpha={alpha[1]:.2f}", color=sns.color_palette('tab10')[1])
             sns.despine()
             plt.title("Posterior NB without mixing weights")
             plt.ylabel("Probability")
@@ -616,7 +670,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
                                  alpha=0.3, label='5%-95% percentile')
                 plt.fill_between(x, np.quantile(prob1_mix, 0.05, axis=0), np.quantile(prob1_mix, 0.95, axis=0),
                                  alpha=0.3, label='5%-95% percentile')
-                plt.legend()
+                plt.legend(frameon=False)
             else:
                 # w.shape = (2,)
                 prob0_mix = prob0 * w[0]
@@ -629,6 +683,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
                             label=f"q={q[1]:.2f} alpha={alpha[1]:.2f}", linewidth=3)
                 sns.lineplot(x=x, y=(prob0_mix + prob1_mix).reshape(-1), linewidth=3, color="k",
                              label=f"mixture w={w_mean[0]:.4f}, {w_mean[1]:.4f}", linestyle="--")
+                plt.legend(frameon=False)
             sns.despine()
             plt.title("Posterior Mixture NB")
             plt.ylabel("Probability")
@@ -641,7 +696,10 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
             labels = np.argmin(dists, axis=1)
 
             plt.subplot(3, 4, 3)
-            sns.histplot(x=self.model.data["x"], hue=labels, discrete=True, element="step", alpha=0.7, stat='percent')
+            ax = sns.histplot(x=self.model.data["x"], hue=labels, discrete=True, element="step", alpha=0.7, stat='percent')
+            leg = ax.get_legend()
+            leg.set_title("KMeans cluster")
+            leg.set_frame_on(False)
             plt.axvline(cluster_means[0], color="red", linestyle="--")
             plt.axvline(cluster_means[1], color="red", linestyle="--")
             sns.despine()
@@ -649,7 +707,10 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
             plt.ylabel("Percent")
 
             plt.subplot(3, 4, 7)
-            sns.histplot(x=self.model.data["x"], hue=labels, discrete=True, element="step", alpha=0.7, stat='percent')
+            ax = sns.histplot(x=self.model.data["x"], hue=labels, discrete=True, element="step", alpha=0.7, stat='percent')
+            leg = ax.get_legend()
+            leg.set_title("KMeans cluster")
+            leg.set_frame_on(False)
             plt.yscale("log")
             plt.axvline(cluster_means[0], color="red", linestyle="--")
             plt.axvline(cluster_means[1], color="red", linestyle="--")
@@ -666,6 +727,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
 
             sns.lineplot(x=x, y=prob0, label=f"q={cluster_means[0]:.2f} alpha={alpha[0]:.2f}")
             sns.lineplot(x=x, y=prob1, label=f"q={cluster_means[1]:.2f} alpha={alpha[1]:.2f}")
+            plt.legend(title="KMeans cluster", frameon=False)
             sns.despine()
             plt.title("kMeans determined distribution")
             plt.ylabel("Probability")
@@ -677,10 +739,12 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
             # if y_true is None or str
             f1 = -1
         plt.suptitle(config.replace("_", " ")
-					 .replace("ncell", "\nncell") + f"\nF1-score {f1:.3f}",)
-        os.makedirs("figs", exist_ok=True)
-        plt.savefig(f"figs/{config}.png")
-        plt.show()
+                     .replace("ncell", "\nncell") + f"\nF1-score {f1:.3f}",)
+        if save_dir is not None:
+            os.makedirs(save_dir, exist_ok=True)
+            plt.savefig(os.path.join(save_dir, f"{config}.png"))
+        if show:
+            plt.show()
         plt.close()
 
         return q, alpha, w

From 2dee937bc3250f94ca7a1bf14b44774b9d50593a Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Tue, 14 Oct 2025 13:48:15 +0200
Subject: [PATCH 05/39] refactor: cleanup unused files

---
 ...create_data_mean_variance_fold_increase.py |  45 ------
 experiments/synthetic_benchmark/run_beamt.py  |  42 ------
 .../synthetic_benchmark/run_dextrademixer.py  | 133 ------------------
 3 files changed, 220 deletions(-)
 delete mode 100644 experiments/synthetic_benchmark/create_data_mean_variance_fold_increase.py
 delete mode 100644 experiments/synthetic_benchmark/run_beamt.py
 delete mode 100644 experiments/synthetic_benchmark/run_dextrademixer.py

diff --git a/experiments/synthetic_benchmark/create_data_mean_variance_fold_increase.py b/experiments/synthetic_benchmark/create_data_mean_variance_fold_increase.py
deleted file mode 100644
index 7c9192d..0000000
--- a/experiments/synthetic_benchmark/create_data_mean_variance_fold_increase.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import sys
-sys.path.append("../../")
-
-import matplotlib.pyplot as plt
-from dextrademixer.utils import DextramerSimulator
-import hashlib
-
-
-def main():
-    output_h5mu = sys.argv[1]
-    output_pdf = sys.argv[2]
-    total_cell = int(sys.argv[3])
-    nclones = int(sys.argv[4])
-    p_outlier = float(sys.argv[5])
-    p = float(sys.argv[6])
-    cov = bool(int(sys.argv[7]))
-    mean_inc = float(sys.argv[8])
-    var_inc = float(sys.argv[9])
-    i = int(sys.argv[10])
-
-    sim_config = f'{total_cell}_{nclones}_{p_outlier}_{p}_{cov}_{mean_inc}_{var_inc}_{i}'
-    # Use sim_hash to ensure reproducibility, but have variance in seed
-    sim_hash = hashlib.sha256(sim_config.encode('utf-8')).digest()
-    seed = int.from_bytes(sim_hash[:4], byteorder='little')
-
-    plt.ioff()
-    sim = DextramerSimulator()
-
-    mdata1, fig = sim.simulate_pmhc_data_from_distribution(total_cells=total_cell,
-                                                            nof_clones=nclones,
-                                                            p_binding_outlier=p_outlier,
-                                                            binding_ratio=p,
-                                                            mean_inc=mean_inc,
-                                                            var_inc=var_inc,
-                                                            simulate_neg_control=True,
-                                                            use_clonotype_cov=cov,
-                                                            plot_data=True,
-                                                            rng_key=seed)
-    mdata1.write(output_h5mu)
-    fig.savefig(output_pdf)
-    plt.close()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/experiments/synthetic_benchmark/run_beamt.py b/experiments/synthetic_benchmark/run_beamt.py
deleted file mode 100644
index 720686e..0000000
--- a/experiments/synthetic_benchmark/run_beamt.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import sys
-sys.path.append("../../")
-
-import pandas as pd
-
-from dextrademixer.model import BEAMT
-import muon as mu
-
-
-def main(f_in, f_out_csv):
-    mdata = mu.read(f_in)
-    true_binder = mdata.mod["airr"].obs["is_binder"]
-    N = len(true_binder)
-
-    d = {"model": [], "thresh": [], "p": [], "assignment": [], "true_binder": []}
-
-    mixer = BEAMT()
-    mixer.preprocess_model_data(mdata, "pmhc1", neg_ctrl_key="neg_control")
-    mixer.fit()
-    p_thr, assignment_thr = mixer.predict_posterior_class(threshold=0.5)
-    p_fdr, assignment_fdr = mixer.predict_posterior_class(target_fdr=0.05)
-
-    d["model"].extend([f"BEAMT"] * N)
-    d["thresh"].extend(["thresh"] * N)
-    d["p"].extend(p_thr)
-    d["assignment"].extend(assignment_thr)
-    d["true_binder"].extend(true_binder)
-
-    d["model"].extend(["BEAMT"] * N)
-    d["thresh"].extend(["fdr"] * N)
-    d["p"].extend(p_fdr)
-    d["assignment"].extend(assignment_fdr)
-    d["true_binder"].extend(true_binder)
-
-    df = pd.DataFrame.from_dict(d)
-    df.to_csv(f_out_csv)
-
-
-if __name__ == "__main__":
-    f_in = sys.argv[1]
-    f_out_csv = sys.argv[2]
-    main(f_in, f_out_csv)
\ No newline at end of file
diff --git a/experiments/synthetic_benchmark/run_dextrademixer.py b/experiments/synthetic_benchmark/run_dextrademixer.py
deleted file mode 100644
index 501e3a0..0000000
--- a/experiments/synthetic_benchmark/run_dextrademixer.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import argparse
-import multiprocessing
-import os
-import warnings
-import pickle
-import pandas as pd
-import numpyro
-from sklearn.metrics import (roc_auc_score, average_precision_score, f1_score, precision_score, recall_score,
-                             accuracy_score, classification_report)
-import sys
-
-sys.path.append("../../")
-from dextrademixer.model import DextraDemixer
-from dextrademixer.utils import convert_str_to_bool_and_none
-import muon as mu
-
-multiprocessing.set_start_method("spawn", force=True)
-
-
-def parse_arguments():
-    parser = argparse.ArgumentParser(description="Run tool on single file.")
-    # Data parameters
-    parser.add_argument("--input_file", type=str, default="simulation/test.h5mu", help="Input .h5mu file")
-    parser.add_argument("--output_file", type=str, default="output.csv",
-                        help="Output CSV file for performance results")
-    parser.add_argument("--gex_key", type=str, default="gex",
-                        help="Key for modality where pMHC counts are stored")
-    parser.add_argument("--airr_key", type=str, default='airr',
-                        help="Key for modality where TCR data is stored")
-    parser.add_argument("--pmhc_key", type=str, default="pmhc1",
-                        help="Key for pMHC counts, expected to be in 'gex' modality")
-    parser.add_argument("--label_key", type=str, default=None,
-                        help="Key for labels, expected to be in 'airr' modality")
-
-    # Model parameters
-    parser.add_argument("--model_type", default='mixturemodelkmeans', help="Model type",
-                        choices=["mixturemodelkmeans", "mixturemodel"])
-    parser.add_argument("--mode", type=str, default="I",
-                        help="Processing mode. 'I' -> independent concentration parameter for signal and noise data. "
-                             "'C' -> clonotype level concentration parameter", choices=["I", "C"])
-    parser.add_argument("--neg_ctrl_key", type=str, default=None,
-                        help="Key for negative control counts. If not None, enables model to use negative control data for "
-                             "fitting. Expected to be in 'gex' modality. If None, no negative control data is used.")
-    parser.add_argument("--ir_clone_key", type=str, default='clone_id',
-                        help="Key for clonotype information. If not None, enables model to have different "
-                             "mixture coefficients for each clonotype. Expected to be in 'airr' modality. ")
-    parser.add_argument("--alpha_model", type=str, default="kmeans",
-                        choices=["overdispersion", "kmeans"],
-                        help="Modeling of the alpha parameter. Options: 'overdispersion', 'kmeans'.")
-    parser.add_argument("--overdispersion_scale_prior", type=float, default=1e-2,
-                        help="Prior for scale parameter of HalfCauchy for overdispersion model. Not used for kmeans.")
-    parser.add_argument("--var_hyperprior", type=float, default=1e0,
-                        help="Prior for scale parameter of kmeans model. Not used for overdispersion.")
-    parser.add_argument("--outlier_threshold", type=float, default=4.0,
-                        help="Threshold for outlier removal based on log transformed z-score.")
-    parser.add_argument("--target_fdr", type=float, default=0.05,
-                        help="Target FDR for posterior class assignment. If None, uses threshold instead.")
-    parser.add_argument("--threshold", type=float, default=None,
-                        help="Threshold for posterior class assignment. If None, uses target_fdr instead.")
-
-    # Optimization parameters
-    parser.add_argument("--seed", type=int, default=42, help="Random seed")
-    parser.add_argument("--maxiter", type=int, default=5000, help="Number of iterations for optimization")
-    parser.add_argument("--lr", type=float, default=1e-2, help="Learning rate")
-    return parser.parse_args()
-
-
-def run_inference(f_in, args):
-    opt_params = {"maxiter": args.maxiter,
-                  "nof_inits": 10,
-                  "adam": {"init_value": args.lr,},
-                  "overdispersion_scale_prior": args.overdispersion_scale_prior,
-                  "var_hyperprior": args.var_hyperprior,
-                  }
-
-    numpyro.set_host_device_count(1)
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-        mdata = mu.read(f_in)
-    if args.label_key is None:
-        y_true = pd.Series(0, index=mdata.mod["airr"].obs.index)
-    else:
-        y_true = mdata.mod[args.airr_key].obs[args.label_key]
-
-    mixer = DextraDemixer(model_type=args.model_type, mode=args.mode, alpha_model=args.alpha_model)
-    mixer.preprocess_model_data(mdata,
-                                pmhc_key=args.pmhc_key,
-                                gex_key=args.gex_key,
-                                neg_ctrl_key=args.neg_ctrl_key,
-                                ir_clone_key=args.ir_clone_key,
-                                outlier_threshold=args.outlier_threshold,)
-
-    mixer.model._model_config["overdispersion_scale_prior"] = opt_params["overdispersion_scale_prior"]
-    mixer.model._model_config["var_hyperprior"] = opt_params["var_hyperprior"]
-
-    trace, best_loss = mixer.fit_svi(svi_config=opt_params,
-                                     nof_inits=opt_params["nof_inits"],
-                                     rng_key=args.seed,
-                                     return_loss=True)
-    p_pred, assignment = mixer.predict_posterior_class(target_fdr=args.target_fdr, threshold=args.threshold)
-
-    config = (f"{args.model_type}_{args.mode}_{args.neg_ctrl_key}_{args.ir_clone_key}_{args.alpha_model}_{opt_params['overdispersion_scale_prior']},{opt_params['var_hyperprior']}_lr={opt_params['adam']['init_value']}\n"
-              f"{f_in.replace('simulation/sim_', '').replace('.h5mu', '').replace('/', '-')}")
-
-    print(classification_report(y_true[y_true.notna()].astype(int), assignment[y_true.notna().values]))
-    mixer.plot_results(assignment, p_pred, y_true, args.seed, config)
-
-    os.makedirs("saved_models", exist_ok=True)
-    with open(f"saved_models/{config}.pkl", "wb") as f:
-        pickle.dump(mixer, f)
-
-    return y_true, p_pred, assignment, best_loss
-
-
-def main():
-    args = parse_arguments()
-    args = convert_str_to_bool_and_none(args)
-
-    y_true, p_pred, assignment, best_loss = run_inference(args.input_file, args)
-
-    results = pd.Series()
-    results['roc_auc'] = roc_auc_score(y_true[y_true.notna()].astype(int), p_pred[y_true.notna()])
-    results['pr_auc'] = average_precision_score(y_true[y_true.notna()].astype(int), p_pred[y_true.notna()])
-    results['f1'] = f1_score(y_true[y_true.notna()].astype(int), assignment[y_true.notna()])
-    results['precision'] = precision_score(y_true[y_true.notna()].astype(int), assignment[y_true.notna()])
-    results['recall'] = recall_score(y_true[y_true.notna()].astype(int), assignment[y_true.notna()])
-    results['accuracy'] = accuracy_score(y_true[y_true.notna()].astype(int), assignment[y_true.notna()])
-    print(results)
-    results.to_csv(args.output_file)
-
-
-if __name__ == "__main__":
-    main()

From 5f25647a1943af9f3afcced0fc231fb26bdabfac Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Tue, 14 Oct 2025 14:39:17 +0200
Subject: [PATCH 06/39] update cluster params

---
 experiments/.slurm/config.yaml             |  8 ++---
 experiments/.slurm_preemptible/config.yaml | 38 ++++++++++++++++++++++
 experiments/.slurm_preemptible/status.py   | 15 +++++++++
 3 files changed, 57 insertions(+), 4 deletions(-)
 create mode 100644 experiments/.slurm_preemptible/config.yaml
 create mode 100755 experiments/.slurm_preemptible/status.py

diff --git a/experiments/.slurm/config.yaml b/experiments/.slurm/config.yaml
index 73b2b13..41b10b2 100644
--- a/experiments/.slurm/config.yaml
+++ b/experiments/.slurm/config.yaml
@@ -4,12 +4,12 @@ cluster:
     --partition=cpu_p
     --qos=cpu_normal
     --nice=0
-    -t 3-00:00:00
+    -t 1-00:00:00
     --mem={resources.mem_mb}
     -c {resources.c}
-    --job-name={rule}-{wildcards} 
-    --output=./logs/slurm_logs/{rule}/{rule}-{wildcards}.out 
-    --error=./logs/slurm_logs/{rule}/{rule}-{wildcards}.err
+    --job-name={rule}-$(date +%Y%m%d_%H%M%S)-{wildcards} 
+    --output=./logs/slurm_logs/{rule}/$(date +%Y%m%d_%H%M%S)-{wildcards}.out 
+    --error=./logs/slurm_logs/{rule}/$(date +%Y%m%d_%H%M%S)-{wildcards}.err
     --parsable
 
 default-resources:
diff --git a/experiments/.slurm_preemptible/config.yaml b/experiments/.slurm_preemptible/config.yaml
new file mode 100644
index 0000000..f3898e6
--- /dev/null
+++ b/experiments/.slurm_preemptible/config.yaml
@@ -0,0 +1,38 @@
+cluster:
+  mkdir -p ./logs/slurm_logs/{rule} &&
+  sbatch
+    --partition=cpu_p
+    --qos=cpu_preemptible
+    --nice=0
+    -t 3-00:00:00
+    --mem={resources.mem_mb}
+    -c {resources.c}
+    --job-name={rule}-$(date +%Y%m%d_%H%M%S)-{wildcards} 
+    --output=./logs/slurm_logs/{rule}/$(date +%Y%m%d_%H%M%S)-{wildcards}.out 
+    --error=./logs/slurm_logs/{rule}/$(date +%Y%m%d_%H%M%S)-{wildcards}.err
+    --parsable
+
+default-resources:
+  - c=16
+  - mem_mb=64000
+  - mem="64G"
+  - disk_mb=250000
+
+set-resources:
+  - dummy_rule:c=32
+  - dummy_rule:mem=128000
+  - dummy_rule:mem_mb=128000
+  - run_simulation:c=1
+
+set-threads:
+  - dummy_rule=20
+
+restart-times: 1
+max-jobs-per-second: 10
+max-status-checks-per-second: 1
+latency-wait: 60
+jobs: 1000
+keep-going: True
+printshellcmds: True
+scheduler: greedy
+use-conda: True
diff --git a/experiments/.slurm_preemptible/status.py b/experiments/.slurm_preemptible/status.py
new file mode 100755
index 0000000..aa8681d
--- /dev/null
+++ b/experiments/.slurm_preemptible/status.py
@@ -0,0 +1,15 @@
+#!/usr/bin/python
+import subprocess
+import sys
+
+jobid = sys.argv[-1]
+
+output = str(subprocess.check_output("sacct -j %s --format State --noheader | head -1 | awk '{print $1}'" % jobid, shell=True).strip())
+
+running_status=["PENDING", "CONFIGURING", "COMPLETING", "RUNNING", "SUSPENDED", "PREEMPTED"]
+if "COMPLETED" in output:
+    print("success")
+elif any(r in output for r in running_status):
+    print("running")
+else:
+    print("failed")

From b227af7483be039395849d1402dffd19c52d0e21 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Tue, 14 Oct 2025 17:41:19 +0200
Subject: [PATCH 07/39] feature: return mean_over_cell posterior parameters

---
 dextrademixer/model/Dextrademixer.py | 25 ++++++++++++++++++++-----
 1 file changed, 20 insertions(+), 5 deletions(-)

diff --git a/dextrademixer/model/Dextrademixer.py b/dextrademixer/model/Dextrademixer.py
index 71f6ebc..aa951ff 100644
--- a/dextrademixer/model/Dextrademixer.py
+++ b/dextrademixer/model/Dextrademixer.py
@@ -460,7 +460,7 @@ def _predict_posterior_class_dist(p_samples,  target_fdr, cred_intvl, nof_thresh
         assignment = (p_mean >= threshold).astype(jnp.int32)
         return p_mean, assignment, threshold
 
-    def get_posterior_samples(self, num_samples: int = 1000, seed: int = 42) -> Dict[str, Array]:
+    def get_posterior_samples(self, num_samples: int = 1000, seed: int = 42) -> Dict:
         """
         Returns posterior samples of model parameters after fitting the model
 
@@ -490,21 +490,36 @@ def get_posterior_samples(self, num_samples: int = 1000, seed: int = 42) -> Dict
         q = posterior_samples["delta_q"].mean(0).cumsum(0)
         w = posterior_samples["w"].mean(0)
 
+        if self.model.data["clone_continuous"] is not None:
+            # w is per clone, transform to per cell and take mean over all cells
+            w_cell = w[self.model.data["clone_continuous"]]
+            w_mean_over_cells = w_cell.mean(0)
+        else:
+            w_mean_over_cells = w
         # extract alpha, which depends on the mode and alpha_model
         if self.alpha_model == 'kmeans':
-            # shape will be defined by mode, mode=='C': (C, 1), mode=='I': (1, 2)
+            # shape will be defined by mode, mode=='C': (C, ), mode=='I': (2, )
             alpha = posterior_samples["alpha"].mean(0)
         else:
             overdispersion = posterior_samples["overdispersion"].mean(0) + 1
             if self.mode == "C":
-                # alpha.shape = (C, 1)
+                # alpha.shape = (C, )
                 q_weighted = (w * q).mean(1)
                 alpha = q_weighted ** 2 / (q_weighted * (overdispersion) - q_weighted)
             elif self.mode == "I":
-                # alpha.shape = (1, 2)
+                # alpha.shape = (2, )
                 alpha = q ** 2 / (q * (overdispersion) - q)
 
-        return {"q": q, "w": w, "alpha": alpha, "posterior_samples": posterior_samples}
+        if self.mode == "C":
+            # alpha is per clone, transform to per cell and take mean over all cells
+            alpha_cell = alpha[self.model.data["clone_continuous"]]
+            alpha_cell = alpha_cell[:, None] * w_cell
+            alpha_mean_over_cells = alpha_cell.mean(0)
+        else:
+            alpha_mean_over_cells = alpha
+
+        return {"q": q, "w": w, "alpha": alpha,
+                "w_mean_over_cells": w_mean_over_cells, "alpha_mean_over_cells": alpha_mean_over_cells}
 
     def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', additional_text=None,
                      save_dir='figs/', show=False):

From d1bb1ad5d97e728f642b6eca72a2e48e5a3afe80 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Tue, 14 Oct 2025 17:53:12 +0200
Subject: [PATCH 08/39] add estimate_sim_params.ipynb

---
 .../estimate_sim_params.ipynb                 | 2539 +++++++++++++++++
 1 file changed, 2539 insertions(+)
 create mode 100644 experiments/synthetic_benchmark/estimate_sim_params.ipynb

diff --git a/experiments/synthetic_benchmark/estimate_sim_params.ipynb b/experiments/synthetic_benchmark/estimate_sim_params.ipynb
new file mode 100644
index 0000000..ac10eda
--- /dev/null
+++ b/experiments/synthetic_benchmark/estimate_sim_params.ipynb
@@ -0,0 +1,2539 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6c23dee3-f16e-408a-9be9-c317b0177da0",
+   "metadata": {},
+   "source": [
+    "Check real BEAM-T dataset\n",
+    "Estimate parameters for simulation\n",
+    "Check if simulation makes sense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a820175b-0368-4969-9eaf-8066fb5f142a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T15:46:33.412581Z",
+     "start_time": "2025-08-14T15:46:33.370004Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import os\n",
+    "import warnings\n",
+    "from collections import defaultdict\n",
+    "from tqdm import tqdm\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.image as mpimg\n",
+    "import muon as mu\n",
+    "import scirpy as ir\n",
+    "import scanpy as sc\n",
+    "from mudata import MuData\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "import numpyro.distributions as npd\n",
+    "import jax\n",
+    "import statsmodels.formula.api as smf\n",
+    "from scipy import stats\n",
+    "from scipy.stats import truncnorm\n",
+    "\n",
+    "\n",
+    "warnings.catch_warnings()\n",
+    "warnings.simplefilter(\"ignore\")\n",
+    "\n",
+    "pd.set_option('display.max_columns', 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c96e60db-9425-483a-9222-f14cdef98c17",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T12:42:11.737315Z",
+     "start_time": "2025-08-14T12:42:11.707443Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6c468fc-85ba-4d58-b8b5-81b08d3eb1d7",
+   "metadata": {},
+   "source": [
+    "# Estimate simulation parameters from real data (BEAM-T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8e3a13b6796b87b3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T12:42:12.126649Z",
+     "start_time": "2025-08-14T12:42:11.831021Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Preprocessing\n",
+    "experiment = '10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein'\n",
+    "if not os.path.exists(f'../../data/BEAMT/{experiment}/preprocessed.h5mu'):\n",
+    "    adata_tcr = ir.io.read_10x_vdj(\n",
+    "        f\"../../data/BEAMT/{experiment}/filtered_contig_annotations.csv\")\n",
+    "\n",
+    "    adata = sc.read_10x_h5(\n",
+    "        f\"../../data/BEAMT/{experiment}/sample_filtered_feature_bc_matrix.h5\",\n",
+    "        gex_only=False)\n",
+    "    adata.var_names_make_unique()\n",
+    "    mdata = mu.MuData({\"gex\": adata, \"airr\": adata_tcr})\n",
+    "    ir.pp.index_chains(mdata)\n",
+    "    ir.tl.chain_qc(mdata)\n",
+    "\n",
+    "    # filter TCRs only and antigen barcodes only\n",
+    "    mdata = mdata[mdata.obs[\"airr:receptor_type\"] == \"TCR\"]\n",
+    "    mdata = mdata[:, mdata.var[\"gex:feature_types\"] == \"Antigen Capture\"]\n",
+    "\n",
+    "    # minimal pMHC QC filtering\n",
+    "    sc.pp.filter_cells(mdata[\"gex\"], min_genes=1)\n",
+    "    sc.pp.filter_genes(mdata[\"gex\"], min_cells=10)\n",
+    "\n",
+    "    mdata.update()\n",
+    "\n",
+    "    mu.pp.filter_obs(mdata, \"airr:chain_pairing\", lambda x: ~np.isin(x, [\"orphan VDJ\", \"orphan VJ\"]))\n",
+    "    mu.pp.intersect_obs(mdata)\n",
+    "\n",
+    "    ir.pp.ir_dist(mdata)  # Adds clone_id\n",
+    "    ir.tl.define_clonotypes(mdata, receptor_arms=\"all\", dual_ir=\"primary_only\")\n",
+    "    \n",
+    "    ir.pp.ir_dist(mdata, metric=\"alignment\", sequence=\"aa\", cutoff=250)\n",
+    "    ir.tl.define_clonotype_clusters(mdata, sequence=\"aa\", metric=\"alignment\", receptor_arms=\"all\",\n",
+    "                                    dual_ir=\"primary_only\")  # Used for distance calculation\n",
+    "    ir.tl.clonotype_network(mdata, min_cells=3, sequence=\"aa\", metric=\"alignment\")\n",
+    "\n",
+    "    dist = mdata.mod[\"airr\"].uns[\"cc_aa_alignment\"][\"distances\"].toarray()\n",
+    "    mdata.mod[\"airr\"].uns[\"ir_dist_aa_full\"] = dist - 1\n",
+    "\n",
+    "    mdata.write(f'../../data/BEAMT/{experiment}/preprocessed.h5mu', compression='gzip')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9d888a41-70af-4ad9-abdc-e6d468524a03",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T13:51:34.417924Z",
+     "start_time": "2025-08-14T13:51:25.536258Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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hg9x6660++BcAAFiVtzIwNjbW/Oa3dOlS2b17t4wYMUJyc3MlOjranI+KijIL49hMnz5dJkyYYBbI0d8UNet00znzSv+OGTNGNm3aZDJTx58DBgyQm2++WSIiInz4LwIAsBLmQwAArIwcBADAM7ISAFDZkE0AAFTN+YVXU5o+mTcIACiOauIjWkDpb3/7m5n8MHToUJkxY4ZTkaXatWubwk3Nmzcv1gQM3VzRCRiX06D2FNa2CRi6uaNFnfT/BAAAUFG8laWOk/8jIyOdJv+7yte2bdvKN99843Rs/Pjx8tNPP8mcOXOkZcuWXvuOAADr8uaYEQAAf0NOAgD8lTcyrqT3O5UugqrnR48ebT+2fv16j4uj/vDDD3L69GkWMgUAVLqxXmpqqlnEbeHChWYB06SkJLPQ2t69e6VJkyYun6sZMmSI9OrVyxSE0gXd7r33Xtm1a5e0aNHCtNFrmTt3rlkUThf81gXftM9vv/3WvAcAAH++n6m5atOpUycz7tRCTwkJCaawryt6btKkSeV4lQAAf89BzaNhw4aZOQzdu3c3Y73LF+vWMZxmkMrPzzdjNtvrI0eOmOJMderUMQtyq6efflpWrFghf//736Vu3br2CYj169c3xX2///57c14nUF577bXy9ddfy3PPPSd33XWXyUQAALyhqo8ZAQCoDBk4aNAgOXXqlMTFxZmxXXh4uKxbt86++ExWVpYEBgba2y9YsMCMFR999FGnfuLj402xw6CgIDMG1N8Gz5w5Y65Bfz+cPHmy23uiAACUBGNBAICVeTMHk5OTZebMmWYsGBYWJvPmzTO/Jbqiz4TquDEzM1MOHTokr732mtP8CaVjwsufd2nTpo3s2bOn1N8XAICSYswIAKhsyCYAAMrOn/KUeYMAgAot8KQTBvVHwYcfftjtA52NGzeWzz77zFeXAABAlebNLC3J5H9dpM1xIKwaNGhg/l5+HACA0mLMCACAe+QkAMBfeSvjSrrY6ahRo6RPnz4ye/Zs6d+/v6xcuVK2bt0qixYtMufPnTtnHrB55JFHpGnTpmZh07Fjx5qFULW4BQAAlSkHExMTJSYmxp57WuhpzZo1kpKSIi+99NIV7ZcvX+60/8Ybb8h7771nih9qZurDspql48ePlwEDBpg2y5YtMwvCrV69WgYPHlyGbw0AsAJvZJye10VFT5w44XRc93Wc5ooe99Te9lePORbv1X1dANUdLaB46dIlOXjwoFnExpVx48Y5FYbKycmRli1buu0TAOC/vDXWK+li3UePHpXbb7/dvq+THXXT+6Ba6Ne2oLe6++67nT7rzTfflMcff9wUNdywYYP9/qpmmd4j1fEhAADewjMwAACr8nYGjhw50myu2MaBNnpv0xMt+vvxxx8X63MBACgNxoIAACvzVg6mpqaaZ1P0GVF9lkV/09O5DXv37pUmTZpc0f78+fPSunVrGThwoDz33HNu+73tttvMb4Q21ar5bPk5AABcYswIAKhsyCYAAKru/MKrKU2fzBsEABTHL7P8vCw+Pt784Hd5oOrE93/961/2H/h0EiEAAPBtlurkf528r5P/deL/jh07rpj8f+zYMR99EwAArsSYEQAA98hJAIC/8lbGlfR+Z69evWTFihWmoFNYWJi8++67pmCFraC9PpDz9ddfy4MPPii33nqrDB8+XLp06SJffPGF24eHAACoiBzMz8+XzMxM6du3r/2YLvCt+xkZGcW6Dp3Ef/HiRWnUqJHZP3DggFk83LHP+vXrmwUBPPWZl5dnHkp13AAA1uSNjNMCEzoO0wKENoWFhWa/Z8+eLt+jxx3bq/Xr19vbt2rVyky0cGyjebV582a3fSodY2q+uloMx0a/a7169Zw2AIA1efN3PV2o+9ChQ2a8pXml4zLHxbqXLFli3w8NDTUFey/fHBf1dnVeNy3upHSS4eeffy6nT5+WCxcuyL59+2TGjBnkGgDAq3gGBgBgVWQgAMDKyEEAgJV5KwcTExMlJiZGoqOjpX379qbQU61atSQlJcVl+27dusnMmTNl8ODBHudA6Gfr8zS2TRc4BQCgPDFmBABUNmQTAABVd37h1ZSmT+YNAgCKo5r4yD333GMWTrt8kvvZs2fNuYKCAl99NAAAfsHbWaqT/3VzxXFSvyuOCwMAAOANjBndu3CxsKIvAQBQwchJAIC/8mbGlfR+pz4MpJsrNWvWlI8//rjYnw0AQEXlYHZ2tmlnK2poo/t79uwp1nW8+OKL0rx5c3tBJy3uZOvj8j5t51xJSEiQSZMmFeszAQD+zVtjvdjYWBk2bJh07dpVunfvLklJSZKbm2sWqlFRUVHSokULk0Fq1KhRZlLH7NmzpX///rJy5UrZunWrKe6rAgICZPTo0TJlyhS55ZZbTMGnCRMmmByMjIw0bbSYoRbQ0OusW7eu2X/uuefk97//vTRs2NDL/1IAAH/E73oAAHhGVgIArIoMBABYGTkIALAyb+Rgfn6+ZGZmyrhx4+zHAgMDzXOf+mxLWezbt888OxMcHGwWMNXncG644Qa37fPy8sxmk5OTU6bPBwCAMSMAoLIhmwAAqLrzC/Ve6rfffmt/feTIEdmxY4fUqVNHbr755mL1CQBApSrwVFRUZCbIX+706dNSu3ZtX30sAAB+gywFAPgzcs69c3mXKvoSAAAVjJwEAPgrMg4AYGWVIQenTZtmil9oMUSdnF8WunCAPtTqOGm/ZcuWXrhKAIBVM27QoEFy6tQpiYuLM0UGw8PDZd26dfYihFlZWWaxGptevXrJihUrZPz48fLyyy+bIk6rV6+WDh062NuMHTvWTLh44okn5MyZM9K7d2/Tpy0Ha9SoYbJx4sSJZjEaLQKlBZ4cMw4AgMo+1gMAoDIjKwEAVkUGAgCsjBwEAFiZN3IwOzvbLHhqe2bGRvf37NlT6mvr0aOHLFmyRNq0aWMWW500aZLceeedsnPnTqlbt67L9+hCqdoOAABvYcwIAKhsyCYAAKru/MKjR4/K7bffbt+fNWuW2fr06WPm0henTwAAKkWBp4cfftj81UB9/PHHzQR4G/3h8OuvvzYT6wEAgGtkKQDAn5FzAAC4R04CAPwVGQcAsDJv5mDjxo0lKChITpw44XRc95s2berxvfpAqhZ42rBhg3Tq1Ml+3PY+7aNZs2ZOfepDqu7o93D8LgAA6/HFWG/kyJFmc8U2qcLRwIEDzeaOXturr75qNlc6d+4smzZtKtE1AgCguOcJAIBnZCUAwKrIQACAlZGDAAArqwo5eN9999lf63OkWvDpxhtvlHfeeUeGDx/u8j3jxo2T2NhY+35OTo60bNmyXK4XAOBfqkJWAgCshWwCAKDqzy8MDQ01xaXK0icAAJWiwFP9+vXNXw22unXrSs2aNe3nqlevLnfccYfExMR4+2P93oWLBRJ8TVBFXwYAoByQpQAAf0bOAQDgHjkJAPBXZBwAwMq8mYPavkuXLpKWliaRkZHmWGFhodn39GDpjBkzZOrUqfLxxx9L165dnc61atXKFHnSPmwFnXQC/ubNm2XEiBGl+s4AAGtgrAcAsDJyEAAAz8hKAIBVkYEAACsjBwEAVubNHGzcuLEEBQXJiRMnnI7rvj7v6S0NGjSQW2+9Vfbv3++2jS7I6rgoKwAApcWYEQBQ2ZBNAACUHXkKALAqrxd4evPNN+3VC1944QWpXbu2tz/Cks7+fJECTwBgEWQpAMCfkXMAALhHTgIA/BUZBwCwMm/nYGxsrAwbNswUaurevbskJSVJbm6uREdHm/NRUVHSokULSUhIMPvTp0+XuLg4WbFihbmG48ePm+N16tQxW0BAgIwePVqmTJkit9xyiyn4NGHCBGnevLm9iBQAAK4w1gMAWBk5CACAZ2QlAMCqyEAAgJWRgwAAK/NmDurCp126dJG0tDT7c5yFhYVmf+TIkV675nPnzsn3338vQ4cO9VqfAAC4w5gRAFDZkE0AAJQdeQoAsCqvF3iyiY+P91XXAABYAlkKAPBn5BwAAO6RkwAAf0XGAQCszFs5OGjQIDl16pQp2qTFmsLDw2XdunUSEhJizmdlZUlgYKC9/YIFCyQ/P18effTRK65n4sSJ5vXYsWNNkagnnnhCzpw5I7179zZ9BgcHe+WaAQD+jbEeAMDKyEEAADwjKwEAVkUGAgCsjBwEAFiZt3IwNjZWhg0bJl27dpXu3btLUlKSec4zOjranI+KipIWLVpIQkKC2dfnRL/99lv76yNHjsiOHTukTp06cvPNN5vjusDqb37zG7nxxhvl6NGj5lqDgoJkyJAhXrlmAACKgzEjAKCyIZv807++OyU/Xbgk/Ts18+nnbM/6P/nnzuNyXZ0aUi0oQHredK20bVrvinbf/HBWvj5yRrqHNpLPvzsl19WtYT/3l/T9ck1goJz5OV+SP/te3hvRS366cFFmf/Kdvc0H249I19BGsmJzlny864Q5NnXtbknfe0qeuvsml9f257W75c3obh6vPzZ1hzR2uJbH3tgkIXWDZfZvwyQgIEA+/Oqo/PObYzKsV6j87/5seerum6Vm9SDTtqioSF5ZvVMKC4tkcmQHmffpfonqeaM0rvNLf2pZxkE58n8/y91tmph/H5vE9d/JziNnJeXxbqav+Z/ul9+ENZf3t/0gj/+qlaz5+qjcfkND6dCifjH+SwCoaOQpAMBqvFrgqXPnzpKWliYNGzaU22+/3fyfcXe2bdvmzY8GAMAvkKUAAH9GzgEA4B45CQDwV2QcAMDKfJWDI0eONJsr6enpTvsHDx68an96Xa+++qrZAAAoDsZ6AAArIwcBAPCMrAQAWBUZCACwMnIQAGBlvsjBQYMGyalTpyQuLk6OHz8u4eHhsm7dOgkJCTHns7KyJDAw0N5eCzbpZ9vMmjXLbH369LE/V/rDDz+YYk6nT5+W6667Tnr37i2bNm0yrwEA8CXGjACAyoZs8n9RKVvM3/6d+vv0c2KWbZXsc/n2/XbN6sk/R915Rbsn386UI2d+ls43NJBtWWeczs1Yt9dp/5EFG694//LNWTL1oY7y8gff2I/9fcdR8zfhn3tcXpt+3tKNnueWvr/9iNP+/+4/bf6Ou7+dKUL17N+2m30tYqXaN6sn93X8b9Es/d5acEr9um0TmZu2Ty4VFMrYfm2d+oz7+y7z9/V//UcOTvvlv4e2t8n5+ZLMXv+dvJN5WA7/+LMEBgZI0oZ90qpxbfnshbs9fgcAFYc8BQBYmVcLPA0YMEBq1PhvpdTIyEhvdg0AgCWQpQAAf0bOAQDgHjkJAPBXZBwAwMrIQQCAvyLjAABWRg4CAOAZWQkAsCoyEABgZeQgAMDKfJWDI0eONJsrtqJNNqGhoVJUVOSxv5UrV3rt2gAAKAnGjACAyoZsgrc4FndSh38877bYkuPf8pKbd8mr/V0sdH3vofD/3ZM4V8bPO/VTnvl7Pr/A/D2QnVum/gD4FnkKALAyrxZ4io+Pd/kaAAAUD1kKAPBn5BwAAO6RkwAAf0XGAQCsjBwEAPgrMg4AYGXkIAAAnpGVAACrIgMBAFZGDgIArIwcBADAM7ISAFDZkE0AAJQdeQoAsLLAir4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD8VUDAf/8eyM6t6EsBAAAAAAAAAAAAAABACVUTL2rYsKEE2J4muYoff/zRmx/t9wqLiir6EgAA5YAsBQD4M3IOAAD3yEkAgL8i4wAAVkYOAgD8FRkHALAychAAAM/ISgCAVZGBAAArIweLL/iaIPO3dnWvLvcDAKhA5CAAAJ6RlQCAyoZsAgCg7MhTAICVefWJj6SkJG92Bwfrdh6X6F+1qujLAAD4GFkKAPBn5BwAAO6RkwAAf0XGAQCsjBwEAPgrMg4AYGXkIAAAnpGVAACrIgMBAFZGDgIArIwcBADAM7ISAFDZkE0AAJQdeQoAsDKvFngaNmyYN7uDgwPZuRV9CQCAckCWAgD8GTkHAIB75CQAwF+RcQAAKyMHAQD+iowDAFgZOQgAgGdkZcmc/fliRV8CAMBLyEAAgJWRgwAAKyMHAQDwjKwEAFQ2ZBMAAGVHngIArMyrBZ5ycnKkXr169tee2NoBAIBfkKUAAH9GzpXcubxLUqeGV4fuAIBKipwEAPgrMg4AYGXkIADAX5FxAAArIwcBAPCMrCyZ/SfPVfQlAAC8hAwEAFgZOQgAsDJyEAAAz8hKAEBlQzYBAFB25CkAwMq8ukp0w4YN5dixY9KkSRNp0KCBBAQEXNGmqKjIHC8oKPDmRwMA4BfIUgCAPyPnSq6wqKiiLwEAUE7ISQCAvyLjAABWRg4CAPwVGQcAsDJyEAAAz8hKAIBVkYEAACvzVQ4mJyfLzJkz5fjx4xIWFibz5s2T7t27u22/atUqmTBhghw8eFBuueUWmT59utx///1ObXbv3i0vvviifP7553Lp0iVp3769vPfee3LDDTeU8FsDAPBfjAcBAPCMrAQAVDZkEwAAZUeeAgCszKsFnj799FNp1KiRef3ZZ595s2sAACyBLAUA+DNyDgAA98hJAIC/IuMAAFZGDgIA/BUZBwCwMnIQAADPyMqScbGmAQCgiiIDAQBW5oscTE1NldjYWFm4cKH06NFDkpKSJCIiQvbu3WsWirvcxo0bZciQIZKQkCAPPPCArFixQiIjI2Xbtm3SoUMH0+b777+X3r17y/Dhw2XSpElSr1492bVrlwQHB3vlmgEA1sR4EAAAz8hKAEBlQzYBAFB25CkAwMq8WuCpT58+Ll8DAIDiIUsBAP6MnAMAwD1yEgDgr8g4AICVkYMAAH9FxgEArIwcBADAM7ISAGBVZCAAwMp8kYOJiYkSExMj0dHRZl8LPa1Zs0ZSUlLkpZdeuqL9nDlzpF+/fjJmzBizP3nyZFm/fr3Mnz/fvFe98sorcv/998uMGTPs77vpppu8cr0AAOtiPAgAgGdkJQCgsiGbAAAoO/IUAGBlXi3wdLn/+7//k7/+9a+ye/dus9++fXvz8IytsiIAAPCMLAUA+DNyDgAA98hJAIC/IuMAAFZGDgIA/BUZBwCwMnKw7H7Mza/oSwAA+BBZCQCwKjIQAGBlZc3B/Px8yczMlHHjxtmPBQYGSt++fSUjI8Ple/R4bGys07GIiAhZvXq1eV1YWGgKRI0dO9Yc3759u7Rq1cp8RmRkpNtrycvLM5tNTk5Osb4DAMC6GA8CAOAZWQkAqGzIJgAAyo48BQBYSaCvOv7Xv/4loaGhMnfuXBOuuulrfcBFzwEAAM/IUgCAPyPnAABwj5wEAPgrMg4AYGXkIADAX5FxAAArIwcBAPCMrAQAWBUZCACwMm/kYHZ2thQUFEhISIjTcd0/fvy4y/focU/tT548KefOnZNp06ZJv3795JNPPpGHHnpIHn74Yfn888/dXktCQoLUr1/fvrVs2bJY3wEAYE2MBwEA8IysBAD4ezYlJyeb/oKDg6VHjx6yZcsWj+1XrVolbdu2Ne07duwoa9eudTpfVFQkcXFx0qxZM6lZs6b07dtX9u3bZz9/8OBBGT58uLlePX/TTTdJfHy85OfnO/Xz9ddfy5133mk+R+9xzpgxo8TfDQAAdxjrAQCsppqvOn766adl0KBBsmDBAgkKCjLH9AGap556ypz75ptvfPXRAAD4BbIUAODPyDkAANwjJwEA/oqMAwBYGTkIAPBXZBwAwMrIQQAAPCMrAQBWRQYCAKyssuZgYWGh+TtgwAB57rnnzOvw8HDZuHGjLFy4UPr06ePyfePGjZPY2Fj7fk5ODkWeAABVLgcBAKgsyEoAgD9nU2pqqrmXqPcbtbhTUlKSREREyN69e6VJkyZXtNd7k0OGDDFF5h944AFZsWKFREZGyrZt26RDhw6mjRZi0gIZS5cuNUUyJkyYYPr89ttvTbGmPXv2mHufr7/+utx8882yc+dOiYmJkdzcXJk1a5b9nua9995rikPptel3+sMf/iANGjSQJ554wmv/lgAA62KsBwCwmkBfdbx//355/vnn7YGq9LUONvUcAADwjCwFAPgzcg4AAPfISQCAvyLjAABWRg4CAPwVGQcAsDJyEAAAz8hKAIBVkYEAACvzRg42btzYvOfEiRNOx3W/adOmLt+jxz211z6rVasm7du3d2rTrl07ycrKcnstNWrUkHr16jltAAC4w3gQAADPyEoAgD9nU2JioimuFB0dbe5DajGlWrVqSUpKisv2c+bMkX79+smYMWPMfcrJkydL586dZf78+eZ8UVGRKRI1fvx4U7i+U6dOsmzZMjl69KisXr3atNH3v/nmm6aAU+vWreXBBx+UF154Qd5//3375yxfvlzy8/PNddx2220yePBgefbZZ831AgDgDYz1AABW47MCTzoo3L179xXH9VhYWJivPhYAAL9BlgIA/Bk5BwCAe+QkAMBfkXEAACsjBwEA/oqMAwBYGTkIAIBnZCUAwKrIQACAlXkjB6tXry5dunSRtLQ0+7HCwkKz37NnT5fv0eOO7dX69evt7bXPbt26yd69e53afPfdd3LjjTcW67oAALgaxoMAAHhGVgIA/DWbtIBSZmam9O3b134sMDDQ7GdkZLh8jx53bK8iIiLs7Q8cOCDHjx93alO/fn3p0aOH2z7V2bNnpVGjRk6fc9ddd5l7pI6fo/dK/+///q/Y3xEAAHcY6wEArKaaNzv7+uuv7a+1Gu+oUaNMhcQ77rjDHNu0aZMkJyfLtGnTvPmxAAD4DbIUAODPyDkAANwjJwEA/oqMAwBYGTkIAPBXZBwAwMrIQQAAPCMrAQBW5csM1PfNnDnTLOCmC9/MmzdPunfv7rLt4sWLZdmyZbJz506zr8Ux/vznPzu1Lyoqkvj4eNP2zJkz8qtf/UoWLFggt9xySym+OQAAvsnB2NhYGTZsmHTt2tXkWFJSkuTm5kp0dLQ5HxUVJS1atJCEhASzr5/Zp08fmT17tvTv319WrlwpW7dulUWLFtn7HDNmjAwaNMgsZnrPPffIunXr5B//+Iekp6d78V8DAGA13BMFAMAzshIAYIVsys7OloKCAgkJCXE6rvt79uxx+R797c9Vez1uO2875q7N5fR76G+Js2bNcvqcVq1aXdGH7VzDhg2dzuXl5ZnNJicnx8M3BwBYFWM9AICVebXAU3h4uAQEBJgHO23Gjh17Rbvf/e535qEXeNa/UzNZ8/Wxir4MAEA5IksBAP6MnCu5M7kXpV7wNRV9GQCAckBOAgD8la8yriQL16hVq1bJhAkT5ODBg2YxmunTp8v999/vsu2TTz4pr7/+urz22msyevToYl8TAACXY6wHAPBXZBwAwMoqwz3PXbt2SVxcnGRmZsqhQ4fc3su8Wp8XLlyQ559/3ix0qpPxIyIi5C9/+csViwEAAFASjBkBAFblqwxMTU01BS4WLlwoPXr0MMUtdPy2d+9eadKkyRXttUDFkCFDpFevXhIcHGyekbn33nvNWFKLYKgZM2bI3LlzZenSpWYxN32mRvv89ttvzXsAAKgMOajtTp06Ze6F6j1O/QwtyGS7f5mVlSWBgYH29pp9K1askPHjx8vLL79snhVdvXq1dOjQwd7moYceMpmqRaF0obk2bdrIe++9J7179y7jvwAAwMq4JwoAgGdkJQCgsvHXbDpy5Ij069dPBg4cKDExMaXuR++fTpo0yavXBgDwP/6apwAAlHuBpwMHDnizO8tr0aBmRV8CAKCckaUAAH9GzpVcgcNNawCAfyMnAQD+yhcZV9KFazZu3GgWrtEHSh944AEzgT8yMlK2bdvmNHFfffDBB7Jp0yZp3ry5168bAGA9jPUAAP6KjAMAWFlluOd5/vx5ad26tZmE/9xzz5W6T33vmjVrZNWqVVK/fn0ZOXKkPPzww/K///u/Xv+OAADrYMwIALAqX2VgYmKiWYAtOjra7Os4T8dyKSkp8tJLL13Rfvny5U77b7zxhilckZaWJlFRUWZhHR0javGLAQMGmDbLli0zxTK0CMbgwYN98j0AAP7NVzmo9yx1c0WLGl5O75nq5skf/vAHswEA4C3cEwUAwDOyEgBghWxq3LixBAUFyYkTJ5yO637Tpk1dvkePe2pv+6vHmjVr5tRGi2o4Onr0qNxzzz3Sq1cvWbRoUbE+x/EzHI0bN848f2qTk5MjLVu2vMq/AADAahjrAQCszKsFnm688UZvdgcAgOWQpQAAf0bOAQDgHjkJAPBXvsi4ki5cM2fOHOnXr5+MGTPG7E+ePFnWr18v8+fPN++1OXLkiDzzzDPy8ccfS//+/b1+3QAA62GsBwDwV77KuOTkZJk5c6YcP35cwsLCZN68edK9e3e37bUYxYQJE+TgwYNyyy23yPTp0+X++++3n9eFSuPj42Xx4sVy5swZ+dWvfiULFiwwbS+Xl5dnil989dVXsn379ismPAIAUJnueXbr1s1sytX54vR59uxZ+etf/yorVqyQX//616bNm2++Ke3atZNNmzbJHXfc4fXvCQCwBu6LAgCsyhcZmJ+fL5mZmWYRNZvAwEDp27evZGRkFKsPLRJ88eJFadSokX2BHb0Hq33YaNFfvT+qfVLgCQBQGowFAQBWRg4CAOAZWQkAsEI2Va9eXbp06SJpaWkSGRlpjhUWFpp9d0Xse/bsac6PHj3afkznv+tx1apVK1OASdvY5jdosaXNmzfLiBEjnObHa3En/Xx9DlR/T7z8c1555RXzm+E111xj/5w2bdpIw4YNr7iuGjVqmA0AAH+YX3ju3Dkzf2L16tVy+vRpk6/PPvusPPnkkz65fgCANXi1wJMr3377rWRlZZmHSB09+OCDvv5oAAD8AlkKAPBn5BwAAO6RkwAAf1WWjCvNwjV6PDY21ulYRESEeQDHRh+SHTp0qCkCddttt0lFOnbm5wr9fACAbzHWAwD4q7JmXGpqqhm7afEJXUg0KSnJjN327t0rTZo0uaL9xo0bZciQIZKQkCAPPPCAKU6hEyG3bdsmHTp0MG1mzJghc+fOlaVLl5rJFzpZQ/vUaw0ODnbqb+zYsdK8eXNT4AkAgMp+z9Mbfep5nazvuKB327Zt5YYbbjBtXBV40oKIutnoQgEAABQH90UBAFZV1gzMzs6WgoICCQkJcTqu+3v27ClWHy+++KK592kb/+kCOLY+Lu/Tdu5yjAcBAKXBWBAAYGXkIAAAnpGVAAB/zCadDzFs2DDp2rWrKUShcyJyc3MlOjranI+KipIWLVqYORBq1KhR0qdPH5k9e7b0799fVq5cKVu3bpVFixaZ8wEBAab405QpU0zBCtucCP3tz1ZESos73X333abIxqxZs+TUqVP269HiUOp3v/udTJo0SYYPH25+O9y5c6fMmTNHXnvtNS/8ywEAULnnF2p/n376qbz99tsSGhoqn3zyiTz11FMmTxmDAgAqXYGn//znP/LQQw/JN998YwaFRUVF5ri+VvpAKQAAcI8sBQD4M2/mXHJyssycOdNMJgwLC5N58+aZHzjdWbVqlfmh8uDBg+aHy+nTp8v9999vPz9x4kTzY+fhw4elevXq0qVLF5k6daq50QsAQHlgPAgA8FfeyLjSLFyj48WrLUqjY8Nq1arJs88+W+zv46vFa2rX8NlPuACACsRYDwDgr7yVcYmJiRITE2OfvKgTMdasWSMpKSny0ksvXdFeJxT269fPFOpVkydPlvXr18v8+fPNe/U6dBLH+PHjZcCAAabNsmXLzHhQC/4OHjzY3tc///lPMznjvffeM68BAKjs9zy90afeH9XnYho0aFDsBb114qNO8gcAoLi4LwoAsKrKkoHTpk0zcyPS09OvKHpfEowHAQBVMQcBAKgI5CAAAJ6RlQAAf86mQYMGmQJLcXFx5jnM8PBwWbdunf1ZTi14ERgYaG/fq1cvU4hC5zy8/PLLZi00netgK0ihxo4da4pEPfHEE3LmzBnp3bu36dP225/Oodi/f7/Zrr/+eqfrsX2X+vXrm/kSTz/9tFlLrXHjxuYatU8AAPx5fqGtCJQWYNSCiErz7/XXX5ctW7ZQ4AkAUGq/jOy8TCsBa3XfkydPSq1atWTXrl3yr3/9y1QS1gdBAQCAZ2QpAMCfeSvnUlNTJTY2VuLj42Xbtm2mwFNERITp1xW9yTpkyBAZPny4bN++XSIjI822c+dOe5tbb73V3JjVm8RffvmlhIaGyr333mt+PAUAoDwwHgQA+KvKmnGZmZnmwZ0lS5bYHxAq7uI1+lCrbWvZsqVPrxMAULVV1hwEAKAyZFx+fr4Zm/Xt29d+TCcu6n5GRobL9+hxx/ZKfye0tT9w4ICZFOnYRsduPXr0cOrzxIkTZuLHW2+9Za4fAICSsNpYb9y4cXL27Fn7dvjw4Yq+JABAJWe1rAQAwNsZqIurBQUFmfuYjnS/adOmHt87a9YsU+BJF2vr1KmT/bjtfSXpk/EgAKAkGAsCAKyMHAQAwDOyEgDg79k0cuRIOXTokOTl5cnmzZvN/AUb7U/nsjsaOHCg7N2717TXNdDuv/9+p/M67/3VV181cyMuXLggGzZsMOuj2Tz++OOmiIarzZH+XvjFF1+YPn744Qd58cUXS/zdAACoavMLbQUVP/zwQzly5IjJx88++0y+++47s7YoAACVrsCThpgOAvXhUQ1C3bTSry509uyzz/rqY/3KTxcuVfQlAAAqEFkKAPBn3sq5xMREs+BadHS0tG/fXhYuXGhu7KakpLhsrwt29+vXT8aMGSPt2rWTyZMnS+fOnU1BJ5vf/e535mZt69at5bbbbjOfkZOTI19//bVXvjsAAFfDeBAA4K+8kXGlWbhGj3tqrw+k6oNCN9xwg1SrVs1s+vDs888/b4r+usPiNQCAkmCsBwDwV97IuOzsbCkoKJCQkBCn47qvExFd0eOe2tv+emqjkzJ0QuOTTz5pJowUl06e1N8PHTcAgDVV1D1Pb/Spf3US5JkzZ4r9uTVq1JB69eo5bQAAlNd90eTkZPPbXXBwsFn8ZsuWLW7b6uIAjzzyiGmvC90kJSVd0WbixInmnOPWtm3bUn1PAAB8lYHVq1eXLl26SFpamv1YYWGh2e/Zs6fb982YMcPMlVi3bt0V9z51QR0d9zn2qfc4dYE5d30yHgQAlATPyAAArIwcBADAM7ISAFDZkE0AAPjv/EI1b948s0bp9ddfb57D0XVI9XnUu+66y2WfzBsEAFRogScNw7p165rXGqxHjx41r2+88UZTHdhXEzDUqlWrzIQKbd+xY0dZu3at03mdlB8XFyfNmjWTmjVrmoW79+3bd0U/a9asMZ+nbRo2bCiRkZFSnv62JatcPw8AULl4M0sBAPDHnNNFZjIzM82YzkZv6Oq+3uh1RY87tlcRERFu2+tnLFq0SOrXry9hYWEu23AjFgDgbYwHAQD+yhsZV5qFa/S4Y3u1fv16e/uhQ4eaor47duywb82bNzfFgT/++GO318LiNQCAkmCsBwDwV1U543SCxk8//WQK+JaETi7R3w9tW8uWLX12jQCAyq2i7nl6o089f8011zi10WvOysoq9ecCAOCrMWNqaqrExsZKfHy8bNu2zTzPqc9+njx50mX78+fPS+vWrWXatGkeCybedtttcuzYMfv25Zdflvg7AgDg6/ummoGLFy+WpUuXyu7du2XEiBGSm5sr0dHR5nxUVJTTPc7p06fLhAkTJCUlxczN10VrdDt37pw5r0UNR48eLVOmTJEPP/xQvvnmG9OHPitT3vPoAQD+qSr/fggAQFmRgwAAeEZWAgAqG7IJAAD/zlOdP7hp0ybzjIyuWzp79mx5+umnZcOGDS7bM28QAFAc1cRHOnToIF999ZW0atXKFEmaMWOGmSioC2PrBImSTsBYuHCh6ScpKclMwNBgbtKkyRXtN27cKEOGDDFB+MADD8iKFSvMA6U6eUOvSem1zJ071zzMqtenD6pqn99++60pCqXee+89iYmJkT//+c/y61//Wi5duiQ7d+704r8QAADlk6UAAPhrzmVnZ5sbuiEhIU7HdX/Pnj0u36MTE1211+OOPvroIxk8eLCZ5K/FgXXhb71h7IqOPydNmlSsawYAoDgYDwIA/JW3Mk5/Oxw2bJh07dpVunfvbn4/vHzhmhYtWpjxmho1apT06dPHPGjTv39/WblypWzdutV8rrr22mvN5kgXNtXF3tq0aePVfwMAgHUx1gMA+CtvZJz+DhcUFCQnTpxwOq777hbi1uOe2tv+6jH9vc+xTXh4uHn96aefSkZGhing60jHm4899ph5xtQVXSxVx6Y2OTk5TNYAAIuqqHue+fn5Zu6D7fWRI0dM4fo6derIzTffXKw+dbLh8OHDTbtGjRqZAvbPPPOMKe50xx13+OBfCwBgRd7KysTERDPPz5ZjOs9wzZo1pnDFSy+9dEX7bt26mU25Om9TrVo1jwWgAACoDL8NDho0SE6dOiVxcXFm3oPe31y3bp19XoQW6g0MDLS3X7BggRkrPvroo079aKHEiRMnmtdjx441Y8QnnnhCzpw5I7179zZ92ubYAwBQFjwjAwCwMnIQAADPyEoAQGVDNgEA4L/zC3/++Wd5+eWX5YMPPjBrzahOnTqZuRezZs2Svn37XtEn8wYBAMXxyxObXjZ+/HgpLCw0r1999VU5cOCA3HnnnbJ27VpTXKk0EzDat29vJmDUqlXLTMBwZc6cOdKvXz8ZM2aMtGvXTiZPniydO3eW+fPnm/NFRUVmcqJe34ABA0ygLlu2zFR1XL16tWmjxZx0obeZM2fKk08+Kbfeeqv57N/+9rde+bcBAKA8s9QmOTlZQkNDzUQLHfBu2bLFbdv333/fTOpv0KCB1K5d20z8eOutt8r0fQAA8GXOeds999xjbr5qEWEdY+p48OTJky7b6o3Ys2fP2rfDhw+X+/UCAPxLZc9JAAAqOuN04Rp9WEYXrtF7lzp+u3zhmmPHjtnb9+rVS1asWGEe/gkLC5N3333X/C6oDwkBAFBeGOsBAPyVNzJOJ2x06dJF0tLS7Me0T93XIhOu6HHH9mr9+vX29johRCdjOLbRCRWbN2+2t9Hr08kjOq7UTa9ZpaamytSpU91erxaE0iIYjhsAwJoq6p6nzn24/fbbzabH9b36+o9//GOx+1SvvfaaPPDAA/LII4/IXXfdZbJTnx8FAKAyZaUWqMjMzHSaSK9FLHRfi/aWxb59+6R58+ZmAQEt9KuZ605eXp4ZVzpuAACU12+DI0eOlEOHDpk80nucOjfQJj09XZYsWWLfP3jwoJlLf/lmK+6kAgICzHVpwagLFy7Ihg0bzFx6AAC8gWdkAABWRg4CAOAZWQkAqGzIJgAA/Hd+4cWLF82mz5w60kJStuu9HPMGAQDFUU18JCIiwv765ptvlj179siPP/4oDRs2NA9+lmQChi6WXdwJGHrcscKh7VpsxZs03PWBU8dJHfXr1zcPs+p7Bw8eLNu2bZMjR46Yz9KJjtpeJzVqwSdPC73pg7G62XhzokZRkde6AgBUEd7IUhtdeEbzUQslauZpsUPtf+/evdKkSZMr2jdq1EheeeUVadu2rRnkfvTRR6bYorZ1vC4AACoy5xo3bmxukJ44ccLpuO7rgjOu6PHitNcCh3pdut1xxx1yyy23yF//+len8anjjVjdAACojONBAAD8NeN04RrdXNGFay43cOBAsxWXLnYDAIA3MdYDAPgrb2WcPtcybNgw6dq1q3Tv3t0825Kbm2ueV1FRUVHSokULSUhIMPujRo2SPn36yOzZs6V///6ycuVK2bp1qynuq/SzR48eLVOmTDG/9WnBpwkTJpiFuyMjI02bG264weka6tSpY/7edNNNcv3113vhXwcA4O8q6p5naGioWZy7LH2q4OBgSU5ONhsAAJU1K7Ozs6WgoMCpSKHSfe2vtHRehRbDaNOmjSmYOGnSJLOgwM6dO6Vu3bpXtNfxqLYBAKA4+G0QAGBl5CAAwMrIQQAAPCMrAQCVDdkEAID/zi/U4kx6fsyYMVKzZk258cYb5fPPP5dly5ZJYmKi1/8dAADW4bMCT44OHz5s/rZs2bJE7yvNBAwtxuSqvR63nbcdc9fmP//5j/k7ceJEE7Q6AVJD+u6775bvvvvOFL1wxZcTNX66cNEn/QIAqobSZqmN5llMTIx9UKqFntasWSMpKSny0ksvXdFeM8+RDlqXLl0qX375JQWeAACVJue0CGGXLl0kLS3NvghbYWGh2Xe3OE3Pnj3NeV3MzWb9+vXmuCfar2NBXwAAqsp4EACAyoqMAwBYGTkIAPBXZcm4QYMGyalTpyQuLs48yxkeHi7r1q2zP+uZlZUlgYGB9va9evWSFStWyPjx4+Xll182RZxWr14tHTp0sLcZO3asmcTxxBNPyJkzZ6R3796mTy1mAQCAtzHWAwCgamXlfffdZ3/dqVMnU/BJJ++/8847Mnz48Cvajxs3ziweYJOTk1NpvgsAoHKrbBkIAEB5IgcBAFZGDgIA4BlZCQCobMgmAAD8b36hFn3S5z8fe+wxU3RKnxOdOnWqPPnkk175vgAAa/oljbzs0qVLMmHCBKlfv74pkKSbvtawu3ixchcr0oW71SuvvCKPPPKIWTD8zTffNNUeV61a5fZ9GtRnz561b7b/M+ENgVRuBgDL8VaW5ufnS2ZmpvTt29d+TAekup+RkXHV9xcVFZlCGHv37pW77rqr1N8HAABf5JxOll+8eLEpRLh7924ZMWKEWaTNVtQwKirKjNUcixbqjVot4quFg7Ww79atW+0FofS9eoN206ZNcujQIZOhf/jDH+TIkSMycOBAH/xLAADgX/dWAQDwhIwDAFgZOQgA8FfezDj9zU5/o8vLy5PNmzebxbVt0tPTZcmSJU7t9fc7fZ5F2+/cuVPuv/9+p/P6zOerr75qJnRcuHBBNmzYILfeeqvbz9dr1+dkdPIHAADFwVgPAADfZ2Xjxo0lKChITpw44XRc95s2beq1a23QoIEZM+7fv9/l+Ro1aki9evWcNgAA3GG8CACwMnIQAGBl5CAAAJ6RlQCAyoZsAgDAv+cX6nOmWltC1xL9+eefzfqjun6pzjsEAKC0qomPPPPMM/L+++/LjBkzpGfPnuaYFpHQxbNPnz4tCxYs8MkEDD3uqb3trx5r1qyZUxvbpHzb8fbt2ztNwmjdurWp0uiOttENAIDKkqUqOztbCgoK7BWHbXRfB5buaLHCFi1amEGq5vFf/vIX+Z//+R+XbbWNbjY5OTnF/JYAAKvyVs4NGjRITp06JXFxcWZhNh3XaQEnW+7pGE4LG9r06tVLVqxYYW74aiGnW265RVavXi0dOnQw5zXzNB+1YJRm6LXXXivdunWTL774Qm677Taf/FsAAOCrnAQAoLIh4wAAVubNHExOTpaZM2eae6JhYWEyb9486d69u8u2u3btMvdPtZi9PtD62muvyejRo53a6DVMmjTJ6VibNm08/pYIAIANYz0AgJWRgwAA+D4rq1evLl26dJG0tDSJjIw0xwoLC82+TuT3lnPnzsn3338vQ4cO9VqfAADrYrwIALAycrB4frrA4rAA4I/IQQAAPCMrAQCVDdkEAEDZkadXt/sY63QDgD/xWYEnXTB75cqVct9999mPderUSVq2bClDhgzx2QQMDXA977gYzfr16+3B3qpVK1PkSdvYCjppEQqtxjhixAizr5+phZq08mLv3r3NMa30ePDgQbnxxhvL9O8CAEB5ZmlZ1K1bV3bs2GEmKmpuaoVhLXZ49913X9E2ISHhikXfAAAor5zT8aG7MWJ6evoVxwYOHGg2V4KDg80NYgAA/CUnS7Lo9+LFi2XZsmWyc+dO+33SP//5z27bAwBQ1e55AgDgDzmYmppqfrdbuHCh9OjRQ5KSkiQiIsI849KkSZMr2p8/f978xqf3RJ977jm3/WqB+w0bNtj3q1Xz2SNFAAA/w1gPAGBl5CAAAOWTlXpPdNiwYdK1a1fzHIveF83NzZXo6GhzPioqSlq0aGHmNaj8/Hz59ttv7a+PHDli5kbUqVNHbr75ZnP8hRdekN/85jdmruDRo0clPj5egoKCzHUBAFBWjBcBAFZGDhbPqXP5FX0JAAAfIAcBAPCMrAQAVDZkEwAAZUeeXt11dWtU9CUAALzIZ6uxaIGk0NDQK45rgSUt3FRcJZ2AMWrUKOnTp4/Mnj1b+vfvb4J969atsmjRInM+ICDAFH+aMmWK3HLLLeZ6JkyYIM2bN7cXkapXr548+eSTZmKG/p8Anaihi6Aqd4uA+0JggEhhUbl9HACgkvFWljZu3NhMNDxx4oTTcd3XoofuBAYG2icvalHE3bt3m7x1VeBp3LhxJrNttHiiZigAAL7OOQAA/JG3crKki35rYUT9QbRXr16m6OH06dPl3nvvlV27dpl7sAAAlBVjQQCAlXkrBxMTEyUmJsb+3IyO+dasWSMpKSny0ksvXdG+W7duZlOuzjsWdPL02yEAAO4w1gMAWBk5CABA+WTloEGD5NSpUxIXFyfHjx838xvWrVsnISEh5nxWVpaZ/2CjBZtuv/12+/6sWbPMpnMO9fkY9cMPP5jnZE6fPi3XXXed9O7dWzZt2mReAwBQVowXAQBWRg4WT+3qQRV9CQAAHyAHAQDwjKwEAFQ2ZBMAAGVHngIArOaXmQteNnLkSJk8ebLk5eXZj+nrqVOnmnMlmYChEyh0AoZOvtixY8cVEzCOHTtmb6+Lj2rFRi3oFBYWJu+++66sXr1aOnToYG8zduxYeeaZZ+SJJ54wi9icO3fO9KmLltpoQafBgwfL0KFDTZtDhw7Jp59+Kg0bNpTy0r9T83L7LABA5eOtLNXBbJcuXSQtLc1+rLCw0Oz37Nmz2P3oexyv5fLBtBZIdNwAACiPnAMAwB95KycdF/1u3769WfS7Vq1aZtFvV5YvXy5PPfWUuQ/btm1beeONN+zjRwAAvIGxIADAyryRg/n5+ZKZmSl9+/a1H9NFS3U/IyOjTNe3b98+ad68ubRu3Voee+wx8zwOAADFwVgPAGBl5CAAAOWXldpe5/fp+zdv3iw9evSwn9OiTUuWLLHv60IBRUVFV2y24k5q5cqVphCU9qfFnnT/pptuKvN3BgBAMV4EAFgZOQgAsDJyEACA8svK5ORk87ugrh+qvx1u2bLFbdvFixfLnXfeadYR1U3nYHhqDwCwDsZxAACUHXkKALCaat7s7OGHH3ba37Bhg1x//fWm0JL66quvzGIz/9//9/+VqF8NYXdB7DixwmbgwIFmcycgIEBeffVVs7lzzTXXmMJSugEAUF58laWxsbEybNgw6dq1q3Tv3l2SkpIkNzfXLPKtoqKipEWLFpKQkGD29a+21QmKOiheu3atvPXWW7JgwQKvfVcAgPX4KucAAPAH3s5J26Lf48aNK/Wi3+fPn5eLFy9Ko0aNSvRdAABwxFgQAGBl3s7B7OxsKSgokJCQEKfjur9nz55SX6dOZtTFT9u0aSPHjh2TSZMmmcmLO3fulLp167p8j/6G6PigbU5OTqk/HwBQ9TDWAwBYGTkIAIBnZCUAwKrIQACAlZGDAAArIwcBACj/rExNTTVrqi1cuNDMh9D11CIiImTv3r3SpEkTl+uVDhkyRHr16mUKQk2fPl3uvfde2bVrl1l3DQBgLYzjAAAoO/IUAGBlXi3wVL9+faf9Rx55xGm/ZcuW3vw4AAD8jq+ydNCgQXLq1CmJi4uT48ePS3h4uKxbt86++FtWVpZZ6NtGiz899dRT8sMPP0jNmjWlbdu28vbbb5t+AAAoLcaMxZN3saCiLwEA4Ac56Y1Fv1988UVp3ry5KQrlDgt6AwCuhrFgyRVV9AUAACyXg/fdd5/9dadOncwExxtvvFHeeecdGT58uMv3JCQkmEJQAABrqioZBwCAL5CDAAB4RlYCAKyKDAQAWBk5CACwMnIQAIDyz8rExESJiYmR6Ohos6+FntasWSMpKSny0ksvXdF++fLlTvtvvPGGvPfee5KWliZRUVEl/nwAQNXGOA4AgLIjTwEAVubVAk9vvvmmN7sDAMByfJmlI0eONJsr6enpTvtTpkwxGwAA3sSYsXhyLlyq6EsAAFSAypaT06ZNk5UrV5rxYnBwsNt2LOgNAKhqGVcVHDqdW9GXAACopDnYuHFjCQoKkhMnTjgd1/2mTZt67XMaNGggt956q+zfv99tm3HjxklsbKxTwV8etgUA62CsBwCwMnIQAADPyEoAgFWRgQAAKyMHAQBWRg4CAFC+WZmfny+ZmZlmToNNYGCg9O3bVzIyMorVx/nz5+XixYvSqFEjr14bAKBqYBwHAEDZkacAACvzaoEnV06dOiV79+41r9u0aSPXXXedrz8SAAC/QpYCAPwZOXelwICKvgIAgD/kZFkW/Z41a5Yp8LRhwwbp1KmTx7Ys6A0AKA3Ggp41ql29oi8BAFBJc7B69erSpUsXSUtLk8jISHOssLDQ7I8cOdJr13ju3Dn5/vvvZejQoW7b1KhRw2wAANgw1gMAWBk5CACAZ2QlAMCqyEAAgJWRgwAAKyMHAQDwXVZmZ2dLQUGBhISEOB3X/T179hSrjxdffFGaN29uikK5k5eXZzbHOfQAAP/FOA4AgLIjTwEAVhHoq45zc3PlD3/4gzRr1kzuuusus+mNzOHDh5uq9QAAwDOyFADgz8g5AAB8m5OOi37b2Bb97tmzp9v3zZgxQyZPnizr1q2Trl27XvVzdDHvevXqOW0AALjDWBAAYGXeykEtsrt48WJZunSp7N69W0aMGGH6jo6ONuejoqJMMV6b/Px82bFjh9n09ZEjR8zr/fv329u88MIL8vnnn8vBgwdl48aN8tBDD5miwUOGDPHyvwIAwB8x1gMAWBk5CACAZ2QlAMCqyEAAgJWRgwAAKyMHAQCo/Fk5bdo0WblypXzwwQcSHBzstl1CQoLUr1/fvrVs2dIrn//+9iPyz2+OeaUvAIB/ZBMAAFUdeQoAsBqfFXjSBWV08Zd//OMfcubMGbP9/e9/N8eef/55X30sAAB+gywFAPgzcg4AAN/nZEkX/Z4+fbpMmDBBUlJSJDQ0VI4fP262c+fO+eR7AgCsh7EgAMDKvJWDgwYNklmzZklcXJyEh4ebYk1apDckJMScz8rKkmPHfpnsd/ToUbn99tvNpsf1vfr6j3/8o73NDz/8YIo5tWnTRn7729/KtddeK5s2bZLrrrvOy/8KAAB/xFgPAGBl5CAAAJ6RlQAAqyIDAQBWRg4CAKyMHAQAwPdZ2bhxYwkKCpITJ044Hdf9pk2benyvzqfQAk+ffPKJdOrUyWNbnYN/9uxZ+3b48GEpq0a1qkvepQLZfODHMvcFAPAOxnEAAJQdeQoAsJpqvur4vffek3fffVfuvvtu+7H7779fatasaRaDWbBgga8+GgAAv0CWAgD8GTkHAIDvc1IX/T516pRZ9FsLNenC35cv+h0YGGhvr/3m5+fLo48+6tRPfHy8TJw40WvfDwBgXYwFAQBW5s0cHDlypNlcSU9Pd9rXAr5FRUUe+1u5cmWxPxsAgMsx1gMAWBk5CACAZ2QlAMCqyEAAgJWRgwAAKyMHAQDwfVZWr15dunTpImlpaRIZGWmOFRYWmn138yzUjBkzZOrUqfLxxx9L165dr/o5NWrUMJs3dW/VSHYePevVPgEAZcM4DgCAsiNPAQBW47MCT+fPn7cvFOqoSZMm5hwAAPCMLAUA+DNyDgCA8snJkiz6ffDgwRJeKQAAJcNYEABgZeQgAMBfkXEAACsjBwEA8IysBABYFRkIALAychAAYGXkIAAA5ZOVsbGxMmzYMFOoqXv37pKUlCS5ubkSHR1tzkdFRUmLFi0kISHB7E+fPl3i4uJkxYoVEhoaKsePHzfH69SpYzYAgHUxjgMAoOzIUwCA1QT6quOePXtKfHy8XLhwwX7s559/lkmTJplzAADAM7IUAODPyDkAANwjJwEA/oqMAwBYGTkIAPBXZBwAwMrIQQAAPCMrAQBWRQYCAKyMHAQAWBk5CABA+WTloEGDZNasWaZoU3h4uOzYsUPWrVtnX1A8KytLjh07Zm+/YMECyc/Pl0cffVSaNWtm37QPAIC1MY4DAKDsyFMAgNVU81XHWsm+X79+cv3110tYWJg59tVXX0lwcLB8/PHHvvpYAAD8BlkKAPBn5BwAAO6RkyJFFX0BAACfIOMAAFZGDgIA/BUZBwCwMnIQAADPyEoAgFWRgQAAKyMHAQBWRg4CAFB+WTly5EizuZKenu60f/DgwTJcNQDAnzGOAwCg7MhTAIDV+KzAU8eOHWXfvn2yfPly2bNnjzk2ZMgQeeyxx6RmzZq++lgAAPwGWQoA8GfkHAAA7lk5J2tcE2j+fnX4jNx0XZ2KvhwAgJdZOeMAACAHAQD+iowDAFgZOQgAgGdkJQDAqshAAICVkYMAACsjBwEA8IysBABUNmQTAABlR54CAKzGJwWeLl68KG3btpWPPvpIYmJifPERAAD4NbIUAODPyLniu1RQWNGXAAAoZ1bPyTYhdWWNHJOgwICKvhQAgJdZPeMAANZGDgIA/BUZBwCwMnIQAADPyEoAgFWRgQAAKyMHAQBW5u0cTE5OlpkzZ8rx48clLCxM5s2bJ927d3fZdteuXRIXFyeZmZly6NAhee2112T06NFl6hMAAG9jzAgAqGzIJgAAyo48BQBYUaAvOr3mmmvkwoULvujasn7Ku1TRlwAAKEdkKQDAn5FzxZd9Lr+iLwEAUM6snpOUdQIA/2X1jAMAWBs5CADwV2Scd53P5zlRAKhKyEEAADwjKwEAVkUGAgCsjBwEAFiZN3MwNTVVYmNjJT4+XrZt22aKMUVERMjJkyddtj9//ry0bt1apk2bJk2bNvVKnwAAeBtjRgBAZUM2AQBQduQpAMCKfFLgST399NMyffp0uXSJCefesP7bExV9CQCAckaWAgD8GTlXPNWCKHMBAFZETgIA/BUZBwCwMnIQAOCvyDjv+fCroxV9CQCAEiIHAQDwjKwEAFgVGQgAsDJyEABgZd7KwcTERImJiZHo6Ghp3769LFy4UGrVqiUpKSku23fr1k1mzpwpgwcPlho1anilTwAAfIExIwDAn7MpOTlZQkNDJTg4WHr06CFbtmzx2H7VqlXStm1b075jx46ydu1ap/NFRUUSFxcnzZo1k5o1a0rfvn1l3759Tm2mTp0qvXr1MuO7Bg0auPycgICAK7aVK1eW+fsCAGDDWA8AYDXVfNXxv//9b0lLS5NPPvnEDBRr167tdP7999/31Uf7lWtrV5fTufkVfRkAgApAlgIA/Bk5VzwXCwor+hIAABWAnAQA+CsyDgBgZeQgAMBfkXHecz6/oKIvAQBQQuQgAACekZUAAKsiAwEAVkYOAgCszBs5mJ+fL5mZmTJu3Dj7scDAQLOQd0ZGRqmuq7R95uXlmc0mJyenVJ8PAIANY0YAgL9mU2pqqsTGxppiulrcKSkpSSIiImTv3r3SpEmTK9pv3LhRhgwZIgkJCfLAAw/IihUrJDIyUrZt2yYdOnQwbWbMmCFz586VpUuXSqtWrWTChAmmz2+//dYUhbKN9wYOHCg9e/aUv/71r26v780335R+/frZ990VgwIAoDQY6wEArMZnBZ50sPbII4/4qnsAAPweWQoA8GfkXPGc+umXh14BANZBTgIA/BUZBwCwMnIQAOCvyDgAgJWRgwAAeEZWAgCsigwEAFgZOQgAsDJv5GB2drYUFBRISEiI03Hd37NnT7n2qQuNT5o0qVSfCQCAK4wZAQD+mk2JiYkSExMj0dHRZl8LPa1Zs0ZSUlLkpZdeuqL9nDlzTMGlMWPGmP3JkyfL+vXrZf78+ea9RUVFpkjU+PHjZcCAAabNsmXLzDhu9erVMnjwYHPMNmZbsmTJVb9n06ZNy/w9AQBwhbEeAMBqvF7gqbCwUGbOnCnfffedqeT761//WiZOnCg1a9b09kcBAOCXyFIAgD8j50qmRrXAir4EAEA5IicBAP6KjAMAWBk5CADwV2QcAMDKyEEAADwjKwEAVuWrDExOTjb9Hj9+XMLCwmTevHnSvXt3l2137dolcXFxkpmZKYcOHZLXXntNRo8e7dRGr+nyBbrbtGlT6oXCAQBQjAUBAFbmrzk4btw4iY2Nte/n5ORIy5YtK/SaAABVk79mJQCg6vJmNun79bc5HUPZBAYGSt++fSUjI8Ple/S443hLRUREmOJN6sCBA+a3Qe3Dpn79+tKjRw/zXluBp+J6+umn5Y9//KO0bt1annzySVOIKiAgoITfFAAAZ4z1AABW5fWVoqdOnSovv/yy1KlTR1q0aCFz5841AzmUzjVBLOYNAFZDlgIA/Bk5BwCAe+QkAMBf+SLjdOGa0NBQCQ4ONg+jbtmyxWP7VatWSdu2bU37jh07ytq1a53O60NCer527drSsGFD88Dr5s2by3SNAAAoxnoAAH9FxgEArKwq3PPUifeuNp1AaaOfd/n5adOmlel7AACgGDMCAKzKFxmYmppqFneLj4+Xbdu2mQJPurjbyZMnXbY/f/68WZhNx3dNmzZ12+9tt90mx44ds29ffvllma4TAADGggAAK/NmDjZu3FiCgoLkxIkTTsd139M4zxd91qhRQ+rVq+e0AQBQGowZAQD+nE3Z2dlSUFAgISEhTsd1X4s0uaLHPbW3/S1Jn+68+uqr8s4778j69evlkUcekaeeekrmzZvntn1eXp4p8Ou4VUaFhUVyqaDQ/NXNUzvblnepQIqKfmmr73fcd2xvO1/gsK9/tX3+pUJzztNnajt9r6PL90vC0+fZ21zW/8VSfl7+//velyso/O+/t16L/lte/rmF/+872/4N3X1/x/9eFx2+12X/Kext9fNsm/676rGLl+1f3q/tv4Gr/v77Wb9cn6v3e+Kq3dXeW5LPKO51AJUBYz0AgFVV83aHy5Ytk7/85S/ypz/9yexv2LBB+vfvL2+88YapIIySCQqkojEAWA1ZCgDwZ+QcAADukZMAAH/l7YyzLVyzcOFCs9BpUlKSWbhm79690qRJkyv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",
+      "text/plain": [
+       "<Figure size 8100x1400 with 60 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>file</th>\n",
+       "      <th>gene</th>\n",
+       "      <th>threshold</th>\n",
+       "      <th>mean_neg</th>\n",
+       "      <th>concentration_neg</th>\n",
+       "      <th>var_nb_neg</th>\n",
+       "      <th>var_data_neg</th>\n",
+       "      <th>mean-</th>\n",
+       "      <th>concentration-</th>\n",
+       "      <th>var_nb-</th>\n",
+       "      <th>var_data-</th>\n",
+       "      <th>std_nb-</th>\n",
+       "      <th>std_data-</th>\n",
+       "      <th>overdisp_nb-</th>\n",
+       "      <th>overdisp_data-</th>\n",
+       "      <th>cof_var_nb-</th>\n",
+       "      <th>cof_var_data-</th>\n",
+       "      <th>N-</th>\n",
+       "      <th>mean+</th>\n",
+       "      <th>concentration+</th>\n",
+       "      <th>var_nb+</th>\n",
+       "      <th>var_data+</th>\n",
+       "      <th>std_nb+</th>\n",
+       "      <th>std_data+</th>\n",
+       "      <th>overdisp_nb+</th>\n",
+       "      <th>overdisp_data+</th>\n",
+       "      <th>cof_var_nb+</th>\n",
+       "      <th>cof_var_data+</th>\n",
+       "      <th>N+</th>\n",
+       "      <th>mean_ratio</th>\n",
+       "      <th>concentration_ratio</th>\n",
+       "      <th>var_nb_ratio</th>\n",
+       "      <th>var_data_ratio</th>\n",
+       "      <th>overdisp_nb_ratio</th>\n",
+       "      <th>overdisp_data_ratio</th>\n",
+       "      <th>N_ratio</th>\n",
+       "      <th>binder_ratio</th>\n",
+       "      <th>noise_to_neg_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>CMV</td>\n",
+       "      <td>197.942</td>\n",
+       "      <td>4.722340</td>\n",
+       "      <td>3.939271e-01</td>\n",
+       "      <td>61.333071</td>\n",
+       "      <td>1039.818115</td>\n",
+       "      <td>25.003214</td>\n",
+       "      <td>2.897500</td>\n",
+       "      <td>240.761868</td>\n",
+       "      <td>338.704865</td>\n",
+       "      <td>15.516503</td>\n",
+       "      <td>18.403936</td>\n",
+       "      <td>9.629237</td>\n",
+       "      <td>13.546453</td>\n",
+       "      <td>0.620580</td>\n",
+       "      <td>0.736063</td>\n",
+       "      <td>1867</td>\n",
+       "      <td>1230.471773</td>\n",
+       "      <td>3.924429</td>\n",
+       "      <td>387034.531887</td>\n",
+       "      <td>4.371280e+05</td>\n",
+       "      <td>622.120995</td>\n",
+       "      <td>661.156539</td>\n",
+       "      <td>314.541577</td>\n",
+       "      <td>355.252334</td>\n",
+       "      <td>0.505596</td>\n",
+       "      <td>0.537320</td>\n",
+       "      <td>3773</td>\n",
+       "      <td>49.212545</td>\n",
+       "      <td>1.354419</td>\n",
+       "      <td>1607.540828</td>\n",
+       "      <td>1290.586670</td>\n",
+       "      <td>32.665265</td>\n",
+       "      <td>26.224749</td>\n",
+       "      <td>2.020889</td>\n",
+       "      <td>0.668972</td>\n",
+       "      <td>5.294666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>EBV_BMLF-1_GLCT</td>\n",
+       "      <td>197.942</td>\n",
+       "      <td>4.722340</td>\n",
+       "      <td>3.939271e-01</td>\n",
+       "      <td>61.333071</td>\n",
+       "      <td>1039.818115</td>\n",
+       "      <td>1.454062</td>\n",
+       "      <td>0.231539</td>\n",
+       "      <td>10.585553</td>\n",
+       "      <td>20.490885</td>\n",
+       "      <td>3.253545</td>\n",
+       "      <td>4.526686</td>\n",
+       "      <td>7.279988</td>\n",
+       "      <td>14.092170</td>\n",
+       "      <td>2.237556</td>\n",
+       "      <td>3.113132</td>\n",
+       "      <td>5638</td>\n",
+       "      <td>251.500000</td>\n",
+       "      <td>24.847288</td>\n",
+       "      <td>2797.139942</td>\n",
+       "      <td>2.756250e+03</td>\n",
+       "      <td>52.887994</td>\n",
+       "      <td>52.500000</td>\n",
+       "      <td>11.121829</td>\n",
+       "      <td>10.959245</td>\n",
+       "      <td>0.210290</td>\n",
+       "      <td>0.208748</td>\n",
+       "      <td>2</td>\n",
+       "      <td>172.963772</td>\n",
+       "      <td>107.313660</td>\n",
+       "      <td>264.241278</td>\n",
+       "      <td>134.511032</td>\n",
+       "      <td>1.527726</td>\n",
+       "      <td>0.777683</td>\n",
+       "      <td>0.000355</td>\n",
+       "      <td>0.000355</td>\n",
+       "      <td>0.307911</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>Flu</td>\n",
+       "      <td>197.942</td>\n",
+       "      <td>4.722340</td>\n",
+       "      <td>3.939271e-01</td>\n",
+       "      <td>61.333071</td>\n",
+       "      <td>1039.818115</td>\n",
+       "      <td>11.730760</td>\n",
+       "      <td>1.432491</td>\n",
+       "      <td>107.794731</td>\n",
+       "      <td>224.974823</td>\n",
+       "      <td>10.382424</td>\n",
+       "      <td>14.999161</td>\n",
+       "      <td>9.189066</td>\n",
+       "      <td>19.178196</td>\n",
+       "      <td>0.885060</td>\n",
+       "      <td>1.278618</td>\n",
+       "      <td>4249</td>\n",
+       "      <td>922.462976</td>\n",
+       "      <td>4.215191</td>\n",
+       "      <td>202796.575052</td>\n",
+       "      <td>2.202642e+05</td>\n",
+       "      <td>450.329407</td>\n",
+       "      <td>469.323150</td>\n",
+       "      <td>219.842509</td>\n",
+       "      <td>238.778384</td>\n",
+       "      <td>0.488182</td>\n",
+       "      <td>0.508772</td>\n",
+       "      <td>1391</td>\n",
+       "      <td>78.636249</td>\n",
+       "      <td>2.942561</td>\n",
+       "      <td>1881.321785</td>\n",
+       "      <td>979.061646</td>\n",
+       "      <td>23.924358</td>\n",
+       "      <td>12.450513</td>\n",
+       "      <td>0.327371</td>\n",
+       "      <td>0.246631</td>\n",
+       "      <td>2.484099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>EBV_BRLF1_YVLD</td>\n",
+       "      <td>197.942</td>\n",
+       "      <td>4.722340</td>\n",
+       "      <td>3.939271e-01</td>\n",
+       "      <td>61.333071</td>\n",
+       "      <td>1039.818115</td>\n",
+       "      <td>2.596270</td>\n",
+       "      <td>0.386810</td>\n",
+       "      <td>20.022444</td>\n",
+       "      <td>45.894730</td>\n",
+       "      <td>4.474645</td>\n",
+       "      <td>6.774565</td>\n",
+       "      <td>7.712004</td>\n",
+       "      <td>17.677179</td>\n",
+       "      <td>1.723490</td>\n",
+       "      <td>2.609345</td>\n",
+       "      <td>5630</td>\n",
+       "      <td>810.800000</td>\n",
+       "      <td>2.834109</td>\n",
+       "      <td>232769.662520</td>\n",
+       "      <td>2.139172e+05</td>\n",
+       "      <td>482.462084</td>\n",
+       "      <td>462.511790</td>\n",
+       "      <td>287.086412</td>\n",
+       "      <td>263.834677</td>\n",
+       "      <td>0.595045</td>\n",
+       "      <td>0.570439</td>\n",
+       "      <td>10</td>\n",
+       "      <td>312.294178</td>\n",
+       "      <td>7.326876</td>\n",
+       "      <td>11625.436849</td>\n",
+       "      <td>4661.039551</td>\n",
+       "      <td>37.225916</td>\n",
+       "      <td>14.925157</td>\n",
+       "      <td>0.001776</td>\n",
+       "      <td>0.001773</td>\n",
+       "      <td>0.549785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>SARS_Cov2</td>\n",
+       "      <td>197.942</td>\n",
+       "      <td>4.722340</td>\n",
+       "      <td>3.939271e-01</td>\n",
+       "      <td>61.333071</td>\n",
+       "      <td>1039.818115</td>\n",
+       "      <td>8.543229</td>\n",
+       "      <td>1.653138</td>\n",
+       "      <td>52.693679</td>\n",
+       "      <td>87.826508</td>\n",
+       "      <td>7.259041</td>\n",
+       "      <td>9.371580</td>\n",
+       "      <td>6.167888</td>\n",
+       "      <td>10.280247</td>\n",
+       "      <td>0.849684</td>\n",
+       "      <td>1.096960</td>\n",
+       "      <td>5066</td>\n",
+       "      <td>1414.221254</td>\n",
+       "      <td>4.004123</td>\n",
+       "      <td>500904.754996</td>\n",
+       "      <td>5.018279e+05</td>\n",
+       "      <td>707.746250</td>\n",
+       "      <td>708.398127</td>\n",
+       "      <td>354.191223</td>\n",
+       "      <td>354.843985</td>\n",
+       "      <td>0.500449</td>\n",
+       "      <td>0.500910</td>\n",
+       "      <td>574</td>\n",
+       "      <td>165.537081</td>\n",
+       "      <td>2.422136</td>\n",
+       "      <td>9505.974155</td>\n",
+       "      <td>5713.854980</td>\n",
+       "      <td>57.425044</td>\n",
+       "      <td>34.517068</td>\n",
+       "      <td>0.113304</td>\n",
+       "      <td>0.101773</td>\n",
+       "      <td>1.809109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>negative_control</td>\n",
+       "      <td>197.942</td>\n",
+       "      <td>4.722340</td>\n",
+       "      <td>3.939271e-01</td>\n",
+       "      <td>61.333071</td>\n",
+       "      <td>1039.818115</td>\n",
+       "      <td>3.867945</td>\n",
+       "      <td>0.489523</td>\n",
+       "      <td>34.430371</td>\n",
+       "      <td>57.992855</td>\n",
+       "      <td>5.867740</td>\n",
+       "      <td>7.615304</td>\n",
+       "      <td>8.901464</td>\n",
+       "      <td>14.993197</td>\n",
+       "      <td>1.517018</td>\n",
+       "      <td>1.968824</td>\n",
+       "      <td>5634</td>\n",
+       "      <td>807.000000</td>\n",
+       "      <td>2.431045</td>\n",
+       "      <td>268695.473466</td>\n",
+       "      <td>2.786387e+05</td>\n",
+       "      <td>518.358441</td>\n",
+       "      <td>527.862346</td>\n",
+       "      <td>332.955977</td>\n",
+       "      <td>345.277145</td>\n",
+       "      <td>0.642328</td>\n",
+       "      <td>0.654105</td>\n",
+       "      <td>6</td>\n",
+       "      <td>208.637941</td>\n",
+       "      <td>4.966156</td>\n",
+       "      <td>7804.024882</td>\n",
+       "      <td>4804.706543</td>\n",
+       "      <td>37.404630</td>\n",
+       "      <td>23.028921</td>\n",
+       "      <td>0.001065</td>\n",
+       "      <td>0.001064</td>\n",
+       "      <td>0.819074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
+       "      <td>Flu_A0201</td>\n",
+       "      <td>173.376</td>\n",
+       "      <td>2.901204</td>\n",
+       "      <td>2.628828e+32</td>\n",
+       "      <td>2.901204</td>\n",
+       "      <td>875.116882</td>\n",
+       "      <td>15.505495</td>\n",
+       "      <td>1.876203</td>\n",
+       "      <td>143.647475</td>\n",
+       "      <td>331.107086</td>\n",
+       "      <td>11.985302</td>\n",
+       "      <td>18.196348</td>\n",
+       "      <td>9.264295</td>\n",
+       "      <td>21.354178</td>\n",
+       "      <td>0.772971</td>\n",
+       "      <td>1.173542</td>\n",
+       "      <td>182</td>\n",
+       "      <td>1950.187696</td>\n",
+       "      <td>4.113499</td>\n",
+       "      <td>926523.693207</td>\n",
+       "      <td>1.047826e+06</td>\n",
+       "      <td>962.561008</td>\n",
+       "      <td>1023.633510</td>\n",
+       "      <td>475.094625</td>\n",
+       "      <td>537.294725</td>\n",
+       "      <td>0.493574</td>\n",
+       "      <td>0.524890</td>\n",
+       "      <td>2227</td>\n",
+       "      <td>125.773976</td>\n",
+       "      <td>2.192459</td>\n",
+       "      <td>6449.982473</td>\n",
+       "      <td>3164.612305</td>\n",
+       "      <td>51.282329</td>\n",
+       "      <td>25.161106</td>\n",
+       "      <td>12.236264</td>\n",
+       "      <td>0.924450</td>\n",
+       "      <td>5.344504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
+       "      <td>CMV_B0702</td>\n",
+       "      <td>87.184</td>\n",
+       "      <td>2.528850</td>\n",
+       "      <td>8.153597e+11</td>\n",
+       "      <td>2.528850</td>\n",
+       "      <td>833.862305</td>\n",
+       "      <td>3.732883</td>\n",
+       "      <td>1.053218</td>\n",
+       "      <td>16.963204</td>\n",
+       "      <td>32.455223</td>\n",
+       "      <td>4.118641</td>\n",
+       "      <td>5.696949</td>\n",
+       "      <td>4.544264</td>\n",
+       "      <td>8.694412</td>\n",
+       "      <td>1.103341</td>\n",
+       "      <td>1.526153</td>\n",
+       "      <td>2220</td>\n",
+       "      <td>649.994709</td>\n",
+       "      <td>3.614533</td>\n",
+       "      <td>117537.315045</td>\n",
+       "      <td>1.443395e+05</td>\n",
+       "      <td>342.837155</td>\n",
+       "      <td>379.920325</td>\n",
+       "      <td>180.828110</td>\n",
+       "      <td>222.062505</td>\n",
+       "      <td>0.527446</td>\n",
+       "      <td>0.584498</td>\n",
+       "      <td>189</td>\n",
+       "      <td>174.126735</td>\n",
+       "      <td>3.431894</td>\n",
+       "      <td>6928.957342</td>\n",
+       "      <td>4447.341309</td>\n",
+       "      <td>39.792610</td>\n",
+       "      <td>25.540830</td>\n",
+       "      <td>0.085135</td>\n",
+       "      <td>0.078456</td>\n",
+       "      <td>1.476119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
+       "      <td>negative_control_A0201</td>\n",
+       "      <td>173.376</td>\n",
+       "      <td>2.901204</td>\n",
+       "      <td>2.628828e+32</td>\n",
+       "      <td>2.901204</td>\n",
+       "      <td>875.116882</td>\n",
+       "      <td>2.114713</td>\n",
+       "      <td>0.873756</td>\n",
+       "      <td>7.232860</td>\n",
+       "      <td>12.402468</td>\n",
+       "      <td>2.689398</td>\n",
+       "      <td>3.521714</td>\n",
+       "      <td>3.420256</td>\n",
+       "      <td>5.864846</td>\n",
+       "      <td>1.271755</td>\n",
+       "      <td>1.665339</td>\n",
+       "      <td>2406</td>\n",
+       "      <td>633.666667</td>\n",
+       "      <td>1.553935</td>\n",
+       "      <td>259031.426601</td>\n",
+       "      <td>2.944109e+05</td>\n",
+       "      <td>508.951301</td>\n",
+       "      <td>542.596449</td>\n",
+       "      <td>408.781841</td>\n",
+       "      <td>464.614792</td>\n",
+       "      <td>0.803185</td>\n",
+       "      <td>0.856281</td>\n",
+       "      <td>3</td>\n",
+       "      <td>299.646619</td>\n",
+       "      <td>1.778455</td>\n",
+       "      <td>35813.138102</td>\n",
+       "      <td>23738.091797</td>\n",
+       "      <td>119.517911</td>\n",
+       "      <td>79.220286</td>\n",
+       "      <td>0.001247</td>\n",
+       "      <td>0.001245</td>\n",
+       "      <td>0.728909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
+       "      <td>negative_control_B0702</td>\n",
+       "      <td>87.184</td>\n",
+       "      <td>2.528850</td>\n",
+       "      <td>8.153597e+11</td>\n",
+       "      <td>2.528850</td>\n",
+       "      <td>833.862305</td>\n",
+       "      <td>1.782627</td>\n",
+       "      <td>0.662888</td>\n",
+       "      <td>6.576434</td>\n",
+       "      <td>18.766962</td>\n",
+       "      <td>2.564456</td>\n",
+       "      <td>4.332085</td>\n",
+       "      <td>3.689182</td>\n",
+       "      <td>10.527701</td>\n",
+       "      <td>1.438583</td>\n",
+       "      <td>2.430169</td>\n",
+       "      <td>2406</td>\n",
+       "      <td>601.000000</td>\n",
+       "      <td>1.064116</td>\n",
+       "      <td>340038.726438</td>\n",
+       "      <td>2.959260e+05</td>\n",
+       "      <td>583.128396</td>\n",
+       "      <td>543.990809</td>\n",
+       "      <td>565.788230</td>\n",
+       "      <td>492.389351</td>\n",
+       "      <td>0.970264</td>\n",
+       "      <td>0.905143</td>\n",
+       "      <td>3</td>\n",
+       "      <td>337.142924</td>\n",
+       "      <td>1.605272</td>\n",
+       "      <td>51705.636364</td>\n",
+       "      <td>15768.455078</td>\n",
+       "      <td>153.364145</td>\n",
+       "      <td>46.770832</td>\n",
+       "      <td>0.001247</td>\n",
+       "      <td>0.001245</td>\n",
+       "      <td>0.704916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10k_BEAM-T_Human_A2402_EBV_CMV_spikein</td>\n",
+       "      <td>EBV</td>\n",
+       "      <td>64.444</td>\n",
+       "      <td>1.961246</td>\n",
+       "      <td>4.752330e-01</td>\n",
+       "      <td>10.055138</td>\n",
+       "      <td>47.815094</td>\n",
+       "      <td>5.349569</td>\n",
+       "      <td>1.177876</td>\n",
+       "      <td>29.645753</td>\n",
+       "      <td>36.547199</td>\n",
+       "      <td>5.444791</td>\n",
+       "      <td>6.045428</td>\n",
+       "      <td>5.541709</td>\n",
+       "      <td>6.831803</td>\n",
+       "      <td>1.017800</td>\n",
+       "      <td>1.130078</td>\n",
+       "      <td>2320</td>\n",
+       "      <td>482.138596</td>\n",
+       "      <td>4.481992</td>\n",
+       "      <td>52346.939110</td>\n",
+       "      <td>7.634764e+04</td>\n",
+       "      <td>228.794535</td>\n",
+       "      <td>276.310768</td>\n",
+       "      <td>108.572389</td>\n",
+       "      <td>158.352061</td>\n",
+       "      <td>0.474541</td>\n",
+       "      <td>0.573094</td>\n",
+       "      <td>570</td>\n",
+       "      <td>90.126625</td>\n",
+       "      <td>3.805148</td>\n",
+       "      <td>1765.748310</td>\n",
+       "      <td>2089.014893</td>\n",
+       "      <td>19.591861</td>\n",
+       "      <td>23.178665</td>\n",
+       "      <td>0.245690</td>\n",
+       "      <td>0.197232</td>\n",
+       "      <td>2.727638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10k_BEAM-T_Human_A2402_EBV_CMV_spikein</td>\n",
+       "      <td>CMV</td>\n",
+       "      <td>64.444</td>\n",
+       "      <td>1.961246</td>\n",
+       "      <td>4.752330e-01</td>\n",
+       "      <td>10.055138</td>\n",
+       "      <td>47.815094</td>\n",
+       "      <td>3.266996</td>\n",
+       "      <td>0.645879</td>\n",
+       "      <td>19.792167</td>\n",
+       "      <td>33.713371</td>\n",
+       "      <td>4.448839</td>\n",
+       "      <td>5.806322</td>\n",
+       "      <td>6.058216</td>\n",
+       "      <td>10.319380</td>\n",
+       "      <td>1.361752</td>\n",
+       "      <td>1.777266</td>\n",
+       "      <td>2633</td>\n",
+       "      <td>1385.941634</td>\n",
+       "      <td>2.885361</td>\n",
+       "      <td>667102.997951</td>\n",
+       "      <td>4.532816e+05</td>\n",
+       "      <td>816.763734</td>\n",
+       "      <td>673.261929</td>\n",
+       "      <td>481.335564</td>\n",
+       "      <td>327.056792</td>\n",
+       "      <td>0.589320</td>\n",
+       "      <td>0.485779</td>\n",
+       "      <td>257</td>\n",
+       "      <td>424.225101</td>\n",
+       "      <td>4.467340</td>\n",
+       "      <td>33705.405280</td>\n",
+       "      <td>13445.158203</td>\n",
+       "      <td>79.451699</td>\n",
+       "      <td>31.693454</td>\n",
+       "      <td>0.097607</td>\n",
+       "      <td>0.088927</td>\n",
+       "      <td>1.665776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10k_BEAM-T_Human_A2402_EBV_CMV_spikein</td>\n",
+       "      <td>negative_control</td>\n",
+       "      <td>64.444</td>\n",
+       "      <td>1.961246</td>\n",
+       "      <td>4.752330e-01</td>\n",
+       "      <td>10.055138</td>\n",
+       "      <td>47.815094</td>\n",
+       "      <td>1.801178</td>\n",
+       "      <td>0.549719</td>\n",
+       "      <td>7.702813</td>\n",
+       "      <td>11.078585</td>\n",
+       "      <td>2.775394</td>\n",
+       "      <td>3.328451</td>\n",
+       "      <td>4.276543</td>\n",
+       "      <td>6.150745</td>\n",
+       "      <td>1.540878</td>\n",
+       "      <td>1.847930</td>\n",
+       "      <td>2887</td>\n",
+       "      <td>156.000000</td>\n",
+       "      <td>2.392657</td>\n",
+       "      <td>10327.117742</td>\n",
+       "      <td>1.164800e+04</td>\n",
+       "      <td>101.622427</td>\n",
+       "      <td>107.925901</td>\n",
+       "      <td>66.199473</td>\n",
+       "      <td>74.666667</td>\n",
+       "      <td>0.651426</td>\n",
+       "      <td>0.691833</td>\n",
+       "      <td>3</td>\n",
+       "      <td>86.610000</td>\n",
+       "      <td>4.352510</td>\n",
+       "      <td>1340.694268</td>\n",
+       "      <td>1051.397827</td>\n",
+       "      <td>15.479671</td>\n",
+       "      <td>12.139451</td>\n",
+       "      <td>0.001039</td>\n",
+       "      <td>0.001038</td>\n",
+       "      <td>0.918385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2k_BEAM-T_Mouse_H2Kb_OT-1_5pv2</td>\n",
+       "      <td>OT-1</td>\n",
+       "      <td>81.580</td>\n",
+       "      <td>11.605676</td>\n",
+       "      <td>1.501462e+96</td>\n",
+       "      <td>11.605676</td>\n",
+       "      <td>11477.687500</td>\n",
+       "      <td>30.833333</td>\n",
+       "      <td>1.718346</td>\n",
+       "      <td>584.094782</td>\n",
+       "      <td>651.805481</td>\n",
+       "      <td>24.168053</td>\n",
+       "      <td>25.530481</td>\n",
+       "      <td>18.943615</td>\n",
+       "      <td>21.139637</td>\n",
+       "      <td>0.783829</td>\n",
+       "      <td>0.828016</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1284.159790</td>\n",
+       "      <td>4.557121</td>\n",
+       "      <td>363149.955646</td>\n",
+       "      <td>3.354067e+05</td>\n",
+       "      <td>602.619246</td>\n",
+       "      <td>579.143036</td>\n",
+       "      <td>282.791876</td>\n",
+       "      <td>261.187633</td>\n",
+       "      <td>0.469271</td>\n",
+       "      <td>0.450990</td>\n",
+       "      <td>1333</td>\n",
+       "      <td>41.648426</td>\n",
+       "      <td>2.652040</td>\n",
+       "      <td>621.731210</td>\n",
+       "      <td>514.580933</td>\n",
+       "      <td>14.928084</td>\n",
+       "      <td>12.355351</td>\n",
+       "      <td>222.166667</td>\n",
+       "      <td>0.995519</td>\n",
+       "      <td>2.656746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2k_BEAM-T_Mouse_H2Kb_OT-1_5pv2</td>\n",
+       "      <td>negative_control</td>\n",
+       "      <td>81.580</td>\n",
+       "      <td>11.605676</td>\n",
+       "      <td>1.501462e+96</td>\n",
+       "      <td>11.605676</td>\n",
+       "      <td>11477.687500</td>\n",
+       "      <td>5.961509</td>\n",
+       "      <td>0.812680</td>\n",
+       "      <td>49.692846</td>\n",
+       "      <td>68.568329</td>\n",
+       "      <td>7.049315</td>\n",
+       "      <td>8.280600</td>\n",
+       "      <td>8.335615</td>\n",
+       "      <td>11.501840</td>\n",
+       "      <td>1.182472</td>\n",
+       "      <td>1.389011</td>\n",
+       "      <td>1325</td>\n",
+       "      <td>545.785714</td>\n",
+       "      <td>0.746879</td>\n",
+       "      <td>399381.561286</td>\n",
+       "      <td>8.029060e+05</td>\n",
+       "      <td>631.966424</td>\n",
+       "      <td>896.050222</td>\n",
+       "      <td>731.755249</td>\n",
+       "      <td>1471.101165</td>\n",
+       "      <td>1.157902</td>\n",
+       "      <td>1.641762</td>\n",
+       "      <td>14</td>\n",
+       "      <td>91.551598</td>\n",
+       "      <td>0.919032</td>\n",
+       "      <td>8037.003192</td>\n",
+       "      <td>11709.575195</td>\n",
+       "      <td>87.786597</td>\n",
+       "      <td>127.901374</td>\n",
+       "      <td>0.010566</td>\n",
+       "      <td>0.010456</td>\n",
+       "      <td>0.513672</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            file                    gene  \\\n",
+       "0   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein                     CMV   \n",
+       "1   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein         EBV_BMLF-1_GLCT   \n",
+       "2   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein                     Flu   \n",
+       "3   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein          EBV_BRLF1_YVLD   \n",
+       "4   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein               SARS_Cov2   \n",
+       "5   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein        negative_control   \n",
+       "6               5k_BEAM-T_Human_A0201_B0702_PBMC               Flu_A0201   \n",
+       "7               5k_BEAM-T_Human_A0201_B0702_PBMC               CMV_B0702   \n",
+       "8               5k_BEAM-T_Human_A0201_B0702_PBMC  negative_control_A0201   \n",
+       "9               5k_BEAM-T_Human_A0201_B0702_PBMC  negative_control_B0702   \n",
+       "10        10k_BEAM-T_Human_A2402_EBV_CMV_spikein                     EBV   \n",
+       "11        10k_BEAM-T_Human_A2402_EBV_CMV_spikein                     CMV   \n",
+       "12        10k_BEAM-T_Human_A2402_EBV_CMV_spikein        negative_control   \n",
+       "13                2k_BEAM-T_Mouse_H2Kb_OT-1_5pv2                    OT-1   \n",
+       "14                2k_BEAM-T_Mouse_H2Kb_OT-1_5pv2        negative_control   \n",
+       "\n",
+       "    threshold   mean_neg  concentration_neg  var_nb_neg  var_data_neg  \\\n",
+       "0     197.942   4.722340       3.939271e-01   61.333071   1039.818115   \n",
+       "1     197.942   4.722340       3.939271e-01   61.333071   1039.818115   \n",
+       "2     197.942   4.722340       3.939271e-01   61.333071   1039.818115   \n",
+       "3     197.942   4.722340       3.939271e-01   61.333071   1039.818115   \n",
+       "4     197.942   4.722340       3.939271e-01   61.333071   1039.818115   \n",
+       "5     197.942   4.722340       3.939271e-01   61.333071   1039.818115   \n",
+       "6     173.376   2.901204       2.628828e+32    2.901204    875.116882   \n",
+       "7      87.184   2.528850       8.153597e+11    2.528850    833.862305   \n",
+       "8     173.376   2.901204       2.628828e+32    2.901204    875.116882   \n",
+       "9      87.184   2.528850       8.153597e+11    2.528850    833.862305   \n",
+       "10     64.444   1.961246       4.752330e-01   10.055138     47.815094   \n",
+       "11     64.444   1.961246       4.752330e-01   10.055138     47.815094   \n",
+       "12     64.444   1.961246       4.752330e-01   10.055138     47.815094   \n",
+       "13     81.580  11.605676       1.501462e+96   11.605676  11477.687500   \n",
+       "14     81.580  11.605676       1.501462e+96   11.605676  11477.687500   \n",
+       "\n",
+       "        mean-  concentration-     var_nb-   var_data-    std_nb-  std_data-  \\\n",
+       "0   25.003214        2.897500  240.761868  338.704865  15.516503  18.403936   \n",
+       "1    1.454062        0.231539   10.585553   20.490885   3.253545   4.526686   \n",
+       "2   11.730760        1.432491  107.794731  224.974823  10.382424  14.999161   \n",
+       "3    2.596270        0.386810   20.022444   45.894730   4.474645   6.774565   \n",
+       "4    8.543229        1.653138   52.693679   87.826508   7.259041   9.371580   \n",
+       "5    3.867945        0.489523   34.430371   57.992855   5.867740   7.615304   \n",
+       "6   15.505495        1.876203  143.647475  331.107086  11.985302  18.196348   \n",
+       "7    3.732883        1.053218   16.963204   32.455223   4.118641   5.696949   \n",
+       "8    2.114713        0.873756    7.232860   12.402468   2.689398   3.521714   \n",
+       "9    1.782627        0.662888    6.576434   18.766962   2.564456   4.332085   \n",
+       "10   5.349569        1.177876   29.645753   36.547199   5.444791   6.045428   \n",
+       "11   3.266996        0.645879   19.792167   33.713371   4.448839   5.806322   \n",
+       "12   1.801178        0.549719    7.702813   11.078585   2.775394   3.328451   \n",
+       "13  30.833333        1.718346  584.094782  651.805481  24.168053  25.530481   \n",
+       "14   5.961509        0.812680   49.692846   68.568329   7.049315   8.280600   \n",
+       "\n",
+       "    overdisp_nb-  overdisp_data-  cof_var_nb-  cof_var_data-    N-  \\\n",
+       "0       9.629237       13.546453     0.620580       0.736063  1867   \n",
+       "1       7.279988       14.092170     2.237556       3.113132  5638   \n",
+       "2       9.189066       19.178196     0.885060       1.278618  4249   \n",
+       "3       7.712004       17.677179     1.723490       2.609345  5630   \n",
+       "4       6.167888       10.280247     0.849684       1.096960  5066   \n",
+       "5       8.901464       14.993197     1.517018       1.968824  5634   \n",
+       "6       9.264295       21.354178     0.772971       1.173542   182   \n",
+       "7       4.544264        8.694412     1.103341       1.526153  2220   \n",
+       "8       3.420256        5.864846     1.271755       1.665339  2406   \n",
+       "9       3.689182       10.527701     1.438583       2.430169  2406   \n",
+       "10      5.541709        6.831803     1.017800       1.130078  2320   \n",
+       "11      6.058216       10.319380     1.361752       1.777266  2633   \n",
+       "12      4.276543        6.150745     1.540878       1.847930  2887   \n",
+       "13     18.943615       21.139637     0.783829       0.828016     6   \n",
+       "14      8.335615       11.501840     1.182472       1.389011  1325   \n",
+       "\n",
+       "          mean+  concentration+        var_nb+     var_data+     std_nb+  \\\n",
+       "0   1230.471773        3.924429  387034.531887  4.371280e+05  622.120995   \n",
+       "1    251.500000       24.847288    2797.139942  2.756250e+03   52.887994   \n",
+       "2    922.462976        4.215191  202796.575052  2.202642e+05  450.329407   \n",
+       "3    810.800000        2.834109  232769.662520  2.139172e+05  482.462084   \n",
+       "4   1414.221254        4.004123  500904.754996  5.018279e+05  707.746250   \n",
+       "5    807.000000        2.431045  268695.473466  2.786387e+05  518.358441   \n",
+       "6   1950.187696        4.113499  926523.693207  1.047826e+06  962.561008   \n",
+       "7    649.994709        3.614533  117537.315045  1.443395e+05  342.837155   \n",
+       "8    633.666667        1.553935  259031.426601  2.944109e+05  508.951301   \n",
+       "9    601.000000        1.064116  340038.726438  2.959260e+05  583.128396   \n",
+       "10   482.138596        4.481992   52346.939110  7.634764e+04  228.794535   \n",
+       "11  1385.941634        2.885361  667102.997951  4.532816e+05  816.763734   \n",
+       "12   156.000000        2.392657   10327.117742  1.164800e+04  101.622427   \n",
+       "13  1284.159790        4.557121  363149.955646  3.354067e+05  602.619246   \n",
+       "14   545.785714        0.746879  399381.561286  8.029060e+05  631.966424   \n",
+       "\n",
+       "      std_data+  overdisp_nb+  overdisp_data+  cof_var_nb+  cof_var_data+  \\\n",
+       "0    661.156539    314.541577      355.252334     0.505596       0.537320   \n",
+       "1     52.500000     11.121829       10.959245     0.210290       0.208748   \n",
+       "2    469.323150    219.842509      238.778384     0.488182       0.508772   \n",
+       "3    462.511790    287.086412      263.834677     0.595045       0.570439   \n",
+       "4    708.398127    354.191223      354.843985     0.500449       0.500910   \n",
+       "5    527.862346    332.955977      345.277145     0.642328       0.654105   \n",
+       "6   1023.633510    475.094625      537.294725     0.493574       0.524890   \n",
+       "7    379.920325    180.828110      222.062505     0.527446       0.584498   \n",
+       "8    542.596449    408.781841      464.614792     0.803185       0.856281   \n",
+       "9    543.990809    565.788230      492.389351     0.970264       0.905143   \n",
+       "10   276.310768    108.572389      158.352061     0.474541       0.573094   \n",
+       "11   673.261929    481.335564      327.056792     0.589320       0.485779   \n",
+       "12   107.925901     66.199473       74.666667     0.651426       0.691833   \n",
+       "13   579.143036    282.791876      261.187633     0.469271       0.450990   \n",
+       "14   896.050222    731.755249     1471.101165     1.157902       1.641762   \n",
+       "\n",
+       "      N+  mean_ratio  concentration_ratio  var_nb_ratio  var_data_ratio  \\\n",
+       "0   3773   49.212545             1.354419   1607.540828     1290.586670   \n",
+       "1      2  172.963772           107.313660    264.241278      134.511032   \n",
+       "2   1391   78.636249             2.942561   1881.321785      979.061646   \n",
+       "3     10  312.294178             7.326876  11625.436849     4661.039551   \n",
+       "4    574  165.537081             2.422136   9505.974155     5713.854980   \n",
+       "5      6  208.637941             4.966156   7804.024882     4804.706543   \n",
+       "6   2227  125.773976             2.192459   6449.982473     3164.612305   \n",
+       "7    189  174.126735             3.431894   6928.957342     4447.341309   \n",
+       "8      3  299.646619             1.778455  35813.138102    23738.091797   \n",
+       "9      3  337.142924             1.605272  51705.636364    15768.455078   \n",
+       "10   570   90.126625             3.805148   1765.748310     2089.014893   \n",
+       "11   257  424.225101             4.467340  33705.405280    13445.158203   \n",
+       "12     3   86.610000             4.352510   1340.694268     1051.397827   \n",
+       "13  1333   41.648426             2.652040    621.731210      514.580933   \n",
+       "14    14   91.551598             0.919032   8037.003192    11709.575195   \n",
+       "\n",
+       "    overdisp_nb_ratio  overdisp_data_ratio     N_ratio  binder_ratio  \\\n",
+       "0           32.665265            26.224749    2.020889      0.668972   \n",
+       "1            1.527726             0.777683    0.000355      0.000355   \n",
+       "2           23.924358            12.450513    0.327371      0.246631   \n",
+       "3           37.225916            14.925157    0.001776      0.001773   \n",
+       "4           57.425044            34.517068    0.113304      0.101773   \n",
+       "5           37.404630            23.028921    0.001065      0.001064   \n",
+       "6           51.282329            25.161106   12.236264      0.924450   \n",
+       "7           39.792610            25.540830    0.085135      0.078456   \n",
+       "8          119.517911            79.220286    0.001247      0.001245   \n",
+       "9          153.364145            46.770832    0.001247      0.001245   \n",
+       "10          19.591861            23.178665    0.245690      0.197232   \n",
+       "11          79.451699            31.693454    0.097607      0.088927   \n",
+       "12          15.479671            12.139451    0.001039      0.001038   \n",
+       "13          14.928084            12.355351  222.166667      0.995519   \n",
+       "14          87.786597           127.901374    0.010566      0.010456   \n",
+       "\n",
+       "    noise_to_neg_mean  \n",
+       "0            5.294666  \n",
+       "1            0.307911  \n",
+       "2            2.484099  \n",
+       "3            0.549785  \n",
+       "4            1.809109  \n",
+       "5            0.819074  \n",
+       "6            5.344504  \n",
+       "7            1.476119  \n",
+       "8            0.728909  \n",
+       "9            0.704916  \n",
+       "10           2.727638  \n",
+       "11           1.665776  \n",
+       "12           0.918385  \n",
+       "13           2.656746  \n",
+       "14           0.513672  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Determine params based on negative control percentile threshold for all BEAM-T datasets and all genes\n",
+    "QUANTILE = 0.999\n",
+    "FILES = ['10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein', '5k_BEAM-T_Human_A0201_B0702_PBMC', '10k_BEAM-T_Human_A2402_EBV_CMV_spikein', '2k_BEAM-T_Mouse_H2Kb_OT-1_5pv2']  #  '10k_BEAM-T_Human_A1101_EBV_spikein', \n",
+    "\n",
+    "params = []\n",
+    "nrows = 4\n",
+    "ncols = 18\n",
+    "plt.figure(figsize=(4.5 * ncols, 3.5 * nrows))\n",
+    "i = 0\n",
+    "for file in FILES:\n",
+    "    mdata = mu.read(f'../../data/BEAMT/{file}/preprocessed.h5mu')\n",
+    "    genes = mdata.var['gex:gene_ids'].unique()\n",
+    "    for gene in genes:\n",
+    "        if file == '5k_BEAM-T_Human_A0201_B0702_PBMC':  # Has two negative controls\n",
+    "            mhc = gene.split('_')[-1]\n",
+    "            neg_ctrl_key = [gene for gene in mdata.var['gex:gene_ids'].unique() if 'negative' in gene and mhc in gene][0]\n",
+    "        else:\n",
+    "            neg_ctrl_key = [gene for gene in mdata.var['gex:gene_ids'].unique() if 'negative' in gene][0]\n",
+    "        x_neg = mdata['gex'][:, neg_ctrl_key].X.toarray().reshape(-1, )\n",
+    "        nbfit_neg = smf.negativebinomial(\"nbdata ~ 1\", data=pd.DataFrame({\"nbdata\": x_neg})).fit(disp=False)\n",
+    "        mean_neg = np.exp(nbfit_neg.params.iloc[0])\n",
+    "        concentration_neg = 1 / nbfit_neg.params.iloc[1]\n",
+    "        var_nb_neg = mean_neg + mean_neg ** 2 / concentration_neg\n",
+    "        var_data_neg = x_neg.var()\n",
+    "\n",
+    "        thr = np.quantile(x_neg, QUANTILE) if file != '2k_BEAM-T_Mouse_H2Kb_OT-1_5pv2' else np.quantile(x_neg, 0.99 )  # 2k only 1339 samples\n",
+    "        x = mdata['gex'][:, gene].X.toarray().reshape(-1, )\n",
+    "        i += 1\n",
+    "        param = {'file': file, 'gene': gene, 'threshold': thr,\n",
+    "\t\t\t\t f'mean_neg': mean_neg, f'concentration_neg': concentration_neg,\n",
+    "\t\t\t\t f'var_nb_neg': var_nb_neg, 'var_data_neg': var_data_neg}\n",
+    "\n",
+    "        for direction in ['-', '+']:\n",
+    "            plt.subplot(nrows, ncols, i + (ncols if direction == '+' else 0))\n",
+    "            x_in_threshold = x[x > thr] if direction == '+' else x[x <= thr]\n",
+    "            nbfit = smf.negativebinomial(\"nbdata ~ 1\", data=pd.DataFrame({\"nbdata\": x_in_threshold})).fit(disp=False)\n",
+    "            mean = np.exp(nbfit.params.iloc[0])\n",
+    "            concentration = 1 / nbfit.params.iloc[1]\n",
+    "            var_nb = mean + mean ** 2 / concentration\n",
+    "            var_data = x_in_threshold.var()\n",
+    "            sns.histplot(x_in_threshold, discrete=True, binrange=(x_in_threshold.min(), x_in_threshold.max()), stat='probability', element='step', fill=True, alpha=0.3, label=gene)\n",
+    "            if direction == '-':\n",
+    "                sns.histplot(x_neg, discrete=True, binrange=(x_in_threshold.min(), x_in_threshold.max()), stat='probability', element='step', fill=True, alpha=0.3, label=neg_ctrl_key)\n",
+    "\n",
+    "\t\t\t# Fitted distribution for visualization\n",
+    "            x_range = np.arange(x_in_threshold.min(), x_in_threshold.max())\n",
+    "            prob = np.exp(npd.NegativeBinomial2(mean, concentration).log_prob(x_range))\n",
+    "            prob = prob / prob.sum()\n",
+    "            sns.lineplot(x=x_range, y=prob, c='black', ls=':')\n",
+    "\n",
+    "            plt.legend(frameon=False)\n",
+    "            sns.despine()\n",
+    "            plt.title(r'$\\mu$ = {:.1f}, $\\alpha$ = {:.1f} $var_{{NB}}$={:.0f} $var_{{data}}$={:.0f}'.format(mean, concentration, var_nb, var_data))\n",
+    "            \n",
+    "            param.update({f'mean{direction}': mean, f'concentration{direction}': concentration, \n",
+    "                          f'var_nb{direction}': var_nb, f'var_data{direction}': var_data, \n",
+    "                          f'std_nb{direction}': var_nb ** 0.5, f'std_data{direction}': var_data ** 0.5, \n",
+    "                          f'overdisp_nb{direction}': var_nb / mean, f'overdisp_data{direction}': var_data / mean, \n",
+    "                          f'cof_var_nb{direction}': var_nb ** 0.5 / mean, f'cof_var_data{direction}': var_data ** 0.5 / mean, \n",
+    "                          f'N{direction}': x_in_threshold.shape[0],})\n",
+    "\n",
+    "        # Signal and noise\n",
+    "        plt.subplot(nrows, ncols, i + (2 * ncols))\n",
+    "        sns.histplot(x, discrete=True, stat='probability', element='step', fill=True, alpha=0.3, label=gene)\n",
+    "        sns.despine()\n",
+    "        plt.title('all')\n",
+    "\n",
+    "        # Close to threshold\n",
+    "        plt.subplot(nrows, ncols, i + (3 * ncols))\n",
+    "        sns.histplot(x, binrange=(thr - thr/2, thr + thr/2), discrete=True, stat='count', element='step', fill=True, alpha=0.3, label=gene)\n",
+    "        sns.despine()\n",
+    "        plt.title(f'between {thr - thr/2:.0f} and {thr + thr/2:.0f}')\n",
+    "        \n",
+    "        params.append(param)\n",
+    "            \n",
+    "plt.suptitle(f'{QUANTILE} Quantile based determination of binder and non-binder', y=1.0)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "params = pd.DataFrame(params)\n",
+    "for key in [f'mean', f'concentration', f'var_nb', f'var_data', f'overdisp_nb', f'overdisp_data', f'N']:\n",
+    "    params[f'{key}_ratio'] = params[f'{key}+'] / params[f'{key}-']\n",
+    "params['binder_ratio'] = params['N+'] / (params['N-'] + params['N+'])\n",
+    "params['noise_to_neg_mean'] = params['mean-'] / params['mean_neg']\n",
+    "params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8c729af-e292-49a7-b5f1-f4b871e4201c",
+   "metadata": {},
+   "source": [
+    "## Negative control distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1ce2c913-6426-43ae-a3d8-00f33f7930e2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T13:42:23.969837Z",
+     "start_time": "2025-08-14T13:42:23.850382Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean-</th>\n",
+       "      <th>concentration-</th>\n",
+       "      <th>var_nb-</th>\n",
+       "      <th>std_nb-</th>\n",
+       "      <th>overdisp_nb-</th>\n",
+       "      <th>cof_var_nb-</th>\n",
+       "      <th>N-</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.867945</td>\n",
+       "      <td>0.489523</td>\n",
+       "      <td>34.430371</td>\n",
+       "      <td>5.867740</td>\n",
+       "      <td>8.901464</td>\n",
+       "      <td>1.517018</td>\n",
+       "      <td>5634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2.114713</td>\n",
+       "      <td>0.873756</td>\n",
+       "      <td>7.232860</td>\n",
+       "      <td>2.689398</td>\n",
+       "      <td>3.420256</td>\n",
+       "      <td>1.271755</td>\n",
+       "      <td>2406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.782627</td>\n",
+       "      <td>0.662888</td>\n",
+       "      <td>6.576434</td>\n",
+       "      <td>2.564456</td>\n",
+       "      <td>3.689182</td>\n",
+       "      <td>1.438583</td>\n",
+       "      <td>2406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1.801178</td>\n",
+       "      <td>0.549719</td>\n",
+       "      <td>7.702813</td>\n",
+       "      <td>2.775394</td>\n",
+       "      <td>4.276543</td>\n",
+       "      <td>1.540878</td>\n",
+       "      <td>2887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>5.961509</td>\n",
+       "      <td>0.812680</td>\n",
+       "      <td>49.692846</td>\n",
+       "      <td>7.049315</td>\n",
+       "      <td>8.335615</td>\n",
+       "      <td>1.182472</td>\n",
+       "      <td>1325</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       mean-  concentration-    var_nb-   std_nb-  overdisp_nb-  cof_var_nb-  \\\n",
+       "5   3.867945        0.489523  34.430371  5.867740      8.901464     1.517018   \n",
+       "8   2.114713        0.873756   7.232860  2.689398      3.420256     1.271755   \n",
+       "9   1.782627        0.662888   6.576434  2.564456      3.689182     1.438583   \n",
+       "12  1.801178        0.549719   7.702813  2.775394      4.276543     1.540878   \n",
+       "14  5.961509        0.812680  49.692846  7.049315      8.335615     1.182472   \n",
+       "\n",
+       "      N-  \n",
+       "5   5634  \n",
+       "8   2406  \n",
+       "9   2406  \n",
+       "12  2887  \n",
+       "14  1325  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "neg_ctrl_params = params[params['gene'].str.contains('negative')].copy()\n",
+    "neg_ctrl_params[['mean-', 'concentration-', 'var_nb-', 'std_nb-', 'overdisp_nb-', 'cof_var_nb-', 'N-']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9100607f-13d1-456c-8e45-a3225e267b8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Truncated log-normal params:  -1.0539178917389445 1.8375518345106903 1.018115879390079 0.4175162931163312\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overdispersion Gamma shape, scale 4.186062616134899 1.2384303396204106\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameterize negative control MEAN with, gamma matching first and second moment\n",
+    "# ns    = neg_ctrl_params['N-']      # matching sample sizes\n",
+    "\n",
+    "# weights = ns / ns.sum()\n",
+    "# mu_hat  = np.sum(weights * means)\n",
+    "# var_hat = np.sum(weights * (means - mu_hat)**2)\n",
+    "\n",
+    "# alpha = mu_hat**2 / var_hat\n",
+    "# theta = var_hat / mu_hat      # scale ; rate = 1/theta\n",
+    "\n",
+    "# print(f\"Mean shape, scale: {alpha}, {theta}\")\n",
+    "# samples = stats.gamma(a=alpha, scale=theta).rvs(10000)\n",
+    "# print(samples.min())\n",
+    "# sns.histplot(samples, stat='density')\n",
+    "# # sns.histplot(neg_ctrl_params['mean-'], stat='density')\n",
+    "# sns.scatterplot(x=neg_ctrl_params['mean-'], y=0.1)\n",
+    "# sns.despine()\n",
+    "# plt.show()\n",
+    "\n",
+    "\n",
+    "# Truncated log-normal\n",
+    "ns, means = neg_ctrl_params['N-'].to_numpy(float), neg_ctrl_params['mean-'].to_numpy(float)\n",
+    "\n",
+    "# Weighted MoM lognormal params\n",
+    "w = ns / ns.sum()\n",
+    "m = np.sum(w * means)\n",
+    "v = np.sum(w * (means - m)**2)\n",
+    "sigma2 = np.log(1 + v / m**2)\n",
+    "sigma  = np.sqrt(sigma2)\n",
+    "mu_ln  = np.log(m) - 0.5 * sigma2\n",
+    "\n",
+    "# Truncate to observed range\n",
+    "low, high = means.min(), means.max()\n",
+    "a, b = (np.log(low) - mu_ln) / sigma, (np.log(high) - mu_ln) / sigma\n",
+    "samples = np.exp(truncnorm(a, b, loc=mu_ln, scale=sigma).rvs(100000, random_state=0))\n",
+    "print(f'Truncated log-normal params: ', a, b, mu_ln, sigma)\n",
+    "# Plot\n",
+    "sns.histplot(samples, stat='density', bins=50)\n",
+    "sns.scatterplot(x=means, y=np.full_like(means, 0.25, dtype=float))\n",
+    "sns.despine()\n",
+    "plt.show()\n",
+    "\n",
+    "# Parameterize negative control OVERDISPERSION with, gamma matching first and second moment\n",
+    "overdisp = neg_ctrl_params['overdisp_nb-'] - 1\n",
+    "\n",
+    "weights = ns / ns.sum()\n",
+    "mu_hat  = np.sum(weights * overdisp)\n",
+    "var_hat = np.sum(weights * (means - mu_hat)**2)\n",
+    "\n",
+    "# mu_hat = overdisp.mean()\n",
+    "# var_hat = overdisp.var()\n",
+    "alpha = mu_hat**2 / var_hat\n",
+    "theta = var_hat / mu_hat      # scale ; rate = 1/theta\n",
+    "print('Overdispersion Gamma shape, scale', alpha, theta)\n",
+    "sns.histplot(stats.gamma(a=alpha, scale=theta).rvs(100000).clip(max=20)+1, stat='density', label='gamma')\n",
+    "\n",
+    "sns.scatterplot(x=overdisp+1, y=0.1)\n",
+    "sns.despine()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4af0a035-0959-4cbc-8508-adcc2c68ae77",
+   "metadata": {},
+   "source": [
+    "## Non-binder distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c9dce22d-bb14-4589-94a0-a036e94552ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean-</th>\n",
+       "      <th>mean_ratio</th>\n",
+       "      <th>mean+</th>\n",
+       "      <th>N-</th>\n",
+       "      <th>N+</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25.003214</td>\n",
+       "      <td>49.212545</td>\n",
+       "      <td>1230.471773</td>\n",
+       "      <td>1867</td>\n",
+       "      <td>3773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.454062</td>\n",
+       "      <td>172.963772</td>\n",
+       "      <td>251.500000</td>\n",
+       "      <td>5638</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11.730760</td>\n",
+       "      <td>78.636249</td>\n",
+       "      <td>922.462976</td>\n",
+       "      <td>4249</td>\n",
+       "      <td>1391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.596270</td>\n",
+       "      <td>312.294178</td>\n",
+       "      <td>810.800000</td>\n",
+       "      <td>5630</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8.543229</td>\n",
+       "      <td>165.537081</td>\n",
+       "      <td>1414.221254</td>\n",
+       "      <td>5066</td>\n",
+       "      <td>574</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>15.505495</td>\n",
+       "      <td>125.773976</td>\n",
+       "      <td>1950.187696</td>\n",
+       "      <td>182</td>\n",
+       "      <td>2227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.732883</td>\n",
+       "      <td>174.126735</td>\n",
+       "      <td>649.994709</td>\n",
+       "      <td>2220</td>\n",
+       "      <td>189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>5.349569</td>\n",
+       "      <td>90.126625</td>\n",
+       "      <td>482.138596</td>\n",
+       "      <td>2320</td>\n",
+       "      <td>570</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>3.266996</td>\n",
+       "      <td>424.225101</td>\n",
+       "      <td>1385.941634</td>\n",
+       "      <td>2633</td>\n",
+       "      <td>257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>30.833333</td>\n",
+       "      <td>41.648426</td>\n",
+       "      <td>1284.159790</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        mean-  mean_ratio        mean+    N-    N+\n",
+       "0   25.003214   49.212545  1230.471773  1867  3773\n",
+       "1    1.454062  172.963772   251.500000  5638     2\n",
+       "2   11.730760   78.636249   922.462976  4249  1391\n",
+       "3    2.596270  312.294178   810.800000  5630    10\n",
+       "4    8.543229  165.537081  1414.221254  5066   574\n",
+       "6   15.505495  125.773976  1950.187696   182  2227\n",
+       "7    3.732883  174.126735   649.994709  2220   189\n",
+       "10   5.349569   90.126625   482.138596  2320   570\n",
+       "11   3.266996  424.225101  1385.941634  2633   257\n",
+       "13  30.833333   41.648426  1284.159790     6  1333"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params[~params['gene'].str.contains('negative')][['mean-', 'mean_ratio', 'mean+', 'N-', 'N+', ]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a91ae8e6dfffbbe",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T15:43:22.538843Z",
+     "start_time": "2025-08-14T15:43:22.441667Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean-</th>\n",
+       "      <th>mean_ratio</th>\n",
+       "      <th>mean+</th>\n",
+       "      <th>N-</th>\n",
+       "      <th>N+</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25.003214</td>\n",
+       "      <td>49.212545</td>\n",
+       "      <td>1230.471773</td>\n",
+       "      <td>1867</td>\n",
+       "      <td>3773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11.730760</td>\n",
+       "      <td>78.636249</td>\n",
+       "      <td>922.462976</td>\n",
+       "      <td>4249</td>\n",
+       "      <td>1391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8.543229</td>\n",
+       "      <td>165.537081</td>\n",
+       "      <td>1414.221254</td>\n",
+       "      <td>5066</td>\n",
+       "      <td>574</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>15.505495</td>\n",
+       "      <td>125.773976</td>\n",
+       "      <td>1950.187696</td>\n",
+       "      <td>182</td>\n",
+       "      <td>2227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.732883</td>\n",
+       "      <td>174.126735</td>\n",
+       "      <td>649.994709</td>\n",
+       "      <td>2220</td>\n",
+       "      <td>189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>5.349569</td>\n",
+       "      <td>90.126625</td>\n",
+       "      <td>482.138596</td>\n",
+       "      <td>2320</td>\n",
+       "      <td>570</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>3.266996</td>\n",
+       "      <td>424.225101</td>\n",
+       "      <td>1385.941634</td>\n",
+       "      <td>2633</td>\n",
+       "      <td>257</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        mean-  mean_ratio        mean+    N-    N+\n",
+       "0   25.003214   49.212545  1230.471773  1867  3773\n",
+       "2   11.730760   78.636249   922.462976  4249  1391\n",
+       "4    8.543229  165.537081  1414.221254  5066   574\n",
+       "6   15.505495  125.773976  1950.187696   182  2227\n",
+       "7    3.732883  174.126735   649.994709  2220   189\n",
+       "10   5.349569   90.126625   482.138596  2320   570\n",
+       "11   3.266996  424.225101  1385.941634  2633   257"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "non_binder_params = params[~params['gene'].str.contains('negative')].copy()\n",
+    "non_binder_params = non_binder_params[(non_binder_params['N+'] > 20) & (non_binder_params['N-'] > 20)].copy()\n",
+    "non_binder_params[['mean-', 'mean_ratio', 'mean+', 'N-', 'N+', ]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8b2caf2f00bf488e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T15:11:17.359570Z",
+     "start_time": "2025-08-14T15:11:17.180587Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(non_binder_params, x='mean-', y='mean_ratio')\n",
+    "sns.despine()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fcb858ab-43d3-4467-b51b-a40223b56fa7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T15:47:43.631651Z",
+     "start_time": "2025-08-14T15:47:43.377680Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trunc log-normal params for noise:  -1.4325807532116341 1.9485510504360735 2.0461540382126118 0.6019089551720753\n",
+      "lowest sampled value 3.267036725146729\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overdispersion Gamma shape, scale 0.7285976325752553 8.167773103845757\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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oOTlZYmJiJDQ0VDp37izp6elu933vvffk1ltvlXr16pklPj6+0P42m00SExMlOjpaatSoYfbZuXNnJVwJAADwB14PQCkpKTJq1CiZNGmSZGZmSrt27aRHjx5y7Ngxl/unpaXJgAEDZN26dbJhwwZp2rSpdO/eXQ4dOuTYZ9q0aTJr1iyZM2eObNq0SWrVqmXOeeHChUq8MgAA4KuCbFpd4kVa49OxY0d5++23zXp+fr4JNc8884yMHz++2OPz8vJMTZAeP2jQIFP706hRI3nhhRdk9OjRZp/Tp09LZGSkzJ8/X/r371/sOXNyciQ8PNwcFxYWJp52U7deEpMwsdD2vSlJsjF1pcffDwAA+FANUG5urmRkZJgmKkeBqlQx61q7UxLnzp2TS5cuSf369c36nj17JCsry+mcGmY0aLk758WLF03oKbgAAIDA5dUAdOLECVODo7UzBem6hpiSGDdunKnxsQce+3GlOeeUKVNMSLIvWgMFAAACV1XxY1OnTpUlS5aYfkHagbqsJkyYYPoh2WkNkDdC0K4dO0zzmCvRDerL8iWLKr1MAAAEIq8GoIiICAkODpajR486bdf1qKioIo+dPn26CUBr166Vtm3bOrbbj9Nz6Ciwguds3769y3NVr17dLN52yRbksm+QvX8QAAAIgCawkJAQiYuLk9TUVMc27QSt6126dHF7nI7ySkpKklWrVkmHDh2cXouNjTUhqOA5tUZHR4MVdU4AAGAdXm8C06anwYMHmyDTqVMnmTlzppw9e1aGDh1qXteRXY0bNzb9dNRrr71m5vhZvHixmTvI3q+ndu3aZgkKCpLnnntOXnnlFWnZsqUJRBMnTjT9hPr06ePVawUAAL7B6wEoISFBjh8/bkKNhhltptKaHXsn5v3795uRYXazZ882o8cefPBBp/PoPEKTJ082X48dO9aEqCeeeEKys7Ola9eu5pzl6ScEAAACh9fnAfJF3poH6LOXB0nPxIUuj2GOIAAAAmgmaAAAgMpGAAIAAJZDAAIAAJZTpgD0yy+/eL4kAAAAvhyAWrRoIXfeeacsWrSIJ6wDAABrBKDMzEwz+7LO4aOTDj755JOSnp7u+dIBAAD4SgDSuXrefPNNOXz4sMydO1eOHDli5tq5/vrrZcaMGWZeHwAAgIDsBF21alXp27evLF261MzQvGvXLhk9erR5kKjO4KzBCAAAIKAC0JYtW2TEiBHmoaNa86PhZ/fu3bJmzRpTO9S7d2/PlRQAAMCbj8LQsDNv3jzZvn279OrVSxYuXGj+tT+yQp+/NX/+fPOsLgAAgIAIQPo8rkcffVSGDBlian9cadiwofzlL38pb/kAAAB8IwBpE9eVV17p9JBSpY8VO3DggHktJCTEPOUdAAAgIPoANW/eXE6cOFFo+6lTp0zzFwAAQMAFIHcPkD9z5oyEhoaWt0wAAAC+0wSmEx+qoKAgSUxMlJo1azpey8vLk02bNpk5ggAAAAImAG3dutVRA/T999+bfj52+nW7du3MUHgAAICACUDr1q0z/w4dOtTMBB0WFlZR5QIAAPCtUWA6BxAAAEDAByB95IVObqi1Pvp1UZYtW+aJsgEAAHg3AIWHh5vOz/avAQAAAj4AFWz2ogkMAABYbh6g8+fPy7lz5xzr+/btk5kzZ8rnn3/uybIBAAD4TgDSp7zrA1BVdna2dOrUSd544w2zXZ8TBgAAEHABKDMzU2699Vbz9ccffyxRUVGmFkhD0axZszxdRgAAAO8Pg9fmrzp16pivtdlLR4Xpg1FvuukmE4Tgebt27JCbuvVy+Vp0g/qyfMmiSi8TAACWCkAtWrSQTz75RO6//35ZvXq1PP/882b7sWPHmByxglyyBUlMwkSXr+1NSar08gAAYLkApM8Be/jhh03w6datm3Tp0sVRG3TDDTd4uowAAMAP3d//v+TI8VM+2XJRpgD04IMPSteuXeXIkSPm+V92Goa0VggAAODI8VMuWy98oeWiTAFIacdnXQrS0WAAAAC+rkwB6OzZszJ16lRJTU01/X7y8/OdXv/ll188VT4AAADfCECPP/64fPnllzJw4ECJjo52PCIDAAAgYAPQZ599JitWrJBbbrnF8yUCAADwxYkQ69WrJ/Xr1/d8aQAAAHw1ACUlJZmh8AWfBwYAABDQTWD63K/du3dLZGSkxMTESLVq1Qo9KgMAACCgAlCfPn08XxIAAABfDkCTJk3yfEkAAAB8uQ+Qys7Olvfff18mTJggp06dcjR9HTp0yJPlAwAA8I0aoO+++07i4+MlPDxc9u7dK8OGDTOjwpYtWyb79++XhQsXer6kAAAA3qwBGjVqlAwZMkR27twpoaGhju29evWSf/7zn54qGwAAgO8EoM2bN8uTTz5ZaHvjxo0lKyvLE+UCAADwrQBUvXp1ycnJKbR9x44d0qBBA0+UCwAAwLcC0H333Scvv/yyXLp0yazrs8C078+4cePkgQce8HQZAQAAvB+AdCLEM2fOmNqe8+fPy+233y4tWrSQOnXqyKuvvlqqcyUnJ5vJFLUvUefOnSU9Pd3tvj/++KMJWLq/hq6ZM2cW2mfy5MnmtYJL69aty3KZAAAgQJVpFJiO/lqzZo18/fXX8u2335owdOONN5qRYaWRkpJiOlTPmTPHhB8NND169JDt27dLw4YNC+2vj95o1qyZ9OvXT55//nm3573uuutk7dq1jvWqVct0mQAAIECVOhnk5+fL/PnzzZB3HQKvNSyxsbESFRUlNpvNrJfUjBkzzBD6oUOHmnUNQvqU+blz58r48eML7d+xY0ezKFevOy6qalVTHgAAgHI3gWnA0f4/jz/+uJnwsE2bNqa2Zd++fWZY/P3331/ic+Xm5kpGRoZTrVGVKlXM+oYNG6Q8dHh+o0aNTG3RI488YvonAQAAlKkGSGt+dJ6f1NRUufPOO51e++KLL8wzwnQSxEGDBhV7rhMnTkheXp55oGpBuv7zzz9LWWlTmpazVatWcuTIEXnppZfk1ltvlR9++MH0UXLl4sWLZrFzNcINAABYtAboww8/lBdffLFQ+FF33XWXaZb64IMPxJt69uxp+gi1bdvW9CdauXKleWzHRx995PaYKVOmmH5N9qVp06aVWmYAAODDNUD6CIxp06YVGT5mzZpVonNFRERIcHCwHD161Gm7rnuy/07dunXl6quvll27drndR59npp2xC9YA+VMI2rVjh9zUrVeh7dEN6svyJYu8UiYAAAImAOlDTy9vsipIX/v1119LdK6QkBCJi4szzWnadGbvYK3rI0eOFE/REWq7d++WgQMHFjmxoy7+6pItSGISJhbavjclySvlAQAgoAKQ9tkpaki51uj8/vvvJT6f1roMHjxYOnToIJ06dTLD4M+ePesYFaZ9ifTxGtpEZe84/dNPPzm+1o7Y33zzjdSuXdvMQ6RGjx4t9957r1x11VVy+PBhmTRpkinXgAEDSnOpAAAggFUt7SgwHe3lrrakYEfikkhISJDjx49LYmKieYZY+/btZdWqVY5aJh29pSPD7DTQ3HDDDY716dOnm0UnYkxLSzPbDh48aMLOyZMnzUSNXbt2lY0bN/KIDgAAULYApLU1xSnJCLCCtLnLXZOXPdTY6QzQGsKKsmTJklK9PwAAsJ5SBaB58+ZVXEkAAAB8+VlgAAAA/owABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALKdUT4OHf9m1Y4fc1K2Xy9eiG9SX5UsWVXqZAADwBQSgAHbJFiQxCRNdvrY3JanSywMAgK+gCQwAAFgOAQgAAFgOAQgAAFgOAQgAAFgOnaAtihFiAAArIwBZFCPEAABWRhMYAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwnKreLgB8z64dO+Smbr1cvhbdoL4sX7Ko0ssEAIAnEYBQyCVbkMQkTHT52t6UpEovDwAAnkYTGAAAsBxqgOCR5jGaxgAA/oQABI80j9E0BgDwJzSBAQAAy/F6AEpOTpaYmBgJDQ2Vzp07S3p6utt9f/zxR3nggQfM/kFBQTJz5sxynxMAAFiPV5vAUlJSZNSoUTJnzhwTVDTQ9OjRQ7Zv3y4NGzYstP+5c+ekWbNm0q9fP3n++ec9ck74ifO/ipw9LnIhRyQ0XKRWhEiNet45t33/89kiIbVEgoJFqlQVqXWF58rkj8z35YRIfp6ILU8k9+y/vx+Xfz8r8l4CgD8EoBkzZsiwYcNk6NChZl1Dy4oVK2Tu3Lkyfvz4Qvt37NjRLMrV62U5J/zA6UMin44U+eWL/2xr3k3kvrdEwhtX7rld7d/sDpHOT4msSRTp9Xr5y+SP9PuyYrRI3CCRTXNEfklz/f2syHsJAP7QBJabmysZGRkSHx//n8JUqWLWN2zY4DPnhJdpbcHlH5hqd6rI35759+uVdW53++uHvX7oR15T/jL5I/v3JerawuGn4Pfzt6yKu5cAUEpeC0AnTpyQvLw8iYyMdNqu61lZWZV6zosXL0pOTo7TAh+hTSWXf2AW/ODU1yvr3EXtrx/6TTqWv0z+yP590eu/PPzY6ffl3MmKu5cAUEoMgxeRKVOmyEsvveTtYvi1Cnt8hvYTKc/rnjx3cfv/frH8ZfJH9uu1X7/b/U6X7DwAEMgBKCIiQoKDg+Xo0aNO23U9KiqqUs85YcIE03HaTmuAmjZtWqYyWFWFPT4jNKx8r3vy3MXtX7V6+cvkj+zXa79+t/uFl+w8ABDITWAhISESFxcnqampjm35+flmvUuXLpV6zurVq0tYWJjTAh9Rq8G/O8m6otv19co6d1H7a0fog5vLXyZ/ZP++6PXr98EVfb3mFRV3LwHAn+YB0lqX9957TxYsWCDbtm2T4cOHy9mzZx0juAYNGmRqZwp2cv7mm2/Mol8fOnTIfL1r164SnxN+RodH6wihyz847SOHyjN8urTndre/fRTY0W3lL5M/sn9f9Pr1+3B5CLJ/P+tEVdy9BAB/6gOUkJAgx48fl8TERNNJuX379rJq1SpHJ+b9+/ebUVx2hw8flhtuuMGxPn36dLPcfvvtkpaWVqJzwg/p8OgH/1Jg7piwf9cWeOIDs7TnLri/0zxAwSJ9kq37Ia7fF71+nQfo7tcKzANU1/n7WZH3EgD8qRP0yJEjzeKKPdTY6ezONputXOeEn9IPyIr6kCztuSuyLP6spN8Xvn8AfIDXH4UBAABQ2QhAAADAcghAAADAcrzeBwiBr8ImSQQAoIwIQPDfSRIBACgjmsAAAIDlEIAAAIDlEIAAAIDlEIAAAIDl0AkaXsUIMQCANxCA4FWMEAMAeANNYAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIYBQa/GyLP8HgAQHkRgOB3Q+QZHg8AKC8CEPwOkycCAMqLAAS/w+SJAIDyohM0AACwHAIQAACwHAIQAACwHAIQAACwHDpBI6AwQgwAUBIEIAQURogBAEqCJjAAAGA5BCAAAGA5BCAAAGA59AGCZRTVQfrAvj3S9KrYQtvpOA0AgYkABMsoqoP0tpcH8eBVALAQmsAAAIDlEIAAAIDlEIAAAIDl0AcIKAIzSwNAYCIAAUVgZmkACEwEIKCMqB0CAP9FAALKiNohAPBfdIIGAACWQwACAACWQwACAACWQx8goBI7SNM5GgB8AwEIqMQO0nSOBgDfQBMYAACwHJ8IQMnJyRITEyOhoaHSuXNnSU9PL3L/pUuXSuvWrc3+bdq0kZUrVzq9PmTIEAkKCnJa7r777gq+CgAA4C+83gSWkpIio0aNkjlz5pjwM3PmTOnRo4ds375dGjZsWGj/9evXy4ABA2TKlCnyxz/+URYvXix9+vSRzMxMuf766x37aeCZN2+eY7169eqVdk2AO0yeCAC+wesBaMaMGTJs2DAZOnSoWdcgtGLFCpk7d66MHz++0P5vvvmmCTdjxowx60lJSbJmzRp5++23zbEFA09UVFQlXglQPCZPBADf4NUmsNzcXMnIyJD4+Pj/FKhKFbO+YcMGl8fo9oL7K60xunz/tLQ0U4PUqlUrGT58uJw8edJtOS5evCg5OTlOCwAACFxerQE6ceKE5OXlSWRkpNN2Xf/5559dHpOVleVyf91upzVEffv2ldjYWNm9e7e8+OKL0rNnTxOSgoODC51Tm9Neeuklj10XUBY0jwGAhZrAKkL//v0dX2sn6bZt20rz5s1NrVC3bt0K7T9hwgTTD8lOa4CaNm1aaeUFFM1jAGCRJrCIiAhTI3P06FGn7brurv+Obi/N/qpZs2bmvXbt2uXyde0vFBYW5rQAAIDA5dUAFBISInFxcZKamurYlp+fb9a7dOni8hjdXnB/pZ2g3e2vDh48aPoARUdHe7D0AADAX3m9CUybngYPHiwdOnSQTp06mWHwZ8+edYwKGzRokDRu3Nj001HPPvus3H777fLGG2/IPffcI0uWLJEtW7bIu+++a14/c+aM6c/zwAMPmFoh7QM0duxYadGiheksDfgj+gcBQIAFoISEBDl+/LgkJiaajszt27eXVatWOTo679+/34wMs7v55pvN3D9//vOfTefmli1byieffOKYA0ib1L777jtZsGCBZGdnS6NGjaR79+5muDxzASEQ+wetTRrIc8cAwN8CkBo5cqRZXNGOy5fr16+fWVypUaOGrF692uNlBHwVzx0DAD99FAYAAEBlIgABAADL8YkmMACV23H6wL490vSqWJev0XcIgBUQgAALdpze9vIgJl0EYGk0gQEAAMuhBgiAE+YcAmAFBCAATphzCIAVEIAAlBhzDgEIFPQBAgAAlkMNEIByo98QAH9DAAJQof2GaB4D4IsIQAB8bkJGao0AVDQCEACfm5CRWiMAFY0ABMDn8BgPABWNAATA5/AYDwAVjQAEIGAwGg1ASRGAAAQMRqMBKCkmQgQAAJZDDRAASzeP0akasCYCEABLN48V1ana3cNfFeEI8G8EIABwgz5FQOCiDxAAALAcaoAAwMPu7/9fcuT4qULb6W8E+A4CEAB4eM6hXb/skfgJcwttZxJHwHcQgACgAmarLi0mcQQqFwEIAPy4w7W75jZCE1A0AhAA+DENP66CE01qQNEYBQYAACyHGiAA8PMO1zGlPIbmMYAABAAB2eHa05M4uutrpAhU8EcEIACwmKJqh9zNVeRuaH9xjwxh7iP4KgIQAFhMcTVK7p6Z5snzKTpqw5voBA0AACyHGiAAgE81xdFshspAAAIAeIW7prOyNpt5uqM2Hb8DGwEIABDQz2ArqqN2UUHG3SSTiv5L/o8ABAAI+GewuTuuqBFs7uZYQmAgAAEALKusgaos/ZfK+pq7Wiqa6MqHAAQAQCX0Xyrra+5qqYpq8qOJrngEIAAA/DRseXKyS6vVHBGAAAAIMGWdnHKtm9qmsjbf+XI/KgIQAACosKY9X8VM0AAAwHJ8IgAlJydLTEyMhIaGSufOnSU9Pb3I/ZcuXSqtW7c2+7dp00ZWrlzp9LrNZpPExESJjo6WGjVqSHx8vOzcubOCrwIAAPgLrweglJQUGTVqlEyaNEkyMzOlXbt20qNHDzl27JjL/devXy8DBgyQxx57TLZu3Sp9+vQxyw8//ODYZ9q0aTJr1iyZM2eObNq0SWrVqmXOeeHChUq8MgAA4Ku8HoBmzJghw4YNk6FDh8q1115rQkvNmjVl7lzXQ/vefPNNufvuu2XMmDFyzTXXSFJSktx4443y9ttvO2p/Zs6cKX/+85+ld+/e0rZtW1m4cKEcPnxYPvnkk0q+OgAA4Iu8GoByc3MlIyPDNFE5ClSlilnfsGGDy2N0e8H9ldbu2Pffs2ePZGVlOe0THh5umtbcnRMAAFiLV0eBnThxQvLy8iQyMtJpu67//PPPLo/RcONqf91uf92+zd0+l7t48aJZ7E6fPm3+zcnJkYrw+++X5NL5s4W22/LzXW4v62uePh/v5XvnC9T38ueyB+p7+XPZK/O9/Lnslfle+jlYUZ+xqk6dOhIUFFT0TjYvOnTokE2LsH79eqftY8aMsXXq1MnlMdWqVbMtXrzYaVtycrKtYcOG5uuvv/7anPPw4cNO+/Tr18/20EMPuTznpEmTzDEsLCwsLCws4vfL6dOni80gXq0BioiIkODgYDl69KjTdl2PiopyeYxuL2p/+7+6TUeBFdynffv2Ls85YcIE0xHbLj8/X06dOiVXXHFF8QnSD2nqbtq0qRw4cEDCwsIkEAX6NXJ9/i/QrzHQr88K15jjx9enNUDF8WoACgkJkbi4OElNTTUjuezhQ9dHjhzp8pguXbqY15977jnHtjVr1pjtKjY21oQg3cceePQm6miw4cOHuzxn9erVzVJQ3bp1JdDpD7S//VCXVqBfI9fn/wL9GgP9+qxwjWEBen1enwlaa14GDx4sHTp0kE6dOpkRXGfPnjWjwtSgQYOkcePGMmXKFLP+7LPPyu233y5vvPGG3HPPPbJkyRLZsmWLvPvuu+Z1rbHRcPTKK69Iy5YtTSCaOHGiNGrUyBGyAACAtXk9ACUkJMjx48fNxIXaSVlrbVatWuXoxLx//34zMszu5ptvlsWLF5th7i+++KIJOTq8/frrr3fsM3bsWBOinnjiCcnOzpauXbuac+rEiQAAAF4PQEqbu9w1eaWlpRXa1q9fP7O4o7VAL7/8sllQmDb36cSTlzf7BZJAv0auz/8F+jUG+vVZ4RqrB/j1BWlPaG8XAgAAwFIzQQMAAFQ2AhAAALAcAhAAALAcAlCA0ekCOnbsaCaBatiwoRn6v3379iKPmT9/vuk4XnDx5RFzkydPLlTe1q1bF3nM0qVLzT56XW3atJGVK1eKr4qJiSl0fbo8/fTTfnv//vnPf8q9995rpqPQ8l3+YGLtiqgjQXXy0ho1aphn+e3cubPY8yYnJ5vvl16vPu8vPT1dfO36Ll26JOPGjTM/d7Vq1TL76PQe+oBmT/+ce+v+DRkypFBZ9aHV/nL/SnKNrn4ndXn99df94h6W5LPhwoUL5v8ZnQS4du3a8sADDxSaePhyZf3d9QUEoADz5Zdfmh/gjRs3mgki9T/f7t27m2kBiqKTXB05csSx7Nu3T3zZdddd51Tef/3rX273Xb9+vQwYMEAee+wx2bp1q/nF1+WHH34QX7R582ana9P7qIoa+ejr909//tq1a2c+8FyZNm2azJo1S+bMmWMmLdWgoA851v+Q3UlJSTHziOkolczMTHN+PebYsWPiS9d37tw5Uz6dj0z/XbZsmfngue+++zz6c+7N+6c08BQs64cffljkOX3p/pXkGgtemy5z5841gUZDgj/cw5J8Njz//PPy97//3fzBqPtrSO/bt2+R5y3L767PKPZhGfBrx44dM89F+fLLL93uM2/ePFt4eLjNX+iz29q1a1fi/fUZcPfcc4/Tts6dO9uefPJJmz949tlnbc2bN7fl5+cHxP3Tn8fly5c71vW6oqKibK+//rpjW3Z2tq169eq2Dz/80O159HmBTz/9tGM9Ly/P1qhRI9uUKVNsvnR9rqSnp5v99u3b57Gfc29e3+DBg229e/cu1Xl89f6V9B7q9d51111F7uOr99DVZ0N2drZ51ubSpUsd+2zbts3ss2HDBpfnKOvvrq+gBijA2Z9sX79+/SL3O3PmjFx11VXmuS+9e/eWH3/8UXyZVrFqVXWzZs3kkUceMRNmurNhwwZTLVuQ/oWi231dbm6uLFq0SB599NEin0vnb/evoD179phJUAveo/DwcNMk4u4e6fclIyPD6RidMFXX/eG+6u+l3s/iHrlTmp9zb9M527RppVWrVuaxQydPnnS7r7/fP20WWrFihalVLo6v3sPTl3026P3QWqGC90Sb66688kq396Qsv7u+hAAUwPS5avpYkFtuucVppuzL6X9YWp376aefmg9bPU5n3D548KD4Iv3l0n4vOrv37NmzzS/hrbfeKr/99pvL/fUX1D6zuJ2u63Zfp/0QdDZz7WMRKPfvcvb7UJp7dOLECcnLy/PL+6pNA9onSJtli3q+Uml/zr1Jm78WLlxonsH42muvmeaTnj17mnsUaPdPLViwwPSlKa55yFfvoavPhqysLPN8zstDeVH3pCy/u77EJ2aCRsXQ9l7t51Jcm7M+SNb+MFmlH57XXHONvPPOO5KUlCS+Rv9jtWvbtq35T0ZrPz766KMS/UXmT/7yl7+Y69W/IAPl/lmZ/oX90EMPmY6j+oEYKD/n/fv3d3ytnb21vM2bNze1Qt26dZNAo39waG1OcYMNfPUelvSzIdBRAxSg9NEi//jHP2TdunXSpEmTUh1brVo1ueGGG2TXrl3iD/QvlquvvtpteaOiogqNZNB13e7LtCPz2rVr5fHHHw/o+2e/D6W5RxERERIcHOxX99UefvS+aifU0j5du7ifc1+izT16j9yV1R/vn91XX31lOrGX9vfSV+6hu8+GqKgo0zSpNc4lvSdl+d31JQSgAKN/WeoP+PLly+WLL76Q2NjYUp9Dq6a///57M6zRH2j/l927d7str9aOaNV8QfoBVLDWxBfNmzfP9Km45557Avr+6c+o/mdZ8B7l5OSYESXu7pFW1cfFxTkdo9X6uu6L99UefrQ/iIZaHWbs6Z9zX6LNr9oHyF1Z/e3+XV4rq2XXEWP+dA+L+2yIi4szfzwVvCca9LTPkrt7UpbfXZ/i7V7Y8Kzhw4ebEUFpaWm2I0eOOJZz58459hk4cKBt/PjxjvWXXnrJtnr1atvu3bttGRkZtv79+9tCQ0NtP/74o80XvfDCC+b69uzZY/v6669t8fHxtoiICDOqwdX16T5Vq1a1TZ8+3Yxq0JEZOtrh+++/t/kqHRFz5ZVX2saNG1foNX+8f7/99ptt69atZtH/dmbMmGG+to+Cmjp1qq1u3bq2Tz/91Pbdd9+ZETaxsbG28+fPO86hI27eeustx/qSJUvMaJP58+fbfvrpJ9sTTzxhzpGVleVT15ebm2u77777bE2aNLF98803Tr+XFy9edHt9xf2c+8r16WujR482I4W0rGvXrrXdeOONtpYtW9ouXLjgF/evuGu0O336tK1mzZq22bNnuzyHL9/Dknw2PPXUU+b/nS+++MK2ZcsWW5cuXcxSUKtWrWzLli1zrJfkd9dXEYACjP7iulp0qLTd7bffboat2j333HPmhz4kJMQWGRlp69Wrly0zM9PmqxISEmzR0dGmvI0bNzbru3btcnt96qOPPrJdffXV5pjrrrvOtmLFCpsv00Cj92379u2FXvPH+7du3TqXP5f269DhtBMnTjTl1w/Fbt26Fbr2q666yoTXgvTDxn7tOqx648aNNl+7Pv3wc/d7qce5u77ifs595fr0A7R79+62Bg0amD8s9DqGDRtWKMj48v0ryc+oeuedd2w1atQwQ71d8eV7WJLPhvPnz9tGjBhhq1evngl6999/vwlJl5+n4DEl+d31VTwNHgAAWA59gAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAEnKCgIPnkk0/M13v37jXr33zzjfiCyZMnS/v27b1dDMDyqnq7AABQkZo2bSpHjhwxTyAHADtqgAD4pdzc3BLtFxwcbJ5YXbUqf+8B+A8CEACPuHjxovz3f/+3NGzYUEJDQ6Vr166yefNmyc/PlyZNmsjs2bOd9t+6datUqVJF9u3bZ9azs7Pl8ccflwYNGkhYWJjcdddd8u233xZqOnr//fclNjbWvIfauXOn3HbbbWb92muvlTVr1ji9z+VNYL/++qs88sgj5n1q1KghLVu2lHnz5jntu2TJErn55pvNOa+//nr58ssvS/Q9SEtLM8enpqZKhw4dpGbNmuY827dvL7TvO++8Y2qndJ+HHnpITp8+XervOYCyIwAB8IixY8fK//3f/8mCBQskMzNTWrRoIT169DDBZsCAAbJ48WKn/T/44AO55ZZb5KqrrjLr/fr1k2PHjslnn30mGRkZcuONN0q3bt3k1KlTjmN27dpl3mPZsmUm0Gi46tu3r4SEhMimTZtkzpw5Mm7cuCLLOXHiRPnpp5/M+2zbts0Es8ubx8aMGSMvvPCCCWldunSRe++9V06ePFni78Wf/vQneeONN2TLli2m5unRRx91el2v46OPPpK///3vsmrVKvM+I0aMKPH5AXiAtx9HD8D/nTlzxlatWjXbBx984NiWm5tra9SokW3atGm2rVu32oKCgmz79u0zr+Xl5dkaN25smz17tln/6quvbGFhYbYLFy44nbd58+a2d955x3w9adIk8x7Hjh1zvL569Wpb1apVbYcOHXJs++yzz2z6X9vy5cvN+p49e8y6lkHde++9tqFDh7q8Dvu+U6dOdWy7dOmSrUmTJrbXXnut2O/DunXrzPFr1651bFuxYoXZdv78ecd1BAcH2w4ePOhU5ipVqtiOHDlS7HsA8AxqgACU2+7du+XSpUumRseuWrVq0qlTJ1PLok1X11xzjaMWSJuUtLZHa32UNnWdOXNGrrjiCqldu7Zj2bNnjzm3ndYWadOVnZ5bm5EaNWrk2KY1NkUZPny4aeLSMmmt1fr16wvtU/AcWoOjzVn6XiXVtm1bx9fR0dHmX71euyuvvFIaN27s9H5am6VNZV999ZXT90BrygB4Hr0CAVQK7XejAWj8+PHm37vvvtsEHqXhR4OC9qG5XN26dR1f16pVq9zl6Nmzp+l3tHLlStNfSJvZnn76aZk+fbp4ioY/O+0TpDTglISGrYJD9iMjIz1WLgD/QQ0QgHJr3ry56Yfz9ddfO7ZpjZB2gtaOyerhhx+WH374wfTv+fjjj00gstP+PllZWaa2RfsOFVyKGr6utUoHDhwww9ztNm7cWGx5tRZp8ODBsmjRIpk5c6a8++67Tq8XPMfvv/9uyqzv5Sn79++Xw4cPO72fdghv1aqV6Zhd8Prr1KnjsfcF8B/UAAEoN62Z0aYl7Txcv35908Qzbdo0OXfunDz22GNmn5iYGDMiStfz8vLkvvvucxwfHx9vmoH69Oljjrv66qtNQFixYoXcf//9plbEFT1O99Uw8/rrr0tOTo7pgFyUxMREiYuLk+uuu86MXPvHP/5RKNwkJyeb0WG6/X/+53/MyLHLOzKXh44u0zJrrZOWWUfP6UgwHa4PoHJQAwTAI6ZOnSoPPPCADBw40NTo6Ein1atXS7169Rz7aK2P9vfRUKM1HQWbibRJSoezDx061ISa/v37m6aqopqAtNZk+fLlcv78edPfSIfRv/rqq0WWU2uqJkyYYPrp6PvpPEHaJ+jya9GlXbt28q9//Uv+9re/eXQiRa3Z0dFrvXr1ku7du5uy/O///q/Hzg+geEHaE7oE+wFAwNN5gHSOIR2WzuMqgMBGDRAAALAcAhAAlNBTTz3lNES94KKvAfAfNIEBQAnpXD7aadkVfXyHPgYEgH8gAAEAAMuhCQwAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAIjV/D/3+2w/Agy1/AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameterize non-binder MEAN with, gamma matching first and second moment\n",
+    "# ns    = non_binder_params['N-']\n",
+    "\n",
+    "# weights = ns / ns.sum()\n",
+    "# mu_hat  = np.sum(weights * means)\n",
+    "# var_hat = np.sum(weights * (means - mu_hat)**2)\n",
+    "\n",
+    "# alpha = mu_hat**2 / var_hat\n",
+    "# theta = var_hat / mu_hat      # scale ; rate = 1/theta\n",
+    "\n",
+    "# print(f\"Mean shape, scale: {alpha}, {theta}\")\n",
+    "# samples = stats.gamma(a=alpha, scale=theta).rvs(10000)\n",
+    "# print(samples.min())\n",
+    "# sns.histplot(samples, stat='density')\n",
+    "# # sns.histplot(non_binder_params['mean-'], stat='density')\n",
+    "# sns.scatterplot(x=non_binder_params['mean-'], y=0.05)\n",
+    "# sns.despine()\n",
+    "# plt.show()\n",
+    "\n",
+    "# Truncated log-normal\n",
+    "non_binder = params[~params['gene'].str.contains('negative', na=False)]\n",
+    "non_binder = non_binder[(non_binder[['N-', 'N+']] > 20).all(1)]\n",
+    "ns, means = non_binder['N-'].to_numpy(float), non_binder['mean-'].to_numpy(float)\n",
+    "\n",
+    "# Weighted MoM lognormal params\n",
+    "w = ns / ns.sum()\n",
+    "m = np.sum(w * means)\n",
+    "v = np.sum(w * (means - m)**2)\n",
+    "sigma2 = np.log(1 + v / m**2)\n",
+    "sigma  = np.sqrt(sigma2)\n",
+    "mu_ln  = np.log(m) - 0.5 * sigma2\n",
+    "\n",
+    "# Truncate to observed range\n",
+    "low, high = means.min(), means.max()\n",
+    "a, b = (np.log(low) - mu_ln) / sigma, (np.log(high) - mu_ln) / sigma\n",
+    "samples = np.exp(truncnorm(a, b, loc=mu_ln, scale=sigma).rvs(100000, random_state=0))\n",
+    "print('trunc log-normal params for noise: ', a, b, mu_ln, sigma)\n",
+    "print('lowest sampled value', samples.min())\n",
+    "# Plot\n",
+    "sns.histplot(samples, stat='density', bins=50)\n",
+    "sns.scatterplot(x=means, y=np.full_like(means, 0.05, dtype=float))\n",
+    "sns.despine()\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Parameterize non-binder OVERDISPERSION with, gamma matching first and second moment\n",
+    "overdisp = non_binder_params['overdisp_nb-'] - 1\n",
+    "\n",
+    "weights = ns / ns.sum()\n",
+    "mu_hat  = np.sum(weights * overdisp)\n",
+    "var_hat = np.sum(weights * (means - mu_hat)**2)\n",
+    "\n",
+    "# mu_hat = overdisp.mean()\n",
+    "# var_hat = overdisp.var()\n",
+    "alpha = mu_hat**2 / var_hat\n",
+    "theta = var_hat / mu_hat      # scale ; rate = 1/theta\n",
+    "print('Overdispersion Gamma shape, scale', alpha, theta)\n",
+    "sns.histplot(stats.gamma(a=alpha, scale=theta).rvs(100000).clip(max=20)+1, stat='density', label='gamma')\n",
+    "\n",
+    "sns.scatterplot(x=overdisp+1, y=0.1)\n",
+    "sns.despine()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a762c483-810d-4b09-a511-5ee8e16aea9c",
+   "metadata": {},
+   "source": [
+    "## Mean and Variance fold increase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "121039f6-14c6-43e2-9b29-ce8f98f32fc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>file</th>\n",
+       "      <th>gene</th>\n",
+       "      <th>threshold</th>\n",
+       "      <th>mean_neg</th>\n",
+       "      <th>concentration_neg</th>\n",
+       "      <th>var_nb_neg</th>\n",
+       "      <th>var_data_neg</th>\n",
+       "      <th>mean-</th>\n",
+       "      <th>concentration-</th>\n",
+       "      <th>var_nb-</th>\n",
+       "      <th>var_data-</th>\n",
+       "      <th>std_nb-</th>\n",
+       "      <th>std_data-</th>\n",
+       "      <th>overdisp_nb-</th>\n",
+       "      <th>overdisp_data-</th>\n",
+       "      <th>cof_var_nb-</th>\n",
+       "      <th>cof_var_data-</th>\n",
+       "      <th>N-</th>\n",
+       "      <th>mean+</th>\n",
+       "      <th>concentration+</th>\n",
+       "      <th>var_nb+</th>\n",
+       "      <th>var_data+</th>\n",
+       "      <th>std_nb+</th>\n",
+       "      <th>std_data+</th>\n",
+       "      <th>overdisp_nb+</th>\n",
+       "      <th>overdisp_data+</th>\n",
+       "      <th>cof_var_nb+</th>\n",
+       "      <th>cof_var_data+</th>\n",
+       "      <th>N+</th>\n",
+       "      <th>mean_ratio</th>\n",
+       "      <th>concentration_ratio</th>\n",
+       "      <th>var_nb_ratio</th>\n",
+       "      <th>var_data_ratio</th>\n",
+       "      <th>overdisp_nb_ratio</th>\n",
+       "      <th>overdisp_data_ratio</th>\n",
+       "      <th>N_ratio</th>\n",
+       "      <th>binder_ratio</th>\n",
+       "      <th>noise_to_neg_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>CMV</td>\n",
+       "      <td>197.942</td>\n",
+       "      <td>4.722340</td>\n",
+       "      <td>3.939271e-01</td>\n",
+       "      <td>61.333071</td>\n",
+       "      <td>1039.818115</td>\n",
+       "      <td>25.003214</td>\n",
+       "      <td>2.897500</td>\n",
+       "      <td>240.761868</td>\n",
+       "      <td>338.704865</td>\n",
+       "      <td>15.516503</td>\n",
+       "      <td>18.403936</td>\n",
+       "      <td>9.629237</td>\n",
+       "      <td>13.546453</td>\n",
+       "      <td>0.620580</td>\n",
+       "      <td>0.736063</td>\n",
+       "      <td>1867</td>\n",
+       "      <td>1230.471773</td>\n",
+       "      <td>3.924429</td>\n",
+       "      <td>387034.531887</td>\n",
+       "      <td>4.371280e+05</td>\n",
+       "      <td>622.120995</td>\n",
+       "      <td>661.156539</td>\n",
+       "      <td>314.541577</td>\n",
+       "      <td>355.252334</td>\n",
+       "      <td>0.505596</td>\n",
+       "      <td>0.537320</td>\n",
+       "      <td>3773</td>\n",
+       "      <td>49.212545</td>\n",
+       "      <td>1.354419</td>\n",
+       "      <td>1607.540828</td>\n",
+       "      <td>1290.586670</td>\n",
+       "      <td>32.665265</td>\n",
+       "      <td>26.224749</td>\n",
+       "      <td>2.020889</td>\n",
+       "      <td>0.668972</td>\n",
+       "      <td>5.294666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>Flu</td>\n",
+       "      <td>197.942</td>\n",
+       "      <td>4.722340</td>\n",
+       "      <td>3.939271e-01</td>\n",
+       "      <td>61.333071</td>\n",
+       "      <td>1039.818115</td>\n",
+       "      <td>11.730760</td>\n",
+       "      <td>1.432491</td>\n",
+       "      <td>107.794731</td>\n",
+       "      <td>224.974823</td>\n",
+       "      <td>10.382424</td>\n",
+       "      <td>14.999161</td>\n",
+       "      <td>9.189066</td>\n",
+       "      <td>19.178196</td>\n",
+       "      <td>0.885060</td>\n",
+       "      <td>1.278618</td>\n",
+       "      <td>4249</td>\n",
+       "      <td>922.462976</td>\n",
+       "      <td>4.215191</td>\n",
+       "      <td>202796.575052</td>\n",
+       "      <td>2.202642e+05</td>\n",
+       "      <td>450.329407</td>\n",
+       "      <td>469.323150</td>\n",
+       "      <td>219.842509</td>\n",
+       "      <td>238.778384</td>\n",
+       "      <td>0.488182</td>\n",
+       "      <td>0.508772</td>\n",
+       "      <td>1391</td>\n",
+       "      <td>78.636249</td>\n",
+       "      <td>2.942561</td>\n",
+       "      <td>1881.321785</td>\n",
+       "      <td>979.061646</td>\n",
+       "      <td>23.924358</td>\n",
+       "      <td>12.450513</td>\n",
+       "      <td>0.327371</td>\n",
+       "      <td>0.246631</td>\n",
+       "      <td>2.484099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>SARS_Cov2</td>\n",
+       "      <td>197.942</td>\n",
+       "      <td>4.722340</td>\n",
+       "      <td>3.939271e-01</td>\n",
+       "      <td>61.333071</td>\n",
+       "      <td>1039.818115</td>\n",
+       "      <td>8.543229</td>\n",
+       "      <td>1.653138</td>\n",
+       "      <td>52.693679</td>\n",
+       "      <td>87.826508</td>\n",
+       "      <td>7.259041</td>\n",
+       "      <td>9.371580</td>\n",
+       "      <td>6.167888</td>\n",
+       "      <td>10.280247</td>\n",
+       "      <td>0.849684</td>\n",
+       "      <td>1.096960</td>\n",
+       "      <td>5066</td>\n",
+       "      <td>1414.221254</td>\n",
+       "      <td>4.004123</td>\n",
+       "      <td>500904.754996</td>\n",
+       "      <td>5.018279e+05</td>\n",
+       "      <td>707.746250</td>\n",
+       "      <td>708.398127</td>\n",
+       "      <td>354.191223</td>\n",
+       "      <td>354.843985</td>\n",
+       "      <td>0.500449</td>\n",
+       "      <td>0.500910</td>\n",
+       "      <td>574</td>\n",
+       "      <td>165.537081</td>\n",
+       "      <td>2.422136</td>\n",
+       "      <td>9505.974155</td>\n",
+       "      <td>5713.854980</td>\n",
+       "      <td>57.425044</td>\n",
+       "      <td>34.517068</td>\n",
+       "      <td>0.113304</td>\n",
+       "      <td>0.101773</td>\n",
+       "      <td>1.809109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
+       "      <td>Flu_A0201</td>\n",
+       "      <td>173.376</td>\n",
+       "      <td>2.901204</td>\n",
+       "      <td>2.628828e+32</td>\n",
+       "      <td>2.901204</td>\n",
+       "      <td>875.116882</td>\n",
+       "      <td>15.505495</td>\n",
+       "      <td>1.876203</td>\n",
+       "      <td>143.647475</td>\n",
+       "      <td>331.107086</td>\n",
+       "      <td>11.985302</td>\n",
+       "      <td>18.196348</td>\n",
+       "      <td>9.264295</td>\n",
+       "      <td>21.354178</td>\n",
+       "      <td>0.772971</td>\n",
+       "      <td>1.173542</td>\n",
+       "      <td>182</td>\n",
+       "      <td>1950.187696</td>\n",
+       "      <td>4.113499</td>\n",
+       "      <td>926523.693207</td>\n",
+       "      <td>1.047826e+06</td>\n",
+       "      <td>962.561008</td>\n",
+       "      <td>1023.633510</td>\n",
+       "      <td>475.094625</td>\n",
+       "      <td>537.294725</td>\n",
+       "      <td>0.493574</td>\n",
+       "      <td>0.524890</td>\n",
+       "      <td>2227</td>\n",
+       "      <td>125.773976</td>\n",
+       "      <td>2.192459</td>\n",
+       "      <td>6449.982473</td>\n",
+       "      <td>3164.612305</td>\n",
+       "      <td>51.282329</td>\n",
+       "      <td>25.161106</td>\n",
+       "      <td>12.236264</td>\n",
+       "      <td>0.924450</td>\n",
+       "      <td>5.344504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
+       "      <td>CMV_B0702</td>\n",
+       "      <td>87.184</td>\n",
+       "      <td>2.528850</td>\n",
+       "      <td>8.153597e+11</td>\n",
+       "      <td>2.528850</td>\n",
+       "      <td>833.862305</td>\n",
+       "      <td>3.732883</td>\n",
+       "      <td>1.053218</td>\n",
+       "      <td>16.963204</td>\n",
+       "      <td>32.455223</td>\n",
+       "      <td>4.118641</td>\n",
+       "      <td>5.696949</td>\n",
+       "      <td>4.544264</td>\n",
+       "      <td>8.694412</td>\n",
+       "      <td>1.103341</td>\n",
+       "      <td>1.526153</td>\n",
+       "      <td>2220</td>\n",
+       "      <td>649.994709</td>\n",
+       "      <td>3.614533</td>\n",
+       "      <td>117537.315045</td>\n",
+       "      <td>1.443395e+05</td>\n",
+       "      <td>342.837155</td>\n",
+       "      <td>379.920325</td>\n",
+       "      <td>180.828110</td>\n",
+       "      <td>222.062505</td>\n",
+       "      <td>0.527446</td>\n",
+       "      <td>0.584498</td>\n",
+       "      <td>189</td>\n",
+       "      <td>174.126735</td>\n",
+       "      <td>3.431894</td>\n",
+       "      <td>6928.957342</td>\n",
+       "      <td>4447.341309</td>\n",
+       "      <td>39.792610</td>\n",
+       "      <td>25.540830</td>\n",
+       "      <td>0.085135</td>\n",
+       "      <td>0.078456</td>\n",
+       "      <td>1.476119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10k_BEAM-T_Human_A2402_EBV_CMV_spikein</td>\n",
+       "      <td>EBV</td>\n",
+       "      <td>64.444</td>\n",
+       "      <td>1.961246</td>\n",
+       "      <td>4.752330e-01</td>\n",
+       "      <td>10.055138</td>\n",
+       "      <td>47.815094</td>\n",
+       "      <td>5.349569</td>\n",
+       "      <td>1.177876</td>\n",
+       "      <td>29.645753</td>\n",
+       "      <td>36.547199</td>\n",
+       "      <td>5.444791</td>\n",
+       "      <td>6.045428</td>\n",
+       "      <td>5.541709</td>\n",
+       "      <td>6.831803</td>\n",
+       "      <td>1.017800</td>\n",
+       "      <td>1.130078</td>\n",
+       "      <td>2320</td>\n",
+       "      <td>482.138596</td>\n",
+       "      <td>4.481992</td>\n",
+       "      <td>52346.939110</td>\n",
+       "      <td>7.634764e+04</td>\n",
+       "      <td>228.794535</td>\n",
+       "      <td>276.310768</td>\n",
+       "      <td>108.572389</td>\n",
+       "      <td>158.352061</td>\n",
+       "      <td>0.474541</td>\n",
+       "      <td>0.573094</td>\n",
+       "      <td>570</td>\n",
+       "      <td>90.126625</td>\n",
+       "      <td>3.805148</td>\n",
+       "      <td>1765.748310</td>\n",
+       "      <td>2089.014893</td>\n",
+       "      <td>19.591861</td>\n",
+       "      <td>23.178665</td>\n",
+       "      <td>0.245690</td>\n",
+       "      <td>0.197232</td>\n",
+       "      <td>2.727638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10k_BEAM-T_Human_A2402_EBV_CMV_spikein</td>\n",
+       "      <td>CMV</td>\n",
+       "      <td>64.444</td>\n",
+       "      <td>1.961246</td>\n",
+       "      <td>4.752330e-01</td>\n",
+       "      <td>10.055138</td>\n",
+       "      <td>47.815094</td>\n",
+       "      <td>3.266996</td>\n",
+       "      <td>0.645879</td>\n",
+       "      <td>19.792167</td>\n",
+       "      <td>33.713371</td>\n",
+       "      <td>4.448839</td>\n",
+       "      <td>5.806322</td>\n",
+       "      <td>6.058216</td>\n",
+       "      <td>10.319380</td>\n",
+       "      <td>1.361752</td>\n",
+       "      <td>1.777266</td>\n",
+       "      <td>2633</td>\n",
+       "      <td>1385.941634</td>\n",
+       "      <td>2.885361</td>\n",
+       "      <td>667102.997951</td>\n",
+       "      <td>4.532816e+05</td>\n",
+       "      <td>816.763734</td>\n",
+       "      <td>673.261929</td>\n",
+       "      <td>481.335564</td>\n",
+       "      <td>327.056792</td>\n",
+       "      <td>0.589320</td>\n",
+       "      <td>0.485779</td>\n",
+       "      <td>257</td>\n",
+       "      <td>424.225101</td>\n",
+       "      <td>4.467340</td>\n",
+       "      <td>33705.405280</td>\n",
+       "      <td>13445.158203</td>\n",
+       "      <td>79.451699</td>\n",
+       "      <td>31.693454</td>\n",
+       "      <td>0.097607</td>\n",
+       "      <td>0.088927</td>\n",
+       "      <td>1.665776</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            file       gene  threshold  \\\n",
+       "0   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein        CMV    197.942   \n",
+       "2   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein        Flu    197.942   \n",
+       "4   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein  SARS_Cov2    197.942   \n",
+       "6               5k_BEAM-T_Human_A0201_B0702_PBMC  Flu_A0201    173.376   \n",
+       "7               5k_BEAM-T_Human_A0201_B0702_PBMC  CMV_B0702     87.184   \n",
+       "10        10k_BEAM-T_Human_A2402_EBV_CMV_spikein        EBV     64.444   \n",
+       "11        10k_BEAM-T_Human_A2402_EBV_CMV_spikein        CMV     64.444   \n",
+       "\n",
+       "    mean_neg  concentration_neg  var_nb_neg  var_data_neg      mean-  \\\n",
+       "0   4.722340       3.939271e-01   61.333071   1039.818115  25.003214   \n",
+       "2   4.722340       3.939271e-01   61.333071   1039.818115  11.730760   \n",
+       "4   4.722340       3.939271e-01   61.333071   1039.818115   8.543229   \n",
+       "6   2.901204       2.628828e+32    2.901204    875.116882  15.505495   \n",
+       "7   2.528850       8.153597e+11    2.528850    833.862305   3.732883   \n",
+       "10  1.961246       4.752330e-01   10.055138     47.815094   5.349569   \n",
+       "11  1.961246       4.752330e-01   10.055138     47.815094   3.266996   \n",
+       "\n",
+       "    concentration-     var_nb-   var_data-    std_nb-  std_data-  \\\n",
+       "0         2.897500  240.761868  338.704865  15.516503  18.403936   \n",
+       "2         1.432491  107.794731  224.974823  10.382424  14.999161   \n",
+       "4         1.653138   52.693679   87.826508   7.259041   9.371580   \n",
+       "6         1.876203  143.647475  331.107086  11.985302  18.196348   \n",
+       "7         1.053218   16.963204   32.455223   4.118641   5.696949   \n",
+       "10        1.177876   29.645753   36.547199   5.444791   6.045428   \n",
+       "11        0.645879   19.792167   33.713371   4.448839   5.806322   \n",
+       "\n",
+       "    overdisp_nb-  overdisp_data-  cof_var_nb-  cof_var_data-    N-  \\\n",
+       "0       9.629237       13.546453     0.620580       0.736063  1867   \n",
+       "2       9.189066       19.178196     0.885060       1.278618  4249   \n",
+       "4       6.167888       10.280247     0.849684       1.096960  5066   \n",
+       "6       9.264295       21.354178     0.772971       1.173542   182   \n",
+       "7       4.544264        8.694412     1.103341       1.526153  2220   \n",
+       "10      5.541709        6.831803     1.017800       1.130078  2320   \n",
+       "11      6.058216       10.319380     1.361752       1.777266  2633   \n",
+       "\n",
+       "          mean+  concentration+        var_nb+     var_data+     std_nb+  \\\n",
+       "0   1230.471773        3.924429  387034.531887  4.371280e+05  622.120995   \n",
+       "2    922.462976        4.215191  202796.575052  2.202642e+05  450.329407   \n",
+       "4   1414.221254        4.004123  500904.754996  5.018279e+05  707.746250   \n",
+       "6   1950.187696        4.113499  926523.693207  1.047826e+06  962.561008   \n",
+       "7    649.994709        3.614533  117537.315045  1.443395e+05  342.837155   \n",
+       "10   482.138596        4.481992   52346.939110  7.634764e+04  228.794535   \n",
+       "11  1385.941634        2.885361  667102.997951  4.532816e+05  816.763734   \n",
+       "\n",
+       "      std_data+  overdisp_nb+  overdisp_data+  cof_var_nb+  cof_var_data+  \\\n",
+       "0    661.156539    314.541577      355.252334     0.505596       0.537320   \n",
+       "2    469.323150    219.842509      238.778384     0.488182       0.508772   \n",
+       "4    708.398127    354.191223      354.843985     0.500449       0.500910   \n",
+       "6   1023.633510    475.094625      537.294725     0.493574       0.524890   \n",
+       "7    379.920325    180.828110      222.062505     0.527446       0.584498   \n",
+       "10   276.310768    108.572389      158.352061     0.474541       0.573094   \n",
+       "11   673.261929    481.335564      327.056792     0.589320       0.485779   \n",
+       "\n",
+       "      N+  mean_ratio  concentration_ratio  var_nb_ratio  var_data_ratio  \\\n",
+       "0   3773   49.212545             1.354419   1607.540828     1290.586670   \n",
+       "2   1391   78.636249             2.942561   1881.321785      979.061646   \n",
+       "4    574  165.537081             2.422136   9505.974155     5713.854980   \n",
+       "6   2227  125.773976             2.192459   6449.982473     3164.612305   \n",
+       "7    189  174.126735             3.431894   6928.957342     4447.341309   \n",
+       "10   570   90.126625             3.805148   1765.748310     2089.014893   \n",
+       "11   257  424.225101             4.467340  33705.405280    13445.158203   \n",
+       "\n",
+       "    overdisp_nb_ratio  overdisp_data_ratio    N_ratio  binder_ratio  \\\n",
+       "0           32.665265            26.224749   2.020889      0.668972   \n",
+       "2           23.924358            12.450513   0.327371      0.246631   \n",
+       "4           57.425044            34.517068   0.113304      0.101773   \n",
+       "6           51.282329            25.161106  12.236264      0.924450   \n",
+       "7           39.792610            25.540830   0.085135      0.078456   \n",
+       "10          19.591861            23.178665   0.245690      0.197232   \n",
+       "11          79.451699            31.693454   0.097607      0.088927   \n",
+       "\n",
+       "    noise_to_neg_mean  \n",
+       "0            5.294666  \n",
+       "2            2.484099  \n",
+       "4            1.809109  \n",
+       "6            5.344504  \n",
+       "7            1.476119  \n",
+       "10           2.727638  \n",
+       "11           1.665776  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ratio params\n",
+    "ratio_binder_cols = [col for col in params.columns if 'ratio' in col]\n",
+    "ratio_params = params[~params['gene'].str.contains('negative')].copy()\n",
+    "ratio_params = ratio_params[(ratio_params['N-'] > 20) & (ratio_params['N+'] > 20)]\n",
+    "# ratio_params = ratio_params[['file', 'gene', 'threshold'] + non_binder_cols]\n",
+    "ratio_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0d00ab18-1113-4f69-a0e2-22a5fae132a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='mean_ratio', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Mean fold increase (binder count with respect to non-binder)\n",
+    "sns.histplot(ratio_params, x='mean_ratio', stat='density')\n",
+    "# samples = stats.norm(*stats.norm.fit(ratio_params['mean_ratio'])).rvs(size=10000)\n",
+    "# sns.histplot(samples, stat='density')\n",
+    "# sns.scatterplot(ratio_params, x='mean_ratio', y=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "95eb274d-4486-4962-8d0b-46015eebf38e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='mean+', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(ratio_params, x='mean+', stat='density')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c995c72c-0806-4bae-ade4-085b62352b06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='overdisp_nb+', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Var fold increase (with respect to mean, basically overdispersion)\n",
+    "sns.histplot(ratio_params, x='overdisp_nb+')\n",
+    "sns.scatterplot(ratio_params, x='overdisp_nb+', y=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "aaa31f58-4c01-4d8c-b14e-c8c2eeeab351",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='mean-', ylabel='mean_ratio'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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21p///GevzUC16chNw48OYG7Xrl3t1AwAAKC+w86oUaPMrQ5OHj9+vFdZSEiI/OhHPzILDQIAADTIsFNZWWlutfVmx44dEhsbW1f1AgAAqBU+LSp45MiR2vntAAAAdcznFZTPnTsnmzZtkoKCAikvL/cq0/2xAAAAGuxsrF27dknHjh1lzJgxMnXqVHn55Zdl+vTp8txzz5kNQm9Wdna23HXXXWbLibi4ODMm6MCBA17X3HvvvWaMUNVj0qRJXtdo4Bo2bJiEh4eb59ENSS9fvuzLSwMAAJbxKezMmDFDRowYIX/961+ladOmsnXrVvnLX/4iffr0kV/+8pc3/TzaMjRlyhTz8zk5OVJRUSGDBw82rUZVTZgwQY4fP+45FixY4DULTIOOti5t2bJFli5dKkuWLJE5c+b48tIAAIBlfFpBOTo6WrZt2yadO3c29/Py8qRr167mnM7S+uabb3yqzPfff29aZjQEDRgwwNOyc/vtt1+zxWjt2rUyfPhwKSwslPj4eHNu0aJFMmvWLPN8oaGhN/y9rKAMAEDDU6crKOs08+Dg//1RDSfajaT0Fx49etTXOpvKqpiYGK/zy5YtMzO/evToIVlZWXL+/HlPmQatnj17eoKOSktLM38Ae/furfH36OKHWl71AAAAdvJpgLLueq5Tzzt16iQ/+clPTJfRyZMn5b/+679MIPGFTmvXcT93332313M8+uij0rZtW0lKSpLdu3ebFhsd1/P++++b8qKiIq+go9yPtexaY4Xmzp3rUz0BAEAAhJ1XXnlFzpw5Y+7/y7/8izz22GMyefJkE37+8z//06eK6Nidr7/+Wj777DOv8xMnTvTc1xacxMREGThwoBw+fFg6dOjg0+/S1qHMzEzPY23ZSU5O9um5AACAZWFHh/ho15W79UXvr1u37pYqoTO61qxZI5s3b5bWrVtf99q+ffua20OHDpmwk5CQINu3b/e65sSJE+ZWy2oSFhZmDgAAYL9gX8KOTju/lbE5VZ9Lg86qVatkw4YNN7WvVn5+vrnVFh6Vmpoqe/bskeLiYs81OrNLByp169btlusIAAACLOzowGTtrjp16tQt/3LtuvrDH/4gy5cvN2vt6BgbPS5cuGDKtavqpZdekp07d5qd1nUjUu0y05laKSkp5hqdqq6hZty4cfLVV1+ZDUpnz55tnpvWGwAA4NPU89WrV5u1bhYuXOjzgGTzy4OCajy/ePFiefzxx03r0T/8wz+YsTy69o6Oq3nooYdMmKk6xUzX+NExQxs3bpRmzZqZ6e/z5s2Txo1vrpeOqecAADQ8N/v97VPYadGihZn+rasU6zo2urBgVadPn5aGhLADAEDDc7Pf3z7NxvohW0IAAAA4yaewo91EN0O7knQfK11lGQAAwAk+raD8Q9bjaWhdWgAAwC51GnZ8GA4EAADQcMIOAACA0wg7AADAaoQdAABgNcIOAACwWp2Gnf79+1+14CAAAIDfr7OjKisrzc7jugGn3q9K965S//M//3PrNQQAAKjvsLN161Z59NFHzZ5U1aeX635XV65cuZU6AQAAOBt2dFXkO++8Uz788ENJTEy85oaeAAAADTLsHDx4UN59913p2LFj7dcIAADA6QHKffv2NeN1AAAArGzZmTZtmjz11FNSVFQkPXv2lJCQEK/ylJSU2qofAADALQly+bCBVXDw1Q1COm5Hn6ohDlAuKyuTqKgoKS0tlcjISKerAwAAavH726eWnSNHjvjyYwAAAPXOp7DTtm3b2q8JAACAPy0qqPbt2ycFBQVSXl7udf7v/u7vbrVeAAAAzoWdb7/9Vh566CHZs2ePZ6yOcq+309DG7AAAAHv5NPX8ySeflHbt2pmtIsLDw2Xv3r2yefNms9Dgxo0ba7+WAAAA9dmyk5eXJxs2bJDY2FgzM0uPe+65R7Kzs+WJJ56QXbt2+VofAAAA51t2tJuqefPm5r4GnsLCQs/A5QMHDtRuDQEAAOq7ZadHjx7y1Vdfma4sXU15wYIFEhoaKr/97W+lffv2t1IfAAAA58PO7Nmz5dy5c+b+iy++KMOHD5f+/ftLy5Yt5b//+79rt4YAAAD1vYJyTU6fPi0tWrRokDugs4IyAABi7fe3T2N23HQz0PXr18uFCxckJibmVp4KAACgTvgUdk6dOiUDBw6U2267TYYOHSrHjx835zMyMswGoQAAAA067MyYMcPsdK6rJ+s6O24PP/ywrFu3rjbrBwAAUP8DlD/66CPTfdW6dWuv8506dZK//OUvt1YjAAAAp1t2dCZW1RadqoOUw8LCaqNeAAAAzoUdnWb+9ttvex7rDKzKykqz3s59991XOzUDAABwqhtLQ40OUP7iiy/MjufPPPOM2R9LW3Y+//zz2qgXAACAcy07uoKybguh+2GNHDnSdGuNHj3a7InVoUOH2qkZAABALfB5nZ0mTZrIAw88II899phMmjRJ/vZv/1Z27Nghf/7zn2/6OXTj0LvuusvssxUXFyejRo26am+tixcvypQpU8zqzBEREZKeni4nTpzwukZnhQ0bNsyMI9LnmTlzply+fNnXlwYAAAK9G0unl48bN850W1VfgFnH7+hGoTdj06ZNJsho4NFw8txzz8ngwYNl37590qxZM8809w8//FBWrlxpVkmcOnWqaUVyd5fp79Kgk5CQIFu2bDFr/mgA06nxr7zyii8vDwAABPp2ETrFXEPJnDlzJD4+vtYq8/3335uWGQ1BAwYMMMs/t2rVSpYvXy4//elPzTXffPONdO3aVfLy8qRfv36ydu1aszeX7rzursuiRYtk1qxZ5vl0g9IbYbsIAAAanjrdLkK7kTIzM2s16CitrHJvPbFz506pqKiQQYMGea7p0qWLtGnTxoQdpbc9e/b0qktaWpr5A9BB0zW5dOmSKa96AAAAO/kUdrSVZePGjbVaEZ26Pn36dLn77rvNAGhVVFRkWmaio6O9rtVgo2Xua6qHLvdj9zU1jRXSJOg+kpOTa/W1AACABj5m59/+7d/kZz/7mXz66aemVUXHx1T1xBNP/ODn1LE7X3/9tXz22WdS17KyskzLlJu27BB4AACwk09hZ8WKFWbLCJ2RpS08OijZTe//0LCjg47XrFkjmzdv9tqCQgcd6zo+JSUlXq072o2mZe5rtm/f7vV87tla7muq01WeWekZAIDA4FM31s9//nOZO3euGWPz3XffyZEjRzzHt99+e9PPo2OjNeisWrVKNmzYIO3atfMq79Onj2k1ys3N9ZzTqek61Tw1NdU81ts9e/ZIcXGx55qcnBwzUKlbt26+vDwAABDoLTva2qI7nAcH+7xMj6frSmda/elPfzJr7bjH2Og4mqZNm5rbjIwM0+Wkg5Y1wEybNs0EHJ2JpXRWmIYanQqvKzvrc8yePds8N603AADAp6nnuvaNTgnXdXFu6ZdX6f6qavHixfL44497FhV86qmnTNeZzqLSmVZvvfWWVxeV7rQ+efJk06Wm6/OMHz9e5s2bJ40b31yWY+o5AAANz81+f/sUdnRMjm4E2qtXL0lJSblqgPKvfvUraUgIOwAANDw3+/3tUzeWjpHp3bu3ua8zqG6mtQYAAMAJPoWdTz75pPZrAgAAUAdubYQxAACAnyPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACr+bRdBGpP6flyOXm2XMouVkhk0xCJbRYqUeGhTlcLAABrEHYcVFhyQWa9t1s+PXjSc25Ap1iZl54iSdFNHa0bAAC2oBvLwRad6kFHbT54Up59b7cpBwAAt46w4xDtuqoedKoGHi0HAAC3jrDjEB2jcz1nblAOAABuDmHHIZFNQq5b3vwG5QAA4OYQdhwSGxFqBiPXRM9rOQAAuHWEHYfo9HKddVU98Ojj+ekpTD8HAKCWMPXcQTq9/I0xvc1gZB2jo11X2qJD0HEO6x4BgH0IOw7TL1K+TP0D6x4BgJ3oxgJY9wgArEbYAVj3CACsRtgBWPcIAKxG2AFY9wgArEbYAVj3CACsRtgBWPcIAKzG1HPg/7DuEQDYibADVMG6RwBgH7qxAACA1Qg7AADAaoQdAABgNcIOAACwmqNhZ/PmzTJixAhJSkqSoKAg+eCDD7zKH3/8cXO+6vHggw96XXP69GkZO3asREZGSnR0tGRkZMjZs2fr+ZUAAAB/5WjYOXfunPTq1UvefPPNa16j4eb48eOeY8WKFV7lGnT27t0rOTk5smbNGhOgJk6cWA+1BwAADYGjU8+HDBlijusJCwuThISEGsv2798v69atkx07dsidd95pzr3xxhsydOhQ+eUvf2lajAAAQGDz+zE7GzdulLi4OOncubNMnjxZTp065SnLy8szXVfuoKMGDRokwcHBsm3btms+56VLl6SsrMzrAAAAdvLrsKNdWG+//bbk5ubK/PnzZdOmTaYl6MqVK6a8qKjIBKGqGjduLDExMabsWrKzsyUqKspzJCcn1/lrAQAAzvDrFZQfeeQRz/2ePXtKSkqKdOjQwbT2DBw40OfnzcrKkszMTM9jbdkh8AAAYCe/btmprn379hIbGyuHDh0yj3UsT3Fxsdc1ly9fNjO0rjXOxz0OSGdvVT0AAICdGlTYOXbsmBmzk5iYaB6npqZKSUmJ7Ny503PNhg0bpLKyUvr27etgTQEAgL9wtBtL18Nxt9KoI0eOSH5+vhlzo8fcuXMlPT3dtNIcPnxYnnnmGenYsaOkpaWZ67t27WrG9UyYMEEWLVokFRUVMnXqVNP9xUwsAACgglwul8upPwode3PfffdddX78+PGycOFCGTVqlOzatcu03mh4GTx4sLz00ksSHx/vuVa7rDTgrF692szC0nD0+uuvS0RExE3XQ8fs6EDl0tJSurQAAGggbvb729Gw4y8IOwAA2Pv93aDG7AAAAPxQhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDVHNwIFAJuUni+Xk2fLpexihUQ2DZHYZqESFR7qdLWAgEfYAYBaUFhyQWa9t1s+PXjSc25Ap1iZl54iSdFNHa0bEOjoxgKAWmjRqR501OaDJ+XZ93abcgDOIewAwC3SrqvqQadq4NFyAM4h7ADALdIxOtdz5gblAOoWYQcAblFkk5Drlje/QTmAukXYAYBbFBsRagYj10TPazkA5xB2AOAW6fRynXVVPfDo4/npKUw/BxzG1HMAqAU6vfyNMb3NYGQdo6NdV9qiQ9ABnEfYAYBaosGGcAP4H7qxAACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKzmaNjZvHmzjBgxQpKSkiQoKEg++OADr3KXyyVz5syRxMREadq0qQwaNEgOHjzodc3p06dl7NixEhkZKdHR0ZKRkSFnz56t51cCAAD8laNh59y5c9KrVy958803ayxfsGCBvP7667Jo0SLZtm2bNGvWTNLS0uTixYueazTo7N27V3JycmTNmjUmQE2cOLEeXwUAAPBnQS5tPvED2rKzatUqGTVqlHms1dIWn6eeekqefvppc660tFTi4+NlyZIl8sgjj8j+/fulW7dusmPHDrnzzjvNNevWrZOhQ4fKsWPHzM/X5NKlS+ZwKysrk+TkZPP82kIEAAD8n35/R0VF3fD722/H7Bw5ckSKiopM15WbvqC+fftKXl6eeay32nXlDjpKrw8ODjYtQdeSnZ1tnst9aNABAAB28tuwo0FHaUtOVfrYXaa3cXFxXuWNGzeWmJgYzzU1ycrKMinQfRw9erROXgMAAHBeYwlAYWFh5gAAAPbz25adhIQEc3vixAmv8/rYXaa3xcXFXuWXL182M7Tc1wAAgMDmt2GnXbt2JrDk5uZ6DUTSsTipqanmsd6WlJTIzp07Pdds2LBBKisrzdgeAAAAR7uxdD2cQ4cOeQ1Kzs/PN2Nu2rRpI9OnT5eXX35ZOnXqZMLP888/b2ZYuWdsde3aVR588EGZMGGCmZ5eUVEhU6dONTO1rjUTCzUrPV8uJ8+WS9nFColsGiKxzUIlKjzU6WoBANCww84XX3wh9913n+dxZmamuR0/fryZXv7MM8+YtXh03RxtwbnnnnvM1PImTZp4fmbZsmUm4AwcONDMwkpPTzdr8+DmFZZckFnv7ZZPD570nBvQKVbmpadIUnRTR+sGAIA16+w0hHn6trboTF2xyyvoVA08b4zpTQsPAMAvNfh1dlA/tOuqpqCjNh88acoBAGjICDsBTsfoXM+ZG5QDAODvCDsBLrJJyHXLm9+gHAAAf0fYCXCxEaFmbE5N9LyWAwDQkBF2ApwOPtZZV9UDjz6en57C4GQAQIMXkNtFwJtOL9dZVzoYWcfoaNeVtugQdAAANiDswNBgQ7gBANiIbiwAAGA1WnYAXIXtQwDYhLADwAvbhwCwDd1YALxadKoHHfdq2s++t9uUA0BDQ9gB4MH2IQBsRNgB4MH2IQBsRNgB4MH2IQBsRNgB4MH2IQBsRNgB4MH2IQBsxNRzAF7YPgSAbQg7AK7C9iEAbEI3FgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1vw87v/jFLyQoKMjr6NKli6f84sWLMmXKFGnZsqVERERIenq6nDhxwtE6AwAAkdLz5XK4+KzsKvirHP7+rHnshMbSAHTv3l0+/vhjz+PGjf9/tWfMmCEffvihrFy5UqKiomTq1KkyevRo+fzzzx2qLQAAKCy5ILPe2y2fHjzpOTegU6zMS0+RpOim9VqXBhF2NNwkJCRcdb60tFR+//vfy/Lly+X+++835xYvXixdu3aVrVu3Sr9+/Wp8vkuXLpnDraysrA5rDwBAYCk9X35V0FGbD56UZ9/bLW+M6S1R4aH1Vh+/78ZSBw8elKSkJGnfvr2MHTtWCgoKzPmdO3dKRUWFDBo0yHOtdnG1adNG8vLyrvl82dnZphXIfSQnJ9fL6wAAIBCcPFt+VdCpGni0vD75fdjp27evLFmyRNatWycLFy6UI0eOSP/+/eXMmTNSVFQkoaGhEh0d7fUz8fHxpuxasrKyTKuQ+zh69Gg9vBIAAAJD2cWK65afuUF5wHVjDRkyxHM/JSXFhJ+2bdvKH//4R2na1Lc+v7CwMHMAAIDaF9kk5LrlzW9QHnAtO9VpK85tt90mhw4dMuN4ysvLpaSkxOsanY1V0xgfAABQ92IjQs1g5JroeS2vTw0u7Jw9e1YOHz4siYmJ0qdPHwkJCZHc3FxP+YEDB8yYntTUVEfrCQBAoIoKDzWzrqoHHn08Pz2lXgcnN4hurKefflpGjBhhuq4KCwvlhRdekEaNGsmYMWPM4OKMjAzJzMyUmJgYiYyMlGnTppmgc62ZWAAAoO7p9HKddaWDkXWMjnZdaYtOfQedBhF2jh07ZoLNqVOnpFWrVnLPPfeYaeV6X7322msSHBxsFhPU6eRpaWny1ltvOV1tAAACXlS4M+GmuiCXy+WSAKfr7Ggrkc7M0tYhAABgz/d3gxuzAwAA8EMQdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAq/n9dhH1wb2ItK7ECAAAGgb39/aNNoMg7IjImTNnzG1ycrLTVQEAAD58j+u2EdfC3lgiUllZaXZUb968uQQFBYnNCVgD3dGjR9kDzA/x/vg33h//xXsTuO+Py+UyQScpKclsCn4ttOzowKXgYGndurUECv3Lxj8I/ov3x7/x/vgv3pvAfH+irtOi48YAZQAAYDXCDgAAsBphJ4CEhYXJCy+8YG7hf3h//Bvvj//ivfFvYX7w/jBAGQAAWI2WHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYCQC/+MUvzMrQVY8uXbo4Xa2AtXnzZhkxYoRZ8VPfiw8++MCrXOcMzJkzRxITE6Vp06YyaNAgOXjwoGP1DSQ3em8ef/zxqz5LDz74oGP1DTTZ2dly1113mdXu4+LiZNSoUXLgwAGvay5evChTpkyRli1bSkREhKSnp8uJEyccq3OgyL6J9+bee++96vMzadKkeqkfYSdAdO/eXY4fP+45PvvsM6erFLDOnTsnvXr1kjfffLPG8gULFsjrr78uixYtkm3btkmzZs0kLS3N/CMOZ98bpeGm6mdpxYoV9VrHQLZp0yYTZLZu3So5OTlSUVEhgwcPNu+b24wZM2T16tWycuVKc71uBTR69GhH6x0INt3Ee6MmTJjg9fnRf+/qhU49h91eeOEFV69evZyuBmqgH8FVq1Z5HldWVroSEhJcr776qudcSUmJKywszLVixQqHahmYqr83avz48a6RI0c6Vid4Ky4uNu/Tpk2bPJ+VkJAQ18qVKz3X7N+/31yTl5fnYE0DT3G190b95Cc/cT355JOO1IeWnQCh3SDaNN++fXsZO3asFBQUOF0l1ODIkSNSVFRkuq6q7vvSt29fycvLc7Ru+F8bN240zfSdO3eWyZMny6lTp5yuUsAqLS01tzExMeZ2586dpkWh6udHu+zbtGnD58fh98Zt2bJlEhsbKz169JCsrCw5f/58vdSHjUADgH5RLlmyxPzjrM2Gc+fOlf79+8vXX39t+lfhPzToqPj4eK/z+thdBudoF5Z2ibRr104OHz4szz33nAwZMsR8kTZq1Mjp6gWUyspKmT59utx9993mi1PpZyQ0NFSio6O9ruXz4/x7ox599FFp27at+Y/37t27ZdasWWZcz/vvv1/ndSLsBAD9x9gtJSXFhB/9C/fHP/5RMjIyHK0b0JA88sgjnvs9e/Y0n6cOHTqY1p6BAwc6WrdAo+ND9D9sjD9sOO/NxIkTvT4/OglDPzf6Hwf9HNUlurECkP6v57bbbpNDhw45XRVUk5CQYG6rzx7Rx+4y+A/tFtYmeT5L9Wvq1KmyZs0a+eSTT6R169ae8/oZKS8vl5KSEq/r+fw4/97URP/jrerj80PYCUBnz541SVpTNfyLdo/oP8q5ubmec2VlZWZWVmpqqqN1w9WOHTtmxuzwWaofOm5cv0xXrVolGzZsMJ+Xqvr06SMhISFenx/tJtExinx+nH1vapKfn29u6+PzQzdWAHj66afN2iHadaXTMHX3WR1fMGbMGKerFrBhs+r/ZHRQsn7odSCfDqTUvu6XX35ZOnXqZP7BeP75500ft65bAefeGz10vJuu26KBVP/D8Mwzz0jHjh3N0gCon+6R5cuXy5/+9Ccz3tA9DkcH8euaVHqrXfOZmZnm/YqMjJRp06aZoNOvXz+nqx/Q783hw4dN+dChQ80aSDpmR5cJGDBggOkOrnOOzAFDvXr44YddiYmJrtDQUNff/M3fmMeHDh1yuloB65NPPjFTMqsfOq3ZPf38+eefd8XHx5sp5wMHDnQdOHDA6Wq7Av29OX/+vGvw4MGuVq1amenNbdu2dU2YMMFVVFTkdLUDRk3vjR6LFy/2XHPhwgXXP//zP7tatGjhCg8Pdz300EOu48ePO1rvQCA3eG8KCgpcAwYMcMXExJh/1zp27OiaOXOmq7S0tF7qF/R/lQQAALASY3YAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgC/cu+995qdqnX39xYtWkh8fLz87ne/k3Pnzsk//uM/mh2VdafxtWvXen7m66+/liFDhkhERIS5fty4cXLy5ElP+bp16+See+6R6Ohos+Py8OHDzS7Mbt99950EBQXJ+++/L/fdd5+Eh4dLr169JC8vr95fP4DaR9gB4HeWLl0qsbGxsn37dhN8Jk+eLD/72c/kxz/+sXz55ZcyePBgE2jOnz8vJSUlcv/990vv3r3liy++MMHmxIkT8vd///ee59OglJmZacpzc3MlODhYHnroIamsrPT6vT//+c/l6aeflvz8fLnttttkzJgxcvnyZQf+BADUJnY9B+B3LTtXrlyRTz/91DzW+1FRUTJ69Gh5++23zbmioiJJTEw0LS8ff/yxuXb9+vWe5zh27JgkJyfLgQMHTGipTlt9WrVqJXv27JEePXqYlp127drJf/zHf0hGRoa5Zt++fdK9e3fZv3+/dOnSpd5eP4DaR8sOAL+TkpLiud+oUSPT9dSzZ0/POe2qUsXFxfLVV1/JJ598Yrqw3Ic7nLi7qg4ePGhaadq3by+RkZHyox/9yJwvKCi45u/VMOX+Harq80+aNKkOXz2A2ta41p8RAG5RSEiI12MdT1P1nD5W2g119uxZGTFihMyfP/+q53EHFi1v27atGfuTlJRkfk5bdMrLy6/5e6v+DqVdW24amAA0HIQdAA3aHXfcIe+9955prWnc+Op/0k6dOmW6szTo9O/f35z77LPPfvDv0UHRABomurEANGhTpkyR06dPm26qHTt2mK4rHb+jM7d0vI/O6NJusN/+9rdy6NAh2bBhgxmsDCBwEHYANGjaLfX555+bYKOztHRsj05b12nmOutKj3feeUd27txpuq5mzJghr776qtPVBlCPmI0FAACsRssOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAMRm/w+NMF2SVG+o6gAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(ratio_params, x='mean-', y='mean_ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "2159dce1-2252-4136-9974-80e2061ded88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='mean_ratio', ylabel='overdisp_nb+'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.regplot(ratio_params, x='mean_ratio', y='overdisp_nb+')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6d62eb0-2c0c-4193-bf85-a513ab3ad0ba",
+   "metadata": {},
+   "source": [
+    "## Test out new simulator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "18830e8a-3c5b-4c73-9907-65b2dac4b73c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Wj8j+88Erc/7+ttn2h4Y5KRoukppiz8yNWe/6mTbpltnn94tUbym50ptf3Qc9XhVZlemh8ezbM/7XG0Z9QJvOJtJ1dO7z14e/5etkvF86SuTA9xnv9aFs7Xb2B736MPeRbVnn8cHN9ld9+LrpPZGrh4jc8LLrZ+qW/4q07pf1+/pb+2KwSNM7Re58P+f06jprZoTD6ASRz/qJHN0t8vg+kTKVXaf/9F57Jkqg3Ew7toem29cWPmLPmNFzmbv5a+bRyhdFwsv69kEHgIKnhZZWvGAvGNTy/yRgfNo3I/NNM6p1WPCQvUDg5TeJlIsW+fDvwkkt/s+eSf7taHtm9E2vu5/nJ3fZMy01s1XPX/q/xnTNhBt5wDfp1szt7yeKFItwHa8Z+Zo+ZzqNZjBrwSYtQKmFrnq9Y/9MM2b1M7V5hsiz8faMXC18WKO1yIDFuV8L/f6dSNIR9+mcN8D1ukwzKnOjGabOGfuZff9axv+OjHCl1wIN3Vy/ZPbNs/ZrW+frW923+5Z7dg3iCS1Akeam8Ohvy9xPrxm1zi4kZ2SEK+dCD3N6i9z3TcZ+btHH/poUbx/0+sKZ7h+9rshtmY5Cn3rsV748Y9wPk0R+Wy5ydJdIyTddr930+mb+A5IjzTB37uP7y6H2eTl7v2vG/7/+vY0O/ZhpPVbYB3cWP+56v3BwrUiNVhnvt34osnp8xnt/XNPAUq8v/1Ue6nSpFC9KI3jIwdkT9tfMhZJChVaUcDz/yfaz3/2chhMZBc/8toy/7K9aqN4fx0fmigK50ftZpdd7gXD/DgS58GJFrU4CACCEcIdoke5Nqrm8L/H/7N0HfBR1+vjxJ7SEXqVIERSkdxuoFEGqHlj4efxVUNE7PTzBgopdUcFDVDwQQQXOgnh4gooIIgqoFKlKERSliQmoQCB0kvm/nu8yy+xmN9nd7Gbb5/16DWFnZ2e+U5+Z+bZiYdoVWitEa9v448yIHjg794vTcPD3cvOSe0RunOXKNPTH/k5rm1zvVeO68ZUBLr+oq2TwgA9d61jzvMB+p9MWLykh0zQHSmtmhZuzRpCTd20ke1pdX3u44X+uGjZVm7hquyutAZeXQAo12C+9taZVXjwywrXQQhClP53pyMnO/aBvv4DOrxa+PR9/6+WvFQR7mfZLhbw4M8JtWuPf33L3nnpBEWoBknjia9s4aaZNsmwLINFpJpj5G2N9wNkZ4c60aa1q14jc09vT5BXj7FZ67Gntv95xryDsOJ9re1r+Y5Z9TT1x2DG5Y3r7e3tcIDHOzC+PbeFdQNE+DgLhbxtrYT5fAsloL1Had1OmmvkcTkWKhu9Y73Oq4IKHYOYdxLTex6z3fgzmXi3QY8JfGoK5f7YzMPzNx1fBBCScnFiLLyh8egxM6S0y//Hwzldb1Rh3gciGWbkL4miLHxoHJ3US+fql4Oe98DmR1y93tWyhPr5b5N3+Ij/Mdi3Tfm70LmCo32krW5HI3NeWUt5yFGJ30tZjJvcUWeCnMkAk7N3qWl9d72B887LIxI6n71PmPhTZPtwBAAAQc6gZHiX5ZDMCAAAAAAAgWFr4S1uG0OHyJ8ObGfvHZpEfPnJ1G2Fb8drpLsp+W+Nqhv2SocHN225JTgtlFS0vsmqy67N2e6HLtLtNc9IuLuzvIsFXqyzOwnjaBYYOXR6VQqHrqeu7eW7glSXUwlGu7aoFpoqf6dmyIgAAAJICmeEAAABB+m3/ETmzQgFaEwEAABHx674jcs4ZZaKdjMShGbtLx4s06SNybre8p9VWI7Q2dovrRGqfH/yyfvpc5JcvRS76h6sLEP1bpX7gvz/wm8gXz4gc+NVz/LJXRYqVEDnvFleG6uxT3ZD5qmH885eubhq0ZY96HVytqGgf0hXrenazoNZ/ILL6P7nnoa2frP+fyMzbRS78++nxswaLVKjjao2m6+OuLlS2fO7ZDd50R7ctdrdntn3bXV1WXHK3yO8/iqx9J/eydf3t8drtSYNurnUqUUrkWNbp5rs1Q79kBZHOD7tahdPa9NrtRvFSrq5HznV0N7JhpkjTq07/X5s+1+5unLQQwNvXuvb9+YN8d/W2fanImrdc21Jrsze9WmTzHJEyVV1VRjo9ID4tGu36jRYMyMu3pwolXHCb6+/Wr0S+fjHwVl+05rh2GWPSkw9tmv3LZ13LqtrY87sZA10FF7RPei2woN3R6fbULvW0C8Mz8+lyTo8d7crGX3/2i0eLbPtG5JzOIhcPcY3TYyitvO+u3YKh3aFoc+9dnxIpUiSw7mB0HbuNEClaPPd3P84Vufyp3N/lR1se+PxxkbqXiJzbPbjfAgAA+EBmeIzYlrFXdmcekYqlTzf3mL1/l/y+N1OqVz3D1XS1Plj+/qMctYrLIUmTyikHfczJcvXv6O1ghuuvlqJFYsmvFPjvQexzbbbVbmItc5fIH1uCa4bSn0yvlxGBNJl9IN3VfGbmztzr4zyOta9WX/2F2uy+0HQ++hIjtYxI+Vq+m7wz029xLdu7mXh9saLN5dp9n9kvDLQ/TefLCW3KtcJZIsW9uiAItElcnafuU63NYDd9q83savcH+vB/8rhI6cquB3WteVCmmivt2v+sNuWuL2v0YVtf3igdp/MrVUWkaDFXOvRFgu3YwdPzVLp++lt9kNa06Ly1uVmdpzd7e+i6ppYTKVJMJK2c7/XSFy8HdomUq+naB96cTfDqcu3tr79zNo+rLwV0nK6nrpu+qNAHa3t76G91O9nN/fuix7b9O3/0uLK3o+573X76Ag045fBxmuwPil5ndq8TqdIw/2n1Wqovh/N64Wlf1+xrzqE/XU17O6/vGsPK1XA1h21fr/XFpH190H6o9Vw/49zc8zexp53nC0+d3he9Rm9f4rpu6nVJX3LrNdnZX7P2+ZxWwbPrEt0m+rJ7/3aRoiVc89frkr5A13XTF7d6zdTrvy5Dr996XdLtoi+hS1Zyde9ix5fsU93k/PGjKzb44t3cub6U1pfpmq6M7z2/01h48DfX/zWNWvMrP8f89L+87WspEL32++Ir7qsdp9Yrr3shva/27ota7fQxriA0c+C4ozn6QB33dZ/vw68rPZeVlyP7A1++xlRfzw72fY+vbniCpfE1z++DuH/UvtT1eNbj35tznLOPe6W/KVXZ1e+s9o+eX0YL4kKXMYtk0bBOclbl0tFOSmLY9InI2rdd14X8MsM1PmktaX2mvOH94Jf10T9dsadsdZGVb7gyXDsOC/z3Go81rd7mnspk1cxwzejO673InGEif/50OiPavh5qV2Pez6bv3+x/Pu/f4vrrzOh2pq35tSJ1LhL5eKjnfLd95X+eWlBAM7jPbO3KtPRl8b88P7/7V8/Pdld5WbtdmaranZ1mlOv9hGa0+4rdyyeezgyfcZP/psj1Wqq1rvVa6qu/8u/f88zA1xjm3Bcd7jv9HOv05dOuvzd8IHmac59nZvi8h3Lf3+RF0/ztxMCm1Xs7PUb1vrLHSM/vtK9xHfSYWTrONW7gxyIrXnd10XLNqUz7/I6dS+9zPcN7++Lp08eDnRn+2SOuvwXNDP9wsOvYuGiw6z46P3MfdN3znn9r7oIrn97vum/Q7yqfE1w69Fha8rLrHHzAxztOJIWsYyflnIfmmP8PuqSePNTLq+AJAABBIDM8SsqV9MyEWf3bEblw5BfSqeEZ7nH/Gv2UTMz+i8wt8X/SqKL9Ylbk5hMPy9KcpvJD6k1SMuV4YJmOW049KK1+03N8lXODezjIT7larpfBezaIlD7Ds0RzXn1Eap+P+hLX+ULX7u9RMxaD4d3vt78XwsGySy/7oy/SAhXOvtr/l0//iet9vITw16+888FbH6K9H6Sd3r4m0BSKfDtJgqIl9P31Azb+Aq95Ox5WfT1w2+O0r+lXLnT9f8h3uaf7+YtTfxeIvNAo9/dfPe8a/NEHwLEtXP/X0t59Tj302p5zZEDnVwrce596r/MTmSLT+7vSfM0buafXmgJdn3D9X0urf/2C6/jUjBN9sXPTJ64S1urVS10FaHSeakxDVw2B+35yldrXF1Kq9kWey9BMnOcbeI7Tl1X3OgoHOL1xuWsbaan5fyzz/E4zqZwZNPpQf85lrv+/fpkrE0NL1SstTa+DrfUNIn3Gi0z/f659p7TJvOt8vASzM9Bebi3SZqDIX172Pc1va0UmdRS59F6RC/4u8kJjV62FflN8Tw8gf/piVGsb6fWxRkv/02lBIL2WaqbU4OW+p9FCPHoe12kncstc17iJl7gy1uxrmWbkjmsr0rC3SP9pIrtWu64nFw893Vyqvnz+frrIXWtyNzv6wW0iza51Zcpr4a/XOvtP86bZrsFWr6NI54dEJjtqsbzto79Nram06Ln8+032ppntdgEuXz651/X3Ch/9lWo8cMrKEBnvp+belB6e1+Zg4r63T4PIyPBl+zfBTa8vqJ3r9d27wRXICyet8RcIPQbzK+DofY+rPnv49P+dBfR80f2ttGCGnbnjd9rdEnGagR0Mra2YFz1m/b2ot/2y0P9v9LnozhXBpQkx6+BRCq0FTAtybfzQVQta78v1mbDW+a7CI00cTYGr3RtcBe3rd3FlxmpmX9VTz04Z608/U9kFtOxMVb1+1WjlqlWshVpVw16naynr77Tglw5O6WtdfWhvnOV6ftFMcn1OrNb0dOzWwrmaYV/7QldmvDdnH/L6DKNpcFr9lisW6/rrc4CdEa7yu1Z6s9+35JzIf1otuKU1ar0z2POat/2+Z937nv2D//RZ4Gl0FjRWWmBZC4LpPY+3Sue47jv0+Uy3U15pcz6/p3s9bx/cLfLTvNz3L94FmPTdlL5P0hrde08VatcWCdxO7Uvdh1e8cLrvdi0E4cxE1/tA3Sbe77rWzXAVhvMuPK/HqO4H7wJxWqtZ11uPVT2mNaZXru96x2C/19LzZc07rnXRbenv2NPCnvZ7Cd0e9nmmtbDrdz1d+Nz5G02/FrDUfa3PuPqdnqtO388QadHPc5ze9+o50+p61/7TfaPn3TldRCrUFp/0fNZj0lf817RrgTvdF96VBry3p56DWvi8Qdfc33ks73NXwZfKDUTOchRAtbsjWPSv0+eg7mctUK/bCQkvJSVFhnVvKPM37pa1O/dLdo7rnJj+7Q4ywwEA8Z8ZPn78eBk9erRkZGRIy5Yt5d///rdccIFX5o/DjBkz5NFHH5Vt27ZJgwYN5LnnnpNevXpJPLmiRQ1pufYxKS1H5bCkyUMnXSVXd+1z1ToqI4flZ8t1M3xASolkni4tqxnh6rgUl5Lip1ZGxwdPP8zoTauvDFGlTWLZDyDzvfp50pvuyx4T+eRu1+ezO7seguy/Nr2h1kyo4qVFej7negjRTG2tKXo8y/WgZmfG29Nv+OD0/1v2d9Uo1YcJLSlrxnd11Y66aqJICR+1SPXl9uZPRNre4noo1pe1mnmuy65Yz3NaLf2tzSoVKyky81QJYWemtPfDoDctEauZfPqQryWNNUO83Z2uTHZ9KTfn1EvnVv/P9XCmmZnVW7gy2zQ9mq6P7zo9vx7P5S4QoC/09TeasesscX7NZNcDlT4QOuehn1vd4L+vK81s0BeMzgelK//tqtWlL4Tzqs2jaanZ9nQJ5kvuFfl6zOnvL7zd9YCjD0qauRpMDXEnLeTQ9mbXA+bOU5mjF/7Ddaw4X0AESzMvvUtz6/7S5dkv4zUjRZthsx+mw0FfLNsPjsG+3HUK9Lfa5Jiz1QcnZ6EAe356jtgPo1oa3ebdkoQeG1rj264J6f1Cw76u+HrBk1cGgl1D0tcLKm/O9Nm1+fy9AE8/9YJj6+LT43Z4ZbY7mX0vni+Pck1zavmaKaHXMDtjBQkXv+esS5dxX+TRAoaXHMfLqU0ZB6V+VR8vDeGbff3J66WYsuNjXpl6doEujUP+apjaL9rta5d9LjtfONvL8JsmyxUPtVZ0MPQ6Yy8vL/oCMNiMcJVXRriTv/XS+xJfBRO96b2cFk7wp+9E172YNrPqi94v6j3Yf2/0P49GV7hqwa+eKiHRl7x2DbNze4r8+KnrnlJfNPtb37+MczX3+/upeNTreVessft79cf7/tfblS+7CoJqYTV7OR/defr/up5H97leNtv3wbbuI10FQDRWf3Cr7/m3+Ktr/rUu8F2j3Re9j5z1d1dGl01fivf8lytDSO/hZg/1nF6Pe70HdNYyt++7G//F1dyuvV66znp/rS3EeO/n829zvbR+9zoJia8Me91GSp8NNAMhr/3hd77lXb/3zrBQvmqVI+SYX9iKF3Vl0JRJLWZqkiEIep+rhWv1mq3XCy0Qq9csrZn5D6+Mh8k9XPfKw3eJvHOtSJnqIvedul/XQmC+4t/U3q6/t33pWYj3st0iHU49n711qtaxN431miGuv9P3Bl0eE3nvBpEz24j87dQ1QJ9vtQZww56ulmK8OeP4mjdFdnoVtrOvaSXK5t0qh7NVKX1X4suXz0jAtJBgMJzP9pqB6uR8FgqW1kIOpIKEvZ18fneqALXNeW+g20r7HvcukGd4PY+/1kWk2TUi6/57etw3Y3O3+qat5+hxaheG1sKNOtj03ZZmfHvTJtC9vXejqzKHk77b0mXo/YOud9cnXc/6y145PU21Zq6/WgP8w39IvuwCC7o9tHC3pl8LiOjxre+cejyb+/lcC2PqvagWTLnnB1crQtoEu5PeN9gF3Z2/U/ouTt/RaSFPpQXOb/QqDGI3Sa7nc161xTXz3lcBUm//udJVKeYRP633KL0HecdRyNIuzOpep7+dvtfRfWCv86N/+q4pj7iO3760qVNRft6TZTLDAQAIlwA6gIms9957T+655x55/PHHZfXq1SYwd+/eXfbs8X3jtGTJEunfv78MGjRI1qxZI3379jXD+vV5ZGzEoOJFi8jZKRlSLWW/1EvJnZmVKgGUJM6LNpulNa90qN7c/3TadKhOa0/vXQq4iqPmp920sk6nLxq9x2sTSvYLJs3Q1f9r857OJpmVNgXq/K2W7tYS4c5+meyaL/p7nY83rbmqtLar/k5rm57R0FVK17tZLW22Sm/YA2niyZdK9Vx9OmnGuW4TpRnhus10mc71qnjqwUCnt9Pj/bBgmoc6VZo25VRadf2rNvFcd7Ocqq7l6Dx0m9o047zGqVrIvmiBADst9oOarod3QQFfdFs695n38aPbQNdLa/MH0mx0+Tq+x2tp/lrnidRsc3qcHlu6vgXhfbzZD5ze/Xjl1Yx2QTj3U6j0xb193Idjfvbx6avJuWAVCbKvLySsRIjfT8/eKBvTAy+80rJ2BSmX5op/q7fzYA4/vGvMxBpfXYXkmqaOK24479lyTVMz7/sK/c5fZoFzGr0/CZRmBjs5Ww+yWwLyLkSp95Iey6x7uiai0nusQNLg/I03XU+dj7NWnfN+RP9vummp7btwly5fM1n0nthf4S/7PlkLmwZKf+M9vwp1XdtIm7AtXTX39LodNSPaSdNlb3/netn31772sxYk1eeMUNn3+r7OLf3OTlN+vNdfj4dgW51C0DE/GiqXSZXHr2wigzsH0b90uGiBLO/CQ1oz1VkQxZtm2moXEHbLaJrJpbVF7cJdWjjU/k4L42mBbC1spfPUeWumoBZg1W4Y7AKsOk/9rN0H2YVbtVa1zlOn0UJSWrNY06pdiWjBT63BaRcO0/TYBUA1I9wu7KrdkNhdLdiFRu2/2uKE/k4HZ0a4ZnbpOGcBLu9C6NqFla6Hry4T7ELGWrjN/r8WeNX0qt9Wn16uXdhW18lOt8dyHIVxNePSH18Z4c5+sXW7a+EgvXbq9vXVhUf1PFq/KSi9ng3w03paQWhtf1/M+lmuQlA2XxUV8qPbylcBbl+0kGBeBSKdXdDkNU/tAiMQesx4Z4T7Ola1sJp3K4HOtPh7/+Es5G+fH3ru2AWz7b/aVLzdHY93KzF29x66Xfyts7OFSOd1Rwt+Oc8vPf71s7kO5LjWX/ezr9rgmnb7/LSbzNfriP1bc01ynPM6D52X3TqkXq/sLuZ0Pvb56ixwbjto//ZP1/L8FfrTNOm0Ouj1SNOi1zWdp7NGPeIqfvtTrKhnlsWBoydlxOyNUvfBT3IN4774SSzLks7PL5RhM3y0BAkAQCzUDH/hhRfktttuk5tvdvW19Oqrr8onn3wikydPlgcfdDRpd8rYsWOlR48eMmyYqwTxiBEjZP78+TJu3DjzWwAAEHnJGL/7ta1thvtmfCdFYjy/EwCAaMX8aGlUvZxs/cPVKsYV//5ato06VSM5krRlCe3S6MI7RHqOco3TDNsXm7hqMl/l4x5HM21Gn+1ZK/LNvqdby7pzpcibfxHJ+l3ksT9cTTNrrc2a57kKpDhbfdMWEzSTTGth6jy1pqkWdtFmx4f/6qoJq31MO2nmpq9mtX31Ve2ssb3d0Ye0diNkG3de7t/puniPX+nV5ZCuh78W7Ox+tjWDULtDsmvMvuFoJtl7/v5aF3P2mb0qyJZInC1faaEAbQlDC69rzWdfLaMUT3U1+xxMS2f5dX8Saf5aqdHxzha7lBZyCqT1Gyd/Lcj4k1ctdef+0/7O/Tngp/tAb9qlTiDbRPuuzjWNn1Z2chyt8DnPM7sLAWeNdbslGz3H/3eryIV/998tjb/WE5TzvHB202P3223TjGr73K1/uWcLjr5qeOdazuWuv3UvPd3Mu22qV2tfox19hE/zasbd25hzJSCveHXd5nTtZFerAojL+O3LZY2qyi+/Z8nK7acKhonI7O99FJ461Wqbtqau9wA6jO4XwYJJAIC4FdXM8OPHj8uqVatk+PDhpysdFikiXbt2laVLfZfk1PFaqs1JS7XNmnWqryAvx44dM4MtM9PV/M6BAwVoxtjhyKGDkn3skBw6mEdzWv44mm/LOdWc58mjKZJz7LBky2E5XuSo5OQcliwrWw4UOV3KUb9XByRHUlL8lH7MOiRiV+A8dFTkmI/pDno1n3nkpOd0+lmnsccdPub6/6FjIket0yUv7fFHsnPP0+N3R13fZx32nKfzN3YaDh3xPS/37457zjNQ3tvhZI5I9qlxOp+jOSInvabJOiKSdmoZR07kXq49T/1sb2tNnzNdzuXqvtH1M+NODYdPuJZjb0fnPG1HHeM1Hc7t6P29Sccxx/Sn9qX7WMjxv/91n9hpMen1Wo6mXafRbWV5L9PHceZMl5Ous0nTMc9l2fs2VO5t6xx3an+5t2uWK/32vg8He7/p+uo6eF9jfK2TTmOn44BeQ0q7tr05v3JOz8/7t+Z3p44dX+ur29Zevn3+FckRKZojcsJybWf7e/u3vj4796l9rqTkuL7TEtj+1skX57Te0+i1xOMc8ZE+f8eXHrf2drSvSbqe/tKh+96cZyfzmOaQ4zw+eHo/hCluBKts2bKm36xYUhjxuzBi+MmjhyTn1PwPZx2UNOt4wL87ejgrbOlICs74aMcY57nurM3ifV3KNa+Duafx/ux97jrPa3saO+7pdaHUAd/XWn8xyTlPb973T/7Y1+dI8RUf8orL3tOY6/6pWOSLdwz3Zt/j5bUs/b34ub77otd3j3sSx2/d29NrGu/11TTZ93Pu9fCzrTzWJ4/7E42Nvu4Lfd3P+ZqPngvFT13nfd2LmnU9ljvt+bHvbT3u70840up1j2eP916GvW2996m/e11f917B8nWc2vek9r1WIPPO9tqeen5aXs88TmG8rsdi/C6smB/p+J197LAcO5zl8xn88KFDp5+XCyNO//6b63ja8f3p4+ePdNe4LSt8H1Pe9746zZalnvP8fdfp7+z5/fGbyJ8Znr/99VSt572/u8ZvW3O6O6g/94hsXZv7eE/fGtn448/xlMgtV/v79m4q/LxbRVae6obNl+6jXF0/aYtncx0Zhnk5+y8itbqIHNojsvh5V23ypte4uoXQWulak1xb+frfLa7pu2hT6PpskiGyYtLp+WgrHFqTVvs51yaytTas1jTWZty1CXjtEkZrLNfrJHJWe5GFz7quZ97XVV/rrS69T+Sr571GnjouLh3maul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+      "text/plain": [
+       "<Figure size 2000x1200 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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x7Qur5bdrIjyQDm7O2wodAAAfpGeelI370+WsmlF8c0i7d/rkbpFGlzvHQ8rDz9bDaz4Q+eNz955fdNymyvVFajV3vo92MzXzPpHV74vc7DKmUEH0PObjO0WaXSdyeLfIwS3OxLJW9LO6U8rKdI4lpeM1XfUvkU/vdlbea3eryOcPi1Sp5+ySy62805zjRFWp7yyrJtXPvUfkqlfcl/vlI+f6ylQQOeNiZ684On6WN1YXTtr62ldfPeas9XvJqS66Vk9znive+JZI6TJ5l9eW6FUbus+bn+zsvqr7qNx52vpbKyYktnHe5Nw4N/e5aTeL9H5bpHzlvOvX8bRSdzrH1AoVR6BqnQOAd89c3Ux+2nJQ5v7m/J3esC/d7iIBgH20i1YdE1S7pA1mMvzABpGUJSJtb/W/AY3rOa0m7nUYohpnB7qEAAAABsnw4nJtke0mt5uEBhPS5D9dykmvxmXzWbS09/nlqzgfdbzxzMMipcs6u5oodWrdh/c4X2v9fVo1ZyLctdWSJ9duz9V/XZLdloObcv+/aa7I8IS83RuMae981KR7frYvdnYp6sl1fZ7z71koUqeN8+/3bnCWRWuyWi3N/322s9v5bqfGjHA90bduTOvY4yumiNz5jUi9js4u5fV1nR4U6fpM/uUFAJQYG/YdNo81K8VJ1NK4qS3AdUwer8lwP80Z5hyT22JVutNKdDppgleT4ZoIVwtf8m29h/eK/PGFswKgntvkZ96zzsdLn3K2NtZxrjQZvkLHY5K8yXBtyb1isvu8nyfmTYZbrddPZOT2rqMJ+da3SEDoeyorGT57qDOxnXFAJL5O3uX3/pI3Gb7wVBLcNRmulQ414a9TbHmRk0dzn9NuybU1fvm2edf/7QjnYyiT4QAQZE0T4+XP9MycZLhWegMABNms+509Nja4UKTqGUVbh15H6HpOby8y4FQvlSjxnv/iN+neorbMX79fbmhXV86oEcWV2AEAIcGY4cV1/IhPi/160IdxNT21vU3kosdEevxH5OLHc8fkbneH+3KaAO/6rEi355zddBakydUiiV5ujHpT7/zClymo9bm2OvKXjldu0Zv32gLM9aa+tgK3xjN39dcW925NzfunnHrdQecNY+3qFAAAF83r+DlMSSB88YjI7GGBW5/GwLe6i2z70fvz2tPMezeKvHmFyO5VzsSzL7Qi3tvXOFtOWy3NXX3zZN7XaOtti9Wq29Oo+s4KcFomfXy/t/f15+eVM08tf1xkXKfc+Uf/dl8uZ6wqD/qe1qRl8JaAP7jRv5bz0/vkjiPlem60+Tv35b55xpkIN6/pKzLtHyK7Vjr3javFY5zP6Tp1fHfLiaMi7/Rytp5xpS3nvZnzlLPlvEXHX7ds/V7k1abOCoPfnKoQkJ91n4pM6Zlb9sL88ZXI5B7OhL9nt+lvXZn/ePW/f+F83ZJxzmMvM4xbdmpPTTPutbsUAFxUjPP+u3/8ZO61+O970mTZtr9CWCoAEWPCxSIf3Jb7975fRUbWEVn7cdHWt2OpyAu1RDa5JHj3/OKcNy2fXogK6vbcOn9d5FGx09UP/xF5qaGzVXgoaKVWlZ3tPE985SyR+SOd58D6Oa1zvpXv5JZfezN6vY3IrMGnXpvlvi5faaOY5CT3+4aIaNUq5jYie/OHrfJ/M9bK2PmbZcpi9jEAoPhIhtspJp8W4RbtOv2MS0Qq1RZpeHFul0OVTyXFXWkLbe0GtHI+A9Nr16VKWyBpjU1fWK9x5UuX59bn8naTvDBzn3F2keR6cm+dMH82KHee3gS2xk3XFlSurJvaG78RmfOk89Gi6543XOS3Wd67d/r8IZF5I0SWTnR226pdm277IXeZPWucNz43znN53XqRGQOdN3Ct7ke1fHoB4O2iSbtHnXK1yFePu9/4X/QvkdWnxlMtrsVviHz7vMjnDzq7Wy2MJiz0s2e4tPhT+jln/1/uxQkAoPiW/y+wY1vv+01kx2KRDadaNXujPb1oF4Y7l+cmjXXok4Kk7hLZulDk+3/5Xhar9XZBsk/klkn9uV6KNf655W+XYV58ZZXBGz1nUBVPjQVeEE3Ma6UDpV2yuyaRXS1+Lff/u1eKrP/SeZ6h+8aVnkPpc7pObd3v2k37lu+cMd4XP72R23JeaVfxFm1Zr5UQ9cbj8jcLXs+Pr4lsWyRyaIfvNye3/+AcT91zf+34KW8lAcuySc7XzX7Ceez5OjSPHfRcVVvlAwgbbetVkS5n18wzf/HmU9eNItLj9e+l9/ifzP/X7UqVT1cWoQI3EEk0SbnyXfcGBD69LuvU63y4n2CdN+n9DK34Zt0n0Xsfeq9BzyP0HoXeZ9FGDsGg51x6v2bNdGeyVSvWWUPrFfi6T0R++dD5f3397y6NKA6lOIfc0ZbPFv18+jmt80Sl20iTvbrNtLKhNuxQ2kuP3kOzepQ8vE/k2+ec87RHJV2XJov1/pT2bqjn3lr2mYNEfnzduf11/+m52o8uFU497xtp5cHlk529IupnORqAbbx7tbMnJr1u0PM6TXTrNYeuX/er7l9Pum+1IuSyN50VLfVzWseP67mfbmezzd52fy5t56nPnOXRhfr7zu1ibQ/Lhm+cSX/XRjWIaKeVjZUBF+X2MGANeXIiy7nfN+1Pl6/X7pFjJ7hHCQAoQd2kjx07Vl555RXZu3evtG7dWt544w0599xz7StQmfIiCXVFKjovvuvEHir8NefdJ7JntUjZiu7dkGvXQN4S0a6aXuP+uoLUbiVyRhdnC2kd11NPkAvT/m7n+OTexnzUcdGtscx1HHGr63Xz+WuJ1L9AZPHrecdA15uansoluLcy0pZYegKtrW08ud4odk2Mays3b3T+nxvc5+lNZa0pKzEiwz320a+funen2vAiZ9em2sLrngXOeZrw1hufelLfqFtuS/Q1U50n/U2vzu1+1NLyRve/Z55qRbTte5GO/xSpdqqF23enbmy3+YcU2zdP5f6/ZnORji4twbzRCyf97E16iDS6LHf+54OdN6LPv8+5fwEg2uK3F3pra3XKIXlw2ip5rY+PvakEgt6w++h251AgZ3XN+7ze6Hr3OpFSZZxxqN8nzoTqBy5doOvNKY0zFWqINL8+7zr0RpuVQP7hVZG1H4m0utkZA/O7Sao38PIb4iSQrEpuBY3VXRDdNp4t21016ek+5nlh5r/ofPQ8l8iPtsK+9g33eavezf1/fhXeXG+wep7jjPKo5Phaa+dj+l6R56vnPwyOp/EX5R1iZum43P/rMaUt0bV7y31rRU4ed1aw8EYrBXomge/+TqTuOc6bvzrMjbfzEVfahb6ep+n76b7tPSW3e3dvtMKgnidVaSjS+Ym8Xdhr5cNP7hKpe67I3S4VHBa+LLL9R5HO/yfy5RCR68aJJLYSn+k+1dZGt7zn3qJLx2ovKu0VQm/a3nTqBrC/9Aa5nn97Hmt6U1mHJ+rylEjjQq4jgCiN3+qei8+Quy5sKL/tSZORXzkrJ90+eZmM/Uc7ST16QrJP5a9enbtBXv/WmbTr2qSWJJxWxs5iA8GjlQ61C+ozu4rc6lFJryBauU9f1+gKkb6nksUF0Xip8dGKxTP+6f1ejQ711+EuCSg9R/7YSy+JmrDt6ZJE9qT3oj6+0/l/vWfmi5n3iez82dmTojWsjcZ2rZRap63I21c75w330puOnnu5VsTUcz7X80Dt6dFziJ+67XOHIMrPlvkiXzxU8Lm0v/R8Ttd35ShnJUUdkuf7f5/qPUlEEluL/HOR99fq6/wpy1eP5v5fjzndjrVb5FYo+Oy+3Of1fmStZkX6SCVVpMRvSxUv8djq+eXxj9fIyh2HZPTNbeS6toVU7AYAIBqS4R988IEMGTJExo8fLx07dpTRo0fLFVdcIevXr5eaNfPWBA++mNyxqFOdNRIrlzom4y8vL/d+4xy/cfneLEnNdMjmQ1nStmZpidFW3o2vMNPq/VnSIN4hlcvFOFuDe96s9ObcQpKbphD1nC14zv2ns9V47Zbuz9ds6rxx5tna+6p/i9Q4O/eCQE+s29/lbDWkyetuHsnepeOdXa7qc+ffn1uLVFsQXf6i8yTZGgNdWzdpd+/ahfkZnUWqnpm3NZJrTVxvPFsl6Y1TbZGlrd4LqhGq449bXahrqkO7k9Vx1rWmq77etQW4Kceplt56AXcszdlFlna3rrRWqtY41i5YrW6f9KJGx0n3pLV9qzdy/t9b7VllVS5QWhlAT/itFvbWGOpq7zpnmbXSRcrPzue0BbvWpNXER73zREoXcCNHP4tu/wrVnTVsNdFfo4mzkoNZ/y/Oigu7ljsrZVjdk2qCxDUZfuLYqTLr3aQY5zoqVPP+nrrtNJGivRfocZ9+wFnjVy9w/KG1hysl5lQ4CaidK5wXsvr980a3lW5v/c6Uixdb6XGo3yVv48wCYS784rd3dauUN4+frdkd2mS4Jl31d1hjjLdkuCYNXbvg1liqy3rGZKUVtDR+e7JaL7uuQ4cR8be1kN00hnjeYPXsJj3QNH5qhT/X1tWuNOnqybXSoJ4j6NA2et6lsdrqHcZqQZVfEj8Q9LiyWil5U76ysyW6L7y1ht6zypkM13jqK9eee6xkeH6sCoN6fqTb0TMZri2WlN6c9lahQc85tRKIxlB/kuHast6TrsO1RwJ/FbdXiNWnEvOeyXA9z9VzJW1pRjIcJTR+W0qXipFSpzpVswyautLtbysRrv7Ym2auar74Zbc81aOZlCtTSA9ugD+09apW2LptpjORp0PbaIJaK9xrK2H93e7/hcjp7fJfh0m2zhYZ9LOzlfDEziKXPinS/lQityBWK9uM/blJ7sndRXr8W6SFSwU2HbZGY/MFg0WaXSvyv1PnohvnOCtFamMO7QFPey285X2RT+52xh79HNqYwerdT2NxQZUotXKalkF7g1FaYU3X+f6NIn2mOf+vw9howxG9T6MV2bTC6FyXIV2unyTS6qbCKwVa9zm0fNpaWWmrab2ncu8PIl+4DCXzr7Ny/+9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h7k+vi7T+t0jFBtnf//FlkfPvFUlIcH/w9tOrIjXbitS/0Pe6N84SWf25SLk6IsVKimya7f6+jme9a4XIakeluknXiZz7X5FydUXOvClr/sxHXQ9Er3jLtd4Np8YZ1y4w9b0K9V2tROyxq7VLcG1dlNsDMm0J/uNIkUaXiNRo4/6AcMGr7q1p7Jbh8190/Z39tEjxsiJn3+bq5lFbXWlXi7al7+X83VqZUNepeR8I3WZtNVWygusBXPVWIuf8X9Z7U/uLlKmW+3p07GvtJvLCB137YMErIpe9KlK3vffjQ/fj/JdceaT76LdPREpWdF/nsgkinR4RKVM1a97S8SKlKrm6i0z+TaTueSJt+rha02mXpe3/6+rqUvfZBfeLFCqce9qXvOvaxiY9s7rb1IfSemw6j9VAaTfyui/b9nO91pZ12mqr0cUiFRtmLefre/T4Sygs0vJaKRArJoqkHRQ5HIKHcrpNsx53PSDW86x8vcA+r8fimmkiF9yX954NVn4sUqhIweVjpLSSXDBSpN4FInXODXdqEAalE4tI8aKFJCEv1zBEGfa1G479+OTPfR8QZrtTsipg3DJhidSpUFIm3X6O1Cp/qjIHAAAhRmF4KGlXXI6uQO878V8pLUflksIeXSYVSRT5a2P22sVzns15/fpg0n4IPOlakX9PEWms3YjNFvnivyL//tT18Fa7I9Lu12+ZIZJUU2KKjmc1+d8i17wnUu/8cKcGABDDihctLMdOZMXpV77fIBVLFcv9gzrWnhb2aYuI7l6GQ9EC3zY3ipSukjVPx8bTXmNKVhJ56A/f656YS0U5Hc/am0Vvuf627p017+c3XX+1ENg5bMvq/4ns+937enTcydzoPc7c4SJ/zBH59yfu733/ZNb/57+Q9X+7FYm9DVoYroXgnl1dLs9l/GbtVlEFWhiuhdjOMc1XTnSlQR8mLh7rvRKjN/bY182uzOoVaFp/kYGO7iy1kFq3S4fMaXuzyLwRrvmlq7m6v/RGx2x3btNXg9zf1/RqYfjEa133lmf9x7XMntWuypUVTss97V/f5/r7VIrI1h+z5ms3qlpJIFh2d6R2Yfjfm1zHhxbc3/i/rOUO7hYpWyP75+18LKhCXL2nDhUdu1YrdNjnWedHAvv8nOdEfv/KVbGkdh67otXjUMVTYbi2oPvhWdfxf4/H2KOIG02rlw13EgAAgMOWvw7LVW8tdJu3ff8ReW/BVnmiV9OwpQsAENvoJj2U2t5iHjgWTcxquTEt3aPAVh96Fime9bpsAIXVOgaQ045TLaW0tYmOpaMPF9WaqSIHtrt3jxUr/trgerBod5sEAEA+uf2C06RbM/fWwH8fPh6asbJ9jb+tXQPmh+LlfL+Xly4pc6Kta21JtfO2rqQ6Wf9PLCMhl3EiuPd8stwrOnh7z3O8dM/uV4MZr925Dm2NHchnC7ol2bHU0IxJH8mc2xTMeWZ3R+s8nhA4HTMVccXKz+uJXrvs7rwjkcZe7SZax0cOhHaBrb0p5Acdgzl1t+/39VmGpjnzmueDdvnsrQts7SVFY96BHSLHD/pIQ4ZrG32mYY+r2/C0Q1lxW/PQrZtpS2T/FlePMc680tfHD2e91jR6dk/tZKVnT4uuT7dDJ80rTYPuS62QZtP/O++tbNqVtPZKk1Os0HNCnw/p+u37Gm1s4e36aJbxciw40+xMl26v5r9+zj73dBvsMdY980Pz2Pl5m25zbseA8zv/2uTKe90nzuNXK9d50t5e7B5f9F7JpO+E69izjys7H52f1+/QcczNe39n5WNO1xhn197Hj7jSl5kvPs4z7a5ej0HP/NC88vZ92kW985hTmhfmO3bl3IW9SUO66//2dutnPe9X7XPCk17/9Dqo6XKeL8HS/Nfvch5f+szP3ld6bDmPez2u9Pv1fLePO0Sdl2dtyPx/YpGsookvVu6SYydOHZ8AAIQYLcNDSbsTFZ1EKhRPkP3HvNwgn3G5yNrp2cf88ZRQyNWCZNqdrtc3f5N9Ge2CVFv+aJeoSrtR/O5xVwt19d4lrr/1O4r0c3yntrT69CaR6z4SqXOO+zr1JnvcRa5uWnt6tMYK1IxHRLbOF7ljHt00AQCizpl1yptp5prkcCcFAAD4qUGV0rLnYJpULeuohB4qr7d2FaIN2SER6c2zXAV8ashO/yqQ6ZAoYy90DfFx0WOhT5P2eqK90fx3kUiVM9zf0wK1V5r5t55PbhLZOFPk4e0ixZNc8/asFRntGIrEF+2RxNkjjZMWeo9s7OrVJXm1yN8bXXn3UkORBhdlLafPZ+yeWS5+Jmu+9h6j+Xf3MldB4UuNRBpe7FqfN9oTkPbAc+fPIlVPtYBc8LLID0Pdl9OKhFpA+OSpwsYXGrh6/hvkMVzNh1eKbP9Z5IoxWfO0sLdsdfdef2YOcf/c+Euzp237QpHpd4tcPFTkvHvc39u1NOv/+jzJpttr6zZcpHE3kTfOdPWw0+XJrPy48dQQPmMvcBXEa080TtpLkD6LsjkbkThpQbrzO22Vm7j+as8zTS/Pfozb69aeeV5uIlLjTJE/l4tUa+kacqZKU5G9a90/92ZbkRbXucaN37lE5PK3XD3I3PCxSJMe2Z/hqV/eFjnrlqwei9Z/I3LVOyKVG/s+z+z8fPAP1zA46gXXs0Wjz2eu4WVs2luinkuXj3LMO9X7U04VCvS4e/dikXMHiFz4gPd8tOk50eQykRsmus/X89WzUobnvgzEpOtdw/p4o71eau9WJcpnzZvxsMgfP4jsd/SkdedCkap+XkcQEbb97arMMem2c+ToiXT5ZtVu+d/yXabi+dh5m2Vg1xyOTQAAgkRheKRKqiWy7sus13pT7hxX0fnj0S5o3jzXdWPvacu87DfApibteu+F4fqDQKe8FoYvGpXV8qlQHsc5BAAgv+kDzH3rRWqdlfVAyxd9+FrFI4Z60gdnW38SqXeel8+vyhoDetvPIiccrTvmjnCNAa3dqGts1wfZRUvmnCb9jN2KIicbvss+z+5Zxuari3RvdPv03kNbvWo339rqxm4FvneNyOEAW6b5w1urKGVXDlTakqVIMZEdPro3P3lM5PevRU7r5GpVog/VPG38zvXwdsNM/9K1d13W/50VHzV/NM061vquZa5WQkp7udHXtjWf+1636Urdcj2QTfFRAKPjvzsfeqee6iFIt00fDLe6QSSxrPvxruNx6wNGzQdnixvnNmv6tev7HYtFytd17evTOmd1na7DAug6tCtqLaTQXo+01ZseGy2v871Nh5JdY9Xb9LPaUsmk81Thjbb68Ze2kNLzRQsudHztP1e67pG1RZeur8qph+Se9JzXFkb6GX97AdD7aO1Kvt6FIqUru7+nLb42zXI9ZHfSdGjvTXrv3qp31sNuvffWIY+qNXcfl16HGbB7edKKrJrHlXJ4MLjlR5Fytf0fl1wLSrSXqcKJIg27us4XTYvukxqt3Ydx8EWPuUJFXWkPBd1m/b2jrcS0lwzG+UaQ/tupoZnisqcBuyBcaYtPfwrD7ZbudsvSUNPrrPmeVP9jek5DtWgctQvD/W2l7yw482S3Jtdrrd0qWK/Zynl/4Oy9x/N77Xspu1WwxiNfheF2fjif3WjPfp48473GVm/LaUG4Z/pMvjoKw721xPbG3q5gexnUxh72dmnFgsz8cBR2akG4L85tLuURX232fZQn5/2rP8eFPvMy6fzN9dezIDxzXQdcBeHKbl3ubf2FimTvjcnupUfX4c95pkMs2fcHbmlI8X7fGWgvFfY5qHnlTyv8bT/l7Zz1h6+CcOc9u7O1vt4/a0t6p0jurQNeFS6UIJ0aV5aEhAQpWayIXNO2ttQoV0Le+GGTvLtgs9zTpaF5DwCAUKIwPL8V9hhb1O9gnuB9DEqvi556OO6tINyTdmH05UDX/7XGr37P+m9dD2Nv+UakvKP26QeXixQp4bqpv+EjkekDRZZPcHUHrz+4mvQU6eJjbEtffhgmsnaaSP8FrrHT/aEPlO2asmf0Cuz7AADw19L3XIWGvV4VqZhzbfR5n7wsDVu4xsP+dtVuee+nLVIoIUEuaFRJ7qolWQ9QdXp0j0hRj9YtH13takWhrcs8W+Zo6yXlrYWKL7tXZsV3X5Xs9EHc57dlf2/tFxK0CR6tYlT3F7P+/9l/XH+1IM9XIW6oOMdL1wfmjbq6Wr/YipbKeiirBeHacqfXa64CYW8PZj++wfW+r158PL3XLev/c59zf2/peJFZj7vP0wer9sNV5SwY96QPv78Y4How/NW93pd5u5P3ca/te0i9B3S2lPp5VNZY8dojkG3lR+7p0p6HnGO9q06PiHQanNUTUbHSrvHhtaWSFirb+exZIdPzwaU9xruyt0sfena4O3vLs9ys+kzky3tErn7XNU762x2z3tMC2yd8DEGgrbT0OwOhvTMtHiPS/i6RbsPc39NKIJOuE2l+jcglz2bN10qudkVRrRhx1dtZlWYmXu0qkLbHUNdWih9ekfVZPXb0Ox/2GDLJpt2Uvn+ZqyBZWyb6Q8dit/fPv6eINL7EVUAz6VrX/fb1H+W+DvuYy0uLMJt2TavbXL2163jXLl6f+EekEKN6AQDCKcACOeczPwrz8of2hnmSrrSjVdrJdNm877C0ru0+jFeHBpVMj2wb9hySYV+vk8cuY+xwAEBo8XQhv2nXRc2vzl5b1Ol8x8Nbf9Q8K/j0eLYa04Lp9V+7Wud41q7U2swbvhX5/VQLdS0IV8vGu2qS6kO5QM1/0TXut7da4b5sXZD1f62tDQBAfrDH1/NjbN/dVsXM/3/w8zZZsvUfWbxlv7z0Xdb4Z24teXzJacxBLeAKFS1o80fpU63Vc3PZq77fc46FrS08tfKbZ7eo+c1zPG5VrGT29zV92urZqVSV7PvgitF5S4/nuI7B0nT7WzjvSStNOvPFeS/mPNbs/1851lWw7Ssdni2Z7HnOsTI97y394WzxZbee84c9Hqm3fZ9Ti+9AC8Kd3+Htu+x5ztaZnss6WzhphRj1j6Og29t6tTKLL/Y1y7OXh5xoIbzn99l/wzH+pp0P+hsjp7FOgQBt339Evl+3R/7vQ0cXzwAAICye+3qdpBw9IX8ecPxmOCWxiKvn03kb9snhtNx/kwMAEAgKw/NJmVMNwhMSS4s07u56Ybcyc453o2p6dKOofnzJNbaQJx1fqOtTWa8XvuFfS6unklzTWx7dDWoLOH+M99LyS41sIvLyGSJT7xT56XXXdwyt4uq6yGaPe25YWeP8jD7f1ZWk3Vrmpcau7kxfayUy81HXWETOLk+90Qeub7QVeb2NyHM1RBaNcXXv+VYH1zq80W6pNN3agshJPzfqXNe6fHW9FUq/f+PaZmdeAQAQ6tYk3iri+aJdJvu1zqIBfH8Ay0YCb2NU+psvgcitK/5wSygcffsOADz8dchVyePHjT56hwgV/S3pSYd+yKnSW8DfccK1Tvv7PNftmQatKKPL629uXdZbGu2hRbytR//a36MVjpzzdZ12WvQ95/v2e5mvT2ZVDvRct7cKiPpZe326zZo+XadbZbvjWZVovOZV2qllvKxfP2d/v35P5nYcy7lSkjO/0718v36fXTFLK2FZGY5lndtqObbz+Kl9lEPFSd125z7S/2s37va+zZzvSL9+ly6XmQY/hwHJPL6OnzpmTnjPC190++18Mtt93D09zu00+yeHddv5Zh8D9j7zJz32fggFt3xNz9oeO23299n/93wvc54j3fZ55blfMvPf89g6dY55ni/O/e/rPHB7fdxxLvqZj3qs2eebPXyAJ51vV/6188tOs32smu+1stal//e1Ps/05jbPj4rMiBwnMlzXiMta1vC5zMa9h6TZkzNlb2oQlVYBAPCBbtLzyR2tEuXRH49JrTKFXK2szrlTpGoz15vnDXKN77P4rawPXPiQq0tP/dFUsqJrnMqcdBri6kbVszVTqOjDT2crGm9jBTlbtvw6SaRcnawb7v2O1iY6PuK1p1qV21afKojW7yiU6BpXUlut63iU2oro5zdd7/82RaR6q5y72HS2gtn9q+vmW7uo1Mmz60ql69d0a9e12o2mTcdM2rcua0wtZwuy/LB9oWubNa/sYwMAEBk0lmTk8qBIu53WFrGerU7tsSed3f/q5O3zOY096mw9Gk2clfR0bEV/x2H2jNV56Vb9wDaRgx694Thf22OZa3fMzjE2PYWqa3dv67EfUAcikDG0s6Vhp+9W5c6xRD3HFfX2QFnXo/vIOZ6nt3FMdWxt5/Gu44wf/DOXdG53rVsL453jWWq6TMHBMVeFBTv/NA06fqW9rJ5r3lo2a1qKlXI/5/wtpNJt067g9Xu0EoPdilvHAj38t+sBsLai13Xb7+mDWV/nt+4L+3Opf7qnR49NXz0JaB7a6dB8KFvDdZ1wXl90f9nXDh0jXq9Rzoq4Zj8luLdc15bY+jl7H2pe2WnRPC9e1pXn+v2a71pRp6RjTFNdRrdJxybWPPYcCkkrmWp6NS2qTHVXF6cZp7bfHvvXk14/7CGnzLGT7DounOO0a1r1GNA0aT7oA3Yde915vOvvBd0vOlyEpsMcOwlZLdB1W/L7vh9hV6RQPnZXrJWsddiHW2aI1G2fNf/5eiK1zha56fO8f8ec4SLzRrj+/2iyyPBaIs2uErl6XNbwHzpUhZNWWld1zxep2tQ1PMhDW1znk23dVyKf9Ml6/dNrIrOeEDnjXyLrpmdPR8vrRX5zDRNjzrWLnnAfmuPcASK7lrp+16s+n2WNCTz+VCMBHRJCh4vxxTmEhjc6/MfvX2X1YuKNboNOem0e5FH5/IehrsmTM/694qV73jnD3BsleHrWY4zr6Xe5/s58xDX5M9SMNzrshz2utfN7Kp/hfi/xg2NojtGO49COrf6wh0XR3gD1+qq9CAZiybisnkd0TO5Xm2dPt6/Xnn561TUFw9v+DZZzyBd7vbpv7f2rJjqeK+mxPzSrB6nMxiA2PT8837e9caZrrHl9huak55jzPMv83hzOIzX7GY/XT2f1AqmNQHKj91bD/Og1yrmMfRzq+WKfM9VauIZK1GuU3ejHeS3xxX4u6KT55+n9XiK9PxE53WPoKUSkkkULS63yJaRdfUcs8uGCF+bI+mdPxQ4AAOK1MHzUqFHy4osvSnJysrRq1UreeOMNadeunUSK08oVkmqlEmTsr8dl3o6T8sVVPSSx8Kkf4BXqu6ZVn7oewOpDtfoXuib7wZqvwnC7FVHd81x//8ql+8izb3f9IPHHR1dl/f/Mm1xjpwbC+RDU86ZWW4z7og88F/vofnTHItfk+fD624dckz7kc9JCeZ180Rq4zu1UX97r+qHW71R38L7MHSHy22SR/gu9PzCbeK3rgd0NE93naxefOo6jjqHazDH+oy8LXnGNLXr1O64HEz1fzj5WurYm13EVtZtaHbvdNvlG10O+G09VNtCW8j+/IXLHPJFSjoeW+UnHh9WxLm/OJT/Ve5eKlKsrcpWPhxgAYioWT5kyRR5//HHZunWrNGrUSJ5//nnp0cPPB4EF7Yu7MguUiicMkWOW+y3TO+k95Z1nfvb60dQpA6TsqZD/v/QLZOwL38qkYsOkkudz+Bfq55yGQFrihIp2pZ1XOpay88GyxkbPwrHcaC8xbvwstCxeztWdtD50dD549NY6xtdDNudy9rboNug9WDAF2Gqlx72Bv4qWdC8QnjlEgqaFGs6CDee9phY6OnsnUvY+8xz/XP36sWty8jZmvfN40uP92vdFpvTLOZ0rPnJNnrRQyZuLn3E97LVbBen42p7js6uXTvXQFIxXW3ifr/fyOnmj969jTt2ve9Ix6l88zX2eFtgsf997PjrzsFrLrEIRHf7As5cnHQvc8yF6btsy/W7XZ7SiqErdKfLhlVkFWJWbiNS7wP03hVbktenDbnv4pNrnitw60339b1/oXoFXe83692SRr+8TWXaqwux9v2dP18jTs/7vPHYG/CJS+XSRnctE3rlIpMsTIi1vyCq80vXrUE9Or7V0VUjW7dTfE1r52K6koveigxwFTXEgpuO3D6nHTsrwb9bJrgNH5ZzTKkrNcsVlT2qaXNmmppxIz5Cvf9st3ZtXl6SSQfSIoT2PeavEptfAP2aHZgOcQ4dp72h6zdPrj10YnlNlqW0LsgooNS47C8OdFXKU9tSmvBWEqz9OXReUVlDxrGSl555e43Ia7kIbBnhToYHI/lN5mRPn9cSzsuEFD2TFMTuvPCt5hZJWBlj4es5DWeSVsyDcSfMq814xwf97pdZ9vN+X6LVeh6qwBVoQbtPKSDnt27I1cx/ypcJp7sdRTs682XV/tuID38u0uFZk1RT/771O6+za/vL1XMMebp7nvRBW6bOahCIia3OIvU6elTW1wtrpPbL2ydovJGAa3/Rctu9JOj8uUjRR5LvHAl+X53q1spk+o/Km7X9EluXy3FDz0N6Xzt4v/1zpffnm12Q1nglEqCqwxqBIe36+5a/DkuGjQmrLWkmyaldK5uu0k0H+9gIAwIsI75/Ru08++UTuu+8+efLJJ2X58uUmmHfr1k327nU8yIsgv+/PkEPHvQT6zo+IdLjH1YrCSVtHtL9LpOcrIuf0d9WY1KntLSI1vHSp3uYm319+Wqes/+tDH1uTyyRf5dayPVStnAJ9aH8y+5g05uZ9t48bcSetJa+tuH390NVttmuoexb2a61q5wOMnPwyztWiTR9E6Oe2eSls0XSY9xa6z9fx3TfNynqtP6j0x2hBti5c/T+RrfP9W3b7z64KBgBiPhYvXLhQevfuLbfeequsWLFCrrjiCjOtXr1aIpLjuvlA4lS5qdBMub74IulaaKl0SMg5zXutrFaYn5zsJBus2rLLyqFCUtMrs/7f+VGRjg+LXPS4SBfHsCi2DgNFuj4t0u05kYu9tHrRile2Ho6Hwd5oC05P5Wq71nHl2657FKWFwP8alfO6/vWmK03edBzsamF2saPFkid9aKffq1O3ESIVG7q/3+ZG132RFrR50kK6zHS8IdLrDVfLNFvdDq6/RUqIXP2eK48v8dJzjK9KCPpQrkQ5kSvGuD5r6/Kk+zbqfml7s+/1trhepLqXVji1zxG53EelQN3mHiOz9oWTVozUVsLBdo3vWTighYv68NduKes5rI+/arUL7AF5XmmL33B0j+mtO31v9+n+0lZTnuOM51Yosmdt9vdNC3wfLc68aXSJ932TvMr9XtbzXvmvDd6PJWcl1sxlPXqysgsHdy3znqYaXs4TZ/q04rA69k/WfbHzN4GvAhRNp95jawUT50NznRdHYj5+52Ds/M3y1W+75fFpq+U/E5bKkM9Xyc+b/zZjij/8+Sr5alUuPVfAuxK5t+7zi/PZRTjWkVQ78M9oAXKdc0OzLm98xXnPIVzqne//Op33TU7e7rGckk71BOhTgkjVFrnvF92m3NZVqbH4TXvZa3mdq1KZp7K1Tn3/RTmvw1lpU3sPrH8qj7SXR703cqbfM+0trsvbcacF9a16Z59fupp/n6/e2lU57sy+WfNqt3PNd3L25mOz88w+h53ncuNLXeutn8O26TK5qd8xq5cXX5x5qvnpj1yPR0Ti83MdB3z273vl4DHv9+7adfqk286RamWz7rPv//TXAkwhACCWRWVh+Msvvyy333673HLLLdK0aVMZM2aMlCxZUt57L8CWzPnsyImsAvAfd6Z7v8G3H0A5ac1Lvams1MhVaK2F3TppjVRvD66beLQatlVp5ipYt3+I6cNc5w1p6xsDOzS0lXlu7GWc4yrl5Kt73VuR57Xmqi/fDhZ5toqrOzlnQbGzxfoIxw/W+S+IvNBAZMN3IovHijxTKatllnY3N+8FV9fuz9XMGo/dNuocken3ZM2fdOpmXlufj6gr8kpz94ed2vr7k76uwm1d3q4l/d2pB+6LRrnGQ9da+G+1dy1jd2X354qs79HW2DZ7nv3AdMz5IjM8WpO93Unki7td/1/9uavFlI6zrp/TbddW9G+28z32upp4nSs/7Lx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",
+      "text/plain": [
+       "<Figure size 2000x1200 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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71+82fD+rsV/7/7jjjrN69eq5z6LPCyD16bgUKSa3adMmYrx/+OGHrWXLli42dOzY0V5++WVfyl2RfPPNN3bllVfa/vvvb1WqVHH7pyjPPvus7bXXXm4ftW7d2p588smEx49ksXHjRteJrqSVJTov+uCDD2zAgAEufivOyf333+/Oa3faaSe3vfUefu7TSL788ks3594OO+zgOuNdc801Ll6Hy87Otv79+7u51qpXr24HHXRQsfOyAUguxIH0Es81YjzX2rFevyczxTbF3JJUgq9evdp9nxXrhgwZ4uK6tlm89SAlUdrr/njOsX/88Ud3vqIOiLo2V93DH3/8kaBPAsCvunIhfic34nd0xO+WFSd+a85woLxs3rw5sGXLlkAy+eeffwL6KRx88MGB++67LzB8+PBA3759A5mZmYEuXboEcnNzCyyvZY8++ujAiy++WOA2d+7cQDrIzs52t2QzcuRIt0/at28f2Geffdx+WLRoUcRl33vvvUBGRkaga9eubn9effXV7rWXX355geVycnIChx56aKBGjRqBu+++O/DUU08F2rZtG6hVq1Zg/vz5JSrnXXfd5cr2xx9/BMqTPkOfPn1K9NqDDjoo0Llz50KPt2jRIlCvXr3AcccdF6hcuXKJ11/Ub69169aBRo0aBR566KHAY489FmjWrFmgadOmgT///LPY15911lluP1900UWBoUOHuvJp219yySUFltP3RI83b97c/ab1b32fAKR2/Bb97rOysgrF5PHjxxda9tZbb807Rig29OzZ0/398ssvB9JBsu4jxcUqVaoE9t9//8Aee+zhtnk0w4YNc8+ffvrpbh+dd9557u8HH3wwofEj2ndJsbQ86VxBn0/bqCR22mmnQO/evQs9rnXuvPPOgWOPPbZU60/EPo1k1qxZgWrVqgX23XdfF79vu+029zvW+Ua4c845x52D3HTTTYGnn346cMghh7i/P/vsswR+IiA9EAdKFweSia5TN23a5O6TTazXiPFca8d6/R4PfW/69etX4teXxCOPPFJkPUVR3n//fffaiRMnlrgepKRKe90f6zn20qVLAw0aNAi0atUq8Pjjjwfuv//+QN26dQN77713UtZBARU5fsdbV078DiJ+E789xO/kRGM4Kjz9aL/44otCjw8cODDiwcyPg3Jpbd26NeUOTuFWr14dWLduXUxBSkFaB2R9bo8qWhWgf/zxx7zHXnnlFbee1157Le+xVatWBXbcccdAr169KkxjeMuWLV3AC7d48eK8E9zSrD8anfhqW33zzTd5j2n/VKpUKTBgwIAiX6vX6LV33HFHgcdvvPFGt5/nzJlT4MJi+fLl7t/Tpk2jMRxII7E2YP7222+uIj40fuv4dvjhh7sL723btgWSUbJeSMdjxYoVgY0bN7p/a/tHawTRMvXr1y8Uj9TYq328Zs2ahMSPdGoMV7yLdE7qnR+Vdv2l3afR9OjRI9C4cePA2rVr8x4bMWKEW88HH3yQ99jXX3/tHtN5X+hvQhfhahQHkBpSJQ6Uh/Xr1wdSXazXiPFca8d6/Z7OlenPP/+8e62uV0taD1JSpbnuj+cc+4orrghUr1498Ouvv+Y9pvo2fSZ1eAOQmnXlxO/UQPyOjvhdceI3adKTdL7h+fPn27nnnmt16tRxKSnuuOMOXTHa0qVLXfoQpRFRSsF///vfEdMJKiXF7rvv7lIHK53JLbfc4h4PNXLkSDvqqKOsUaNGbrm2bdu69AqR5i454YQT7PPPP7cDDzzQpU1QOuIXXnih1POgKOWKPu8XX3xhN9xwQ176jVNPPTViqoX333/fjjzySKtVq5bbBp06dbIxY8ZYaVStWtWlrAinMnhpICLZtGlT1NQwsVKaZ6WW6Nu3b6Hn1q1b57b1TTfd5P7esmWL3XnnnS69nL4X2k6HH364TZo0qcDrlHJD2/TRRx91c0e2atXK7d8ffvihRHOGe/OdvPrqqy7tZtOmTV25unXrZgsXLiz0+q+//tqOP/54q1u3riujUmw8/vjjVlraTtrvxdHn1E3pXCpXrpz3uFLz6Tf0v//9L+8x/VspRE877bS8x/QdVHqTt956q9BvJh6aM1zr0fdUc3lee+21Eb8vL730ktunSqeiz3jOOee433moBQsW2Omnn+5+89r22gdabu3ate557Z8NGzbY888/n5ciOJbUKN6+XbRokb377rt5r/XStrRo0aJEqU9jpe2v37BuHqU21ndL37eifPbZZ+5e2yGU/tZ+fuWVV/Ie0/df2w4oS8Tv8o/foXJyclzcjEbHdMVcxQKPyq/pIX777Tf76quv4p6rWTFGUzWEUzosrfupp55yf69Zs8bF8g4dOriUUvr8PXr0sDlz5kQ8Jo8dO9Zuv/1222WXXVwK6aI+VyrsI8VZxbji6HxGKcZC95H069fPxTjFqUTEj+IoRd+xxx7rtpVSc99zzz3uNxyeTkznWO3atXO/K33Gyy67zP76668Cy02fPt2tq0GDBm4bKP3YhRde6J5TrNU+EX2PvBgcS0pzb9+qXErF5r029LtQlmLdp5Ho+6w05zpO6nvmOf/8893vI3T/aT9XqlTJndN5tL0vuugi95sNP18CikOsJg5Eo2tnbatff/210HOaikJ1Bt4xXtchXkpK7zugNJiqHwilfaHj2s8//+yuj7Vde/fuXeI5R3WNrhS1utZVulKdI+hcQektw+m6U9/3PfbYw32nGjdu7K55VZbSivUaMdZr7Xiu30ti9OjRtueee7rtoOvuKVOmREzZq/is8mqfKr4/99xzhZZTyl89p22v+o4DDjgg7zei7X3zzTe7fyveh19bF0X7tk+fPu7f+k6HXs/HWg9SGqW57o/nHPv11193xzv9djzdu3d339PSnr8hvRG/k7uunPidj/hN/CZ+J7f8byqSytlnn+3m2dA81AoampNDP6Knn37aBeWHHnrIHRRUuaof2xFHHJFXOaf5PBSMdTDSOr777jv7z3/+404aQucUVjDXgUDL66D19ttvux+A1qGAFUqNnmeccYarfNKPXAcW/bh1MNI6Suvqq692ByOdmOhgowrGq666qkCDlk4GdIDT+ymgaW6RWbNm2YQJE+xf//pX3tyLuhVHFWt6v6KsWLHC3asCM5zK8t///tcd3LWNVWntlSEemjNNJxJvvPGG27cK0h7tKwUYr7FPlYfPPPOM9erVyy655BL7559/3HwsqmTVPGz77LNPoRM4BVF9DxQQ9P0pDX0XMzMz3XdOjbAK2joZUOO3R5WbOjgqWKvxVyehOkF655133N+iz6SyxyLSti+OvhOiwBZKFdpqRPae95bVvCb6XKF0Ijt8+HD3m1HjRUnoJEEntIMGDbKpU6faE0884U7AQk+M1blAJ+9a9uKLL3YntQrQ+j2rbPqOqxOE9rG2m34n2qYK9tqmmstEFwGaj0SvV7m9CmN1giiOvrt6rU7+tG1uvPFG97hXOR+rkuxTHWe+/fbbvEaBUPocH374oVtntBMH7+QrvGJNJzYyY8aMuD4DkCjE7/KP33qdLvp1r+cUJ7WddQHr0fupAkHbNfx44z2v+YtjpYstVTjoxF+fPZQ+u8qpC22vcVX7T3/r4koN6fo+6PW6eFR8CnXvvfe68wF9R3SsCz03SNV9VJr4re+q4rSeV+VXaeNHcZ0qNA/WwQcf7M5z9Pm9Oc/UKO5Rw7e2mTozaq5rdSpT5weVUZVWOr9btWqVHXPMMS6m3nrrrW6bavvrnE/0uH7LutjUuaBX2aBOhMXRcUPxW/N0HX300a4huSTKep9GouOatmf4ftb3XOey4edpusAObTQP/d3Onj3bVWIB8SJWEwfC6XpMjSKK616FqEeP6XjufabXXnvNbQcdv9XpWdfiuoZTxaGeC6Xjna7ldI6hCnvvWqWkdD2pOKWYoTKrorl///7umlUd7bxYpmvyjz/+2NUl6Dpc20PX6nPnzs27TtS6tGxxVOaSlDvWa+14rt/j9emnn7rvuWK16kRUh6Ptp32mhgnReZniviqA9btQfFbDkn6PqoO57rrr3HIjRoxw69Fv1evoru+h6kP0G9E+0WfSXJs6JnjXvbFcW992222uwl/bRecbOl+M5Xo+XFnv00hiPcdWHYbOjcL3s7fse++9l5DyIL0Rv5Ozrpz4XTzid3yI35ERvxPA76HpiJxi+dJLL817TGkJlJ5AKSZC59r466+/XIqC0BQImidT8xGEz6Pnzd0RmuLES1UWSnML7rbbboXmH9Brp0yZUiA9hub2U0rieGhdoeVVqmKtu3v37gXmG7n++utdipS///7b/a17zU2huY2VHjFU6Ou87VfcTeUojspUu3Ztt51Dad6MwYMHB9566y03z6HmftA6//vf/wZKQukg9fq33367wOPHH398gX2h70F4qnOVTfNFXnjhhYXmR1bZtZ/ideSRR7qbZ9KkSW59e+21V4H31xwRevy7777LK5/SbWvbhm+z0H3k7fNYbtEUlV7Ee27JkiWFnuvUqZOb78ajFCKh287z7rvvunVMmDAhEC/vO3jSSScVePzKK690j3vpu5XKRN9xzbMRSttT83x4j2tezfD0NJGUJo259lmkNOmxrr8k+9RL3XrPPfcUWt+QIUPccz/99FPU8rz++utuGR3zIh3r9LuMhDTpKCvEb3/it+Y46t+/v0vlpXmNVEYt17lz5wKpunSMC98+smHDBre81hMvpYMKjYOhqcKOOuqoAlM1hKc6V/zSfgg9BnrxVuWMtI9TdR+FKio9rp5TuSJp2LChmzs6EfEjGu+7o3nOQj+/vjtVq1bNm/5Ev1EtN3r06AKv1zlD6OPjxo2LmC4tVGnTmBeXQq649Zf1Po1E5zPhxyXPmWee6eY697Rr167Ab8nz/fffu3Xo+AjEg1gdRByITNMvaF7zSFMzvfDCC0Xu20GDBrnvUGj6SC+ulOQcwzsn0L1H1+jhZdH1uY6bmqPV89xzz7nlNA9ruND96X33irsVFaOKukaM9Vo7nuv3eHjlnz59et5j2j/VqlULnHrqqXmPXXTRRW7qjvC5avV9q1OnTt7+Pvnkk11cKkpp0qB6v9eizhuKW39Z79NIYj3H9q7DQ7+/nptvvtk9p3NmIBLid3LXlRO/8xG/g4jf8a2f+F1+GBmepDTKM7RnlnpfqKeSerd41NtLvU804sijnkzq0aEUI0rT7FEPOS91iZfmJHREpUb6KjWCRil98MEH7m+NOPUoLYxScnvUOyb8vUtDPfNC0zrovdQbR2lONEJGvaDUG0oja5QeI1To6zQyJpaRXcWlaXvggQfso48+cj2PtJ1DacRPKPVoU2+3//u//3M9AONNG6l9o15H6vGkHmBejyB9Zi9Fuvc90E3Um06jgnWv78bMmTMLrVdpteMd4VsUjX4KHZ3mfR/0HVCvLPUY0sgo7bfwbRa6j9SzTp+trHjpbdRzLJy+O6HpZrVstOVC11US4T1G1aNT3yf1mNJ3WiPDtP/UIzD0t6qR361bt3a/VX2nvN+hfpdKz5OoHmCJVJJ9Wtx+Cl0mEm0LpYPRb0TbRL9B9epTrzz13i3NvgNKg/hdvvFb2TdCqQe1RpLqWKDe1l52lbI43qsHsY71it9e72T12NZoby8bioS+r3r7Kn5r1Lr2Q6T4rVEFJU1BnYz7KFbaB9FGwatc3j4qbfwojnqVe7xe5hp5ovNCfZ/0W9VvTCOyQ3+rikPar/qtqoe5dy6kTC577723Gy2ebMp6n0ZS3P4L3XdleZ6Gio1YTRyINuJQo4iUitQb1aMYr/dR+t1In0/pX/Ve2u+qv9U1cWgKSdEItERRnNHoOo+2l0blhH5XlMpS9Qu6/gwXuj81ejKW7aS0vyUR6zE8nuv3eB1yyCEuPnu0b7QvNdJT52Qa9abtpWty7b/Q37WucTV1jc7VOnfu7I4JOk5MmzatQHrfZFLW+7Qs93NR6wI8xO/krCsnfheP+B0f4ndkxO/SozE8SYUfgBVs9QULTxutxzUvR+jcwkpLHa0RVGkNQht1lWpFcwCEp0sJD/Dh5RGlGQmfG7GkwtfvpTDx1u/NjeFVNhd1UCjtgUEBU2nPdTIVS+BTAFNF6eWXX+5SM8eTZlXUcKeGa81VoXSoOniooVQnXArooTQntOa++emnn9zzHqXhCBfpsWTYR0qhrltZ8U5uIs33rVQooSc/+ne05ULXVRJq0A6lEzIFa2/OEf1WFbDDl/N4lebaj5oj6LHHHnPBUSe/StfkzZOUDEqyT4vbT6HLRKLjoRondOKj34/ot6O0tko/H5oeGShPxG//4rdHUz9oCgqv8bKsjvfap97cZkpt7p1DKK6Hzq+ljk+PP/64qzRQp7HQ9FdKz5Yq8TuR+ygS7QNNDRJJaPwubfwoiuJ0+GdU5woJjd/6nWkewaJ+q6o0U3zSfOCqtNJ8YqeccoprKE+WC8Wy3qfJfJ6Gio1YTRyIRNOZ6LpLsVydknWtpgYUpS8Nna5hyZIlduedd9r48eML7SPt21A6J1Cq0ETRusLnhtT+VLpPj/anGmNC5++MRBXEZSnWY3g8cSFeka61Fdf1m9QUZYr76qSo9Ka6FfW7VjpbnVuq8UJzDiv1rmJ6WW/HePhRlkTt59BlgGiI38lZV078Lh7xOz7E77JXvYLGbxrDk5Q3Ari4xySYQSK/wlXzNqjhLBJvXj0dYFWBq15xWlaPq1FXo1ZVYaf1xPvepZGo9a9fv97dYnm/SCdB6lWnHnM9e/a0YcOGxfy+3nZds2aNlYQq6zXHjea2UEWpKta1bzSSyPPSSy+5ked6XvOgqBJWn0Oj4rwToFCJPhAlah+pt1D4SUY0GiUdL69Rdvny5YXmkdRj3twX3rJ6LJz3WPg8rqURftKj35ge0z6PtG1DG3PVAUL7/q233nJz6GiuE28u8kSenJVUSfap5nVSY0Bptr/mRPJGYepEVr1y9b1XQ5gaIQA/EL/9id+hdBxQI3NoTNbxXj3+Va7Q43Fpj/eK38qcormLNd+x4rf2T2iFjHrQq3FemWTUaK7jny7e1Hs9fH955U+kZNxHkWgfqaOALlpDG5pVsaLKLG8fJSJ+lIb2mcqnDmqReJ9d3zNlJ1CsVg92jSbRd0AxXY8lQ6etst6nxZ2nhdNjoftOy2qOskjLlfV+RnojVpds/ekeB/QadTxWLFdluo7VqjjXHLQelU+ZQXSOocpV7WPNt6hjla7Xwvetyhk+52ZpJPK7osrkWOanVLwqScyK9Vo7nuv3RPP2lzqaKzNPJBp9KRpVOm/ePJfxRXPxakSaOjqqYUUd35JBWe/TSGI9xy4u/nu/a6AoxO/krCsnfheP+J1YxO/Sa1xB4zeN4WlGo0/nzJnjgnd441soVcqpR4d6Q4X2NNOPIBl5aU7U8KVePNE8+uijMR3IlF7ZG+HjUYrlU0891aXZUQAtridWKC+tSUkrCo844gh3cFEvNo0s/+STT1yK11CqUFVPPo0aD9236rGYbPuoe/fuUZfTZ1TDQSxKclKgxgiZPn16gcD7+++/u7QoSjMUuuxnn33mgmjoSY6+C0q97Y0GKwn1PA0d3bdw4UL3Prvuumve9tLn0zKxvI9O3HVTT8wvv/zS9RrTSeh9993nni/q917WSrJPtb31ebSfwmn767teq1atYtenz61GcY8uUrSdi/oOAsmI+F3y+B1OqeKUJis0Jut4/8wzz7gRAeo4E3q88Z4vCXVQu+yyy9xxUObPn28DBgwoFL+7du1qzz77bIHH1ZM5fBRDRdlHxcVvTYXh0d86rnvPJyp+RKL30TldaFzWPpXQ+K2e5YrDsXRcOPjgg91NWUuUBah3794ubZvSPPoZu8tjn0aikSs6x9b+U3aX0MoydSoJfUz7XMc2pdgLHdVR2t8tUFLE6vSPA8rMduWVV7pKU8V2XROeeOKJec9/9913Li4oY5saBjxlOQ1YSfantoMyyRU1RYdShSrVbnFU33D33XfHXY5Yr7XjuX4vyTV5OO0/vb93nqjviiqgY7l+VMOJviO6KW4pE5Diu879NDrV77he1vs0kljPsXfZZRe3zSP9br/55htiOsoU8bts68qJ34lB/M5H/I6M+F16NIanGVUgqTFoxIgRhQ46Gr2pA5kOAF6PpNDGRo3sHDlypCUjpbDQQU6jYY877rgCc6GE9mAp6Two+uGrh5sqOtVTKFrlpnrqhDd4q9J98ODBrkI7dD6LeCi4nHHGGfbcc8+5ALJt27ZCKdJD95n3eXWAUuqeSKl5ytt+++3nGna1LdSrLnT+mNAyl/Wc4WoYVQ8/pUlRA4W33YYOHerKoO3s0b/VSKEOBt7jakBROh2dNJWmZ9OQIUPc99bz5JNPunul6REFXgVdnZBq1H9o4NX2Um9FjWxUBbCCfegJp04e9Z0JTVGi37UaVvxQ0n2qba65jRRQdWItOnFVZxDNBR5KUwNoOxT1XdcxTqMv1bGkV69eJfgkgH+I3/HHb6Vk0oVi+AWzRl9r3Xo/j+aXUtYI9QB+6qmn8t5fnYp0cu/NERcvxTodA1UxoPVp5IAayENpn4V37lKcUS/0oiot0uUcK1aas0+9ihWvQytR9LeO/zpPK0n8iJe+H0888UTe59ffqoxQ5Zn3W9X3SN8zjfoPpfM3jbrQ90IZS3QfGt+9C0UvfutziV/xuzzmDA+P30otqQoLnfsoZnu/3xdffNFtO6U5DN3PqrzTOZ23X7XtdLw76KCDCo1AAMoasTr944Cmt9BcnS+//LKL1SeccILbp55I+1b/1nQoyUKfQdNJKX7p3CdU6P4s6/kpY73Wjuf6PV6qK9GcoaqrkKVLl7psa/qee+/jTVmnxqTwVMOh9T8a3Rg6vY3O+VR5rExvOh/V78b7rvgV18t6n+o4plFgut72UkXHc46tba2GKO0HL4Z//PHHroEj/LsKJBLxu2zryonfiUH8zkf8joz4XXo0hqeZ8847z1XIav5q9VzTqBX1klFFlB5XikYFGwVM/fh1ENMBS5VPOilQOpNIaQ/8ptEgSkmjUTTqLaO5HTS3hnr2ab4I/SBLOg+KGrNVka1KS6UfV+AJ75l1yCGH5DVwvvnmm267qVJP20oN2Eq/oko8bVPP5MmT3UiwWHvtqPFbDaZaXo2dSuMRSoFcgUg98nQioXlHdYDSATyWdDdlTY2zCnjaNqrs1UhhHWT13fv+++/dd680c4brwO01KGsOH9HBWhXNumneds8jjzzi5tXW91wpbBUYtay+P6HbVYFZo7VUVqXaVocGBQH9ZsJ7TaqBX98zbXdvdFhRtJzKoECtIK5KX31vvdT3+l5pVLcaxNXzUo0nOonV68aNG+dO0HUiqBNCfTZVDqv3nSra9V1T8PfmyhZ1xNBINaVyUioTdUxQRXFpqFesfmOiEwTNZeONRNdn81LOlHSfqtemjjv6PuuzqrFB5d9pp53sxhtvLLCs9ptSn+t3FXpBo8+q34A6Dei3qBF9+g2HN45p/+ukRj0Uvc+mnoqiE+ZkmX8dFRfxO/74vWLFCtt3331d5xddhIm2kyo6dOzVyb1HU0ooLbnig45nKoviuXo868IjNG3ZqFGjXFxQpYeO/bHEb6XnUvzQ+URoZzAvft9zzz1unbqgUK90vWd5z9Xsxz4S9XBW3BKvN7EXSzT6QN99UeWKGpj79evnYp62pfaP4qd6bauCpSTxQ3N1f/rppzFlm9GFsFKnKdWaYqgukBVTlG7Pu5hWLNJvT5VOGsms36TeX73XVUGgChWdX2i76Tuh8zbFfJ1vqsza3l4lkT6zYphGLyjG6zPqQr64ef+Ko+2t7e7NczhlypS8ba7tre1eHvs0WvzW/tRvQY/rfEfxWOnjtS1DO7FoH+i7oHMlpV1U5xFtV503hWdaAMoDsTr944D2ka7htS4dt8M7qOt8Q8d0vac6tWnbKd1mouaHTQQ1erzwwgtu/lSN2FHq2A0bNrhrRW037/yopPNTxnq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ZmSlvv/22XHPNNXLrrbfK3r17zXgs5557rhmHrWPHjl7rHjt2rBnHRc8DHU9Zz5+S0HOxTJky5pzLyMgwY0r069dP5s+f715Gx3C84IILpH79+nLXXXeZMSB1TImvv/7avFb6nTTtgfC374s7RnpOqBNPPNFrWR1rslGjRu737WV1XBP9Xp50DJU333zT/GaOP/54CYWOF9KsWTMZNmyYzJs3T1599VXZvXu3vP/+++5ldEzPRx55xCx7yy23mLE2dEwd/T1r2vQcP3jwoDnGut/0d6L7VMfX0X2qY5mkpqaa8Uj085puPd7qmGOOKTaNeu7qZ3UME903Oga70jFhghHKMdXrzG+//WZ+0770e3z//fdmndWqVSt0m0rHZPGkY4+qxYsXu+fpvjz22GPNWES+21FLly41YyAB4UD8Lv34rZ/T37f+1fc0Tup+1rGMbLo9HbdJ96u/64C+r+MXB0rHYu7evbt8+umn5rt70u+u6dSxm5SOf6bHT1/rWEzbtm0z54N+ftWqVSY+eRo6dKi5H9BzRK91nvcGsXqMShK/9VzVOK3vX3vttSWOH0XJzc0142Cdcsop5j5Hv7895tmTTz7pXu7f//632Wc33nijGet6/fr18vrrr5s0/vzzz+b+bvv27XLOOeeYmPrAAw+Yfar7X+/5lM7X3/Ltt99u7gUvvfRSM1/H6SqOXjc0fus4XWeffbZcf/31EopwHtNA6XVN96fvcdbzXO9lfe/TiN+IBGK183HAHyfjgD6P3X///Sau33fffV7v6Ty9ntvf6bPPPjP7Qa/ftWrVMs/i+gy3efNm854nvd7ps5zeYzz//PPuZ5VQ6fOkximNGZrm//3vfzJ48GDzzNq7d293LNNn8unTp5u8BH0O1/2hz+orVqxwPyfqunTZ4miai0u3xnXf5QJ91g7m+T1Ys2bNMue5xmrNE9E8HN1/esx07Ful92Ua93WsTf1daHz+7rvvzO9R82AGDhxolnvrrbfMevS3qvtU81r0PNT8EP2N6DHR76Rjbeo1wb7HCeTZ+qGHHpJWrVqZ/aL3G3q/GMjzvK9wHtNABXqPrXkYem/ke5ztZb/99tuwpAfxjfjtfPyOtuc44nfhiN/E76IsSdT47XRpPLzZtVG0to1NWztpbSVtHeQ51oa2FNIaLZ61PrS2lbYC9m3hZI/d4dnFiW/NJ6VjC2pLYt8aavpZrUFl05ponq2WS1rbrVevXl6tbrTmm7YEsVu+6l+t3aZjG/u2rvX8XGG1eXwnTUdxNE1a29C3RZaOm6Etnb/88kszzqG29NJ1+qspGAjtDlI//9VXX3nNP//8872OhZ4Hvl2da9q0ttZNN93knme3NNK0+6sxGGrLcK0N6bl9HSNC5y9fvtydPq0FqfvWd595HqPCWjMF0sLJl7Ye0vPEc8wSu7aVv9ZCWvtQx7vxrDXlue98azVOmTLFCpZ9DmqtVE//+c9/zHy7+26ttahp13E2POn+1BZT9nxt0e/bPY0/JWm5bddoDXX9oRxTu7Xak08+WWjtNG1dWZjPP/+8QA1Tz2ud/i5tWgNTezLwZddi1M8AJUX8diZ+a23vwYMHm95LtEW1plGXO/XUU7266tJrnO/+UdnZ2WZ5XU+wtDsozzho0xrHntccrTntW6taY7UeB89roB1vNZ3+jnGsHqNA77G09remyx9tmaFjR4cjfhTGPnd0nDPP76/njtYyt7uV09+oLufb2kLvGTznT5w40W93aeFsuW3Xmg91/eE4psG2DNf7Gd/rkk1rm2vrBxvxG+FGrHY+DhR1zXA6DujwC9pSyN/QTO+//36Rx3bYsGHmHPLsPtKOK6HcYxTWssw3Lfp8rtdNHaPVs1WjLqfjsPryPJ72uVfcVFyM0t7itPcTHR/WU6DP2sE8vwfDTv+iRYvc8/T4aFq1lZ1nnoL2BOM7Vq2eb6mpqe7jra0CNS4VpSQtv+zfa1H3DcWtPxzHNNh8hUDvse0e3DzPX5u2ztX3QumZD4mB+O3Cc5x/xG/ityfid2D6JGj8pmV4lNJWnp41s7T2hdZU0totNq3tpbVPtMWRTWsyaY2O1q1byz///OOerzXk1IwZM6Rbt24FWlRqS99Dhw6ZVkpTp041r7XFqa1NmzZy+umnu19r7RjfbZeE1szTmjw23ZbWxtm4caNpIaO1oLQ2lLasqVixotdnPT+nLWMCadnl25rU1zPPPCM//PCDqXmk+9mTtvjxpDXatLbbgw8+aGoAFrduX3pstNaR1njSGmB2jSD9zlqb0fM80ElpbTptFax/9dz49ddfC6z3sssuC7qFb1G09ZNn6zT7fNBzQGtlaY0hbRmlx813n3keI61Zp9+tpMaPH29axmsNwJYtW7rn79+/3/zVmmO+9NzR2mGeyxa2nOe6QuFbY1RrdOr5pDWm9JzWlmF6/LRGoOdvVVt+6/fR36qeU/bvUH+X559/fthqgIVTKMe0uOPkuYw/ui+aNm1qfiO6T/Q3qLX6tFae1t71/GwkjzPgi/hduvFbe9/wpDWotSWpXgu0trXdu0okrgNag1iv9Rq/7drJWmNbW3vbvaEoz+1qbV+N39pqXY+Dv/itrQqCvZeI5mMU6D2WHoPCWsFruuxjVNL4URytVW6za5lryxNNs55P+lvV35i2yPb8rWoc0uOqv1WtYW5/N+3JpUOHDqa1eLQJ1zENRnHHj/iN0kCsdjYOFMbpOKAtDrUV0V9//eVu1aMxXrfTt29fv98vOzvbbEuPu+bf6jNxkyZNvNarLdDCReOMtq6z6f7SVjme58rnn39u8hf0+dOX5/HU1pOB7Kejjz660Pe0hZ32fKP7RFtqegr0Gh7M83uwunbtauKzTY+NHktt6an3ZNrqTfeXPpPr8fP8Xesz7oQJE8y92qmnnmquCXqdWLhwoWlxGo3CcUyDFa7jXNS6ABvxm+c4f4jf/hG/id9F2Z+g8ZvC8CjlewHWYKsnmG+30Tp/586d7td//vmn6Za6sEJQ7dbAs1BXu1r55ZdfCnSX4hvgfdOjtJsRLbQNB9/1212Y2OvXgKbszOaiLgolvTBowNRuz/VmKpDApwFMM0pvu+020zVzMN2sKi2404JrLdzV7lD14qEFpXrDpQHd03vvvScvvPCC/PHHH+Z9m3bD4cvfvGg4RtqFuk4l8dNPP5njowFOuxr3ZN/c2N1oe9KuUDxvfvT/hS3nua5QeBbQK70h02CtXRvZv1UN2L7L2exMcz2OgwYNkhdffNEER7351e6a9CbK8zfqpFCOaXHHyXMZf/R6qIUTeuOjvx+lvx3t1lbPCc/ukSN5nAFfxG/n4rdNh37QISjswstIXQf0mJ511lmm+zXt2ty+h9C4bnd5rbTi0yuvvGIyDbTSmGf3V9o9W6zE70jfY+kx0KFB/PGM3yWNH0XROO37HbVyhfKM3/o7q1OnTpG/Vc000/j0xBNPmEyrHj16yMUXX2wKyqPlQTGcv7tARct9GhIbsdr5WO2P03FAM4X1uUtjlVZK1mc1LUDR7ks9h2vYtGmTPProozJ58uQCx0iPrSe9J9CuQsNF1+WZIW4fT+3u06bHUwtjdNtF0QziktD7Gb3P0kqA2i2p77AvgV7Dg4kLwfL3rK1xXX+TOkSZxn2tpKjdm+pU1O9au7PVe0stvGjRooXpeldjekn3Yzg5kZZwHWfPZYDCEL95jvOH+B0c4jfxO5HjN4XhUcpuAVzcPOXqQSI/w1XHbdCCM3/scfX0AqsZuForTpfV+Vqoq61WNcNO1xPstksiXOvPysoyUyDb83cTpLXqtMZcnz59ZPTo0QFv196vu3btklBoENIxbjQIaUapZqzrsdGWRLYPP/zQtDzX93UcFM2E1e+hreLsGyBP4b4QhesYaW0h35uMwmgraV/Lli0zhcF6s6et/nxvEuxC2a1btxYYR1Ln2WNf2MvqPF/2PN8bgpLwvenR35jO02Pub996FuZqBQg99l9++aUZQ0fHOrHHIg/nzVmoQjmmOq6TFgaUZP/rmEh2K0y9kdVauXrea0GYFkJ4Hmcd4yTU7QDBIH47E7896XVAC5k9Y7JeB7TGv6bL83pc0uuAxm/tOUXHLtbxjjV+6/HxzJDRGvRaOK89yWihuV7/9OFNa6/7Hi87/eEUK/dYeoz0wVwfWj0LmjVjRTOz7GMUjvhREnrMNH1aQc0f+7vreab3KRqrtQa7tibRc0Bjus7zjPNOCefvLlCe92m+dJ7nsSN+I1KI1aGtP9LXDKfjgH5GKx5rLNfMdL1Wa8a5jkFr0/RpzyB6j6GZq3qMdbxFvVbp85rvsdV0+o65WRLhPFc0MzmQ8Sk1XvmLWbfeeqvp/UTjod260lOgz9rBPL+Hm328tKK59szjj7a+VNqqdPXq1eY761i82iJNKzpqwYpWfIsGJT2moQj0Hru4+G//roGiEL9DW3+8P8cRv/0jfhO/i1I/QeM3heFxRlufamGhBm/fwjdPmimnNTq0NpRnTTP9EUQju5sTLfjSWjyFef755wO6kGn3ynYLH5t2sXzJJZeYbnY0gBZXE8uT3a1JqBmFZ5xxhrm4aC02bVn+448/mi5ePWmGqtbk01bjnsdWayxG2zHq1atXocvpd9SCg0D43hTojel5551nbq70ZtRfANDCCLVo0SKvwPv333+bblG0myHPZbWVuQZRz5scPRe06227NVgotOapZ+u+tWvXmu00a9bMvb/0++kygWxHb9x10pqYc+fONbXG9Cb0qaeeMu8X9XuPtFCOqe5v/T56nHzp/tdzvVq1asWuT7+3Forb9LzQ/ex5Dupx1mubdtHjWStUt2O/DziN+B16/PalXcVpN1meMVl/52+//bZpEaAVZ8J1HdAKav/+97/NdVCtWbNGhgwZUiB+9+zZ0wzt4UlrMvu2YkjkeyzP+K1DYdj0tV7X7ffDFT/80e3oPZ1nXNZjqjzjt9Ys1zgcSMWFU045xUzaa4n2AtSvXz/TbZt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",
+      "text/plain": [
+       "<Figure size 2000x1200 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1200 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1200 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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hhESAa4ZHIilJJDXgmC5bQZJFrXvxfVZjGbH7PHnQmi0pSR7ZJNtdsqI+f0j9v3N1+MTVSXKKmpB/Flh7vGJNkbsXe5QbTRJ5sY1Iw7NELn8pfD8b54u8eZlI2Yoih3ar/dy1UGWjI8MMC9vfs0SkTDm1/ex/ivzwoioBq53a5jaIAn/pTLX2AfYH2vZTGXF+wBqiiJC/7TspcmDUOO4kkWtjPJfJ1ytD+01fKPlj7be6baTY8dqFIhWPE/lrPhg+YmX+KyLfDBe5/XuRaicW3nFJwYHS/3Y/v1qk+/Cibg0hsVGuqkjHoervXz8LfW5Xe4lS7vv0q0R+niJSprxI18Eis0aqzyNlkGZuCZVQ7/uhMjrnF9UbxpfxZrfnDyl09Pk7l48pTPZv9S5nT4jJ/u3qf/YVQkoGezfac3LJDa1FWSlbrVv4SU4nuWZrIIO6RlORpGSRXb+pAHTM6ZERjcAX/B7jA6dOPbxX/fb45kp/Yv6MZwQC3OH8rXW6SMYf4WsrogrKcY3VPp3jZix7hqA8jCMObBc54XT3bU5ortqHv8tXU+swVqwRyiJ38ucSkVN6ipQtH/55TiAwff0cVaUG8z9nBrUZvI3lvWBTgHySA/kR+B2y31EVB/Pi1EAJeq8g+GiBRMiAh03EPkYgWAHV87DmYY0mebfHGAvbQ+awPaDazPafVWJCStnQdvu2KTkllwmdN7LnkR1ernLe/WKZuconqOuB/eMap5QJl83mRaoqYFrjwDGbqzbDVuO8xjt+FalaJzxgUhPp/Hb+Gi4/Zz/A73B9qtcP/x36Ha4JzgHovuK8xm5geT3045ouiRr5BfpMapVgNUUSB+gLeL7o5xQhRCzHfBtriP+yJVPOOrlm/DuDHoHerFJbpFpg7XFUdzlyQD3D8XyPVM0Dz2gE1kN31GymnuN1Wqn7FftJrSxSJVBR1q0CVfUGah1kvKBXoT9w3B2/KFv78c2U7tu2Qs1HqtQVqXy8d5uwLOeJ7VXgPOz70IM4foU0dU5blivdDbsEjhNtWTKMT+y2/hSqbgv9ggS82i1Deg7PKsigdis1ZsBzPylFzfmhv3DcMqkiWwJl7eEvgD+izhkh/Wy3NXAdsFRsxTSROm2ULKrVDw9yw5gJ4zLs276OOarq3ZqZofaiDdgnvkOVH20HQLWYBh3Cz3PHKrWcLcYSWN7Oufwc2o9xEWz62NZc0xm/a3mtGlOiP1WqqZawBdDLCK4E8KHov4PXa5b6HwkDG35Q4xC9TrU5roH+z1PZx1LnjHEBxoVO0K9NIOfyVfNWENJtBeaSrpCB11gT42DIN9i+7XkTOdG/4ROpcJw6F/QXVDXCthhzYzyGMTDG45AZzgP9H/0ECaDop7oKY/nqqmKirqaM66GpfYY6B7PaopYz2oC+vu1nJVYcD2N8jHuwf7QD9wfuQ2yD46E9uH8hb/QjfV9h7In+pMfJbmA8mblZ7ad+B9VXEOCI+9jsU3s3RV42EfcqwPlWcrnf7fvYZTlHzH/Qv/GMQHUmnKO+h/W9A3kF388MJdD+sTB0DU9MV/cxxpNaBngGafAZxsYnnKreQ57oD7jG2oepz8+5jOLW5d79CvcqKmzh/mrQUckP/RVjbIyDnHObAoTO8Di4oll5Gb0kW3IlWcZvO0Wub/4Xqb/ug4I5mOkkx2QdDmiNnogD3PDoaHg5neF44OJG1jcz9oOHrC61ipsYN6F2hi+bHP7Q19tAceltoABM/DrCwcqPpNiAGxavWFn1SUjh7ftTZO5LIlcXw4j0PxYU/jGReYCBC5QSneGJgd3Pt4jMHUNnOCmhuBuYMq1KcnySS4UWLzCR9cq6QlWQj+6QgsFS+vqpWmoQed0kkVO6h76e9Zy7URpZ6H8sUpOH5pepQSwyuRBAZ/8+SS1z4gRLsWAyjsGz6Sj86HaRVdPVJLn9zcp4bJYmRZAbMNuCCX60iilYYmXFFKU7MA7BYBgGBkyOT+4mcu1bIaM8JqHImofBv+/7aokSjHP2bxM5696QI/6FViKtrxc55SI1oTj/YTXxQXu+eVqkdguR0y5X3816Rhm9T75QGbC/G6WM4ucNVZOqyX3UhP+cB0Sm3KLGZFe+ItKsp8j/Hle/6eTI5Mcx5o8TuXqCCsaAsfPMv6tJD6oLNb1QGQneuVYkfYBqn3Oys2iiSPv+6rqhekCX+9RkLhIor4+xXpdAxh7K90O+5z8iUqG6+gyyxbjlnEHKwIM+hSV4UBHp5AtC+8J4EfK98Mlwx0E8YHz6w2iRLveLLJogUq+dSPNL1OR+6SS78pLdn6BnIEsEfpx1jxQ4S98Jr7RUElj+gcje9eq6xQJKJmPCfOET6vphXDb7WZGOdxasY4SQggI6CLS6Tv1/5KC0m3+/iDwiWVJWOamRQd3xDmWgRn9H4DIMLqgYsuwd9Ts4y53P0lUz1LPq0hdEPhkY+hwGI8ynbnRZxmxsJ6ULLxkV/vnng0UWvx7+WZ/J4e+/GKqeibfPVQa4F1ur5x/0J47pBXQBntle43EYxSb0ELkryhwQS0R8MUSk739FmnZTn00ZoAxp4IzrRK6KIVs+EljKBUvBYY7cMhAg+M51Iht/EBm8UT37TaAXsHxc57tFLnpKZM1XSkf2fFakw62h7Ua3DRn3Lnk+NN7BeKW3Y4kZOPixzBz07pk3q6VqLh8j0ub60DZzRol8O0L9Dd0+9RaRS18UqddW9aczB4j0+qf6HsbOMWeKnNLDPdh80jVKzw/epIzS0fhimMpqv/0HldmPsczjDnvMv09Xjo6H1qvy/ugrCBC402UJPifvXa+M8Q+ui815Hi8YU2G8hSCCgVzbN27Q7+b8W+Tm/4nUP7OoW0NIsWDF5gxZtmlv/uxs3bfq2Vq9kci9y0I6RON83pq83Ek5dS94TGTmP5QTHPOqm75Uzlbsp0odkQdW5f0tnGHjzhI59RKRTQuUbh76p9IfJkO3KJs7dA2AwxzJaF5gWU444cwlWBt0FrnpM5FZz6pKdiYYV3ihg+TA+y4V5dJvFbn4WfU37ICQwQ1T1XwRz31No7NF1jsS3KCT8Wp8Tt7lQY5rEloqDuMDjBOaXRz6/u2rQ38PWqsCrVZ9Gt5GzLHxgmPTGdSMajFI4kFQmwa2fixn68X4s9X/V72qxkImqKiDOWO0ZU4QSDf5r+GfmdV8Jvb0/i3aZvp8tBMe1f68MKsMAox58XIukYPldjVIWjMrDHr5QGCXMcexWJ4X4xAT9G+Ty0ar8bNbkDkcu9oBHAvmebtdE+1LgkN2VMBhGwm3Pmqy4kP1igSCJWxZWt7t0jzfIvK+dECusxKkdrjr/ugG+rfnfneJrDWWUNwwx12mf/tY5KRz1XhSg2UGNRgrg4c2KPsRKmBiLAkb4obvvathgyVvqZcbsCPqdsDeZyYRn/2AsgsVEnSGx0HnE1Nl0i85svtwoPO3uUFEO8NhRI1l/a9YwIPWGVFkPqSMaPgwp/k3I5ThEg8ZPIDwMHYC5W0C4yciWRAlo7OpnEwfKNKuvzKqRQNG9UZdRH75RA0aECGEB279jmqiPW+seohqIwb4ZbpIw84i345UBm0MABB5AiM0HhKYxCFqDooO7YBhWwOjBQYgMBTXPEWdB6JKMFHt+mD4pG/mE8qQ2PtNFdH2zVPqZjYNv98+I3LuQ6E1V9EODFwQXdM2jpKziEDCeSNjHIZfGBY2zFXGbkRwwZCBCTIMmjDGINIJAwy097yHQxH6zskmJkz12ofLIL9BO+HQxvWDsRiRPuhPcAjEI4N4QNQ6zq1VHxU9COaNU0aS1n3Uezg+YJS3+1U+P7pgWPj6CeUIaRxB8ZQ08HxAlYnml6p+/sNLysFzxjVS4sBA7usnRU6/UkXD4nmECD44eNzuF0JMMGlDtKx2Bgb4tO7dck/5z0XWG5MbRH17gWhWp27VWeQ2VsH1f6CD26beJvLQutD30Gdu6IEswET8z8A64hgkmwNlJ27rk+sBLIDDGsZjJ27jDmeQnRvOsupmNCn0J5zi0KUA4wLt0IDjF05dr2uHtdjxAnAkI0IZMtQTRRhA8IzU5wXndFrDkIEBE3iMWfT795aFMgJhLL9xRqg0q9MZDgc7gCFfR+VruS57T2TQb+q6IQBg9nN5neEAgQtwhuN5B6dJ3bbR9bCeVGlnOMZdy95VegCTHoAxGvYH4wn0AYwwkOPKaSL3BaKDwYwHVTUjOPHdstxiAWvU4li4NugzyWVFHt2pKhHBUYJnOsaNyLDUk7DCcIZrp1pJYsrf1f+xOsPh/MO9hGALZL7AAYVrgUCXrjHug5DiiM56SWsoSXvWS7LkeK+3DecijNKmXkNwtzNgF5nGbjpLrw2esdm9Lcj0wW/QJsxVkZHizNaxj7km/FgwcgLMw/VyLjqQLFowMzLkMS+EoxdzNBzXBM9tBEUhEwfgbz0+wW8QVKYD3PH8RxsqnRCe1YJgN2QQ2XJJUtlcMLKZ2UkIYsJvcc72NbHUPBzywHxDGz5NA6iZQQVgP8F8D7YUnbGyfZU6P5ynlh3misjEQdaIuUycmcGD4DadFY+2o016PAEjHuaZAHYDZIZBdtjWHHPov7EN7A/676DsN+W1qeBYOA8Ye/V4AbpdO8PRdp1YYL/PUEZVfK6PBxmhr5roa2z2Tz2eQDYNghHtbOIkNU/HNjo7G+3BcXRWGpIp7POtofaL9sXqHNfnB3niNzgm2oXrpvsekjA0aAe2RR+ALQWZVmbWOLKx9H7s9ztVu8zMaHMbOGxg8HYGT+g5PAIunUZ65zawc+F6Qs7oa85qDia2XJO9KyO4Yd5vbmh54dhoD/o6zk8/V7yWPkQ/Rb8yMwjDjrtLzYMR/OO1TTzXGONkXD9k+nvZW3BMZIsiQNicV+k+h3NE1h+y/44F7Cs/9kNKBJVSQ/1tz8GjcvmYgMPlWNGZsF627kjoaqjaSbkr8LzWc0HgFVirjwv7tM7cNDNbNfgM95LGK6PSxLnUBjI+zWOGtcPYtxOd0Ym5meljCP7WOE8tA9i5vSptuZEn21lCjnD7+52hrFRkneosVY3OPjXPDWMBLUuv6l6w2ZjO8FhxOqSD7fT43AmyfBHMN/XWYz+uPudmvZR9PhZ6Pify2SD3a4znNgKjNV6Zy027qzGOmQkOHaXHRF5gTON1PeJxhGO8ZGa363EQsCsBGJnI0OVu/d5NvhgnObO+m1wg8nug2kA0bH+fFb2v+AHVqPS9GumedeKUhxcYI5jjjCMxHkNfB339zHmCOS9C4MwCRwAtgiQxd4Dc9LzGvFbOatqo+lSIFAtn+JgxY+S5556TrVu3SqtWrWT06NGSnh4hIroICTrCnTgd4XDuYpKLgb1buVBEpDuzrDGAxoAXnRkTHGekdDR0GddI6LIlGq+IDRMYTaH43Yzc5k0L8HD98mGlkFv/VU2CYOTF4B8R4IiUB6YzHIZaDN6dN89plykDBBRisHzFbJGhxk2HY3kB5x8yrwAmHd/9K1QOvte/1HsY0v8vULIMfPt0yBluRs7g4RGPIxhOSBiWUX7u+g9FptyqDPjptyh56AixOq1VlDoM8Ysnqs+wDR4aTjBphXG9bCWRYQW41hgyFeAwQUadMxKtoJzhCPaY97IaSP3lP+HLCmhn+IxB6oGJ0vz5ndGUsVEFKKz+PHomRUkCjg30KzyDcD98OUx9XhKd4Xgm4p5CX+n3icjHdykDHpw0uuQVKVKKtS5HFjBeNqGJaBJKGrU9I9wZDqemlzNYg+f0gldUoFf7m5ShVDuaYwUTNExCTCNrrGhjvdug3otYJgpu56hp2EUFojgjzouSSGXrY8WcQMD4GUlObkaMaLiVp9OTSzMiv6CIZYKkcZY/i2fiGiva2AJHAClYzKoNYRRQ0A5JGIq1PgfINjLIlRQZnX2lXJMyS8olZStHrnYsmkFhJs6AMG30RpC22zMcAVBuIFhrZJSKHc45q5kZhgyPSEF4XmAeiyBRzZWOefRzTUT6TQ/PJAMIBts0L5ThpOd6+N4skQgHNDJgdEU6VGlBcBoMrbBzAIxfRnkYm1v8RQWUefHWVaGl49r+TWUU6YAoZIR/90+RrwOBfhiL4AVD+W1GlosTBBM8iwzlZarCjXN8pcdLmIvjBR5YHb6OuranIHjLGfSHwAe3zC5k9EFOZkYLlpZ7ONAHR9ZXc34N1qnXoLyoF6YM3EA2lC5BCrsLMozvmKfKymKepAMHgc4ygg0ETgLYXe5ZGrlEsGbsWSHnBQL3kJyA/tXyGpUZpbEDI6qIPNNIvW96kchvX6q/71qs5u8IWEBbzh0icu5gkd+/UevPX/ZSyM6AsqfIOOv6kEqOwD2C/vrw9vCgAvBBP2VHcas0oJnxgMpwQ4UBJEfA5oY+EkmucK7f63DKRJpvI2Ox9yRV+cYJDMb/Pi3Uz+EkQQYaMh4jAWP1Mw3VvXf9f/N+jyBCVKfQQb/I2PTL2M7KyI37AdcPzpc+Rv/RoBSx2X+R6apLuU6/L1Q1Egkn/Y2MyHiB7Q7P1QadRG5yqWBFEkOXG9Q/rqKM+WtbqVyujPSbWAD2ODiNiDsIyIu27EmhkBR7e/3YTwqDyrWjO43jJR5bpw6udPvcOa9H1Ts3ULLcGQzlDLqMBMY1ekztRfk0kcOBID8n8CHpYEgnkK3ZVxHI6dV37YA8w8EKH4uz3+T3tfILgij83INOeXjuv6Z30F0sRFvGxU2O+A0SV4/luAVIkWuE9957T+6//34ZN26cdOjQQZ5//nnp3r27rF69Wk44IbAuUjG1qf65L1ccKzqFOHdoKBMLGafO8h2YiGDtbjOa5OJRKkIUkTzOtcTd1hb3A8qL+MHNEQ7gCNfl4wCcbnqdUhxLZxLBcG9GUKNsa3DfK0PRbFgrBOtOAEy44Ww3wY2kM8nd1ukysUu5VlcRq+bDFNmkOmrbzhZwPGixf+e+cSOjXAUcciiPozGjoIPHnRGKoMGkG/vXZTDg1DejaVDeA5MtM9gBEeeQDx7WyITDxBJOFh1Bjwg9rA+BCEMYk9MaqQkqInixDZw7buulYT8ILoBhHHJFhhnOC9GKmAgjQ10fy2vdex1tj5sFbcYaKbiutU4LZfQjmh+gT9RtrZQsjouIcGyjQdYE1rnB77V8cD0gI5TMMIHDQLcH+8J1wIMV9xT6HO4vKGz0RR2FD4cG5Ig1/nSbcUyU88A2yIqAsQzt0xN8yA8yXTtL9bVaLVQUKYJdUFoFkde6H+C61U8PRWHrdWzgcIMcMVlH1gmMI2iLXfq3uZowYoIHOaMvoa8gSAXr06Df4Xrgf0RG4fq7ZT3jOsARDKOQU07YF5Qp+pnun5CD2c/xXMLx0Ha0DUYHHX2P36GcEvoQIlPxt7MkEGSAzAydVYPnkx3Rn6KiY1G6T58fIjfxNyo34LxsuS8OyQnfm2vjoZ24Bri+uOa4hyE7nVGjM2QROKSvMwZ2uGbox3pdIVuuy9V7yBSgPeY6JLjX4NyKlPGI/eD5hPbrtfpIsdbl8fCvhVmyZk+O/JSlyl8+XuYN+WxxJbm7UgV542gfabetilx0/F5vXagn1n7KLcOA7rZGUCzAOD+5b+yOcH28YyHeNctjBUu0ROPV89XzEM8aMwBv/tjYj4MqM5AXMtg0T9UOj4rGsid4aZzOD7eoec1rKDOLiYKlnqmRQDveuCx8jafHPYy45uc/faCMxV8+KrJyqtJ9eEYjKhqR9eazFL+r1VJkW8CYC8cAssPxDIWjwXzmPt8yFHQHWaN87MwnwyeyeBbCqYRn9jWvq2x+RPwiwxgZ9aiGc/Fzahsd0NlrlMhnD+Y9p/Fdw4MzoZ+RBa95+kSlIxA4uWKqyFWvqJLqOpAQWUvoByj9j/P58XXlyMGkF4Z3yBd6COMhnVWFINSLnnCXMXQBljjAdTNL+kNe0EF6GSLomTcvE6l3psjNX4TWwMWYDGXSEEijgzCnP6DGD4iSx3FRmQU6SGe9Y40ulJKDoR+BmZD9oQyRns+oMVW0voxAVZQZRGldAEM8SgKawaYaOAGwxqGeh6BSAALJPrxZJP3vIqf2Uv0AzhNknZsVcjBuQn/rOVJVoMIyQYj+xlgSehnXGMGKyDqHkwTljZ2grWgzjnOdYVzXMoBTpGHAoO8ERotp96ixPPo7lh3A2Bkyu/YNNa7Q28ARgn6BcQbaA2M8+qpZqhjjl0/vV1VlWJK25OpzPPPsSmhq8t4oaYust+rIGquenJ60wT0Q3Y3ml4v88nH81UzyG+0UjhXcy2amuVeGiq1jkvJmy7uV+jRLrrotzaYz2RFAbHWM3kYEG+hnCcYsuC9tY5qV95xRlQzf62xdBKHjXnWCOSLmUCZuWUiQhZuh3C3Yzcw48kK3X2ew6XkfPsP56Gthyhh6yE6WCMgfOs/N8e1mMNZJFgiqd35uJmBg3qrtA3rcgXNHu1BRzQ20UdtdcGyrkbqP9DIyet4ZPE5SeBYfrosOnINODDuXPeEGc9O+YR/LCt2bmOOhnfr8Ma+15WlkwWP+ht/o/mpndZcJ2dXwnXa2Qw5IGNDHx3noc9DXDbLB/F4bZvXx7HPPDVR6zA3JVfdZ00ho7zfwOy0rfU44l2A/D8gQ+9Tng3vIPp+A3KD3TfDboPxzQ2N82MT0NQq22wrtF+gKjrptZvvMz8LOIycgS8uoUBCQDeSq26/3geM7+ysc9tq2AfuCRpdRdTs/HM+saBHW73Bdc0PjbV1JQv82eC6Ba6LbR0qmLndwXCVVBSA5SSTXYZsf+dkq+eH3XbJ+5wFpXreqNKlZSe7tdookY2NCCCGkJDnDR40aJQMGDJD+/fvb76GsP/30U5kwYYIMHhzIIi4kPt6sHIjlUrwVamqKyJHAuLr3Jwfl/YqtJb38H+FGYhinnLhN1OBo04Nhk8Mua2uahssgxoQyVtyct8cKDFFwOGLCkpPlvh4FGG9Eh79mlCbHoFpHZ2tHOMBnKE/u5L2+KkMNGdWRgPHPuV4byMoIzxLQRmBz/04QdY31xpz850Jjv4GJIRx/2qEMw5y5f+f6DpjcOQ0S/wms2wZuna3WAnEaZhCRi0kjgIPZLKdjrmtmMu7s8H6KdVkwofxlmnI+3zFX5JVACVWAYA0nWM8FUcfIHMCabiYoN4u26hJEyDCHsxvtwznBAT8s4DCCgcI8lgbreCC63pnd8NP7ob+9MjNAp7tCa+ghgwDLALS8VuTqV5Wh4tXzVKYCss9h9JnYI/z3MLau+K97WZu/TRM5qWt4P7jqtVCWNa4jItvPG6b6Pu6LIX+oSGt9/bCGYY8R6n5+9zqRHiOVARpZ8Z3vURHw2F7T/3N3AzGcYDD0Otd/Q2bDtLvybo9rYvZD9Nu/TFCG8znP560qAYM9giNgwHZbXwUygENpyCblIHm5o8pqwTkjwOCKcSIf3aYi8FH+Fs4VVBq4YozaF5wJXQer4zorHYxupxzUWF/WrQyzea0QGIK+AWOAzmB9ZJcKjMBaiMiE12s7gW6Pq1LJmjEd1D4irRUFBwwi3b3WBiTFTpdHI71OimzelyvLdii9/PEaGFZVFO0j2f1lw9oKckr9ejI+p7VUXJYjK7vtDQVrwbkGYy3WnkQlFL3MhnaGwwFnB7EYw6uWvUVWTVPPQKex2sxwQXALjIwV08LLnyEjBAYjbfDT6NLeyMxBEEukdZDipUk35Uh1OgxxPzl1Dspu4dmI+1CXk3UDAUQ4Dy2Dk85TxnKtA+FkxbgIDm+3sl1u+/YqxeWGW1l4r/JgfoCz8cQzQ+utRsNt/BcNHdGNKhk6W9E0+OP5a6Id4Ro80504gy0hZziYnQZPGHv1+vIwluLZCOAMx+c4HxhjzcpGCwOVXqJVKbKX9jEC8OCQhgEUz244NyFbOEDxHuM/7XjAWnFejhy0w1llycsZDhk4lycy5aWd4csDY5HNC5UMdBYaKj2hf+HZoJ3hi14LD8LUa5FpZzjGUZAZggbNKk0YP0Zzhut2wUFhR7wHHAJwhrg5w3U1BzyfAO5DzAfWfq0MznBSQwZoDwIGTWf4+u9V5Qzcp3CEA7OSE/o79qMzX92c4VjSyC0gV8sAzxUvZzjmFua9csVYVeEJgUlw1NkBbyvd7yeUvcO8ovzpoc+2Lgv01bPoDC8J+twriEs/CwPZEANSpsuw7AFyw5HBcnbyCtlrVZKnyk6U+snKSTUhu4f8nNtQ/ll2fHhyg1smw87VMiMnXabkdJEXy46RiknG3Law8SpdqvW/CeZdTrDGoybWErEIbnVzrGsnGYKf9D0dCexDr72OcfRnD6lAbDfwDH0iTc059HjAq0Tzk0a5ba/zxrzDDayR7kRn1kYCug3tcwYaOD9z8oTD+R0pUwoZy16/i/a5aSt6vVfkNmGJElNOWCIMjnM9Hrr1OzVvRTUBL376b0gGsCNozDksMO0Ops1EV3eA/JDRDLBcml633ezn5pImyPr3Auuqm7S7Ma8d6LtRebO1TZta676hsQ1A+xqfq9Yc1mA+q5ffGYaAtwwVYAUQzD2insgp3VVwHsYo5j2Mscw/jLLiWG9Yr7mJOS/sCMjWR9AHAtzO6B0ecDkgUMpV9ztn5QUE9en2Y3zy4Hql6831kQFKj/d+W9mlnCVWdaUHtBn9rcPtynYB2SPo0Fmy/J8nu8saIBhRl1GH3kYlC2TkY6knjLlOu1zZDrB00OANyrH+QksVtHKKUYEB2yOI79IX1DU0s+B0hQFSvHX5MaAt3tm5lny9SgURr92pxgC90xtIveoRljwghBBCipsz/MiRI7J48WIZMiQ0MUlOTpZu3brJ3Ll5jVJZWVn2S5ORoRwYmZmOso4+2bP/sORmHZSmlXLlwAF3h/jw9FzZsM+SFxer0pZbOt0jmfVTRP73D5E9v6uMcBhm9gUm6QcOi2RZap1GDLp1aXR83+pWkdOuVwM8C2sZHRTJKSfSsLsyoiEzp9E5as0/GAbhvNRZMQBZihc+pYyD8wKGug53iswfo/7u8awqhwDHEhxTJt2fEfkiUIb6sjEi0/Ral4iudJT0TCqj2udFi4DTdOH4vI7dM64T2bRQZaDuXBV+DiiNAefgtw6nucm+g0p+KeXCHe3rlog03a2+i6VMrD6HSOttIMOoeiORucYkUVO+nDpWJNAfnduUPT767yIZPXbvFMlwRA2DHUak/hYjWtt+vw43hfpbHxvv9+zI215cF2yTsUdtsyuKoWTDcrUd2uU8L3z+h8NotWeXSFJVtW3WgVC73H5vsnOL+r7i8YG2B651NCArfYy9ewPnlhFo8w71/o9VgX26tKHKKarNbsfC9vid+R22DR4v0MYdmwPnm6m+M6/f1vWBYweOsWubyP49gXbuzdsmtDnN5fm27keRbERx7xDJNh7je13a3rSHyG8u5cUgU7RlZ6C9JjiXzAOhNprfaxkgqAR/Y+KK95tXhaYruwLXb9d2kaqBc/rzN7W9bqM+Lsq4ms/wg4H7fPsf7tfhULbaPq2lug8OZIlsWx/aFnLERF23e6vx3c6t4cfaHzDsRdIhe/X1CZxvPlKlShVJilZypgQQry4vDH2ek3VQsg4fkgMH8pZ5PrGcyN0tRW76LK8j9GjSYcm1DsqhQ2o8gB6SqfX4CekquMbeEEt5XB/S57qPdXxQBXiYnzW9Sr3Au9c6DpgqkpymDKQnXiDS+WHlWJ8eMCCDMwYoJxr213mgynaEsUtns510iUhKeZFy9USWv+suEJSQMrOQnSD4RVePaP93VdrSzowx7sEcZGpUFDkX2aHTRZa8KdLxThUYoMFSHn86nNb6mdG+nyo/j+cc1ixufZvI1g3qnsb3F48IVZuY8y/l3ETJ0R8nqfGVCYJsUPnBWdb7wKHwNkNWbtkpfnAz9pnX+cybVNn8nPIhp6ETVHAxM4/j5XCOkufRwJqT0XCOnZxg3JmdkvdZq6+JZt8+kZQj4WMK8299jzjlb+7H+V1w34Gxga3za4Ybz9HnoOuwzeFckXrni7T6m8ivPgM/zOeL2Ravtjl/d9AhgyNJ4f3g4NG84y97/wH5mPuC7G2ZZYVvu/+Qt65xa2Mm+pMVGCsfCf8tZGb+Bv0H77Hk0779gTYHfrNfn0NW+D60bLxk5Pzcre2Qi/P8nTLwOme9jbl/fR1wDuUz825jgm0quB3zcL7q9NKqzwtalx/cuVlysyrLgWaXSDKCyswAqAYdg9nK1a2tck7uHPk2t61MlTb2ZwvqXSPV0rbZJSsf/1Z9NjT3VSmblKOcstAj1Zq69p1ncq+RdUfSZHVOdWmSHM2JHCU4vcsDKiN4yxL1P/QWzkMH+sLB5JaVXa6aSJUTQxVpENCJgBeMB5wg0Fd/XrddXj1sbodsT1QWcaP9AJF6bVSQLoJVUP3DGUiPcbaW2cndRdZ8IbGRJbIWFcosbzuDHrsnIQs0LXQcjKvMtUuLK3h+RtIlbvLUrHbJ1i8IsNxYmYoiCwKVdX52BFrCrgAnptd54PmbYszLN62Mz9bhZP2yyL9f8I6qxoIgtHj4ZXbe/VZAFb1AoIc5d9T377JP8/5mlSOQcvkMY165TeSAMU/OzFC69McpIk0uDHweZTmcHX+KHLVEVmFcg3nrOmVPwW/XLQ3te+3CvOOLVY4gsGXTQ99l7VX2nS1r855T1kGRjT8FPo+yZA3OpdNgta+Mz0VOvSTv/n6d634Nt28SSQtkfK+cqbaBneq3QEWbJR+FbI87tqhMeeho8Oea0D4XTxHJsUR+X6ium8mKL0Xa3iH5RWnV5YWhz7MPH5AjB1PkAOYUUcAcHKri3m4nyxn1VPDHP6avkA27wufu+zIzJTM5Sh/W4z5UFXAbo0c6v+AcJiu0D9yv5tzFax96nK3H3no7Nxuq3tatTeYYWmPuE0Bn2uNjFx2UEpgLQA7ONms7nnN/wWMb8wotA+d+wCGP37vNKb3mEWgDxgJ55LNPJMkx1vdqr/O45vwrVrzmO9HOw9zOtA8cC7ot0cYW0eZNWr5HcsPnsl7XDds4ZYzzMn8bqb3oj1HbG2Eb57HNa+L8LlJfKGN8h/Znu2wb7ZwOBe4/3KMVM+OzH8SDeR7O50Gsv4tnu/0u97EbuP+sCqH+b+4nO1fpZuB2r2FbyPeI8QyLJDPzeVMY+twqQjZv3gwpWD/88EPY54MGDbLS09PzbP/YY4/Z2/PFF1988cVXSX9lZGRYiUC8uhxQn/PFF1988ZUor9Kqz6nL+eKLL774SpRXadXlgPqcL7744osvKSX6vMjLpMcDItuw7okmNzdXdu/eLTVq1MiXCD5EvdWvX182bdokVasa68uRiFBu/qHs/EG5+YNyK16yQ7RaaaUg9Tn7uX8oO39Qbv6g3PxD2RUvuZVWfc65efGEcvMPZecPys0flJt/ODfPX6jPiyeUmz8oN39Qbv6h7ErW3LxIneE1a9aUlJQU2bYN692FwPvatWvn2b5cuXL2y6R6dWPdnXwCF4CdN34oN/9Qdv6g3PxBufmHsjt2XV5Y+pzXyj+UnT8oN39Qbv6h7PxBubnDuXliQbn5h7LzB+XmD8rNP5RdyZmbA14vf1Bu/qDc/EG5+YeyKxlyCyziUjSkpqZKu3btZObMmWERaHjfqVOnomwaIYQQQmKAupwQQggp+VCfE0IIISUb6nJCCCHEmyIvk45SLP369ZP27dtLenq6PP/883LgwAHp379/UTeNEEIIITFAXU4IIYSUfKjPCSGEkJINdTkhhBBSTJ3hvXv3lh07dsijjz4qW7duldatW8vnn38utWrVKvS2oCzMY489lqc8DIkM5eYfys4flJs/KDf/UHaRoS5PDCg7f1Bu/qDc/EPZ+YNyiw71ecmHcvMPZecPys0flJt/KLuSo8sBr5c/KDd/UG7+oNz8Q9mVLLklWZZlFeoRCSGEEEIIIYQQQgghhBBCCCGEkEReM5wQQgghhBBCCCGEEEIIIYQQQggpCOgMJ4QQQgghhBBCCCGEEEIIIYQQknDQGU4IIYQQQgghhBBCCCGEEEIIISThoDOcEEIIIYQQQgghhBBCCCGEEEJIwkFneIAxY8ZIo0aNpHz58tKhQwdZsGCBlGZGjBghZ555plSpUkVOOOEEueKKK2T16tVh2xw+fFjuvPNOqVGjhlSuXFmuvvpq2bZtW9g2GzdulF69eknFihXt/QwaNEiys7OltDBy5EhJSkqSe++9N/gZ5ebN5s2b5frrr7dlU6FCBWnZsqUsWrQo+L1lWfLoo49KnTp17O+7desmv/32W9g+du/eLX379pWqVatK9erV5eabb5b9+/dLopKTkyOPPPKING7c2JZJkyZN5Mknn7RlpaHcFLNnz5ZLL71U6tata9+XH330Udj3+SWn5cuXy9lnn23rk/r168uzzz5bKOdHFNTn4VCf5w/U57FDXe4P6vPYoC4vPVCfh6Auzx+oy+OD+jx+qMtjh/q8dEBdHg71ef5AfR471OX+oD5PYF1uEWvy5MlWamqqNWHCBOvnn3+2BgwYYFWvXt3atm2bVVrp3r27NXHiRGvFihXW0qVLrYsvvthq0KCBtX///uA2t912m1W/fn1r5syZ1qJFi6yOHTtanTt3Dn6fnZ1ttWjRwurWrZu1ZMkSa8aMGVbNmjWtIUOGWKWBBQsWWI0aNbLOOOMMa+DAgcHPKTd3du/ebTVs2NC68cYbrfnz51tr1661vvjiC2vNmjXBbUaOHGlVq1bN+uijj6xly5ZZl112mdW4cWPr0KFDwW169OhhtWrVypo3b5713XffWSeffLLVp08fK1EZPny4VaNGDWv69OnWunXrrA8++MCqXLmy9cILLwS3odwUuJeGDRtmTZkyBaMXa+rUqWHf54ecMjIyrFq1all9+/a1n5/vvvuuVaFCBWv8+PGFeq6lFerzvFCfHzvU57FDXe4f6vPYoC4vHVCfh0NdfuxQl8cH9bk/qMtjh/o88aEuzwv1+bFDfR471OWLL4t+AAAI20lEQVT+oT5PXF1OZ7hlWenp6dadd94ZfJ+Tk2PVrVvXGjFiRJG2qzixfft2u1PPmjXLfr93716rbNmy9sNA88svv9jbzJ07N3hDJCcnW1u3bg1uM3bsWKtq1apWVlaWlcjs27fPatq0qfXVV19ZXbt2DSpoys2bhx56yOrSpYvn97m5uVbt2rWt5557LvgZ5FmuXDn7QQhWrlxpy3LhwoXBbT777DMrKSnJ2rx5s5WI9OrVy7rpppvCPrvqqqtsJQEoN3ecSjq/5PTyyy9baWlpYfcq+nazZs0K6cxKN9Tn0aE+jw/q8/igLvcP9Xn8UJcnLtTnkaEujw/q8vihPvcHdbk/qM8TE+ry6FCfxwf1eXxQl/uH+jxxdXmpL5N+5MgRWbx4sZ2mr0lOTrbfz507t0jbVpzIyMiw/z/uuOPs/yGzo0ePhsnt1FNPlQYNGgTlhv9RfqNWrVrBbbp37y6ZmZny888/SyKD0iwovWLKB1Bu3kybNk3at28v11xzjV2upk2bNvLqq68Gv1+3bp1s3bo1THbVqlWzSy2ZskNJDexHg+1xT8+fP18Skc6dO8vMmTPl119/td8vW7ZM5syZIz179rTfU26xkV9ywjbnnHOOpKamht2/KH21Z8+eQj2n0gb1eWxQn8cH9Xl8UJf7h/r82KEuTwyoz6NDXR4f1OXxQ33uD+ry/IH6vORDXR4b1OfxQX0eH9Tl/qE+T1xdXkZKOTt37rTXATAfhgDvV61aVWTtKk7k5uba63CcddZZ0qJFC/szdGZ0QnRYp9zwnd7GTa76u0Rl8uTJ8uOPP8rChQvzfEe5ebN27VoZO3as3H///TJ06FBbfvfcc48tr379+gXP3U02puyg4E3KlCljDywTVXaDBw+2B28Y6KWkpNjPs+HDh9vrbQDKLTbyS074H2vKOPehv0tLSyvQ8yjNUJ9Hh/o8PqjP44e63D/U58cOdXliQH0eGery+KAu9wf1uT+oy/MH6vOSD3V5dKjP44P6PH6oy/1DfZ64urzUO8NJbJFXK1assCNgSGQ2bdokAwcOlK+++krKly9f1M0pcQNBRAI9/fTT9ntErKHfjRs3zlbSxJ33339fJk2aJO+8846cfvrpsnTpUntAXbduXcqNEBIG9XnsUJ/7g7rcP9TnhJBYoC6PHepy/1Cf+4O6nBASK9TnsUN97g/qcv9Qnycupb5Mes2aNe0Ij23btoV9jve1a9eW0s5dd90l06dPl2+++UZOPPHE4OeQDcre7N2711Nu+N9Nrvq7RASlWbZv3y5t27a1I1nwmjVrlrz44ov234hcodzcqVOnjpx22mlhnzVv3lw2btwYdu6R7lX8D/mbZGdny+7duxNWdoMGDbIj1q677jq75M8NN9wg9913n4wYMcL+nnKLjfySU2m9f4sD1OeRoT6PD+pzf1CX+4f6/NihLk8MqM+9oS6PD+py/1Cf+4O6PH+gPi/5UJdHhvo8PqjP/UFd7h/q88TV5aXeGY7SEO3atbPXATAjZ/C+U6dOUlrBuvdQzlOnTpWvv/46TzkCyKxs2bJhckOtfjxQtdzw/08//RTWqRHFVbVq1TwP40ThggsusM8ZEUP6hSgslNHQf1Nu7qA0EGRhgrU5GjZsaP+NPoiHnCk7lCzBGhKm7DD4wUBJg/6LexprUiQiBw8etNfSMMGkA+cMKLfYyC85YZvZs2fb6xWZ92+zZs1Yhq2AoT53h/rcH9Tn/qAu9w/1+bFDXZ4YUJ/nhbrcH9Tl/qE+9wd1ef5AfV7yoS53h/rcH9Tn/qAu9w/1eQLrcotYkydPtsqVK2e9/vrr1sqVK61bbrnFql69urV161artHL77bdb1apVs7799ltry5YtwdfBgweD29x2221WgwYNrK+//tpatGiR1alTJ/ulyc7Otlq0aGFddNFF1tKlS63PP//cOv74460hQ4ZYpYmuXbtaAwcODL6n3NxZsGCBVaZMGWv48OHWb7/9Zk2aNMmqWLGi9fbbbwe3GTlypH1vfvzxx9by5cutyy+/3GrcuLF16NCh4DY9evSw2rRpY82fP9+aM2eO1bRpU6tPnz5WotKvXz+rXr161vTp061169ZZU6ZMsWrWrGk9+OCDwW0oN8W+ffusJUuW2C+ov1GjRtl/b9iwId/ktHfvXqtWrVrWDTfcYK1YscLWL+jH48ePL5JzLm1Qn+eF+jz/oD6PDnW5f6jPY4O6vHRAfR4OdXn+QV0eG9Tn/qAujx3q88SHujwv1Of5B/V5dKjL/UN9nri6nM7wAKNHj7YfmqmpqVZ6ero1b948qzSDDuz2mjhxYnAbdNw77rjDSktLszvhlVdeaStxk/Xr11s9e/a0KlSoYD80HnjgAevo0aNWaVbQlJs3n3zyiT04wYD51FNPtV555ZWw73Nzc61HHnnEfghimwsuuMBavXp12Da7du2yH5qVK1e2qlatavXv399+OCcqmZmZdv/C86t8+fLWSSedZA0bNszKysoKbkO5Kb755hvX5xoGOfkpp2XLllldunSx94HBE5Q/KTyoz8OhPs8/qM9jg7rcH9TnsUFdXnqgPg9BXZ5/UJfHDvV5/FCXxw71eemAujwc6vP8g/o8NqjL/UF9nri6PAn/HFvSOyGEEEIIIYQQQgghhBBCCCGEEFK8KPVrhhNCCCGEEEIIIYQQQgghhBBCCEk86AwnhBBCCCGEEEIIIYQQQgghhBCScNAZTgghhBBCCCGEEEIIIYQQQgghJOGgM5wQQgghhBBCCCGEEEIIIYQQQkjCQWc4IYQQQgghhBBCCCGEEEIIIYSQhIPOcEIIIYQQQgghhBBCCCGEEEIIIQkHneGEEEIIIYQQQgghhBBCCCGEEEISDjrDCSGEEEIIIYQQQgghhBBCCCGEJBx0hhNCCCGEEEIIIYQQQgghhBBCCEk46AwnhBBCCCGEEEIIIYQQQgghhBCScNAZTgghhBBCCCGEEEIIIYQQQgghJOGgM5wQQgghhBBCCCGEEEIIIYQQQogkGv8P2AUnTUNhTSgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 2000x1200 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Original values\n",
+    "# Scenario 1: Signal-to-noise effects\n",
+    "config_scenario_1 = {\n",
+    "    'total_cell': [2000],\n",
+    "    # 'total_cell': [500, 1000, 2000, 5000, 10000, 25000],\n",
+    "    'nclones': [0.4],\n",
+    "    'p_outlier': [0.0],\n",
+    "    'p': [0.5],\n",
+    "    'cov': [0],\n",
+    "    'mean_inc': [25, 50, 100, 200],\n",
+    "    'var_inc': [100, 200, 500],\n",
+    "    'i': [0, 1, 2, 3, 4, 5],\n",
+    "}\n",
+    "\n",
+    "import sys\n",
+    "sys.path.append(\"../../\")\n",
+    "\n",
+    "from sklearn.model_selection import ParameterGrid\n",
+    "from dextrademixer.utils import DextramerSimulator\n",
+    "import hashlib\n",
+    "\n",
+    "params = ParameterGrid(config_scenario_1)[0]\n",
+    "total_cell = params['total_cell']\n",
+    "nclones = int(params['nclones'] * params['total_cell'])\n",
+    "p_outlier = params['p_outlier']\n",
+    "p = params['p']\n",
+    "cov = params['cov']\n",
+    "mean_inc = params['mean_inc']\n",
+    "var_inc = params['var_inc']\n",
+    "reps = params['i']\n",
+    "\n",
+    "\n",
+    "for rep in config_scenario_1['i']:\n",
+    "    fig = plt.figure(figsize=(5 * len(config_scenario_1['mean_inc']), 4 * len(config_scenario_1['var_inc']), ))\n",
+    "    i = 1\n",
+    "    \n",
+    "    for _, var_inc in enumerate(config_scenario_1['var_inc']):\n",
+    "        for _, mean_inc in enumerate(config_scenario_1['mean_inc']):\n",
+    "            params['mean_inc'] = mean_inc\n",
+    "            params['var_inc'] = var_inc\n",
+    "        \n",
+    "            sim_config = f'{total_cell}_{nclones}_{p_outlier}_{p}_{cov}_{mean_inc}_{var_inc}_{rep}'\n",
+    "            title = f'total_cell={total_cell}_nclones={nclones}_p_outlier={p_outlier}_p={p}_cov={cov}_mean_inc={mean_inc}_var_inc={var_inc}_i={rep}'\n",
+    "            # Use sim_hash to ensure reproducibility, but have variance in seed\n",
+    "            sim_hash = hashlib.sha256(sim_config.encode('utf-8')).digest()\n",
+    "            seed = int.from_bytes(sim_hash[:4], byteorder='little')\n",
+    "        \n",
+    "            \n",
+    "            sim = DextramerSimulator()\n",
+    "        \n",
+    "            mdata1 = sim.simulate_pmhc_data_from_distribution(total_cells=total_cell,\n",
+    "                                                            nof_clones=nclones,\n",
+    "                                                            p_binding_outlier=p_outlier,\n",
+    "                                                            binding_ratio=p,\n",
+    "                                                            mean_inc=mean_inc,\n",
+    "                                                            var_inc=var_inc,\n",
+    "                                                            simulate_neg_control=True,\n",
+    "                                                            use_clonotype_cov=cov,\n",
+    "                                                            plot_data=False,\n",
+    "                                                            rng_key=seed)\n",
+    "            x = mdata1['gex'].X[:, 0]\n",
+    "            x_neg_ctrl = mdata1['gex'][:, 'neg_control'].X\n",
+    "            hue = mdata1['airr'].obs['is_binder']\n",
+    "            x_log = np.log(x + 1)  # Transform to log scale, roughly normal distributed\n",
+    "            zscore = (x_log - x_log.mean()) / x_log.std()\n",
+    "            x_no_outliers = x[zscore < 4]\n",
+    "            hue_no_outliers = hue[zscore < 4]\n",
+    "            outlier_thr = x_no_outliers.max()\n",
+    "    \n",
+    "            # Best theoretical F1\n",
+    "            precision, recall, thresholds = precision_recall_curve(hue, x)\n",
+    "            f1 = 2 * precision * recall / (precision + recall + 1e-12)\n",
+    "            best_idx = np.argmax(f1)\n",
+    "            best_threshold = (thresholds[best_idx] if best_idx < len(thresholds) else scores.max() + 1e-6)\n",
+    "            best_f1 = f1[best_idx]\n",
+    "    \n",
+    "            plt.subplot(len(config_scenario_1['var_inc']), len(config_scenario_1['mean_inc']), i)\n",
+    "            i += 1\n",
+    "        \n",
+    "            # sns.histplot(x=x, stat='percent', element='step', binrange=(0, 100), hue=hue, legend=False, log_scale=False, discrete=True)\n",
+    "            sns.histplot(x=x, stat='percent', element='step', binrange=(0, 1000), hue=hue, legend=False, log_scale=False, discrete=True)\n",
+    "            # sns.histplot(x_neg_ctrl, discrete=True, binrange=(0, 50), stat='percent', element='step', label='neg_ctrl')\n",
+    "            # plt.axvline(best_threshold, c='r', ls=':')\n",
+    "            sns.despine()\n",
+    "            plt.title(f'{sim_config}\\nmean_inc={mean_inc}, var_inc={var_inc}, best_f1={best_f1:.2f}')\n",
+    "    plt.suptitle(f'Repetition {rep}')\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:dextrademixer]",
+   "language": "python",
+   "name": "conda-env-dextrademixer-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From b009fe5a4c775e7b6311d1659d297032af7f8f2d Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Tue, 14 Oct 2025 17:57:11 +0200
Subject: [PATCH 09/39] feature: new version of synthetic benchmark snakemake
 pipeline. 1. Optimized for HPC to avoid spawning many small jobs, instead
 uses multiprocessing to run in parallel. 2. Additional logging of figs and
 metrics 3. Changed folder structure depending on scenario and configuration
 in a yaml

---
 .gitignore                                    |   2 +-
 dextrademixer/utils/utils.py                  |  57 ++++-
 .../benchmarks/scenario1/config.yaml          |   8 +
 .../benchmarks/scenario_test/config.yaml      |   8 +
 .../synthetic_benchmark/run_beamt_mp.py       |  67 ++++++
 .../run_dextrademixer_mp.py                   | 199 ++++++++++++++++++
 .../synthetic_benchmark/simulate_scenarios.py |  74 +++++++
 .../slurm_run_simulation.sh                   |  36 ++--
 .../snakefile_run_simulation                  | 107 ----------
 .../snakefile_run_simulation.smk              |  88 ++++++++
 ...le_run_timing => snakefile_run_timing.smk} |   0
 11 files changed, 522 insertions(+), 124 deletions(-)
 create mode 100644 experiments/synthetic_benchmark/benchmarks/scenario1/config.yaml
 create mode 100644 experiments/synthetic_benchmark/benchmarks/scenario_test/config.yaml
 create mode 100644 experiments/synthetic_benchmark/run_beamt_mp.py
 create mode 100644 experiments/synthetic_benchmark/run_dextrademixer_mp.py
 create mode 100644 experiments/synthetic_benchmark/simulate_scenarios.py
 delete mode 100644 experiments/synthetic_benchmark/snakefile_run_simulation
 create mode 100644 experiments/synthetic_benchmark/snakefile_run_simulation.smk
 rename experiments/synthetic_benchmark/{snakefile_run_timing => snakefile_run_timing.smk} (100%)

diff --git a/.gitignore b/.gitignore
index 5e99b95..5b86d1f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -12,7 +12,7 @@ experiments/*/saved_models
 experiments/*/simulation
 *.db
 *pkl
-
+experiments/*/benchmarks
 # Editors
 .vscode/
 .idea/
diff --git a/dextrademixer/utils/utils.py b/dextrademixer/utils/utils.py
index 77d1e26..7a51e82 100644
--- a/dextrademixer/utils/utils.py
+++ b/dextrademixer/utils/utils.py
@@ -1,4 +1,5 @@
 import itertools
+import os
 from collections import defaultdict
 from typing import Any
 
@@ -7,11 +8,13 @@
 import numpy as np
 import optax
 import pandas as pd
+import scirpy as ir
+
 from jax import pure_callback
 from numpy import ndarray, dtype, bool_
 from scipy.stats import ortho_group, random_correlation
-
-import scirpy as ir
+from sklearn.metrics import (roc_auc_score, average_precision_score, f1_score, precision_score, recall_score,
+                             accuracy_score, )
 
 
 def gower_centering(distance_matrix):
@@ -314,3 +317,53 @@ def str_to_bool(s):
         setattr(args, key, str_to_bool(value))
 
     return args
+
+
+def get_slurm_cpu_count():
+    # Check for SLURM-provided variables
+    for var in ("SLURM_CPUS_PER_TASK", "SLURM_CPUS_ON_NODE", "SLURM_NTASKS", "SLURM_JOB_CPUS_PER_NODE"):
+        if var in os.environ:
+            value = os.environ[var]
+            # SLURM_JOB_CPUS_PER_NODE can be something like "4(x2)" meaning 2 nodes with 4 CPUs each
+            if "(" in value:
+                value = value.split("(")[0]
+            try:
+                return int(value)
+            except ValueError:
+                pass
+    # Fallback
+    try:
+        import multiprocessing
+        return multiprocessing.cpu_count()
+    except NotImplementedError:
+        return 1
+
+
+def guess_worker_mem_limit_mb(nworkers: int):
+    # If SLURM ressources are present
+    if "SLURM_MEM_PER_NODE" in os.environ:
+        return int(int(os.environ["SLURM_MEM_PER_NODE"]) * 0.95 // nworkers)
+    if "SLURM_MEM_PER_CPU" in os.environ:
+        return int(int(os.environ["SLURM_MEM_PER_CPU"]) * 0.95)
+    return None  # no good signal; skip limiting
+
+
+def init_worker(worker_mem_limit_mb=None):
+    if worker_mem_limit_mb is None:
+        return
+    try:
+        import resource
+        limit_bytes = int(worker_mem_limit_mb) * 1024 * 1024
+        # Address space cap → allocations above this raise MemoryError
+        resource.setrlimit(resource.RLIMIT_AS, (limit_bytes, limit_bytes))
+    except Exception:
+        # If we can't set it, just proceed; kernel OOM may still occur.
+        pass
+
+
+def calculate_metrics(y_true: np.ndarray, p_pred: np.ndarray, assignment: np.ndarray) -> dict:
+    results_dict = {'auroc': roc_auc_score(y_true, p_pred), 'aps': average_precision_score(y_true, p_pred),
+                    'f1': f1_score(y_true, assignment), 'precision': precision_score(y_true, assignment),
+                    'recall': recall_score(y_true, assignment), 'accuracy': accuracy_score(y_true, assignment)}
+
+    return results_dict
diff --git a/experiments/synthetic_benchmark/benchmarks/scenario1/config.yaml b/experiments/synthetic_benchmark/benchmarks/scenario1/config.yaml
new file mode 100644
index 0000000..36800b0
--- /dev/null
+++ b/experiments/synthetic_benchmark/benchmarks/scenario1/config.yaml
@@ -0,0 +1,8 @@
+total_cell: [500, 1000, 2000, 5000, 10000, 25000]
+frac_clone: [0.4]
+binding_ratio: [0.1]
+use_clonotype_cov: [False]
+p_binding_outlier: [0.0]
+mean_inc: [25, 50, 100, 200]
+var_inc: [100, 200, 500]
+i: [0, 1, 2, 3, 4]
diff --git a/experiments/synthetic_benchmark/benchmarks/scenario_test/config.yaml b/experiments/synthetic_benchmark/benchmarks/scenario_test/config.yaml
new file mode 100644
index 0000000..c54d6dc
--- /dev/null
+++ b/experiments/synthetic_benchmark/benchmarks/scenario_test/config.yaml
@@ -0,0 +1,8 @@
+total_cell: [500, 1000, 2000, 5000]
+frac_clone: [0.4]
+binding_ratio: [0.1]
+use_clonotype_cov: [False]
+p_binding_outlier: [0.0]
+mean_inc: [25, 50]
+var_inc: [100, 200]
+i: [0, 1, 2]
diff --git a/experiments/synthetic_benchmark/run_beamt_mp.py b/experiments/synthetic_benchmark/run_beamt_mp.py
new file mode 100644
index 0000000..c2baef7
--- /dev/null
+++ b/experiments/synthetic_benchmark/run_beamt_mp.py
@@ -0,0 +1,67 @@
+import argparse
+import os
+import warnings
+
+import pandas as pd
+import muon as mu
+
+from tqdm import tqdm
+
+import sys
+sys.path.append("../../")
+from dextrademixer.model import BEAMT
+from dextrademixer.utils import calculate_metrics
+
+
+def main(args):
+    input_dir = os.path.join('benchmarks', args.scenario, 'simulation')
+    input_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith('.h5mu')]
+    base_dir = os.path.join('benchmarks', args.scenario)
+
+    for f_in in tqdm(input_files, desc="Running inference"):
+        data_config = os.path.basename(f_in).replace('.h5mu', '')
+        config = "BEAMT" + '_' + data_config
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            mdata = mu.read(f_in)
+
+        true_binder = mdata.mod["airr"].obs["is_binder"]
+        y_true = mdata.mod["airr"].obs["is_binder"]
+        N = len(true_binder)
+
+        d = {"model": [], "thresh": [], "p": [], "assignment": [], "true_binder": []}
+
+        mixer = BEAMT()
+        mixer.preprocess_model_data(mdata, "pmhc1", neg_ctrl_key="neg_control")
+        mixer.fit()
+
+
+        results = []
+        posterior_params = [(0.01, None), (0.02, None), (0.05, None), (0.1, None), (0.2, None), (None, 0.5)]
+
+        for target_fdr, threshold in posterior_params:
+            p_pred, assignment = mixer.predict_posterior_class(target_fdr=target_fdr, threshold=threshold)
+            results_dict = {}
+            results_dict['config'] = config
+            results_dict['model_config'] = "BEAMT"
+            results_dict['data_config'] = data_config
+            results_dict.update({'sim_' + k: v for k, v in mdata['gex'].uns['sim_params'].items()})
+            results_dict.update({'posterior_target_fdr': target_fdr, 'posterior_threshold': threshold,
+                                 "posterior_config": f"{target_fdr}_{threshold}"})
+
+            results_dict.update(calculate_metrics(y_true, p_pred, assignment))
+            results.append(results_dict)
+
+        results = pd.DataFrame(results)
+        csv_dir = os.path.join(base_dir, 'csv')
+        os.makedirs(csv_dir, exist_ok=True)
+        results.to_csv(csv_dir + f"/{config}.csv")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run tool on all data from a scenario.")
+    # IO parameters
+    parser.add_argument("--scenario", type=str, default="scenario_test", help="Name of scenario")
+    args = parser.parse_args()
+    main(args)
diff --git a/experiments/synthetic_benchmark/run_dextrademixer_mp.py b/experiments/synthetic_benchmark/run_dextrademixer_mp.py
new file mode 100644
index 0000000..108d53b
--- /dev/null
+++ b/experiments/synthetic_benchmark/run_dextrademixer_mp.py
@@ -0,0 +1,199 @@
+import argparse
+import multiprocessing
+import os
+import warnings
+import pickle
+
+import pandas as pd
+import numpyro
+import muon as mu
+
+from tqdm import tqdm
+
+import sys
+sys.path.append("../../")
+from dextrademixer.model import DextraDemixer
+from dextrademixer.utils import (convert_str_to_bool_and_none, get_slurm_cpu_count, calculate_metrics,
+                                 guess_worker_mem_limit_mb, init_worker)
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(description="Run tool on all data from a scenario.")
+    # IO parameters
+    parser.add_argument("--scenario", type=str, default="scenario_test",
+                        help="Name of scenario")
+    parser.add_argument("--use_mp", type=str, default=True,
+                        help="Whether to use multiprocessing")
+
+    # Data parameters
+    parser.add_argument("--gex_key", type=str, default="gex",
+                        help="Key for modality where pMHC counts are stored")
+    parser.add_argument("--airr_key", type=str, default='airr',
+                        help="Key for modality where TCR data is stored")
+    parser.add_argument("--pmhc_key", type=str, default="pmhc1",
+                        help="Key for pMHC counts, expected to be in 'gex' modality")
+    parser.add_argument("--label_key", type=str, default="is_binder",
+                        help="Key for labels, expected to be in 'airr' modality")
+
+    # Model parameters
+    parser.add_argument("--model_type", default='mixturemodelkmeans', help="Model type",
+                        choices=["mixturemodelkmeans", "mixturemodel"])
+    parser.add_argument("--mode", type=str, default="C",
+                        help="Processing mode. 'I' -> independent concentration parameter for signal and noise data. "
+                             "'C' -> clonotype level concentration parameter", choices=["I", "C"])
+    parser.add_argument("--neg_ctrl_key", type=str, default=None,
+                        help="Key for negative control counts. If not None, enables model to use negative control data for "
+                             "fitting. Expected to be in 'gex' modality. If None, no negative control data is used.")
+    parser.add_argument("--ir_clone_key", type=str, default='clone_id',
+                        help="Key for clonotype information. If not None, enables model to have different "
+                             "mixture coefficients for each clonotype. Expected to be in 'airr' modality. ")
+    parser.add_argument("--alpha_model", type=str, default="kmeans",
+                        choices=["overdispersion", "kmeans"],
+                        help="Modeling of the alpha parameter. Options: 'overdispersion', 'kmeans'.")
+    parser.add_argument("--hyperprior", type=float, default=1e0,
+                        help="Prior for scale parameter of kmeans model and HalfCauchy for overdispersion model")
+    parser.add_argument("--outlier_threshold", type=float, default=4.0,
+                        help="Threshold for outlier removal based on log transformed z-score.")
+
+    # Posterior class assignment parameters
+    # parser.add_argument("--target_fdr", type=float, default=None,
+    #                     help="Target FDR for posterior class assignment. If None, uses threshold instead.")
+    # parser.add_argument("--threshold", type=float, default=None,
+    #                     help="Threshold for posterior class assignment. If None, uses target_fdr instead.")
+
+    # Optimization parameters
+    parser.add_argument("--seed", type=int, default=42, help="Random seed")
+    parser.add_argument("--maxiter", type=int, default=50, help="Number of iterations for optimization")
+    parser.add_argument("--lr", type=float, default=1e-2, help="Learning rate")
+    return parser.parse_args()
+
+
+def parse_data_config_from_name(filename):
+    data_config_dict = {}
+    parts = filename.replace('.h5mu', '').split('_')
+    data_config_dict['total_cell'] = int(parts[0])
+    data_config_dict['nof_clones'] = int(parts[1])
+    data_config_dict['p_binding_outlier'] = float(parts[2])
+    data_config_dict['binding_ratio'] = float(parts[3])
+    data_config_dict['use_clonotype_cov'] = parts[4] == 'True'
+    data_config_dict['mean_inc'] = int(parts[5])
+    data_config_dict['var_inc'] = int(parts[6])
+    data_config_dict['i'] = int(parts[7])
+    return data_config_dict
+
+
+def run_inference_star(t):
+    """ Helper for multiprocessing; unpack args """
+    f, args = t
+    run_inference(f, args)
+
+
+def run_inference(f_in, args):
+    model_config = (f"{args.model_type}_{args.mode}_{args.neg_ctrl_key}_{args.ir_clone_key}_"
+                    f"{args.alpha_model}_{args.hyperprior}_{args.lr}")
+    data_config = os.path.basename(f_in).replace('.h5mu', '')
+    config = model_config + '_' + data_config
+
+    base_dir = os.path.join('benchmarks', args.scenario)
+    if os.path.exists(os.path.join(base_dir, 'csv', f"{config}.csv")):
+        print(f"Results for {config} already exist, skipping...")
+        return
+
+    numpyro.set_host_device_count(1)
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        mdata = mu.read(f_in)
+
+    mixer = DextraDemixer(model_type=args.model_type, mode=args.mode, alpha_model=args.alpha_model)
+    mixer.preprocess_model_data(mdata, pmhc_key=args.pmhc_key, gex_key=args.gex_key, neg_ctrl_key=args.neg_ctrl_key,
+                                ir_clone_key=args.ir_clone_key, outlier_threshold=args.outlier_threshold, )
+
+    mixer.model._model_config["overdispersion_scale_prior"] = args.hyperprior
+    mixer.model._model_config["var_hyperprior"] = args.hyperprior
+    opt_params = {"maxiter": args.maxiter, "nof_inits": 10, "adam": {"init_value": args.lr, }, }
+    mixer.fit_svi(svi_config=opt_params, nof_inits=opt_params["nof_inits"], rng_key=args.seed, return_loss=True)
+
+    # Save model
+    save_model_dir = os.path.join(base_dir, 'saved_models')
+    os.makedirs(save_model_dir, exist_ok=True)
+    with open(os.path.join(save_model_dir, f"{config}.pkl"), "wb") as f:
+        pickle.dump(mixer, f)
+
+    # Log metrics
+    y_true = mdata.mod[args.airr_key].obs[args.label_key].astype(int).values
+    posterior_samples = mixer.get_posterior_samples(num_samples=1000, seed=args.seed)
+
+    results = []
+    # Predict posterior class with different methods, (target_fdr, threshold, quantile, cred_intvl)
+    posterior_params = [
+        (0.01, None, None, None), (0.02, None, None, None), (0.05, None, None, None), (0.10, None, None, None),
+        (0.20, None, None, None), (None, 0.50, None, None), (0.05, None, 0.50, None), (0.05, None, 0.40, None),
+        (0.05, None, 0.30, None), (0.05, None, 0.20, None), (0.05, None, 0.10, None), (0.05, None, None, 0.50),
+        (0.05, None, None, 0.60), (0.05, None, None, 0.70), (0.05, None, None, 0.80), (0.05, None, None, 0.90),]
+
+    for target_fdr, threshold, quantile, cred_intvl in posterior_params:
+        p_pred, assignment = mixer.predict_posterior_class(target_fdr=target_fdr, threshold=threshold,
+                                                          quantile=quantile, cred_intvl=cred_intvl)
+        posterior_config = f"{target_fdr}_{threshold}_{quantile}_{cred_intvl}"
+        # Plot results
+        os.makedirs(os.path.join(base_dir, 'figs', config), exist_ok=True)
+        sim_params = mdata[args.gex_key].uns['sim_params']
+        additional_text = (f"p_binder: {sim_params['binding_ratio']}\n"
+                           f"q_noise: {sim_params['mean_non_binder']:.2f}, "
+                           f"alpha_noise: {sim_params['concentration_non_binder']:.2f}\n"
+                           f"q_signal: {sim_params['mean_pos']:.2f}, "
+                           f"alpha_signal: {sim_params['concentration_pos']:.2f}")
+        mixer.plot_results(assignment, p_pred, y_true if args.label_key is not None else None, args.seed,
+                           config + '/' + posterior_config, additional_text=additional_text,
+                           save_dir=os.path.join(base_dir, 'figs'), show=False)
+
+        results_dict = {'config': config, 'model_config': model_config, 'data_config': data_config,
+                        'posterior_target_fdr': target_fdr, 'posterior_threshold': threshold,
+                        'posterior_quantile': quantile, 'posterior_cred_intvl': cred_intvl,
+                        "posterior_config": posterior_config,
+                        "posterior_q0": posterior_samples["q"][0],
+                        "posterior_q1": posterior_samples["q"][1],
+                        "posterior_w0": posterior_samples["w_mean_over_cells"][0],
+                        "posterior_w1": posterior_samples["w_mean_over_cells"][1],
+                        "posterior_alpha0": posterior_samples["alpha_mean_over_cells"][0],
+                        "posterior_alpha1": posterior_samples["alpha_mean_over_cells"][1],
+                        }
+        results_dict.update({'model_'+k: v for k, v in vars(args).items()})
+        results_dict.update({'sim_'+k: v for k, v in mdata[args.gex_key].uns['sim_params'].items()})
+        results_dict.update(calculate_metrics(y_true, p_pred, assignment))
+        results.append(results_dict)
+
+    results = pd.DataFrame(results)
+    csv_dir = os.path.join(base_dir, 'csv')
+    os.makedirs(csv_dir, exist_ok=True)
+    results.to_csv(csv_dir + f"/{config}.csv")
+
+
+def main():
+    args = parse_arguments()
+    args = convert_str_to_bool_and_none(args)
+    input_dir = os.path.join('benchmarks', args.scenario, 'simulation')
+    input_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith('.h5mu')]
+
+    if args.use_mp:
+        nworkers = get_slurm_cpu_count()
+        mem_per_worker_mb = guess_worker_mem_limit_mb(nworkers)
+
+        ctx = multiprocessing.get_context("spawn")
+        with ctx.Pool(nworkers, initializer=init_worker, initargs=(mem_per_worker_mb,), maxtasksperchild=1,) as pool:
+            try:
+                list(tqdm(pool.imap_unordered(run_inference_star, [(f, args) for f in input_files]),
+                          total=len(input_files), desc="Running inference"))
+            except Exception as e:
+                # ensures .finished is NOT written and SLURM sees non-zero
+                print(f"ERROR: worker failed (likely OOM): {e}", file=sys.stderr)
+                sys.exit(1)
+    else:
+        for f in tqdm(input_files, desc="Running inference"):
+            run_inference_star((f, args))
+
+    print("DONE!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/synthetic_benchmark/simulate_scenarios.py b/experiments/synthetic_benchmark/simulate_scenarios.py
new file mode 100644
index 0000000..12c24ae
--- /dev/null
+++ b/experiments/synthetic_benchmark/simulate_scenarios.py
@@ -0,0 +1,74 @@
+import sys
+import hashlib
+import argparse
+import os
+import yaml
+
+sys.path.append("../../")
+
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+from sklearn.model_selection import ParameterGrid
+
+from dextrademixer.utils import DextramerSimulator, convert_str_to_bool_and_none
+
+import warnings
+warnings.filterwarnings(
+    "ignore",
+    message=".*pull obs/var columns from individual modalities.*",
+    category=FutureWarning,
+)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--scenario", type=str, help="Scenario name", default="scenario_test")
+
+    args = parser.parse_args()
+    args = convert_str_to_bool_and_none(args)
+
+    save_path = os.path.join('benchmarks', args.scenario, 'simulation')
+    os.makedirs(save_path, exist_ok=True)
+
+    with open(os.path.join('benchmarks', args.scenario, 'config.yaml'), "r") as f:
+        sim_config = yaml.safe_load(f)
+    param_grid = ParameterGrid(sim_config)
+    print("Number of parameter sets: ", len(param_grid))
+
+    for params in tqdm(param_grid):
+        if 'nof_clones' not in params and 'frac_clone' in params:
+            params['nof_clones'] = int(params['frac_clone'] * params['total_cell'])
+        params = argparse.Namespace(**params)
+        sim_config = (f"{params.total_cell}_{params.nof_clones}_{params.p_binding_outlier}_{params.binding_ratio}_"
+                      f"{params.use_clonotype_cov}_{params.mean_inc}_{params.var_inc}_{params.i}")
+        if os.path.exists(os.path.join(save_path, f"{sim_config}.h5mu")):
+            print(f"Simulation with config {sim_config} already exists, skipping...")
+            continue
+
+        # Use sim_hash to ensure reproducibility, but have variance in seed
+        sim_hash = hashlib.sha256(sim_config.encode('utf-8')).digest()
+        seed = int.from_bytes(sim_hash[:4], byteorder='little')
+
+        plt.ioff()
+        sim = DextramerSimulator()
+        mdata, fig = sim.simulate_pmhc_data_from_distribution(total_cells=params.total_cell,
+                                                              nof_clones=params.nof_clones,
+                                                              p_binding_outlier=params.p_binding_outlier,
+                                                              binding_ratio=params.binding_ratio,
+                                                              mean_inc=params.mean_inc,
+                                                              var_inc=params.var_inc,
+                                                              simulate_neg_control=True,
+                                                              use_clonotype_cov=params.use_clonotype_cov,
+                                                              plot_data=True,
+                                                              rng_key=seed)
+
+        mdata.write(os.path.join(save_path, f"{sim_config}.h5mu"))
+        fig.savefig(os.path.join(save_path, f"{sim_config}.pdf"))
+        plt.close()
+
+    print(f"Finished simulations for scenario {args.scenario}.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/synthetic_benchmark/slurm_run_simulation.sh b/experiments/synthetic_benchmark/slurm_run_simulation.sh
index a43e882..89c2436 100755
--- a/experiments/synthetic_benchmark/slurm_run_simulation.sh
+++ b/experiments/synthetic_benchmark/slurm_run_simulation.sh
@@ -1,32 +1,40 @@
 #!/bin/bash
+# Usage:
+#   bash slurm_run_smk.sh                # cpu_normal
+#   bash slurm_run_smk.sh --preemptible  # cpu_preemptible
 
 
 PATH_BASE="$(dirname "$(realpath "$0")")/../.."
-PATH_LOGS="${PATH_BASE}/logs/slurm_logs"
+PATH_LOGS="."
 
-mkdir -p "${PATH_LOGS}"
-mkdir -p "${PATH_BASE}/logs/jobs"
+PROFILE_DIR="${PATH_BASE}/experiments/.slurm"
+[[ "${1-}" == "--preemptible" ]] && PROFILE_DIR="${PATH_BASE}/experiments/.slurm_preemptible"
+
+
+mkdir -p "${PATH_LOGS}/logs/slurm_logs"
+mkdir -p "${PATH_LOGS}/logs/jobs"
 
 DATE_TIME=$(date +"%Y-%m-%d_%H-%M-%S")
-JOB_NAME="SKM_simSyn_${DATE_TIME}"
+JOB_NAME="SKM_dex_benchmark_${DATE_TIME}"
 
 echo "#!/bin/bash
 #SBATCH -J ${JOB_NAME}
-#SBATCH -o ${PATH_BASE}/logs/slurm_logs/${JOB_NAME}.out
-#SBATCH -e ${PATH_BASE}/logs/slurm_logs/${JOB_NAME}.err
+#SBATCH -o ${PATH_LOGS}/logs/slurm_logs/${JOB_NAME}.out
+#SBATCH -e ${PATH_LOGS}/logs/slurm_logs/${JOB_NAME}.err
 #SBATCH -p cpu_p
-#SBATCH -t 10-00:00:00
-#SBATCH -c 8 
+#SBATCH -t 1-00:00:00
+#SBATCH -c 4
 #SBATCH --mem=24G
-#SBATCH --qos=cpu_long
-#SBATCH --nice=10000
+#SBATCH --qos=cpu_normal
+#SBATCH --nice=0
 
 source ~/.bash_profile
-conda activate dextraDemixer 
+conda activate dextrademixer
 cd ${PATH_BASE}/experiments/synthetic_benchmark
 
-snakemake aggregate_results -s snakefile_run_simulation -c8 --profile ${PATH_BASE}/experiments/.slurm/ --cluster-status ${PATH_BASE}/experiments/.slurm/status.py --conda-frontend conda
+snakemake -s snakefile_run_simulation.smk --unlock
+snakemake -s snakefile_run_simulation.smk -c4 --profile ${PROFILE_DIR} --cluster-status ${PROFILE_DIR}/status.py --conda-frontend conda -p
 
 
-" > ${PATH_BASE}/logs/jobs/${JOB_NAME}.cmd
-sbatch ${PATH_BASE}/logs/jobs/${JOB_NAME}.cmd
+" > ${PATH_LOGS}/logs/jobs/${JOB_NAME}.cmd
+sbatch ${PATH_LOGS}/logs/jobs/${JOB_NAME}.cmd
diff --git a/experiments/synthetic_benchmark/snakefile_run_simulation b/experiments/synthetic_benchmark/snakefile_run_simulation
deleted file mode 100644
index 8745e3a..0000000
--- a/experiments/synthetic_benchmark/snakefile_run_simulation
+++ /dev/null
@@ -1,107 +0,0 @@
-from snakemake.utils import min_version
-min_version("6.0")
-
-
-# Configuration
-tools = ["dextramixer", "dextramixerkmeans", "beamt"]
-reps = 5
-
-# Scenario 1: Signal-to-noise effects
-config_scenario_1 = {
-    "cell_list": [500, 1000, 2000, 5000, 1000, 25000],
-    "frac_clone_list": [0.4],
-    "p_list": [0.1],
-    "cov_list": [None],
-    "p_outlier_list": [0.0],
-    "mean_fold_increase": [1, 2, 5, 10, 20, 50],
-    "var_fold_increase": [1.2, 2, 5, 10],
-}
-
-# Scenario 2: Specificity distribution
-config_scenario_2 = {
-    "cell_list": [5000],
-    "frac_clone_list": [0.4],
-    "p_list": [0.0, 0.001, 0.1, 0.25, 0.5, 0.75, 1.0],
-    "cov_list": [None],
-    "p_outlier_list": [0.0],
-    "mean_fold_increase": [2, 5, 10],
-    "var_fold_increase": [2, 5],
-}
-
-
-# Scenario 3: outlier influnce 
-config_scenario_3 = { 
-    "cell_list": [5000],
-    "frac_clone_list": [0.4],
-    "p_list":  [0.1],
-    "cov_list": [None],
-    "p_outlier_list": [0.01, 0.1, 0.15, 0.2],
-    "mean_fold_increase": [5, 20], 
-    "var_fold_increase": [2, 10],
-}
-
-
-def aggregate_simulation_by_scenario(scenario):
-    sim_name = [f"ncell{cell}_nclone{int(clone*cell)}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}"
-                for cell in scenario["cell_list"]
-                for clone in scenario["frac_clone_list"]
-                for po in scenario["p_outlier_list"]
-                for p in scenario["p_list"]
-                for cov in scenario["cov_list"]
-                for mean in scenario["mean_fold_increase"]
-                for var in scenario["var_fold_increase"]
-                for i in range(reps)]
-    return sim_name
-
-
-def aggregate_all_simulations():
-    simulations = []
-    scenarios = [config_scenario_1, config_scenario_2, config_scenario_3]
-    for scenario in scenarios:
-        simulations += aggregate_simulation_by_scenario(scenario)
-    return simulations
-
-
-rule all:
-    input:
-        "results/aggregated_results_simulation.csv"
-
-rule run_simulation:
-    priority: 30
-    input:
-        # No input files required for this script
-    output:
-        h5mu = "simulation/sim_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.h5mu",
-        pdf = "simulation/sim_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.pdf"
-    params:
-        reps = reps
-    conda:
-        "environment.yaml"
-    shell:
-        "python create_data_mean_variance_fold_increase.py {output.h5mu} {output.pdf} "
-        "{wildcards.cell} {wildcards.clone} {wildcards.po} {wildcards.p} {wildcards.cov} "
-        "{wildcards.mean} {wildcards.var} {params.reps}"
-
-rule run_tool:
-    priority: 20
-    input:
-        "simulation/sim_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.h5mu"
-    output:
-        "prediction/{tool}_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.pred.csv"
-    conda:
-        "environment.yaml"
-    shell:
-        "python run_{wildcards.tool}.py {input} {output}"
-
-rule aggregate_results:
-    priority: 10
-    input:
-        expand("prediction/{tool}_{sim_name}.pred.csv",
-               tool=tools,
-               sim_name=aggregate_all_simulations())
-    output:
-        "results/aggregated_results.csv"
-    conda:
-        "environment.yaml"
-    shell:
-        "python aggregate_results.py prediction {output}"
diff --git a/experiments/synthetic_benchmark/snakefile_run_simulation.smk b/experiments/synthetic_benchmark/snakefile_run_simulation.smk
new file mode 100644
index 0000000..ce2339f
--- /dev/null
+++ b/experiments/synthetic_benchmark/snakefile_run_simulation.smk
@@ -0,0 +1,88 @@
+from snakemake.utils import min_version
+min_version("6.0")
+
+
+# Configuration
+SCENARIOS = ["scenario1"]
+NUM_THREADS = 16
+RAM_PER_THREAD = 32
+USE_MP = True
+if not USE_MP:
+    NUM_THREADS = 1
+
+TOOL_CONFIGS = ([f"{model_type}-{mode}-{neg_ctrl_key}-{ir_clone_key}-{alpha_model}-{hyperprior}-{lr}"
+                for model_type in ["mixturemodelkmeans"]
+                for mode in ["I", "C"]
+                for neg_ctrl_key in [None, "neg_control"]
+                for ir_clone_key in [None, "clone_id"]
+                for alpha_model in ["kmeans", "overdispersion"]
+                for hyperprior in [1e0, 1e-2]
+                for lr in [1e-2]
+                if (mode == "C" and ir_clone_key is not None) or mode != "C"
+                ])
+
+# TOOL_CONFIGS = ([f"{model_type}_{mode}_{neg_ctrl_key}_{ir_clone_key}_{alpha_model}_{hyperprior}_{lr}"
+#                 for model_type in ["mixturemodelkmeans"]
+#                 for mode in ["I"]
+#                 for neg_ctrl_key in ["neg_control"]
+#                 for ir_clone_key in ["clone_id"]
+#                 for alpha_model in ["overdispersion"]
+#                 for hyperprior in [1e-1, 1e-2]
+#                 for lr in [1e-2]
+#                 if (mode == "C" and ir_clone_key is not None) or mode != "C"
+#                 ])
+
+rule all:
+    input:
+        expand("benchmarks/{scenario}/aggregated_results.csv",scenario=SCENARIOS)
+
+
+rule run_simulation:
+    priority: 30
+    input:
+        # No input files required for this script
+    output:
+        "benchmarks/{scenario}/simulation.finished"
+    conda:
+        "environment.yaml"
+    shell:
+        "python simulate_scenarios.py --scenario {wildcards.scenario} "
+        "&& touch {output}"
+
+
+rule run_dextrademixer:
+    priority: 20
+    input:
+        "benchmarks/{scenario}/simulation.finished"
+    output:
+        "benchmarks/{scenario}/{model_type}-{mode}-{neg_ctrl_key}-{ir_clone_key}-{alpha_model}-{hyperprior}-{lr}.finished"
+    conda:
+        "environment.yaml"
+    threads:
+        min(32, NUM_THREADS)  # Cluster can only request 32 cores at once
+    params:
+        use_mp=USE_MP
+    resources:
+        c=min(32, NUM_THREADS),
+        mem_mb=RAM_PER_THREAD * 1000 * min(32, NUM_THREADS),
+        mem=f"{RAM_PER_THREAD * min(32, NUM_THREADS)}G",
+    shell:
+        "python run_dextrademixer_mp.py --scenario {wildcards.scenario} --model_type {wildcards.model_type} "
+        "--mode {wildcards.mode} --neg_ctrl_key {wildcards.neg_ctrl_key} --ir_clone_key {wildcards.ir_clone_key} "
+        "--alpha_model {wildcards.alpha_model} --hyperprior {wildcards.hyperprior} --lr {wildcards.lr} --maxiter 5000 --use_mp {params.use_mp} "
+        "&& touch {output}"
+
+
+rule aggregate_results:
+    priority: 10
+    input:
+        lambda wildcards: expand(
+            f"benchmarks/{wildcards.scenario}/{{tool_config}}.finished",
+            tool_config=TOOL_CONFIGS
+        )
+    output:
+        "benchmarks/{scenario}/aggregated_results.csv"
+    conda:
+        "environment.yaml"
+    shell:
+        "python aggregate_results.py --scenario {wildcards.scenario}"
diff --git a/experiments/synthetic_benchmark/snakefile_run_timing b/experiments/synthetic_benchmark/snakefile_run_timing.smk
similarity index 100%
rename from experiments/synthetic_benchmark/snakefile_run_timing
rename to experiments/synthetic_benchmark/snakefile_run_timing.smk

From 1c53378ae7b8790ea0b44f079b5209e5a0e140e8 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Wed, 15 Oct 2025 13:46:20 +0200
Subject: [PATCH 10/39] refactor: Remove duplicate line

---
 dextrademixer/model/Dextrademixer.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/dextrademixer/model/Dextrademixer.py b/dextrademixer/model/Dextrademixer.py
index aa951ff..743bebb 100644
--- a/dextrademixer/model/Dextrademixer.py
+++ b/dextrademixer/model/Dextrademixer.py
@@ -480,12 +480,6 @@ def get_posterior_samples(self, num_samples: int = 1000, seed: int = 42) -> Dict
         else:  # mcmc inference
             posterior_samples = self.sampler.get_samples(num_samples)
 
-        if self.is_svi: # svi
-            predictive = npy.infer.Predictive(self.guide, params=self.svi_result.params, num_samples=1000)
-            posterior_samples = predictive(jax.random.PRNGKey(seed), data=None)
-        else: # mcmc inference
-            posterior_samples = self.sampler.get_samples()
-
         # Extract mean from posterior samples
         q = posterior_samples["delta_q"].mean(0).cumsum(0)
         w = posterior_samples["w"].mean(0)

From ea58ad4bb2b2974ee5ede0614190527c999a4907 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Wed, 15 Oct 2025 13:47:07 +0200
Subject: [PATCH 11/39] feature: if y_true is None, create dummy zero values

---
 dextrademixer/model/Dextrademixer.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/dextrademixer/model/Dextrademixer.py b/dextrademixer/model/Dextrademixer.py
index 743bebb..e070642 100644
--- a/dextrademixer/model/Dextrademixer.py
+++ b/dextrademixer/model/Dextrademixer.py
@@ -521,6 +521,8 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
         if self.trace is None and self.svi_result is None:
             raise RuntimeError("Model has not been fit yet. Please call `fit` or `fit_svi` first.")
 
+        if y_true is None:
+            y_true = np.zeros_like(assignment)
         plt.figure(figsize=(16, 12))
 
         # Plot data colored in predicted class assignment

From e69d36133cf421d302df1399472c395bc3300bf0 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Wed, 15 Oct 2025 13:48:07 +0200
Subject: [PATCH 12/39] feature: save and load models

---
 dextrademixer/model/Dextrademixer.py | 36 ++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/dextrademixer/model/Dextrademixer.py b/dextrademixer/model/Dextrademixer.py
index e070642..ba92691 100644
--- a/dextrademixer/model/Dextrademixer.py
+++ b/dextrademixer/model/Dextrademixer.py
@@ -3,6 +3,7 @@
 import abc
 import warnings
 import os
+import pickle
 
 from typing import TYPE_CHECKING, Union, Dict, Tuple
 
@@ -760,6 +761,41 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
 
         return q, alpha, w
 
+    def save_model(self, filepath):
+        """
+        Save the fitted model to a file using pickle.
+        Args:
+            filepath (str): The path to the file where the model should be saved.
+        """
+        with open(filepath, 'wb') as f:
+            pickle.dump(vars(self), f)
+
+    def load_model(self, filepath):
+        """
+        Load model state into this instance.
+        Usage: model = DextraDemixer(); model.load_model(filepath)
+        Args:
+            filepath (str): The path to ckpt file.
+        """
+        with open(filepath, 'rb') as f:
+            ckpt = pickle.load(f)
+        self.__dict__.update(ckpt)
+        return self
+
+    @classmethod
+    def from_ckpt(cls, filepath):
+        """
+        Create a new instance directly from ckpt file (no __init__ call).
+        Usage: model = DextraDemixer.from_file(filepath)
+        Args:
+            filepath (str): The path to ckpt file.
+        """
+        with open(filepath, 'rb') as f:
+            ckpt = pickle.load(f)
+        self = cls.__new__(cls)
+        self.__dict__.update(ckpt)
+        return self
+
 
 class ADextraDemixerModel(metaclass=RegisteredModel):
     """

From bcc96dd4000be3c9c89bda82a4c1ca2c894bfbe7 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Wed, 15 Oct 2025 13:48:07 +0200
Subject: [PATCH 13/39] feature: save and load models

---
 dextrademixer/model/Dextrademixer.py          | 36 +++++++++++++++++++
 .../run_dextrademixer_mp.py                   |  3 +-
 2 files changed, 37 insertions(+), 2 deletions(-)

diff --git a/dextrademixer/model/Dextrademixer.py b/dextrademixer/model/Dextrademixer.py
index e070642..ba92691 100644
--- a/dextrademixer/model/Dextrademixer.py
+++ b/dextrademixer/model/Dextrademixer.py
@@ -3,6 +3,7 @@
 import abc
 import warnings
 import os
+import pickle
 
 from typing import TYPE_CHECKING, Union, Dict, Tuple
 
@@ -760,6 +761,41 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
 
         return q, alpha, w
 
+    def save_model(self, filepath):
+        """
+        Save the fitted model to a file using pickle.
+        Args:
+            filepath (str): The path to the file where the model should be saved.
+        """
+        with open(filepath, 'wb') as f:
+            pickle.dump(vars(self), f)
+
+    def load_model(self, filepath):
+        """
+        Load model state into this instance.
+        Usage: model = DextraDemixer(); model.load_model(filepath)
+        Args:
+            filepath (str): The path to ckpt file.
+        """
+        with open(filepath, 'rb') as f:
+            ckpt = pickle.load(f)
+        self.__dict__.update(ckpt)
+        return self
+
+    @classmethod
+    def from_ckpt(cls, filepath):
+        """
+        Create a new instance directly from ckpt file (no __init__ call).
+        Usage: model = DextraDemixer.from_file(filepath)
+        Args:
+            filepath (str): The path to ckpt file.
+        """
+        with open(filepath, 'rb') as f:
+            ckpt = pickle.load(f)
+        self = cls.__new__(cls)
+        self.__dict__.update(ckpt)
+        return self
+
 
 class ADextraDemixerModel(metaclass=RegisteredModel):
     """
diff --git a/experiments/synthetic_benchmark/run_dextrademixer_mp.py b/experiments/synthetic_benchmark/run_dextrademixer_mp.py
index 108d53b..d3494e5 100644
--- a/experiments/synthetic_benchmark/run_dextrademixer_mp.py
+++ b/experiments/synthetic_benchmark/run_dextrademixer_mp.py
@@ -116,8 +116,7 @@ def run_inference(f_in, args):
     # Save model
     save_model_dir = os.path.join(base_dir, 'saved_models')
     os.makedirs(save_model_dir, exist_ok=True)
-    with open(os.path.join(save_model_dir, f"{config}.pkl"), "wb") as f:
-        pickle.dump(mixer, f)
+    mixer.save_model(os.path.join(save_model_dir, f"{config}.pkl"))
 
     # Log metrics
     y_true = mdata.mod[args.airr_key].obs[args.label_key].astype(int).values

From e610cac75489c5644a9a06932b6b819e7184e7ae Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Wed, 22 Oct 2025 15:42:27 +0200
Subject: [PATCH 14/39] feature: parallelize BEAMT

---
 experiments/synthetic_benchmark/run_beamt_mp.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/experiments/synthetic_benchmark/run_beamt_mp.py b/experiments/synthetic_benchmark/run_beamt_mp.py
index c2baef7..5c1d048 100644
--- a/experiments/synthetic_benchmark/run_beamt_mp.py
+++ b/experiments/synthetic_benchmark/run_beamt_mp.py
@@ -2,6 +2,8 @@
 import os
 import warnings
 
+from concurrent.futures import ThreadPoolExecutor
+
 import pandas as pd
 import muon as mu
 
@@ -18,7 +20,7 @@ def main(args):
     input_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith('.h5mu')]
     base_dir = os.path.join('benchmarks', args.scenario)
 
-    for f_in in tqdm(input_files, desc="Running inference"):
+    def run_beamt(f_in):
         data_config = os.path.basename(f_in).replace('.h5mu', '')
         config = "BEAMT" + '_' + data_config
 
@@ -26,17 +28,12 @@ def main(args):
             warnings.simplefilter("ignore")
             mdata = mu.read(f_in)
 
-        true_binder = mdata.mod["airr"].obs["is_binder"]
         y_true = mdata.mod["airr"].obs["is_binder"]
-        N = len(true_binder)
-
-        d = {"model": [], "thresh": [], "p": [], "assignment": [], "true_binder": []}
 
         mixer = BEAMT()
         mixer.preprocess_model_data(mdata, "pmhc1", neg_ctrl_key="neg_control")
         mixer.fit()
 
-
         results = []
         posterior_params = [(0.01, None), (0.02, None), (0.05, None), (0.1, None), (0.2, None), (None, 0.5)]
 
@@ -58,6 +55,9 @@ def main(args):
         os.makedirs(csv_dir, exist_ok=True)
         results.to_csv(csv_dir + f"/{config}.csv")
 
+    with ThreadPoolExecutor() as ex:
+        list(tqdm(ex.map(run_beamt, input_files), total=len(input_files)))
+
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Run tool on all data from a scenario.")

From 8f579ec8ac89068a23ec65a0568a461295a05726 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Wed, 22 Oct 2025 15:43:17 +0200
Subject: [PATCH 15/39] refactor: rename mixer to model

---
 .../run_dextrademixer_mp.py                    | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/experiments/synthetic_benchmark/run_dextrademixer_mp.py b/experiments/synthetic_benchmark/run_dextrademixer_mp.py
index d3494e5..d313f55 100644
--- a/experiments/synthetic_benchmark/run_dextrademixer_mp.py
+++ b/experiments/synthetic_benchmark/run_dextrademixer_mp.py
@@ -104,23 +104,23 @@ def run_inference(f_in, args):
         warnings.simplefilter("ignore")
         mdata = mu.read(f_in)
 
-    mixer = DextraDemixer(model_type=args.model_type, mode=args.mode, alpha_model=args.alpha_model)
-    mixer.preprocess_model_data(mdata, pmhc_key=args.pmhc_key, gex_key=args.gex_key, neg_ctrl_key=args.neg_ctrl_key,
+    model = DextraDemixer(model_type=args.model_type, mode=args.mode, alpha_model=args.alpha_model)
+    model.preprocess_model_data(mdata, pmhc_key=args.pmhc_key, gex_key=args.gex_key, neg_ctrl_key=args.neg_ctrl_key,
                                 ir_clone_key=args.ir_clone_key, outlier_threshold=args.outlier_threshold, )
 
-    mixer.model._model_config["overdispersion_scale_prior"] = args.hyperprior
-    mixer.model._model_config["var_hyperprior"] = args.hyperprior
+    model.model._model_config["overdispersion_scale_prior"] = args.hyperprior
+    model.model._model_config["var_hyperprior"] = args.hyperprior
     opt_params = {"maxiter": args.maxiter, "nof_inits": 10, "adam": {"init_value": args.lr, }, }
-    mixer.fit_svi(svi_config=opt_params, nof_inits=opt_params["nof_inits"], rng_key=args.seed, return_loss=True)
+    model.fit_svi(svi_config=opt_params, nof_inits=opt_params["nof_inits"], rng_key=args.seed, return_loss=True)
 
     # Save model
     save_model_dir = os.path.join(base_dir, 'saved_models')
     os.makedirs(save_model_dir, exist_ok=True)
-    mixer.save_model(os.path.join(save_model_dir, f"{config}.pkl"))
+    model.save_model(os.path.join(save_model_dir, f"{config}.pkl"))
 
     # Log metrics
     y_true = mdata.mod[args.airr_key].obs[args.label_key].astype(int).values
-    posterior_samples = mixer.get_posterior_samples(num_samples=1000, seed=args.seed)
+    posterior_samples = model.get_posterior_samples(num_samples=1000, seed=args.seed)
 
     results = []
     # Predict posterior class with different methods, (target_fdr, threshold, quantile, cred_intvl)
@@ -131,7 +131,7 @@ def run_inference(f_in, args):
         (0.05, None, None, 0.60), (0.05, None, None, 0.70), (0.05, None, None, 0.80), (0.05, None, None, 0.90),]
 
     for target_fdr, threshold, quantile, cred_intvl in posterior_params:
-        p_pred, assignment = mixer.predict_posterior_class(target_fdr=target_fdr, threshold=threshold,
+        p_pred, assignment = model.predict_posterior_class(target_fdr=target_fdr, threshold=threshold,
                                                           quantile=quantile, cred_intvl=cred_intvl)
         posterior_config = f"{target_fdr}_{threshold}_{quantile}_{cred_intvl}"
         # Plot results
@@ -142,7 +142,7 @@ def run_inference(f_in, args):
                            f"alpha_noise: {sim_params['concentration_non_binder']:.2f}\n"
                            f"q_signal: {sim_params['mean_pos']:.2f}, "
                            f"alpha_signal: {sim_params['concentration_pos']:.2f}")
-        mixer.plot_results(assignment, p_pred, y_true if args.label_key is not None else None, args.seed,
+        model.plot_results(assignment, p_pred, y_true if args.label_key is not None else None, args.seed,
                            config + '/' + posterior_config, additional_text=additional_text,
                            save_dir=os.path.join(base_dir, 'figs'), show=False)
 

From 3d20156615c6b697df3183c3e8f8c5c23d85d221 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Fri, 24 Oct 2025 19:13:46 +0200
Subject: [PATCH 16/39] feature: sample variance based on mean, instead of
 sampling overdispersion

---
 dextrademixer/utils/simulation.py | 49 ++++++++++++++++++++++++++-----
 1 file changed, 42 insertions(+), 7 deletions(-)

diff --git a/dextrademixer/utils/simulation.py b/dextrademixer/utils/simulation.py
index 549aa47..16dfb41 100644
--- a/dextrademixer/utils/simulation.py
+++ b/dextrademixer/utils/simulation.py
@@ -37,6 +37,42 @@ def generate_nb_val(mu, alpha, size):
     return stats.poisson.rvs(g)
 
 
+def sample_var_from_mean(mean: Union[float, np.ndarray],
+                         a: float = 2.0221541172111164, b: float = 1.6969075027280063,
+                         resid_std: float = 0.31049623532404225, rng: Union[int, np.random.RandomState] = 42
+                         ) -> Union[float, np.ndarray]:
+    """
+    Sample a realistic variance given a mean using the fitted power-law model:
+        log(var) = a + b*log(mean) + Normal(0, resid_std^2)
+
+    Args:
+        mean : float or np.ndarray
+            Mean(s) at which to sample the variance. Must be > 0; broadcasting allowed.
+        a : float, default 2.0221541172111164
+            Proportionality constant (exp(intercept) from log–log OLS).
+        b : float, default 1.6969075027280063
+            Scaling exponent (slope from log–log OLS).
+        resid_std : float, default 0.31049623532404225
+            Residual standard deviation on the *log-variance* scale (σ from OLS residuals).
+        rng : int | np.random.RandomState, default 42
+            Source of randomness. If int, used as the seed. If None, uses SciPy/Numpy default RNG.
+    Returns:
+        float or np.ndarray
+            A sample of variance values with the same broadcasted shape as `mean`.
+    """
+
+    if isinstance(rng, int):
+        rng = np.random.RandomState(seed=rng)
+    if isinstance(mean, np.ndarray):
+        size = mean.shape
+    else:
+        size = None
+    log_var = np.log(a) + b*np.log(mean) + stats.norm(0, resid_std).rvs(size=size, random_state=rng)
+    var = np.exp(log_var)
+
+    return var
+
+
 def t_cell_simulation(n_clones=3,
                       mean_binder_range=None,
                       shape_binder_range=None,
@@ -461,22 +497,21 @@ def simulate_pmhc_data_from_distribution(self,
         if mean_neg_ctrl is None:
             mean_neg_ctrl = np.exp(stats.truncnorm(-1.0539178917389445, 1.8375518345106903, loc=1.018115879390079, scale=0.4175162931163312).rvs(random_state=rng))
         if concentration_neg_ctrl is None:
-            overdisp_neg_ctrl = stats.gamma(a=4.186062616134899, scale=1.2384303396204106).rvs(random_state=rng) + 1
-            var_neg_ctrl = mean_neg_ctrl * overdisp_neg_ctrl
+            var_neg_ctrl = sample_var_from_mean(mean_neg_ctrl, rng=rng)
             concentration_neg_ctrl = convert_to_invdispersion(mean_neg_ctrl, var_neg_ctrl)
         if mean_non_binder is None:
             mean_non_binder = np.exp(stats.truncnorm(-1.4325807532116341, 1.9485510504360735, loc=2.0461540382126118, scale=0.6019089551720753).rvs(random_state=rng))
         if concentration_non_binder is None:
-            overdisp_non_binder = stats.gamma(a=0.802396044662406, scale=6.554415080004114).rvs(random_state=rng) + 1
-            var_non_binder = mean_non_binder * overdisp_non_binder
+            var_non_binder = sample_var_from_mean(mean_non_binder, rng=rng)
             concentration_non_binder = convert_to_invdispersion(mean_non_binder, var_non_binder)
         if mean_inc is None:
             mean_inc = stats.uniform(50, 450).rvs(random_state=rng)  # between [50, 450+50]
         mean_pos = mean_inc * mean_non_binder
         if var_inc is None:
-            var_inc = stats.uniform(100, 400).rvs(random_state=rng)  # between [100, 400+100]
-        assert var_inc > 1, "`var_inc` must be larger than 1"
-        concentration_pos = convert_to_invdispersion(mean_pos, mean_pos * var_inc)
+            var_pos = sample_var_from_mean(mean_pos, rng=rng)
+        else:
+            var_pos = var_inc * mean_non_binder
+        concentration_pos = convert_to_invdispersion(mean_pos, var_pos)
 
         binder_assignment = rng.binomial(1, binding_ratio, size=nof_clones)
         K = None

From aa7ae48e273ebca673d8da5dd9efbdedfd8bdb4b Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Fri, 24 Oct 2025 19:35:22 +0200
Subject: [PATCH 17/39] fix jax, jaxlib and numpyro versions

---
 experiments/synthetic_benchmark/environment.yaml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/experiments/synthetic_benchmark/environment.yaml b/experiments/synthetic_benchmark/environment.yaml
index 4aa42bb..a74493d 100644
--- a/experiments/synthetic_benchmark/environment.yaml
+++ b/experiments/synthetic_benchmark/environment.yaml
@@ -11,9 +11,9 @@ dependencies:
       - pandas>=2.1.4
       - statsmodels>=0.14.1
       - matplotlib>=3.8.3
-      - jax>=0.4.26
-      - jaxlib>=0.4.26
-      - numpyro>=0.14.0
+      - jax==0.5.0
+      - jaxlib==0.5.0
+      - numpyro==0.16.1
       - arviz>=0.17.1
       - mudata>=0.2.3
       - muon>=0.1.6

From ff646cd590f08a9e386b163a55ea3b966c3eacd5 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Fri, 24 Oct 2025 19:42:24 +0200
Subject: [PATCH 18/39] scenario config: Update to version with variance
 sampled in relationship to mean

---
 .../benchmarks/scenario1/config.yaml                   | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/experiments/synthetic_benchmark/benchmarks/scenario1/config.yaml b/experiments/synthetic_benchmark/benchmarks/scenario1/config.yaml
index 36800b0..ea4f532 100644
--- a/experiments/synthetic_benchmark/benchmarks/scenario1/config.yaml
+++ b/experiments/synthetic_benchmark/benchmarks/scenario1/config.yaml
@@ -1,8 +1,8 @@
-total_cell: [500, 1000, 2000, 5000, 10000, 25000]
+total_cell: [500, 1000, 2000, 5000]
 frac_clone: [0.4]
-binding_ratio: [0.1]
+binding_ratio: [0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.9]
 use_clonotype_cov: [False]
 p_binding_outlier: [0.0]
-mean_inc: [25, 50, 100, 200]
-var_inc: [100, 200, 500]
-i: [0, 1, 2, 3, 4]
+mean_inc: [10, 20, 50, 100, 200]
+var_inc: [null]
+i: [0, 1, 2,]

From 36a39a8420bbe67bd0feb814f7367d404e2b800e Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Fri, 24 Oct 2025 19:53:20 +0200
Subject: [PATCH 19/39] feature: add flags to sbatch script and snakemake file

---
 .../slurm_run_simulation.sh                   | 55 +++++++++++++++++--
 .../snakefile_run_simulation.smk              | 14 +++--
 2 files changed, 57 insertions(+), 12 deletions(-)

diff --git a/experiments/synthetic_benchmark/slurm_run_simulation.sh b/experiments/synthetic_benchmark/slurm_run_simulation.sh
index 89c2436..163cb81 100755
--- a/experiments/synthetic_benchmark/slurm_run_simulation.sh
+++ b/experiments/synthetic_benchmark/slurm_run_simulation.sh
@@ -1,21 +1,63 @@
 #!/bin/bash
-# Usage:
+# Usage examples:
 #   bash slurm_run_smk.sh                # cpu_normal
 #   bash slurm_run_smk.sh --preemptible  # cpu_preemptible
+#   bash slurm_run_smk.sh --scenario scenarioX --threads 8 --ram 32 --no-mp
 
+set -euo pipefail
 
+# ---- Default values
+SCENARIO="scenario1"
+NUM_THREADS=16
+RAM_PER_THREAD=16
+USE_MP=True
+PROFILE_SUFFIX=".slurm"
+
+# ---- Parse flags
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --scenario)
+            SCENARIO="$2"
+            shift 2
+            ;;
+        --threads)
+            NUM_THREADS="$2"
+            shift 2
+            ;;
+        --ram)
+            RAM_PER_THREAD="$2"
+            shift 2
+            ;;
+        --preemptible)
+            PROFILE_SUFFIX=".slurm_preemptible"
+            shift
+            ;;
+        --no-mp)
+            USE_MP=False
+            shift
+            ;;
+        -h|--help)
+            echo "Usage: bash $0 [--scenario NAME] [--threads N] [--ram GB] [--preemptible] [--no-mp]"
+            exit 0
+            ;;
+        *)
+            echo "Unknown argument: $1" >&2
+            exit 1
+            ;;
+    esac
+done
+
+# ---- Paths
 PATH_BASE="$(dirname "$(realpath "$0")")/../.."
 PATH_LOGS="."
-
-PROFILE_DIR="${PATH_BASE}/experiments/.slurm"
-[[ "${1-}" == "--preemptible" ]] && PROFILE_DIR="${PATH_BASE}/experiments/.slurm_preemptible"
+PROFILE_DIR="${PATH_BASE}/experiments/${PROFILE_SUFFIX}"
 
 
 mkdir -p "${PATH_LOGS}/logs/slurm_logs"
 mkdir -p "${PATH_LOGS}/logs/jobs"
 
 DATE_TIME=$(date +"%Y-%m-%d_%H-%M-%S")
-JOB_NAME="SKM_dex_benchmark_${DATE_TIME}"
+JOB_NAME="SKM_dex_benchmark_${SCENARIO}_${DATE_TIME}"
 
 echo "#!/bin/bash
 #SBATCH -J ${JOB_NAME}
@@ -33,7 +75,8 @@ conda activate dextrademixer
 cd ${PATH_BASE}/experiments/synthetic_benchmark
 
 snakemake -s snakefile_run_simulation.smk --unlock
-snakemake -s snakefile_run_simulation.smk -c4 --profile ${PROFILE_DIR} --cluster-status ${PROFILE_DIR}/status.py --conda-frontend conda -p
+snakemake -s snakefile_run_simulation.smk -c4 --profile ${PROFILE_DIR} --cluster-status ${PROFILE_DIR}/status.py --conda-frontend conda -p --config SCENARIO=${SCENARIO} NUM_THREADS=${NUM_THREADS} RAM_PER_THREAD=${RAM_PER_THREAD} USE_MP=${USE_MP}
+
 
 
 " > ${PATH_LOGS}/logs/jobs/${JOB_NAME}.cmd
diff --git a/experiments/synthetic_benchmark/snakefile_run_simulation.smk b/experiments/synthetic_benchmark/snakefile_run_simulation.smk
index ce2339f..480437f 100644
--- a/experiments/synthetic_benchmark/snakefile_run_simulation.smk
+++ b/experiments/synthetic_benchmark/snakefile_run_simulation.smk
@@ -3,10 +3,10 @@ min_version("6.0")
 
 
 # Configuration
-SCENARIOS = ["scenario1"]
-NUM_THREADS = 16
-RAM_PER_THREAD = 32
-USE_MP = True
+SCENARIO = config.get("SCENARIO", "scenario1")
+NUM_THREADS = int(config.get("NUM_THREADS", 16))
+RAM_PER_THREAD = int(config.get("RAM_PER_THREAD", 16))
+USE_MP = config.get("USE_MP", "False") == "True"
 if not USE_MP:
     NUM_THREADS = 1
 
@@ -16,9 +16,11 @@ TOOL_CONFIGS = ([f"{model_type}-{mode}-{neg_ctrl_key}-{ir_clone_key}-{alpha_mode
                 for neg_ctrl_key in [None, "neg_control"]
                 for ir_clone_key in [None, "clone_id"]
                 for alpha_model in ["kmeans", "overdispersion"]
-                for hyperprior in [1e0, 1e-2]
+                for hyperprior in [1e2, 1e1, 1e0]
                 for lr in [1e-2]
-                if (mode == "C" and ir_clone_key is not None) or mode != "C"
+                if ((mode == "C" and ir_clone_key is not None) or mode != "C")
+                   and
+                   ((alpha_model == "kmeans" and hyperprior > 1e0) or (alpha_model == "overdispersion" and hyperprior <= 1e1))
                 ])
 
 # TOOL_CONFIGS = ([f"{model_type}_{mode}_{neg_ctrl_key}_{ir_clone_key}_{alpha_model}_{hyperprior}_{lr}"

From 7b0472d0543cbb1c89955cf7d2d18dc74d221952 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Mon, 1 Dec 2025 09:51:43 +0100
Subject: [PATCH 20/39] feature: resample so that sampled N_binder / N_total ~
 p_binding_ratio

---
 dextrademixer/utils/simulation.py | 35 +++++++++++++++++++++++++------
 1 file changed, 29 insertions(+), 6 deletions(-)

diff --git a/dextrademixer/utils/simulation.py b/dextrademixer/utils/simulation.py
index 16dfb41..27fa4ca 100644
--- a/dextrademixer/utils/simulation.py
+++ b/dextrademixer/utils/simulation.py
@@ -446,7 +446,8 @@ def simulate_pmhc_data_from_distribution(self,
                                              use_clonotype_cov: bool = False,
                                              simulate_neg_control: bool = False,
                                              plot_data: bool = False,
-                                             rng_key: int = 42
+                                             rng_key: int = 42,
+                                             rep: int = 0,
                                              ) -> Union[Tuple[MuData, Any], MuData]:
         """
         Given distribution parameters generate binding data for one pMHC. If certain parameters are not specified,
@@ -513,7 +514,31 @@ def simulate_pmhc_data_from_distribution(self,
             var_pos = var_inc * mean_non_binder
         concentration_pos = convert_to_invdispersion(mean_pos, var_pos)
 
-        binder_assignment = rng.binomial(1, binding_ratio, size=nof_clones)
+        # Sample binder assignments and cells per clone until empirical binding ratio is close to target
+        max_trials = 20
+        best_err = 10000
+        for _ in range(max_trials):
+            total_le = total_cells - nof_clones
+            raw_cells_per_clone = stats.boltzmann.rvs(*cells_per_clonotype, size=nof_clones, random_state=rng)
+            cells_per_clone_p = raw_cells_per_clone / raw_cells_per_clone.sum()
+            cells_per_clone_trial = (rng.multinomial(total_le, cells_per_clone_p) + np.ones(nof_clones)).astype("int32")
+
+            # Sample multiple binder assignments and pick the one that gives empirical binding ratio closest to target
+            binder_assignment_trial = rng.binomial(1, binding_ratio, size=(10000, nof_clones))
+            empirical_binding_ratio = ((cells_per_clone_trial * binder_assignment_trial).sum(1) / total_cells)
+            # mean of error from empirical cell and clone level binder ratio
+            err = ((np.abs(empirical_binding_ratio - binding_ratio) +
+                   np.abs(binder_assignment_trial.mean(1) - binding_ratio))
+                   / 2)
+
+            if err.min() < best_err:
+                best_idx = err.argmin()
+                binder_assignment = binder_assignment_trial[best_idx]
+                cells_per_clone = cells_per_clone_trial
+
+            if err.min() < binding_ratio * 0.05:
+                break
+
         K = None
         cc_assignment = None
 
@@ -529,10 +554,6 @@ def simulate_pmhc_data_from_distribution(self,
 
         # generate cell per clonotype following a discrete exponentially decreasing distribution normalized to
         # specified total cell count
-        total_le = total_cells - nof_clones
-        raw_cells_per_clone = np.array([stats.boltzmann.rvs(*cells_per_clonotype,random_state=rng) for _ in range(nof_clones)])
-        cells_per_clone_p = raw_cells_per_clone/raw_cells_per_clone.sum()
-        cells_per_clone = (rng.multinomial(total_le, cells_per_clone_p) + np.ones(nof_clones)).astype("int32")
 
         d = {"x": [], "binder": [], "clone": [], "fold_increase": [], "outlier":[]}
         if simulate_neg_control:
@@ -599,6 +620,7 @@ def simulate_pmhc_data_from_distribution(self,
             'mean_inc': mean_inc,
             'var_inc': var_inc,
             'mean_pos': mean_pos,
+            'var_pos': var_pos,
             'concentration_pos': concentration_pos,
             'total_cells': total_cells,
             'nof_clones': nof_clones,
@@ -608,6 +630,7 @@ def simulate_pmhc_data_from_distribution(self,
             'rng_key': rng_key,
             'best_f1': best_f1,
             'best_threshold': best_threshold,
+            'rep': rep,
         }
         mdat['gex'].uns['sim_params'] = sim_params
 

From 43a951bc67f27c85fd548a2ebdc48b2fd6968674 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Tue, 2 Dec 2025 10:57:13 +0100
Subject: [PATCH 21/39] bugfix: Use noise mean for binder outliers instead of
 binder mean. Add outlier mask vector to final mdata

---
 dextrademixer/utils/simulation.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/dextrademixer/utils/simulation.py b/dextrademixer/utils/simulation.py
index 27fa4ca..cecb7ec 100644
--- a/dextrademixer/utils/simulation.py
+++ b/dextrademixer/utils/simulation.py
@@ -588,7 +588,7 @@ def simulate_pmhc_data_from_distribution(self,
                                                     scale=concentration_non_binder / 3, random_state=rng)
 
                 x = x.at[outlier_idx].set(
-                    DextramerSimulator.generate_nb_val(mean, concentration, size=np.sum(outlier), rng_key=key)
+                    DextramerSimulator.generate_nb_val(mean_non_binder, concentration, size=np.sum(outlier), rng_key=key)
                 )
                 d["outlier"].extend(outlier.tolist())
             else:
@@ -824,6 +824,7 @@ def __generate_mdata(d, simulate_neg_control, cov, cc_assignment) -> MuData:
         adata_tcr = ad.AnnData()
         adata_tcr.obs["is_binder"] = d["binder"]
         adata_tcr.obs["clone_id"] = d["clone"]
+        adata_tcr.obs["outlier"] = d["outlier"]
 
         if cov is not None:
             adata_tcr.obs["cc_aa_sim"] = cc_assignment[d["clone"]]

From 211ce03a41d0b41139cc6af3d92072c0a7ef6340 Mon Sep 17 00:00:00 2001
From: Yang <yang.an@outlook.com>
Date: Tue, 2 Dec 2025 11:01:12 +0100
Subject: [PATCH 22/39] feature: ensure real outlier ratio roughly matches the
 specified ratio

---
 dextrademixer/utils/simulation.py | 39 +++++++++++++++++++------------
 1 file changed, 24 insertions(+), 15 deletions(-)

diff --git a/dextrademixer/utils/simulation.py b/dextrademixer/utils/simulation.py
index cecb7ec..ccbbce3 100644
--- a/dextrademixer/utils/simulation.py
+++ b/dextrademixer/utils/simulation.py
@@ -579,21 +579,6 @@ def simulate_pmhc_data_from_distribution(self,
 
             x = DextramerSimulator.generate_nb_val(mean, concentration, size=n_cells, rng_key=key)
 
-            if p_binding_outlier > 0 and is_binder:
-                outlier = stats.binom.rvs(1, p_binding_outlier, size=n_cells, random_state=rng)
-                outlier_idx = np.where(outlier)
-
-                a = (0.001 - concentration_non_binder) / (concentration_non_binder / 3)
-                concentration = stats.truncnorm.rvs(a, np.inf, loc=concentration_non_binder,
-                                                    scale=concentration_non_binder / 3, random_state=rng)
-
-                x = x.at[outlier_idx].set(
-                    DextramerSimulator.generate_nb_val(mean_non_binder, concentration, size=np.sum(outlier), rng_key=key)
-                )
-                d["outlier"].extend(outlier.tolist())
-            else:
-                d["outlier"].extend([0]*n_cells)
-
             if simulate_neg_control:
                 key, subkey = jax.random.split(key)
                 x_neg = DextramerSimulator.generate_nb_val(mean_neg_ctrl, concentration_neg_ctrl, size=n_cells, rng_key=key)
@@ -604,6 +589,30 @@ def simulate_pmhc_data_from_distribution(self,
             d["clone"].extend([i] * n_cells)
             d["fold_increase"].extend([mean_inc] * n_cells)
 
+        if p_binding_outlier > 0:
+            outlier = np.zeros(total_cells, dtype=int)
+            binder_mask = np.array(d["binder"], dtype=bool)
+            n_binder = binder_mask.sum()
+
+            binder_outlier_trial = rng.binomial(1, p_binding_outlier, size=(10000, n_binder))
+
+            err = np.abs(p_binding_outlier - binder_outlier_trial.mean(1))
+            best_idx = err.argmin()
+            binder_outlier = binder_outlier_trial[best_idx]
+            outlier[binder_mask] = binder_outlier
+
+            a = (0.001 - concentration_non_binder) / (concentration_non_binder / 3)
+            concentration = stats.truncnorm.rvs(a, np.inf, loc=concentration_non_binder,
+                                                scale=concentration_non_binder / 3, random_state=rng)
+            x = np.array(d["x"])
+            x[outlier.astype(bool)] = (
+                DextramerSimulator.generate_nb_val(mean_non_binder, concentration, size=np.sum(outlier), rng_key=key)
+            )
+            d["x"] = x.tolist()
+            d["outlier"] = outlier.tolist()
+        else:
+            d["outlier"] = [0]*total_cells
+
         mdat = DextramerSimulator.__generate_mdata(d, simulate_neg_control, K, cc_assignment)
         # Best theoretical F1
         precision, recall, thresholds = precision_recall_curve(mdat['airr'].obs['is_binder'], mdat['gex'].X[:, 0])

From 30a41819a5519a2f1b1d718d6d883048b0b8f4b9 Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Fri, 20 Feb 2026 09:52:35 +0100
Subject: [PATCH 23/39] feature: resample binding assignment to get close to
 target binding ratio

---
 dextrademixer/utils/simulation.py | 75 ++++++++++++++++++++++---------
 1 file changed, 54 insertions(+), 21 deletions(-)

diff --git a/dextrademixer/utils/simulation.py b/dextrademixer/utils/simulation.py
index 16dfb41..ccbbce3 100644
--- a/dextrademixer/utils/simulation.py
+++ b/dextrademixer/utils/simulation.py
@@ -446,7 +446,8 @@ def simulate_pmhc_data_from_distribution(self,
                                              use_clonotype_cov: bool = False,
                                              simulate_neg_control: bool = False,
                                              plot_data: bool = False,
-                                             rng_key: int = 42
+                                             rng_key: int = 42,
+                                             rep: int = 0,
                                              ) -> Union[Tuple[MuData, Any], MuData]:
         """
         Given distribution parameters generate binding data for one pMHC. If certain parameters are not specified,
@@ -513,7 +514,31 @@ def simulate_pmhc_data_from_distribution(self,
             var_pos = var_inc * mean_non_binder
         concentration_pos = convert_to_invdispersion(mean_pos, var_pos)
 
-        binder_assignment = rng.binomial(1, binding_ratio, size=nof_clones)
+        # Sample binder assignments and cells per clone until empirical binding ratio is close to target
+        max_trials = 20
+        best_err = 10000
+        for _ in range(max_trials):
+            total_le = total_cells - nof_clones
+            raw_cells_per_clone = stats.boltzmann.rvs(*cells_per_clonotype, size=nof_clones, random_state=rng)
+            cells_per_clone_p = raw_cells_per_clone / raw_cells_per_clone.sum()
+            cells_per_clone_trial = (rng.multinomial(total_le, cells_per_clone_p) + np.ones(nof_clones)).astype("int32")
+
+            # Sample multiple binder assignments and pick the one that gives empirical binding ratio closest to target
+            binder_assignment_trial = rng.binomial(1, binding_ratio, size=(10000, nof_clones))
+            empirical_binding_ratio = ((cells_per_clone_trial * binder_assignment_trial).sum(1) / total_cells)
+            # mean of error from empirical cell and clone level binder ratio
+            err = ((np.abs(empirical_binding_ratio - binding_ratio) +
+                   np.abs(binder_assignment_trial.mean(1) - binding_ratio))
+                   / 2)
+
+            if err.min() < best_err:
+                best_idx = err.argmin()
+                binder_assignment = binder_assignment_trial[best_idx]
+                cells_per_clone = cells_per_clone_trial
+
+            if err.min() < binding_ratio * 0.05:
+                break
+
         K = None
         cc_assignment = None
 
@@ -529,10 +554,6 @@ def simulate_pmhc_data_from_distribution(self,
 
         # generate cell per clonotype following a discrete exponentially decreasing distribution normalized to
         # specified total cell count
-        total_le = total_cells - nof_clones
-        raw_cells_per_clone = np.array([stats.boltzmann.rvs(*cells_per_clonotype,random_state=rng) for _ in range(nof_clones)])
-        cells_per_clone_p = raw_cells_per_clone/raw_cells_per_clone.sum()
-        cells_per_clone = (rng.multinomial(total_le, cells_per_clone_p) + np.ones(nof_clones)).astype("int32")
 
         d = {"x": [], "binder": [], "clone": [], "fold_increase": [], "outlier":[]}
         if simulate_neg_control:
@@ -558,21 +579,6 @@ def simulate_pmhc_data_from_distribution(self,
 
             x = DextramerSimulator.generate_nb_val(mean, concentration, size=n_cells, rng_key=key)
 
-            if p_binding_outlier > 0 and is_binder:
-                outlier = stats.binom.rvs(1, p_binding_outlier, size=n_cells, random_state=rng)
-                outlier_idx = np.where(outlier)
-
-                a = (0.001 - concentration_non_binder) / (concentration_non_binder / 3)
-                concentration = stats.truncnorm.rvs(a, np.inf, loc=concentration_non_binder,
-                                                    scale=concentration_non_binder / 3, random_state=rng)
-
-                x = x.at[outlier_idx].set(
-                    DextramerSimulator.generate_nb_val(mean, concentration, size=np.sum(outlier), rng_key=key)
-                )
-                d["outlier"].extend(outlier.tolist())
-            else:
-                d["outlier"].extend([0]*n_cells)
-
             if simulate_neg_control:
                 key, subkey = jax.random.split(key)
                 x_neg = DextramerSimulator.generate_nb_val(mean_neg_ctrl, concentration_neg_ctrl, size=n_cells, rng_key=key)
@@ -583,6 +589,30 @@ def simulate_pmhc_data_from_distribution(self,
             d["clone"].extend([i] * n_cells)
             d["fold_increase"].extend([mean_inc] * n_cells)
 
+        if p_binding_outlier > 0:
+            outlier = np.zeros(total_cells, dtype=int)
+            binder_mask = np.array(d["binder"], dtype=bool)
+            n_binder = binder_mask.sum()
+
+            binder_outlier_trial = rng.binomial(1, p_binding_outlier, size=(10000, n_binder))
+
+            err = np.abs(p_binding_outlier - binder_outlier_trial.mean(1))
+            best_idx = err.argmin()
+            binder_outlier = binder_outlier_trial[best_idx]
+            outlier[binder_mask] = binder_outlier
+
+            a = (0.001 - concentration_non_binder) / (concentration_non_binder / 3)
+            concentration = stats.truncnorm.rvs(a, np.inf, loc=concentration_non_binder,
+                                                scale=concentration_non_binder / 3, random_state=rng)
+            x = np.array(d["x"])
+            x[outlier.astype(bool)] = (
+                DextramerSimulator.generate_nb_val(mean_non_binder, concentration, size=np.sum(outlier), rng_key=key)
+            )
+            d["x"] = x.tolist()
+            d["outlier"] = outlier.tolist()
+        else:
+            d["outlier"] = [0]*total_cells
+
         mdat = DextramerSimulator.__generate_mdata(d, simulate_neg_control, K, cc_assignment)
         # Best theoretical F1
         precision, recall, thresholds = precision_recall_curve(mdat['airr'].obs['is_binder'], mdat['gex'].X[:, 0])
@@ -599,6 +629,7 @@ def simulate_pmhc_data_from_distribution(self,
             'mean_inc': mean_inc,
             'var_inc': var_inc,
             'mean_pos': mean_pos,
+            'var_pos': var_pos,
             'concentration_pos': concentration_pos,
             'total_cells': total_cells,
             'nof_clones': nof_clones,
@@ -608,6 +639,7 @@ def simulate_pmhc_data_from_distribution(self,
             'rng_key': rng_key,
             'best_f1': best_f1,
             'best_threshold': best_threshold,
+            'rep': rep,
         }
         mdat['gex'].uns['sim_params'] = sim_params
 
@@ -801,6 +833,7 @@ def __generate_mdata(d, simulate_neg_control, cov, cc_assignment) -> MuData:
         adata_tcr = ad.AnnData()
         adata_tcr.obs["is_binder"] = d["binder"]
         adata_tcr.obs["clone_id"] = d["clone"]
+        adata_tcr.obs["outlier"] = d["outlier"]
 
         if cov is not None:
             adata_tcr.obs["cc_aa_sim"] = cc_assignment[d["clone"]]

From eb04360a6e34c750b2596ba70866132e4c1ce23f Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Fri, 20 Feb 2026 09:53:16 +0100
Subject: [PATCH 24/39] feature: Add MCC

---
 dextrademixer/utils/utils.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/dextrademixer/utils/utils.py b/dextrademixer/utils/utils.py
index 7a51e82..e6b3e8b 100644
--- a/dextrademixer/utils/utils.py
+++ b/dextrademixer/utils/utils.py
@@ -14,7 +14,7 @@
 from numpy import ndarray, dtype, bool_
 from scipy.stats import ortho_group, random_correlation
 from sklearn.metrics import (roc_auc_score, average_precision_score, f1_score, precision_score, recall_score,
-                             accuracy_score, )
+                             accuracy_score, matthews_corrcoef)
 
 
 def gower_centering(distance_matrix):
@@ -364,6 +364,7 @@ def init_worker(worker_mem_limit_mb=None):
 def calculate_metrics(y_true: np.ndarray, p_pred: np.ndarray, assignment: np.ndarray) -> dict:
     results_dict = {'auroc': roc_auc_score(y_true, p_pred), 'aps': average_precision_score(y_true, p_pred),
                     'f1': f1_score(y_true, assignment), 'precision': precision_score(y_true, assignment),
-                    'recall': recall_score(y_true, assignment), 'accuracy': accuracy_score(y_true, assignment)}
+                    'recall': recall_score(y_true, assignment), 'accuracy': accuracy_score(y_true, assignment),
+                    'mcc': matthews_corrcoef(y_true, assignment)}
 
     return results_dict

From 2a66d7cd99371027256a7e35100f7e2e0b415d3a Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Fri, 20 Feb 2026 10:00:50 +0100
Subject: [PATCH 25/39] feature: Multiple enhancements - size factor
 calculation - alpha offset - incorporating negative control in a more
 flexible way - exponential lr decay - flag to log performance during training
 - various clonotype info incorporations - small improvements of plotting

---
 dextrademixer/model/Dextrademixer.py | 439 ++++++++++++---------------
 1 file changed, 197 insertions(+), 242 deletions(-)

diff --git a/dextrademixer/model/Dextrademixer.py b/dextrademixer/model/Dextrademixer.py
index ba92691..ba8d777 100644
--- a/dextrademixer/model/Dextrademixer.py
+++ b/dextrademixer/model/Dextrademixer.py
@@ -8,7 +8,7 @@
 from typing import TYPE_CHECKING, Union, Dict, Tuple
 
 import arviz as az
-
+import tqdm
 import numpy as np
 import pandas as pd
 import mudata as md
@@ -27,10 +27,10 @@
 from numpyro.infer.svi import SVIRunResult
 from sklearn.cluster import KMeans
 from sklearn.metrics import f1_score
-import tqdm
+from optax import exponential_decay
 
 from dextrademixer.model import ApMHCDeconvolution
-from dextrademixer.utils import RegisteredModel
+from dextrademixer.utils import RegisteredModel, calculate_metrics
 
 if TYPE_CHECKING:
     from jax._src.typing import Array
@@ -63,7 +63,8 @@ class DextraDemixer(ApMHCDeconvolution):
 
     """
 
-    def __init__(self, model_type: str = "mixturemodel", mode: str = "H", alpha_model="overdispersion"):
+    def __init__(self, model_type: str = "mixturemodel", mode: str = "H", alpha_model="overdispersion", 
+                 model_config: Dict = None):
         super().__init__()
 
         if mode.upper() not in ("H", "I", "C"):
@@ -78,10 +79,14 @@ def __init__(self, model_type: str = "mixturemodel", mode: str = "H", alpha_mode
         self.guide = None
         self.mode = mode.upper()
         self.alpha_model = alpha_model
+        self.model_config = model_config if model_config is not None else {}
 
         if model_type not in ADextraDemixerModel.registry.keys():
             raise warnings.warn(f"`model_type` {model_type} not supported using the standard model.")
-        self.model = ADextraDemixerModel.registry.get(model_type, DextraDemixerMixtureModel)()
+        self.model = ADextraDemixerModel.registry.get(model_type, DextraDemixerKmeansModel)()
+        self.model.mode = mode
+        self.model.alpha_model = alpha_model
+        self.model._model_config.update(self.model_config)
 
     @property
     def version(self):
@@ -108,6 +113,7 @@ def preprocess_model_data(self,
                               ir_key: str = "airr",
                               ir_clone_key: str = None,
                               ir_cov_key: str = None,
+                              use_size_factor: bool = None,
                               **kwargs):
         """
         Preprocesses the data and initializes the model
@@ -121,6 +127,7 @@ def preprocess_model_data(self,
             ir_clone_key: (Optional) a string specifying the field in `obs` of `ir_key` that holds clonotype ids
             ir_cov_key: (Optional) the key in AIRR module's `.uns` that contains a full, symmetric and square distance matrix
                          for all clonotype cluster
+            use_size_factor: (Optional) if wanting to use size factors, provide keys of pMHCs to use, is use all
             kwargs: dictionary of additional information pasted to the Model object (used for custom model prior)
         """
         gex = mdata.mod[gex_key]
@@ -137,9 +144,36 @@ def preprocess_model_data(self,
             if c is None:
                 raise ValueError("If `mode`= C a clonotype vector `ir_clone_key` must be specified.")
 
+        if use_size_factor:  #TODO
+            pmhc_list = use_size_factor if isinstance(use_size_factor, list) else mdata[gex_key].var_names.tolist()
+            x_plus = jnp.array(gex[:, pmhc_list].X.toarray(),
+                               dtype=FLOAT_DTYPE)  # only used for size factor calculation
+            s = self.calculate_size_factors(x_plus)
+            del x_plus
+        else:
+            s = jnp.ones(x.shape[0], dtype=FLOAT_DTYPE)
+
         self._check_parameters(x, x_neg, c, sigma)
-        self.model.preprocess_model_data(x=x, neg_cont=x_neg, c=c, sigma=sigma, mode=self.mode,
+        self.model.preprocess_model_data(x=x, s=s, neg_cont=x_neg, c=c, sigma=sigma, mode=self.mode,
                                          alpha_model=self.alpha_model, **kwargs)
+    
+    @staticmethod
+    def calculate_size_factors(counts: jnp.ndarray) -> jnp.ndarray:
+        """
+        DESeq2 size factor calculation
+        """
+
+        log_counts = jnp.log(counts)
+        log_counts = jnp.where(jnp.isinf(log_counts), jnp.nan, log_counts)
+        log_means = jnp.nanmean(log_counts, axis=0)
+
+        mask = jnp.isfinite(log_means) # Only use genes with non-zero geometric mean
+        log_ratios = log_counts[:, mask] - log_means[mask]
+        log_medians = jnp.nanmedian(log_ratios, axis=1)
+        size_factors = jnp.exp(log_medians)
+        size_factors = jnp.where(jnp.isnan(size_factors), 1.0, size_factors)  # Handle cells with all zero/nan counts
+
+        return size_factors
 
     @staticmethod
     def get_default_sampler_config():
@@ -195,18 +229,21 @@ def fit(self, sampler_config: Dict[str, Union[int, float]] = None, rng_key: int
 
         return self.__make_arvis()
 
-    def fit_svi(self, guide=npy.infer.autoguide.AutoNormal, svi_config: Dict[str, Union[int, float]] = None,
+    def fit_svi(self, guide='normal', svi_config: Dict[str, Union[int, float]] = None,
                 nof_inits: int = 100, use_minimal_loss: bool = True, rng_key: int = 998777,
-                return_loss: bool = False) \
+                y_true: Array = None) \
             -> az.InferenceData:
         """
         Implements stochastic variational inference
 
-        guide: The guide to use for variational inference. If None, self.model object will be checked for a guide function
+        guide: The guide to use for variational inference.
+               If None, self.model object will be checked for a guide function,
+               elif 'normal', AutoNormal guide will be used, elif 'mvnormal', AutoMultivariateNormal guide will be used
         svi_config: configuration for optimizer (Adam) and posterior samples
         nof_inits: number of initializations tried with different seeds to find gut init values
         use_minimal_loss: boolean indicating whether to report the parameters with the lowest loss instead
         rng_key: integer seed to initialize numpyros RNG-Key store
+        y_true: (Optional) ground truth labels to monitor performance during training
         """
 
         if self.model.data is None:
@@ -224,11 +261,12 @@ def fit_svi(self, guide=npy.infer.autoguide.AutoNormal, svi_config: Dict[str, Un
         svi_config.pop("adam", None)
         svi_config.pop("tracer", None)
 
+        optimizer = npy.optim.ClippedAdam(exponential_decay(**adam_config),)
         # check for custom guide in self.model otherwise use autoguide
-        # optimizer = npy.optim.ClippedAdam(exponential_decay(**adam_config),
-        #                                   b1=svi_config["adam_beta"]["b1"], b2=svi_config["adam_beta"]["b2"])
-        optimizer = npy.optim.ClippedAdam(adam_config["init_value"])
-
+        if guide == 'normal':
+            guide = npy.infer.autoguide.AutoNormal
+        elif (guide == 'mvnormal') or (guide == 'multivariatenormal'):
+            guide = npy.infer.autoguide.AutoMultivariateNormal
         # find good random initialization
         random_init = []
         for i, key in enumerate(random.split(random.PRNGKey(rng_key), nof_inits)):
@@ -259,11 +297,17 @@ def body_fn(svi_state, step):
         svi_state = svi.init(rng_key=best_key)
         losses = []
         params = []
+        logs = []
+        best_f1 = 0
+        best_it = 0
+        compiled_body_fn = jit(body_fn)
+
         with tqdm.trange(1, svi_config.get("maxiter", 1000) + 1,
                          disable=(not svi_config.get("progress_bar", False)), mininterval=10) as t:
-            batch = max(svi_config.get("maxiter", 1000) // 20, 1)
+            # batch = max(svi_config.get("maxiter", 1000) // 100, 1)
+            batch = 10
             for i in t:
-                svi_state, loss, param = jit(body_fn)(svi_state, i)
+                svi_state, loss, param = compiled_body_fn(svi_state, i)
 
                 losses.append(loss)
                 params.append(param)
@@ -274,16 +318,20 @@ def body_fn(svi_state, step):
                         avg_loss = float("nan")
                     else:
                         avg_loss = sum(valid_losses) / num_valid
-                    t.set_postfix_str(
-                        "init loss: {:.4f}, avg. loss [{}-{}]: {:.4f}".format(
-                            losses[0], i - batch + 1, i, avg_loss
-                        ),
-                        refresh=False,
-                    )
+                    if y_true is not None:
+                        self.svi_result = SVIRunResult(params=params[-1], losses=jnp.stack(losses), state=svi_state)
+                        p_pred, assignment = self.predict_posterior_class(threshold=0.5, )
+                        results = calculate_metrics(y_true, p_pred, assignment)
+                        if results["f1"] > best_f1:
+                            best_f1 = results["f1"]
+                            best_it = i
+                        results.update({"it": i, "loss": loss, "avg_loss": avg_loss, "best_f1": best_f1, "best_it": best_it})
+                        logs.append(results)
+
+                    t.set_postfix_str(f"avg. loss [{i - batch + 1}-{i}]: {avg_loss:.4f}", refresh=False,)
         losses = jnp.stack(losses)
         params = params[jnp.nanargmin(losses)] if use_minimal_loss else params[-1]
         self.svi_result = SVIRunResult(params=params, losses=losses, state=svi_state)
-
         posterior_samples = self.guide.sample_posterior(random.PRNGKey(self.rng_key), self.svi_result.params,
                                                         sample_shape=(500,))
 
@@ -291,12 +339,8 @@ def body_fn(svi_state, step):
         posterior_samples_np = {k: np.array(v)[np.newaxis, ...] for k, v in posterior_samples.items()}
         inference_data = az.from_dict(posterior=posterior_samples_np)
 
-        if return_loss:
-            converged = not jnp.isnan(losses).all()
-            best_iteration = jnp.nanargmin(losses)
-            best_loss = losses[best_iteration]
-            return inference_data, {"init_loss": losses[0], "converged": converged,
-                                    "best_loss": best_loss, "best_iteration": best_iteration}
+        if y_true is not None:
+            return inference_data, logs
         else:
             return inference_data
 
@@ -305,7 +349,10 @@ def predict_posterior_class(self,
                                 target_fdr: float = None,
                                 quantile: float = None,
                                 cred_intvl: float = None,
-                                clonotype_adherence: bool = False
+                                clonotype_adherence: bool = False,
+                                clonotype_majority_voting: bool = False,
+                                clonotype_mean_p: bool = False,
+                                clone_id: Array = None,
                                 ) -> Tuple[Array, Array]:
         """
         Returns the binder assignments based on the inferred posterior class probabilities.
@@ -323,25 +370,50 @@ class probability
                         over Pr(FDR(t)≤alpha|posterior)≥cred_intvl
             clonotype_adherence: (Optional) instead of using posterior class assignment per cell use clonotype
                                  probability vector if available.
+            clonotype_majority_voting: (Optional) after initial assignment perform majority voting within clonotypes
+            clonotype_mean_p: (Optional) after initial probability, use mean within each clonotype for each cell
+            clone_id: (Optional) map each cell id to clone id, shape=(n_cells)
         Returns:
             A tuple (p, assignment) of arrays with p being the posterior probability of binding and assignment the
             class assignment decision
         """
         def __return_p_summary(p_sample):
             if quantile:
-                return jnp.quantile(p_sample, quantile, axis=0)[:, 1]
+                p = jnp.quantile(p_sample, quantile, axis=0)[:, 1]
             elif cred_intvl:
-                return p_sample
+                p = p_sample
             else:
-                return jnp.nanmean(p_sample, axis=0)[:, 1]
+                p = jnp.nanmean(p_sample, axis=0)[:, 1]
+
+            if clonotype_mean_p:
+                if clone_id is None:
+                    raise ValueError("If `clonotype_mean_p`= True a clonotype vector `clone_id` must be specified.")
+                unique_ids = np.unique(clone_id)
+
+                if cred_intvl:
+                    # mean for each clone while keeping posterior samples, shape (num_clones, num_samples, 2)
+                    mean_p = np.stack([p[:, clone_id == cid].mean(axis=[1]) for cid in unique_ids])
+                    p = mean_p[clone_id].transpose(1, 0, 2)  # shape (num_posterior_samples, num_cells, 2)
+
+                else:
+                    df = pd.DataFrame({"p": p, "clone_id": clone_id})
+                    mean_p = df.groupby("clone_id")["p"].mean()
+                    p = mean_p.values[clone_id]
+            return p
+
+        if self.model.data["clone_continuous"] is not None and clone_id is None:
+            clone_id = self.model.data["clone_continuous"]
+
+        if clone_id is not None:
+            # Make clone_id continuous
+            clone_id, _ = pd.factorize(clone_id)
 
         if self.sampler is None and self.svi_result is None:
             raise RuntimeError("Model has not been fit yet. Please call first `fit` or `fit_svi`.")
 
         # posterior probability of belonging to the binding class
         if self.is_svi:
-            if clonotype_adherence and self.model.data["clone_continuous"] is not None:
-                #TODO ALTERNATIVE USE MAJORITY VOTING IN CLONE?
+            if clonotype_adherence and clone_id is not None:
                 posterior_samples = self.guide.sample_posterior(random.PRNGKey(self.rng_key), self.svi_result.params,
                                                                 sample_shape=(500,))
                 p = __return_p_summary(posterior_samples["w"])
@@ -353,7 +425,7 @@ def __return_p_summary(p_sample):
                 p = __return_p_summary(jnp.exp(samples["log_p"]))
 
         else:
-            if clonotype_adherence and self.model.data["clone_continuous"] is not None:
+            if clonotype_adherence and clone_id is not None:
                 p = __return_p_summary(self.sampler.get_samples()["w"])
             else:
                 p = __return_p_summary(jnp.exp(self.sampler.get_samples()["log_p"][..., [0,1]]))
@@ -363,9 +435,16 @@ def __return_p_summary(p_sample):
         else:
             assignment = self._predict_posterior_class(p, threshold, target_fdr)
 
-        if clonotype_adherence and self.model.data["clone_continuous"] is not None:
-            assignment = assignment[self.model.data["clone_continuous"]]
-            p = p[self.model.data["clone_continuous"]]
+        if clonotype_majority_voting:
+            if clone_id is None:
+                raise ValueError("If `clonotype_mean_p`= True a clonotype vector `clone_id` must be specified.")
+            df = pd.DataFrame({"assignment": assignment, "clone_id": clone_id})
+            majority_assignment = df.groupby("clone_id")["assignment"].agg(lambda x: x.mode()[0])
+            assignment = majority_assignment.values[clone_id]
+
+        if clonotype_adherence and clone_id is not None:
+            assignment = assignment[clone_id]
+            p = p[clone_id]
 
         return p, assignment
 
@@ -485,7 +564,7 @@ def get_posterior_samples(self, num_samples: int = 1000, seed: int = 42) -> Dict
         q = posterior_samples["delta_q"].mean(0).cumsum(0)
         w = posterior_samples["w"].mean(0)
 
-        if self.model.data["clone_continuous"] is not None:
+        if w.ndim > 2:
             # w is per clone, transform to per cell and take mean over all cells
             w_cell = w[self.model.data["clone_continuous"]]
             w_mean_over_cells = w_cell.mean(0)
@@ -504,6 +583,9 @@ def get_posterior_samples(self, num_samples: int = 1000, seed: int = 42) -> Dict
             elif self.mode == "I":
                 # alpha.shape = (2, )
                 alpha = q ** 2 / (q * (overdispersion) - q)
+                if self.model._model_config['alpha_offset']:
+                    alpha = alpha + jnp.array([0, self.model._model_config['alpha_offset']])
+                
 
         if self.mode == "C":
             # alpha is per clone, transform to per cell and take mean over all cells
@@ -513,11 +595,29 @@ def get_posterior_samples(self, num_samples: int = 1000, seed: int = 42) -> Dict
         else:
             alpha_mean_over_cells = alpha
 
-        return {"q": q, "w": w, "alpha": alpha,
-                "w_mean_over_cells": w_mean_over_cells, "alpha_mean_over_cells": alpha_mean_over_cells}
+        posterior_samples_mean = {"q": q, "w": w, "alpha": alpha,
+                                  "w_mean_over_cells": w_mean_over_cells, "alpha_mean_over_cells": alpha_mean_over_cells}
+        if self.alpha_model == 'overdispersion':
+            posterior_samples_mean["overdispersion"] = overdispersion
+        # Negative control model
+        if 'noise_mean_inv_inc' in posterior_samples:
+            s = jnp.ones(self.model.data["x"].shape[0]) if self.model.data["s"] is None else self.model.data["s"]
+
+            posterior_samples_mean['noise_mean_inv_inc'] = posterior_samples['noise_mean_inv_inc'].mean(0)
+            posterior_samples_mean['noise_overdisp_inv_inc'] = posterior_samples['noise_overdisp_inv_inc'].mean(0)
+
+            q_neg = jnp.clip(s * q[0] / posterior_samples_mean['noise_mean_inv_inc'], a_min=1e-3)
+            overdispersion_neg = jnp.clip(
+                posterior_samples_mean['overdispersion'][0] / posterior_samples_mean['noise_overdisp_inv_inc'],
+                a_min=1.0 + 1e-3)
+            alpha_neg = q_neg ** 2 / (q_neg * overdispersion_neg - q_neg)
+            posterior_samples_mean['q_neg'] = q_neg.mean()
+            posterior_samples_mean['alpha_neg'] = alpha_neg.mean()
+            posterior_samples_mean['overdispersion_neg'] = overdispersion_neg
+        return posterior_samples_mean
 
     def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', additional_text=None,
-                     save_dir='figs/', show=False):
+                     save_dir='figs/', show=False, return_plt=False):
 
         if self.trace is None and self.svi_result is None:
             raise RuntimeError("Model has not been fit yet. Please call `fit` or `fit_svi` first.")
@@ -529,7 +629,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
         # Plot data colored in predicted class assignment
         plt.subplot(3, 4, 1)
         ax = sns.histplot(x=self.model.data["x"], hue=assignment,
-                          discrete=True, element="step", alpha=0.7)
+                          discrete=True, element="step", alpha=0.3)
         leg = ax.get_legend()
         leg.set_title("Pred class")
         leg.set_frame_on(False)
@@ -539,7 +639,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
 
         plt.subplot(3, 4, 5)
         ax = sns.histplot(x=self.model.data["x"], hue=assignment,
-                          discrete=True, element="step", alpha=0.7)
+                          discrete=True, element="step", alpha=0.3)
         leg = ax.get_legend()
         leg.set_title("Pred class")
         leg.set_frame_on(False)
@@ -549,7 +649,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
 
         plt.subplot(3, 4, 9)
         ax = sns.scatterplot(x=self.model.data["x"], y=p_pred, hue=assignment,
-                             markers={0: ".", 1: "X"})
+                             markers={0: ".", 1: "X"}, alpha=0.3)
         leg = ax.get_legend()
         leg.set_title("Pred class")
         leg.set_frame_on(False)
@@ -561,7 +661,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
         # Plot data colored in true class assignment
         plt.subplot(3, 4, 2)
         ax = sns.histplot(x=self.model.data["x"], hue=y_true,
-                          discrete=True, element="step", alpha=0.7)
+                          discrete=True, element="step", alpha=0.3)
         leg = ax.get_legend()
         leg.set_title("True class")
         leg.set_frame_on(False)
@@ -570,7 +670,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
 
         plt.subplot(3, 4, 6)
         ax = sns.histplot(x=self.model.data["x"], hue=y_true,
-                          discrete=True, element="step", alpha=0.7)
+                          discrete=True, element="step", alpha=0.3)
         leg = ax.get_legend()
         leg.set_title("True class")
         leg.set_frame_on(False)
@@ -579,7 +679,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
         plt.title("True class assignment log-scale")
 
         plt.subplot(3, 4, 10)
-        ax = sns.scatterplot(x=self.model.data["x"], y=p_pred, hue=y_true)
+        ax = sns.scatterplot(x=self.model.data["x"], y=p_pred, hue=y_true, alpha=0.3)
         leg = ax.get_legend()
         leg.set_title("True class")
         leg.set_frame_on(False)
@@ -621,7 +721,10 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
                          label=f"q={q[1]:.2f} alpha={alpha_weighted[1]:.2f}")
             plt.fill_between(x, np.quantile(prob1, 0.05, axis=0), np.quantile(prob1, 0.95, axis=0), alpha=0.3,
                              label='5%-95% percentile')
-            plt.legend(frameon=False)
+            handles = ax1.lines + ax2.lines
+            labels = [h.get_label() for h in handles]
+            ax1.legend(handles, labels, frameon=False, loc='best')
+            ax2.get_legend().remove()
             sns.despine()
             plt.title("Posterior NB without mixing weights")
             plt.ylabel("Probability")
@@ -649,7 +752,6 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
             plt.ylabel("Probability")
 
         elif self.mode == "I":
-            npd.NegativeBinomial2(mean=q, concentration=alpha)
             prob0 = jnp.exp(npd.NegativeBinomial2(q[0], alpha[0]).log_prob(x))
             prob1 = jnp.exp(npd.NegativeBinomial2(q[1], alpha[1]).log_prob(x))
 
@@ -660,6 +762,10 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
             ax2 = ax1.twinx()
             sns.lineplot(x=np.arange(0, self.model.data["x"].max()), y=prob1, ax=ax2,
                          label=f"q={q[1]:.2f} alpha={alpha[1]:.2f}", color=sns.color_palette('tab10')[1])
+            handles = ax1.lines + ax2.lines
+            labels = [h.get_label() for h in handles]
+            ax1.legend(handles, labels, frameon=False, loc='best')
+            ax2.get_legend().remove()
             sns.despine()
             plt.title("Posterior NB without mixing weights")
             plt.ylabel("Probability")
@@ -708,7 +814,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
             labels = np.argmin(dists, axis=1)
 
             plt.subplot(3, 4, 3)
-            ax = sns.histplot(x=self.model.data["x"], hue=labels, discrete=True, element="step", alpha=0.7, stat='percent')
+            ax = sns.histplot(x=self.model.data["x"], hue=labels, discrete=True, element="step", alpha=0.3, stat='percent')
             leg = ax.get_legend()
             leg.set_title("KMeans cluster")
             leg.set_frame_on(False)
@@ -719,7 +825,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
             plt.ylabel("Percent")
 
             plt.subplot(3, 4, 7)
-            ax = sns.histplot(x=self.model.data["x"], hue=labels, discrete=True, element="step", alpha=0.7, stat='percent')
+            ax = sns.histplot(x=self.model.data["x"], hue=labels, discrete=True, element="step", alpha=0.3, stat='percent')
             leg = ax.get_legend()
             leg.set_title("KMeans cluster")
             leg.set_frame_on(False)
@@ -757,6 +863,8 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
             plt.savefig(os.path.join(save_dir, f"{config}.png"))
         if show:
             plt.show()
+        if return_plt:
+            return
         plt.close()
 
         return q, alpha, w
@@ -831,9 +939,6 @@ def preprocess_model_data(self,
                      "sigma": None if sigma is None else jnp.array(sigma, dtype=FLOAT_DTYPE),
                      }
 
-        self.mode = mode
-        self.alpha_model = alpha_model
-
     def _init_kmeans(self, scale_factor=1.0, outlier_threshold=4.0) -> Dict:
         """
         Initialize KMeans with 2 clusters and compute all necessary prior parameters
@@ -973,180 +1078,6 @@ def data(self, value):
         self._data = value
 
 
-class DextraDemixerMixtureModel(ADextraDemixerModel):
-    """
-    Implements a two-component negative-binomial mixture model of the form:
-
-    The generative model is wlog. specified for data of an individual epitope $j$.
-
-    $$\begin{align}
-    \text{\# Hyperprior}\\
-    &\mu_q \sim \text{N}(0,10)\\
-    &\sigma_q \sim \text{HC}(5)\\
-    &q^b \sim \text{N}(\mu_q, \sigma_q) &\forall b \in \{0,1\}\\
-    \text{IF mode == "H":}\\
-    &\alpha \sim \text{HC}(10)\\
-    \text{ELSE:}\\
-    &\alpha^b \sim \text{HC}(10) &\forall b \in \{0,1\}\\
-    \text{IF C defined:}&\\
-    \text{IF $\Sigma$ defined:}&\\
-    &w \sim \text{MvLogitN}(0, \Sigma)\\
-    \text{ELSE:}\\
-    &w_c \sim \text{Dir}([1,1]) &\forall c \in C\\
-    \text{IF mode == "H":}\\
-    &X_{ij}\sim (1-w^0_{C(i)})\text{NegBinom}({s_i}*e^{q^0}, \alpha) + w^1_{C(i)}\text{NegBinom}({s_i}*e^{q^1}, \alpha)&\forall i \in N\\
-    \text{ELIF mode == "C":}\\
-    &X_{ij}\sim (1-w^0_{C(i)})\text{NegBinom}({s_i}*e^{q^0}, \alpha_{C(i)}) + w^1_{C(i)}\text{NegBinom}({s_i}*e^{q^1}, \alpha_{C(i)})&\forall i \in N\\
-    \text{ELSE:}\\
-    &X_{ij}\sim (1-w^0_{C(i)})\text{NegBinom}({s_i}*e^{q^0}, \alpha^0) + w^1_{C(i)}\text{NegBinom}({s_i}*e^{q^1}, \alpha^1)&\forall i \in N\\
-    \text{ELSE:}\\
-    &w \sim \text{Dir}([1,1])\\
-    \text{IF mode == "H":}\\
-    &X_{ij}\sim (1-w^0)\text{NegBinom}({s_i}*e^{q^0}, \alpha) + w^1\text{NegBinom}({s_i}*e^{q^1}, \alpha)&\forall i \in N\\
-    \text{ELSE:}\\
-    &X_{ij}\sim (1-w^0)\text{NegBinom}({s_i}*e^{q^0}, \alpha^0) + w^1\text{NegBinom}({s_i}*e^{q^1}, \alpha^1)&\forall i \in N\\
-    \text{IF $X_{.j}^{\text{neg}}$ defined:}\\
-    \text{IF mode == "H":}\\
-    &X_{.j}^{\text{neg}} \sim \text{NegBinom}({s_i}^{\text{neg}}*e^{q^0}, \alpha)&\forall i \in N_{\text{neg}}\\
-    \text{ELIF mode == "C":}\\
-    &X_{.j}^{\text{neg}} \sim \text{NegBinom}({s_i}^{\text{neg}}*e^{q^0}, \alpha[C_{\text{neg}}(i)])&\forall i \in N_{\text{neg}}\\
-    \text{ELSE:}\\
-    &X_{.j}^{\text{neg}} \sim \text{NegBinom}({s_i}^{\text{neg}}*e^{q^0}, \alpha^0)&\forall i \in N_{\text{neg}}\\
-    \text{s.t.  }  q_{\text{raw}}^0 &< q_{\text{raw}}^1\\
-    \end{align}$$
-
-    with $C(i)$ being the clone index of T cell $i$ and $\hat{s}_i$ a cell-specific scaling factor accounting for
-    differences in sequencing depth. $q_c$ is the expectation value of avidity for clone $c\in C$ or the expacted
-    value of unspecific binding of epitope $i$.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self._name = "mixturemodel"
-        self._version = "0.0.3"
-        self._model_config = {
-            "mu_w_mean_prior": 0.0,
-            "mu_w_var_prior": 10.0,
-            "mu_q_mean_prior": 0.0,
-            "mu_q_var_prior": 5.0,
-            "sigma_q_var_prior": 10.0,
-            "alpha_var_prior": 10.0,
-        }
-
-    def get_default_model_config(self) -> Dict:
-        return self._model_config
-
-    @property
-    def name(self) -> str:
-        return self._name
-
-    @property
-    def version(self) -> str:
-        return self._version
-
-    def model(  # type: ignore
-            self,
-            **kwargs):
-
-        if self.data is None:
-            raise RuntimeError("Model was not properly initialized. Please call `preprocess_model_data` first")
-
-        model_config = {**self.get_default_model_config(), **kwargs["model_config"]} if (
-                "model_config" in kwargs) else self.get_default_model_config()
-
-        x = self.data["x"]
-        x_neg = self.data["x_neg"]
-        clone = self.data["clone_continuous"]
-        sigma = self.data["sigma"]
-        s =  jnp.ones(x.shape[0]) if self.data["s"] is None else self.data["s"]
-
-        # plates
-        N_sample = x.shape[0]
-        c_nof = np.unique(clone).size if clone is not None else 0
-        K = 2
-
-        # hyperprior parameters
-        mu_w_mean_prior = model_config.get("mu_w_mean_prior", 0.0)
-        mu_w_var_prior = model_config.get("mu_w_var_prior", 10.0)
-        mu_q_mean_prior = model_config.get("mu_q_mean_prior", 0.0)
-        mu_q_var_prior = model_config.get("mu_q_var_prior", 5.0)
-        sigma_q_var_prior = model_config.get("sigma_q_var_prior", 10.0)
-        alpha_var_prior = model_config.get("alpha_var_prior", 10.0)
-
-        # hyperprior
-        mu_q = npy.sample("mu_q", npd.Normal(mu_q_mean_prior, mu_q_var_prior))
-        sigma_q = npy.sample("sigma_q", npd.HalfCauchy(sigma_q_var_prior))
-
-        #shape prior
-        if self.mode == "H":
-            alpha = npy.sample("alpha", npd.TransformedDistribution(npd.HalfCauchy(alpha_var_prior),
-                                                                    npd.transforms.PowerTransform(-2.0)))
-        elif self.mode == "C":
-            with npy.plate("clone_axis", c_nof):
-                alpha = npy.sample("alpha", npd.TransformedDistribution(npd.HalfCauchy(alpha_var_prior),
-                                                                        npd.transforms.PowerTransform(-2.0)))
-        else:
-            with npy.plate("cluster_axis", K):
-                alpha = npy.sample("alpha", npd.TransformedDistribution(npd.HalfCauchy(alpha_var_prior),
-                                                                        npd.transforms.PowerTransform(-2.0)))
-
-        # cluster probability prior
-        if clone is not None:
-            if sigma is not None:
-                # non-centered multivariat parametrization
-                mu_w = npy.sample("mu_w", npd.Normal(mu_w_mean_prior, mu_w_var_prior))
-                with npy.plate("clone_axis", c_nof):
-                    gamma_w = npy.sample("gamma_w", npd.Normal(loc=0, scale=1))
-                L = jnp.linalg.cholesky(sigma)
-                w_raw = jax.scipy.special.ndtr(jnp.clip(mu_w + jnp.dot(L, gamma_w), -5, 5))
-                #w_raw = jax.scipy.special.expit(mu_w + jnp.dot(L, gamma_w))
-                w = npy.deterministic("w", jnp.stack([1 - w_raw, w_raw], axis=-1))
-            else:
-                with npy.plate("clone_axis", c_nof):
-                    w = npy.sample("w", npd.Dirichlet(jnp.ones(K)))
-            z = npd.Categorical(probs=w[clone])
-        else:
-            w = npy.sample("w", npd.Dirichlet(jnp.ones(K)))
-            z = npd.Categorical(probs=w)
-
-        q = npy.sample("q",
-                       npd.TransformedDistribution(npd.LogNormal(loc=mu_q, scale=sigma_q).expand((K,)),
-                                                   npd.transforms.OrderedTransform()))
-
-        with npy.plate("sample_axis", N_sample):
-            if x_neg is not None:
-                if self.mode == "H":
-                    yhat_neg = npy.sample("yhat_neg",
-                                          npd.NegativeBinomial2(mean=s*q[0], concentration=alpha),
-                                          obs=x_neg)
-                elif self.mode == "C":
-                    yhat_neg = npy.sample("yhat_neg",
-                                          npd.NegativeBinomial2(mean=s*q[0],
-                                                                concentration=alpha[clone]),
-                                          obs=x_neg)
-                else:
-                    yhat_neg = npy.sample("yhat_neg",
-                                          npd.NegativeBinomial2(mean=s*q[0], concentration=alpha[0]),
-                                          obs=x_neg)
-
-            # target pMHC
-            if self.mode == "C":
-                mixture = npd.MixtureSameFamily(z, npd.NegativeBinomial2(mean=s[:,None]*q,
-                                                                         concentration=alpha[clone].reshape(
-                                                                             (N_sample, 1))
-                                                                         )
-                                                )
-            else:
-                mixture = npd.MixtureSameFamily(z, npd.NegativeBinomial2(mean=s[:,None]*q,
-                                                                         concentration=alpha))
-
-            yhat = npy.sample("yhat", mixture, obs=x)
-
-            # Until here, where we can track the membership probability of each sample
-            log_probs = mixture.component_log_probs(yhat)
-            p = npy.deterministic("log_p", log_probs - logsumexp(log_probs, axis=-1, keepdims=True))
-
-
 class DextraDemixerKmeansModel(ADextraDemixerModel):
     """
     Dextramixer version who is initialized and prior parametrized by K-means++ results
@@ -1165,7 +1096,9 @@ def __init__(self):
             "sigma_q_var_prior": 10.0,
             "alpha_var_prior": 10.0,
             "var_hyperprior": 10.0,
-            "overdispersion_scale_prior": 1.0,
+            "scale_var_prior": 1.0,
+            "overdispersion_scale_prior": 1e-2,
+            "alpha_offset": 0.0,
         }
 
     @property
@@ -1190,8 +1123,7 @@ def preprocess_model_data(self,
         super().preprocess_model_data(x=x, s=s, neg_cont=neg_cont, c=c, sigma=sigma, mode=mode,
                                       alpha_model=alpha_model, **kwargs)
         self._kmeans_dict = self._init_kmeans(scale_factor=scale_factor,
-                                              outlier_threshold=kwargs['outlier_threshold']
-                                              if 'outlier_threshold' in kwargs else 4.0)
+                                              outlier_threshold=kwargs.get('outlier_threshold', 4.0))
         self._model_config.update(self._kmeans_dict)
 
     def get_default_model_config(self) -> Dict:
@@ -1223,6 +1155,8 @@ def model(self, **kwargs):
         var_hp = model_config["var_hyperprior"]  # Variance for priors if using alpha_model = kmeans
         tau_concentration_prior = model_config["tau_concentration_prior"]
         overdispersion_scale_prior = model_config["overdispersion_scale_prior"]
+        scale_var_prior = model_config["scale_var_prior"]
+        alpha_offset = model_config.get("alpha_offset", False)
 
         clone_means = model_config.get("clone_means", None)  # Mean for each clonotype
         clone_variances = model_config.get("clone_variances", None)
@@ -1250,11 +1184,15 @@ def model(self, **kwargs):
             # Convert kmeans priors to deltas, due to cumsum ordering
             mean_deltas = jnp.array([max(cluster_means[0], 1e-1), cluster_means[1] - cluster_means[0]])
             var_deltas = jnp.array([max(cluster_variances[0], 1e-1), max(cluster_variances[1] - cluster_variances[0], 1)])
+            if scale_var_prior == 'sqrt':
+                var_deltas = jnp.sqrt(var_deltas)
+            else:
+                var_deltas *= scale_var_prior
 
             # Convert kmeans parameters to lognormal parameters with target mean and variance
             # NB mean parameter: q_prior ~ LogNormal(mu_q, sigma_q), with cluster means and variances
             sigma2_q_prior = jnp.log(var_deltas / mean_deltas ** 2 + 1)
-            sigma_q_prior = jnp.sqrt(sigma2_q_prior)
+            sigma_q_prior = jnp.maximum(jnp.sqrt(sigma2_q_prior), 0.01)  # avoid sigma=0
             mu_q_prior = jnp.log(mean_deltas) - sigma2_q_prior / 2
 
             # Sample delta_q from lognormal distribution and cumsum to create ordered q
@@ -1276,7 +1214,12 @@ def model(self, **kwargs):
                 # For each mixture component, we have one alpha parameter
                 with npy.plate("cluster_axis", K):
                     overdispersion = npy.sample("overdispersion", overdispersion_prior_dist) + 1
-                alpha = npy.deterministic("alpha", q**2 / (q * overdispersion - q))
+                #TODO make sure that alpha > 1 to prevent exponential dist for the second component
+                if alpha_offset:
+                    alpha = npy.deterministic("alpha", q**2 / (q * overdispersion - q) + jnp.array([0, alpha_offset]))
+                else:
+                    alpha = npy.deterministic("alpha", q**2 / (q * overdispersion - q))
+
 
         elif self.alpha_model == "kmeans":
             # NB mean parameter: q ~ LogNormal(mu_q, sigma_q),
@@ -1341,18 +1284,30 @@ def model(self, **kwargs):
         else:
             raise NotImplementedError(f" {self.alpha_model} not implemented")
 
-        # Sample from the mixture model
-        with npy.plate("sample_axis", N_sample):
-            if x_neg is not None:
+        if x_neg is not None:
+            if self.alpha_model == 'kmeans':
+                raise NotImplementedError("Not implemented, kmeans model will be killed")
+            s_q = npy.sample("s_q", npd.LogNormal(0.9692917285815055, 0.6293977074906485))
+            q_neg = npy.deterministic("q_neg", jnp.clip(s * q[0] / s_q, a_min=1e-3))
+            s_alpha = npy.sample("s_alpha", npd.LogNormal(0.19724303327974974, 0.43970806321879075))
+            overdispersion_neg = npy.deterministic("overdispersion_neg", jnp.clip(overdispersion[0] / s_alpha, a_min=1.0 + 1e-3))
+            with npy.plate("sample_axis", N_sample):
                 if self.mode == "C":
-                    yhat_neg = npy.sample("yhat_neg", obs=x_neg,
-                                          fn=npd.NegativeBinomial2(mean=s*q[0],
-                                                                concentration=alpha[clone]),)
-
+                    raise NotImplementedError
+                    # Not sure how to implement: If each clonotype has its own alpha, then we need to compute alpha_neg
+                    # for each clonotype weighted by its belonging to noise
+
+                    # q_neg_weighted = (w[:, 0] * q_neg)
+                    # alpha_neg = npy.deterministic("alpha_neg", q_neg_weighted ** 2 / (q_neg_weighted * overdispersion - q_neg_weighted))
+                    # yhat_neg = npy.sample("yhat_neg", obs=x_neg,
+                    #                       fn=npd.NegativeBinomial2(mean=q_neg, concentration=alpha_neg[clone]))
                 else:
+                    alpha_neg = npy.deterministic("alpha_neg", q_neg ** 2 / (q_neg * overdispersion_neg - q_neg))
                     yhat_neg = npy.sample("yhat_neg", obs=x_neg,
-                                          fn=npd.NegativeBinomial2(mean=s*q[0], concentration=alpha[0]),)
+                                          fn=npd.NegativeBinomial2(mean=q_neg, concentration=alpha_neg, ))
 
+        # Sample from the mixture model
+        with npy.plate("sample_axis", N_sample):
             # target pMHC
             if self.mode == "C":
                 mixture = npd.MixtureSameFamily(z, npd.NegativeBinomial2(mean=s[:,None]*q, concentration=alpha[clone, None]))

From 0f6c8a73601183723d69e821d47cdba401e3949c Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Fri, 20 Feb 2026 10:01:24 +0100
Subject: [PATCH 26/39] update .gitignore

---
 .gitignore | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/.gitignore b/.gitignore
index 5b86d1f..b997b17 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,12 +10,19 @@ experiments/*/optuna_study
 experiments/*/optuna
 experiments/*/saved_models
 experiments/*/simulation
+experiments/*/logs
+experiments/Ioanna_data/
+experiments/Ioanna's experiments with Dextrademixer
 *.db
 *pkl
 experiments/*/benchmarks
 # Editors
 .vscode/
 .idea/
+*.png
+*.h5mu
+*.csv
+*.xlsx
 
 # Vagrant
 .vagrant/
@@ -138,3 +145,4 @@ venv.bak/
 .mypy_cache/
 .dmypy.json
 dmypy.json
+experiments/Ioanna_data/mudata_fromseurat-Copy1.h5mu

From 915d50b5d3d8891292ac9eb73dc177f0fcf32982 Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Mon, 16 Mar 2026 17:45:23 +0100
Subject: [PATCH 27/39] feature: remove kmeans outlier threshold, instead
 remove outlier from training data if zvalue > 100, but keep it for prediction

---
 dextrademixer/model/Dextrademixer.py | 153 +++++++++++++++------------
 1 file changed, 86 insertions(+), 67 deletions(-)

diff --git a/dextrademixer/model/Dextrademixer.py b/dextrademixer/model/Dextrademixer.py
index ba8d777..31bb77c 100644
--- a/dextrademixer/model/Dextrademixer.py
+++ b/dextrademixer/model/Dextrademixer.py
@@ -114,6 +114,7 @@ def preprocess_model_data(self,
                               ir_clone_key: str = None,
                               ir_cov_key: str = None,
                               use_size_factor: bool = None,
+                              outlier_threshold: float = None,
                               **kwargs):
         """
         Preprocesses the data and initializes the model
@@ -144,7 +145,7 @@ def preprocess_model_data(self,
             if c is None:
                 raise ValueError("If `mode`= C a clonotype vector `ir_clone_key` must be specified.")
 
-        if use_size_factor:  #TODO
+        if use_size_factor:
             pmhc_list = use_size_factor if isinstance(use_size_factor, list) else mdata[gex_key].var_names.tolist()
             x_plus = jnp.array(gex[:, pmhc_list].X.toarray(),
                                dtype=FLOAT_DTYPE)  # only used for size factor calculation
@@ -155,7 +156,7 @@ def preprocess_model_data(self,
 
         self._check_parameters(x, x_neg, c, sigma)
         self.model.preprocess_model_data(x=x, s=s, neg_cont=x_neg, c=c, sigma=sigma, mode=self.mode,
-                                         alpha_model=self.alpha_model, **kwargs)
+                                         alpha_model=self.alpha_model, outlier_threshold=outlier_threshold, **kwargs)
     
     @staticmethod
     def calculate_size_factors(counts: jnp.ndarray) -> jnp.ndarray:
@@ -345,6 +346,7 @@ def body_fn(svi_state, step):
             return inference_data
 
     def predict_posterior_class(self,
+                                data: Dict = None,
                                 threshold: float = None,
                                 target_fdr: float = None,
                                 quantile: float = None,
@@ -401,12 +403,9 @@ def __return_p_summary(p_sample):
                     p = mean_p.values[clone_id]
             return p
 
-        if self.model.data["clone_continuous"] is not None and clone_id is None:
-            clone_id = self.model.data["clone_continuous"]
-
-        if clone_id is not None:
-            # Make clone_id continuous
-            clone_id, _ = pd.factorize(clone_id)
+        data = data if data is not None else self.model.data_full
+        clone_id = clone_id if clone_id is not None else data.get("clone_continuous", None)
+        clone_id = pd.factorize(clone_id)[0] if clone_id is not None else None
 
         if self.sampler is None and self.svi_result is None:
             raise RuntimeError("Model has not been fit yet. Please call first `fit` or `fit_svi`.")
@@ -414,6 +413,7 @@ def __return_p_summary(p_sample):
         # posterior probability of belonging to the binding class
         if self.is_svi:
             if clonotype_adherence and clone_id is not None:
+                # TODO Not used, so did not match outlier filtered data
                 posterior_samples = self.guide.sample_posterior(random.PRNGKey(self.rng_key), self.svi_result.params,
                                                                 sample_shape=(500,))
                 p = __return_p_summary(posterior_samples["w"])
@@ -421,10 +421,11 @@ def __return_p_summary(p_sample):
             else:
                 predictive = npy.infer.Predictive(self.model.model, guide=self.guide, params=self.svi_result.params,
                                                   num_samples=500)
-                samples = predictive(jax.random.PRNGKey(self.rng_key))  # self.rng_key
+                samples = predictive(jax.random.PRNGKey(self.rng_key), data=data)  # self.rng_key
                 p = __return_p_summary(jnp.exp(samples["log_p"]))
 
         else:
+            # TODO Unused, did not update with outlier filtered data
             if clonotype_adherence and clone_id is not None:
                 p = __return_p_summary(self.sampler.get_samples()["w"])
             else:
@@ -617,7 +618,7 @@ def get_posterior_samples(self, num_samples: int = 1000, seed: int = 42) -> Dict
         return posterior_samples_mean
 
     def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', additional_text=None,
-                     save_dir='figs/', show=False, return_plt=False):
+                     save_dir='figs/', show=False, return_plt=False, data=None):
 
         if self.trace is None and self.svi_result is None:
             raise RuntimeError("Model has not been fit yet. Please call `fit` or `fit_svi` first.")
@@ -626,9 +627,11 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
             y_true = np.zeros_like(assignment)
         plt.figure(figsize=(16, 12))
 
+        data = data if data is not None else self.model.data_full
+
         # Plot data colored in predicted class assignment
         plt.subplot(3, 4, 1)
-        ax = sns.histplot(x=self.model.data["x"], hue=assignment,
+        ax = sns.histplot(x=data["x"], hue=assignment,
                           discrete=True, element="step", alpha=0.3)
         leg = ax.get_legend()
         leg.set_title("Pred class")
@@ -638,7 +641,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
         plt.title("Predicted class assignment")
 
         plt.subplot(3, 4, 5)
-        ax = sns.histplot(x=self.model.data["x"], hue=assignment,
+        ax = sns.histplot(x=data["x"], hue=assignment,
                           discrete=True, element="step", alpha=0.3)
         leg = ax.get_legend()
         leg.set_title("Pred class")
@@ -648,7 +651,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
         plt.title("Predicted class assignment log-scale")
 
         plt.subplot(3, 4, 9)
-        ax = sns.scatterplot(x=self.model.data["x"], y=p_pred, hue=assignment,
+        ax = sns.scatterplot(x=data["x"], y=p_pred, hue=assignment,
                              markers={0: ".", 1: "X"}, alpha=0.3)
         leg = ax.get_legend()
         leg.set_title("Pred class")
@@ -660,7 +663,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
 
         # Plot data colored in true class assignment
         plt.subplot(3, 4, 2)
-        ax = sns.histplot(x=self.model.data["x"], hue=y_true,
+        ax = sns.histplot(x=data["x"], hue=y_true,
                           discrete=True, element="step", alpha=0.3)
         leg = ax.get_legend()
         leg.set_title("True class")
@@ -669,7 +672,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
         plt.title("True class assignment")
 
         plt.subplot(3, 4, 6)
-        ax = sns.histplot(x=self.model.data["x"], hue=y_true,
+        ax = sns.histplot(x=data["x"], hue=y_true,
                           discrete=True, element="step", alpha=0.3)
         leg = ax.get_legend()
         leg.set_title("True class")
@@ -679,7 +682,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
         plt.title("True class assignment log-scale")
 
         plt.subplot(3, 4, 10)
-        ax = sns.scatterplot(x=self.model.data["x"], y=p_pred, hue=y_true, alpha=0.3)
+        ax = sns.scatterplot(x=data["x"], y=p_pred, hue=y_true, alpha=0.3)
         leg = ax.get_legend()
         leg.set_title("True class")
         leg.set_frame_on(False)
@@ -698,17 +701,17 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
         q = posterior_samples["q"]
         w = posterior_samples["w"]
         alpha = posterior_samples["alpha"]
-        x = np.arange(0, self.model.data["x"].max())
+        x = np.arange(0, data["x"].max())
 
         if self.mode == "C":
             # alpha_weighted is the mean of alpha weighted by w for each cell with shape (2, )
             # This reflects better the contribution of each alpha on average for the binder and non-binder NB component
-            alpha_weighted = (w[self.model.data["clone_continuous"]] * alpha[self.model.data["clone_continuous"]][:, None])
+            alpha_weighted = (w[data["clone_continuous"]] * alpha[data["clone_continuous"]][:, None])
             alpha_weighted = alpha_weighted.mean(0) / w.mean(0)
 
             # pdf for each cell
-            prob0 = jnp.exp(npd.NegativeBinomial2(mean=q[0], concentration=alpha[self.model.data["clone_continuous"]][:, np.newaxis]).log_prob(x))
-            prob1 = jnp.exp(npd.NegativeBinomial2(mean=q[1], concentration=alpha[self.model.data["clone_continuous"]][:, np.newaxis]).log_prob(x))
+            prob0 = jnp.exp(npd.NegativeBinomial2(mean=q[0], concentration=alpha[data["clone_continuous"]][:, np.newaxis]).log_prob(x))
+            prob1 = jnp.exp(npd.NegativeBinomial2(mean=q[1], concentration=alpha[data["clone_continuous"]][:, np.newaxis]).log_prob(x))
 
             # Individual Negative Binomial
             plt.subplot(3, 4, 8)
@@ -731,9 +734,9 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
 
             # Mixture model
             # Use different w for each clonotype and hence cell: w.shape = (#clonotypes, 2)
-            prob0_mix = prob0 * w[self.model.data["clone_continuous"]][:, 0:1]
-            prob1_mix = prob1 * w[self.model.data["clone_continuous"]][:, 1:2]
-            w_mean = w[self.model.data["clone_continuous"]].mean(0)  # mean over all clonotypes
+            prob0_mix = prob0 * w[data["clone_continuous"]][:, 0:1]
+            prob1_mix = prob1 * w[data["clone_continuous"]][:, 1:2]
+            w_mean = w[data["clone_continuous"]].mean(0)  # mean over all clonotypes
 
             plt.subplot(3, 4, 4)
             sns.lineplot(x=x, y=prob0_mix.mean(0), c=sns.color_palette('tab10')[0],
@@ -757,10 +760,10 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
 
             # Individual Negative Binomial
             plt.subplot(3, 4, 8)
-            ax1 = sns.lineplot(x=np.arange(0, self.model.data["x"].max()), y=prob0,
+            ax1 = sns.lineplot(x=np.arange(0, data["x"].max()), y=prob0,
                                label=f"q={q[0]:.2f} alpha={alpha[0]:.2f}", color=sns.color_palette('tab10')[0])
             ax2 = ax1.twinx()
-            sns.lineplot(x=np.arange(0, self.model.data["x"].max()), y=prob1, ax=ax2,
+            sns.lineplot(x=np.arange(0, data["x"].max()), y=prob1, ax=ax2,
                          label=f"q={q[1]:.2f} alpha={alpha[1]:.2f}", color=sns.color_palette('tab10')[1])
             handles = ax1.lines + ax2.lines
             labels = [h.get_label() for h in handles]
@@ -772,7 +775,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
 
             # Mixture model
             plt.subplot(3, 4, 4)
-            if self.model.data["clone_continuous"] is not None:
+            if data["clone_continuous"] is not None:
                 # Use different w for each clonotype: w.shape = (#clonotypes, 2)
                 # probX = (max UMI)
                 prob0_mix = prob0[None,] * w[:, 0:1]
@@ -810,11 +813,11 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
         if self.model_type == "mixturemodelkmeans":
             cluster_means = self.model._kmeans_dict["cluster_means"]
             cluster_vars = self.model._kmeans_dict["cluster_variances"]
-            dists = jnp.abs(self.model.data["x"][:, None].repeat(2, axis=1) - cluster_means)
+            dists = jnp.abs(data["x"][:, None].repeat(2, axis=1) - cluster_means)
             labels = np.argmin(dists, axis=1)
 
             plt.subplot(3, 4, 3)
-            ax = sns.histplot(x=self.model.data["x"], hue=labels, discrete=True, element="step", alpha=0.3, stat='percent')
+            ax = sns.histplot(x=data["x"], hue=labels, discrete=True, element="step", alpha=0.3, stat='percent')
             leg = ax.get_legend()
             leg.set_title("KMeans cluster")
             leg.set_frame_on(False)
@@ -825,7 +828,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
             plt.ylabel("Percent")
 
             plt.subplot(3, 4, 7)
-            ax = sns.histplot(x=self.model.data["x"], hue=labels, discrete=True, element="step", alpha=0.3, stat='percent')
+            ax = sns.histplot(x=data["x"], hue=labels, discrete=True, element="step", alpha=0.3, stat='percent')
             leg = ax.get_legend()
             leg.set_title("KMeans cluster")
             leg.set_frame_on(False)
@@ -839,7 +842,7 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config='', addi
             plt.subplot(3, 4, 12)
             alpha = cluster_means**2 / (cluster_vars - cluster_means)
             alpha[alpha < 0] = 100
-            x = np.arange(0, self.model.data["x"].max())
+            x = np.arange(0, data["x"].max())
             prob0 = jnp.exp(npd.NegativeBinomial2(cluster_means[0], alpha[0]).log_prob(x))
             prob1 = jnp.exp(npd.NegativeBinomial2(cluster_means[1], alpha[1]).log_prob(x))
 
@@ -930,16 +933,27 @@ def preprocess_model_data(self,
         """
         """
         clone = None if c is None else jnp.array(c, dtype=INT_DTYPE)
-        self.data = {"x": jnp.array(x, dtype=INT_DTYPE),
-                     "s": None if s is None else jnp.array(s, dtype=FLOAT_DTYPE),
-                     "x_neg": None if neg_cont is None else jnp.array(neg_cont, dtype=FLOAT_DTYPE),
-                     "clone": clone,
-                     # If clone is not contiuous, then there will be problems with indexing
-                     "clone_continuous": None if clone is None else jnp.searchsorted(jnp.unique(clone), clone),
-                     "sigma": None if sigma is None else jnp.array(sigma, dtype=FLOAT_DTYPE),
+        zscore = jnp.abs((x - jnp.mean(x)) / jnp.std(x))
+        outlier_threshold = 100 # TODO Hardcoded
+
+        self.data_full = {"x": jnp.array(x, dtype=INT_DTYPE),
+                          "s": None if s is None else jnp.array(s, dtype=FLOAT_DTYPE),
+                          "x_neg": None if neg_cont is None else jnp.array(neg_cont, dtype=FLOAT_DTYPE),
+                          "clone": clone,
+                          # If clone is not contiuous, then there will be problems with indexing
+                          "clone_continuous": None if clone is None else jnp.searchsorted(jnp.unique(clone), clone),
+                          "sigma": None if sigma is None else jnp.array(sigma, dtype=FLOAT_DTYPE),
+                          }
+        
+        self.data = {"x": jnp.array(x[jnp.where(zscore < outlier_threshold)], dtype=INT_DTYPE),
+                     "s": jnp.array(s[jnp.where(zscore < outlier_threshold)], dtype=FLOAT_DTYPE) if s is not None else None,
+                     "x_neg": jnp.array(neg_cont[jnp.where(zscore < outlier_threshold)], dtype=FLOAT_DTYPE) if neg_cont is not None else None,
+                     "clone": jnp.array(clone[jnp.where(zscore < outlier_threshold)], dtype=INT_DTYPE) if clone is not None else None,
+                     "clone_continuous": None if clone is None else jnp.searchsorted(jnp.unique(clone), clone[jnp.where(zscore < outlier_threshold)]),
+                     "sigma": None if sigma is None else jnp.array(sigma, dtype=FLOAT_DTYPE)[jnp.where(zscore < outlier_threshold)],
                      }
 
-    def _init_kmeans(self, scale_factor=1.0, outlier_threshold=4.0) -> Dict:
+    def _init_kmeans(self, scale_factor=1.0, outlier_threshold=None) -> Dict:
         """
         Initialize KMeans with 2 clusters and compute all necessary prior parameters
         for mu_q, sigma_q, alpha, and tau priors.
@@ -952,18 +966,21 @@ def _init_kmeans(self, scale_factor=1.0, outlier_threshold=4.0) -> Dict:
         returns: Dict with params estimates and  k-mean labels,
         """
         x = self.data["x"].copy()
-        # remove outliers
-        x_log = np.log(x + 1)  # Transform to log scale, roughly normal distributed
-        zscore = (x_log - x_log.mean()) / x_log.std()
-        x_no_outliers = x[zscore < outlier_threshold]
-        x_no_outliers = x_no_outliers.sort()
-        diffs = np.diff(x_no_outliers, prepend=x_no_outliers[0])
-        # TODO What value to define a huge gap?
-        huge_gap_indices = np.where(diffs > x.max() / 3)[0]
-        if len(huge_gap_indices) > 0:
-            first_gap_index = huge_gap_indices[0]
-            x_no_outliers = x_no_outliers[:first_gap_index]
-
+        # # remove outliers
+        # if outlier_threshold is not None:
+        #     x_log = np.log(x + 1)  # Transform to log scale, roughly normal distributed
+        #     zscore = (x_log - x_log.mean()) / x_log.std()
+        #     print(zscore.max())
+        #     x_no_outliers = x[zscore < outlier_threshold]
+        #     x_no_outliers = x_no_outliers.sort()
+        #     diffs = np.diff(x_no_outliers, prepend=x_no_outliers[0])
+        #     # TODO What value to define a huge gap?
+        #     huge_gap_indices = np.where(diffs > x.max() / 3)[0]
+        #     if len(huge_gap_indices) > 0:
+        #         first_gap_index = huge_gap_indices[0]
+        #         x_no_outliers = x_no_outliers[:first_gap_index]
+        # else:
+        x_no_outliers = x
         clone = self.data.get("clone_continuous", None)
         sigma = self.data.get("sigma", None)
         n_clusters = 2  # KMeans with 2 clusters
@@ -972,6 +989,11 @@ def _init_kmeans(self, scale_factor=1.0, outlier_threshold=4.0) -> Dict:
         kmeans = KMeans(n_clusters=n_clusters, init=np.vstack([np.min(x_no_outliers), np.max(x_no_outliers)]), n_init="auto").fit(x_no_outliers.reshape(-1, 1))
         labels = kmeans.predict(x.reshape(-1, 1))
 
+        if labels.sum() <= 3:
+            # Assign highest three values to cluster 1 and the rest to cluster 0
+            sorted_indices = np.argsort(x)
+            labels[sorted_indices[-3:]] = 1
+        
         # Initialize lists for cluster attributes
         cluster_means = []
         cluster_variances = []
@@ -1096,7 +1118,6 @@ def __init__(self):
             "sigma_q_var_prior": 10.0,
             "alpha_var_prior": 10.0,
             "var_hyperprior": 10.0,
-            "scale_var_prior": 1.0,
             "overdispersion_scale_prior": 1e-2,
             "alpha_offset": 0.0,
         }
@@ -1118,31 +1139,34 @@ def preprocess_model_data(self,
                               alpha_model="overdispersion",
                               mode: str = "H",
                               scale_factor: float = 1.0,
+                              outlier_threshold: float = None,
                               **kwargs):
 
         super().preprocess_model_data(x=x, s=s, neg_cont=neg_cont, c=c, sigma=sigma, mode=mode,
                                       alpha_model=alpha_model, **kwargs)
         self._kmeans_dict = self._init_kmeans(scale_factor=scale_factor,
-                                              outlier_threshold=kwargs.get('outlier_threshold', 4.0))
+                                              outlier_threshold=outlier_threshold)
         self._model_config.update(self._kmeans_dict)
 
     def get_default_model_config(self) -> Dict:
         return self._model_config
 
-    def model(self, **kwargs):
+    def model(self, data=None, **kwargs):
         """
         Define the probabilistic model based on the preprocessed data and KMeans initialization.
         """
-        if self.data is None:
-            raise RuntimeError("Model was not properly initialized. Please call `preprocess_model_data` first.")
 
         model_config = {**self.get_default_model_config(), **kwargs.get("model_config", {})}
-
-        x = self.data["x"]
-        s = jnp.ones(x.shape[0]) if self.data["s"] is None else self.data["s"]
-        x_neg = self.data["x_neg"]
-        clone = self.data["clone_continuous"]
-        sigma = self.data["sigma"]
+        if data is None:
+            if self.data is None:
+                raise RuntimeError("Model was not properly initialized. Please call `preprocess_model_data` first.")
+            data = self.data
+
+        x = data["x"]
+        s = data["s"]
+        x_neg = data["x_neg"]
+        clone = data["clone_continuous"]
+        sigma = data["sigma"]
         N_sample = x.shape[0]
         c_nof = np.unique(clone).size if clone is not None else 0
         K = 2
@@ -1155,7 +1179,6 @@ def model(self, **kwargs):
         var_hp = model_config["var_hyperprior"]  # Variance for priors if using alpha_model = kmeans
         tau_concentration_prior = model_config["tau_concentration_prior"]
         overdispersion_scale_prior = model_config["overdispersion_scale_prior"]
-        scale_var_prior = model_config["scale_var_prior"]
         alpha_offset = model_config.get("alpha_offset", False)
 
         clone_means = model_config.get("clone_means", None)  # Mean for each clonotype
@@ -1184,10 +1207,6 @@ def model(self, **kwargs):
             # Convert kmeans priors to deltas, due to cumsum ordering
             mean_deltas = jnp.array([max(cluster_means[0], 1e-1), cluster_means[1] - cluster_means[0]])
             var_deltas = jnp.array([max(cluster_variances[0], 1e-1), max(cluster_variances[1] - cluster_variances[0], 1)])
-            if scale_var_prior == 'sqrt':
-                var_deltas = jnp.sqrt(var_deltas)
-            else:
-                var_deltas *= scale_var_prior
 
             # Convert kmeans parameters to lognormal parameters with target mean and variance
             # NB mean parameter: q_prior ~ LogNormal(mu_q, sigma_q), with cluster means and variances
@@ -1214,7 +1233,7 @@ def model(self, **kwargs):
                 # For each mixture component, we have one alpha parameter
                 with npy.plate("cluster_axis", K):
                     overdispersion = npy.sample("overdispersion", overdispersion_prior_dist) + 1
-                #TODO make sure that alpha > 1 to prevent exponential dist for the second component
+                # Make sure that alpha > 1 to prevent exponential dist for the second component
                 if alpha_offset:
                     alpha = npy.deterministic("alpha", q**2 / (q * overdispersion - q) + jnp.array([0, alpha_offset]))
                 else:

From b1bb15a67c2ba3c83c4d323782d365075977a382 Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Mon, 16 Mar 2026 17:46:49 +0100
Subject: [PATCH 28/39] feature: float_or_none for argparse

---
 dextrademixer/utils/utils.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/dextrademixer/utils/utils.py b/dextrademixer/utils/utils.py
index e6b3e8b..0570e61 100644
--- a/dextrademixer/utils/utils.py
+++ b/dextrademixer/utils/utils.py
@@ -319,6 +319,15 @@ def str_to_bool(s):
     return args
 
 
+def float_or_none(value):
+    if value is None or value.lower() == 'none':
+        return None
+    try:
+        return float(value)
+    except ValueError:
+        raise ValueError(f"'{value}' is not a valid float or 'None'")
+    
+
 def get_slurm_cpu_count():
     # Check for SLURM-provided variables
     for var in ("SLURM_CPUS_PER_TASK", "SLURM_CPUS_ON_NODE", "SLURM_NTASKS", "SLURM_JOB_CPUS_PER_NODE"):

From a2950de9965556d493059746b5686b2ee169d905 Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Mon, 16 Mar 2026 17:49:38 +0100
Subject: [PATCH 29/39] experiment: Gemuend 2025 CMV data

---
 .../Ioanna_data/Benchmark_Ioanna.ipynb        | 1404 +++++++++++++++++
 1 file changed, 1404 insertions(+)
 create mode 100644 experiments/Ioanna_data/Benchmark_Ioanna.ipynb

diff --git a/experiments/Ioanna_data/Benchmark_Ioanna.ipynb b/experiments/Ioanna_data/Benchmark_Ioanna.ipynb
new file mode 100644
index 0000000..de4dde8
--- /dev/null
+++ b/experiments/Ioanna_data/Benchmark_Ioanna.ipynb
@@ -0,0 +1,1404 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2df647af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Changed directory to: /lustre/groups/imm01/workspace/yang/DextraDemixer/experiments/Ioanna_data\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Change cwd to the notebook's directory for VS Code \n",
+    "try:\n",
+    "    notebook_path = globals()['__vsc_ipynb_file__']\n",
+    "    notebook_dir = os.path.dirname(notebook_path)\n",
+    "    os.chdir(notebook_dir)\n",
+    "    print(f\"Changed directory to: {notebook_dir}\")\n",
+    "except:\n",
+    "    print(\"Could not find notebook path. Running from:\", os.getcwd())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d771bfc7-e30e-4d9f-ab47-1d80d0086f25",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/dextrademixer/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "/opt/conda/envs/dextrademixer/lib/python3.13/site-packages/muon/_core/preproc.py:31: FutureWarning: `__version__` is deprecated, use `importlib.metadata.version('scanpy')` instead\n",
+      "  if Version(scanpy.__version__) < Version(\"1.10\"):\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys \n",
+    "\n",
+    "sys.path.append(\"../../\")\n",
+    "\n",
+    "import numpyro as npy\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scanpy as sc\n",
+    "import scirpy as ir\n",
+    "import itertools as itr\n",
+    "import seaborn as sns\n",
+    "import resource\n",
+    "\n",
+    "from matplotlib import pyplot as plt, cm as mpl_cm\n",
+    "import matplotlib as mpl\n",
+    "from cycler import cycler\n",
+    "from statsmodels.stats.multitest import fdrcorrection\n",
+    "from sklearn.metrics import classification_report, average_precision_score, precision_score, recall_score, accuracy_score, confusion_matrix, f1_score, roc_auc_score, matthews_corrcoef\n",
+    "\n",
+    "import mudata as md\n",
+    "from mudata import MuData, AnnData\n",
+    "import muon as mu\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"mudata\")\n",
+    "\n",
+    "from IPython.display import Markdown\n",
+    "\n",
+    "from dextrademixer.model import DextraDemixer, BEAMT #, ITRAP,  ICON\n",
+    "from dextrademixer.utils import calculate_metrics\n",
+    "#from dextrademixer.model.ITRAP import ITRAP\n",
+    "\n",
+    "pd.set_option('display.max_columns', 200)\n",
+    "# mu.set_options(pull_on_update=True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5cbbc2c8-b9fa-434c-8ac4-54af11f1c4e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "abbc614b-f949-4856-a740-1d1841c70045",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper function:\n",
+    "dex_map ={\"HLA-A*0201\": \"DexA\", \"HLA-C*0702\":\"DexC\", \"HLA-B*0801\":\"DexN\", \"HLA-B*0702\":\"DexB\"}\n",
+    "\n",
+    "\n",
+    "def run_BEAMT(mdat,\n",
+    "              y_true,\n",
+    "              df,\n",
+    "              pmhc_key=\"HLA-A*0201\",\n",
+    "              gex_key=\"rio\",\n",
+    "              ir_key=\"airr\",\n",
+    "              neg_ctrl_key=\"HLA-B*0801\",\n",
+    "              threshold=0.5,\n",
+    "              target_fdr=None):\n",
+    "\n",
+    "    model_config = f\"beamt\"\n",
+    "    #config = f\"Data_{data_config}_model_{model_config}\"\n",
+    "    mdat = mdat[~np.isnan(y_true),:]\n",
+    "    \n",
+    "    mixer = BEAMT()\n",
+    "    mixer.preprocess_model_data(mdat, pmhc_key=pmhc_key, gex_key=gex_key, neg_ctrl_key=neg_ctrl_key, ir_key=ir_key)\n",
+    "\n",
+    "    mixer.fit()\n",
+    "    p_pred, assignment = mixer.predict_posterior_class(target_fdr=target_fdr, threshold=threshold)\n",
+    "    p_pred = p_pred.__array__()\n",
+    "    assignment = assignment.__array__()\n",
+    "    \n",
+    "    df.loc[~np.isnan(y_true),f\"{dex_map.get(pmhc_key, 'DexB')}_ppred_\"+model_config] = p_pred\n",
+    "    df.loc[~np.isnan(y_true),f\"{dex_map.get(pmhc_key, 'DexB')}_assignment_\"+model_config] = assignment\n",
+    "        \n",
+    "    metrics = eval_results(y_true[~np.isnan(y_true)], assignment, p_pred, None)\n",
+    "    # print(f\"{setting} BEAMT F1 {metrics['f1']:.3f}, Precision {metrics['precision']:.3f}, Recall {metrics['recall']:.3f} FDR {metrics['fdr']:.3f} MCC {metrics['mcc']:.3f}\\n\")\n",
+    "    return metrics\n",
+    "\n",
+    "\n",
+    "def eval_results(y_true, y_pred, p_pred, config):\n",
+    "    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n",
+    "    tpr = tp / (tp + fn)\n",
+    "    fdr = fp / (tp + fp)\n",
+    "\n",
+    "    mask = y_true.notna().to_numpy()\n",
+    "    \n",
+    "    results = pd.Series()\n",
+    "    results['roc_auc'] = roc_auc_score(y_true[mask].astype(int), p_pred[mask])\n",
+    "    results['pr_auc'] = average_precision_score(y_true[mask].astype(int), p_pred[mask])\n",
+    "    results['f1'] = f1_score(y_true[mask].astype(int), y_pred[mask])\n",
+    "    results['precision'] = precision_score(y_true[mask].astype(int), y_pred[mask])\n",
+    "    results['recall'] = recall_score(y_true[mask].astype(int), y_pred[mask])\n",
+    "    results['accuracy'] = accuracy_score(y_true[mask].astype(int), y_pred[mask])\n",
+    "    results['mcc'] = matthews_corrcoef(y_true[mask].astype(int), y_pred[mask])\n",
+    "    results['fdr'] = fdr\n",
+    "        \n",
+    "    #results.to_csv(config+\"_performance.csv\")\n",
+    "    return results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7d171cd9-677e-476e-bf99-017f94d30ca1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/dextrademixer/lib/python3.13/site-packages/anndata/utils.py:362: ExperimentalFeatureWarning: Support for Awkward Arrays is currently experimental. Behavior may change in the future. Please report any issues you may encounter!\n",
+      "  warnings.warn(msg, category, stacklevel=stacklevel)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre>MuData object with n_obs × n_vars = 9229 × 20154\n",
+       "  uns:\t&#x27;Cell_colors&#x27;, &#x27;Dex_colors&#x27;, &#x27;Sample_colors&#x27;, &#x27;cartridge_colors&#x27;, &#x27;cluster_colors&#x27;, &#x27;pca&#x27;\n",
+       "  obsm:\t&#x27;AB&#x27;, &#x27;HTO&#x27;, &#x27;HTOflex&#x27;, &#x27;RNA&#x27;, &#x27;RiO&#x27;, &#x27;RiO_bg&#x27;, &#x27;X_pca&#x27;, &#x27;X_umap&#x27;, &#x27;X_wnn.umap&#x27;, &#x27;wnn.umap&#x27;\n",
+       "  varm:\t&#x27;AB&#x27;, &#x27;HTO&#x27;, &#x27;HTOflex&#x27;, &#x27;PCs&#x27;, &#x27;RNA&#x27;, &#x27;RiO&#x27;, &#x27;RiO_bg&#x27;\n",
+       "  obsp:\t&#x27;wknn&#x27;, &#x27;wsnn&#x27;\n",
+       "  7 modalities\n",
+       "    airr:\t2835 x 0\n",
+       "      obs:\t&#x27;receptor_type&#x27;, &#x27;receptor_subtype&#x27;, &#x27;chain_pairing&#x27;, &#x27;cluster&#x27;, &#x27;Dex&#x27;, &#x27;clone_id&#x27;, &#x27;clone_id_size&#x27;, &#x27;antigen.species&#x27;, &#x27;antigen.gene&#x27;\n",
+       "      uns:\t&#x27;chain_indices&#x27;, &#x27;clone_id&#x27;, &#x27;clonotype_network&#x27;, &#x27;ir_dist_VDJDB_aa_identity&#x27;, &#x27;ir_dist_nt_identity&#x27;, &#x27;ir_query_VDJDB_aa_identity&#x27;, &#x27;scirpy_version&#x27;\n",
+       "      obsm:\t&#x27;X_clonotype_network&#x27;, &#x27;airr&#x27;, &#x27;chain_indices&#x27;\n",
+       "    rna:\t9229 x 20107\n",
+       "      obs:\t&#x27;orig.ident&#x27;, &#x27;nCount_RNA&#x27;, &#x27;nFeature_RNA&#x27;, &#x27;nCount_HTO&#x27;, &#x27;nFeature_HTO&#x27;, &#x27;nCount_HTOflex&#x27;, &#x27;nFeature_HTOflex&#x27;, &#x27;nReads_ALL&#x27;, &#x27;nReads_RNA&#x27;, &#x27;percent.mito&#x27;, &#x27;meanCount_RNA&#x27;, &#x27;meanCount_HTO&#x27;, &#x27;meanCount_HTOflex&#x27;, &#x27;HTOflex_maxID&#x27;, &#x27;HTOflex_secondID&#x27;, &#x27;HTOflex_margin&#x27;, &#x27;HTOflex_classification&#x27;, &#x27;HTOflex_classification.global&#x27;, &#x27;hash.ID&#x27;, &#x27;hash.ID.flex&#x27;, &#x27;HTO_maxID&#x27;, &#x27;HTO_secondID&#x27;, &#x27;HTO_margin&#x27;, &#x27;HTO_classification&#x27;, &#x27;HTO_classification.global&#x27;, &#x27;tag.no&#x27;, &#x27;Sample&#x27;, &#x27;SpikeIn.pct&#x27;, &#x27;Background&#x27;, &#x27;Background.Donor&#x27;, &#x27;Background.pct&#x27;, &#x27;flex.tag.no&#x27;, &#x27;Cell&#x27;, &#x27;Type&#x27;, &#x27;Donor&#x27;, &#x27;Donor.long&#x27;, &#x27;Origin&#x27;, &#x27;Specificity&#x27;, &#x27;peptide.stim&#x27;, &#x27;CMV.peptide&#x27;, &#x27;Peptide.Seq&#x27;, &#x27;HLA.A.02.01&#x27;, &#x27;HLA.B.07.02&#x27;, &#x27;HLA.C.07.02&#x27;, &#x27;nCount_AB&#x27;, &#x27;nFeature_AB&#x27;, &#x27;nCount_RiO&#x27;, &#x27;nFeature_RiO&#x27;, &#x27;cartridge&#x27;, &#x27;S.Score&#x27;, &#x27;G2M.Score&#x27;, &#x27;Phase&#x27;, &#x27;RNA_snn_res.0.5&#x27;, &#x27;seurat_clusters&#x27;, &#x27;nCount_SCT&#x27;, &#x27;nFeature_SCT&#x27;, &#x27;SCT.weight&#x27;, &#x27;AB.weight&#x27;, &#x27;wsnn_res.0.5&#x27;, &#x27;wsnn_res.0.5_anno&#x27;, &#x27;wsnn_res.0.5_old&#x27;, &#x27;wsnn_res.0.52&#x27;, &#x27;wsnn_res.0.52_anno&#x27;, &#x27;nCount_RiO_bg&#x27;, &#x27;nFeature_RiO_bg&#x27;, &#x27;cell_id&#x27;, &#x27;DexA&#x27;, &#x27;DexB&#x27;, &#x27;DexC&#x27;, &#x27;Dex&#x27;, &#x27;flex_tag_label&#x27;, &#x27;cluster&#x27;, &#x27;category&#x27;\n",
+       "      var:\t&#x27;vst.mean&#x27;, &#x27;vst.variance&#x27;, &#x27;vst.variance.expected&#x27;, &#x27;vst.variance.standardized&#x27;, &#x27;vst.variable&#x27;, &#x27;highly_variable&#x27;\n",
+       "      uns:\t&#x27;pca&#x27;\n",
+       "      obsm:\t&#x27;X_pca&#x27;, &#x27;wnn.umap&#x27;\n",
+       "      varm:\t&#x27;PCs&#x27;\n",
+       "      layers:\t&#x27;counts&#x27;, &#x27;data&#x27;\n",
+       "    ab:\t9229 x 31\n",
+       "      var:\t&#x27;highly_variable&#x27;\n",
+       "      uns:\t&#x27;apca&#x27;\n",
+       "      obsm:\t&#x27;X_apca&#x27;\n",
+       "      varm:\t&#x27;apca&#x27;\n",
+       "      layers:\t&#x27;counts&#x27;, &#x27;data&#x27;\n",
+       "    rio:\t9229 x 4\n",
+       "      layers:\t&#x27;counts&#x27;\n",
+       "    rio_bg:\t9229 x 3\n",
+       "      layers:\t&#x27;counts&#x27;, &#x27;data&#x27;\n",
+       "    hto:\t9229 x 4\n",
+       "      layers:\t&#x27;counts&#x27;\n",
+       "    hto_flex:\t9229 x 5\n",
+       "      layers:\t&#x27;counts&#x27;</pre>"
+      ],
+      "text/plain": [
+       "MuData object with n_obs × n_vars = 9229 × 20154\n",
+       "  uns:\t'Cell_colors', 'Dex_colors', 'Sample_colors', 'cartridge_colors', 'cluster_colors', 'pca'\n",
+       "  obsm:\t'AB', 'HTO', 'HTOflex', 'RNA', 'RiO', 'RiO_bg', 'X_pca', 'X_umap', 'X_wnn.umap', 'wnn.umap'\n",
+       "  varm:\t'AB', 'HTO', 'HTOflex', 'PCs', 'RNA', 'RiO', 'RiO_bg'\n",
+       "  obsp:\t'wknn', 'wsnn'\n",
+       "  7 modalities\n",
+       "    airr:\t2835 x 0\n",
+       "      obs:\t'receptor_type', 'receptor_subtype', 'chain_pairing', 'cluster', 'Dex', 'clone_id', 'clone_id_size', 'antigen.species', 'antigen.gene'\n",
+       "      uns:\t'chain_indices', 'clone_id', 'clonotype_network', 'ir_dist_VDJDB_aa_identity', 'ir_dist_nt_identity', 'ir_query_VDJDB_aa_identity', 'scirpy_version'\n",
+       "      obsm:\t'X_clonotype_network', 'airr', 'chain_indices'\n",
+       "    rna:\t9229 x 20107\n",
+       "      obs:\t'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'nCount_HTO', 'nFeature_HTO', 'nCount_HTOflex', 'nFeature_HTOflex', 'nReads_ALL', 'nReads_RNA', 'percent.mito', 'meanCount_RNA', 'meanCount_HTO', 'meanCount_HTOflex', 'HTOflex_maxID', 'HTOflex_secondID', 'HTOflex_margin', 'HTOflex_classification', 'HTOflex_classification.global', 'hash.ID', 'hash.ID.flex', 'HTO_maxID', 'HTO_secondID', 'HTO_margin', 'HTO_classification', 'HTO_classification.global', 'tag.no', 'Sample', 'SpikeIn.pct', 'Background', 'Background.Donor', 'Background.pct', 'flex.tag.no', 'Cell', 'Type', 'Donor', 'Donor.long', 'Origin', 'Specificity', 'peptide.stim', 'CMV.peptide', 'Peptide.Seq', 'HLA.A.02.01', 'HLA.B.07.02', 'HLA.C.07.02', 'nCount_AB', 'nFeature_AB', 'nCount_RiO', 'nFeature_RiO', 'cartridge', 'S.Score', 'G2M.Score', 'Phase', 'RNA_snn_res.0.5', 'seurat_clusters', 'nCount_SCT', 'nFeature_SCT', 'SCT.weight', 'AB.weight', 'wsnn_res.0.5', 'wsnn_res.0.5_anno', 'wsnn_res.0.5_old', 'wsnn_res.0.52', 'wsnn_res.0.52_anno', 'nCount_RiO_bg', 'nFeature_RiO_bg', 'cell_id', 'DexA', 'DexB', 'DexC', 'Dex', 'flex_tag_label', 'cluster', 'category'\n",
+       "      var:\t'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'highly_variable'\n",
+       "      uns:\t'pca'\n",
+       "      obsm:\t'X_pca', 'wnn.umap'\n",
+       "      varm:\t'PCs'\n",
+       "      layers:\t'counts', 'data'\n",
+       "    ab:\t9229 x 31\n",
+       "      var:\t'highly_variable'\n",
+       "      uns:\t'apca'\n",
+       "      obsm:\t'X_apca'\n",
+       "      varm:\t'apca'\n",
+       "      layers:\t'counts', 'data'\n",
+       "    rio:\t9229 x 4\n",
+       "      layers:\t'counts'\n",
+       "    rio_bg:\t9229 x 3\n",
+       "      layers:\t'counts', 'data'\n",
+       "    hto:\t9229 x 4\n",
+       "      layers:\t'counts'\n",
+       "    hto_flex:\t9229 x 5\n",
+       "      layers:\t'counts'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mdata = mu.read_h5mu(\"./mudata_scanpy_new.h5mu\")\n",
+    "mdata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "555c9cd9-0925-420f-92e8-1f5e2754edb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "count",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "eb13d510-261d-4695-aeb7-06ef165e523c",
+       "rows": [
+        [
+         "('05-95-D2',)",
+         "2754"
+        ],
+        [
+         "('05-95-D1',)",
+         "2348"
+        ],
+        [
+         "('50-50-D1',)",
+         "2161"
+        ],
+        [
+         "('50-50-D2',)",
+         "1966"
+        ]
+       ],
+       "shape": {
+        "columns": 1,
+        "rows": 4
+       }
+      },
+      "text/plain": [
+       "rna:Sample\n",
+       "05-95-D2      2754\n",
+       "05-95-D1      2348\n",
+       "50-50-D1      2161\n",
+       "50-50-D2      1966\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# These are the four samples: Donor 1 & 2, each with 5 or 50% theoretical spikein over all 3 clones (detected is less)\n",
+    "mdata.obs[['rna:Sample', ]].value_counts(dropna=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "968d3df4-4149-4e27-93da-e58dd3b5bbcf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Spike-in clones are True, HLA-mismatched BG are False (BG_D1 False for NLV, BG_D2 False for CRV)\n",
+    "# HLA-matched BG are nan because we don't know if there are some clonotypes that may bind\n",
+    "mdata.obs['NLV_true'] = np.where(mdata.obs[\"rna:flex_tag_label\"] == 'NLV_Clone', 1, np.where(mdata.obs[\"rna:flex_tag_label\"] =='BG_D1', np.nan, 0))\n",
+    "mdata.obs['CRV_true'] = np.where(mdata.obs[\"rna:flex_tag_label\"] == 'CRV_Clone', 1, np.where(mdata.obs[\"rna:flex_tag_label\"] == 'BG_D2', np.nan, 0))\n",
+    "mdata.obs['TPR_true'] = np.where((mdata.obs[\"rna:flex_tag_label\"] == 'TPR_enriched') & (mdata.obs['rna:wsnn_res.0.5_anno'] == 'TPR_enriched_CD8_other'), 1, np.where(mdata.obs[\"rna:flex_tag_label\"] == 'BG_D2', np.nan, 0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6711ec3f-2366-40f4-bd0d-f8e05cf1c319",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HLA-A*0201 05-95-D2 clone_avail_only=True Mean increase: 36.0 ratio 0.0084 N=835\n",
+      "HLA-A*0201 05-95-D2 clone_avail_only=False Mean increase: 19.8 ratio 0.0087 N=2752\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HLA-A*0201 50-50-D2 clone_avail_only=True Mean increase: 23.2 ratio 0.0494 N=587\n",
+      "HLA-A*0201 50-50-D2 clone_avail_only=False Mean increase: 18.5 ratio 0.0407 N=1966\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HLA-C*0702 05-95-D1 clone_avail_only=True Mean increase: 9.8 ratio 0.0042 N=709\n",
+      "HLA-C*0702 05-95-D1 clone_avail_only=False Mean increase: 13.4 ratio 0.0047 N=2348\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HLA-C*0702 50-50-D1 clone_avail_only=True Mean increase: 14.9 ratio 0.0697 N=703\n",
+      "HLA-C*0702 50-50-D1 clone_avail_only=False Mean increase: 13.3 ratio 0.0569 N=2161\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize \n",
+    "true_labels = {\"HLA-A*0201\":\"NLV_true\", \"HLA-C*0702\":\"CRV_true\", \"HLA-B*0702\":\"TPR_true\"}\n",
+    "\n",
+    "for dex_key, sample in [('HLA-A*0201', '05-95-D2'), ('HLA-A*0201', '50-50-D2'), ('HLA-C*0702', '05-95-D1'), ('HLA-C*0702', '50-50-D1')]:\n",
+    "    plt.figure(figsize=(6, 3))\n",
+    "    i = 0\n",
+    "    for tcr_info_sequenced_only in [True, False]:\n",
+    "        i += 1\n",
+    "        plt.subplot(1, 2, i)\n",
+    "        mdata_sample = mdata[mdata.obs[f'rna:Sample'] == sample].copy()\n",
+    "        if tcr_info_sequenced_only:\n",
+    "            mdata_sample = mdata_sample[mdata_sample['airr'].obs_names]\n",
+    "        mdata_sample = mdata_sample[mdata_sample.obs[true_labels[dex_key]].notna()].copy()\n",
+    "        sns.histplot(x=mdata_sample['rio'][:, dex_key].X.toarray()[:, 0], hue=mdata_sample.obs[true_labels[dex_key]], bins=100)\n",
+    "        n_samples = mdata_sample.shape[0]\n",
+    "        plt.yscale('log')\n",
+    "        sns.despine()\n",
+    "\n",
+    "        # Train models\n",
+    "        y_true = mdata_sample.obs[true_labels[dex_key]]\n",
+    "        # mdata_tmp = mdata_sample[~np.isnan(y_true),:].copy()\n",
+    "        df = pd.DataFrame({\"barcode\":mdata_sample.obs_names, \"NLV_true\":mdata_sample.obs.NLV_true, \"CRV_true\":mdata_sample.obs.CRV_true, \"TPR_true\":mdata_sample.obs.TPR_true})\n",
+    "\n",
+    "        # # BEAMT\n",
+    "        setting = f'{dex_key},{sample},{tcr_info_sequenced_only}'\n",
+    "        # result = {'dex_key': dex_key, 'model_config': 'BEAMT', 'sample': sample, 'tcr_info_sequenced_only': tcr_info_sequenced_only, 'setting': setting, }\n",
+    "        result_ = run_BEAMT(mdata_sample, y_true, df, pmhc_key=dex_key, gex_key=\"rio\", ir_key=\"airr\", neg_ctrl_key=\"HLA-B*0801\", threshold=0.5, target_fdr=None)\n",
+    "        \n",
+    "        pos = mdata_sample[y_true.astype(bool)]\n",
+    "        neg = mdata_sample[~y_true.astype(bool)]\n",
+    "        pos_mean = pos['rio'][:, dex_key].X.toarray().mean()\n",
+    "        neg_mean = neg['rio'][:, dex_key].X.toarray().mean()\n",
+    "        print(f'{dex_key} {sample} clone_avail_only={tcr_info_sequenced_only} Mean increase: {(pos_mean / neg_mean):.1f} ratio {pos.shape[0]/n_samples:.4f} N={n_samples}')\n",
+    "\n",
+    "        plt.title(f'clone_avail_only={tcr_info_sequenced_only} N={n_samples}\\nBEAMT F1={result_[\"f1\"]:.2f}, MCC={result_[\"mcc\"]:.2f}')\n",
+    "        \n",
+    "    plt.suptitle(f'{dex_key} {sample}')\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ccaea043-f350-4c32-8dee-722e7503a415",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "dex_key",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "model_config",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "sample",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "tcr_info_sequenced_only",
+         "rawType": "bool",
+         "type": "boolean"
+        },
+        {
+         "name": "setting",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "roc_auc",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "pr_auc",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "f1",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "precision",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "recall",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "accuracy",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "mcc",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fdr",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "ref": "ff9f4923-3500-48fb-9903-1947b62735f3",
+       "rows": [
+        [
+         "0",
+         "HLA-A*0201",
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+         "1.0",
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+         "0.9994654203323559",
+         "0.9683386259473217",
+         "0.9130434782608695",
+         "0.9545454545454546",
+         "0.875",
+         "0.998546511627907",
+         "0.9131901392356188",
+         "0.045454545454545456"
+        ],
+        [
+         "2",
+         "HLA-A*0201",
+         "BEAMT",
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+         "True",
+         "DexA,50-50-D2,True",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "0.0"
+        ],
+        [
+         "3",
+         "HLA-A*0201",
+         "BEAMT",
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+         "0.9570410217991486",
+         "0.9281045751633987",
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+         "0.8875",
+         "0.9944048830111902",
+         "0.9262500139199894",
+         "0.0273972602739726"
+        ],
+        [
+         "4",
+         "HLA-C*0702",
+         "BEAMT",
+         "05-95-D1",
+         "True",
+         "DexC,05-95-D1,True",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
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+         "0.0"
+        ],
+        [
+         "5",
+         "HLA-C*0702",
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+         "DexC,05-95-D1,False",
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+         "1.0",
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+         "0.9995741056218058",
+         "0.9532586618367267",
+         "0.0"
+        ],
+        [
+         "6",
+         "HLA-C*0702",
+         "BEAMT",
+         "50-50-D1",
+         "True",
+         "DexC,50-50-D1,True",
+         "0.9949759720401922",
+         "0.9637538364950332",
+         "0.8470588235294118",
+         "1.0",
+         "0.7346938775510204",
+         "0.9815078236130867",
+         "0.8487487888793945",
+         "0.0"
+        ],
+        [
+         "7",
+         "HLA-C*0702",
+         "BEAMT",
+         "50-50-D1",
+         "False",
+         "DexC,50-50-D1,False",
+         "0.9755399443101398",
+         "0.899231795670309",
+         "0.8113207547169812",
+         "0.9662921348314607",
+         "0.6991869918699187",
+         "0.98149005090236",
+         "0.8134723615037478",
+         "0.033707865168539325"
+        ]
+       ],
+       "shape": {
+        "columns": 13,
+        "rows": 8
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dex_key</th>\n",
+       "      <th>model_config</th>\n",
+       "      <th>sample</th>\n",
+       "      <th>tcr_info_sequenced_only</th>\n",
+       "      <th>setting</th>\n",
+       "      <th>roc_auc</th>\n",
+       "      <th>pr_auc</th>\n",
+       "      <th>f1</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>mcc</th>\n",
+       "      <th>fdr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>HLA-A*0201</td>\n",
+       "      <td>BEAMT</td>\n",
+       "      <td>05-95-D2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>DexA,05-95-D2,True</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>HLA-A*0201</td>\n",
+       "      <td>BEAMT</td>\n",
+       "      <td>05-95-D2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>DexA,05-95-D2,False</td>\n",
+       "      <td>0.999465</td>\n",
+       "      <td>0.968339</td>\n",
+       "      <td>0.913043</td>\n",
+       "      <td>0.954545</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.998547</td>\n",
+       "      <td>0.913190</td>\n",
+       "      <td>0.045455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HLA-A*0201</td>\n",
+       "      <td>BEAMT</td>\n",
+       "      <td>50-50-D2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>DexA,50-50-D2,True</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HLA-A*0201</td>\n",
+       "      <td>BEAMT</td>\n",
+       "      <td>50-50-D2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>DexA,50-50-D2,False</td>\n",
+       "      <td>0.989346</td>\n",
+       "      <td>0.957041</td>\n",
+       "      <td>0.928105</td>\n",
+       "      <td>0.972603</td>\n",
+       "      <td>0.887500</td>\n",
+       "      <td>0.994405</td>\n",
+       "      <td>0.926250</td>\n",
+       "      <td>0.027397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HLA-C*0702</td>\n",
+       "      <td>BEAMT</td>\n",
+       "      <td>05-95-D1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>DexC,05-95-D1,True</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>HLA-C*0702</td>\n",
+       "      <td>BEAMT</td>\n",
+       "      <td>05-95-D1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>DexC,05-95-D1,False</td>\n",
+       "      <td>0.999844</td>\n",
+       "      <td>0.975758</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.909091</td>\n",
+       "      <td>0.999574</td>\n",
+       "      <td>0.953259</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>HLA-C*0702</td>\n",
+       "      <td>BEAMT</td>\n",
+       "      <td>50-50-D1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>DexC,50-50-D1,True</td>\n",
+       "      <td>0.994976</td>\n",
+       "      <td>0.963754</td>\n",
+       "      <td>0.847059</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.734694</td>\n",
+       "      <td>0.981508</td>\n",
+       "      <td>0.848749</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>HLA-C*0702</td>\n",
+       "      <td>BEAMT</td>\n",
+       "      <td>50-50-D1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>DexC,50-50-D1,False</td>\n",
+       "      <td>0.975540</td>\n",
+       "      <td>0.899232</td>\n",
+       "      <td>0.811321</td>\n",
+       "      <td>0.966292</td>\n",
+       "      <td>0.699187</td>\n",
+       "      <td>0.981490</td>\n",
+       "      <td>0.813472</td>\n",
+       "      <td>0.033708</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      dex_key model_config    sample  tcr_info_sequenced_only  \\\n",
+       "0  HLA-A*0201        BEAMT  05-95-D2                     True   \n",
+       "1  HLA-A*0201        BEAMT  05-95-D2                    False   \n",
+       "2  HLA-A*0201        BEAMT  50-50-D2                     True   \n",
+       "3  HLA-A*0201        BEAMT  50-50-D2                    False   \n",
+       "4  HLA-C*0702        BEAMT  05-95-D1                     True   \n",
+       "5  HLA-C*0702        BEAMT  05-95-D1                    False   \n",
+       "6  HLA-C*0702        BEAMT  50-50-D1                     True   \n",
+       "7  HLA-C*0702        BEAMT  50-50-D1                    False   \n",
+       "\n",
+       "               setting   roc_auc    pr_auc        f1  precision    recall  \\\n",
+       "0   DexA,05-95-D2,True  1.000000  1.000000  1.000000   1.000000  1.000000   \n",
+       "1  DexA,05-95-D2,False  0.999465  0.968339  0.913043   0.954545  0.875000   \n",
+       "2   DexA,50-50-D2,True  1.000000  1.000000  1.000000   1.000000  1.000000   \n",
+       "3  DexA,50-50-D2,False  0.989346  0.957041  0.928105   0.972603  0.887500   \n",
+       "4   DexC,05-95-D1,True  1.000000  1.000000  1.000000   1.000000  1.000000   \n",
+       "5  DexC,05-95-D1,False  0.999844  0.975758  0.952381   1.000000  0.909091   \n",
+       "6   DexC,50-50-D1,True  0.994976  0.963754  0.847059   1.000000  0.734694   \n",
+       "7  DexC,50-50-D1,False  0.975540  0.899232  0.811321   0.966292  0.699187   \n",
+       "\n",
+       "   accuracy       mcc       fdr  \n",
+       "0  1.000000  1.000000  0.000000  \n",
+       "1  0.998547  0.913190  0.045455  \n",
+       "2  1.000000  1.000000  0.000000  \n",
+       "3  0.994405  0.926250  0.027397  \n",
+       "4  1.000000  1.000000  0.000000  \n",
+       "5  0.999574  0.953259  0.000000  \n",
+       "6  0.981508  0.848749  0.000000  \n",
+       "7  0.981490  0.813472  0.033708  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# BEAMT\n",
+    "\n",
+    "results = []\n",
+    "for dex_key, sample in [('HLA-A*0201', '05-95-D2'), ('HLA-A*0201', '50-50-D2'), ('HLA-C*0702', '05-95-D1'), ('HLA-C*0702', '50-50-D1')]:\n",
+    "    for tcr_info_sequenced_only in [True, False]:\n",
+    "        mdata_sample = mdata[mdata.obs[f'rna:Sample'] == sample]\n",
+    "        if tcr_info_sequenced_only:\n",
+    "            mdata_sample = mdata_sample[mdata_sample['airr'].obs_names]\n",
+    "\n",
+    "        df = pd.DataFrame({\"barcode\":mdata_sample.obs_names, \n",
+    "                           \"NLV_true\":mdata_sample.obs.NLV_true,\n",
+    "                           \"CRV_true\":mdata_sample.obs.CRV_true,\n",
+    "                           \"TPR_true\":mdata_sample.obs.TPR_true})\n",
+    "\n",
+    "        true_labels = {\"HLA-A*0201\":\"NLV_true\", \"HLA-C*0702\":\"CRV_true\", \"HLA-B*0702\":\"TPR_true\"}\n",
+    "\n",
+    "        # Train models\n",
+    "        mask = mdata_sample.obs[true_labels[dex_key]]\n",
+    "        mdata_tmp = mdata_sample[~np.isnan(mask),:].copy()\n",
+    "        y_true = mdata_tmp.obs[true_labels[dex_key]]\n",
+    "        \n",
+    "        # BEAMT\n",
+    "        model_config = f\"beamt\"\n",
+    "        setting = f'{dex_map[dex_key]},{sample},{tcr_info_sequenced_only}'\n",
+    "        result = {'dex_key': dex_key, 'model_config': 'BEAMT', 'sample': sample, 'tcr_info_sequenced_only': tcr_info_sequenced_only, 'setting': setting, }\n",
+    "        mixer = BEAMT()\n",
+    "        mixer.preprocess_model_data(mdata_tmp, pmhc_key=dex_key, gex_key='rio', neg_ctrl_key='HLA-B*0801', ir_key='airr')    \n",
+    "        \n",
+    "        mixer.fit()\n",
+    "        p_pred, assignment = mixer.predict_posterior_class(target_fdr=None, threshold=0.5)\n",
+    "        p_pred = p_pred.__array__()\n",
+    "        assignment = assignment.__array__()\n",
+    "        metrics = eval_results(y_true, assignment, p_pred, None)\n",
+    "        result.update(metrics)\n",
+    "        results.append(result)\n",
+    "\n",
+    "results_beamt = results.copy()\n",
+    "results_beamt = pd.DataFrame(results_beamt)\n",
+    "results_beamt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9063fa1",
+   "metadata": {},
+   "source": [
+    "# DextraDemixer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "423ac5e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_confusion_hist(df, assignment, title=''):\n",
+    "    confusion_df = pd.DataFrame({ 'x_umi': df['x_umi'], 'True': df['y_true'], 'Predicted': assignment })\n",
+    "    conditions = [ (confusion_df['True'] == 0.0) & (confusion_df['Predicted'] == 0.0), (confusion_df['True'] == 1.0) & (confusion_df['Predicted'] == 1.0), (confusion_df['True'] == 0.0) & (confusion_df['Predicted'] == 1.0), (confusion_df['True'] == 1.0) & (confusion_df['Predicted'] == 0.0) ]\n",
+    "\n",
+    "    choices = [ 'True Negative', 'True Positive', 'False Positive', 'False Negative' ]\n",
+    "    confusion_df['Outcome'] = np.select(conditions, choices, default='Unknown')\n",
+    "\n",
+    "    plt.figure(figsize=(4, 3))\n",
+    "    custom_palette = { 'True Negative': '#8DB4E2', 'True Positive': '#1F4E79', 'False Positive': '#C00000', 'False Negative': '#FFC000' }\n",
+    "    ax = sns.histplot( data=confusion_df, x='x_umi', hue='Outcome', palette=custom_palette, hue_order=choices, bins=100, multiple=\"stack\", \n",
+    "                edgecolor=\"white\", \n",
+    "                linewidth=0.5 )\n",
+    "    sns.move_legend(ax, 'upper left', bbox_to_anchor=(0.5, 1), frameon=False, ncols=1)\n",
+    "    plt.yscale('log')\n",
+    "    sns.despine()\n",
+    "    plt.title(title)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d7c10d8e-a0d6-4c7a-a0e3-930b82ed9f0e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "seed = 42\n",
+    "alpha_offset = 5\n",
+    "hyperprior = 1.0\n",
+    "use_size_factor = True\n",
+    "outlier_threshold = None\n",
+    "results = []\n",
+    "pred_dfs = []\n",
+    "\n",
+    "\n",
+    "if os.path.exists('results.csv'):\n",
+    "    pred_dfs = pd.read_csv('pred_df.csv')\n",
+    "    results_df = pd.read_csv('results.csv')\n",
+    "\n",
+    "else:\n",
+    "    for dex_key, sample in [('HLA-A*0201', '05-95-D2'), ('HLA-A*0201', '50-50-D2'), ('HLA-C*0702', '05-95-D1'), ('HLA-C*0702', '50-50-D1')]:\n",
+    "        for tcr_info_sequenced_only in [False, True]:\n",
+    "            mdata_sample = mdata[mdata.obs[f'rna:Sample'] == sample]\n",
+    "            if tcr_info_sequenced_only:\n",
+    "                mdata_sample = mdata_sample[mdata_sample['airr'].obs_names]\n",
+    "        \n",
+    "            true_labels = {\"HLA-A*0201\":\"NLV_true\", \"HLA-C*0702\":\"CRV_true\", \"HLA-B*0702\":\"TPR_true\"}\n",
+    "        \n",
+    "            # Train models\n",
+    "            mask = mdata_sample.obs[true_labels[dex_key]]\n",
+    "            mdata_sample = mdata_sample[~np.isnan(mask),:].copy()\n",
+    "            y_true = mdata_sample.obs[true_labels[dex_key]]\n",
+    "        \n",
+    "            setting = f'{dex_map[dex_key]},{sample},{tcr_info_sequenced_only}'\n",
+    "            pred_df = mdata_sample.obs.copy()\n",
+    "            pred_df['setting'] = setting\n",
+    "            pred_df['x_umi'] = mdata_sample['rio'][:, dex_key].X.toarray()[:, 0]\n",
+    "            pred_df['y_true'] = y_true\n",
+    "\n",
+    "            for neg_ctrl_key in [None, 'HLA-B*0801']:\n",
+    "                model = DextraDemixer(model_type='mixturemodelkmeans', mode='I', alpha_model='overdispersion',\n",
+    "                                    model_config={\"overdispersion_scale_prior\": hyperprior, \"alpha_offset\": alpha_offset})\n",
+    "                model.preprocess_model_data(mdata_sample, pmhc_key=dex_key, gex_key=\"rio\", neg_ctrl_key=neg_ctrl_key,\n",
+    "                                            ir_clone_key=None, outlier_threshold=outlier_threshold, use_size_factor=use_size_factor)\n",
+    "                opt_params = {\"maxiter\": 1000, \"nof_inits\": 10, \"adam\": {\"init_value\": 3e-1, \"end_value\": 3e-3, \"decay_rate\": 0.995, \"transition_steps\": 1}, }\n",
+    "            \n",
+    "                model.fit_svi(guide='normal', svi_config=opt_params, nof_inits=opt_params[\"nof_inits\"], rng_key=42, y_true=None)\n",
+    "                samples = model.get_posterior_samples()\n",
+    "                for clone_mean in ['clone_id', False]:\n",
+    "                    # Fill nan clones with a unique clone id\n",
+    "                    if clone_mean:\n",
+    "                        clone_id = mdata_sample.obs[f'airr:{clone_mean}'].copy()\n",
+    "                        clone_id = clone_id.astype(float)\n",
+    "                        max_id = clone_id.max()\n",
+    "                        num_nans = clone_id.isna().sum()\n",
+    "                        clone_id[clone_id.isna()] = np.arange(max_id, max_id+num_nans)\n",
+    "                    else:\n",
+    "                        clone_id = None\n",
+    "\n",
+    "                    model_config = f\"{'no-neg' if neg_ctrl_key is None else 'neg'},{clone_mean if clone_mean else 'no-clone'}\"\n",
+    "            \n",
+    "                    p_pred, assignment = model.predict_posterior_class(threshold=0.5, clonotype_mean_p=bool(clone_mean), clone_id=clone_id)\n",
+    "                    pred_df[f'p_pred_{model_config}'] = p_pred\n",
+    "                    pred_df[f'assignment_{model_config}'] = assignment\n",
+    "\n",
+    "                    metrics = eval_results(y_true, assignment, p_pred, None)\n",
+    "                    # model.plot_results(assignment, p_pred, y_true[~np.isnan(y_true)], seed,\n",
+    "                    #                 f\"{setting} {model_config}\", save_dir='figs', show=False)\n",
+    "            \n",
+    "                    result = {'sample': sample, 'tcr_info_sequenced_only': tcr_info_sequenced_only, 'setting': setting, \n",
+    "                            'dex_key': dex_key, 'clone_mean': clone_mean, 'neg_ctrl_key': neg_ctrl_key, 'model_config': model_config, \n",
+    "                            'hyperprior': hyperprior, 'alpha_offset': alpha_offset,\n",
+    "                            'outlier_threshold': outlier_threshold,\n",
+    "                            'use_size_factor': use_size_factor}\n",
+    "\n",
+    "                    print(f\"{setting} {model_config} F1 {metrics['f1']:.3f}, Precision {metrics['precision']:.3f}, Recall {metrics['recall']:.3f} FDR {metrics['fdr']:.3f} MCC {metrics['mcc']:.3f}\\n\")\n",
+    "                    result.update(metrics)\n",
+    "                    samples_clean = {k: np.array(v) for k, v in samples.items()}\n",
+    "                    result.update(samples_clean)\n",
+    "                    results.append(result)\n",
+    "            \n",
+    "            pred_dfs.append(pred_df)\n",
+    "\n",
+    "    pred_dfs = pd.concat(pred_dfs)\n",
+    "    pred_dfs.to_csv('pred_df.csv')\n",
+    "    results_df = pd.DataFrame(results)\n",
+    "    results_df.to_csv('results.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "20892b4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nIyOD+/nNmzesXbt2rE+fPqy4uJidPn2aAWC+vr5cnatXrzIAbP/+/eXG99133zGhUMieP39eaT+cnJyYnp4ed1GaMcZ+/fVXBoCdPn263PhKRUVFMRkZGfbFF19U2kZFMjIymKGhIbOysuJdcC2vrVOnTjEAbP78+VUet7r3IZSOjZeaO3cuA8B2795d475UdR+CjY0NGzx4MK+sdKzYzs5OIvbS8eP8/HyJaxAikYjp6Ohw14mys7OZqqoqs7OzY0VFRRJtl/d5llXR73Jpny5evFjjdrp161bri8oikahOF5Vnz57N22fZsmXlXlQeOXKkRNt2dna8f4+1a9cyACw0NFSi7ps3b9j79+8r7UtZzf2iMp0h1IPTp0/jwYMHKC4uRnp6Oi5cuIBz587ByMgIJ0+ehLy8PFc3ICAAAwYMgKWlJWbNmgUTExOkp6fj5s2bePbsGTcnfcGCBXj9+jXOnz8PKSkpODk5YebMmdiwYQOcnZ1hbW2N4OBgSElJYeTIkeXG9cUXX2DVqlU4dOgQNz2udA502emB3333Hfr16wc7OzvMnj0bz549w5YtWzB8+HA4OTlx9VxcXKCgoIB+/fqhbdu2iIuLwy+//AJFRUVs3LixWp+VnZ0dbG1tYWpqirS0NPzyyy/Iy8vDX3/9BaHw/2dB9+vXDz169ECvXr2gpqaGO3fuYO/evTAwMCj3juaK3LlzB7///jvEYjGysrIQHh6O//znPxAIBDhw4ADvLvJt27bh559/hq2tLRQVFSXuHxk7diyUlJQAfHhMhb29fY2GqEaNGoV169Zh+vTp6NevH2JiYhAcHFzlWc0///yDoUOHYtKkSbCwsIC0tDRCQkKQnp7OTaVUVVXFzp07MW3aNNjY2GDy5MnQ1tZGSkoKTp06hf79++Onn36q9udWkZq08/3338PZ2Rn9+/fH9OnT8ebNG/z000/o1q2bxGNRPDw8sG/fPiQlJcHY2BhCoRA7d+7E6NGj0b17d0yfPh16enp48OAB7t+/zw2lfsza2hru7u745ZdfkJWVBTs7O9y+fRv79u3DmDFjYG9vX+M+L126FCdPnsSoUaPg4eGBnj17Ij8/HzExMTh69CiSk5NrNC25rNGjR6N///5YsWIFkpOTYWFhgWPHjiE7O7tWx6uzRk0/LUzpX1WlL1lZWaarq8uGDRvGtm/fzvuLu6xHjx4xNzc3pqury2RkZFi7du3YqFGj2NGjRxljjJ04cYIBYFu2bOHtl5OTw4yMjJi1tTUrKipibdq0YQMHDqw0xg4dOvCmvWppabHPPvtMot7Vq1dZv379mLy8PNPW1maenp4S8W/fvp316dOHaWpqMmlpaaanp8emTp3KEhMTq/V5MfZhGqOJiQmTk5Nj2tra7Msvv2SPHj2SqLdq1SrWvXt3pqamxmRkZJihoSGbM2cOS0tLq1Y7pX9ll76kpaWZpqYm69u3L1u5ciV78uSJxD7u7u4VThtFydTRUv/9738ZALZr1y6urKozhHfv3rHFixczPT09pqCgwPr3789u3rwp8Rfpx2cIr169Yp6enqxz585MSUmJqampsb59+0rMCCqNwdHRkampqTF5eXnWsWNH5uHhwSIiIir9vKp7hlDTdg4dOsQ6d+7M5OTkWLdu3djJkyfZ+PHjWefOnXn1xo8fzxQUFCTOBq5du8aGDRvGVFRUmJKSErOysmI7duzgtpd3p/L79++Zr68v69ChA5ORkWEGBgZs5cqVvKnejFX/DIGxD9NtV65cyUxNTZmsrCzT0tJi/fr1Y5s3by73TKkiH58hMPZhNuG0adOYqqoqU1NTY9OmTWN3795tkjMEAWNlrp6QFi0uLg5du3bFX3/9VeFZBameZcuW4Y8//sDDhw8hJyfX1OF8Urp37w5tbW3eTYE6Ojpwc3PDjz/+2ISREbpTuRW5ePEibG1tKRnUg4sXL2L16tWUDCrx/v17icebXLp0CdHR0Rg8eDBXdv/+fRQUFNTqKb2kftEZAiGkQSQnJ8PBwQFTp06Fvr4+Hjx4gF27dkFNTQ2xsbGV3nvxqfn43puPSUlJVXgfQ3NCCYEQ0iCys7Mxe/ZsXL9+HS9fvoSSkhKGDh2KjRs3ctNKW4rBgwfz7r35mJGRUbWfJ9WUKCEQQkgdRUZG8u69+ZiCggL69+/fiBHVDiUEQgghAOiiMiGEkBKtPiEwxpCTkwM6USKEtHatPiHk5uZCTU0Nubm5TR0KIYQ0qVafEAghhHxACYEQQggASgiEEEJKUEIghBACgBICIYSQEpQQCCGEAKCEQAghpAQlBEIIIQAoIRBCCClBayrXwbsiEYpEDLJSAsjLSjV1OIQQUief/BlCVlYWevXqhe7du6Nbt27Ys2dPo7VdJGIIjcpEkYieg0QI+fR98mcIKioquHLlChQVFZGfn49u3bph3LhxLWo1JkIIaQyffEKQkpKCoqIiAKCwsBCMsUZ/cqlQAOQUfFg7loaPCCGfqiYfMrpy5QpGjx4NfX19CAQCHD9+XKJOQEAAjI2NIS8vj759++L27du87VlZWbC2tkb79u2xdOlSaGlpNVL0HxSXDB3R8BFprZ4+fYoZM2ZAX18fsrKyMDIywoIFC/D69etqHyM5ORkCgQBRUVENFyipVJMnhPz8fFhbWyMgIKDc7YcPH4a3tzd8fHxw584dWFtbw9HRERkZGVwddXV1REdHIykpCQcPHkR6enqF7RUWFiInJ4f3IoTU3uPHj9GrVy8kJibijz/+wMOHD7Fr1y6EhYXB1tYWmZmZTR0iqS7WjABgISEhvLI+ffowT09P7r1IJGL6+vrMz8+v3GPMmTOHHTlypMI2fHx8GACJV3Z2do3jzX77nh2+kc7e5BWxwzfS2eEb6Sz77fsaH4eQT5mTkxNr3749e/v2La88NTWVKSoqsq+//poxVv7/bzU1NRYYGMhtL/uys7Pj6v3222/MwsKCycrKMl1dXd53wpMnT9gXX3zBlJSUmIqKCps4cSJLS0vjtvv4+DBra2v222+/MQMDA6akpMTmzJnDiouL2aZNm5iOjg7T1tZmGzZs4MX25s0b9tVXXzEtLS2moqLC7O3tWVRUVD18Ys1Xk58hVKaoqAiRkZFwcHDgyoRCIRwcHHDz5k0AQHp6Ore4TXZ2Nq5cuQJzc/MKj7ly5UpkZ2dzr6dPnzZsJwhpwTIzM3HmzBnMnTsXCgoKvG26urpwdXXF4cOHq3Vdr3Qo+Pz580hNTcWxY8cAADt37oSnpydmz56NmJgYnDx5EqampgAAsVgMZ2dnZGZm4vLlyzh37hweP34MFxcX3rEfPXqE06dPIzQ0FH/88Qd+++03jBw5Es+ePcPly5exadMmfPvtt7h16xa3z8SJE5GRkYHTp08jMjISNjY2GDp0aIs+42nWF5VfvXoFkUgEHR0dXrmOjg4ePHgAAHjy5Almz57NXUyeN28eLC0tKzymnJwc5OTkGjRuQlqLxMREMMbQpUuXcrd36dIFb968wcuXL6s8lra2NgCgTZs20NXV5co3bNiAxYsXY8GCBVxZ7969AQBhYWGIiYlBUlISDAwMAAD79+9H165dER4eztUTi8XYu3cvVFRUYGFhAXt7eyQkJODvv/+GUCiEubk5Nm3ahIsXL6Jv3764du0abt++jYyMDO77YvPmzTh+/DiOHj2K2bNn1+LTav6adUKojj59+tBFKEKaWHXOAGojIyMDL168wNChQ8vdHh8fDwMDAy4ZAICFhQXU1dURHx/PJQRjY2OoqKhwdXR0dCAlJQWhUMgrK702GR0djby8PInp6wUFBXj06FG99a+5adYJQUtLC1JSUhIXidPT03l/QdRGQEAAAgICIBKJ6nQcQlozU1NTCAQCxMfHY+zYsRLb4+PjoaGhAW1tbQgEAonE8f79+0qP//EwVG3JyMjw3gsEgnLLxGIxACAvLw96enq4dOmSxLHU1dXrJabmqFlfQ5CVlUXPnj0RFhbGlYnFYm72Ql14enoiLi4O4eHhdQ2TkFarTZs2GDZsGH7++WcUFBTwtqWlpSE4OBguLi4QCATQ1tZGamoqtz0xMRFv377l3svKygIA7480FRUVGBsb874DyurSpQuePn3KuxYYFxeHrKwsWFhY1LpfNjY2SEtLg7S0NExNTXmvxp7W3piaPCHk5eUhKiqKG/ZJSkpCVFQUUlJSAADe3t7Ys2cP9u3bh/j4eMyZMwf5+fmYPn16E0ZNCCn1008/obCwEI6Ojrhy5QqePn2K0NBQDBs2DO3atcN3330HABgyZAh++ukn3L17FxEREfj66695f6W3bdsWCgoKCA0NRXp6OrKzswEAa9euxZYtW+Dv74/ExETcuXMHO3bsAAA4ODjA0tISrq6uuHPnDm7fvg03NzfY2dmhV69ete6Tg4MDbG1tMWbMGJw9exbJycm4ceMGVq1ahYiIiDp8Ws1cU05xYoyxixcvljsN1N3dnauzY8cOZmhoyGRlZVmfPn3Y//73vzq3+9NPP7EuXbqwTp060bRTQuooOTmZubu7Mx0dHSYjI8MMDAzYvHnz2KtXr7g6z58/Z8OHD2dKSkrMzMyM/f3337xpp4wxtmfPHmZgYMCEQiFv2umuXbuYubk5k5GRYXp6emzevHnctupOOy3L3d2dOTs788rs7OzYggULuPc5OTls3rx5TF9fn+uTq6srS0lJqdNn1ZwJGGvk5zw0Mzk5OVBTU0N2djZUVVVrtm9BMUKjMjHcSgNn770BADh114SqQrO+NEMIIeVq8iEjQgghzQMlBEIIIQBacUIICAiAhYUFN0+ZEEJau1abEGjaKSGE8LXahEAIIYSPEgIhhBAAlBAIIYSUaLUJgS4qE0IIX6tNCHRRmRBC+OiWWkKasXdFokZdp1tWSgB5WalGa6+1EQgECAkJwZgxY5o6lHJRQiCkGSsSMYRGNd4KXU7dNSFfzboCgaDS7T4+Pli7dm2dY6qOwYMH4/Lly/jjjz8wefJkrnzbtm3Ytm0bkpOTGyWOUmvXrsXx48cl1mpJTU2FhoZGo8ZSE5QQCCG1UvZR1ocPH8aaNWuQkJDAlSkrK3M/M8YgEokgLd1wXzny8vL49ttvMX78eIm1DpqLuq7j0tBa7TUEuqhMSN3o6upyLzU1NQgEAu79gwcPoKKigtOnT6Nnz56Qk5PDtWvX4OHhITFcsnDhQgwePJh7LxaL4efnhw4dOkBBQQHW1tY4evRolfFMmTIFWVlZ2LNnT6X1Tpw4ARsbG8jLy8PExAS+vr4oLi7mtj948AADBgyAvLw8LCwscP78eQgEAhw/fpyrs3z5cnTq1AmKioowMTHB6tWrucV+goKC4Ovri+joaAgEAggEAgQFBQEA7zj9+vXD8uXLebG9fPkSMjIyuHLlCgCgsLAQS5YsQbt27aCkpIS+ffuWu2hPfWm1Zwienp7w9PTknnZKCKl/K1aswObNm2FiYlLtoRI/Pz/8/vvv2LVrF8zMzHDlyhVMnToV2trasLOzq3A/VVVVrFq1CuvWrYO7uzuUlJQk6ly9ehVubm7w9/fHwIED8ejRI259ZB8fH4hEIowZMwaGhoa4desWcnNzsXjxYonjqKioICgoCPr6+oiJicGsWbOgoqKCZcuWwcXFBbGxsQgNDcX58+cBoNzvGFdXV/zwww/YuHEjN/x2+PBh6OvrY+DAgQAALy8vxMXF4dChQ9DX10dISAicnJwQExMDMzOzan2eNdFqzxAIIQ1v3bp1GDZsGDp27AhNTc0q6xcWFuL777/H3r174ejoCBMTE3h4eGDq1KnYvXt3lfvPnTsX8vLy2Lp1a7nbfX19sWLFCri7u8PExATDhg3D+vXruWOfO3cOjx49wv79+2FtbY0BAwZwC/yU9e2336Jfv34wNjbG6NGjsWTJEvz5558APiz7qaysDGlpae6MqbylQCdNmoQXL17g2rVrXNnBgwcxZcoUCAQCpKSkIDAwEEeOHMHAgQPRsWNHLFmyBAMGDEBgYGCVn0VttNozBEJIw6vpqmUPHz7E27dvMWzYMF55UVERevToUeX+cnJyWLduHebNm4c5c+ZIbI+Ojsb169d5X/IikQjv3r3D27dvkZCQAAMDA95Yf58+fSSOc/jwYfj7++PRo0fIy8tDcXFxjddT0dbWxvDhwxEcHIyBAwciKSkJN2/e5JJTTEwMRCIROnXqxNuvsLAQbdq0qVFb1UUJgRDSYD4ethEKhfh4Ta7SsXfgw5K6AHDq1Cm0a9eOV09OTq5abU6dOhWbN2/Ghg0bYGxszNuWl5cHX19fjBs3TmI/efnqza+6efMmXF1d4evrC0dHR6ipqeHQoUPYsmVLtfYvy9XVFfPnz8eOHTtw8OBBWFpawtLSkotVSkoKkZGRkJLiTwUue8G+PlFCIIQ0Gm1tbcTGxvLKoqKiuFlBFhYWkJOTQ0pKSqXXCyojFArh5+eHcePGSZwl2NjYICEhAaampuXua25ujqdPnyI9PR06OjoAIHHz6o0bN2BkZIRVq1ZxZU+ePOHVkZWVhUgkqjJWZ2dnzJ49G6GhoTh48CDc3Ny4bT169IBIJEJGRgZ3TaGhtdqEEBAQgICAgGr9oxFC6seQIUPw448/Yv/+/bC1tcXvv/+O2NhYbjhIRUUFS5YswaJFiyAWizFgwABkZ2fj+vXrUFVVhbu7e7XaGTlyJPr27Yvdu3dzX+wAsGbNGowaNQqGhoaYMGEChEIhoqOjERsbiw0bNnDXO9zd3fHDDz8gNzcX3377LYD/v+/CzMwMKSkpOHToEHr37o1Tp04hJCSE176xsTGSkpIQFRWF9u3bQ0VFpdwzHCUlJYwZMwarV69GfHw8pkyZwm3r1KkTXF1d4ebmhi1btqBHjx54+fIlwsLCYGVlhZEjR9bsw6+Opl3SuellZ2czACw7O7vm+759zw7fSGdv8orY4Rvp7PCNdJb99n0DRElaq4LCYpb99n2jvQoKi2sVZ2BgIFNTU+PeX7x4kQFgb968kai7Zs0apqOjw9TU1NiiRYuYl5cXs7Oz47aLxWK2bds2Zm5uzmRkZJi2tjZzdHRkly9frrB9Ozs7tmDBAl7ZjRs3GABmZGTEKw8NDWX9+vVjCgoKTFVVlfXp04f98ssv3Pb4+HjWv39/Jisryzp37sz++9//MgAsNDSUq7N06VLWpk0bpqyszFxcXNi///1vXv/fvXvHxo8fz9TV1RkAFhgYyBhjDAALCQnhxfP3338zAGzQoEES/SoqKmJr1qxhxsbGTEZGhunp6bGxY8eye/fuVfhZ1IWgJMhWq3TaaXZ2do0vCuUUFCM0KhPDrTRw9t4bAB/u9FRVaLUnXoS0ONevX8eAAQPw8OFDdOzYsanDaVD0zUUIIWWEhIRAWVkZZmZmePjwIRYsWID+/fu3+GQAUEIghBCe3NxcLF++HCkpKdDS0oKDg0OtZhB9iighEEJIGW5ubrzZPq0J3alMCCEEACUEQgghJWjIqJ4JBR9mHwG02Agh5NPSas8QGurx18UlC5qERmU26kpXhBBSV602IdCayoQQwtdqEwIhhBA+uoZASDOWlfsWb98VNVp7ivKyUFdRbLT2PgVBQUFYuHAhsrKyKq0nEAgQEhIisSLcp4QSAiHN2Nt3RfDedqTR2tu6cGK1E0Lpw94q4uPjg7Vr19ZDVFUbPHgwLl++DODDY7JNTEzg5eWFuXPn1vnYLi4u+Pzzz7n3a9euxfHjxxEVFcWrl5qaWu1V4ZorSgiEkFpJTU3lfj58+DDWrFmDhIQErqzsM/sZYxCJRJCWbrivnFmzZmHdunV4+/Yt9u/fD09PT2hoaPCeIFobCgoK5a549rGyi+p8qugaAiGkVkqXh9TV1YWamhoEAgH3/sGDB1BRUcHp06fRs2dPyMnJ4dq1a/Dw8JAYUlm4cCEGDx7MvReLxfDz80OHDh2goKAAa2trHD16tMp4FBUVoaurCxMTE6xduxZmZmY4efIkACAlJQXOzs5QVlaGqqoqJk2ahPT0dG7f6Oho2NvbQ0VFBaqqqujZsyciIiIAfBgyUldX53729fVFdHQ0BAIBBAIBgoKCAHw4Yzp+/DgAoF+/fli+fDkvvpcvX0JGRgZXrlwB8GHlsyVLlqBdu3ZQUlJC3759cenSpWp++g2DEgIhpMGsWLECGzduRHx8PKysrKq1j5+fH/bv349du3bh/v37WLRoEaZOncoNCVWXgoICioqKIBaL4ezsjMzMTFy+fBnnzp3D48eP4eLiwtV1dXVF+/btER4ejsjISKxYsYJbtKcsFxcXLF68GF27dkVqaipSU1N5xyl7vEOHDvFWhzt8+DD09fW5xW68vLxw8+ZNHDp0CPfu3cPEiRPh5OSExMTEGvWzPtGQESGkwaxbt05ifeTKFBYW4vvvv8f58+dha2sLADAxMcG1a9ewe/fuaq2iJhKJ8Mcff+DevXuYPXs2wsLCEBMTg6SkJBgYGAAA9u/fj65duyI8PBy9e/dGSkoKli5dis6dOwP4sAhOeRQUFKCsrAxpaelKh4gmTZqEhQsX4tq1a1wCOHjwIKZMmQKBQICUlBQEBgYiJSUF+vr6AIAlS5YgNDQUgYGB+P7776v9mdUnOkMghDSYXr161aj+w4cP8fbtWwwbNgzKysrca//+/Xj06FGl+/78889QVlaGgoICZs2ahUWLFmHOnDmIj4+HgYEBlwyAD0t1qqurIz4+HgDg7e2NmTNnwsHBARs3bqyyrapoa2tj+PDhCA4OBgAkJSVxazEDQExMDEQiETp16sTr5+XLl+vcdl3QGQIhpMEoKSnx3guFQny8Jtf79++5n/Py8gAAp06dQrt27Xj1yluCsixXV1esWrUKCgoK0NPTg1BY/b93165diy+//BKnTp3C6dOn4ePjg0OHDmHs2LHVPkZ58cyfPx87duzAwYMHYWlpCUtLSwAf+iklJYXIyEhISfEfb1P2Ynxjo4RACGk02traiI2N5ZVFRUVx4/UWFhaQk5NDSkpKtYaHylJTU4OpqalEeZcuXfD06VM8ffqUO0uIi4tDVlYWLCwsuHqdOnVCp06dsGjRIkyZMgWBgYHlJgRZWdlqrcXu7OyM2bNnIzQ0FAcPHuQ9UrtHjx4QiUTIyMjghpSag1Y7ZNRQzzIihFRsyJAhiIiIwP79+5GYmAgfHx9eglBRUcGSJUuwaNEi7Nu3D48ePcKdO3ewY8cO7Nu3r1ZtOjg4wNLSEq6urrhz5w5u374NNzc32NnZoVevXigoKICXlxcuXbqEJ0+e4Pr16wgPD0eXLl3KPZ6xsTGSkpIQFRWFV69eobCwsNx6SkpKGDNmDFavXo34+Hje9NdOnTrB1dUVbm5uOHbsGJKSknD79m34+fnh1KlTtepnfWi1Zwienp7w9PTk1lQmpDlSlJfF1oUTG7W9huTo6IjVq1dj2bJlePfuHWbMmAE3NzfExMRwddavXw9tbW34+fnh8ePHUFdXh42NDb755ptatSkQCHDixAnMmzcPgwYNglAohJOTE3bs2AEAkJKSwuvXr+Hm5ob09HRoaWlh3Lhx8PX1Lfd448ePx7Fjx2Bvb4+srCwEBgbCw8Oj3Lqurq74/PPPMWjQIBgaGvK2BQYGYsOGDVi8eDGeP38OLS0tfPbZZxg1alSt+lkfBOzjAb1WpjQhZGdnQ1VVtWb7FhQjNCoTw600cPbeGwDg/ezUXROqCq025xJCPjGtdsiIEEIIHyUEQgghACghEEIIKUEJgRBCCABKCIQQQkpQQiCEEAKAEgIhhJASlBAIIYQAoIRACCGkBN1GS0gz9j4zE6KSJ4A2BillZchoajZKW9VdvL65EggECAkJkVgBriwPDw9kZWVxK6k1d5QQCGnGRHl5eODu3mjtdd63r0YJwcPDo9yHziUmJpb75NHGFBQUhOnTpwP48OWtr6+PYcOGYdOmTWjbtm2dj5+amgoNDQ0AQHJyMjp06IC7d++ie/fuXJ3t27dLPO67OaOEQAipEycnJwQGBvLKtLW1mygaPlVVVSQkJEAsFiM6OhrTp0/HixcvcObMmTofu7IV00p9ag/O/OSvITx9+hSDBw+GhYUFrKyscOTIkaYOiZBWRU5ODrq6uryXlJQUtm7dCktLSygpKcHAwABz587lFsApT2UL3QPglqNUUFCAgYEB5s+fj/z8/EpjEwgE0NXVhb6+PkaMGIH58+fj/PnzKCgogFgsxrp169C+fXvIycmhe/fuCA0N5fYtKiqCl5cX9PT0IC8vDyMjI/j5+fGOXToU1KFDBwAf1jkQCAQYPHgwgA9nUKVDSr/88gv09fUhFot5MTo7O2PGjBnc+xMnTsDGxgby8vIwMTGBr68viouLK+1nffnkE4K0tDS2bduGuLg4nD17FgsXLqzyl4QQ0vCEQiH8/f1x//597Nu3DxcuXMCyZcsqrF/ZQvePHj2Ck5MTxo8fj3v37uHw4cO4du0avLy8ahSTgoICxGIxiouLsX37dmzZsgWbN2/GvXv34OjoiC+++IJb5N7f3x8nT57En3/+iYSEBAQHB8PY2Ljc496+fRsAcP78eaSmpuLYsWMSdSZOnIjXr1/j4sWLXFlmZiZCQ0O5pTWvXr0KNzc3LFiwAHFxcdi9ezeCgoLw3Xff1aiftfXJJwQ9PT1uzE5XVxdaWlrIzMxs2qAIaUX++usv3rrAEyd+WL9h4cKFsLe3h7GxMYYMGYINGzbgzz//rPA4KSkpcHBwQOfOnWFmZoaJEyfC2toaAODn5wdXV1csXLgQZmZm6NevH/z9/bF//368e/euWnEmJiZi165d6NWrF1RUVLB582YsX74ckydPhrm5OTZt2oTu3btj27ZtXDxmZmYYMGAAjIyMMGDAAN4iN2WVDpG1adMGurq60CznOoyGhgZGjBiBgwcPcmVHjx6FlpYW7O3tAQC+vr5YsWIF3N3dYWJigmHDhmH9+vXYvXt3tfpYV02eEK5cuYLRo0dDX1+fdwpWVkBAAIyNjSEvL4++ffty2fhjkZGREIlEvMW0CSENy97eHlFRUdzL398fwIe/locOHYp27dpBRUUF06ZNw+vXr/H27dtyj1PZQvfR0dEICgriJR5HR0eIxWIkJSVVGFt2djaUlZWhqKgIc3Nz6OjoIDg4GDk5OXjx4gX69+/Pq9+/f3/Ex8cD+DDcExUVBXNzc8yfPx9nz56t60cFV1dX/Oc//+FWWQsODsbkyZO59Z+jo6Oxbt06Xj9nzZqF1NTUCj+3+tTkCSE/Px/W1tYICAgod/vhw4fh7e0NHx8f3LlzB9bW1nB0dERGRgavXmZmJtzc3PDLL79U2l5hYSFycnJ4L0JI7SkpKcHU1JR76enpITk5GaNGjYKVlRX+85//IDIykvs/XlRUVO5x1q5di/v372PkyJG4cOECLCwsEBISAuDDovT/+te/eIknOjoaiYmJ6NixY4WxqaioICoqCrGxscjPz8eVK1fQqVOnavXLxsYGSUlJWL9+PQoKCjBp0iRMmDChhp8O3+jRo8EYw6lTp/D06VNcvXqVGy4q7aevry+vnzExMUhMTIS8vHyd2q6OJp9lNGLECIwYMaLC7Vu3bsWsWbO46WO7du3CqVOnsHfvXqxYsQLAhy/5MWPGYMWKFejXr1+l7fn5+VW4NB4hpH5ERkZCLBZjy5Yt3F+/lQ0XlapooXsbGxvExcXVeCqrUCgsdx9VVVXo6+vj+vXrsLOz48qvX7+OPn368Oq5uLjAxcUFEyZMgJOTEzIzMyWGhGRlPyw9KhKJKo1HXl4e48aNQ3BwMB4+fAhzc3PY2Nhw221sbJCQkNBkU3abPCFUpqioCJGRkVi5ciVXJhQK4eDggJs3bwIAGGPw8PDAkCFDMG3atCqPuXLlSnh7e3Pvc3JyaIiJkHpmamqK9+/fY8eOHRg9ejSuX7+OXbt2VVi/oKAAS5cuxYQJE9ChQwc8e/YM4eHhGD9+PABg+fLl+Oyzz+Dl5YWZM2dCSUkJcXFxOHfuHH766adaxbh06VL4+PigY8eO6N69OwIDAxEVFYXg4GAAH/4Y1dPTQ48ePSAUCnHkyBHo6upCXV1d4lht27aFgoICQkND0b59e8jLy1c45dTV1RWjRo3C/fv3MXXqVN62NWvWYNSoUTA0NMSECRMgFAoRHR2N2NhYbNiwoVb9rIlmnRBevXoFkUgEHR0dXrmOjg4ePHgA4ENGP3z4MKysrLjrDwcOHIClpWW5x5STk4OcnFyDxk1IfZFSVkbncm78asj26oO1tTW2bt2KTZs2YeXKlRg0aBD8/Pzg5uZWfrtVLHRvZWWFy5cvY9WqVRg4cCAYY+jYsSNcXFxqHeP8+fORnZ2NxYsXIyMjAxYWFjh58iTMzMwAfBhu+uGHH5CYmAgpKSn07t0bf//9N3fGU5a0tDT8/f2xbt06rFmzBgMHDsSlS5fKbXfIkCHQ1NREQkICvvzyS942R0dH/PXXX1i3bh02bdoEGRkZdO7cGTNnzqx1P2tCwJrRbXQf3wr+4sULtGvXDjdu3ICtrS1Xb9myZbh8+TJu3bpV67YCAgIQEBAAkUiEf/75B9nZ2VBVVa3RMXIKihEalYnhVho4e+8NAPB+duquCVWFZp1zCSGE0+QXlSujpaUFKSkppKen88rT09OrdZdgZTw9PREXF4fw8PA6HYcQQlqKZp0QZGVl0bNnT4SFhXFlYrEYYWFhvDMGQgghddfk4xl5eXl4+PAh9z4pKQlRUVHQ1NSEoaEhvL294e7ujl69eqFPnz7Ytm0b8vPzuVlHhBBC6keTJ4SIiAjuLj0A3Awgd3d3BAUFwcXFBS9fvsSaNWuQlpbGPW/k4wvNNVX2GgIhhJBmdlG5KeTk5EBNTY0uKhNCWr1mfQ2BEEJI46GEQAghBEArTggBAQGwsLBA7969mzoUQghpFlptQqD7EAghhK9WVzxNTEwQHh6ONm3a8MqzsrJgY2ODx48f10twhLR67zMBUcWrjNU7KWVApvprKtdFUFAQFi5ciKysrEZpr7ny8PBAVlZWuY/+b2y1SgjJycnlTtcsLCzE8+fP6xwUIaSEKA944N547XXeV6OE4OHhgX3lPGspMTGxyZ7YWSooKAjTp0+Ho6Mjb2nMrKwsaGho4OLFi9xSl40hOTkZHTp0wN27d7lFvQBg+/btaC6TPWuUEE6ePMn9fObMGd7T/EQiEcLCwipcYq65ofsQCKkfTk5OCAwM5JWVriDW1KSlpXH+/HlcvHiRd79Tc1LRU1GbQo2uIYwZMwZjxoyBQCCAu7s7937MmDGYPHkyzp07hy1btjRUrPWKriEQUj/k5OSgq6vLe0lJSWHr1q2wtLSEkpISDAwMMHfuXOTlVTz8FR0dDXt7e6ioqEBVVRU9e/ZEREQEt/3atWsYOHAgFBQUYGBggPnz51e5frqSkhJmzJjBrZ1SkadPn2LSpElQV1eHpqYmnJ2dkZyczG0vLi7G/Pnzoa6ujjZt2mD58uXcd2Cp0NBQDBgwgKszatQo3qpvHTp0AAD06NEDAoGAOzvx8PDgjvPLL79AX18fYrGYF5+zszNmzJjBvT9x4gRsbGwgLy8PExMT+Pr6ori4uNI+VkeNEoJYLIZYLIahoSEyMjK492KxGIWFhUhISMCoUaPqHBQh5NMnFArh7++P+/fvY9++fbhw4QKWLVtWYX1XV1e0b98e4eHhiIyMxIoVKyAjIwMAePToEZycnDB+/Hjcu3cPhw8fxrVr1+Dl5VVlHGvXrkVMTAyOHj1a7vb379/D0dERKioquHr1Kq5fvw5lZWU4OTlxq7tt2rQJwcHBCAwMxPXr15GTkyMx5p+fnw9vb29EREQgLCwMQqEQY8eO5b7cS5f+PX/+PFJTU3Hs2DGJWCZOnIjXr1/j4sWLXFlmZiZCQ0O5ldWuXr0KNzc3LFiwAHFxcdi9ezeCgoLw3XffVflZVIm1ctnZ2QwAy87Orvm+b9+zwzfS2Zu8Inb4RrrEz9lv3zdAxKRVKXjC2N3BjfcqeFKj8Nzd3ZmUlBRTUlLiXhMmTCi37pEjR1ibNm2494GBgUxNTY17r6KiwoKCgsrd96uvvmKzZ8/mlV29epUJhUJWUFBQ7j5lj79ixQrWqVMn9v79e/bmzRsGgF28eJExxtiBAweYubk5E4vF3L6FhYVMQUGBnTlzhjHGmI6ODvvxxx+57cXFxczQ0JA5OzuX2zZjjL18+ZIBYDExMYwxxpKSkhgAdvfuXV49d3d33nGcnZ3ZjBkzuPe7d+9m+vr6TCQSMcYYGzp0KPv+++95xzhw4ADT09OrMJbqqvVzFcLCwhAWFsadKZS1d+/eOiWplkIo+PB4CwCQlRJAXlaqiSMipP7Z29tj586d3HslJSUAH/4S9vPzw4MHD5CTk4Pi4mK8e/cOb9++haKiosRxvL29MXPmTBw4cAAODg6YOHEit15ydHQ07t27x61mBnxYLVEsFiMpKQldunSpNMbly5dj9+7d2Lt3LyZNmsTbFh0djYcPH0JFRYVX/u7dOzx69AjZ2dlIT0/nLa0pJSWFnj178r77EhMTsWbNGty6dQuvXr3itqWkpKBbt26VxleWq6srZs2ahZ9//hlycnIIDg7G5MmTuYV5oqOjcf36dd4ZgUgkqvSzra5aJQRfX1+sW7cOvXr1gp6eHgQCQa0DaMmKRYz3XKOGXyKbkManpKQkMaMoOTkZo0aNwpw5c/Ddd99BU1MT165dw1dffYWioqJyv7TWrl2LL7/8EqdOncLp06fh4+ODQ4cOYezYscjLy8O//vUvzJ8/X2I/Q0PDKmNUV1fHypUr4evrKzGsnZeXh549e/KSTamaXBwfPXo0jIyMsGfPHu46QLdu3bhhp5ochzGGU6dOoXfv3rh69Sr+/e9/8+L19fXFuHHjJPaVl6/bt0ytEsKuXbsQFBRUrTWMmyuaZURIw4mMjIRYLMaWLVu4v2z//PPPKvfr1KkTOnXqhEWLFmHKlCkIDAzE2LFjYWNjg7i4uDpNZZ03bx78/f2xfft2XrmNjQ0OHz6Mtm3bVviASx0dHYSHh2PQoEEAPvxFfufOHW766OvXr5GQkIA9e/Zg4MCBAD5cBC9LVlaW27cy8vLyGDduHIKDg/Hw4UOYm5vDxsaGF29CQkKDTOut1Z3KRUVF6NevX33H0qholhEhDcfU1BTv37/Hjh078PjxYxw4cAC7du2qsH5BQQG8vLxw6dIlPHnyBNevX0d4eDg3FLR8+XLcuHEDXl5eiIqKQmJiIk6cOFGti8ql5OXl4evrC39/f165q6srtLS04OzsjKtXryIpKQmXLl3C/Pnz8ezZMwAfkomfnx9OnDiBhIQELFiwAG/evOFGRzQ0NNCmTRv88ssvePjwIS5cuMA9yr9U27ZtoaCggNDQUKSnpyM7O7vCWF1dXXHq1Cns3buXu5hcas2aNdi/fz98fX1x//59xMfH49ChQ/j222+r/VlUpFZnCDNnzsTBgwexevXqOgdACKmElPKHm8Uas716YG1tja1bt2LTpk1YuXIlBg0aBD8/P7i5uZXfrJQUXr9+DTc3N6Snp0NLSwvjxo2Dr68vAMDKygqXL1/GqlWrMHDgQDDG0LFjR7i4uNQoLnd3d2zZsgVxcXFcmaKiIq5cuYLly5dj3LhxyM3NRbt27TB06FDujGH58uVIS0uDm5sbpKSkMHv2bDg6OkJK6sN1QaFQiEOHDmH+/Pno1q0bzM3N4e/vz7vxTVpaGv7+/li3bh3WrFmDgQMH4tKlS+XGOWTIEGhqaiIhIQFffvklb5ujoyP++usvrFu3Dps2bYKMjAw6d+6MmTNn1uizKE+t1kNYsGAB9u/fDysrK1hZWXFTw0pt3bq1zoE1loZcD4HWRiCkZRKLxejSpQsmTZqE9evXN3U49aZW31D37t3jxs5iY2N52+gCMyGkpXny5AnOnj0LOzs7FBYW4qeffkJSUpLEX++fulolhLI3TRBCSEsnFAoRFBSEJUuWgDGGbt264fz581VOd/3UtNoxDJplRAipLgMDA1y/fr2pw2hwtUoI9vb2lQ4NXbhwodYBNRZPT094enpy1xAIIaS1q1VCKPvoVuDDs0CioqIQGxsLd/dGfFQvIYSQelOrhFD2rrmy1q5dW+nTDAkhhDRf9bqE5tSpU+k5RoQQ8omq14Rw8+bNOj9LgxBCSNOo1ZDRxw9VYowhNTUVERERdPcyIYR8omqVED6elSMUCmFubo5169Zh+PDh9RIYIYSQxlWrhPDx+qmEEEI+fXW6MS0yMhLx8fEAgK5du6JHjx71ElRjoBvTCCGEr1YJISMjA5MnT8alS5egrq4OAMjKyoK9vT0OHTpUo0UlmgrdmEYIIXy1mmU0b9485Obm4v79+8jMzERmZiZiY2ORk5NT7opGhBBCmr9anSGEhoZKPNjJwsICAQEBdFGZEEI+UbU6QxCLxRJrIACAjIwMb9FpQgghn45aJYQhQ4ZgwYIFePHiBVf2/PlzLFq0CEOHDq234AghhDSeWiWEn376CTk5OTA2NkbHjh3RsWNHdOjQATk5OdixY0d9x0gIIaQR1OoagoGBAe7cuYPz58/jwYMHAIAuXbrAwcGhXoMjhBDSeGp0hnDhwgVYWFggJycHAoEAw4YNw7x58zBv3jz07t0bXbt2xdWrVxsqVkIIIQ2oRglh27ZtmDVrVrmL0aupqeFf//oXtm7dWm/BEUIIaTw1SgjR0dFwcnKqcPvw4cMRGRlZ56AIIYQ0vholhPT09HKnm5aSlpbGy5cv6xwUIYSQxlejhNCuXTvExsZWuP3evXvQ09Orc1CNISAgABYWFujdu3dTh0IIIc1CjRLC559/jtWrV+Pdu3cS2woKCuDj44NRo0bVW3ANydPTE3FxcQgPD2/qUAghpFmo0bTTb7/9FseOHUOnTp3g5eUFc3NzAMCDBw+4J4euWrWqQQIlhBDSsGqUEHR0dHDjxg3MmTMHK1euBGMMACAQCODo6IiAgADo6Og0SKCEEEIaVo1vTDMyMsLff/+NN2/e4OHDh2CMwczMDBoaGg0RHyGEkEZS6wVyNDQ06IIsIYS0ILV6lhEhhJCWhxICIYQQAJQQCCGElKCEQAghBAAlBEIIISUoIRBCCAFACYEQQkgJSgiEEEIAUEIghBBSokUkhLFjx0JDQwMTJkxo6lAIIeST1SISwoIFC7B///6mDoMQQj5pLSIhDB48GCoqKk0dBiGEfNKaPCFcuXIFo0ePhr6+PgQCAY4fPy5RJyAgAMbGxpCXl0ffvn1x+/btxg+UEEJauCZPCPn5+bC2tkZAQEC52w8fPgxvb2/4+Pjgzp07sLa2hqOjIzIyMmrVXmFhIXJycngvQgghzSAhjBgxAhs2bMDYsWPL3b5161bMmjUL06dPh4WFBXbt2gVFRUXs3bu3Vu35+flBTU2NexkYGNQlfEIIaTGaPCFUpqioCJGRkXBwcODKhEIhHBwccPPmzVodc+XKlcjOzuZeT58+ra9wCSHkk1brBXIaw6tXryASiSSW5dTR0cGDBw+49w4ODoiOjkZ+fj7at2+PI0eOwNbWttxjysnJQU5OrkHjJoSQT1GzTgjVdf78+RrvExAQgICAAIhEogaIiBBCPj3NeshIS0sLUlJSSE9P55Wnp6dDV1e3Tsf29PREXFwcwsPD63QcQghpKZp1QpCVlUXPnj0RFhbGlYnFYoSFhVU4JEQIIaR2mnzIKC8vDw8fPuTeJyUlISoqCpqamjA0NIS3tzfc3d3Rq1cv9OnTB9u2bUN+fj6mT5/ehFETQkjL0+QJISIiAvb29tx7b29vAIC7uzuCgoLg4uKCly9fYs2aNUhLS0P37t0RGhoqcaG5phr7GoJQAOQUFAMAZKUEkJeVapR2CSGkugSMMdbUQTSlnJwcqKmpITs7G6qqqjXbt6AYoVGZGG6lgbP33gBAtX526q4JVYUmz8WEEMLTrK8hEEIIaTyUEAghhABoxQkhICAAFhYW6N27d1OHQgghzUKrTQh0HwIhhPC12oRACCGEjxICIYQQAK04IdA1BEII4Wu1CYGuIRBCCF+rTQiEEEL4KCEQQggBQAmBEEJICUoIhBBCALTihECzjAghhK/VJgSaZUQIIXytNiEQQgjho4RACCEEACUEQgghJSghEEIIAdAM1lRuKg21prJQKMDQbmr1ekxCCGkMrfYMoaFmGb0vFsFz4354btwPkVhcr8cmhJCG1GoTAiGEED5KCIQQQgBQQiCEEFKCEgIhhBAAlBAIIYSUoIRACCEEACUEQgghJVptQqiPx1/LCsVwslaHoB7jIoSQptJqE0J93Jgmz9Kg+o8jBHhfj5ERQkjTaLUJgRBCCB8lBEIIIQAoIRBCCClBCYEQQggASgiEEEJKUEIghBACgBICIYSQEpQQCCGEAKCEQAghpAQlBEIIIQAA6aYOoKkEBAQgICAAIpGoqUOpN/kFhZASfsjxIrEYSgpyTdJeY8dBCKkfrfYMoT6eZdTcZOcVwGNdEDzWBSE7r6DJ2mvsOAgh9aPVJgRCCCF8lBAIIYQAoIRACCGkBCUEQgghACghEEIIKUEJgRBCCABKCIQQQkpQQiCEEAKAEgIhhJASlBAIIYQAoIRACCGkBCUEQgghACghEEIIKdEiEsJff/0Fc3NzmJmZ4ddff23qcAgh5JP0ya+HUFxcDG9vb1y8eBFqamro2bMnxo4dizZt2jR1aIQQ8kn55M8Qbt++ja5du6Jdu3ZQVlbGiBEjcPbs2aYOixBCPjlNnhCuXLmC0aNHQ19fHwKBAMePH5eoExAQAGNjY8jLy6Nv3764ffs2t+3Fixdo164d975du3Z4/vx5Y4ROCCEtSpMnhPz8fFhbWyMgIKDc7YcPH4a3tzd8fHxw584dWFtbw9HRERkZGbVqr7CwEDk5ObwXIYSQZpAQRowYgQ0bNmDs2LHlbt+6dStmzZqF6dOnw8LCArt27YKioiL27t0LANDX1+edETx//hz6+voVtufn5wc1NTXuZWBgUL8dqgahAHhX+B7vCt8jv6BQYnt+QWGl28vWqWh7dY5XH8doKI3dXn34FGNuqWr7b9Fc/g1r8n+zPjV5QqhMUVERIiMj4eDgwJUJhUI4ODjg5s2bAIA+ffogNjYWz58/R15eHk6fPg1HR8cKj7ly5UpkZ2dzr6dPnzZ4Pz5W+F5U6ZrD1VmTuLROddYsrmrt47oco6F8iusyf4oxt1S1/bdoLv+GNfm/WZ+a9SyjV69eQSQSQUdHh1euo6ODBw8eAACkpaWxZcsW2NvbQywWY9myZZXOMJKTk4OcnFyDxk0IIZ+iZp0QquuLL77AF198UaN9AgICEBAQAJFI1EBREULIp6VZDxlpaWlBSkoK6enpvPL09HTo6urW6dienp6Ii4tDeHh4nY5DCCEtRbNOCLKysujZsyfCwsK4MrFYjLCwMNja2jZhZIQQ0vI0+ZBRXl4eHj58yL1PSkpCVFQUNDU1YWhoCG9vb7i7u6NXr17o06cPtm3bhvz8fEyfPr0JoyaEkJanyRNCREQE7O3tuffe3t4AAHd3dwQFBcHFxQUvX77EmjVrkJaWhu7duyM0NFTiQnNN0TUEQgjha/KEMHjwYDDGKq3j5eUFLy+vem3X09MTnp6eyMnJgZqaWr0emxBCPkXN+hoCIYSQxtPkZwhNrfTspFaPsHiXC+QVIycnB2/z8wEAuTkCvC8sKPk5B2/zc0uOL8X9zKuTm4McOX5ezs3NqXR72Tplt1e0X1XlFbVRVUw1ba8m6uMYje1TjLmlqu2/RXP5N6zJ/83KqKioQCAQVLu+gFU1XtPCPXv2rEkeX0EIIQ0tOzsbqqqq1a7f6hOCWCzGixcvapxJc3JyYGBggKdPn9boA//UUb+p361BS+l3Tb/XWv2QkVAoRPv27Wu9v6qq6if9C1Nb1O/WhfrdOtAgJyGEEACUEAghhJSghFBLcnJy8PHxaXVPTqV+U79bg9ba71Z/UZkQQsgHdIZACCEEACUEQgghJSghEEIIAUAJgRBCSAlKCLUQEBAAY2NjyMvLo2/fvrh9+3ZTh1QjV65cwejRo6Gvrw+BQIDjx4/ztjPGsGbNGujp6UFBQQEODg5ITEzk1cnMzISrqytUVVWhrq6Or776Cnl5ebw69+7dw8CBAyEvLw8DAwP88MMPDd21Cvn5+aF3795QUVFB27ZtMWbMGCQkJPDqvHv3Dp6enmjTpg2UlZUxfvx4idX6UlJSMHLkSCgqKqJt27ZYunQpiouLeXUuXboEGxsbyMnJwdTUFEFBQQ3dvUrt3LkTVlZW3E1Wtra2OH36NLe9pfa7rI0bN0IgEGDhwoVcWWvod40xUiOHDh1isrKybO/evez+/fts1qxZTF1dnaWnpzd1aNX2999/s1WrVrFjx44xACwkJIS3fePGjUxNTY0dP36cRUdHsy+++IJ16NCBFRQUcHWcnJyYtbU1+9///seuXr3KTE1N2ZQpU7jt2dnZTEdHh7m6urLY2Fj2xx9/MAUFBbZ79+7G6iaPo6MjCwwMZLGxsSwqKop9/vnnzNDQkOXl5XF1vv76a2ZgYMDCwsJYREQE++yzz1i/fv247cXFxaxbt27MwcGB3b17l/39999MS0uLrVy5kqvz+PFjpqioyLy9vVlcXBzbsWMHk5KSYqGhoY3a37JOnjzJTp06xf755x+WkJDAvvnmGyYjI8NiY2MZYy2336Vu377NjI2NmZWVFVuwYAFX3tL7XRuUEGqoT58+zNPTk3svEomYvr4+8/Pza8Koau/jhCAWi5muri778ccfubKsrCwmJyfH/vjjD8YYY3FxcQwACw8P5+qcPn2aCQQC9vz5c8YYYz///DPT0NBghYWFXJ3ly5czc3PzBu5R9WRkZDAA7PLly4yxD32UkZFhR44c4erEx8czAOzmzZuMsQ+JVCgUsrS0NK7Ozp07maqqKtfPZcuWsa5du/LacnFxYY6Ojg3dpRrR0NBgv/76a4vvd25uLjMzM2Pnzp1jdnZ2XEJo6f2uLRoyqoGioiJERkbCwcGBKxMKhXBwcMDNmzebMLL6k5SUhLS0NF4f1dTU0LdvX66PN2/ehLq6Onr16sXVcXBwgFAoxK1bt7g6gwYNgqysLFfH0dERCQkJePPmTSP1pmLZ2dkAAE1NTQBAZGQk3r9/z+t3586dYWhoyOu3paUlb7U+R0dH5OTk4P79+1ydsscordNcfj9EIhEOHTqE/Px82Nratvh+e3p6YuTIkRKxtfR+11arf7hdTbx69QoikUhi+U4dHR08ePCgiaKqX2lpaQBQbh9Lt6WlpaFt27a87dLS0tDU1OTV6dChg8QxSrdpaGg0SPzVIRaLsXDhQvTv3x/dunXjYpKVlYW6ujqv7sf9Lu9zKd1WWZ2cnBwUFBRAQUGhIbpUpZiYGNja2uLdu3dQVlZGSEgILCwsEBUV1WL7fejQIdy5cwfh4eES21r6v3dtUUIgrY6npydiY2Nx7dq1pg6l0ZibmyMqKgrZ2dk4evQo3N3dcfny5aYOq8E8ffoUCxYswLlz5yAvL9/U4XwyaMioBrS0tCAlJSUxEyE9PR26urpNFFX9Ku1HZX3U1dVFRkYGb3txcTEyMzN5dco7Rtk2moKXlxf++usvXLx4kffYc11dXRQVFSErK4tX/+N+V9Wniuqoqqo26V+LsrKyMDU1Rc+ePeHn5wdra2ts3769xfY7MjISGRkZsLGxgbS0NKSlpXH58mX4+/tDWloaOjo6LbLfdUUJoQZkZWXRs2dPhIWFcWVisRhhYWGwtbVtwsjqT4cOHaCrq8vrY05ODm7dusX10dbWFllZWYiMjOTqXLhwAWKxGH379uXqXLlyBe/fv+fqnDt3Dubm5k0yXMQYg5eXF0JCQnDhwgWJ4ayePXtCRkaG1++EhASkpKTw+h0TE8NLhufOnYOqqiosLCy4OmWPUVqnuf1+iMViFBYWtth+Dx06FDExMYiKiuJevXr1gqurK/dzS+x3nTX1Ve1PzaFDh5icnBwLCgpicXFxbPbs2UxdXZ03E6G5y83NZXfv3mV3795lANjWrVvZ3bt32ZMnTxhjH6adqqursxMnTrB79+4xZ2fncqed9ujRg926dYtdu3aNmZmZ8aadZmVlMR0dHTZt2jQWGxvLDh06xBQVFZts2umcOXOYmpoau3TpEktNTeVeb9++5ep8/fXXzNDQkF24cIFFREQwW1tbZmtry20vnYY4fPhwFhUVxUJDQ5m2tna50xCXLl3K4uPjWUBAQJNPQ1yxYgW7fPkyS0pKYvfu3WMrVqxgAoGAnT17ljHWcvv9sbKzjBhrPf2uCUoItbBjxw5maGjIZGVlWZ8+fdj//ve/pg6pRi5evMgASLzc3d0ZYx+mnq5evZrp6OgwOTk5NnToUJaQkMA7xuvXr9mUKVOYsrIyU1VVZdOnT2e5ubm8OtHR0WzAgAFMTk6OtWvXjm3cuLGxuiihvP4CYIGBgVydgoICNnfuXKahocEUFRXZ2LFjWWpqKu84ycnJbMSIEUxBQYFpaWmxxYsXs/fv3/PqXLx4kXXv3p3JysoyExMTXhtNYcaMGczIyIjJysoybW1tNnToUC4ZMNZy+/2xjxNCa+l3TdDjrwkhhACgawiEEEJKUEIghBACgBICIYSQEpQQCCGEAKCEQAghpAQlBEIIIQAoIRBCCClBCYEQQggASgiEfFIuXboEgUAg8VA2QuoD3alMyCekqKgImZmZ0NHRgUAgaOpwSAtDCYEQQggAGjIipEIvX76Erq4uvv/+e67sxo0bkJWVlXjk8cc8PDwwZswYXtnChQsxePBg7v3gwYMxb948LFy4EBoaGtDR0cGePXuQn5+P6dOnQ0VFBaampjh9+jS3Dw0ZkYZECYGQCmhra2Pv3r1Yu3YtIiIikJubi2nTpsHLywtDhw6tlzb27dsHLS0t3L59G/PmzcOcOXMwceJE9OvXD3fu3MHw4cMxbdo0vH37tl7aI6QylBAIqcTnn3+OWbNmwdXVFV9//TWUlJTg5+dXb8e3trbGt99+CzMzM6xcuRLy8vLQ0tLCrFmzYGZmhjVr1uD169e4d+9evbVJSEUoIRBShc2bN6O4uBhHjhxBcHAw5OTk6u3YVlZW3M9SUlJo06YNLC0tubLSBdw/XrKUkIZACYGQKjx69AgvXryAWCxGcnJytfYRCoX4eL5G2eVES8nIyPDeCwQCXlnpTCKxWFzDqAmpOemmDoCQ5qyoqAhTp06Fi4sLzM3NMXPmTMTExKBt27aV7qetrY3Y2FheWVRUlEQCIKQ5oTMEQiqxatUqZGdnw9/fH8uXL0enTp0wY8aMKvcbMmQIIiIisH//fiQmJsLHx0ciQRDS3FBCIKQCly5dwrZt23DgwAGoqqpCKBTiwIEDuHr1Knbu3Fnpvo6Ojli9ejWWLVuG3r17Izc3F25ubo0UOSG1QzemEUIIAUBnCIQQQkpQQiCkFrp27QplZeVyX8HBwU0dHiG1QkNGhNTCkydPyp1GCny4d0BFRaWRIyKk7ighEEIIAUBDRoQQQkpQQiCEEAKAEgIhhJASlBAIIYQAoIRACCGkBCUEQgghACghEEIIKfF/o37ekZETcTkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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BA2uspxyqOan8+vVrNnjwYCYQCNiZM2eq3b+p70OYMmUKMzU15Z3s/Ouvv5hAIGBGRkaV2l5+QrGkpIRlZWVVKq9///6sX79+jDHGxGIxMzMzYxYWFiw3N7dS3oqfraqUn1Q+cuRIlX0KDg6udz3jxo1rtZPKjo6OvHw7duyo8qRyVffEuLm58f4eISEhDADbvXt3pbz5+fksLy+v1r6Ue/+k8u3bt+mk8ofmzJkzuH//PkpKSpCeno7z58/j3LlzMDIywm+//cadCAWAoKAgDBkyBL169YKHhwdMTU2Rnp6Oq1ev4smTJ4iNjQVQOuzy+vVrhIeHQ0pKCo6Ojpg/fz6+/PJLTJw4EdbW1gAAZ2dn3L17F1999RVu376NWbNmcXcqh4WFISIigndyys7ODhcuXOAOiV+8eIG+ffti1qxZ3BDUH3/8gdOnT8PR0ZF3ErtLly7w9vbGd999h+LiYvTv3x8nTpzApUuXcODAgWoPv8vdunULs2bNwqxZs2Bubo6CggIcP34cV65cwYIFC2BjY8PlHThwIKZPn461a9ciIyMD5ubm2LdvHx4+fFjpxHRN/v77b/zyyy9gjCEnJwexsbE4cuQI8vLysGXLlkpTZvz3v//l/gbFxcW4c+cOvvzySwDAhAkTuLvOHz58CBMTk3oNUY0bNw7Hjh3D5MmT8cknnyA1NRW7du2ClZVVjfdk5ObmokuXLpg2bRqsra2hpKSE8PBw3LhxA5s3bwYACIVC7NmzB05OTujRowfmzZsHfX19PH36FJGRkVBRUan1RG5d1KceX19fnD17Fh9//DEWLlwIsViM7du3cyenK/L19YWfnx8iIyNhZ2cHAAgMDMSQIUNgY2ODBQsWwMTEBA8fPsSpU6eqvUxXS0sLa9euhZ+fHxwdHTFhwgQkJiZix44d6N+/P+/Ita7mzp2LX3/9FZ999hkiIyPx8ccfQywW4/79+/j111/xxx9/oF+/fvUuFyg90p01axZ27NiB7OxsDB48GBEREXU6imoWLRp+PjDlv07KH7KyskxXV5eNHj2a/fDDD1X+8maMsZSUFObq6sp0dXWZjIwM09fXZ+PGjWNHjx5ljP3vctLNmzfz9svJyWFGRkbM2tq60l3JERERbOLEiUxbW5tJS0szLS0tNn78eHby5ElePltbW6arq8u9fvPmDZszZw4zNzdnCgoKTCQSsR49erCvv/66yjufxWIx+/rrr5mRkRGTlZVlPXr0YL/88kud3q8HDx6w6dOnM2NjYyYnJ8cUFBSYra0t27VrF+/XXLmCggK2YsUKpqury0QiEevfvz8LCwurU12MMd7fRigUMjU1Nda3b1+2ZMkSdu/evSr3Kb/ktqpH+a9kxhi7e/dupUttaztCkEgk3HsnEolY37592e+//17pF2l528t/HRYWFrKVK1cya2trpqyszBQVFZm1tTXbsWNHpTpu377NpkyZwjp16sREIhEzMjJiM2bMYBERETW+V3U9QqhvPREREaxv375MVlaWmZmZsT179rDly5czOTk5Xr7ly5czgUDAEhISeOlxcXFs8uTJTE1NjcnJyTFLS0u2YcMGbnt1dypv376ddevWjcnIyDAdHR22cOFC9ubNG16euh4hMMZYUVER27RpE+vRowcTiURMXV2d2draMj8/P5adnV2pjOq8f4TAWOnnfPHixaxTp05MUVGRjR8/nj1+/LhVjhAEjFU4e0I+aLm5udDQ0MDWrVvh6enZ2s1p13bs2IFVq1YhJSUFOjo6rd2cdmXSpEm4d+8ed8USUHrDoZGREY4cOdKKLSN0p3IHcvHiRejr68PDw6O1m9LuRUZGYvHixRQMavH+/SJJSUk4ffo0NywEgBvK8/f3b+HWkffREQIhpNl07twZ7u7u3D0AO3fuRGFhIW7fvg0LC4vWbl6Tef/em6ro6uq2UGsajk4qE0KajaOjIw4dOoQXL15AJBJh0KBB+Prrrz+oYABUvvemKu3htzcdIRBCSCPFx8fj2bNnNeZpzvtDmgoFBEIIIQDopDIhhJAyHT4gsLIbluhAiRDS0XX4gJCbmwtVVdVmmTGUEELakw4fEAghhJTqsAEhKCgIVlZW6N+/f2s3hRBC2oQOf5VRTk4OVFVVkZ2dXWn9XkII6Ug67BECIYQQPgoIhBBCAHTggEDnEAghhI/OIdA5BEIIAdCBjxAIIYTwUUAghBACgKa/bhLvisQoEpeOvMlKCSAnW/PawoQQ0hZ12COEpjypXCRmCIvJRFhMJhcYCCGkvaGTyk1wUjmnoARhMZkAAMc+GlCRpwMvQkj7Q99cjVA+VNSxQyoh5EPRYYeMmkL5UFEHP8giBI8fP8ann34KPT09yMrKwsjICEuWLMHr16/rXMbDhw8hEAgQExPTfA0lNaKAQAhplAcPHqBfv35ISkrCoUOHkJycjF27diEiIgKDBg1CZmZmazeR1BEFBEJIo3h6ekJWVhZnz57F8OHDYWhoCCcnJ4SHh+Pp06dYt24dAEAgEODEiRO8fdXU1BASEgIAMDExAQD07dsXAoEAdnZ2XL69e/eiR48eEIlE6Ny5M7y8vLhtaWlpmDhxIpSUlKCiooIZM2YgPT2d2+7r64s+ffpg7969MDQ0hJKSEhYtWgSxWIxvv/0Wurq60NbWxldffcVrW1ZWFubPnw8tLS2oqKhg5MiRiI2NbcJ3ru3psAGBpq4gpPEyMzPxxx9/YNGiRZCXl+dt09XVhYuLC0JDQ+s0rHr9+nUAQHh4OJ4/f45jx44BAHbu3AlPT08sWLAAd+/exW+//QZzc3MAgEQiwcSJE5GZmYkLFy7g3LlzePDgAZydnXllp6Sk4MyZMwgLC8OhQ4fwf//3f/jkk0/w5MkTXLhwAZs2bcL69etx7do1bp/p06cjIyMDZ86cQXR0NGxsbDBq1KgP+4iHdXDZ2dkMAMvOzq7/vvnFLPTPdPYmr4iF/pnOQv9MZ9n5xc3QSkLapr/++osBYMePH69y+5YtWxgAlp6eXmU+VVVVFhwczBhjLDU1lQFgt2/f5uXR09Nj69atq7L8s2fPMikpKZaWlsal3bt3jwFg169fZ4wx5uPjwxQUFFhOTg6Xx8HBgRkbGzOxWMylWVpasoCAAMYYY5cuXWIqKirs3bt3vPrMzMzY7t27q30/2ju6yogQ0mismS6syMjIwLNnzzBq1KgqtyckJMDAwAAGBgZcmpWVFdTU1JCQkMCNABgbG0NZWZnLo6OjAykpKQiFQl5aRkYGACA2NhZ5eXno1KkTr76CggKkpKQ0Wf/aGgoIhJAGMzc3h0AgQEJCAiZPnlxpe0JCAtTV1aGlpQWBQFApcBQXF9dY/vvDUA0lIyPDey0QCKpMk0gkAIC8vDx07twZUVFRlcpSU1Nrkja1RR32HAIhpPE6deqE0aNHY8eOHSgoKOBte/HiBQ4cOABnZ2cIBAJoaWnh+fPn3PakpCTk5+dzr2VlZQEAYrGYS1NWVoaxsTEiIiKqrL979+54/PgxHj9+zKXFx8cjKysLVlZWDe6XjY0NXrx4AWlpaZibm/MempqaDS63raOAQAhplO3bt6OwsBAODg64ePEiHj9+jLCwMIwePRr6+vrc1TsjR47E9u3bcfv2bdy8eROfffYZ71e6trY25OXlERYWhvT0dGRnZwMovUpo8+bNCAwMRFJSEm7duoVt27YBAOzt7dGrVy+4uLjg1q1buH79OlxdXTF8+HD069evwX2yt7fHoEGDMGnSJJw9exYPHz7En3/+iXXr1uHmzZuNeLfaNgoITUwoKJ3KIqegBO+KxLXvQEg7Z2FhgZs3b8LU1BQzZsyAmZkZFixYgBEjRuDq1avQ0NAAAGzevBkGBgYYOnQoZs+ejRUrVkBBQYErR1paGoGBgdi9ezf09PQwceJEAICbmxu2bt2KHTt2oEePHhg3bhySkpIAlA7znDx5Eurq6hg2bBjs7e1hamqK0NDQRvVJIBDg9OnTGDZsGObNm4euXbti5syZePToEXR0dBpVdlvWYecyCgoKQlBQEMRiMf7+++8GzWVUPofRmN7qOHvnDQDwntO8RoSQ9qTDHiF4enoiPj4eN27caO2mEEJIm9BhAwIhhBA+CgiEEEIAUEAghBBShgICIYQQABQQCCGElKGAQAghBAAFBEIIIWUoIBBCCAHQgWc7rXinMiFt1bsiMYrELTeZgKyUAHKyUi1WX0cjEAhw/PhxTJo0qbWbUqUOGxA8PT3h6emJnJwcqKqqtnZzCKlSkZghLKblVuhy7KMBuTrmFQgENW738fGBr69vo9tUF3Z2drhw4QIOHTqEmTNnculbt27F1q1b8fDhwxZpRzlfX1+cOHECMTExvPTnz59DXV29RdtSHx02IBBCGqfiVNahoaHYuHEjEhMTuTQlJSXuOWMMYrEY0tLN95UjJyeH9evXY+rUqZXWOmgrdHV1W7sJNaJzCISQBtHV1eUeqqqqEAgE3Ov79+9DWVkZZ86cga2tLUQiES5fvgx3d/dKwyXe3t6ws7PjXkskEgQEBMDExATy8vKwtrbG0aNHa23PrFmzkJWVhZ9++qnGfCdPnoSNjQ3k5ORgamoKPz8/lJSUcNvv37+PIUOGQE5ODlZWVggPD4dAIMCJEye4PKtXr0bXrl2hoKAAU1NTbNiwgVvsJyQkBH5+foiNjYVAIIBAIEBISAgA8MoZPHgwVq9ezWvby5cvISMjg4sXLwIACgsLsWLFCujr60NRUREDBw6sctGepkJHCISQZrNmzRp8//33MDU1rfNQSUBAAH755Rfs2rULFhYWuHjxIubMmQMtLS0MHz682v1UVFSwbt06+Pv7w83NDYqKipXyXLp0Ca6urggMDMTQoUORkpKCBQsWACgd4hKLxZg0aRIMDQ1x7do15ObmYvny5ZXKUVZWRkhICPT09HD37l14eHhAWVkZq1atgrOzM+Li4hAWFobw8HAAqHJY2sXFBd9++y2++eYbbvgtNDQUenp6GDp0KADAy8sL8fHxOHz4MPT09HD8+HE4Ojri7t27sLCwqNP7WR90hEAIaTb+/v4YPXo0zMzMuHURalJYWIivv/4ae/fuhYODA0xNTeHu7o45c+Zg9+7dte6/aNEiyMnJYcuWLVVu9/Pzw5o1a+Dm5gZTU1OMHj0aX3zxBVf2uXPnkJKSgv3798Pa2hpDhgzhFvipaP369Rg8eDCMjY0xfvx4rFixAr/++iuA0mU/lZSUIC0tzR0xVbUU6IwZM/Ds2TNcvnyZSzt48CBmzZoFgUCAtLQ0BAcH48iRIxg6dCjMzMywYsUKDBkyBMHBwbW+Fw1BRwiEkGZT31XLkpOTkZ+fj9GjR/PSi4qK0Ldv31r3F4lE8Pf3x+eff46FCxdW2h4bG4srV67wvuTFYjHevXuH/Px8JCYmwsDAgDfWP2DAgErlhIaGIjAwECkpKcjLy0NJSUm911PR0tLCmDFjcODAAQwdOhSpqam4evUqF5zu3r0LsViMrl278vYrLCxEp06d6lVXXVFAaEblq6cBdDkf6ZjeH7YRCoV4f02u8rF3oHRxewA4deoU9PX1eflEIlGd6pwzZw6+//57fPnllzA2NuZty8vLg5+fH6ZMmVJpPzm5ul1fdfXqVbi4uMDPzw8ODg5QVVXF4cOHsXnz5jrtX5GLiwsWL16Mbdu24eDBg+jVqxd69erFtVVKSgrR0dGQkuJ/d1Q8Yd+UKCA0oxIx462eVtfL+Qj5UGlpaSEuLo6XFhMTw10VZGVlBZFIhLS0tBrPF9REKBQiICAAU6ZMqXSUYGNjg8TERJibm1e5r6WlJR4/foz09HRuqcz3F9H6888/YWRkhHXr1nFpjx494uWRlZWt0z1OEydOxIIFCxAWFoaDBw/C1dWV29a3b1+IxWJkZGRw5xSaGwUEQkiLGTlyJL777jvs378fgwYNwi+//IK4uDhuOEhZWRkrVqzA0qVLIZFIMGTIEGRnZ+PKlStQUVGBm5tbner55JNPMHDgQOzevZu3BvLGjRsxbtw4GBoaYtq0aRAKhYiNjUVcXBy+/PJL7nyHm5sbvv32W+Tm5mL9+vUA/nffhYWFBdLS0nD48GH0798fp06dwvHjx3n1GxsbIzU1FTExMejSpQuUlZWrPMJRVFTEpEmTsGHDBiQkJGDWrFnctq5du8LFxQWurq7YvHkz+vbti5cvXyIiIgK9e/fGJ598Ur83vy5YB5ednc0AsOzs7Prvm1/MQv9MZ2/yiljon+k1Ps/OL26G1pMPXUFhCcvOL26xR0FhSYPaGRwczFRVVbnXkZGRDAB78+ZNpbwbN25kOjo6TFVVlS1dupR5eXmx4cOHc9slEgnbunUrs7S0ZDIyMkxLS4s5ODiwCxcuVFv/8OHD2ZIlS3hpf/75JwPAjIyMeOlhYWFs8ODBTF5enqmoqLABAwawH3/8kduekJDAPv74YyYrK8u6devG/vvf/zIALCwsjMuzcuVK1qlTJ6akpMScnZ3Zv//9b17/3717x6ZOncrU1NQYABYcHMwYYwwAO378OK89p0+fZgDYsGHDKvWrqKiIbdy4kRkbGzMZGRnWuXNnNnnyZHbnzp1q34vGEJQ1ssMqv1M5Ozu73ieFcgpKEBaTiTG91bmhoeqeO/bRgIo8HZAR0t5cuXIFQ4YMQXJyMszMzFq7Oc2KvqEIIaSC48ePQ0lJCRYWFkhOTsaSJUvw8ccff/DBAOjAAYEmtyOEVCU3NxerV69GWloaNDU1YW9v36AriNqjDhsQaHI7QkhVXF1deVf7dCR0pzIhhBAAFBAIIYSUoYBACCEEAAUEQgghZSggEEIIAUABgRBCSJkOe9kpIe1BVm4+8t8VtVh9CnKyUFNWaLH62oOQkBB4e3sjKyurxnwCgQDHjx+vtCJce0IBgZA2LP9dEZZtPdJi9W3xnl7ngFA+2Vt1fHx84Ovr2wStqp2dnR0uXLgAoHSabFNTU3h5eWHRokWNLtvZ2Rljx47lXvv6+uLEiROIiYnh5Xv+/HmdV4VrqyggEEIa5Pnz59zz0NBQbNy4EYmJiVxaxTn7GWMQi8WQlm6+rxwPDw/4+/sjPz8f+/fvh6enJ9TV1XkziDaEvLx8lSueva/iojrtFZ1DIIQ0SPnykLq6ulBVVYVAIOBe379/H8rKyjhz5gxsbW0hEolw+fJluLu7VxpS8fb2hp2dHfdaIpEgICAAJiYmkJeXh7W1NY4ePVprexQUFKCrqwtTU1P4+vrCwsICv/32GwAgLS0NEydOhJKSElRUVDBjxgykp6dz+8bGxmLEiBFQVlaGiooKbG1tcfPmTQClQ0Zqamrccz8/P8TGxkIgEEAgECAkJARA6RHTiRMnAACDBw/G6tWree17+fIlZGRkcPHiRQClK5+tWLEC+vr6UFRUxMCBAxEVFVXHd795UEAghDSbNWvW4JtvvkFCQgJ69+5dp30CAgKwf/9+7Nq1C/fu3cPSpUsxZ84cbkioruTl5VFUVASJRIKJEyciMzMTFy5cwLlz5/DgwQM4OztzeV1cXNClSxfcuHED0dHRWLNmDbdoT0XOzs5Yvnw5evTogefPn+P58+e8ciqWd/jwYd7qcKGhodDT0+MWu/Hy8sLVq1dx+PBh3LlzB9OnT4ejoyOSkpLq1c+mRENGhJBm4+/vX2l95JoUFhbi66+/Rnh4OAYNGgQAMDU1xeXLl7F79+46raImFotx6NAh3LlzBwsWLEBERATu3r2L1NRUGBgYAAD279+PHj164MaNG+jfvz/S0tKwcuVKdOvWDUDpIjhVkZeXh5KSEqSlpWscIpoxYwa8vb1x+fJlLgAcPHgQs2bNgkAgQFpaGoKDg5GWlgY9PT0AwIoVKxAWFobg4GB8/fXXdX7PmhIdIbSQ8vWVcwpK8K6IZlglHUO/fv3qlT85ORn5+fkYPXo0lJSUuMf+/fuRkpJS4747duyAkpIS5OXl4eHhgaVLl2LhwoVISEiAgYEBFwyA0qU61dTUkJCQAABYtmwZ5s+fD3t7e3zzzTe11lUbLS0tjBkzBgcOHAAApKamcmsxA8Ddu3chFovRtWtXXj8vXLjQ6Lobg44QWgitr0w6IkVFRd5roVCI99fkKi4u5p7n5eUBAE6dOgV9fX1evqqWoKzIxcUF69atg7y8PDp37gyhsO6/d319fTF79mycOnUKZ86cgY+PDw4fPozJkyfXuYyq2rN48WJs27YNBw8eRK9evdCrVy8Apf2UkpJCdHQ0pKSkePtVPBnf0iggEEJajJaWFuLi4nhpMTEx3Hi9lZUVRCIR0tLS6jQ8VJGqqirMzc0rpXfv3h2PHz/G48ePuaOE+Ph4ZGVlwcrKisvXtWtXdO3aFUuXLsWsWbMQHBxcZUCQlZWt0zoqEydOxIIFCxAWFoaDBw/yptTu27cvxGIxMjIyuCGltqDdDxk9fvwYdnZ2sLKyQu/evXHkSMtds00IqZ+RI0fi5s2b2L9/P5KSkuDj48MLEMrKylixYgWWLl2Kffv2ISUlBbdu3cK2bduwb9++BtVpb2+PXr16wcXFBbdu3cL169fh6uqK4cOHo1+/figoKICXlxeioqLw6NEjXLlyBTdu3ED37t2rLM/Y2BipqamIiYnBq1evUFhYWGU+RUVFTJo0CRs2bEBCQgLv8teuXbvCxcUFrq6uOHbsGFJTU3H9+nUEBATg1KlTDepnU2j3RwjS0tLYunUr+vTpgxcvXsDW1hZjx46tdKhKSHukICeLLd7TW7S+5uTg4IANGzZg1apVePfuHT799FO4urri7t27XJ4vvvgCWlpaCAgIwIMHD6CmpgYbGxv861//alCdAoEAJ0+exOeff45hw4ZBKBTC0dER27ZtAwBISUnh9evXcHV1RXp6OjQ1NTFlyhT4+flVWd7UqVNx7NgxjBgxAllZWQgODoa7u3uVeV1cXDB27FgMGzYMhoaGvG3BwcH48ssvsXz5cjx9+hSampr46KOPMG7cuAb1sykI2PsDeu2ctbU1fv/9d94JpJqUr5iWnZ0NFRWVetWVU1CCsJhMjOmtzp0fqMtzxz4aUJFv97GYEPKBafUho4sXL2L8+PHQ09Pj3dhRUVBQEIyNjSEnJ4eBAwfi+vXrVZYVHR0NsVhc52BACCHkf1o9ILx9+xbW1tYICgqqcntoaCiWLVsGHx8f3Lp1C9bW1nBwcEBGRgYvX2ZmJlxdXfHjjz+2RLMJIeSD0+rjFk5OTnBycqp2+5YtW+Dh4YF58+YBAHbt2oVTp05h7969WLNmDYDSm1kmTZqENWvWYPDgwTXWV1hYyDsJlJOT0wS9IISQ9q/VjxBqUlRUhOjoaNjb23NpQqEQ9vb2uHr1KoDSSbPc3d0xcuRIzJ07t9YyAwICoKqqyj1oeIkQQkq16YDw6tUriMVi6Ojo8NJ1dHTw4sULAMCVK1cQGhqKEydOoE+fPujTpw/vioX3rV27FtnZ2dzj8ePHzdoHQghpL1p9yKixhgwZAolEUuf8IpGo1jseCSGkI2rTRwiampqQkpLiTVMLAOnp6Y2eezwoKAhWVlbo379/o8ohhJAPRZsOCLKysrC1tUVERASXJpFIEBERwc2E2FCenp6Ij4/HjRs3GttMQgj5ILT6kFFeXh6Sk5O51+W3hGtoaMDQ0BDLli2Dm5sb+vXrhwEDBmDr1q14+/Ytd9URIYSQptHqAeHmzZsYMWIE93rZsmUAADc3N4SEhMDZ2RkvX77Exo0b8eLFC/Tp0wdhYWGVTjQT8iEqzsyEuGwG0JYgpaQEGQ2NFqmrrovXt1UCgQDHjx+vtAJcRe7u7sjKyqryhtu2qNUDgp2dXaXpcN/n5eUFLy+vJq03KCgIQUFBdZq1kJDWIs7Lw303txarr9u+ffUKCO7u7lVOOpeUlFTlzKMtKSQkhBtJEAgE0NPTw+jRo7Fp0yZoa2s3uvznz59DXV0dAPDw4UOYmJjg9u3b6NOnD5fnhx9+qPX7rS1p9YDQWjw9PeHp6cnNZUQIaRhHR0cEBwfz0rS0tFqpNXwqKipITEyERCJBbGws5s2bh2fPnuGPP/5odNl1ubClvX23tOmTyoSQtk8kEkFXV5f3kJKSwpYtW9CrVy8oKirCwMAAixYt4hbAqUpNC90D4JajlJeXh4GBARYvXoy3b9/W2DaBQABdXV3o6enByckJixcvRnh4OAoKCiCRSODv748uXbpAJBJxw9HlioqK4OXlhc6dO0NOTg5GRkYICAjglV0+FGRiYgKgdJ0DgUAAOzs7AKVHUOVDSj/++CP09PQqXSY/ceJEfPrpp9zrkydPwsbGBnJycjA1NYWfnx9KSkpq7GdToYBACGkWQqEQgYGBuHfvHvbt24fz589j1apV1eavaaH7lJQUODo6YurUqbhz5w5CQ0Nx+fLleg8ly8vLQyKRoKSkBD/88AM2b96M77//Hnfu3IGDgwMmTJjALXIfGBiI3377Db/++isSExNx4MABGBsbV1lu+YSb4eHheP78OY4dO1Ypz/Tp0/H69WtERkZyaZmZmQgLC+OW1rx06RJcXV2xZMkSxMfHY/fu3QgJCcFXX31Vr342VIcNCHQfAiFN4/fff+etCzx9eun6Dd7e3hgxYgSMjY0xcuRIfPnll/j111+rLSctLQ329vbo1q0bLCwsMH36dFhbWwMonXLGxcUF3t7esLCwwODBgxEYGIj9+/fj3bt3dWpnUlISdu3ahX79+kFZWRnff/89Vq9ejZkzZ8LS0hKbNm1Cnz59sHXrVq49FhYWGDJkCIyMjDBkyBDeIjcVlQ+RderUCbq6utCo4jyMuro6nJyccPDgQS7t6NGj0NTU5C6s8fPzw5o1a+Dm5gZTU1OMHj0aX3zxBXbv3l2nPjZWhw0IdB8CIU1jxIgRiImJ4R6BgYEASn8tjxo1Cvr6+lBWVsbcuXPx+vVr5OfnV1lOTQvdx8bGIiQkhBd4HBwcIJFIkJqaWm3bsrOzoaSkBAUFBVhaWkJHRwcHDhxATk4Onj17ho8//piX/+OPP0ZCQgKA0uGemJgYWFpaYvHixTh79mxj3yq4uLjgP//5DzfB5oEDBzBz5kxu/efY2Fj4+/vz+unh4YHnz59X+741pQ4bEAghTUNRURHm5ubco3Pnznj48CHGjRuH3r174z//+Q+io6O5Ke6LioqqLMfX1xf37t3DJ598gvPnz8PKygrHjx8HUHq/0j//+U9e4ImNjUVSUhLMzMyqbZuysjJiYmIQFxeHt2/f4uLFi+jatWud+mVjY4PU1FR88cUXKCgowIwZMzBt2rR6vjt848ePB2MMp06dwuPHj3Hp0iVuuKi8n35+frx+3r17F0lJSZCTk2tU3XXRYa8yIoQ0n+joaEgkEmzevJn79VvTcFG56ha6t7GxQXx8fL0vZRUKhVXuo6KiAj09PVy5cgXDhw/n0q9cuYIBAwbw8jk7O8PZ2RnTpk2Do6MjMjMzKw0JycqWLj1a22XscnJymDJlCg4cOIDk5GRYWlrCxsaG225jY4PExMRWu2SXAgIhpMmZm5ujuLgY27Ztw/jx43HlyhXs2rWr2vwFBQVYuXIlpk2bBhMTEzx58gQ3btzA1KlTAQCrV6/GRx99BC8vL8yfPx+KioqIj4/HuXPnsH379ga1ceXKlfDx8YGZmRn69OmD4OBgxMTE4MCBAwBK12Lp3Lkz+vbtC6FQiCNHjkBXVxdqamqVytLW1oa8vDzCwsLQpUsXyMnJVXvJqYuLC8aNG4d79+5hzpw5vG0bN27EuHHjYGhoiGnTpkEoFCI2NhZxcXH48ssvG9TP+uiwAYFuTCPtgZSSErpVceNXc9bXFKytrbFlyxZs2rQJa9euxbBhwxAQEABXV9eq661lofvevXvjwoULWLduHYYOHQrGGMzMzODs7NzgNi5evBjZ2dlYvnw5MjIyYGVlhd9++w0WFhYASoebvv32WyQlJUFKSgr9+/fH6dOnuSOeiqSlpREYGAh/f39s3LgRQ4cORVRUVJX1jhw5EhoaGkhMTMTs2bN52xwcHPD777/D398fmzZtgoyMDLp164b58+c3uJ/1IWDt6Ta6ZlB+Y1p2djZUVFTqt29BCcJiMjGmtzrO3nkDAHV67thHAyryHTYWE0LaKDqpTAghBAAFBEIIIWUoIBBCCAFAAYEQQkiZDhsQaOoKQgjh67ABgaauIIQQvg4bEAghhPBRQCCEEAKAAgIhhJAyDbpd1tTUFDdu3ECnTp146VlZWbCxscGDBw+apHGEdHjFmYC4+lXGmpyUEiBT9zWVGyMkJATe3t7IyspqkfraKnd3d2RlZXGrr7WmBgWEhw8fVjkHUGFhIZ4+fdroRhFCyojzgPtuLVdft331Cgju7u7YV8VcS0lJSa02Y2e5kJAQzJs3Dw4ODrylMbOysqCuro7IyEhuqcuW8PDhQ5iYmOD27dvo06cPl/7DDz+grcwgVK+A8Ntvv3HP//jjD95sfmKxGBEREdUuMUf+RygonQcJAGSlBJCTlWrlFhHScI6OjggODualla8g1tqkpaURHh6OyMhIblWytqa6WVFbQ73OIUyaNAmTJk2CQCCAm5sb93rSpEmYOXMmzp07h82bNzdXW5tUa96HUCJmCIvJRFhMJorEbeOXASENJRKJoKury3tISUlhy5Yt6NWrFxQVFWFgYIBFixYhL6/64a/Y2FiMGDECysrKUFFRga2tLW7evMltv3z5MoYOHQp5eXkYGBhg8eLFePv2bY1tU1RUxKeffoo1a9bUmO/x48eYMWMG1NTUoKGhgYkTJ+Lhw4fc9pKSEixevBhqamro1KkTVq9ezX0HlgsLC8OQIUO4POPGjeOt+mZiYgIA6Nu3LwQCAXd04u7uzpXz448/Qk9PDxKJhNe+iRMn4tNPP+Venzx5EjY2NpCTk4OpqSn8/PxQUlJSYx/rol4BQSKRQCKRwNDQEBkZGdxriUSCwsJCJCYmYty4cY1uVEug+xAIaV5CoRCBgYG4d+8e9u3bh/Pnz2PVqlXV5ndxcUGXLl1w48YNREdHY82aNZCRkQEApKSkwNHREVOnTsWdO3cQGhqKy5cvw8vLq9Z2+Pr64u7duzh69GiV24uLi+Hg4ABlZWVcunQJV65cgZKSEhwdHbnV3TZt2oQDBw4gODgYV65cQU5OTqUx/7dv32LZsmW4efMmIiIiIBQKMXnyZO7L/fr16wBKlxZ9/vw5jh07Vqkt06dPx+vXrxEZGcmlZWZmIiwsjFtZ7dKlS3B1dcWSJUsQHx+P3bt3IyQkBF999VWt70VtGnQOoaY1TAkhHcvvv/8OpQrrKDg5OeHIkSPw9vbm0oyNjfHll1/is88+w44dO6osJy0tDStXrkS3bt0AgFuXAAACAgLg4uLClWlhYYHAwEAMHz4cO3furHF5ST09PSxZsgTr1q3j/aIvFxoaColEgj179kAgEAAAgoODoaamhqioKIwZMwbbtm3D2rVrMXnyZADA9u3bcfr0aV455Yv5lNu7dy+0tLQQHx+Pnj17csNonTp1gq6ubpVtVVdXh5OTEw4ePIhRo0YBAI4ePQpNTU1uyMvPzw9r1qyBm1vpuSVTU1N88cUXWLVqFXx8fKp9H+qiwZPyR0REICIigjtSqGjv3r2NahQhpP0YMWIEdu7cyb1WVFQEUPpLOCAgAPfv30dOTg5KSkrw7t075OfnQ0FBoVI5y5Ytw/z58/Hzzz/D3t4e06dP59ZLjo2NxZ07d7jVzACAMQaJRILU1FR07969xjauXr0au3fvxt69ezFjxgzettjYWCQnJ0NZWZmX/u7dO6SkpCA7Oxvp6em8pTWlpKRga2vL++5LSkrCxo0bce3aNbx69YrblpaWhp49e9bYvopcXFzg4eGBHTt2QCQS4cCBA5g5cya3ME9sbCyuXLnCOyIQi8U1vrd11aCA4OfnB39/f/Tr1w+dO3fmoiohpONRVFSsdEXRw4cPMW7cOCxcuBBfffUVNDQ0cPnyZfzjH/9AUVFRlV9avr6+mD17Nk6dOoUzZ87Ax8cHhw8fxuTJk5GXl4d//vOfWLx4caX9DA0Na22jmpoa1q5dCz8/v0rD2nl5ebC1teUFm3L1OTk+fvx4GBkZ4aeffuLOA/Ts2ZMbdqpPOYwxnDp1Cv3798elS5fw73//m9dePz8/TJkypdK+NR0p1UWDAsKuXbsQEhKCuXPnNqpyQsiHKTo6GhKJBJs3b+Z+2f7666+17te1a1d07doVS5cuxaxZsxAcHIzJkyfDxsYG8fHxjbqU9fPPP0dgYCB++OEHXrqNjQ1CQ0Ohra1d7aqJOjo6uHHjBoYNGwag9Bf5rVu3uMtHX79+jcTERPz0008YOnQogNKT4BXJyspy+9ZETk4OU6ZMwYEDB5CcnAxLS0vY2Njw2puYmNgsl/U26E7loqIiDB48uKnbQgj5QJibm6O4uBjbtm3DgwcP8PPPP2PXrl3V5i8oKICXlxeioqLw6NEjXLlyBTdu3OCGglavXo0///wTXl5eiImJQVJSEk6ePFmnk8rl5OTk4Ofnh8DAQF66i4sLNDU1MXHiRFy6dAmpqamIiorC4sWL8eTJEwClwSQgIAAnT55EYmIilixZgjdv3nCjI+rq6ujUqRN+/PFHJCcn4/z581i2bBmvHm1tbcjLyyMsLAzp6enIzs6utq0uLi44deoU9u7dy51MLrdx40bs378ffn5+uHfvHhISEnD48GGsX7++zu9FdRp0hDB//nwcPHgQGzZsaHQDCCE1kFIqvVmsJetrAtbW1tiyZQs2bdqEtWvXYtiwYQgICICrq2vV1UpJ4fXr13B1dUV6ejo0NTUxZcoU+Pn5AQB69+6NCxcuYN26dRg6dCgYYzAzM4Ozs3O92uXm5obNmzcjPj6eS1NQUMDFixexevVqTJkyBbm5udDX18eoUaO4I4bVq1fjxYsXcHV1hZSUFBYsWAAHBwdISZXeQyQUCnH48GEsXrwYPXv2hKWlJQIDA3k3vklLSyMwMBD+/v7YuHEjhg4diqioqCrbOXLkSGhoaCAxMRGzZ8/mbXNwcMDvv/8Of39/bNq0CTIyMujWrRvmz59fr/eiKgLWgFvklixZgv3796N3797o3bs3d2lYuS1btjS6YS0lJycHqqqqyM7OrvZwsdp9C0oQFpOJMb3VcfbOGwCo93PHPhpQkW/wuX1CSCuQSCTo3r07ZsyYgS+++KK1m9NkGvRNdOfOHW7sLC4ujretvZxgDgoKQlBQUK3jec2t/K5lumOZkLbr0aNHOHv2LIYPH47CwkJs374dqamplX69t3cNCggVb5porzw9PeHp6ckdIbSWEjHD2Ttv4NhHA427PoAQ0lyEQiFCQkKwYsUKMMbQs2dPhIeH13q5a3tDYxWEEFILAwMDXLlypbWb0ewaFBBGjBhR49DQ+fPnG9wgQgghraNBAaHi1K1A6VwgMTExiIuL426nJoQQ0r40KCBUvGuuIl9f3xpnMySEENJ2NekSmnPmzKF5jAghpJ1q0oBw9erVRs+lQQghpHU0aMjo/UmVGGN4/vw5bt68SXcvE0JIO9WggPD+dftCoRCWlpbw9/fHmDFjmqRhhBBCWlaDAsL766cSQghp/xp1Y1p0dDQSEhIAAD169EDfvn2bpFGEEEJaXoMCQkZGBmbOnImoqCioqakBALKysjBixAgcPny4XotKEEIIaRsadJXR559/jtzcXNy7dw+ZmZnIzMxEXFwccnJyqlzRqC0KCgqClZUV+vfv39pNIYSQNqFBASEsLAw7duzgTexkZWWFoKAgnDlzpska15w8PT0RHx+PGzdutHZTCCGkTWhQQJBIJJXWQAAAGRkZ3qLThBBC2o8GBYSRI0diyZIlePbsGZf29OlTLF26FKNGjWqyxhFCCGk5DQoI27dvR05ODoyNjWFmZgYzMzOYmJggJycH27Zta+o2EkIIaQENusrIwMAAt27dQnh4OO7fvw8A6N69O+zt7Zu0cYQQQlpOvY4Qzp8/DysrK+Tk5EAgEGD06NH4/PPP8fnnn6N///7o0aMHLl261FxtJYQQ0ozqFRC2bt0KDw+PKhejV1VVxT//+U9s2bKlyRpHCCGk5dQrIMTGxsLR0bHa7WPGjEF0dHSjG0UIIaTl1SsgpKenV3m5aTlpaWm8fPmy0Y0ihBDS8uoVEPT19REXF1ft9jt37qBz586NbhQhhJCWV6+AMHbsWGzYsAHv3r2rtK2goAA+Pj4YN25ckzWOEEJIy6nXZafr16/HsWPH0LVrV3h5ecHS0hIAcP/+fQQFBUEsFmPdunXN0tAPnVAA5BSUAABkpQSQk5Vq5RYRQjqaegUEHR0d/Pnnn1i4cCHWrl0LxhgAQCAQwMHBAUFBQdDR0WmWhn7oSsQMZ++8AQA49tEALURKCGlp9b4xzcjICKdPn8abN2+QnJwMxhgsLCygrq7eHO0jhBDSQhq8QI66ujpNHU0IIR+QBs1lRAgh5MPzQQSEyZMnQ11dHdOmTWvtphBCSLv1QQSEJUuWYP/+/a3dDEIIadc+iIBgZ2cHZWXl1m4GIYS0a60eEC5evIjx48dDT08PAoEAJ06cqJQnKCgIxsbGkJOTw8CBA3H9+vWWbyghhHzgWj0gvH37FtbW1ggKCqpye2hoKJYtWwYfHx/cunUL1tbWcHBwQEZGRgu3lBBCPmwNvuy0qTg5OcHJyana7Vu2bIGHhwfmzZsHANi1axdOnTqFvXv3Ys2aNfWur7CwEIWFhdzrnJyc+jeaEEI+QK1+hFCToqIiREdH81ZiEwqFsLe3x9WrVxtUZkBAAFRVVbmHgYFBUzWXEELatTYdEF69egWxWFxpOgwdHR28ePGCe21vb4/p06fj9OnT6NKlS43BYu3atcjOzuYejx8/brb2E0JIe9LqQ0ZNITw8vM55RSIRRCJRM7aGEELapzZ9hKCpqQkpKSmkp6fz0tPT06Grq9uosoOCgmBlZUXTbxBCSJk2HRBkZWVha2uLiIgILk0ikSAiIgKDBg1qVNmenp6Ij4/HjRs3GttMQgj5ILT6kFFeXh6Sk5O516mpqYiJiYGGhgYMDQ2xbNkyuLm5oV+/fhgwYAC2bt2Kt2/fclcdEUIIaRqtHhBu3ryJESNGcK+XLVsGAHBzc0NISAicnZ3x8uVLbNy4ES9evECfPn0QFhZG6y4QQkgTa/WAYGdnxy20Ux0vLy94eXk1ab1BQUHcKm+EEELa+DmE5kTnEAghhK/DBgRCCCF8FBAIIYQA6MABge5DIIQQvg4bEOgcAiGE8HXYgEAIIYSPAgIhhBAAFBAIIYSUoYBACCEEQBu4U7m1tPSdykKhAKN6qtYpXSgAcgpKAACyUgLIyUq1SBvbk7cFhZASlv6eEUskUJSnKc0JaawOe4TQ0lcZFZeI4fnNfnh+sx9iiaRSesW0EjFDWEwmwmIyUSSueVqPjio7rwDu/iFw9w9Bdl5BazeHkA9Chw0IhBBC+CggEEIIAUABgRBCSBk6qdxGpr+WFoI7uSxV4bmASarMX35SlU6oti/0dyNtWYc9QmhrU1dk5eRxJ50zs3K457lv86vMX35SlU6oti/0dyNtWYcNCIQQQvgoIBBCCAFAAYEQQkgZCgiEEEIAUEAghBBShgICIYQQAHQfQpPchyAtFMCxtxoAoOq7BtqOlpgUrro6aEI6Qtq2DnuE0JT3IYiLivBgnAMejHOAoKS4CVrXfFpiUrjq6qAJ6Qhp2zpsQCCEEMJHAYEQQggACgiEEELKUEAghBACgAICIYSQMhQQCCGEAKCAQAghpAzdmNaIG9OkIYFjbzVIs/+VIZAScovbVLtfhQVwhALW4PoBQEFOFu8KS+99kDAGoUAAoGVu/GqKG83qchNbS/erpbXmDXt0syCpqMMGBE9PT3h6eiInJweqqjV/gVdH+PIFHri5wXLPHi4tJzsHK3acBAAErXGtcr+snDys3n4MALDJa0qD6i6XnVeAz7Yf4MoqL3eL9/Rm/8+dnVeAZVuPNKq+6sqomN7S/WppTfE+tse6SdtDQ0aEEEIAUEAghBBShgICIYQQABQQCCGElKGAQAghBAAFBEIIIWUoIBBCCAFAAYEQQkgZCgiEEEIAUEAghBBShgICIYQQAB14LqOmmNyunFBOhG4hpfMZ5Ytk8P2iiQD4k9hJC4Et3lMBAPWpsrrJ60SyNf/pKu7XEpOWVayvtrY1Z93V9bWqyfKqm0yPJnkjHVWHDQhNMbldOVZSCLk388te7UGae9nzX0K5ie62LJkIvWfOAIBneqF1Lru6yetqmxSv4n4tNdFdxXa2pLr0tarJ8qqbTI8meSMdFQ0ZEUIIAUABgRBCSBkKCIQQQgBQQCCEEFKGAgIhhBAAFBAIIYSUoYBACCEEAAUEQgghZSggEEIIAUABgRBCSBkKCIQQQgBQQCCEEFKGAgIhhBAAFBAIIYSUoYBACCEEAAUEQgghZT6IgPD777/D0tISFhYW2LNnT2s3hxBC2qV2v2JaSUkJli1bhsjISKiqqsLW1haTJ09Gp06dWrtphBDSrrT7I4Tr16+jR48e0NfXh5KSEpycnHD27NnWbhYhhLQ7rR4QLl68iPHjx0NPTw8CgQAnTpyolCcoKAjGxsaQk5PDwIEDcf36dW7bs2fPoK+vz73W19fH06dPW6LphBDyQWn1gPD27VtYW1sjKCioyu2hoaFYtmwZfHx8cOvWLVhbW8PBwQEZGRkNqq+wsBA5OTm8ByGEkDZwDsHJyQlOTk7Vbt+yZQs8PDwwb948AMCuXbtw6tQp7N27F2vWrIGenh7viODp06cYMGBAteUFBATAz8+vSdoupSBCt5A9kFIQAW/K0hTl0S2k9MT2a2Ht8VYkI43vF02ESFT6LwBIBJW3v59eHwpysnhXWFxanqx0pXQJYxAKSguvz3Mledla21Zb3RXT6tOX6varWJ9YIoGivKjWst5vW0VvCwohVfZ3LO93Xd6jinVXLKO2/lZsU3XlVdW22vpaF/V576p6X97fr6o8TdHOjqD8vWvp96vVjxBqUlRUhOjoaNjb23NpQqEQ9vb2uHr1KgBgwIABiIuLw9OnT5GXl4czZ87AwcGh2jLXrl2L7Oxs7vH48eMGt09GqRByb+ZDRqnwf2mKBZB7Mx9yb+ZDICWptYySwkK8muMMpTev8GqOM17NcYZQLK60/f30+sjOK4C7fwjc/UOQl19YKf3lm1xue32e52Tn1dq22uqumFafvlS3X8X6svMKGvS+VJenvN91eY8q1l2Xemqqr7q+1KevddHQ964u/S7P0xTt7AjK37uWfr9a/QihJq9evYJYLIaOjg4vXUdHB/fv3wcASEtLY/PmzRgxYgQkEglWrVpV4xVGIpEIIhH9QiGEkPe16YBQVxMmTMCECRNauxmEENKutekhI01NTUhJSSE9PZ2Xnp6eDl1d3UaVHRQUBCsrK/Tv379R5RBCyIeiTQcEWVlZ2NraIiIigkuTSCSIiIjAoEGDGlW2p6cn4uPjcePGjcY2kxBCPgitPmSUl5eH5ORk7nVqaipiYmKgoaEBQ0NDLFu2DG5ubujXrx8GDBiArVu34u3bt9xVR4QQQppGqweEmzdvYsSIEdzrZcuWAQDc3NwQEhICZ2dnvHz5Ehs3bsSLFy/Qp08fhIWFVTrRXF9BQUEICgqCuIFX7hBCyIem1QOCnZ0dGGM15vHy8oKXl1eT1uvp6QlPT0/k5ORAVVW1ScsmhJD2qE2fQyCEENJyWv0IobWVH500aAqLd7lAXgmQU/YvwHuem5uL4sIC7nlONel5JSXIKfsXAERVbK8qvbiwoFJZDXnemP1qalv9685BjkjIPW98m2sur+n3q08Zde/r++WV4+9XeXtdVF93zeXVZb/a+k2qV/7eNfb9UlZWhkBQ9ykOBKy28ZoP3JMnT2BgYNDazSCEkCaXnZ0NFRWVOufv8AFBIpHg2bNn9Y6kOTk5MDAwwOPHj+v1hrc1H0o/AOpLW/Sh9ANon32p7/dahx8yEgqF6NKlS4P3V1FRaTcfjpp8KP0AqC9t0YfSD+DD6sv7aDCPEEIIAAoIhBBCylBAaCCRSAQfH592P3Pqh9IPgPrSFn0o/QA+rL5Up8OfVCaEEFKKjhAIIYQAoIBACCGkDAUEQgghACggEEIIKUMBoQGCgoJgbGwMOTk5DBw4ENevX2/tJvH4+vpCIBDwHt26deO2v3v3Dp6enujUqROUlJQwderUSqvSpaWl4ZNPPoGCggK0tbWxcuVKlJTNW9ScLl68iPHjx0NPTw8CgQAnTpzgbWeMYePGjejcuTPk5eVhb2+PpKQkXp7MzEy4uLhARUUFampq+Mc//oG8vDxenjt37mDo0KGQk5ODgYEBvv322xbvi7u7e6W/k6OjY5vrS0BAAPr37w9lZWVoa2tj0qRJSExM5OVpqs9UVFQUbGxsIBKJYG5ujpCQkBbvi52dXaW/y2effdbm+tIsGKmXw4cPM1lZWbZ3715279495uHhwdTU1Fh6enprN43j4+PDevTowZ4/f849Xr58yW3/7LPPmIGBAYuIiGA3b95kH330ERs8eDC3vaSkhPXs2ZPZ29uz27dvs9OnTzNNTU22du3aZm/76dOn2bp169ixY8cYAHb8+HHe9m+++YapqqqyEydOsNjYWDZhwgRmYmLCCgoKuDyOjo7M2tqa/fXXX+zSpUvM3NyczZo1i9uenZ3NdHR0mIuLC4uLi2OHDh1i8vLybPfu3S3aFzc3N+bo6Mj7O2VmZvLytIW+ODg4sODgYBYXF8diYmLY2LFjmaGhIcvLy+PyNMVn6sGDB0xBQYEtW7aMxcfHs23btjEpKSkWFhbWon0ZPnw48/Dw4P1dsrOz21xfmgMFhHoaMGAA8/T05F6LxWKmp6fHAgICWrFVfD4+Psza2rrKbVlZWUxGRoYdOXKES0tISGAA2NWrVxljpV9kQqGQvXjxgsuzc+dOpqKiwgoLC5u17RW9/yUqkUiYrq4u++6777i0rKwsJhKJ2KFDhxhjjMXHxzMA7MaNG1yeM2fOMIFAwJ4+fcoYY2zHjh1MXV2d15fVq1czS0vLFusLY6UBYeLEidXu01b7kpGRwQCwCxcuMMaa7jO1atUq1qNHD15dzs7OzMHBocX6wlhpQFiyZEm1+7TVvjQFGjKqh6KiIkRHR8Pe3p5LEwqFsLe3x9WrV1uxZZUlJSVBT08PpqamcHFxQVpaGgAgOjoaxcXFvD5069YNhoaGXB+uXr2KXr168Valc3BwQE5ODu7du9eyHakgNTUVL1684LVdVVUVAwcO5LVdTU0N/fr14/LY29tDKBTi2rVrXJ5hw4ZBVlaWy+Pg4IDExES8efOmhXpTKioqCtra2rC0tMTChQvx+vVrbltb7Ut2djYAQENDA0DTfaauXr3KK6M8T3P+33q/L+UOHDgATU1N9OzZE2vXrkV+fj63ra32pSl0+Mnt6uPVq1cQi8WVlu/U0dHB/fv3W6lVlQ0cOBAhISGwtLTE8+fP4efnh6FDhyIuLg4vXryArKws1NTUePvo6OjgxYsXAIAXL15U2cfyba2lvO6q2lax7dra2rzt0tLS0NDQ4OUxMTGpVEb5NnV19WZp//scHR0xZcoUmJiYICUlBf/617/g5OSEq1evQkpKqk32RSKRwNvbGx9//DF69uzJ1dMUn6nq8uTk5KCgoADy8vLN3hcAmD17NoyMjKCnp4c7d+5g9erVSExMxLFjx9psX5oKBYQPkJOTE/e8d+/eGDhwIIyMjPDrr7+22Q9iRzRz5kzuea9evdC7d2+YmZkhKioKo0aNasWWVc/T0xNxcXG4fPlyazel0arry4IFC7jnvXr1QufOnTFq1CikpKTAzMyspZvZomjIqB40NTUhJSVV6eqJ9PR06OrqtlKraqempoauXbsiOTkZurq6KCoqQlZWFi9PxT7o6upW2cfyba2lvO6a3n9dXV1kZGTwtpeUlCAzM7PN98/U1BSamppITk7m2tKW+uLl5YXff/8dkZGRvCnjm+ozVV0eFRWVJv8hU11fqjJw4EAA4P1d2lJfmhIFhHqQlZWFra0tIiIiuDSJRIKIiAgMGjSoFVtWs7y8PKSkpKBz586wtbWFjIwMrw+JiYlIS0vj+jBo0CDcvXuX92V07tw5qKiowMrKqsXbX87ExAS6urq8tufk5ODatWu8tmdlZSE6OprLc/78eUgkEu4/9qBBg3Dx4kUUFxdzec6dOwdLS8sWGy6qypMnT/D69Wt07twZQNvpC2MMXl5eOH78OM6fP19piKqpPlODBg3ilVGepyn/b9XWl6rExMQAAO/v0hb60ixa+6x2e3P48GEmEolYSEgIi4+PZwsWLGBqamq8Kw5a2/Lly1lUVBRLTU1lV65cYfb29kxTU5NlZGQwxkovETQ0NGTnz59nN2/eZIMGDWKDBg3i9i+/rG7MmDEsJiaGhYWFMS0trRa57DQ3N5fdvn2b3b59mwFgW7ZsYbdv32aPHj1ijJVedqqmpsZOnjzJ7ty5wyZOnFjlZad9+/Zl165dY5cvX2YWFha8SzWzsrKYjo4Omzt3LouLi2OHDx9mCgoKTX7ZaU19yc3NZStWrGBXr15lqampLDw8nNnY2DALCwv27t27NtWXhQsXMlVVVRYVFcW7FDM/P5/L0xSfqfJLNVeuXMkSEhJYUFBQk1+qWVtfkpOTmb+/P7t58yZLTU1lJ0+eZKampmzYsGFtri/NgQJCA2zbto0ZGhoyWVlZNmDAAPbXX3+1dpN4nJ2dWefOnZmsrCzT19dnzs7OLDk5mdteUFDAFi1axNTV1ZmCggKbPHkye/78Oa+Mhw8fMicnJyYvL880NTXZ8uXLWXFxcbO3PTIykgGo9HBzc2OMlV56umHDBqajo8NEIhEbNWoUS0xM5JXx+vVrNmvWLKakpMRUVFTYvHnzWG5uLi9PbGwsGzJkCBOJRExfX5998803LdqX/Px8NmbMGKalpcVkZGSYkZER8/DwqPTDoi30pao+AGDBwcFcnqb6TEVGRrI+ffowWVlZZmpqyqujJfqSlpbGhg0bxjQ0NJhIJGLm5uZs5cqVvPsQ2kpfmgNNf00IIQQAnUMghBBShgICIYQQABQQCCGElKGAQAghBAAFBEIIIWUoIBBCCAFAAYEQQkgZCgiEEEIAUEAgpF2JioqCQCCoNJEcIU2B7lQmpB0pKipCZmYmdHR0IBAIWrs55ANDAYEQQggAGjIipFovX76Erq4uvv76ay7tzz//hKysbKWpjd/n7u6OSZMm8dK8vb1hZ2fHvbazs8Pnn38Ob29vqKurQ0dHBz/99BPevn2LefPmQVlZGebm5jhz5gy3Dw0ZkeZEAYGQamhpaWHv3r3w9fXFzZs3kZubi7lz58LLy6vJVjTbt28fNDU1cf36dXz++edYuHAhpk+fjsGDB+PWrVsYM2YM5s6dy1vTl5DmQgGBkBqMHTsWHh4ecHFxwWeffQZFRUUEBAQ0WfnW1tZYv349LCwssHbtWsjJyUFTUxMeHh6wsLDAxo0b8fr1a9y5c6fJ6iSkOhQQCKnF999/j5KSEhw5cgQHDhyASCRqsrJ79+7NPZeSkkKnTp3Qq1cvLq18ofb3l9IkpDlQQCCkFikpKXj27BkkEgkePnxYp32EQiHev16j4jKX5WRkZHivBQIBL638SiKJRFLPVhNSf9Kt3QBC2rKioiLMmTMHzs7OsLS0xPz583H37l1oa2vXuJ+Wlhbi4uJ4aTExMZUCACFtCR0hEFKDdevWITs7G4GBgVi9ejW6du2KTz/9tNb9Ro4ciZs3b2L//v1ISkqCj49PpQBBSFtDAYGQakRFRWHr1q34+eefoaKiAqFQiJ9//hmXLl3Czp07a9zXwcEBGzZswKpVq9C/f3/k5ubC1dW1hVpOSMPQjWmEEEIA0BECIYSQMhQQCGmAHj16QElJqcrHgQMHWrt5hDQIDRkR0gCPHj2q8jJSoPTeAWVl5RZuESGNRwGBEEIIABoyIoQQUoYCAiGEEAAUEAghhJShgEAIIQQABQRCCCFlKCAQQggBQAGBEEJImf8HiFNTj4kG0u4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for setting in pred_dfs['setting'].unique():\n",
+    "    for model_config in ['neg,clone_id',]:# 'no-neg,clone_id', 'neg,no-clone', 'no-neg,no-clone']:\n",
+    "        plot_confusion_hist(pred_dfs[pred_dfs['setting'] == setting], pred_dfs[pred_dfs['setting'] == setting][f'assignment_{model_config}'], title=setting + ' ' + model_config)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c7fc2dca-b49f-43da-a7ac-a8853e2c73f8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "results_merged = pd.concat([results_df, results_beamt])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d2a0e428",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "roc_auc 0.84375\n",
+      "pr_auc 0.84375\n",
+      "f1 0.15625\n",
+      "precision 0.625\n",
+      "recall 0.1875\n",
+      "accuracy 0.3125\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import wilcoxon\n",
+    "\n",
+    "metrics_list = ['roc_auc', 'pr_auc', 'f1', 'precision', 'recall', 'accuracy']\n",
+    "\n",
+    "# Show all model configurations or only neg,clone_id model\n",
+    "for show_all in [True, False]:\n",
+    "    results_melt = results_merged.sort_values(['setting', 'model_config']).melt( id_vars=['model_config', 'setting'], value_vars=metrics_list, var_name='metric', value_name='value' )\n",
+    "    if show_all:\n",
+    "        plt.figure(figsize=(6, 2))\n",
+    "        ax = sns.boxplot(data=results_melt, x='metric', y='value', hue='model_config', order=metrics_list)\n",
+    "        sns.move_legend(ax, 'upper left', bbox_to_anchor=(1, 1), frameon=False)\n",
+    "        sns.despine()\n",
+    "        plt.show()\n",
+    "        \n",
+    "    else:\n",
+    "        plt.figure(figsize=(4, 2))\n",
+    "        models_to_compare = ['BEAMT', 'neg,clone_id']\n",
+    "        df_filtered = results_melt[results_melt['model_config'].isin(models_to_compare)]\n",
+    "        \n",
+    "        ax = sns.boxplot(data=df_filtered, x='metric', y='value', hue='model_config', order=metrics_list)\n",
+    "        sns.move_legend(ax, 'upper left', bbox_to_anchor=(1, 1), frameon=False)\n",
+    "        \n",
+    "        y_max = df_filtered['value'].max()\n",
+    "        y_offset = (y_max - df_filtered['value'].min()) * 0.05\n",
+    "        \n",
+    "        for i, metric in enumerate(metrics_list):\n",
+    "            df_metric = results_merged.sort_values('setting')\n",
+    "            vec_1 = df_metric[df_metric['model_config'] == models_to_compare[0]][metric].values\n",
+    "            vec_2 = df_metric[df_metric['model_config'] == models_to_compare[1]][metric].values\n",
+    "            \n",
+    "            try:\n",
+    "                stat, p = wilcoxon(vec_1, vec_2)\n",
+    "                if p < 0.001: sig = '***'\n",
+    "                elif p < 0.01: sig = '**'\n",
+    "                elif p < 0.05: sig = '*'\n",
+    "                else: sig = 'ns'\n",
+    "            except:\n",
+    "                sig = 'ns'\n",
+    "            \n",
+    "            print(metric, p)\n",
+    "            ax.text(i, y_max + y_offset, sig, ha='center', va='bottom', )\n",
+    "        \n",
+    "        ax.set_ylim(top=y_max + (y_offset * 4))\n",
+    "        \n",
+    "        sns.despine()\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5dc84b2d-8b64-4667-bc9c-e89cd892bbf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for show_all in [True, False]:\n",
+    "    results_melt = results_merged.sort_values(['setting', 'model_config']).melt(id_vars=['model_config'], value_vars=['roc_auc', 'pr_auc', 'f1', 'precision', 'recall', 'accuracy'], var_name='metric', value_name='value', )\n",
+    "    if show_all:\n",
+    "        plt.figure(figsize=(6, 2))\n",
+    "    else:\n",
+    "        results_melt = results_melt[results_melt['model_config'].isin(['BEAMT', 'neg,clone_id'])]\n",
+    "        plt.figure(figsize=(4, 2))\n",
+    "    \n",
+    "    ax = sns.boxplot(results_melt, x='metric', y='value', hue='model_config')\n",
+    "    sns.move_legend(ax, 'upper left', bbox_to_anchor=(1, 1), frameon=False)\n",
+    "    \n",
+    "    for container in ax.containers:\n",
+    "        try:\n",
+    "            ax.bar_label(container, fmt=\"%.3f\", fontsize=10, label_type='edge', padding=5, rotation=90)\n",
+    "        except:\n",
+    "            pass\n",
+    "    \n",
+    "    # plt.xticks(rotation=10, ha='center')\n",
+    "    sns.despine()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "883c4caa-c6af-403f-b88c-102fa8e7621a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for show_all in [True, False]:\n",
+    "    results_sub = results_merged.copy()\n",
+    "    if show_all:\n",
+    "        plt.figure(figsize=(10, 2))\n",
+    "    else:\n",
+    "        plt.figure(figsize=(5, 2))\n",
+    "        results_sub = results_sub[results_sub['model_config'].isin(['BEAMT', 'neg,clone_id'])]\n",
+    "\n",
+    "\n",
+    "    ax = sns.barplot(results_sub.sort_values(['setting', 'model_config']), x='setting', y='f1', hue='model_config')\n",
+    "    sns.move_legend(ax, 'upper left', bbox_to_anchor=(1, 1), frameon=False)\n",
+    "    \n",
+    "    for container in ax.containers:\n",
+    "        ax.bar_label(container, fmt=\"%.2f\", fontsize=10, label_type='edge', padding=5, rotation=90)\n",
+    "    \n",
+    "    plt.xticks(rotation=20, ha='center')\n",
+    "    sns.despine()\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9ee1aec1-7a48-4974-b007-01d47a70ec74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x200 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results_sub_plot = results_sub[results_sub['model_config'].isin(['BEAMT', 'neg,clone_id'])].copy()\n",
+    "metrics = ['pr_auc', 'roc_auc', 'f1', 'precision', 'recall', 'accuracy', ]\n",
+    "fig, axes = plt.subplots(1, len(metrics), figsize=(2 * len(metrics), 2))\n",
+    "legend_handles, legend_labels = None, None\n",
+    "\n",
+    "for i, metric in enumerate(metrics):\n",
+    "    ax = axes[i]\n",
+    "    results_pivot = results_sub_plot.pivot( columns='model_config', values=metric, index=['setting', 'dex_key', 'sample', 'tcr_info_sequenced_only'] ).reset_index()\n",
+    "    results_pivot['dex'] = results_pivot['dex_key'].map(dex_map)\n",
+    "    results_pivot['dataset'] = results_pivot['dex'] + ',' + results_pivot['sample']\n",
+    "    sns.scatterplot( results_pivot, x='neg,clone_id', y='BEAMT', \n",
+    "                    # hue='dataset', \n",
+    "                    # hue='tcr_info_sequenced_only',\n",
+    "                    # style='tcr_info_sequenced_only', \n",
+    "                    ax=ax, legend=(i == 0) )\n",
+    "\n",
+    "    if i == 0:\n",
+    "        legend_handles, legend_labels = ax.get_legend_handles_labels()\n",
+    "        if ax.legend_ is not None:\n",
+    "            ax.legend_.remove()\n",
+    "\n",
+    "    sns.despine(ax=ax)\n",
+    "    min_value = results_sub_plot[metric].min()\n",
+    "    max_value = results_sub_plot[metric].max()\n",
+    "    diff = max_value - min_value\n",
+    "    ax.plot((min_value - diff * 0.1, 1), (min_value - diff * 0.1, 1), ls=':', c='grey')\n",
+    "    ax.set_title(metric)\n",
+    "\n",
+    "fig.legend( legend_handles, legend_labels, loc='upper center', bbox_to_anchor=(0.5, 1.25), ncol=4, frameon=False )\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 61854bed2e59a8b75522520601efe14218535920 Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Wed, 18 Mar 2026 14:24:25 +0100
Subject: [PATCH 30/39] feature: save BEAMT results to csv

---
 .../Ioanna_data/Benchmark_Ioanna.ipynb        | 499 ++----------------
 1 file changed, 48 insertions(+), 451 deletions(-)

diff --git a/experiments/Ioanna_data/Benchmark_Ioanna.ipynb b/experiments/Ioanna_data/Benchmark_Ioanna.ipynb
index de4dde8..cfb45dd 100644
--- a/experiments/Ioanna_data/Benchmark_Ioanna.ipynb
+++ b/experiments/Ioanna_data/Benchmark_Ioanna.ipynb
@@ -267,7 +267,7 @@
          "type": "integer"
         }
        ],
-       "ref": "eb13d510-261d-4695-aeb7-06ef165e523c",
+       "ref": "cb458682-ec96-4983-93f2-46b28aa0920e",
        "rows": [
         [
          "('05-95-D2',)",
@@ -450,462 +450,59 @@
    "execution_count": 9,
    "id": "ccaea043-f350-4c32-8dee-722e7503a415",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
-       "columns": [
-        {
-         "name": "index",
-         "rawType": "int64",
-         "type": "integer"
-        },
-        {
-         "name": "dex_key",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "model_config",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "sample",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "tcr_info_sequenced_only",
-         "rawType": "bool",
-         "type": "boolean"
-        },
-        {
-         "name": "setting",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "roc_auc",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "pr_auc",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "f1",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "precision",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "recall",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "accuracy",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "mcc",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "fdr",
-         "rawType": "float64",
-         "type": "float"
-        }
-       ],
-       "ref": "ff9f4923-3500-48fb-9903-1947b62735f3",
-       "rows": [
-        [
-         "0",
-         "HLA-A*0201",
-         "BEAMT",
-         "05-95-D2",
-         "True",
-         "DexA,05-95-D2,True",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "0.0"
-        ],
-        [
-         "1",
-         "HLA-A*0201",
-         "BEAMT",
-         "05-95-D2",
-         "False",
-         "DexA,05-95-D2,False",
-         "0.9994654203323559",
-         "0.9683386259473217",
-         "0.9130434782608695",
-         "0.9545454545454546",
-         "0.875",
-         "0.998546511627907",
-         "0.9131901392356188",
-         "0.045454545454545456"
-        ],
-        [
-         "2",
-         "HLA-A*0201",
-         "BEAMT",
-         "50-50-D2",
-         "True",
-         "DexA,50-50-D2,True",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "0.0"
-        ],
-        [
-         "3",
-         "HLA-A*0201",
-         "BEAMT",
-         "50-50-D2",
-         "False",
-         "DexA,50-50-D2,False",
-         "0.9893458377518558",
-         "0.9570410217991486",
-         "0.9281045751633987",
-         "0.9726027397260274",
-         "0.8875",
-         "0.9944048830111902",
-         "0.9262500139199894",
-         "0.0273972602739726"
-        ],
-        [
-         "4",
-         "HLA-C*0702",
-         "BEAMT",
-         "05-95-D1",
-         "True",
-         "DexC,05-95-D1,True",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "0.0"
-        ],
-        [
-         "5",
-         "HLA-C*0702",
-         "BEAMT",
-         "05-95-D1",
-         "False",
-         "DexC,05-95-D1,False",
-         "0.9998444003578791",
-         "0.9757575757575758",
-         "0.9523809523809523",
-         "1.0",
-         "0.9090909090909091",
-         "0.9995741056218058",
-         "0.9532586618367267",
-         "0.0"
-        ],
-        [
-         "6",
-         "HLA-C*0702",
-         "BEAMT",
-         "50-50-D1",
-         "True",
-         "DexC,50-50-D1,True",
-         "0.9949759720401922",
-         "0.9637538364950332",
-         "0.8470588235294118",
-         "1.0",
-         "0.7346938775510204",
-         "0.9815078236130867",
-         "0.8487487888793945",
-         "0.0"
-        ],
-        [
-         "7",
-         "HLA-C*0702",
-         "BEAMT",
-         "50-50-D1",
-         "False",
-         "DexC,50-50-D1,False",
-         "0.9755399443101398",
-         "0.899231795670309",
-         "0.8113207547169812",
-         "0.9662921348314607",
-         "0.6991869918699187",
-         "0.98149005090236",
-         "0.8134723615037478",
-         "0.033707865168539325"
-        ]
-       ],
-       "shape": {
-        "columns": 13,
-        "rows": 8
-       }
-      },
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dex_key</th>\n",
-       "      <th>model_config</th>\n",
-       "      <th>sample</th>\n",
-       "      <th>tcr_info_sequenced_only</th>\n",
-       "      <th>setting</th>\n",
-       "      <th>roc_auc</th>\n",
-       "      <th>pr_auc</th>\n",
-       "      <th>f1</th>\n",
-       "      <th>precision</th>\n",
-       "      <th>recall</th>\n",
-       "      <th>accuracy</th>\n",
-       "      <th>mcc</th>\n",
-       "      <th>fdr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>HLA-A*0201</td>\n",
-       "      <td>BEAMT</td>\n",
-       "      <td>05-95-D2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>DexA,05-95-D2,True</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>HLA-A*0201</td>\n",
-       "      <td>BEAMT</td>\n",
-       "      <td>05-95-D2</td>\n",
-       "      <td>False</td>\n",
-       "      <td>DexA,05-95-D2,False</td>\n",
-       "      <td>0.999465</td>\n",
-       "      <td>0.968339</td>\n",
-       "      <td>0.913043</td>\n",
-       "      <td>0.954545</td>\n",
-       "      <td>0.875000</td>\n",
-       "      <td>0.998547</td>\n",
-       "      <td>0.913190</td>\n",
-       "      <td>0.045455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>HLA-A*0201</td>\n",
-       "      <td>BEAMT</td>\n",
-       "      <td>50-50-D2</td>\n",
-       "      <td>True</td>\n",
-       "      <td>DexA,50-50-D2,True</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>HLA-A*0201</td>\n",
-       "      <td>BEAMT</td>\n",
-       "      <td>50-50-D2</td>\n",
-       "      <td>False</td>\n",
-       "      <td>DexA,50-50-D2,False</td>\n",
-       "      <td>0.989346</td>\n",
-       "      <td>0.957041</td>\n",
-       "      <td>0.928105</td>\n",
-       "      <td>0.972603</td>\n",
-       "      <td>0.887500</td>\n",
-       "      <td>0.994405</td>\n",
-       "      <td>0.926250</td>\n",
-       "      <td>0.027397</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>HLA-C*0702</td>\n",
-       "      <td>BEAMT</td>\n",
-       "      <td>05-95-D1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>DexC,05-95-D1,True</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>HLA-C*0702</td>\n",
-       "      <td>BEAMT</td>\n",
-       "      <td>05-95-D1</td>\n",
-       "      <td>False</td>\n",
-       "      <td>DexC,05-95-D1,False</td>\n",
-       "      <td>0.999844</td>\n",
-       "      <td>0.975758</td>\n",
-       "      <td>0.952381</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.909091</td>\n",
-       "      <td>0.999574</td>\n",
-       "      <td>0.953259</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>HLA-C*0702</td>\n",
-       "      <td>BEAMT</td>\n",
-       "      <td>50-50-D1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>DexC,50-50-D1,True</td>\n",
-       "      <td>0.994976</td>\n",
-       "      <td>0.963754</td>\n",
-       "      <td>0.847059</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.734694</td>\n",
-       "      <td>0.981508</td>\n",
-       "      <td>0.848749</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>HLA-C*0702</td>\n",
-       "      <td>BEAMT</td>\n",
-       "      <td>50-50-D1</td>\n",
-       "      <td>False</td>\n",
-       "      <td>DexC,50-50-D1,False</td>\n",
-       "      <td>0.975540</td>\n",
-       "      <td>0.899232</td>\n",
-       "      <td>0.811321</td>\n",
-       "      <td>0.966292</td>\n",
-       "      <td>0.699187</td>\n",
-       "      <td>0.981490</td>\n",
-       "      <td>0.813472</td>\n",
-       "      <td>0.033708</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      dex_key model_config    sample  tcr_info_sequenced_only  \\\n",
-       "0  HLA-A*0201        BEAMT  05-95-D2                     True   \n",
-       "1  HLA-A*0201        BEAMT  05-95-D2                    False   \n",
-       "2  HLA-A*0201        BEAMT  50-50-D2                     True   \n",
-       "3  HLA-A*0201        BEAMT  50-50-D2                    False   \n",
-       "4  HLA-C*0702        BEAMT  05-95-D1                     True   \n",
-       "5  HLA-C*0702        BEAMT  05-95-D1                    False   \n",
-       "6  HLA-C*0702        BEAMT  50-50-D1                     True   \n",
-       "7  HLA-C*0702        BEAMT  50-50-D1                    False   \n",
-       "\n",
-       "               setting   roc_auc    pr_auc        f1  precision    recall  \\\n",
-       "0   DexA,05-95-D2,True  1.000000  1.000000  1.000000   1.000000  1.000000   \n",
-       "1  DexA,05-95-D2,False  0.999465  0.968339  0.913043   0.954545  0.875000   \n",
-       "2   DexA,50-50-D2,True  1.000000  1.000000  1.000000   1.000000  1.000000   \n",
-       "3  DexA,50-50-D2,False  0.989346  0.957041  0.928105   0.972603  0.887500   \n",
-       "4   DexC,05-95-D1,True  1.000000  1.000000  1.000000   1.000000  1.000000   \n",
-       "5  DexC,05-95-D1,False  0.999844  0.975758  0.952381   1.000000  0.909091   \n",
-       "6   DexC,50-50-D1,True  0.994976  0.963754  0.847059   1.000000  0.734694   \n",
-       "7  DexC,50-50-D1,False  0.975540  0.899232  0.811321   0.966292  0.699187   \n",
-       "\n",
-       "   accuracy       mcc       fdr  \n",
-       "0  1.000000  1.000000  0.000000  \n",
-       "1  0.998547  0.913190  0.045455  \n",
-       "2  1.000000  1.000000  0.000000  \n",
-       "3  0.994405  0.926250  0.027397  \n",
-       "4  1.000000  1.000000  0.000000  \n",
-       "5  0.999574  0.953259  0.000000  \n",
-       "6  0.981508  0.848749  0.000000  \n",
-       "7  0.981490  0.813472  0.033708  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# BEAMT\n",
     "\n",
-    "results = []\n",
-    "for dex_key, sample in [('HLA-A*0201', '05-95-D2'), ('HLA-A*0201', '50-50-D2'), ('HLA-C*0702', '05-95-D1'), ('HLA-C*0702', '50-50-D1')]:\n",
-    "    for tcr_info_sequenced_only in [True, False]:\n",
-    "        mdata_sample = mdata[mdata.obs[f'rna:Sample'] == sample]\n",
-    "        if tcr_info_sequenced_only:\n",
-    "            mdata_sample = mdata_sample[mdata_sample['airr'].obs_names]\n",
+    "if os.path.exists('results_beamt.csv'):\n",
+    "    pred_dfs_beamt = pd.read_csv('pred_df_beamt.csv')\n",
+    "    results_beamt = pd.read_csv('results_beamt.csv')\n",
     "\n",
-    "        df = pd.DataFrame({\"barcode\":mdata_sample.obs_names, \n",
-    "                           \"NLV_true\":mdata_sample.obs.NLV_true,\n",
-    "                           \"CRV_true\":mdata_sample.obs.CRV_true,\n",
-    "                           \"TPR_true\":mdata_sample.obs.TPR_true})\n",
+    "else:\n",
+    "    results = []\n",
+    "    pred_dfs = []\n",
+    "    for dex_key, sample in [('HLA-A*0201', '05-95-D2'), ('HLA-A*0201', '50-50-D2'), ('HLA-C*0702', '05-95-D1'), ('HLA-C*0702', '50-50-D1')]:\n",
+    "        for tcr_info_sequenced_only in [True, False]:\n",
+    "            mdata_sample = mdata[mdata.obs[f'rna:Sample'] == sample]\n",
+    "            if tcr_info_sequenced_only:\n",
+    "                mdata_sample = mdata_sample[mdata_sample['airr'].obs_names]\n",
     "\n",
-    "        true_labels = {\"HLA-A*0201\":\"NLV_true\", \"HLA-C*0702\":\"CRV_true\", \"HLA-B*0702\":\"TPR_true\"}\n",
+    "            true_labels = {\"HLA-A*0201\":\"NLV_true\", \"HLA-C*0702\":\"CRV_true\", \"HLA-B*0702\":\"TPR_true\"}\n",
     "\n",
-    "        # Train models\n",
-    "        mask = mdata_sample.obs[true_labels[dex_key]]\n",
-    "        mdata_tmp = mdata_sample[~np.isnan(mask),:].copy()\n",
-    "        y_true = mdata_tmp.obs[true_labels[dex_key]]\n",
-    "        \n",
-    "        # BEAMT\n",
-    "        model_config = f\"beamt\"\n",
-    "        setting = f'{dex_map[dex_key]},{sample},{tcr_info_sequenced_only}'\n",
-    "        result = {'dex_key': dex_key, 'model_config': 'BEAMT', 'sample': sample, 'tcr_info_sequenced_only': tcr_info_sequenced_only, 'setting': setting, }\n",
-    "        mixer = BEAMT()\n",
-    "        mixer.preprocess_model_data(mdata_tmp, pmhc_key=dex_key, gex_key='rio', neg_ctrl_key='HLA-B*0801', ir_key='airr')    \n",
-    "        \n",
-    "        mixer.fit()\n",
-    "        p_pred, assignment = mixer.predict_posterior_class(target_fdr=None, threshold=0.5)\n",
-    "        p_pred = p_pred.__array__()\n",
-    "        assignment = assignment.__array__()\n",
-    "        metrics = eval_results(y_true, assignment, p_pred, None)\n",
-    "        result.update(metrics)\n",
-    "        results.append(result)\n",
+    "            # Train models\n",
+    "            mask = mdata_sample.obs[true_labels[dex_key]]\n",
+    "            mdata_sample = mdata_sample[~np.isnan(mask),:].copy()\n",
+    "            y_true = mdata_sample.obs[true_labels[dex_key]]\n",
+    "            \n",
+    "            pred_df = mdata_sample.obs.copy()\n",
+    "            pred_df['setting'] = setting\n",
+    "            pred_df['x_umi'] = mdata_sample['rio'][:, dex_key].X.toarray()[:, 0]\n",
+    "            pred_df['y_true'] = y_true\n",
+    "\n",
+    "            # BEAMT\n",
+    "            model_config = f\"beamt\"\n",
+    "            setting = f'{dex_map[dex_key]},{sample},{tcr_info_sequenced_only}'\n",
+    "            result = {'dex_key': dex_key, 'model_config': 'BEAMT', 'sample': sample, 'tcr_info_sequenced_only': tcr_info_sequenced_only, 'setting': setting, }\n",
+    "            mixer = BEAMT()\n",
+    "            mixer.preprocess_model_data(mdata_sample, pmhc_key=dex_key, gex_key='rio', neg_ctrl_key='HLA-B*0801', ir_key='airr')    \n",
+    "            \n",
+    "            mixer.fit()\n",
+    "            p_pred, assignment = mixer.predict_posterior_class(target_fdr=None, threshold=0.5)\n",
+    "            p_pred = p_pred.__array__()\n",
+    "            assignment = assignment.__array__()\n",
+    "            pred_df[f'p_pred_{model_config}'] = p_pred\n",
+    "            pred_df[f'assignment_{model_config}'] = assignment\n",
+    "\n",
+    "            metrics = eval_results(y_true, assignment, p_pred, None)\n",
+    "            result.update(metrics)\n",
+    "            results.append(result)\n",
+    "            \n",
+    "            pred_dfs.append(pred_df)\n",
     "\n",
-    "results_beamt = results.copy()\n",
-    "results_beamt = pd.DataFrame(results_beamt)\n",
-    "results_beamt"
+    "    pred_dfs_beamt = pd.concat(pred_dfs)\n",
+    "    pred_dfs_beamt.to_csv('pred_df_beamt.csv')\n",
+    "    results_beamt = pd.DataFrame(results)\n",
+    "    results_beamt.to_csv('results_beamt.csv')"
    ]
   },
   {
@@ -1141,7 +738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "id": "d2a0e428",
    "metadata": {},
    "outputs": [

From 9fa73eff3c87e7292dd34d2c9f6cc2e9737c0e34 Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Tue, 24 Mar 2026 15:20:20 +0100
Subject: [PATCH 31/39] Update Figure design

---
 .../Ioanna_data/Benchmark_Ioanna.ipynb        | 269 ++++++++----------
 1 file changed, 116 insertions(+), 153 deletions(-)

diff --git a/experiments/Ioanna_data/Benchmark_Ioanna.ipynb b/experiments/Ioanna_data/Benchmark_Ioanna.ipynb
index cfb45dd..28589e3 100644
--- a/experiments/Ioanna_data/Benchmark_Ioanna.ipynb
+++ b/experiments/Ioanna_data/Benchmark_Ioanna.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 19,
    "id": "2df647af",
    "metadata": {},
    "outputs": [
@@ -29,21 +29,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 20,
    "id": "d771bfc7-e30e-4d9f-ab47-1d80d0086f25",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/envs/dextrademixer/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "/opt/conda/envs/dextrademixer/lib/python3.13/site-packages/muon/_core/preproc.py:31: FutureWarning: `__version__` is deprecated, use `importlib.metadata.version('scanpy')` instead\n",
-      "  if Version(scanpy.__version__) < Version(\"1.10\"):\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import sys \n",
     "\n",
@@ -82,10 +71,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 21,
    "id": "5cbbc2c8-b9fa-434c-8ac4-54af11f1c4e4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -93,7 +91,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 22,
+   "id": "7a7b131f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.reset_defaults()\n",
+    "\n",
+    "global_settings = {\n",
+    "'font.size': 12, 'axes.titlesize': 'large', 'axes.labelsize': 'medium', 'xtick.labelsize': 'medium', 'ytick.labelsize': 'medium', 'legend.fontsize': 'medium', 'figure.titlesize': 'large',\n",
+    "    'figure.figsize': (3, 2.5), 'figure.dpi': 100, 'savefig.dpi': 300, 'savefig.bbox': 'tight', 'savefig.transparent': True,\n",
+    "}\n",
+    "plt.rcParams.update(global_settings)\n",
+    "os.makedirs('figures', exist_ok=True)\n",
+    "\n",
+    "hue_order = ['BEAMT', 'Ours', 'Ours+control', 'Ours+clone', 'Ours+control+clone', ]\n",
+    "palette = {'BEAMT': \"#7adeff\", 'Ours': \"#0A44D5\", 'Ours+control': \"#21C3A8\", 'Ours+clone': \"#0e8fcb\", 'Ours+control+clone': \"#8900bf\", \"DextraDemixer\":  \"#8900bf\"}\n",
+    "\n",
+    "plt.rcParams.update({ 'axes.spines.top': False, 'axes.spines.right': False, })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "id": "abbc614b-f949-4856-a740-1d1841c70045",
    "metadata": {},
    "outputs": [],
@@ -155,18 +175,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 24,
    "id": "7d171cd9-677e-476e-bf99-017f94d30ca1",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/envs/dextrademixer/lib/python3.13/site-packages/anndata/utils.py:362: ExperimentalFeatureWarning: Support for Awkward Arrays is currently experimental. Behavior may change in the future. Please report any issues you may encounter!\n",
-      "  warnings.warn(msg, category, stacklevel=stacklevel)\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -236,7 +248,7 @@
        "      layers:\t'counts'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -248,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 25,
    "id": "555c9cd9-0925-420f-92e8-1f5e2754edb7",
    "metadata": {},
    "outputs": [
@@ -267,7 +279,7 @@
          "type": "integer"
         }
        ],
-       "ref": "cb458682-ec96-4983-93f2-46b28aa0920e",
+       "ref": "ba655f65-70ae-4f27-8e10-430fa11a06b1",
        "rows": [
         [
          "('05-95-D2',)",
@@ -300,7 +312,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -312,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 26,
    "id": "968d3df4-4149-4e27-93da-e58dd3b5bbcf",
    "metadata": {},
    "outputs": [],
@@ -326,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 27,
    "id": "6711ec3f-2366-40f4-bd0d-f8e05cf1c319",
    "metadata": {},
    "outputs": [
@@ -340,7 +352,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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ggIMHD0p9Nsq6cuWK5PPJy8vD69evERERgZiYGPB4PKxfvx7ff/+91DYrV65EYGAgBAIBXFxc8OOPP8rU6+LiItOPlRBSzVR2JkpIeYEGrgiW9Lpz5w6rV68eA8D++eefEveTk5PDTE1NmZ6eHnv79q3cMunp6czAwIDp6+uzlJSUEuuLj49nOjo6zNLSkgmFwhLLylNQUMA2btzInJ2dGZ/PZ2ZmZqxPnz4sIiJCpmx4eHipx0LRcY6Pj2deXl7M2tqa6enpMXt7e/bdd9+x9+/fyy3v6elZ4j5UnfVDlf2HhISwr7/+mjk5OTFjY2Omp6fHrK2t2VdffcVCQ0NV2q/YvHnzmLOzMzM2NmZ8Pp999tlnbMGCBezdu3cKy3fp0oXZ2NgwHo/HBAIB++yzz5i3tzd78eKFyvsXXxEUvzgcDjMyMmL29vasT58+7Mcff1Q4801pnwUA5unpqXKbCCFVC4exEsbWIIQQQgghNRY9NUwIIYQQoqUoESSEEEII0VKUCBJCCCGEaClKBAkhhBBCtBQlgoQQQgghWooSQUIIIYQQLUWJICGEEEKIlqJEkBBCCCFES1EiSAghhBCipSgRJIQQQgjRUpQIEkIIIYRoKUoECSGEEEK0FCWChBBCCCFaihJBQgghhBAtRYkgIYQQQoiWokSQEEIIIURLUSJICCGEEKKlKBEkhBBCCNFSlAgSQgghhGgpSgQJIYQQQrQUJYKEEEIIIVqKEkFCCCGEEC1FiSAhhBBCiJaiRJAQQgghREtRIkgIIYQQoqUoESSEEEII0VKUCBJCCCGEaClKBAkhhBBCtBQlgoQQQgghWooSQUIIIYQQLUWJICGEEEKIlqJEkBBCCCFES1EiSAghhBCipapkIhgbGwsOhwMvL6/KbkqNtnz5cnA4HFy5ckVqOYfDQbdu3SqlTcrYu3cvOBwO9u7dW9lNITUMxZ6KQbGnarpy5Qo4HA6WL19e2U0hFahKJoKEVGXiZEGV16f/4FUlDg4O4HA4MDY2xtu3b+WW6datGzgcDp4/f67x/RcVFeHgwYPo3LkzrK2tYWBggMaNG2P8+PH4999/Zco/ePAAkyZNwueffw5LS0vweDzUq1cP//vf/3DixAkwxmS2ESceil5nz57V+PsipLwoE4OqcsxRRvHv7Pbt2+WWESfmP/zwg0b3XVBQgJCQEEycOBHOzs4wMTGBgYEBWrRogaVLlyIrK0thW0p6cblcqW1K+xxHjBghs5+IiAjMnz8fbdu2lcQ/R0dHTJo0Se34rKvWVqRGmD59OkaMGAF7e/vKbkq1YmZmhmXLlsksX7FiBQDIXefg4FDezSqz7OxsLFu2DDt37qzQ/Y4aNQq//fYb7OzsMGjQIBgbG+PBgwfYt28fDh06hLCwMLi7u0vK37lzBydPnkT79u3h5uYGU1NTvHnzBqdOncLgwYMxduxY7N+/X+6+PD095X4WDRs2LK+3R+Sg2KMZpqammDVrltx11SHmKGvFihUYO3YsjI2NK2R/L168wKBBg2BoaIju3bvDw8MD2dnZOHfuHFatWoVff/0VERERsLCwkGzj4uIiN/YDwLVr13D58mX06dNH7vpWrVph4MCBMsudnZ1llg0ePBgpKSlwc3PD6NGjoauri5s3b2L37t04cuQILly4gA4dOqj2hlkVFBMTwwAwT0/Pym6KVgLAunbtWtnNUCgoKIgBYEFBQZXdFCkAWBX9SpWofv36DABr2LAh43K57NGjRzJlunbtygCwZ8+eaXTfUVFRDABr3rw5y8nJkVq3Z88eBoB1795danl+fr7cujIyMljTpk0ZABYZGSm1btmyZQwACw8PL7E9FHsqF8Ue5YjP0/r162u03vDwcAaALVu2TKP1qkP8nW3YsCEDwBYtWiRTRvx5LF68WKP7TkhIYH5+fiw7O1tquVAoZB4eHgwAmz59utL1tW/fngFgoaGhUsvViTc//vgjS0xMlFm+Zs0aBoA5OzsrXZdYhd8ajoqKwvDhw1G3bl3weDzY2NigZ8+e+O2335TaPikpCd7e3nBwcIC+vj4sLS0xaNAg3LlzR6Zs8f4c4eHh6NatG4yNjWFiYgIPDw88fvxY7j5yc3Oxbt06uLi4wNDQEEZGRujQoQMOHz5cpve+d+9eDB48GA0aNIBAIICJiQk6duyIAwcOyJRt0qQJ9PX1kZqaKreu9evXg8PhYNu2bZJl4eHhmDJlCpo1awYTExMIBAI4OztjxYoVyM/Pl6lDUT+dshIKhfjxxx/RokULGBgYwMTEBJ07d5b7GRfvkxUbG4sRI0bAwsICfD4fbdq0wenTp0vdX1FREerVqwcTExNkZ2fLLTNjxgxwOBwcO3aszO9PVQ4ODnBwcEBmZibmzJkDBwcH6OnpSfrheHl5gcPhIDY2VmbbkvrsvH//Hj4+PmjatCkEAgFMTU3Ro0cPnD9/Xq12rlu3DkVFRZg/f75a26vj5cuXAIAePXrAwMBAat2AAQMAACkpKVLLeTye3LpMTEzQq1cvAMCzZ8/kllmxYgXFHoo9ALQj9kRHR2PhwoVo06aN5DZi/fr1MWXKFCQkJChdz8uXLzFlyhQ0bNgQAoEA5ubmaNGiBaZOnYp3797JlD98+DC6d+8OMzMz8Pl8NG3aFKtXr4ZQKFT5PcyYMQO2trbYtGmTSm0ui7p162LatGkwNDSUWq6vr49FixYBgNLn7oMHD/DXX3+hbt268PDwKHPbFixYAFtbW7nLBQIBHj58KPczKUmFJoIBAQFwc3PDyZMn4ebmhrlz58LDwwPJyckK+wAUFxMTgzZt2mD79u1wcnLC3Llz0atXL/zxxx9wc3NT+MU9ffo0evbsCRMTE0ydOhWdO3fGmTNn0LVrV5lgl56ejk6dOmHRokXgcrmYMGECPD09kZKSglGjRpWpL8K3336LuLg4dOnSBbNmzcKIESMQFxeHsWPHYsmSJVJlPT09UVBQoPAfgH379kFfXx+jRo2SLFu/fj3Onz8PFxcXfPPNN5g0aRL09fWxfPly9OnTB0VFRWq3XVkfPnxAr1694OPjg8LCQnh7e2Ps2LGIjo7G8OHDJV+iT8XFxaFdu3aIjY3F2LFjMXz4cDx8+BADBgxAeHh4ifvkcrmYPHkysrKy5B6vvLw8HDhwANbW1pLkoqJ9+PAB7u7uOHnyJHr27InvvvsOjo6OatcXFxeH1q1b48cff4SlpSWmTp2K4cOH4/Hjx+jduzcCAgJUrnPgwIHo0qULTp8+Xeox15TmzZsDAC5fvoy8vDypdeLv8//+9z+l6srNzcXly5cBAC1atJBaJ07Wrl27BgsLC7i5uaFp06Z4/fo1xR6KPTU29pw4cQI7d+5EvXr1MHLkSMyYMQPNmjVDYGAg2rZti8TExFLrSEpKQtu2bREUFITmzZtj5syZGDt2LBwdHREcHIykpCSp8hMmTMCoUaPw/PlzDB48GN7e3jA3N8eSJUvQu3dvFBYWqvQeDAwMsGrVKuTl5WHx4sUqbVse9PT0AAC6usr1rPP39wcATJw4UaaPoNjr16+xa9curF27Frt27cL9+/dVbheHw5G0SdF+FFL5GqKa/v33X6arq8tq1arFHj58KLP+1atXkv9XdLm0Z8+eDABbvXq11PKIiAjG5XKZubk5y8rKkiwXXzbmcrns4sWLUtssXLiQAWDr16+XWu7p6Sl3eV5eHuvVqxfjcDjs7t27qrx1iefPn8ssEwqFzN3dnenq6rKEhATJ8levXjEdHR3WunVrmW3Et9MGDRoktfzFixdMJBLJlP/hhx8YAHbkyBGp5Ypul6EMt2fWrl3LALA+ffqwgoICyfK3b99KbkFGRERIlos/awBs+fLlUnWdPXtWUldx8m7PvH79munq6so9XuLyn95aCAoKYsuWLVP6VdrtIPH7+JT4fffo0UPmVgNj/51zMTExMusU3arp2rUr43A47PDhw1LL09LSWKtWrRifz2dv3rwpsb2ftq+goIBFRUUxDofDWrduLXUuKbo1rIljOHv2bAaA2dvbs2nTprEFCxawr776iunq6rIRI0bIPWaMMfbs2TO2bNky9sMPP7DJkyczW1tbBoD5+PhIlfv333+Zjo6O5PMp/uLxeGzmzJmS90qxh2IPY1U/9ojbbmpqKrd8SEgIY+zjLU55XSnOnTvHdHR02NSpU6WWy4s3W7duZQDY5s2bZerJzs5mubm5Mu/366+/llrO2H+fubx65BGXDwgIYEVFRaxFixZMR0dH6jug6Nbw3bt3VTq+y5YtY2lpaUq1a+rUqQwAW7hwYallc3NzmZmZGeNyuSw+Pl5mffFz8NNXt27dWFxcnFJtYoyxI0eOMACsffv2Sm8jVmGJ4PTp0xkAtnHjxlLLygvGr169kvxj8eHDB5ltxowZwwCwffv2SZaJT5LRo0fLlH/58iUDwAYPHixZlpqayrhcLmvTpo3cdt27d48BYN9//32p70EVx48fl2k7Y4x9+eWXDIBM4uzt7S23v4Ei7969YwDY+PHjpZaXRzBu2LAh43A47PHjxzLrAgMDZdpRvK9LYWGhzDb29vasdu3aUssU9dMZMmQIA8Bu374ttbx9+/ZMR0dHJtESJzfKvko7JqUlgvfu3ZO7naqJoPg8HDJkiNz6Tp48yQAwPz+/Etv7afvE/3iOGDGCAWDBwcGSMooSQU0dw507dzKBQCBVtnXr1iwsLExhu8PCwqTK6+vrM19fX5mERBx7RowYwV6+fMny8vJYXFwcCwgIYFZWVlLJI8Wejyj2VO3YU1ICIe+HjDwtWrRgjo6OUstKSgR37dpVap0uLi5MV1dXblJVWFjIateuzdq2bVtqPYxJJ4KM/Zec/+9//5OUUZQIiper8pIXfz8VGhrKOBwOs7OzY+/fvy+1/N69exkA5uHhIXf927dv2ZIlS9idO3dYWloaS0tLY1evXmXdu3dnwMf+kYp+CBf38uVLZmlpyXR1ddmNGzdKLf+pCntq+K+//gIAhU/NlObu3bsAgM6dO0suzRbn7u6OAwcO4O7duxg3bpzUujZt2siUr1evHgAgLS1NsuzWrVsoKipS2CeroKAAABT27ylNfHw81q9fj0uXLiE+Pl7mVtinl+m9vLxw4cIF7Nu3Dz/99BOAj7c/Dh8+jDp16qBv375S5XNycrBlyxaEhIQgOjoaWVlZUkNpKHMboCyysrLw/Plz1K1bF02aNJFZL37yU/xZFufi4iL3cna9evVw8+ZNpfY/bdo0HDt2DLt27ZJcjhf3z+jTp4/MU3QVObwCn89Hy5YtNVKX+HhkZGTIPU/FferUPU/XrVuHkJAQLF68GEOGDAGfz1dYtqzHkDGG7777Dtu3b8fq1asxZswYmJmZ4d69e5g9ezb69OmDbdu2wdvbW2bb3r17gzGGgoICxMfH4+DBg1i0aBGuXr2K48ePQ19fH8B/sWfZsmWS2/H29vaYNGkSvvjiC7Rv3x4bNmzAnDlz5LaRYg/FntJUVuypX7++3L7FYowxHDx4EHv37sU///yDtLQ0qdv04u9ISfr3749FixbB29sb586dQ69evdCxY0c0a9YMHA5HUi43Nxf//PMPLCwssHnzZrl18Xg8tc/hXr16oWfPnjh//jzOnDkjcw4W5+XlpfGxQG/cuIFRo0bB0NAQx48fR61atUrdRnwufPPNN3LX16lTBytXrpRa1qVLF5w/fx6dOnVCZGQkAgMD8d133yncR3JyMvr06YOUlBT4+fmp/sQwKnD4mPT0dAAfO2GqIyMjAwBgY2Mjd714uXg/xZmZmcksE99LL/6lEHewvHXrFm7duqWwLYo6BZfk5cuXaNeuHdLS0tC5c2f07NkTpqam4HK5iI2Nxb59+2Q60n799dcwMTHBgQMHsG7dOnC5XJw+fRrv37/HrFmzpPooFBQUwN3dHVFRUXB2dsbw4cNhaWkp+YdrxYoVanXUVYWmPyPg4+ckEomU2n/37t3RtGlTHD58GD///DOMjY1L/SJWlDp16kgFzbIQn6cXLlzAhQsXFJZT5zwFPj7cMmPGDGzYsAFbtmzBggUL1KpHGfv27cMvv/yC2bNnY+HChZLlnTp1wqlTp9CgQQMsXLgQnp6eMDIykluHnp4enJycsHTpUujr68PHxwdbt27FvHnzAJQce7744gu0a9cOERERuHnzpkzfQoBiD8We0lXV2DNnzhxs3rwZNjY26NWrF+rWrQuBQADg4wNEcXFxpdZRv359REVFYfny5Th79ixOnDgB4GOiPG/ePMycORPAxx82jDGkpKRIhtLSNF9fX1y8eBHz58+XPBhWEW7evIk+ffpAR0cHYWFhaNeuXanb/Pvvv7hx4wbs7OxKTFrl0dXVxaRJkxAZGYk///xTYSKYnJwMd3d3PH36FFu2bMG0adNU2o9kf2ptpQbxly0xMVHuL7bSmJqaAgDevHkjd724w6q4nDrE286ePRsbN25Uux55Nm7ciHfv3iEoKEjml8rhw4exb98+mW0EAgGGDRuGwMBAXLhwAb1795aU8/T0lCobGhqKqKgoeHl5ISgoSGpdUlJSuX0xi6uIz6g0U6dOxXfffYeDBw/C09MTBw4cQN26dfHVV1/JlN27d2+Jv6Y/5eDgoPavzJKSQB2dj89syetELe8fLvHx27JliyQIa9rixYuxZ88erFu3DhMnTlRYrqzHUPyQRffu3WXKWltbo0mTJrh79y6ePn2K1q1bl1p/nz594OPjgytXrkgSwdJij6WlJYCPV7XkodhDsUcZVS32JCcnY+vWrXB2dsaNGzdkxuBT5Un0pk2b4tdff0VhYSH++ecfXLx4Eb/88gu+++47GBoaYuLEiZJj+/nnn+Pvv/9Wqa3KatmyJTw9PREUFIQ9e/bIvUIPAPfu3cPJkydVqnvWrFlyfxRcu3YNHh4e0NHRwblz59C+fXul6lPmIZGSlBaXkpKS0KNHDzx58gR+fn5qJ4FABSaC7du3x+3btxEWFqZWIvj5558DAK5fv47CwkKZJ3bET3d98cUXarexXbt20NHRwbVr19SuQxHxiN+DBw+WWXf16lWF23l5eSEwMBD79u1D69atERYWhpYtW8LFxUVu/YMGDVKpfk0yNjaGk5MTXr58iWfPnqFRo0ZS6zXxGZXG09MTPj4+8Pf3B5/PR3p6OmbOnCn3i7h3716Vjk3Xrl3LZeox8S2GV69eyQxsfPv2bZny4kB07dq1cksEzczMsGTJEsyePbvEf8jLegzFV4o+HSJGTLxcmVtYwH+3IIvHh5JiT0FBgeQfrQYNGsitk2IPxR5lVLXY8/LlS4hEIvTs2VMmCUxISJAM3aQKXV1dtG7dGq1bt4abmxu6dOmCkydPYuLEiTAyMkLz5s3x77//4v379zA3N1e5fmWsXr0av/76K5YuXarwSfp79+6p/APEy8tLJhG8fPky+vXrBx6Ph3PnzqFt27ZK1ZWfn4/g4GBwudwSf0iXRNylRV5cSkhIgLu7O54/f46dO3diypQpau1DrMKGj/n222+hq6uLVatW4dGjRzLrSxsfyM7ODl9++SViY2Nl+h9ERkbi0KFDqFWrFr7++mu121inTh2MHj0at2/fxqpVq+QOefDixQvExMSoXLe4j8infUPOnTuHwMBAhdt17NgRjRo1QmhoKHbu3ImCggK5AUFR/S9fvizXW3ufmjBhAhhj+P7776WOX2pqKlatWiUpU15MTU0xatQo3L17Fz/88INkeAd5rly5AvbxgSmlXuXVp1B8m+HTIV8ePHiALVu2yJRv06YNOnfujBMnTmDPnj1y63zw4AGSk5PL1K5p06bByckJu3btUnj1oqzHsHPnzgA+XrUS394T27lzJxISEmBtbY1mzZpJlstLjoGPSaP49nLx8brGjRsnN/Z8+PABs2bNQnx8PJo0aSK3Px9AsYdij3KqWuwRfy7Xr1+XOh7Z2dmYPHmy0sO43LlzR+a7CUAyHWXx8T/nzJmDDx8+YMKECXLvZqSlpZX5aqGtrS3mzp2LN2/eKOyL6OXlpdLxZYzJ9OM8f/48vvrqKwgEAly6dEnpJBAAjh49irS0NPTp00fSJ1iev//+W273g0uXLmHTpk0AgDFjxkitEw8D9eLFC+zZs6fMSSBQgVcEmzVrhu3bt2Pq1Kn4/PPPMWDAADRq1Ajv3r3DrVu3YGJiUuqYTTt37kTHjh3x/fff4/z582jTpg1evXqFo0ePQkdHB0FBQWWegmbbtm149uwZli5diuDgYHTq1AlWVlZ4/fo1Hj9+jFu3buHw4cMqjwE3bdo0BAUFYejQoRgyZAhsbW3x8OFDnD17FsOGDcOvv/6qcNtx48ZhyZIlWLVqFXR1dTF69GiZMv369UPDhg2xceNGPHjwAJ9//jni4+Nx+vRpeHh4ID4+XuVjoY558+YhLCwMoaGhaNWqFfr27Yvc3FwcPXoUycnJmD9/Pjp16lSubZg2bRoCAwORmJiIfv36wc7Orlz3V1bi78Lhw4eRkJAAV1dXxMfHIzQ0FAMGDJA7GO6hQ4fg7u6OiRMnYuvWrXB1dYWZmRkSEhJw//59PHz4EDdv3kSdOnXUbpe+vj7WrVuHYcOGKdWXSB3Tpk3DwYMHcf/+fTRu3Bj9+/eHmZkZ/v77b1y+fBlcLhd+fn5SV1UmTZqEd+/eoV27drC3t5f0dTtz5gzy8vIwcOBAqX/wLS0tUVhYiLS0NLRo0QKNGjUCj8dDTEwMsrKyoKenh8OHD0tu0ctDsYdijzKqUuyxtrbGiBEjcOTIEbi4uKBnz57IyMjAhQsXwOfz4eLignv37pVaT3BwMHbt2oVOnTrByckJtWrVwosXL3Dq1CnweDypKe4mTJiAO3fuSMbb7NWrF+zt7fH+/XvExMTgzz//xPjx48s8jeX8+fPh7+9fLnOfA8DTp08xYMAA5Ofno2/fvggNDUVoaKhMOXkPdgH/3RYuLUmbM2cOnj17Bjc3N8m5cv/+fcl4qKtWrYKbm5vUNt26dUNsbCxat26N2NhYuW3w8vJSbYpBlZ8zLqMbN26wQYMGMUtLS6anp8dsbGxYr1692NGjRyVlSpp2JSEhgU2dOpXZ29szPT09Vrt2bTZgwAAWFRUlU7a06YCgYKgCoVDIfvnlF9ahQwdmYmLC9PX1Wb169Zi7uzvbtGkTS01NVeu9R0REsO7duzMzMzNmZGTEOnbsyEJCQkqd1icuLk4yDtpXX32lsP74+Hg2atQoZmtry/h8PmvWrBlbv349KygokPtey2MIB8Y+jnu2Zs0a1rx5c8bn8yXv9dChQzJlS5tiRzzMQnHKTPPk4uLCALDTp0+r/T5UhRKGjyltKqj4+Hg2bNgwVqtWLcbn81mbNm3Y8ePHSzw3MjMz2Zo1a9gXX3zBDA0NGZ/PZw4ODqxv375s165dSg07IG4fig0f86kOHTpI3pump5hjjLGsrCy2YsUK1qpVK2ZgYMB0dXWZjY0NGzp0qMxUcYwxFhwczAYPHswcHR2ZoaGhJI54eHiwI0eOyAwfk5GRwWbMmMGaNWvGeDye5L3o6uoyR0dHFhgYKClLsUcaxZ6qGXuUnWIuJyeHLVq0iDk5OTEej8fs7OzYtGnTWGpqqtz3J+98+Ouvv9jUqVNZy5YtJfHJycmJeXl5sQcPHsjd76lTp5iHh4fk33krKyvWtm1btnjxYrnD+8jz6fAxn9q5c6fku6zpKebEx6G0lzyPHj1iAJidnZ3cYYmKCwwMZB4eHqx+/frM0NBQ8n0fNmwY+/PPP+Vuo0y7SptK81Oc/6+YkBojKysLtra2MDc3R0xMTIlXegghRFMo9pDqiM5SUuPs2LED2dnZmDZtGgViQkiFodhDqiO6IkhqhIyMDOzYsQOJiYkICAiAubk5nj59WuZ+W4QQUhKKPaS6o0RQTbGxsdi7d69SZRWNT1QdXLlyRakn1szMzKQ6DVe02NhYODo6gsfjoXXr1vjll1/KdagIQioLxR5pFHsIKRtKBNV05coVuYPgyhMTE6PaEzxVyPLly5Uaj6m0qY4IIZpBsUcaxR5CyoYSQUIIIYQQLUW9WQkhhBBCtBQlgoQQQgghWooSQUIIIYQQLVXlEkEOhyPz4vF4cHBwgKenJx4/fiyzzZUrV+Ru9+lLkVevXoHL5YLD4WDRokVKtU9HRwcvXrxQWK579+6SsuIn/Ly8vJRqp/jVrVu3Uo+Xg4NDiXUUn34mPT0dvr6+GD16NJo1awZdXV1wOBxcvHix1P2UxYULFzB37lz06NEDtWvXBofDKdNUT+/fv8esWbPg4OAAHo8HW1tbTJgwocT5qhMSEjBhwgTY2tpKzqdZs2YhLS1N7XYUt3fvXskx79Kli8JysbGx0NHRKfWcfPLkCWbMmAFnZ2eYmppCX18ftra28PDwwO7duyEUCjW6nboKCgqwZcsWjB8/Hi4uLtDX1weHwylxDtvS3LhxA3379oW5uTkEAgFatmyJzZs3y51/V+z06dPo1q0bTE1NYWRkBFdXV+zbt0/tNpSG4lTNi1OAerFFkfDwcPTt2xe1a9cGj8dDw4YNsXDhQmRlZcktv3v3bnzzzTdwdXWFgYEBOBwOfvjhh7K+JSnFP9ulS5cqLLdv3z6lPt8LFy5g9OjRcHR0hIGBAQQCARo2bIixY8ciLCxM49uV1b59+9CuXTsYGRnB1NQU3bp1w+nTp1Wu5+3bt5gxY4bkaXFLS0t8/fXXCudRjoqKgo+PD/r06QNra2twOJwqNfVphc01rKply5ZJ/j8jIwNRUVHYv38/jh8/juvXr8PFxUVmm/r168udFL00gYGBEIlE4HA4CAoKwsqVK6Grq/jQ6OrqorCwELt378batWtl1j979gxXrlyRlBMbOHCgzBN8V65cwdWrV9G1a1eZL5wqT/t99913coeJKF5nbGws5s+fDwCws7ODhYWFZOLw8uTn54fQ0FDw+Xw0bNgQ79+/V7uud+/ewc3NDdHR0XB3d8eIESPw5MkTBAUF4Y8//sDNmzfRoEEDqW1evHgBNzc3JCcnY8CAAWjSpAmioqKwZcsWnD17FhEREahdu3ZZ3yaAj+fGtWvX8PTpU3z22Wcy6wMDA8EYkzk3ilu5ciVWrFgBkUiEDh06wNPTE0ZGRnj79i2uXLmCSZMmYceOHbh9+7ZGtiuLnJwcydAdVlZWsLa2xqtXr9SuLzQ0FIMHDwafz8fw4cNhbm6OU6dOYfbs2YiIiMDRo0dlttm2bRtmzJiB2rVrY8yYMdDX18exY8fg5eWFBw8eYMOGDWq3pzQUp2pOnFIntiiyc+dOTJs2Dbq6uhg0aBDs7Oxw584drF+/HmfOnMG1a9dgamoqtc3cuXORkZGBWrVqwdbWtsQEvqx0dXURFBSEZcuWSc3jLRYQEFBijMrKysK4ceNw8uRJ8Pl8uLu7Y9CgQdDT00NMTAzOnDmDAwcOYO7cuVLfP3W304R58+bh559/hp2dHSZPnowPHz7gyJEj6NevH3755RdMnz5dqXpiY2Ph5uaGpKQktGvXDoMGDUJKSgpOnDiBP/74A6dOnUKvXr2ktjl06BC2bNkCPT09NGvWrELOZ5WoNCFdBUAJc/hNnz5d7tyQ4nkB1ZmjsrCwkNnZ2TETExM2bdo0BoAdP368xPbVrVuXtWnThllbW8udn3X+/PkMAPv6669LnZdSPJ+iork+SyOeJzYmJqbUsu/fv2cXL15k7969Y4wx5unpyQCwCxcuqLVvZd24cYM9fPiQFRYWSubI7Nixo1p1TZkyhQFgc+bMkVq+ZcsWBoD16tVLZpuePXsyAGzr1q1Sy2fPns0AsG+++UatthQnnoN04MCBDACbN2+eTJnCwkJma2vL2rZty+rWrSv3PF+zZg0DwOrVq8f++usvufs6deoU69atm0a2KyuhUMjOnDnDXr9+zRgrfX7QkmRkZDBLS0umr6/Pbt26JVmel5cnme/48OHDUtvExMQwHo/HzM3Npb4D79+/Z05OTgwAu3HjhnpvrgQUp1RTHeKUOrFFntevXzM+n890dXVl5speu3YtA8BmzJghs11YWBiLjY1ljP0XTzQ9h674WIrjlLz5kMVz5YrPi0/P16KiItarVy8GgHXv3p0lJibK1JGfn882b97Mpk2bVubtNCEiIoIBYE5OTuz9+/eS5TExMczc3JzxeDylzk3GGOvfvz8DwGbOnCk1r/nTp0+ZsbExs7GxkZnn/e7du+zvv/9mQqGQMfbf97OqqFaJ4O+//84AsL59+0otL0uAPXXqFAPAJk+ezB48eMAAsN69e5fYvrp167Jdu3YxACwkJERq/YcPH1idOnWYm5sbW7x4cZUKsJ+qqABbXFkSwaysLCYQCJihoSHLzMyUWldUVCQ5Fi9evJAsf/78OQPAHBwcWFFRkdQ2mZmZzNDQkBkYGMh8cVVVPHB36NCBWVpasg8fPkiVCQ0NZQCYv7+/3EQwJiaG6enpMT09PYWTuYvl5+eXebvyUJZEcPfu3QwAGzdunMy6S5cuMQCsS5cuUsuXLFnCALClS5eqVF9ZUZxSTVWPU+rEFkXE592QIUNk1hUWFjJzc3NmaGjIcnJyFNZR3ong0aNHmUAgYAMGDJApI/6B/Mcff8g9Xw8cOMAAsIYNG5YaN4vHG3W304SxY8cyAGzPnj0y60qKIZ/Ky8tjenp6TEdHR+Y8YYyx7777jgFg+/btK7GeqpYIVrk+giUR9xFp06aNxur09/cH8LHvhLOzM1q3bo3z588jLi6uxO1GjhwJQ0NDmb5Qv//+O5KTkzF58mSNtZF89NdffyEvLw8dO3aUmb5JR0dHcjk+PDxcslz8/z179pSZ+9PY2BgdO3ZEbm4u/vrrL421c/LkyUhJSUFoaKjU8oCAABgZGWHkyJFytwsKCkJBQQEGDx4MZ2fnEvfB4/HKvF1Vc/nyZQBA7969ZdZ16dIFBgYGuHHjhlQ/x5K26dOnj1SZikJxqvpRJ7Yo8ubNGwCQexuZy+Wifv36yMnJQWRkpAZarh4zMzMMHToUf/zxh6S9ACAUCrF//3507doVjRs3lrut+FycN28eDA0NS9xP8Xij7naaoKk48f79exQUFMDCwkLuFILiz/zSpUtlaW6Fq7J9BIt3Hs7MzMStW7cQERGBr776CvPmzZO7TWxsrNR2xTVp0gQjRoyQWpaYmIgzZ86gcePGcHNzA/Ax0N65cweBgYFYtWqVwvYZGxtjxIgR2Lt3LxISEiQdPwMCAmBiYoJhw4bJ7ZdTXjZv3izT90YTUy8pO81TcYo+g7J6+vQpACgMUI0aNQIAREdHq7TN+fPnER0djR49emikncOGDcOsWbMQEBCAIUOGAPh4roWFhWH8+PEwMjKSu93169cBQOV2qLsd8PG8SU9PV7q8i4sLBg4cqPJ+lFHSZ6WrqwtHR0f8+++/ePnyJZo2bVrqNjY2NjA0NERCQgJyc3NhYGCg8TZTnFJNVY1T6sQWRSwsLAB8nNXlUyKRSJK8P336VOkZYsrD5MmTsX//fgQFBcHHxwcAEBISgnfv3in8gVBYWCj50axKvFF3O+Djw0ObN29WaZuBAwdK+ufm5OQgMTERRkZGsLGxkSmrymdbq1YtcLlcpKamIjs7WyaWv3z5EsB/51N1UWUTQXlTCzVr1gwjR45UOJl3XFycwimJBgwYIBNg9+zZg6KiIqmO26NGjcLcuXOxZ88eLF++XG5HWrHJkydj9+7d2LNnD5YuXYq4uDhcuHAB33zzTbn8o1OSLVu2yCyrX7++RgKsMtM8FVdeiWBGRgYAyHSyFhMvL57YqLNNWRkaGmLUqFHYtWsXYmNj4eDgIDnXSroCk5SUBAAqP02m7nbAx3+YS7uqVJynp2e5JYLl9fnm5OQgIyOjXL6TFKdUU1XjlCbjRK9evaCrq4uTJ0/i9u3bUleGN2zYIHlYTlMjFqirU6dOaNKkCQIDA7Fw4UJwOBwEBASgVq1aGDx4sNwnpd+/f48PHz4AUC3eqLsd8PGYq/rZOjg4SBJBTX62AoEA3bt3x8WLF7F06VJs3LhRsu758+fYs2cPgMr/bFVVZW8Ns4/9F8EYQ3Z2NiIjI2FlZYXRo0dj8eLFcrfp2rWr1HbFXydPnpQqKxKJsHv3bujo6GDcuHGS5ebm5ujXrx9ev36NP/74o8Q2urq6okWLFtizZw9EIpHkqb7KuN0SExMj8541Mf/m8uXLFR5TRS/y8R9fxhh2794tOddatmyJdu3aVXbTpMTGxqr02YqHGCEfUZxSjTbEqfr162PZsmUoKChAx44dMWrUKHz//ffo0aMHFi5ciJYtWwKATFeVyjB58mS8fPkSly9fxvPnzxEeHo6xY8eCz+dXdtMkHBwcVP5s1XkqX1mbN2+GqakpNm3ahA4dOmDevHnw9PSEi4sLnJycAFSNz1YV1aK1hoaGaNeuHU6cOAFDQ0P89NNPZRqiAgDOnTuHuLg4fPnll6hbt67UOvFJJO7TUJLJkycjLi4OYWFhCAoKQuvWrfH555+XqW1EPvEvN/EvvE+Jlxe/9aTONprwxRdf4IsvvkBQUBDCwsIQFxdX6j+84tsWiYmJKu1L3e2qmvL8fBVdDdAkilPVl6bjxA8//IBjx47B1dUVp06dgp+fH3Jzc3H69Gl07twZAFCnTp2yN7yMxo0bBx6Ph8DAQMnQViXFKXNzc+jr6wNQLd6ou50maPqzbd68Oe7cuYNx48YhLi4OW7duxdWrVzF79mz88ssvAKrGZ6uKKntrWB4zMzN89tln+Pvvv/H333+jXr16atclDp7nzp1TOIjr2bNn8erVqxL3M3bsWCxYsABTp05FYmJiiYN0VkdVqY+geFw+RX05nj17BkC6n48622jKlClTMHXqVEydOhUCgQBjxowpsXynTp1w+fJlXLp0CRMnTlR6P+puB1StPoKfffYZbt++jejoaLRu3VpqXWFhIWJiYqCrqyvVCf+zzz5DamoqoqOj0aFDB6ltkpKSkJOTAzs7uwq9BUpxquKVNU6VR5wYPHgwBg8eLLP8xx9/BAC0bdtW6brKi4WFBb7++muEhITAxMQEHTp0KPGBM11dXbRv3x5//vknLl26JLkCVhp1twPK3kfQ0NAQdevWRWJiIpKSkmT6Carz2To5OckdsF58a7gqfLaqqFaJIPDfvXeRSKR2HW/evMHp06dhYmKCoUOHyi3z5MkTREREYM+ePVKDxn7KzMwMQ4YMQXBwMAwNDRU+EVpdVaU+gu3bt4dAIEBERASysrKk+mCJRCKcP38eAKQ6YIv///z58xCJRFKX7LOyshAREQEDAwO0b99e4+0V9+NKSEjAuHHjSv3FOX78eKxbtw7Hjx/Ho0eP0KxZM4VlhUKh5Mk6dbcDqlYfQXd3dxw8eBBnz56V+R79+eefyM3NRZcuXaTa7+7ujoiICJw9e1YmERTPUODu7l4u7S0JxamKVdY4pU5sUceLFy8QERGBFi1alPqEf0WZPHkyjhw5gpSUFKxfv77U8lOmTMGff/6JDRs2YMyYMSX+yCoeb9Tdrqx9BIGPMSA4OBhnz57F+PHjpcpqMk4EBwcD+Bj7qxU1h50pNyhhfK6QkBAGgOnp6bE3b95Ilqs6Ppd48N1vv/1WYZlnz54xDofD6tWrJzX+HOSM/xMXF8dCQkLY9evXpZbT+FyylB1H8PHjx+zx48cyyytqQOmuXbsyACw8PFyJd6V43K9r166xkJAQFh8fL7W8tAGlHRwcpAZVLi4sLIx1795dI9tpmjLjCKanp7PHjx9LBqEWy8jIYBYWFioNKP3y5csqN6A0xSlZ1SFOqRNbnj9/zh4/fiwzZmhGRoZM2dTUVObq6soAsDNnzpTYFmXHESzpPJRH3rEUiUQsNDSUhYSEsNzcXMnyZ8+elTqg9P/+9z+Z7zFjHwea37ZtG/P29i7zdpqgzoDSKSkp7PHjxywlJUVqeX5+vsw4hyKRiK1evZoBYMOHDy+1PfK+n5Wpyl4RLP5rLScnB48ePZJk7mvXroWVlZXMNiUNywAAs2bNgqmpqWRMrUmTJiks27BhQ3Tt2hVXrlxBWFgYPDw8FJa1t7eHvb19Ke+oapg3bx5SU1MB/DfsiK+vLw4cOADg4yV1TV/xuX79uuSYZ2dnA/h4Ob54h95PH0IQDw/CPunUvXbtWly5cgUbN27EvXv30K5dOzx+/BihoaGoU6cO/Pz8ZPa/fft2uLm5YebMmbh06RKaNm2KyMhIhIeHo3HjxlizZo3MNuIrOSVN4aUMVedUXrRoEQoLC7FixQq0bdsWbm5uaNOmjWSquD///BPPnj2TGaNO3e004ccff8STJ08AAPfu3QPwcWxD8fnVqVMnqe9aSEgIxo8fD09PT6nP3cTERDLkTrdu3TBixAiYm5vj999/x9OnTzFkyBAMHz5cat+Ojo7w9fXFzJkz0aZNGwwfPlwyxVxCQgLmzp0rc6VQkyhOlY/KiFPqxJYePXogLi4OMTExUlPtrVy5UnKVuk6dOkhMTMTvv/+O9PR0/Pzzz5Kx64oLDAyUvNfnz58DAE6dOiV5erdJkyZYuHChpLw4RpX0xLgyOBwO+vfvr3R5HR0dHD16FGPHjkVoaCgaNGiAHj16oGnTpuByuYiNjcXly5eRkpIiNYSSuttpgpubG+bMmYONGzeiZcuWGDJkCD58+IBff/0V79+/xy+//CIzVeK2bduwYsUKLFu2TOr7+uzZM3Tu3BlffvklHBwcUFBQgEuXLuHBgwfo1KmT3D67T548kXQJEEtLS5P6N3DDhg2SoYcqXGVnop/C///CKf7icrnM2tqa9e/fn50/f15mG/Ev7dJeMTEx7Pz58wwA+/zzz0tty8GDBxkA1r9/f6n2KZvJV8Vf2uLyil7qtqMk4l+3Jb0+pWg5Y4y9e/eOzZw5k9nb2zM9PT1mbW3Nxo8fz169eqWwDfHx8czLy4tZW1szPT09Zm9vz7777jupX4diIpGImZubMwcHB7lTc5X0HpWdCUDRFUGxR48esenTp7PmzZszY2Njyfvs3bs3CwwMVDjyvrrblYX46qmi16dTrYmP1afLxa5fv8769OnDzMzMGJ/PZ87Ozmzjxo2ssLBQYRt+//131qVLF2ZkZMQMDAxYmzZt2N69ezX4LqVRnFJNdYhTjKkeWxS9r9OnT7Pu3bszS0tLpqenx6ysrNiQIUPYzZs3Fe5bfLVO0evTK3P37t1jANjo0aOVfn+qXF1VdEWwuHPnzrGRI0cyBwcHxufzGY/HY46OjmzkyJEsLCxM49uVVVBQEGvTpg0zMDBgRkZGrEuXLuzUqVNyyyo655OTk9moUaOYo6Mj4/P5zNjYmLm6urJt27Yp/PdCme++OlfLNYXDGI33QUhx9+/fR6tWreDn54dp06ZVdnMIIUTG1q1bMWvWLDx48ADNmzev7OaQaqxaDB9DSEW6evUqrKysMGHChMpuCiGEyHX16lX079+fkkBSZnRFkBBCCCFES9EVQUIIIYQQLUWJICGEEEKIlqJEkBBCCCFES1EiSAghhBCipSgRLAVjDJmZmTIDGxNCSFVAMYoQUhaUCJYiKysLpqamyMrKquymEEKIDIpRhJCyoESQEEIIIURLUSJICCGEEKKlKBEkhJBKNmXKFNjY2MDExAQtWrTAqVOnKrtJhBAtQYkgIYRUsjlz5iA2NhaZmZnYs2cPxowZg3fv3lV2swghWoASQQX8/PzQrFkztG3btrKbQgip4Zo0aQIejwcA4HA4+PDhAxITEyu5VYQQbUCJoALe3t549OgRbt26pfQ2IpEISUlJSEpKgkgkKsfWEUIqS3Z2NpYtW4bevXvD3NwcHA4He/fulVtWKBRiwYIFsLW1hUAggKurKy5cuCC37LRp0yAQCNC2bVu4u7ujRYsWGm03xSdCiDyUCGrQ27dv8XNoFH4OjcLbt28ruzmEkHKQmpqKlStX4vHjx2jVqlWJZb28vLBx40aMHj0aW7ZsAZfLRd++fXH9+nWZstu3b0d2djYuXryInj17gsPhaLTdFJ8IIfLoVnYDahpjs9qV3QTy/xhjKCoqQmFhYWU3pdrT09MDl8ut7GZUCTY2NkhKSoK1tTVu376tsPtIVFQUjhw5Al9fX8ybNw8AMG7cODg7O2P+/Pm4ceOGzDZcLhc9evTA5s2b0ahRI/Tt21ejbTc2qw2RSIS3b9/CysoKOjp0LaCyFRUVoaCgoLKbUe3p6uqCy+Vq/AeUNqBEkNQ4jDGkp6cjJSUFRUVFld2cGsPMzAzW1tZaH2h5PB6sra1LLXfs2DFwuVxMmTJFsozP52PixIlYtGgRXr16hXr16sndtrCwEM+fP9dYm4vLyXiPjSfuY72VFWxsbMplH6R0jDG8efMG6enpld2UGoPL5aJOnTowNTXV+jilCkoESY0jDq4mJiYwMTGBrq4uBYUyYIwhNzcXycnJAEDJg5Lu3r2Lxo0bw8TERGp5u3btAAD37t1DvXr1kJGRgT/++AP9+/cHn89HSEgIwsPDsW7dOrn1CoVCCIVCyd+ZmZkqt83QpJbK2xDNEsepOnXqwMDAgGJUGTDGUFhYiMzMTCQlJSEvL4/ilAooEdQA8a2Wt2/fgjFGX+hKVFRUhIyMDFhaWsLCwqKym1NjCAQCAEBycjLq1KlDt4mVkJSUJPcfI/Gy169fA/j4lHBAQACmTZsGxhgaNmyIQ4cOwcXFRW6969atw4oVK8qt3aT8FRUVSZLA2rWpO5GmGBsbg8fjITU1leKUCigRVMDPzw9+fn5K3VoUd8LOzngPEwtbGBoaVkALiTwFBQVgjNFnUA4MDAwAfDzGFGBLl5eXJxkSpjg+ny9ZDwAmJiYIDw9Xul4fHx/MmTNH8ndmZqbCW8ykahL3CRR/p4jmGBoaIiUlheKUCigRVMDb2xve3t7IzMyEqalpqeWNzWoDjFVAy4gy6Kqs5tExVY1AIJC6hSuWn58vWa8OHo8nN8Ek1Q99pzSPjqnq6JExQggpB+Kniz8lXmZra1um+mnQe0KIJlAiSAgh5cDFxQXR0dEyD3NERkZK1peFOoPeE0LIpygRJISQcjBkyBAUFRXB399fskwoFCIoKAiurq5l7tdHVwQJIZpAiSDRSnv37gWHwwGfz5c7p2u3bt3g7Ows+dvBwQFfffWVTLmWLVvC3t4erIT+oR07doSVlZXSA1tv375d4ZRlpGrYtm0bVq9ejT179gAATp06hdWrV2P16tXIyMgAALi6umLo0KHw8fHB/Pnz4e/vD3d3d8TGxuKnn34qcxvoimDNRjGKVBRKBIlWEwqF+PHHH9XefvTo0Xj16hWuXbsmd31sbCxu3ryJ4cOHQ1dXuWezKMhWfRs2bMCSJUuwY8cOAMCJEyewZMkSLFmyBGlpaZJy+/fvx6xZsxAcHIyZM2eioKAAp0+fRpcuXcrcBroiqB0oRpHyRokg0WouLi4ICAiQjOmmqlGjRoHD4eDQoUNy1x8+fBiMMYwePboszVQoJyenXOolJYuNjQVjTO7LwcFBUo7P58PX1xdJSUnIz89HVFQUevXqpZE20BVB7UAxipQ3SgSJVlu0aBGKiorU/sVdr149dOnSBceOHZM7X+ihQ4fg5OQEV1dXpepzcHDAv//+i6tXr4LD4YDD4aBbt24A/rtVdPXqVUybNg116tSBnZ0dAMDLy0sqARFbvny53OEUDhw4gNatW0MgEMDc3BwjRozAq1evlH/jhJAKQTGKYlR5o0RQAbrtoh0cHR0xbty4Mv3iHj16NN69e4dz585JLX/w4AEePnyo0i/tzZs3w87ODk2aNEFwcDCCg4OxePFiqTLTpk3Do0ePsHTpUixcuFDl9q5Zswbjxo1Do0aNsHHjRsyaNQuXLl1Cly5daN7TaoRilHagGEUxqrxRIqgA3XbRHosXL0ZhYSHWr1+v1vZDhgwBj8eTufUi/luVIDtw4ECYmprCysoKY8aMwZgxY/Dll19KlTE3N8elS5cwffp0LFiwQKW2xsXFYdmyZVi9ejWOHDmCb7/9FkuXLkV4eDgSEhKwfft2leojlYdilPagGEUxqjxRIki0XoMGDTB27Fj4+/vLHQC4NLVq1ULfvn3x+++/S/rDMMZw5MgRtGnTBo0bN9ZoeydPnqz21EknTpyASCTCsGHDkJqaKnlZW1ujUaNGKk11RgipGBSjKEaVJ0oECQHwww8/oLCwUO1+OKNHj0ZOTg5CQ0MBADdu3EBsbGy5dMB2dHRUe9tnz56BMYZGjRrB0tJS6vX48WMkJydrsKWEEE2hGEUxqrzQXMOE4OMv7jFjxsDf31+tPi1fffUVTE1NcejQIYwaNQqHDh0Cl8vFiBEjNN5WeXPUKppfs6ioSOpvkUgEDoeDsLAwub/YjYyMNNNIUu78/Pzg5+cn8xmTmoli1EcUozSPEkFC/t8PP/yAAwcOqNUPh8fjYciQIdi/fz/evn2Lo0ePwt3dHdbW1irXpc6k6bVq1ZLbiTouLk7qbycnJzDG4OjoqPHbQaRieXt7w9vbG5mZmTA1Na3s5pAKQDGKlAe6NUzI/3NycsKYMWOwa9cuvHnzRuXtR48ejYKCAnzzzTdISUlR+5aLoaGhyk/GOTk5ISMjA/fv35csS0pKQkhIiFS5QYMGgcvlYsWKFTIzDTDG8O7dO7XaTAgpfxSjKEaVB7oiSEgxixcvRnBwMJ4+fYrmzZtLrXv+/DlWr14ts83nn38ODw8PdO3aFXZ2dggNDYVAIMCgQYPUakPr1q2xY8cOrF69Gg0bNkSdOnXg7u5e4jYjRozAggUL8PXXX2PmzJnIzc3Fjh070LhxY/z999+Sck5OTli9ejV8fHwQGxuLgQMHwtjYGDExMQgJCcGUKVMwb948tdpNCCl/FKMoRmlajU8EhUIhvv32W1y8eBHp6elo1qwZNm3ahA4dOlR200gV1LBhQ4wZMwb79u2TWff06VMsWbJEZvnEiRPh4eEBHR0djBw5Er6+vujXrx+MjY3VasPSpUsRFxeHn376CVlZWejatWupQbZ27doICQnBnDlzMH/+fDg6OmLdunV49uyZVJAFgIULF6Jx48bYtGkTVqxYAeDjoLM9e/ZE//791WozqXjUR1A7UYyiGKVpHFbSTNQ1QE5ODn7++Wd4eXnBzs4Ov/32G6ZPn47Y2FilOp2K+99kZGTAxMREbpmkpCT4X32BrLRU6OgLIBAI8HUzE1hZWcHKygo6OnQHvqLk5+cjJiYGjo6O4PP5ld2cGoWObdWkTIwCpONUTk4Olo52h42NTQW2lIjRd6n80LFVXY3PUAwNDbF06VLY29tDR0cHI0aMgL6+Pp4+fVpu+8zJeA//y4/wc2gU3r59W277IYQQQggpiyp3azg7Oxu+vr6IjIxEVFQU0tLSEBQUBC8vL5myQqEQS5cuRXBwMNLS0tCyZUusXr1aZpTz4p49e4b379+jYcOG5fguACPT2jA0NCzXfZDqJSUlpcTbePr6+jA3N6/AFhFCyH8oRmmnKpcIpqamYuXKlbC3t0erVq1w5coVhWW9vLxw7NgxzJo1C40aNcLevXvRt29fhIeHo1OnTjLl8/LyMGbMGPj4+NBwC6TCtW3bVmaohOK6du1a4vlOiKYwkUhyt4K6rxAxilHaqcolgjY2NkhKSoK1tTVu376tcEL1qKgoHDlyBL6+vpIniMaNGwdnZ2fMnz8fN27ckCpfUFCAoUOHomHDhli6dGm5vw9CPnXw4EHk5eUpXF+rVq0KbA3RZrnZGfC//AgGBnGYO6Ad9RUkAChGaasqlwjyeDylBrg8duwYuFwupkyZIlnG5/MxceJELFq0CK9evUK9evUAfBypfOzYseBwONi3b59ag2ESUlYdO3as7CaQGqSsTw1T9xXyKYpR2qna3g+4e/cuGjduLPOUXLt27QAA9+7dkyz75ptvkJSUhKNHj0JXt8rlvoQQojJvb288evQIt27dquymEEKqsWqbFSUlJcm9nSFe9vr1awAfp68JDAwEn8+HhYWFpFxYWBg6d+4ss71QKIRQKJT8nZmZqemmE0IIIYRUCdU2EczLywOPx5NZLh43SNzPoX79+jLT1JRk3bp1kgEsCSGEEEJqsmp7a1ggEEhduRPLz8+XrFeHj48PMjIyJK9Xr16VqZ2EEEIIIVVVtb0iaGNjg8TERJnlSUlJAABbW1u16uXxeODxeDR9EyGEEEJqvGp7RdDFxQXR0dEyffgiIyMl68uCOmITQgghpKartongkCFDUFRUBH9/f8kyoVCIoKAguLq6SoaOIYQQQggh8lXJW8Pbtm1Denq65MnfU6dOISEhAQAwY8YMmJqawtXVFUOHDoWPjw+Sk5PRsGFD7Nu3D7Gxsdi9e3eZ20C3hmum+Ph4pKamVnYzYGFhAXt7e7W2VWdqRbHExETMnj0b58+fh0gkQvfu3bFp0yY0aNBArbaQykMxquaqCnGKYpT24DBVHqmtIA4ODgqnuYmJiYGDgwOAjw+GLFmyBAcOHJCcbKtWrUKvXr001pbMzEyYmpoiIyNDZsxCsaSkJPhffYGstFTo6Asg+pAHHX0BDA0NMaWrE43aX4Hy8/MRExMDR0dHyRPkYvHx8WjStCnycnMrqXX/ERgY4Mnjx2oF2pEjR8pMrXjr1i2FUyuKZWdn44svvkBGRgbmzp0LPT09bNq0CYwx3Lt3D7Vr1y5xvyUdW1J5lIlRgHScSk5KgHX9RhSjKkl1iFPVMUYBFKfUUSWvCMbGxipVjs/nw9fXF76+vuXbIFIjpKamIi83F6MX+MLK3qnS2vE2/gUOrv8eqampKgdZVadWLG779u149uwZoqKiJFM39unTB87Ozvj555+xdu1a9d8UIUQjqkKcohilXapkIlgV0G2XmsvK3gl2jZpXdjPUosrUivK2bdu2rdT83U2aNEGPHj3w22+/UZAlpAqprnGKYlT1U20fFilv9NQwqYpUmVqxOJFIhPv376NNmzYy69q1a4cXL14gKytL4+0lpRMKhZgwYQLs7e1hYmKC9u3b4+bNm5XdLELUQjGq+qFEkJBqRNmpFT/1/v17CIVCtbYl5auwsBAODg64fv060tPTMWvWLPTr1w/Z2dmV3TRCVEYxqvqhRJCQakTZqRXlbQdArW1J+TI0NMTSpUthb28PHR0djBgxAvr6+nj69GllN40QlVGMqn4oEVTAz88PzZo1k+qrQEhlU3dqRfHy8piWUdtkZ2dj2bJl6N27N8zNzcHhcLB37165ZYVCIRYsWABbW1sIBAK4urriwoULJdb/7NkzvH//Hg0bNiyH1hNSvihGVT+UCCpAfQRJVWRjYyOZRrG40qZWNDc3B4/HU2tbIi01NRUrV67E48eP0apVqxLLenl5YePGjRg9ejS2bNkCLpeLvn374vr163LL5+XlYcyYMfDx8YGpqWl5NJ+QckUxqvqhRJCQakTdqRV1dHTQokUL3L59W2ZdZGQkGjRoAGNjY423tyYS/0MXFxdX4tBV4mE01q1bB19fX0yZMgWXL19G/fr1MX/+fJnyBQUFGDp0KBo2bIilS5eW51sgpNxQjKp+KBEkpBpRdmrF+Ph4PHnyRGbbW7duSQXap0+f4vLlyxg6dGjFvIEagMfjwdrautRyJQ2jcfPmTbx69UqyXCQSYezYseBwONi3bx84HE65tJ2Q8kYxqvpROxF0d3fHpUuXFK4PDw+Hu7u7utVXOuojSKqi4lMrzp8/H/7+/nB3d0dsbCx++uknSblx48ahadOmUttOmzYNTk5O8PDwgK+vLzZv3owvv/wSVlZWmDt3bkW/lXJX2TFKlWE0vvnmGyQlJeHo0aPQ1aXhXUn1RTGq+lE74ly5cgWTJk1SuD45ORlXr15Vt/pK5+3tDW9vb8n0TaTmeBv/olrvf//+/ViyZInUPJ6nT59Gly5dStzO2NgYV65cwezZs7F69WqIRCJ069YNmzZtgqWlZZnaVBVVdoxSdhiNuLg4BAYGgs/nw8LCQlIuLCwMnTt3ltleKBRKdaj/9BYcqRkqM05RjNIuZfrpWdLti+fPn9P9fFKlWFhYQGBggIPrv6/spkBgYCD1j74qlJla8cqVK3KX29nZ4ejRo2rttzqqzBil7DAa9evXhypTvq9btw4rVqzQTCNJlVNV4hTFKO2hUiK4b98+7Nu3T/L36tWrERAQIFMuPT0d9+/fR9++fcveQkI0xN7eHk8eP0ZqamplNwUWFhZqTeZOSlaVYpS6w2iUxsfHB3PmzJH8nZmZqXDKLlL9VJU4RTFKe6iUCObm5iIlJUXyd1ZWFnR0pLsZcjgcGBoaYurUqfTkG6ly7O3tKbjVYFUpRtnY2CAxMVFmeVmHwuDxeODxeDQfeg1GcYpUJJUSwW+//RbffvstAMDR0RFbtmxB//79y6VhlY2CLCHVT1WKUS4uLggPD0dmZqbUAyOlDaOhLOrHTAjRBLWfGo6JiamxSSBAA0oTUt1VdoxSdhgNQgipTGUepyArKwtxcXFIS0uT2+G5tKeEajKRSIS3b98CAKysrGRuURFCyl95xKht27YhPT1d8uTvqVOnkJCQAACYMWMGTE1NpYbRSE5ORsOGDbFv3z7ExsZi9+7dZXtToLsWhBDNUDsRTE1NxYwZM3D8+HG5gYgxBg6Ho9VBKifjPfwvJ8DAIA5zB7STO5QEIaR8lGeM2rBhA+Li4iR/nzhxAidOnAAAjBkzRnKrVt1hNJRBt4YJIZqgdiI4ZcoUnDp1CjNnzkTnzp1Rq1YtTbarxjAyrQ1DQ8PKbgYhWqc8Y1RsbKxS5ZQZRkNddEWQEKIJaieC58+fx+zZs6VGCieEkKqipscouiJICNEEtTutGRgYwMHBQYNNIYQQzaEYRQghpVM7ERwzZgxCQkI02ZYqheYaJqR6oxhFCCGlU/vW8JAhQ3D16lX07t0bU6ZMQb169cDlcmXKffHFF2VqYGWh2y6EVG8Uo0pHIxsQQtROBDt16iT5/wsXLsisp6eGCSGViWJU6WhkA0KI2olgUFCQJttBCFFSdnY2fH19ERkZiaioKKSlpSEoKAheXl5KbZ+eno758+cjJCQEubm5aNeuHX7++edqe2VMEYpRyqGRDYimUYyqXtROBD09PTXZDkIqRHx8fKVP5g6UbUL31NRUrFy5Evb29mjVqhWuXLmi9LYikQgeHh74559/8P3338PCwgLbt29Ht27dcOfOHTRq1EitNlVFNT1G0fAxNVdViFMUo7RHmWcWIaS6iI+PR9OmTZCbm1fZTYGBgQCPHz9RK9Da2NggKSkJ1tbWuH37tkoPCxw7dgw3btzA0aNHMWTIEADAsGHD0LhxYyxbtgyHDh1SuT2kclA/5pqpqsQpilHaQ+1EcMKECaWW4XA4GplKiRBNSE1NRW5uHg4sGoam9paV1o7H8SkYs/Y3pKamqhVkeTwerK2t1dr3sWPHYGVlhUGDBkmWWVpaYtiwYThw4ACEQiF4PJ5adVc1FKNIdVQV4hTFKO2idiJ4+fJlcDgcqWVFRUVISkpCUVERLC0tqd8JqZKa2lvii8Z1K7sZleLu3bv44osvZJ4ObdeuHfz9/REdHY0WLVpUUus0i2IUqc60NU5pU4yqKtQeKyA2NhYxMTFSr/j4eOTm5mLr1q0wNjbGpUuXNNlWQkgZJSUlyX0yVLzs9evXFd2kclPTYxSNI0hqIm2KUVWFxgeN0tPTw/Tp09GzZ09Mnz5d09UTQsogLy9P7m0VPp8vWV/T1ZQY5e3tjUePHuHWrVuV3RRCNIZiVMUrt9FDW7VqhT///LO8qi939Gub1EQCgQBCoVBmeX5+vmS9tqjuMYqQmohiVMUrt0TwwoULMDAwKK/qyx392iY1kfhpvk+Jl9na2lZ0kypNdY9RhNREFKMqntoPi6xcuVLu8vT0dPz555/4+++/sXDhQrUbRgjRPBcXF1y7dg0ikUiqM3ZkZCQMDAzQuHHjSmydZlGMIqT60aYYVVWonQguX75c7vJatWrByckJO3fuxOTJk9WtnhBSRklJScjIyICTkxP09PQAfJx/99ixYzhx4oRkjK7U1FQcPXoU/fr1q1HDMlCMIqRq0/YYVVWonQiKRCJNtoMQooJt27YhPT1d8gTdqVOnkJCQAACYMWMGTE1N4ePjg3379iEmJgYODg4APgbZ9u3bY/z48Xj06JFk1P6ioiKsWLGist5OuajpMYpmFiFVGcWo6oNmFiFa53F8SrXf/4YNGxAXFyf5+8SJEzhx4gQAYMyYMQpnmuByuThz5gy+//57bN26FXl5eWjbti327t2Lzz77rMztIhWHZhap2SozTlGM0i5lTgSvXr2KP/74Q/KB169fHx4eHujatWuZG0eIJllYWMDAQIAxa3+r7KbAwEAACwsLtbePjY0ttczevXuxd+9emeW1atVCYGAgAgMD1d5/dUIxilQnVSVOUYzSHmongh8+fMDIkSNx8uRJMMZgZmYG4GNH7J9//hlff/01Dh8+LLnvX5l27NiBgIAAPHjwAIsXL1bYd4jUbPb29nj8+EmlT+YOlG1Cd6Kc6hSjCBGrKnGKYpT2UDsRXLFiBUJCQjBv3jzMnTsXVlZWAIDk5GT8/PPP8PX1xcqVK7Fq1SqNNVZdNjY2WL58OU1WTWBvb0/BTUtUpxhFSHEUp0hFUnscwUOHDsHT0xM//fSTJMACQJ06dbB+/XqMGzcOwcHBGmlkWQ0cOBD9+/eXXBEghNR81SlGEUJIZVE7EUxKSoKrq6vC9a6urnjz5o3K9WZnZ2PZsmXo3bs3zM3NweFw5PYhAAChUIgFCxbA1tYWAoEArq6uuHDhgsr7JITUPOUVowghpCZROxG0s7PDlStXFK6/evUq7OzsVK43NTUVK1euxOPHj9GqVasSy3p5eWHjxo0YPXo0tmzZAi6Xi759++L69esq75cQUrOUV4wihJCaRO1E0NPTE7/99humTp2Kp0+foqioCCKRCE+fPsW3336Lo0ePwsvLS+V6xdPLxMXFwdfXV2G5qKgoHDlyBOvWrYOvry+mTJmCy5cvo379+pg/f766b4sQUkOUV4yqKmg+dEKIJqj9sMiiRYvw4sUL+Pv7IyAgQDIVjEgkAmMMnp6eWLRokcr18ng8WFtbl1ru2LFj4HK5mDJlimQZn8/HxIkTsWjRIrx69Qr16tVTef+EkJqhvGJUVUHjCBJCNEHtRJDL5WLv3r2YM2cOzpw5IzVGV9++fdGyZUuNNVKeu3fvonHjxjAxMZFa3q5dOwDAvXv3JIlgYWEhCgsLUVRUhMLCQuTn50NPTw9cLrdc20gqD2OssptQ41S3Y1rZMYqQ0lS371R1QMdUdSolgvn5+Zg1axaaN2+OGTNmAABatmwpE1C3bt2KnTt3YsuWLeU2RldSUhJsbGxklouXiae1AYDVq1dLTU2zZs0aBAUFyb0tJBQKIRQKJX9nZmZqsNWkvOnp6YHD4SAnJwcCgaCym1Oj5ObmAkCVHnevKsUoQhQRn3O5ubkUpzQsJycHHA6HvtcqUCkR9Pf3x969e/Ho0aMSy3l4eGD+/Plo0aIFvv322zI1UJG8vDy5k0/z+XzJerHly5crPYj0unXraD7DaozL5cLU1BQpKSkQCoUwMTGBrq4uOBxOZTet2mKMITc3F8nJyTAzM6vSV9KrUoxSBQ16r124XC7MzMyQnJwMADAwMKAYVQaMMRQWFiIzMxOZmZlVPk5VNSolgr/99hsGDx6MBg0alFjOyckJQ4cOxeHDh8styAoEAqkrd2L5+fmS9erw8fHBnDlzJH9nZmZSX8NqxtraGgKBAMnJyXRFV4PMzMyU6r9bmapSjFIFDXqvfcTfJXEySMqOy+XCxsaG+syqSKVE8MGDBxg9erRSZd3c3HDq1Cm1GqUMGxsbJCYmyixPSkoCANja2qpVL4/HA4/Hg5+fH/z8/FBUVFSmdpKKx+FwYGZmBlNTU0m/UFI21aVPbVWKUaoYOHAgAODMmTOV2xBSYTgcDmxsbFCnTh0UFBRUdnOqPV1dXXC5XLqyqgaVEsEPHz5AX19fqbL6+vpyr9hpiouLC8LDw5GZmSn1wEhkZKRkfVnQE3nVH4fDga6uLnR11X4milQzFRGjsrOz4evri8jISERFRSEtLa3EPsdLly5FcHAw0tLS0LJlS6xevRpffvmlyvslNROXy60WP7JIzaXSOIK2trZ4+PChUmUfPnyo9lU5ZQwZMgRFRUXw9/eXLBMKhQgKCoKrqyvdziVEC1VEjKJB7wkhNYlKl0r+97//Yf/+/fDx8UGdOnUUlktOTsb+/fsxdOhQtRq1bds2pKenS578PXXqFBISEgAAM2bMgKmpKVxdXTF06FD4+PggOTkZDRs2xL59+xAbG4vdu3ertd/i6NYwIdVPRcQo8aD31tbWuH37tsIBncWD3vv6+mLevHkAgHHjxsHZ2Rnz58/HjRs3VN43IYRomkpXBBcsWID8/Hy4u7tLbsF+KjIyEj169EB+fj6+//57tRq1YcMGLFmyBDt27AAAnDhxAkuWLMGSJUuQlpYmKbd//37MmjULwcHBmDlzJgoKCnD69Gl06dJFrf0W5+3tjUePHuHWrVtlrosQUjEqIkZpYtD7mzdv4tWrVyrvmxBCNE2lK4INGjTAb7/9hpEjR8LNzQ0NGjRAixYtYGxsjKysLDx8+BAvXryAgYEBjhw5AicnJ7UaFRsbq1Q5Pp8PX1/fEqeiI4Roj4qKUcqgQe8JIdWBynMNe3h44P79+5gyZQry8/Nx8uRJBAcH4+TJk8jNzcXkyZPxzz//oF+/fuXR3gpD83gSUj1VlRil6qD3AoEAgYGBWLNmDQQCAYKDg+XWKxQKJeOliV+EEKIutR6ndHBwwI4dO7Bjxw5kZWVJntw1NjbWdPsqDT01TEj1VRViFA16TwipDlS+IvgpY2Nj1K1bt0YlgYSQmqOyYlR5DnqfkZEheVFfQ0JIWdAAa4QQUg5o0HtCSHVQ5iuCNRX1ESSElIWLiwuio6Nl+vBpctB7GtmAEFJWlAgqQEGWEFIW1WnQe5FIhLdv3yIpKQkikaiym0MIqUB0a5gQQlRU0wa9z8l4D//LCTAwiMPcAe3kPu1MCKmZKBGsAOJf2wBgZWUFHR26EEtIdbZhwwbExcVJ/j5x4gROnDgBABgzZoxkpIH9+/djyZIlUnMNa3LQe02ObGBkWhuGhoZlrocQUr1QIqgA/domhChSFQa9p4dFCCGaQJemFNB0H0Ej09owNqutkboIIYT6MRNCNIESQUIIIYQQLUWJICGEVEM0xBUhRBMoESSEkGqIbg0TQjSBEkFCCCGEEC1FiaACdNuFEEIIITUdJYIK0G0XQkhVRj9WCSGaQIkgIYRUQ/RjlRCiCZQIEkIIIYRoKUoECSFEyzAAObm5yM3NBQOr7OYQQioRJYKEEFINlaWPYH5+PvLi/kZu/F3k5uaVQ+sIIdUFJYIKUEdsQkhVVtY+ggKeHgz09TTcKkJIdUOJoALUEZsQQgghNR0lgoQQQgghWooSQUIIIYQQLUWJICGEaAGRSIScnBzk5uYCjJ4UJoR8pFvZDSCEEKI6Pz8/+Pn5oaioSKnyKSkpyHkZidzsLAhhCuiXcwMJIdUCXREkhJBqSJ0H2gx5+vSkMCFECiWChBBCCCFaihJBQgghhBAtRYkgIYQQQoiWokRQAZpZhBBCCCE1HSWCCtDMIoQQQgip6Wj4GEIIIWUmEonw9u1bAICVlRV0dNS7zvBpPQA0Ui8hRD5KBAkhpBpSdRzB8vb27Vu8Pb324x9fLYKNjY1G6gGgkXoJIfJRIkgIIdWQt7c3vL29kZmZCVNT08puDgDAqpZRudSjqXoJIbLoGjshhBBCiJaiRJAQQgghREtpRSKYkpICDw8PGBoa4rPPPsOlS5cqu0mEECJBMYoQUlm0oo+gt7c3rK2tkZKSgosXL2LYsGF49uwZzM3NK7tphBBCMYoQUmlq/BXB7OxsnDx5EitWrICBgQH69++PFi1aIDQ0tLKbRgghFKMIIZWqyiWC2dnZWLZsGXr37g1zc3NwOBzs3btXblmhUIgFCxbA1tYWAoEArq6uuHDhglSZZ8+ewcjICHZ2dpJlLVq0wL///lueb4MQUkNRjCKE1CRVLhFMTU3FypUr8fjxY7Rq1arEsl5eXti4cSNGjx6NLVu2gMvlom/fvrh+/bqkTHZ2NkxMTKS2MzExQXZ2drm0nxBSs1GMIoTUJFWuj6CNjQ2SkpJgbW2N27dvK5zrNyoqCkeOHIGvry/mzZsHABg3bhycnZ0xf/583LhxAwBgZGSEzMxMqW0zMzNhZETjUhFCVEcxihBSk1S5K4I8Hg/W1talljt27Bi4XC6mTJkiWcbn8zFx4kTcvHkTr169AgA0atQI2dnZSExMlJR7+PAhmjdvrvnGE0JqPIpRhJCapMolgsq6e/cuGjduLHNLpV27dgCAe/fuAfj4a3vAgAFYtmwZ8vLycPr0ady/fx8DBgyo6CYTQrQIxShCSHVQ5W4NKyspKUnunJPiZa9fv5Ys2759Ozw9PVG7dm3Y2dnh119/VTgsg1AohFAolPz96S2bsijLpOzibctr0vXi9QPqT/KuqYnn1VXex6kmUfazquzPtLqqjjGKMSbzWSv6/D9dXpxIJEJSUpLUupLOoU/jj7oq61zV9rhT2bGEYlTZVNujlZeXBx6PJ7Ocz+dL1otZWlrizJkzyM3NRXR0NP73v/8prHfdunUwNTWVvOrVq6exNudkvIf/5Uf4OTRKctIq6+3bt7gfvEjl7dSpXzzp+9vTa9Vqp7rbakJ5H6eaRNnPqrI/0+qqOsao3NxcpFzYKvVZK/r8SzovUlJSpNaVdg5p6ntbWeeqtsedyo4lFKPKptpeERQIBFK/isXy8/Ml69Xh4+ODOXPmSP7OzMzUaKA1Mq0NQ0NDtba1NFVvO3XqL8sk75U9QXx5H6eaRNnPqrI/0+qousYoSzNDmc9b0edf0nmhbB2S/Wroe1tZ56q2x53KjiUUo9RXbRNBGxsbqc7VYuLbEba2tmrVy+Px5P6KJ4QQVZR3jPLz84Ofnx+KiorK1E5CiHartreGXVxcEB0dLdM/JjIyUrK+LPz8/NCsWTOFQ0MQQkhJyjtGeXt749GjR7h161aZ6iGEaLdqmwgOGTIERUVF8Pf3lywTCoUICgqCq6trmW+VUJAlhJRFeccoQgjRhCp5a3jbtm1IT0+XPFV36tQpJCQkAABmzJgBU1NTuLq6YujQofDx8UFycjIaNmyIffv2ITY2Frt3767M5hNCariqEKPo1jAhRBOqZCK4YcMGxMXFSf4+ceIETpw4AQAYM2YMTE1NAQD79+/HkiVLEBwcjLS0NLRs2RKnT59Gly5dytwGcZAtLCwEUPIQDVlZWcjPzYEwLxc6RSKIPuSX+F8u5+M2qjw0kpWVhey8Dypvp079AIBcoXiFyu1Ud1tNKO/jVJMo+1mp85kaGxuDw+FoqqlVTlWIUd7e3vD29kZGRgbMzMxKHUYmOzsbwvx8fBAKUfBBBx/0RGAfhODk5UJHlwOdItHHcqIPMOAJJZ+1os//0+UAJH9nZ2fDQME6eedQSfGntG0/racy4o+2x53yjCWa3H9xNT1GqYLDGGOV3YiqLCEhgW7hEKKijIwMmYGUSfmgGEWI6ihG/YcSwVKIRCK8fv26xF8P4uEbXr16RSdWBaFjXvFUOeb0a7viKBOjAPrOaAIdQ82oCseRYtR/quSt4apER0cHdnZ2SpU1MTGh4FDB6JhXPDrmVYsqMQqgz08T6BhqBh3HqqHaPjVMCCGEEELKhhJBQgghhBAtRYmgBvB4PCxbtoxmJKlAdMwrHh3z6o0+v7KjY6gZdByrFnpYhBBCCCFES9EVQUIIIYQQLUWJICGEEEKIlqJEkBBCCCFES1EiSAghhBCipSgRLAOhUIgFCxbA1tYWAoEArq6uuHDhQmU3q0q7desWpk+fjubNm8PQ0BD29vYYNmwYoqOjZco+fvwYvXv3hpGREczNzTF27FikpKTIlBOJRPjpp5/g6OgIPp+Pli1b4vDhw3L3r2ydNdmaNWvA4XDg7Owss+7GjRvo1KkTDAwMYG1tjZkzZyI7O1umnCrnvrJ1kvKh7XGKYk75oVhSQzCithEjRjBdXV02b948tmvXLtahQwemq6vLrl27VtlNq7IGDx7MrK2t2YwZM1hAQABbtWoVs7KyYoaGhuzBgweScq9evWIWFhbMycmJbdmyha1Zs4bVqlWLtWrVigmFQqk6Fy5cyACwyZMnM39/f+bh4cEAsMOHD0uVU6XOmurVq1fMwMCAGRoasubNm0utu3v3LuPz+ezzzz9nO3bsYIsXL2Y8Ho/17t1bph5lz31V6iTlQ9vjFMWc8kGxpOagRFBNkZGRDADz9fWVLMvLy2NOTk6sQ4cOldiyqi0iIkImAEZHRzMej8dGjx4tWfbtt98ygUDA4uLiJMsuXLjAALBdu3ZJliUkJDA9PT3m7e0tWSYSiVjnzp2ZnZ0dKywsVLnOmmz48OHM3d2dde3aVSZ49+nTh9nY2LCMjAzJsoCAAAaAnTt3TrJMlXNf2TpJ+aA4RTGnvFAsqTkoEVTT999/z7hcrtRJyRhja9euZQBYfHx8JbWsevriiy/YF198Ifm7Tp06bOjQoTLlGjduzHr06CH528/PjwFg//77r1S5Q4cOMQBSvyqVrbOmunr1KuNyuez+/fsywTsjI4Pp6uqy77//XmoboVDIjIyM2MSJEyXLlD33VamTlA+KU4pRzFEfxZKahfoIqunu3bto3LixzITZ7dq1AwDcu3evElpVPTHG8PbtW1hYWAAAEhMTkZycjDZt2siUbdeuHe7evSv5++7duzA0NETTpk1lyonXq1pnTVRUVIQZM2Zg0qRJaNGihcz6Bw8eoLCwUOb46Ovrw8XFReaYK3Puq1InKR8Up+SjmKM+iiU1DyWCakpKSoKNjY3McvGy169fV3STqq2DBw8iMTERw4cPB/Dx2AJQeHzfv38PoVAoKWtlZQUOhyNTDvjvc1Clzppo586diIuLw6pVq+SuL+34FD+flT33VamTlA+KU/JRzFEfxZKahxJBNeXl5cmdJ5HP50vWk9I9efIE3t7e6NChAzw9PQH8d+yUOb7Kfg6q1FnTvHv3DkuXLsWSJUtgaWkpt0xpx6f4sdHUMa+px7sqoTgli2KO+iiW1EyUCKpJIBDI/TWXn58vWU9K9ubNG3h4eMDU1BTHjh0Dl8sF8N+xU+b4Kvs5qFJnTfPDDz/A3NwcM2bMUFimtONT/Nho6pjX1ONdlVCckkYxp2woltRMupXdgOrKxsYGiYmJMsvFl7BtbW0ruknVSkZGBvr06YP09HRcu3ZN6niJL/+Lj2VxSUlJMDc3l/wytLGxQXh4OBhjUrdqPv0cVKmzJnn27Bn8/f2xefNmqdsn+fn5KCgoQGxsLExMTEo9Pp9+Psqc+6rUScoHxan/UMwpG4olNRddEVSTi4sLoqOjkZmZKbU8MjJSsp7Il5+fj379+iE6OhqnT59Gs2bNpNbXrVsXlpaWuH37tsy2UVFRUsfWxcUFubm5ePz4sVS5Tz8HVeqsSRITEyESiTBz5kw4OjpKXpGRkYiOjoajoyNWrlwJZ2dn6OrqyhyfDx8+4N69ezLHXJlzX5U6SfmgOPURxZyyo1hSg1XuQ8vV119//SUz/lF+fj5r2LAhc3V1rcSWVW2FhYWsf//+TFdXl/3xxx8Ky02dOpUJBAKp4S0uXrzIALAdO3ZIlr169UrhmF5169aVGtNL2TprkpSUFBYSEiLzat68ObO3t2chISHs/v37jDHGevfuzWxsbFhmZqZk+8DAQAaAhYWFSZapcu4rWycpHxSnKOZoCsWSmosSwTIYOnSoZGyjXbt2MTc3N6arq8uuXr1a2U2rsr777jsGgPXr148FBwfLvMTi4+NZ7dq1mZOTE9u6dStbu3Ytq1WrFmvRogXLz8+XqvP7779nANiUKVNYQECAZJT/gwcPSpVTpc6aTt4gsHfu3GE8Hk9q5H4+n8969uwps72y574qdZLyoe1ximJO+aJYUv1RIlgGeXl5bN68ecza2prxeDzWtm1bdvbs2cpuVpXWtWtXBkDhq7iHDx+ynj17MgMDA2ZmZsZGjx7N3rx5I1NnUVERW7t2Latfvz7T19dnzZs3ZwcOHJC7f2XrrOnkBW/GGLt27Rpzc3NjfD6fWVpaMm9vb6lf4GKqnPvK1knKh7bHKYo55YtiSfXHYYyx8r8BTQghhBBCqhp6WIQQQgghREtRIkgIIYQQoqUoESSEEEII0VKUCBJCCCGEaClKBAkhhBBCtBQlgoQQQgghWooSQUIIIYQQLUWJICGEEEKIlqJEkBBCCCFES1EiSAghhBCipSgRJIQQQgjRUpQIEkIIIYRoKUoECSGEEEK01P8BL6pfRxTcT8EAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 600x300 with 2 Axes>"
       ]
@@ -358,7 +370,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 600x300 with 2 Axes>"
       ]
@@ -376,7 +388,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x300 with 2 Axes>"
       ]
@@ -394,7 +406,7 @@
     },
     {
      "data": {
-      "image/png": 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u3TqZfeHh4eDxeIiPj6/2eZycnBTeg9atW8uk//nnn+Hi4gIjIyO0bt0amzZtkklz6NAhBAQEwMHBAQKBAE2bNsXQoUNx8+bNapeXkPpmwoQJCn/HTpw4oe3iVUpS9rfeeguMMZn9PB4P06dPr/Z57t69i3nz5sHDwwONGjWCvb09AgMD5ca4e/fu4bPPPkPXrl1hZGQEHo+Hp0+fKsw7JycH8+bNg7OzMwQCAZo0aYKhQ4ciPz+fS5OcnIz58+fDz88PjRo1qtYXLn21jiI6o0ePHigoKIChoaG2i6I1Li4u2LVrl9S2BQsWwMTEBF988YWWSlW5tWvXYtq0aRCJRDWS/8aNG5Gbmyu17dmzZ1i0aBH69Okjtf3HH3/E1KlTMWTIEISEhODcuXP49NNPkZ+fj88//5xLd+PGDVhYWGDmzJmwsrJCSkoKduzYAS8vL8TGxsLd3b1GroXUHRRvpAkEAmzfvl1me335Xbhx4wZ+/fVXDBkypEby3759O37++WcMGTIEn3zyCbKysvDjjz/inXfewYkTJ+Dv78+ljY2Nxffffw9XV1e4uLjg6tWrCvPNysqCj48PXrx4gSlTpqBVq1ZIT0/HuXPnUFRUxMXVe/fuYc2aNWjdujXc3NwQGxur/sUwHRUTE8MAsJiYGG0Xpd4JCgpizZs313YxGAC2dOlSrZy7ffv2zMfHp9I0paWlrKCgoHYK9B8AzMPDgwFg69atk9oXFhbGALBLly7VyLm//PJLBoBduHCB25afn88aN27MAgMDpdKOGTOGGRsbs8zMzErzTElJYfr6+uzjjz+ukTLXFoo36tPVeBMUFMSMjY2rnc/SpUtZbf+pDwoKYkKhkLVp04a99dZbTCwWS+0HwIKDg6t9nvj4eJaTkyO1LSMjg1lbW7Nu3bpJbX/16hXLzs5mjDG2du1aBoA9efJEbr7Tpk1j5ubm7PHjx5WePzs7m7169YoxxlhUVFS1fscb7GPKpKQkTJo0iXvk4ezsjGnTpqG4uLjS46KiouDp6QmhUAgrKyuMHTsWSUlJUmkmTJgAExMTJCUlYeDAgTAxMYG1tTXmzJmD0tJSqbRisRgbN25E+/btYWRkBFtbW3z88cf4999/VbqeZ8+e4ZNPPkHbtm0hFArRuHFjDBs2TKqZNT4+HjweDzt37pQ5/s8//wSPx8Nvv/2mdH6AZvu6aPrelhcTEwMej4dDhw7J7NuzZw94PF71vrUoQdL0HhERgfbt20MgEODEiRMK7+HTp0/B4/EQHh4utf3u3bsYOnQoLC0tYWRkhE6dOuHo0aNKl6Nbt27o2bMnvvnmGxQUFGjgypSzZ88eODs7o2vXrty2mJgYvHr1Cp988olU2uDgYOTl5eH48eOV5mljYwORSITXr1/XRJE1huKNNIo3NR9vzp07h2HDhsHR0RECgQDNmjXDZ599ptTv/KlTp9C9e3eYm5vDxMQEbdu2xcKFC6XSFBUVYenSpWjVqhWX/7x581BUVKRU+fh8PhYtWoTr16/LvU+a4OnpCRMTE6ltjRs3xrvvvos7d+5Ibbe0tESjRo2qzPP169cICwvDlClT4OzsjOLiYoXX3KhRI1haWqp/AeU0yMeUL1++hJeXF16/fo0pU6agXbt2SEpKwoEDB5Cfn6+wCTw8PBwTJ05E586dsWrVKqSmpuK7777DhQsXcOXKFZibm3NpS0tLERAQgC5duuDbb7/FX3/9hXXr1qFly5aYNm0al+7jjz/m8v3000/x5MkT/PDDD7hy5QouXLgAAwMDpa7p0qVL+OeffzBy5Eg0bdoUT58+xZYtW+Dr64vbt29DJBKhU6dOaNGiBfbv34+goCCp4yMjI2FhYYGAgACl89Okmri35fn6+qJZs2aIiIjAoEGDpPZFRESgZcuW8Pb2BlAWZHJycpQqt5WVlUrXefr0aezfvx/Tp0+HlZUVnJycVKpI3Lp1C926dUOTJk0wf/58GBsbY//+/Rg4cCAOHjwoc22KLFu2DD169MCWLVsQEhKiMJ2m7sWVK1dw584dmce2V65cAQB06tRJarunpyf4fD6uXLmCsWPHSu17/fo1SkpKkJKSgo0bNyI7Oxu9evVSqozaQPGG4k15mo43GRkZUu8NDAxgZmaGqKgo5OfnY9q0aWjcuDHi4uKwadMmvHjxAlFRUQrPcevWLfTv3x9vvfUWVqxYAYFAgIcPH+LChQtcGrFYjPfffx/nz5/HlClT4OLighs3bmDDhg24f/8+Dh8+rNT1jB49Gl9++SVWrFiBQYMGVdqvtuJ1KtKoUSMIBIJK06SkpKgcuyXOnz+PwsJCtGrVCkOHDsXhw4chFovh7e2N0NBQeHh4qJVvldRqT6vjxo8fz/h8vtzHMZLm0oqPDYqLi5mNjQ3r0KGD1KOl3377jQFgS5Ys4bYFBQUxAGzFihVSeXfs2JF5enpy78+dO8cAsIiICKl0J06ckLu9Mvn5+TLbYmNjGQD2yy+/cNsWLFjADAwMpB7/FBUVMXNzc/bhhx+qnJ+8xyuqPjaoiXvLmOxjgwULFjCBQMBev37NbUtLS2P6+vpS6SSP65R5KSLvMSUAxufz2a1bt6S2K3pE9eTJEwaAhYWFcdt69erF3NzcWGFhIbdNLBazrl27statWyssT/kySJr//fz8mJ2dHfdZy3tMqYl7wRhjs2fPZgDY7du3pbYHBwczPT09ucdYW1uzkSNHymxv27Ytd04TExO2aNEiVlpaWuW1awvFG4o3EpqMN5KyVXxJ4o68e7pq1SrG4/HYs2fPuG0VH1Nu2LCBAWDp6ekK7+GuXbsYn89n586dk9q+detWma4I8pR/xLpz504GgP3666/c/vJxqvw2ZV7l46U8Z8+eZTwejy1evFhhmsoeU65fv54BYI0bN2ZeXl4sIiKCbd68mdna2jILCwv28uVLuXlW9zFlg2sZE4vFOHz4MAYMGCDzbRyAwpp5fHw80tLSsGzZMhgZGXHbAwMD0a5dOxw/fhzLly+XOmbq1KlS7999912pjt5RUVEwMzND7969pWr9kqbVmJgYjB49WqnrEgqF3P9LSkqQnZ2NVq1awdzcHJcvX8a4ceMAACNGjMCqVavw66+/YtKkSQCAkydP4vXr1xgxYoTK+WlCTdxbecaPH49Vq1bhwIED3LVHRkbizZs3Uq0vAQEBOHXqVHUvSy4fHx+4urqqdWxmZiZOnz6NFStWICcnR+rbdEBAAJYuXYqkpCQ0adJEqfyWLVsGHx8fbN26FZ999pncNJq4F2KxGPv27UPHjh3h4uIita+yzthGRkZyH6mEhYUhOzsbjx8/RlhYGAoKClBaWgo+v+71qqB4Q/GmJuONkZERjh07JrXNwsICgPQ9zcvLQ0FBAbp27QrGGK5cuQJHR0e5eUpaBY8cOYKJEyfK/b2KioqCi4sL2rVrJ/Wz1LNnTwBlj2nLd0eozJgxY/DVV19hxYoVGDhwoMLfCWXvUfv27RXuS0tLw+jRo+Hs7Ix58+YplV9FkoFJPB4P0dHR3GPQjh07cq1jX331lVp5V6bBVcbS09ORnZ2NDh06qHTcs2fPAABt27aV2deuXTucP39eapuRkRGsra2ltllYWEj1zXjw4AGysrJgY2Mj95xpaWlKl6+goACrVq1CWFgYkpKSpIYLZ2Vlcf93d3dHu3btEBkZKRUgrKysuF8kVfLThJq4t/K0a9cOnTt3RkREBHftEREReOedd9CqVSsunb29Pezt7dW6lqo4OzurfezDhw/BGMPixYuxePFiuWnS0tKUroz16NEDfn5++Oabb2T+2Eho4l78/fffSEpKklvhEwqFCvtNFRYWSv1BkZA83gGAkSNHchW8b7/9tlrlrAkUbyje1GS80dPTkxoRWF5iYiKWLFmCo0ePypS1sns6YsQIbN++HR999BHmz5+PXr16YfDgwRg6dChXMXvw4AHu3Lkjc18kVPlZ0tPTw6JFixAUFITDhw8r7Gqh6DqVlZeXh/79+yMnJwfnz5+X6UumLElMGjBggFQe77zzDpydnfHPP/9Uq5yKNLjKWG3R09OrMo1YLIaNjQ0iIiLk7lf0gy7PjBkzEBYWhlmzZsHb2xtmZmbg8XgYOXIkxGKxVNoRI0Zg5cqVyMjIQKNGjXD06FGMGjUK+vr6auVX25S5t4qMHz8eM2fOxIsXL1BUVIT//e9/+OGHH6TSFBQUKP0HwM7OTqXzy6tcKPomKK/zNQDMmTOH62tTUfkgr4ylS5fC19cXP/74o1Q/GQlN3IuIiAjw+XyMGjVKZp+9vT1KS0uRlpYmVUkoLi7Gq1ev4ODgUOk5LSws0LNnT0RERNTJylhtoXhTc+pjvCktLUXv3r2RmZmJzz//HO3atYOxsTGSkpIwYcKESu+pUCjE2bNnERMTg+PHj+PEiROIjIxEz549cfLkSejp6UEsFsPNzQ3r16+Xm0ezZs2UKqfEmDFjuL5jAwcOlJsmJSVFqbzMzMxk4mxxcTEGDx6M69ev488//1T5y1F5kphka2srs8/GxkblwTDKanCVMWtra5iamqo8UWTz5s0BlM0bUv4bnWSbZL8qWrZsib/++gvdunWT+0daFQcOHEBQUJDUZJ6FhYVyO4ePGDECy5cvx8GDB2Fra4vs7GyMHDlS7fyqqyburSIjR45ESEgI9u7di4KCAhgYGEg9LgHKvrlPnDhRqfzKf4NXl+SxQsV7K/kGL9GiRQsAZR10q/stUcLHxwe+vr5Ys2YNlixZIrO/uveiqKgIBw8ehK+vr9yKlaSza3x8PN577z1ue3x8PMRisVKdYVX5Y1bbKN5QvNFGvLlx4wbu37+PnTt3Yvz48dx2ZR/18fl89OrVC7169cL69evx9ddf44svvkBMTAz8/f3RsmVLXLt2Db169dLIZNaS1rEJEybgyJEjctMo23oYFhaGCRMmcO/FYjHGjx+P6Oho7N+/Hz4+PtUqq6enJwDIjLwFygbrtGvXrlr5K9LgKmN8Ph8DBw7E7t27ER8fL9OPgzEm94erU6dOsLGxwdatW/Hhhx9yozX++OMP3LlzR+4fsqoMHz4cmzdvxpdffomvv/5aat+bN2+Qm5srt7VCHj09PZlf1E2bNskdfu3i4gI3NzdERkbC1tYW9vb26NGjh9r5VVdN3FtFrKys0K9fP+zevRuFhYXo27evzKiamuwzJk/z5s2hp6eHs2fPSn0r3Lx5s1Q6GxsbrhVrxowZMsEpPT1dpdYNiWXLlsHX1xfbtm2T2Vfde/H777/j9evXGDNmjNz9PXv2hKWlJbZs2SJVGduyZQtEIhECAwO5bRVbz4Cy6T+io6Pl9seqCyjeULzRRryRtOaVv6eMMXz33XdVHpuZmSkzHYPkS5FkCofhw4fj999/x08//YQpU6ZIpS0oKIBYLIaxsbFKZR47diy++uormf56Eur2GZsxYwYiIyPx448/YvDgwSqVSZ62bdvC3d0dR44cQUZGBvd5njx5Es+fP8eMGTOqfQ55GlxlDAC+/vprnDx5Ej4+Ptyw3OTkZERFReH8+fNyA5KBgQHWrFmDiRMnwsfHB6NGjeKGQzs5OSnsAF0ZHx8ffPzxx1i1ahWuXr2KPn36wMDAAA8ePEBUVBS+++47DB06VKm8+vfvj127dsHMzAyurq6IjY3FX3/9hcaNG8tNP2LECCxZsgRGRkaYNGmSTCdNVfOrjpq4t5UZP348d1+//PJLmf012WdMHjMzMwwbNgybNm0Cj8dDy5Yt8dtvv8ntdxEaGoru3bvDzc0NkydPRosWLZCamorY2Fi8ePEC165dU/n8Pj4+8PHxwd9//y2zr7r3IiIiAgKBQOEM20KhEF9++SWCg4MxbNgwBAQE4Ny5c9i9ezdWrlwp9UfBzc0NvXr1goeHBywsLPDgwQP8/PPPKCkpwerVq9UuY02jeEPxprbjTbt27dCyZUvMmTMHSUlJMDU1xcGDB5V6hLZixQqcPXsWgYGBaN68OdLS0rB582Y0bdoU3bt3BwCMGzcO+/fvx9SpUxETE4Nu3bqhtLQUd+/exf79+/Hnn3+q/AVJT08PX3zxhcJWQnWeBmzcuBGbN2+Gt7c3RCIRdu/eLbV/0KBBXKUxKyuLW4ZNMo3HDz/8AHNzc5ibm0stz7Rhwwb07t0b3bt3x8cff4ysrCysX78ebdq0kZnuRNKZ/9atWwCAXbt2cf0SFy1apPzFqDUGsx549uwZGz9+PLO2tmYCgYC1aNGCBQcHs6KiIsaY4ukGIiMjWceOHZlAIGCWlpZszJgx7MWLF1JpFM2MrGim423btjFPT08mFApZo0aNmJubG5s3b57CIbLy/Pvvv2zixInMysqKmZiYsICAAHb37l3WvHlzFhQUJJP+wYMH3FDg8+fPq52fJoaaS2j63kLBjNhFRUXMwsKCmZmZ1dgM+IqmtlA0q3R6ejobMmQIE4lEzMLCgn388cfs5s2bcodqP3r0iI0fP57Z2dkxAwMD1qRJE9a/f3924MCBKsulqAySzxEanIE/KyuLGRkZscGDB1eZdtu2baxt27bM0NCQtWzZkm3YsEFmVu6lS5eyTp06MQsLC6avr88cHBzYyJEj2fXr1zVS3ppE8YbijabjTVUz8N++fZv5+/szExMTZmVlxSZPnsyuXbsmE1MqXkt0dDT74IMPmIODAzM0NGQODg5s1KhR7P79+1L5FxcXszVr1rD27dszgUDALCwsmKenJ1u+fDnLyspSq+wlJSWsZcuWlcZKVSia/kPyKj91hWQqIXkveT9fp06dYu+88w4zMjJilpaWbNy4cSw5OVkmXWXnVwXvv8wIaTDevHkDBwcHDBgwAD///LO2i0MIacAo3hBNqHsT9xBSTYcPH0Z6erpUx1ZCCKkJFG+IJlDLmJbl5uZyk8wpYm1tXa3h1zUpMzOz0vX39PT01Op0ro6LFy/i+vXr+PLLL2FlZYXLly/XynkJqS8o3mgOxRuiUSo91CQaJ3meX9lL0crydYGPj0+lZVenr4e6goKCmJ6eHvP09GQ3btyotfMSUl9QvNEcijdEk6hlTMseP36Mx48fV5qme/fuUst61CUJCQmVjuARCoXo1q1bLZaIEKIIxRtC6iaqjBFCCCGEaBF14CeEEEII0SKqjBFCCCGEaJFOVsbCw8PB4/GkXjY2NvDz88Mff/whk75i2vKvqVOnyj3H8OHDwePx8Pnnn8vdf+bMGS6PirMGS3Tr1g08Ho9b9HTZsmWVlkXy8vX1VenaJa/58+dz6U6ePIlJkyahQ4cO0NPTg5OTk8I81VVUVITPP/8cDg4OEAqF6NKli0rLhvz111/w8/ODlZUVzM3N4eXlhV27dsmkU3S91ZnVXZLHRx99JHf/F198waXJyMiQ2X/mzBkMHjwYdnZ2MDQ0hI2NDQYMGIBff/1VJm12djaWL18Od3d3mJiYQCgUokOHDvj888/x8uVLta+hvDt37qBv374wMTGBpaUlxo0bh/T0dKWOLSwsxKpVq+Dq6gqRSIQmTZpg2LBh3IzUEpX97Cm7SHBDRPFIt+LRli1bMGzYMDg6OoLH40mts6guJycn8Hg8hbPY//TTT9x9jY+Pl9l/9epVjB07Fs2aNYNAIIClpSX8/f0RFhYms2RVYWEhNmzYgC5dusDMzAxGRkZo06YNpk+fjvv371f7WoCydSGHDx8Oc3NzmJqa4oMPPqiyr6OEWCzG1q1b4eHhARMTE9ja2qJfv374559/pNLl5uZi6dKl6Nu3LywtLcHj8RAeHq6R8qujQS6HpKwVK1bA2dkZjDGkpqYiPDwc7733Ho4dO4b+/ftLpe3du7fceWTatGkjsy07OxvHjh2Dk5MT9u7di9WrVytcbNXIyAh79uzB2LFjpbY/ffoU//zzj1RH2sGDB6NVq1bc+9zcXEybNg2DBg2SWpNL3mrziq69vPIr3e/ZsweRkZF4++235S4ArQkTJkzAgQMHMGvWLLRu3Zq7/zExMdyyHIocPXoUAwcOhLe3N/dHYf/+/Rg/fjwyMjJkljyR9/l17NixWuU3MjLCwYMHsXnzZhgaGkrt27t3L4yMjFBYWChz3NKlS7FixQq0bt0aH3/8MZo3b45Xr17h999/x5AhQxAREYHRo0cDKOtw7e/vj8TERAwbNgxTpkyBoaEhrl+/jp9//hmHDh2qdgB88eIFevToATMzM3z99dfIzc3Ft99+ixs3biAuLk7m2ioaM2YMjh49ismTJ+Ptt9/Gy5cvERoaCm9vb9y4cUNmYWZ5P3vKrpnYkFE80o14tGbNGuTk5MDLywvJyckaK7+RkRFiYmKQkpICOzs7qX0REREK49H27dsxdepU2NraYty4cWjdujVycnIQHR2NSZMmITk5GQsXLgQAZGRkoG/fvkhISED//v0xevRomJiY4N69e9i3bx+2bdtW6dQjysjNzYWfnx+ysrKwcOFCGBgYYMOGDfDx8cHVq1erXEJr7ty5WL9+PcaOHYtPPvkEr1+/xo8//ggfHx9cuHABXl5e3LWsWLECjo6OcHd3x5kzZ6pV7mrT5lBObQkLC5O7LExmZiYzMDBgo0ePltoOFZdu2LFjBzMwMGCnT59mANiZM2dk0kiW/Rg8eDDT19dn6enpUvtXrlzJbG1tWffu3Vn79u3lnic9PV3hEh2KKLr2ipKSklhxcTFjjLHAwECNDxm/ePEiA8DWrl3LbSsoKGAtW7Zk3t7eVR7fu3dv5uDgwAoLC7ltkqU23nrrLam0qn5+ygDABg4cyPh8Pjt8+LDUvgsXLjAAbMiQIQyA1GcbFRXFALChQ4dy97e8EydOsGPHjnHX4+7uzkQiETt37pxM2qysLLZw4cJqX8u0adOYUChkz54947adOnWKAWA//vhjpce+ePGCAWBz5syR2i752V+/fj23TdmfPV1D8Ui34tHTp0+5pcCMjY3lLi+lqubNm7NevXoxU1NTtnHjRql9z58/Z3w+n4tH5e91bGws09PTY927d2fZ2dky+V66dElqaaXAwEDG5/PlLs1WWFjIZs+eXe1rWbNmDQPA4uLiuG137txhenp6bMGCBZUeW1JSwoRCIRs6dKjU9sePHzMA7NNPP5Uqr2R5o0uXLsldmq426eRjSkXMzc0hFAqhr1+9BsOIiAj07t0bfn5+cHFxQUREhMK0H3zwAQQCAaKioqS279mzB8OHD9fa5IsODg4wMDCosfwPHDgAPT09TJkyhdsmWWQ4NjYWz58/r/T47OxsWFhYQCAQcNv09fVhZWUFoVAo95iCggK53wzV1aRJE/To0QN79uyR2h4REQE3Nzepb/YSixcvhqWlJXbs2CH3/gYEBHCtIAcPHsS1a9fwxRdfyP1mbmpqipUrV1b7Og4ePIj+/fvD0dGR2+bv7482bdpg//79lR6bk5MDQLb1Q7IwsqLPIicnR+bxB5FG8ej/NaR41Lx5c4Utk9VhZGSEwYMHy8SjvXv3wsLCAgEBATLHLF++HDweDxEREWjUqJHM/k6dOnGPUS9evIjjx49j0qRJGDJkiExagUCAb7/9ttrXceDAAXTu3BmdO3fmtrVr1w69evWqMh6VlJSgoKBAJh7Z2NiAz+dLfRYCgUCmBVGbdLoylpWVhYyMDKSnp+PWrVuYNm0acnNzZZrogbLn5BkZGTKvik2yL1++RExMDEaNGgUAGDVqFA4cOKCw6VYkEuGDDz7A3r17uW3Xrl3DrVu3uEdVNUFy7eVf6hCLxXLvi7xXSUkJd9yVK1fQpk0bmJqaSuUnaUK+evVqpef19fXFrVu3sHjxYjx8+BCPHj3Cl19+ifj4eMybN08mfXh4OIyNjSEUCuHq6ioTsNQ1evRoHDt2jJvV/M2bN4iKipL72T148AB3797FwIED5Qa+io4ePQoAGDdunFJlyc/PV+pzKD9PU1JSEtLS0tCpUyeZ/Ly8vHDlypVKz9myZUs0bdoU69atw7Fjx/DixQvExcVh6tSpcHZ2xsiRI2WO8fPzg6mpKUQiEd5//308ePBAqetr6Cge6U48qimjR49GXFwcHj16xG3bs2cPhg4dKlOZzc/PR3R0NHr06CH1RUwRVeNRUVGR0p+FhFgsxvXr1xXGo0ePHnFfAOWR9PULDw9HREQEEhMTcf36dUyYMAEWFhZSle06R2ttclokaRqv+BIIBCw8PFwmvby0ktfevXul0n777bdMKBRyTb73799nANihQ4ek0kkeC0RFRbHffvuN8Xg8lpiYyBhjbO7cuaxFixaMsbIZp2visYC8lyKVPRZ48uRJlTN6S14xMTHcce3bt2c9e/aUye/WrVsMANu6dWul15Gbm8uGDx/OeDwel79IJJJ5ZMgYY127dmUbN25kR44cYVu2bGEdOnRgANjmzZsrPUdl8N+joszMTGZoaMh27drFGGPs+PHjjMfjsadPn3KznUse+Rw5coQBYBs2bFDqHB07dmRmZmZKl0mZ2dVRYZZySfP8L7/8IpPf3LlzGQCpRy/yXLx4kbVs2VLqHJ6entwjAInIyEg2YcIEtnPnTnbo0CG2aNEiJhKJmJWVFfezr4soHulWPCpPk48pAwMD2Zs3b5idnR378ssvGWOM3b59mwFgf//9t8wj4WvXrjEAbObMmUqdY9CgQQwA+/fff5VKX9lnq+izlvwMrVixQia/0NBQBoDdvXu30vM+ePCAvf3221L5t2jRotLj6sJjSp3uwB8aGsp1eE1NTcXu3bvx0UcfoVGjRlIdUIGy5vvp06fL5OHm5ib1PiIiAoGBgVzLR+vWreHp6YmIiAgMHDhQbjn69OkDS0tL7Nu3D3PmzMG+fftqfNHZ8tdeHXZ2dkqPOHJ3d+f+X1BQINWkLyHpIFxQUFBpXgKBAG3atMHQoUMxePBglJaWYtu2bRg7dixOnTqFd955h0t74cIFqWM//PBDeHp6YuHChZgwYYLCR2nKsLCwQN++fbF3716MHTsWe/bsQdeuXWU6rQNljzIAKNUqJkmvbFoAGD9+fJUdjQHpR4eS+1zVZyFvv4SFhQU8PDwwbNgwvPPOO3j48CFWrVqFYcOG4dSpU1w+w4cPx/Dhw7njBg4ciICAAPTo0QMrV67E1q1blbvQBorikW7Eo5qkp6eH4cOHY+/evVi0aBEiIiLQrFkzvPvuuzKjEdWJR6qkDwgIUGk0KqB8PKpMo0aN0L59e3h7e6NXr15ISUnB6tWrMXDgQJw7dw5WVlYqlam26HRlzMvLS6o5dNSoUejYsSOmT5+O/v37S40ia9q0qcJhwxJ37tzBlStXMH78eDx8+JDb7uvri9DQUGRnZ8s0gwOAgYEBhg0bhj179sDLywvPnz+v0UcCgOy1q8vIyKjK+yKPUChEUVGRzHZJn66qKkjTp0/H//73P1y+fBl8ftnT9uHDh6N9+/aYOXMmLl68qPBYQ0NDTJ8+HVOnTkVCQoJSFZjKjB49GuPGjUNiYiIOHz6Mb775Rm46yWdfWTN7xfTKDucGgBYtWqBFixZKpwf+/z6r+1lkZWXh3Xffxdy5czF79mxue6dOneDr64uwsDBMmzZN4fHdu3dHly5d8Ndff6lU7oaI4pFuxiNNGz16NL7//ntcu3YNe/bswciRI+X2UVMnHknSKzP62d7enus7qqzqxqM3b97A398fvr6+2LRpE7fd398f7du3x9q1a7FmzRqVylRbdLrPWEV8Ph9+fn5ITk5Wqx+LZH6ezz77DK1bt+Ze69atQ2FhIQ4ePKjw2NGjR+Pq1atYtmwZ3N3d4erqqvZ11KbS0lKkpKQo9SrfT8Xe3l7usG7JtsqGrxcXF+Pnn39GYGAgF/iAsj8i/fr1Q3x8fJXDq5s1awYAyMzMVOl65Xn//fchEAgQFBSEoqIiqdaf8tq1awcAuHHjhlL5tmvXDllZWVV2HpbIzc1V6nMoP3+YJFgq+iwsLS0rbRU7ePAgUlNT8f7770tt9/HxgampqUyrpDzNmjXTyOfQ0FA8Ul19jUea1KVLF7Rs2RKzZs3CkydPFFakW7VqBX19fZXiEaB8/CooKFD6s5CQxBt1P4uzZ8/i5s2bMvGodevWcHFxUSoeaYtOt4zJ8+bNGwDgOmQrizGGPXv2wM/PD5988onM/i+//BIRERGYOHGi3OO7d+8OR0dHnDlzps7W3OV5/vy5zPxAisTExHATQHp4eCAmJkbm27nkG6SHh4fCfF69eoU3b97IHY1XUlICsVhc5Ug9SYuTtbW1UmWvjFAoxMCBA7F7927069dPYTN4mzZt0LZtWxw5cgTfffcdTExMKs13wIAB2Lt3L3bv3o0FCxZUWY5vv/0Wy5cvrzJd8+bN8fTpUwBlI0Ktra3lTgQZFxdX6ecAlD1OAyBzvxljKC0t5X6fKvP48WONfA4NEcUj1dTXeKRpo0aNwldffQUXFxeFZReJROjZsydOnz6N58+fc19QFRkwYABWrVqF3bt34913362yDJGRkQp/vipi/y2Rzefz4ebmJjceXbx4ES1atKj0MamieASUfRbKxCNtocpYOSUlJTh58iQMDQ3h4uKi0rEXLlzA06dPsWLFCgwdOlRm//3797F48WK8fPlSbs2ex+Ph+++/x5UrV5QerVIXqNtHY+jQofj222+xbds2zJkzB0BZ03RYWBi6dOkiFRgSExORn5/PfTOzsbGBubk5Dh06hBUrVnCPb3Jzc3Hs2DG0a9eOa8pOT0+X+UOfk5ODjRs3wsrKCp6enupffDlz5sxBy5Yt5Q4fL2/58uUYOXIkPvroI+zevVtm2oKTJ0+iuLgY/fv3x9ChQ7Fq1SqsXLkSvr6+8Pb2lrmO1atXc9NbqNNnDACGDBmCnTt3SgXk6Oho3L9/X2qyypKSEjx69AhmZmZci5qkn8++ffuwbNkyLu3Ro0eRl5cnNbGuvM/i999/R0JCAj799NMqy61rKB6prq7Ho9ry0UcfQU9PD126dKk03dKlSxEdHY1x48bht99+k/mCmJCQgJs3byIoKAje3t7o27cvtm/fjn79+sn0OSwuLsbChQu56S3U6TMGlH0W8+fPR3x8PPfo+t69ezh9+jT32UjcvXsXIpGIGw1aPh717duXS3f58mXcu3evTo+m1OnK2B9//IG7d+8CANLS0rBnzx48ePAA8+fPl+lLcf/+fbnLhNja2qJ3796IiIiAnp4eAgMD5Z7r/fffxxdffIF9+/YhJCREbpoPPvgAH3zwQTWvSjOuX7/ODWV++PAhsrKy8NVXXwEoC2IDBgwAoH4fjS5dumDYsGFYsGAB0tLS0KpVK+zcuRNPnz7Fzz//LJV2/Pjx+Pvvv7lvT3p6epgzZw4WLVqEd955B+PHj0dpaSl+/vlnvHjxQupzCg0NxeHDhzFgwAA4OjoiOTkZO3bsQGJiInbt2iXVD+fMmTPw8/PD0qVLpSoWynB3d5cK7oqMGDECN27cwMqVK3HlyhWMGjWKm4H/xIkTiI6O5qbdMDAwwK+//gp/f3/06NEDw4cPR7du3WBgYIBbt25hz549sLCw4Cpj6vQZA4CFCxciKioKfn5+mDlzJnJzc7F27Vq4ublJfbNNSkqCi4sLgoKCuGVDBgwYgPbt22PFihV49uwZ14H/hx9+gL29PSZNmsQd37VrV3Ts2BGdOnWCmZkZLl++jB07dqBZs2bcDN+6jOKRYg0lHgHAsWPHcO3aNQBlFe7r169z1/L+++/jrbfeAlC26oGzs7PU75uymjdvrlQM69q1K0JDQ/HJJ5+gXbt2UjPwnzlzBkePHuXKBgC//PIL+vTpg8GDB2PAgAHo1asXjI2N8eDBA+zbtw/JyclcZUydPmMA8Mknn+Cnn35CYGAg5syZAwMDA6xfvx62trZS/VIBwMXFBT4+Ptzs+Z6enujduzd27tyJ7Oxs9OnTB8nJydi0aROEQiFmzZoldfwPP/yA169fc8vKSabnAYAZM2bAzMxM5fKrTWvjOLVI3pBbIyMj5uHhwbZs2cLNjixRMW35l4+PDysuLmaNGzdm7777bqXndXZ2Zh07dmSMSQ8lr0xNDSWvasbryoYla2IoNmNlM1zPmTOH2dnZMYFAwDp37sxOnDghk87Hx0fuUPeIiAjm5eXFzM3NmVAoZF26dJGZGfrkyZOsd+/ezM7OjhkYGDBzc3PWp08fFh0dLZPfsWPHlBrGzphys6BXnNqivOjoaPbBBx8wGxsbpq+vz6ytrdmAAQPYkSNHZNL++++/bMmSJczNzY2JRCJmZGTEOnTowBYsWCAzfYS6bt68yfr06cNEIhEzNzdnY8aMYSkpKVJpJNMGVPz8MzMz2WeffcbatGnDBAIBs7KyYiNHjmSPHz+WSvfFF18wDw8PZmZmxgwMDJijoyObNm2azHl0DcUj3YlHjDEWFBSk8FrKT61w48YNBoDNnz+/yrJLpraoTGX3OiEhgY0ePZo5ODgwAwMDZmFhwXr16sV27tzJSktLpdLm5+ezb7/9lnXu3JmZmJgwQ0ND1rp1azZjxgz28OHDKsuqjOfPn7OhQ4cyU1NTZmJiwvr3788ePHggk07yM1+xfCtWrGCurq5MKBQyMzMz1r9/f3blyhWZ45s3b67ws3jy5IlGrkVZPMb+q94TouPmzZuHvXv34uHDh5V2WieEkJq2efNmzJs3D48ePVJqfU9Sv9FoSkL+ExMTg8WLF1NFjBCidTExMfj000+pIqYjqGWMEEIIIUSLqGWMEEIIIUSLqDJGCCGEEKJFVBkjhBBCCNEiqowRQgghhGiRzlfGGGPIzs4GjWMghGgbxSNCdJPOV8ZycnJgZmam9Mr1hBBSUygeEaKbdL4yRgghhBCiTVQZI4QQLQsNDYWrqys6d+6s7aIQQrRA5yd9zc7OhpmZGbKysmQW4yWEkNpE8YgQ3UQtY4QQQgghWkSVMUII0TJ6TEmIbqPHlPRYgFRDaWkpSkpKtF2MOsnAwAB6enraLka9QvGIVAfFI8XqejzS13YBtCU0NBShoaEoLS1V6TixWIzU1FQAgK2tLfh8alzURYwxpKSk4PXr19ouSp1mbm4OOzs78Hg8bRelQaJ4RACKR8qqy/GIWsZU/CaanJyMdUfiAACzP/CCvb19TReR1EHJycl4/fo1bGxsIBKJ6uQvtzYxxpCfn4+0tDSYm5vT74mSKB4RdVA8qlx9iEc62zJWHY3MG2u7CESLSktLucDXuDH9LCgiFAoBAGlpabCxsanTjwi0TdWWekmLWGpqKkzMLOmPrw6jeKScuh6PqE2bEBVJ+mSIRCItl6Tuk9wj6sdSueDgYNy+fRuXLl1SKn1qairWHYnD1j+vID8/v4ZLR+oyikfKq8vxiFrGCFETtUZUje5RzWlk3hjQ7V4mpBz6XataXb5H1DJGCCGEEKJFVBlTklgsRnJyMlJTU6HjYx4IIRpG84wRotuoMqYk6qNBVJGSkoIZM2agRYsWEAgEaNasGQYMGIDo6GgAgJOTE3g8Hng8HkQiEdzc3LB9+3YAZT9rBgYG2Ldvn9y8J02ahLfffrvKMkyYMAEDBw7U2DWRmqNqnzFCVEUxqW6jypgKGpk3homphbaLQeq4p0+fwtPTE6dPn8batWtx48YNnDhxAn5+fggODubSrVixAsnJybh58ybGjh2LyZMn448//oCtrS0CAwOxY8cOmbzz8vKwf/9+TJo0SWPlrYudWQkhmkMxqe6jyhghGvbJJ5+Ax+MhLi4OQ4YMQZs2bdC+fXuEhITgf//7H5euUaNGsLOzQ4sWLfD555/D0tISp06dAlD2TTM6OhqJiYlSeUdFReHNmzcYM2ZMpWVYtmwZdu7ciSNHjnDfds+cOYOnT5+Cx+MhMjISPj4+MDIyQkREBJYtWwYPDw+pPDZu3AgnJyepbdu3b4eLiwuMjIzQrl07bN68Wf0bRQipFRST6j6qjBGiQZmZmThx4gSCg4NhbGwss9/c3Fxmm1gsxsGDB/Hvv//C0NAQAPDee+/B1tYW4eHhUmnDwsIwePBgufmUN2fOHAwfPhx9+/ZFcnIykpOT0bVrV27//PnzMXPmTNy5cwcBAQFKXVtERASWLFmClStX4s6dO/j666+xePFi7Ny5U6njSc2RzDuWnJwMsVis7eKQOoRiUv1AU1uoiZYhIfI8fPgQjDG0a9euyrSff/45Fi1ahKKiIrx58waWlpb46KOPAAB6enoICgpCeHg4Fi9eDB6Ph0ePHuHcuXPcN9XKmJiYQCgUoqioCHZ2djL7Z82ahcGDB6t0bUuXLsW6deu445ydnXH79m38+OOPCAoKUikvoll5WZnYdvoFRKJnNBM/kUIxqX6gGoSayoLfbaw7EsdVyghRZaTt3LlzcfXqVZw+fRpdunTBhg0b0KpVK27/hx9+iCdPniAmJgZA2TdQJycn9OzZs9rl7NSpk0rp8/Ly8OjRI0yaNAkmJibc66uvvsKjR4+qXR5dp4nRlCZmjWl1ECKDYlL9QC1j1WBi1lhusy/RXa1btwaPx8Pdu3erTGtlZYVWrVqhVatWiIqKgpubGzp16gRXV1cur3fffRdhYWHw9fXFL7/8gsmTJ2tk4sKKP7d8Pl8maJfvRJubmwsA+Omnn9ClSxepdHVtWZH6KDg4GMHBwdzalIRoCsWk+qHet4w9f/4cvr6+cHV1xVtvvYWoqChtF4noMEtLSwQEBCA0NBR5eXky+1+/fi33uGbNmmHEiBFYsGCB1PZJkybh4MGDOHjwIJKSkjBhwgSly2JoaKj0WofW1tZISUmRCn5Xr17l/m9rawsHBwc8fvyYC9aSl7Ozs9JlIoTULopJ9UO9r4zp6+tj48aNuH37Nk6ePIlZs2bJ/YEjpLZIFnz28vLCwYMH8eDBA9y5cwfff/89vL29FR43c+ZMHDt2DPHx8dy2YcOGwcDAAB9//DH69OmDZs2aKV0OJycnXL9+Hffu3UNGRkalw8V9fX2Rnp6Ob775Bo8ePUJoaCj++OMPqTTLly/HqlWr8P333+P+/fu4ceMGwsLCsH79eqXLRAipfRST6r56Xxmzt7fnhr/a2dnBysoKmZmZ2i0U0WktWrTA5cuX4efnh9mzZ6NDhw7o3bs3oqOjsWXLFoXHubq6ok+fPliyZAm3TSQSYeTIkfj333/x4YcfqlSOyZMno23btujUqROsra1x4cIFhWldXFywefNmhIaGwt3dHXFxcZgzZ45Umo8++gjbt29HWFgY3Nzc4OPjg/Dw8Hr5LZQQXUIxqe7jMS2v7XP27FmsXbsWCQkJSE5OxqFDh2Rm6A0NDcXatWuRkpICd3d3bNq0CV5eXjJ5JSQkICgoCDdv3lT6/JI+GllZWTA1NVWYLjk5Gdv+foScfzPANxRCXFwAvqEQxsbGmOLTkkYv6ZDCwkI8efIEzs7OMDIy0nZx6jS6V6qheERURb9jyqvL90rrLWN5eXlwd3dHaGio3P2RkZEICQnB0qVLcfnyZbi7uyMgIABpaWlS6TIzMzF+/Hhs27atNopNCCEaQ2tTEqLbtF4Z69evH7766isMGjRI7v7169dj8uTJmDhxIlxdXbF161aIRCKpZRmKioowcOBAzJ8/X2oSOXmKioqQnZ0t9SKkPio/nLvi69y5c9ouHlEBrU1JGgKKSeqr01NbFBcXIyEhQWo0B5/Ph7+/P2JjYwGUzaEyYcIE9OzZE+PGjasyz1WrVmH58uU1VmZCakv5kUUVNWnSpPYKQgghoJhUHXW6MpaRkYHS0lLY2tpKbbe1teXmTLlw4QIiIyPx1ltv4fDhwwCAXbt2wc3NTW6eCxYsQEhICPc+OztbpdEgFdFM/ERbyk/GSAgh2kYxSX11ujKmjO7du6u0FptAIIBAINDY+WkZEkIIIYRUR51uxrGysoKenp7MckOpqaly17ZShSY7zNIyJISQuoAWDCekfqrTlTFDQ0N4enoiOjqa2yYWixEdHV3pRHXKoA6zhJCGhtbMJaR+0vpjytzcXDx8+JB7/+TJE1y9ehWWlpZwdHRESEgIgoKC0KlTJ3h5eWHjxo3Iy8vDxIkTtVhqQgipm2jNXELqH61XxuLj4+Hn58e9l3SuDwoKQnh4OEaMGIH09HQsWbIEKSkp8PDwwIkTJ2Q69RNCCCGE1Edar4z5+vrKrMxe0fTp0zF9+nSNnjc0NJRbr4sQTUpMTERGRkatnc/KygqOjo61dj5CSP1B8ah+0HplTFuCg4MRHBzMLT9CiCYkJiainYsLCvLza+2cQpEId+/cUTkAKrvMmERUVBQWL16Mp0+fonXr1lizZg3ee++96hafgL4ckppB8aj+0NnKGCE1ISMjAwX5+Rjz+VrYOras8fOlJj5CxJq5yMjIUCn4SZYZ27p1K7p06YKNGzciICAA9+7dg42NjUz6f/75B6NGjcKqVavQv39/7NmzBwMHDsTly5fRoUMHTV6STqIvh6QmUDyqP6gyRkgNsHVsiaat22u7GAqVX2YMALZu3Yrjx49jx44dmD9/vkz67777Dn379sXcuXMBAF9++SVOnTqFH374AVu3bq3VshNCVEPxqO6r01Nb1CRamJfoKskyY/7+/ty2isuMVRQbGyuVHgACAgIUpieEEGVQPCqjs5UxmmeM6KrKlhlLSUmRe0xKSopK6QkhRBkUj8robGWMEEIIIaQuoMoYITpGnWXG7OzsamRZMkKIbqN4VEZnK2PUZ4zoKnWWGfP29pZKDwCnTp2q9rJkhBDdRvGojM6OpqSh5ESXVbXM2Pjx49GkSROsWrUKADBz5kz4+Phg3bp1CAwMxL59+xAfH49t27Zp8zIIIQ0AxSMdrowRUpNSEx/V6fNUtcxYYmIi+Pz/bzjv2rUr9uzZg0WLFmHhwoVo3bo1Dh8+XG/n9CFEl1A8qvuoMkaIBllZWUEoEiFizdxaO6dQJIKVlZXKx1W2zNiZM2dktg0bNgzDhg1T+TyEEO2geFR/UGWMEA1ydHTE3Tt3aC04HfX8+XOMGzcOaWlp0NfXx+LFi7XyB0MsFnMdnG1tbaVaFYjuoHhUf1BlTEMo+BEJR0dHCkY6Sl9fHxs3boSHhwdSUlLg6emJ9957D8bGxrVajrysTGw7/QIi0TPM/sAL9vb2tXp+UndQPKofdLbGoOnRlGXB7zbWHYmTGXJLCNEN9vb28PDwAFA2/N7KygqZmZlaKYuJWWM0Mm+slXMTQlSjs5WxmpiBn4IfIfXb2bNnMWDAADg4OIDH4+Hw4cMyaUJDQ+Hk5AQjIyN06dIFcXFxcvNKSEhAaWkpmjVrVsOlJoTUdzpbGSOEkIry8vLg7u6O0NBQufsjIyMREhKCpUuX4vLly3B3d0dAQADS0tKk0mVmZmL8+PH1eqg9IaT2UJ8xQgj5T79+/dCvXz+F+9evX4/Jkydz8x9t3boVx48fx44dOzB//nwAQFFREQYOHIj58+eja9eulZ6vqKgIRUVF3Pvs7GwNXAUhpL6hljFCCFFCcXExEhIS4O/vz23j8/nw9/dHbGwsAIAxhgkTJqBnz54YN25clXmuWrUKZmZm3IseaRKim6gyRgghSsjIyEBpaSk3EaWEra0tUlJSAAAXLlxAZGQkDh8+DA8PD3h4eODGjRsK81ywYAGysrK41/Pnz2v0GgghdZPOPqYMDQ1FaGgoSktLtV0UQkgD0b17d4jFYqXTCwQCCAQCikeE6DidrYzR2pSkpiQmJtIkiw2QlZUV9PT0ZKauSU1NhZ2dXbXypnhEagrFo/pBZytjhNSExMREuLi0Q35+Qa2dUyQS4s6du0oHwLNnz2Lt2rVISEhAcnIyDh06hIEDB1Z6zJkzZxASEoJbt26hWbNmWLRoESZMmFD9wtcjhoaG8PT0RHR0NHe/xGIxoqOjFS7jQog21Yd4BFBMAqgyRohGZWRkID+/ALsXDoeLo3WNn+9OYjrGfr0fGRkZSgc/yfQNH374IQYPHlxl+idPniAwMBBTp05FREQEoqOj8dFHH8He3h4BAQHVvYQ6JTc3Fw8fPuTeP3nyBFevXoWlpSUcHR0REhKCoKAgdOrUCV5eXti4cSPy8vK40ZXqoseUpCbUh3gEUEwCqDJGSI1wcbTG222aaLsYclU1fUNFW7duhbOzM9atWwcAcHFxwfnz57Fhw4Z6G/gUiY+Ph5+fH/c+JCQEABAUFITw8HCMGDEC6enpWLJkCVJSUuDh4YETJ07IdOpXFT2mJDWpLscjgGISQJUxQkgVYmNjpaZzAICAgADMmjVLOwWqQb6+vmCMVZpm+vTp9FiSEC1qiDGJprYghFQqJSVF7nQO2dnZKCiovb4oDZmm18olpCFriDGJKmOEEKJlNbFWLiGk/qDKGCGkUnZ2dnKnczA1NYVQKNRSqQghuqohxiSdrYzRYwFClOPt7Y3o6GipbadOnYK3t7eWSkQI0WUNMSbpbGWMHgsQXZWbm4urV6/i6tWrAP5/+obExEQAZUv0jB8/nks/depUPH78GPPmzcPdu3exefNm7N+/H5999pk2it8g0ZdDossoJtFoSkJqxJ3E9Dp7nqqmb0hOTuaCIAA4Ozvj+PHj+Oyzz/Ddd9+hadOm2L59e70dQl4X0dQWpCbV5XgEUEwCqDJGiEZZWVlBJBJi7Nf7a+2cIpEQVlZWSqevavqG8PBwucdcuXJFneIRQrSkPsQjgGISoGZlrEWLFrh06RIaN24stf3169d4++238fjxY40UjpD6xtHREXfu3KW14GoRxSNC5KN4VH+oVRl7+vSp3GU7ioqKkJSUVO1CEVKfOTo6UjCqRQ0hHtFySKSmUDyqH1SqjB09epT7/59//inVt6G0tBTR0dFwcnLSWOEIIUSRhhSPqM8YIbpNpcqYZBV1Ho+HoKAgqX0GBgZwcnLi1ooihJCaRPGIENJQqFQZE4vFAMpGMly6dEnlTnqEEKIpFI8IIQ2FWn3Gnjx5oulyEFLvSCoDRLHauEcUjwiheKSMunyP1J7aIjo6GtHR0UhLS5O5wB07dlS7YITUVYaGhuDz+Xj58iWsra1haGgIHo+n7WLVKYwxFBcXIz09HXw+H4aGhjV6PopHRFdRPKpabccjdahVGVu+fDlWrFiBTp06wd7evl5+8DR6iaiLz+fD2dkZycnJePnypbaLU6eJRCI4OjqCz6+5xT4oHikmFou5NfxsbW1r9HMg2kHxSHm1EY/UxWOVzbSmgL29Pb755huMGzeuJspUqySjl7KysmBqaqowXXJyMrb9/Qg5/2aAbyiEuLhA7r/GxsaY4tMS9vb2tXgVRBsYY3jz5g1V6BXQ09ODvr5+jVeOKB4pjkfi4gLkF+RDJDLG7A+8KC41YBSPKldb8UhdarWMFRcXo2vXrpouCyH1Co/Hg4GBAQwMDLRdFJ1G8ahyJmaNYWxsrO1ikBpG8ah+U6ut7qOPPsKePXs0XZYGQfJYIDk5uU53FiSkoaB4RAip79RqGSssLMS2bdvw119/4a233pKpia9fv14jhauP8rIyse30C4hEz+ixACG1gOIRIaS+U6sydv36dXh4eAAAbt68KbWvrj6PrU30WICQ2kPxiBBS36lVGYuJidF0OQghRC0Ujwgh9V3dG99JCCE6JjQ0FK6urujcubO2i0II0QK1Wsb8/Pwqbf4/ffq02gUihBBVNIR4RAuFE6Lb1KqMSfpnSJSUlODq1au4efOmzIK9hBBSkygeEULqO7UqYxs2bJC7fdmyZcjNza1WgQghRBUUj6pGM/ETUrdp9Ddy7NixtA4cIaROoHj0/8qm3LmNdUfiuEoZIaTuUHuhcHliY2NhZGSkySwJIUQtFI+k0ZQ7hNRdalXGBg8eLPWeMYbk5GTEx8dj8eLFGikYIYQog+IRIaS+U6syVnG0D5/PR9u2bbFixQr06dNHIwUjhBBlUDwihNR3alXGwsLCNF2Oahk0aBDOnDmDXr164cCBA9ouDiGkFtW1eEQIIaqqVp+xhIQE3LlzBwDQvn17dOzYUSOFUtXMmTPx4YcfYufOnVo5PyFE++pKPCKEEFWpVRlLS0vDyJEjcebMGZibmwMAXr9+DT8/P+zbtw/W1taaLGOVfH19cebMmVo9JyGkbqhr8UgdoaGhCA0NRWlpqbaLQgjRArWmtpgxYwZycnJw69YtZGZmIjMzEzdv3kR2djY+/fRTlfI6e/YsBgwYAAcHB/B4PBw+fFgmTWhoKJycnGBkZIQuXbogLi5OnWITQhogTcYjbQkODsbt27dx6dIlbReFEKIFalXGTpw4gc2bN8PFxYXb5urqitDQUPzxxx8q5ZWXlwd3d3eEhobK3R8ZGYmQkBAsXboUly9fhru7OwICApCWlqZO0QkhDYwm4xEhhGiDWo8pxWIxDAwMZLYbGBhALBarlFe/fv3Qr18/hfvXr1+PyZMnY+LEiQCArVu34vjx49ixYwfmz5+vWsEBFBUVoaioiHufnZ2tch6EkLpDk/GIEEK0Qa2WsZ49e2LmzJl4+fIlty0pKQmfffYZevXqpbHCFRcXIyEhAf7+/tw2Pp8Pf39/xMbGqpXnqlWrYGZmxr2aNWumqeISQrSgtuIRIYTUFLUqYz/88AOys7Ph5OSEli1bomXLlnB2dkZ2djY2bdqkscJlZGSgtLQUtra2UtttbW2RkpLCvff398ewYcPw+++/o2nTppVW1BYsWICsrCzu9fz5c42VlxBS+2orHhFCSE1R6zFls2bNcPnyZfz111+4e/cuAMDFxUWqBas2/fXXX0qnFQgEEAgENVgaQkhtqmvxiBBCVKVSy9jp06fh6uqK7Oxs8Hg89O7dGzNmzMCMGTPQuXNntG/fHufOndNY4aysrKCnpyezsG1qairs7OyqlXdoaChcXV3RuXPnauVDCNGO2o5HhBBSU1SqjG3cuBGTJ0+GqampzD4zMzN8/PHHWL9+vcYKZ2hoCE9PT0RHR3PbxGIxoqOj4e3tXa28aSg5IfVbbccjQgipKSpVxq5du4a+ffsq3N+nTx8kJCSoVIDc3FxcvXoVV69eBQA8efIEV69eRWJiIgAgJCQEP/30E3bu3Ik7d+5g2rRpyMvL40ZXEkJ0U03EI0II0QaV+oylpqbKHULOZaavj/T0dJUKEB8fDz8/P+59SEgIACAoKAjh4eEYMWIE0tPTsWTJEqSkpMDDwwMnTpyQ6dSvKprxmpD6rSbikSbQWrmEEFWp1DLWpEkT3Lx5U+H+69evw97eXqUC+Pr6gjEm8woPD+fSTJ8+Hc+ePUNRUREuXryILl26qHQOeegxJSH1W03EI02YOXMmfvnll1o/LyGk/lKpMvbee+9h8eLFKCwslNlXUFCApUuXon///horHCGEKFJX45Gvry8aNWpU6+clhNRfKlXGFi1ahMzMTLRp0wbffPMNjhw5giNHjmDNmjVo27YtMjMz8cUXX9RUWesVsViM1NRUJCcn0yzghNSAmohHtFYuIUQbVOozZmtri3/++QfTpk3DggULwBgDAPB4PAQEBCA0NLTafblqS033GcvLysS20y8gEj3D7A+8tPK4hJCGrCbikWSt3A8//BCDBw+W2S9ZK3fr1q3o0qULNm7ciICAANy7dw82NjYauS5CiO5RedLX5s2b4/fff8e///6Lhw8fgjGG1q1bw8LCoibKV2OCg4MRHByM7OxsmJmZ1cg5TMwaw9jYuEbyJoRoPh419LVyJS32QFllls9XaxEWQoiGqTUDPwBYWFjQhKmEkDqhNuKRZK3cBQsWcNs0sVbu8uXLNVXEKlGLPSF1E30tIoQQJTSUtXJNzBqjkXnjGj8PIUR5areMEUIIkUVr5RJCVKWzLWO0NiUhRBW0Vi4hpKbobGWMJn0lhKiC1solhNQUekxJCCH/yc3NxcOHD7n3krVyLS0t4ejoiJCQEAQFBaFTp07w8vLCxo0baa1cQki1UWWMEEL+Q2vlEkK0gSpjhBDyH8lauZWZPn06pk+frtHz1sa8h4SQuktn+4xRh1lCCCGE1AU6WxmjDrOEkLqCvhwSott0tjJGCCF1BX05JES3UZ8xQghpYBhjyMvPBysugLGhkbaLUyto3U1Sn1FljBBCtEzToykLCgpQmHwH4pJC8Ay7QqjP00i+dVlqaipSf/u67E3/hbTuJqlX6KsDIYRoWU08phQKDCAyNNBYfvWBrYUJbC1MtF0MQlRGLWNaRk3rhBBCiG7T2b/8dWX0kqRpPfW3r2XWvCOEEEJIw6ezLWN1aZJFalYnhNSkih36GWNyW+TLt9RX3EeqRk86iLp0tjJGCCF1RU0vh1SxQz8PPKSfCgPMjaU6u0ta6m0tTJD6by51hFcRDSIg6qJqOyGEaFltzDNWsUO/tbmx3FZ5WwsT2Dc2pRZ7NdEgAqIOqowRQgghhGgRVcYIIYQQQrSI+owRQoiOYIyhsCC/7P8CVq28NNVZvWI+AKgTPNE5VBmrJ2pqlA6N/iFE+2q6A79EQckblCbdBAz0kd+sAEAjtfPSVGf1ivkAoE7wROfobGWstoKfptTUKB0a/UOI9tXmVDtCIwMY6Gsm9Guqo3rFfKgDPNE1OtsMUhujlzStpkbp0OgfQgghRHt0tjJGCCGEEFIXUGWMEEIIIUSLqDJGCCGEEKJFOtuBnxBCGjrJVBbi4kIYM9WmshCLGdJTUyEWiwEAfD5fKyOuFU19UbFcdRGNgifKosoYIYRoWU2N7pZMZVFSVABDM1OVjk3PykPe35uQXswgMuTBWCTSyohrRVNfpL/OkypXXUSj4ImyqDJGCCFaVpNTWwiNDKCHN2oda2NujLxiBmNDHkyMjTVaLlUomvpC2+VSRk2NVKcR8A0LtW0SQgghhGgRVcYIIYQQQrSIKmOEEEIIIVpEfcZqmGTUi7yRP6mpqUhNTYUNY+DxeNosJqf8KB2g6tFL6ozoqakFhmtqRBGNXCKEEFKTdLYyVltrU+ZlZWLb6RcAgEGlf8Ha3FhmRJCxjUmd6YQqGaVja2GC1H9zqxy9pM6InppaYLimRhTRyCVCCCE1SWcrY7W5MK+JWWMAgLXYWGoETF0dDWNrYQL7xqYy2wDZ0UvqXkNNLTBcU+rqZ0UIIaT+o+cthBBCCCFapLMtY4QQUt8xxpCXnw9xcQF4JWKwkkLwSsTILyhQacZ9Sb9ISR9W+WnKZuQHUK0Z7yv2o01PT9dov9mK5ZT08ayq/27F9ITUJqqMEUKIlqnbh7WgoACFyXeQn5sDA4EQJUUF3L+qzLgv6Rcp6cMKyHbdkMzIn1rNGe/Ln0tkyEN+MdNov9mK5ZT08ax43ur2fSVEk6gyRgghWladPqxCgQHEJQYw/G+mfUM1Z9xXpl+kjbmxRipN5fug5hWrtmamMhSVU9N9XwnRFGqPJYQQQgjRIqqMEUIIIYRoEVXGCCGEEEK0iCpjhBBCCCFaRJUxQgghhBAtosoYIYQQQogWUWWMEEIIIUSLqDJGCCGEEKJFDaIy9ttvv6Ft27Zo3bo1tm/fru3iEEJ0GMUjQoiq6v0M/G/evEFISAhiYmJgZmYGT09PDBo0CI0bN9Z20QghOobiESFEHfW+ZSwuLg7t27dHkyZNYGJign79+uHkyZPaLhYhRAdRPCKEqEPrlbGzZ89iwIABcHBwAI/Hw+HDh2XShIaGwsnJCUZGRujSpQvi4uK4fS9fvkSTJk24902aNEFSUlJtFJ0Q0sBQPCKEaIPWK2N5eXlwd3dHaGio3P2RkZEICQnB0qVLcfnyZbi7uyMgIABpaWm1XFJCSENH8YgQog1ar4z169cPX331FQYNGiR3//r16zF58mRMnDgRrq6u2Lp1K0QiEXbs2AEAcHBwkPrmmZSUBAcHh1opOyGkYaF4RAjRhjrdgb+4uBgJCQlYsGABt43P58Pf3x+xsbEAAC8vL9y8eRNJSUkwMzPDH3/8gcWLFyvMs6ioCEVFRdz77OxsjZSVMYa8/HyIiwvAKxGDlRRK/QvGkFacCwCwFovB50vXg8VihvTUVACAra0tt18sFiM1NRWpqamwYQw8Hq/SckjSi8Vi7n16ejr4fD46dOgAff3qf+Tly6pKmYCya6uqzHw+X+oeKF8u2fOkyrmn6lL0GckrQ8VrkZSlutdYV1W89w3lusqrS/FILBYjLy8P+QUFMGZM5WthTIyMrHwIDLPx6vp18Pl8uJSKlTy39O8/Y0B6uZ/tqqSmpoKXmYOMrHwIrYyrzFfyXlGcqXicvO3y4478OFbxd1iR8r/DVcVpybkU/f6r+/tTVUzS1XgE1L+YVKcrYxkZGSgtLZX5RbK1tcXdu3cBAPr6+li3bh38/PwgFosxb968SkcurVq1CsuXL9d4WQsKClCYfAf5uTkwEAhRUlQg8+8veuYwFQkxpXO6zDWlZ+Uh7+9NSBWJgP4LYW9vD6DsFyb1t6+R/joPxjYmMDE2lnd6Tvn0IkMenmfk4bdMe+jr62PWxDHw8PCo9rVKyppfzFQqEwCg/8Iqy2xc4R4oS955yr9XNb+KFH1GylyLpCzVvca6quK9byjXVV5dikfp6enIe3wRha8yYWhmqvLx+Xl5iMqyQ3FiIcQJv0FfXx8zXLLLfi6rOneF3/+8Yoa8vzchvZhBZMhDfhX/Ps/IQ3RuM5SIhZhsmMmdU1G+VcWZisdV3J5a7newsuMk+Vf8HVZ0HeV/h6uK05Jzpcs5tvw5Aaj0+1NVTNLVeATUv5hUpytjynr//ffx/vvvK5V2wYIFCAkJ4d5nZ2ejWbNmGimHUGAAcYkBDI0MoIc3cv4VwsRYcbCzMTeWG3BsLUzkpFZMkt74v8BhxbOAob6hahdTBRtzY+QVK/+NvKprKF/mqip3qpxH1XtXFUWfkbxzVrwWTV1jXaXpe11f1VY8MhYYIk9goFYZAUBk0gj6+vowNBLCQF8fgPJPCSr+/kveGxvyqvw3v5jBmNcIJaUAkKlUvqqWp/z2yn7XFB1X/ndV0XVUzLeqn//y90gTcb58vpVdo67GI6B+xaQ6XRmzsrKCnp4e19QokZqaCjs7O7XyFAgEEAgEmigeIUSH1GQ8Cg0NRWhoKEpLSzVRVEJIPVOnH6IaGhrC09MT0dHR3DaxWIzo6Gh4e3tXK+/Q0FC4urqic+fO1S0mIUQH1GQ8Cg4Oxu3bt3Hp0qXqFpMQUg9pvWUsNzcXDx8+5N4/efIEV69ehaWlJRwdHRESEoKgoCB06tQJXl5e2LhxI/Ly8jBx4sRqnTc4OBjBwcHIzs6GmZlZdS+DENIAaCseEUJ0m9YrY/Hx8fDz8+PeS/pPBAUFITw8HCNGjEB6ejqWLFmClJQUeHh44MSJE3JHxxBCSHVoKx7RY0pCdJvWK2O+vr5gVQzNnj59OqZPn67R80qC35s3bwBUPaQ8JycHhfl5KCrIB79UDHFxodS/xYX54BUVorioCODxFf5bpMdHbm4uRCIRkF+E3IJisNKyTqGslAfG0wdycmD8X4fKnJwcqXQV98srp1S+hcUoKioEeyNGbm6uzHVK0v/3puzf/CIYGxUiJ79IalvFsioqs6IyVTxHxffKXqOq+UreV5VfVedRpnyK0qp7jfVFxc9C2etq1KhRlVOj1CZtxSNJS31WVhbMzc2rjEe5ubkoKqw63ij7Lyt9g7zCYjCePlgpD9l5hQp/36v9738x6U0ppM5Z1XGKfpc0dZyq+Zf/HVb2976qOP/fG6VjX1XxRFfjEVD/YhKPVRV5GrgXL15obDQlIUQ1WVlZMDVVfWqGhoriESHapa2YpPOVMbFYjJcvX1ZZG5YMOX/+/Dn98VCA7lHV6B5Jq2stY9pG8Uhz6B5Vje6RLG3FJK0/ptQ2Pp+Ppk2bKp3e1NSUfmirQPeoanSPiDwUjzSP7lHV6B5pX52e2oIQQgghpKGjyhghhBBCiBZRZUxJAoEAS5cupdn7K0H3qGp0j4gm0M9R1egeVY3uUd2h8x34CSGEEEK0iVrGCCGEEEK0iCpjhBBCCCFaRJUxQgghhBAtosoYIYQQQogWUWVMCaGhoXBycoKRkRG6dOmCuLg4bRepxpw9exYDBgyAg4MDeDweDh8+LLWfMYYlS5bA3t4eQqEQ/v7+ePDggVSazMxMjBkzBqampjA3N8ekSZOQm5srleb69et49913YWRkhGbNmuGbb76p6UvTiFWrVqFz585o1KgRbGxsMHDgQNy7d08qTWFhIYKDg9G4cWOYmJhgyJAhSE1NlUqTmJiIwMBAiEQi2NjYYO7cudw6qRJnzpzB22+/DYFAgFatWiE8PLymL4/UE7oSkygeVY1iUgPBSKX27dvHDA0N2Y4dO9itW7fY5MmTmbm5OUtNTdV20WrE77//zr744gv266+/MgDs0KFDUvtXr17NzMzM2OHDh9m1a9fY+++/z5ydnVlBQQGXpm/fvszd3Z3973//Y+fOnWOtWrVio0aN4vZnZWUxW1tbNmbMGHbz5k22d+9eJhQK2Y8//lhbl6m2gIAAFhYWxm7evMmuXr3K3nvvPebo6Mhyc3O5NFOnTmXNmjVj0dHRLD4+nr3zzjusa9eu3P43b96wDh06MH9/f3blyhX2+++/MysrK7ZgwQIuzePHj5lIJGIhISHs9u3bbNOmTUxPT4+dOHGiVq+X1D26FJMoHlWNYlLDQJWxKnh5ebHg4GDufWlpKXNwcGCrVq3SYqlqR8XgJxaLmZ2dHVu7di237fXr10wgELC9e/cyxhi7ffs2A8AuXbrEpfnjjz8Yj8djSUlJjDHGNm/ezCwsLFhRURGX5vPPP2dt27at4SvSvLS0NAaA/f3334yxsvthYGDAoqKiuDR37txhAFhsbCxjrOwPDJ/PZykpKVyaLVu2MFNTU+6ezJs3j7Vv317qXCNGjGABAQE1fUmkjtPVmETxSDkUk+onekxZieLiYiQkJMDf35/bxufz4e/vj9jYWC2WTDuePHmClJQUqfthZmaGLl26cPcjNjYW5ubm6NSpE5fG398ffD4fFy9e5NL06NEDhoaGXJqAgADcu3cP//77by1djWZkZWUBACwtLQEACQkJKCkpkbpH7dq1g6Ojo9Q9cnNzg62tLZcmICAA2dnZuHXrFpemfB6SNLr4c0f+H8Wk/0fxSD6KSfUTVcYqkZGRgdLSUqkfUACwtbVFSkqKlkqlPZJrrux+pKSkwMbGRmq/vr4+LC0tpdLIy6P8OeoDsViMWbNmoVu3bujQoQOAsvIbGhrC3NxcKm3Fe1TV9StKk52djYKCgpq4HFIPUEz6fxSPZFFMqr/0tV0AQuqr4OBg3Lx5E+fPn9d2UQghhGJSPUYtY5WwsrKCnp6ezKiT1NRU2NnZaalU2iO55sruh52dHdLS0qT2v3nzBpmZmVJp5OVR/hx13fTp0/Hbb78hJiYGTZs25bbb2dmhuLgYr1+/lkpf8R5Vdf2K0piamkIoFGr6ckg9QTHp/1E8kkYxqX6jylglDA0N4enpiejoaG6bWCxGdHQ0vL29tVgy7XB2doadnZ3U/cjOzsbFixe5++Ht7Y3Xr18jISGBS3P69GmIxWJ06dKFS3P27FmUlJRwaU6dOoW2bdvCwsKilq5GPYwxTJ8+HYcOHcLp06fh7Owstd/T0xMGBgZS9+jevXtITEyUukc3btyQ+iNx6tQpmJqawtXVlUtTPg9JGl38uSP/j2LS/6N4VIZiUgOh7REEdd2+ffuYQCBg4eHh7Pbt22zKlCnM3NxcatRJQ5KTk8OuXLnCrly5wgCw9evXsytXrrBnz54xxsqGkpubm7MjR46w69evsw8++EDuUPKOHTuyixcvsvPnz7PWrVtLDSV//fo1s7W1ZePGjWM3b95k+/btYyKRqF4MJZ82bRozMzNjZ86cYcnJydwrPz+fSzN16lTm6OjITp8+zeLj45m3tzfz9vbm9kuGkffp04ddvXqVnThxgllbW8sdRj537lx2584dFhoaSsPICWNMt2ISxaOqUUxqGKgypoRNmzYxR0dHZmhoyLy8vNj//vc/bRepxsTExDAAMq+goCDGWNlw8sWLFzNbW1smEAhYr1692L1796TyePXqFRs1ahQzMTFhpqambOLEiSwnJ0cqzbVr11j37t2ZQCBgTZo0YatXr66tS6wWefcGAAsLC+PSFBQUsE8++YRZWFgwkUjEBg0axJKTk6Xyefr0KevXrx8TCoXMysqKzZ49m5WUlEiliYmJYR4eHszQ0JC1aNFC6hxEt+lKTKJ4VDWKSQ0DjzHGaq8djhBCCCGElEd9xgghhBBCtIgqY4QQQgghWkSVMUIIIYQQLaLKGCGEEEKIFlFljBBCCCFEi6gyRgghhBCiRVQZI4QQQgjRIqqMEUIIIYRoEVXGCCGEEEK0iCpjhBBCCCFaRJUxQgghhBAtosoYIYQQQogW/R+XntpZoHVcjQAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 600x300 with 2 Axes>"
       ]
@@ -447,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 28,
    "id": "ccaea043-f350-4c32-8dee-722e7503a415",
    "metadata": {},
    "outputs": [],
@@ -515,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 29,
    "id": "423ac5e0",
    "metadata": {},
    "outputs": [],
@@ -532,16 +544,20 @@
     "    ax = sns.histplot( data=confusion_df, x='x_umi', hue='Outcome', palette=custom_palette, hue_order=choices, bins=100, multiple=\"stack\", \n",
     "                edgecolor=\"white\", \n",
     "                linewidth=0.5 )\n",
-    "    sns.move_legend(ax, 'upper left', bbox_to_anchor=(0.5, 1), frameon=False, ncols=1)\n",
+    "    sns.move_legend(ax, 'upper left', bbox_to_anchor=(0.5, 1), frameon=False, ncols=1, title='', fontsize='small')\n",
     "    plt.yscale('log')\n",
+    "    plt.xlabel('UMI count')\n",
     "    sns.despine()\n",
     "    plt.title(title)\n",
+    "\n",
+    "    os.makedirs('../figures', exist_ok=True)\n",
+    "    plt.savefig(f'../figures/cmv_{title}.pdf')\n",
     "    plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 30,
    "id": "d7c10d8e-a0d6-4c7a-a0e3-930b82ed9f0e",
    "metadata": {
     "scrolled": true
@@ -633,13 +649,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 31,
    "id": "20892b4c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -649,7 +665,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -659,7 +675,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -669,7 +685,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -679,7 +695,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -689,7 +705,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -699,7 +715,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -709,7 +725,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -721,37 +737,29 @@
    "source": [
     "for setting in pred_dfs['setting'].unique():\n",
     "    for model_config in ['neg,clone_id',]:# 'no-neg,clone_id', 'neg,no-clone', 'no-neg,no-clone']:\n",
-    "        plot_confusion_hist(pred_dfs[pred_dfs['setting'] == setting], pred_dfs[pred_dfs['setting'] == setting][f'assignment_{model_config}'], title=setting + ' ' + model_config)\n"
+    "        title = setting.replace('DexA,05-95-D2,', 'DexA 5% spike-in').replace('DexA,50-50-D2,', 'DexA 50% spike-in').replace('DexC,05-95-D1,', 'DexC 5% spike-in').replace('DexC,50-50-D1,', 'DexC 50% spike-in').replace('True', ' (has clonotype)').replace('False', ' (all cells)')\n",
+    "        plot_confusion_hist(pred_dfs[pred_dfs['setting'] == setting], pred_dfs[pred_dfs['setting'] == setting][f'assignment_{model_config}'], title=title)\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "c7fc2dca-b49f-43da-a7ac-a8853e2c73f8",
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 32,
+   "id": "f77605d3",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "results_merged = pd.concat([results_df, results_beamt])"
+    "results_merged = pd.concat([results_df, results_beamt])\n",
+    "results_merged['model_config'] = results_merged['model_config'].replace({'neg,clone_id': 'DextraDemixer', 'no-neg,clone_id': 'Ours+clone', 'neg,no-clone': 'Ours+control', 'no-neg,no-clone': 'Ours', 'BEAMT': 'BEAMT'})\n",
+    "results_merged['title'] = results_merged['setting'].apply(lambda x: x.replace('DexA,05-95-D2,', 'DexA 95% spike-in').replace('DexA,50-50-D2,', 'DexA 50% spike-in').replace('DexC,05-95-D1,', 'DexC 5% spike-in').replace('DexC,50-50-D1,', 'DexC 50% spike-in').replace('True', ' (has clonotype)').replace('False', ' (all cells)'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 33,
    "id": "d2a0e428",
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "image/png": 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hUn0Pun37Nps3b2b79u2a171x40bN66upJAguQeFaiUlJSbRt27bI+cKFzR9cU1HfXld9f2IrK39DlFEROi8qn1vW5s9ClE7uNSGELu5f9s3U1BRnZ2fatWunOVY46Tw9PZ24uDi6du2qGRcL0K1bN7Kysvjrr7+4efMmeXl5dOnSRXPeyclJqyPpzJkzFBQU0LJlS6165Obmlmsd5pMnTxbpyCoUFxdHnTp1tOrj7OxMq1atiIuL0xyztrbWBMBwbzJkenq6znVITEwkPz+fzp07a445ODjU+A40CYJLUN41FfXtddX3J7ay8jdEGRVhWQU77dV2tX3GflXu6iiEqPkKhwQUUqlUWscKA161Wrfx72XJysrC1NSU48ePF5kzZGur/2RTKys9VvgpQXFt8LCv/KALCYJLUN41FSu717Ws/A1RRkXkNPNDsXIsM51JxmUs/jpe+RV6CNX2Gfv6bmBQeFwIIcri5eXFzp07URRFExzHxsZiZ2fHI488gpOTE2ZmZhw+fJgmTZoA9yamnT9/XrNaVMeOHSkoKCA9PZ2nnnqqwnXy9vYmOjqasWPHFlvff/75h8OHD2uGQ1y/fp34+HjatGlT4bILeXp6YmZmxtGjRzWv+9atW5w/f57u3bsbrJyqJkFwKcqzpmJl97pW98kZipUjapt6ZaZT3c2o/Mo8pGTGftWsm63KVGl20CszbbZuE7selJmje6+4PmmFEOUzZcoUVq1axbRp05g6dSrx8fEEBwcTEBCAiYkJtra2jB8/ntmzZ+Ps7EyDBg2YP3++1q/FLVu2ZMSIEYwePZoVK1bQsWNHrl69SnR0NN7e3vTr10+vOgUHB9OzZ0+aNWvGsGHD+Oeff9izZw+BgYG0aNGCgQMHMnHiRN577z3s7OyYO3cujRo1YuDAgQZrFzs7O8aMGcPs2bNxcnKiQYMGmoUD7h86UtNIEFwGWVNRPzJOs/LJjP3KZV1HQUHB5Ej5l+Ury70R+yoOJ+r3ZcDC3EIzgUUIYXiNGjViz549zJ49m/bt2+Pk5MT48eM1E+sAli1bRlZWFgMGDMDOzo7XXnuNW7duaeWzceNGwsLCeO2117hy5Qr16tXjX//6F/3799e7Tn5+fnz66acsXryYN954A3t7e63e140bNzJjxgz69+9PXl4e3bt3Z8+ePUWGQFTUypUrefnll+nfv79mibTLly/X6NWlJAjWQW1aU7G8HqZxmtI7V7s5WiioUBW7HbmhhlzYAaCUmE95tkIXQhQVExNT5NjFixeLHLt/fKyvry9HjhwpMU9bW1u2bt3K1q1bNcdmz56tlcbMzIxFixZprehwP39/f/z9/Uuv/H2GDBnCkCFDij1Xt25dtmzZUuK1xZU1aNAgrdf84HO4t77w/ezs7IiKitI8z87OZtGiRUyaNEnHV1H9SBAsDOJhGKcpvXPifqUNqzDUkIuS8qnJW6ELIR5OJ06c4I8//qBz587cunVLs8SaIYddVDUJgoXBFI7TvH8Zt+JUZOKeKlOFUqBoVjEokY3+4zTv751zdXUlNTUVgJSUFCIjIxk/fjxubm7AvbUUC7eUlN45IYQQ5dW2bdsS/2a+9957jBgxooprVLLly5cTHx+Pubk5jz32GD/99BP16pU9D6i6kiBYGJwuy8TpqyrGaRYqDNIf7KmOjIzU/Fs2GKkedFmXWwghqrM9e/aQn59f7Lnq1MHSsWNHjh9/uFZ1kiBYGJwuy8SV1lNcnPvHad7fS1uSwjRhYWE6r3l79YE6VtUGI6L89PnC9Xe2bpNZdU1XnPLca0KI2k3+nhiPBMHC4PRZxk3fwKRwrOSDm5QUx8XFBQtzcz7Ly9OpDAALc3McHR2r/VJ04h5dvqzcunULC3Nz1v+ue76F94GuHB0dy32vCSGEMA4JgoVRFAYNlRmYuLi4sC0qSufJeoX1qk4/P4nS6fJlxdLSstLvA7nXhBCi5pEgWBhFVQUNVbGpgqj+quI+kHtNCCFqFgmChdFI0FB1dN39rLw7n4HuaybL2spCCCGqAwmChahh9Jm4dTvPpFyraug6wQvgDqDv+sqytnLNoM+9dvXuvbT6fuHS5167+b//yhcuIYQhSBAshJHpvNV0fg6gsP53G73ytzAzY3FYGE5OTppjJQ07uX79OkELF/JZCcv1lMTcrI7OZYCMh632TM1RleNeQ4VeX7hUwGf6lYBKvnDVSGlpaUWGv1Um+YwRupAgWAgjs9Rrq2n9tvOF0v8YFDfsJGr7dr3GapenDFG9KebWKOW41/Lz8zEzM6u09OW5RoIh40tLS2PEiJHk5eVWWZnm5hZERW3T+f+9v78/mzdv1jx3cnLi8ccf56233tKsRqRSFT9c7KOPPmLYsGFax1q3bk1SUhKXLl3C1dVV65yfnx8HDx5k6dKlzJ07V+tcv3792LNnD8HBwfj7+9O0adNS671x40a9tl8W2iQIFsLIcpr5oVg5lplOdTcDy4SYSt/OV8Zqi0KGvNcqO315rxGVLyMjg7y8XJ0/6ypKdTcDEmLIyMjQ6wtQnz592LhxIwCpqaksWLCA/v37k5ycrEmzceNG+vTpo3Xdg780/Pzzz9y9e5f//Oc/bN68mcDAwCJlNW7cmE2bNmkFwVeuXCE6OlqzM2njxo1JSUnRnF++fDl79+7l+++/1xxzcHDQ+fWJoiQIFsLIFCtH1DZlbzv54I/MuuyWZmlpWWnpDVGGEKL20PWzrqLKu6+ohYWFptfW1dWVuXPn8tRTT3H16lXq168P3At4H+zZfVBkZCTDhw/H19eXGTNmFBsE9+/fn08++YTY2Fi6desGwObNm3n22Wc1QbepqalWWba2ttSpU6fM8oXuJAgWoobSZbe0+3vFDJ3eEGUIIUR1lJWVxbZt22jevDnOzs46X3f79m0+/fRTDh8+TOvWrbl16xY//fQTTz31lFY6c3NzRowYwcaNGzVB8KZNm3jrrbcICQkx5EsRpZAgWIgaSt+tnQ2d3hBlCCFEdfHNN99ga2sLQHZ2Nm5ubnzzzTeYmPx/3/JLL72Eqan2qinnzp2jSZMmAHz88ce0aNGCtm3bAjBs2DAiIyOLBMEA48aN46mnnmL16tUcP36cW7du0b9/fwmCq5AEwULUUPpu7VzZ6ct7jRBCVAdPP/0069evB+DmzZu8++67PPfccxw5ckTzBf7tt9+mV69eWtc1bNhQ8+8PP/yQkSNHap6PHDkSX19f1qxZg52dndZ17du3p0WLFnz22WccOHCAUaNGUaeOhGVVSVpbCCGEELWejY0NzZs31zz/4IMPcHBwICIiQjOsy9XVVSvN/c6dO8evv/7KkSNHtMYBFxQU8PHHHxcZKgb3eoPXrVvHuXPnOHLkiIFfkShLecePCyGEEEI8tFQqFSYmJty9e1en9JGRkXTv3p1Tp05x8uRJzSMgIIDIyMhirxk+fDhnzpzh0UcfpU2bNoasvtCB9AQLIYQQotLpujGQIcopj9zcXFJTU4F7wyHWrl1LVlYWAwYM0KTJyMjQpClkZ2eHubk5W7duJTQ0lEcffVTr/IQJE1i5ciW///67Zqxwobp165KSklJk7WtRNSQIFkIIIUSlcXR0xNzcAvTaGKhizMuxU+DevXs1a/Ta2dnRunVrPv30U/z8/DRpxo4dW+S6pUuX0qJFC65fv87gwYOLnPfy8sLLy4vIyEhWrlxZ5LzsaGg8EgQLIWqNiqyVXJ5rKiO9EDWNi4sLUVHbqvW2yZs2bWLTpk2lplEUpdTzBQUFJZ47d+6c5t8xMTGl5nPy5Mlij4eEhMjKEQYmQbAQotYwxFrJ5bnGkOmFqIlK24lSCGORIFgIUWtUx7WSZW1lIYQwDgmChRC1RnVcK1nWVhZCCOMw+hJp69atw8PDA0tLS7p06VLqOnn5+fmEhobSrFkzLC0tad++PXv37q3C2gp9FBQUcOLECb7//ntOnDhR6ngpIYQQQoiqZNSe4B07dhAQEMCGDRvo0qULq1atonfv3sTHx9OgQYMi6RcsWMC2bduIiIigdevW7Nu3j8GDB/PLL7/QsWNHI7wCUZKDBw+ybt06raVkXF1deeWVV/D19TVizYQQQgghjNwTvHLlSiZOnMjYsWNp06YNGzZswNramg8//LDY9Fu3bmXevHn07dsXT09PJk+eTN++fVmxYkUV11yU5uDBgwQFBeHp6cn69evZu3cv69evx9PTk6CgIA4ePGjsKgohhBCiljNaEJyXl8fx48e19uA2MTGhV69eHDp0qNhrcnNziywVZGVlxc8//1xiObm5uWRmZmo9ROUpKChg3bp1dO3alfDwcNq2bYu1tTVt27YlPDycrl278u6778rQCCGEEEIYldGC4GvXrlFQUFBkyRQXF5ciu7EU6t27NytXruTChQuo1Wr279/P559/TkpKSonlLF26FAcHB82jcePGBn0dQtvp06dJTU1l1KhRmJho314mJiaMHDmSlJQUTp8+baQaClF7qe5mYJJ9rcxHeXfcEkKImqRGrQ6xevVqJk6cSOvWrVGpVDRr1oyxY8eWOHwC4PXXXycgIEDzPDMzUwLhSnT9+nUAmjZtWux5T09PrXQVVVBQwOnTp7l+/TrOzs54e3tjampqkLyrIn/QfStRCUxERVlW4Y5dQghR3RktCK5Xrx6mpqakpaVpHU9LS8PV1bXYa+rXr8+uXbvIycnh+vXrNGzYkLlz52oCq+JYWFhgYWFh0LqLkjk7OwOQlJRUZI90gMTERK10FVHZk+8qO//ybCVanq1AhSiU08wPxcqxzHSquxkSMAuDSktLq9Y7xlVH/v7+ZGRksGvXLmNXRUOlUvHFF18waNCgYs9fvHiRpk2bcuLECTp06FCldSsPowXB5ubmPPbYY0RHR2saU61WEx0dzdSpU0u91tLSkkaNGpGfn8/OnTsZOnRoFdRY6MLb2xtXV1e2bt1KeHi41pAItVrNtm3bcHNzw9vbu0LlFE6+69q1K8HBwTRt2pSkpCS2bt1KUFAQoaGhFQpUKzt/KHkr0UuXLhEWFsaCBQuKbJTwMHywC+NRrBxR29QrM53R184UD5W0tDRGjhhBbl5elZVpYW7Otqgo+bw0sJSUFOrWrWvsahiMUYdDBAQEMGbMGHx8fOjcuTOrVq0iOzubsWPHAjB69GgaNWrE0qVLATh8+DBXrlyhQ4cOXLlyhZCQENRqNXPmzDHmyxD3MTU15ZVXXiEoKIh58+YxcuRIPD09SUxMZNu2bRw6dIjQ0NAKDSl4cPJdYaBdOPlu3rx5vPvuuzz55JPlKqey879faVuJuru7yyYKQogaLyMjg9y8PCa3zaahTeVPiv4725T1v98rV4Jgwyrpl/qayqhf+F988UWWL19OUFAQHTp04OTJk+zdu1dz0yYnJ2tNesvJyWHBggW0adOGwYMH06hRI37++Wf5ebia8fX1JTQ0lMTERKZMmUKfPn2YMmUKSUlJBulBrezJdzK5TwghDK+hTQFN7Sv/UZ5A28/Pj+nTpzNnzhycnJxwdXUlJCREcz4jI4MJEyZQv3597O3t6dGjB6dOndLKIywsjAYNGmBnZ8eECROYO3dumUMCfv/9d/r374+9vT12dnY89dRTJCQkFJs2NzeX6dOn06BBAywtLXnyySc5evSo5nxMTAwqlYro6Gh8fHywtrbmiSeeID4+XiufL7/8kk6dOmFpaYmnpyeLFi3in3/+0amdVCqV1vCMI0eO0LFjRywtLfHx8eHEiRM65VNdGH1i3NSpU0sc/hATE6P13NfXl3PnzlVBrURF+fr68uSTT1bKpLLKnnxX1ZP7hBBCGN/mzZsJCAjg8OHDHDp0CH9/f7p168YzzzzDCy+8gJWVFd9++y0ODg6899579OzZk/Pnz+Pk5ERUVBRLlizh3XffpVu3bnz88cesWLGixL8jAFeuXKF79+74+fnxww8/YG9vT2xsbIkB6Zw5c9i5cyebN2/G3d2dt956i969e/Pnn3/i5OSkSTd//nxWrFhB/fr1efnllxk3bhyxsbEA/PTTT4wePZp33nlHE3BPmjQJgODgYL3aKysri/79+/PMM8+wbds2kpKSmDFjhl55GFu5g+A///yThIQEunfvjpWVFYqioFKpDFk3UcOZmprqtJNfTk4Oly5dAijyX7g3LOD+9aHLM/lOnzKqcnKfEEKI6sHb21sTCLZo0YK1a9cSHR2NlZUVR44cIT09XTPRfvny5ezatYvPPvuMSZMmsWbNGsaPH68ZzhkUFMR3331HVlZWieWtW7cOBwcHPv74Y8zMzABo2bJlsWmzs7NZv349mzZt4rnnngMgIiKC/fv3ExkZyezZszVplyxZovnFde7cufTr14+cnBwsLS1ZtGgRc+fOZcyYMcC9Tp3FixczZ84cvYPg7du3o1ariYyMxNLSkrZt2/LXX38xefJkvfIxJr2D4OvXr/Piiy/yww8/oFKpuHDhAp6enowfP566devK7m1Cb5cuXWLixIlax8LCwjT/joiI0BobW57Jd/qUUVWT+4QQQlQfD36mu7m5kZ6ezqlTp8jKyirS8XH37l3N0IX4+HimTJmidb5z58788MMPJZZ38uRJnnrqKU0AXJqEhATy8/Pp1q2b5piZmRmdO3cmLi6uxNfh5uYGQHp6Ok2aNOHUqVPExsayZMkSTZqCggJycnK4c+cO1tbWZdalUFxcHN7e3lqdVF27dtX5+upA7yD41VdfpU6dOiQnJ+Pl5aU5/uKLLxIQECBBsNCbu7s7ERERpZ6/X3km3+lTRlVM7hNCCFG9PBiMqlQq1Go1WVlZuLm5FRmiCVRoTpKVlVW5ry3N/a+j8Bd6tVoN3BvCsGjRIoYMGVLkugd35K0N9A6Cv/vuO/bt28cjjzyidbxFixZaPy8LoStLS0u9V0EonHy3bt06rW/fbm5uxU6+07cMffMXQgjxcOrUqROpqanUqVMHDw+PYtO0atWKo0ePMnr0aM2x+yetFcfb25vNmzeTn59fZm9ws2bNMDc3JzY2VtNpk5+fz9GjR5k5c6ZeryU+Pp7mzZvrfE1JvLy82Lp1q2aoBcCvv/5a4Xyrkt5BcHZ2drHd5Tdu3JBNKUSVqszJd1WRvxA1hb7j9g2d3hBlCOP7O7tqPjsNXU6vXr3o2rUrgwYN4q233qJly5b8/fff7N69m8GDB+Pj48O0adOYOHEiPj4+PPHEE+zYsYPTp0+XupnX1KlTWbNmDcOGDeP111/HwcGBX3/9lc6dOxfptLGxsWHy5MnMnj0bJycnmjRpwltvvcWdO3cYP368zq8lKCiI/v3706RJE/7zn/9gYmLCqVOnOHv2rNYQQV0MHz6c+fPnM3HiRF5//XUuXrzI8uXL9crD2PQOgp966im2bNnC4sWLgf//ueCtt97i6aefNngFhSiNrpPvqmv+QtQE+o7bN3R6Q5QhjMfR0RELc3PW/151ZVqYmxts+VSVSsWePXuYP38+Y8eO5erVq7i6utK9e3fNkq4jRowgMTGRWbNmkZOTw9ChQ/H39+fIkSOafGJiYnj66adJSkrCw8MDZ2dnfvjhB2bPno2vry+mpqZ06NBBa9zv/d544w3UajWjRo3i9u3b+Pj4sG/fPr02r+jduzfffPMNoaGhvPnmm5iZmdG6dWsmTJigd7vY2try9ddf8/LLL9OxY0fatGnDm2++yfPPP693XsaiUhRF0eeCs2fP0rNnTzp16sQPP/zAv//9b37//Xdu3LhBbGwszZo1q6y6GkRmZiYODg7cunULe3t7Y1dHiCLi4+OZOHGi/BEXBlN4T919dJBuO8ZlX8Pq7C7NPXh/r2txSuulNUR6Q5QhjKs2bpv8zDPPaCZZA2zcuJHw8HDOnTun02Q4Ufn07gl+9NFHOX/+PGvXrsXOzo6srCyGDBnCK6+8opmFKIQQ4uGh75j6yk5f3muE8ZS2O+bD4M6dO2zYsIHevXtjamrKRx99xPfff8/+/fs1afbs2UN4eLgEwNVIudYJdnBwYP78+YauixBCCCFEjVM4ZGLJkiXk5OTQqlUrdu7cSa9evTRpPv30UyPWsGxRUVH897//Lfacu7s7v/9eheNZqojeQfCPP/5Y6vnu3buXuzJCCCGEEDWNlZUV33//vbGrUSH//ve/6dKlS7HnHtbea72DYD8/vyLH7t8prqBA/z27hRBCCCGE8djZ2WFnZ2fsalQpk7KTaLt586bWIz09nb179/L444/z3XffVUYdhRBCCCGEMCi9e4IdHByKHHvmmWcwNzcnICCA48ePG6RiQgghhBBCVBa9e4JL4uLiQnx8vKGyE0IIIYQQotLo3RN8+vRpreeKopCSksIbb7xBhw4dDFUvIYQQQgghKo3eQXCHDh1QqVQ8uMfGv/71Lz788EODVUwIIYQQQojKoncQnJSUpPXcxMSE+vXry848QgghhChWbdwxrroq3L755s2bBttauqbSOwh2d3evjHoIIYQQ4iGUlpbGiJEjyMvNq7IyzS3MidoWJYGwKJVOQfA777yjc4bTp08vd2WEEEII8XDJyMggLzcPdWc1ir1S9gUVpMpUkXckj4yMDAmCRal0Wh3i7bff1umxatWqSq6uEEIIIWoixV6BulT6ozyBtp+fH9OnT2fOnDk4OTnh6upKSEiI5nxGRgYTJkygfv362Nvb06NHD06dOqWVR1hYGA0aNMDOzo4JEyYwd+7cUhcMiImJQaVSER0djY+PD9bW1jzxxBNFVtpav349zZo1w9zcnFatWrF169YyX09ubi6BgYE0btwYCwsLmjdvTmRkZInpd+7cSdu2bbGwsMDDw4MVK1Zonffw8CA8PJxx48ZhZ2dHkyZNeP/997XSXL58maFDh+Lo6IiTkxMDBw7k4sWLZdbVmHQKgpOSknR6JCYmVnZ9hRBClJPqbgYm2dfKfKjuZhi7qkJUuc2bN2NjY8Phw4d56623CA0NZf/+/QC88MILpKen8+2333L8+HE6depEz549uXHjBgBRUVEsWbKEN998k+PHj9OkSRPWr1+vU7nz589nxYoVHDt2jDp16jBu3DjNuS+++IIZM2bw2muvcfbsWf773/8yduxYDhw4UGqeo0eP5qOPPuKdd94hLi6O9957D1tb22LTHj9+nKFDhzJs2DDOnDlDSEgICxcuZNOmTVrpVqxYgY+PDydOnGDKlClMnjxZE7Dn5+fTu3dv7Ozs+Omnn4iNjcXW1pY+ffqQl1d1w2D0pfeYYCGEEDWLo6Mj5uYWkBCj8zXm5ha1ftKMqF28vb0JDg4GoEWLFqxdu5bo6GisrKw4cuQI6enpWFhYALB8+XJ27drFZ599xqRJk1izZg3jx49n7NixAAQFBfHdd9+RlZVVZrlLlizB19cXgLlz59KvXz9ycnKwtLRk+fLl+Pv7M2XKFAACAgL49ddfWb58OU8//XSx+Z0/f55PPvmE/fv306tXLwA8PT1LLH/lypX07NmThQsXAtCyZUvOnTvHsmXL8Pf316Tr27evph6BgYG8/fbbHDhwgFatWrFjxw7UajUffPABKpUKgI0bN+Lo6EhMTAzPPvtsme1gDOUKgv/66y+++uorkpOTi0T4K1euNEjFhBBCGIaLiwtRUduKzM6/dOkSYWFhLFiwoMikZ5ldL2obb29vredubm6kp6dz6tQpsrKycHZ21jp/9+5dEhISAIiPj9cEiIU6d+7MDz/8oFe5bm5uAKSnp9OkSRPi4uKYNGmSVvpu3bqxevVq4F4P9H//+1/NuW+//ZaUlBRMTU01gXVZ4uLiGDhwYJEyVq1aRUFBAaampkXqqVKpcHV1JT09HYBTp07x559/Ymdnp5VPTk6Opo2qI72D4OjoaP7973/j6enJH3/8waOPPsrFixdRFIVOnTpVRh2FEEJUkIuLS4lBrbu7O61atariGglRvZiZmWk9V6lUqNVqsrKycHNzIyYmpsg1hvi15P5yC3tR1Wq1Ttf++9//pkuXLprnjRo14vvvv69wnYpTUvsAZGVl8dhjjxEVFVXkuvr161dKfQxB722TX3/9dWbNmsWZM2ewtLRk586dXL58GV9fX1544YXKqKMQQgghhFF06tSJ1NRU6tSpQ/PmzbUe9erVA6BVq1YcPXpU67oHn5eHl5cXsbGxWsdiY2Np06YNAHZ2dlr1sbKyol27dqjVag4ePFihMlq2bKnpBS5Lp06duHDhAg0aNCjSRg4ODjrlYQx6B8FxcXGMHj0agDp16nD37l1sbW0JDQ3lzTffNHgFhRBCCFHzqTJVcJNKf6gyVQatd69evejatSuDBg3iu+++4+LFi/zyyy/Mnz+fY8eOATBt2jQiIyPZvHkzFy5cICwsjNOnT2t6dstr9uzZbNq0ifXr13PhwgVWrlzJ559/zqxZs0q8xsPDgzFjxjBu3Dh27dpFUlISMTExfPLJJ8Wmf+2114iOjmbx4sWcP3+ezZs3s3bt2lLLeNCIESOoV68eAwcO5KefftKUOX36dP766y+9X3dV0Xs4hI2NjWYcsJubGwkJCbRt2xaAa9euGbZ2QgghhKjRHB0dMbcwJ+9I1W6WYaiJnSqVij179jB//nzGjh3L1atXcXV1pXv37pohRiNGjCAxMZFZs2aRk5PD0KFD8ff358iRI5p8CndqS0pKwsPDQ6eyBw0axOrVq1m+fDkzZsygadOmbNy4ET8/v1KvW79+PfPmzWPKlClcv36dJk2aMG/evGLTdurUiU8++YSgoCAWL16Mm5sboaGhWpPiymJtbc2PP/5IYGAgQ4YM4fbt2zRq1IiePXtib2+vcz5VTaUoil4L6g0aNIh+/foxceJEZs2axZdffom/vz+ff/45devWrbSxKIaSmZmJg4MDt27dqtb/Y0TtFR8fz8SJE4mIiJBxmqJSyb0mqkpt3Db5mWeewdXVVbOu78aNGwkPD+fcuXNFxtcK49C7J3jlypWaJT8WLVpEVlYWO3bsoEWLFrIyhBBCCCGKKG1i5sPgzp07bNiwgd69e2NqaspHH33E999/r1lnGGDPnj2Eh4dLAFyN6B0Eh4eHM3LkSODe0IgNGzYYvFJCCCGEEDVF4ZCJJUuWkJOTQ6tWrdi5c6dmnV6ATz/91Ig1FMXROwi+evUqffr0oX79+gwbNoyRI0fSvn37yqibEEIIIUS1Z2VlVe2Hg4qi9F4d4ssvvyQlJYWFCxdy9OhROnXqRNu2bQkPD6/2e0QLIYQQQggB5QiCAerWrcukSZOIiYnh0qVL+Pv7s3XrVpo3b27o+gkhhBBCCGFw5QqCC+Xn53Ps2DEOHz7MxYsXH+pB70IIIYQQ4uGh95hggAMHDrB9+3Z27tyJWq1myJAhfPPNN/To0cPQ9ROiVsjJyeHSpUsARf4L97a1tbS0NErdhBBCiIeR3kFwo0aNuHHjBn369OH9999nwIABWFhYVEbdhKg1Ll26xMSJE7WOhYWFaf4t67gKIYQQhqV3EBwSEsILL7xgsJ1Y1q1bx7Jly0hNTaV9+/asWbOGzp07l5h+1apVrF+/nuTkZOrVq8d//vMfli5dKr1kokZzd3cnIiKi1PNCCCGEMBy9g+AHe6sqYseOHQQEBLBhwwa6dOnCqlWr6N27N/Hx8TRo0KBI+u3btzN37lw+/PBDnnjiCc6fP4+/vz8qlUo26hA1mqWlpfT0CiEeWrVxx7jqqnD75ps3bxqsQ7Oi/P39ycjIYNeuXSWm8fPzo0OHDqxatcpg5ZZrTLChrFy5kokTJzJ27FgANmzYwO7du/nwww+ZO3dukfS//PIL3bp1Y/jw4QB4eHjw0ksvcfjw4SqttxBCCCF0k5aWxsgRI8jNy6uyMi3MzdkWFSWBcA2xevVqFEWp8nKNFgTn5eVx/PhxXn/9dc0xExMTevXqxaFDh4q95oknnmDbtm0cOXKEzp07k5iYyJ49exg1alSJ5eTm5pKbm6t5npmZabgXIYQQQohSZWRkkJuXx3+A+lVQ3lXgs7w8MjIyJAiuIRwcHIxSboWWSKuIa9euUVBQUOQGdXFxITU1tdhrhg8fTmhoKE8++SRmZmY0a9YMPz8/5s2bV2I5S5cuxcHBQfNo3LixQV+HEEIIIcpWH2iIqtIf5Qm0/fz8mD59OnPmzMHJyQlXV1dCQkI055OTkxk4cCC2trbY29szdOhQ0tLSSs1z06ZNODo6sm/fPry8vLC1taVPnz6kpKRopfvggw/w8vLC0tKS1q1b8+6772qd/+WXX+jQoQOWlpb4+Piwa9cuVCoVJ0+eLLHs3NxcAgMDady4MRYWFjRv3pzIyMgS0+/cuZO2bdtiYWGBh4cHK1as0Drv4eFBeHg448aNw87OjiZNmvD+++9rpbl8+TJDhw7F0dERJycnBg4cqPMmav7+/gwaNEjzPDs7m9GjR2Nra4ubm1uR+hiK0YLg8oiJiSE8PJx3332X3377jc8//5zdu3ezePHiEq95/fXXuXXrluZx+fLlKqyxEEIIIWqCzZs3Y2Njw+HDh3nrrbcIDQ1l//79qNVqBg4cyI0bNzh48CD79+8nMTGRF198scw879y5w/Lly9m6dSs//vgjycnJzJo1S3M+KiqKoKAglixZQlxcHOHh4SxcuJDNmzcD9369HjBgAO3ateO3335j8eLFBAYGllnu6NGj+eijj3jnnXeIi4vjvffew9bWtti0x48fZ+jQoQwbNowzZ84QEhLCwoUL2bRpk1a6FStW4OPjw4kTJ5gyZQqTJ08mPj4euLdvRO/evbGzs+Onn34iNjZWE/TnlWMYzOzZszl48CBffvkl3333HTExMfz2229651MWow2HqFevHqampkW+SaWlpeHq6lrsNQsXLmTUqFFMmDABgHbt2pGdnc2kSZOYP38+JiZFY3oLCwtZwk0IIYQQpfL29iY4OBiAFi1asHbtWqKjowE4c+YMSUlJml+Tt2zZQtu2bTl69CiPP/54iXnm5+ezYcMGmjVrBsDUqVMJDQ3VnA8ODmbFihUMGTIEgKZNm3Lu3Dnee+89xowZw/bt21GpVERERGBpaUmbNm24cuVKqYsUnD9/nk8++YT9+/fTq1cvADw9PUtMv3LlSnr27MnChQsBaNmyJefOnWPZsmX4+/tr0vXt25cpU6YAEBgYyNtvv82BAwdo1aoVO3bsQK1W88EHH6BSqQDYuHEjjo6OxMTE8Oyzz5ZY/oOysrKIjIxk27Zt9OzZE7j3BeWRRx7ROQ9dGa0n2NzcnMcee0xzgwGo1Wqio6Pp2rVrsdfcuXOnSKBramoKYJQB1UIIIYR4OHh7e2s9d3NzIz09nbi4OBo3bqw1nLJNmzY4OjoSFxcHQNu2bbG1tcXW1pbnnntOk87a2loTAN+fJ9z7yT8hIYHx48drrrW1tSUsLIyEhAQA4uPj8fb21loGtrRlZAFOnjyJqakpvr6+Or3uuLg4unXrpnWsW7duXLhwgYKCgmLbR6VS4erqqnktp06d4s8//8TOzk7zOpycnMjJydG8Fl0lJCSQl5dHly5dNMecnJwqZQUlo64OERAQwJgxY/Dx8aFz586sWrWK7OxszWoRo0ePplGjRixduhSAAQMGsHLlSjp27EiXLl34888/WbhwIQMGDNAEw0IIIYQQ+jIzM9N6rlKpUKvVOl27Z88e8vPzAbCysio1z8JOu6ysLODeZkj3B3xAhWKa+8s3pNLaJysri8cee4yoqKgi19WvXxXTIcvHqEHwiy++yNWrVwkKCiI1NZUOHTqwd+9ezWS55ORkrZ7fBQsWoFKpWLBgAVeuXKF+/foMGDCAJUuWGOslCCGEEOIh5uXlxeXLl7l8+bKmN/jcuXNkZGTQpk0boHwbGrm4uNCwYUMSExMZMWJEsWlatWrFtm3byM3N1QztPHr0aKn5tmvXDrVazcGDBzXDIUrj5eVFbGys1rHY2FhatmypczDeqVMnduzYQYMGDbC3t9fpmpI0a9YMMzMzDh8+TJMmTQC4efMm58+f17l3W1dGDYLh3viYqVOnFnsuJiZG63mdOnUIDg7WjNkRQgghRM1wFYDKH7p41cD59erVi3bt2jFixAhWrVrFP//8w5QpU/D19cXHx6dCeS9atIjp06fj4OBAnz59yM3N5dixY9y8eZOAgACGDx/O/PnzmTRpEnPnziU5OZnly5cDaMbePsjDw4MxY8Ywbtw43nnnHdq3b8+lS5dIT09n6NChRdK/9tprPP744yxevJgXX3yRQ4cOsXbt2iKrVJRmxIgRLFu2jIEDBxIaGsojjzzCpUuX+Pzzz5kzZ45e43ltbW0ZP348s2fPxtnZmQYNGpQ476uijB4ECyGEEOLh5ejoiIW5OZ9V8WYZhtoNTaVS8eWXXzJt2jS6d++OiYkJffr0Yc2aNRXOe8KECVhbW7Ns2TJmz56NjY0N7dq1Y+bMmQDY29vz9ddfM3nyZDp06EC7du0ICgpi+PDhWuOEPTw88Pf31yzrtn79eubNm8eUKVO4fv06TZo0KXE52U6dOvHJJ58QFBTE4sWLcXNzIzQ0VGtSXFmsra358ccfCQwMZMiQIdy+fZtGjRrRs2fPcvUML1u2jKysLAYMGICdnR2vvfYat27d0jufsqiUWjajLDMzEwcHB27dulXhLnshhKjJ4uPjmThxIhEREbJtt6hUsm2y4URFRTF27Fhu3bqFlZUVd+7cwdnZmW+//RY/Pz9jV69GkZ5gIYQQQlQqFxeXhzYorWxbtmzB09OTRo0acerUKQIDAxk6dKhmAtyBAwfo0aOHBMDlIEGwEEIIIUQ1lZqaqllAwM3NjRdeeEFrQYB+/frRr18/I9awbCVt1AHw7bff8tRTT1Vhbf6fBMFCCCGEENXUnDlzmDNnjrGrUSGlbfHcqFGjqqvIAyQIFkIIIYQQlaZ58+bGrkKxjLZjnBBCCCGEEMYiQbAQQgghhKh1JAgWQgghhBC1jgTBQgghhBCi1pEgWAghhBBC1DqyOoQQQgghKpXsGFc7eHh4MHPmTM22z9WdBMFCCCGEqDRpaWmMHDGS3LzcKivTwtyCbVHbJBAWpZIgWAghhBCVJiMjg9y8XLp49sPe0rnSy8vMuc7hxN1kZGRIECxKJWOChRBCCFHp7C2dqWvjUumP8gTafn5+TJ8+nTlz5uDk5ISrqyshISGa88nJyQwcOBBbW1vs7e0ZOnQoaWlppea5adMmHB0d2bdvH15eXtja2tKnTx9SUlK00n3wwQd4eXlhaWlJ69ateffdd7XO//LLL3To0AFLS0t8fHzYtWsXKpWq1F3YPDw8CA8PZ9y4cdjZ2dGkSRPef/99rTRnzpyhR48eWFlZ4ezszKRJk8jKyiqzrb7++msef/xxLC0tqVevHoMHDy4xbVntFhISQocOHdi6dSseHh44ODgwbNgwbt++rUmjVqtZunQpTZs2xcrKivbt2/PZZ5+VWU9dSBAshBBCiFpv8+bN2NjYcPjwYd566y1CQ0PZv38/arWagQMHcuPGDQ4ePMj+/ftJTEzkxRdfLDPPO3fusHz5crZu3cqPP/5IcnIys2bN0pyPiooiKCiIJUuWEBcXR3h4OAsXLmTz5s0AZGZmMmDAANq1a8dvv/3G4sWLCQwM1On1rFixAh8fH06cOMGUKVOYPHky8fHxAGRnZ9O7d2/q1q3L0aNH+fTTT/n++++ZOnVqqXnu3r2bwYMH07dvX06cOEF0dDSdO3cuNq2u7ZaQkMCuXbv45ptv+Oabbzh48CBvvPGG5vzSpUvZsmULGzZs4Pfff+fVV19l5MiRHDx4UKd2KI0MhxBCCCFEreft7U1wcDAALVq0YO3atURHRwP3ek2TkpJo3LgxAFu2bKFt27YcPXqUxx9/vMQ88/Pz2bBhA82aNQNg6tSphIaGas4HBwezYsUKhgwZAkDTpk05d+4c7733HmPGjGH79u2oVCoiIiKwtLSkTZs2XLlyhYkTJ5b5evr27cuUKVMACAwM5O233+bAgQO0atWK7du3k5OTw5YtW7CxsQFg7dq1DBgwgDfffLPEYSRLlixh2LBhLFq0SHOsffv2xaaNjo7Wqd3UajWbNm3Czs4OgFGjRhEdHc2SJUvIzc0lPDyc77//nq5duwLg6enJzz//zHvvvYevr2+Z7VAa6QkWQgghRK3n7e2t9dzNzY309HTi4uJo3LixJpADaNOmDY6OjsTFxQHQtm1bbG1tsbW15bnnntOks7a21gTA9+cJ93pjExISGD9+vOZaW1tbwsLCSEhIACA+Ph5vb28sLS01eZTU81ra61GpVLi6umrKjouLo3379poAGKBbt26o1WpNb/H9dXr55ZcBOHnyJD179tSpfF3aDe4N3SgMgB9soz///JM7d+7wzDPPaNVny5YtmjaqCOkJFkIIIUStZ2ZmpvVcpVKhVqt1unbPnj3k5+cDYGVlVWqeiqIAaMbfRkRE0KVLF610pqam+lW+GBV5PYDWmGN7e3tA+7UZSmn1LGyj3bt306hRI610FhYWFS5bgmAhhBBCiBJ4eXlx+fJlLl++rOnVPHfuHBkZGbRp0wYAd3d3vfN1cXGhYcOGJCYmMmLEiGLTtGrVim3btpGbm6sJ+o4ePVrOV/L/vLy82LRpE9nZ2Zre4NjYWExMTGjVqhUAzZs3L3Kdt7c30dHRjB07Vqcyymq3srRp0wYLCwuSk5MrPPShOBIECyGEEKLSZeZcr5Hl9OrVi3bt2jFixAhWrVrFP//8w5QpU/D19cXHx6dCeS9atIjp06fj4OBAnz59yM3N5dixY9y8eZOAgACGDx/O/PnzmTRpEnPnziU5OZnly5cD93pMy2vEiBEEBwczZswYQkJCuHr1KtOmTWPUqFGlLisXHBxMz549adasGcOGDeOff/5hz549xU7WM0S72dnZMWvWLF599VXUajVPPvkkt27dIjY2Fnt7e8aMGVPuNgAJgoUQQghRiRwdHbEwt+Bw4u4qK9PC3AJHR0eD5KVSqfjyyy+ZNm0a3bt3x8TEhD59+rBmzZoK5z1hwgSsra1ZtmwZs2fPxsbGhnbt2ml2XLO3t+frr79m8uTJdOjQgXbt2hEUFMTw4cO1xgl7eHjg7++vtaxbaaytrdm3bx8zZszg8ccfx9ramueff56VK1eWep2fnx+ffvopixcv5o033sDe3p7u3bsXm9ZQ7bZ48WLq16/P0qVLSUxMxNHRkU6dOjFv3jy98im2jkrh4JRaIjMzEwcHB27duqUZ4yKEELVRfHw8EydOJCIiQvMTqBCVQbZNNpyoqCjGjh3LrVu3sLKy4s6dOzg7O/Ptt9/i5+dn7OrVKNITLIQQQohK5eLi8tAGpZVty5YteHp60qhRI06dOkVgYCBDhw7VTFI7cOAAPXr0kAC4HCQIFkIIIYSoplJTUwkKCiI1NRU3NzdeeOEFlixZojnfr18/+vXrZ8Qa1lwSBAshhBBCVFNz5sxhzpw5xq7GQ0k2yxBCCCGEELWOBMFCCCGEEKLWkSBYCCGEEELUOhIECyGEEEKIWkeCYCGEEEIIUetIECyEEEIIIWodCYKFEEIIIUStI0GwEEIIIYSodSQIFkIIIYQQtU61CILXrVuHh4cHlpaWdOnShSNHjpSY1s/PD5VKVeQhWwYKIYQQQghdGT0I3rFjBwEBAQQHB/Pbb7/Rvn17evfuTXp6erHpP//8c1JSUjSPs2fPYmpqygsvvFDFNRdCCCGEEDWV0YPglStXMnHiRMaOHUubNm3YsGED1tbWfPjhh8Wmd3JywtXVVfPYv38/1tbWEgQLIYQQQgidGTUIzsvL4/jx4/Tq1UtzzMTEhF69enHo0CGd8oiMjGTYsGHY2NgUez43N5fMzEythxBCCCGEqN3qGLPwa9euUVBQgIuLi9ZxFxcX/vjjjzKvP3LkCGfPniUyMrLENEuXLmXRokUVrqsQQjwMcnJyuHTpEkCR/wK4u7tjaWlplLoJIURVMmoQXFGRkZG0a9eOzp07l5jm9ddfJyAgQPM8MzOTxo0bV0X1hBCi2rl06RITJ07UOhYWFqb5d0REBK1atarqagkhRJUzahBcr149TE1NSUtL0zqelpaGq6trqddmZ2fz8ccfExoaWmo6CwsLLCwsKlxXIYR4GLi7uxMREVHqeSGEqA2MGgSbm5vz2GOPER0dzaBBgwBQq9VER0czderUUq/99NNPyc3NZeTIkVVQUyGEeDhYWlpKT68QQlANhkMEBAQwZswYfHx86Ny5M6tWrSI7O5uxY8cCMHr0aBo1asTSpUu1rouMjGTQoEE4Ozsbo9pCCCGEEKIGM3oQ/OKLL3L16lWCgoJITU2lQ4cO7N27VzNZLjk5GRMT7UUs4uPj+fnnn/nuu++MUWUhhBBCCFHDqRRFUYxdiaqUmZmJg4MDt27dwt7e3tjVEUIIIYQQRmD0nuCqVhjzy3rBQgghRPnY2dmhUqmMXQ0hKqTWBcG3b98GkGXShBBCiHKSX1PFw6DWDYdQq9X8/fff1fJbbOEaxpcvX5YPFz1Iu+lP2qx8pN30J21WPtW93arj31Ah9FXreoJNTEx45JFHjF2NUtnb21fLD73qTtpNf9Jm5SPtpj9ps/KRdhOi8piUnUQIIYQQQoiHiwTBQgghhBCi1pEguBqxsLAgODhYtnnWk7Sb/qTNykfaTX/SZuUj7SZE5at1E+OEEEIIIYSQnmAhhBBCCFHrSBAshBBCCCFqHQmChRBCCCFErSNBsBAPOUVRmDRpEk5OTqhUKk6ePGnsKtVIMTExqFQqMjIyDJpWaAsJCaFDhw6a5/7+/gwaNMho9RFCPLwkCBbiIbd37142bdrEN998Q0pKCpmZmQwYMICGDRuiUqnYtWuXsatYIzzxxBOkpKTg4OBg0LRCCCGMQ4LgcsrLyzN2FR560saGkZCQgJubG0888QSurq5kZ2fTvn171q1bZ+yqVRlD3Evm5ua4urrqtFWsPmlrEnlPVg/5+fnGroIQDwUJgnXk5+fH1KlTmTlzJvXq1aN3794cPHiQzp07Y2FhgZubG3PnzuWff/7RXKNWq3nrrbdo3rw5FhYWNGnShCVLluhUXmBgIC1btsTa2hpPT08WLlyo9cFX3E+EM2fOxM/PzyDlV4bCNpw6dSoODg7Uq1ePhQsXUrhKn4eHB4sXL2b06NHY29szadKkMvN8GNvJkPz9/Zk2bRrJycmoVCo8PDx47rnnCAsLY/DgwcauXrmV9176+eefeeqpp7CysqJx48ZMnz6d7OxsTb65ubkEBgbSuHFjLCwsaN68OZGRkUDRIQ6XLl1iwIAB1K1bFxsbG9q2bcuePXuKTQuwc+dO2rZti4WFBR4eHqxYsULrNXl4eBAeHs64ceOws7OjSZMmvP/++5XVhDop7nPv7NmzPPfcc9ja2uLi4sKoUaO4du2a5pqy3k9lvWero7179/Lkk0/i6OiIs7Mz/fv3JyEhQXP+r7/+4qWXXsLJyQkbGxt8fHw4fPiw5vzXX3/N448/jqWlJfXq1dN67xX3a4yjoyObNm0C4OLFi6hUKnbs2IGvry+WlpZERUVx/fp1XnrpJRo1aoS1tTXt2rXjo48+0sqntP8XPXr0YOrUqVrpr169irm5OdHR0YZoNiGqPQmC9bB582bMzc2JjY0lJCSEvn378vjjj3Pq1CnWr19PZGQkYWFhmvSvv/46b7zxBgsXLuTcuXNs374dFxcXncqys7Nj06ZNnDt3jtWrVxMREcHbb7+tV30rUn5l2bx5M3Xq1OHIkSOsXr2alStX8sEHH2jOL1++nPbt23PixAkWLlxYZn4PazsZyurVqwkNDeWRRx4hJSWFo0ePGrtKBqPvvZSQkECfPn14/vnnOX36NDt27ODnn3/WCgRGjx7NRx99xDvvvENcXBzvvfcetra2xZb/yiuvkJuby48//siZM2d48803S0x7/Phxhg4dyrBhwzhz5gwhISEsXLhQE+gUWrFiBT4+Ppw4cYIpU6YwefJk4uPjK95YFXD/594bb7xBjx496NixI8eOHWPv3r2kpaUxdOhQTfqy3k+GeM9WtezsbAICAjh27BjR0dGYmJgwePBg1Go1WVlZ+Pr6cuXKFb766itOnTrFnDlzUKvVAOzevZvBgwfTt29fTpw4QXR0NJ07d9a7DnPnzmXGjBnExcXRu3dvcnJyeOyxx9i9ezdnz55l0qRJjBo1iiNHjmiuKe3/xYQJE9i+fTu5ubma9Nu2baNRo0b06NGjgi0mRA2hCJ34+voqHTt21DyfN2+e0qpVK0WtVmuOrVu3TrG1tVUKCgqUzMxMxcLCQomIiDBI+cuWLVMee+wxzfMxY8YoAwcO1EozY8YMxdfXV1EUxeDlG4Kvr6/i5eWl1WaBgYGKl5eXoiiK4u7urgwaNKhCZTwM7WRob7/9tuLu7l7sOUD54osvqrQ+hlCee2n8+PHKpEmTtI799NNPiomJiXL37l0lPj5eAZT9+/cXW+aBAwcUQLl586aiKIrSrl07JSQkRKe0w4cPV5555hmtNLNnz1batGmjee7u7q6MHDlS81ytVisNGjRQ1q9fX0pLVK4HP/cWL16sPPvss1ppLl++rABKfHx8ud5PD75ng4ODlfbt22ueF/ceNrarV68qgHLmzBnlvffeU+zs7JTr168Xm7Zr167KiBEjSsyruPegg4ODsnHjRkVRFCUpKUkBlFWrVpVZr379+imvvfaaoihlf7bdvXtXqVu3rrJjxw7NMW9v7xLvaSEeRnWMF37XPI899pjm33FxcXTt2lVrzF+3bt3Iysrir7/+IjU1ldzcXHr27Fmusnbs2ME777xDQkICWVlZ/PPPP9jb2+t8fVxcXIXKryz/+te/tNqsa9eurFixgoKCAgB8fHz0yu9hbSdRNn3vpVOnTnH69GmioqI0xxRFQa1Wk5SUxJkzZzA1NcXX11en8qdPn87kyZP57rvv6NWrF88//zze3t7Fpo2Li2PgwIFax7p168aqVasoKCjA1NQUQOt6lUqFq6sr6enpOtWnstz/uXfq1CkOHDhQbI93QkICGRkZZb6fKvqeNYYLFy4QFBTE4cOHuXbtmqaXNzk5mZMnT9KxY0ecnJyKvfbkyZNMnDixwnV48H4uKCggPDycTz75hCtXrpCXl0dubi7W1tZA2Z9tlpaWjBo1ig8//JChQ4fy22+/cfbsWb766qsK11WImkKGQ+jBxsZG57RWVlblLufQoUOMGDGCvn378s0333DixAnmz5+vNSnFxMREM/6x0P3j6ipSvjHp08a1uZ1E2R68l7Kysvjvf//LyZMnNY9Tp05x4cIFmjVrpve9MGHCBBITExk1ahRnzpzBx8eHNWvWVKjOZmZmWs9VKpUm4DKW+9sxKyuLAQMGaLXhyZMnuXDhAt27dy+zDXV5z1ZHAwYM4MaNG0RERHD48GHNeN+8vLwyX3NZ51UqVamfUYUevJ+XLVvG6tWrCQwM5MCBA5w8eZLevXtr2lKX+3nChAns37+fv/76i40bN9KjRw/c3d3LvE6Ih4UEweXk5eXFoUOHtD68YmNjsbOz45FHHqFFixZYWVmVa4LBL7/8gru7O/Pnz8fHx4cWLVpw6dIlrTT169cnJSVF69j9679WpPzKdP9kEYBff/2VFi1aaHrC9PEwt5Mom773UqdOnTh37hzNmzcv8jA3N6ddu3ao1WoOHjyocx0aN27Myy+/zOeff85rr71GREREsem8vLyIjY3VOhYbG0vLli3Lde8bS6dOnfj999/x8PAo0oY2NjZlvp90ec9WN9evXyc+Pp4FCxbQs2dPvLy8uHnzpua8t7c3J0+e5MaNG8Ve7+3tXerny4OfURcuXODOnTtl1is2NpaBAwcycuRI2rdvj6enJ+fPn9ec1+WzrV27dvj4+BAREcH27dsZN25cmeUK8TCRILicpkyZwuXLl5k2bRp//PEHX375JcHBwQQEBGBiYoKlpSWBgYHMmTOHLVu2kJCQwK+//qqZaV6aFi1akJyczMcff0xCQgLvvPMOX3zxhVaaHj16cOzYMbZs2cKFCxcIDg7m7NmzmvMVKb8yJScnExAQQHx8PB999BFr1qxhxowZ5crrYW6nypSVlaXpwQNISkri5MmTJCcnG7dietL3XgoMDOSXX35h6tSpmt7LL7/8UjMxzsPDgzFjxjBu3Dh27dpFUlISMTExfPLJJ8XmN3PmTPbt20dSUhK//fYbBw4cwMvLq9i0r732GtHR0SxevJjz58+zefNm1q5dy6xZsyreEFXolVde4caNG7z00kscPXqUhIQE9u3bx9ixYykoKCjz/aTLe7a6qVu3Ls7Ozrz//vv8+eef/PDDDwQEBGjOv/TSS7i6ujJo0CBiY2NJTExk586dHDp0CIDg4GA++ugjgoODiYuL00yiLNSjRw/Wrl3LiRMnOHbsGC+//HKRXwSK06JFC/bv388vv/xCXFwc//3vf0lLS9Oc1/WzbcKECbzxxhsoilKjV4wRolyMOSC5JvH19VVmzJihdSwmJkZ5/PHHFXNzc8XV1VUJDAxU8vPzNecLCgqUsLAwxd3dXTEzM1OaNGmihIeH61Te7NmzFWdnZ8XW1lZ58cUXlbfffltxcHDQShMUFKS4uLgoDg4OyquvvqpMnTpVM+GrouVXBl9fX2XKlCnKyy+/rNjb2yt169ZV5s2bp5nc5O7urrz99tt65fkwtpOhPTgxrnDS1oOPMWPGGK2O+irvvXTkyBHlmWeeUWxtbRUbGxvF29tbWbJkieb83bt3lVdffVVxc3NTzM3NlebNmysffvihoihFJ7tNnTpVadasmWJhYaHUr19fGTVqlHLt2rVi0yqKonz22WdKmzZtNPfYsmXLtOpWXJ3bt2+vBAcHV6yxKqC4z73z588rgwcPVhwdHRUrKyuldevWysyZMzVtX9b7qaz3bHWcGLd//37Fy8tLsbCwULy9vZWYmBitCW0XL15Unn/+ecXe3l6xtrZWfHx8lMOHD2uu37lzp9KhQwfF3NxcqVevnjJkyBDNuStXrijPPvusYmNjo7Ro0ULZs2dPsRPjTpw4oVWn69evKwMHDlRsbW2VBg0aKAsWLFBGjx6t1Va6fLbdvn1bsba2VqZMmWLQNhOiJlApygODkYSoJH5+fnTo0IFVq1YZuyqihpN7SQjDuHjxIs2aNePo0aN06tTJ2NURokrJ6hBCCCFELZOfn8/169dZsGAB//rXvyQAFrWSjAk2gvDwcGxtbYt9PPfcc8auXrUh7SSEEJUjNjYWNzc3jh49yoYNG4xdHSGMQoZDGMGNGzdKnElsZWVFo0aNqrhG1ZO0kxBCCCEqiwTBQgghhBCi1pHhEEIIIYQQotaRIFgIIYQQQtQ6EgQLIYQQQohaR4JgIUSNFhISQocOHYxdDSGEEDWMTIwTQlQ7+myGkZWVRW5uLs7OzpVfMSGEEA8N2SxDCFEjKYpCQUGBZu1oIYQQQh8yHEIIUSF+fn5MmzaNmTNnUrduXVxcXIiIiCA7O5uxY8diZ2dH8+bN+fbbbzXXnD17lueeew5bW1tcXFwYNWoU165dA8Df35+DBw+yevVqVCoVKpWKixcvEhMTg0ql4ttvv+Wxxx7DwsKCn3/+udjhEB9++CFt27bFwsICNzc3pk6dWpVNIoQQogaQIFgIUWGbN2+mXr16HDlyhGnTpjF58mReeOEFnnjiCX777TeeffZZRo0axZ07d8jIyKBHjx507NiRY8eOsXfvXtLS0hg6dCgAq1evpmvXrkycOJGUlBRSUlJo3Lixpqy5c+fyxhtvEBcXh7e3d5G6rF+/nldeeYVJkyZx5swZvvrqK5o3b15lbSGEEKJmkDHBQogK8fPzo6CggJ9++gmAgoICHBwcGDJkCFu2bAEgNTUVNzc3Dh06xPfff89PP/3Evn37NHn89ddfNG7cmPj4eFq2bFnsmOCYmBiefvppdu3axcCBAzXHQ0JC2LVrFydPngSgUaNGjB07lrCwsMp/8UIIIWosGRMshKiw+3tkTU1NcXZ2pl27dppjLi4uAKSnp3Pq1CkOHDhQ7DjehIQEWrZsWWpZPj4+JZ5LT0/n77//pmfPnvq+BCGEELWMBMFCiAozMzPTeq5SqbSOqVQqANRqNVlZWQwYMIA333yzSD5ubm5llmVjY1PiOSsrK12rLIQQopaTIFgIUaU6derEzp078fDwoE6d4j+CzM3NKSgo0DtvOzs7PDw8iI6O5umnn65oVYUQQjzEZGKcEKJKvfLKK9y4cYOXXnqJo0ePkpCQwL59+xg7dqwm8PXw8ODw4cNcvHiRa9euoVardc4/JCSEFStW8M4773DhwgV+++031qxZU1kvRwghRA0lQbAQoko1bNiQ2NhYCgoKePbZZ2nXrh0zZ87E0dERE5N7H0mzZs3C1NSUNm3aUL9+fZKTk3XOf8yYMaxatYp3332Xtm3b0r9/fy5cuFBZL0cIIUQNJatDCCGEEEKIWkd6goUQQgghRK0jQbAQQgghhKh1JAgWQgghhBC1jgTBQgghhBCi1pEgWAghhBBC1DoSBAshhBBCiFpHgmAhhBBCCFHrSBAshBBCCCFqHQmChRBCCCFErSNBsBBCCCGEqHUkCBZCCCGEELWOBMFCCCGEEKLW+T8qJm5AQfVv/gAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 600x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -766,9 +774,9 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 400x200 with 1 Axes>"
+       "<Figure size 600x300 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -783,21 +791,21 @@
     "metrics_list = ['roc_auc', 'pr_auc', 'f1', 'precision', 'recall', 'accuracy']\n",
     "\n",
     "# Show all model configurations or only neg,clone_id model\n",
-    "for show_all in [True, False]:\n",
+    "for show_all in [False]:\n",
     "    results_melt = results_merged.sort_values(['setting', 'model_config']).melt( id_vars=['model_config', 'setting'], value_vars=metrics_list, var_name='metric', value_name='value' )\n",
     "    if show_all:\n",
-    "        plt.figure(figsize=(6, 2))\n",
+    "        plt.figure(figsize=(6, 3))\n",
     "        ax = sns.boxplot(data=results_melt, x='metric', y='value', hue='model_config', order=metrics_list)\n",
     "        sns.move_legend(ax, 'upper left', bbox_to_anchor=(1, 1), frameon=False)\n",
     "        sns.despine()\n",
     "        plt.show()\n",
     "        \n",
     "    else:\n",
-    "        plt.figure(figsize=(4, 2))\n",
-    "        models_to_compare = ['BEAMT', 'neg,clone_id']\n",
+    "        plt.figure(figsize=(6, 3))\n",
+    "        models_to_compare = ['BEAMT', 'DextraDemixer']\n",
     "        df_filtered = results_melt[results_melt['model_config'].isin(models_to_compare)]\n",
     "        \n",
-    "        ax = sns.boxplot(data=df_filtered, x='metric', y='value', hue='model_config', order=metrics_list)\n",
+    "        ax = sns.boxplot(data=df_filtered, x='metric', y='value', hue='model_config', order=metrics_list, palette=palette)\n",
     "        sns.move_legend(ax, 'upper left', bbox_to_anchor=(1, 1), frameon=False)\n",
     "        \n",
     "        y_max = df_filtered['value'].max()\n",
@@ -828,75 +836,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "5dc84b2d-8b64-4667-bc9c-e89cd892bbf6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for show_all in [True, False]:\n",
-    "    results_melt = results_merged.sort_values(['setting', 'model_config']).melt(id_vars=['model_config'], value_vars=['roc_auc', 'pr_auc', 'f1', 'precision', 'recall', 'accuracy'], var_name='metric', value_name='value', )\n",
-    "    if show_all:\n",
-    "        plt.figure(figsize=(6, 2))\n",
-    "    else:\n",
-    "        results_melt = results_melt[results_melt['model_config'].isin(['BEAMT', 'neg,clone_id'])]\n",
-    "        plt.figure(figsize=(4, 2))\n",
-    "    \n",
-    "    ax = sns.boxplot(results_melt, x='metric', y='value', hue='model_config')\n",
-    "    sns.move_legend(ax, 'upper left', bbox_to_anchor=(1, 1), frameon=False)\n",
-    "    \n",
-    "    for container in ax.containers:\n",
-    "        try:\n",
-    "            ax.bar_label(container, fmt=\"%.3f\", fontsize=10, label_type='edge', padding=5, rotation=90)\n",
-    "        except:\n",
-    "            pass\n",
-    "    \n",
-    "    # plt.xticks(rotation=10, ha='center')\n",
-    "    sns.despine()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 34,
    "id": "883c4caa-c6af-403f-b88c-102fa8e7621a",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x200 with 1 Axes>"
+       "<Figure size 600x250 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -904,37 +852,40 @@
     }
    ],
    "source": [
-    "for show_all in [True, False]:\n",
+    "for show_all in [ False, ]:\n",
     "    results_sub = results_merged.copy()\n",
     "    if show_all:\n",
     "        plt.figure(figsize=(10, 2))\n",
     "    else:\n",
-    "        plt.figure(figsize=(5, 2))\n",
-    "        results_sub = results_sub[results_sub['model_config'].isin(['BEAMT', 'neg,clone_id'])]\n",
-    "\n",
+    "        plt.figure(figsize=(6, 2.5))\n",
+    "        results_sub = results_sub[results_sub['model_config'].isin(['BEAMT', 'DextraDemixer'])]\n",
     "\n",
-    "    ax = sns.barplot(results_sub.sort_values(['setting', 'model_config']), x='setting', y='f1', hue='model_config')\n",
-    "    sns.move_legend(ax, 'upper left', bbox_to_anchor=(1, 1), frameon=False)\n",
+    "    \n",
+    "    ax = sns.barplot(results_sub.sort_values(['setting', 'model_config']), x='title', y='f1', hue='model_config', palette=palette)\n",
+    "    sns.move_legend(ax, 'upper left', bbox_to_anchor=(1, 1), frameon=False, title='', fontsize='small')\n",
     "    \n",
     "    for container in ax.containers:\n",
-    "        ax.bar_label(container, fmt=\"%.2f\", fontsize=10, label_type='edge', padding=5, rotation=90)\n",
+    "        ax.bar_label(container, fmt=\"%.2f\", fontsize='small', label_type='edge', padding=5, rotation=90)\n",
     "    \n",
-    "    plt.xticks(rotation=20, ha='center')\n",
+    "    plt.xticks(rotation=-20, ha='left', fontsize='small')\n",
+    "    plt.xlabel('')\n",
+    "    plt.ylabel('F1 Score')\n",
     "    sns.despine()\n",
+    "    plt.savefig('../figures/cmv_f1_comparison.pdf')\n",
     "    plt.show()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 35,
    "id": "9ee1aec1-7a48-4974-b007-01d47a70ec74",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1200x200 with 6 Axes>"
+       "<Figure size 1500x250 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -942,9 +893,11 @@
     }
    ],
    "source": [
-    "results_sub_plot = results_sub[results_sub['model_config'].isin(['BEAMT', 'neg,clone_id'])].copy()\n",
+    "results_sub_plot = results_sub[results_sub['model_config'].isin(['BEAMT', 'DextraDemixer'])].copy()\n",
     "metrics = ['pr_auc', 'roc_auc', 'f1', 'precision', 'recall', 'accuracy', ]\n",
-    "fig, axes = plt.subplots(1, len(metrics), figsize=(2 * len(metrics), 2))\n",
+    "# metrics = ['f1', 'precision', 'recall', ]\n",
+    "title_map = {'pr_auc': 'PR AUC', 'roc_auc': 'ROC AUC', 'f1': 'F1 Score', 'precision': 'Precision', 'recall': 'Recall', 'accuracy': 'Accuracy'}\n",
+    "fig, axes = plt.subplots(1, len(metrics), figsize=(2.5 * len(metrics), 2.5))\n",
     "legend_handles, legend_labels = None, None\n",
     "\n",
     "for i, metric in enumerate(metrics):\n",
@@ -952,11 +905,11 @@
     "    results_pivot = results_sub_plot.pivot( columns='model_config', values=metric, index=['setting', 'dex_key', 'sample', 'tcr_info_sequenced_only'] ).reset_index()\n",
     "    results_pivot['dex'] = results_pivot['dex_key'].map(dex_map)\n",
     "    results_pivot['dataset'] = results_pivot['dex'] + ',' + results_pivot['sample']\n",
-    "    sns.scatterplot( results_pivot, x='neg,clone_id', y='BEAMT', \n",
+    "    sns.scatterplot( results_pivot, x='DextraDemixer', y='BEAMT', \n",
     "                    # hue='dataset', \n",
     "                    # hue='tcr_info_sequenced_only',\n",
     "                    # style='tcr_info_sequenced_only', \n",
-    "                    ax=ax, legend=(i == 0) )\n",
+    "                    ax=ax, legend=(i == 0), color=palette['Ours'])\n",
     "\n",
     "    if i == 0:\n",
     "        legend_handles, legend_labels = ax.get_legend_handles_labels()\n",
@@ -968,13 +921,23 @@
     "    max_value = results_sub_plot[metric].max()\n",
     "    diff = max_value - min_value\n",
     "    ax.plot((min_value - diff * 0.1, 1), (min_value - diff * 0.1, 1), ls=':', c='grey')\n",
-    "    ax.set_title(metric)\n",
+    "    ax.set_title(title_map[metric])\n",
+    "    ax.set_ylabel('BEAMT' if i == 0 else '')\n",
     "\n",
     "fig.legend( legend_handles, legend_labels, loc='upper center', bbox_to_anchor=(0.5, 1.25), ncol=4, frameon=False )\n",
     "\n",
     "plt.tight_layout()\n",
+    "plt.savefig('../figures/cmv_scatter_comparison.pdf')\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ff97616",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 155e8d5106fc1034b86d7e0a8b8484da6dd82f67 Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Wed, 8 Apr 2026 12:56:26 +0200
Subject: [PATCH 32/39] feature: clonotype median aggregation model

---
 dextrademixer/model/Dextrademixer.py | 37 +++++++++++++++++++++++++---
 dextrademixer/utils/utils.py         | 16 ++++++++++++
 2 files changed, 49 insertions(+), 4 deletions(-)

diff --git a/dextrademixer/model/Dextrademixer.py b/dextrademixer/model/Dextrademixer.py
index 31bb77c..4edc09a 100644
--- a/dextrademixer/model/Dextrademixer.py
+++ b/dextrademixer/model/Dextrademixer.py
@@ -354,7 +354,11 @@ def predict_posterior_class(self,
                                 clonotype_adherence: bool = False,
                                 clonotype_majority_voting: bool = False,
                                 clonotype_mean_p: bool = False,
+                                clonotype_max_p: bool = False,
+                                clonotype_median_p: bool = False,
+                                clonotype_quantile_p: float = False,
                                 clone_id: Array = None,
+                                predict_at_least_one_binder: bool = False
                                 ) -> Tuple[Array, Array]:
         """
         Returns the binder assignments based on the inferred posterior class probabilities.
@@ -387,25 +391,39 @@ def __return_p_summary(p_sample):
             else:
                 p = jnp.nanmean(p_sample, axis=0)[:, 1]
 
-            if clonotype_mean_p:
+            if clonotype_mean_p or clonotype_max_p or clonotype_quantile_p:
+                assert not (clonotype_mean_p and clonotype_max_p), "Cannot use both `clonotype_mean_p` and `clonotype_max_p` at the same time."
                 if clone_id is None:
                     raise ValueError("If `clonotype_mean_p`= True a clonotype vector `clone_id` must be specified.")
                 unique_ids = np.unique(clone_id)
 
                 if cred_intvl:
                     # mean for each clone while keeping posterior samples, shape (num_clones, num_samples, 2)
-                    mean_p = np.stack([p[:, clone_id == cid].mean(axis=[1]) for cid in unique_ids])
+                    if clonotype_mean_p:
+                        mean_p = np.stack([p[:, clone_id == cid].mean(axis=[1]) for cid in unique_ids])
+                    elif clonotype_max_p:
+                        mean_p = np.stack([p[:, clone_id == cid].max(axis=[1]) for cid in unique_ids])
+                    elif clonotype_quantile_p:
+                        mean_p = np.stack([jnp.quantile(p[:, clone_id == cid], q=clonotype_quantile_p, axis=1, method='higher') for cid in unique_ids])
                     p = mean_p[clone_id].transpose(1, 0, 2)  # shape (num_posterior_samples, num_cells, 2)
 
                 else:
                     df = pd.DataFrame({"p": p, "clone_id": clone_id})
-                    mean_p = df.groupby("clone_id")["p"].mean()
-                    p = mean_p.values[clone_id]
+                    if clonotype_mean_p:
+                        mean_p = df.groupby("clone_id")["p"].mean()
+                    elif clonotype_max_p:
+                        mean_p = df.groupby("clone_id")["p"].max()
+                    elif clonotype_quantile_p:
+                        mean_p = df.groupby("clone_id")["p"].quantile(clonotype_quantile_p, interpolation='higher')
+                    p = jnp.array(mean_p.values)[clone_id]
             return p
 
         data = data if data is not None else self.model.data_full
         clone_id = clone_id if clone_id is not None else data.get("clone_continuous", None)
         clone_id = pd.factorize(clone_id)[0] if clone_id is not None else None
+        
+        if clonotype_median_p and ~clonotype_quantile_p:
+            clonotype_quantile_p = 0.5
 
         if self.sampler is None and self.svi_result is None:
             raise RuntimeError("Model has not been fit yet. Please call first `fit` or `fit_svi`.")
@@ -446,6 +464,17 @@ def __return_p_summary(p_sample):
         if clonotype_adherence and clone_id is not None:
             assignment = assignment[clone_id]
             p = p[clone_id]
+        
+        if predict_at_least_one_binder and assignment.sum() == 0:
+            # if no binder is assigned, assign the cell with highest p as binder
+            if clonotype_mean_p:
+                # assign the clone with highest mean p as binder
+                clone_p = p.mean(axis=0) if p.ndim == 3 else p
+                clone_p_mean = pd.DataFrame({"p": clone_p, "clone_id": clone_id}).groupby("clone_id")["p"].mean()
+                best_clone = clone_p_mean.idxmax()
+                assignment = assignment.at[clone_id == best_clone].set(1)
+            else:
+                assignment = assignment.at[jnp.argmax(p)].set(1)
 
         return p, assignment
 
diff --git a/dextrademixer/utils/utils.py b/dextrademixer/utils/utils.py
index 0570e61..d4ce509 100644
--- a/dextrademixer/utils/utils.py
+++ b/dextrademixer/utils/utils.py
@@ -376,4 +376,20 @@ def calculate_metrics(y_true: np.ndarray, p_pred: np.ndarray, assignment: np.nda
                     'recall': recall_score(y_true, assignment), 'accuracy': accuracy_score(y_true, assignment),
                     'mcc': matthews_corrcoef(y_true, assignment)}
 
+    tp = np.sum(assignment.astype(bool) & y_true.astype(bool))
+    fp = np.sum(assignment.astype(bool) & ~y_true.astype(bool))
+    tn = np.sum(~assignment.astype(bool) & ~y_true.astype(bool))
+    fn = np.sum(~assignment.astype(bool) & y_true.astype(bool))
+
+    if (tp + fp) == 0:
+        fdr = 0.0
+    else:
+        fdr = fp / (tp + fp)
+    
+    results_dict['fdr'] = fdr
+    results_dict['tp'] = tp
+    results_dict['fp'] = fp
+    results_dict['tn'] = tn
+    results_dict['fn'] = fn
+    
     return results_dict

From 3eae5884c808819e9164753ce9e5da80a6a7584e Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Thu, 18 Jun 2026 19:43:47 +0200
Subject: [PATCH 33/39] feature: memory-efficient
 `_predict_posterior_class_dist` through lax.scan, minor cleanup

---
 dextrademixer/model/Dextrademixer.py | 105 +++++++--------------------
 1 file changed, 26 insertions(+), 79 deletions(-)

diff --git a/dextrademixer/model/Dextrademixer.py b/dextrademixer/model/Dextrademixer.py
index 4edc09a..d035727 100644
--- a/dextrademixer/model/Dextrademixer.py
+++ b/dextrademixer/model/Dextrademixer.py
@@ -63,7 +63,7 @@ class DextraDemixer(ApMHCDeconvolution):
 
     """
 
-    def __init__(self, model_type: str = "mixturemodel", mode: str = "H", alpha_model="overdispersion", 
+    def __init__(self, model_type: str = "mixturemodelkmeans", mode: str = "I", alpha_model="overdispersion", 
                  model_config: Dict = None):
         super().__init__()
 
@@ -233,7 +233,7 @@ def fit(self, sampler_config: Dict[str, Union[int, float]] = None, rng_key: int
     def fit_svi(self, guide='normal', svi_config: Dict[str, Union[int, float]] = None,
                 nof_inits: int = 100, use_minimal_loss: bool = True, rng_key: int = 998777,
                 y_true: Array = None) \
-            -> az.InferenceData:
+                -> az.InferenceData:
         """
         Implements stochastic variational inference
 
@@ -354,11 +354,8 @@ def predict_posterior_class(self,
                                 clonotype_adherence: bool = False,
                                 clonotype_majority_voting: bool = False,
                                 clonotype_mean_p: bool = False,
-                                clonotype_max_p: bool = False,
                                 clonotype_median_p: bool = False,
-                                clonotype_quantile_p: float = False,
                                 clone_id: Array = None,
-                                predict_at_least_one_binder: bool = False
                                 ) -> Tuple[Array, Array]:
         """
         Returns the binder assignments based on the inferred posterior class probabilities.
@@ -391,8 +388,8 @@ def __return_p_summary(p_sample):
             else:
                 p = jnp.nanmean(p_sample, axis=0)[:, 1]
 
-            if clonotype_mean_p or clonotype_max_p or clonotype_quantile_p:
-                assert not (clonotype_mean_p and clonotype_max_p), "Cannot use both `clonotype_mean_p` and `clonotype_max_p` at the same time."
+            if clonotype_mean_p or clonotype_median_p:
+                assert not (clonotype_mean_p and clonotype_median_p), "Cannot use both `clonotype_mean_p` and `clonotype_median_p` at the same time."
                 if clone_id is None:
                     raise ValueError("If `clonotype_mean_p`= True a clonotype vector `clone_id` must be specified.")
                 unique_ids = np.unique(clone_id)
@@ -401,20 +398,16 @@ def __return_p_summary(p_sample):
                     # mean for each clone while keeping posterior samples, shape (num_clones, num_samples, 2)
                     if clonotype_mean_p:
                         mean_p = np.stack([p[:, clone_id == cid].mean(axis=[1]) for cid in unique_ids])
-                    elif clonotype_max_p:
-                        mean_p = np.stack([p[:, clone_id == cid].max(axis=[1]) for cid in unique_ids])
-                    elif clonotype_quantile_p:
-                        mean_p = np.stack([jnp.quantile(p[:, clone_id == cid], q=clonotype_quantile_p, axis=1, method='higher') for cid in unique_ids])
+                    elif clonotype_median_p:
+                        mean_p = np.stack([jnp.quantile(p[:, clone_id == cid], q=0.5, axis=1, method='higher') for cid in unique_ids])
                     p = mean_p[clone_id].transpose(1, 0, 2)  # shape (num_posterior_samples, num_cells, 2)
 
                 else:
                     df = pd.DataFrame({"p": p, "clone_id": clone_id})
                     if clonotype_mean_p:
                         mean_p = df.groupby("clone_id")["p"].mean()
-                    elif clonotype_max_p:
-                        mean_p = df.groupby("clone_id")["p"].max()
-                    elif clonotype_quantile_p:
-                        mean_p = df.groupby("clone_id")["p"].quantile(clonotype_quantile_p, interpolation='higher')
+                    elif clonotype_median_p:
+                        mean_p = df.groupby("clone_id")["p"].quantile(0.5, interpolation='higher')
                     p = jnp.array(mean_p.values)[clone_id]
             return p
 
@@ -422,9 +415,6 @@ def __return_p_summary(p_sample):
         clone_id = clone_id if clone_id is not None else data.get("clone_continuous", None)
         clone_id = pd.factorize(clone_id)[0] if clone_id is not None else None
         
-        if clonotype_median_p and ~clonotype_quantile_p:
-            clonotype_quantile_p = 0.5
-
         if self.sampler is None and self.svi_result is None:
             raise RuntimeError("Model has not been fit yet. Please call first `fit` or `fit_svi`.")
 
@@ -464,17 +454,6 @@ def __return_p_summary(p_sample):
         if clonotype_adherence and clone_id is not None:
             assignment = assignment[clone_id]
             p = p[clone_id]
-        
-        if predict_at_least_one_binder and assignment.sum() == 0:
-            # if no binder is assigned, assign the cell with highest p as binder
-            if clonotype_mean_p:
-                # assign the clone with highest mean p as binder
-                clone_p = p.mean(axis=0) if p.ndim == 3 else p
-                clone_p_mean = pd.DataFrame({"p": clone_p, "clone_id": clone_id}).groupby("clone_id")["p"].mean()
-                best_clone = clone_p_mean.idxmax()
-                assignment = assignment.at[clone_id == best_clone].set(1)
-            else:
-                assignment = assignment.at[jnp.argmax(p)].set(1)
 
         return p, assignment
 
@@ -498,7 +477,7 @@ def __make_arvis(self):
         return self.trace
 
     @staticmethod
-    def _predict_posterior_class_dist(p_samples,  target_fdr, cred_intvl, nof_thresh=100):
+    def _predict_posterior_class_dist(p_samples, target_fdr, cred_intvl, nof_thresh=100):
         """
         Posterior BFDR thresholding (Newton et al. 2004, extended with posterior uncertainty).
 
@@ -530,42 +509,24 @@ def _predict_posterior_class_dist(p_samples,  target_fdr, cred_intvl, nof_thresh
                 - Hard assignments (0/1), shape (n_samples,)
                 - Selected threshold \(\tau\)
         """
-        p_samples = p_samples[:,:, 1]
-        p_mean = jnp.mean(p_samples, axis=0) # (n, )
-
-        # Candidate thresholds = unique posterior means
-        # or even grid in (0, 1)
-        #candidate_thresh = jnp.sort(jnp.unique(p_mean))  # (T,)
-        candidate_thresh = jnp.linspace(0.0, 1.0, nof_thresh+2)[1:-1]
-
-        # Expand shapes: (n_draws, n) vs (T,)
-        # -> disc_per_thr_draw: shape (T, n_draws, n)
-        # contains the binder assignment/discoveries for each candidate threshold and draw
-        disc_per_thr_draw = p_samples[None, :, :] >= candidate_thresh[:, None, None]
-
-        # number of discoveries per draw: (T, n_draws)
-        n_disc = disc_per_thr_draw.sum(axis=2)
+        p_samples = p_samples[:, :, 1]
+        p_mean = jnp.mean(p_samples, axis=0)
+        lfdr = 1.0 - p_samples
+        candidate_thresh = jnp.linspace(0.0, 1.0, nof_thresh + 2)[1:-1]
 
-        # local fdr = 1 - p
-        lfdr = 1.0 - p_samples  # (n_draws, n)
+        def eval_threshold(_, tau):
+            disc = p_samples >= tau
+            n_disc = disc.sum(axis=1)
+            sum_lfdr = jnp.sum(jnp.where(disc, lfdr, 0.0), axis=1)
+            gfdr = jnp.where(n_disc > 0, sum_lfdr / n_disc, 0.0)
+            valid = jnp.mean(gfdr <= target_fdr) >= cred_intvl
+            mean_n_disc = jnp.mean(n_disc)
+            return None, (valid, mean_n_disc)
 
-        # sum of lfdr among discoveries: (T, n_draws), sum of local FDR per threshold and draw
-        sum_lfdr_per_thr_draw = jnp.einsum("tns,ns->tn", disc_per_thr_draw, lfdr)
+        _, (valid_thr, n_discoveries) = jax.lax.scan(eval_threshold, None, candidate_thresh)
 
-        # global FDR per threshold and draw, avoid div-by-zero: set gfdr_per_thr_draw=0 where n_disc=0: (T, n_draws)
-        gfdr_per_thr_draw = jnp.where(n_disc > 0, sum_lfdr_per_thr_draw / n_disc, 0.0)
-
-        # posterior probability that FDR ≤ target: (T,)
-        # keep thresholds that pass cred_level
-        valid = (gfdr_per_thr_draw <= target_fdr).mean(axis=1) >= cred_intvl
-
-        if valid.any():
-            n_discoveries = n_disc.mean(axis=1)
-            # select largest threshold that passes credibility interval on FDR
-            threshold_idx = jnp.argmax(jnp.where(valid, n_discoveries, -1))
-            threshold = candidate_thresh[threshold_idx]
-        else:
-            threshold = 1.0
+        threshold_idx = jnp.argmax(jnp.where(valid_thr, n_discoveries, -1.0))
+        threshold = jnp.where(jnp.any(valid_thr), candidate_thresh[threshold_idx], 1.0)
 
         assignment = (p_mean >= threshold).astype(jnp.int32)
         return p_mean, assignment, threshold
@@ -964,7 +925,7 @@ def preprocess_model_data(self,
         clone = None if c is None else jnp.array(c, dtype=INT_DTYPE)
         zscore = jnp.abs((x - jnp.mean(x)) / jnp.std(x))
         outlier_threshold = 100 # TODO Hardcoded
-
+        # With outliers
         self.data_full = {"x": jnp.array(x, dtype=INT_DTYPE),
                           "s": None if s is None else jnp.array(s, dtype=FLOAT_DTYPE),
                           "x_neg": None if neg_cont is None else jnp.array(neg_cont, dtype=FLOAT_DTYPE),
@@ -973,7 +934,7 @@ def preprocess_model_data(self,
                           "clone_continuous": None if clone is None else jnp.searchsorted(jnp.unique(clone), clone),
                           "sigma": None if sigma is None else jnp.array(sigma, dtype=FLOAT_DTYPE),
                           }
-        
+        # Without outliers
         self.data = {"x": jnp.array(x[jnp.where(zscore < outlier_threshold)], dtype=INT_DTYPE),
                      "s": jnp.array(s[jnp.where(zscore < outlier_threshold)], dtype=FLOAT_DTYPE) if s is not None else None,
                      "x_neg": jnp.array(neg_cont[jnp.where(zscore < outlier_threshold)], dtype=FLOAT_DTYPE) if neg_cont is not None else None,
@@ -995,20 +956,6 @@ def _init_kmeans(self, scale_factor=1.0, outlier_threshold=None) -> Dict:
         returns: Dict with params estimates and  k-mean labels,
         """
         x = self.data["x"].copy()
-        # # remove outliers
-        # if outlier_threshold is not None:
-        #     x_log = np.log(x + 1)  # Transform to log scale, roughly normal distributed
-        #     zscore = (x_log - x_log.mean()) / x_log.std()
-        #     print(zscore.max())
-        #     x_no_outliers = x[zscore < outlier_threshold]
-        #     x_no_outliers = x_no_outliers.sort()
-        #     diffs = np.diff(x_no_outliers, prepend=x_no_outliers[0])
-        #     # TODO What value to define a huge gap?
-        #     huge_gap_indices = np.where(diffs > x.max() / 3)[0]
-        #     if len(huge_gap_indices) > 0:
-        #         first_gap_index = huge_gap_indices[0]
-        #         x_no_outliers = x_no_outliers[:first_gap_index]
-        # else:
         x_no_outliers = x
         clone = self.data.get("clone_continuous", None)
         sigma = self.data.get("sigma", None)

From 359630247946562c205b1615793f1377e49c696a Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Thu, 18 Jun 2026 19:48:35 +0200
Subject: [PATCH 34/39] feature: add to utils.py `mean_ci_t_interval`,
 `aggregate_csv`, `get_cpu_model`

---
 dextrademixer/utils/utils.py | 86 +++++++++++++++++++++++++++++++++---
 1 file changed, 80 insertions(+), 6 deletions(-)

diff --git a/dextrademixer/utils/utils.py b/dextrademixer/utils/utils.py
index d4ce509..fb33cc3 100644
--- a/dextrademixer/utils/utils.py
+++ b/dextrademixer/utils/utils.py
@@ -12,9 +12,11 @@
 
 from jax import pure_callback
 from numpy import ndarray, dtype, bool_
-from scipy.stats import ortho_group, random_correlation
+from scipy.stats import ortho_group, random_correlation, t
 from sklearn.metrics import (roc_auc_score, average_precision_score, f1_score, precision_score, recall_score,
                              accuracy_score, matthews_corrcoef)
+from concurrent.futures import ThreadPoolExecutor
+from tqdm import tqdm
 
 
 def gower_centering(distance_matrix):
@@ -370,11 +372,22 @@ def init_worker(worker_mem_limit_mb=None):
         pass
 
 
-def calculate_metrics(y_true: np.ndarray, p_pred: np.ndarray, assignment: np.ndarray) -> dict:
-    results_dict = {'auroc': roc_auc_score(y_true, p_pred), 'aps': average_precision_score(y_true, p_pred),
-                    'f1': f1_score(y_true, assignment), 'precision': precision_score(y_true, assignment),
-                    'recall': recall_score(y_true, assignment), 'accuracy': accuracy_score(y_true, assignment),
-                    'mcc': matthews_corrcoef(y_true, assignment)}
+def calculate_metrics(y_true: np.ndarray, p_pred: np.ndarray, assignment: np.ndarray, full_metrics: bool = True) -> dict:
+    """
+    Calculates performance metrics based on true labels, predicted probabilities, and binary assignments.
+    Args:
+        y_true (np.ndarray): True binary labels (0 or 1).
+        p_pred (np.ndarray): Predicted probabilities for the positive class.
+        assignment (np.ndarray): Binary predictions based on a threshold applied to p_pred.
+        full_metrics (bool): If True, calculates additionally AUROC, accuracy and MCC. Default is True.
+    Returns:
+        dict: A dictionary containing calculated metrics.
+    """
+    results_dict = {'aps': average_precision_score(y_true, p_pred), 'f1': f1_score(y_true, assignment), 
+                    'precision': precision_score(y_true, assignment), 'recall': recall_score(y_true, assignment), }
+    
+    if full_metrics:
+        results_dict.update({'auroc': roc_auc_score(y_true, p_pred), 'accuracy': accuracy_score(y_true, assignment), 'mcc': matthews_corrcoef(y_true, assignment)})
 
     tp = np.sum(assignment.astype(bool) & y_true.astype(bool))
     fp = np.sum(assignment.astype(bool) & ~y_true.astype(bool))
@@ -393,3 +406,64 @@ def calculate_metrics(y_true: np.ndarray, p_pred: np.ndarray, assignment: np.nda
     results_dict['fn'] = fn
     
     return results_dict
+
+
+def mean_ci_t_interval(x, confidence=0.95):
+    x = x.dropna()
+    n = len(x)
+    mean = x.mean()
+
+    alpha = 1 - confidence
+    q = 1 - alpha / 2  # for 95% CI: 1 - 0.05/2 = 0.975
+
+    tcrit = t.ppf(q, df=n - 1)
+    se = x.std(ddof=1) / np.sqrt(n)
+    ci = tcrit * se
+
+    ci_low = mean - ci
+    ci_high = mean + ci
+
+    return f"{mean:.3f} [{ci_low:.3f}, {ci_high:.3f}]"
+
+
+def aggregate_csv(experiment_path='.', output_path='agg_results.csv', rerun=False, paths=None, fps=None) -> pd.DataFrame:
+    """
+    Aggregates CSV files from single experiment outputs into a single DataFrame and saves it as a CSV file using multiprocessing.
+    Args:
+        experiment_path (str): The base directory where the CSV files are located.
+        agg_fp (str): The file path for the aggregated CSV file to be saved.
+        rerun (bool): If True, forces re-aggregation even if the aggregated file already exists. Default is False.
+        paths (list of str): A list of subdirectories within experiment_path to search for CSV files. If None, it defaults to ['csv'].
+        fps (list of str): Alternative instead of using directories, use list of file paths to aggregate. If provided, paths will be ignored.
+    Returns:
+        df (pd.DataFrame): The aggregated DataFrame containing data from all CSV files.
+    """
+    def read_csv(fp):
+        return pd.read_csv(fp, index_col=0)
+    
+    if os.path.exists(output_path) and not rerun:
+        df = pd.read_csv(output_path, index_col=0)
+    else:
+        paths = paths if paths is not None else ['csv']
+        dfs = []
+        if fps is None:
+            fps = [os.path.join(experiment_path, path, f) for path in paths for f in os.listdir(os.path.join(experiment_path, path)) if f.endswith('.csv') and 'intermediate.csv' not in f]
+            
+        with ThreadPoolExecutor() as ex:
+            df = list(tqdm(ex.map(read_csv, fps), total=len(fps)))
+        dfs.extend(df)
+    
+        df = pd.concat(dfs, ignore_index=True)
+        df.to_csv(output_path)
+        
+    return df
+
+
+def get_cpu_model():
+    try:
+        with open("/proc/cpuinfo") as f:
+            for line in f:
+                if "model name" in line:
+                    return line.split(":", 1)[1].strip()
+    except:
+        return "Unknown"

From a7b6fcafcc9ea9fc8c1a6e76b91245f72c0a795c Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Thu, 18 Jun 2026 19:50:36 +0200
Subject: [PATCH 35/39] cleanup: remove hyperparameter tuning

---
 .../aggregate_results.py                      |  28 ---
 ...create_data_mean_variance_fold_increase.py |  45 ----
 .../environment_optuna.yaml                   |  25 ---
 .../optuna_dextrademixer.py                   | 195 ------------------
 .../slurm_run_optuna_multi_at_once.sh         |  33 ---
 .../snakefile_run_optuna                      |  81 --------
 .../snakefile_run_optuna_multi_at_once        |  85 --------
 7 files changed, 492 deletions(-)
 delete mode 100644 experiments/hyperparameter_tuning/aggregate_results.py
 delete mode 100644 experiments/hyperparameter_tuning/create_data_mean_variance_fold_increase.py
 delete mode 100644 experiments/hyperparameter_tuning/environment_optuna.yaml
 delete mode 100644 experiments/hyperparameter_tuning/optuna_dextrademixer.py
 delete mode 100755 experiments/hyperparameter_tuning/slurm_run_optuna_multi_at_once.sh
 delete mode 100644 experiments/hyperparameter_tuning/snakefile_run_optuna
 delete mode 100644 experiments/hyperparameter_tuning/snakefile_run_optuna_multi_at_once

diff --git a/experiments/hyperparameter_tuning/aggregate_results.py b/experiments/hyperparameter_tuning/aggregate_results.py
deleted file mode 100644
index 131487f..0000000
--- a/experiments/hyperparameter_tuning/aggregate_results.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import glob
-import sys
-import os
-
-import pandas as pd
-
-
-def main(f_in_dir, f_out):
-    files = glob.glob(os.path.join(f_in_dir, "*"))
-    dfs = [pd.read_csv(f) for f in files if ".csv" in f]
-
-    extracted_rows=[]
-    for name, df in zip(files, dfs):
-        if 'value' not in df.columns:
-                raise ValueError("Each DataFrame must have a 'value' column.")
-
-        max_row = df.loc[df['value'].idxmax()]
-        param_cols = ["model", "value"]+[col for col in df.columns if col.startswith('param')]
-        max_row["model"] = name
-        extracted_rows.append(max_row[param_cols])
-
-    pd.DataFrame(extracted_rows).to_csv(f_out, index=False)
-
-
-if __name__ == "__main__":
-    f_in_dir = sys.argv[1]
-    f_out = sys.argv[2]
-    main(f_in_dir, f_out)
\ No newline at end of file
diff --git a/experiments/hyperparameter_tuning/create_data_mean_variance_fold_increase.py b/experiments/hyperparameter_tuning/create_data_mean_variance_fold_increase.py
deleted file mode 100644
index 7c9192d..0000000
--- a/experiments/hyperparameter_tuning/create_data_mean_variance_fold_increase.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import sys
-sys.path.append("../../")
-
-import matplotlib.pyplot as plt
-from dextrademixer.utils import DextramerSimulator
-import hashlib
-
-
-def main():
-    output_h5mu = sys.argv[1]
-    output_pdf = sys.argv[2]
-    total_cell = int(sys.argv[3])
-    nclones = int(sys.argv[4])
-    p_outlier = float(sys.argv[5])
-    p = float(sys.argv[6])
-    cov = bool(int(sys.argv[7]))
-    mean_inc = float(sys.argv[8])
-    var_inc = float(sys.argv[9])
-    i = int(sys.argv[10])
-
-    sim_config = f'{total_cell}_{nclones}_{p_outlier}_{p}_{cov}_{mean_inc}_{var_inc}_{i}'
-    # Use sim_hash to ensure reproducibility, but have variance in seed
-    sim_hash = hashlib.sha256(sim_config.encode('utf-8')).digest()
-    seed = int.from_bytes(sim_hash[:4], byteorder='little')
-
-    plt.ioff()
-    sim = DextramerSimulator()
-
-    mdata1, fig = sim.simulate_pmhc_data_from_distribution(total_cells=total_cell,
-                                                            nof_clones=nclones,
-                                                            p_binding_outlier=p_outlier,
-                                                            binding_ratio=p,
-                                                            mean_inc=mean_inc,
-                                                            var_inc=var_inc,
-                                                            simulate_neg_control=True,
-                                                            use_clonotype_cov=cov,
-                                                            plot_data=True,
-                                                            rng_key=seed)
-    mdata1.write(output_h5mu)
-    fig.savefig(output_pdf)
-    plt.close()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/experiments/hyperparameter_tuning/environment_optuna.yaml b/experiments/hyperparameter_tuning/environment_optuna.yaml
deleted file mode 100644
index 3969ba4..0000000
--- a/experiments/hyperparameter_tuning/environment_optuna.yaml
+++ /dev/null
@@ -1,25 +0,0 @@
-name: dextrademixer_optuna
-channels:
-  - conda-forge
-  - bioconda
-dependencies:
-  - python=3.10
-  - pip
-  - pip:
-      - numpy>=1.25.2
-      - scipy>=1.11.4
-      - pandas>=2.1.4
-      - statsmodels>=0.14.1
-      - matplotlib>=3.8.3
-      - jax>=0.4.26
-      - jaxlib>=0.4.26
-      - numpyro>=0.14.0
-      - arviz>=0.17.1
-      - mudata>=0.2.3
-      - muon>=0.1.6
-      - optax>=0.2.2
-      - scanpy>=1.9.8
-      - scirpy>=0.13.0
-      - scikit-learn>=1.6.1
-      - optuna>=4.2.0
-      - torch>=2.6.0
diff --git a/experiments/hyperparameter_tuning/optuna_dextrademixer.py b/experiments/hyperparameter_tuning/optuna_dextrademixer.py
deleted file mode 100644
index 949c6a2..0000000
--- a/experiments/hyperparameter_tuning/optuna_dextrademixer.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import argparse
-import multiprocessing
-import os
-import warnings
-import pickle
-from time import strftime
-
-import numpyro
-import optuna
-import numpy as np
-from sklearn.metrics import (roc_auc_score, average_precision_score, f1_score, precision_score, recall_score,
-                             accuracy_score)
-from optuna.storages import JournalStorage
-from optuna.storages.journal import JournalFileBackend
-
-import sys
-
-sys.path.append("../../")
-from dextrademixer.model import DextraDemixer
-from dextrademixer.utils import convert_str_to_bool_and_none
-import muon as mu
-
-multiprocessing.set_start_method("spawn", force=True)
-
-
-def parse_arguments():
-    parser = argparse.ArgumentParser(description="Run tool on multiple simulation files and average results.")
-    # Data parameters
-    parser.add_argument("input_files", nargs="+", help="List of input .h5mu files")
-    parser.add_argument("output_file", help="Output CSV file for averaged results")
-    parser.add_argument("--pmhc_key", type=str, default="pmhc1", help="Key for pMHC counts")
-    parser.add_argument("--gex_key", type=str, default="gex", help="Key for pMHC count modality. "
-                                                                   "Usually saved in 'gex' modality.")
-    parser.add_argument("--label_key", type=str, default="is_binder", help="Key for binder labels")
-
-    # Model parameters
-    parser.add_argument("--model_type", default='mixturemodelkmeans', help="Model type",
-                        choices=["mixturemodelkmeans", "mixturemodel"])
-    parser.add_argument("--mode", type=str, default="C", help="Processing mode")
-    parser.add_argument("--neg_ctrl_key", type=str, default=None, help="Negative control key. "
-                                                                       "If none, no negative control is used.")
-    parser.add_argument("--ir_clone_key", type=str, default='clone_id', help="Clonotype id key. "
-                                                                             "If None, no clonotype id is used.")
-    parser.add_argument("--alpha_model", type=str, default="overdispersion",
-                        choices=["overdispersion", "kmeans"],
-                        help="Modeling of the alpha parameter. Options: 'overdispersion', 'kmeans'.")
-    parser.add_argument("--overdispersion_scale_prior", type=float, default=None,
-                        help="Prior for scale parameter of HalfCauchy for overdispersion model. "
-                             "Not used if alpha_model == kmeans. None for optuna to suggest.")
-    parser.add_argument("--prior_value", type=float, default=None,
-                        help="Prior for scale parameter if alpha_model == kmeans "
-                             "or scale for HalfCauchy if alpha_model == overdispersion. "
-                             "None for optuna to suggest.")
-    parser.add_argument("--outlier_threshold", type=float, default=4.0,
-                        help="Threshold for outlier removal based on log transformed z-score.")
-    parser.add_argument("--target_fdr", type=float, default=0.05,
-                        help="Target FDR for posterior class assignment. If None, uses threshold instead.")
-    parser.add_argument("--threshold", type=float, default=None,
-                        help="Threshold for posterior class assignment. If None, uses target_fdr instead.")
-
-    # Optimization parameters
-    parser.add_argument("--threads", type=int, default=None, help="Number of threads")
-    parser.add_argument("--n_trials", type=int, default=100, help="Number of optuna trials")
-    parser.add_argument("--seed", type=int, default=42, help="Random seed")
-    parser.add_argument("--n_inits", type=int, default=10, help="Number of initializations for SVI."
-                                                                "Can be set low, if using k-means to initialize.")
-    parser.add_argument("--maxiter", type=int, default=10000,
-                        help="Maximum number of iterations for optimization."
-                             "If None, uses optuna to suggest.")
-    parser.add_argument("--lr", type=float, default=None,
-                        help="Learning rate for Adam optimizer. If None, uses optuna to suggest.")
-    parser.add_argument("--mp", type=str, default=True, help="Use multiprocessing")
-    return parser.parse_args()
-
-
-def run_inference(f_in: str, args: argparse.Namespace, opt_params: dict, trial_number: int=-1):
-    numpyro.set_host_device_count(1)
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-        mdata = mu.read(f_in)
-    y_true = mdata.mod["airr"].obs[args.label_key]
-
-    mixer = DextraDemixer(model_type=args.model_type, mode=args.mode, alpha_model=args.alpha_model)
-    mixer.preprocess_model_data(mdata,
-                                pmhc_key=args.pmhc_key,
-                                gex_key=args.gex_key,
-                                neg_ctrl_key=args.neg_ctrl_key,
-                                ir_clone_key=args.ir_clone_key,
-                                outlier_threshold=args.outlier_threshold,)
-
-    mixer.model._model_config["overdispersion_scale_prior"] = opt_params["prior_value"]
-    mixer.model._model_config["var_hyperprior"] = opt_params["prior_value"]
-
-    trace, best_loss = mixer.fit_svi(svi_config=opt_params,
-                                     nof_inits=opt_params["n_inits"],
-                                     rng_key=args.seed,
-                                     return_loss=True)
-    p_pred, assignment = mixer.predict_posterior_class(target_fdr=args.target_fdr, threshold=args.threshold)
-
-    config = (f"{args.model_type}_{args.mode}_neg={args.neg_ctrl_key}_clone={args.ir_clone_key}_{args.alpha_model}_"
-              f"prior={opt_params['prior_value']}_"
-              f"lr{opt_params['adam']['init_value']}_"
-              f"{f_in.replace('simulation/sim_', '').replace('.h5mu', '')}_"
-              f"_Trial={trial_number}")
-    config = config.replace("/", "_").replace(":", "_")
-
-    mixer.plot_results(assignment, p_pred, y_true, args.seed, config)
-
-    os.makedirs("saved_models", exist_ok=True)
-    with open(f"saved_models/{config}.pkl", "wb") as f:
-        pickle.dump(mixer, f)
-
-    return y_true, p_pred, assignment, best_loss
-
-
-def worker(f_in, args, opt_params, trial_number=-1):
-    # If multiprocessing is enabled, the following is needed to track which dataset failed
-    if args.mp:
-        try:
-            y_true, p_pred, assignment_fdr, best_loss = run_inference(f_in, args, opt_params, trial_number=trial_number)
-        except Exception as e:
-            raise RuntimeError(f"Failed on {f_in}") from e
-    else:
-        y_true, p_pred, assignment_fdr, best_loss = run_inference(f_in, args, opt_params, trial_number=trial_number)
-    return y_true, p_pred, assignment_fdr, best_loss
-
-
-def main():
-    args = parse_arguments()
-    args = convert_str_to_bool_and_none(args)
-
-    def objective(trial: optuna.Trial) -> float:
-        """Optuna objective function."""
-        # Evaluate over multiple datasets
-        opt_params = {"maxiter": trial.suggest_int("maxiter", 500, 50000, log=True)
-                                 if args.maxiter is None else args.maxiter,
-                      "n_inits": args.n_inits,
-                      "adam": {"init_value": trial.suggest_float("init_value", 1e-4, 1e0, log=True)
-                                             if args.lr is None else args.lr, },
-                      "prior_value": trial.suggest_float("prior_value", 1e-2, 1e1, log=True)
-                                     if args.prior_value is None else args.prior_value,
-                      }
-        if args.mp:
-            with multiprocessing.Pool(processes=args.threads) as pool:
-                results = pool.starmap(worker, [(f_in, args, opt_params, trial.number)
-                                                for f_in in args.input_files], chunksize=1)
-        else:
-            results = []
-            for f_in in args.input_files:
-                results.append(worker(f_in, args, opt_params, trial.number))
-
-        y_true, p_pred, assignment_fdr, best_loss = zip(*results)
-
-        f1_list = []
-        for i, dataset in enumerate(args.input_files):
-            if not np.isfinite(p_pred[i]).any() or not np.isfinite(assignment_fdr[i]).any():
-                f1 = 0.0
-                for metric in ["f1", "precision", "recall", "aps", "acc"]:
-                    trial.set_user_attr(f"{metric}_{dataset}", 0.0)
-            else:
-                trial.set_user_attr(f"auc_{dataset}", roc_auc_score(y_true[i], p_pred[i]))
-                f1 = f1_score(y_true[i], assignment_fdr[i])
-                trial.set_user_attr(f"f1_{dataset}", f1)
-                trial.set_user_attr(f"precision_{dataset}", precision_score(y_true[i], assignment_fdr[i]))
-                trial.set_user_attr(f"recall_{dataset}", recall_score(y_true[i], assignment_fdr[i]))
-                trial.set_user_attr(f"aps_{dataset}", average_precision_score(y_true[i], p_pred[i]))
-                trial.set_user_attr(f"acc_{dataset}", accuracy_score(y_true[i], assignment_fdr[i]))
-            f1_list.append(f1)
-            trial.set_user_attr(f"converged_{dataset}", best_loss[i]["converged"])
-            trial.set_user_attr(f"best_loss_{dataset}", float(best_loss[i]["best_loss"]))
-            trial.set_user_attr(f"init_loss_{dataset}", float(best_loss[i]["init_loss"]))
-            trial.set_user_attr(f"best_iteration_{dataset}", int(best_loss[i]["best_iteration"]))
-
-        mean_f1 = np.mean(f1_list)
-
-        return mean_f1
-
-    sampler = optuna.samplers.GPSampler(seed=args.seed)
-    study_name = (f"{strftime('%Y%m%d-%H%M%S')}_mode={args.mode}_neg={args.neg_ctrl_key}_clone={args.ir_clone_key}_"
-                  f"lr={args.lr}_alpha_model={args.alpha_model}_"
-                  f"prior={args.prior_value}")
-    os.makedirs("optuna_study", exist_ok=True)
-
-    storage = JournalStorage(JournalFileBackend(f"optuna_study/{study_name}.log"))
-    study = optuna.create_study(storage=storage,
-                                sampler=sampler, direction="maximize", study_name=study_name,)
-    study.optimize(objective, n_trials=args.n_trials)
-
-    df = study.trials_dataframe()
-    os.makedirs(os.path.dirname(args.output_file), exist_ok=True)
-    df.to_csv(args.output_file)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/experiments/hyperparameter_tuning/slurm_run_optuna_multi_at_once.sh b/experiments/hyperparameter_tuning/slurm_run_optuna_multi_at_once.sh
deleted file mode 100755
index 0d1fc61..0000000
--- a/experiments/hyperparameter_tuning/slurm_run_optuna_multi_at_once.sh
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/bin/bash
-
-
-PATH_BASE="$(dirname "$(realpath "$0")")/../.."
-PATH_LOGS="."
-
-mkdir -p "${PATH_LOGS}/logs/slurm_logs"
-mkdir -p "${PATH_LOGS}/logs/jobs"
-
-DATE_TIME=$(date +"%Y-%m-%d_%H-%M-%S")
-JOB_NAME="SKM_dex_optuna_${DATE_TIME}"
-
-echo "#!/bin/bash
-#SBATCH -J ${JOB_NAME}
-#SBATCH -o ${PATH_LOGS}/logs/slurm_logs/${JOB_NAME}.out
-#SBATCH -e ${PATH_LOGS}/logs/slurm_logs/${JOB_NAME}.err
-#SBATCH -p cpu_p
-#SBATCH -t 3-00:00:00
-#SBATCH -c 4
-#SBATCH --mem=24G
-#SBATCH --qos=cpu_normal
-#SBATCH --nice=0
-
-source ~/.bash_profile
-conda activate dextrademixer
-cd ${PATH_BASE}/experiments/hyperparameter_tuning
-
-snakemake -s snakefile_run_optuna_multi_at_once --unlock
-snakemake -s snakefile_run_optuna_multi_at_once -c4 --profile ${PATH_BASE}/experiments/.slurm/ --cluster-status ${PATH_BASE}/experiments/.slurm/status.py --conda-frontend conda
-
-
-" > ${PATH_LOGS}/logs/jobs/${JOB_NAME}.cmd
-sbatch ${PATH_LOGS}/logs/jobs/${JOB_NAME}.cmd
diff --git a/experiments/hyperparameter_tuning/snakefile_run_optuna b/experiments/hyperparameter_tuning/snakefile_run_optuna
deleted file mode 100644
index 7e35312..0000000
--- a/experiments/hyperparameter_tuning/snakefile_run_optuna
+++ /dev/null
@@ -1,81 +0,0 @@
-from itertools import product
-from snakemake.utils import min_version
-
-min_version("6.0")
-
-# Configuration
-cell_list = [5000]
-clone_list = [100]
-p_list = [0.1]
-cov_list = [0]
-p_outlier_list = [0.0]
-mean_fold_increase = [2]
-var_fold_increase = [1.2]
-reps = 1
-
-model_types = ["mixturemodel", "mixturemodelkmeans"]
-# Parameter dictionary
-param_dict = {
-    "mode": ["I", "H", "C"],
-    "neg": [None, "neg_control"],
-    "clonotype": [None, "clone_id"],
-}
-
-# Create a list of all possible combinations of parameters
-results_list = [f"optuna/{model_type}_mode{mode}_neg{neg}_clonotype{clonotype}.pred.csv"
-                for model_type in model_types
-                for mode in param_dict["mode"]
-                for neg in param_dict["neg"]
-                for clonotype in param_dict["clonotype"] if (mode == "C" and clonotype is not None) or mode != "C"]
-
-rule all:
-    input:
-        "results/aggregated_results.csv"
-
-rule run_simulation:
-    output:
-        h5mu = "simulation/sim_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.h5mu",
-        pdf = "simulation/sim_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.pdf"
-    conda:
-        "environment_optuna.yaml"
-    shell:
-        "python create_data_mean_variance_fold_increase.py {output.h5mu} {output.pdf} "
-        "{wildcards.cell} {wildcards.clone} {wildcards.po} {wildcards.p} {wildcards.cov} "
-        "{wildcards.mean} {wildcards.var} {wildcards.i}"
-
-rule run_tool:
-    input:
-        expand(
-            "simulation/sim_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.h5mu",
-            cell=cell_list,
-            clone=clone_list,
-            po=p_outlier_list,
-            p=p_list,
-            cov=cov_list,
-            mean=mean_fold_increase,
-            var=var_fold_increase,
-            i=range(reps)
-        )
-    output:
-        "optuna/{model_type}_mode{mode}_neg{neg}_clonotype{clonotype}.pred.csv"
-    params:
-        mode="{mode}",
-        neg="{neg}",
-        clonotype="{clonotype}",
-        model_type="{model_type}"
-    conda:
-        "environment_optuna.yaml"
-    threads:
-        1  # TODO higher number of threads decreases performance
-    shell:
-        "python optuna_dextrademixer.py {input} {output} --model_type {params.model_type} --mode {params.mode} --neg {params.neg} --clonotype {params.clonotype} --threads {threads}"
-
-rule aggregate_results:
-    input:
-        results_list
-    output:
-        "results/aggregated_results.csv"
-    conda:
-        "environment_optuna.yaml"
-    shell:
-        "python aggregate_results.py optuna {output}"
diff --git a/experiments/hyperparameter_tuning/snakefile_run_optuna_multi_at_once b/experiments/hyperparameter_tuning/snakefile_run_optuna_multi_at_once
deleted file mode 100644
index 320bbfd..0000000
--- a/experiments/hyperparameter_tuning/snakefile_run_optuna_multi_at_once
+++ /dev/null
@@ -1,85 +0,0 @@
-# For each set of hyperparameter evaluate on multiple simulation datasets and use mean to determine best hparams
-
-from itertools import product
-from snakemake.utils import min_version
-from pprint import pprint
-
-min_version("6.0")
-
-# Configuration
-cell_list = [10000]
-clone_list = [2000, 4000]
-p_list = [0.1, 0.01]
-cov_list = [0]
-p_outlier_list = [0.0]
-mean_fold_increase = [10, 25]
-var_fold_increase = [2]
-reps = [2]
-
-# Manually create list to avoid invalid combinations
-tool_list = [
-    f"optuna/{model_type}_mode{mode}_neg{neg}_clonotype{clonotype}.csv"
-    for model_type in ["mixturemodelkmeans", "mixturemodel"]
-    for mode in ["I", "H", "C"]
-    for neg in [None, "neg_control"]
-    for clonotype in [None, "clone_id"]
-    if (mode == "C" and clonotype is not None) or mode != "C"
-]
-
-num_simulations = len(cell_list) * len(clone_list) * len(p_list) * len(cov_list) * len(p_outlier_list) * len(mean_fold_increase) * len(var_fold_increase) * len(reps)
-
-rule all:
-    input:
-        "results/aggregated_results.csv"
-
-rule run_simulation:
-    output:
-        h5mu = "simulation/sim_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.h5mu",
-        pdf = "simulation/sim_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.pdf"
-    conda:
-       "environment_optuna.yaml"
-    shell:
-        "python create_data_mean_variance_fold_increase.py {output.h5mu} {output.pdf} "
-        "{wildcards.cell} {wildcards.clone} {wildcards.po} {wildcards.p} {wildcards.cov} "
-        "{wildcards.mean} {wildcards.var} {wildcards.i}"
-
-rule run_tool:
-    input:
-        expand(
-            "simulation/sim_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.h5mu",
-            cell=cell_list,
-            clone=clone_list,
-            po=p_outlier_list,
-            p=p_list,
-            cov=cov_list,
-            mean=mean_fold_increase,
-            var=var_fold_increase,
-            i=reps
-        )
-    output:
-        "optuna/{model_type}_mode{mode}_neg{neg}_clonotype{clonotype}.csv"
-    params:
-        mode="{mode}",
-        neg="{neg}",
-        clonotype="{clonotype}",
-        model_type="{model_type}",
-    conda:
-       "environment_optuna.yaml"
-    threads:
-        min(32, num_simulations)  # Cluster can only request 32 cores at once
-    resources:
-        c=min(32, num_simulations),
-        mem_mb=8000 * min(32, num_simulations),
-        mem=f"{8 * min(32, num_simulations)}G",
-    shell:
-        "python optuna_dextrademixer.py {input} {output} --model_type {params.model_type} --mode {params.mode} --neg {params.neg} --clonotype {params.clonotype} --threads {threads}"
-
-rule aggregate_results:
-    input:
-        tool_list
-    output:
-        "results/aggregated_results.csv"
-    conda:
-       "environment_optuna.yaml"
-    shell:
-        "python aggregate_results.py optuna {output}"

From f42668bd4e0a6490442f3cebed66f1bdc26933bc Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Thu, 18 Jun 2026 19:55:22 +0200
Subject: [PATCH 36/39] cleanup: synthetic_benchmark unused files

---
 .../benchmarks/scenario1/config.yaml          |    8 -
 .../benchmarks/scenario_test/config.yaml      |    8 -
 .../estimate_sim_params.ipynb                 | 2539 -----------------
 .../synthetic_benchmark/run_beamt_mp.py       |   67 -
 .../run_dextrademixer_mp.py                   |  198 --
 .../synthetic_benchmark/simulate_scenarios.py |   74 -
 .../synthetic_benchmark/simulation/test.h5mu  |  Bin 747792 -> 0 bytes
 .../slurm_run_simulation.sh                   |   83 -
 .../synthetic_benchmark/slurm_run_timing.sh   |   32 -
 .../snakefile_run_simulation.smk              |   90 -
 .../snakefile_run_timing.smk                  |   76 -
 11 files changed, 3175 deletions(-)
 delete mode 100644 experiments/synthetic_benchmark/benchmarks/scenario1/config.yaml
 delete mode 100644 experiments/synthetic_benchmark/benchmarks/scenario_test/config.yaml
 delete mode 100644 experiments/synthetic_benchmark/estimate_sim_params.ipynb
 delete mode 100644 experiments/synthetic_benchmark/run_beamt_mp.py
 delete mode 100644 experiments/synthetic_benchmark/run_dextrademixer_mp.py
 delete mode 100644 experiments/synthetic_benchmark/simulate_scenarios.py
 delete mode 100644 experiments/synthetic_benchmark/simulation/test.h5mu
 delete mode 100755 experiments/synthetic_benchmark/slurm_run_simulation.sh
 delete mode 100755 experiments/synthetic_benchmark/slurm_run_timing.sh
 delete mode 100644 experiments/synthetic_benchmark/snakefile_run_simulation.smk
 delete mode 100644 experiments/synthetic_benchmark/snakefile_run_timing.smk

diff --git a/experiments/synthetic_benchmark/benchmarks/scenario1/config.yaml b/experiments/synthetic_benchmark/benchmarks/scenario1/config.yaml
deleted file mode 100644
index ea4f532..0000000
--- a/experiments/synthetic_benchmark/benchmarks/scenario1/config.yaml
+++ /dev/null
@@ -1,8 +0,0 @@
-total_cell: [500, 1000, 2000, 5000]
-frac_clone: [0.4]
-binding_ratio: [0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.9]
-use_clonotype_cov: [False]
-p_binding_outlier: [0.0]
-mean_inc: [10, 20, 50, 100, 200]
-var_inc: [null]
-i: [0, 1, 2,]
diff --git a/experiments/synthetic_benchmark/benchmarks/scenario_test/config.yaml b/experiments/synthetic_benchmark/benchmarks/scenario_test/config.yaml
deleted file mode 100644
index c54d6dc..0000000
--- a/experiments/synthetic_benchmark/benchmarks/scenario_test/config.yaml
+++ /dev/null
@@ -1,8 +0,0 @@
-total_cell: [500, 1000, 2000, 5000]
-frac_clone: [0.4]
-binding_ratio: [0.1]
-use_clonotype_cov: [False]
-p_binding_outlier: [0.0]
-mean_inc: [25, 50]
-var_inc: [100, 200]
-i: [0, 1, 2]
diff --git a/experiments/synthetic_benchmark/estimate_sim_params.ipynb b/experiments/synthetic_benchmark/estimate_sim_params.ipynb
deleted file mode 100644
index ac10eda..0000000
--- a/experiments/synthetic_benchmark/estimate_sim_params.ipynb
+++ /dev/null
@@ -1,2539 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6c23dee3-f16e-408a-9be9-c317b0177da0",
-   "metadata": {},
-   "source": [
-    "Check real BEAM-T dataset\n",
-    "Estimate parameters for simulation\n",
-    "Check if simulation makes sense"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a820175b-0368-4969-9eaf-8066fb5f142a",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-08-14T15:46:33.412581Z",
-     "start_time": "2025-08-14T15:46:33.370004Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import os\n",
-    "import warnings\n",
-    "from collections import defaultdict\n",
-    "from tqdm import tqdm\n",
-    "from sklearn.metrics import precision_recall_curve\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.image as mpimg\n",
-    "import muon as mu\n",
-    "import scirpy as ir\n",
-    "import scanpy as sc\n",
-    "from mudata import MuData\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "import numpyro.distributions as npd\n",
-    "import jax\n",
-    "import statsmodels.formula.api as smf\n",
-    "from scipy import stats\n",
-    "from scipy.stats import truncnorm\n",
-    "\n",
-    "\n",
-    "warnings.catch_warnings()\n",
-    "warnings.simplefilter(\"ignore\")\n",
-    "\n",
-    "pd.set_option('display.max_columns', 1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c96e60db-9425-483a-9222-f14cdef98c17",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-08-14T12:42:11.737315Z",
-     "start_time": "2025-08-14T12:42:11.707443Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e6c468fc-85ba-4d58-b8b5-81b08d3eb1d7",
-   "metadata": {},
-   "source": [
-    "# Estimate simulation parameters from real data (BEAM-T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8e3a13b6796b87b3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-08-14T12:42:12.126649Z",
-     "start_time": "2025-08-14T12:42:11.831021Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Preprocessing\n",
-    "experiment = '10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein'\n",
-    "if not os.path.exists(f'../../data/BEAMT/{experiment}/preprocessed.h5mu'):\n",
-    "    adata_tcr = ir.io.read_10x_vdj(\n",
-    "        f\"../../data/BEAMT/{experiment}/filtered_contig_annotations.csv\")\n",
-    "\n",
-    "    adata = sc.read_10x_h5(\n",
-    "        f\"../../data/BEAMT/{experiment}/sample_filtered_feature_bc_matrix.h5\",\n",
-    "        gex_only=False)\n",
-    "    adata.var_names_make_unique()\n",
-    "    mdata = mu.MuData({\"gex\": adata, \"airr\": adata_tcr})\n",
-    "    ir.pp.index_chains(mdata)\n",
-    "    ir.tl.chain_qc(mdata)\n",
-    "\n",
-    "    # filter TCRs only and antigen barcodes only\n",
-    "    mdata = mdata[mdata.obs[\"airr:receptor_type\"] == \"TCR\"]\n",
-    "    mdata = mdata[:, mdata.var[\"gex:feature_types\"] == \"Antigen Capture\"]\n",
-    "\n",
-    "    # minimal pMHC QC filtering\n",
-    "    sc.pp.filter_cells(mdata[\"gex\"], min_genes=1)\n",
-    "    sc.pp.filter_genes(mdata[\"gex\"], min_cells=10)\n",
-    "\n",
-    "    mdata.update()\n",
-    "\n",
-    "    mu.pp.filter_obs(mdata, \"airr:chain_pairing\", lambda x: ~np.isin(x, [\"orphan VDJ\", \"orphan VJ\"]))\n",
-    "    mu.pp.intersect_obs(mdata)\n",
-    "\n",
-    "    ir.pp.ir_dist(mdata)  # Adds clone_id\n",
-    "    ir.tl.define_clonotypes(mdata, receptor_arms=\"all\", dual_ir=\"primary_only\")\n",
-    "    \n",
-    "    ir.pp.ir_dist(mdata, metric=\"alignment\", sequence=\"aa\", cutoff=250)\n",
-    "    ir.tl.define_clonotype_clusters(mdata, sequence=\"aa\", metric=\"alignment\", receptor_arms=\"all\",\n",
-    "                                    dual_ir=\"primary_only\")  # Used for distance calculation\n",
-    "    ir.tl.clonotype_network(mdata, min_cells=3, sequence=\"aa\", metric=\"alignment\")\n",
-    "\n",
-    "    dist = mdata.mod[\"airr\"].uns[\"cc_aa_alignment\"][\"distances\"].toarray()\n",
-    "    mdata.mod[\"airr\"].uns[\"ir_dist_aa_full\"] = dist - 1\n",
-    "\n",
-    "    mdata.write(f'../../data/BEAMT/{experiment}/preprocessed.h5mu', compression='gzip')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9d888a41-70af-4ad9-abdc-e6d468524a03",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-08-14T13:51:34.417924Z",
-     "start_time": "2025-08-14T13:51:25.536258Z"
-    }
-   },
-   "outputs": [
-    {
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hg9x6660++BcAAFiVtzIwNjbW/Oa3dOlS2b17t4wYMUJyc3MlOjranI+KijIL49hMnz5dJkyYYBbI0d8UNet00znzSv+OGTNGNm3aZDJTx58DBgyQm2++WSIiInz4LwIAsBLmQwAArIwcBADAM7ISAFDZkE0AAFTN+YVXU5o+mTcIACiOauIjWkDpb3/7m5n8MHToUJkxY4ZTkaXatWubwk3Nmzcv1gQM3VzRCRiX06D2FNa2CRi6uaNFnfT/BAAAUFG8laWOk/8jIyOdJv+7yte2bdvKN99843Rs/Pjx8tNPP8mcOXOkZcuWXvuOAADr8uaYEQAAf0NOAgD8lTcyrqT3O5UugqrnR48ebT+2fv16j4uj/vDDD3L69GkWMgUAVLqxXmpqqlnEbeHChWYB06SkJLPQ2t69e6VJkyYun6sZMmSI9OrVyxSE0gXd7r33Xtm1a5e0aNHCtNFrmTt3rlkUThf81gXftM9vv/3WvAcAAH++n6m5atOpUycz7tRCTwkJCaawryt6btKkSeV4lQAAf89BzaNhw4aZOQzdu3c3Y73LF+vWMZxmkMrPzzdjNtvrI0eOmOJMderUMQtyq6efflpWrFghf//736Vu3br2CYj169c3xX2///57c14nUF577bXy9ddfy3PPPSd33XWXyUQAALyhqo8ZAQCoDBk4aNAgOXXqlMTFxZmxXXh4uKxbt86++ExWVpYEBgba2y9YsMCMFR999FGnfuLj402xw6CgIDMG1N8Gz5w5Y65Bfz+cPHmy23uiAACUBGNBAICVeTMHk5OTZebMmWYsGBYWJvPmzTO/Jbqiz4TquDEzM1MOHTokr732mtP8CaVjwsufd2nTpo3s2bOn1N8XAICSYswIAKhsyCYAAMrOn/KUeYMAgAot8KQTBvVHwYcfftjtA52NGzeWzz77zFeXAABAlebNLC3J5H9dpM1xIKwaNGhg/l5+HACA0mLMCACAe+QkAMBfeSvjSrrY6ahRo6RPnz4ye/Zs6d+/v6xcuVK2bt0qixYtMufPnTtnHrB55JFHpGnTpmZh07Fjx5qFULW4BQAAlSkHExMTJSYmxp57WuhpzZo1kpKSIi+99NIV7ZcvX+60/8Ybb8h7771nih9qZurDspql48ePlwEDBpg2y5YtMwvCrV69WgYPHlyGbw0AsAJvZJye10VFT5w44XRc93Wc5ooe99Te9lePORbv1X1dANUdLaB46dIlOXjwoFnExpVx48Y5FYbKycmRli1buu0TAOC/vDXWK+li3UePHpXbb7/dvq+THXXT+6Ba6Ne2oLe6++67nT7rzTfflMcff9wUNdywYYP9/qpmmd4j1fEhAADewjMwAACr8nYGjhw50myu2MaBNnpv0xMt+vvxxx8X63MBACgNxoIAACvzVg6mpqaaZ1P0GVF9lkV/09O5DXv37pUmTZpc0f78+fPSunVrGThwoDz33HNu+73tttvMb4Q21ar5bPk5AABcYswIAKhsyCYAAKru/MKrKU2fzBsEABTHL7P8vCw+Pt784Hd5oOrE93/961/2H/h0EiEAAPBtlurkf528r5P/deL/jh07rpj8f+zYMR99EwAArsSYEQAA98hJAIC/8lbGlfR+Z69evWTFihWmoFNYWJi8++67pmCFraC9PpDz9ddfy4MPPii33nqrDB8+XLp06SJffPGF24eHAACoiBzMz8+XzMxM6du3r/2YLvCt+xkZGcW6Dp3Ef/HiRWnUqJHZP3DggFk83LHP+vXrmwUBPPWZl5dnHkp13AAA1uSNjNMCEzoO0wKENoWFhWa/Z8+eLt+jxx3bq/Xr19vbt2rVyky0cGyjebV582a3fSodY2q+uloMx0a/a7169Zw2AIA1efN3PV2o+9ChQ2a8pXml4zLHxbqXLFli3w8NDTUFey/fHBf1dnVeNy3upHSS4eeffy6nT5+WCxcuyL59+2TGjBnkGgDAq3gGBgBgVWQgAMDKyEEAgJV5KwcTExMlJiZGoqOjpX379qbQU61atSQlJcVl+27dusnMmTNl8ODBHudA6Gfr8zS2TRc4BQCgPDFmBABUNmQTAABVd37h1ZSmT+YNAgCKo5r4yD333GMWTrt8kvvZs2fNuYKCAl99NAAAfsHbWaqT/3VzxXFSvyuOCwMAAOANjBndu3CxsKIvAQBQwchJAIC/8mbGlfR+pz4MpJsrNWvWlI8//rjYnw0AQEXlYHZ2tmlnK2poo/t79uwp1nW8+OKL0rx5c3tBJy3uZOvj8j5t51xJSEiQSZMmFeszAQD+zVtjvdjYWBk2bJh07dpVunfvLklJSZKbm2sWqlFRUVHSokULk0Fq1KhRZlLH7NmzpX///rJy5UrZunWrKe6rAgICZPTo0TJlyhS55ZZbTMGnCRMmmByMjIw0bbSYoRbQ0OusW7eu2X/uuefk97//vTRs2NDL/1IAAH/E73oAAHhGVgIArIoMBABYGTkIALAyb+Rgfn6+ZGZmyrhx4+zHAgMDzXOf+mxLWezbt888OxMcHGwWMNXncG644Qa37fPy8sxmk5OTU6bPBwCAMSMAoLIhmwAAqLrzC/Ve6rfffmt/feTIEdmxY4fUqVNHbr755mL1CQBApSrwVFRUZCbIX+706dNSu3ZtX30sAAB+gywFAPgzcs69c3mXKvoSAAAVjJwEAPgrMg4AYGWVIQenTZtmil9oMUSdnF8WunCAPtTqOGm/ZcuWXrhKAIBVM27QoEFy6tQpiYuLM0UGw8PDZd26dfYihFlZWWaxGptevXrJihUrZPz48fLyyy+bIk6rV6+WDh062NuMHTvWTLh44okn5MyZM9K7d2/Tpy0Ha9SoYbJx4sSJZjEaLQKlBZ4cMw4AgMo+1gMAoDIjKwEAVkUGAgCsjBwEAFiZN3IwOzvbLHhqe2bGRvf37NlT6mvr0aOHLFmyRNq0aWMWW500aZLceeedsnPnTqlbt67L9+hCqdoOAABvYcwIAKhsyCYAAKru/MKjR4/K7bffbt+fNWuW2fr06WPm0henTwAAKkWBp4cfftj81UB9/PHHzQR4G/3h8OuvvzYT6wEAgGtkKQDAn5FzAAC4R04CAPwVGQcAsDJv5mDjxo0lKChITpw44XRc95s2berxvfpAqhZ42rBhg3Tq1Ml+3PY+7aNZs2ZOfepDqu7o93D8LgAA6/HFWG/kyJFmc8U2qcLRwIEDzeaOXturr75qNlc6d+4smzZtKtE1AgCguOcJAIBnZCUAwKrIQACAlZGDAAArqwo5eN9999lf63OkWvDpxhtvlHfeeUeGDx/u8j3jxo2T2NhY+35OTo60bNmyXK4XAOBfqkJWAgCshWwCAKDqzy8MDQ01xaXK0icAAJWiwFP9+vXNXw22unXrSs2aNe3nqlevLnfccYfExMR4+2P93oWLBRJ8TVBFXwYAoByQpQAAf0bOAQDgHjkJAPBXZBwAwMq8mYPavkuXLpKWliaRkZHmWGFhodn39GDpjBkzZOrUqfLxxx9L165dnc61atXKFHnSPmwFnXQC/ubNm2XEiBGl+s4AAGtgrAcAsDJyEAAAz8hKAIBVkYEAACsjBwEAVubNHGzcuLEEBQXJiRMnnI7rvj7v6S0NGjSQW2+9Vfbv3++2jS7I6rgoKwAApcWYEQBQ2ZBNAACUHXkKALAqrxd4evPNN+3VC1944QWpXbu2tz/Cks7+fJECTwBgEWQpAMCfkXMAALhHTgIA/BUZBwCwMm/nYGxsrAwbNswUaurevbskJSVJbm6uREdHm/NRUVHSokULSUhIMPvTp0+XuLg4WbFihbmG48ePm+N16tQxW0BAgIwePVqmTJkit9xyiyn4NGHCBGnevLm9iBQAAK4w1gMAWBk5CACAZ2QlAMCqyEAAgJWRgwAAK/NmDurCp126dJG0tDT7c5yFhYVmf+TIkV675nPnzsn3338vQ4cO9VqfAAC4w5gRAFDZkE0AAJQdeQoAsCqvF3iyiY+P91XXAABYAlkKAPBn5BwAAO6RkwAAf0XGAQCszFs5OGjQIDl16pQp2qTFmsLDw2XdunUSEhJizmdlZUlgYKC9/YIFCyQ/P18effTRK65n4sSJ5vXYsWNNkagnnnhCzpw5I7179zZ9BgcHe+WaAQD+jbEeAMDKyEEAADwjKwEAVkUGAgCsjBwEAFiZt3IwNjZWhg0bJl27dpXu3btLUlKSec4zOjranI+KipIWLVpIQkKC2dfnRL/99lv76yNHjsiOHTukTp06cvPNN5vjusDqb37zG7nxxhvl6NGj5lqDgoJkyJAhXrlmAACKgzEjAKCyIZv807++OyU/Xbgk/Ts18+nnbM/6P/nnzuNyXZ0aUi0oQHredK20bVrvinbf/HBWvj5yRrqHNpLPvzsl19WtYT/3l/T9ck1goJz5OV+SP/te3hvRS366cFFmf/Kdvc0H249I19BGsmJzlny864Q5NnXtbknfe0qeuvsml9f257W75c3obh6vPzZ1hzR2uJbH3tgkIXWDZfZvwyQgIEA+/Oqo/PObYzKsV6j87/5seerum6Vm9SDTtqioSF5ZvVMKC4tkcmQHmffpfonqeaM0rvNLf2pZxkE58n8/y91tmph/H5vE9d/JziNnJeXxbqav+Z/ul9+ENZf3t/0gj/+qlaz5+qjcfkND6dCifjH+SwCoaOQpAMBqvFrgqXPnzpKWliYNGzaU22+/3fyfcXe2bdvmzY8GAMAvkKUAAH9GzgEA4B45CQDwV2QcAMDKfJWDI0eONJsr6enpTvsHDx68an96Xa+++qrZAAAoDsZ6AAArIwcBAPCMrAQAWBUZCACwMnIQAGBlvsjBQYMGyalTpyQuLk6OHz8u4eHhsm7dOgkJCTHns7KyJDAw0N5eCzbpZ9vMmjXLbH369LE/V/rDDz+YYk6nT5+W6667Tnr37i2bNm0yrwEA8CXGjACAyoZs8n9RKVvM3/6d+vv0c2KWbZXsc/n2/XbN6sk/R915Rbsn386UI2d+ls43NJBtWWeczs1Yt9dp/5EFG694//LNWTL1oY7y8gff2I/9fcdR8zfhn3tcXpt+3tKNnueWvr/9iNP+/+4/bf6Ou7+dKUL17N+2m30tYqXaN6sn93X8b9Es/d5acEr9um0TmZu2Ty4VFMrYfm2d+oz7+y7z9/V//UcOTvvlv4e2t8n5+ZLMXv+dvJN5WA7/+LMEBgZI0oZ90qpxbfnshbs9fgcAFYc8BQBYmVcLPA0YMEBq1PhvpdTIyEhvdg0AgCWQpQAAf0bOAQDgHjkJAPBXZBwAwMrIQQCAvyLjAABWRg4CAOAZWQkAsCoyEABgZeQgAMDKfJWDI0eONJsrtqJNNqGhoVJUVOSxv5UrV3rt2gAAKAnGjACAyoZsgrc4FndSh38877bYkuPf8pKbd8mr/V0sdH3vofD/3ZM4V8bPO/VTnvl7Pr/A/D2QnVum/gD4FnkKALAyrxZ4io+Pd/kaAAAUD1kKAPBn5BwAAO6RkwAAf0XGAQCsjBwEAPgrMg4AYGXkIAAAnpGVAACrIgMBAFZGDgIArIwcBADAM7ISAFDZkE0AAJQdeQoAsLLAir4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD8VUDAf/8eyM6t6EsBAAAAAAAAAAAAAABACVUTL2rYsKEE2J4muYoff/zRmx/t9wqLiir6EgAA5YAsBQD4M3IOAAD3yEkAgL8i4wAAVkYOAgD8FRkHALAychAAAM/ISgCAVZGBAAArIweLL/iaIPO3dnWvLvcDAKhA5CAAAJ6RlQCAyoZsAgCg7MhTAICVefWJj6SkJG92Bwfrdh6X6F+1qujLAAD4GFkKAPBn5BwAAO6RkwAAf0XGAQCsjBwEAPgrMg4AYGXkIAAAnpGVAACrIgMBAFZGDgIArIwcBADAM7ISAFDZkE0AAJQdeQoAsDKvFngaNmyYN7uDgwPZuRV9CQCAckCWAgD8GTkHAIB75CQAwF+RcQAAKyMHAQD+iowDAFgZOQgAgGdkZcmc/fliRV8CAMBLyEAAgJWRgwAAKyMHAQDwjKwEAFQ2ZBMAAGVHngIArMyrBZ5ycnKkXr169tee2NoBAIBfkKUAAH9GzpXcubxLUqeGV4fuAIBKipwEAPgrMg4AYGXkIADAX5FxAAArIwcBAPCMrCyZ/SfPVfQlAAC8hAwEAFgZOQgAsDJyEAAAz8hKAEBlQzYBAFB25CkAwMq8ukp0w4YN5dixY9KkSRNp0KCBBAQEXNGmqKjIHC8oKPDmRwMA4BfIUgCAPyPnSq6wqKiiLwEAUE7ISQCAvyLjAABWRg4CAPwVGQcAsDJyEAAAz8hKAIBVkYEAACvzVQ4mJyfLzJkz5fjx4xIWFibz5s2T7t27u22/atUqmTBhghw8eFBuueUWmT59utx///1ObXbv3i0vvviifP7553Lp0iVp3769vPfee3LDDTeU8FsDAPBfjAcBAPCMrAQAVDZkEwAAZUeeAgCszKsFnj799FNp1KiRef3ZZ595s2sAACyBLAUA+DNyDgAA98hJAIC/IuMAAFZGDgIA/BUZBwCwMnIQAADPyMqScbGmAQCgiiIDAQBW5oscTE1NldjYWFm4cKH06NFDkpKSJCIiQvbu3WsWirvcxo0bZciQIZKQkCAPPPCArFixQiIjI2Xbtm3SoUMH0+b777+X3r17y/Dhw2XSpElSr1492bVrlwQHB3vlmgEA1sR4EAAAz8hKAEBlQzYBAFB25CkAwMq8WuCpT58+Ll8DAIDiIUsBAP6MnAMAwD1yEgDgr8g4AICVkYMAAH9FxgEArIwcBADAM7ISAGBVZCAAwMp8kYOJiYkSExMj0dHRZl8LPa1Zs0ZSUlLkpZdeuqL9nDlzpF+/fjJmzBizP3nyZFm/fr3Mnz/fvFe98sorcv/998uMGTPs77vpppu8cr0AAOtiPAgAgGdkJQCgsiGbAAAoO/IUAGBlXi3wdLn/+7//k7/+9a+ye/dus9++fXvz8IytsiIAAPCMLAUA+DNyDgAA98hJAIC/IuMAAFZGDgIA/BUZBwCwMnKw7H7Mza/oSwAA+BBZCQCwKjIQAGBlZc3B/Px8yczMlHHjxtmPBQYGSt++fSUjI8Ple/R4bGys07GIiAhZvXq1eV1YWGgKRI0dO9Yc3759u7Rq1cp8RmRkpNtrycvLM5tNTk5Osb4DAMC6GA8CAOAZWQkAqGzIJgAAyo48BQBYSaCvOv7Xv/4loaGhMnfuXBOuuulrfcBFzwEAAM/IUgCAPyPnAABwj5wEAPgrMg4AYGXkIADAX5FxAAArIwcBAPCMrAQAWBUZCACwMm/kYHZ2thQUFEhISIjTcd0/fvy4y/focU/tT548KefOnZNp06ZJv3795JNPPpGHHnpIHn74Yfn888/dXktCQoLUr1/fvrVs2bJY3wEAYE2MBwEA8IysBAD4ezYlJyeb/oKDg6VHjx6yZcsWj+1XrVolbdu2Ne07duwoa9eudTpfVFQkcXFx0qxZM6lZs6b07dtX9u3bZz9/8OBBGT58uLlePX/TTTdJfHy85OfnO/Xz9ddfy5133mk+R+9xzpgxo8TfDQAAdxjrAQCsppqvOn766adl0KBBsmDBAgkKCjLH9AGap556ypz75ptvfPXRAAD4BbIUAODPyDkAANwjJwEA/oqMAwBYGTkIAPBXZBwAwMrIQQAAPCMrAQBWRQYCAKyssuZgYWGh+TtgwAB57rnnzOvw8HDZuHGjLFy4UPr06ePyfePGjZPY2Fj7fk5ODkWeAABVLgcBAKgsyEoAgD9nU2pqqrmXqPcbtbhTUlKSREREyN69e6VJkyZXtNd7k0OGDDFF5h944AFZsWKFREZGyrZt26RDhw6mjRZi0gIZS5cuNUUyJkyYYPr89ttvTbGmPXv2mHufr7/+utx8882yc+dOiYmJkdzcXJk1a5b9nua9995rikPptel3+sMf/iANGjSQJ554wmv/lgAA62KsBwCwmkBfdbx//355/vnn7YGq9LUONvUcAADwjCwFAPgzcg4AAPfISQCAvyLjAABWRg4CAPwVGQcAsDJyEAAAz8hKAIBVkYEAACvzRg42btzYvOfEiRNOx3W/adOmLt+jxz211z6rVasm7du3d2rTrl07ycrKcnstNWrUkHr16jltAAC4w3gQAADPyEoAgD9nU2JioimuFB0dbe5DajGlWrVqSUpKisv2c+bMkX79+smYMWPMfcrJkydL586dZf78+eZ8UVGRKRI1fvx4U7i+U6dOsmzZMjl69KisXr3atNH3v/nmm6aAU+vWreXBBx+UF154Qd5//3375yxfvlzy8/PNddx2220yePBgefbZZ831AgDgDYz1AABW47MCTzoo3L179xXH9VhYWJivPhYAAL9BlgIA/Bk5BwCAe+QkAMBfkXEAACsjBwEA/oqMAwBYGTkIAIBnZCUAwKrIQACAlXkjB6tXry5dunSRtLQ0+7HCwkKz37NnT5fv0eOO7dX69evt7bXPbt26yd69e53afPfdd3LjjTcW67oAALgaxoMAAHhGVgIA/DWbtIBSZmam9O3b134sMDDQ7GdkZLh8jx53bK8iIiLs7Q8cOCDHjx93alO/fn3p0aOH2z7V2bNnpVGjRk6fc9ddd5l7pI6fo/dK/+///q/Y3xEAAHcY6wEArKaaNzv7+uuv7a+1Gu+oUaNMhcQ77rjDHNu0aZMkJyfLtGnTvPmxAAD4DbIUAODPyDkAANwjJwEA/oqMAwBYGTkIAPBXZBwAwMrIQQAAPCMrAQBW5csM1PfNnDnTLOCmC9/MmzdPunfv7rLt4sWLZdmyZbJz506zr8Ux/vznPzu1Lyoqkvj4eNP2zJkz8qtf/UoWLFggt9xySym+OQAAvsnB2NhYGTZsmHTt2tXkWFJSkuTm5kp0dLQ5HxUVJS1atJCEhASzr5/Zp08fmT17tvTv319WrlwpW7dulUWLFtn7HDNmjAwaNMgsZnrPPffIunXr5B//+Iekp6d78V8DAGA13BMFAMAzshIAYIVsys7OloKCAgkJCXE6rvt79uxx+R797c9Vez1uO2875q7N5fR76G+Js2bNcvqcVq1aXdGH7VzDhg2dzuXl5ZnNJicnx8M3BwBYFWM9AICVebXAU3h4uAQEBJgHO23Gjh17Rbvf/e535qEXeNa/UzNZ8/Wxir4MAEA5IksBAP6MnCu5M7kXpV7wNRV9GQCAckBOAgD8la8yriQL16hVq1bJhAkT5ODBg2YxmunTp8v999/vsu2TTz4pr7/+urz22msyevToYl8TAACXY6wHAPBXZBwAwMoqwz3PXbt2SVxcnGRmZsqhQ4fc3su8Wp8XLlyQ559/3ix0qpPxIyIi5C9/+csViwEAAFASjBkBAFblqwxMTU01BS4WLlwoPXr0MMUtdPy2d+9eadKkyRXttUDFkCFDpFevXhIcHGyekbn33nvNWFKLYKgZM2bI3LlzZenSpWYxN32mRvv89ttvzXsAAKgMOajtTp06Ze6F6j1O/QwtyGS7f5mVlSWBgYH29pp9K1askPHjx8vLL79snhVdvXq1dOjQwd7moYceMpmqRaF0obk2bdrIe++9J7179y7jvwAAwMq4JwoAgGdkJQCgsvHXbDpy5Ij069dPBg4cKDExMaXuR++fTpo0yavXBgDwP/6apwAAlHuBpwMHDnizO8tr0aBmRV8CAKCckaUAAH9GzpVcgcNNawCAfyMnAQD+yhcZV9KFazZu3GgWrtEHSh944AEzgT8yMlK2bdvmNHFfffDBB7Jp0yZp3ry5168bAGA9jPUAAP6KjAMAWFlluOd5/vx5ad26tZmE/9xzz5W6T33vmjVrZNWqVVK/fn0ZOXKkPPzww/K///u/Xv+OAADrYMwIALAqX2VgYmKiWYAtOjra7Os4T8dyKSkp8tJLL13Rfvny5U77b7zxhilckZaWJlFRUWZhHR0javGLAQMGmDbLli0zxTK0CMbgwYN98j0AAP7NVzmo9yx1c0WLGl5O75nq5skf/vAHswEA4C3cEwUAwDOyEgBghWxq3LixBAUFyYkTJ5yO637Tpk1dvkePe2pv+6vHmjVr5tRGi2o4Onr0qNxzzz3Sq1cvWbRoUbE+x/EzHI0bN848f2qTk5MjLVu2vMq/AADAahjrAQCszKsFnm688UZvdgcAgOWQpQAAf0bOAQDgHjkJAPBXvsi4ki5cM2fOHOnXr5+MGTPG7E+ePFnWr18v8+fPN++1OXLkiDzzzDPy8ccfS//+/b1+3QAA62GsBwDwV77KuOTkZJk5c6YcP35cwsLCZN68edK9e3e37bUYxYQJE+TgwYNyyy23yPTp0+X++++3n9eFSuPj42Xx4sVy5swZ+dWvfiULFiwwbS+Xl5dnil989dVXsn379ismPAIAUJnueXbr1s1sytX54vR59uxZ+etf/yorVqyQX//616bNm2++Ke3atZNNmzbJHXfc4fXvCQCwBu6LAgCsyhcZmJ+fL5mZmWYRNZvAwEDp27evZGRkFKsPLRJ88eJFadSokX2BHb0Hq33YaNFfvT+qfVLgCQBQGowFAQBWRg4CAOAZWQkAsEI2Va9eXbp06SJpaWkSGRlpjhUWFpp9d0Xse/bsac6PHj3afkznv+tx1apVK1OASdvY5jdosaXNmzfLiBEjnObHa3En/Xx9DlR/T7z8c1555RXzm+E111xj/5w2bdpIw4YNr7iuGjVqmA0AAH+YX3ju3Dkzf2L16tVy+vRpk6/PPvusPPnkkz65fgCANXi1wJMr3377rWRlZZmHSB09+OCDvv5oAAD8AlkKAPBn5BwAAO6RkwAAf1WWjCvNwjV6PDY21ulYRESEeQDHRh+SHTp0qCkCddttt0lFOnbm5wr9fACAbzHWAwD4q7JmXGpqqhm7afEJXUg0KSnJjN327t0rTZo0uaL9xo0bZciQIZKQkCAPPPCAKU6hEyG3bdsmHTp0MG1mzJghc+fOlaVLl5rJFzpZQ/vUaw0ODnbqb+zYsdK8eXNT4AkAgMp+z9Mbfep5nazvuKB327Zt5YYbbjBtXBV40oKIutnoQgEAABQH90UBAFZV1gzMzs6WgoICCQkJcTqu+3v27ClWHy+++KK592kb/+kCOLY+Lu/Tdu5yjAcBAKXBWBAAYGXkIAAAnpGVAAB/zCadDzFs2DDp2rWrKUShcyJyc3MlOjranI+KipIWLVqYORBq1KhR0qdPH5k9e7b0799fVq5cKVu3bpVFixaZ8wEBAab405QpU0zBCtucCP3tz1ZESos73X333abIxqxZs+TUqVP269HiUOp3v/udTJo0SYYPH25+O9y5c6fMmTNHXnvtNS/8ywEAULnnF2p/n376qbz99tsSGhoqn3zyiTz11FMmTxmDAgAqXYGn//znP/LQQw/JN998YwaFRUVF5ri+VvpAKQAAcI8sBQD4M2/mXHJyssycOdNMJgwLC5N58+aZHzjdWbVqlfmh8uDBg+aHy+nTp8v9999vPz9x4kTzY+fhw4elevXq0qVLF5k6daq50QsAQHlgPAgA8FfeyLjSLFyj48WrLUqjY8Nq1arJs88+W+zv46vFa2rX8NlPuACACsRYDwDgr7yVcYmJiRITE2OfvKgTMdasWSMpKSny0ksvXdFeJxT269fPFOpVkydPlvXr18v8+fPNe/U6dBLH+PHjZcCAAabNsmXLzHhQC/4OHjzY3tc///lPMznjvffeM68BAKjs9zy90afeH9XnYho0aFDsBb114qNO8gcAoLi4LwoAsKrKkoHTpk0zcyPS09OvKHpfEowHAQBVMQcBAKgI5CAAAJ6RlQAAf86mQYMGmQJLcXFx5jnM8PBwWbdunf1ZTi14ERgYaG/fq1cvU4hC5zy8/PLLZi00netgK0ihxo4da4pEPfHEE3LmzBnp3bu36dP225/Oodi/f7/Zrr/+eqfrsX2X+vXrm/kSTz/9tFlLrXHjxuYatU8AAPx5fqGtCJQWYNSCiErz7/XXX5ctW7ZQ4AkAUGq/jOy8TCsBa3XfkydPSq1atWTXrl3yr3/9y1QS1gdBAQCAZ2QpAMCfeSvnUlNTJTY2VuLj42Xbtm2mwFNERITp1xW9yTpkyBAZPny4bN++XSIjI822c+dOe5tbb73V3JjVm8RffvmlhIaGyr333mt+PAUAoDwwHgQA+KvKmnGZmZnmwZ0lS5bYHxAq7uI1+lCrbWvZsqVPrxMAULVV1hwEAKAyZFx+fr4Zm/Xt29d+TCcu6n5GRobL9+hxx/ZKfye0tT9w4ICZFOnYRsduPXr0cOrzxIkTZuLHW2+9Za4fAICSsNpYb9y4cXL27Fn7dvjw4Yq+JABAJWe1rAQAwNsZqIurBQUFmfuYjnS/adOmHt87a9YsU+BJF2vr1KmT/bjtfSXpk/EgAKAkGAsCAKyMHAQAwDOyEgDg79k0cuRIOXTokOTl5cnmzZvN/AUb7U/nsjsaOHCg7N2717TXNdDuv/9+p/M67/3VV181cyMuXLggGzZsMOuj2Tz++OOmiIarzZH+XvjFF1+YPn744Qd58cUXS/zdAACoavMLbQUVP/zwQzly5IjJx88++0y+++47s7YoAACVrsCThpgOAvXhUQ1C3bTSry509uyzz/rqY/3KTxcuVfQlAAAqEFkKAPBn3sq5xMREs+BadHS0tG/fXhYuXGhu7KakpLhsrwt29+vXT8aMGSPt2rWTyZMnS+fOnU1BJ5vf/e535mZt69at5bbbbjOfkZOTI19//bVXvjsAAFfDeBAA4K+8kXGlWbhGj3tqrw+k6oNCN9xwg1SrVs1s+vDs888/b4r+usPiNQCAkmCsBwDwV97IuOzsbCkoKJCQkBCn47qvExFd0eOe2tv+emqjkzJ0QuOTTz5pJowUl06e1N8PHTcAgDVV1D1Pb/Spf3US5JkzZ4r9uTVq1JB69eo5bQAAlNd90eTkZPPbXXBwsFn8ZsuWLW7b6uIAjzzyiGmvC90kJSVd0WbixInmnOPWtm3bUn1PAAB8lYHVq1eXLl26SFpamv1YYWGh2e/Zs6fb982YMcPMlVi3bt0V9z51QR0d9zn2qfc4dYE5d30yHgQAlATPyAAArIwcBADAM7ISAFDZkE0AAPjv/EI1b948s0bp9ddfb57D0XVI9XnUu+66y2WfzBsEAFRogScNw7p165rXGqxHjx41r2+88UZTHdhXEzDUqlWrzIQKbd+xY0dZu3at03mdlB8XFyfNmjWTmjVrmoW79+3bd0U/a9asMZ+nbRo2bCiRkZFSnv62JatcPw8AULl4M0sBAPDHnNNFZjIzM82YzkZv6Oq+3uh1RY87tlcRERFu2+tnLFq0SOrXry9hYWEu23AjFgDgbYwHAQD+yhsZV5qFa/S4Y3u1fv16e/uhQ4eaor47duywb82bNzfFgT/++GO318LiNQCAkmCsBwDwV1U543SCxk8//WQK+JaETi7R3w9tW8uWLX12jQCAyq2i7nl6o089f8011zi10WvOysoq9ecCAOCrMWNqaqrExsZKfHy8bNu2zTzPqc9+njx50mX78+fPS+vWrWXatGkeCybedtttcuzYMfv25Zdflvg7AgDg6/ummoGLFy+WpUuXyu7du2XEiBGSm5sr0dHR5nxUVJTTPc7p06fLhAkTJCUlxczN10VrdDt37pw5r0UNR48eLVOmTJEPP/xQvvnmG9OHPitT3vPoAQD+qSr/fggAQFmRgwAAeEZWAgAqG7IJAAD/zlOdP7hp0ybzjIyuWzp79mx5+umnZcOGDS7bM28QAFAc1cRHOnToIF999ZW0atXKFEmaMWOGmSioC2PrBImSTsBYuHCh6ScpKclMwNBgbtKkyRXtN27cKEOGDDFB+MADD8iKFSvMA6U6eUOvSem1zJ071zzMqtenD6pqn99++60pCqXee+89iYmJkT//+c/y61//Wi5duiQ7d+704r8QAADlk6UAAPhrzmVnZ5sbuiEhIU7HdX/Pnj0u36MTE1211+OOPvroIxk8eLCZ5K/FgXXhb71h7IqOPydNmlSsawYAoDgYDwIA/JW3Mk5/Oxw2bJh07dpVunfvbn4/vHzhmhYtWpjxmho1apT06dPHPGjTv39/WblypWzdutV8rrr22mvN5kgXNtXF3tq0aePVfwMAgHUx1gMA+CtvZJz+DhcUFCQnTpxwOq777hbi1uOe2tv+6jH9vc+xTXh4uHn96aefSkZGhing60jHm4899ph5xtQVXSxVx6Y2OTk5TNYAAIuqqHue+fn5Zu6D7fWRI0dM4fo6derIzTffXKw+dbLh8OHDTbtGjRqZAvbPPPOMKe50xx13+OBfCwBgRd7KysTERDPPz5ZjOs9wzZo1pnDFSy+9dEX7bt26mU25Om9TrVo1jwWgAACoDL8NDho0SE6dOiVxcXFm3oPe31y3bp19XoQW6g0MDLS3X7BggRkrPvroo079aKHEiRMnmtdjx441Y8QnnnhCzpw5I7179zZ92ubYAwBQFjwjAwCwMnIQAADPyEoAQGVDNgEA4L/zC3/++Wd5+eWX5YMPPjBrzahOnTqZuRezZs2Svn37XtEn8wYBAMXxyxObXjZ+/HgpLCw0r1999VU5cOCA3HnnnbJ27VpTXKk0EzDat29vJmDUqlXLTMBwZc6cOdKvXz8ZM2aMtGvXTiZPniydO3eW+fPnm/NFRUVmcqJe34ABA0ygLlu2zFR1XL16tWmjxZx0obeZM2fKk08+Kbfeeqv57N/+9rde+bcBAKA8s9QmOTlZQkNDzUQLHfBu2bLFbdv333/fTOpv0KCB1K5d20z8eOutt8r0fQAA8GXOeds999xjbr5qEWEdY+p48OTJky7b6o3Ys2fP2rfDhw+X+/UCAPxLZc9JAAAqOuN04Rp9WEYXrtF7lzp+u3zhmmPHjtnb9+rVS1asWGEe/gkLC5N3333X/C6oDwkBAFBeGOsBAPyVNzJOJ2x06dJF0tLS7Me0T93XIhOu6HHH9mr9+vX29johRCdjOLbRCRWbN2+2t9Hr08kjOq7UTa9ZpaamytSpU91erxaE0iIYjhsAwJoq6p6nzn24/fbbzabH9b36+o9//GOx+1SvvfaaPPDAA/LII4/IXXfdZbJTnx8FAKAyZaUWqMjMzHSaSK9FLHRfi/aWxb59+6R58+ZmAQEt9KuZ605eXp4ZVzpuAACU12+DI0eOlEOHDpk80nucOjfQJj09XZYsWWLfP3jwoJlLf/lmK+6kAgICzHVpwagLFy7Ihg0bzFx6AAC8gWdkAABWRg4CAOAZWQkAqGzIJgAA/Hd+4cWLF82mz5w60kJStuu9HPMGAQDFUU18JCIiwv765ptvlj179siPP/4oDRs2NA9+lmQChi6WXdwJGHrcscKh7VpsxZs03PWBU8dJHfXr1zcPs+p7Bw8eLNu2bZMjR46Yz9KJjtpeJzVqwSdPC73pg7G62XhzokZRkde6AgBUEd7IUhtdeEbzUQslauZpsUPtf+/evdKkSZMr2jdq1EheeeUVadu2rRnkfvTRR6bYorZ1vC4AACoy5xo3bmxukJ44ccLpuO7rgjOu6PHitNcCh3pdut1xxx1yyy23yF//+len8anjjVjdAACojONBAAD8NeN04RrdXNGFay43cOBAsxWXLnYDAIA3MdYDAPgrb2WcPtcybNgw6dq1q3Tv3t0825Kbm2ueV1FRUVHSokULSUhIMPujRo2SPn36yOzZs6V///6ycuVK2bp1qynuq/SzR48eLVOmTDG/9WnBpwkTJpiFuyMjI02bG264weka6tSpY/7edNNNcv3113vhXwcA4O8q6p5naGioWZy7LH2q4OBgSU5ONhsAAJU1K7Ozs6WgoMCpSKHSfe2vtHRehRbDaNOmjSmYOGnSJLOgwM6dO6Vu3bpXtNfxqLYBAKA4+G0QAGBl5CAAwMrIQQAAPCMrAQCVDdkEAID/zi/U4kx6fsyYMVKzZk258cYb5fPPP5dly5ZJYmKi1/8dAADW4bMCT44OHz5s/rZs2bJE7yvNBAwtxuSqvR63nbcdc9fmP//5j/k7ceJEE7Q6AVJD+u6775bvvvvOFL1wxZcTNX66cNEn/QIAqobSZqmN5llMTIx9UKqFntasWSMpKSny0ksvXdFeM8+RDlqXLl0qX375JQWeAACVJue0CGGXLl0kLS3NvghbYWGh2Xe3OE3Pnj3NeV3MzWb9+vXmuCfar2NBXwAAqsp4EACAyoqMAwBYGTkIAPBXZcm4QYMGyalTpyQuLs48yxkeHi7r1q2zP+uZlZUlgYGB9va9evWSFStWyPjx4+Xll182RZxWr14tHTp0sLcZO3asmcTxxBNPyJkzZ6R3796mTy1mAQCAtzHWAwCgamXlfffdZ3/dqVMnU/BJJ++/8847Mnz48Cvajxs3ziweYJOTk1NpvgsAoHKrbBkIAEB5IgcBAFZGDgIA4BlZCQCobMgmAAD8b36hFn3S5z8fe+wxU3RKnxOdOnWqPPnkk175vgAAa/oljbzs0qVLMmHCBKlfv74pkKSbvtawu3ixchcr0oW71SuvvCKPPPKIWTD8zTffNNUeV61a5fZ9GtRnz561b7b/M+ENgVRuBgDL8VaW5ufnS2ZmpvTt29d+TAekup+RkXHV9xcVFZlCGHv37pW77rqr1N8HAABf5JxOll+8eLEpRLh7924ZMWKEWaTNVtQwKirKjNUcixbqjVot4quFg7Ww79atW+0FofS9eoN206ZNcujQIZOhf/jDH+TIkSMycOBAH/xLAADgX/dWAQDwhIwDAFgZOQgA8FfezDj9zU5/o8vLy5PNmzebxbVt0tPTZcmSJU7t9fc7fZ5F2+/cuVPuv/9+p/P6zOerr75qJnRcuHBBNmzYILfeeqvbz9dr1+dkdPIHAADFwVgPAADfZ2Xjxo0lKChITpw44XRc95s2beq1a23QoIEZM+7fv9/l+Ro1aki9evWcNgAA3GG8CACwMnIQAGBl5CAAAJ6RlQCAyoZsAgDAv+cX6nOmWltC1xL9+eefzfqjun6pzjsEAKC0qomPPPPMM/L+++/LjBkzpGfPnuaYFpHQxbNPnz4tCxYs8MkEDD3uqb3trx5r1qyZUxvbpHzb8fbt2ztNwmjdurWp0uiOttENAIDKkqUqOztbCgoK7BWHbXRfB5buaLHCFi1amEGq5vFf/vIX+Z//+R+XbbWNbjY5OTnF/JYAAKvyVs4NGjRITp06JXFxcWZhNh3XaQEnW+7pGE4LG9r06tVLVqxYYW74aiGnW265RVavXi0dOnQw5zXzNB+1YJRm6LXXXivdunWTL774Qm677Taf/FsAAOCrnAQAoLIh4wAAVubNHExOTpaZM2eae6JhYWEyb9486d69u8u2u3btMvdPtZi9PtD62muvyejRo53a6DVMmjTJ6VibNm08/pYIAIANYz0AgJWRgwAA+D4rq1evLl26dJG0tDSJjIw0xwoLC82+TuT3lnPnzsn3338vQ4cO9VqfAADrYrwIALAycrB4frrA4rAA4I/IQQAAPCMrAQCVDdkEAEDZkadXt/sY63QDgD/xWYEnXTB75cqVct9999mPderUSVq2bClDhgzx2QQMDXA977gYzfr16+3B3qpVK1PkSdvYCjppEQqtxjhixAizr5+phZq08mLv3r3NMa30ePDgQbnxxhvL9O8CAEB5ZmlZ1K1bV3bs2GEmKmpuaoVhLXZ49913X9E2ISHhikXfAAAor5zT8aG7MWJ6evoVxwYOHGg2V4KDg80NYgAA/CUnS7Lo9+LFi2XZsmWyc+dO+33SP//5z27bAwBQ1e55AgDgDzmYmppqfrdbuHCh9OjRQ5KSkiQiIsI849KkSZMr2p8/f978xqf3RJ977jm3/WqB+w0bNtj3q1Xz2SNFAAA/w1gPAGBl5CAAAOWTlXpPdNiwYdK1a1fzHIveF83NzZXo6GhzPioqSlq0aGHmNaj8/Hz59ttv7a+PHDli5kbUqVNHbr75ZnP8hRdekN/85jdmruDRo0clPj5egoKCzHUBAFBWjBcBAFZGDhbPqXP5FX0JAAAfIAcBAPCMrAQAVDZkEwAAZUeeXt11dWtU9CUAALzIZ6uxaIGk0NDQK45rgSUt3FRcJZ2AMWrUKOnTp4/Mnj1b+vfvb4J969atsmjRInM+ICDAFH+aMmWK3HLLLeZ6JkyYIM2bN7cXkapXr548+eSTZmKG/p8Anaihi6Aqd4uA+0JggEhhUbl9HACgkvFWljZu3NhMNDxx4oTTcd3XoofuBAYG2icvalHE3bt3m7x1VeBp3LhxJrNttHiiZigAAL7OOQAA/JG3crKki35rYUT9QbRXr16m6OH06dPl3nvvlV27dpl7sAAAlBVjQQCAlXkrBxMTEyUmJsb+3IyO+dasWSMpKSny0ksvXdG+W7duZlOuzjsWdPL02yEAAO4w1gMAWBk5CABA+WTloEGD5NSpUxIXFyfHjx838xvWrVsnISEh5nxWVpaZ/2CjBZtuv/12+/6sWbPMpnMO9fkY9cMPP5jnZE6fPi3XXXed9O7dWzZt2mReAwBQVowXAQBWRg4WT+3qQRV9CQAAHyAHAQDwjKwEAFQ2ZBMAAGVHngIArOaXmQteNnLkSJk8ebLk5eXZj+nrqVOnmnMlmYChEyh0AoZOvtixY8cVEzCOHTtmb6+Lj2rFRi3oFBYWJu+++66sXr1aOnToYG8zduxYeeaZZ+SJJ54wi9icO3fO9KmLltpoQafBgwfL0KFDTZtDhw7Jp59+Kg0bNpTy0r9T83L7LABA5eOtLNXBbJcuXSQtLc1+rLCw0Oz37Nmz2P3oexyv5fLBtBZIdNwAACiPnAMAwB95KycdF/1u3769WfS7Vq1aZtFvV5YvXy5PPfWUuQ/btm1beeONN+zjRwAAvIGxIADAyryRg/n5+ZKZmSl9+/a1H9NFS3U/IyOjTNe3b98+ad68ubRu3Voee+wx8zwOAADFwVgPAGBl5CAAAOWXldpe5/fp+zdv3iw9evSwn9OiTUuWLLHv60IBRUVFV2y24k5q5cqVphCU9qfFnnT/pptuKvN3BgBAMV4EAFgZOQgAsDJyEACA8svK5ORk87ugrh+qvx1u2bLFbdvFixfLnXfeadYR1U3nYHhqDwCwDsZxAACUHXkKALCaat7s7OGHH3ba37Bhg1x//fWm0JL66quvzGIz/9//9/+VqF8NYXdB7DixwmbgwIFmcycgIEBeffVVs7lzzTXXmMJSugEAUF58laWxsbEybNgw6dq1q3Tv3l2SkpIkNzfXLPKtoqKipEWLFpKQkGD29a+21QmKOiheu3atvPXWW7JgwQKvfVcAgPX4KucAAPAH3s5J26Lf48aNK/Wi3+fPn5eLFy9Ko0aNSvRdAABwxFgQAGBl3s7B7OxsKSgokJCQEKfjur9nz55SX6dOZtTFT9u0aSPHjh2TSZMmmcmLO3fulLp167p8j/6G6PigbU5OTqk/HwBQ9TDWAwBYGTkIAIBnZCUAwKrIQACAlZGDAAArIwcBACj/rExNTTVrqi1cuNDMh9D11CIiImTv3r3SpEkTl+uVDhkyRHr16mUKQk2fPl3uvfde2bVrl1l3DQBgLYzjAAAoO/IUAGBlXi3wVL9+faf9Rx55xGm/ZcuW3vw4AAD8jq+ydNCgQXLq1CmJi4uT48ePS3h4uKxbt86++FtWVpZZ6NtGiz899dRT8sMPP0jNmjWlbdu28vbbb5t+AAAoLcaMxZN3saCiLwEA4Ac56Y1Fv1988UVp3ry5KQrlDgt6AwCuhrFgyRVV9AUAACyXg/fdd5/9dadOncwExxtvvFHeeecdGT58uMv3JCQkmEJQAABrqioZBwCAL5CDAAB4RlYCAKyKDAQAWBk5CACwMnIQAIDyz8rExESJiYmR6Ohos6+FntasWSMpKSny0ksvXdF++fLlTvtvvPGGvPfee5KWliZRUVEl/nwAQNXGOA4AgLIjTwEAVubVAk9vvvmmN7sDAMByfJmlI0eONJsr6enpTvtTpkwxGwAA3sSYsXhyLlyq6EsAAFSAypaT06ZNk5UrV5rxYnBwsNt2LOgNAKhqGVcVHDqdW9GXAACopDnYuHFjCQoKkhMnTjgd1/2mTZt67XMaNGggt956q+zfv99tm3HjxklsbKxTwV8etgUA62CsBwCwMnIQAADPyEoAgFWRgQAAKyMHAQBWRg4CAFC+WZmfny+ZmZlmToNNYGCg9O3bVzIyMorVx/nz5+XixYvSqFEjr14bAKBqYBwHAEDZkacAACvzaoEnV06dOiV79+41r9u0aSPXXXedrz8SAAC/QpYCAPwZOXelwICKvgIAgD/kZFkW/Z41a5Yp8LRhwwbp1KmTx7Ys6A0AKA3Ggp41ql29oi8BAFBJc7B69erSpUsXSUtLk8jISHOssLDQ7I8cOdJr13ju3Dn5/vvvZejQoW7b1KhRw2wAANgw1gMAWBk5CACAZ2QlAMCqyEAAgJWRgwAAKyMHAQDwXVZmZ2dLQUGBhISEOB3X/T179hSrjxdffFGaN29uikK5k5eXZzbHOfQAAP/FOA4AgLIjTwEAVhHoq45zc3PlD3/4gzRr1kzuuusus+mNzOHDh5uq9QAAwDOyFADgz8g5AAB8m5OOi37b2Bb97tmzp9v3zZgxQyZPnizr1q2Trl27XvVzdDHvevXqOW0AALjDWBAAYGXeykEtsrt48WJZunSp7N69W0aMGGH6jo6ONuejoqJMMV6b/Px82bFjh9n09ZEjR8zr/fv329u88MIL8vnnn8vBgwdl48aN8tBDD5miwUOGDPHyvwIAwB8x1gMAWBk5CACAZ2QlAMCqyEAAgJWRgwAAKyMHAQCo/Fk5bdo0WblypXzwwQcSHBzstl1CQoLUr1/fvrVs2dIrn//+9iPyz2+OeaUvAIB/ZBMAAFUdeQoAsBqfFXjSBWV08Zd//OMfcubMGbP9/e9/N8eef/55X30sAAB+gywFAPgzcg4AAN/nZEkX/Z4+fbpMmDBBUlJSJDQ0VI4fP262c+fO+eR7AgCsh7EgAMDKvJWDgwYNklmzZklcXJyEh4ebYk1apDckJMScz8rKkmPHfpnsd/ToUbn99tvNpsf1vfr6j3/8o73NDz/8YIo5tWnTRn7729/KtddeK5s2bZLrrrvOy/8KAAB/xFgPAGBl5CAAAJ6RlQAAqyIDAQBWRg4CAKyMHAQAwPdZ2bhxYwkKCpITJ044Hdf9pk2benyvzqfQAk+ffPKJdOrUyWNbnYN/9uxZ+3b48GEpq0a1qkvepQLZfODHMvcFAPAOxnEAAJQdeQoAsJpqvur4vffek3fffVfuvvtu+7H7779fatasaRaDWbBgga8+GgAAv0CWAgD8GTkHAIDvc1IX/T516pRZ9FsLNenC35cv+h0YGGhvr/3m5+fLo48+6tRPfHy8TJw40WvfDwBgXYwFAQBW5s0cHDlypNlcSU9Pd9rXAr5FRUUe+1u5cmWxPxsAgMsx1gMAWBk5CACAZ2QlAMCqyEAAgJWRgwAAKyMHAQDwfVZWr15dunTpImlpaRIZGWmOFRYWmn138yzUjBkzZOrUqfLxxx9L165dr/o5NWrUMJs3dW/VSHYePevVPgEAZcM4DgCAsiNPAQBW47MCT+fPn7cvFOqoSZMm5hwAAPCMLAUA+DNyDgCA8snJkiz6ffDgwRJeKQAAJcNYEABgZeQgAMBfkXEAACsjBwEA8IysBABYFRkIALAychAAYGXkIAAA5ZOVsbGxMmzYMFOoqXv37pKUlCS5ubkSHR1tzkdFRUmLFi0kISHB7E+fPl3i4uJkxYoVEhoaKsePHzfH69SpYzYAgHUxjgMAoOzIUwCA1QT6quOePXtKfHy8XLhwwX7s559/lkmTJplzAADAM7IUAODPyDkAANwjJwEA/oqMAwBYGTkIAPBXZBwAwMrIQQAAPCMrAQBWRQYCAKyMHAQAWBk5CABA+WTloEGDZNasWaZoU3h4uOzYsUPWrVtnX1A8KytLjh07Zm+/YMECyc/Pl0cffVSaNWtm37QPAIC1MY4DAKDsyFMAgNVU81XHWsm+X79+cv3110tYWJg59tVXX0lwcLB8/PHHvvpYAAD8BlkKAPBn5BwAAO6RkyJFFX0BAACfIOMAAFZGDgIA/BUZBwCwMnIQAADPyEoAgFWRgQAAKyMHAQBWRg4CAFB+WTly5EizuZKenu60f/DgwTJcNQDAnzGOAwCg7MhTAIDV+KzAU8eOHWXfvn2yfPly2bNnjzk2ZMgQeeyxx6RmzZq++lgAAPwGWQoA8GfkHAAA7lk5J2tcE2j+fnX4jNx0XZ2KvhwAgJdZOeMAACAHAQD+iowDAFgZOQgAgGdkJQDAqshAAICVkYMAACsjBwEA8IysBABUNmQTAABlR54CAKzGJwWeLl68KG3btpWPPvpIYmJifPERAAD4NbIUAODPyLniu1RQWNGXAAAoZ1bPyTYhdWWNHJOgwICKvhQAgJdZPeMAANZGDgIA/BUZBwCwMnIQAADPyEoAgFWRgQAAKyMHAQBW5u0cTE5OlpkzZ8rx48clLCxM5s2bJ927d3fZdteuXRIXFyeZmZly6NAhee2112T06NFl6hMAAG9jzAgAqGzIJgAAyo48BQBYUaAvOr3mmmvkwoULvujasn7Ku1TRlwAAKEdkKQDAn5FzxZd9Lr+iLwEAUM6snpOUdQIA/2X1jAMAWBs5CADwV2Scd53P5zlRAKhKyEEAADwjKwEAVkUGAgCsjBwEAFiZN3MwNTVVYmNjJT4+XrZt22aKMUVERMjJkyddtj9//ry0bt1apk2bJk2bNvVKnwAAeBtjRgBAZUM2AQBQduQpAMCKfFLgST399NMyffp0uXSJCefesP7bExV9CQCAckaWAgD8GTlXPNWCKHMBAFZETgIA/BUZBwCwMnIQAOCvyDjv+fCroxV9CQCAEiIHAQDwjKwEAFgVGQgAsDJyEABgZd7KwcTERImJiZHo6Ghp3769LFy4UGrVqiUpKSku23fr1k1mzpwpgwcPlho1anilTwAAfIExIwDAn7MpOTlZQkNDJTg4WHr06CFbtmzx2H7VqlXStm1b075jx46ydu1ap/NFRUUSFxcnzZo1k5o1a0rfvn1l3759Tm2mTp0qvXr1MuO7Bg0auPycgICAK7aVK1eW+fsCAGDDWA8AYDXVfNXxv//9b0lLS5NPPvnEDBRr167tdP7999/31Uf7lWtrV5fTufkVfRkAgApAlgIA/Bk5VzwXCwor+hIAABWAnAQA+CsyDgBgZeQgAMBfkXHecz6/oKIvAQBQQuQgAACekZUAAKsiAwEAVkYOAgCszBs5mJ+fL5mZmTJu3Dj7scDAQLOQd0ZGRqmuq7R95uXlmc0mJyenVJ8PAIANY0YAgL9mU2pqqsTGxppiulrcKSkpSSIiImTv3r3SpEmTK9pv3LhRhgwZIgkJCfLAAw/IihUrJDIyUrZt2yYdOnQwbWbMmCFz586VpUuXSqtWrWTChAmmz2+//dYUhbKN9wYOHCg9e/aUv/71r26v780335R+/frZ990VgwIAoDQY6wEArMZnBZ50sPbII4/4qnsAAPweWQoA8GfkXPGc+umXh14BANZBTgIA/BUZBwCwMnIQAOCvyDgAgJWRgwAAeEZWAgCsigwEAFgZOQgAsDJv5GB2drYUFBRISEiI03Hd37NnT7n2qQuNT5o0qVSfCQCAK4wZAQD+mk2JiYkSExMj0dHRZl8LPa1Zs0ZSUlLkpZdeuqL9nDlzTMGlMWPGmP3JkyfL+vXrZf78+ea9RUVFpkjU+PHjZcCAAabNsmXLzDhu9erVMnjwYHPMNmZbsmTJVb9n06ZNy/w9AQBwhbEeAMBqvF7gqbCwUGbOnCnfffedqeT761//WiZOnCg1a9b09kcBAOCXyFIAgD8j50qmRrXAir4EAEA5IicBAP6KjAMAWBk5CADwV2QcAMDKyEEAADwjKwEAVuWrDExOTjb9Hj9+XMLCwmTevHnSvXt3l2137dolcXFxkpmZKYcOHZLXXntNRo8e7dRGr+nyBbrbtGlT6oXCAQBQjAUBAFbmrzk4btw4iY2Nte/n5ORIy5YtK/SaAABVk79mJQCg6vJmNun79bc5HUPZBAYGSt++fSUjI8Ple/S443hLRUREmOJN6sCBA+a3Qe3Dpn79+tKjRw/zXluBp+J6+umn5Y9//KO0bt1annzySVOIKiAgoITfFAAAZ4z1AABW5fWVoqdOnSovv/yy1KlTR1q0aCFz5841AzmUzjVBLOYNAFZDlgIA/Bk5BwCAe+QkAMBf+SLjdOGa0NBQCQ4ONg+jbtmyxWP7VatWSdu2bU37jh07ytq1a53O60NCer527drSsGFD88Dr5s2by3SNAAAoxnoAAH9FxgEArKwq3PPUifeuNp1AaaOfd/n5adOmlel7AACgGDMCAKzKFxmYmppqFneLj4+Xbdu2mQJPurjbyZMnXbY/f/68WZhNx3dNmzZ12+9tt90mx44ds29ffvllma4TAADGggAAK/NmDjZu3FiCgoLkxIkTTsd139M4zxd91qhRQ+rVq+e0AQBQGowZAQD+nE3Z2dlSUFAgISEhTsd1X4s0uaLHPbW3/S1Jn+68+uqr8s4778j69evlkUcekaeeekrmzZvntn1eXp4p8Ou4VUaFhUVyqaDQ/NXNUzvblnepQIqKfmmr73fcd2xvO1/gsK9/tX3+pUJzztNnajt9r6PL90vC0+fZ21zW/8VSfl7+//velyso/O+/t16L/lte/rmF/+872/4N3X1/x/9eFx2+12X/Kext9fNsm/676rGLl+1f3q/tv4Gr/v77Wb9cn6v3e+Kq3dXeW5LPKO51AJUBYz0AgFVV83aHy5Ytk7/85S/ypz/9yexv2LBB+vfvL2+88YapIIySCQqkojEAWA1ZCgDwZ+QcAADukZMAAH/l7YyzLVyzcOFCs9BpUlKSWbhm79690qRJkyv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",
-      "text/plain": [
-       "<Figure size 8100x1400 with 60 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>file</th>\n",
-       "      <th>gene</th>\n",
-       "      <th>threshold</th>\n",
-       "      <th>mean_neg</th>\n",
-       "      <th>concentration_neg</th>\n",
-       "      <th>var_nb_neg</th>\n",
-       "      <th>var_data_neg</th>\n",
-       "      <th>mean-</th>\n",
-       "      <th>concentration-</th>\n",
-       "      <th>var_nb-</th>\n",
-       "      <th>var_data-</th>\n",
-       "      <th>std_nb-</th>\n",
-       "      <th>std_data-</th>\n",
-       "      <th>overdisp_nb-</th>\n",
-       "      <th>overdisp_data-</th>\n",
-       "      <th>cof_var_nb-</th>\n",
-       "      <th>cof_var_data-</th>\n",
-       "      <th>N-</th>\n",
-       "      <th>mean+</th>\n",
-       "      <th>concentration+</th>\n",
-       "      <th>var_nb+</th>\n",
-       "      <th>var_data+</th>\n",
-       "      <th>std_nb+</th>\n",
-       "      <th>std_data+</th>\n",
-       "      <th>overdisp_nb+</th>\n",
-       "      <th>overdisp_data+</th>\n",
-       "      <th>cof_var_nb+</th>\n",
-       "      <th>cof_var_data+</th>\n",
-       "      <th>N+</th>\n",
-       "      <th>mean_ratio</th>\n",
-       "      <th>concentration_ratio</th>\n",
-       "      <th>var_nb_ratio</th>\n",
-       "      <th>var_data_ratio</th>\n",
-       "      <th>overdisp_nb_ratio</th>\n",
-       "      <th>overdisp_data_ratio</th>\n",
-       "      <th>N_ratio</th>\n",
-       "      <th>binder_ratio</th>\n",
-       "      <th>noise_to_neg_mean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
-       "      <td>CMV</td>\n",
-       "      <td>197.942</td>\n",
-       "      <td>4.722340</td>\n",
-       "      <td>3.939271e-01</td>\n",
-       "      <td>61.333071</td>\n",
-       "      <td>1039.818115</td>\n",
-       "      <td>25.003214</td>\n",
-       "      <td>2.897500</td>\n",
-       "      <td>240.761868</td>\n",
-       "      <td>338.704865</td>\n",
-       "      <td>15.516503</td>\n",
-       "      <td>18.403936</td>\n",
-       "      <td>9.629237</td>\n",
-       "      <td>13.546453</td>\n",
-       "      <td>0.620580</td>\n",
-       "      <td>0.736063</td>\n",
-       "      <td>1867</td>\n",
-       "      <td>1230.471773</td>\n",
-       "      <td>3.924429</td>\n",
-       "      <td>387034.531887</td>\n",
-       "      <td>4.371280e+05</td>\n",
-       "      <td>622.120995</td>\n",
-       "      <td>661.156539</td>\n",
-       "      <td>314.541577</td>\n",
-       "      <td>355.252334</td>\n",
-       "      <td>0.505596</td>\n",
-       "      <td>0.537320</td>\n",
-       "      <td>3773</td>\n",
-       "      <td>49.212545</td>\n",
-       "      <td>1.354419</td>\n",
-       "      <td>1607.540828</td>\n",
-       "      <td>1290.586670</td>\n",
-       "      <td>32.665265</td>\n",
-       "      <td>26.224749</td>\n",
-       "      <td>2.020889</td>\n",
-       "      <td>0.668972</td>\n",
-       "      <td>5.294666</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
-       "      <td>EBV_BMLF-1_GLCT</td>\n",
-       "      <td>197.942</td>\n",
-       "      <td>4.722340</td>\n",
-       "      <td>3.939271e-01</td>\n",
-       "      <td>61.333071</td>\n",
-       "      <td>1039.818115</td>\n",
-       "      <td>1.454062</td>\n",
-       "      <td>0.231539</td>\n",
-       "      <td>10.585553</td>\n",
-       "      <td>20.490885</td>\n",
-       "      <td>3.253545</td>\n",
-       "      <td>4.526686</td>\n",
-       "      <td>7.279988</td>\n",
-       "      <td>14.092170</td>\n",
-       "      <td>2.237556</td>\n",
-       "      <td>3.113132</td>\n",
-       "      <td>5638</td>\n",
-       "      <td>251.500000</td>\n",
-       "      <td>24.847288</td>\n",
-       "      <td>2797.139942</td>\n",
-       "      <td>2.756250e+03</td>\n",
-       "      <td>52.887994</td>\n",
-       "      <td>52.500000</td>\n",
-       "      <td>11.121829</td>\n",
-       "      <td>10.959245</td>\n",
-       "      <td>0.210290</td>\n",
-       "      <td>0.208748</td>\n",
-       "      <td>2</td>\n",
-       "      <td>172.963772</td>\n",
-       "      <td>107.313660</td>\n",
-       "      <td>264.241278</td>\n",
-       "      <td>134.511032</td>\n",
-       "      <td>1.527726</td>\n",
-       "      <td>0.777683</td>\n",
-       "      <td>0.000355</td>\n",
-       "      <td>0.000355</td>\n",
-       "      <td>0.307911</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
-       "      <td>Flu</td>\n",
-       "      <td>197.942</td>\n",
-       "      <td>4.722340</td>\n",
-       "      <td>3.939271e-01</td>\n",
-       "      <td>61.333071</td>\n",
-       "      <td>1039.818115</td>\n",
-       "      <td>11.730760</td>\n",
-       "      <td>1.432491</td>\n",
-       "      <td>107.794731</td>\n",
-       "      <td>224.974823</td>\n",
-       "      <td>10.382424</td>\n",
-       "      <td>14.999161</td>\n",
-       "      <td>9.189066</td>\n",
-       "      <td>19.178196</td>\n",
-       "      <td>0.885060</td>\n",
-       "      <td>1.278618</td>\n",
-       "      <td>4249</td>\n",
-       "      <td>922.462976</td>\n",
-       "      <td>4.215191</td>\n",
-       "      <td>202796.575052</td>\n",
-       "      <td>2.202642e+05</td>\n",
-       "      <td>450.329407</td>\n",
-       "      <td>469.323150</td>\n",
-       "      <td>219.842509</td>\n",
-       "      <td>238.778384</td>\n",
-       "      <td>0.488182</td>\n",
-       "      <td>0.508772</td>\n",
-       "      <td>1391</td>\n",
-       "      <td>78.636249</td>\n",
-       "      <td>2.942561</td>\n",
-       "      <td>1881.321785</td>\n",
-       "      <td>979.061646</td>\n",
-       "      <td>23.924358</td>\n",
-       "      <td>12.450513</td>\n",
-       "      <td>0.327371</td>\n",
-       "      <td>0.246631</td>\n",
-       "      <td>2.484099</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
-       "      <td>EBV_BRLF1_YVLD</td>\n",
-       "      <td>197.942</td>\n",
-       "      <td>4.722340</td>\n",
-       "      <td>3.939271e-01</td>\n",
-       "      <td>61.333071</td>\n",
-       "      <td>1039.818115</td>\n",
-       "      <td>2.596270</td>\n",
-       "      <td>0.386810</td>\n",
-       "      <td>20.022444</td>\n",
-       "      <td>45.894730</td>\n",
-       "      <td>4.474645</td>\n",
-       "      <td>6.774565</td>\n",
-       "      <td>7.712004</td>\n",
-       "      <td>17.677179</td>\n",
-       "      <td>1.723490</td>\n",
-       "      <td>2.609345</td>\n",
-       "      <td>5630</td>\n",
-       "      <td>810.800000</td>\n",
-       "      <td>2.834109</td>\n",
-       "      <td>232769.662520</td>\n",
-       "      <td>2.139172e+05</td>\n",
-       "      <td>482.462084</td>\n",
-       "      <td>462.511790</td>\n",
-       "      <td>287.086412</td>\n",
-       "      <td>263.834677</td>\n",
-       "      <td>0.595045</td>\n",
-       "      <td>0.570439</td>\n",
-       "      <td>10</td>\n",
-       "      <td>312.294178</td>\n",
-       "      <td>7.326876</td>\n",
-       "      <td>11625.436849</td>\n",
-       "      <td>4661.039551</td>\n",
-       "      <td>37.225916</td>\n",
-       "      <td>14.925157</td>\n",
-       "      <td>0.001776</td>\n",
-       "      <td>0.001773</td>\n",
-       "      <td>0.549785</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
-       "      <td>SARS_Cov2</td>\n",
-       "      <td>197.942</td>\n",
-       "      <td>4.722340</td>\n",
-       "      <td>3.939271e-01</td>\n",
-       "      <td>61.333071</td>\n",
-       "      <td>1039.818115</td>\n",
-       "      <td>8.543229</td>\n",
-       "      <td>1.653138</td>\n",
-       "      <td>52.693679</td>\n",
-       "      <td>87.826508</td>\n",
-       "      <td>7.259041</td>\n",
-       "      <td>9.371580</td>\n",
-       "      <td>6.167888</td>\n",
-       "      <td>10.280247</td>\n",
-       "      <td>0.849684</td>\n",
-       "      <td>1.096960</td>\n",
-       "      <td>5066</td>\n",
-       "      <td>1414.221254</td>\n",
-       "      <td>4.004123</td>\n",
-       "      <td>500904.754996</td>\n",
-       "      <td>5.018279e+05</td>\n",
-       "      <td>707.746250</td>\n",
-       "      <td>708.398127</td>\n",
-       "      <td>354.191223</td>\n",
-       "      <td>354.843985</td>\n",
-       "      <td>0.500449</td>\n",
-       "      <td>0.500910</td>\n",
-       "      <td>574</td>\n",
-       "      <td>165.537081</td>\n",
-       "      <td>2.422136</td>\n",
-       "      <td>9505.974155</td>\n",
-       "      <td>5713.854980</td>\n",
-       "      <td>57.425044</td>\n",
-       "      <td>34.517068</td>\n",
-       "      <td>0.113304</td>\n",
-       "      <td>0.101773</td>\n",
-       "      <td>1.809109</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
-       "      <td>negative_control</td>\n",
-       "      <td>197.942</td>\n",
-       "      <td>4.722340</td>\n",
-       "      <td>3.939271e-01</td>\n",
-       "      <td>61.333071</td>\n",
-       "      <td>1039.818115</td>\n",
-       "      <td>3.867945</td>\n",
-       "      <td>0.489523</td>\n",
-       "      <td>34.430371</td>\n",
-       "      <td>57.992855</td>\n",
-       "      <td>5.867740</td>\n",
-       "      <td>7.615304</td>\n",
-       "      <td>8.901464</td>\n",
-       "      <td>14.993197</td>\n",
-       "      <td>1.517018</td>\n",
-       "      <td>1.968824</td>\n",
-       "      <td>5634</td>\n",
-       "      <td>807.000000</td>\n",
-       "      <td>2.431045</td>\n",
-       "      <td>268695.473466</td>\n",
-       "      <td>2.786387e+05</td>\n",
-       "      <td>518.358441</td>\n",
-       "      <td>527.862346</td>\n",
-       "      <td>332.955977</td>\n",
-       "      <td>345.277145</td>\n",
-       "      <td>0.642328</td>\n",
-       "      <td>0.654105</td>\n",
-       "      <td>6</td>\n",
-       "      <td>208.637941</td>\n",
-       "      <td>4.966156</td>\n",
-       "      <td>7804.024882</td>\n",
-       "      <td>4804.706543</td>\n",
-       "      <td>37.404630</td>\n",
-       "      <td>23.028921</td>\n",
-       "      <td>0.001065</td>\n",
-       "      <td>0.001064</td>\n",
-       "      <td>0.819074</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
-       "      <td>Flu_A0201</td>\n",
-       "      <td>173.376</td>\n",
-       "      <td>2.901204</td>\n",
-       "      <td>2.628828e+32</td>\n",
-       "      <td>2.901204</td>\n",
-       "      <td>875.116882</td>\n",
-       "      <td>15.505495</td>\n",
-       "      <td>1.876203</td>\n",
-       "      <td>143.647475</td>\n",
-       "      <td>331.107086</td>\n",
-       "      <td>11.985302</td>\n",
-       "      <td>18.196348</td>\n",
-       "      <td>9.264295</td>\n",
-       "      <td>21.354178</td>\n",
-       "      <td>0.772971</td>\n",
-       "      <td>1.173542</td>\n",
-       "      <td>182</td>\n",
-       "      <td>1950.187696</td>\n",
-       "      <td>4.113499</td>\n",
-       "      <td>926523.693207</td>\n",
-       "      <td>1.047826e+06</td>\n",
-       "      <td>962.561008</td>\n",
-       "      <td>1023.633510</td>\n",
-       "      <td>475.094625</td>\n",
-       "      <td>537.294725</td>\n",
-       "      <td>0.493574</td>\n",
-       "      <td>0.524890</td>\n",
-       "      <td>2227</td>\n",
-       "      <td>125.773976</td>\n",
-       "      <td>2.192459</td>\n",
-       "      <td>6449.982473</td>\n",
-       "      <td>3164.612305</td>\n",
-       "      <td>51.282329</td>\n",
-       "      <td>25.161106</td>\n",
-       "      <td>12.236264</td>\n",
-       "      <td>0.924450</td>\n",
-       "      <td>5.344504</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
-       "      <td>CMV_B0702</td>\n",
-       "      <td>87.184</td>\n",
-       "      <td>2.528850</td>\n",
-       "      <td>8.153597e+11</td>\n",
-       "      <td>2.528850</td>\n",
-       "      <td>833.862305</td>\n",
-       "      <td>3.732883</td>\n",
-       "      <td>1.053218</td>\n",
-       "      <td>16.963204</td>\n",
-       "      <td>32.455223</td>\n",
-       "      <td>4.118641</td>\n",
-       "      <td>5.696949</td>\n",
-       "      <td>4.544264</td>\n",
-       "      <td>8.694412</td>\n",
-       "      <td>1.103341</td>\n",
-       "      <td>1.526153</td>\n",
-       "      <td>2220</td>\n",
-       "      <td>649.994709</td>\n",
-       "      <td>3.614533</td>\n",
-       "      <td>117537.315045</td>\n",
-       "      <td>1.443395e+05</td>\n",
-       "      <td>342.837155</td>\n",
-       "      <td>379.920325</td>\n",
-       "      <td>180.828110</td>\n",
-       "      <td>222.062505</td>\n",
-       "      <td>0.527446</td>\n",
-       "      <td>0.584498</td>\n",
-       "      <td>189</td>\n",
-       "      <td>174.126735</td>\n",
-       "      <td>3.431894</td>\n",
-       "      <td>6928.957342</td>\n",
-       "      <td>4447.341309</td>\n",
-       "      <td>39.792610</td>\n",
-       "      <td>25.540830</td>\n",
-       "      <td>0.085135</td>\n",
-       "      <td>0.078456</td>\n",
-       "      <td>1.476119</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
-       "      <td>negative_control_A0201</td>\n",
-       "      <td>173.376</td>\n",
-       "      <td>2.901204</td>\n",
-       "      <td>2.628828e+32</td>\n",
-       "      <td>2.901204</td>\n",
-       "      <td>875.116882</td>\n",
-       "      <td>2.114713</td>\n",
-       "      <td>0.873756</td>\n",
-       "      <td>7.232860</td>\n",
-       "      <td>12.402468</td>\n",
-       "      <td>2.689398</td>\n",
-       "      <td>3.521714</td>\n",
-       "      <td>3.420256</td>\n",
-       "      <td>5.864846</td>\n",
-       "      <td>1.271755</td>\n",
-       "      <td>1.665339</td>\n",
-       "      <td>2406</td>\n",
-       "      <td>633.666667</td>\n",
-       "      <td>1.553935</td>\n",
-       "      <td>259031.426601</td>\n",
-       "      <td>2.944109e+05</td>\n",
-       "      <td>508.951301</td>\n",
-       "      <td>542.596449</td>\n",
-       "      <td>408.781841</td>\n",
-       "      <td>464.614792</td>\n",
-       "      <td>0.803185</td>\n",
-       "      <td>0.856281</td>\n",
-       "      <td>3</td>\n",
-       "      <td>299.646619</td>\n",
-       "      <td>1.778455</td>\n",
-       "      <td>35813.138102</td>\n",
-       "      <td>23738.091797</td>\n",
-       "      <td>119.517911</td>\n",
-       "      <td>79.220286</td>\n",
-       "      <td>0.001247</td>\n",
-       "      <td>0.001245</td>\n",
-       "      <td>0.728909</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
-       "      <td>negative_control_B0702</td>\n",
-       "      <td>87.184</td>\n",
-       "      <td>2.528850</td>\n",
-       "      <td>8.153597e+11</td>\n",
-       "      <td>2.528850</td>\n",
-       "      <td>833.862305</td>\n",
-       "      <td>1.782627</td>\n",
-       "      <td>0.662888</td>\n",
-       "      <td>6.576434</td>\n",
-       "      <td>18.766962</td>\n",
-       "      <td>2.564456</td>\n",
-       "      <td>4.332085</td>\n",
-       "      <td>3.689182</td>\n",
-       "      <td>10.527701</td>\n",
-       "      <td>1.438583</td>\n",
-       "      <td>2.430169</td>\n",
-       "      <td>2406</td>\n",
-       "      <td>601.000000</td>\n",
-       "      <td>1.064116</td>\n",
-       "      <td>340038.726438</td>\n",
-       "      <td>2.959260e+05</td>\n",
-       "      <td>583.128396</td>\n",
-       "      <td>543.990809</td>\n",
-       "      <td>565.788230</td>\n",
-       "      <td>492.389351</td>\n",
-       "      <td>0.970264</td>\n",
-       "      <td>0.905143</td>\n",
-       "      <td>3</td>\n",
-       "      <td>337.142924</td>\n",
-       "      <td>1.605272</td>\n",
-       "      <td>51705.636364</td>\n",
-       "      <td>15768.455078</td>\n",
-       "      <td>153.364145</td>\n",
-       "      <td>46.770832</td>\n",
-       "      <td>0.001247</td>\n",
-       "      <td>0.001245</td>\n",
-       "      <td>0.704916</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>10k_BEAM-T_Human_A2402_EBV_CMV_spikein</td>\n",
-       "      <td>EBV</td>\n",
-       "      <td>64.444</td>\n",
-       "      <td>1.961246</td>\n",
-       "      <td>4.752330e-01</td>\n",
-       "      <td>10.055138</td>\n",
-       "      <td>47.815094</td>\n",
-       "      <td>5.349569</td>\n",
-       "      <td>1.177876</td>\n",
-       "      <td>29.645753</td>\n",
-       "      <td>36.547199</td>\n",
-       "      <td>5.444791</td>\n",
-       "      <td>6.045428</td>\n",
-       "      <td>5.541709</td>\n",
-       "      <td>6.831803</td>\n",
-       "      <td>1.017800</td>\n",
-       "      <td>1.130078</td>\n",
-       "      <td>2320</td>\n",
-       "      <td>482.138596</td>\n",
-       "      <td>4.481992</td>\n",
-       "      <td>52346.939110</td>\n",
-       "      <td>7.634764e+04</td>\n",
-       "      <td>228.794535</td>\n",
-       "      <td>276.310768</td>\n",
-       "      <td>108.572389</td>\n",
-       "      <td>158.352061</td>\n",
-       "      <td>0.474541</td>\n",
-       "      <td>0.573094</td>\n",
-       "      <td>570</td>\n",
-       "      <td>90.126625</td>\n",
-       "      <td>3.805148</td>\n",
-       "      <td>1765.748310</td>\n",
-       "      <td>2089.014893</td>\n",
-       "      <td>19.591861</td>\n",
-       "      <td>23.178665</td>\n",
-       "      <td>0.245690</td>\n",
-       "      <td>0.197232</td>\n",
-       "      <td>2.727638</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>10k_BEAM-T_Human_A2402_EBV_CMV_spikein</td>\n",
-       "      <td>CMV</td>\n",
-       "      <td>64.444</td>\n",
-       "      <td>1.961246</td>\n",
-       "      <td>4.752330e-01</td>\n",
-       "      <td>10.055138</td>\n",
-       "      <td>47.815094</td>\n",
-       "      <td>3.266996</td>\n",
-       "      <td>0.645879</td>\n",
-       "      <td>19.792167</td>\n",
-       "      <td>33.713371</td>\n",
-       "      <td>4.448839</td>\n",
-       "      <td>5.806322</td>\n",
-       "      <td>6.058216</td>\n",
-       "      <td>10.319380</td>\n",
-       "      <td>1.361752</td>\n",
-       "      <td>1.777266</td>\n",
-       "      <td>2633</td>\n",
-       "      <td>1385.941634</td>\n",
-       "      <td>2.885361</td>\n",
-       "      <td>667102.997951</td>\n",
-       "      <td>4.532816e+05</td>\n",
-       "      <td>816.763734</td>\n",
-       "      <td>673.261929</td>\n",
-       "      <td>481.335564</td>\n",
-       "      <td>327.056792</td>\n",
-       "      <td>0.589320</td>\n",
-       "      <td>0.485779</td>\n",
-       "      <td>257</td>\n",
-       "      <td>424.225101</td>\n",
-       "      <td>4.467340</td>\n",
-       "      <td>33705.405280</td>\n",
-       "      <td>13445.158203</td>\n",
-       "      <td>79.451699</td>\n",
-       "      <td>31.693454</td>\n",
-       "      <td>0.097607</td>\n",
-       "      <td>0.088927</td>\n",
-       "      <td>1.665776</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>10k_BEAM-T_Human_A2402_EBV_CMV_spikein</td>\n",
-       "      <td>negative_control</td>\n",
-       "      <td>64.444</td>\n",
-       "      <td>1.961246</td>\n",
-       "      <td>4.752330e-01</td>\n",
-       "      <td>10.055138</td>\n",
-       "      <td>47.815094</td>\n",
-       "      <td>1.801178</td>\n",
-       "      <td>0.549719</td>\n",
-       "      <td>7.702813</td>\n",
-       "      <td>11.078585</td>\n",
-       "      <td>2.775394</td>\n",
-       "      <td>3.328451</td>\n",
-       "      <td>4.276543</td>\n",
-       "      <td>6.150745</td>\n",
-       "      <td>1.540878</td>\n",
-       "      <td>1.847930</td>\n",
-       "      <td>2887</td>\n",
-       "      <td>156.000000</td>\n",
-       "      <td>2.392657</td>\n",
-       "      <td>10327.117742</td>\n",
-       "      <td>1.164800e+04</td>\n",
-       "      <td>101.622427</td>\n",
-       "      <td>107.925901</td>\n",
-       "      <td>66.199473</td>\n",
-       "      <td>74.666667</td>\n",
-       "      <td>0.651426</td>\n",
-       "      <td>0.691833</td>\n",
-       "      <td>3</td>\n",
-       "      <td>86.610000</td>\n",
-       "      <td>4.352510</td>\n",
-       "      <td>1340.694268</td>\n",
-       "      <td>1051.397827</td>\n",
-       "      <td>15.479671</td>\n",
-       "      <td>12.139451</td>\n",
-       "      <td>0.001039</td>\n",
-       "      <td>0.001038</td>\n",
-       "      <td>0.918385</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2k_BEAM-T_Mouse_H2Kb_OT-1_5pv2</td>\n",
-       "      <td>OT-1</td>\n",
-       "      <td>81.580</td>\n",
-       "      <td>11.605676</td>\n",
-       "      <td>1.501462e+96</td>\n",
-       "      <td>11.605676</td>\n",
-       "      <td>11477.687500</td>\n",
-       "      <td>30.833333</td>\n",
-       "      <td>1.718346</td>\n",
-       "      <td>584.094782</td>\n",
-       "      <td>651.805481</td>\n",
-       "      <td>24.168053</td>\n",
-       "      <td>25.530481</td>\n",
-       "      <td>18.943615</td>\n",
-       "      <td>21.139637</td>\n",
-       "      <td>0.783829</td>\n",
-       "      <td>0.828016</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1284.159790</td>\n",
-       "      <td>4.557121</td>\n",
-       "      <td>363149.955646</td>\n",
-       "      <td>3.354067e+05</td>\n",
-       "      <td>602.619246</td>\n",
-       "      <td>579.143036</td>\n",
-       "      <td>282.791876</td>\n",
-       "      <td>261.187633</td>\n",
-       "      <td>0.469271</td>\n",
-       "      <td>0.450990</td>\n",
-       "      <td>1333</td>\n",
-       "      <td>41.648426</td>\n",
-       "      <td>2.652040</td>\n",
-       "      <td>621.731210</td>\n",
-       "      <td>514.580933</td>\n",
-       "      <td>14.928084</td>\n",
-       "      <td>12.355351</td>\n",
-       "      <td>222.166667</td>\n",
-       "      <td>0.995519</td>\n",
-       "      <td>2.656746</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2k_BEAM-T_Mouse_H2Kb_OT-1_5pv2</td>\n",
-       "      <td>negative_control</td>\n",
-       "      <td>81.580</td>\n",
-       "      <td>11.605676</td>\n",
-       "      <td>1.501462e+96</td>\n",
-       "      <td>11.605676</td>\n",
-       "      <td>11477.687500</td>\n",
-       "      <td>5.961509</td>\n",
-       "      <td>0.812680</td>\n",
-       "      <td>49.692846</td>\n",
-       "      <td>68.568329</td>\n",
-       "      <td>7.049315</td>\n",
-       "      <td>8.280600</td>\n",
-       "      <td>8.335615</td>\n",
-       "      <td>11.501840</td>\n",
-       "      <td>1.182472</td>\n",
-       "      <td>1.389011</td>\n",
-       "      <td>1325</td>\n",
-       "      <td>545.785714</td>\n",
-       "      <td>0.746879</td>\n",
-       "      <td>399381.561286</td>\n",
-       "      <td>8.029060e+05</td>\n",
-       "      <td>631.966424</td>\n",
-       "      <td>896.050222</td>\n",
-       "      <td>731.755249</td>\n",
-       "      <td>1471.101165</td>\n",
-       "      <td>1.157902</td>\n",
-       "      <td>1.641762</td>\n",
-       "      <td>14</td>\n",
-       "      <td>91.551598</td>\n",
-       "      <td>0.919032</td>\n",
-       "      <td>8037.003192</td>\n",
-       "      <td>11709.575195</td>\n",
-       "      <td>87.786597</td>\n",
-       "      <td>127.901374</td>\n",
-       "      <td>0.010566</td>\n",
-       "      <td>0.010456</td>\n",
-       "      <td>0.513672</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                            file                    gene  \\\n",
-       "0   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein                     CMV   \n",
-       "1   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein         EBV_BMLF-1_GLCT   \n",
-       "2   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein                     Flu   \n",
-       "3   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein          EBV_BRLF1_YVLD   \n",
-       "4   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein               SARS_Cov2   \n",
-       "5   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein        negative_control   \n",
-       "6               5k_BEAM-T_Human_A0201_B0702_PBMC               Flu_A0201   \n",
-       "7               5k_BEAM-T_Human_A0201_B0702_PBMC               CMV_B0702   \n",
-       "8               5k_BEAM-T_Human_A0201_B0702_PBMC  negative_control_A0201   \n",
-       "9               5k_BEAM-T_Human_A0201_B0702_PBMC  negative_control_B0702   \n",
-       "10        10k_BEAM-T_Human_A2402_EBV_CMV_spikein                     EBV   \n",
-       "11        10k_BEAM-T_Human_A2402_EBV_CMV_spikein                     CMV   \n",
-       "12        10k_BEAM-T_Human_A2402_EBV_CMV_spikein        negative_control   \n",
-       "13                2k_BEAM-T_Mouse_H2Kb_OT-1_5pv2                    OT-1   \n",
-       "14                2k_BEAM-T_Mouse_H2Kb_OT-1_5pv2        negative_control   \n",
-       "\n",
-       "    threshold   mean_neg  concentration_neg  var_nb_neg  var_data_neg  \\\n",
-       "0     197.942   4.722340       3.939271e-01   61.333071   1039.818115   \n",
-       "1     197.942   4.722340       3.939271e-01   61.333071   1039.818115   \n",
-       "2     197.942   4.722340       3.939271e-01   61.333071   1039.818115   \n",
-       "3     197.942   4.722340       3.939271e-01   61.333071   1039.818115   \n",
-       "4     197.942   4.722340       3.939271e-01   61.333071   1039.818115   \n",
-       "5     197.942   4.722340       3.939271e-01   61.333071   1039.818115   \n",
-       "6     173.376   2.901204       2.628828e+32    2.901204    875.116882   \n",
-       "7      87.184   2.528850       8.153597e+11    2.528850    833.862305   \n",
-       "8     173.376   2.901204       2.628828e+32    2.901204    875.116882   \n",
-       "9      87.184   2.528850       8.153597e+11    2.528850    833.862305   \n",
-       "10     64.444   1.961246       4.752330e-01   10.055138     47.815094   \n",
-       "11     64.444   1.961246       4.752330e-01   10.055138     47.815094   \n",
-       "12     64.444   1.961246       4.752330e-01   10.055138     47.815094   \n",
-       "13     81.580  11.605676       1.501462e+96   11.605676  11477.687500   \n",
-       "14     81.580  11.605676       1.501462e+96   11.605676  11477.687500   \n",
-       "\n",
-       "        mean-  concentration-     var_nb-   var_data-    std_nb-  std_data-  \\\n",
-       "0   25.003214        2.897500  240.761868  338.704865  15.516503  18.403936   \n",
-       "1    1.454062        0.231539   10.585553   20.490885   3.253545   4.526686   \n",
-       "2   11.730760        1.432491  107.794731  224.974823  10.382424  14.999161   \n",
-       "3    2.596270        0.386810   20.022444   45.894730   4.474645   6.774565   \n",
-       "4    8.543229        1.653138   52.693679   87.826508   7.259041   9.371580   \n",
-       "5    3.867945        0.489523   34.430371   57.992855   5.867740   7.615304   \n",
-       "6   15.505495        1.876203  143.647475  331.107086  11.985302  18.196348   \n",
-       "7    3.732883        1.053218   16.963204   32.455223   4.118641   5.696949   \n",
-       "8    2.114713        0.873756    7.232860   12.402468   2.689398   3.521714   \n",
-       "9    1.782627        0.662888    6.576434   18.766962   2.564456   4.332085   \n",
-       "10   5.349569        1.177876   29.645753   36.547199   5.444791   6.045428   \n",
-       "11   3.266996        0.645879   19.792167   33.713371   4.448839   5.806322   \n",
-       "12   1.801178        0.549719    7.702813   11.078585   2.775394   3.328451   \n",
-       "13  30.833333        1.718346  584.094782  651.805481  24.168053  25.530481   \n",
-       "14   5.961509        0.812680   49.692846   68.568329   7.049315   8.280600   \n",
-       "\n",
-       "    overdisp_nb-  overdisp_data-  cof_var_nb-  cof_var_data-    N-  \\\n",
-       "0       9.629237       13.546453     0.620580       0.736063  1867   \n",
-       "1       7.279988       14.092170     2.237556       3.113132  5638   \n",
-       "2       9.189066       19.178196     0.885060       1.278618  4249   \n",
-       "3       7.712004       17.677179     1.723490       2.609345  5630   \n",
-       "4       6.167888       10.280247     0.849684       1.096960  5066   \n",
-       "5       8.901464       14.993197     1.517018       1.968824  5634   \n",
-       "6       9.264295       21.354178     0.772971       1.173542   182   \n",
-       "7       4.544264        8.694412     1.103341       1.526153  2220   \n",
-       "8       3.420256        5.864846     1.271755       1.665339  2406   \n",
-       "9       3.689182       10.527701     1.438583       2.430169  2406   \n",
-       "10      5.541709        6.831803     1.017800       1.130078  2320   \n",
-       "11      6.058216       10.319380     1.361752       1.777266  2633   \n",
-       "12      4.276543        6.150745     1.540878       1.847930  2887   \n",
-       "13     18.943615       21.139637     0.783829       0.828016     6   \n",
-       "14      8.335615       11.501840     1.182472       1.389011  1325   \n",
-       "\n",
-       "          mean+  concentration+        var_nb+     var_data+     std_nb+  \\\n",
-       "0   1230.471773        3.924429  387034.531887  4.371280e+05  622.120995   \n",
-       "1    251.500000       24.847288    2797.139942  2.756250e+03   52.887994   \n",
-       "2    922.462976        4.215191  202796.575052  2.202642e+05  450.329407   \n",
-       "3    810.800000        2.834109  232769.662520  2.139172e+05  482.462084   \n",
-       "4   1414.221254        4.004123  500904.754996  5.018279e+05  707.746250   \n",
-       "5    807.000000        2.431045  268695.473466  2.786387e+05  518.358441   \n",
-       "6   1950.187696        4.113499  926523.693207  1.047826e+06  962.561008   \n",
-       "7    649.994709        3.614533  117537.315045  1.443395e+05  342.837155   \n",
-       "8    633.666667        1.553935  259031.426601  2.944109e+05  508.951301   \n",
-       "9    601.000000        1.064116  340038.726438  2.959260e+05  583.128396   \n",
-       "10   482.138596        4.481992   52346.939110  7.634764e+04  228.794535   \n",
-       "11  1385.941634        2.885361  667102.997951  4.532816e+05  816.763734   \n",
-       "12   156.000000        2.392657   10327.117742  1.164800e+04  101.622427   \n",
-       "13  1284.159790        4.557121  363149.955646  3.354067e+05  602.619246   \n",
-       "14   545.785714        0.746879  399381.561286  8.029060e+05  631.966424   \n",
-       "\n",
-       "      std_data+  overdisp_nb+  overdisp_data+  cof_var_nb+  cof_var_data+  \\\n",
-       "0    661.156539    314.541577      355.252334     0.505596       0.537320   \n",
-       "1     52.500000     11.121829       10.959245     0.210290       0.208748   \n",
-       "2    469.323150    219.842509      238.778384     0.488182       0.508772   \n",
-       "3    462.511790    287.086412      263.834677     0.595045       0.570439   \n",
-       "4    708.398127    354.191223      354.843985     0.500449       0.500910   \n",
-       "5    527.862346    332.955977      345.277145     0.642328       0.654105   \n",
-       "6   1023.633510    475.094625      537.294725     0.493574       0.524890   \n",
-       "7    379.920325    180.828110      222.062505     0.527446       0.584498   \n",
-       "8    542.596449    408.781841      464.614792     0.803185       0.856281   \n",
-       "9    543.990809    565.788230      492.389351     0.970264       0.905143   \n",
-       "10   276.310768    108.572389      158.352061     0.474541       0.573094   \n",
-       "11   673.261929    481.335564      327.056792     0.589320       0.485779   \n",
-       "12   107.925901     66.199473       74.666667     0.651426       0.691833   \n",
-       "13   579.143036    282.791876      261.187633     0.469271       0.450990   \n",
-       "14   896.050222    731.755249     1471.101165     1.157902       1.641762   \n",
-       "\n",
-       "      N+  mean_ratio  concentration_ratio  var_nb_ratio  var_data_ratio  \\\n",
-       "0   3773   49.212545             1.354419   1607.540828     1290.586670   \n",
-       "1      2  172.963772           107.313660    264.241278      134.511032   \n",
-       "2   1391   78.636249             2.942561   1881.321785      979.061646   \n",
-       "3     10  312.294178             7.326876  11625.436849     4661.039551   \n",
-       "4    574  165.537081             2.422136   9505.974155     5713.854980   \n",
-       "5      6  208.637941             4.966156   7804.024882     4804.706543   \n",
-       "6   2227  125.773976             2.192459   6449.982473     3164.612305   \n",
-       "7    189  174.126735             3.431894   6928.957342     4447.341309   \n",
-       "8      3  299.646619             1.778455  35813.138102    23738.091797   \n",
-       "9      3  337.142924             1.605272  51705.636364    15768.455078   \n",
-       "10   570   90.126625             3.805148   1765.748310     2089.014893   \n",
-       "11   257  424.225101             4.467340  33705.405280    13445.158203   \n",
-       "12     3   86.610000             4.352510   1340.694268     1051.397827   \n",
-       "13  1333   41.648426             2.652040    621.731210      514.580933   \n",
-       "14    14   91.551598             0.919032   8037.003192    11709.575195   \n",
-       "\n",
-       "    overdisp_nb_ratio  overdisp_data_ratio     N_ratio  binder_ratio  \\\n",
-       "0           32.665265            26.224749    2.020889      0.668972   \n",
-       "1            1.527726             0.777683    0.000355      0.000355   \n",
-       "2           23.924358            12.450513    0.327371      0.246631   \n",
-       "3           37.225916            14.925157    0.001776      0.001773   \n",
-       "4           57.425044            34.517068    0.113304      0.101773   \n",
-       "5           37.404630            23.028921    0.001065      0.001064   \n",
-       "6           51.282329            25.161106   12.236264      0.924450   \n",
-       "7           39.792610            25.540830    0.085135      0.078456   \n",
-       "8          119.517911            79.220286    0.001247      0.001245   \n",
-       "9          153.364145            46.770832    0.001247      0.001245   \n",
-       "10          19.591861            23.178665    0.245690      0.197232   \n",
-       "11          79.451699            31.693454    0.097607      0.088927   \n",
-       "12          15.479671            12.139451    0.001039      0.001038   \n",
-       "13          14.928084            12.355351  222.166667      0.995519   \n",
-       "14          87.786597           127.901374    0.010566      0.010456   \n",
-       "\n",
-       "    noise_to_neg_mean  \n",
-       "0            5.294666  \n",
-       "1            0.307911  \n",
-       "2            2.484099  \n",
-       "3            0.549785  \n",
-       "4            1.809109  \n",
-       "5            0.819074  \n",
-       "6            5.344504  \n",
-       "7            1.476119  \n",
-       "8            0.728909  \n",
-       "9            0.704916  \n",
-       "10           2.727638  \n",
-       "11           1.665776  \n",
-       "12           0.918385  \n",
-       "13           2.656746  \n",
-       "14           0.513672  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Determine params based on negative control percentile threshold for all BEAM-T datasets and all genes\n",
-    "QUANTILE = 0.999\n",
-    "FILES = ['10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein', '5k_BEAM-T_Human_A0201_B0702_PBMC', '10k_BEAM-T_Human_A2402_EBV_CMV_spikein', '2k_BEAM-T_Mouse_H2Kb_OT-1_5pv2']  #  '10k_BEAM-T_Human_A1101_EBV_spikein', \n",
-    "\n",
-    "params = []\n",
-    "nrows = 4\n",
-    "ncols = 18\n",
-    "plt.figure(figsize=(4.5 * ncols, 3.5 * nrows))\n",
-    "i = 0\n",
-    "for file in FILES:\n",
-    "    mdata = mu.read(f'../../data/BEAMT/{file}/preprocessed.h5mu')\n",
-    "    genes = mdata.var['gex:gene_ids'].unique()\n",
-    "    for gene in genes:\n",
-    "        if file == '5k_BEAM-T_Human_A0201_B0702_PBMC':  # Has two negative controls\n",
-    "            mhc = gene.split('_')[-1]\n",
-    "            neg_ctrl_key = [gene for gene in mdata.var['gex:gene_ids'].unique() if 'negative' in gene and mhc in gene][0]\n",
-    "        else:\n",
-    "            neg_ctrl_key = [gene for gene in mdata.var['gex:gene_ids'].unique() if 'negative' in gene][0]\n",
-    "        x_neg = mdata['gex'][:, neg_ctrl_key].X.toarray().reshape(-1, )\n",
-    "        nbfit_neg = smf.negativebinomial(\"nbdata ~ 1\", data=pd.DataFrame({\"nbdata\": x_neg})).fit(disp=False)\n",
-    "        mean_neg = np.exp(nbfit_neg.params.iloc[0])\n",
-    "        concentration_neg = 1 / nbfit_neg.params.iloc[1]\n",
-    "        var_nb_neg = mean_neg + mean_neg ** 2 / concentration_neg\n",
-    "        var_data_neg = x_neg.var()\n",
-    "\n",
-    "        thr = np.quantile(x_neg, QUANTILE) if file != '2k_BEAM-T_Mouse_H2Kb_OT-1_5pv2' else np.quantile(x_neg, 0.99 )  # 2k only 1339 samples\n",
-    "        x = mdata['gex'][:, gene].X.toarray().reshape(-1, )\n",
-    "        i += 1\n",
-    "        param = {'file': file, 'gene': gene, 'threshold': thr,\n",
-    "\t\t\t\t f'mean_neg': mean_neg, f'concentration_neg': concentration_neg,\n",
-    "\t\t\t\t f'var_nb_neg': var_nb_neg, 'var_data_neg': var_data_neg}\n",
-    "\n",
-    "        for direction in ['-', '+']:\n",
-    "            plt.subplot(nrows, ncols, i + (ncols if direction == '+' else 0))\n",
-    "            x_in_threshold = x[x > thr] if direction == '+' else x[x <= thr]\n",
-    "            nbfit = smf.negativebinomial(\"nbdata ~ 1\", data=pd.DataFrame({\"nbdata\": x_in_threshold})).fit(disp=False)\n",
-    "            mean = np.exp(nbfit.params.iloc[0])\n",
-    "            concentration = 1 / nbfit.params.iloc[1]\n",
-    "            var_nb = mean + mean ** 2 / concentration\n",
-    "            var_data = x_in_threshold.var()\n",
-    "            sns.histplot(x_in_threshold, discrete=True, binrange=(x_in_threshold.min(), x_in_threshold.max()), stat='probability', element='step', fill=True, alpha=0.3, label=gene)\n",
-    "            if direction == '-':\n",
-    "                sns.histplot(x_neg, discrete=True, binrange=(x_in_threshold.min(), x_in_threshold.max()), stat='probability', element='step', fill=True, alpha=0.3, label=neg_ctrl_key)\n",
-    "\n",
-    "\t\t\t# Fitted distribution for visualization\n",
-    "            x_range = np.arange(x_in_threshold.min(), x_in_threshold.max())\n",
-    "            prob = np.exp(npd.NegativeBinomial2(mean, concentration).log_prob(x_range))\n",
-    "            prob = prob / prob.sum()\n",
-    "            sns.lineplot(x=x_range, y=prob, c='black', ls=':')\n",
-    "\n",
-    "            plt.legend(frameon=False)\n",
-    "            sns.despine()\n",
-    "            plt.title(r'$\\mu$ = {:.1f}, $\\alpha$ = {:.1f} $var_{{NB}}$={:.0f} $var_{{data}}$={:.0f}'.format(mean, concentration, var_nb, var_data))\n",
-    "            \n",
-    "            param.update({f'mean{direction}': mean, f'concentration{direction}': concentration, \n",
-    "                          f'var_nb{direction}': var_nb, f'var_data{direction}': var_data, \n",
-    "                          f'std_nb{direction}': var_nb ** 0.5, f'std_data{direction}': var_data ** 0.5, \n",
-    "                          f'overdisp_nb{direction}': var_nb / mean, f'overdisp_data{direction}': var_data / mean, \n",
-    "                          f'cof_var_nb{direction}': var_nb ** 0.5 / mean, f'cof_var_data{direction}': var_data ** 0.5 / mean, \n",
-    "                          f'N{direction}': x_in_threshold.shape[0],})\n",
-    "\n",
-    "        # Signal and noise\n",
-    "        plt.subplot(nrows, ncols, i + (2 * ncols))\n",
-    "        sns.histplot(x, discrete=True, stat='probability', element='step', fill=True, alpha=0.3, label=gene)\n",
-    "        sns.despine()\n",
-    "        plt.title('all')\n",
-    "\n",
-    "        # Close to threshold\n",
-    "        plt.subplot(nrows, ncols, i + (3 * ncols))\n",
-    "        sns.histplot(x, binrange=(thr - thr/2, thr + thr/2), discrete=True, stat='count', element='step', fill=True, alpha=0.3, label=gene)\n",
-    "        sns.despine()\n",
-    "        plt.title(f'between {thr - thr/2:.0f} and {thr + thr/2:.0f}')\n",
-    "        \n",
-    "        params.append(param)\n",
-    "            \n",
-    "plt.suptitle(f'{QUANTILE} Quantile based determination of binder and non-binder', y=1.0)\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "plt.show()\n",
-    "\n",
-    "params = pd.DataFrame(params)\n",
-    "for key in [f'mean', f'concentration', f'var_nb', f'var_data', f'overdisp_nb', f'overdisp_data', f'N']:\n",
-    "    params[f'{key}_ratio'] = params[f'{key}+'] / params[f'{key}-']\n",
-    "params['binder_ratio'] = params['N+'] / (params['N-'] + params['N+'])\n",
-    "params['noise_to_neg_mean'] = params['mean-'] / params['mean_neg']\n",
-    "params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8c729af-e292-49a7-b5f1-f4b871e4201c",
-   "metadata": {},
-   "source": [
-    "## Negative control distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "1ce2c913-6426-43ae-a3d8-00f33f7930e2",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-08-14T13:42:23.969837Z",
-     "start_time": "2025-08-14T13:42:23.850382Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mean-</th>\n",
-       "      <th>concentration-</th>\n",
-       "      <th>var_nb-</th>\n",
-       "      <th>std_nb-</th>\n",
-       "      <th>overdisp_nb-</th>\n",
-       "      <th>cof_var_nb-</th>\n",
-       "      <th>N-</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>3.867945</td>\n",
-       "      <td>0.489523</td>\n",
-       "      <td>34.430371</td>\n",
-       "      <td>5.867740</td>\n",
-       "      <td>8.901464</td>\n",
-       "      <td>1.517018</td>\n",
-       "      <td>5634</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2.114713</td>\n",
-       "      <td>0.873756</td>\n",
-       "      <td>7.232860</td>\n",
-       "      <td>2.689398</td>\n",
-       "      <td>3.420256</td>\n",
-       "      <td>1.271755</td>\n",
-       "      <td>2406</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1.782627</td>\n",
-       "      <td>0.662888</td>\n",
-       "      <td>6.576434</td>\n",
-       "      <td>2.564456</td>\n",
-       "      <td>3.689182</td>\n",
-       "      <td>1.438583</td>\n",
-       "      <td>2406</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>1.801178</td>\n",
-       "      <td>0.549719</td>\n",
-       "      <td>7.702813</td>\n",
-       "      <td>2.775394</td>\n",
-       "      <td>4.276543</td>\n",
-       "      <td>1.540878</td>\n",
-       "      <td>2887</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>5.961509</td>\n",
-       "      <td>0.812680</td>\n",
-       "      <td>49.692846</td>\n",
-       "      <td>7.049315</td>\n",
-       "      <td>8.335615</td>\n",
-       "      <td>1.182472</td>\n",
-       "      <td>1325</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       mean-  concentration-    var_nb-   std_nb-  overdisp_nb-  cof_var_nb-  \\\n",
-       "5   3.867945        0.489523  34.430371  5.867740      8.901464     1.517018   \n",
-       "8   2.114713        0.873756   7.232860  2.689398      3.420256     1.271755   \n",
-       "9   1.782627        0.662888   6.576434  2.564456      3.689182     1.438583   \n",
-       "12  1.801178        0.549719   7.702813  2.775394      4.276543     1.540878   \n",
-       "14  5.961509        0.812680  49.692846  7.049315      8.335615     1.182472   \n",
-       "\n",
-       "      N-  \n",
-       "5   5634  \n",
-       "8   2406  \n",
-       "9   2406  \n",
-       "12  2887  \n",
-       "14  1325  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "neg_ctrl_params = params[params['gene'].str.contains('negative')].copy()\n",
-    "neg_ctrl_params[['mean-', 'concentration-', 'var_nb-', 'std_nb-', 'overdisp_nb-', 'cof_var_nb-', 'N-']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "9100607f-13d1-456c-8e45-a3225e267b8b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Truncated log-normal params:  -1.0539178917389445 1.8375518345106903 1.018115879390079 0.4175162931163312\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overdispersion Gamma shape, scale 4.186062616134899 1.2384303396204106\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Parameterize negative control MEAN with, gamma matching first and second moment\n",
-    "# ns    = neg_ctrl_params['N-']      # matching sample sizes\n",
-    "\n",
-    "# weights = ns / ns.sum()\n",
-    "# mu_hat  = np.sum(weights * means)\n",
-    "# var_hat = np.sum(weights * (means - mu_hat)**2)\n",
-    "\n",
-    "# alpha = mu_hat**2 / var_hat\n",
-    "# theta = var_hat / mu_hat      # scale ; rate = 1/theta\n",
-    "\n",
-    "# print(f\"Mean shape, scale: {alpha}, {theta}\")\n",
-    "# samples = stats.gamma(a=alpha, scale=theta).rvs(10000)\n",
-    "# print(samples.min())\n",
-    "# sns.histplot(samples, stat='density')\n",
-    "# # sns.histplot(neg_ctrl_params['mean-'], stat='density')\n",
-    "# sns.scatterplot(x=neg_ctrl_params['mean-'], y=0.1)\n",
-    "# sns.despine()\n",
-    "# plt.show()\n",
-    "\n",
-    "\n",
-    "# Truncated log-normal\n",
-    "ns, means = neg_ctrl_params['N-'].to_numpy(float), neg_ctrl_params['mean-'].to_numpy(float)\n",
-    "\n",
-    "# Weighted MoM lognormal params\n",
-    "w = ns / ns.sum()\n",
-    "m = np.sum(w * means)\n",
-    "v = np.sum(w * (means - m)**2)\n",
-    "sigma2 = np.log(1 + v / m**2)\n",
-    "sigma  = np.sqrt(sigma2)\n",
-    "mu_ln  = np.log(m) - 0.5 * sigma2\n",
-    "\n",
-    "# Truncate to observed range\n",
-    "low, high = means.min(), means.max()\n",
-    "a, b = (np.log(low) - mu_ln) / sigma, (np.log(high) - mu_ln) / sigma\n",
-    "samples = np.exp(truncnorm(a, b, loc=mu_ln, scale=sigma).rvs(100000, random_state=0))\n",
-    "print(f'Truncated log-normal params: ', a, b, mu_ln, sigma)\n",
-    "# Plot\n",
-    "sns.histplot(samples, stat='density', bins=50)\n",
-    "sns.scatterplot(x=means, y=np.full_like(means, 0.25, dtype=float))\n",
-    "sns.despine()\n",
-    "plt.show()\n",
-    "\n",
-    "# Parameterize negative control OVERDISPERSION with, gamma matching first and second moment\n",
-    "overdisp = neg_ctrl_params['overdisp_nb-'] - 1\n",
-    "\n",
-    "weights = ns / ns.sum()\n",
-    "mu_hat  = np.sum(weights * overdisp)\n",
-    "var_hat = np.sum(weights * (means - mu_hat)**2)\n",
-    "\n",
-    "# mu_hat = overdisp.mean()\n",
-    "# var_hat = overdisp.var()\n",
-    "alpha = mu_hat**2 / var_hat\n",
-    "theta = var_hat / mu_hat      # scale ; rate = 1/theta\n",
-    "print('Overdispersion Gamma shape, scale', alpha, theta)\n",
-    "sns.histplot(stats.gamma(a=alpha, scale=theta).rvs(100000).clip(max=20)+1, stat='density', label='gamma')\n",
-    "\n",
-    "sns.scatterplot(x=overdisp+1, y=0.1)\n",
-    "sns.despine()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4af0a035-0959-4cbc-8508-adcc2c68ae77",
-   "metadata": {},
-   "source": [
-    "## Non-binder distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "c9dce22d-bb14-4589-94a0-a036e94552ed",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mean-</th>\n",
-       "      <th>mean_ratio</th>\n",
-       "      <th>mean+</th>\n",
-       "      <th>N-</th>\n",
-       "      <th>N+</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>25.003214</td>\n",
-       "      <td>49.212545</td>\n",
-       "      <td>1230.471773</td>\n",
-       "      <td>1867</td>\n",
-       "      <td>3773</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.454062</td>\n",
-       "      <td>172.963772</td>\n",
-       "      <td>251.500000</td>\n",
-       "      <td>5638</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>11.730760</td>\n",
-       "      <td>78.636249</td>\n",
-       "      <td>922.462976</td>\n",
-       "      <td>4249</td>\n",
-       "      <td>1391</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.596270</td>\n",
-       "      <td>312.294178</td>\n",
-       "      <td>810.800000</td>\n",
-       "      <td>5630</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>8.543229</td>\n",
-       "      <td>165.537081</td>\n",
-       "      <td>1414.221254</td>\n",
-       "      <td>5066</td>\n",
-       "      <td>574</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>15.505495</td>\n",
-       "      <td>125.773976</td>\n",
-       "      <td>1950.187696</td>\n",
-       "      <td>182</td>\n",
-       "      <td>2227</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>3.732883</td>\n",
-       "      <td>174.126735</td>\n",
-       "      <td>649.994709</td>\n",
-       "      <td>2220</td>\n",
-       "      <td>189</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>5.349569</td>\n",
-       "      <td>90.126625</td>\n",
-       "      <td>482.138596</td>\n",
-       "      <td>2320</td>\n",
-       "      <td>570</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>3.266996</td>\n",
-       "      <td>424.225101</td>\n",
-       "      <td>1385.941634</td>\n",
-       "      <td>2633</td>\n",
-       "      <td>257</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>30.833333</td>\n",
-       "      <td>41.648426</td>\n",
-       "      <td>1284.159790</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1333</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        mean-  mean_ratio        mean+    N-    N+\n",
-       "0   25.003214   49.212545  1230.471773  1867  3773\n",
-       "1    1.454062  172.963772   251.500000  5638     2\n",
-       "2   11.730760   78.636249   922.462976  4249  1391\n",
-       "3    2.596270  312.294178   810.800000  5630    10\n",
-       "4    8.543229  165.537081  1414.221254  5066   574\n",
-       "6   15.505495  125.773976  1950.187696   182  2227\n",
-       "7    3.732883  174.126735   649.994709  2220   189\n",
-       "10   5.349569   90.126625   482.138596  2320   570\n",
-       "11   3.266996  424.225101  1385.941634  2633   257\n",
-       "13  30.833333   41.648426  1284.159790     6  1333"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "params[~params['gene'].str.contains('negative')][['mean-', 'mean_ratio', 'mean+', 'N-', 'N+', ]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "a91ae8e6dfffbbe",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-08-14T15:43:22.538843Z",
-     "start_time": "2025-08-14T15:43:22.441667Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mean-</th>\n",
-       "      <th>mean_ratio</th>\n",
-       "      <th>mean+</th>\n",
-       "      <th>N-</th>\n",
-       "      <th>N+</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>25.003214</td>\n",
-       "      <td>49.212545</td>\n",
-       "      <td>1230.471773</td>\n",
-       "      <td>1867</td>\n",
-       "      <td>3773</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>11.730760</td>\n",
-       "      <td>78.636249</td>\n",
-       "      <td>922.462976</td>\n",
-       "      <td>4249</td>\n",
-       "      <td>1391</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>8.543229</td>\n",
-       "      <td>165.537081</td>\n",
-       "      <td>1414.221254</td>\n",
-       "      <td>5066</td>\n",
-       "      <td>574</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>15.505495</td>\n",
-       "      <td>125.773976</td>\n",
-       "      <td>1950.187696</td>\n",
-       "      <td>182</td>\n",
-       "      <td>2227</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>3.732883</td>\n",
-       "      <td>174.126735</td>\n",
-       "      <td>649.994709</td>\n",
-       "      <td>2220</td>\n",
-       "      <td>189</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>5.349569</td>\n",
-       "      <td>90.126625</td>\n",
-       "      <td>482.138596</td>\n",
-       "      <td>2320</td>\n",
-       "      <td>570</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>3.266996</td>\n",
-       "      <td>424.225101</td>\n",
-       "      <td>1385.941634</td>\n",
-       "      <td>2633</td>\n",
-       "      <td>257</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        mean-  mean_ratio        mean+    N-    N+\n",
-       "0   25.003214   49.212545  1230.471773  1867  3773\n",
-       "2   11.730760   78.636249   922.462976  4249  1391\n",
-       "4    8.543229  165.537081  1414.221254  5066   574\n",
-       "6   15.505495  125.773976  1950.187696   182  2227\n",
-       "7    3.732883  174.126735   649.994709  2220   189\n",
-       "10   5.349569   90.126625   482.138596  2320   570\n",
-       "11   3.266996  424.225101  1385.941634  2633   257"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "non_binder_params = params[~params['gene'].str.contains('negative')].copy()\n",
-    "non_binder_params = non_binder_params[(non_binder_params['N+'] > 20) & (non_binder_params['N-'] > 20)].copy()\n",
-    "non_binder_params[['mean-', 'mean_ratio', 'mean+', 'N-', 'N+', ]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "8b2caf2f00bf488e",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-08-14T15:11:17.359570Z",
-     "start_time": "2025-08-14T15:11:17.180587Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.scatterplot(non_binder_params, x='mean-', y='mean_ratio')\n",
-    "sns.despine()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "fcb858ab-43d3-4467-b51b-a40223b56fa7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-08-14T15:47:43.631651Z",
-     "start_time": "2025-08-14T15:47:43.377680Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trunc log-normal params for noise:  -1.4325807532116341 1.9485510504360735 2.0461540382126118 0.6019089551720753\n",
-      "lowest sampled value 3.267036725146729\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overdispersion Gamma shape, scale 0.7285976325752553 8.167773103845757\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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oOTlZYmJiJDQ0VDp37izp6elu933vvffk1ltvlXr16pklPj6+0P42m00SExMlOjpaatSoYfbZuXNnJVwJAADwB14PQCkpKTJq1CiZNGmSZGZmSrt27aRHjx5y7Ngxl/unpaXJgAEDZN26dbJhwwZp2rSpdO/eXQ4dOuTYZ9q0aTJr1iyZM2eObNq0SWrVqmXOeeHChUq8MgAA4KuCbFpd4kVa49OxY0d5++23zXp+fr4JNc8884yMHz++2OPz8vJMTZAeP2jQIFP706hRI3nhhRdk9OjRZp/Tp09LZGSkzJ8/X/r371/sOXNyciQ8PNwcFxYWJp52U7deEpMwsdD2vSlJsjF1pcffDwAA+FANUG5urmRkZJgmKkeBqlQx61q7UxLnzp2TS5cuSf369c36nj17JCsry+mcGmY0aLk758WLF03oKbgAAIDA5dUAdOLECVODo7UzBem6hpiSGDdunKnxsQce+3GlOeeUKVNMSLIvWgMFAAACV1XxY1OnTpUlS5aYfkHagbqsJkyYYPoh2WkNkDdC0K4dO0zzmCvRDerL8iWLKr1MAAAEIq8GoIiICAkODpajR486bdf1qKioIo+dPn26CUBr166Vtm3bOrbbj9Nz6Ciwguds3769y3NVr17dLN52yRbksm+QvX8QAAAIgCawkJAQiYuLk9TUVMc27QSt6126dHF7nI7ySkpKklWrVkmHDh2cXouNjTUhqOA5tUZHR4MVdU4AAGAdXm8C06anwYMHmyDTqVMnmTlzppw9e1aGDh1qXteRXY0bNzb9dNRrr71m5vhZvHixmTvI3q+ndu3aZgkKCpLnnntOXnnlFWnZsqUJRBMnTjT9hPr06ePVawUAAL7B6wEoISFBjh8/bkKNhhltptKaHXsn5v3795uRYXazZ882o8cefPBBp/PoPEKTJ082X48dO9aEqCeeeEKys7Ola9eu5pzl6ScEAAACh9fnAfJF3poH6LOXB0nPxIUuj2GOIAAAAmgmaAAAgMpGAAIAAJZDAAIAAJZTpgD0yy+/eL4kAAAAvhyAWrRoIXfeeacsWrSIJ6wDAABrBKDMzEwz+7LO4aOTDj755JOSnp7u+dIBAAD4SgDSuXrefPNNOXz4sMydO1eOHDli5tq5/vrrZcaMGWZeHwAAgIDsBF21alXp27evLF261MzQvGvXLhk9erR5kKjO4KzBCAAAIKAC0JYtW2TEiBHmoaNa86PhZ/fu3bJmzRpTO9S7d2/PlRQAAMCbj8LQsDNv3jzZvn279OrVSxYuXGj+tT+yQp+/NX/+fPOsLgAAgIAIQPo8rkcffVSGDBlian9cadiwofzlL38pb/kAAAB8IwBpE9eVV17p9JBSpY8VO3DggHktJCTEPOUdAAAgIPoANW/eXE6cOFFo+6lTp0zzFwAAQMAFIHcPkD9z5oyEhoaWt0wAAAC+0wSmEx+qoKAgSUxMlJo1azpey8vLk02bNpk5ggAAAAImAG3dutVRA/T999+bfj52+nW7du3MUHgAAICACUDr1q0z/w4dOtTMBB0WFlZR5QIAAPCtUWA6BxAAAEDAByB95IVObqi1Pvp1UZYtW+aJsgEAAHg3AIWHh5vOz/avAQAAAj4AFWz2ogkMAABYbh6g8+fPy7lz5xzr+/btk5kzZ8rnn3/uybIBAAD4TgDSp7zrA1BVdna2dOrUSd544w2zXZ8TBgAAEHABKDMzU2699Vbz9ccffyxRUVGmFkhD0axZszxdRgAAAO8Pg9fmrzp16pivtdlLR4Xpg1FvuukmE4Tgebt27JCbuvVy+Vp0g/qyfMmiSi8TAACWCkAtWrSQTz75RO6//35ZvXq1PP/882b7sWPHmByxglyyBUlMwkSXr+1NSar08gAAYLkApM8Be/jhh03w6datm3Tp0sVRG3TDDTd4uowAAMAP3d//v+TI8VM+2XJRpgD04IMPSteuXeXIkSPm+V92Goa0VggAAODI8VMuWy98oeWiTAFIacdnXQrS0WAAAAC+rkwB6OzZszJ16lRJTU01/X7y8/OdXv/ll188VT4AAADfCECPP/64fPnllzJw4ECJjo52PCIDAAAgYAPQZ599JitWrJBbbrnF8yUCAADwxYkQ69WrJ/Xr1/d8aQAAAHw1ACUlJZmh8AWfBwYAABDQTWD63K/du3dLZGSkxMTESLVq1Qo9KgMAACCgAlCfPn08XxIAAABfDkCTJk3yfEkAAAB8uQ+Qys7Olvfff18mTJggp06dcjR9HTp0yJPlAwAA8I0aoO+++07i4+MlPDxc9u7dK8OGDTOjwpYtWyb79++XhQsXer6kAAAA3qwBGjVqlAwZMkR27twpoaGhju29evWSf/7zn54qGwAAgO8EoM2bN8uTTz5ZaHvjxo0lKyvLE+UCAADwrQBUvXp1ycnJKbR9x44d0qBBA0+UCwAAwLcC0H333Scvv/yyXLp0yazrs8C078+4cePkgQce8HQZAQAAvB+AdCLEM2fOmNqe8+fPy+233y4tWrSQOnXqyKuvvlqqcyUnJ5vJFLUvUefOnSU9Pd3tvj/++KMJWLq/hq6ZM2cW2mfy5MnmtYJL69aty3KZAAAgQJVpFJiO/lqzZo18/fXX8u2335owdOONN5qRYaWRkpJiOlTPmTPHhB8NND169JDt27dLw4YNC+2vj95o1qyZ9OvXT55//nm3573uuutk7dq1jvWqVct0mQAAIECVOhnk5+fL/PnzzZB3HQKvNSyxsbESFRUlNpvNrJfUjBkzzBD6oUOHmnUNQvqU+blz58r48eML7d+xY0ezKFevOy6qalVTHgAAgHI3gWnA0f4/jz/+uJnwsE2bNqa2Zd++fWZY/P3331/ic+Xm5kpGRoZTrVGVKlXM+oYNG6Q8dHh+o0aNTG3RI488YvonAQAAlKkGSGt+dJ6f1NRUufPOO51e++KLL8wzwnQSxEGDBhV7rhMnTkheXp55oGpBuv7zzz9LWWlTmpazVatWcuTIEXnppZfk1ltvlR9++MH0UXLl4sWLZrFzNcINAABYtAboww8/lBdffLFQ+FF33XWXaZb64IMPxJt69uxp+gi1bdvW9CdauXKleWzHRx995PaYKVOmmH5N9qVp06aVWmYAAODDNUD6CIxp06YVGT5mzZpVonNFRERIcHCwHD161Gm7rnuy/07dunXl6quvll27drndR59npp2xC9YA+VMI2rVjh9zUrVeh7dEN6svyJYu8UiYAAAImAOlDTy9vsipIX/v1119LdK6QkBCJi4szzWnadGbvYK3rI0eOFE/REWq7d++WgQMHFjmxoy7+6pItSGISJhbavjclySvlAQAgoAKQ9tkpaki51uj8/vvvJT6f1roMHjxYOnToIJ06dTLD4M+ePesYFaZ9ifTxGtpEZe84/dNPPzm+1o7Y33zzjdSuXdvMQ6RGjx4t9957r1x11VVy+PBhmTRpkinXgAEDSnOpAAAggFUt7SgwHe3lrrakYEfikkhISJDjx49LYmKieYZY+/btZdWqVY5aJh29pSPD7DTQ3HDDDY716dOnm0UnYkxLSzPbDh48aMLOyZMnzUSNXbt2lY0bN/KIDgAAULYApLU1xSnJCLCCtLnLXZOXPdTY6QzQGsKKsmTJklK9PwAAsJ5SBaB58+ZVXEkAAAB8+VlgAAAA/owABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALKdUT4OHf9m1Y4fc1K2Xy9eiG9SX5UsWVXqZAADwBQSgAHbJFiQxCRNdvrY3JanSywMAgK+gCQwAAFgOAQgAAFgOAQgAAFgOAQgAAFgOnaAtihFiAAArIwBZFCPEAABWRhMYAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwnKreLgB8z64dO+Smbr1cvhbdoL4sX7Ko0ssEAIAnEYBQyCVbkMQkTHT52t6UpEovDwAAnkYTGAAAsBxqgOCR5jGaxgAA/oQABI80j9E0BgDwJzSBAQAAy/F6AEpOTpaYmBgJDQ2Vzp07S3p6utt9f/zxR3nggQfM/kFBQTJz5sxynxMAAFiPV5vAUlJSZNSoUTJnzhwTVDTQ9OjRQ7Zv3y4NGzYstP+5c+ekWbNm0q9fP3n++ec9ck74ifO/ipw9LnIhRyQ0XKRWhEiNet45t33/89kiIbVEgoJFqlQVqXWF58rkj8z35YRIfp6ILU8k9+y/vx+Xfz8r8l4CgD8EoBkzZsiwYcNk6NChZl1Dy4oVK2Tu3Lkyfvz4Qvt37NjRLMrV62U5J/zA6UMin44U+eWL/2xr3k3kvrdEwhtX7rld7d/sDpHOT4msSRTp9Xr5y+SP9PuyYrRI3CCRTXNEfklz/f2syHsJAP7QBJabmysZGRkSHx//n8JUqWLWN2zY4DPnhJdpbcHlH5hqd6rI35759+uVdW53++uHvX7oR15T/jL5I/v3JerawuGn4Pfzt6yKu5cAUEpeC0AnTpyQvLw8iYyMdNqu61lZWZV6zosXL0pOTo7TAh+hTSWXf2AW/ODU1yvr3EXtrx/6TTqWv0z+yP590eu/PPzY6ffl3MmKu5cAUEoMgxeRKVOmyEsvveTtYvi1Cnt8hvYTKc/rnjx3cfv/frH8ZfJH9uu1X7/b/U6X7DwAEMgBKCIiQoKDg+Xo0aNO23U9KiqqUs85YcIE03HaTmuAmjZtWqYyWFWFPT4jNKx8r3vy3MXtX7V6+cvkj+zXa79+t/uFl+w8ABDITWAhISESFxcnqampjm35+flmvUuXLpV6zurVq0tYWJjTAh9Rq8G/O8m6otv19co6d1H7a0fog5vLXyZ/ZP++6PXr98EVfb3mFRV3LwHAn+YB0lqX9957TxYsWCDbtm2T4cOHy9mzZx0juAYNGmRqZwp2cv7mm2/Mol8fOnTIfL1r164SnxN+RodH6wihyz847SOHyjN8urTndre/fRTY0W3lL5M/sn9f9Pr1+3B5CLJ/P+tEVdy9BAB/6gOUkJAgx48fl8TERNNJuX379rJq1SpHJ+b9+/ebUVx2hw8flhtuuMGxPn36dLPcfvvtkpaWVqJzwg/p8OgH/1Jg7piwf9cWeOIDs7TnLri/0zxAwSJ9kq37Ia7fF71+nQfo7tcKzANU1/n7WZH3EgD8qRP0yJEjzeKKPdTY6ezONputXOeEn9IPyIr6kCztuSuyLP6spN8Xvn8AfIDXH4UBAABQ2QhAAADAcghAAADAcrzeBwiBr8ImSQQAoIwIQPDfSRIBACgjmsAAAIDlEIAAAIDlEIAAAIDlEIAAAIDl0AkaXsUIMQCANxCA4FWMEAMAeANNYAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIYBQa/GyLP8HgAQHkRgOB3Q+QZHg8AKC8CEPwOkycCAMqLAAS/w+SJAIDyohM0AACwHAIQAACwHAIQAACwHAIQAACwHDpBI6AwQgwAUBIEIAQURogBAEqCJjAAAGA5BCAAAGA5BCAAAGA59AGCZRTVQfrAvj3S9KrYQtvpOA0AgYkABMsoqoP0tpcH8eBVALAQmsAAAIDlEIAAAIDlEIAAAIDl0AcIKAIzSwNAYCIAAUVgZmkACEwEIKCMqB0CAP9FAALKiNohAPBfdIIGAACWQwACAACWQwACAACWQx8goBI7SNM5GgB8AwEIqMQO0nSOBgDfQBMYAACwHJ8IQMnJyRITEyOhoaHSuXNnSU9PL3L/pUuXSuvWrc3+bdq0kZUrVzq9PmTIEAkKCnJa7r777gq+CgAA4C+83gSWkpIio0aNkjlz5pjwM3PmTOnRo4ds375dGjZsWGj/9evXy4ABA2TKlCnyxz/+URYvXix9+vSRzMxMuf766x37aeCZN2+eY7169eqVdk2AO0yeCAC+wesBaMaMGTJs2DAZOnSoWdcgtGLFCpk7d66MHz++0P5vvvmmCTdjxowx60lJSbJmzRp5++23zbEFA09UVFQlXglQPCZPBADf4NUmsNzcXMnIyJD4+Pj/FKhKFbO+YcMGl8fo9oL7K60xunz/tLQ0U4PUqlUrGT58uJw8edJtOS5evCg5OTlOCwAACFxerQE6ceKE5OXlSWRkpNN2Xf/5559dHpOVleVyf91upzVEffv2ldjYWNm9e7e8+OKL0rNnTxOSgoODC51Tm9Neeuklj10XUBY0jwGAhZrAKkL//v0dX2sn6bZt20rz5s1NrVC3bt0K7T9hwgTTD8lOa4CaNm1aaeUFFM1jAGCRJrCIiAhTI3P06FGn7brurv+Obi/N/qpZs2bmvXbt2uXyde0vFBYW5rQAAIDA5dUAFBISInFxcZKamurYlp+fb9a7dOni8hjdXnB/pZ2g3e2vDh48aPoARUdHe7D0AADAX3m9CUybngYPHiwdOnSQTp06mWHwZ8+edYwKGzRokDRu3Nj001HPPvus3H777fLGG2/IPffcI0uWLJEtW7bIu+++a14/c+aM6c/zwAMPmFoh7QM0duxYadGiheksDfgj+gcBQIAFoISEBDl+/LgkJiaajszt27eXVatWOTo679+/34wMs7v55pvN3D9//vOfTefmli1byieffOKYA0ib1L777jtZsGCBZGdnS6NGjaR79+5muDxzASEQ+wetTRrIc8cAwN8CkBo5cqRZXNGOy5fr16+fWVypUaOGrF692uNlBHwVzx0DAD99FAYAAEBlIgABAADL8YkmMACV23H6wL490vSqWJev0XcIgBUQgAALdpze9vIgJl0EYGk0gQEAAMuhBgiAE+YcAmAFBCAATphzCIAVEIAAlBhzDgEIFPQBAgAAlkMNEIByo98QAH9DAAJQof2GaB4D4IsIQAB8bkJGao0AVDQCEACfm5CRWiMAFY0ABMDn8BgPABWNAATA5/AYDwAVjQAEIGAwGg1ASRGAAAQMRqMBKCkmQgQAAJZDDRAASzeP0akasCYCEABLN48V1ana3cNfFeEI8G8EIABwgz5FQOCiDxAAALAcaoAAwMPu7/9fcuT4qULb6W8E+A4CEAB4eM6hXb/skfgJcwttZxJHwHcQgACgAmarLi0mcQQqFwEIAPy4w7W75jZCE1A0AhAA+DENP66CE01qQNEYBQYAACyHGiAA8PMO1zGlPIbmMYAABAAB2eHa05M4uutrpAhU8EcEIACwmKJqh9zNVeRuaH9xjwxh7iP4KgIQAFhMcTVK7p6Z5snzKTpqw5voBA0AACyHGiAAgE81xdFshspAAAIAeIW7prOyNpt5uqM2Hb8DGwEIABDQz2ArqqN2UUHG3SSTiv5L/o8ABAAI+GewuTuuqBFs7uZYQmAgAAEALKusgaos/ZfK+pq7Wiqa6MqHAAQAQCX0Xyrra+5qqYpq8qOJrngEIAAA/DRseXKyS6vVHBGAAAAIMGWdnHKtm9qmsjbf+XI/KgIQAACosKY9X8VM0AAAwHJ8IgAlJydLTEyMhIaGSufOnSU9Pb3I/ZcuXSqtW7c2+7dp00ZWrlzp9LrNZpPExESJjo6WGjVqSHx8vOzcubOCrwIAAPgLrweglJQUGTVqlEyaNEkyMzOlXbt20qNHDzl27JjL/devXy8DBgyQxx57TLZu3Sp9+vQxyw8//ODYZ9q0aTJr1iyZM2eObNq0SWrVqmXOeeHChUq8MgAA4Ku8HoBmzJghw4YNk6FDh8q1115rQkvNmjVl7lzXQ/vefPNNufvuu2XMmDFyzTXXSFJSktx4443y9ttvO2p/Zs6cKX/+85+ld+/e0rZtW1m4cKEcPnxYPvnkk0q+OgAA4Iu8GoByc3MlIyPDNFE5ClSlilnfsGGDy2N0e8H9ldbu2Pffs2ePZGVlOe0THh5umtbcnRMAAFiLV0eBnThxQvLy8iQyMtJpu67//PPPLo/RcONqf91uf92+zd0+l7t48aJZ7E6fPm3+zcnJkYrw+++X5NL5s4W22/LzXW4v62uePh/v5XvnC9T38ueyB+p7+XPZK/O9/Lnslfle+jlYUZ+xqk6dOhIUFFT0TjYvOnTokE2LsH79eqftY8aMsXXq1MnlMdWqVbMtXrzYaVtycrKtYcOG5uuvv/7anPPw4cNO+/Tr18/20EMPuTznpEmTzDEsLCwsLCws4vfL6dOni80gXq0BioiIkODgYDl69KjTdl2PiopyeYxuL2p/+7+6TUeBFdynffv2Ls85YcIE0xHbLj8/X06dOiVXXHFF8QnSD2nqbtq0qRw4cEDCwsIkEAX6NXJ9/i/QrzHQr88K15jjx9enNUDF8WoACgkJkbi4OElNTTUjuezhQ9dHjhzp8pguXbqY15977jnHtjVr1pjtKjY21oQg3cceePQm6miw4cOHuzxn9erVzVJQ3bp1JdDpD7S//VCXVqBfI9fn/wL9GgP9+qxwjWEBen1enwlaa14GDx4sHTp0kE6dOpkRXGfPnjWjwtSgQYOkcePGMmXKFLP+7LPPyu233y5vvPGG3HPPPbJkyRLZsmWLvPvuu+Z1rbHRcPTKK69Iy5YtTSCaOHGiNGrUyBGyAACAtXk9ACUkJMjx48fNxIXaSVlrbVatWuXoxLx//34zMszu5ptvlsWLF5th7i+++KIJOTq8/frrr3fsM3bsWBOinnjiCcnOzpauXbuac+rEiQAAAF4PQEqbu9w1eaWlpRXa1q9fP7O4o7VAL7/8sllQmDb36cSTlzf7BZJAv0auz/8F+jUG+vVZ4RqrB/j1BWlPaG8XAgAAwFIzQQMAAFQ2AhAAALAcAhAAALAcAlCA0ekCOnbsaCaBatiwoRn6v3379iKPmT9/vuk4XnDx5RFzkydPLlTe1q1bF3nM0qVLzT56XW3atJGVK1eKr4qJiSl0fbo8/fTTfnv//vnPf8q9995rpqPQ8l3+YGLtiqgjQXXy0ho1aphn+e3cubPY8yYnJ5vvl16vPu8vPT1dfO36Ll26JOPGjTM/d7Vq1TL76PQe+oBmT/+ce+v+DRkypFBZ9aHV/nL/SnKNrn4ndXn99df94h6W5LPhwoUL5v8ZnQS4du3a8sADDxSaePhyZf3d9QUEoADz5Zdfmh/gjRs3mgki9T/f7t27m2kBiqKTXB05csSx7Nu3T3zZdddd51Tef/3rX273Xb9+vQwYMEAee+wx2bp1q/nF1+WHH34QX7R582ana9P7qIoa+ejr909//tq1a2c+8FyZNm2azJo1S+bMmWMmLdWgoA851v+Q3UlJSTHziOkolczMTHN+PebYsWPiS9d37tw5Uz6dj0z/XbZsmfngue+++zz6c+7N+6c08BQs64cffljkOX3p/pXkGgtemy5z5841gUZDgj/cw5J8Njz//PPy97//3fzBqPtrSO/bt2+R5y3L767PKPZhGfBrx44dM89F+fLLL93uM2/ePFt4eLjNX+iz29q1a1fi/fUZcPfcc4/Tts6dO9uefPJJmz949tlnbc2bN7fl5+cHxP3Tn8fly5c71vW6oqKibK+//rpjW3Z2tq169eq2Dz/80O159HmBTz/9tGM9Ly/P1qhRI9uUKVNsvnR9rqSnp5v99u3b57Gfc29e3+DBg229e/cu1Xl89f6V9B7q9d51111F7uOr99DVZ0N2drZ51ubSpUsd+2zbts3ss2HDBpfnKOvvrq+gBijA2Z9sX79+/SL3O3PmjFx11VXmuS+9e/eWH3/8UXyZVrFqVXWzZs3kkUceMRNmurNhwwZTLVuQ/oWi231dbm6uLFq0SB599NEin0vnb/evoD179phJUAveo/DwcNMk4u4e6fclIyPD6RidMFXX/eG+6u+l3s/iHrlTmp9zb9M527RppVWrVuaxQydPnnS7r7/fP20WWrFihalVLo6v3sPTl3026P3QWqGC90Sb66688kq396Qsv7u+hAAUwPS5avpYkFtuucVppuzL6X9YWp376aefmg9bPU5n3D548KD4Iv3l0n4vOrv37NmzzS/hrbfeKr/99pvL/fUX1D6zuJ2u63Zfp/0QdDZz7WMRKPfvcvb7UJp7dOLECcnLy/PL+6pNA9onSJtli3q+Uml/zr1Jm78WLlxonsH42muvmeaTnj17mnsUaPdPLViwwPSlKa55yFfvoavPhqysLPN8zstDeVH3pCy/u77EJ2aCRsXQ9l7t51Jcm7M+SNb+MFmlH57XXHONvPPOO5KUlCS+Rv9jtWvbtq35T0ZrPz766KMS/UXmT/7yl7+Y69W/IAPl/lmZ/oX90EMPmY6j+oEYKD/n/fv3d3ytnb21vM2bNze1Qt26dZNAo39waG1OcYMNfPUelvSzIdBRAxSg9NEi//jHP2TdunXSpEmTUh1brVo1ueGGG2TXrl3iD/QvlquvvtpteaOiogqNZNB13e7LtCPz2rVr5fHHHw/o+2e/D6W5RxERERIcHOxX99UefvS+aifU0j5du7ifc1+izT16j9yV1R/vn91XX31lOrGX9vfSV+6hu8+GqKgo0zSpNc4lvSdl+d31JQSgAKN/WeoP+PLly+WLL76Q2NjYUp9Dq6a///57M6zRH2j/l927d7str9aOaNV8QfoBVLDWxBfNmzfP9Km45557Avr+6c+o/mdZ8B7l5OSYESXu7pFW1cfFxTkdo9X6uu6L99UefrQ/iIZaHWbs6Z9zX6LNr9oHyF1Z/e3+XV4rq2XXEWP+dA+L+2yIi4szfzwVvCca9LTPkrt7UpbfXZ/i7V7Y8Kzhw4ebEUFpaWm2I0eOOJZz58459hk4cKBt/PjxjvWXXnrJtnr1atvu3bttGRkZtv79+9tCQ0NtP/74o80XvfDCC+b69uzZY/v6669t8fHxtoiICDOqwdX16T5Vq1a1TZ8+3Yxq0JEZOtrh+++/t/kqHRFz5ZVX2saNG1foNX+8f7/99ptt69atZtH/dmbMmGG+to+Cmjp1qq1u3bq2Tz/91Pbdd9+ZETaxsbG28+fPO86hI27eeustx/qSJUvMaJP58+fbfvrpJ9sTTzxhzpGVleVT15ebm2u77777bE2aNLF98803Tr+XFy9edHt9xf2c+8r16WujR482I4W0rGvXrrXdeOONtpYtW9ouXLjgF/evuGu0O336tK1mzZq22bNnuzyHL9/Dknw2PPXUU+b/nS+++MK2ZcsWW5cuXcxSUKtWrWzLli1zrJfkd9dXEYACjP7iulp0qLTd7bffboat2j333HPmhz4kJMQWGRlp69Wrly0zM9PmqxISEmzR0dGmvI0bNzbru3btcnt96qOPPrJdffXV5pjrrrvOtmLFCpsv00Cj92379u2FXvPH+7du3TqXP5f269DhtBMnTjTl1w/Fbt26Fbr2q666yoTXgvTDxn7tOqx648aNNl+7Pv3wc/d7qce5u77ifs595fr0A7R79+62Bg0amD8s9DqGDRtWKMj48v0ryc+oeuedd2w1atQwQ71d8eV7WJLPhvPnz9tGjBhhq1evngl6999/vwlJl5+n4DEl+d31VTwNHgAAWA59gAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAEnKCgIPnkk0/M13v37jXr33zzjfiCyZMnS/v27b1dDMDyqnq7AABQkZo2bSpHjhwxTyAHADtqgAD4pdzc3BLtFxwcbJ5YXbUqf+8B+A8CEACPuHjxovz3f/+3NGzYUEJDQ6Vr166yefNmyc/PlyZNmsjs2bOd9t+6datUqVJF9u3bZ9azs7Pl8ccflwYNGkhYWJjcdddd8u233xZqOnr//fclNjbWvIfauXOn3HbbbWb92muvlTVr1ji9z+VNYL/++qs88sgj5n1q1KghLVu2lHnz5jntu2TJErn55pvNOa+//nr58ssvS/Q9SEtLM8enpqZKhw4dpGbNmuY827dvL7TvO++8Y2qndJ+HHnpITp8+XervOYCyIwAB8IixY8fK//3f/8mCBQskMzNTWrRoIT169DDBZsCAAbJ48WKn/T/44AO55ZZb5KqrrjLr/fr1k2PHjslnn30mGRkZcuONN0q3bt3k1KlTjmN27dpl3mPZsmUm0Gi46tu3r4SEhMimTZtkzpw5Mm7cuCLLOXHiRPnpp5/M+2zbts0Es8ubx8aMGSMvvPCCCWldunSRe++9V06ePFni78Wf/vQneeONN2TLli2m5unRRx91el2v46OPPpK///3vsmrVKvM+I0aMKPH5AXiAtx9HD8D/nTlzxlatWjXbBx984NiWm5tra9SokW3atGm2rVu32oKCgmz79u0zr+Xl5dkaN25smz17tln/6quvbGFhYbYLFy44nbd58+a2d955x3w9adIk8x7Hjh1zvL569Wpb1apVbYcOHXJs++yzz2z6X9vy5cvN+p49e8y6lkHde++9tqFDh7q8Dvu+U6dOdWy7dOmSrUmTJrbXXnut2O/DunXrzPFr1651bFuxYoXZdv78ecd1BAcH2w4ePOhU5ipVqtiOHDlS7HsA8AxqgACU2+7du+XSpUumRseuWrVq0qlTJ1PLok1X11xzjaMWSJuUtLZHa32UNnWdOXNGrrjiCqldu7Zj2bNnjzm3ndYWadOVnZ5bm5EaNWrk2KY1NkUZPny4aeLSMmmt1fr16wvtU/AcWoOjzVn6XiXVtm1bx9fR0dHmX71euyuvvFIaN27s9H5am6VNZV999ZXT90BrygB4Hr0CAVQK7XejAWj8+PHm37vvvtsEHqXhR4OC9qG5XN26dR1f16pVq9zl6Nmzp+l3tHLlStNfSJvZnn76aZk+fbp4ioY/O+0TpDTglISGrYJD9iMjIz1WLgD/QQ0QgHJr3ry56Yfz9ddfO7ZpjZB2gtaOyerhhx+WH374wfTv+fjjj00gstP+PllZWaa2RfsOFVyKGr6utUoHDhwww9ztNm7cWGx5tRZp8ODBsmjRIpk5c6a8++67Tq8XPMfvv/9uyqzv5Sn79++Xw4cPO72fdghv1aqV6Zhd8Prr1KnjsfcF8B/UAAEoN62Z0aYl7Txcv35908Qzbdo0OXfunDz22GNmn5iYGDMiStfz8vLkvvvucxwfHx9vmoH69Oljjrv66qtNQFixYoXcf//9plbEFT1O99Uw8/rrr0tOTo7pgFyUxMREiYuLk+uuu86MXPvHP/5RKNwkJyeb0WG6/X/+53/MyLHLOzKXh44u0zJrrZOWWUfP6UgwHa4PoHJQAwTAI6ZOnSoPPPCADBw40NTo6Ein1atXS7169Rz7aK2P9vfRUKM1HQWbibRJSoezDx061ISa/v37m6aqopqAtNZk+fLlcv78edPfSIfRv/rqq0WWU2uqJkyYYPrp6PvpPEHaJ+jya9GlXbt28q9//Uv+9re/eXQiRa3Z0dFrvXr1ku7du5uy/O///q/Hzg+geEHaE7oE+wFAwNN5gHSOIR2WzuMqgMBGDRAAALAcAhAAlNBTTz3lNES94KKvAfAfNIEBQAnpXD7aadkVfXyHPgYEgH8gAAEAAMuhCQwAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAIjV/D/3+2w/Agy1/AAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Parameterize non-binder MEAN with, gamma matching first and second moment\n",
-    "# ns    = non_binder_params['N-']\n",
-    "\n",
-    "# weights = ns / ns.sum()\n",
-    "# mu_hat  = np.sum(weights * means)\n",
-    "# var_hat = np.sum(weights * (means - mu_hat)**2)\n",
-    "\n",
-    "# alpha = mu_hat**2 / var_hat\n",
-    "# theta = var_hat / mu_hat      # scale ; rate = 1/theta\n",
-    "\n",
-    "# print(f\"Mean shape, scale: {alpha}, {theta}\")\n",
-    "# samples = stats.gamma(a=alpha, scale=theta).rvs(10000)\n",
-    "# print(samples.min())\n",
-    "# sns.histplot(samples, stat='density')\n",
-    "# # sns.histplot(non_binder_params['mean-'], stat='density')\n",
-    "# sns.scatterplot(x=non_binder_params['mean-'], y=0.05)\n",
-    "# sns.despine()\n",
-    "# plt.show()\n",
-    "\n",
-    "# Truncated log-normal\n",
-    "non_binder = params[~params['gene'].str.contains('negative', na=False)]\n",
-    "non_binder = non_binder[(non_binder[['N-', 'N+']] > 20).all(1)]\n",
-    "ns, means = non_binder['N-'].to_numpy(float), non_binder['mean-'].to_numpy(float)\n",
-    "\n",
-    "# Weighted MoM lognormal params\n",
-    "w = ns / ns.sum()\n",
-    "m = np.sum(w * means)\n",
-    "v = np.sum(w * (means - m)**2)\n",
-    "sigma2 = np.log(1 + v / m**2)\n",
-    "sigma  = np.sqrt(sigma2)\n",
-    "mu_ln  = np.log(m) - 0.5 * sigma2\n",
-    "\n",
-    "# Truncate to observed range\n",
-    "low, high = means.min(), means.max()\n",
-    "a, b = (np.log(low) - mu_ln) / sigma, (np.log(high) - mu_ln) / sigma\n",
-    "samples = np.exp(truncnorm(a, b, loc=mu_ln, scale=sigma).rvs(100000, random_state=0))\n",
-    "print('trunc log-normal params for noise: ', a, b, mu_ln, sigma)\n",
-    "print('lowest sampled value', samples.min())\n",
-    "# Plot\n",
-    "sns.histplot(samples, stat='density', bins=50)\n",
-    "sns.scatterplot(x=means, y=np.full_like(means, 0.05, dtype=float))\n",
-    "sns.despine()\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "# Parameterize non-binder OVERDISPERSION with, gamma matching first and second moment\n",
-    "overdisp = non_binder_params['overdisp_nb-'] - 1\n",
-    "\n",
-    "weights = ns / ns.sum()\n",
-    "mu_hat  = np.sum(weights * overdisp)\n",
-    "var_hat = np.sum(weights * (means - mu_hat)**2)\n",
-    "\n",
-    "# mu_hat = overdisp.mean()\n",
-    "# var_hat = overdisp.var()\n",
-    "alpha = mu_hat**2 / var_hat\n",
-    "theta = var_hat / mu_hat      # scale ; rate = 1/theta\n",
-    "print('Overdispersion Gamma shape, scale', alpha, theta)\n",
-    "sns.histplot(stats.gamma(a=alpha, scale=theta).rvs(100000).clip(max=20)+1, stat='density', label='gamma')\n",
-    "\n",
-    "sns.scatterplot(x=overdisp+1, y=0.1)\n",
-    "sns.despine()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a762c483-810d-4b09-a511-5ee8e16aea9c",
-   "metadata": {},
-   "source": [
-    "## Mean and Variance fold increase"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "121039f6-14c6-43e2-9b29-ce8f98f32fc8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>file</th>\n",
-       "      <th>gene</th>\n",
-       "      <th>threshold</th>\n",
-       "      <th>mean_neg</th>\n",
-       "      <th>concentration_neg</th>\n",
-       "      <th>var_nb_neg</th>\n",
-       "      <th>var_data_neg</th>\n",
-       "      <th>mean-</th>\n",
-       "      <th>concentration-</th>\n",
-       "      <th>var_nb-</th>\n",
-       "      <th>var_data-</th>\n",
-       "      <th>std_nb-</th>\n",
-       "      <th>std_data-</th>\n",
-       "      <th>overdisp_nb-</th>\n",
-       "      <th>overdisp_data-</th>\n",
-       "      <th>cof_var_nb-</th>\n",
-       "      <th>cof_var_data-</th>\n",
-       "      <th>N-</th>\n",
-       "      <th>mean+</th>\n",
-       "      <th>concentration+</th>\n",
-       "      <th>var_nb+</th>\n",
-       "      <th>var_data+</th>\n",
-       "      <th>std_nb+</th>\n",
-       "      <th>std_data+</th>\n",
-       "      <th>overdisp_nb+</th>\n",
-       "      <th>overdisp_data+</th>\n",
-       "      <th>cof_var_nb+</th>\n",
-       "      <th>cof_var_data+</th>\n",
-       "      <th>N+</th>\n",
-       "      <th>mean_ratio</th>\n",
-       "      <th>concentration_ratio</th>\n",
-       "      <th>var_nb_ratio</th>\n",
-       "      <th>var_data_ratio</th>\n",
-       "      <th>overdisp_nb_ratio</th>\n",
-       "      <th>overdisp_data_ratio</th>\n",
-       "      <th>N_ratio</th>\n",
-       "      <th>binder_ratio</th>\n",
-       "      <th>noise_to_neg_mean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
-       "      <td>CMV</td>\n",
-       "      <td>197.942</td>\n",
-       "      <td>4.722340</td>\n",
-       "      <td>3.939271e-01</td>\n",
-       "      <td>61.333071</td>\n",
-       "      <td>1039.818115</td>\n",
-       "      <td>25.003214</td>\n",
-       "      <td>2.897500</td>\n",
-       "      <td>240.761868</td>\n",
-       "      <td>338.704865</td>\n",
-       "      <td>15.516503</td>\n",
-       "      <td>18.403936</td>\n",
-       "      <td>9.629237</td>\n",
-       "      <td>13.546453</td>\n",
-       "      <td>0.620580</td>\n",
-       "      <td>0.736063</td>\n",
-       "      <td>1867</td>\n",
-       "      <td>1230.471773</td>\n",
-       "      <td>3.924429</td>\n",
-       "      <td>387034.531887</td>\n",
-       "      <td>4.371280e+05</td>\n",
-       "      <td>622.120995</td>\n",
-       "      <td>661.156539</td>\n",
-       "      <td>314.541577</td>\n",
-       "      <td>355.252334</td>\n",
-       "      <td>0.505596</td>\n",
-       "      <td>0.537320</td>\n",
-       "      <td>3773</td>\n",
-       "      <td>49.212545</td>\n",
-       "      <td>1.354419</td>\n",
-       "      <td>1607.540828</td>\n",
-       "      <td>1290.586670</td>\n",
-       "      <td>32.665265</td>\n",
-       "      <td>26.224749</td>\n",
-       "      <td>2.020889</td>\n",
-       "      <td>0.668972</td>\n",
-       "      <td>5.294666</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
-       "      <td>Flu</td>\n",
-       "      <td>197.942</td>\n",
-       "      <td>4.722340</td>\n",
-       "      <td>3.939271e-01</td>\n",
-       "      <td>61.333071</td>\n",
-       "      <td>1039.818115</td>\n",
-       "      <td>11.730760</td>\n",
-       "      <td>1.432491</td>\n",
-       "      <td>107.794731</td>\n",
-       "      <td>224.974823</td>\n",
-       "      <td>10.382424</td>\n",
-       "      <td>14.999161</td>\n",
-       "      <td>9.189066</td>\n",
-       "      <td>19.178196</td>\n",
-       "      <td>0.885060</td>\n",
-       "      <td>1.278618</td>\n",
-       "      <td>4249</td>\n",
-       "      <td>922.462976</td>\n",
-       "      <td>4.215191</td>\n",
-       "      <td>202796.575052</td>\n",
-       "      <td>2.202642e+05</td>\n",
-       "      <td>450.329407</td>\n",
-       "      <td>469.323150</td>\n",
-       "      <td>219.842509</td>\n",
-       "      <td>238.778384</td>\n",
-       "      <td>0.488182</td>\n",
-       "      <td>0.508772</td>\n",
-       "      <td>1391</td>\n",
-       "      <td>78.636249</td>\n",
-       "      <td>2.942561</td>\n",
-       "      <td>1881.321785</td>\n",
-       "      <td>979.061646</td>\n",
-       "      <td>23.924358</td>\n",
-       "      <td>12.450513</td>\n",
-       "      <td>0.327371</td>\n",
-       "      <td>0.246631</td>\n",
-       "      <td>2.484099</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
-       "      <td>SARS_Cov2</td>\n",
-       "      <td>197.942</td>\n",
-       "      <td>4.722340</td>\n",
-       "      <td>3.939271e-01</td>\n",
-       "      <td>61.333071</td>\n",
-       "      <td>1039.818115</td>\n",
-       "      <td>8.543229</td>\n",
-       "      <td>1.653138</td>\n",
-       "      <td>52.693679</td>\n",
-       "      <td>87.826508</td>\n",
-       "      <td>7.259041</td>\n",
-       "      <td>9.371580</td>\n",
-       "      <td>6.167888</td>\n",
-       "      <td>10.280247</td>\n",
-       "      <td>0.849684</td>\n",
-       "      <td>1.096960</td>\n",
-       "      <td>5066</td>\n",
-       "      <td>1414.221254</td>\n",
-       "      <td>4.004123</td>\n",
-       "      <td>500904.754996</td>\n",
-       "      <td>5.018279e+05</td>\n",
-       "      <td>707.746250</td>\n",
-       "      <td>708.398127</td>\n",
-       "      <td>354.191223</td>\n",
-       "      <td>354.843985</td>\n",
-       "      <td>0.500449</td>\n",
-       "      <td>0.500910</td>\n",
-       "      <td>574</td>\n",
-       "      <td>165.537081</td>\n",
-       "      <td>2.422136</td>\n",
-       "      <td>9505.974155</td>\n",
-       "      <td>5713.854980</td>\n",
-       "      <td>57.425044</td>\n",
-       "      <td>34.517068</td>\n",
-       "      <td>0.113304</td>\n",
-       "      <td>0.101773</td>\n",
-       "      <td>1.809109</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
-       "      <td>Flu_A0201</td>\n",
-       "      <td>173.376</td>\n",
-       "      <td>2.901204</td>\n",
-       "      <td>2.628828e+32</td>\n",
-       "      <td>2.901204</td>\n",
-       "      <td>875.116882</td>\n",
-       "      <td>15.505495</td>\n",
-       "      <td>1.876203</td>\n",
-       "      <td>143.647475</td>\n",
-       "      <td>331.107086</td>\n",
-       "      <td>11.985302</td>\n",
-       "      <td>18.196348</td>\n",
-       "      <td>9.264295</td>\n",
-       "      <td>21.354178</td>\n",
-       "      <td>0.772971</td>\n",
-       "      <td>1.173542</td>\n",
-       "      <td>182</td>\n",
-       "      <td>1950.187696</td>\n",
-       "      <td>4.113499</td>\n",
-       "      <td>926523.693207</td>\n",
-       "      <td>1.047826e+06</td>\n",
-       "      <td>962.561008</td>\n",
-       "      <td>1023.633510</td>\n",
-       "      <td>475.094625</td>\n",
-       "      <td>537.294725</td>\n",
-       "      <td>0.493574</td>\n",
-       "      <td>0.524890</td>\n",
-       "      <td>2227</td>\n",
-       "      <td>125.773976</td>\n",
-       "      <td>2.192459</td>\n",
-       "      <td>6449.982473</td>\n",
-       "      <td>3164.612305</td>\n",
-       "      <td>51.282329</td>\n",
-       "      <td>25.161106</td>\n",
-       "      <td>12.236264</td>\n",
-       "      <td>0.924450</td>\n",
-       "      <td>5.344504</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
-       "      <td>CMV_B0702</td>\n",
-       "      <td>87.184</td>\n",
-       "      <td>2.528850</td>\n",
-       "      <td>8.153597e+11</td>\n",
-       "      <td>2.528850</td>\n",
-       "      <td>833.862305</td>\n",
-       "      <td>3.732883</td>\n",
-       "      <td>1.053218</td>\n",
-       "      <td>16.963204</td>\n",
-       "      <td>32.455223</td>\n",
-       "      <td>4.118641</td>\n",
-       "      <td>5.696949</td>\n",
-       "      <td>4.544264</td>\n",
-       "      <td>8.694412</td>\n",
-       "      <td>1.103341</td>\n",
-       "      <td>1.526153</td>\n",
-       "      <td>2220</td>\n",
-       "      <td>649.994709</td>\n",
-       "      <td>3.614533</td>\n",
-       "      <td>117537.315045</td>\n",
-       "      <td>1.443395e+05</td>\n",
-       "      <td>342.837155</td>\n",
-       "      <td>379.920325</td>\n",
-       "      <td>180.828110</td>\n",
-       "      <td>222.062505</td>\n",
-       "      <td>0.527446</td>\n",
-       "      <td>0.584498</td>\n",
-       "      <td>189</td>\n",
-       "      <td>174.126735</td>\n",
-       "      <td>3.431894</td>\n",
-       "      <td>6928.957342</td>\n",
-       "      <td>4447.341309</td>\n",
-       "      <td>39.792610</td>\n",
-       "      <td>25.540830</td>\n",
-       "      <td>0.085135</td>\n",
-       "      <td>0.078456</td>\n",
-       "      <td>1.476119</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>10k_BEAM-T_Human_A2402_EBV_CMV_spikein</td>\n",
-       "      <td>EBV</td>\n",
-       "      <td>64.444</td>\n",
-       "      <td>1.961246</td>\n",
-       "      <td>4.752330e-01</td>\n",
-       "      <td>10.055138</td>\n",
-       "      <td>47.815094</td>\n",
-       "      <td>5.349569</td>\n",
-       "      <td>1.177876</td>\n",
-       "      <td>29.645753</td>\n",
-       "      <td>36.547199</td>\n",
-       "      <td>5.444791</td>\n",
-       "      <td>6.045428</td>\n",
-       "      <td>5.541709</td>\n",
-       "      <td>6.831803</td>\n",
-       "      <td>1.017800</td>\n",
-       "      <td>1.130078</td>\n",
-       "      <td>2320</td>\n",
-       "      <td>482.138596</td>\n",
-       "      <td>4.481992</td>\n",
-       "      <td>52346.939110</td>\n",
-       "      <td>7.634764e+04</td>\n",
-       "      <td>228.794535</td>\n",
-       "      <td>276.310768</td>\n",
-       "      <td>108.572389</td>\n",
-       "      <td>158.352061</td>\n",
-       "      <td>0.474541</td>\n",
-       "      <td>0.573094</td>\n",
-       "      <td>570</td>\n",
-       "      <td>90.126625</td>\n",
-       "      <td>3.805148</td>\n",
-       "      <td>1765.748310</td>\n",
-       "      <td>2089.014893</td>\n",
-       "      <td>19.591861</td>\n",
-       "      <td>23.178665</td>\n",
-       "      <td>0.245690</td>\n",
-       "      <td>0.197232</td>\n",
-       "      <td>2.727638</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>10k_BEAM-T_Human_A2402_EBV_CMV_spikein</td>\n",
-       "      <td>CMV</td>\n",
-       "      <td>64.444</td>\n",
-       "      <td>1.961246</td>\n",
-       "      <td>4.752330e-01</td>\n",
-       "      <td>10.055138</td>\n",
-       "      <td>47.815094</td>\n",
-       "      <td>3.266996</td>\n",
-       "      <td>0.645879</td>\n",
-       "      <td>19.792167</td>\n",
-       "      <td>33.713371</td>\n",
-       "      <td>4.448839</td>\n",
-       "      <td>5.806322</td>\n",
-       "      <td>6.058216</td>\n",
-       "      <td>10.319380</td>\n",
-       "      <td>1.361752</td>\n",
-       "      <td>1.777266</td>\n",
-       "      <td>2633</td>\n",
-       "      <td>1385.941634</td>\n",
-       "      <td>2.885361</td>\n",
-       "      <td>667102.997951</td>\n",
-       "      <td>4.532816e+05</td>\n",
-       "      <td>816.763734</td>\n",
-       "      <td>673.261929</td>\n",
-       "      <td>481.335564</td>\n",
-       "      <td>327.056792</td>\n",
-       "      <td>0.589320</td>\n",
-       "      <td>0.485779</td>\n",
-       "      <td>257</td>\n",
-       "      <td>424.225101</td>\n",
-       "      <td>4.467340</td>\n",
-       "      <td>33705.405280</td>\n",
-       "      <td>13445.158203</td>\n",
-       "      <td>79.451699</td>\n",
-       "      <td>31.693454</td>\n",
-       "      <td>0.097607</td>\n",
-       "      <td>0.088927</td>\n",
-       "      <td>1.665776</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                            file       gene  threshold  \\\n",
-       "0   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein        CMV    197.942   \n",
-       "2   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein        Flu    197.942   \n",
-       "4   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein  SARS_Cov2    197.942   \n",
-       "6               5k_BEAM-T_Human_A0201_B0702_PBMC  Flu_A0201    173.376   \n",
-       "7               5k_BEAM-T_Human_A0201_B0702_PBMC  CMV_B0702     87.184   \n",
-       "10        10k_BEAM-T_Human_A2402_EBV_CMV_spikein        EBV     64.444   \n",
-       "11        10k_BEAM-T_Human_A2402_EBV_CMV_spikein        CMV     64.444   \n",
-       "\n",
-       "    mean_neg  concentration_neg  var_nb_neg  var_data_neg      mean-  \\\n",
-       "0   4.722340       3.939271e-01   61.333071   1039.818115  25.003214   \n",
-       "2   4.722340       3.939271e-01   61.333071   1039.818115  11.730760   \n",
-       "4   4.722340       3.939271e-01   61.333071   1039.818115   8.543229   \n",
-       "6   2.901204       2.628828e+32    2.901204    875.116882  15.505495   \n",
-       "7   2.528850       8.153597e+11    2.528850    833.862305   3.732883   \n",
-       "10  1.961246       4.752330e-01   10.055138     47.815094   5.349569   \n",
-       "11  1.961246       4.752330e-01   10.055138     47.815094   3.266996   \n",
-       "\n",
-       "    concentration-     var_nb-   var_data-    std_nb-  std_data-  \\\n",
-       "0         2.897500  240.761868  338.704865  15.516503  18.403936   \n",
-       "2         1.432491  107.794731  224.974823  10.382424  14.999161   \n",
-       "4         1.653138   52.693679   87.826508   7.259041   9.371580   \n",
-       "6         1.876203  143.647475  331.107086  11.985302  18.196348   \n",
-       "7         1.053218   16.963204   32.455223   4.118641   5.696949   \n",
-       "10        1.177876   29.645753   36.547199   5.444791   6.045428   \n",
-       "11        0.645879   19.792167   33.713371   4.448839   5.806322   \n",
-       "\n",
-       "    overdisp_nb-  overdisp_data-  cof_var_nb-  cof_var_data-    N-  \\\n",
-       "0       9.629237       13.546453     0.620580       0.736063  1867   \n",
-       "2       9.189066       19.178196     0.885060       1.278618  4249   \n",
-       "4       6.167888       10.280247     0.849684       1.096960  5066   \n",
-       "6       9.264295       21.354178     0.772971       1.173542   182   \n",
-       "7       4.544264        8.694412     1.103341       1.526153  2220   \n",
-       "10      5.541709        6.831803     1.017800       1.130078  2320   \n",
-       "11      6.058216       10.319380     1.361752       1.777266  2633   \n",
-       "\n",
-       "          mean+  concentration+        var_nb+     var_data+     std_nb+  \\\n",
-       "0   1230.471773        3.924429  387034.531887  4.371280e+05  622.120995   \n",
-       "2    922.462976        4.215191  202796.575052  2.202642e+05  450.329407   \n",
-       "4   1414.221254        4.004123  500904.754996  5.018279e+05  707.746250   \n",
-       "6   1950.187696        4.113499  926523.693207  1.047826e+06  962.561008   \n",
-       "7    649.994709        3.614533  117537.315045  1.443395e+05  342.837155   \n",
-       "10   482.138596        4.481992   52346.939110  7.634764e+04  228.794535   \n",
-       "11  1385.941634        2.885361  667102.997951  4.532816e+05  816.763734   \n",
-       "\n",
-       "      std_data+  overdisp_nb+  overdisp_data+  cof_var_nb+  cof_var_data+  \\\n",
-       "0    661.156539    314.541577      355.252334     0.505596       0.537320   \n",
-       "2    469.323150    219.842509      238.778384     0.488182       0.508772   \n",
-       "4    708.398127    354.191223      354.843985     0.500449       0.500910   \n",
-       "6   1023.633510    475.094625      537.294725     0.493574       0.524890   \n",
-       "7    379.920325    180.828110      222.062505     0.527446       0.584498   \n",
-       "10   276.310768    108.572389      158.352061     0.474541       0.573094   \n",
-       "11   673.261929    481.335564      327.056792     0.589320       0.485779   \n",
-       "\n",
-       "      N+  mean_ratio  concentration_ratio  var_nb_ratio  var_data_ratio  \\\n",
-       "0   3773   49.212545             1.354419   1607.540828     1290.586670   \n",
-       "2   1391   78.636249             2.942561   1881.321785      979.061646   \n",
-       "4    574  165.537081             2.422136   9505.974155     5713.854980   \n",
-       "6   2227  125.773976             2.192459   6449.982473     3164.612305   \n",
-       "7    189  174.126735             3.431894   6928.957342     4447.341309   \n",
-       "10   570   90.126625             3.805148   1765.748310     2089.014893   \n",
-       "11   257  424.225101             4.467340  33705.405280    13445.158203   \n",
-       "\n",
-       "    overdisp_nb_ratio  overdisp_data_ratio    N_ratio  binder_ratio  \\\n",
-       "0           32.665265            26.224749   2.020889      0.668972   \n",
-       "2           23.924358            12.450513   0.327371      0.246631   \n",
-       "4           57.425044            34.517068   0.113304      0.101773   \n",
-       "6           51.282329            25.161106  12.236264      0.924450   \n",
-       "7           39.792610            25.540830   0.085135      0.078456   \n",
-       "10          19.591861            23.178665   0.245690      0.197232   \n",
-       "11          79.451699            31.693454   0.097607      0.088927   \n",
-       "\n",
-       "    noise_to_neg_mean  \n",
-       "0            5.294666  \n",
-       "2            2.484099  \n",
-       "4            1.809109  \n",
-       "6            5.344504  \n",
-       "7            1.476119  \n",
-       "10           2.727638  \n",
-       "11           1.665776  "
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Ratio params\n",
-    "ratio_binder_cols = [col for col in params.columns if 'ratio' in col]\n",
-    "ratio_params = params[~params['gene'].str.contains('negative')].copy()\n",
-    "ratio_params = ratio_params[(ratio_params['N-'] > 20) & (ratio_params['N+'] > 20)]\n",
-    "# ratio_params = ratio_params[['file', 'gene', 'threshold'] + non_binder_cols]\n",
-    "ratio_params"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "0d00ab18-1113-4f69-a0e2-22a5fae132a9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='mean_ratio', ylabel='Density'>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Mean fold increase (binder count with respect to non-binder)\n",
-    "sns.histplot(ratio_params, x='mean_ratio', stat='density')\n",
-    "# samples = stats.norm(*stats.norm.fit(ratio_params['mean_ratio'])).rvs(size=10000)\n",
-    "# sns.histplot(samples, stat='density')\n",
-    "# sns.scatterplot(ratio_params, x='mean_ratio', y=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "95eb274d-4486-4962-8d0b-46015eebf38e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='mean+', ylabel='Density'>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.histplot(ratio_params, x='mean+', stat='density')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "c995c72c-0806-4bae-ade4-085b62352b06",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='overdisp_nb+', ylabel='Count'>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Var fold increase (with respect to mean, basically overdispersion)\n",
-    "sns.histplot(ratio_params, x='overdisp_nb+')\n",
-    "sns.scatterplot(ratio_params, x='overdisp_nb+', y=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "aaa31f58-4c01-4d8c-b14e-c8c2eeeab351",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='mean-', ylabel='mean_ratio'>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.scatterplot(ratio_params, x='mean-', y='mean_ratio')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "2159dce1-2252-4136-9974-80e2061ded88",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='mean_ratio', ylabel='overdisp_nb+'>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.regplot(ratio_params, x='mean_ratio', y='overdisp_nb+')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6d62eb0-2c0c-4193-bf85-a513ab3ad0ba",
-   "metadata": {},
-   "source": [
-    "## Test out new simulator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "18830e8a-3c5b-4c73-9907-65b2dac4b73c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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k/fffl0aNGpl91KBBA/nvf//rdrrDhw/LP//5TylXrpxERkbKrbfeKn/88YcEkzNnzphCdHl9WKIxdNmyZTJq1CgTvzUeqpdeeslc11atWtVsb12GlfvUnXXr1pk+90qVKmUK4z3yyCOmcF5mKSkpMnLkSNPXWsmSJeWqq67KtV82AIGFOBBavLlH9OZe29P790CmsU1jbl4egh8/ftwczxrrJk+ebOK6bjNf3DPnJr/3/d5cY3t6/w4guJ6VK+J3YCN+Z4/4Xa/wxG/tMxwoKOfOnbOdP3/eFkhOnz5t05/C1VdfbXvxxRdt06dPtw0cONBWpEgRW6dOnWxpaWku0+u0119/ve2jjz5yGXbu3GkLBSkpKWYINDNnzjT7pGnTpraWLVua/bB//3630y5ZssQWFhZm69y5s9mfDz/8sPnugw8+6DJdamqqrX379rbSpUvbRo8ebXv77bdtjRs3tpUtW9b266+/5imdzz//vEnbsWPHbAVJ16F///55+u5VV11l69ChQ5bxderUsVWoUMF2ww032IoVK5bn+ftin7oTGxtrq1Spku3SSy+1vfnmm7aXXnrJVr58eVuLFi2yHMNPPfWUmf/gwYPNMdGzZ0/z/pNPPvHpOgGhKhDjt9LzUnh4eJaYvHDhwizThvp5IFD3kcbF4sWL29q0aWO77LLLzDbPztSpU83nd9xxh9lH9957r3k/fvz4LNduDRo0sFWpUsX2yiuv2F5//XVbdHS0rVatWra//vorz8eSxtKCpNcKun66jfKiatWqtr59+2YZr/OsVq2arXv37vmavy/2qTvbtm2zRURE2Fq1amWbMmWK7ZlnnjG/Y73eyOzuu+821yCPP/64bdq0abZ27dqZ999//70P1wgIDcSB/MWBQKL3qWfPnjWvgcbTe0Rv7rU9vX/3hh43Q4YMyfP382LChAle39M6fP311+a7y5cv9+k9syfye9/v6TW2N/fvQGESCs/Kid92xG/itwPxOzCRGY5CT3+0P/zwQ5bxY8aMcXsys+KknF8XLlwIupNTZsePH7clJiZ6FKQ0SOsJWdfbQR+0aoD+5Zdf0sd9+umnZj7z5s1LH3f06FFbuXLlbH369Ck0meH16tUzAS+zAwcOpF/g5mf+vtin7jz00EO2kiVL2g4ePJg+Tn+vOh99YO5w6NAh8wDO+Xer63XNNdeYC+6LFy/6bJ0AFCxPMzCD9TwQqDfS3oiPj7edOXPG/K/bP7tMEJ2mYsWKWeKRZvbqPj5x4kT6OH1wovP58ccf08dpfC9atKht1KhRhSYzXK9r3F2TOmJpfuef332anR49etiqV69uS0hISB/37rvvmvksW7YsfdzGjRvNOL1GcP5N6E24ZooDCA7BEgcKQlJSki3YeXqP6M29tqf376H8MP2DDz4w3920aZNP75k9kZ/7fm+usT29fwcQXM/Kid/BgfidPeJ34YnfNJMeoP0N//rrr3LPPfdIVFSUaZLiP//5j94xSmxsrGk+RJsR0SYFJ06c6LY5QW2Son79+hIeHm6aM3nyySfNeGczZ86U6667TqpUqWKma9y4sWlewV3fJTfddJOsXbtWrrzyStNswiWXXCIffvhhvvtB0SZXdH1/+OEHGTFiRHrzG7fddpvbpha+/vpr6dixo5QtW9Zsg7Zt28qcOXMkP0qUKGGarMhM0+BoBsKds2fPZts0jKcuXLhgmpYYOHBgls8SExPNtn788cfN+/Pnz8tzzz1nmpfT40K30zXXXCMrV650+Z42uaHb9LXXXjN9R1566aVm/+7evTtPfYY7+jv57LPPTLObtWrVMunq0qWL7Nu3L8v3N27cKDfeeKOUL1/epFGb2HjzzTclv3Q76X7Pja6nDtqcS7FixdLHa9N8+hv63//+lz5O/9cmRG+//fb0cXoMavMmX375ZZbfjDe0z3Cdjx6n2pfnsGHD3B4vs2fPNvtUm1PRdbz77rvN79zZb7/9JnfccYf5zeu2132g0yUkJJjPdf8kJyfLBx98kN5EsCdNozj27f79+2Xx4sXp33U021KnTp08NX3q632anc8//9ycm7Q5GIeuXbvKZZddZo5XB92X+lvTY8BB10ubhT906JCsX78+H2sB2BG/Cz5+O0tNTTVxMzu+Pg9oX80aY8aMGZPlM20OS+f99ttvm/cnTpwwsbxZs2amSSld/x49eshPP/3k9pw8d+5cefbZZ6VmzZqmCemc1isY9pHGWY1xudHrGW1izHkfqSFDhpgYp3HKOX5r+nRw0Kbx9drE+fyfF9pEX/fu3c220qa5X3jhBfMbztycmF5jNWnSxPyudB3/9a9/ycmTJ12m27x5s5lXpUqVzDbQ5sfuu+8+85nGWt0nSo8jRwz2pElzx77VdGlTbI7vOh8L/uTpPnVHj2dt5lzPk3qcOfTr18/8Ppz3n+7nokWLmms6B93e999/v/nNZr5eAnJDrCYOZEfvnXVbHTx4MMtn2hWFPjNwnOO///779CYpHceANoOpzwec6b7Q89rvv/9u7o91u/bt2zfPfY7qPbo2Uav3utpcqV4j6LWCNm+Zmd536vGu90V6TFWvXt3c82pa8svTe0RP77W9uX/Pi48//lgaNmxotoPed69Zs8Ztk70anzW9uk81vs+YMSPLdNrkr36m216fd1xxxRXpvxHd3k888YT5X+N95nvrnOi+7d+/v/lfj2nn+/n83jN7Ij/3/d5cY3t6/w5kRvwO7GflxO8MxG/iN/E7sGUcqQgod911l+lnQ/uh1qChfXLoj2jatGkmKL/yyivmpKAPV/XHdu2116Y/nNP+PDQY68lI57Fjxw554403zEWDc5/CGsz1RKDT60nrq6++Mj8AnYcGLGea6XnnnXeah0/6I9cTi/649WSk88ivhx9+2JyM9MJETzb6gHHo0KHy6aefulwM6AlOl6cBTfsW2bZtmyxdulT+7//+L73vRR1yow/WdHk5iY+PN6/6ADMzTcs777xjTu66jfWhtSMN3tA+0/RC4osvvjD7VoO0g+4rDTCa6el4ePjee+9Jnz59ZPDgwXL69GnTH4s+ZNV+2Fq2bJnlAk6DqB4HGhD0+MkPPRaLFClijjnNhNWgrRcDmvntoA839eSowVozf/UiVC+QFi1aZN4rXSdNuyfcbfvc6DGhNLA50wfamons+NwxrfZrouvlTC9kp0+fbn4zmnmRF3qRoBe048aNkw0bNshbb71lLsCcL4y1cIFevOu0gwYNMhe1GqD196xp02NcC0HoPtbtpr8T3aYa7HWbal8mehOg/ZHo9zXdjgfGWggiN3rs6nf14k+3zWOPPWbGOx7Oe8rf+9Qd3QZHjx7Nsp+VboclS5akv9dtqTcOur6Zp3N8rv2WAr5A/C74+K3f05t+fdXPNE7qdtYbWH+dB/RmSx846IW/rrszXXdNp95oOzJXdf/pe7250ox0PR70+3rzqPHJ2dixY831gB4jen51vjYI1n2Un/itx6rGaf1cH37pcf7zzz+nZypn3p/ffPONiUl5ufHUQhXaD9bVV19trnN0/R19nmmmuINmfOs208KM2te1FirTwg+aRn1opdd3GqO6detmYupTTz1ltqluf73mUzpef8t6s6nXgo6HDVqIMDd63tD4rf10XX/99SYjOS/8vU/d0fOabs/M+1mPc72WzXydpjfYzpnmzr/b7du3m4dYgLeI1cSBzPR+TDNFNK47Hog66Dg9nzvWad68eWY76PlbCz3rvbjew+mDQ/3MmZ7v9F5OrzH0gb0+hM0PvZ/UOKUxQ9OsD5pHjhxp7lm1oJ0jluk9+YoVK8yzBL0P1+2h9+o7d+5Mv0/Ueem0udE05yXdnt5re3P/7q3Vq1eb41xjtT4T0Wc4uv10n2nGhNLrMo37+gBYfxcanzVjSX+P+gxm+PDhZrp3333XzEd/q46C7noc6vMQ/Y3oPtF10r429ZzguO/15N76mWeeMQ/8dbvo9YZeL3pyP5+Zv/epO55eY3tz/w5kh/gdmM/Kid+5I357h/jtHvHbB6yumg73TSw/8MAD6eO0WQJtnkCbmHDua+PkyZOmiQLnJhC0n0ztjyBzP3qOvjucmzhxNFXmTPsWvOSSS7L0P6DfXbNmjUvzGNq332OPPebV+um8nNOr/SfovLt27erS38ijjz5qmkg5deqUea+v2jeF9m2szSM6c/6eY/vlNmg6cqNpioyMNNvZmfabMWnSJNuXX35p+jnUvh90nu+8844tL7Q5SP3+V1995TL+xhtvdNkXehxkbupc06b9Rd53333p47TJDZ2fpl33k7c6duxoBoeVK1ea+TVq1Mhl+dpHhI7fsWNHevq0uW3dtpm3mfM+cuxzT4bs5NS8iOOzmJiYLJ+1bdvW9HfjoE2IOG87h8WLF5t5LF261OYtxzF4yy23uIz/97//bcb/9NNP6U2Z6DGu/Ww40+2p/Xw4xmu/mpmbp3EnP82Y6z5z10y6p/P39z51R5uf0ek//PDDLJ898cQT5jPtd0npumU+r6nk5GQznfaRAuQX8dua+K2/35EjR5qmvLRfI02jTtehQweXprr8cR7Q5qCc46BzU2HXXXdd+ns9F2Vu6lzPdbofXnjhhSzxVtPpbh8H6z5yllPzuPqZpsudypUrm76jnZv+dt52DpMnTzaf7dmzx+Ytx7Gj/Zw5r78eOyVKlEjv/kR/ozrdxx9/7PJ9vWZwHj9//ny3zaU5y28z5rk1IZfb/P29T93R65nM5yWH3r17m77OHZo0aeLyW3LYtWuXmYeeHwFvEKvtiAPuafcL2q+5M23GNfM9h7t9O27cOHMMOTcf6YgrebnGcFwT6KuD3qNnToven+t5U/todZgxY4aZTvthzcx5fzqOvdyGnGJUTveInt5re3P/7g1H+jdv3pw+TvdPRESE7bbbbksfd//995uuOzL3VavHW1RUVPr+vvXWW01cykl+mkF1/F5zum7Ibf7+3qfueHqN7c39O5AZ8Tuwn5U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Wj8j+88Erc/7+ttn2h4Y5KRoukppiz8yNWe/6mTbpltnn94tUbym50ptf3Qc9XhVZlemh8ezbM/7XG0Z9QJvOJtJ1dO7z14e/5etkvF86SuTA9xnv9aFs7Xb2B736MPeRbVnn8cHN9ld9+LrpPZGrh4jc8LLrZ+qW/4q07pf1+/pb+2KwSNM7Re58P+f06jprZoTD6ASRz/qJHN0t8vg+kTKVXaf/9F57Jkqg3Ew7toem29cWPmLPmNFzmbv5a+bRyhdFwsv69kEHgIKnhZZWvGAvGNTy/yRgfNo3I/NNM6p1WPCQvUDg5TeJlIsW+fDvwkkt/s+eSf7taHtm9E2vu5/nJ3fZMy01s1XPX/q/xnTNhBt5wDfp1szt7yeKFItwHa8Z+Zo+ZzqNZjBrwSYtQKmFrnq9Y/9MM2b1M7V5hsiz8faMXC18WKO1yIDFuV8L/f6dSNIR9+mcN8D1ukwzKnOjGabOGfuZff9axv+OjHCl1wIN3Vy/ZPbNs/ZrW+frW923+5Z7dg3iCS1Akeam8Ohvy9xPrxm1zi4kZ2SEK+dCD3N6i9z3TcZ+btHH/poUbx/0+sKZ7h+9rshtmY5Cn3rsV748Y9wPk0R+Wy5ydJdIyTddr930+mb+A5IjzTB37uP7y6H2eTl7v2vG/7/+vY0O/ZhpPVbYB3cWP+56v3BwrUiNVhnvt34osnp8xnt/XNPAUq8v/1Ue6nSpFC9KI3jIwdkT9tfMhZJChVaUcDz/yfaz3/2chhMZBc/8toy/7K9aqN4fx0fmigK50ftZpdd7gXD/DgS58GJFrU4CACCEcIdoke5Nqrm8L/H/7N0HfBR1+vjxJ7SEXqVIERSkdxuoFEGqHlj4efxVUNE7PTzBgopdUcFDVDwQQQXOgnh4gooIIgqoFKlKERSliQmoQCB0kvm/nu8yy+xmN9nd7Gbb5/16DWFnZ2e+U5+Z+bZiYdoVWitEa9v448yIHjg794vTcPD3cvOSe0RunOXKNPTH/k5rm1zvVeO68ZUBLr+oq2TwgA9d61jzvMB+p9MWLykh0zQHSmtmhZuzRpCTd20ke1pdX3u44X+uGjZVm7hquyutAZeXQAo12C+9taZVXjwywrXQQhClP53pyMnO/aBvv4DOrxa+PR9/6+WvFQR7mfZLhbw4M8JtWuPf33L3nnpBEWoBknjia9s4aaZNsmwLINFpJpj5G2N9wNkZ4c60aa1q14jc09vT5BXj7FZ67Gntv95xryDsOJ9re1r+Y5Z9TT1x2DG5Y3r7e3tcIDHOzC+PbeFdQNE+DgLhbxtrYT5fAsloL1Had1OmmvkcTkWKhu9Y73Oq4IKHYOYdxLTex6z3fgzmXi3QY8JfGoK5f7YzMPzNx1fBBCScnFiLLyh8egxM6S0y//Hwzldb1Rh3gciGWbkL4miLHxoHJ3US+fql4Oe98DmR1y93tWyhPr5b5N3+Ij/Mdi3Tfm70LmCo32krW5HI3NeWUt5yFGJ30tZjJvcUWeCnMkAk7N3qWl9d72B887LIxI6n71PmPhTZPtwBAAAQc6gZHiX5ZDMCAAAAAAAgWFr4S1uG0OHyJ8ObGfvHZpEfPnJ1G2Fb8drpLsp+W+Nqhv2SocHN225JTgtlFS0vsmqy67N2e6HLtLtNc9IuLuzvIsFXqyzOwnjaBYYOXR6VQqHrqeu7eW7glSXUwlGu7aoFpoqf6dmyIgAAAJICmeEAAABB+m3/ETmzQgFaEwEAABHx674jcs4ZZaKdjMShGbtLx4s06SNybre8p9VWI7Q2dovrRGqfH/yyfvpc5JcvRS76h6sLEP1bpX7gvz/wm8gXz4gc+NVz/LJXRYqVEDnvFleG6uxT3ZD5qmH885eubhq0ZY96HVytqGgf0hXrenazoNZ/ILL6P7nnoa2frP+fyMzbRS78++nxswaLVKjjao2m6+OuLlS2fO7ZDd50R7ctdrdntn3bXV1WXHK3yO8/iqx9J/eydf3t8drtSYNurnUqUUrkWNbp5rs1Q79kBZHOD7tahdPa9NrtRvFSrq5HznV0N7JhpkjTq07/X5s+1+5unLQQwNvXuvb9+YN8d/W2fanImrdc21Jrsze9WmTzHJEyVV1VRjo9ID4tGu36jRYMyMu3pwolXHCb6+/Wr0S+fjHwVl+05rh2GWPSkw9tmv3LZ13LqtrY87sZA10FF7RPei2woN3R6fbULvW0C8Mz8+lyTo8d7crGX3/2i0eLbPtG5JzOIhcPcY3TYyitvO+u3YKh3aFoc+9dnxIpUiSw7mB0HbuNEClaPPd3P84Vufyp3N/lR1se+PxxkbqXiJzbPbjfAgAA+EBmeIzYlrFXdmcekYqlTzf3mL1/l/y+N1OqVz3D1XS1Plj+/qMctYrLIUmTyikHfczJcvXv6O1ghuuvlqJFYsmvFPjvQexzbbbVbmItc5fIH1uCa4bSn0yvlxGBNJl9IN3VfGbmztzr4zyOta9WX/2F2uy+0HQ++hIjtYxI+Vq+m7wz029xLdu7mXh9saLN5dp9n9kvDLQ/TefLCW3KtcJZIsW9uiAItElcnafuU63NYDd9q83savcH+vB/8rhI6cquB3WteVCmmivt2v+sNuWuL2v0YVtf3igdp/MrVUWkaDFXOvRFgu3YwdPzVLp++lt9kNa06Ly1uVmdpzd7e+i6ppYTKVJMJK2c7/XSFy8HdomUq+naB96cTfDqcu3tr79zNo+rLwV0nK6nrpu+qNAHa3t76G91O9nN/fuix7b9O3/0uLK3o+573X76Ag045fBxmuwPil5ndq8TqdIw/2n1Wqovh/N64Wlf1+xrzqE/XU17O6/vGsPK1XA1h21fr/XFpH190H6o9Vw/49zc8zexp53nC0+d3he9Rm9f4rpu6nVJX3LrNdnZX7P2+ZxWwbPrEt0m+rJ7/3aRoiVc89frkr5A13XTF7d6zdTrvy5Dr996XdLtoi+hS1Zyde9ix5fsU93k/PGjKzb44t3cub6U1pfpmq6M7z2/01h48DfX/zWNWvMrP8f89L+87WspEL32++Ir7qsdp9Yrr3shva/27ota7fQxriA0c+C4ozn6QB33dZ/vw68rPZeVlyP7A1++xlRfzw72fY+vbniCpfE1z++DuH/UvtT1eNbj35tznLOPe6W/KVXZ1e+s9o+eX0YL4kKXMYtk0bBOclbl0tFOSmLY9InI2rdd14X8MsM1PmktaX2mvOH94Jf10T9dsadsdZGVb7gyXDsOC/z3Go81rd7mnspk1cxwzejO673InGEif/50OiPavh5qV2Pez6bv3+x/Pu/f4vrrzOh2pq35tSJ1LhL5eKjnfLd95X+eWlBAM7jPbO3KtPRl8b88P7/7V8/Pdld5WbtdmaranZ1mlOv9hGa0+4rdyyeezgyfcZP/psj1Wqq1rvVa6qu/8u/f88zA1xjm3Bcd7jv9HOv05dOuvzd8IHmac59nZvi8h3Lf3+RF0/ztxMCm1Xs7PUb1vrLHSM/vtK9xHfSYWTrONW7gxyIrXnd10XLNqUz7/I6dS+9zPcN7++Lp08eDnRn+2SOuvwXNDP9wsOvYuGiw6z46P3MfdN3znn9r7oIrn97vum/Q7yqfE1w69Fha8rLrHHzAxztOJIWsYyflnIfmmP8PuqSePNTLq+AJAABBIDM8SsqV9MyEWf3bEblw5BfSqeEZ7nH/Gv2UTMz+i8wt8X/SqKL9Ylbk5hMPy9KcpvJD6k1SMuV4YJmOW049KK1+03N8lXODezjIT7larpfBezaIlD7Ds0RzXn1Eap+P+hLX+ULX7u9RMxaD4d3vt78XwsGySy/7oy/SAhXOvtr/l0//iet9vITw16+888FbH6K9H6Sd3r4m0BSKfDtJgqIl9P31Azb+Aq95Ox5WfT1w2+O0r+lXLnT9f8h3uaf7+YtTfxeIvNAo9/dfPe8a/NEHwLEtXP/X0t59Tj302p5zZEDnVwrce596r/MTmSLT+7vSfM0buafXmgJdn3D9X0urf/2C6/jUjBN9sXPTJ64S1urVS10FaHSeakxDVw2B+35yldrXF1Kq9kWey9BMnOcbeI7Tl1X3OgoHOL1xuWsbaan5fyzz/E4zqZwZNPpQf85lrv+/fpkrE0NL1SstTa+DrfUNIn3Gi0z/f659p7TJvOt8vASzM9Bebi3SZqDIX172Pc1va0UmdRS59F6RC/4u8kJjV62FflN8Tw8gf/piVGsb6fWxRkv/02lBIL2WaqbU4OW+p9FCPHoe12kncstc17iJl7gy1uxrmWbkjmsr0rC3SP9pIrtWu64nFw893Vyqvnz+frrIXWtyNzv6wW0iza51Zcpr4a/XOvtP86bZrsFWr6NI54dEJjtqsbzto79Nram06Ln8+032ppntdgEuXz651/X3Ch/9lWo8cMrKEBnvp+belB6e1+Zg4r63T4PIyPBl+zfBTa8vqJ3r9d27wRXICyet8RcIPQbzK+DofY+rPnv49P+dBfR80f2ttGCGnbnjd9rdEnGagR0Mra2YFz1m/b2ot/2y0P9v9LnozhXBpQkx6+BRCq0FTAtybfzQVQta78v1mbDW+a7CI00cTYGr3RtcBe3rd3FlxmpmX9VTz04Z608/U9kFtOxMVb1+1WjlqlWshVpVw16naynr77Tglw5O6WtdfWhvnOV6ftFMcn1OrNb0dOzWwrmaYV/7QldmvDdnH/L6DKNpcFr9lisW6/rrc4CdEa7yu1Z6s9+35JzIf1otuKU1ar0z2POat/2+Z937nv2D//RZ4Gl0FjRWWmBZC4LpPY+3Sue47jv0+Uy3U15pcz6/p3s9bx/cLfLTvNz3L94FmPTdlL5P0hrde08VatcWCdxO7Uvdh1e8cLrvdi0E4cxE1/tA3Sbe77rWzXAVhvMuPK/HqO4H7wJxWqtZ11uPVT2mNaZXru96x2C/19LzZc07rnXRbenv2NPCnvZ7Cd0e9nmmtbDrdz1d+Nz5G02/FrDUfa3PuPqdnqtO388QadHPc5ze9+o50+p61/7TfaPn3TldRCrUFp/0fNZj0lf817RrgTvdF96VBry3p56DWvi8Qdfc33ks73NXwZfKDUTOchRAtbsjWPSv0+eg7mctUK/bCQkvJSVFhnVvKPM37pa1O/dLdo7rnJj+7Q4ywwEA8Z8ZPn78eBk9erRkZGRIy5Yt5d///rdccIFX5o/DjBkz5NFHH5Vt27ZJgwYN5LnnnpNevXpJPLmiRQ1pufYxKS1H5bCkyUMnXSVXd+1z1ToqI4flZ8t1M3xASolkni4tqxnh6rgUl5Lip1ZGxwdPP8zoTauvDFGlTWLZDyDzvfp50pvuyx4T+eRu1+ezO7seguy/Nr2h1kyo4qVFej7negjRTG2tKXo8y/WgZmfG29Nv+OD0/1v2d9Uo1YcJLSlrxnd11Y66aqJICR+1SPXl9uZPRNre4noo1pe1mnmuy65Yz3NaLf2tzSoVKyky81QJYWemtPfDoDctEauZfPqQryWNNUO83Z2uTHZ9KTfn1EvnVv/P9XCmmZnVW7gy2zQ9mq6P7zo9vx7P5S4QoC/09TeasesscX7NZNcDlT4QOuehn1vd4L+vK81s0BeMzgelK//tqtWlL4Tzqs2jaanZ9nQJ5kvuFfl6zOnvL7zd9YCjD0qauRpMDXEnLeTQ9mbXA+bOU5mjF/7Ddaw4X0AESzMvvUtz6/7S5dkv4zUjRZthsx+mw0FfLNsPjsG+3HUK9Lfa5Jiz1QcnZ6EAe356jtgPo1oa3ebdkoQeG1rj264J6f1Cw76u+HrBk1cGgl1D0tcLKm/O9Nm1+fy9AE8/9YJj6+LT43Z4ZbY7mX0vni+Pck1zavmaKaHXMDtjBQkXv+esS5dxX+TRAoaXHMfLqU0ZB6V+VR8vDeGbff3J66WYsuNjXpl6doEujUP+apjaL9rta5d9LjtfONvL8JsmyxUPtVZ0MPQ6Yy8vL/oCMNiMcJVXRriTv/XS+xJfBRO96b2cFk7wp+9E172YNrPqi94v6j3Yf2/0P49GV7hqwa+eKiHRl7x2DbNze4r8+KnrnlJfNPtb37+MczX3+/upeNTreVessft79cf7/tfblS+7CoJqYTV7OR/defr/up5H97leNtv3wbbuI10FQDRWf3Cr7/m3+Ktr/rUu8F2j3Re9j5z1d1dGl01fivf8lytDSO/hZg/1nF6Pe70HdNYyt++7G//F1dyuvV66znp/rS3EeO/n829zvbR+9zoJia8Me91GSp8NNAMhr/3hd77lXb/3zrBQvmqVI+SYX9iKF3Vl0JRJLWZqkiEIep+rhWv1mq3XCy0Qq9csrZn5D6+Mh8k9XPfKw3eJvHOtSJnqIvedul/XQmC+4t/U3q6/t33pWYj3st0iHU49n711qtaxN431miGuv9P3Bl0eE3nvBpEz24j87dQ1QJ9vtQZww56ulmK8OeP4mjdFdnoVtrOvaSXK5t0qh7NVKX1X4suXz0jAtJBgMJzP9pqB6uR8FgqW1kIOpIKEvZ18fneqALXNeW+g20r7HvcukGd4PY+/1kWk2TUi6/57etw3Y3O3+qat5+hxaheG1sKNOtj03ZZmfHvTJtC9vXejqzKHk77b0mXo/YOud9cnXc/6y145PU21Zq6/WgP8w39IvuwCC7o9tHC3pl8LiOjxre+cejyb+/lcC2PqvagWTLnnB1crQtoEu5PeN9gF3Z2/U/ouTt/RaSFPpQXOb/QqDGI3Sa7nc161xTXz3lcBUm//udJVKeYRP633KL0HecdRyNIuzOpep7+dvtfRfWCv86N/+q4pj7iO3760qVNRft6TZTLDAQAIlwA6gIms9957T+655x55/PHHZfXq1SYwd+/eXfbs8X3jtGTJEunfv78MGjRI1qxZI3379jXD+vV5ZGzEoOJFi8jZKRlSLWW/1EvJnZmVKgGUJM6LNpulNa90qN7c/3TadKhOa0/vXQq4iqPmp920sk6nLxq9x2sTSvYLJs3Q1f9r857OJpmVNgXq/K2W7tYS4c5+meyaL/p7nY83rbmqtLar/k5rm57R0FVK17tZLW22Sm/YA2niyZdK9Vx9OmnGuW4TpRnhus10mc71qnjqwUCnt9Pj/bBgmoc6VZo25VRadf2rNvFcd7Ocqq7l6Dx0m9o047zGqVrIvmiBADst9oOarod3QQFfdFs695n38aPbQNdLa/MH0mx0+Tq+x2tp/lrnidRsc3qcHlu6vgXhfbzZD5ze/Xjl1Yx2QTj3U6j0xb193Idjfvbx6avJuWAVCbKvLySsRIjfT8/eKBvTAy+80rJ2BSmX5op/q7fzYA4/vGvMxBpfXYXkmqaOK24479lyTVMz7/sK/c5fZoFzGr0/CZRmBjs5Ww+yWwLyLkSp95Iey6x7uiai0nusQNLg/I03XU+dj7NWnfN+RP9vummp7btwly5fM1n0nthf4S/7PlkLmwZKf+M9vwp1XdtIm7AtXTX39LodNSPaSdNlb3/netn31772sxYk1eeMUNn3+r7OLf3OTlN+vNdfj4dgW51C0DE/GiqXSZXHr2wigzsH0b90uGiBLO/CQ1oz1VkQxZtm2moXEHbLaJrJpbVF7cJdWjjU/k4L42mBbC1spfPUeWumoBZg1W4Y7AKsOk/9rN0H2YVbtVa1zlOn0UJSWrNY06pdiWjBT63BaRcO0/TYBUA1I9wu7KrdkNhdLdiFRu2/2uKE/k4HZ0a4ZnbpOGcBLu9C6NqFla6Hry4T7ELGWrjN/r8WeNX0qt9Wn16uXdhW18lOt8dyHIVxNePSH18Z4c5+sXW7a+EgvXbq9vXVhUf1PFq/KSi9ng3w03paQWhtf1/M+lmuQlA2XxUV8qPbylcBbl+0kGBeBSKdXdDkNU/tAiMQesx4Z4T7Ola1sJp3K4HOtPh7/+Es5G+fH3ru2AWz7b/aVLzdHY93KzF29x66Xfyts7OFSOd1Rwt+Oc8vPf71s7kO5LjWX/ezr9rgmnb7/LSbzNfriP1bc01ynPM6D52X3TqkXq/sLuZ0Pvb56ixwbjto//ZP1/L8FfrTNOm0Ouj1SNOi1zWdp7NGPeIqfvtTrKhnlsWBoydlxOyNUvfBT3IN4774SSzLks7PL5RhM3y0BAkAQCzUDH/hhRfktttuk5tvdvW19Oqrr8onn3wikydPlgcfdDRpd8rYsWOlR48eMmyYqwTxiBEjZP78+TJu3DjzWwAAEHnJGL/7ta1thvtmfCdFYjy/EwCAaMX8aGlUvZxs/cPVKsYV//5ato06VSM5krRlCe3S6MI7RHqOco3TDNsXm7hqMl/l4x5HM21Gn+1ZK/LNvqdby7pzpcibfxHJ+l3ksT9cTTNrrc2a57kKpDhbfdMWEzSTTGth6jy1pqkWdtFmx4f/6qoJq31MO2nmpq9mtX31Ve2ssb3d0Ye0diNkG3de7t/puniPX+nV5ZCuh78W7Ox+tjWDULtDsmvMvuFoJtl7/v5aF3P2mb0qyJZInC1faaEAbQlDC69rzWdfLaMUT3U1+xxMS2f5dX8Saf5aqdHxzha7lBZyCqT1Gyd/Lcj4k1ctdef+0/7O/Tngp/tAb9qlTiDbRPuuzjWNn1Z2chyt8DnPM7sLAWeNdbslGz3H/3eryIV/998tjb/WE5TzvHB202P3223TjGr73K1/uWcLjr5qeOdazuWuv3UvPd3Mu22qV2tfox19hE/zasbd25hzJSCveHXd5nTtZFerAojL+O3LZY2qyi+/Z8nK7acKhonI7O99FJ461Wqbtqau9wA6jO4XwYJJAIC4FdXM8OPHj8uqVatk+PDhpysdFikiXbt2laVLfZfk1PFaqs1JS7XNmnWqryAvx44dM4MtM9PV/M6BAwVoxtjhyKGDkn3skBw6mEdzWv44mm/LOdWc58mjKZJz7LBky2E5XuSo5OQcliwrWw4UOV3KUb9XByRHUlL8lH7MOiRiV+A8dFTkmI/pDno1n3nkpOd0+lmnsccdPub6/6FjIket0yUv7fFHsnPP0+N3R13fZx32nKfzN3YaDh3xPS/37457zjNQ3tvhZI5I9qlxOp+jOSInvabJOiKSdmoZR07kXq49T/1sb2tNnzNdzuXqvtH1M+NODYdPuJZjb0fnPG1HHeM1Hc7t6P29Sccxx/Sn9qX7WMjxv/91n9hpMen1Wo6mXafRbWV5L9PHceZMl5Ous0nTMc9l2fs2VO5t6xx3an+5t2uWK/32vg8He7/p+uo6eF9jfK2TTmOn44BeQ0q7tr05v3JOz8/7t+Z3p44dX+ur29Zevn3+FckRKZojcsJybWf7e/u3vj4796l9rqTkuL7TEtj+1skX57Te0+i1xOMc8ZE+f8eXHrf2drSvSbqe/tKh+96cZyfzmOaQ4zw+eHo/hCluBKts2bKm36xYUhjxuzBi+MmjhyTn1PwPZx2UNOt4wL87ejgrbOlICs74aMcY57nurM3ifV3KNa+Duafx/ux97jrPa3saO+7pdaHUAd/XWn8xyTlPb973T/7Y1+dI8RUf8orL3tOY6/6pWOSLdwz3Zt/j5bUs/b34ub77otd3j3sSx2/d29NrGu/11TTZ93Pu9fCzrTzWJ4/7E42Nvu4Lfd3P+ZqPngvFT13nfd2LmnU9ljvt+bHvbT3u70840up1j2eP916GvW2996m/e11f917B8nWc2vek9r1WIPPO9tqeen5aXs88TmG8rsdi/C6smB/p+J197LAcO5zl8xn88KFDp5+XCyNO//6b63ja8f3p4+ePdNe4LSt8H1Pe9746zZalnvP8fdfp7+z5/fGbyJ8Znr/99VSt572/u8ZvW3O6O6g/94hsXZv7eE/fGtn448/xlMgtV/v79m4q/LxbRVae6obNl+6jXF0/aYtncx0Zhnk5+y8itbqIHNojsvh5V23ypte4uoXQWulak1xb+frfLa7pu2hT6PpskiGyYtLp+WgrHFqTVvs51yaytTas1jTWZty1CXjtEkZrLNfrJHJWe5GFz7quZ97XVV/rrS69T+Sr571GnjouLh3maul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-      "text/plain": [
-       "<Figure size 2000x1200 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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x7Qur5bdrIjyQDm7O2wodAAAfpGeelI370+WsmlF8c0i7d/rkbpFGlzvHQ8rDz9bDaz4Q+eNz955fdNymyvVFajV3vo92MzXzPpHV74vc7DKmUEH0PObjO0WaXSdyeLfIwS3OxLJW9LO6U8rKdI4lpeM1XfUvkU/vdlbea3eryOcPi1Sp5+ySy62805zjRFWp7yyrJtXPvUfkqlfcl/vlI+f6ylQQOeNiZ684On6WN1YXTtr62ldfPeas9XvJqS66Vk9znive+JZI6TJ5l9eW6FUbus+bn+zsvqr7qNx52vpbKyYktnHe5Nw4N/e5aTeL9H5bpHzlvOvX8bRSdzrH1AoVR6BqnQOAd89c3Ux+2nJQ5v7m/J3esC/d7iIBgH20i1YdE1S7pA1mMvzABpGUJSJtb/W/AY3rOa0m7nUYohpnB7qEAAAABsnw4nJtke0mt5uEBhPS5D9dykmvxmXzWbS09/nlqzgfdbzxzMMipcs6u5oodWrdh/c4X2v9fVo1ZyLctdWSJ9duz9V/XZLdloObcv+/aa7I8IS83RuMae981KR7frYvdnYp6sl1fZ7z71koUqeN8+/3bnCWRWuyWi3N/322s9v5bqfGjHA90bduTOvY4yumiNz5jUi9js4u5fV1nR4U6fpM/uUFAJQYG/YdNo81K8VJ1NK4qS3AdUwer8lwP80Z5hyT22JVutNKdDppgleT4ZoIVwtf8m29h/eK/PGFswKgntvkZ96zzsdLn3K2NtZxrjQZvkLHY5K8yXBtyb1isvu8nyfmTYZbrddPZOT2rqMJ+da3SEDoeyorGT57qDOxnXFAJL5O3uX3/pI3Gb7wVBLcNRmulQ414a9TbHmRk0dzn9NuybU1fvm2edf/7QjnYyiT4QAQZE0T4+XP9MycZLhWegMABNms+509Nja4UKTqGUVbh15H6HpOby8y4FQvlSjxnv/iN+neorbMX79fbmhXV86oEcWV2AEAIcGY4cV1/IhPi/160IdxNT21vU3kosdEevxH5OLHc8fkbneH+3KaAO/6rEi355zddBakydUiiV5ujHpT7/zClymo9bm2OvKXjldu0Zv32gLM9aa+tgK3xjN39dcW925NzfunnHrdQecNY+3qFAAAF83r+DlMSSB88YjI7GGBW5/GwLe6i2z70fvz2tPMezeKvHmFyO5VzsSzL7Qi3tvXOFtOWy3NXX3zZN7XaOtti9Wq29Oo+s4KcFomfXy/t/f15+eVM08tf1xkXKfc+Uf/dl8uZ6wqD/qe1qRl8JaAP7jRv5bz0/vkjiPlem60+Tv35b55xpkIN6/pKzLtHyK7Vjr3javFY5zP6Tp1fHfLiaMi7/Rytp5xpS3nvZnzlLPlvEXHX7ds/V7k1abOCoPfnKoQkJ91n4pM6Zlb9sL88ZXI5B7OhL9nt+lvXZn/ePW/f+F83ZJxzmMvM4xbdmpPTTPutbsUAFxUjPP+u3/8ZO61+O970mTZtr9CWCoAEWPCxSIf3Jb7975fRUbWEVn7cdHWt2OpyAu1RDa5JHj3/OKcNy2fXogK6vbcOn9d5FGx09UP/xF5qaGzVXgoaKVWlZ3tPE985SyR+SOd58D6Oa1zvpXv5JZfezN6vY3IrMGnXpvlvi5faaOY5CT3+4aIaNUq5jYie/OHrfJ/M9bK2PmbZcpi9jEAoPhIhtspJp8W4RbtOv2MS0Qq1RZpeHFul0OVTyXFXWkLbe0GtHI+A9Nr16VKWyBpjU1fWK9x5UuX59bn8naTvDBzn3F2keR6cm+dMH82KHee3gS2xk3XFlSurJvaG78RmfOk89Gi6543XOS3Wd67d/r8IZF5I0SWTnR226pdm277IXeZPWucNz43znN53XqRGQOdN3Ct7ke1fHoB4O2iSbtHnXK1yFePu9/4X/QvkdWnxlMtrsVviHz7vMjnDzq7Wy2MJiz0s2e4tPhT+jln/1/uxQkAoPiW/y+wY1vv+01kx2KRDadaNXujPb1oF4Y7l+cmjXXok4Kk7hLZulDk+3/5Xhar9XZBsk/klkn9uV6KNf655W+XYV58ZZXBGz1nUBVPjQVeEE3Ma6UDpV2yuyaRXS1+Lff/u1eKrP/SeZ6h+8aVnkPpc7pObd3v2k37lu+cMd4XP72R23JeaVfxFm1Zr5UQ9cbj8jcLXs+Pr4lsWyRyaIfvNye3/+AcT91zf+34KW8lAcuySc7XzX7Ceez5OjSPHfRcVVvlAwgbbetVkS5n18wzf/HmU9eNItLj9e+l9/ifzP/X7UqVT1cWoQI3EEk0SbnyXfcGBD69LuvU63y4n2CdN+n9DK34Zt0n0Xsfeq9BzyP0HoXeZ9FGDsGg51x6v2bNdGeyVSvWWUPrFfi6T0R++dD5f3397y6NKA6lOIfc0ZbPFv18+jmt80Sl20iTvbrNtLKhNuxQ2kuP3kOzepQ8vE/k2+ec87RHJV2XJov1/pT2bqjn3lr2mYNEfnzduf11/+m52o8uFU497xtp5cHlk529IupnORqAbbx7tbMnJr1u0PM6TXTrNYeuX/er7l9Pum+1IuSyN50VLfVzWseP67mfbmezzd52fy5t56nPnOXRhfr7zu1ibQ/Lhm+cSX/XRjWIaKeVjZUBF+X2MGANeXIiy7nfN+1Pl6/X7pFjJ7hHCQAoQd2kjx07Vl555RXZu3evtG7dWt544w0599xz7StQmfIiCXVFKjovvuvEHir8NefdJ7JntUjZiu7dkGvXQN4S0a6aXuP+uoLUbiVyRhdnC2kd11NPkAvT/m7n+OTexnzUcdGtscx1HHGr63Xz+WuJ1L9AZPHrecdA15uansoluLcy0pZYegKtrW08ud4odk2Mays3b3T+nxvc5+lNZa0pKzEiwz320a+funen2vAiZ9em2sLrngXOeZrw1hufelLfqFtuS/Q1U50n/U2vzu1+1NLyRve/Z55qRbTte5GO/xSpdqqF23enbmy3+YcU2zdP5f6/ZnORji4twbzRCyf97E16iDS6LHf+54OdN6LPv8+5fwEg2uK3F3pra3XKIXlw2ip5rY+PvakEgt6w++h251AgZ3XN+7ze6Hr3OpFSZZxxqN8nzoTqBy5doOvNKY0zFWqINL8+7zr0RpuVQP7hVZG1H4m0utkZA/O7Sao38PIb4iSQrEpuBY3VXRDdNp4t21016ek+5nlh5r/ofPQ8l8iPtsK+9g33eavezf1/fhXeXG+wep7jjPKo5Phaa+dj+l6R56vnPwyOp/EX5R1iZum43P/rMaUt0bV7y31rRU4ed1aw8EYrBXomge/+TqTuOc6bvzrMjbfzEVfahb6ep+n76b7tPSW3e3dvtMKgnidVaSjS+Ym8Xdhr5cNP7hKpe67I3S4VHBa+LLL9R5HO/yfy5RCR68aJJLYSn+k+1dZGt7zn3qJLx2ovKu0VQm/a3nTqBrC/9Aa5nn97Hmt6U1mHJ+rylEjjQq4jgCiN3+qei8+Quy5sKL/tSZORXzkrJ90+eZmM/Uc7ST16QrJP5a9enbtBXv/WmbTr2qSWJJxWxs5iA8GjlQ61C+ozu4rc6lFJryBauU9f1+gKkb6nksUF0Xip8dGKxTP+6f1ejQ711+EuCSg9R/7YSy+JmrDt6ZJE9qT3oj6+0/l/vWfmi5n3iez82dmTojWsjcZ2rZRap63I21c75w330puOnnu5VsTUcz7X80Dt6dFziJ+67XOHIMrPlvkiXzxU8Lm0v/R8Ttd35ShnJUUdkuf7f5/qPUlEEluL/HOR99fq6/wpy1eP5v5fjzndjrVb5FYo+Oy+3Of1fmStZkX6SCVVpMRvSxUv8djq+eXxj9fIyh2HZPTNbeS6toVU7AYAIBqS4R988IEMGTJExo8fLx07dpTRo0fLFVdcIevXr5eaNfPWBA++mNyxqFOdNRIrlzom4y8vL/d+4xy/cfneLEnNdMjmQ1nStmZpidFW3o2vMNPq/VnSIN4hlcvFOFuDe96s9ObcQpKbphD1nC14zv2ns9V47Zbuz9ds6rxx5tna+6p/i9Q4O/eCQE+s29/lbDWkyetuHsnepeOdXa7qc+ffn1uLVFsQXf6i8yTZGgNdWzdpd+/ahfkZnUWqnpm3NZJrTVxvPFsl6Y1TbZGlrd4LqhGq449bXahrqkO7k9Vx1rWmq77etQW4Kceplt56AXcszdlFlna3rrRWqtY41i5YrW6f9KJGx0n3pLV9qzdy/t9b7VllVS5QWhlAT/itFvbWGOpq7zpnmbXSRcrPzue0BbvWpNXER73zREoXcCNHP4tu/wrVnTVsNdFfo4mzkoNZ/y/Oigu7ljsrZVjdk2qCxDUZfuLYqTLr3aQY5zoqVPP+nrrtNJGivRfocZ9+wFnjVy9w/KG1hysl5lQ4CaidK5wXsvr980a3lW5v/c6Uixdb6XGo3yVv48wCYS784rd3dauUN4+frdkd2mS4Jl31d1hjjLdkuCYNXbvg1liqy3rGZKUVtDR+e7JaL7uuQ4cR8be1kN00hnjeYPXsJj3QNH5qhT/X1tWuNOnqybXSoJ4j6NA2et6lsdrqHcZqQZVfEj8Q9LiyWil5U76ysyW6L7y1ht6zypkM13jqK9eee6xkeH6sCoN6fqTb0TMZri2WlN6c9lahQc85tRKIxlB/kuHast6TrsO1RwJ/FbdXiNWnEvOeyXA9z9VzJW1pRjIcJTR+W0qXipFSpzpVswyautLtbysRrv7Ym2auar74Zbc81aOZlCtTSA9ugD+09apW2LptpjORp0PbaIJaK9xrK2H93e7/hcjp7fJfh0m2zhYZ9LOzlfDEziKXPinS/lQityBWK9uM/blJ7sndRXr8W6SFSwU2HbZGY/MFg0WaXSvyv1PnohvnOCtFamMO7QFPey285X2RT+52xh79HNqYwerdT2NxQZUotXKalkF7g1FaYU3X+f6NIn2mOf+vw9howxG9T6MV2bTC6FyXIV2unyTS6qbCKwVa9zm0fNpaWWmrab2ncu8PIl+4DCXzr7Ny/+9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",
-      "text/plain": [
-       "<Figure size 2000x1200 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x1200 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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p79PriEjUqlXL6t+/f575us7atWtbZ555ZoHWH8imTZus6tWrW8cee6z1wgsvWI8//rhVpUoVq23btvleTx45csRch+n13NChQ61//etf1nnnnWfSqetxmjx5spn/6KOP5jnGDxw4ENXfBCSSWIwHSs9FpUqVsjp27Gg1bdrUHN+BuBkPYoneu+r5Tl9jyUsvvWSVLVvWuuKKK8z99NixY60OHTqYbfTqq69GfP8d6j19ODRNgwYNsorS008/bb53/fr1YX/2008/NZ+dO3dunpioedGqVSurXbt2Ea8/mII+Cwj1Wrsg1xFAcRCLcTye7utiCXGcOG4jjsc+CsORsPTA/Oabb/LMHzlypN8Tlhsn3oI6fPhwXJ+A7AJNX6+99prZHlo47qSBWE+6+rtt999/vwnCv/76a868//73v+bzb7/9ds68HTt2WJUrVzYXAcWlMLxRo0YmqPnasGFDzkVsQdYfiF7cal798MMPOfN0+5QsWdIaMWJE0M/qZ/SzWjnC6c477zTb+aeffspTGO7czgAST6gFmJs3bzYP4J2xXM91p5xyirnR1gK6WBSLN86Fdb2lldy0EpxvbNLCXt3Gu3btikosSaTCcI19/q5P7Zvqgq4/kFtuucU8PPnjjz9y5um21O+aOHFi0M++9dZbZrn//Oc/XvP1QVmZMmWs7du35ykMX7RoUVTTD8Ad27Zty6nQrOeuQIXhbseDorB3714rnmmjAN97Xy280Urrel3lFM79d6j39In8EN1+3uEb+3bu3GllZGQUeP3BFORZQDjX2gW5jgDgjni6rysKxHHieCDE8YlWvKIwvIjYBWmrVq0ygaFSpUqmdsUDDzxgdrqNGzda5557rqlxoy1AnnnmmTzr0BPWQw89ZGpkaEsL3UnvvvtuM99Ja/do7ZwaNWqY5Y4//njTIsNfTRINWl999ZXVqVMn0+pKC8/0gA6Xrst58NkPtr7++mvTIkR/a7ly5azzzz/fnEx9aa2i7t27WxUqVDB5cMIJJ1hTp061CsPPP/9s0vbiiy/6PfFqQC9oSxSt3ae1Za655po876Wnp5u81sI9+2JDC/60dpbuF5pP3bp1s7744guvz+nJU9OoJ9Pnn3/eaty4salttHTp0pDSdOqpp5rJtyBRA57W+DvmmGNMuk477TRrzZo1eT6/cOFCq0+fPiYQahq1NbfWKisMGjg0bcOGDcuZ98svv5h548aN81p2y5YtZv6oUaNy5l1yySXmOPItXLjxxhtN2n2PmXCOYQ3sun7dT7UmlrZK9re/aIsm3ab60Ff3hcsuu8wc505au+7CCy80adW8122gy+3evdu8r9/nO4US5Oxt6zv5C8CFURiu5xOdfJ1xxhnm/BXMs88+a9Kq29tJLzB0/n333ee3MFz3GeeFGFAYiOXuxHK7AFMvzDWGBqLxwd/5Y9q0aWa+5lG4D/T1Bl1rRvv67bffzDq1trV906NxXWsAa1r192tN3WXLlnl9zj5vaa1bvWGsW7euuWn8559/4nobhXq99fHHH5t5+ur07bffmvkaO6MRS/Lbl37//XezHs2rOnXqmAc8vi0d9BpCr7f0Zl+PK23JoNcRzgc7dnzSdenDII35DRs2NC0onNduvlMoBdf2tvWdfBVWYbj+Xr3e8aUtPXv16hX0s9o6QNO0b98+r/kar3W+1kD3VxiusTxWK60g/hCz3Y8HwQrD3YwH9sNJfZjor7WMPiS0z/ULFiywLr74YtOCzd4HhgwZkqcHMzu+rF271twza75qjxihsK8NnJXE9b69ZcuW5ppGW+Ppw0i9ZtACBV96L6r7u7a6031Kew254IILTFoKg96ja3rth73h3H+Hc08fDvtZzptvvmnilOaD3ovPnz/f74NgjdMa53Sbapz3rbyl9PpF39O812cg2tuBfYzY55dQ7rd96bYN5R6/sB6iF+RZQDjX2gW5jgAUcdz9OB6r93XE8YIhjhPHfynGcTzZ7W7aixsdG1nH0tBxqD/++GMz7ob2uT9x4kQ57bTT5Mknn5SpU6fKXXfdJZ06dZLu3bvn9OevY3Z8/fXXZvwGXcfy5cvl+eefl9WrV3uNKaxjB7Rs2dIsn5ycLB9++KEZt0vXMWjQIK/0rF27Vi6++GK57rrrZMCAAfLqq6+asQZ0jC9dR0HdfvvtZlwKHTtCx0gYO3as3HbbbfLf//43Zxkdj+Taa68136fjIer4IUuXLpXZs2fL//3f/+WMt6hTfkqWLGm+Lxh7/EEdK8OXpuVf//qX3rGbPH7ggQdy0hAOHR/tggsuMONh67YtXbp0znu6rTIzM+Xyyy83f2dkZMi///1vueKKK+SGG26QPXv2mDFXzjzzTDPmWrt27bzWPXnyZDNWi+4HOgaj7j8FoftiiRIlzD6Xnp5uxo3o37+/fP/99znLzJ07V8455xypU6eO3HHHHVK7dm0zbsRHH31k/lb6mzTtofCX9/ltI90n1AknnOC1bN26dc14lPb79rI6don+LicdJ+WVV14xx0zr1q0lEjomSMOGDWX06NFmXOoXX3xR/vnnH3n99ddzltFxPB988EGz7PXXX2/G09Bxc/R41rTpPn7o0CGzjTXf9DjRPNUxdDRPdbyS1NRUM+aIfl7TrdtbHXvssfmmUfdd/ayOU6J5o2OwKx33JRyRbFM9z/z888/mmPalv+N///ufWWfFihUDfqfScVecdDx5tWTJkjyfGThwoBmbVI//U045RZ5++uk8+wkQTcTyoo/l+jkdW0xf9T2NmZrPOnaRTb9Px2nSfPU999jvd+vWLeTfrWMxn3rqqfLWW2+Z3+6kv13TqWM1KR3vTLef/q1jL23fvt3sD/r5lStXmljlNGrUKHNtoPuInvec1wnxuo0KEst1X9WYre9feeWVBY4lwWRlZZlxr04++WRzzaO/3x7j7NFHH81Z7qabbjJ5pjFm8ODBsn79enn55ZdNGr/55htzrbdjxw4544wzTHy99957TZ5q/uv1n9L5eizfcsst5rrwwgsvNPN1XK786HlDY7mOy3X66afL1VdfLZGIZJvq9Yj+Nn+xVPP/k08+Cbou3ad1fb77tTOW6zWvk45Jp7FcP6PXR88++6wZdxAoKGK2+/HAHzfjgd6j3XPPPSa+33333V7v6Tw9r9u/6e233zb5oOfxatWqmftzva/bvHmzec9J44iev/Ra45lnnsk550VK7zE1Xmns0DS/8847Mnz4cHMf26dPn5yYpvfpn3/+uXm+oPfmmh96/75ixYqce0ddly6bH01zfunW+O67XKj33+Hc04dr/vz5Zj/XmK3PSfS5juafbjMd81bp9ZnGfx1jU48LjdOffvqpOR71ucyQIUPMcpMmTTLr0WNV81Sfv+h+qM9I9BjRbaK/ScfY1HOCfa0Tyv32/fffL82aNTP5otcdet0Yyj2+r2hu01CFeq1d0OsIwIk47n4cj7X7OuJ4YMRx4ngwS4njjBleVOwaJ1qjxqYtH7RGkrYIco6noa2DtNaKs2aH1qjSVsC+rZrs8Tmc3Zj41m5SOp6gtiT2rYWmn9VaUjatbeZstVzQGm29e/f2ammjtdu0lZXd8lVftQabjm3s27rW+blANXZ8J01HfjRNWqPQtxWWjo2hLZ0/+OADM06xtu7SdfqrDRiKOXPmmM9/+OGHXvPPPvtsr22h+4FvV+eaNq2Rde211+bMs1sXadr91QqMtGW41nh0fr+OA6Hzly9fnpM+remoeeubZ85tFKgFUyitmnzpWNG6nzjHJbFrVPm2rlZaw1DHtHHWjHLmnW/NxdmzZ1vhsvdBrXnqdOutt5r5dvfdWjNR0+47Jqbmp47lYc/XFv12q+ZgCtJy2661Gun6I9mmdgs1HfszUA00bVEZyLvvvpunFqnzXKfHpU3Pe9rlqtYA1ON29OjROS3zfvzxx6C/G4gEsdydWK61u4cPH256MtEW1ZpGXa5r165ePULo+c43f5S2TtXldT3h0u6fnDHRpjWMtScVm9aU9q1FrXFbt4PzfGjHXk2nv20cr9so1Ostre2t6fJHW2JcfvnlUYklgdj7jrZcdv5+3Xe0VrndjZweo7qcb+sKvX5wzp85c2ZOq+ZACtpy264lH+n6I9mmdm8sr7/+ep71aWsafS9YLzt2Ly++5zp7fLJzzjknZ54e19qTkrao0fzU1j7a+kBbw/i75gNCRcx2Px4Eaxnudjzo3LmzaSHkb7gm57nP37bVew7dh5zdRtrxJZJrjUAtynzTovfs2lpM73+crRl1OR1/1Zdze9r7Xn5TfrFKe5DTey0dF9Yp1PvvcO7pw2Gnf/HixTnzdPtoWrV1nfM5g/YI4ztGre5vqampOdtbWwNqi75gCtLiK5QhQvJbfzS2abjPGkK91i7odQSgiOMe3Nf5RxwnjjsRx0PTlzhOy/Cipq08nbWvtIaF1kbSGiw2rdGlNUy0lZFNaytprY3mzZvL33//nTNfa8GpL7/8Urp06ZKnRaW29D18+LBpmTRnzhzzt7Y4tbVo0cK0pLRpDRjf7y4IrX2ntXVs+l1a4+aPP/4wrWK0ppPWeNLWNGXKlPH6rPNz2homlNZcvq1JfT3xxBPy2WefmdpFms9O2srHSWutaY22++67z9Tyy2/dvnTbaM0irdWktbzsWj/6m7XGonM/0ElpjTltFayvum/8+OOPedZ70UUXhd3CNxht8eRsuWPvD7oPaM0rrRWkraF0u/nmmXMbae05/W0FNW3aNNMyXmv5OVsEHThwwLxq7TBfuu9oDTDnsoGWc64rEr61QrXWpu5PWitK92ltDabbT2v9OY9Vbfmtv0ePVd2n7ONQj8uzzz47arW8oimSbZrfdnIu44/mRYMGDcwxonmix6DW3NOad1pD1/lZPefZ5z2ltXi11p9uB60dq7VigcJALC/aWK49cThpjemmTZua84LWrrZ7WimMc7/WGNbzvsZyuzay1tDW1t52zyjK+b1au1djubZa1+3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nh/pQPVBButP7t3gqrIRy7gAQHu2BxG64FqgwXI93neyeQrSQzVmu4O+5uNJYnJ+MrXnHHHby7anETquzYNm3t11boOFM7TGynY307EJhZ2G4PV6zFm5uO9q7gfbKq9d4K97xFIZrBUClr1o4an7TFs/1lk0LVbWSX7DC8PTN3n/rOvLrXtuXfsbmr9B72bT816EV6NTP//X/vnPYXS0QLV3B+/1A2zEQzVvlLAg33xNkzPBQ+Bub3F/PNVrepZPv/la9af6NRfWa2f69mvcHduVeY2k++lYEcQ5f7I/vGOi639iSy3iupZWzIFzZBeFaMUHTYP8u34J8f/ucbkOt5KGVMwMdR87x2+3f5xy6R6+/Y7gwnCp0/uiBq4Wu1Y4zf9aT3I382PIQain60gtrNxza42mp7Utv4uzus/RiVWs/6U1AsIfyvhfmekP5vwc8B7neUDmDka5TD3ItFP/vVbkF9P5qeOo6QjH9cs+Ny1fPiGz72fs9rRmttY6cJ6tYouOwaZ74jiuvadZ8tE/0+dHaP3rj/nUUWpRrTwL63Vp7GACKgfZVfS6mbfOf9jy0sVsraAsqbR0UinlPiix/J/REaM3bj4bm3iAF8uHg3AeikdLWXzpMiKYxkO2/iHw0LP7HawQAJLxG1T0PDCuWKeT6/L5jIxeU3VWgrjdYt9XaqjIrxALmYPR7dD32pPSBVk53idpF4mHvYcW80pHl3XW1b374yx/9jH7X4YPezwWcy/r+Pq+HnFbusrqMWdbns76F75qXuozmr36vFgp7dUV6OPfV7so6UP7aadaH2bouv3nqWJ/dBeQRP8va79vpM78ny3/rJfO7AlTad/5+5/+d3V5aAZaz88pf1+bOxhX6/ZpvNnu7a5fueT7rk7/+6G/X1vL+0u07z94W5rjw2W982fnpXIedr5p+s//5fJe9/XW+ve9pq2nnfu+br777h/6tv0e/394H9LucaXUeS17fH+Kx7Lv9guVZsM/7bhv72PCad/TYMuk7+nvtz9v56Pv78hz/R7z3GTtv7HUoLRTz+9uOeO+/zjRmh/i77e/TcxoAAEUlpLY1SZ5Kp4EaakYzESXyuR+q1drTC4ufj4ZMW8MnIFqG+6M1c9oO8nTNsXmRNGneWhpvLiHr0rNlz2E/9Qe0qwGtTepLawVrYbF2N1WlkcibF4SXjsoNRc57WeTd60X2Hq2ZVVBt/0/kp6M1f+xaXEq7a/j9C/+fSd+Y+3/thsEf37Ge7BpherNljzOleaQ1sas28hRca4UDrREdyjhbzotjrfHlW1tL7deH+cd5r08LA+waOZomnac3WxVriTTv5xmrSm9UtDu0Y47WHNaaNVpArX9rJQE9wWgLPa2lqjVetFuqtBNzv0cvxnV+gy6ei3Nn4bZ2HaGF4f78uSy3lrN+Ttev3VdoK3dtKa4t+LUWWdOzRCrUdHyf4+ajoPSGf+9fub/zuF65NdO05p6OR9akd+Trt/NGH3Zoflau77/bFs3TGq72uRCcbh/9HXXaefad4sa5j4fSiwHgNu1qb940TzdNWoNz4QTpuu5LeVlyW3yM/Wy1DGlf8v/Zuw/4KOr0j+NPaAHpXRBQsKEgiB27ggV7/auHip56p4f99BQ9C2cB9fTsiJyCpyiWs5+iqBQbCChIsaCCRKVJSQglQDL/13eWSWY3u8nuZpPZ3Xzer9cSspmd/e3M7Dwzv/L8zE2VKYpVSs3njRZSOiilAtV5XMe9zl99t4648CqRJt0d+r9GqGhEgypb1Vv3g9vMznym/KilnyabzXjabNs9QqN8dG4ZPySUprNXjBREfhNuC8Us9b494iazBk3N3r85lJJL6Zc0cksxSGnCJtxa1gNWI12iXZCrs5s6Zin+aASQn+Lz5HtCI8n8aSOV9ksVklrn3FdDKc/0ftFSFE7/d2j7HXlz5Z8NAIAK7L19S+vbrbX99HslmdKqQp251Yn5mnkVp7pOxBdbU03PHB16nDYqesx/+pjQ6OTr4xg1FYvi890dy8+ZqIZXb9SorumVvtMTWc+g1KjRRqJ6I6TUkf6siA7VIw40W/Ft2aiYK78KXfP856SyZZSi0e+B7mX/f+Uis7ypZjf9Fkq16Y1K0/vsdoLZ6GNDdQAe3Se/PMgst5lZl77RR7p4dQtvXVVWA/jYvqFU77HqO3Tt5F0/eTR124itKUZFKZ41as3ZEsqSF0mdHn+ZET4CK1omNtVnPHtK2Sg1f12I6iu0Hz13tjM76dHwUWleeTUdneeONmbn+gZD+Och1/R3+ruf6qf8aVi9RsxXLjTrHDGiZ3iXUAr4o7deN/t5nQIe3CP000sFqpH//lFXqvfQfhv437L6I6V+FV1nXx1jhJRSuXpZBPRed7avPPOfvk9K6aqshtFoFLIeO/YL/a7RyvruD/k1dE2rhts7Wkd/bdvdQqP6NbjkmYjU9B7/do2l4DezB3YLpfLXdfxLW1PQ9vSNfFdqeI3Q//PHZh16lU+fqkEj2uf+wS1q9Nfx0+sss9O2puT1vnM6Fi9632zM8WXztg96K/Q5NBgoZ+v1/OXTy9IaK8W+Nxrf2yaH3Wh2xJBQB15tY6VWVfphUR3Wwo/NnjnB7IynQ/dlGogRSftx95PMzhxT/m+aQkH3IVfNDtXVeZQtQVM4AAAAZGpj+GOPPWb33XefLV261Hr37m2PPPKI7bdfxE1YuohWmaw045GUH99rDNeIaF1UKoVBtN7DFY3sdtffMfHGcM3d4d2QeppsG55ePBle43Ik3dj4+S/GvRs23VypgSBRXjoVT+QNqkej0bvsHz4fhP+m0ru58OiiXR0ePF6apknDQ5UWugHUTbnnlBGhdEz+Zb1R1poDRKnf1BCiyn+PKgu8lBGRvJsPjXjXw1v/DoeY9TnP7H9b514/4C9mxw6zajPhlrLU+Sc+HJrPTl69JJTi/uq5Zi06J7duvV7bRpQy/89Twv+uhiBVbqgiY0iepfWcOZoro/sJZmfXwtH03n7UdAhH+ebFA9IxjqsCeNUPZvmfmi36NDw1kM+DHyywo6YMsR5eG+7nj4anKfLSQfmpMVzpf/731/AUW+NvKt9ZTJV9s48NnVfViUbL/DAhfBlV7HopP/V+x94Tfr594uBQhZYapFUB7U89psZ7VTapzDqHq+Je6S3VWN77nPJl//adUKxRY706pqlBXeuMRplMFENF6Y38jeFeBboawxWzdF3jph4dFOp84KftJKoIPUUd4hpahb5/P5TC8pTHzXKbllU6a5qQo4aGftc+nfq42Qn/Cu8o5lGHNlXMqYOA0nwmS9cC6gjU75bk1wEAGSKt4ngFirYU27KCIltesNHaNaskpiTDa8BSJ+tUNYbXaxQ7ZaXfL19U/b3UGB7ZEB4r/WgsFaUw9e7LIzum++sdvPtvr0E7Ht59scrvf53SvIu/Idy/DYsKKk/56HLK10007xLe6T8WNSJHUva9jlsbcWOldPeL1enffy3pTzOqkdn+xl7tUz0Xyd8QXtl7eak0/fwpLSNFy2KkeZqjNYZH0tzcHv/c8mt+3rruhdGzJ8VTH+Qe43FOgbc44ripbJpBd7vr+1Mn9ohvWfFN4sd4NO5gjq3zefqvab1MjuJlRYxMler/Huj76N9G3gh4peT1N4brGPOOM+87536e76J/973vebRjypsK0bs38R9L6qDibRsdR9HSErfZOfQaHR/RGsNVx+aWeWV4Y3i82btqqUyJ5SmnQTwjDwvdH0abYzpRIw42a76d2R+ipIVWtoiH9gj9vPrrsnvWqlJWON3nnvRIqKOUOql0PTT28o/sHfrOqnO813agjjGaivSzh0JTYajjujqUaFqIWNQZTem9pe/loek1vMwt6mivhzrEqOP/r1vnVvY6EVWUflqeOiY06EpzanuUhU+P814PnxLh2VNDA/RUT+HFO93zqw3GPx2J2ljujJJ915vOxW9oK7OzniufbjzSuzeW1Yk/d1r565vbm4fX8UfWFT0U0VHJ86Uvy260rH/371pxuTTftJ+/TUODDb1Bmdu0CU9b7fffi8xeu7SCa8NKMr74+TPLaB/q/Zu0L99eFTm9r/ajRx2sKhMrFbm37eMRuR8r4t+/FT3vryuM9Rp/2aOVN3IqAW1DHUv6jqmTmrs9l8XeFv6U89rO6uQZS+R1jKYEqswbvmPO66zo+adv8KnaRvyiXaP4j39/Z1tdU/i333H/LD8lTLY3hr/44ot27bXX2hNPPGH777+/Pfjgg3bMMcfYd999Z+3aRankrGbrrOLG4tWb64dO2updrAZuXYjr5K7nNJpMjcD9/xGaf0l/13yfXuqCc14MXZi+uLVx0LPPRaGRWRp55k/3o3mOpL/SjZ8butmTnY8J3fQdflOoovbjrXOPKRWDd4NxzN3hX37Ns73DoaFe4XovT7NOZgWVBIZ4RBup7T8pqxJgSwVp4aLRZ9HFc2Xpz3c9LhTwdTOhxlUFOjUGVDTSXPyj0r0bRDXYL5lV/sZI/PtGo6ndm7Acs1U/lp0YI9M1+beLbvAiG/b91my92dcxpBsIjy5ENLLOu4H1n2T0vDpbKPjos+siR8eEGjv0vHoba1vo2GrYLPQaNdh7N0hKD+Y/0er48J73Rq677781uOk9dELVVAKaM967IVVKLL2ftr06fTRpWz61lXeTpdGUOiZ0w+elxdKxre3TuF2oDE23Lft8Wi7Hl89D76e5QlRBpWX1edTRQ+to7Ovtrue99/fKqvfT51Xjuz6HvptuSr2Srd9nxyy3SWj7eK/xtoH/mNT2VSeV0nUuLxsxrm2lAKZ16pjUssos4O+IomV0keftEz+NBNDFn45FlU+fU5/fK7vWrW2l+erq+eaL8Zb3zzmjcuk7GG3EpvuagtDFZbR5arz3077QfvDeI1G6SdCNurav3kudh/z7001Rt6niC/RI3jGn1+g7p/NLZY1syVKaOO3HaI1uSMs4XkrnA50rfA3hf677pn1f0slO3L2lXTuvq00v2dV61NkaNyuaQ8pP8+ZELhcra4oyg9y/S+j86P/+TPmn2Xt/D8+q8u3/QnNV6mY+8oI72s1TZIe3f/UoP+eV/8bJX7EW6YWzzLocaHbGU6Fzp9cQ7p0LdJOsDk3+aV+eObmsg58q8fRQ+XXe0MgajR7yqKLriL+btfFdQEejhm81BqihXbFLHQi8Tmn6vEffZTbmuNDv2k5qfI9srFasUUWZHupccMCloed1LaHtqPVoJLuuz7RPNC+qGvI18ksxu8+5obk3vX2smwydx4++w1JO1xLqeKbPocqKg68qG02VrGlPhioQzxwd3oFzsuYv+9lsnwvNPhhqdsIDoYrIdKLjRvvVX3ELoPbG8SiO6bGtfbl4jS1fW1T1xnCN3lVM6H58aM5EVRx7NC2VMssovaAaQ3W/rutXxUiNSlQnLXVK03n2iJvN2u9evtFQDbgaEenNS+1R7FGWFV0T65pW9zBeGm9RDNI9lbKF6fpB96a6Vtfc1+rMrWtTxeQdDgpdJ+g+Q/fCKn9FFefx8O7/YtEcgJGN01/4OoJ75r8Zmqs8UZGVpONvjJ69rrI5FiviVQZ2Py56dj8/3bN97qvE9eheLVpDtKeyOSWTqVvRMePR/WGsyv14K4vVoF9RGs9ogzF0jaVrWPGnHI8Uq37JG9QQaw5KHX9qVFn5k9nOR4U6kWqEtjfPtzdKPF7e8VoRr8OFR9eZDVuYtfNlLojmo7vKRquLrls1ittv4rCyxuztDwrdV+s6Wdd6GvXspSPXZ5zuq6PzD6D56rmt/3HMPvaNrtZ++GjrtekXo0qneAy7b1B9hDJS6TrW33DvzTHviVZvpsYnb8CL/q86N39DtOqs9N3w7m+8404ZMXVset9/lTmyYSSy8Vzl8eai1efS4BKvsV2ZHTTqXB11Ve/mdSDw6L2YMzzjYrnnvXlLrX2zhlavTo713C6JKUk9qmdVBk118khFY7jO4bHO4zr/e98nzRWdqsZwL9OI1ylF964VxXRvOa8h3H3NT6GGbV1T6Lsv+s5UVNfmNYSLMrpEztPsvZfXEB6t8duLR7pO8fMPLovkdoryNYZ7sT6y49ePcWTZiEV19svnRe+4pAGMXozwGsIr0u2IUB2u4q+X8acqVPcdrbNdMvwN4aqnVjuAX6zOXftfVjYwI5LX7hSL22HJSV0WY1Gb1aKtA+iUlVTZSUQDFXUMR8u6q7YJXSN77Rr7/Sn0XfLaz1JB+16xbebW71RVqLwa6KLvk9cwruPTqwP0t89UlQbJRsbMSNu0rnyZaCLrFisrtwbrxPqeVdQxM1sbwx944AG75JJL7MILL3R/V+D+3//+Z08//bTdeOONNVqWmcsd22C5OlOU+1v7xjn2U77ZooIS+2CxY/132Lrpcrb+VCOUV0msRi8vvZA/h79uNnSz6vFGq+mnqJHL35iqE637HnVCFZZe5bZGgnnr9v7vXz70ovAPUG+b0PvrQtivcevQzUrd3OS/BKKL+YpOym5K9AQPcI0+86dwi0UX+ToB6wJaFyNx9Q7f2gDrd2/X8N8jRxL6y+LvEeOJnBNc/A2gulny3zBF8tLtesv6bzrVmOLRjYHSTqlCRj2HYtEovNe3NgL4R7MrfZl3nCmI+yveVSGg0b9PHFTWSKPKlVcvLr/+m5eFGiCfOipUIaGeTOp1Jtd+G/um5INbQzdUf5ka3pjjpVUTdS7RtlN6M41EVpk8modeN8ZKufXKH8PXfd0PZQ3xSjnmVSx5Zf3mzfDedP4OJB6lc9d37aYlZg22KV/x8lDv0P//8LLZLkeHPotG1184PnQBrvRg6tmkC1GvB1y73UKf1xspMPKQUOPqdVG+E9qn/gtNpfJT+jClVVTavd1ODN2I9jrb7DTfRdi93ULp5ZSW0OuRr15iBww2O9Z3bPn9c+fQeesaX+8yfy8u3XyqJ+Vrf7KkaR3e1AzRen2NOmJrb8sEGtq9Y+7WVWb37GDWcW+zP8WY5qGq3v97aD8Onp7eqfwDlE5xPKzDkHos6/9qpN3q8Dqz3Ye5fZ1usdu3XOBGy2FbzrGNlmvjG9xg3ev4KsV26h9+bn/8gPDG6nhFdiSJNSqsskrZeEVWhFbUEO5R5YHOm5G8LCX+G2VZGOXGVDex2uaq5IysvNboq0f3Lts3buVVx9A5/vW/hDogeaPi1CgRuS108+O/AVKF2Mf/rHjk9vgbQrFyxuiyUTxeJeEe/xeqtNTNvP+GPrIHq1cOnbd0jlWjtbII9Dw99P+BL4cqTceeYdZ5/60xenKooUKp8jU1TKQXzgkflb/481DFp+Luh3eEGlYipyhRh7ixZ5rt/+eKU+q/e33opyoe1YjiUfpP0U2IKgbUiOM1hiuO6Vznp3Sse50Xu5OTOkwotX7fiB7rnl9mmv3vmtDoBm2Lyrx0gdn8raPHFD+1XROZluODf4QqNJLN3qJOX9611mWfl2/YyiTqnKvrxHP/W43zlSGbpF0cr0BuvdAxfcIjn9jLl/a1fXfwdV5NlLI+6d5XFb+6z/OPmlFDoZ+uBzQ6UZV/kedLVQBd/EH8I1LUUO41kMXqGBZPzI6swFSs/1slnchT0XgYOfI5cro0eSlG/KhMZEc/iZVlLVlelh51dlDnYjU8a07EaJXFaoiMt24hlaKN3vGPpo7WEB7rM1SU8S/aFGZeZ9JodP2gBtjIQRyJ8qbRi3aP5zXEKIV5NMnWV8XL6+hR2X6PrNfRtb5/dL9MHl72f/91nzry+xsQ/NeosajO4sOtWZLEawgXdzCHb3Sf/xpc9QeVnWN+LLtXKuU/z6ne7M3Lw7MqRk6h4Oc/v0VrCK+oPJGdTNQgqVFz3v1ItO9z5wpGxtUimRLLGzcouzb987NlHbg/u/FI69iiiplMESw19FUHdbiP7LhUVZqCZeov0a9hPE07lk0vqyn0vGx5375VvsE5UWq0/f7dsnYete+oU1VVqH5b099NHxXf8moLiNUYrsGZqtNQHYU6fPg7bcbDq1dPhOo5vMZwdazwGsNVB676BHXsj4yXeo3qYlQnr8+z20mhtq3KOs4lch2j94/VGB5r2uRYtH869gk9tE1jTVdcGS+Dgv/aT9OgqB7F6zDZasfKr5m6H19+OpmcuvFn4ImH2iH3HBi9MVzbNQCBNoZv2rTJZs6caUOGlF1E1alTx/r372+ff/55ueWLiorchyc/P1S5XFCQmh4fv65abyVFJbZXs2Jbty7HbP1G5WIz21BkZ++42T5dGOqh+euqYitoHdGoLDudbNZ6L7OGnczWVnBCLdp6oXrY1jk8dWI/9I7QzYhGI2s0q55v26NsPX0Gm22nucs6h+YG0jrWbQzNYZyjEWdrzHY8yeyXrfNC6XXe+/QfatZ8l63rqmN2xD2h0a/rV5u12C7U81aVtd1ODI3e9i40jxlu9l4FF00NW5p12s/sh4gbBfVe63r41gt0x+zAq0LzoX79stl3b5u13iU055AqwXUjr0pvBc29LwhVMMtRd5lN2DrHqEbx+huu1Qjo7x287YFmc94N/X/RV2Wfe6djypetUetQT8HPHi7/WTauDu8dJvUbVxwcK9Ooc1l5olFKdH9K+WhW/15+Hb8r8OZUvO4VeeF/974na3yN/Eqps2Gzb7lmoeWW+EbN/74k+vusXhGqaP95683Oit/KltNrTL2zfMfhlpLQurW8ntPyK2KsW59PI8z1t98WlJVdVq0IPb/81+jbpWRrhxP/a7QNNQo78v2WRUn/9tPWhuhVGlW9tYJtbWH099pW2+qH0N/0u0Yr6/9LF4ZuFr3X5M0vK8+KreUuWhZextL3j+gpr+2k5ZYuCr3u163vtzLi9dqPG34s/z4/z4r+PrJOo+wXR//7ssWh1y9ZWPY51hfFXlcs3v7yLPkpfB2/bL2YSWS93jG3ZnVo3QtnJF6ueHnnFG3P3K0ZC6pJ06ZNLcc/aj4DJBrHayKWb8lfYptyimzdhiKz7Q4y+yYiNflWJVt7Hd9SpIZFXeyttxOLbrQit1OcWbdG681+aGLX7Hi4HbFS6cXr2+VLT7FJa7ezfXO+sadbj7UczSmu7AvfvW23bTrf8nM72oN7bc3koXOOUn4nYudjzRZsbfBVpXu0FJWppJi4IYWViuN9I00+Gx3+t3/75k+fv7XR4Ndvzb6JcgMwJcZNWTRDmpm13DF03bQyIqOLvL61cTjSzQlko5B7ekaPFXfsUP5zyaJZZvepQXyM2ZdjzX7cehyqA1JkZWnR1sw5P27tDLAwohFF13pe1hrFiBcuDt0AKRW8bk5nvxT62ypfB6u3bwllG9ANjnrblp6Ht95kF643e29YaPSeesxHenlw6KGRTAddHWpkP/nx0HyV4waGbo6/m2z2pq8i9LKpoVEIS2eHUmdpGzx8iNmOR5md/mQonZ+/o+EZo0MdCJQWz3/tpe1w/z6hTkhqUNrh4NB1oxrM1UlN6cF2Ozl0w6uGfKXV/3DrSLX6fzXb72Kzx7dOm3PRB9FHd6ljnUZlDhgeyn5wz25l2Wq0bzdsCk0PohtJ9d72ZzqQCbeH5mI9Z1yoI4Q6cuhz7jUwNPJeHfa8zgYTh4c6PCilriqI2+8W2ie6+VOHVHU6kwUTQinndMx4lS0fDQut+8i/m711hdkJD5t13ie8Y4JSKe50lFndeqERq17P7Hv3MNvvz6FsAH4a9aFKbo3AV2e9ctvm/FCHxgExOrrGS+lpdU+ha0Xtd3XSVIOjMgSdr06PCY7q/fThUGNC18NCnU7UcVKfOY0Qx1MTx9cXrrWSovW2rnCt1Y/IbtRxG8f9m+QtW2m7tqrCMaD7AynYep2v6/0NW6Lfm+gz1i2Ifk+wcmn569CK7tHW+++9rOy+W5VYOh/7G7wSpXJo3dE6/Ho2FJd/f92L+xutVLHoZQzznPOS2QtbO2NtinEfut0+ocZMnet0jlUHOVXSdTsyVN/gz1DnDQw45LpQnYK37mj2+WPZKLdolCVKHdAk2np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",
-      "text/plain": [
-       "<Figure size 2000x1200 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x1200 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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hhESAa4ZHIilJJDXgmC5bQZJFrXvxfVZjGbH7PHnQmi0pSR7ZJNtdsqI+f0j9v3N1+MTVSXKKmpB/Flh7vGJNkbsXe5QbTRJ5sY1Iw7NELn8pfD8b54u8eZlI2Yoih3ar/dy1UGWjI8MMC9vfs0SkTDm1/ex/ivzwoioBq53a5jaIAn/pTLX2AfYH2vZTGXF+wBqiiJC/7TspcmDUOO4kkWtjPJfJ1ytD+01fKPlj7be6baTY8dqFIhWPE/lrPhg+YmX+KyLfDBe5/XuRaicW3nFJwYHS/3Y/v1qk+/Cibg0hsVGuqkjHoervXz8LfW5Xe4lS7vv0q0R+niJSprxI18Eis0aqzyNlkGZuCZVQ7/uhMjrnF9UbxpfxZrfnDyl09Pk7l48pTPZv9S5nT4jJ/u3qf/YVQkoGezfac3LJDa1FWSlbrVv4SU4nuWZrIIO6RlORpGSRXb+pAHTM6ZERjcAX/B7jA6dOPbxX/fb45kp/Yv6MZwQC3OH8rXW6SMYf4WsrogrKcY3VPp3jZix7hqA8jCMObBc54XT3bU5ortqHv8tXU+swVqwRyiJ38ucSkVN6ipQtH/55TiAwff0cVaUG8z9nBrUZvI3lvWBTgHySA/kR+B2y31EVB/Pi1EAJeq8g+GiBRMiAh03EPkYgWAHV87DmYY0mebfHGAvbQ+awPaDazPafVWJCStnQdvu2KTkllwmdN7LnkR1ernLe/WKZuconqOuB/eMap5QJl83mRaoqYFrjwDGbqzbDVuO8xjt+FalaJzxgUhPp/Hb+Gi4/Zz/A73B9qtcP/x36Ha4JzgHovuK8xm5geT3045ouiRr5BfpMapVgNUUSB+gLeL7o5xQhRCzHfBtriP+yJVPOOrlm/DuDHoHerFJbpFpg7XFUdzlyQD3D8XyPVM0Dz2gE1kN31GymnuN1Wqn7FftJrSxSJVBR1q0CVfUGah1kvKBXoT9w3B2/KFv78c2U7tu2Qs1HqtQVqXy8d5uwLOeJ7VXgPOz70IM4foU0dU5blivdDbsEjhNtWTKMT+y2/hSqbgv9ggS82i1Deg7PKsigdis1ZsBzPylFzfmhv3DcMqkiWwJl7eEvgD+izhkh/Wy3NXAdsFRsxTSROm2ULKrVDw9yw5gJ4zLs276OOarq3ZqZofaiDdgnvkOVH20HQLWYBh3Cz3PHKrWcLcYSWN7Oufwc2o9xEWz62NZc0xm/a3mtGlOiP1WqqZawBdDLCK4E8KHov4PXa5b6HwkDG35Q4xC9TrU5roH+z1PZx1LnjHEBxoVO0K9NIOfyVfNWENJtBeaSrpCB11gT42DIN9i+7XkTOdG/4ROpcJw6F/QXVDXCthhzYzyGMTDG45AZzgP9H/0ECaDop7oKY/nqqmKirqaM66GpfYY6B7PaopYz2oC+vu1nJVYcD2N8jHuwf7QD9wfuQ2yD46E9uH8hb/QjfV9h7In+pMfJbmA8mblZ7ad+B9VXEOCI+9jsU3s3RV42EfcqwPlWcrnf7fvYZTlHzH/Qv/GMQHUmnKO+h/W9A3kF388MJdD+sTB0DU9MV/cxxpNaBngGafAZxsYnnKreQ57oD7jG2oepz8+5jOLW5d79CvcqKmzh/mrQUckP/RVjbIyDnHObAoTO8Di4oll5Gb0kW3IlWcZvO0Wub/4Xqb/ug4I5mOkkx2QdDmiNnogD3PDoaHg5neF44OJG1jcz9oOHrC61ipsYN6F2hi+bHP7Q19tAceltoABM/DrCwcqPpNiAGxavWFn1SUjh7ftTZO5LIlcXw4j0PxYU/jGReYCBC5QSneGJgd3Pt4jMHUNnOCmhuBuYMq1KcnySS4UWLzCR9cq6QlWQj+6QgsFS+vqpWmoQed0kkVO6h76e9Zy7URpZ6H8sUpOH5pepQSwyuRBAZ/8+SS1z4gRLsWAyjsGz6Sj86HaRVdPVJLn9zcp4bJYmRZAbMNuCCX60iilYYmXFFKU7MA7BYBgGBkyOT+4mcu1bIaM8JqHImofBv+/7aokSjHP2bxM5696QI/6FViKtrxc55SI1oTj/YTXxQXu+eVqkdguR0y5X3816Rhm9T75QGbC/G6WM4ucNVZOqyX3UhP+cB0Sm3KLGZFe+ItKsp8j/Hle/6eTI5Mcx5o8TuXqCCsaAsfPMv6tJD6oLNb1QGQneuVYkfYBqn3Oys2iiSPv+6rqhekCX+9RkLhIor4+xXpdAxh7K90O+5z8iUqG6+gyyxbjlnEHKwIM+hSV4UBHp5AtC+8J4EfK98Mlwx0E8YHz6w2iRLveLLJogUq+dSPNL1OR+6SS78pLdn6BnIEsEfpx1jxQ4S98Jr7RUElj+gcje9eq6xQJKJmPCfOET6vphXDb7WZGOdxasY4SQggI6CLS6Tv1/5KC0m3+/iDwiWVJWOamRQd3xDmWgRn9H4DIMLqgYsuwd9Ts4y53P0lUz1LPq0hdEPhkY+hwGI8ynbnRZxmxsJ6ULLxkV/vnng0UWvx7+WZ/J4e+/GKqeibfPVQa4F1ur5x/0J47pBXQBntle43EYxSb0ELkryhwQS0R8MUSk739FmnZTn00ZoAxp4IzrRK6KIVs+EljKBUvBYY7cMhAg+M51Iht/EBm8UT37TaAXsHxc57tFLnpKZM1XSkf2fFakw62h7Ua3DRn3Lnk+NN7BeKW3Y4kZOPixzBz07pk3q6VqLh8j0ub60DZzRol8O0L9Dd0+9RaRS18UqddW9aczB4j0+qf6HsbOMWeKnNLDPdh80jVKzw/epIzS0fhimMpqv/0HldmPsczjDnvMv09Xjo6H1qvy/ugrCBC402UJPifvXa+M8Q+ui815Hi8YU2G8hSCCgVzbN27Q7+b8W+Tm/4nUP7OoW0NIsWDF5gxZtmlv/uxs3bfq2Vq9kci9y0I6RON83pq83Ek5dS94TGTmP5QTHPOqm75Uzlbsp0odkQdW5f0tnGHjzhI59RKRTQuUbh76p9IfJkO3KJs7dA2AwxzJaF5gWU444cwlWBt0FrnpM5FZz6pKdiYYV3ihg+TA+y4V5dJvFbn4WfU37ICQwQ1T1XwRz31No7NF1jsS3KCT8Wp8Tt7lQY5rEloqDuMDjBOaXRz6/u2rQ38PWqsCrVZ9Gt5GzLHxgmPTGdSMajFI4kFQmwa2fixn68X4s9X/V72qxkImqKiDOWO0ZU4QSDf5r+GfmdV8Jvb0/i3aZvp8tBMe1f68MKsMAox58XIukYPldjVIWjMrDHr5QGCXMcexWJ4X4xAT9G+Ty0ar8bNbkDkcu9oBHAvmebtdE+1LgkN2VMBhGwm3Pmqy4kP1igSCJWxZWt7t0jzfIvK+dECusxKkdrjr/ugG+rfnfneJrDWWUNwwx12mf/tY5KRz1XhSg2UGNRgrg4c2KPsRKmBiLAkb4obvvathgyVvqZcbsCPqdsDeZyYRn/2AsgsVEnSGx0HnE1Nl0i85svtwoPO3uUFEO8NhRI1l/a9YwIPWGVFkPqSMaPgwp/k3I5ThEg8ZPIDwMHYC5W0C4yciWRAlo7OpnEwfKNKuvzKqRQNG9UZdRH75RA0aECGEB279jmqiPW+seohqIwb4ZbpIw84i345UBm0MABB5AiM0HhKYxCFqDooO7YBhWwOjBQYgMBTXPEWdB6JKMFHt+mD4pG/mE8qQ2PtNFdH2zVPqZjYNv98+I3LuQ6E1V9EODFwQXdM2jpKziEDCeSNjHIZfGBY2zFXGbkRwwZCBCTIMmjDGINIJAwy097yHQxH6zskmJkz12ofLIL9BO+HQxvWDsRiRPuhPcAjEI4N4QNQ6zq1VHxU9COaNU0aS1n3Uezg+YJS3+1U+P7pgWPj6CeUIaRxB8ZQ08HxAlYnml6p+/sNLysFzxjVS4sBA7usnRU6/UkXD4nmECD44eNzuF0JMMGlDtKx2Bgb4tO7dck/5z0XWG5MbRH17gWhWp27VWeQ2VsH1f6CD26beJvLQutD30Gdu6IEswET8z8A64hgkmwNlJ27rk+sBLIDDGsZjJ27jDmeQnRvOsupmNCn0J5zi0KUA4wLt0IDjF05dr2uHtdjxAnAkI0IZMtQTRRhA8IzU5wXndFrDkIEBE3iMWfT795aFMgJhLL9xRqg0q9MZDgc7gCFfR+VruS57T2TQb+q6IQBg9nN5neEAgQtwhuN5B6dJ3bbR9bCeVGlnOMZdy95VegCTHoAxGvYH4wn0AYwwkOPKaSL3BaKDwYwHVTUjOPHdstxiAWvU4li4NugzyWVFHt2pKhHBUYJnOsaNyLDUk7DCcIZrp1pJYsrf1f+xOsPh/MO9hGALZL7AAYVrgUCXrjHug5DiiM56SWsoSXvWS7LkeK+3DecijNKmXkNwtzNgF5nGbjpLrw2esdm9Lcj0wW/QJsxVkZHizNaxj7km/FgwcgLMw/VyLjqQLFowMzLkMS+EoxdzNBzXBM9tBEUhEwfgbz0+wW8QVKYD3PH8RxsqnRCe1YJgN2QQ2XJJUtlcMLKZ2UkIYsJvcc72NbHUPBzywHxDGz5NA6iZQQVgP8F8D7YUnbGyfZU6P5ynlh3misjEQdaIuUycmcGD4DadFY+2o016PAEjHuaZAHYDZIZBdtjWHHPov7EN7A/676DsN+W1qeBYOA8Ye/V4AbpdO8PRdp1YYL/PUEZVfK6PBxmhr5roa2z2Tz2eQDYNghHtbOIkNU/HNjo7G+3BcXRWGpIp7POtofaL9sXqHNfnB3niNzgm2oXrpvsekjA0aAe2RR+ALQWZVmbWOLKx9H7s9ztVu8zMaHMbOGxg8HYGT+g5PAIunUZ65zawc+F6Qs7oa85qDia2XJO9KyO4Yd5vbmh54dhoD/o6zk8/V7yWPkQ/Rb8yMwjDjrtLzYMR/OO1TTzXGONkXD9k+nvZW3BMZIsiQNicV+k+h3NE1h+y/44F7Cs/9kNKBJVSQ/1tz8GjcvmYgMPlWNGZsF627kjoaqjaSbkr8LzWc0HgFVirjwv7tM7cNDNbNfgM95LGK6PSxLnUBjI+zWOGtcPYtxOd0Ym5meljCP7WOE8tA9i5vSptuZEn21lCjnD7+52hrFRkneosVY3OPjXPDWMBLUuv6l6w2ZjO8FhxOqSD7fT43AmyfBHMN/XWYz+uPudmvZR9PhZ6Pify2SD3a4znNgKjNV6Zy027qzGOmQkOHaXHRF5gTON1PeJxhGO8ZGa363EQsCsBGJnI0OVu/d5NvhgnObO+m1wg8nug2kA0bH+fFb2v+AHVqPS9GumedeKUhxcYI5jjjCMxHkNfB339zHmCOS9C4MwCRwAtgiQxd4Dc9LzGvFbOatqo+lSIFAtn+JgxY+S5556TrVu3SqtWrWT06NGSnh4hIroICTrCnTgd4XDuYpKLgb1buVBEpDuzrDGAxoAXnRkTHGekdDR0GddI6LIlGq+IDRMYTaH43Yzc5k0L8HD98mGlkFv/VU2CYOTF4B8R4IiUB6YzHIZaDN6dN89plykDBBRisHzFbJGhxk2HY3kB5x8yrwAmHd/9K1QOvte/1HsY0v8vULIMfPt0yBluRs7g4RGPIxhOSBiWUX7u+g9FptyqDPjptyh56AixOq1VlDoM8Ysnqs+wDR4aTjBphXG9bCWRYQW41hgyFeAwQUadMxKtoJzhCPaY97IaSP3lP+HLCmhn+IxB6oGJ0vz5ndGUsVEFKKz+PHomRUkCjg30KzyDcD98OUx9XhKd4Xgm4p5CX+n3icjHdykDHpw0uuQVKVKKtS5HFjBeNqGJaBJKGrU9I9wZDqemlzNYg+f0gldUoFf7m5ShVDuaYwUTNExCTCNrrGhjvdug3otYJgpu56hp2EUFojgjzouSSGXrY8WcQMD4GUlObkaMaLiVp9OTSzMiv6CIZYKkcZY/i2fiGiva2AJHAClYzKoNYRRQ0A5JGIq1PgfINjLIlRQZnX2lXJMyS8olZStHrnYsmkFhJs6AMG30RpC22zMcAVBuIFhrZJSKHc45q5kZhgyPSEF4XmAeiyBRzZWOefRzTUT6TQ/PJAMIBts0L5ThpOd6+N4skQgHNDJgdEU6VGlBcBoMrbBzAIxfRnkYm1v8RQWUefHWVaGl49r+TWUU6YAoZIR/90+RrwOBfhiL4AVD+W1GlosTBBM8iwzlZarCjXN8pcdLmIvjBR5YHb6OuranIHjLGfSHwAe3zC5k9EFOZkYLlpZ7ONAHR9ZXc34N1qnXoLyoF6YM3EA2lC5BCrsLMozvmKfKymKepAMHgc4ygg0ETgLYXe5ZGrlEsGbsWSHnBQL3kJyA/tXyGpUZpbEDI6qIPNNIvW96kchvX6q/71qs5u8IWEBbzh0icu5gkd+/UevPX/ZSyM6AsqfIOOv6kEqOwD2C/vrw9vCgAvBBP2VHcas0oJnxgMpwQ4UBJEfA5oY+EkmucK7f63DKRJpvI2Ox9yRV+cYJDMb/Pi3Uz+EkQQYaMh4jAWP1Mw3VvXf9f/N+jyBCVKfQQb/I2PTL2M7KyI37AdcPzpc+Rv/RoBSx2X+R6apLuU6/L1Q1Egkn/Y2MyHiB7Q7P1QadRG5yqWBFEkOXG9Q/rqKM+WtbqVyujPSbWAD2ODiNiDsIyIu27EmhkBR7e/3YTwqDyrWjO43jJR5bpw6udPvcOa9H1Ts3ULLcGQzlDLqMBMY1ekztRfk0kcOBID8n8CHpYEgnkK3ZVxHI6dV37YA8w8EKH4uz3+T3tfILgij83INOeXjuv6Z30F0sRFvGxU2O+A0SV4/luAVIkWuE9957T+6//34ZN26cdOjQQZ5//nnp3r27rF69Wk44IbAuUjG1qf65L1ccKzqFOHdoKBMLGafO8h2YiGDtbjOa5OJRKkIUkTzOtcTd1hb3A8qL+MHNEQ7gCNfl4wCcbnqdUhxLZxLBcG9GUKNsa3DfK0PRbFgrBOtOAEy44Ww3wY2kM8nd1ukysUu5VlcRq+bDFNmkOmrbzhZwPGixf+e+cSOjXAUcciiPozGjoIPHnRGKoMGkG/vXZTDg1DejaVDeA5MtM9gBEeeQDx7WyITDxBJOFh1Bjwg9rA+BCEMYk9MaqQkqInixDZw7buulYT8ILoBhHHJFhhnOC9GKmAgjQ10fy2vdex1tj5sFbcYaKbiutU4LZfQjmh+gT9RtrZQsjouIcGyjQdYE1rnB77V8cD0gI5TMMIHDQLcH+8J1wIMV9xT6HO4vKGz0RR2FD4cG5Ig1/nSbcUyU88A2yIqAsQzt0xN8yA8yXTtL9bVaLVQUKYJdUFoFkde6H+C61U8PRWHrdWzgcIMcMVlH1gmMI2iLXfq3uZowYoIHOaMvoa8gSAXr06Df4Xrgf0RG4fq7ZT3jOsARDKOQU07YF5Qp+pnun5CD2c/xXMLx0Ha0DUYHHX2P36GcEvoQIlPxt7MkEGSAzAydVYPnkx3Rn6KiY1G6T58fIjfxNyo34LxsuS8OyQnfm2vjoZ24Bri+uOa4hyE7nVGjM2QROKSvMwZ2uGbox3pdIVuuy9V7yBSgPeY6JLjX4NyKlPGI/eD5hPbrtfpIsdbl8fCvhVmyZk+O/JSlyl8+XuYN+WxxJbm7UgV542gfabetilx0/F5vXagn1n7KLcOA7rZGUCzAOD+5b+yOcH28YyHeNctjBUu0ROPV89XzEM8aMwBv/tjYj4MqM5AXMtg0T9UOj4rGsid4aZzOD7eoec1rKDOLiYKlnqmRQDveuCx8jafHPYy45uc/faCMxV8+KrJyqtJ9eEYjKhqR9eazFL+r1VJkW8CYC8cAssPxDIWjwXzmPt8yFHQHWaN87MwnwyeyeBbCqYRn9jWvq2x+RPwiwxgZ9aiGc/Fzahsd0NlrlMhnD+Y9p/Fdw4MzoZ+RBa95+kSlIxA4uWKqyFWvqJLqOpAQWUvoByj9j/P58XXlyMGkF4Z3yBd6COMhnVWFINSLnnCXMXQBljjAdTNL+kNe0EF6GSLomTcvE6l3psjNX4TWwMWYDGXSEEijgzCnP6DGD4iSx3FRmQU6SGe9Y40ulJKDoR+BmZD9oQyRns+oMVW0voxAVZQZRGldAEM8SgKawaYaOAGwxqGeh6BSAALJPrxZJP3vIqf2Uv0AzhNknZsVcjBuQn/rOVJVoMIyQYj+xlgSehnXGMGKyDqHkwTljZ2grWgzjnOdYVzXMoBTpGHAoO8ERotp96ixPPo7lh3A2Bkyu/YNNa7Q28ARgn6BcQbaA2M8+qpZqhjjl0/vV1VlWJK25OpzPPPsSmhq8t4oaYust+rIGquenJ60wT0Q3Y3ml4v88nH81UzyG+0UjhXcy2amuVeGiq1jkvJmy7uV+jRLrrotzaYz2RFAbHWM3kYEG+hnCcYsuC9tY5qV95xRlQzf62xdBKHjXnWCOSLmUCZuWUiQhZuh3C3Yzcw48kK3X2ew6XkfPsP56Gthyhh6yE6WCMgfOs/N8e1mMNZJFgiqd35uJmBg3qrtA3rcgXNHu1BRzQ20UdtdcGyrkbqP9DIyet4ZPE5SeBYfrosOnINODDuXPeEGc9O+YR/LCt2bmOOhnfr8Ma+15WlkwWP+ht/o/mpndZcJ2dXwnXa2Qw5IGNDHx3noc9DXDbLB/F4bZvXx7HPPDVR6zA3JVfdZ00ho7zfwOy0rfU44l2A/D8gQ+9Tng3vIPp+A3KD3TfDboPxzQ2N82MT0NQq22wrtF+gKjrptZvvMz8LOIycgS8uoUBCQDeSq26/3geM7+ysc9tq2AfuCRpdRdTs/HM+saBHW73Bdc0PjbV1JQv82eC6Ba6LbR0qmLndwXCVVBSA5SSTXYZsf+dkq+eH3XbJ+5wFpXreqNKlZSe7tdookY2NCCCGkJDnDR40aJQMGDJD+/fvb76GsP/30U5kwYYIMHhzIIi4kPt6sHIjlUrwVamqKyJHAuLr3Jwfl/YqtJb38H+FGYhinnLhN1OBo04Nhk8Mua2uahssgxoQyVtyct8cKDFFwOGLCkpPlvh4FGG9Eh79mlCbHoFpHZ2tHOMBnKE/u5L2+KkMNGdWRgPHPuV4byMoIzxLQRmBz/04QdY31xpz850Jjv4GJIRx/2qEMw5y5f+f6DpjcOQ0S/wms2wZuna3WAnEaZhCRi0kjgIPZLKdjrmtmMu7s8H6KdVkwofxlmnI+3zFX5JVACVWAYA0nWM8FUcfIHMCabiYoN4u26hJEyDCHsxvtwznBAT8s4DCCgcI8lgbreCC63pnd8NP7ob+9MjNAp7tCa+ghgwDLALS8VuTqV5Wh4tXzVKYCss9h9JnYI/z3MLau+K97WZu/TRM5qWt4P7jqtVCWNa4jItvPG6b6Pu6LIX+oSGt9/bCGYY8R6n5+9zqRHiOVARpZ8Z3vURHw2F7T/3N3AzGcYDD0Otd/Q2bDtLvybo9rYvZD9Nu/TFCG8znP560qAYM9giNgwHZbXwUygENpyCblIHm5o8pqwTkjwOCKcSIf3aYi8FH+Fs4VVBq4YozaF5wJXQer4zorHYxupxzUWF/WrQyzea0QGIK+AWOAzmB9ZJcKjMBaiMiE12s7gW6Pq1LJmjEd1D4irRUFBwwi3b3WBiTFTpdHI71OimzelyvLdii9/PEaGFZVFO0j2f1lw9oKckr9ejI+p7VUXJYjK7vtDQVrwbkGYy3WnkQlFL3MhnaGwwFnB7EYw6uWvUVWTVPPQKex2sxwQXALjIwV08LLnyEjBAYjbfDT6NLeyMxBEEukdZDipUk35Uh1OgxxPzl1Dspu4dmI+1CXk3UDAUQ4Dy2Dk85TxnKtA+FkxbgIDm+3sl1u+/YqxeWGW1l4r/JgfoCz8cQzQ+utRsNt/BcNHdGNKhk6W9E0+OP5a6Id4Ro80504gy0hZziYnQZPGHv1+vIwluLZCOAMx+c4HxhjzcpGCwOVXqJVKbKX9jEC8OCQhgEUz244NyFbOEDxHuM/7XjAWnFejhy0w1llycsZDhk4lycy5aWd4csDY5HNC5UMdBYaKj2hf+HZoJ3hi14LD8LUa5FpZzjGUZAZggbNKk0YP0Zzhut2wUFhR7wHHAJwhrg5w3U1BzyfAO5DzAfWfq0MznBSQwZoDwIGTWf4+u9V5Qzcp3CEA7OSE/o79qMzX92c4VjSyC0gV8sAzxUvZzjmFua9csVYVeEJgUlw1NkBbyvd7yeUvcO8ovzpoc+2Lgv01bPoDC8J+twriEs/CwPZEANSpsuw7AFyw5HBcnbyCtlrVZKnyk6U+snKSTUhu4f8nNtQ/ll2fHhyg1smw87VMiMnXabkdJEXy46RiknG3Law8SpdqvW/CeZdTrDGoybWErEIbnVzrGsnGYKf9D0dCexDr72OcfRnD6lAbDfwDH0iTc059HjAq0Tzk0a5ba/zxrzDDayR7kRn1kYCug3tcwYaOD9z8oTD+R0pUwoZy16/i/a5aSt6vVfkNmGJElNOWCIMjnM9Hrr1OzVvRTUBL376b0gGsCNozDksMO0Ops1EV3eA/JDRDLBcml633ezn5pImyPr3Auuqm7S7Ma8d6LtRebO1TZta676hsQ1A+xqfq9Yc1mA+q5ffGYaAtwwVYAUQzD2insgp3VVwHsYo5j2Mscw/jLLiWG9Yr7mJOS/sCMjWR9AHAtzO6B0ecDkgUMpV9ztn5QUE9en2Y3zy4Hql6831kQFKj/d+W9mlnCVWdaUHtBn9rcPtynYB2SPo0Fmy/J8nu8saIBhRl1GH3kYlC2TkY6knjLlOu1zZDrB00OANyrH+QksVtHKKUYEB2yOI79IX1DU0s+B0hQFSvHX5MaAt3tm5lny9SgURr92pxgC90xtIveoRljwghBBCipsz/MiRI7J48WIZMiQ0MUlOTpZu3brJ3Ll5jVJZWVn2S5ORoRwYmZmOso4+2bP/sORmHZSmlXLlwAF3h/jw9FzZsM+SFxer0pZbOt0jmfVTRP73D5E9v6uMcBhm9gUm6QcOi2RZap1GDLp1aXR83+pWkdOuVwM8C2sZHRTJKSfSsLsyoiEzp9E5as0/GAbhvNRZMQBZihc+pYyD8wKGug53iswfo/7u8awqhwDHEhxTJt2fEfkiUIb6sjEi0/Ral4iudJT0TCqj2udFi4DTdOH4vI7dM64T2bRQZaDuXBV+DiiNAefgtw6nucm+g0p+KeXCHe3rlog03a2+i6VMrD6HSOttIMOoeiORucYkUVO+nDpWJNAfnduUPT767yIZPXbvFMlwRA2DHUak/hYjWtt+vw43hfpbHxvv9+zI215cF2yTsUdtsyuKoWTDcrUd2uU8L3z+h8NotWeXSFJVtW3WgVC73H5vsnOL+r7i8YG2B651NCArfYy9ewPnlhFo8w71/o9VgX26tKHKKarNbsfC9vid+R22DR4v0MYdmwPnm6m+M6/f1vWBYweOsWubyP49gXbuzdsmtDnN5fm27keRbERx7xDJNh7je13a3rSHyG8u5cUgU7RlZ6C9JjiXzAOhNprfaxkgqAR/Y+KK95tXhaYruwLXb9d2kaqBc/rzN7W9bqM+Lsq4ms/wg4H7fPsf7tfhULbaPq2lug8OZIlsWx/aFnLERF23e6vx3c6t4cfaHzDsRdIhe/X1CZxvPlKlShVJilZypgQQry4vDH2ek3VQsg4fkgMH8pZ5PrGcyN0tRW76LK8j9GjSYcm1DsqhQ2o8gB6SqfX4CekquMbeEEt5XB/S57qPdXxQBXiYnzW9Sr3Au9c6DpgqkpymDKQnXiDS+WHlWJ8eMCCDMwYoJxr213mgynaEsUtns510iUhKeZFy9USWv+suEJSQMrOQnSD4RVePaP93VdrSzowx7sEcZGpUFDkX2aHTRZa8KdLxThUYoMFSHn86nNb6mdG+nyo/j+cc1ixufZvI1g3qnsb3F48IVZuY8y/l3ETJ0R8nqfGVCYJsUPnBWdb7wKHwNkNWbtkpfnAz9pnX+cybVNn8nPIhp6ETVHAxM4/j5XCOkufRwJqT0XCOnZxg3JmdkvdZq6+JZt8+kZQj4WMK8299jzjlb+7H+V1w34Gxga3za4Ybz9HnoOuwzeFckXrni7T6m8ivPgM/zOeL2Ravtjl/d9AhgyNJ4f3g4NG84y97/wH5mPuC7G2ZZYVvu/+Qt65xa2Mm+pMVGCsfCf8tZGb+Bv0H77Hk0779gTYHfrNfn0NW+D60bLxk5Pzcre2Qi/P8nTLwOme9jbl/fR1wDuUz825jgm0quB3zcL7q9NKqzwtalx/cuVlysyrLgWaXSDKCyswAqAYdg9nK1a2tck7uHPk2t61MlTb2ZwvqXSPV0rbZJSsf/1Z9NjT3VSmblKOcstAj1Zq69p1ncq+RdUfSZHVOdWmSHM2JHCU4vcsDKiN4yxL1P/QWzkMH+sLB5JaVXa6aSJUTQxVpENCJgBeMB5wg0Fd/XrddXj1sbodsT1QWcaP9AJF6bVSQLoJVUP3DGUiPcbaW2cndRdZ8IbGRJbIWFcosbzuDHrsnIQs0LXQcjKvMtUuLK3h+RtIlbvLUrHbJ1i8IsNxYmYoiCwKVdX52BFrCrgAnptd54PmbYszLN62Mz9bhZP2yyL9f8I6qxoIgtHj4ZXbe/VZAFb1AoIc5d9T377JP8/5mlSOQcvkMY165TeSAMU/OzFC69McpIk0uDHweZTmcHX+KHLVEVmFcg3nrOmVPwW/XLQ3te+3CvOOLVY4gsGXTQ99l7VX2nS1r855T1kGRjT8FPo+yZA3OpdNgta+Mz0VOvSTv/n6d634Nt28SSQtkfK+cqbaBneq3QEWbJR+FbI87tqhMeeho8Oea0D4XTxHJsUR+X6ium8mKL0Xa3iH5RWnV5YWhz7MPH5AjB1PkAOYUUcAcHKri3m4nyxn1VPDHP6avkA27wufu+zIzJTM5Sh/W4z5UFXAbo0c6v+AcJiu0D9yv5tzFax96nK3H3no7Nxuq3tatTeYYWmPuE0Bn2uNjFx2UEpgLQA7ONms7nnN/wWMb8wotA+d+wCGP37vNKb3mEWgDxgJ55LNPJMkx1vdqr/O45vwrVrzmO9HOw9zOtA8cC7ot0cYW0eZNWr5HcsPnsl7XDds4ZYzzMn8bqb3oj1HbG2Eb57HNa+L8LlJfKGN8h/Znu2wb7ZwOBe4/3KMVM+OzH8SDeR7O50Gsv4tnu/0u97EbuP+sCqH+b+4nO1fpZuB2r2FbyPeI8QyLJDPzeVMY+twqQjZv3gwpWD/88EPY54MGDbLS09PzbP/YY4/Z2/PFF1988cVXSX9lZGRYiUC8uhxQn/PFF1988ZUor9Kqz6nL+eKLL774SpRXadXlgPqcL7744osvKSX6vMjLpMcDItuw7okmNzdXdu/eLTVq1MiXCD5EvdWvX182bdokVasa68uRiFBu/qHs/EG5+YNyK16yQ7RaaaUg9Tn7uX8oO39Qbv6g3PxD2RUvuZVWfc65efGEcvMPZecPys0flJt/ODfPX6jPiyeUmz8oN39Qbv6h7ErW3LxIneE1a9aUlJQU2bYN692FwPvatWvn2b5cuXL2y6R6dWPdnXwCF4CdN34oN/9Qdv6g3PxBufmHsjt2XV5Y+pzXyj+UnT8oN39Qbv6h7PxBubnDuXliQbn5h7LzB+XmD8rNP5RdyZmbA14vf1Bu/qDc/EG5+YeyKxlyCyziUjSkpqZKu3btZObMmWERaHjfqVOnomwaIYQQQmKAupwQQggp+VCfE0IIISUb6nJCCCHEmyIvk45SLP369ZP27dtLenq6PP/883LgwAHp379/UTeNEEIIITFAXU4IIYSUfKjPCSGEkJINdTkhhBBSTJ3hvXv3lh07dsijjz4qW7duldatW8vnn38utWrVKvS2oCzMY489lqc8DIkM5eYfys4flJs/KDf/UHaRoS5PDCg7f1Bu/qDc/EPZ+YNyiw71ecmHcvMPZecPys0flJt/KLuSo8sBr5c/KDd/UG7+oNz8Q9mVLLklWZZlFeoRCSGEEEIIIYQQQgghhBBCCCGEkEReM5wQQgghhBBCCCGEEEIIIYQQQggpCOgMJ4QQQgghhBBCCCGEEEIIIYQQknDQGU4IIYQQQgghhBBCCCGEEEIIISThoDOcEEIIIYQQQgghhBBCCCGEEEJIwkFneIAxY8ZIo0aNpHz58tKhQwdZsGCBlGZGjBghZ555plSpUkVOOOEEueKKK2T16tVh2xw+fFjuvPNOqVGjhlSuXFmuvvpq2bZtW9g2GzdulF69eknFihXt/QwaNEiys7OltDBy5EhJSkqSe++9N/gZ5ebN5s2b5frrr7dlU6FCBWnZsqUsWrQo+L1lWfLoo49KnTp17O+7desmv/32W9g+du/eLX379pWqVatK9erV5eabb5b9+/dLopKTkyOPPPKING7c2JZJkyZN5Mknn7RlpaHcFLNnz5ZLL71U6tata9+XH330Udj3+SWn5cuXy9lnn23rk/r168uzzz5bKOdHFNTn4VCf5w/U57FDXe4P6vPYoC4vPVCfh6Auzx+oy+OD+jx+qMtjh/q8dEBdHg71ef5AfR471OX+oD5PYF1uEWvy5MlWamqqNWHCBOvnn3+2BgwYYFWvXt3atm2bVVrp3r27NXHiRGvFihXW0qVLrYsvvthq0KCBtX///uA2t912m1W/fn1r5syZ1qJFi6yOHTtanTt3Dn6fnZ1ttWjRwurWrZu1ZMkSa8aMGVbNmjWtIUOGWKWBBQsWWI0aNbLOOOMMa+DAgcHPKTd3du/ebTVs2NC68cYbrfnz51tr1661vvjiC2vNmjXBbUaOHGlVq1bN+uijj6xly5ZZl112mdW4cWPr0KFDwW169OhhtWrVypo3b5713XffWSeffLLVp08fK1EZPny4VaNGDWv69OnWunXrrA8++MCqXLmy9cILLwS3odwUuJeGDRtmTZkyBaMXa+rUqWHf54ecMjIyrFq1all9+/a1n5/vvvuuVaFCBWv8+PGFeq6lFerzvFCfHzvU57FDXe4f6vPYoC4vHVCfh0NdfuxQl8cH9bk/qMtjh/o88aEuzwv1+bFDfR471OWLL4t+AAAI20lEQVT+oT5PXF1OZ7hlWenp6dadd94ZfJ+Tk2PVrVvXGjFiRJG2qzixfft2u1PPmjXLfr93716rbNmy9sNA88svv9jbzJ07N3hDJCcnW1u3bg1uM3bsWKtq1apWVlaWlcjs27fPatq0qfXVV19ZXbt2DSpoys2bhx56yOrSpYvn97m5uVbt2rWt5557LvgZ5FmuXDn7QQhWrlxpy3LhwoXBbT777DMrKSnJ2rx5s5WI9OrVy7rpppvCPrvqqqtsJQEoN3ecSjq/5PTyyy9baWlpYfcq+nazZs0K6cxKN9Tn0aE+jw/q8/igLvcP9Xn8UJcnLtTnkaEujw/q8vihPvcHdbk/qM8TE+ry6FCfxwf1eXxQl/uH+jxxdXmpL5N+5MgRWbx4sZ2mr0lOTrbfz507t0jbVpzIyMiw/z/uuOPs/yGzo0ePhsnt1FNPlQYNGgTlhv9RfqNWrVrBbbp37y6ZmZny888/SyKD0iwovWLKB1Bu3kybNk3at28v11xzjV2upk2bNvLqq68Gv1+3bp1s3bo1THbVqlWzSy2ZskNJDexHg+1xT8+fP18Skc6dO8vMmTPl119/td8vW7ZM5syZIz179rTfU26xkV9ywjbnnHOOpKamht2/KH21Z8+eQj2n0gb1eWxQn8cH9Xl8UJf7h/r82KEuTwyoz6NDXR4f1OXxQ33uD+ry/IH6vORDXR4b1OfxQX0eH9Tl/qE+T1xdXkZKOTt37rTXATAfhgDvV61aVWTtKk7k5uba63CcddZZ0qJFC/szdGZ0QnRYp9zwnd7GTa76u0Rl8uTJ8uOPP8rChQvzfEe5ebN27VoZO3as3H///TJ06FBbfvfcc48tr379+gXP3U02puyg4E3KlCljDywTVXaDBw+2B28Y6KWkpNjPs+HDh9vrbQDKLTbyS074H2vKOPehv0tLSyvQ8yjNUJ9Hh/o8PqjP44e63D/U58cOdXliQH0eGery+KAu9wf1uT+oy/MH6vOSD3V5dKjP44P6PH6oy/1DfZ64urzUO8NJbJFXK1assCNgSGQ2bdokAwcOlK+++krKly9f1M0pcQNBRAI9/fTT9ntErKHfjRs3zlbSxJ33339fJk2aJO+8846cfvrpsnTpUntAXbduXcqNEBIG9XnsUJ/7g7rcP9TnhJBYoC6PHepy/1Cf+4O6nBASK9TnsUN97g/qcv9Qnycupb5Mes2aNe0Ij23btoV9jve1a9eW0s5dd90l06dPl2+++UZOPPHE4OeQDcre7N2711Nu+N9Nrvq7RASlWbZv3y5t27a1I1nwmjVrlrz44ov234hcodzcqVOnjpx22mlhnzVv3lw2btwYdu6R7lX8D/mbZGdny+7duxNWdoMGDbIj1q677jq75M8NN9wg9913n4wYMcL+nnKLjfySU2m9f4sD1OeRoT6PD+pzf1CX+4f6/NihLk8MqM+9oS6PD+py/1Cf+4O6PH+gPi/5UJdHhvo8PqjP/UFd7h/q88TV5aXeGY7SEO3atbPXATAjZ/C+U6dOUlrBuvdQzlOnTpWvv/46TzkCyKxs2bJhckOtfjxQtdzw/08//RTWqRHFVbVq1TwP40ThggsusM8ZEUP6hSgslNHQf1Nu7qA0EGRhgrU5GjZsaP+NPoiHnCk7lCzBGhKm7DD4wUBJg/6LexprUiQiBw8etNfSMMGkA+cMKLfYyC85YZvZs2fb6xWZ92+zZs1Yhq2AoT53h/rcH9Tn/qAu9w/1+bFDXZ4YUJ/nhbrcH9Tl/qE+9wd1ef5AfV7yoS53h/rcH9Tn/qAu9w/1eQLrcotYkydPtsqVK2e9/vrr1sqVK61bbrnFql69urV161artHL77bdb1apVs7799ltry5YtwdfBgweD29x2221WgwYNrK+//tpatGiR1alTJ/ulyc7Otlq0aGFddNFF1tKlS63PP//cOv74460hQ4ZYpYmuXbtaAwcODL6n3NxZsGCBVaZMGWv48OHWb7/9Zk2aNMmqWLGi9fbbbwe3GTlypH1vfvzxx9by5cutyy+/3GrcuLF16NCh4DY9evSw2rRpY82fP9+aM2eO1bRpU6tPnz5WotKvXz+rXr161vTp061169ZZU6ZMsWrWrGk9+OCDwW0oN8W+ffusJUuW2C+ov1GjRtl/b9iwId/ktHfvXqtWrVrWDTfcYK1YscLWL+jH48ePL5JzLm1Qn+eF+jz/oD6PDnW5f6jPY4O6vHRAfR4OdXn+QV0eG9Tn/qAujx3q88SHujwv1Of5B/V5dKjL/UN9nri6nM7wAKNHj7YfmqmpqVZ6ero1b948qzSDDuz2mjhxYnAbdNw77rjDSktLszvhlVdeaStxk/Xr11s9e/a0KlSoYD80HnjgAevo0aNWaVbQlJs3n3zyiT04wYD51FNPtV555ZWw73Nzc61HHnnEfghimwsuuMBavXp12Da7du2yH5qVK1e2qlatavXv399+OCcqmZmZdv/C86t8+fLWSSedZA0bNszKysoKbkO5Kb755hvX5xoGOfkpp2XLllldunSx94HBE5Q/KTyoz8OhPs8/qM9jg7rcH9TnsUFdXnqgPg9BXZ5/UJfHDvV5/FCXxw71eemAujwc6vP8g/o8NqjL/UF9nri6PAn/HFvSOyGEEEIIIYQQQgghhBBCCCGEEFK8KPVrhhNCCCGEEEIIIYQQQgghhBBCCEk86AwnhBBCCCGEEEIIIYQQQgghhBCScNAZTgghhBBCCCGEEEIIIYQQQgghJOGgM5wQQgghhBBCCCGEEEIIIYQQQkjCQWc4IYQQQgghhBBCCCGEEEIIIYSQhIPOcEIIIYQQQgghhBBCCCGEEEIIIQkHneGEEEIIIYQQQgghhBBCCCGEEEISDjrDCSGEEEIIIYQQQgghhBBCCCGEJBx0hhNCCCGEEEIIIYQQQgghhBBCCEk46AwnhBBCCCGEEEIIIYQQQgghhBCScNAZTgghhBBCCCGEEEIIIYQQQgghJOGgM5wQQgghhBBCCCGEEEIIIYQQQogkGv8P2AUnTUNhTSgAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 2000x1200 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Original values\n",
-    "# Scenario 1: Signal-to-noise effects\n",
-    "config_scenario_1 = {\n",
-    "    'total_cell': [2000],\n",
-    "    # 'total_cell': [500, 1000, 2000, 5000, 10000, 25000],\n",
-    "    'nclones': [0.4],\n",
-    "    'p_outlier': [0.0],\n",
-    "    'p': [0.5],\n",
-    "    'cov': [0],\n",
-    "    'mean_inc': [25, 50, 100, 200],\n",
-    "    'var_inc': [100, 200, 500],\n",
-    "    'i': [0, 1, 2, 3, 4, 5],\n",
-    "}\n",
-    "\n",
-    "import sys\n",
-    "sys.path.append(\"../../\")\n",
-    "\n",
-    "from sklearn.model_selection import ParameterGrid\n",
-    "from dextrademixer.utils import DextramerSimulator\n",
-    "import hashlib\n",
-    "\n",
-    "params = ParameterGrid(config_scenario_1)[0]\n",
-    "total_cell = params['total_cell']\n",
-    "nclones = int(params['nclones'] * params['total_cell'])\n",
-    "p_outlier = params['p_outlier']\n",
-    "p = params['p']\n",
-    "cov = params['cov']\n",
-    "mean_inc = params['mean_inc']\n",
-    "var_inc = params['var_inc']\n",
-    "reps = params['i']\n",
-    "\n",
-    "\n",
-    "for rep in config_scenario_1['i']:\n",
-    "    fig = plt.figure(figsize=(5 * len(config_scenario_1['mean_inc']), 4 * len(config_scenario_1['var_inc']), ))\n",
-    "    i = 1\n",
-    "    \n",
-    "    for _, var_inc in enumerate(config_scenario_1['var_inc']):\n",
-    "        for _, mean_inc in enumerate(config_scenario_1['mean_inc']):\n",
-    "            params['mean_inc'] = mean_inc\n",
-    "            params['var_inc'] = var_inc\n",
-    "        \n",
-    "            sim_config = f'{total_cell}_{nclones}_{p_outlier}_{p}_{cov}_{mean_inc}_{var_inc}_{rep}'\n",
-    "            title = f'total_cell={total_cell}_nclones={nclones}_p_outlier={p_outlier}_p={p}_cov={cov}_mean_inc={mean_inc}_var_inc={var_inc}_i={rep}'\n",
-    "            # Use sim_hash to ensure reproducibility, but have variance in seed\n",
-    "            sim_hash = hashlib.sha256(sim_config.encode('utf-8')).digest()\n",
-    "            seed = int.from_bytes(sim_hash[:4], byteorder='little')\n",
-    "        \n",
-    "            \n",
-    "            sim = DextramerSimulator()\n",
-    "        \n",
-    "            mdata1 = sim.simulate_pmhc_data_from_distribution(total_cells=total_cell,\n",
-    "                                                            nof_clones=nclones,\n",
-    "                                                            p_binding_outlier=p_outlier,\n",
-    "                                                            binding_ratio=p,\n",
-    "                                                            mean_inc=mean_inc,\n",
-    "                                                            var_inc=var_inc,\n",
-    "                                                            simulate_neg_control=True,\n",
-    "                                                            use_clonotype_cov=cov,\n",
-    "                                                            plot_data=False,\n",
-    "                                                            rng_key=seed)\n",
-    "            x = mdata1['gex'].X[:, 0]\n",
-    "            x_neg_ctrl = mdata1['gex'][:, 'neg_control'].X\n",
-    "            hue = mdata1['airr'].obs['is_binder']\n",
-    "            x_log = np.log(x + 1)  # Transform to log scale, roughly normal distributed\n",
-    "            zscore = (x_log - x_log.mean()) / x_log.std()\n",
-    "            x_no_outliers = x[zscore < 4]\n",
-    "            hue_no_outliers = hue[zscore < 4]\n",
-    "            outlier_thr = x_no_outliers.max()\n",
-    "    \n",
-    "            # Best theoretical F1\n",
-    "            precision, recall, thresholds = precision_recall_curve(hue, x)\n",
-    "            f1 = 2 * precision * recall / (precision + recall + 1e-12)\n",
-    "            best_idx = np.argmax(f1)\n",
-    "            best_threshold = (thresholds[best_idx] if best_idx < len(thresholds) else scores.max() + 1e-6)\n",
-    "            best_f1 = f1[best_idx]\n",
-    "    \n",
-    "            plt.subplot(len(config_scenario_1['var_inc']), len(config_scenario_1['mean_inc']), i)\n",
-    "            i += 1\n",
-    "        \n",
-    "            # sns.histplot(x=x, stat='percent', element='step', binrange=(0, 100), hue=hue, legend=False, log_scale=False, discrete=True)\n",
-    "            sns.histplot(x=x, stat='percent', element='step', binrange=(0, 1000), hue=hue, legend=False, log_scale=False, discrete=True)\n",
-    "            # sns.histplot(x_neg_ctrl, discrete=True, binrange=(0, 50), stat='percent', element='step', label='neg_ctrl')\n",
-    "            # plt.axvline(best_threshold, c='r', ls=':')\n",
-    "            sns.despine()\n",
-    "            plt.title(f'{sim_config}\\nmean_inc={mean_inc}, var_inc={var_inc}, best_f1={best_f1:.2f}')\n",
-    "    plt.suptitle(f'Repetition {rep}')\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:dextrademixer]",
-   "language": "python",
-   "name": "conda-env-dextrademixer-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/experiments/synthetic_benchmark/run_beamt_mp.py b/experiments/synthetic_benchmark/run_beamt_mp.py
deleted file mode 100644
index 5c1d048..0000000
--- a/experiments/synthetic_benchmark/run_beamt_mp.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import argparse
-import os
-import warnings
-
-from concurrent.futures import ThreadPoolExecutor
-
-import pandas as pd
-import muon as mu
-
-from tqdm import tqdm
-
-import sys
-sys.path.append("../../")
-from dextrademixer.model import BEAMT
-from dextrademixer.utils import calculate_metrics
-
-
-def main(args):
-    input_dir = os.path.join('benchmarks', args.scenario, 'simulation')
-    input_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith('.h5mu')]
-    base_dir = os.path.join('benchmarks', args.scenario)
-
-    def run_beamt(f_in):
-        data_config = os.path.basename(f_in).replace('.h5mu', '')
-        config = "BEAMT" + '_' + data_config
-
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore")
-            mdata = mu.read(f_in)
-
-        y_true = mdata.mod["airr"].obs["is_binder"]
-
-        mixer = BEAMT()
-        mixer.preprocess_model_data(mdata, "pmhc1", neg_ctrl_key="neg_control")
-        mixer.fit()
-
-        results = []
-        posterior_params = [(0.01, None), (0.02, None), (0.05, None), (0.1, None), (0.2, None), (None, 0.5)]
-
-        for target_fdr, threshold in posterior_params:
-            p_pred, assignment = mixer.predict_posterior_class(target_fdr=target_fdr, threshold=threshold)
-            results_dict = {}
-            results_dict['config'] = config
-            results_dict['model_config'] = "BEAMT"
-            results_dict['data_config'] = data_config
-            results_dict.update({'sim_' + k: v for k, v in mdata['gex'].uns['sim_params'].items()})
-            results_dict.update({'posterior_target_fdr': target_fdr, 'posterior_threshold': threshold,
-                                 "posterior_config": f"{target_fdr}_{threshold}"})
-
-            results_dict.update(calculate_metrics(y_true, p_pred, assignment))
-            results.append(results_dict)
-
-        results = pd.DataFrame(results)
-        csv_dir = os.path.join(base_dir, 'csv')
-        os.makedirs(csv_dir, exist_ok=True)
-        results.to_csv(csv_dir + f"/{config}.csv")
-
-    with ThreadPoolExecutor() as ex:
-        list(tqdm(ex.map(run_beamt, input_files), total=len(input_files)))
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run tool on all data from a scenario.")
-    # IO parameters
-    parser.add_argument("--scenario", type=str, default="scenario_test", help="Name of scenario")
-    args = parser.parse_args()
-    main(args)
diff --git a/experiments/synthetic_benchmark/run_dextrademixer_mp.py b/experiments/synthetic_benchmark/run_dextrademixer_mp.py
deleted file mode 100644
index d313f55..0000000
--- a/experiments/synthetic_benchmark/run_dextrademixer_mp.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import argparse
-import multiprocessing
-import os
-import warnings
-import pickle
-
-import pandas as pd
-import numpyro
-import muon as mu
-
-from tqdm import tqdm
-
-import sys
-sys.path.append("../../")
-from dextrademixer.model import DextraDemixer
-from dextrademixer.utils import (convert_str_to_bool_and_none, get_slurm_cpu_count, calculate_metrics,
-                                 guess_worker_mem_limit_mb, init_worker)
-
-
-def parse_arguments():
-    parser = argparse.ArgumentParser(description="Run tool on all data from a scenario.")
-    # IO parameters
-    parser.add_argument("--scenario", type=str, default="scenario_test",
-                        help="Name of scenario")
-    parser.add_argument("--use_mp", type=str, default=True,
-                        help="Whether to use multiprocessing")
-
-    # Data parameters
-    parser.add_argument("--gex_key", type=str, default="gex",
-                        help="Key for modality where pMHC counts are stored")
-    parser.add_argument("--airr_key", type=str, default='airr',
-                        help="Key for modality where TCR data is stored")
-    parser.add_argument("--pmhc_key", type=str, default="pmhc1",
-                        help="Key for pMHC counts, expected to be in 'gex' modality")
-    parser.add_argument("--label_key", type=str, default="is_binder",
-                        help="Key for labels, expected to be in 'airr' modality")
-
-    # Model parameters
-    parser.add_argument("--model_type", default='mixturemodelkmeans', help="Model type",
-                        choices=["mixturemodelkmeans", "mixturemodel"])
-    parser.add_argument("--mode", type=str, default="C",
-                        help="Processing mode. 'I' -> independent concentration parameter for signal and noise data. "
-                             "'C' -> clonotype level concentration parameter", choices=["I", "C"])
-    parser.add_argument("--neg_ctrl_key", type=str, default=None,
-                        help="Key for negative control counts. If not None, enables model to use negative control data for "
-                             "fitting. Expected to be in 'gex' modality. If None, no negative control data is used.")
-    parser.add_argument("--ir_clone_key", type=str, default='clone_id',
-                        help="Key for clonotype information. If not None, enables model to have different "
-                             "mixture coefficients for each clonotype. Expected to be in 'airr' modality. ")
-    parser.add_argument("--alpha_model", type=str, default="kmeans",
-                        choices=["overdispersion", "kmeans"],
-                        help="Modeling of the alpha parameter. Options: 'overdispersion', 'kmeans'.")
-    parser.add_argument("--hyperprior", type=float, default=1e0,
-                        help="Prior for scale parameter of kmeans model and HalfCauchy for overdispersion model")
-    parser.add_argument("--outlier_threshold", type=float, default=4.0,
-                        help="Threshold for outlier removal based on log transformed z-score.")
-
-    # Posterior class assignment parameters
-    # parser.add_argument("--target_fdr", type=float, default=None,
-    #                     help="Target FDR for posterior class assignment. If None, uses threshold instead.")
-    # parser.add_argument("--threshold", type=float, default=None,
-    #                     help="Threshold for posterior class assignment. If None, uses target_fdr instead.")
-
-    # Optimization parameters
-    parser.add_argument("--seed", type=int, default=42, help="Random seed")
-    parser.add_argument("--maxiter", type=int, default=50, help="Number of iterations for optimization")
-    parser.add_argument("--lr", type=float, default=1e-2, help="Learning rate")
-    return parser.parse_args()
-
-
-def parse_data_config_from_name(filename):
-    data_config_dict = {}
-    parts = filename.replace('.h5mu', '').split('_')
-    data_config_dict['total_cell'] = int(parts[0])
-    data_config_dict['nof_clones'] = int(parts[1])
-    data_config_dict['p_binding_outlier'] = float(parts[2])
-    data_config_dict['binding_ratio'] = float(parts[3])
-    data_config_dict['use_clonotype_cov'] = parts[4] == 'True'
-    data_config_dict['mean_inc'] = int(parts[5])
-    data_config_dict['var_inc'] = int(parts[6])
-    data_config_dict['i'] = int(parts[7])
-    return data_config_dict
-
-
-def run_inference_star(t):
-    """ Helper for multiprocessing; unpack args """
-    f, args = t
-    run_inference(f, args)
-
-
-def run_inference(f_in, args):
-    model_config = (f"{args.model_type}_{args.mode}_{args.neg_ctrl_key}_{args.ir_clone_key}_"
-                    f"{args.alpha_model}_{args.hyperprior}_{args.lr}")
-    data_config = os.path.basename(f_in).replace('.h5mu', '')
-    config = model_config + '_' + data_config
-
-    base_dir = os.path.join('benchmarks', args.scenario)
-    if os.path.exists(os.path.join(base_dir, 'csv', f"{config}.csv")):
-        print(f"Results for {config} already exist, skipping...")
-        return
-
-    numpyro.set_host_device_count(1)
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-        mdata = mu.read(f_in)
-
-    model = DextraDemixer(model_type=args.model_type, mode=args.mode, alpha_model=args.alpha_model)
-    model.preprocess_model_data(mdata, pmhc_key=args.pmhc_key, gex_key=args.gex_key, neg_ctrl_key=args.neg_ctrl_key,
-                                ir_clone_key=args.ir_clone_key, outlier_threshold=args.outlier_threshold, )
-
-    model.model._model_config["overdispersion_scale_prior"] = args.hyperprior
-    model.model._model_config["var_hyperprior"] = args.hyperprior
-    opt_params = {"maxiter": args.maxiter, "nof_inits": 10, "adam": {"init_value": args.lr, }, }
-    model.fit_svi(svi_config=opt_params, nof_inits=opt_params["nof_inits"], rng_key=args.seed, return_loss=True)
-
-    # Save model
-    save_model_dir = os.path.join(base_dir, 'saved_models')
-    os.makedirs(save_model_dir, exist_ok=True)
-    model.save_model(os.path.join(save_model_dir, f"{config}.pkl"))
-
-    # Log metrics
-    y_true = mdata.mod[args.airr_key].obs[args.label_key].astype(int).values
-    posterior_samples = model.get_posterior_samples(num_samples=1000, seed=args.seed)
-
-    results = []
-    # Predict posterior class with different methods, (target_fdr, threshold, quantile, cred_intvl)
-    posterior_params = [
-        (0.01, None, None, None), (0.02, None, None, None), (0.05, None, None, None), (0.10, None, None, None),
-        (0.20, None, None, None), (None, 0.50, None, None), (0.05, None, 0.50, None), (0.05, None, 0.40, None),
-        (0.05, None, 0.30, None), (0.05, None, 0.20, None), (0.05, None, 0.10, None), (0.05, None, None, 0.50),
-        (0.05, None, None, 0.60), (0.05, None, None, 0.70), (0.05, None, None, 0.80), (0.05, None, None, 0.90),]
-
-    for target_fdr, threshold, quantile, cred_intvl in posterior_params:
-        p_pred, assignment = model.predict_posterior_class(target_fdr=target_fdr, threshold=threshold,
-                                                          quantile=quantile, cred_intvl=cred_intvl)
-        posterior_config = f"{target_fdr}_{threshold}_{quantile}_{cred_intvl}"
-        # Plot results
-        os.makedirs(os.path.join(base_dir, 'figs', config), exist_ok=True)
-        sim_params = mdata[args.gex_key].uns['sim_params']
-        additional_text = (f"p_binder: {sim_params['binding_ratio']}\n"
-                           f"q_noise: {sim_params['mean_non_binder']:.2f}, "
-                           f"alpha_noise: {sim_params['concentration_non_binder']:.2f}\n"
-                           f"q_signal: {sim_params['mean_pos']:.2f}, "
-                           f"alpha_signal: {sim_params['concentration_pos']:.2f}")
-        model.plot_results(assignment, p_pred, y_true if args.label_key is not None else None, args.seed,
-                           config + '/' + posterior_config, additional_text=additional_text,
-                           save_dir=os.path.join(base_dir, 'figs'), show=False)
-
-        results_dict = {'config': config, 'model_config': model_config, 'data_config': data_config,
-                        'posterior_target_fdr': target_fdr, 'posterior_threshold': threshold,
-                        'posterior_quantile': quantile, 'posterior_cred_intvl': cred_intvl,
-                        "posterior_config": posterior_config,
-                        "posterior_q0": posterior_samples["q"][0],
-                        "posterior_q1": posterior_samples["q"][1],
-                        "posterior_w0": posterior_samples["w_mean_over_cells"][0],
-                        "posterior_w1": posterior_samples["w_mean_over_cells"][1],
-                        "posterior_alpha0": posterior_samples["alpha_mean_over_cells"][0],
-                        "posterior_alpha1": posterior_samples["alpha_mean_over_cells"][1],
-                        }
-        results_dict.update({'model_'+k: v for k, v in vars(args).items()})
-        results_dict.update({'sim_'+k: v for k, v in mdata[args.gex_key].uns['sim_params'].items()})
-        results_dict.update(calculate_metrics(y_true, p_pred, assignment))
-        results.append(results_dict)
-
-    results = pd.DataFrame(results)
-    csv_dir = os.path.join(base_dir, 'csv')
-    os.makedirs(csv_dir, exist_ok=True)
-    results.to_csv(csv_dir + f"/{config}.csv")
-
-
-def main():
-    args = parse_arguments()
-    args = convert_str_to_bool_and_none(args)
-    input_dir = os.path.join('benchmarks', args.scenario, 'simulation')
-    input_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith('.h5mu')]
-
-    if args.use_mp:
-        nworkers = get_slurm_cpu_count()
-        mem_per_worker_mb = guess_worker_mem_limit_mb(nworkers)
-
-        ctx = multiprocessing.get_context("spawn")
-        with ctx.Pool(nworkers, initializer=init_worker, initargs=(mem_per_worker_mb,), maxtasksperchild=1,) as pool:
-            try:
-                list(tqdm(pool.imap_unordered(run_inference_star, [(f, args) for f in input_files]),
-                          total=len(input_files), desc="Running inference"))
-            except Exception as e:
-                # ensures .finished is NOT written and SLURM sees non-zero
-                print(f"ERROR: worker failed (likely OOM): {e}", file=sys.stderr)
-                sys.exit(1)
-    else:
-        for f in tqdm(input_files, desc="Running inference"):
-            run_inference_star((f, args))
-
-    print("DONE!")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/experiments/synthetic_benchmark/simulate_scenarios.py b/experiments/synthetic_benchmark/simulate_scenarios.py
deleted file mode 100644
index 12c24ae..0000000
--- a/experiments/synthetic_benchmark/simulate_scenarios.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import sys
-import hashlib
-import argparse
-import os
-import yaml
-
-sys.path.append("../../")
-
-import matplotlib.pyplot as plt
-from tqdm import tqdm
-from sklearn.model_selection import ParameterGrid
-
-from dextrademixer.utils import DextramerSimulator, convert_str_to_bool_and_none
-
-import warnings
-warnings.filterwarnings(
-    "ignore",
-    message=".*pull obs/var columns from individual modalities.*",
-    category=FutureWarning,
-)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--scenario", type=str, help="Scenario name", default="scenario_test")
-
-    args = parser.parse_args()
-    args = convert_str_to_bool_and_none(args)
-
-    save_path = os.path.join('benchmarks', args.scenario, 'simulation')
-    os.makedirs(save_path, exist_ok=True)
-
-    with open(os.path.join('benchmarks', args.scenario, 'config.yaml'), "r") as f:
-        sim_config = yaml.safe_load(f)
-    param_grid = ParameterGrid(sim_config)
-    print("Number of parameter sets: ", len(param_grid))
-
-    for params in tqdm(param_grid):
-        if 'nof_clones' not in params and 'frac_clone' in params:
-            params['nof_clones'] = int(params['frac_clone'] * params['total_cell'])
-        params = argparse.Namespace(**params)
-        sim_config = (f"{params.total_cell}_{params.nof_clones}_{params.p_binding_outlier}_{params.binding_ratio}_"
-                      f"{params.use_clonotype_cov}_{params.mean_inc}_{params.var_inc}_{params.i}")
-        if os.path.exists(os.path.join(save_path, f"{sim_config}.h5mu")):
-            print(f"Simulation with config {sim_config} already exists, skipping...")
-            continue
-
-        # Use sim_hash to ensure reproducibility, but have variance in seed
-        sim_hash = hashlib.sha256(sim_config.encode('utf-8')).digest()
-        seed = int.from_bytes(sim_hash[:4], byteorder='little')
-
-        plt.ioff()
-        sim = DextramerSimulator()
-        mdata, fig = sim.simulate_pmhc_data_from_distribution(total_cells=params.total_cell,
-                                                              nof_clones=params.nof_clones,
-                                                              p_binding_outlier=params.p_binding_outlier,
-                                                              binding_ratio=params.binding_ratio,
-                                                              mean_inc=params.mean_inc,
-                                                              var_inc=params.var_inc,
-                                                              simulate_neg_control=True,
-                                                              use_clonotype_cov=params.use_clonotype_cov,
-                                                              plot_data=True,
-                                                              rng_key=seed)
-
-        mdata.write(os.path.join(save_path, f"{sim_config}.h5mu"))
-        fig.savefig(os.path.join(save_path, f"{sim_config}.pdf"))
-        plt.close()
-
-    print(f"Finished simulations for scenario {args.scenario}.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/experiments/synthetic_benchmark/simulation/test.h5mu b/experiments/synthetic_benchmark/simulation/test.h5mu
deleted file mode 100644
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diff --git a/experiments/synthetic_benchmark/slurm_run_simulation.sh b/experiments/synthetic_benchmark/slurm_run_simulation.sh
deleted file mode 100755
index 163cb81..0000000
--- a/experiments/synthetic_benchmark/slurm_run_simulation.sh
+++ /dev/null
@@ -1,83 +0,0 @@
-#!/bin/bash
-# Usage examples:
-#   bash slurm_run_smk.sh                # cpu_normal
-#   bash slurm_run_smk.sh --preemptible  # cpu_preemptible
-#   bash slurm_run_smk.sh --scenario scenarioX --threads 8 --ram 32 --no-mp
-
-set -euo pipefail
-
-# ---- Default values
-SCENARIO="scenario1"
-NUM_THREADS=16
-RAM_PER_THREAD=16
-USE_MP=True
-PROFILE_SUFFIX=".slurm"
-
-# ---- Parse flags
-while [[ $# -gt 0 ]]; do
-    case "$1" in
-        --scenario)
-            SCENARIO="$2"
-            shift 2
-            ;;
-        --threads)
-            NUM_THREADS="$2"
-            shift 2
-            ;;
-        --ram)
-            RAM_PER_THREAD="$2"
-            shift 2
-            ;;
-        --preemptible)
-            PROFILE_SUFFIX=".slurm_preemptible"
-            shift
-            ;;
-        --no-mp)
-            USE_MP=False
-            shift
-            ;;
-        -h|--help)
-            echo "Usage: bash $0 [--scenario NAME] [--threads N] [--ram GB] [--preemptible] [--no-mp]"
-            exit 0
-            ;;
-        *)
-            echo "Unknown argument: $1" >&2
-            exit 1
-            ;;
-    esac
-done
-
-# ---- Paths
-PATH_BASE="$(dirname "$(realpath "$0")")/../.."
-PATH_LOGS="."
-PROFILE_DIR="${PATH_BASE}/experiments/${PROFILE_SUFFIX}"
-
-
-mkdir -p "${PATH_LOGS}/logs/slurm_logs"
-mkdir -p "${PATH_LOGS}/logs/jobs"
-
-DATE_TIME=$(date +"%Y-%m-%d_%H-%M-%S")
-JOB_NAME="SKM_dex_benchmark_${SCENARIO}_${DATE_TIME}"
-
-echo "#!/bin/bash
-#SBATCH -J ${JOB_NAME}
-#SBATCH -o ${PATH_LOGS}/logs/slurm_logs/${JOB_NAME}.out
-#SBATCH -e ${PATH_LOGS}/logs/slurm_logs/${JOB_NAME}.err
-#SBATCH -p cpu_p
-#SBATCH -t 1-00:00:00
-#SBATCH -c 4
-#SBATCH --mem=24G
-#SBATCH --qos=cpu_normal
-#SBATCH --nice=0
-
-source ~/.bash_profile
-conda activate dextrademixer
-cd ${PATH_BASE}/experiments/synthetic_benchmark
-
-snakemake -s snakefile_run_simulation.smk --unlock
-snakemake -s snakefile_run_simulation.smk -c4 --profile ${PROFILE_DIR} --cluster-status ${PROFILE_DIR}/status.py --conda-frontend conda -p --config SCENARIO=${SCENARIO} NUM_THREADS=${NUM_THREADS} RAM_PER_THREAD=${RAM_PER_THREAD} USE_MP=${USE_MP}
-
-
-
-" > ${PATH_LOGS}/logs/jobs/${JOB_NAME}.cmd
-sbatch ${PATH_LOGS}/logs/jobs/${JOB_NAME}.cmd
diff --git a/experiments/synthetic_benchmark/slurm_run_timing.sh b/experiments/synthetic_benchmark/slurm_run_timing.sh
deleted file mode 100755
index c8a1e3f..0000000
--- a/experiments/synthetic_benchmark/slurm_run_timing.sh
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/bin/bash
-
-
-PATH_BASE="$(dirname "$(realpath "$0")")/../.."
-PATH_LOGS="${PATH_BASE}/logs/slurm_logs"
-
-mkdir -p "${PATH_LOGS}"
-mkdir -p "${PATH_BASE}/logs/jobs"
-
-DATE_TIME=$(date +"%Y-%m-%d_%H-%M-%S")
-JOB_NAME="SKM_time_${DATE_TIME}"
-
-echo "#!/bin/bash
-#SBATCH -J ${JOB_NAME}
-#SBATCH -o ${PATH_BASE}/logs/slurm_logs/${JOB_NAME}.out
-#SBATCH -e ${PATH_BASE}/logs/slurm_logs/${JOB_NAME}.err
-#SBATCH -p cpu_p
-#SBATCH -t 10-00:00:00
-#SBATCH -c 8 
-#SBATCH --mem=24G
-#SBATCH --qos=cpu_long
-#SBATCH --nice=10000
-
-source ~/.bash_profile
-conda activate dextraDemixer 
-cd ${PATH_BASE}/experiments/synthetic_benchmark
-
-snakemake aggregate_results -s snakefile_run_timing -c8 --profile ${PATH_BASE}/experiments/.slurm/ --conda-frontend conda
-
-
-" > ${PATH_BASE}/logs/jobs/${JOB_NAME}.cmd
-sbatch ${PATH_BASE}/logs/jobs/${JOB_NAME}.cmd
diff --git a/experiments/synthetic_benchmark/snakefile_run_simulation.smk b/experiments/synthetic_benchmark/snakefile_run_simulation.smk
deleted file mode 100644
index 480437f..0000000
--- a/experiments/synthetic_benchmark/snakefile_run_simulation.smk
+++ /dev/null
@@ -1,90 +0,0 @@
-from snakemake.utils import min_version
-min_version("6.0")
-
-
-# Configuration
-SCENARIO = config.get("SCENARIO", "scenario1")
-NUM_THREADS = int(config.get("NUM_THREADS", 16))
-RAM_PER_THREAD = int(config.get("RAM_PER_THREAD", 16))
-USE_MP = config.get("USE_MP", "False") == "True"
-if not USE_MP:
-    NUM_THREADS = 1
-
-TOOL_CONFIGS = ([f"{model_type}-{mode}-{neg_ctrl_key}-{ir_clone_key}-{alpha_model}-{hyperprior}-{lr}"
-                for model_type in ["mixturemodelkmeans"]
-                for mode in ["I", "C"]
-                for neg_ctrl_key in [None, "neg_control"]
-                for ir_clone_key in [None, "clone_id"]
-                for alpha_model in ["kmeans", "overdispersion"]
-                for hyperprior in [1e2, 1e1, 1e0]
-                for lr in [1e-2]
-                if ((mode == "C" and ir_clone_key is not None) or mode != "C")
-                   and
-                   ((alpha_model == "kmeans" and hyperprior > 1e0) or (alpha_model == "overdispersion" and hyperprior <= 1e1))
-                ])
-
-# TOOL_CONFIGS = ([f"{model_type}_{mode}_{neg_ctrl_key}_{ir_clone_key}_{alpha_model}_{hyperprior}_{lr}"
-#                 for model_type in ["mixturemodelkmeans"]
-#                 for mode in ["I"]
-#                 for neg_ctrl_key in ["neg_control"]
-#                 for ir_clone_key in ["clone_id"]
-#                 for alpha_model in ["overdispersion"]
-#                 for hyperprior in [1e-1, 1e-2]
-#                 for lr in [1e-2]
-#                 if (mode == "C" and ir_clone_key is not None) or mode != "C"
-#                 ])
-
-rule all:
-    input:
-        expand("benchmarks/{scenario}/aggregated_results.csv",scenario=SCENARIOS)
-
-
-rule run_simulation:
-    priority: 30
-    input:
-        # No input files required for this script
-    output:
-        "benchmarks/{scenario}/simulation.finished"
-    conda:
-        "environment.yaml"
-    shell:
-        "python simulate_scenarios.py --scenario {wildcards.scenario} "
-        "&& touch {output}"
-
-
-rule run_dextrademixer:
-    priority: 20
-    input:
-        "benchmarks/{scenario}/simulation.finished"
-    output:
-        "benchmarks/{scenario}/{model_type}-{mode}-{neg_ctrl_key}-{ir_clone_key}-{alpha_model}-{hyperprior}-{lr}.finished"
-    conda:
-        "environment.yaml"
-    threads:
-        min(32, NUM_THREADS)  # Cluster can only request 32 cores at once
-    params:
-        use_mp=USE_MP
-    resources:
-        c=min(32, NUM_THREADS),
-        mem_mb=RAM_PER_THREAD * 1000 * min(32, NUM_THREADS),
-        mem=f"{RAM_PER_THREAD * min(32, NUM_THREADS)}G",
-    shell:
-        "python run_dextrademixer_mp.py --scenario {wildcards.scenario} --model_type {wildcards.model_type} "
-        "--mode {wildcards.mode} --neg_ctrl_key {wildcards.neg_ctrl_key} --ir_clone_key {wildcards.ir_clone_key} "
-        "--alpha_model {wildcards.alpha_model} --hyperprior {wildcards.hyperprior} --lr {wildcards.lr} --maxiter 5000 --use_mp {params.use_mp} "
-        "&& touch {output}"
-
-
-rule aggregate_results:
-    priority: 10
-    input:
-        lambda wildcards: expand(
-            f"benchmarks/{wildcards.scenario}/{{tool_config}}.finished",
-            tool_config=TOOL_CONFIGS
-        )
-    output:
-        "benchmarks/{scenario}/aggregated_results.csv"
-    conda:
-        "environment.yaml"
-    shell:
-        "python aggregate_results.py --scenario {wildcards.scenario}"
diff --git a/experiments/synthetic_benchmark/snakefile_run_timing.smk b/experiments/synthetic_benchmark/snakefile_run_timing.smk
deleted file mode 100644
index 016f0e4..0000000
--- a/experiments/synthetic_benchmark/snakefile_run_timing.smk
+++ /dev/null
@@ -1,76 +0,0 @@
-from snakemake.utils import min_version
-min_version("6.0")
-
-
-# Configuration
-tools = ["dextramixer"] 
-reps = 5
-
-# Scenario 4: Timing analyse on reserved node
-scenario = {
-    "cell_list": [2000, 5000, 1000, 25000, 50000],
-    "frac_clone_list": [0.01, 0.1, 0.25, 0.5, 0.75, 1.0],
-    "p_list": [0.1],
-    "cov_list": [None],
-    "p_outlier_list": [0], 
-    "mean_fold_increase": [5],
-    "var_fold_increase": [2], 
-}
-
-
-def aggregate_simulation():
-    sim_name = [f"ncell{cell}_nclone{int(clone*cell)}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}"
-                for cell in scenario["cell_list"]
-                for clone in scenario["frac_clone_list"]
-                for po in scenario["p_outlier_list"]
-                for p in scenario["p_list"]
-                for cov in scenario["cov_list"]
-                for mean in scenario["mean_fold_increase"]
-                for var in scenario["var_fold_increase"]
-                for i in range(reps)]
-    return sim_name
-
-
-rule all:
-    input:
-        "results/aggregated_results_timing.csv"
-
-rule run_simulation:
-    priority: 30
-    input:
-        # No input files required for this script
-    output:
-        h5mu = "simulation/sim_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.h5mu",
-        pdf = "simulation/sim_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.pdf"
-    params:
-        reps = reps
-    conda:
-        "environment.yaml"
-    shell:
-        "python create_data_mean_variance_fold_increase.py {output.h5mu} {output.pdf} "
-        "{wildcards.cell} {wildcards.clone} {wildcards.po} {wildcards.p} {wildcards.cov} "
-        "{wildcards.mean} {wildcards.var} {params.reps}"
-
-rule run_tool:
-    priority: 20
-    input:
-        "simulation/sim_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.h5mu"
-    output:
-        "prediction/{tool}_ncell{cell}_nclone{clone}_poutlier{po}_pbinder{p}_negctr1_cov{cov}_meaninc{mean}_varinc{var}_rep{i}.pred.csv"
-    conda:
-        "environment.yaml"
-    shell:
-        "python run_{wildcards.tool}.py {input} {output}"
-
-rule aggregate_results:
-    priority: 10
-    input:
-        expand("prediction/{tool}_{sim_name}.pred.csv",
-               tool=tools,
-               sim_name=aggregate_simulation())
-    output:
-        "results/aggregated_results.csv"
-    conda:
-        "environment.yaml"
-    shell:
-        "python aggregate_results.py prediction {output}"

From 30b1f270c33d60eb25940ae0a53d3243985b5791 Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Thu, 18 Jun 2026 19:57:42 +0200
Subject: [PATCH 37/39] feature: new way of running synthetic benchmark by
 using apptainer

---
 .../synthetic_benchmark/aggregate_results.py  |  97 +---------
 experiments/synthetic_benchmark/env.def       |  16 ++
 .../synthetic_benchmark/environment.yaml      |  14 +-
 experiments/synthetic_benchmark/run_beam.py   |  58 ++++++
 .../synthetic_benchmark/run_dextrademixer.py  | 166 ++++++++++++++++++
 .../synthetic_benchmark/simulate_data.py      |  71 ++++++++
 .../synthetic_benchmark/slurm_benchmark.sh    | 100 +++++++++++
 .../snakefile_benchmark.smk                   | 129 ++++++++++++++
 8 files changed, 557 insertions(+), 94 deletions(-)
 create mode 100644 experiments/synthetic_benchmark/env.def
 create mode 100644 experiments/synthetic_benchmark/run_beam.py
 create mode 100644 experiments/synthetic_benchmark/run_dextrademixer.py
 create mode 100644 experiments/synthetic_benchmark/simulate_data.py
 create mode 100644 experiments/synthetic_benchmark/slurm_benchmark.sh
 create mode 100644 experiments/synthetic_benchmark/snakefile_benchmark.smk

diff --git a/experiments/synthetic_benchmark/aggregate_results.py b/experiments/synthetic_benchmark/aggregate_results.py
index 7112e1d..cc0c93a 100644
--- a/experiments/synthetic_benchmark/aggregate_results.py
+++ b/experiments/synthetic_benchmark/aggregate_results.py
@@ -1,94 +1,13 @@
-import glob
+import argparse
 import sys
-import os
-import re
+sys.path.append('../..')
 
-import pandas as pd
-import numpy as np
-
-from sklearn.metrics import classification_report, roc_auc_score, matthews_corrcoef, confusion_matrix
-
-
-def parse_filename(filename):
-    # Remove the extension from the filename
-    base_name = filename.rsplit('.', 1)[0]
-
-    # Define the regex pattern
-    pattern = r"(?P<method>^[a-zA-Z]+)_(?P<key_values>(?:[a-zA-Z]+[\d\.]+_*)+)"
-
-    # Match the pattern in the base name
-    match = re.match(pattern, base_name)
-
-    if not match:
-        return None
-
-    method = match.group("method")
-    key_values_str = match.group("key_values")
-
-    # Define another regex pattern to extract key-value pairs
-    key_value_pattern = r"([a-zA-Z]+)([\d\.]+)"
-    key_value_matches = re.findall(key_value_pattern, key_values_str)
-
-    # Convert key-value matches to a dictionary
-    key_values = {key: float(value) if '.' in value else int(value) for key, value in key_value_matches}
-
-    return method, key_values
-
-
-def main(f_in_dir, f_out):
-    d = {"model":[], "threshold":[], "rep":[], "ncell":[], "nclone":[], "pbinder":[], "cov":[], "meaninc":[], "varinc":[],
-         "wprecision":[], "wrecall":[], "wf1":[], "acc":[], "wauc":[], "mcc":[], "tpr":[], "fdr":[], }
-
-    for f in glob.glob(os.path.join(f_in_dir, "*")):
-        model_name, sim_params = parse_filename(os.path.basename(f))
-
-        errors = {"file": [], "model": []}
-        df = pd.read_csv(f)
-        for name, group in df.groupby(["model", "thresh"]):
-
-            y_true = group["true_binder"]
-            y_pred = group["assignment"]
-            p_pred = group["p"]
-
-            if np.isnan(p_pred).any():
-                errors["file"].append(f)
-                errors["model"].append(name[0])
-                continue
-
-            cr = classification_report(y_true, y_pred, output_dict=True, labels=[0,1])
-            auc = roc_auc_score(y_true, p_pred, average="weighted")
-            mcc = matthews_corrcoef(y_true, y_pred)
-            tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
-            tpr = tp / (tp + fn)
-            fdr = fp / (tp + fp)
-
-            d["model"].append(name[0])
-            d["threshold"].append(name[1])
-            d["rep"].append(sim_params.get("rep", None))
-            d["ncell"].append(sim_params.get("ncell", None))
-            d["nclone"].append(sim_params.get("nclone", None))
-            d["pbinder"].append(sim_params.get("pbinder", None))
-            d["cov"].append(sim_params.get("cov", None))
-            d["meaninc"].append(sim_params.get("meaninc", None))
-            d["varinc"].append(sim_params.get("varinc", None))
-
-            d["wprecision"].append(cr["weighted avg"]["precision"])
-            d["wrecall"].append(cr["weighted avg"]["recall"])
-            d["wf1"].append(cr["weighted avg"]["f1-score"])
-            d["acc"].append(cr["accuracy"])
-            d["wauc"].append(auc)
-            d["mcc"].append(mcc)
-            d["tpr"].append(tpr)
-            d["fdr"].append(fdr)
-
-    df = pd.DataFrame.from_dict(d)
-    df.to_csv(f_out)
-
-    df_error = pd.DataFrame.from_dict(errors)
-    df_error.to_csv(f_out+".errors")
+from dextrademixer.utils import aggregate_csv
 
 
 if __name__ == "__main__":
-    f_in_dir = sys.argv[1]
-    f_out = sys.argv[2]
-    main(f_in_dir, f_out)
\ No newline at end of file
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--f_ins", nargs="+", required=True)
+    parser.add_argument("--f_out", required=True)
+    args = parser.parse_args()
+    aggregate_csv(fps=args.f_ins, output_path=args.f_out, rerun=True)
diff --git a/experiments/synthetic_benchmark/env.def b/experiments/synthetic_benchmark/env.def
new file mode 100644
index 0000000..4d21695
--- /dev/null
+++ b/experiments/synthetic_benchmark/env.def
@@ -0,0 +1,16 @@
+Bootstrap: docker
+From: continuumio/miniconda3
+
+%files
+    environment.yaml /environment.yaml
+
+%post
+    conda env create -f /environment.yaml
+    conda clean --all -y
+
+%environment
+    source /opt/conda/etc/profile.d/conda.sh
+    conda activate dextrademixer
+
+%runscript
+    python "$@"
\ No newline at end of file
diff --git a/experiments/synthetic_benchmark/environment.yaml b/experiments/synthetic_benchmark/environment.yaml
index a74493d..b5165c7 100644
--- a/experiments/synthetic_benchmark/environment.yaml
+++ b/experiments/synthetic_benchmark/environment.yaml
@@ -1,19 +1,21 @@
-name: dextraMixer_syntheticBenchmark
+name: dextrademixer
 channels:
   - conda-forge
   - bioconda
 dependencies:
-  - python=3.10
+  - python=3.13
   - pip
+  - snakemake
+  - jupyterlab
   - pip:
       - numpy>=1.25.2
       - scipy>=1.11.4
       - pandas>=2.1.4
       - statsmodels>=0.14.1
       - matplotlib>=3.8.3
-      - jax==0.5.0
-      - jaxlib==0.5.0
-      - numpyro==0.16.1
+      - jax>=0.5.0
+      - jaxlib>=0.5.0
+      - numpyro>=0.16.1
       - arviz>=0.17.1
       - mudata>=0.2.3
       - muon>=0.1.6
@@ -21,3 +23,5 @@ dependencies:
       - scanpy>=1.9.8
       - scirpy>=0.13.0
       - scikit-learn>=1.6.1  # TODO: fix scikit-learn version
+      - psutil
+
diff --git a/experiments/synthetic_benchmark/run_beam.py b/experiments/synthetic_benchmark/run_beam.py
new file mode 100644
index 0000000..8f44b15
--- /dev/null
+++ b/experiments/synthetic_benchmark/run_beam.py
@@ -0,0 +1,58 @@
+import argparse
+import os
+import warnings
+
+import pandas as pd
+import muon as mu
+
+import sys
+sys.path.append("../../")
+from dextrademixer.model import BEAMT
+from dextrademixer.utils import calculate_metrics
+
+
+def run_inference(args):
+    f_in = args.f_in
+    f_out = args.f_out
+
+    base_dir = os.path.dirname(f_out)
+    sim_config = os.path.basename(f_in).replace('.h5mu', '')
+    config = "BEAM" + '_' + sim_config
+    csv_dir = os.path.join(base_dir, 'csv')
+    if os.path.exists(csv_dir + f"/{config}.csv"):
+        return
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        mdata = mu.read(f_in)
+
+    y_true = mdata.mod["airr"].obs["is_binder"]
+
+    model = BEAMT()
+    model.preprocess_model_data(mdata, "pmhc1", neg_ctrl_key="neg_control")
+    model.fit()
+
+    p_pred, assignment = model.predict_posterior_class(threshold=0.5)
+    results_dict = {'config': config, 'model_config': "BEAM", 'sim_config': sim_config, 'threshold': 0.5}
+    results_dict.update(calculate_metrics(y_true, p_pred, assignment, full_metrics=False))
+    results_dict.update({'sim_'+k: mdata['gex'].uns['sim_params'][k] for k in ['binding_ratio', 'mean_inc', 'nof_clones', 'p_binding_outlier', 'rep', 'rng_key', 'total_cells']})
+
+    results = pd.DataFrame([results_dict])
+    os.makedirs(csv_dir, exist_ok=True)
+    results.to_csv(f_out)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Run tool on a single file.")
+    parser.add_argument("--f_in", type=str, default="benchmarks/scenario_test/simulation/2000_800_0.0_0.9_False_500_None_1.h5mu",
+                        help="Path to input h5mu file")
+    parser.add_argument("--f_out", type=str, default="benchmarks/scenario_test/csv/2000_800_0.0_0.9_False_500_None_1.csv",
+                        help="Path to output csv file")
+    args = parser.parse_args()
+    
+    run_inference(args)
+    print("DONE!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/synthetic_benchmark/run_dextrademixer.py b/experiments/synthetic_benchmark/run_dextrademixer.py
new file mode 100644
index 0000000..91fd89c
--- /dev/null
+++ b/experiments/synthetic_benchmark/run_dextrademixer.py
@@ -0,0 +1,166 @@
+from time import time
+t = time()
+
+import argparse
+import os
+import warnings
+import resource
+
+import pandas as pd
+import numpyro
+import muon as mu
+
+import sys
+sys.path.append("../../")
+
+here = os.path.dirname(os.path.abspath(__file__))
+root = os.path.abspath(os.path.join(here, "..", ".."))
+sys.path.insert(0, root)
+
+from dextrademixer.model import DextraDemixer
+from dextrademixer.utils import convert_str_to_bool_and_none, calculate_metrics, float_or_none, get_cpu_model
+
+
+posterior_params_full = [
+            # Thresholding
+            (None, 0.50, None, None,),  # cell-level
+            (None, 0.50, True, None,),  # clone-level
+            # FDR control
+            (0.01, None, None, None,),
+            (0.05, None, None, None,),
+            (0.10, None, None, None,),
+            (0.01, None, True, None,),
+            (0.05, None, True, None,),
+            (0.10, None, True, None,),
+            # credible interval bounded FDR control
+            (0.01, None, None, 0.5,),
+            (0.05, None, None, 0.5,),
+            (0.10, None, None, 0.5,),
+            (0.01, None, True, 0.5,),
+            (0.05, None, True, 0.5,),
+            (0.10, None, True, 0.5,),
+        ]
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(description="Run tool on a single file.")
+    # IO parameters
+    parser.add_argument("--f_in", type=str, default="benchmarks/scenario_test/simulation/2000_800_0.0_0.9_False_500_None_1.h5mu",
+                        help="Path to input h5mu file")
+    parser.add_argument("--f_out", type=str, default="benchmarks/scenario_test/csv/2000_800_0.0_0.9_False_500_None_1.h5mu",
+                        help="Path to output directory")
+
+    # mudata keys
+    parser.add_argument("--gex_key", type=str, default="gex",
+                        help="Key for modality where pMHC counts are stored")
+    parser.add_argument("--airr_key", type=str, default='airr',
+                        help="Key for modality where TCR data is stored")
+    parser.add_argument("--pmhc_key", type=str, default="pmhc1",
+                        help="Key for pMHC counts, expected to be in 'gex' modality")
+    parser.add_argument("--label_key", type=str, default="is_binder",
+                        help="Key for labels, expected to be in 'airr' modality")
+
+    # Model parameters
+    parser.add_argument("--neg_ctrl_key", type=str, default=None,
+                        help="Key for negative control counts. If not None, enables model to use negative control data for "
+                             "fitting. Expected to be in 'gex' modality. If None, no negative control data is used.")
+    parser.add_argument("--hc_scale_prior", type=float, default=1.0,
+                        help="Prior for scale parameter of kmeans model and HalfCauchy for overdispersion model")
+    parser.add_argument("--alpha_offset", type=float, default=5.0,
+                        help="Additive offset for inverse dispersion parameter of signal component")
+
+    # Optimization parameters
+    parser.add_argument("--seed", type=int, default=42, help="Random seed")
+    parser.add_argument("--maxiter", type=int, default=1000, help="Number of iterations for optimization")
+    parser.add_argument("--lr_init_value", type=float, default=3e-1, help="Learning rate")
+    parser.add_argument("--lr_end_value", type=float, default=3e-3, help="Learning rate")
+    parser.add_argument("--lr_decay_rate", type=float, default=0.995, help="Learning rate")
+    parser.add_argument("--lr_transition_steps", type=int, default=1, help="Learning rate")
+    parser.add_argument("--guide", type=str, default="normal", help="Type of guide for SVI", choices=["normal", "mvnormal"])
+
+    # Logging parameters
+    parser.add_argument("--scaling_test", type=str, default=False,
+                        help="Use short list of posterior parameters")
+    
+    # Util
+    parser.add_argument("-f", type=str, default=None,
+                        help="Unused, for compatibility with Jupyter")
+
+    return parser.parse_args()
+
+
+def run_inference(args):
+    config = os.path.basename(args.f_out.replace('.csv', ''))
+    model_config = config.split('-')[0]
+    sim_config = config.split('-')[1]
+    base_dir = os.path.dirname(args.f_out.replace('csv/', ''))
+    if os.path.exists(args.f_out):
+        print(f"Results for {args.f_out} already exist, skipping...")
+        return
+
+    save_model_dir = os.path.join(base_dir, 'saved_models')
+    os.makedirs(save_model_dir, exist_ok=True)
+    csv_dir = os.path.join(base_dir, 'csv')
+    os.makedirs(csv_dir, exist_ok=True)
+
+    print(f"Running inference for config: {config}")
+    numpyro.set_host_device_count(1)
+
+    # Load data
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        mdata = mu.read(args.f_in)
+    
+    # Initialize and fit model
+    model = DextraDemixer(model_type='mixturemodelkmeans', mode='I', alpha_model='overdispersion', 
+                          model_config={"overdispersion_scale_prior": args.hc_scale_prior, "alpha_offset": args.alpha_offset})
+    model.preprocess_model_data(mdata, pmhc_key=args.pmhc_key, gex_key=args.gex_key, neg_ctrl_key=args.neg_ctrl_key,
+                                ir_clone_key=None, )
+    opt_params = {"maxiter": args.maxiter, "nof_inits": 10,
+                  "adam": {"init_value": args.lr_init_value, "end_value": args.lr_end_value,
+                           "decay_rate": args.lr_decay_rate, "transition_steps": args.lr_transition_steps}, }
+    model.fit_svi(guide=args.guide, svi_config=opt_params, nof_inits=opt_params["nof_inits"], rng_key=args.seed)
+    model.save_model(os.path.join(save_model_dir, f"{config}.pkl"))
+    
+    # Log metrics using different posterior prediction methods
+    y_true = mdata.mod[args.airr_key].obs[args.label_key].astype(int).values
+
+    results = []
+    # Predict posterior class with different methods, (target_fdr, threshold, clone_median_p, cred_intvl)
+    if args.scaling_test:
+        posterior_params = [(None, 0.50, True, None,)]  # clone-level thresholding only to test scaling
+    else:
+        posterior_params = posterior_params_full
+
+    for target_fdr, threshold, clone_median_p, cred_intvl in posterior_params:
+        p_pred, assignment = model.predict_posterior_class(target_fdr=target_fdr, threshold=threshold,
+                                                           cred_intvl=cred_intvl,
+                                                           clonotype_median_p=clone_median_p,
+                                                           clone_id=mdata[args.airr_key].obs['clone_id'].values
+                                                           )
+
+        results_dict = {'config': config, 'model_config': f'{model_config}{"+clone" if clone_median_p else ""}', 'sim_config': sim_config,
+                        'target_fdr': target_fdr, 'threshold': threshold, 'cred_intvl': cred_intvl, 'clone_median_p': clone_median_p, }
+        results_dict.update(calculate_metrics(y_true, p_pred, assignment, full_metrics=False))
+        if args.scaling_test:
+            results_dict.update({"total_time": time() - t, "cpu_model": get_cpu_model(), 
+                                 "peak_mem_resource": resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024,})
+        results_dict.update({'model_'+k: v for k, v in vars(args).items()})
+        results_dict.update({'sim_'+k: mdata[args.gex_key].uns['sim_params'][k] for k in ['binding_ratio', 'mean_inc', 'nof_clones', 'p_binding_outlier', 'rep', 'rng_key', 'total_cells']})
+        results.append(results_dict)
+        pd.DataFrame(results).to_csv(args.f_out.replace('.csv', '_intermediate.csv'))
+
+    results = pd.DataFrame(results)
+    results.to_csv(args.f_out)
+    os.remove(args.f_out.replace('.csv', '_intermediate.csv'))
+
+
+def main():
+    args = parse_arguments()
+    args = convert_str_to_bool_and_none(args)
+
+    run_inference(args)
+    print("DONE!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/synthetic_benchmark/simulate_data.py b/experiments/synthetic_benchmark/simulate_data.py
new file mode 100644
index 0000000..80683a3
--- /dev/null
+++ b/experiments/synthetic_benchmark/simulate_data.py
@@ -0,0 +1,71 @@
+import sys
+import hashlib
+import argparse
+import os
+
+sys.path.append("../../")
+
+from dextrademixer.utils import DextramerSimulator, convert_str_to_bool_and_none
+
+import warnings
+warnings.filterwarnings("ignore", message=".*pull obs/var columns from individual modalities.*", category=FutureWarning,)
+
+
+def main(params):
+    if params.f_out is not None:
+        base_path = os.path.dirname(params.f_out)
+        sim_config = os.path.basename(params.f_out.replace('.h5mu', ''))
+        os.makedirs(base_path, exist_ok=True)
+    else:
+        base_path = 'simulation'
+        os.makedirs(base_path, exist_ok=True)
+        sim_config = (f"{params.total_cells},{params.nof_clones if params.nof_clones is not None else params.frac_clones},"
+                      f"{params.p_binding_outlier},{params.binding_ratio},False,{params.mean_inc},None,{params.i}")
+        params.f_out = os.path.join(base_path, f"{sim_config}.h5mu")
+    if params.nof_clones is None:
+        params.nof_clones = int(params.frac_clones * params.total_cells)
+
+    if os.path.exists(params.f_out):
+        print(f"Simulation with config {sim_config} already exists, skipping...")
+        return
+
+    # Use sim_hash to ensure reproducibility, but have variance in seed
+    sim_hash = hashlib.sha256(sim_config.encode('utf-8')).digest()
+    seed = int.from_bytes(sim_hash[:4], byteorder='little')
+
+    sim = DextramerSimulator()
+    mdata = sim.simulate_pmhc_data_from_distribution(total_cells=params.total_cells,
+                                                     nof_clones=params.nof_clones,
+                                                     p_binding_outlier=params.p_binding_outlier,
+                                                     binding_ratio=params.binding_ratio,
+                                                     mean_inc=params.mean_inc,
+                                                     simulate_neg_control=True,
+                                                     plot_data=False,
+                                                     rep=params.i,
+                                                     rng_key=seed,
+                                                     )
+
+    mdata.write(params.f_out)
+
+    print(f"Finished simulations.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--f_out", type=str, default="benchmarks/scenario_test/simulation/test.h5mu",
+                        help="Output file path")
+    parser.add_argument("--total_cells", type=int, default=5000, help="Number of cells")
+    parser.add_argument("--nof_clones", type=int, default=None, help="Number of clones")
+    parser.add_argument("--frac_clones", type=float, default=0.4,
+                        help="Fraction of clones, only used when nof_clones is None")
+    parser.add_argument("--p_binding_outlier", type=float, default=0.0,
+                        help="Probability that cell from binding clone has low UMI count", )
+    parser.add_argument("--binding_ratio", type=float, help="Ratio of binder cells", default=0.1)
+    parser.add_argument("--mean_inc", type=float, help="Signal to noise ratio", default=20)
+    parser.add_argument("--i", type=int, help="Repetition index", default=0)
+
+    params = parser.parse_args()
+    params = convert_str_to_bool_and_none(params)
+
+    main(params)
diff --git a/experiments/synthetic_benchmark/slurm_benchmark.sh b/experiments/synthetic_benchmark/slurm_benchmark.sh
new file mode 100644
index 0000000..5c5cd93
--- /dev/null
+++ b/experiments/synthetic_benchmark/slurm_benchmark.sh
@@ -0,0 +1,100 @@
+#!/bin/bash
+# Usage examples:
+#   bash slurm_benchmark.sh --scenario test
+#   bash slurm_benchmark.sh --scenario synth_benchmark
+#   bash slurm_benchmark.sh --scenario scaling --node cpusrv100 --scaling_test --no-preemptible
+#   bash slurm_benchmark.sh --scenario dropout
+
+set -euo pipefail
+
+# ---- Default values
+SCENARIO="test"
+RAM_PER_CELL_NUM=0.05
+ADDITIONAL_FLAGS=""
+NODE=""
+SCALING_TEST=False
+PREEMPTIBLE=True
+
+# ---- Parse flags
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --scenario)
+            SCENARIO="$2"
+            shift 2
+            ;;
+        --ram)
+            RAM_PER_CELL_NUM="$2"
+            shift 2
+            ;;
+        --no-preemptible)
+            PREEMPTIBLE=False
+            shift
+            ;;
+        --scaling_test)
+            SCALING_TEST=True
+            # PROFILE_SUFFIX=".slurm_one_node"
+            shift
+            ;;
+        --node)
+            NODE="$2"
+            shift 2
+            ;;
+        --additional_flags)
+            ADDITIONAL_FLAGS="$2"
+            shift 2
+            ;;
+        -h|--help)
+            echo "Usage: bash $0 [--scenario NAME] [--ram MB_per_cell] [--no-preemptible] [--additional_flags 'QUOTED_FLAGS'] [--node NODE_NAME] [--scaling_test]"
+            exit 0
+            ;;
+        *)
+            echo "Unknown argument: $1" >&2
+            exit 1
+            ;;
+    esac
+done
+
+PATH_BASE="$(dirname "$(realpath "$0")")/../.."
+PATH_LOGS="."
+PROFILE_DIR="${PATH_BASE}/experiments/.slurm"
+
+
+mkdir -p "${PATH_LOGS}/logs/slurm_logs"
+mkdir -p "${PATH_LOGS}/logs/jobs"
+
+DATE_TIME=$(date +"%Y-%m-%d_%H-%M-%S")
+JOB_NAME="SKM_${SCENARIO}_${DATE_TIME}"
+
+echo "Submitting job ${JOB_NAME} to Slurm with scenario=${SCENARIO}, RAM per cell=${RAM_PER_CELL_NUM}MB, node=${NODE}, scaling_test=${SCALING_TEST}, additional_flags='${ADDITIONAL_FLAGS}'"
+
+cat > "${PATH_LOGS}/logs/jobs/${JOB_NAME}.cmd" <<EOF
+#!/bin/bash
+#SBATCH -J ${JOB_NAME}
+#SBATCH -o ${PATH_LOGS}/logs/${JOB_NAME}.out
+#SBATCH -e ${PATH_LOGS}/logs/${JOB_NAME}.err
+#SBATCH -p cpu_p
+#SBATCH -t 1-00:00:00
+#SBATCH -c 4
+#SBATCH --mem=10G
+#SBATCH --qos=cpu_normal
+#SBATCH --nice=0
+
+source ~/.bash_profile
+conda activate dextrademixer
+cd ${PATH_BASE}/experiments/synthetic_benchmark
+
+snakemake -s snakefile_benchmark.smk --unlock --config SCENARIO=${SCENARIO} RAM_PER_CELL_NUM=${RAM_PER_CELL_NUM} NODE=${NODE} SCALING_TEST=${SCALING_TEST} PREEMPTIBLE=${PREEMPTIBLE}
+
+snakemake -s snakefile_benchmark.smk \\
+    -c4 \\
+    --profile ${PROFILE_DIR} \\
+    --cluster-status ${PROFILE_DIR}/status.py \\
+    --conda-frontend conda \\
+    --config SCENARIO=${SCENARIO} RAM_PER_CELL_NUM=${RAM_PER_CELL_NUM} NODE=${NODE} SCALING_TEST=${SCALING_TEST} PREEMPTIBLE=${PREEMPTIBLE} \\
+    --jobs 1000 \\
+    --resources job_token=1000 \\
+    -p \\
+    ${ADDITIONAL_FLAGS}
+EOF
+
+sbatch ${PATH_LOGS}/logs/jobs/${JOB_NAME}.cmd
diff --git a/experiments/synthetic_benchmark/snakefile_benchmark.smk b/experiments/synthetic_benchmark/snakefile_benchmark.smk
new file mode 100644
index 0000000..38daa00
--- /dev/null
+++ b/experiments/synthetic_benchmark/snakefile_benchmark.smk
@@ -0,0 +1,129 @@
+import yaml
+import itertools
+
+# Configuration
+SCENARIO = config.get("SCENARIO", "test")
+RAM_PER_CELL_NUM = float(config.get("RAM_PER_CELL_NUM", 0.05))
+SCALING_TEST = str(config.get("SCALING_TEST", False)).lower() in {"1", "true", "yes", "y"}
+NODE = config.get("NODE", None)
+PREEMPTIBLE = str(config.get("PREEMPTIBLE", False)).lower() in {"1", "true", "yes", "y"}
+
+with open(f"benchmarks/{SCENARIO}/config.yaml") as f:
+    config_yaml = yaml.safe_load(f)
+
+print(f"Running scenario: {SCENARIO} on Node={NODE} as scaling test={SCALING_TEST} and RAM per cell = {RAM_PER_CELL_NUM} on {'preemptible' if PREEMPTIBLE else 'non-preemptible'} cluster")
+print(config_yaml)
+
+target_files = []
+keys = config_yaml.keys()
+values = config_yaml.values()
+for combo in itertools.product(*values):
+    kwargs = dict(zip(keys, combo))
+    # Filter out configurations where N * p < 1
+    if float(kwargs["total_cells"]) * float(kwargs["binding_ratio"]) >= 1:
+        target_files.append(
+            f"benchmarks/{SCENARIO}/csv/{kwargs['model_config']}-"
+            f"{kwargs['total_cells']},0.4,{kwargs['p_binding_outlier']},{kwargs['binding_ratio']},False,{kwargs['mean_inc']},None,{kwargs['i']}.csv"
+        )
+
+print(target_files)
+
+rule all:
+    input:
+        f"benchmarks/{SCENARIO}/aggregated_results.csv"
+
+
+rule aggregate_results:
+    input:
+        lambda wc: target_files
+    output:
+        "benchmarks/{scenario}/aggregated_results.csv"
+    resources:
+        c=1,
+        mem="8000M",
+        node="",
+        qos="cpu_preemptible" if PREEMPTIBLE else "cpu_normal",
+    shell:
+        r"""
+        apptainer exec --bind "$PWD/../..":"$PWD/../.." \
+                       --pwd "$PWD" dextrademixer.sif \
+        python aggregate_results.py \
+            --f_ins {input} \
+            --f_out {output}
+        """
+
+rule run_simulation:
+    priority: 30
+    output:
+        "benchmarks/{scenario}/simulation/{N},0.4,{po},{p},False,{mean_inc},None,{i}.h5mu"
+    resources:
+        c=1,
+        mem=lambda wc: str(float(wc.N) * RAM_PER_CELL_NUM + 1500) + "M",
+        node="",  # always use all nodes
+        qos="cpu_preemptible" if PREEMPTIBLE else "cpu_normal",
+    shell:
+        r"""
+        apptainer exec --bind "$PWD/../..":"$PWD/../.." \
+                       --pwd "$PWD" dextrademixer.sif \
+        python simulate_data.py \
+            --f_out {output} \
+            --total_cells {wildcards.N} \
+            --frac_clones 0.4 \
+            --p_binding_outlier {wildcards.po} \
+            --binding_ratio {wildcards.p} \
+            --mean_inc {wildcards.mean_inc} \
+            --i {wildcards.i}
+        """
+
+
+rule run_dextrademixer:
+    priority: 20
+    input:
+        "benchmarks/{scenario}/simulation/{N},0.4,{po},{p},False,{mean_inc},None,{i}.h5mu"
+    output:
+        protected(
+            "benchmarks/{scenario}/csv/Dextra{model_config}-"  # wildcard cannot be empty, therefore have to use a small hack here
+            "{N},0.4,{po},{p},False,{mean_inc},None,{i}.csv",
+        )
+    params:
+        neg_ctrl_key=lambda wc: "neg_control" if wc.model_config == "Demixer+neg." else "None"
+    resources:
+        c=1,
+        node=NODE if NODE is not None else "",
+        qos="cpu_preemptible" if PREEMPTIBLE else "cpu_normal",
+        mem=lambda wc: str(float(wc.N) * RAM_PER_CELL_NUM + 3000) + "M",
+        job_token=1000 if SCALING_TEST else 1,  # allow only 1 concurrent job for scaling test, by using 1000 out of 1000 tokens. For non-scaling test, allow all jobs to run concurrently. Need to set job tokens to 1000
+    shell:
+        r"""
+        apptainer exec --bind "$PWD/../..":"$PWD/../.." \
+                       --pwd "$PWD" dextrademixer.sif \
+        python run_dextrademixer.py \
+            --f_in {input} \
+            --f_out {output} \
+            --neg_ctrl_key {params.neg_ctrl_key} \
+            --scaling_test {SCALING_TEST}
+        """
+
+
+rule run_beam:
+    priority: 20
+    input:
+        "benchmarks/{scenario}/simulation/{N},0.4,{po},{p},False,{mean_inc},None,{i}.h5mu"
+    output:
+        protected(
+            "benchmarks/{scenario}/csv/BEAM-"  # wildcard cannot be empty, therefore have to use a small hack here
+            "{N},0.4,{po},{p},False,{mean_inc},None,{i}.csv",
+        )
+    resources:
+        c=1,
+        mem="1000M",
+        qos="cpu_preemptible" if PREEMPTIBLE else "cpu_normal",
+        node="",
+    shell:
+        r"""
+        apptainer exec --bind "$PWD/../..":"$PWD/../.." \
+                       --pwd "$PWD" dextrademixer.sif \
+        python run_beam.py \
+            --f_in {input} \
+            --f_out {output}
+        """

From 269ab9f853b9455408059dca4e1ece5b7faa089e Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Thu, 18 Jun 2026 19:58:04 +0200
Subject: [PATCH 38/39] feature: update slurm config

---
 experiments/.slurm/config.yaml                | 32 +++++++------
 experiments/.slurm_one_node/config.yaml       | 45 +++++++++++++++++++
 .../status.py                                 |  0
 experiments/.slurm_preemptible/config.yaml    | 38 ----------------
 4 files changed, 64 insertions(+), 51 deletions(-)
 create mode 100644 experiments/.slurm_one_node/config.yaml
 rename experiments/{.slurm_preemptible => .slurm_one_node}/status.py (100%)
 delete mode 100644 experiments/.slurm_preemptible/config.yaml

diff --git a/experiments/.slurm/config.yaml b/experiments/.slurm/config.yaml
index 41b10b2..a5d510c 100644
--- a/experiments/.slurm/config.yaml
+++ b/experiments/.slurm/config.yaml
@@ -1,34 +1,40 @@
 cluster:
-  mkdir -p ./logs/slurm_logs/{rule} &&
+  NODEFLAG="";
+  if [ "{resources.node}" != "" ]; then
+      NODEFLAG="--nodelist={resources.node}";
+  fi;
+
   sbatch
     --partition=cpu_p
-    --qos=cpu_normal
+    --qos={resources.qos}
     --nice=0
     -t 1-00:00:00
     --mem={resources.mem_mb}
     -c {resources.c}
+    --exclude=cpusrv101
     --job-name={rule}-$(date +%Y%m%d_%H%M%S)-{wildcards} 
-    --output=./logs/slurm_logs/{rule}/$(date +%Y%m%d_%H%M%S)-{wildcards}.out 
-    --error=./logs/slurm_logs/{rule}/$(date +%Y%m%d_%H%M%S)-{wildcards}.err
+    $NODEFLAG
+    --output=./benchmarks/{wildcards.scenario}/logs/{rule}/$(date +%Y%m%d_%H%M%S)-{wildcards}.out 
+    --error=./benchmarks/{wildcards.scenario}/logs/{rule}/$(date +%Y%m%d_%H%M%S)-{wildcards}.err
     --parsable
 
 default-resources:
-  - c=16
-  - mem_mb=64000
-  - mem="64G"
-  - disk_mb=250000
+  - c=1
+  - mem_mb=8000
+  - mem="8G"
+  - disk_mb=8000
 
 set-resources:
-  - dummy_rule:c=32
-  - dummy_rule:mem=128000
-  - dummy_rule:mem_mb=128000
+  - dummy_rule:c=1
+  - dummy_rule:mem=8000
+  - dummy_rule:mem_mb=8000
   - run_simulation:c=1
 
 set-threads:
   - dummy_rule=20
 
-restart-times: 1
-max-jobs-per-second: 10
+restart-times: 0
+max-jobs-per-second: 500
 max-status-checks-per-second: 1
 latency-wait: 60
 jobs: 1000
diff --git a/experiments/.slurm_one_node/config.yaml b/experiments/.slurm_one_node/config.yaml
new file mode 100644
index 0000000..48fa750
--- /dev/null
+++ b/experiments/.slurm_one_node/config.yaml
@@ -0,0 +1,45 @@
+cluster:
+  mkdir -p ./logs/{rule} &&
+  
+  NODEFLAG="";
+  if [ "{resources.node}" != "" ]; then
+      NODEFLAG="--nodelist={resources.node}";
+  fi;
+
+  sbatch
+    --partition=cpu_p
+    --qos=cpu_preemptible
+    --nice=0
+    -t 1-00:00:00
+    --mem={resources.mem_mb}
+    -c {resources.c}
+    --job-name={rule}-$(date +%Y%m%d_%H%M%S)-{wildcards} 
+    $NODEFLAG
+    --output=./benchmarks/{wildcards.scenario}/logs/{rule}/$(date +%Y%m%d_%H%M%S)-{wildcards}.out 
+    --error=./benchmarks/{wildcards.scenario}/logs/{rule}/$(date +%Y%m%d_%H%M%S)-{wildcards}.err
+    --parsable
+
+default-resources:
+  - c=2
+  - mem_mb=16000
+  - mem="16G"
+  - disk_mb=160000
+
+set-resources:
+  - dummy_rule:c=2
+  - dummy_rule:mem=16000
+  - dummy_rule:mem_mb=16000
+  - run_simulation:c=1
+
+set-threads:
+  - dummy_rule=20
+
+restart-times: 0
+max-jobs-per-second: 500
+max-status-checks-per-second: 1
+latency-wait: 60
+jobs: 1000
+keep-going: True
+printshellcmds: True
+scheduler: greedy
+use-conda: True
diff --git a/experiments/.slurm_preemptible/status.py b/experiments/.slurm_one_node/status.py
similarity index 100%
rename from experiments/.slurm_preemptible/status.py
rename to experiments/.slurm_one_node/status.py
diff --git a/experiments/.slurm_preemptible/config.yaml b/experiments/.slurm_preemptible/config.yaml
deleted file mode 100644
index f3898e6..0000000
--- a/experiments/.slurm_preemptible/config.yaml
+++ /dev/null
@@ -1,38 +0,0 @@
-cluster:
-  mkdir -p ./logs/slurm_logs/{rule} &&
-  sbatch
-    --partition=cpu_p
-    --qos=cpu_preemptible
-    --nice=0
-    -t 3-00:00:00
-    --mem={resources.mem_mb}
-    -c {resources.c}
-    --job-name={rule}-$(date +%Y%m%d_%H%M%S)-{wildcards} 
-    --output=./logs/slurm_logs/{rule}/$(date +%Y%m%d_%H%M%S)-{wildcards}.out 
-    --error=./logs/slurm_logs/{rule}/$(date +%Y%m%d_%H%M%S)-{wildcards}.err
-    --parsable
-
-default-resources:
-  - c=16
-  - mem_mb=64000
-  - mem="64G"
-  - disk_mb=250000
-
-set-resources:
-  - dummy_rule:c=32
-  - dummy_rule:mem=128000
-  - dummy_rule:mem_mb=128000
-  - run_simulation:c=1
-
-set-threads:
-  - dummy_rule=20
-
-restart-times: 1
-max-jobs-per-second: 10
-max-status-checks-per-second: 1
-latency-wait: 60
-jobs: 1000
-keep-going: True
-printshellcmds: True
-scheduler: greedy
-use-conda: True

From 331c37ee9307496f9d070255f10367f5c61567dd Mon Sep 17 00:00:00 2001
From: ArcaneEmergence <yang.an@outlook.com>
Date: Thu, 18 Jun 2026 19:59:15 +0200
Subject: [PATCH 39/39] Figure: Add notebooks for figure plotting and add
 environment.yaml files for a minimal and full setup.

---
 .../env.def => env_full.def                   |    6 +-
 env_minimal.def                               |   16 +
 environment_full.yaml                         |  303 ++++
 environment_minimal.yaml                      |   32 +
 .../Figure2_synthetic_benchmark.ipynb         |  773 ++++++++
 .../SuppFig2_Estimate_sim_params.ipynb        | 1596 +++++++++++++++++
 ...pFig3_Examples_of_simulated_datasets.ipynb |  190 ++
 .../benchmarks/dropout/config.yaml            |    7 +
 .../benchmarks/scaling/config.yaml            |    7 +
 .../benchmarks/synth_benchmark/config.yaml    |    7 +
 .../synthetic_benchmark/environment.yaml      |   27 -
 11 files changed, 2934 insertions(+), 30 deletions(-)
 rename experiments/synthetic_benchmark/env.def => env_full.def (58%)
 create mode 100644 env_minimal.def
 create mode 100644 environment_full.yaml
 create mode 100644 environment_minimal.yaml
 create mode 100644 experiments/synthetic_benchmark/Figure2_synthetic_benchmark.ipynb
 create mode 100644 experiments/synthetic_benchmark/SuppFig2_Estimate_sim_params.ipynb
 create mode 100644 experiments/synthetic_benchmark/SuppFig3_Examples_of_simulated_datasets.ipynb
 create mode 100644 experiments/synthetic_benchmark/benchmarks/dropout/config.yaml
 create mode 100644 experiments/synthetic_benchmark/benchmarks/scaling/config.yaml
 create mode 100644 experiments/synthetic_benchmark/benchmarks/synth_benchmark/config.yaml
 delete mode 100644 experiments/synthetic_benchmark/environment.yaml

diff --git a/experiments/synthetic_benchmark/env.def b/env_full.def
similarity index 58%
rename from experiments/synthetic_benchmark/env.def
rename to env_full.def
index 4d21695..2e702e9 100644
--- a/experiments/synthetic_benchmark/env.def
+++ b/env_full.def
@@ -1,11 +1,11 @@
 Bootstrap: docker
-From: continuumio/miniconda3
+From: condaforge/miniforge3
 
 %files
-    environment.yaml /environment.yaml
+    environment_full.yaml /environment_full.yaml
 
 %post
-    conda env create -f /environment.yaml
+    conda env create -f /environment_full.yaml
     conda clean --all -y
 
 %environment
diff --git a/env_minimal.def b/env_minimal.def
new file mode 100644
index 0000000..012891c
--- /dev/null
+++ b/env_minimal.def
@@ -0,0 +1,16 @@
+Bootstrap: docker
+From: condaforge/miniforge3
+
+%files
+    environment_minimal.yaml /environment_minimal.yaml
+
+%post
+    conda env create -f /environment_minimal.yaml
+    conda clean --all -y
+
+%environment
+    source /opt/conda/etc/profile.d/conda.sh
+    conda activate dextrademixer
+
+%runscript
+    python "$@"
\ No newline at end of file
diff --git a/environment_full.yaml b/environment_full.yaml
new file mode 100644
index 0000000..b8162b3
--- /dev/null
+++ b/environment_full.yaml
@@ -0,0 +1,303 @@
+name: dextrademixer
+channels:
+  - conda-forge
+  - bioconda
+  - nvidia
+  - pytorch
+  - nodefaults
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1
+  - _openmp_mutex=4.5
+  - _python_abi3_support=1.0
+  - amply=0.1.6
+  - annotated-types=0.7.0
+  - anyio=4.11.0
+  - appdirs=1.4.4
+  - argon2-cffi=25.1.0
+  - argon2-cffi-bindings=25.1.0
+  - argparse-dataclass=2.0.0
+  - arrow=1.4.0
+  - asttokens=3.0.1
+  - async-lru=2.0.5
+  - attrs=25.4.0
+  - babel=2.17.0
+  - beautifulsoup4=4.14.2
+  - bleach=6.3.0
+  - bleach-with-css=6.3.0
+  - brotli-python=1.2.0
+  - bzip2=1.0.8
+  - ca-certificates=2025.11.12
+  - cached-property=1.5.2
+  - cached_property=1.5.2
+  - certifi=2025.11.12
+  - cffi=2.0.0
+  - charset-normalizer=3.4.4
+  - click=8.3.1
+  - coin-or-cbc=2.10.12
+  - coin-or-cgl=0.60.9
+  - coin-or-clp=1.17.10
+  - coin-or-osi=0.108.11
+  - coin-or-utils=2.11.12
+  - colorama=0.4.6
+  - coloredlogs=15.0.1
+  - comm=0.2.3
+  - conda-inject=1.3.2
+  - configargparse=1.7.1
+  - connection_pool=0.0.3
+  - cpython=3.13.9
+  - debugpy=1.8.17
+  - decorator=5.2.1
+  - defusedxml=0.7.1
+  - docutils=0.22.3
+  - dpath=2.2.0
+  - eido=0.2.4
+  - exceptiongroup=1.3.1
+  - executing=2.2.1
+  - fqdn=1.5.1
+  - gitdb=4.0.12
+  - gitpython=3.1.45
+  - h11=0.16.0
+  - h2=4.3.0
+  - hpack=4.1.0
+  - httpcore=1.0.9
+  - httpx=0.28.1
+  - humanfriendly=10.0
+  - hyperframe=6.1.0
+  - icu=75.1
+  - idna=3.11
+  - immutables=0.21
+  - importlib-metadata=8.7.0
+  - iniconfig=2.3.0
+  - ipykernel=7.1.0
+  - ipython=9.7.0
+  - ipython_pygments_lexers=1.1.1
+  - isoduration=20.11.0
+  - jedi=0.19.2
+  - jinja2=3.1.6
+  - json5=0.12.1
+  - jsonpointer=3.0.0
+  - jsonschema=4.25.1
+  - jsonschema-specifications=2025.9.1
+  - jsonschema-with-format-nongpl=4.25.1
+  - jupyter-lsp=2.3.0
+  - jupyter_client=8.6.3
+  - jupyter_core=5.9.1
+  - jupyter_events=0.12.0
+  - jupyter_server=2.17.0
+  - jupyter_server_terminals=0.5.3
+  - jupyterlab=4.5.0
+  - jupyterlab_pygments=0.3.0
+  - jupyterlab_server=2.28.0
+  - keyutils=1.6.3
+  - krb5=1.21.3
+  - lark=1.3.1
+  - ld_impl_linux-64=2.45.1
+  - libblas=3.11.0
+  - libcblas=3.11.0
+  - libedit=3.1.20250104
+  - libexpat=2.7.3
+  - libffi=3.5.2
+  - libgcc=15.2.0
+  - libgcc-ng=15.2.0
+  - libgfortran=15.2.0
+  - libgfortran5=15.2.0
+  - libgomp=15.2.0
+  - liblapack=3.11.0
+  - liblapacke=3.11.0
+  - liblzma=5.8.1
+  - libmpdec=4.0.0
+  - libopenblas=0.3.30
+  - libsodium=1.0.20
+  - libsqlite=3.51.0
+  - libstdcxx=15.2.0
+  - libstdcxx-ng=15.2.0
+  - libuuid=2.41.2
+  - libzlib=1.3.1
+  - logmuse=0.2.8
+  - markdown-it-py=4.0.0
+  - markupsafe=3.0.3
+  - matplotlib-inline=0.2.1
+  - mdurl=0.1.2
+  - mistune=3.1.4
+  - nbclient=0.10.2
+  - nbconvert-core=7.16.6
+  - nbformat=5.10.4
+  - ncurses=6.5
+  - nest-asyncio=1.6.0
+  - notebook-shim=0.2.4
+  - numpy=2.3.5
+  - openssl=3.6.0
+  - overrides=7.7.0
+  - packaging=25.0
+  - pandas=2.3.3
+  - pandocfilters=1.5.0
+  - parso=0.8.5
+  - pephubclient=0.4.4
+  - peppy=0.40.8
+  - pexpect=4.9.0
+  - pip=25.3
+  - plac=1.4.5
+  - platformdirs=4.5.0
+  - pluggy=1.6.0
+  - prometheus_client=0.23.1
+  - prompt-toolkit=3.0.52
+  - psutil=7.1.3
+  - ptyprocess=0.7.0
+  - pulp=2.8.0
+  - pure_eval=0.2.3
+  - pycparser=2.22
+  - pydantic=2.12.4
+  - pydantic-core=2.41.5
+  - pygments=2.19.2
+  - pyparsing=3.2.5
+  - pysocks=1.7.1
+  - pytest=9.0.1
+  - python=3.13.9
+  - python-dateutil=2.9.0.post0
+  - python-fastjsonschema=2.21.2
+  - python-gil=3.13.9
+  - python-json-logger=2.0.7
+  - python-tzdata=2025.2
+  - python_abi=3.13
+  - pytz=2025.2
+  - pyyaml=6.0.3
+  - pyzmq=27.1.0
+  - readline=8.2
+  - referencing=0.37.0
+  - requests=2.32.5
+  - reretry=0.11.8
+  - rfc3339-validator=0.1.4
+  - rfc3986-validator=0.1.1
+  - rfc3987-syntax=1.1.0
+  - rich=14.2.0
+  - rpds-py=0.29.0
+  - send2trash=1.8.3
+  - setuptools=80.9.0
+  - shellingham=1.5.4
+  - six=1.17.0
+  - slack-sdk=3.39.0
+  - slack_sdk=3.39.0
+  - smart_open=7.5.0
+  - smmap=5.0.2
+  - snakemake=9.13.7
+  - snakemake-interface-common=1.22.0
+  - snakemake-interface-executor-plugins=9.3.9
+  - snakemake-interface-logger-plugins=2.0.0
+  - snakemake-interface-report-plugins=1.3.0
+  - snakemake-interface-scheduler-plugins=2.0.2
+  - snakemake-interface-storage-plugins=4.2.3
+  - snakemake-minimal=9.13.7
+  - sniffio=1.3.1
+  - soupsieve=2.8.3
+  - stack_data=0.6.3
+  - tabulate=0.9.0
+  - terminado=0.18.1
+  - throttler=1.2.2
+  - tinycss2=1.5.0
+  - tk=8.6.13
+  - tomli=2.3.0
+  - tornado=6.5.2
+  - traitlets=5.14.3
+  - typer=0.20.0
+  - typer-slim=0.20.0
+  - typer-slim-standard=0.20.0
+  - typing-extensions=4.15.0
+  - typing-inspection=0.4.2
+  - typing_extensions=4.15.0
+  - typing_utils=0.1.0
+  - tzdata=2025b
+  - ubiquerg=0.8.0
+  - uri-template=1.3.0
+  - urllib3=2.5.0
+  - veracitools=0.1.3
+  - wcwidth=0.2.14
+  - webcolors=25.10.0
+  - webencodings=0.5.1
+  - websocket-client=1.9.0
+  - wrapt=1.17.3
+  - yaml=0.2.5
+  - yte=1.8.1
+  - zeromq=4.3.5
+  - zipp=3.23.0
+  - zstandard=0.25.0
+  - zstd=1.5.7
+  - pip:
+      - absl-py==2.3.1
+      - adjusttext==1.3.0
+      - airr==1.5.1
+      - anndata==0.12.6
+      - array-api-compat==1.12.0
+      - arviz==0.22.0
+      - awkward==2.8.10
+      - awkward-cpp==50
+      - chex==0.1.91
+      - contourpy==1.3.3
+      - cycler==0.12.1
+      - dill==0.4.1
+      - donfig==0.8.1.post1
+      - feather-format==0.4.1
+      - fishersapi==1.0
+      - fonttools==4.60.1
+      - fsspec==2025.10.0
+      - google-crc32c==1.7.1
+      - h5netcdf==1.7.3
+      - h5py==3.15.1
+      - hierdiff==0.85
+      - igraph==1.0.0
+      - jax==0.8.1
+      - jaxlib==0.8.1
+      - joblib==1.5.2
+      - kiwisolver==1.4.9
+      - legacy-api-wrap==1.5
+      - levenshtein==0.27.3
+      - llvmlite==0.45.1
+      - logomaker==0.8.7
+      - matplotlib==3.10.7
+      - ml-dtypes==0.5.4
+      - mudata==0.3.2
+      - multipledispatch==1.0.0
+      - muon==0.1.7
+      - natsort==8.4.0
+      - networkx==3.6
+      - numba==0.62.1
+      - numcodecs==0.16.5
+      - numpyro==0.19.0
+      - olga==1.3.0
+      - opt-einsum==3.4.0
+      - optax==0.2.6
+      - palmotif==0.4
+      - parasail==1.3.4
+      - parmap==1.7.0
+      - patsy==1.0.2
+      - pillow==12.0.0
+      - pooch==1.8.2
+      - progress==1.6.1
+      - protobuf==6.33.1
+      - pwseqdist==0.6
+      - pyarrow==23.0.1
+      - pynndescent==0.5.13
+      - python-levenshtein==0.27.3
+      - rapidfuzz==3.14.3
+      - scanpy==1.11.5
+      - scikit-learn==1.7.2
+      - scipy==1.16.3
+      - scirpy==0.22.3
+      - seaborn==0.13.2
+      - session-info2==0.2.3
+      - squarify==0.4.4
+      - statsmodels==0.14.5
+      - svgwrite==1.4.3
+      - tcrdist3==0.3
+      - tcrsampler==0.1.9
+      - texttable==1.7.0
+      - threadpoolctl==3.6.0
+      - toolz==1.1.0
+      - tqdm==4.67.1
+      - umap-learn==0.5.9.post2
+      - xarray==2025.11.0
+      - xarray-einstats==0.9.1
+      - yamlordereddictloader==0.4.2
+      - zarr==3.1.5
+      - zipdist==0.1.5
+prefix: /opt/conda/envs/dextrademixer
diff --git a/environment_minimal.yaml b/environment_minimal.yaml
new file mode 100644
index 0000000..bf94830
--- /dev/null
+++ b/environment_minimal.yaml
@@ -0,0 +1,32 @@
+# Minimal environment for DextraDemixer with fixed package versions to ensure reproducibility and avoid dependency conflicts. 
+# This environment is used for testing and benchmarking purposes, and may not include all the packages required for full functionality of DextraDemixer. 
+# For a complete environment, please refer to environment.yaml.
+
+name: dextrademixer
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - python=3.13
+  - pip
+  - snakemake
+  - jupyterlab
+  - pip:
+      - arviz==0.22.0
+      - jax==0.8.1
+      - jaxlib==0.8.1
+      - matplotlib==3.10.7
+      - mudata==0.3.2
+      - muon==0.1.7
+      - numpy==2.3.5
+      - numpyro==0.19.0
+      - optax==0.2.6
+      - pandas==2.3.3
+      - psutil==7.1.3
+      - scanpy==1.11.5
+      - scikit-learn==1.7.2
+      - scipy==1.16.3
+      - scirpy==0.22.3
+      - statsmodels==0.14.5
+      - tcrdist3==0.3
+      
diff --git a/experiments/synthetic_benchmark/Figure2_synthetic_benchmark.ipynb b/experiments/synthetic_benchmark/Figure2_synthetic_benchmark.ipynb
new file mode 100644
index 0000000..ab6ece1
--- /dev/null
+++ b/experiments/synthetic_benchmark/Figure2_synthetic_benchmark.ipynb
@@ -0,0 +1,773 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "df16e128",
+   "metadata": {},
+   "source": [
+    "# Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "06f47777",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Changed directory to: /lustre/groups/imm01/workspace/yang/DextraDemixer/experiments/synthetic_benchmark\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Only needed for VS Code - change cwd to the notebook's directory for VS Code \n",
+    "import os\n",
+    "\n",
+    "try:\n",
+    "    notebook_path = globals()['__vsc_ipynb_file__']\n",
+    "    notebook_dir = os.path.dirname(notebook_path)\n",
+    "    os.chdir(notebook_dir)\n",
+    "    print(f\"Changed directory to: {notebook_dir}\")\n",
+    "except:\n",
+    "    print(\"ERROR. Could not find notebook path. Running from:\", os.getcwd())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1df32e8d-f1e8-44a6-abdb-11beec363d2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/dextrademixer/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from scipy.stats import wilcoxon\n",
+    "\n",
+    "import sys\n",
+    "sys.path.append('../../')\n",
+    "from dextrademixer.utils.utils import mean_ci_t_interval, aggregate_csv\n",
+    "\n",
+    "pd.set_option('display.max_columns', 200)\n",
+    "pd.set_option('display.max_rows', 200)\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3ee91fe5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Figure settings\n",
+    "FIGURE_PATH = '../../figures/Figure2'\n",
+    "os.makedirs(FIGURE_PATH, exist_ok=True)\n",
+    "\n",
+    "sns.reset_defaults()\n",
+    "global_settings = {'font.size': 12, 'axes.titlesize': 'large', 'axes.labelsize': 'medium', 'xtick.labelsize': 'medium', 'ytick.labelsize': 'medium', \n",
+    "                   'legend.fontsize': 'medium', 'figure.titlesize': 'large', 'figure.figsize': (3, 2.5), 'figure.dpi': 100, 'savefig.dpi': 300, \n",
+    "                   'savefig.bbox': 'tight', 'savefig.transparent': True, 'axes.spines.top': False, 'axes.spines.right': False, }\n",
+    "plt.rcParams.update(global_settings)\n",
+    "\n",
+    "hue_order = ['BEAM', 'DextraDemixer', 'DextraDemixer+neg.', 'DextraDemixer+clone', 'DextraDemixer+neg.+clone', ]\n",
+    "palette = {'ITRAP': \"#ffa52e\", 'ICON': \"#ffeb11\", 'BEAM': \"#02c102\", \n",
+    "           'DextraDemixer': \"#24c8c8\", 'DextraDemixer+neg.': \"#0f83b8\", 'DextraDemixer+clone': \"#0A44D5\", 'DextraDemixer+neg.+clone': \"#8900bf\", \n",
+    "           'DextraDemixer(+clone)': \"#0A44D5\", 'DextraDemixer+neg.(+clone)': \"#8900bf\",  # Scaling colors\n",
+    "           }\n",
+    "\n",
+    "# Names were changed for the manuscript\n",
+    "naming_dict = {'sim_total_cells': 'Total cell count', 'sim_binding_ratio': 'Fraction of binders', 'sim_mean_inc': 'Signal-to-noise ratio'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e11288a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "EXPERIMENT_PATH = 'benchmarks/synth_benchmark'\n",
+    "RESULTS_PATH = 'results'\n",
+    "os.makedirs(RESULTS_PATH, exist_ok=True)\n",
+    "RERUN = False  # True: Rerun data loading (requires Zenodo data), False: Load from preprocessed csv, will run if agg_fp not found"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a43417ad-86a9-492b-87c0-c2b234cf33c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_bench_fp = os.path.join(RESULTS_PATH, 'results_benchmark.csv')\n",
+    "df_fdr_fp = os.path.join(RESULTS_PATH, 'results_FDR.csv')\n",
+    "\n",
+    "if not os.path.exists(df_bench_fp) or not os.path.exists(df_fdr_fp):\n",
+    "    df_all = aggregate_csv(experiment_path=EXPERIMENT_PATH, rerun=RERUN, output_path=os.path.join(RESULTS_PATH, 'results_all.csv'), paths=['csv', ])\n",
+    "\n",
+    "    def get_clean_posterior_config_name(x):\n",
+    "        prefix = 'median@' if x['clone_median_p'] and pd.notna(x['clone_median_p']) else ''\n",
+    "        if pd.isna(x['target_fdr']):\n",
+    "            core = f'T={x[\"threshold\"]}'\n",
+    "        else:\n",
+    "            core = f'FDR={x[\"target_fdr\"]}'\n",
+    "            if pd.notna(x['cred_intvl']):\n",
+    "                core += f',cred_intvl={x[\"cred_intvl\"]}'            \n",
+    "        return prefix + core\n",
+    "\n",
+    "    df_all['posterior_config_clean'] = df_all.apply(get_clean_posterior_config_name, axis=1)\n",
+    "    df_all['sim_num_binder'] = df_all['sim_binding_ratio'] * df_all['sim_total_cells']\n",
+    "    # Rename for manuscript\n",
+    "    df_all['model_config'] = df_all['model_config'].str.replace('control', 'neg.', regex=False)\n",
+    "\n",
+    "    df_bench = df_all[df_all['posterior_config_clean'].isin(['median@T=0.5', 'T=0.5'])].copy()\n",
+    "    df_fdr = df_all[df_all['posterior_config_clean'].str.contains('FDR=')].copy()\n",
+    "\n",
+    "    df_bench.to_csv(df_bench_fp, index=False)\n",
+    "    df_fdr.to_csv(df_fdr_fp, index=False)\n",
+    "\n",
+    "else:\n",
+    "    df_bench = pd.read_csv(df_bench_fp)\n",
+    "    df_fdr = pd.read_csv(df_fdr_fp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abc94911-214f-4fb5-848a-343ca7b1a114",
+   "metadata": {},
+   "source": [
+    "# Synthetic benchmark"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92c2a94f",
+   "metadata": {},
+   "source": [
+    "## Performance and robustness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "810e60f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "model_config\n",
+       "BEAM                        0.666 [0.654, 0.678]\n",
+       "DextraDemixer               0.857 [0.848, 0.867]\n",
+       "DextraDemixer+clone         0.900 [0.891, 0.910]\n",
+       "DextraDemixer+neg.          0.868 [0.859, 0.877]\n",
+       "DextraDemixer+neg.+clone    0.912 [0.904, 0.921]\n",
+       "Name: f1, dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Performance mean + 95% CI\n",
+    "df_bench[df_bench['posterior_config_clean'].isin(['T=0.5', 'median@T=0.5'])].groupby('model_config')['f1'].apply(mean_ci_t_interval, confidence=0.95)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "af1fc8d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BEAM and DextraDemixer: stat=187962, p-value=9e-237\n",
+      "DextraDemixer and DextraDemixer+neg.: stat=16819, p-value=6e-28\n",
+      "DextraDemixer+neg. and DextraDemixer+clone: stat=148584, p-value=5e-127\n",
+      "DextraDemixer+clone and DextraDemixer+neg.+clone: stat=5436, p-value=8e-22\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Paired Wilcoxon signed-rank test between neighbors\n",
+    "df_sub = df_bench.copy()\n",
+    "df_sub = df_sub.sort_values(['model_config', 'sim_config'])\n",
+    "    \n",
+    "# Wilcoxon\n",
+    "for idx in range(len(hue_order) - 1):\n",
+    "    data_1 = df_sub[df_sub['model_config'] == hue_order[idx]]['f1'].values\n",
+    "    data_2 = df_sub[df_sub['model_config'] == hue_order[idx + 1]]['f1'].values\n",
+    "    stat, pval = wilcoxon(data_1, data_2)\n",
+    "    print(f\"{hue_order[idx]} and {hue_order[idx + 1]}: stat={stat:.0f}, p-value={pval:.0e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "13f2ef8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 880x250 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure 2a - Classification performance across all synthetic benchmark configurations\n",
+    "fig = plt.figure(figsize=(2.2 * 4, 2.5))\n",
+    "i = 0\n",
+    "for metric in ['f1', 'precision', 'recall', 'aps']:\n",
+    "    i += 1        \n",
+    "    ax = plt.subplot(1, 4, i)\n",
+    "\n",
+    "    df_sub = df_bench.copy()\n",
+    "    df_sub = df_sub.sort_values(['model_config', 'sim_config'])\n",
+    "\n",
+    "    sns.barplot(data=df_sub, y=metric, hue='model_config', palette=palette, legend=i==4, hue_order=hue_order)\n",
+    "    if ax.get_legend():\n",
+    "        ax.get_legend().remove()\n",
+    "        \n",
+    "    plt.ylabel(metric.title() if metric != 'aps' else 'APS')\n",
+    "    plt.tick_params(labelleft=i==1)\n",
+    "    plt.ylim(0, 1.25)\n",
+    "    if i==1:\n",
+    "        plt.yticks([0, 0.25, 0.5, 0.75, 1.0])\n",
+    "    for c in ax.containers:\n",
+    "        ax.bar_label(c, fmt=\"%.2f\", label_type='edge', padding=5, rotation=90, fontsize='small')\n",
+    "        \n",
+    "    # Wilcoxon signed rank test\n",
+    "    for idx in range(len(hue_order) - 1):\n",
+    "        data_1 = df_sub[df_sub['model_config'] == hue_order[idx]][metric].values\n",
+    "        data_2 = df_sub[df_sub['model_config'] == hue_order[idx + 1]][metric].values\n",
+    "        stat, pval = wilcoxon(data_1, data_2)\n",
+    "                                \n",
+    "        if pval < 0.001: star = '***'\n",
+    "        elif pval < 0.01: star = '**'\n",
+    "        elif pval < 0.05: star = '*'\n",
+    "        else: star = 'ns'\n",
+    "        \n",
+    "        rect_1 = ax.containers[idx][0]\n",
+    "        rect_2 = ax.containers[idx + 1][0]\n",
+    "        x1 = rect_1.get_x() + rect_1.get_width() / 2 + 0.02\n",
+    "        x2 = rect_2.get_x() + rect_2.get_width() / 2 - 0.02\n",
+    "        y_bracket = 1.23\n",
+    "        bracket_drop = 0.02\n",
+    "        \n",
+    "        ax.plot([x1, x1, x2, x2], \n",
+    "                [y_bracket - bracket_drop, y_bracket, y_bracket, y_bracket - bracket_drop], \n",
+    "                lw=1.2, c='black')\n",
+    "        ax.text((x1 + x2) * 0.5, y_bracket, star, ha='center', va='bottom', color='black')\n",
+    "\n",
+    "# One legend for whole figure\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "if handles:\n",
+    "    fig.legend(handles, labels, loc='upper left', bbox_to_anchor=(1.0, 1.0), ncol=1, frameon=False, title=\"\", fontsize='medium')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f'{FIGURE_PATH}/Fig2a_synth_benchmark_bar.pdf', bbox_inches='tight')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "11ee2e6e-f3bf-4300-9572-68bd0ee8aa2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x250 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure 2b - Classification performance stratified by simulation parameters\n",
+    "plt.figure(figsize=(2.5 * 3, 2.5))\n",
+    "i = 0\n",
+    "for parameter in ['sim_total_cells', 'sim_binding_ratio', 'sim_mean_inc']:\n",
+    "    df_sub = df_bench.copy()\n",
+    "    if parameter == 'sim_total_cells':\n",
+    "        # When stratifying by total cell count, we remove fraction of binder < 0.01, \n",
+    "        # since 0.01 is the lowest value used for all total cell counts, \n",
+    "        # as with ncells=100, we need binder_ratio>=0.01 to have >=1 binder\n",
+    "        df_sub = df_sub[df_sub['sim_binding_ratio'].between(0.01, 1)]    \n",
+    "    df_sub = df_sub[df_sub['model_config'] != 'BEAM']\n",
+    "\n",
+    "    i += 1\n",
+    "    plt.subplot(1, 3, i)\n",
+    "    ax = sns.lineplot(df_sub, x=parameter, y='f1', hue='model_config', hue_order=hue_order, palette=palette, legend=False, \n",
+    "                      linewidth=1.8, err_kws={'alpha': 0.1}, marker='.', markerfacecolor='none', markeredgecolor=None, markersize=8, ) \n",
+    "    plt.xscale('log')\n",
+    "    plt.xlabel(naming_dict[parameter])\n",
+    "    if ax.get_legend():\n",
+    "        sns.move_legend(ax, 'upper left', bbox_to_anchor=(1, 1), frameon=False)\n",
+    "    plt.ylim(0.5, 1.01)\n",
+    "    plt.ylabel('F1' if i == 1 else '')\n",
+    "    plt.tick_params(labelleft=i==1)\n",
+    "\n",
+    "    sns.despine()\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f'{FIGURE_PATH}/Fig2b_synth_stratified.pdf')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "cd58d156",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure 2c - Heatmap with contour showing pairwise stratified performance\n",
+    "params = ['sim_mean_inc', 'sim_binding_ratio']\n",
+    "df_sub = df_bench.copy()\n",
+    "df_sub = df_sub[df_sub['model_config'] == 'DextraDemixer+neg.+clone']\n",
+    "means = df_sub.pivot_table(index=params[0], columns=params[1], values='f1')\n",
+    "\n",
+    "# Heatmap\n",
+    "plt.figure(figsize=(4, 3))\n",
+    "ax = sns.heatmap( means, annot=False, fmt=\"\", cmap='viridis_r', cbar_kws={'label': 'F1'} , )\n",
+    "x = np.arange(means.shape[1]) + 0.5\n",
+    "y = np.arange(means.shape[0]) + 0.5\n",
+    "\n",
+    "# Contour\n",
+    "thresholds = np.array([0.85, 0.9, 0.95, 0.99])\n",
+    "contour = ax.contour(x, y, means.values, levels=thresholds, colors=\"orange\", linewidths=2, linestyles='dashed')\n",
+    "ax.clabel(contour, inline=True, fontsize='medium', )\n",
+    "plt.xticks(rotation=45, ha='center')\n",
+    "plt.xlabel(naming_dict[params[1]])\n",
+    "plt.ylabel(naming_dict[params[0]])\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f'{FIGURE_PATH}/Fig2c_synth_heatmap_contour.pdf')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a8a6c5ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 350x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure 2d -Difference between negative and non-negative stratified by SNR and Total cell count\n",
+    "params = ['sim_mean_inc', 'sim_total_cells']\n",
+    "df_sub = df_bench.copy()\n",
+    "df_sub = df_sub[df_sub['model_config'].isin([f'DextraDemixer+clone', f'DextraDemixer+neg.+clone'])]\n",
+    "\n",
+    "pivot_table_neg = df_sub[df_sub['model_config'] == f'DextraDemixer+neg.+clone'].pivot_table(index=params[0], columns=params[1], values='f1')\n",
+    "pivot_table_no_neg = df_sub[df_sub['model_config'] == f'DextraDemixer+clone'].pivot_table(index=params[0], columns=params[1], values='f1')\n",
+    "diff = pivot_table_neg - pivot_table_no_neg\n",
+    "\n",
+    "plt.figure(figsize=(3.5, 3))\n",
+    "ax = sns.heatmap( diff, annot=None, fmt=\".2f\", cmap='viridis_r', )\n",
+    "cbar = ax.collections[0].colorbar\n",
+    "cbar.ax.set_ylabel(r'$\\Delta$F1(   $-$   )', fontsize='small')\n",
+    "cbar.ax.plot(7.6, 0.5, marker='s', color=palette[\"DextraDemixer+neg.+clone\"], transform=cbar.ax.transAxes, clip_on=False, markersize=6, linestyle='None')\n",
+    "cbar.ax.plot(7.6, 0.64, marker='s', color=palette[\"DextraDemixer+clone\"], transform=cbar.ax.transAxes, clip_on=False, markersize=6, linestyle='None')\n",
+    "plt.xticks(rotation=45, ha='center')\n",
+    "plt.xlabel(naming_dict[params[1]])\n",
+    "plt.ylabel(naming_dict[params[0]])\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f'{FIGURE_PATH}/Fig2d_synth_heatmap_neg_control.pdf')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f43a65b",
+   "metadata": {},
+   "source": [
+    "## FDR control"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6450f590",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ktm3bRo0aNbjzzjt5/PHHS1TnnZWVRWxsLJmZmdL6J5FIqgTyuiQ5UzDNXIdRUFguoxBk+0xy/McFI8MQoIBdU3DYwG5TcNhBUxUysk3cToWLGjsq+1DOCH766SfatWvH3LlzGTp06CmXj4qK4h//+Adz5szJN/3LL7+kT58+LFu2jGuvvbbAeoU5kpKSkuQ1qRwwTZNnnnmGOXPmsG/fvvD0pKQk7rnnHh599NECpYmloap3K3vwwQd54IEHCu0+NW/ePObPn8/y5cvLZd9CiHyZURKJRHKuUOUcSUOHDmXx4sWMGTOGxo0bM2/ePHr37s3q1avp1KlTket99913vPzyyzRv3pxmzZrxyy+/FLns0qVL6d+/P126dOGVV15hy5YtPP300yQnJ/PGG2+Uw1FJJBKJRHJuYponOIxClkjk8Zpk+wV6bqmaEALBccHI5VCIjlDQ/nYTLEwwgoJgyCSQbqC6ZdRjeZHX8e3v5E2rU6dOoes5nc5zIoS7KrhxxowZw9q1a5kxYwZt27YlLi6OzMxMNm3axLPPPktKSgovvvjiae9n5MiR+Hw+JkyYwIIFC8LdyhYtWsSrr77K2LFj+frrr0//gErJyy+/XOS8gQMHcvPNN5fbvhVFkSKSRCI5J6lSjqQffviBDh06MGPGDP75z38C4Pf7admyJTVq1GD9+vVFrpuWlobdbic6OpoXXniBcePGFelIatGiBXa7nZ9++insQJo0aRLPPPMM27Zt48ILLyzWeOWTf4lEUtWQ1yVJZZEnGFnuIoE/IMj2C3L8glDIch9ZvzgENs1yFjlsCnab5TA6ESHADAnMoMAICoyAwPAKdJ/A1E1ECLwGJNbVaNc5olKO90yjpI6kgQMHsnbtWg4dOpTP1XLvvffywQcfkJaWVizB6Ey/JlVlN05iYiK//fYbNWvWLDDvyJEjtGjRgmPHjp32fmrUqBEukcnIyCAxMZHs7GxcLhd+v5+6deuWyX7KmooW+0aMGMHrr79eYfuTSCSSyqRKPcpbvHgxmqZx7733hqe5XC6GDRvGd999l69LxN+pVq1asWrCt23bxrZt27j33nvzlbGNGDECIUSZdXaQSCQSieRsxDAF3oAgI9vkSJrB3iM6W3eH+GlniI2/B9n0R4jfduvsOqyTlmVimgK3U6F6rEKtaiq1qmkkxqrERKg47QoYCqEcgT/dxHfUIGu3TsaOIBnbg2TsCOLZFSLngE4wywQB0Qk2mnZy0+2GKBq0dBLSBb5AlXkmdkZy+PBhduzYQSgUCk+76aabOHr0KJ988kl4WmpqKosWLaJv377nhOsILDdOdnY2EyZMYPPmzWE3zr59+7j11ltLnWdSFpimWWS3KUVRKKtnxVW9W1lep7bWrVszadIksrOzufzyy0lMTKR+/fps3ry5QsZRhZ7NSyQSSblTpUrbNm3aRJMmTQo8scoLQvvll19ISko67X0ABTor1KlTh3r16oXnlwTDMDAMo8B0RVHyPcUrbJkT0TTtnFjWNM2TftmWZFlVVcM/YOSyJV9WCIFpmkUue+I5XJJlJRLJmY+R5zDKzTDyBwXZPoHXLwjqgpAOpgBFETg0y1nkdirERoKqFryxNHXQA5a7yAzlOYxMjKBAhAQi9/KiaAqKXUF1KNgiVZQTtuWKUGjSxsHcr3zMX+nD4xVERyjcdo2bYb0jLGFKko9XX32VjIwMDh06BMB///tfDhw4AMCoUaOIjY3l0Ucf5Z133snn5L7pppu47LLLuPPOO9m2bRuJiYm8/vrrGIZxToVpr1q1KuzG6d27N4mJiaxYsQKXy8XkyZOpW7dupY1t0KBB9OzZk8cff5w2bdqES9t++eUXpk2bxm233VYm+6nq3coqo/SusHBzGY8hkUjOJaqUkHT48OFwW9kTObHVbFns48Rt/n0/J9tHUS1t169fX2gifrVq1WjdunX49bffflvkjXhcXBxt27YNv/7+++/zPRk8kejo6HCLR4Aff/wRv99f6LIRERH5OhL8/PPPeL3eQpd1uVxcdtll4de//PILHo+n0GXtdjtXXHFF+PWWLVvIyMgodFlVVfO1EN66dStpaWmFLgvk64Kwfft2UlJSily2c+fOYeHp999/58iRI0Uu27FjRxwOK5T1zz//POnf+rLLLgt3ptm9e/dJ3XDt2rUL//337dvHnj17ilz24osvDgulBw4c4K+//ipy2by8A7DO2z/++KPIZVu1ahWu0U9OTmbHjh1FLtu8eXNq1KgBQEpKCtu2bSty2QsvvJBatWoBlkV8y5YtRS7buHHjSv1BLZFISoduWLlFeSVpvoDA47WEo0CuYCROEIwcdoh0WcKRWogTQZig+62yNDMo0P0C3Wti5gpIZu5zB1VVUGyg2hUUt4qqnVoEqlXfxtyvfMz+7/HvMY9X8Obn1us7e0bgdkox6UReeOEF9u7dG379ySefhF1Gt99+O7GxsYWul9d5aNy4cbz88sv4fD7atWvHvHnzCg01Plupym6cF198kalTpzJ69GgOHDgQdiHVq1cv3ESmLKjq3coqSuyrqHBziUQiOROoUkJSaVvNlnQfQJH7yROHCkO2tJVIJBLJmYpuCMtdlNspzR8QeHwCXzDXYRQCASgIHHYr9DrapWArSjASlsMoTzAygoJQtonpF5i6QOi521OwHEY2BVuUimor5Y23KohP0Ji/svDfAh987eOePjIv6e+c7OFGHvPmzWPevHkFpsfHx/Of//yH//znP2U/sDOEquzGsdlsTJkyhSlTppCRkUF2djZRUVHhh1BlRZ06dfjss8/yTfvrr7+qTLeyihL7KircXCKRSM4EqlTYdsuWLalZsyYrV67MN33btm20aNGCN998k+HDh59yOycL286bt2/fvgJlcu3bt0fTNL777rtCt1tUS9u0tLRCAyRlaVvhy8rStqqzrCxtO/s404NtJadPSBe5HdKsTmm+gInHh+UwClmd0gSgKgK7zRKM8kKvC7vhEsIqSzMDAiNkHg++9uYKRqHj1xvFpqDarX9KEdsrDlk+k/0ZJgfSrX/7002iohVeGhlLrwlFO1rXvJhAtWh5HapKlNc1acOGDXTo0CH8+qWXXuKjjz4CrLK8hx9+uEz2c+jQIUaMGMHu3bvzuXH27t0bduMUt0lLRVEVOspVJAMGDEBRlLDYl5ycTOfOncNi388//8yyZctOez8VFW4ukUgkZwJVypFUu3ZtDh48WGD6qVrNlnQfedv8u5B0+PDhfGVgf6eolraapuUTP4qiOMucC8uWRGyQy5bvsoqiFPtvV5JlJRJJ+RPSRW45muUw8gVNPF5LMLIyjCx/kaqIsFAUE6Fg14oWeEydsLvIDFpike4zLdeRbuUYKeQKRjYFm1NBicqfY1QSdFNwNOu4WHQgVzzK8hcUw+MNhWoxKtERCh5vwfnREQrRblnWdq7QvXv3sIt85syZvPTSS0yYMAFFUXjuuecQQpRJEHZVduNUlY5yld2trKJK7yoq3FwikUjOBKqUkNS2bVtWr15NVlZWvqdWGzZsCM8vi32A1Qb3RNHo0KFDHDhwIF/HOIlEIpFIKpuwYBS0coy8AZNsL/hCllgU0gVKrmBkt1kZRpZgVHRJh6ljBV4HBSIvxygnN/haFwgj17V0QvC1FlG8HKOiyAkI9qcbYbFof7rJ4UwTvRCjowJUj1aoF6+SFKdRN07hPBcIUzCom4vZ/ytY3jaom4uQbr0HkrOfE2/a586dy8KFC8MOpUsvvZTBgweXW0c1RVEqXUSCygmZLozKFlAqSuyrqHBziUQiOROoUqVtGzZs4LLLLmPGjBn885//BKxyspYtW5KQkMD3338PWIHGXq+3SCvxyUrbAJo1a4bT6eTnn38OOywef/xxpk2bxm+//UazZs2KNV5ZQiKRSKoa8rp05hLSrYDrYAj8IYHXb+LxWplGoZAgZOQKRupxh5HDpmA7icNImFjuopDADAj0gCUY5QVfi7yqZIXjJWl25bQEI9MUJGcLDqQblsso12mUXoiLCMBpg3pxKvXiVerFa9SLVahlgHrEILQ/ROiATuiATlRtGx3fqI5wKcxb5WP+an+4a9ugri6GdnNjBxxx0jlZlSiva1JMTEzYkZSYmEhKSkq+z8GJ80+Htm3bcuutt3LHHXeEm09UFWrUqBEOmc7IyCAxMZHs7GxcLhd+v5+6deuWealVYd3KzhV0XWfq1KnMnTu3yHBzm61KPaOXSCSScqNKXe06dOjAwIEDefTRR0lOTuaCCy7gnXfeYc+ePcyZMye83JAhQ/jmm2/yPQHJzMzklVdeAazuaGC1vI2LiyMuLo6RI0eGl50xYwb9+vWjR48e3HLLLWzdupVXX32Vu+++u9gikkQikUgkJUUIqwtauCQtJMjxmWT7rEwjXS8oGDlsChGRCnZb0WWrQliCkch1GYVzjHymJRjlBV+TG3xtV7BFqqeVYwSWK8rKMbKcRvvTTQ5lmASLiM1LiMx1GcWr1IvTqBejEJtjYhw0LMHoOz+hgzqZhZS2+dMMHLEqa+86ynXDY7l7RjWyMk1iYlUOf+fnh/uTuerdqnWjLyk/AoFAvhykE90nHo+nzG7ot23bxvz583n88cfp1asX99xzD717964SmYAVFTJ9pncrK6vSu4oKN5dIJJIzgSolJAG8++67PP7447z33nukp6fTunVr/ve//+VrH18Y6enpBdqc/utf/wKgfv36+YSk6667jk8++YQpU6YwatQoqlevzmOPPcYTTzxR9gckkZzh+AICmwaZOSYxkSqGgWyvLZGcAiEEwVzBKBiycou8fhOPzxKQQiGBblqCkaZiuYvsClEuBZt2csHoePC1VZYW8gp0b65glBt8LQA1N/jaFqGiaJQ6xwjAFIJj2SJXLDLCLqPU7MJdRnYN6sapYadRUrxG3UgF27FcwWh70PrvIZ1joUI2YAN7HRv2JBv2ern/6tjITDOpeYWbn0em4ohXcSVq+FMNgukmF94fg6kLVLu8Pp0LDBo0iPT0dAD69u3L/v37w0LSsmXLaNWqVZnsx+VysXnzZn744Qfefvttbr/9diIjIxk6dCh33XUXjRo1KpP9lIaK6ih3pncrK4/ii7wH1RLJucCaNWvo2rUrq1evpkuXLpU9nNNCURSefPJJJk+eXNlDOeMpl9K29PR0XnnllbNemJElJJKznUBI8J8vvMxf6QuXkNx2jZthvSNwypu1Kom8LlUOHq+Z2xUNy2HktzKNQnp+wchhJ9wpzXaK8rF8wdehvE5pVo6RmZtjpCig5OYYaTbLbXQ6ghFAQBccPLFjWobBwXQTv1748nHu4y6jpNzytEQHGIcMQvt1Qges8jT9sAGF5SE5leNiUT1LPLLV1FAKeX9cEQrNWzv48+0sds33EMoS2GMUGt0WTdO7Y9GkyF3lONOvSX8vkfN6vSxcuJA5c+awfv16rrrqKlatWlUpY6uojnJnUreyc7n07lxj3rx53HnnnYXOe+SRR5g+fToNGjRg7969gCUgxMTEkJSUxOWXX86wYcPydX7M4+9OvujoaC666CLGjx9Pnz59yv5Ayoj169ezfPlyxowZU+YipxSSJIVRYkeSEILk5GTi4uIKdDA7cOAAM2fO5D//+Q85OTlnvZAkkZzN5PhN5i3zMfu/3vA0j1fw5ufW6zt7RkhnkuScx+sXHDqmczjNJBSyisdsmuUwctoVotwndxgBmMZxwUiEcoOvvcdzjEwdK8NIVVDslEnwNVjf5+lekSsWmeEStWSPoLAnTDYVasfmiUV5biONCF2Ec4xCmwOE9uskp+Smdf8NNVIJi0V5wpGWqBVb/PJ7Bdt+DZI0IIom98QSzDJxxamYupAikqRCiIiIYOjQoQwdOpSdO3fy9ttvV9pYKipkuqp3KzvTS+8qi8tnflHZQ+C7h09fmHnqqac4//zz801r2bJl+P/btm0bDt73eDxs376dRYsW8dZbb/HQQw8xc+bMAtvs3r07Q4YMQQjB3r17eeONN+jbty9Lly7l2muvPe0xlwfr169nypQpDB06VLrlJBVCsYUkIQRPPPEEr7zyCh6PB0VR6NOnD3PnzsXlcvHYY48xe/ZsgsEgvXv3Zty4ceU5bolEUo5kZJtEOBXmryzYGQngg6993NMnooJHJZFUHUK64Eiawf4UE19AEB+l4Io++Y2KEGAEjgdfG0FBKMfE9Oc6jHRrGUW1nEWaXcHmOn3BCCBkCA5lnugyskQjb7Dw5WNcSlgsyhOOasaoKB7TEoz2WC6jnAM6WWmF2IwANVbNJxg5kmyocepp57b4vYI/dobw/OgnrppKuysjZDnbOUhFhWCfTCRp2rQpzz33XLntuzSUR0e5qt6t7EwvvZOcHr169TppGWfdunW5/fbb80177rnnGDRoELNmzaJx48bcf//9+eY3adIk3zo33ngjzZs356WXXqqyQlJJME2TYDCIy+Uq0+0qisLcuXMZOnRomW4XLDdoRIS896hKFFtIevnll5k2bRr169enR48e7N69m//+978MGzaMlJQUNmzYwO2338748eNlYLVEcoaiG4JDqQbJmSb1a2h4iuiy5PEKPD5BtWh58yY5tzBMQWqmyf5kg4xsk2i3Qu1q+QUkIbDEolyXkRHMK0sTmLqJCOUKRgooJ+YYnWbwdR6ZvlyxKDfH6EC6yZEsE7Mwh5ACtWJOcBnFqyTFqUS7FIw00ypN+83KM0rdH8L0FH5N0BJV7PXs+dxG2imEtdKim1b+VFaOid1eLruQnAFUVAi2x+Mp0+1VJGUVMv3iiy8ydepURo8eXWS3sspk/vz5BUrvEhMTadSoEZ06daJFixanLSS9f/WPJV7n9pXtTmufkvLD7Xbz3nvvUb9+faZNm8Z999130u/fZs2akZiYyK5du4q1fb/fz/Tp05k/fz779u0jPj6eyy+/nBkzZoRz1fKqdxYuXEhycjINGjTgnnvuYezYsfnGoigKDzzwANdccw2TJk3ijz/+4IILLuBf//oXPXv2BGDy5MlMmTIFIJ87K6+Ded42Lr/8cp555hl+//13Fi1aRP/+/dm0aROPPfYY3377LaZp0qFDB6ZNm8Zll11W4ve1uCxdupTp06ezceNGFEWhadOmPPTQQwwaNAiALl26kJqayjvvvMNDDz3ETz/9xL333suLL75IIBDgmWee4YMPPmD//v3UqFGDW2+9lalTp+arlgoEAkyYMIH3338fv99P165dy+R6KDlOsYWkt99+m/bt2/PNN9+E/0jjx4/nhRdeoF69emzcuLHMgg0lEknFk+0z2XPE4GiaQVw0REfYiY5QChWToiMUot1SRJKcOwghyMgW7E8xSM00cNgUalVTURUFYUIwyypF0325ndJyc43yzAyqTUG1KdhcGkrU6QVf56GbgiOZx8WiPOHIU0jHM4BIB9SL18JiUb14ldqxKjYF9OTcPKOdQYIHdI4c0BG+QrajgK2Whr1u/vI01V0+opFhCoKGldsU0q3Qb5um4NAgIUIhMUpeh85VqnIIdlWhrErOqnq3sqpeeicpXzIzM0lNTc03LTEx8ZTrRUVFMWDAAObMmcO2bdto0aLFSfeRnp5erOuKYRhcd911rFy5kltuuYXRo0fj8XhYsWIFW7dupVGjRggh6NevH6tXr2bYsGG0bduWr776inHjxnHw4EFmzZqVb5vr1q3jk08+YcSIEURHR/Pyyy9z4403sm/fPhISErjhhhv4/fffWbBgAbNmzQoff/Xq1cPbWLVqFQsXLmTkyJEkJibSoEEDfvvtNzp37kxMTAzjx4/Hbrcze/ZsunTpwjfffFNohtTpMm/ePO666y5atGjBo48+SlxcHJs2bWLZsmVhIQng2LFj9OrVi1tuuYXbb7+dmjVrYpom/fr1Y926ddx77700a9aMLVu2MGvWLH7//fd8pb53330377//PoMGDaJjx46sWrWqSmdcnYkUW0j6448/mD59ej6l7+677+aFF15g4sSJUkSSSM5QhBAcTTfZfcTA5zdRVXjhIx/3Xqdwazc3//6ft8A6t13jRjesHBiJ5Gwnx2dyMNXg8DETgSAxRsWmKQhhCUi+ozrBDNPqlJYbfK06FGyRapkIRgAev+BAxvFuaQfSTQ5nmuiFhVcDNWIUkuK04y6jeJU4twIG6Id1ggd0Qht1Mvbr6Id0RGElblpu57QTgrBtdW2ojvIRb8xc0SioCwK6dW1SVSuYPMKuEBsDbruGy67gsoOZaWI7i4SkgwcP8n//938kJydz4403Uq9ePQzDIDMzk9jYWDRNq+whVknat29P+/btmTlzZjgEe/r06RUWgl1Wrp/TpbCQ6TfeeKPM91MVu5VV9dI7SflyzTXXFJhWXPEwL0tp165d+YQkv99PamoqQgj27dvHpEmTMAyDm2666ZTbfPfdd1m5ciUzZ87koYceCk+fMGFCeFyff/45q1at4umnn2bixIkAPPDAAwwcOJCXXnqJkSNH5hOttm/fzrZt28LTunbtSps2bViwYAEjR46kdevWXHzxxSxYsID+/fvToEGDAuPauXMnW7ZsoXnz5uFpAwYMIBQKsW7dOho2bAjAkCFDaNq0KePHj+ebb74p1vtYXDIzM3nwwQdp3749a9asyVda9/e/2ZEjR3jzzTcZPnx4eNr777/P119/zTfffEOnTp3C01u2bMl9993H+vXr6dixI5s3b+b9999nxIgRvPbaa4D1/t522238+uuvZXpM5zLFvg30+/0F1N28Gmz51EciOTMJhAR7j+gcTDVx2mH3YYP/fOkjxy947bMc5k2IQ1XgA9m1TXIOEggJjhwzOJhqdWSLi1JwOSznje4V+JINAsd0bHaF2Ho2dB30wlrZlwDTFBz1CA6kG/lK0zIKcwcBLhu5YpFmlafFqdSJU3HaFMshdUgntMvKM0o9oBM6rINRcDuKA2x1rRyjvBI1Wy0NxVZOopEQhHKdRkHdch6piiUauewKiVEQ6dRw5b62KVi5Un6BkWOiC1DsoDnP/ABdIQRjx47l1VdfRdd1FEWhVatW1KtXj+zsbBo0aMBTTz3FmDFjKnuoVZrKCsGuTLeLDJm2qOqld5Ly5bXXXqNJkyalWjcqKgooWMI6Z84c5syZE35tt9sZP348Dz/88Cm3+fHHH5OYmMioUaMKzMtzzn355ZdomsaDDz6Yb/7YsWNZvHgxS5cuZeTIkeHp11xzTb777datWxMTE8Nff/1VjKO0uOqqq/KJSIZhsHz5cvr37x8WkQBq167NoEGDeOutt8jKyiqy26bX68XrLfiwOTs7O59DTNM04uPjAVixYgUej4cJEyYUyGf6u6vQ6XQW6Mq3aNEimjVrxoUXXphvH926dQNg9erVdOzYkS+//BKgwPs7ZswY5s+fX+jxSEpOifwERdlG5VMyieTMI81jsvuwToZH4HJYAdprt1h3wQ1ra4y6IQKHQ+H6Hg7uui6CTK9JbITKsaCOKp1IkrMY3Tieg5SZI4iJgFq5OUh6QOBPNfCnGLhdCo0vd5NQXSMz2yQ2SiUt1eDIXh1/EfliJ+INilyH0XGn0cEMk1AhQg9AYpSSKxZpYZdRQqSCoiiY3twQ7F8DeA/oZO7X0ZML75ymRCjHXUa55Wm26sXvnFZShMhzGlluI0MAQuC0KTjsCgmREOnQcNnBZVOwqwIRsoLJDY/AMAWGpqA5FOzRCpHxGrYIDZtbwXYWlNjOmDGDl156iUceeYSrr76a7t27h+fFxsZyww038PHHH0sh6W9URgh2Rbl+iosMmbao6qV3kvKlffv2Jw3bPhnZ2dkA4c9zHtdffz0jR44kGAzy448/8swzz+D1evMJsykpKRjG8S/sqKgooqKi2LVrF02bNsVmK/rH8t69e6lTp06B/eblDO/duzff9PPOO6/ANuLj40lPTy/mkVKgs11KSgper5emTZsWWLZZs2aYpsn+/fuLLPl7/vnnw7lMJzJq1Kh8Ilr9+vXZs2cPQDhj6sSuekVRt25dHA5Hvml//PEH27dvz1eydyLJycmA9f6pqlrA7FLYsUpKT4luBydMmMCzzz4bfp334bn77ruJjIzMt6yiKGzevLkMhiiRSMoS3RAcTDHYm3uTmZ5t8PoSL6mZAkWB/lc4ufFKF3Vqa6z15fCN10uEohClqmSnm3iFoIsZQafISBxlEAwskVQVhBCkeQQHcnOQ3A6FWtUUVEXB1CGQbuA7oqN7BTG1NC5s72LuVz7mn+DYG3S1mzuvdfP75mBYTDKFIDVbWA6jdCPsMjqWU/iNsEODunF5JWka9eJU6saruHNdgEaWSehAiND2AOn7dUIHdIxjRXROi1HzCUb2eja0aqffOe1khAyrNC2oW64jVRHYVAWnTaFGtEKUSw07jZw2MEO5bqMcgREyMVVQHQqaS8FdQ8MWqWKLUNDcKlo5ldVVJm+99RZDhgzhmWee4dixYwXmt27dmqVLl1bCyKo2FRWCXVGunyNHjuTrPrdkyRI++ugjAG666SZuuOGGAutURMj0mUZVLL2TVF22bt0KwAUXXJBver169cIlc7179yYxMZGRI0fStWvX8GexXbt2+QSfJ598ksmTJ5fLOIsybZTEEel2u8tqOIBVAndieRlA9+7dGTduHD169Djt/Ra2nmmatGrVipkzZxa6TlJSUqn2JSkdxRaSrrzyykJ/eNaoUaNMBySRSMoPj9fKQkpON4hyKXyxIcDn3wYQQI04lQf6R9Cong2v0Il12dlwzAeAVwi8Jzx1+d7n46q/iccSyZmMx2vlIB1JM1GA6rHHc5D86Qb+IwZBj4nmVnEmqtRp4mDuVz5m/9d7wjZE+HW3Vg7mLMpmf7rlMgrohe83PkIJl6TlladVj1JQVas0w0jP7Zz2o473gCUamZmFi0ZaNTWfYGSvZ0OLLV/HcMiwStOChuU6UnLDsJ02SIhUiHapuOzgtFm5RhiW08j0C0yPif+EEjV3TQ17tIrNraC5LSGpPAWvqsL+/fvp2LFjkfMjIyPJysqqwBFVDY4ePUqNGjUq/RyoKNdPkyZNwn/n9957j1GjRjF8+HBUVWXYsGF4PB7uuOOOfOtURMh0VehWVlXOBcnZRXZ2Np9++ilJSUmn7Dg+fPhwZs2axaRJkxgwYACKovDBBx/g8/nCy+SVhzVq1IgNGzYQCoWwF9FatH79+nz99ddhh2MeO3bsCM8vKSX9fFSvXp2IiAh27txZYN6OHTtQVfWkwkzDhg3zlcTl0bx580Jzq+B4HM7WrVsLiHfFoVGjRmzevJmrr776pMdbv359TNMMu8PyKOxYJaWn2ELSmjVrynEYEomkPDFNQXKGyV+HDfwBE10XPDvfy+4jljh0VRsHd1zrBgccDYUIhgRew8RfxI9QvxD4hSBS/qiTnOH4g4LDxwwOpRr4Q4KEaBWHPTdI25MbpJ1uotgUnNWs8i+bHaolasxf6St0m/NX+rirVwS/HjZIz7Y+QzYV6sRZglFSbgB2vTiNSKf1GRKmwEgxCP0RxHNAt8SjAzqisBI5BWw1tPxOo7o21MjyzULRTREuTwvqQK7TyGGDOJdCjFvFaQd3rtNIBYygJRoZHpOAKcCmYHMq2GMUHHH5S9QU7dy8ntSoUYP9+/cXOf/nn38utKzhbKdVq1Zs3ryZ2rVrl2r9sgrBrijXz4miz4svvsjChQvDT/WvueYaxo4dW0BIOldCpk/3XJBI/o7P52Pw4MGkpaXxzDPPnFKEsdlsjB07lhEjRrBkyRL69+/PFVdcUeiyN954I1988QWvvvpqvrBtsD7niqLQu3dv/v3vf/Pqq6/y6KOPhufPmjULRVHo1atXiY8przooIyOjWMtrmkaPHj1YsmQJe/bsCQd0Hz16lPnz59OpU6ci85FKS48ePYiOjubZZ5+lZ8+eBcK2T/V3uPnmm/nyyy956623uPfee/PN8/l8mKZJZGQkvXr14rHHHuPll18Oh20DhV6rvV4v+/btIzExsVjd/iTHkUknEslZjj94PFDb5RD8+pfO+yt8BHWIdCnc08fNpc0cZBk6x3wmmVmCWrEKETYVl6IUKia5FAWXFJEkZzC6IUjJMNmXbODJEcRGQXy05d7RfQLfUStIWwD2OA31BJHD7lDIzDbxFJGD5PEKMrJN+l3swKZDUrxKjRgVW24GkTAE+hGD0C8BMvfrVpnaQQMRKGR7Kthq54Vgn9A5zVm+nz8jt4NaQBeEdKs8z6YpODSIcirExClE2BWcuR3UNMUqUTP8AtNrEtAFigqa03IXuWvllqi5rTI1VYb1h7nhhht48803GTp0KLGxscDxJ8vLly9n3rx5jB8/vjKHWK5cfPHFhU5PT0+nR48e2O12Nm7cWOLtllUIdkW1lj9xH3v37uXqq68Ov+7atWs4Y+REzraQ6fI6FyTnNgcPHuT9998HLBfStm3bWLRoEUeOHGHs2LH5uoKdjKFDh/LEE0/w3HPP0b9//yKXGzJkCO+++y4PP/wwP/zwA507dyYnJ4evv/6aESNGcP3119O3b1+6du3KxIkT2bNnD23atGH58uUsWbKEMWPGlKqR1SWXXALAxIkTueWWW7Db7fTt27dA/MyJPP3006xYsYJOnToxYsQIbDYbs2fPJhAI8Pzzz5d4DKciJiaGWbNmcffdd9OuXTsGDRpEfHw8mzdvxuv18s4775x0/cGDB7Nw4ULuu+8+Vq9ezRVXXIFhGOzYsYOFCxfy1Vdfcemll9K2bVtuvfVWXn/9dTIzM+nYsSMrV67kzz//LLDNH374ga5du5ZraeLZSqmEpD///JMVK1awa9eusCXvggsuoHv37rKDm0RShTiWlRuonS2wa4LZ//Wx6U+rxqbV+Tbu7xeBK1ohORQiJdtENaBabcGvEX6SgjY6uN18U0hHhsvcbgxAxuxLzjSEEBzLEhxINjjmMYhwKtROsEqojKAVpO1LNjCDAkeMVmir+1BQEBOpEh2hFComRUcoJMSoXNXQTihHEDqkE9gaJDu3NC10SIdCSt0Uu9U5LV8Qdi0bSjmLLqaZF4ZtZRsJIVBznUYRdoXYGHDbNVy5opFdUzB1YZWo5ZiEgoKgANWRW6JWS8MRo4bL086VErXSMmXKFFavXk3btm3p3LkziqLw3HPP8fjjj/Pdd99x0UUX8dhjj1X2MMuNPXv2cMkllzB48OCwKCOE4MEHH2TYsGHhbj8nozxDsCvK9RMMBnnllVdyP38qXq83fCyBQKDQdc62kOnSnAuXz/yixPv57uE+pz3Ws4Fz5X345ZdfGDx4MIqiEB0dTVJSEn379uXuu++mffv2xd6O2+1m5MiRTJ48mTVr1tClS5dCl9M0jS+//JJp06Yxf/58Pv74YxISEujUqROtWrUCQFVVPv/8c5544gk++ugj5s6dS4MGDZgxYwZjx44t1XG2a9eOqVOn8uabb7Js2TJM02T37t0nFZJatGjB2rVrefTRR3n22WcxTZMOHTrw/vvv06FDh1KN41QMGzaMGjVqMH36dKZOnYrdbufCCy8s4N4qDFVV+eyzz5g1axbvvvsun376KRERETRs2JDRo0fn69739ttvU716dT744AM+++wzunXrxhdffCFzlMoQRZTgUUp2djb33HMPixYtwjQLZjSoqsott9zC7NmzT3rSni1kZWURGxtLZmZmmVv/JJLTIaRbgcH7k63Std1HdN76n48sr8Cuwa1Xu7mmnYNs0yAtZLmQYiIgo6bOLoIAJNlsDImL41uvl+99PvxC4FIULnO7uTIyEpu8MaySyOtS0WTlWDlIR9NNVAXioxU01QrSDqYbeHODtG1RlnOmMLxBwUc/BbilTwS/7dH59/8KCq33XRfBoE5O/m/QUfSjBhQSaaS4CumcVkMr9/IuU1gB2IHc8jTDFKiKJRq57ArRToh0Hg/DdtgUq+wukCscBQRCgKqB5lKwRSrYYzXsEZZwZHOduyVqp4PP5+Nf//oXixcv5o8//sA0TRo1asTNN9/MuHHjyjwktSI51TUpOTmZkSNHkp6ezptvvhl+IFm9enU2b95MnTp1Ct1uRYVg67rO1KlTmTt3bpGun5N1ZyouXbp0ySe4Tp8+PXwjt3TpUqZMmcL3339/2vspKRWZkVSac6GihKSqkBUlkUgkVYlif/MJIejXrx9r1qyhR48eDB48mJYtWxIVFUV2djZbt27l3XffZf78+Rw9epQVK1aU57glEkkRZHlN9uQGarsdCov/z8/KjZY4dF4NlVEDIkmornJMN0jNMTADEFlDsDMqQLaw7ngvdDi43O0m0zBolysc+UwTt6pighSRJGcUvoDg0DGdQ8dMQrqgWrRqCSR5QdpHDYJZJppLwZmoFeme2XZY553vA6R7Bcmf5DBvXByKEMxf7T/eta2rizu6uXA6FFS/ABPUKOWEEGy7FYKdoKKo5fs5EiLPaWS5jQwBCIHTpuCwKyREQqRDw2UHl80Sk+CEErVsE69hdXPUnFaOkaO2hj1SRXNbodiyRK1scLvdTJo0iUmTJlX2UCqcGjVqsHDhQpYsWcK1117LXXfdVaxSvooKwa4o18/Jskg7duzI//73vzLdX1WktOeCRCKRSCqeYgtJn376KWvWrGH69OmFXtTbtGnDbbfdxvTp05k4cSKfffbZSetHJRJJ2WKagqPpJrsPG/hDJtlek+c/9HEkzRKHrrvcyY1XufApBoeDITIzBREOSK8fYrsaAgFRqspVEREk5XaZCApBuq6jAfGqiltVcUgRSXKGENKtkPn9yQbZPkFclEJCtHo8SDvZIJhm5AvSLoyALvh4U5A1v4cAqBGj8Mj1Efxw+xGueziOu2dUIyvTJCZW5fB3ftYPPsoV/65BzftiCLpV1Fi1Qkq7QoZVmhbULdeRmhuG7bQp1IhWiHIddxo5bVYmi6kLDL/A8Jr4QpZBWXOA+rcSNZtbQXXKErXyoFu3bkycODFfJs6JrF69mqlTp7Jq1aoKHlnFcv3119OlSxceeeQRLrroonzdkAqjokKwT6SiW8v7fD6EEOHsrHOFkp4LEolEIql4ii0kLViwgDZt2pzyycCECRP48MMPmT9/vhSSJJIKwhcQ7D2qc+iYgdMG324J8fH/+TEFVItWGHF9BA0b2MjQDdK8BiEfOKsLfo/y48W6eWzhdNLB7Q4LRQEh8JkmkapKgs1GVBmUCEgkFYFpCo5lWQJSmsckyqVQu5olgug+gS85N0hbFAzS/ju7Ugzmfucn2WN9Tro0tnPzpXYubelgaarBzyNTccSruBI1/KkGwXQTe4yCM15DOc+OFiqfYwwZuR3UDMt1pOSGYTttkBCpEO1ScdnBabNyjVQlt0TNLzByTPx5JWo2y23kqqbiiNUs0SgiN9eonB1TEos1a9Zw9913Fzk/OTmZb775pgJHVHnExsby5ptvsnbtWlatWnXS8tyKCsGuKP7973/TpUsXmjRpwpEjR7j99ttZvXo1iqJwzTXX8O6771KjRo3KHmaFUZJzQSKRSCQVT7GFpI0bNxY7ULBfv3588MEHpR6URCIpPscyTf46opOVI9ANwWuf+fjjgJWNdFlzO3f2cmM4TI4EQ2R6BA4beM4LclCz0n5jVJUuERHUyXUhmUKQbZqoikJ1TSPeZpOlbJIzhoxskwMpBsnpJnYb1IpXUdW8IG3dCtIOCRxRKqqzaHE0ZAj++2uQr7aHEALi3ApDL3fSxKGQ8UoWyapCo1uj2TE7i2C6STD9eBBSo0HRZKQa6GUkIummCJenBXUg12nksEGcSyHGreK0gzvXaaSp1k20GbRK1AIZJsIQKKqC5lSwRyrYZYlaleJkTq8///wzHLp8rnDppZdyySWXEBERUeQyFRWCXVE89dRT/OMf/wBg9OjR1K5dmz/++AMhBE899RSjRo3io48+Ci9/roRMF+dckEgkEknFU2whKTk5mfr16xdr2fr165OcnFzqQUkkklMT0q3OU/uSDRRFsGOvzjvLffiD4HbCXT0juLiljSzDJMNvEMgBNdHk9+gAAQQK0Nrp5FK3G3vuTYzfNPELQbSmkaBpREgXkuQMwes/noNkmIKEGBW7zQrS9h/LH6TtiDl5v8H96QZz1wc4kGGJQ5edb+MflzhQfgmSsjgbERBsey2TK+fVBAV2zfcQyhLYYxQaDYrmgrti2PZrsFTHYeR2UAvogpBuCbs2TcGhQbRLIdqlEGFXcOZ2ULPluobMkBWGHcoSBHQr10h1gOZScSXYsEfnlaipaE4pGlU277zzTr42x08//TRvvfVWgeUyMjL49ddf6d27d0UOr0IpzImTlxd0MifOiy++yNSpUxk9enSRIdjFJS0tjWrVqpXVIZWKjIyMsOtm7dq1/Pnnn2Hx5M033yz2b/AzmdKeCxKJRCKpeIotJOXk5BS7a4jL5cJbSMtwiURSNmR5rSyklAwDmwbvLffzww7L/nDheRrD+0XgjBYk6zpZ2QIVyDgvyJFcF1K8qtIlMpKauZ1mjFwXkk1RqGmzEadpaNKFBFhtl5944gnee+890tPTad26NU8//TTdu3c/5boffvghzz//PNu2bSM6Opp+/frx3HPPkZiYWGDZOXPm8MILL7B7926SkpJ48MEHGTVqVHkc0llFSBccSTPYn2LiCwjioxRcDjU3SNvEf1QvVpA2WCLOV9tC/HdLEMOEaKfC7R2ctI5XyXzfg3+zJQ45Gtpw/SOabb8FSRoQRdN7YglkmThjVDJSDbb9GsTvPXVZjWnmhWFb2UZW22/LaRRhV4iNAbddw5UrGtlzS/DCJWpeQTCYG+htzy1RS8wtUXPJErWqjNfrJSUlJfza4/EU6DCmKAqRkZHcd999PPHEExU9xAqjMCfO77//XqQTJ4/ShGCnpqYyevRotmzZQr9+/ZgwYQLdu3dnw4YNJCUl8fnnn9OmTZvw8hXZqatx48asW7eOzp07Ex0dTVpaWlhISk9PP+NK9UpDac8FiUQikVQ8JepXKkM2JZLKxTQFR9Ksrmz+kMnRdIN//9dHerZAU+HmLi6uvsyBxzRJCRj4ssFIMNgbEySY60K6yOXiEpcrLBT5TJPgCS4kt3Qh5WPo0KEsXryYMWPG0LhxY+bNm0fv3r1ZvXo1nTp1KnK9N954gxEjRnD11Vczc+ZMDhw4wEsvvcRPP/3Ehg0bcLlc4WVnz57Nfffdx4033sjDDz/M2rVrefDBB/F6vTzyyCMVcZhnHIYpSM20cpAysk2i3Qq1q5U8SDuPo1kmc7/z81eq5UJqW0/j9g5OHLtCpLyZgekRoEF070iirnajqAp+r+CPnSFsf4WwOxRCQVFkOZsprADsQG55mmEKVMUSjVx2hcQoiHRq4TBshy1XNBICMyAwsgW+gLBK1LTcErUohcg4DVuEajmN3AqqTX5Pnwncf//93H///QCcf/75vPTSS/Tr16+SR1U5lIUTp7gh2CNHjsTn8zFhwgQWLFhAr169uPLKK1m0aBGvvvoqY8eO5euvvz6t4yktU6ZM4bbbbuOJJ57g1ltvpW/fvowePRohBK+88gpDhw6tlHFVJNKVJZFIJGcOJRKShg0bxvDhw0+5nK7rpR6QRCIpHG9AsPeIFaht12DZDwGW/WA5JOokqNzfP4KEGpBi6GR7rSDSY0khUmxWXlKCptE1IoLEXBeSnutCcqoqtW02YlQVVYrF+fjhhx/48MMPmTFjBv/85z8BGDJkCC1btmT8+PGsX7++0PWCwSCPPfYYV155JStWrAiL8B07dqRv37689dZbYbeRz+dj4sSJ9OnTh8WLFwNwzz33YJomU6dO5d577yU+Pr4CjvbMQAhBRrZgf4pBaqaBw6ZQq5p17uYL0jbBHqudUlgxhWDN7yE+3hQkZIDLDrde6qR9HQ3PkhzSvvUDYKulET84GnuSvcA29BDooeNuASHynEaW28gQgBA4bQoOu0JCJEQ6NFx2cNksMSnvHDFDltvI7zcx/16iVl3DHqXmOo1kidrZwu7duyt7CJVKRTpxVq1axe7du4mMjKR3794kJiayYsUKXC4XkydPpm7dumW2r5LSr18/HA4HTzzxBD///DNCCO666y7OO+887r33XiZMmFBpY6sopCtLIpFIzhyKLSTdcccd5TkOiURSBEIIjmUJdh/WyfIKsn2C2Z97OZDrnOhxqYMBV7vwKyapIQOvBwLxBgdiA4QAFbjE5aJtrgtJCIFPCEJCEJfrQnJKF1KhLF68GE3TuPfee8PTXC4Xw4YN47HHHmP//v0kJSUVWG/r1q1kZGTwj3/8I5+T87rrriMqKooPP/wwLCStXr2aY8eOMWLEiHzbeOCBB/jggw/44osvuP3228vpCM8scnwmB1MNDh8zEQgSY1RsmhWknXNMx59sYARPHaSdx7Eck3e+D7DjiCW2NqulccdlTqJSDFJnZGCkWNMju7iJ6RuJcpJA6pyAlU2km6BghWE77Qo1ohWiXGrYaeQ8UTQyLLdR0CMwggJFgGIHzaXgqm5lOVm5RgqaW5Gu4HMAj8dDZmYmpmkWmHfeeedVwojKn4p04pimGS4hVFUVIUT4c6Uolf8Z69mzJz179sTr9ZKenk5UVBSxsbHlvl+fz4cQotIDraUrSyKRSM4cii0kzZ07tzzHIZFICiEvUHtvsoGqCH7cEeKj1X4ME2IjFe7pG0HD8xUyhUGOTxAMmSTXC5Ge60Kqrml0jYykmmaFC4eEIMc0cakqdWw2oqUL6aRs2rSJJk2aFGg73L59ewB++eWXQoWkQCAAUGiunNvtZtOmTeEbmk2bNgFWZ5oTueSSS8Lzz3UhKRASHDlmcDDVxB8UxOXmIJk6+FNzg7RzBLZIFVfCyYO0wRJnv9ut8+FPAfwhsGtw00UOrmxkI2e5j9TlXit3KE4l/rZonE0dRW7LFIK0bIHdBgmRCtEuFZcdnDYr1yjv8yVMgRkUhLxWKDamAE3B5lSwRytExmvYIjRLNHLJErVzjTfeeIOZM2fy119/FbmMYRgVOKKKoyKdOJ07d+a2227j5ptvZuHChVx++eVMnjyZUaNG8dprrxW4DlcWERER5SbqFBZovXr1ahRFqfRAa+nKkkgkkjOHEpW2SSSSiiMrx+SvwwapmSamKXh7qY9te62y0Uub2Li9jxvhFBzTDXweyI41OFQzgK6ABrRzu2ntdKLmupByhMAEqmka1Ww2HFJAOiWHDx+mdu3aBabnTTt06FCh6zVu3BhFUfj222+58847w9N37twZDthNT08nISGBw4cPo2lagR/uDoeDhISEIveRRyAQCAtXAFlZWcU7uDMA3Tieg5SZI4iJgFrVTgjSTtYJZhQvSDuPLJ/Jez8E2HzAuilvmKhy5+UuEnwmaS9mEtpnfcbclziJHRiFGlG0sylkCNJyBHERCucnaES7ju/fCFq5RsGAiRkSKCqoDkskctfQsEXmlqi5VTSH/Cyey7z55ps88MADXHvttdx1111MnDiRhx56CJfLxbx586hZsyYPPvhgZQ+zXCmpE+fymV+UeB/fPdyH1157jREjRvDss88yduxYOnbsSPfu3Xnuuedo3LgxS5YsOZ3DKFdGjBjB66+/ftrbKSzQ+o8//qgygdaV5cqSSCQSSckotpDUsWNHpkyZEu5UpOs669evp02bNvICL5GUIUZeoPZhnaAh+OuQzttLfXgD4LTD7T3cXNxKxYOJ3y/ICZok1w2RmetCqmWz0SUigri/uZAiVJUETSNKVSvdvn+m4PP5cDqdBabnBWX7fL5C10tMTOTmm2/mnXfeoVmzZgwYMICDBw8yatQo7HY7oVAovK7P58PhKNzx4nK5itxHHs8++yxTpkwpyWFVeYQQpHkEB3JzkNwOhVrVFFRFIegx8ScbBHKDtB3VNFSteOfzz/t0PvjBT3YANBX6tXbQ40Ib/vUBUj7LRoRAcSvE3RyF+xLXSbflCwkyfSa1Y1TqV9PQdEEgzRKNyCtRc6q4a2rYo9VweZrmqvzyGUnV4pVXXuHaa69l6dKlHDt2LJyZ1q1bN8aPH8+ll17KsWPHKnuYFUJ5OnEA6tSpw2effZZv2l9//UVaWhoJCQnltt+yoKzygc6UQOvyPhckEknxWbNmDV27dmX16tV06dKl3Pc3dOhQ1qxZw549e8p9X5LSU+xglO+//z5fq9rMzEy6du3Kzz//XC4Dk0jORbwBwe/7dXbsCxEMCRat9vPqZ5aI1KiOxuR7omjZSiPdMPFkmqS4Quyq5yfTZmADOrndXB8VRZymYQqBxzTxC0GiplHXbidaK55rQ2LhdrvzuX3y8Pv94flFMXv2bHr37s0///lPGjVqxJVXXkmrVq3o27cvAFFRUeFtBIPBQrfh9/tPug+ARx99lMzMzPC//fv3F+vYqioer8nO/Tpb/gqR4TGpHqsSF6Vi+iF7n07WH0EC6Qb2WA1HbPFEpJyAYM63fmavtUSkenEqE3u66VFPI2N2FpmLLBHJ0dROjQnxpxSRMn0mOQFBw0SNhokqItPE8JnYYxSiG9mIb+0k8WIX1S91Enehg8i6NpzVNGxuKeJKCrJr167wdcFut8Lc864JsbGx3H333WXiRDlT+Xt+XFmjKEqVE5E8Hg+HDh3C4/GEp73xxhtlsu28QGsgHGidR1UPtC7vc0FyZjFv3rxwttnf/+WVQTZo0CA8TVVV4uLiaNWqFffeey8bNmwodLt/31ZMTAxXXXUVX3xRcidkRbJ+/XomT55MRkZGZQ9Fco5wWqVtVfnLRiI5kxDCKuHZfdjA4xOkZJj8+39eUjOtrk39OznpdrmNHFWQFRRkBUySawfx2K1A1ro2G1dFRBCT60IKCIHPNIlUVRJtNiJlmHapqF27NgcPHiww/fDhw4D1dLsoYmNjWbJkCfv27WPPnj3Ur1+f+vXr07FjR6pXrx5uVV27dm0MwyA5OTlfeVswGOTYsWMn3QeA0+ks1DV1puEPCg4fMziUauAPCRKiVRx2BSMEOcnHg7TtUSpaMYK08/jtsM473wXI8FmfpZ7N7fRt5SD0a5DkjzwIrwA7xPSLIrKzC0UtWujJy0Ny2KFpTY1qbgikmtijVGIucOCsdup8Jonk78TGxoa73cbExBAREZFPEI6OjubIkSOVNbxKpyx/a27YsIEOHTqEt/vyyy+Hy7huuukmHn744TLbV0kxTZNnnnmGOXPmsG/fvvD0pKQk7rnnHh599NFwUPjpcCYHWsv7jrKlx6gtlT0Elr/S6rS38dRTT3H++efnm9ayZcvw/7dt25axY8cClki7fft2Fi1axFtvvcVDDz3EzJkzC2yze/fuDBkyBCEEe/fu5Y033qBv374sXbqUa6+99rTHXB6sX7+eKVOmMHTo0PBvTImkPJEZSRJJJRPSBfuSdfYdNVEUwepNQf67PoAAasSr3HO9m8RaClmmgd8DqVEhjiSGEAo4gMsjIrjQ4UBRFEwhyDZNVEWhuqYRb7Nhkw6IUtO2bVtWr15NVlZWvsDtvKdYbdu2PeU2zjvvvHC3pYyMDH7++WduvPHGfPsA+Omnn+jdu3d4+k8//YRpmsXax5mMbljC6b5kA0+OIDYK4qM1TMMK0vYd1QllFz9IO4+ALli8McA3f1g36DWiFe7q6KJBpELmfA++Hy2nmb2ejbjB0dhrn/zr8O95SJEa+I/quKprxDR2YI+UYq2kdLRs2ZLNmzeHX1922WW88cYb9O7dG9M0mT17Nk2aNKnEEVYcHo8Hj8dDdHQ00dHRQNk5ccC6OczLkZs1axYvvfQSEyZMQFEUnnvuOYQQ4RvOimbMmDGsXbuWGTNm0LZtW+Li4sjMzGTTpk08++yzpKSk8OKLL572fs6UQOvyPhd0Xef555/njz/+YNSoUdSsWZOhQ4fy559/0rNnT2bNmhUuY5dUbXr16nXSoPy6desWaFry3HPPMWjQIGbNmkXjxo25//77881v0qRJvnVuvPFGmjdvzksvvVRlhaSSYJomwWCwzM9xRVGYO3dulRakJWWH/OUrkVQimdkmv+3W2X3YJNtnMnOhl89zRaSr2jqYcKeb6FqQHTI55jX5q6afw3GWiHSezcbNsbE0czpRFAW/aZJlmkRqGvXsdqrb7VJEOk1uuukmDMPg3//+d3haIBBg7ty5dOjQIdyxbd++fezYseOU23v00UfRdZ2HHnooPK1bt25Uq1atwA/kN954g4iICPr06VNGR1O1yHPhbf1LZ9veEKYpqJ2gEOFUCWSYZO0K4vkrhKmDM1HDdpLQ67/zZ7LBU194wyJSt6Z2Hu8dQd10g5Tp6ZaIpEBUjwgSH447pYjkCwmO5ZjUilFoWkPDLQT+FIPIJDtxzZxSRJKcFrfffjtbt24Nl9FOmTKF7du3c95559GgQQN27tzJ008/XcmjLD9M0+Tpp5/m/PPPJy4ujqSkJOLi4mjQoAHTpk3DNM0y29eJjpa5c+eycOFC7r//fu677z4WLVrEf/7znzLbV0mZP38+y5Yt46abbuKCCy4gMTGRRo0acdNNN/HFF1/w3nvvldm+evbsyQ8//IDH42H//v2kp6ezZ88eHnvssTJxPZWWijwXxo8fz9dff83+/fvp2bMns2fPZvjw4UybNo3/+7//Y/LkyWW2L0nVw+12895771GtWjWmTZt2Srdbs2bNSExMZNeuXcXavt/vZ/LkyTRp0gSXy0Xt2rW54YYb8q2fk5PD2LFjSUpKwul00rRpU1544YUCY1EUhZEjR/LZZ5/RsmVLnE4nLVq0YNmyZeFlJk+ezLhx4wA4//zzw2V5eRlDedv44IMPaNGiBU6nM7z+pk2b6NWrFzExMURFRXH11Vfz/fffF+s4S8vSpUu56qqriI6OJiYmhnbt2jF//vyTrlOW71ceBw8e5K677qJmzZrh5d5+++0yPdazmRI5kr788suwvdrr9aIoCosWLeKXX34psKyiKPluliQSyXEMU3Ao1WDvUYOQbrL5T535q/yEdIh2K9zRx02jC1S8pkEwB45EhEhOsAQkp6JwhdtN41wXkpEbpq0pCrVsNmI1DU0KSGVChw4dGDhwII8++ijJyclccMEFvPPOO+zZs4c5c+aElxsyZAjffPNNvi+z6dOns3XrVjp06IDNZuOzzz5j+fLlPP3007Rr1y68nNvtZurUqTzwwAMMHDiQa6+9lrVr1/L+++8zbdo0qlWrVqHHXBFk5ZgcTDU4mm6iKlAjTkVTFULZJr5kncAxA0VTsMcXP0gbLNfQ578GWb4thADiIxSGXu7kwgSNrP/lkLPaBwK0BJX4wTE4GtpPuc1Mn0nIgIaJGnViVYxsge43iW5kI7q+HaUE45OULQHDuql0ame2kHfnnXfm6+54xRVX8Ntvv/Hf//4XTdPo0aPHWe1IqignDpAvo+zw4cO0b98+/PrSSy8ttJS5ojBNs8gMNSW3+2pZU9UCrSvyXFi0aBGbN2/GMAxq1qzJbbfdRtOmTQFo1aoVAwYMYPr06WWyL0n5kpmZSWpqar5piYmJp1wvKiqKAQMGMGfOHLZt20aLFi1Ouo/09HQaNWp0yu0ahsF1113HypUrueWWWxg9ejQej4cVK1awdetWGjVqhBCCfv36sXr1aoYNG0bbtm356quvGDduHAcPHmTWrFn5trlu3To++eQTRowYQXR0NC+//DI33ngj+/btIyEhgRtuuIHff/+dBQsWMGvWrPDxV69ePbyNVatWsXDhQkaOHEliYiINGjTgt99+o3PnzsTExDB+/HjsdjuzZ8+mS5cufPPNN+FS4LJk3rx53HXXXbRo0YJHH32UuLg4Nm3axLJlyxg0aFCh65T1+wVw9OhRLrvssrDwVL16dZYuXcqwYcPIyspizJgxZX7sZxslEpLmz59fQC2cPXt2octKIUkiKRyvX7DniM7hNAPDELy3ws/mXZZzolVDG4P6OFAiFXIMk3S/yaEaQXw264bpfLudzhERROQ+MfSZJgEhiNE0EjQNt8xCKnPeffddHn/8cd577z3S09Np3bo1//vf/7jyyitPul6rVq349NNP+fzzzzEMg9atW7Nw4UIGDhxYYNkRI0Zgt9v517/+xeeff05SUhKzZs1i9OjR5XVYlYIvIDh0TOfQMZOQLqgWreKwKeg+QXaKjj9VRxhgj9VQbSUTaPalGby9PsChTOuz0rGhjZsvcWJPMUj5Vzr6IaurYURHFzEDolCdJ99+gTykCAimmSiaIPZCB+5aMri+ohBC4DVMskMGnpCJJ6ST6tfJChnEOzS61Ymr7CGWOQ0bNsz3+d+9e3eBDJCzhfnz5/Pbb79Rs2bN8LQ8N06nTp1o0aJFmYkHgUAgXw7Sid3aPB4PNlvlJT4MGjSInj178vjjj9OmTZuwiPLLL78wbdo0brvttjLZT1XOiarIcyErKyv8oCYyMjIsIoH1/Z2cnFwm+5GUP9dcc02BacUVXvOylHbt2pVPSPL7/aSmpiKEYN++fUyaNAnDMLjppptOuc13332XlStXMnPmzHz3whMmTAiP6/PPP2fVqlU8/fTTTJw4ESD8QPGll15i5MiR+USr7du3s23btvC0rl270qZNGxYsWMDIkSNp3bo1F198MQsWLKB///40aNCgwLh27tzJli1baN68eXjagAEDCIVCrFu3joYNGwLWw9GmTZsyfvx4vvnmm2K9j8UlMzOTBx98kPbt27NmzZp8pXUn+5uV9fsFMHHiRAzDYMuWLeHvgfvuu49bb72VyZMnM3z48FM2vDnXKfY35u7du8tzHBLJWY8QVhbM7iMGOT7BnsMGby/14fEJ7Da4+WoXl7TV8CMI+gT73UFSa4dAAZei0Dkigka5beL13Cwkp6pSx2YjRlVR5U1tueByuZgxYwYzZswocpk1a9YUmNanT58SlaXdc8893HPPPaUZYpUnpAuSM0z2Jxtk+wRxUQoJ0aoVpH1Ex3+0dEHaYLn7lv0W4r9bgpgCol0Kg9s7aVNXI2e1j5T/5YABarRC3C3RuFqdOpj873lIUQ7wpxjYoxRiLnDKUO1yRDdNcnSTbN3EEzLICIRIC+hkh0x8holumKgoODSFkClwnSQc/Wzg119/Zfr06SxevLjI7o5nOhXpxBk0aBDp6ekA9O3bl/3794dvIJYtW0arVqcf/FtaXnzxRaZOncro0aM5cOBA+Njr1avHnXfeyeOPP14m+6nKOVEVeS7Exsbi8/lwu91MmjQp37zMzMyzoonFucJrr71WatdmXgfdEzskAsyZMyef89xutzN+/PhiCa0ff/wxiYmJjBo1qsC8vPP7yy+/RNM0HnzwwXzzx44dy+LFi1m6dGlY8ABLLDtRKGndujUxMTH89ddfxThKi6uuuiqfiGQYBsuXL6d///5hEQmsJjCDBg3irbfeKpAReiJerxev11tgenZ2dj6HmKZpxMfHA7BixQo8Hg8TJkwokM90sodzZf1+CSH4+OOPufnmm62ohRPGe+211/Lhhx+yceNGrrjiiiLHJCmBkFS/fv3yHIdEclYTDFmB2gdSTAxD8Pl6P6t/CQFQv6bG4L4OohJVfKbgWMDgYGKAgM36wdTY4aCj241bVRFC4BOCkBDE5bqQnNKFJKmimKbgWJYlIKV5TKJcCrWrKQhTyQ3SNgjlmNgiShakncfhTJO53/nZc8xyIV2UpHF7exfuHJNjr2QS3GV9xlwtHcTeGo0WferPii8kyPKZ1IpRqZ+gYRfgO5Ibqn2BA3uU/LyVFX7DJEc38ISsf2kBg/SAjk838OsCIQSaCi5NxaWqxLns2E8oY0v2ndnCym+//cYbb7zBrl27iI+PZ+DAgQwYMACAjRs3MmnSJL766ivsdnuBoNiziYpy4oCVi1QUAwcOLNQxWlHYbDamTJnClClTyMjIIDs7m6ioqDLvvlRYTlSeQ+nSSy9l8ODBlSYkVeS50L9/f/bt20fTpk155JFH8s379NNPueiii8psX5LypX379icN2z4Z2dnZAOFA9zyuv/56Ro4cSTAY5Mcff+SZZ57B6/XmyxBLSUnBMIzw66ioKKKioti1axdNmzY9qcNx79691KlTp8B+mzVrFp5/InlNW04kPj4+LIwXh7+7WlNSUvB6vfnceCeOwzRN9u/fX2TJ3/PPP8+UKVMKTB81alQ+Ea1+/frhrKa8jKgTu+oVh7J+v1JSUsjIyODf//53vhzUE5GuxFMju7ZJJOVMRrbJ7sMGx7JM0rIM3vrCx9F0EwXoebmDblfYCWmCYECwxxkgraYOCkQoCldGRNAg14UUys1CcqkqdW02oqQLSVKFycg2OZBikJxuYrdBrXgVRVEIZpr4kkOEMkxUp4KzmoZSQleJKQSrd4b45JcgIQMiHHDrpU7a1dfw/xggZXE2IiBQnAqxN0TivsxVrDK0vDyk83PzkERA4M8wiaxnI7qhA+0U5XCSwjFPKE3LDplkhnTS/DqZQQO/YRLULSHQpipE2BSibRrVnQraWSySf//993Tr1g2/3x+e9tFHHzFz5kx0XeeRRx4hOjqacePGMXr0aGrXrl2Joy1fKsqJcyYRFxdXbu27q3JOVEWeCy+//HKR8wYOHMjNN99cZvuSVF22bt0KwAUXXJBver169cIlc7179yYxMZGRI0fStWtXbrjhBgDatWuXT8B48sknyy2kXdMKf9hWEpdeWZdpDRkyhE6dOuWb1r17d8aNG0ePHj3Kbb/F4VTvV15w/+23384dd9xR6LKtW7cun8GdRUghSSIpJ/ICtfccNQiGTNZtCfHZugCmgIQYhcHXOal1nkbIFKQGDA4kBAjmupAudDi43O3GmetCyhECE6imaVSz2XBIAUlSRfH6j+cgGaYgIUbFbjsepB1MM0AteZB2HseyTeZ9H2DnUespYPPaGndc5iTGhIy5HvybLZeKo6GNuNtjsCWe2umUl4fkzM1DSoxSCWaZGD6T6IY2os6zlziz6VxFN4+XpWXrJhmBEKl+HW/IxG+YGEKgCHDaFFyaRoJDw+m2lypvyqkqRNvPzDLDp556CpfLxaeffkrnzp3ZvXs3d955J0888QQ+n4+HH36YiRMnEhsbW9lDLXcqyolTHEaMGMHrr79e4futSKpyTlRVORciIyMrdH+SyiE7O5tPP/2UpKSksLOlKIYPH86sWbOYNGkSAwYMQFEUPvjgA3w+X3iZvPKwRo0asWHDBkKhEHZ74U096tevz9dff43H48nnssnrAFyaSqCSfo9Wr16diIgIdu7cWWDejh07UFU13J24MBo2bJivJC6P5s2bF5pbBYTLzbZu3VpAvDsZZf1+Va9enejoaAzDKHKsklNz9j7uk0gqkRyfyc59Or8f0MnwmLz2mY9P1loi0mUt7Iy9K4IaSSqhkOAPW4C/avoJ2gRRqkqfqCi6REbiVFWCQpBpmjgUhbo2GzWliCSpooR0wf5knV92hdh71CpjqxmnoeiQvV8n848ggWMGtmgNR1zJRSQhBOt2hZjyhZedRw0cGgxq52R0Vxfu3SFSnk2zRCQVoq+LJOHBuGKJSCFDkOIRRLsVmta0kRCp4D9mIHSTuAsdRJ8vRaTCEELgN0xS/CF2Z/vZnJbNmiNZ/Hd/Bl/uT2flgQy+O5zF7xl+vCEDt6ZQ223n/GgXDWJc1I5wEu+04bKVPLQ8wqbSPD6CPvUTuKh6FIYQBMuho1V5smHDBh544AGuvfZaIiIiaNGiBTNnzsTj8fDggw/y/PPPnxMi0t+Ji4ujXr16lSIiQcme7p+p5OVEpaenh3Oi8qjsnKgTKe9zYcOGDflev/TSS3Ts2JGOHTsyc+bMctmnpOrg8/kYPHgwaWlpTJw48ZTfQzabjbFjx7J9+3aWLFkCWB02r7nmmvC/PFHlxhtvJDU1lVdffbXAdvKuMb1798YwjALLzJo1C0VR6NWrV4mPKU8AzcjIKNbyeV1BlyxZEi49A6ub2fz58+nUqVOR+UilpUePHkRHR/Pss8/mc+TCya+/Zf1+aZrGjTfeyMcffxx2pZ1ISkpKibZ3riIdSRJJGSKEFSq8+7BBts/gtz0G76/wEQhBhBP+ca2TC5vbMIUgRdfZHxcklOtCauF00sHtxqEomLkuJIBETSPeZsMuBaQC7Nu3r1TrFVY7LSkdhilIzbRykDKyTaLdCrWr/S1IOyCwR5c8SDuPTJ/JexsC/HrQciE1qq5y5+UuEh0KmQuz8X5r/Rix1dKIHxyNPanwJ4B/5+95SA4VfMkG9kgrVLs0uU1nI6YQVpaRbpITMskMhjjm1/EEDfym1YFPAA5NwaUpxNg03C57uZTeRthU2iZEkhrSMYQgO2BgdykcC+nUcNrPmOtkRkZGgXDYvNfdunWrjCGdc3g8nvDT7bwn3G+88UYlj6r8qco5URXJiaHjM2fOrFKh45Ky5eDBg7z//vuA5ULatm0bixYt4siRI4wdO5bhw4cXaztDhw7liSee4LnnnqN///5FLjdkyBDeffddHn74YX744Qc6d+5MTk4OX3/9NSNGjOD666+nb9++dO3alYkTJ7Jnzx7atGnD8uXLWbJkCWPGjMkXFF1cLrnkEsDqRnbLLbdgt9vp27fvSR12Tz/9NCtWrKBTp06MGDECm83G7NmzCQQCPP/88yUew6mIiYlh1qxZ3H333bRr145BgwYRHx/P5s2b8Xq9vPPOO4WuVx7v1/Tp01m9ejUdOnTgnnvuoXnz5qSlpbFx40a+/vpr0tLSTvdwz3qkkCSRlBGB3EDtgykmgaDJR6v9/PS7DkDT8zT+0ceJK8ZybvzpDJARZ82LUVW6RERQJ9f+GhACn2kSqaok2mxEnsU5IadLgwYNSlUSc2I4oqR0CCHIyBbsTzFIzTRw2BRqVVOhsCDtYjiDiuKnvTof/OgnJwA2Ffq1dtCjmR19n07Kex6MFOtvGdnFTcx1kSiO4p0Pf89DUgzwHTVwJarndKh2yDxelpYdMkgPWnlG3pCJXzfRhUDDKk1zaxoJDhtOt1qqz2FpaBTjQheCRZuTeXfLUbICBjFOjSGtanLPJbURmnpGuDaFEAUyHPJe/72TjaTsME2TZ555hjlz5uR7EJGUlMQ999zDo48+mi9Mt6J44c8/K3yf5zpVOXRcUrb88ssvDB48GEVRiI6OJikpib59+3L33Xfnywg7FW63m5EjRzJ58mTWrFlDly5dCl1O0zS+/PJLpk2bxvz58/n4449JSEigU6dOYcefqqp8/vnnPPHEE3z00UfMnTuXBg0aMGPGjFKfd+3atWPq1Km8+eabLFu2DNM02b1790mFpBYtWrB27VoeffRRnn32WUzTpEOHDrz//vvhz0NZM2zYMGrUqMH06dOZOnUqdrudCy+8kIceeqjIdcrj/apZsyY//PADTz31FJ988gmvv/46CQkJtGjRgueee660h3dOoYhzwcdbTmRlZREbG0tmZmaZW/8kZxbpHpPdh3XSPIIDKTpzvvSRmWN1HOp3lYP27WwoCiSbOvtig+ia9bFr7XTSzu3GnutCyjZNVEUhXtOI1zRsZ8ANUWUyb968Ut3AFhWsdzZQEdelHJ/JwVSDw8dMBIJq0SqamhekrRPKNFEcCvZItcRB2uF9BAQLfgzww15LcE2KV7nzcid1Y1Q8X3nJXu4FE9Q4lfjbonE2dRRruyfmITVIsPKQdL9JMN0ksq52zoRqm0LgN0VuALaBJ6ST6tfJCpn4dIOAbiIEaKqCW1NwaypOTc3XNa2isasKFyVE8tbGw7z606EC80deWod7L65NxBmQm6SqKoMGDeLiiy8OT/N6vTz55JPce++9NG7cON/yiqKc9Ed2VaY8rkmXz/yixOt893AfHnzwQdauXcvEiRNp27ZtuCvYpk2bePbZZ+ncuTMvvvhieJ33r/6xxPu5fWW7Eq9TGiHp488L5pqciu8e7nPKZf6eE1VR70FpKe25EBMTE3YkJSYmkpKSku/3xInzoeq/DxKJRFLRFMuR9NRTT5V4w4qinJOdNiTnFrpxPFA7EBAs+ynAip+ssN86iSqD+jqpVkPBEII/7AEyIy33RJyq0jUykpq5oZZ+08QvBNGaRoKmESFdSMVi6NChlT2Ec4pASHDkmMHBVBN/UBAXpeByqISyTTwnBmmXIgPpRLYc1HlvQ4AMn0BVoFcLO31aOiDVIHVWBqF9lrjkvsRJ7MAo1IjifV5ChiAtRxAXoXB+gka0SyHkMdG9Z3eotpFbmpadF4Ad1Dnm18kJGvgMk5AhUMgrTVPLtTStJAgEHsPkzyw/f2X68fsMLrmyIe9uOVro8u9uOcqIS+tU8ChLz/z585k/f36B6bNnzy4w7UwWkk5GRTtx5s+fz2+//UbNmjXD0xITE2nUqBGdOnWiRYsW+YSkc5HKfL5ckedDVQ4dl0gkkjOBYl0lC2tlmKfa//0LJ69VpxSSJGc72T6TPUcMjqYZpGebzPnSx8FUq51kl0vtXHOVHc0mSEVnT0wAQwMFaOtycYnLhU2xBKYc00RTFGrZbMRqGpp0IUmqGLpxPAcpM0cQEwG1qqnofkH2fh1/qo7QwR6jodpLf/76Q4JFGwOs/dMSimrGKNx1uYsGCSredX6yPstGhEBxK8TdHIX7kuKXAHmDAo/fpHasynnVNBwa+I8ZKIog7kIH7tolD32uaojc0GnLZWSSHdJJCxpkBHRyQgZ+XWAIS5xzayouTaWG04Zdq7jStCLHjiDLMPgzK8BfGX72Z/g5khEkIzNI3s+MJtXcpPp0sgKFl6ZmBQyygwbV3FVfiN+9e3dlD+GcxDTNIs/1vN+v5xLnak4UHA8dB8Kh43lCUlUKHZdIJJKqSrGEJNM0870+ePAgffr0oWXLlowZM4amTZsCVgu+F198kW3btvHFFyW3mkokZwJCCI6mm+w+YpDtNfhhh87ib/wYJsRGKdzS20n981VMBNsdAbIirJuearkupOq5T7l8pklACGI0jURNwyVdSCVGuiXLFyFEbrmmlYPkdijUqqYgdAXvER1fsoHhP70g7Tz+SDaY+52f1GzrRu7qpnYGtHWg5ZikvZlJYHsIAEdTO/GDotHii1++9Pc8JJXcUO0IhZjGZ2aotikEPsMkR7cyjbKCOscCOlm5LqOAbqLklqa5NIVITSXBqWErZalhWSIQZOSKRrtzRaOj6QEyskIUdh/vcqjUinNyQfVIakTYiXFqhYpJMU6NKMeZ8bcsTWtnyekzaNAgevbsyeOPP06bNm3CpW2//PIL06ZN47bbbqvsIZY7VTUnqqKRoeMSiURyepTKt/nAAw/QuHHjcAJ+Hu3ateODDz7gpptu4oEHHuDTTz8tk0FKJFWFQEiw96gVqJ3tM3l/hY/t+6wbmjZNNK7v6cTtFqQoOnuiA5gqqMAlLhdtXS40RUHPzUJyqip1bDZiVLXSS0jOVApzS54KKSQVn91HDPYdNVCA6rEqKgrBYwa+ZINQtonNfXpB2mCVmy3ZHGTF9hACqBahMPRyJxfWsuH7JcCxDz0IrwA7xPSLIrKzq9i5SyfmITWtaeUhmbrAl2zgqq4S08iBPbrq3zDpQuDVjXAIdnpAJz1gdU0LGCYhExQhcGoKTk0l3qbhrAKlaZBfNPorw8+B4ohG8U7Oi3PRKNZF43gXiW572EWSHtC5o3VNXvmxYEbSHa1ropuCM0RLklQCL774IlOnTmX06NEcOHAg7EKqV68ed9555znx3TBmzBjWrl3LjBkzCs2JSklJOefL+yQSiURyakolJK1ateqkaeZXX301jzzySKkHJZFURdJyA7XTs0x2HtB5d7kPXwBcDuh/jZNWLVWEKtjmCOBxW+JSdVWja1Qk1TQNIQRe0yQkBHG5WUjOc+CpX3nyd7ekpGzJyhbYNYiLUglmmmQnhwhmmKhOBWc1rdRB2nnsPWbw9ncBDmdaf8crGtm4+RInTl2Q/n4Wvh8CANjr2YgbHI29dvG/sgrLQwqHatfTiD7fjuaqWp8/IQQBIcgJGXhCJlkhnYygQbpfx6dbpWkmuV3TNBW3phJbRUrTAEwEGbrBH1l+dmcELNEoI0BmUaKRU6V2nJOkOBeN4lw0iXORcIJoVBh7cwLcc1FtEPDOCV3b7mhdk/suroPTVrX+ppKqhc1mY8qUKUyZMoWMjAyys7OJiooiLi6usodWYcicqOLx99BxiUQikeSnVEKSy+Xiu+++4/777y90/vr162X7WslZg24IDqYY7E028PoEn67z8902q8zm/LoqA69zEh8HKZrBnsjjLqT2bjetnU5URSGUm4XkUlXq2mxESReS5AzB8AqyU0NWnpCq4Ig/vSBtAN0ULPstxP+2BDEFxLgUBndw0qaejcAfQVLe92Ckm6BAVPcIontGoJQgBDsvD6lOrEpSNQ2n7YRQ7fNtRNWv/FBtQwj8holHN8nOLU1LC+hkBqzStKBhogiBTbW6pVWl0jT4u2jkZ39GgOT0AJmewkUjt1OldpyLpDhnWDSqdgrRqDC8usmWdC83tajOfZfUISuoE+e0ETKFFJEkJSIuLu6cEpDykDlRxUO+DxKJRHJySiUk3Xbbbbz88svExcUxatQoGjVqBMCuXbt4+eWXmT9/Pg8++GCZDlQiqQzyArWPpFmtzucs9ZKWZYXVXtvJQcfLNBQb/Ob0k+20XBU1VI1uUZHE5bqQsk0TE6imaVSz2XBIAUlyhuBN1snaHyJKU3CcZpB2HoczTd5e72dvmvV5ueQ8G4PaOYnSIPOzbHJW+0CAlqASPzgGR0N7ibaf6TPRzeN5SJqqEEgzEEIQ29RBRJ2KD9XW8wKwdYOskEFW0OBYILdrmm5imFbXNKem4NY0qtk1nO6qUZoGlmiUphv8mSsaHcgIcDQ9QFaWVY74d9xOjdrxTs6Lc9Io1kWTeBfxrpKLRkXh1U1+z/LzY4qHai47V9aOxS7L2SSSYiFzogpyLoeOSyQSSWkplZD03HPPkZqayquvvsprr70WDuUzTRMhBLfeeutJS98kkqqOaQqSM0z+OmwFaq/aFGTphiACqB6vcHNfJ/VqKxy16eyNCCJU0IDL3G5aOp0oikIwt5QtQlVJ0DSi1KpRfnK28+uvv/LKK6+wceNGMjMzC5S/KYrCrl27Kml0ZxahTANQcCWcfhtkUwhW7gjx6S9BdBMiHDConZN29W3ohwxS3stCP2SVhEZ0dBEzIArVWfzPi2kKjuUIXHZoUsPKQxKmwHdUxxahEHOB87TznE45htyuaZ6QQU7IJDNkZRllBAy8ukFQF5hCoCngyu2aFuuy4bBVHRXERHBMN/gz87holJxRtGgU4dKoHZcrGsW5aBxXtqLRyQiY1nstkUiKj8yJspCh4xKJRHJ6lOruwOFw8N577zFu3Di++OKL8AW4fv369OrVizZt2pTpICWSisQfFOw7qnMgxSTNYzBvmY99yZYYcVkbG9d2s2N3w1annxyHNb22aqNrVAQxmoaZ60ICSNQ04m027FJAqhDWrFlDz549iY+P59JLL2XTpk1069YNv9/Pd999R4sWLbjkkksqe5hnFGVx6qZmm8z9zs8fuZ+jFrU17rjMSaxLIWeVj6z/5YABarRC3C3RuFo5S7R9Kw/JJD5CpUFuHpKpC/zJBq5ElZgLyjdUOyOoszvbT7JfxxMw8OsGIRMQJjbVyjKK0VRcTg2tipSmARi5TqM/wqKRn+T0IFmeUKHLnygaXRDntkQjd8kcY5LiEwwGCYVCREZGVvZQJGcRMifKQoaOSyQSyelxWo+ZW7duTevWrctqLBJJpXMsywrUTvOYbPo9xMI1fkIGRLnhhl5OmjVWOerQ2esOIhTrA3S5203zXBdSQAh8pkmkqpJosxEpn2ZVKE888QQNGzbk+++/JxgMUqNGDR577DG6devGhg0b6NWrl3RLViBCCNbt0ln4c4CADk4bDLzYSecLbBhpJsfeyiK4yxItXC0dxN4ajVZCwaewPCTDbxLIMImooxHTqPxCtbNDOn9mB/gj3UdWwCDCpuDSNBIcNhy2qpWDZiBIDeWVp/k4mBE4qWgU6corT3NxQayLC3LL06oKAd3Abwiiqs6QSs2HH37Ihg0bmDVrVnjalClTmDZtGkIIrrvuOt577z2ioqIqcZRnPj1GbSnxOkPKYRxViXM1Jwpk6LhEIpGcLqclJH3//fesXr2a5ORkRowYQePGjfF6vezYsYMmTZrIHz2SM4aQLjiQYrA/xSDDY/LRaj9bdusANGuo0b+XA3csbHX5ybFbroq6qo2u0ZFEqSqmEHgMA1VRqG6zEa9p2KrQTeS5wsaNG5kyZQoxMTGkp6cDYBhW6UuHDh0YPnw4jz/+OL169arMYZ4TZPhM3v0+wNbccrXG1VWGXu4iMUrB92OAzEXZiIBAcSrE3hCJ+zJXicuhMnwmxt/ykELZJnqOSXSD8gvVDpgmf3n87Ej3keYPEWu30SDGWWWEIwNBSkjnz8wAezJ8HMgIkJIRIMujF7p8pFujTlyuaJT7L64KiUaGaeLTBT7DwKubGALsmkKkXSOxCo2ztPzrX//ioosuCr9ev349U6ZMoU+fPjRr1oxXXnmFadOm8eyzz1biKCWSswsZOi6RSCSnR6mEpGAwyC233MKSJUsQQqAoCn379qVx48aoqkqPHj146KGHmDhxYlmPVyIpczxek91HDFIyDP7Yb/DuCh/ZPoHdBn26OWjXVuWoK8R2VwgUsAu4IiKCpk4HiqLgN038QhCtaSRoGhFnqQsp77NelbHZbOGgzLi4OOx2O8nJyeH5DRs2ZNu2bZU1vHOGH/eEmP9jgJwg2FTo39bBNU3tCK8g/e0s/JuDADga2oi7PQZbCbOLTsxDapSbhwSUe6h2yDTZnxNkR6aPIzlBIjSF+lGuSi1X08OikZ89Gf5cp1EAT3bholGU2ypPq3+CaBRbxcQYv251rsvRTUKGQFHAZVOJsmnUj3aS4LITbdeIddhwaWf+9XbXrl3ccccd4dfz58+nVq1afPrpp9hsNkzT5OOPP5ZCkkRShsjQcYlEIjk9SiUkPf744/zvf//jjTfeoGvXrjRt2jQ8z+VyMXDgQJYsWSKFJEmVxjQFR9MtESkj2+DL7wP8369WmUe9mio3XecgvpbCby4/XpvlQkpSbHSNjSRCVTGEINswsCkKtWw2YjUNrYoLLSVFCEF2yCTDr5OSE+S8WBeJEVXrpvNELrjgAv744w/AeqJ44YUX8umnn4Z/EH7xxRfUqlWrMod4VpMdEMz/McBPey0R47xqKndd7qROnIZ/W4CM+dmYWSaoEN07kqhr3CglFGEKy0MSpsCfYpRbqLYhBId9IXZk+jiQFcCmQF23vcJDsnUEyX8TjVJOIhpFR9ioHeekQW6mUcM4J7HOqvX5NUwTnyHw6ZbbyBRg0xQibBp1o5xUd9mIddiItWtE2VS0s1CoDwQCuFyu8Ovly5fTq1cvbDbrJ1rz5s15/fXXK2t4EslZiQwdl0iKz5o1a+jatSurV6+mS5cu5b6/oUOHsmbNGvbs2VPu+zpbmTx5MlOmTClXd2WphKQFCxZw//33c++993Ls2LEC85s1a8aiRYtOe3ASSXnhCwj2HtU5dMzgcKrB3K/8JKebKECXy+10vUIjJVJnk9NyITmEQqcIN41zXUg+0yQgBDGaRqKm4TrLbm4Cukm6Xyc5J0iaX0cIgU1VMMyqbfXu3bs3b7/9Ns8++yw2m42HH36YO++8k8aNGwPWk3/5VL982HJQ553vA2T5BaoCvVva6d3SgRqCjIUevOv8ANhqacQPjsaeVHJBo7A8JFO3RCRnNStU2xFTdp9FUwhSAzo7s3zszfRjmIKaLjuuCug1H8p1Gv2R4WNPRsASjTICZJ9ENKoT56R+rmh0QZyLaOfpd9sra/LcRl7dJGBY58rf3UYxdo2Ys8RtVBzOP/98vv76a+6++25++ukn/vzzT6ZNmxaef/ToURkVICkTZE7UcWToOLx/9Y+VPQRuX9mu1OvOmzePO++8s9B5jzzyCNOnT6dBgwbs3bsXsB4wxsTEkJSUxOWXX86wYcPo0KFDgXX/7maOjo7moosuYvz48fTp06fU4y1v1q9fz/LlyxkzZsw5dR5LKo9S/cpMTk6mVatWRc7XNA2v11vqQUkk5cmxTJO/juikZZqs2xrk8/UBhID4GIWbrnNQ53yF7S4/Ppslmpyn2OkaG4FbVdFzXUhOVaWOzUaMWrUCdU8H3RRk+nVSfSFSckJ4QwaxLo1mCRHEu21k+HXiXDa8IYOICriRLg2PP/44o0ePRtOs8d1xxx1omsbHH3+MpmlMnDiRoUOHVu4gzzL8IcHCnwOs22UJHLVjFO7s6KJBgkZwT4hj73kwUqycpMgubmKui0RxlPwzU1gekuE3CaQbRNS2lWmothCCTN3g90wff2X68YVMqrtsRDoK/8p0ayqRNpUc3cRnmCXeXwjB0ZDOnxk+9mYEOJjhJyU9SHZO4aJRTJ5oFO+kcaybhlVUNMpzG3l1A59uYgJ2VcFt06gd6aCm20GMQyPOYSNKU87ZVtvDhw9n9OjRbNu2jQMHDlCvXj2uu+668Pxvv/2WFi1aVOIIJZKzm3M5dPxs4KmnnuL888/PN61ly5bh/2/bti1jx44FwOPxsH37dhYtWsRbb73FQw89xMyZMwtss3v37gwZMgQhBHv37uWNN96gb9++LF26lGuvvbZ8D6iU5OXrDR06VJ7PkgqhVL88k5KS2LFjR5Hzv/32Wy644IJSD0oiKQ/CgdrJBskZBh987WdXbhDwxS1s9OluIy1OZ7PDciE5hUIndwSN3Q6EEOSYJroQxOVmITnPgpseIQRZAYN0f4ijOSGyAjooCtF2jQaxTpJiXczeeJh3txwlK2AQ49S4o3VN7ru4Dk5b1Tt+u91OQkJCvmm33347t99+eyWN6Oxm51GDed/5OZYjUIBrLrRzfRsHdgWyvswhe7kXTFDjVOJvi8bZ1FHifRSVh3Q8VNtuhWrby0bQ9egGf3n8/J7hxxPQqeawUSPaXmjeUjWnjfpRTiJsavjz4dVN9mYHSAsULgIFc8vT/jhBNEo9hWhUN/54plGjWDdRzqop5AYMk5xc0SiYm23ktqlE2jTOj3ZSzWknxmFlGznPEbdRcRg1ahQul4svv/ySSy65hEceeQS32w1AWloaR44c4b777qvkUUokEknVpFevXlx66aVFzq9bt26B34HPPfccgwYNYtasWTRu3Jj7778/3/wmTZrkW+fGG2+kefPmvPTSS1VWSCoJpmkSDAbzlVWXBYqiMHfu3LP+oe3kyZOZN29euZTe+f1+HA7HGfFwrVQjHDRoELNnz+a7774LT8v7kf3WW2+xcOFChgw5W82wkjORLK/Jtr06fx4M8cOOEM8tyGHXIQO3E27p56BPfxt/JPo5mFvK1gA7t8ZG09jtICQEmaaJpijUtdupZbOd8SKSN2RwyBNg05FsNh7x8HuaH90U1IhwUDvSQZRDI95tZ/bGw7z60yGyApbglhUweOXHQ7y58RDekFHJR1GQtLQ0fv311yLnb9myJdzNTVJ6grrlQpr5tY9jOYKESIWHr3Ez8BIn6jGD1FkZZC+zRCT3JU5qTIgvlYgUMgQp2SaxboWmNW35QrUNv0lMYzvRDctGRPIZBtszvaw6lMHPRz0opqB+tJNYl61IEalprJt5vxzhsrmbwv/m/XKEprFuqjltBBHsCwZZlZzF278n8/QP+xn/1V9M+d9fzF93mG+3prHngDcsIsVG2mhWN5JeLRJ48Iq6vNjnAv51bSMebl+PG5sk0qZGVJURkQzTxBMySPYF2ePxszvLT4o/BCjUiXTQoWY019SLo1dSPH2S4mhfI4YLYt3UcDukiFQI99xzD59++ilz587lwgsvDE+vVq0aP/30E3fffXepthsIBHjkkUeoU6cObrebDh06sGLFilOuN3nyZBRFKfCvrG86JBKJpDJwu9289957VKtWjWnTpp0yR6ZZs2YkJiaya9euYm3f7/czefJkmjRpgsvlonbt2txwww351s/JyWHs2LEkJSXhdDpp2rQpL7zwQoGxKIrCyJEj+eyzz2jZsiVOp5MWLVqwbNmy8DKTJ09m3LhxgFUunXfNzhM68rbxwQcf0KJFC5xOZ3j9TZs20atXL2JiYoiKiuLqq6/m+++/L9ZxlpalS5dy1VVXER0dTUxMDO3atWP+/PknXacs3688Dh48yF133UXNmjXDy7399ttldpwZGRk89NBDNGjQAKfTSb169RgyZAipqamAlT2lKAoffvghkyZNom7dukRERJCVlQXAhg0b6NmzJ7GxsURERHDVVVfx7bffFtjPunXraNeuHS6Xi0aNGjF79uwyO4aTUSpH0sSJE/n++++58soradasGYqi8NBDD5GWlsaBAwfo3bs3Dz30UFmPVSIpMaYpOJJmsueIQUqmzqdrA2z8w7ppa1Rf5YbeDnJq6mxxBgBwmQqd3RE0irBcSNmmVY5RTdOoZrPhOIPL2IJGbmi2N8QxbwifbuKyqcQ6bTj+dmOnIohx2nh3y9FCt/XOr0cZcUmdihh2iXjooYfYuXNnkV+Aw4cPp1mzZsyZM6eCR3b2sOeYwdvr/RzJsr64OzWycfMlTpw2yFnrI+uzbEQIFLdC3M1RuC8p3Y1nYXlIwhT4Uw1s7rIL1Q4Jwb6cADszfRzNDuJWVZKiXNhOEQJeP8rJmz8f4tWfDoWnZQWM8Osrzotl/Fe7yPEWLrjGRtqoG+ekQbyLC3LL0yIdVUMkKow8t5FXNwmZoCBw52Yb5bmNYh1WtpEUik4fIQSrV68mEAjQqVOncDfKkjJ06FAWL17MmDFjaNy4MfPmzaN3796sXr2aTp06nXL9N954I18+U17ZsEQikVQVMjMzwzfmeSQmJp5yvaioKAYMGMCcOXPYtm3bSUuIMzMzSU9Pp1GjRqfcrmEYXHfddaxcuZJbbrmF0aNH4/F4WLFiBVu3bqVRo0YIIejXrx+rV69m2LBhtG3blq+++opx48Zx8OBBZs2alW+b69at45NPPmHEiBFER0fz8ssvc+ONN7Jv3z4SEhK44YYb+P3331mwYAGzZs0KH3/16tXD21i1ahULFy5k5MiRJCYm0qBBA3777Tc6d+5MTEwM48ePx263M3v2bLp06cI333xTaIbU6TJv3jzuuusuWrRowaOPPkpcXBybNm1i2bJlDBo0qNB1yvr9Ait/8LLLLgsLT9WrV2fp0qUMGzaMrKwsxowZc1rHmZ2dTefOndm+fTt33XUXF198MampqXz++eccOHAg3zk6depUHA4H//znPwkEAjgcDlatWkWvXr245JJLePLJJ1FVlblz59KtWzfWrl1L+/btAesheY8ePahevTqTJ09G13WefPJJataseVrjLw6lEpIcDgfLli3jgw8+YPHixRiGQSAQoHXr1jz99NMMHjy4yrcJl5z95AVqH0w12HVI573lfjJzBJoG115p56KOKn9G+Amo1g1xQ+Hgyjg3Lk0lKARe0yRCVUnQNKJU9Yw8pw1TkBXUSfPpHM0JkhM0UBWFGIdGfCFOi8PZAf5vXyYHPUGmXNUg7ET6O1kBA0/QoJq7at0wrlq1qoA9+UT69u3Lm2++WYEjOrOJjFYJBq3MH90UfLk1yJdbQ5gCYlwKQy5z0rquDSPTIG2+h8B2q+uho6md+EHRaPGlu+nMy0NqmKhROzcPqaxDtXUhOOIPsTOvExuCuhEO7MUQQdyaSoRNLVJofXfLUe69uDZOUyEHiIu0USfeRYNYF43jXTSMdRFRhUUj/e/ZRgKcmpVtVDfSQQ23g1iHFu6kdibYr6syEydOZP369axevRqwfjD36NGDVatWIYTgvPPOY+XKlcW6gTmRH374gQ8//JAZM2bwz3/+E4AhQ4bQsmVLxo8fz/r160+5jZtuuqlYN2QSyZmEDB0/u7jmmmsKTCtup6q8LKVdu3blE5L8fj+pqakIIdi3bx+TJk3CMAxuuummU27z3XffZeXKlcycOTOfsWLChAnhcX3++eesWrWKp59+Otzl/IEHHmDgwIG89NJLjBw5Mt81f/v27Wzbti08rWvXrrRp04YFCxYwcuRIWrduzcUXX8yCBQvo378/DRo0KDCunTt3smXLFpo3bx6eNmDAAEKhEOvWraNhw4aA9T3RtGlTxo8fzzfffFOs97G4ZGZm8uCDD9K+fXvWrFmTz+V6sr9ZWb9fYH33GobBli1bwuLSfffdx6233srkyZMZPnx4uMy8NMyYMYOtW7fyySefMGDAgPD0SZMmFThWv9/PTz/9FN6fEIL77ruPrl27snTp0vD92vDhw2nRogWTJk1i+fLlADzxxBMIIVi7di3nnff/7d15fFTV+fjxz7131iQz2QNh3/dN2dxAwK9SF6pVUAuK1rVVW+tSW0VcSq0LrdifC4pFVNBai1VELYqCFkVAVFTKvm9hyTpJZr/3/P6YZGDMQhJCwvK8Xy9eL+bec++cezM5mXnmOc9pB8SmYtZWz7qxNLg6p6ZpUntEHJOUUhT4FFv3RskvtvjwqxCLvg0D0DJbY+wYB9H2Uf7niG1zWRrDXUl0SnZgKUWpaYKmkWUYpNts2I+zAJJSirJILPtob1mYklAUSylS7AYtkh1VioPn+yN8sauEJTtK2FQUW1krw2Uj023D6zSqDSZ5nQaeY/CD8IEDB2r90JOZmcn+/fubsEfHp0BIYTOgfR8nfZN19u6L8ugb5SxdFwsUDWpvY/xgJylOjcCqEMVvlKL8Cuzg/WkKycNcaIfJ6KnOofWQuuQYZFZMZTNDilBhlKRcG55OdmxHEMC0lOJAOMqGkgDbfUEsE7JdNtz1KCCfXFET6XCB1j8Mb4vTbhzTQSOIZRv5oyblUZOIpaFBPNuos8dJekVtozRH1exFceTeeustLr744vjjuXPn8sknn/DII4/Qv39/br75Zh566CFmz55dr/POnTsXwzC46aab4ttcLhfXX3899913Hzt37qRt27a1nkMphc/nw+PxHJdfpgghTnzPPvss3bp1a9CxlRmXpaWlCdtnzpyZkL1ut9u55557uPPOOw97zrfeeousrCx+/etfV9lXOY5+8MEHGIbBb37zm4T9d911F3PnzuU///lPPOABsWDZoYGSfv364fV62bJlSx2uMubss89OCCKZpslHH33EJZdcEg8iAeTm5jJ+/HhefPFFfD4fXq+32vP5/f5qF9cqKytLyBAzDIP09HQAFi5cSGlpKX/4wx+qTJWu7W9MY98vpRRvvfUWl19+OUqphP6OHj2aN954g2+++YYzzzwToErGm9/vx7KsKts9Hg9OpxOIvQ769++fEESq6VqvueaahKDVqlWr2LhxI/fffz8FBQUJbc855xxmz56NZVkopfjwww+55JJL4kEkiE3FHD16NB988EGV525MDQok3XPPPfz85z/nlFNOaez+EAqFeOCBB5g9ezZFRUXxLKdzzz33sMfu3r2bO+64g48++gjLshg5ciTTpk1L+OWAml+ojz76KH/4wx8a5TpE84hEFbv2m+zYb7L7gMmrCwPsKYhlVJw5yMaZ/6ezzRMirCtQ0Fk5GFaRhRSyLAJKkazrZNlsJB9n37IHo7Hg0f7yMIXBKKGoRZJdJ9Ntw/6jaykJRVm6y8fnO0pYk++nMi6ua9AvJ5lh7VLxhaJc07cFTx8ydafSNf1aELEUx9ribbm5uXz77bc17v/6668T0nxFVaGIYtZ//Lz2SYBSv8KTpPHzUW7+/EsvtzxZzJltbQzuYMcKWBTNKSWwIjYt1N7GRtrVHuy5Dft+ImIqCsst0pN0OmQaeFyxcbqximpbFSuxbSwNsbnITyhikeG04Umqf3/zIhG6prprDbSmu2y08roatIrb0fTjbCOlwFGRbdQm2UlOxUpqqXYDj92Q4EET2L17d8ICJf/+97/p1asX9957LwC/+tWvmD59er3P++2339KtW7cqHwIq0+FXrVp12EBSp06dKCsrIzk5mUsuuYS//vWvTZIuL4QQdTVkyJBai23XpqysDKDK9OGLL76Y2267jXA4zFdffcWf//xn/H5/QgbugQMHMM2D7wFSUlJISUlh8+bNdO/eHZut5vcX27dvp1WrVlWet2fPnvH9hzo0SFApPT29XnU/f7yy3YEDB/D7/XTv3r1K2549e2JZFjt37qxxyt8TTzzBww8/XGX7r3/964QgWvv27eO1miprRB26ql5dNPb9OnDgAMXFxcyYMYMZM2ZU+5yHfvFc02eHH28/tND45s2bueyyy2q5qoN+/LPZuHEjEAsw1aSkpIRQKEQgEKBr165V9nfv3v3YDCQ9/fTT/PWvf6VTp05ceeWVXH755Y2WPtXQ+fxlZWWMHDmSkpIS7rvvPux2O9OmTePss89m1apVVVZyqlzW8VBHIzAmmo6v3GJrnsm+IpMv14R55/MQpgXeFI1LL7Lj6GmywRH70Jtk6pzldNPJE8tC8pmxKV85NhtphoHtOPnwFLUUJcEo+YEIB8oj+CMmdkPH4zDIctsT2vojJst2l/L5zhJW7SvDOiSrsmdWEsPapnJGGy9prtiwUBoyuenUXABeOU5Wbbvkkkt49tlnOf/88/npT3+asG/evHnMmjWr1qlvJ7tAKBZEen7+wW+YSv2KGe/FHk+9wcPu9VFCG8MUzynFLLJAg5Rzk/D8JAnN1rDfm+rqIQGEikyUqfB2tZPc2tagLCelFGWmxZayEJuKA/iCUVIdBi08znoHSSwUX5UFeOurfTx6Rnuu7tuCZ6sJtE7s2wJ/1Gr2IJJSirClDmYbmbEvUtw2Ha/dRiePjUynDa/DwGs3cNqOscjwScJmsxEKxf42KaX45JNPEt6ftGjRosq3nnWRl5dHbm5ule2V2/bsqfrarZSens5tt93G6aefjtPpZMmSJTz77LOsWLGClStX1vgNNcS+EKy8HiBeNFQIIY41q1evBqiy2nibNm3iU+YuuOACsrKyuO222xg5ciSXXnopAIMHD04IYDz44IM89NBDR6WfNdWnq+sUPuCIpmlVZ+LEiVU+m5977rn87ne/47zzzjtqz1sXh7tflhV7f3bVVVfVGKzp169f/P8/XqTi1Vdf5aOPPmLOnDkJ22urs1WbH9+jyv5NnTqVAQMGVHtMSkpKwt/a5tCgQNL+/ft5++23+ec//8kTTzzBn//8Z3r06BEPKlUX2ayLI5nP/9xzz7Fx40ZWrFjB4MGDgdhykH369OGvf/0rf/7znxPa/3hZR3H8MisKam/fa7KnMMo/FwVZvzP2DUHv7jrnjLGRlx6iWCeWhWQ6GJbuxmXTCVoWQaXwGAaZhkHScZCFpJTCFzIpCkbYWxahNBwFTcNjN8hNcSR8OA6ZFiv3lLJkZwlf55UROSR61CnNxfB2qZzZNpXsJHuV5wmZil2+EOP75PCrQa0oCUZJddmIWuqYDCJBbNWKjz/+mJ/97Gf0798//o3H6tWr+e677+jZs2e1356IGJsBr30SqHbfPxYFuOmiJDY+W0LxfD8oMDJ10q/24uhU9fVTV9XVQ1KWIlhgYnNqeLo7cWc3LMARME22lYfYUBKksDxCik2nncdZZXpnnfqpTOZuLuSr/xViWfD/Vu7mHxf3RCNWE6ky0Dqxbwt+ObAV60uqv49Hk2lZ+E1FedQkWFHbyKFrJNsN2iY7yXY78NoN0hwGKZJtdMzo06cPc+bMYcKECbz99tsUFBRw4YUXxvdv3769QXWKAoFAPMX+UJXTCQKBml+jt99+e8Ljyy67jCFDhjBhwgSee+65WrO3H330URlnhRDHvLKyMt5++23atm0bz2ypyc0338y0adO4//77+dnPfoamabz22msJ42jlDJjOnTuzfPlyIpEIdnv174/at2/Pxx9/TGlpaUKWzbp16+L766u+f9Ozs7NJSkpi/fr1VfatW7cOXddrzVrt1KlTlVk/AL169aq2bhUQn262evXqKsG72jT2/crOzsbj8WCaZo19PdSP23z++ee4XK5aj+3cuXM8UFlflffJ6/XW+hzZ2dm43e54BtOhqvu5NrYGfRr0eDxMnDiR999/n3379jFjxgzatGnDlClT6NWrFwMGDOCxxx6r93lrm8//5ZdfsnPnzlqPHTx4cDyIBNCjRw/OOecc3nzzzWqPCQQCBIPBevfzx0zTrPZfZTTxcO0q/50sbS3LarS25UGLDTujrN0eZvnaAFP/4WPDzjAuh8ml5+sMvcJkR5qfiDJxRzTOs6VwbnYyhqYoDIcJmybZmkYLTcOpVPy8h0b4D9eHpmpbGgyzs9jP17tLWLm7mHUHyolEo2Q5DVom2UlxxD4YhqNRvtpdzLQvt3PtvLX89csdrNhVgmmatE2x8fNeWTz7ky48eW5nLu6WSaZTxzLNKv8ikSj7y0Ks2lfG0p0l7PaF8AXCOPXqf94/fr03h9TUVJYtW8b9999PJBJh7ty5zJ07l0gkwuTJk1m+fDlpaWnN3c1jVqlfUeqv/tutUr+ipNjCWhsBBUlnuMj+Q0aDg0iWpThQamHo0D3HoE26ES+qHdhv4vDqpPVqWBAprBSbywJ8utfH8rwyAgGTNilOspKq1gg7HBPFSn+AJz7fyfIfYkGk7i2TuHZgSzaVBrl2QEuW/eIUll93Cst+cQrXDmjB+pIAhaFovftdH0opgqZFUSjC7vIQW3xBdpZH8EctvHYbfdKTGJHr5Sdt07iwbRojWqXROz2JtilOPI6qhfZF83nggQdYtWoVWVlZ3HjjjZx55pmMHDkyvv/9999PeG9TV263u9pvKyvf99T3W+Lx48fTsmVLPv7441rb3XvvvZSUlMT/1fbeTQghmkMgEODqq6+msLCQSZMmHfZvos1m46677mLt2rXMmzcPgDPPPJP/+7//i/+rDKpcdtll5Ofn88wzz1Q5T+V7+wsuuADTNKu0mTZtGpqmcf7559f7mpKTk4HYkvN1YRgG5513HvPmzYtPPYPYamavv/46Z511Vq3Zpw1x3nnn4fF4ePTRR6t8Bq8tu6qx75dhGFx22WW89dZb1QZ7Dhw4UK/zVeeyyy7ju+++4+23366y73CZZAMHDqRz58785S9/iU+/rK5/hmEwevRo3nnnHXbs2BHfv3btWj788MMqx+3YsSMefGsMDS62XSktLY3rr7+e66+/noKCAmbPns2DDz7IpEmT6l1vqKHz+S3L4vvvv+e6666rsm/IkCF89NFHVSKYL7/8Ms899xxKKXr27Mn9999f45KDlWpK1166dGn8l/dQGRkZCWlxX3zxRY0fttPS0hJS15YtW0YkEqm2rcfjYeDAgfHHX331VY0BsaSkpPj9g1iNmOoKo0EsaHfaaafFH69atapK8blKdrs9XoAMYksP1jRw6brO8OHD449Xr15NYWFhtW0BRowYEf//2rVrq/1lVkoRCCky257OvhKN+UtD7Ny2ng7ufNJyNDr30Am4TIpWg64g3TL4ybAz8aTYCVgW2zZvpnzvXpJ0nb3V/PE47bTT4t/abt26tdY3woMHD47//Hfs2JEwGP/YqaeeGn9979q1q9YieQMGDCDJ46U4GGX91h1s2bSJiKWw6RpOQ8PQNSonDLTu0oMdERf/3VHC2m07ySjLA6ArkGzXaZfqol2qkzSnjba5mXg9sW+pS4sK2LX5YMTaUhA2LSKWQgPadOpK53atSXPZiJQWs3rl1zX2t2vXrrRu3brG/U0lOTmZhx9+WL4RbwBPkoYnSas2mORJ0vB6NMJBi4wbvbj6Vs10qKtD6yF1zDJIccZ+B82QIlgQK6rt7Vz/otoRpdgXDLO+JMie0hC6gtwke4MLRBcpk3k7ilj+XSFRU2G3aYzrm8OI9qlomkZhKEphKIrb0Em26ZT7jt50tlhtI4vyqFUl26hNkoOcJAceyTY6Lp177rl88803LFy4kLS0NK644or4vqKiIoYPH55QjLuucnNz2b17d5XteXmxvw+tWrWq9znbtm1b699vAKfTWW0mlBBCNIfdu3fHpyCVlZWxZs0a/vWvf7F3717uuusubr755jqd59prr+WBBx7g8ccf55JLLqmx3cSJE3n11Ve58847WbFiBcOGDaO8vJyPP/6YW265hYsvvpgxY8YwcuRIJk2axLZt2+jfvz8fffQR8+bN47e//W29V+kE4p8NJ02axJVXXondbmfMmDHVfkat9Kc//YmFCxdy1llnccstt2Cz2XjhhRcIhUI88cQT9e7D4Xi9XqZNm8YNN9zA4MGDGT9+POnp6Xz33Xf4/X5eeeWVao87GvfrscceY/HixQwdOpQbb7yRXr16UVhYyDfffMPHH3982L91h/O73/2OuXPnMm7cOK677joGDhxIYWEh7777Ls8//zz9+/ev8Vhd1/n73//O+eefT+/evfnFL35B69at2b17N4sXL8br9TJ//nwAHn74YRYsWMCwYcO45ZZbiEajPP300/Tu3Zvvv/8+4bwTJ07ks88+q9eUyNoccSAJIBKJ8J///Id//vOfzJ8/n7KyssMWcKxOQ+fzFxYWEgqFDnts5ZS7M844g8svv5yOHTuyZ88enn32WSZMmEBJSUmt9VMkXbv5RaIKXYdQWOG0a6R5NJ59N8CqTVFaJ0HHdjqpHRXltlhmk93UaGfYSUvWsdk1ik0Tp66TYRjoun5MftiylCIUtdhWHMRfplEeNikPRLDpGm77wQ/ESkFhIMJ2X4jndm5jlxkLfKVHLVrZdNp5nbRLdZLptlPbZf44eOQwdDJcBk6bTq+sJNqkx761LvAfm9PZapKXl8f+/fvp0qVLrX9ExUGRiGLCKDfPv1c12DxhlJuAzyL1l2kYnoa/FmqqhxQpt4iUWng71r+otqkUBZHKldhCmBGLLLe9XiuxHSqK4vtQiHe+2U/e3ljaescsFzeemkt2sqNK+4DZuAGkQ2sb+aMWIVOhV9Q2SrXb6Oq1k+604bXrUtvoBNGrV6+E1XQqpaenM23atAadc8CAASxevLjKqjvLly+P768PpRTbtm2TepJCnCCu+qT+mY7Ho1WrVnH11VejaRoej4e2bdsyZswYbrjhhoQv2w/H7XZz22238dBDD/Hpp58mfPF9KMMw+OCDD3jkkUd4/fXXeeutt8jMzOSss86K1xTWdZ13332XBx54gH/+85/MmjWLDh06MHXqVO66664GXefgwYOZMmUKzz//PAsWLMCyLLZu3Vrre+DevXuzZMkS7r33Xh599FEsy2Lo0KHMmTOHoUOHNqgfh3P99deTk5PDY489xpQpU7Db7fTo0YM77rijxmOOxv1q0aIFK1as4I9//CP//ve/ee6558jMzKR37948/vjjDb28uJSUFJYsWcKDDz7I22+/zSuvvEJOTg7nnHMObdq0OezxI0aM4Msvv2TKlCk888wzlJWV0bJlS4YOHZoQ/OzXrx8ffvghd955Jw888ABt2rTh4YcfJi8vr0ogqbFpqoEhqWg0ykcffcQ///lP5s2bh8/nIzc3l7Fjx3LFFVdwxhln1PucnTt3rrbC+JYtW+jcuTPTpk3jt7/9bZXjdu7cSbt27Xj88ce55557Eva99NJLXH/99Xz77bc1vmkKh8MMHDiQXbt2sWfPnhrTvavLSKr8dq661D9N0xKq+/942taPHVoY7ERuW7lcYX3bhiKKWR/4+cfig6tJXTEymQnnJnHn8yX0ON0i1CmMqYGmoHPYwdB0N26Hhl8pLE0jzWYj0zCwU3ta4aFBpsP1tzHaKqUoi8RWXdtXFqYkFKt7lOKwxaasKRVvu60kyBc7S/hil4/95bGsNQuNZIfB6W28nNXGQ++sJIwaokearhNVivKwRXk4ioaKFedOspPmsuF12rBVFDU+9DWslKp1+tqPX+/NZd68efz+97+PzxdeuHAho0aNIj8/n3PPPZcHH3yw1m+Sjnc+n4/U1FRKSkrqnZJsRRQBv8XLnwR4fXEw/ns2fqSLa89x43TrrFoeJFp9suRhVdZDapeux+shwcGi2p6OdpLb1L2otqUUxVGTTaVBthYHCIYt0p22+DTPhijULP6zu5il3xYQDlsYusYlvbI4r0t6g2or1UVltpHftAhEKlZS0zWS7AaZDoPspFhtI69DVlI7kS1btozFixezf/9+brnlFrp27Yrf72fdunV069Ytvkx1XS1fvpzTTjstoe5kKBSiT58+ZGZmsmzZMiCWSev3++nRo0f82AMHDlRZjea5557j1ltv5cknn6z1Df+P1TYm/WXTpnpdU6W33q1/7QfP1qqr+BzOxDX1L3/QkA/nDbkPJ9o9ALkPQghxPGlQRtL111/PO++8Q1FREVlZWfz85z/nyiuvZPjw4Uf0Breh8/krtze0FoDD4eC2227jl7/8JV9//XWNq8PVlK5tGEaN1eF/3K6uTuS29Qk2VLYNhCxmLQjywvshKkt7+fzw4vsBdE3j9zcm8Y9wCWCQEtY5w55Eh1wbpgZlloXLMMgyDFJ0vd4fBhvS37q2DUYtioJRDpSHKQxGCUUtkuw62SlO7IecK688zJIdPj7fWcIO38HXudNmMKSVl2HtUjmlZXLCMT8WsSzKwxb+SBhD1/A4DNpkJZHqsuF12OIf6muiaVq9fs7NYf78+Vx66aWcfvrpjB8/PmH1jKysLFq3bs2sWbNO6EDSkYiUWiy9dh8X3ZnGDVMz8JVYeFN18r4MsvTqfZw5swV2h0Y0Ur/vHyxLUVCucDmgS5ZBZkpFcLKiqLbh0EitR1FtpRSllsXWshCbi/34giZem0FWAwtpQywL6YdoiPdX5bN9ZzkArdOc3Dgwl9bexpumU5ltVF6RbRSuyDZKssWyi7p63KQ5baQ6DLw2HYdkG53wwuEwV155JfPmzUMphaZpjBkzhq5du6LrOueddx533HEHkyZNqtd5hw4dyrhx47j33nvj2ZmvvPIK27ZtY+bMmfF21aW6t2/fniuuuIK+ffvicrn4/PPPeeONNxgwYECdp4EIIYQQ4sTVoEDSO++8w89+9jOuuOIKRo0a1WgfLhs6nz8jIwOn0xlvV59jK1VOxTvS+ZCi8UVNhaFrvF7DalKvLwpw/ZhMkgs0WoUcDE1zkezSKFcKS0GGYZBhs+E4Rr7Fj1qKkmCU/ECEA+UR/BETu6HjdRhkuQ8WLi4IRPh8Zwmf7/Cxsejgtdt0jVNbpjC8XSqDcj24allBLWJalEVMAlGFTYMUh0Ebr5s0lx2Pwzhs8Oh488c//pHhw4ezePFiCgoKqizDevrpp/PCCy/U65yhUIgHHniA2bNnU1RURL9+/fjTn/7Eueeee9hjP/74Yx555BF++OEHotEo3bp149e//jVXX311QruaAvCPPvpovWvNHQm7RyeYb/L1bfk40nVcWQbBfJNwkYXdq+H06kTC9QsiVdZDykjW6ZB5sB6SFVUE802caTreLg4cqXULwpabJtv9YTaXBCksD+PWddokO4/otZyvmSza5+PzrwsIBE00Dc7vnsGY7lnx7LyGilqxTCO/aRGMxGobOStqG7VLcsSzjVKlttFJa/Lkybz33ntMnz6dkSNHJqx863K5GDduHPPmzat3IAliSxRPnjw5Yfx67733EuoWVmfChAksXbqUt956i2AwSPv27bnnnnuYNGkSSUlJ9e6HEEIIIU4s9Q4khUIhXnjhBbp165ZQSLoxNHQ+v67r9O3bl5UrV1bZt3z5cjp16pRQaLs6lUWPf5zKLZqXP6jYnR8lLUWvdTUpn9/iHC2FFi0MTA1KLIskXSezIgupuT+cWUpRGjIpCkbYWxahNBybuuaxG+SmOOL984WiLN3lY8nOEtYc8FN5xTrQt0Uyw9umMrS1lxRHzcHbsGlRHjEJRC1smobHaaOt13bCBo8OtXr1ap588ska97do0YL9+/fX65zXXnstc+fO5be//S1du3bl5Zdf5oILLmDx4sU1Zi8CvPvuu1xyySWcfvrpPPTQQ2iaxptvvsnEiRPJz8+vMjXk3HPPZeLEiQnbmroWiRVVdJ7gYd10H+Eii3DRwamMncd7KM436zWtraZ6SGZIESqM4m5pw9vJji3p8EGkoGWxOxBmU0mQvWVhHBrkJjmwN7CQNkAExf+sMB/+kM+mLbFVMXJS7NwwKJeO6fVb0Qpiv+eH1jYKmwqbpuGy6aTZDXI8btKdNjx2g1S7ZBuJmH/84x/86le/4qabbqKgoKDK/p49e/Kvf/2rQed2uVxMnTqVqVOn1tjm008/rbLtxRdfbNDzCSGEEOLkUO9AksPhYMKECfztb39r9EDS2LFj+ctf/sKMGTMS5vPPmjWLoUOHxrOGqpvPP3bsWP7whz+wcuVKBg0aBMD69etZtGhR/FxQ/bz/0tJSnnrqKbKyshJWQxPNq6DEYktelA07I1w6PKnW1aTSknSiLoNSywI0sgyDdJsNezMHkMrDJiWhKHsr6h5FTEWywyAnyREP6PgjJst3l7JkZwnf7SvDPOQSe2YmMaydlzPapJLmqvnXNWxalIVNglELu6Hhcdho53XGgkdOo1FquyhL1bl2TXNJSkqivLy8xv1btmwhMzOzzudbsWIFb7zxRkKNkYkTJ9KnTx/uueceli5dWuOxzzzzDLm5uSxatCg+Jfbmm2+mR48evPzyy1UCSd26deOqq66qc9+OBptbp/sNqQBsfq2UiE9h92p0Hu+hy3Ve1nwfrvO5KushdcoyEuohVRbVTmlnx9Px8EW1w0qxNxhmky+2EptmQQuXHWctmXh1sV8z+bywjCVf5VNWHgVgVOc0LuudXe9V3sKmxd5AmKgFzooV3CqzjVIrahtJtpGoyf79++MFWKtjGEaNq60KIYQQQjSHegeSNE2ja9eu5OfnN3pnjmQ+/y233MKLL77IhRdeyN13343dbufJJ5+kRYsWCdXcn332Wd555x3GjBlDu3btyMvL46WXXmLHjh3Mnj0bh6PqijyiaZmWYk++yea8KAu/CvP+shCtsmz8fJSbGdWsJjX+HDdRU1GqLFy6TpbNRnIzFn0Om7Gi2Qf8EQr8EQJRC5dNJ9Vpi39ADZkWy3f5+O+OEr7OKyNiHXwtd0pzMaxdKme19ZKdVPPrMWRalIdjmUeOiuBRx3Q3qU6DFMeRB4+UpYgGFNEyi0i5RTSgSGppw5V57GZRjBw5kldeeaXaovx79+7lxRdf5KKLLqrz+ebOnYthGNx0003xbS6Xi+uvv5777ruPnTt31rhCpc/nIz09PaGums1mIysrq8bnCwQCaJqGy+Wqcx8bm+HU6Hadlx43pRIstnB4dUryTdZ8HyZYQ1bgoWqqhwQQKjZREUVq18MX1Y4qRX44wuayEDtKQkQjFhlOG0m1ZOPVRRjFGhVm0ZpC1q73AZDmtnH9wFx6ZNd/yk5ZJMp+f5SOHicdvK5YUWzJNhL10LZtW9atW1fj/i+++IIuXbo0YY+EEEIIIWrXoBpJ9913H3feeSfjxo1LmMvfGBo6n9/j8fDpp59yxx138Kc//QnLshgxYgTTpk1LyEA688wzWbp0KX//+98pKCggOTmZIUOG8NJLLzFq1KhGvRZRf8GwYmtelPU7I/xjUZC122OrwS3fEOaWS5LRNMXrnxyymtQ5Lq6+wI0yFDnKhscwsDXDt/6mpSgJRSnwRzgQiFAeNtE1Da/DIKOi7lHUUqzMK2XJjhKW7yklGD04bai1x8GwtqkMa5dKa0/NhX1D0di0taBpYdc1vE4bndLdeBsheKSUwgwoIuWKSKlJqNACpUhqZSO5tZ2wz8Tu0YkGLGzu5l+drTqPPPIIp512GoMHD2bcuHFomsaHH37IokWLeOGFF7AsiwcffLDO5/v222/p1q1blZWGKpeLXbVqVY2BpBEjRvD4448zefJkrrnmGjRN4/XXX2flypW8+eabVdq//PLLPPfccyil6NmzJ/fffz/jx48/bB+rW03ySFX+fNesC1FUYOHx1i0oUlM9JKVi9ZAMh0ZqLwfunJr/9JgVK7FtKQuytThIMGSS6jDwHDIFtCEUiv2axbKScpZ8lU+JLzZH74x2Xq7sl4PbXv/AT34oQnnYok9GEv0zk3BJ8Eg0wPjx43nyySe57LLL6NatG3CwbtqLL77Im2++yWOPPdacXRRCCCGESNCgQNKyZcvIzMykT58+jBgxgg4dOlRZFU3TNP72t7/V+9wNnc8P0KZNm8PWETj33HPrVCRXNL3iMovNe6IsWxPhH4sC+MoVNhsMG6NxxwXJvFxczNnnJHP9RcmU+C1Sk3Q2BsL8o6yY69PTSW7iAJJSirJILPuocuqaUooUh0GLZAe6pmEqxQ/7y1mys4Qvd/koDZvx47OT7JzVNrbiWsdUV40fkkPRWMHsYNTCaYsV5e6UfDDz6Eg+XJtBKxY4KosVVI6UKayQQmngTNdJ6+Fkw0wfm18/ZJrTBA/db0jFcB5703S6d+/O559/zu23387kyZNRSsXHkhEjRvDcc8/Rvn37Op8vLy+P3NzcKtsrt+3Zs6fGYydPnszWrVt55JFH+NOf/gTEpt699dZbXHzxxQltzzjjDC6//HI6duzInj17ePbZZ5kwYQIlJSX86le/qrWPjz76KA8//HCdr6k+ykstwqG6Fdf2hxVl1dRDskxF8ICJI1UntasdR2r1wRZLKUpNk63lIbaWBCkNmKQYOq2PYCW2SiEUa4mwZH0RP6wpRilIcRpcc0pLBuTWb0l1ANOyyAtEsGkap+Uk0y0tqVGmj4qT06RJk1i2bBnDhw+nZ8+eaJrGHXfcQWFhIbt27eKCCy6oMhVWCCGEEKI5NSiQ9Mwzz8T//8knn1TbpqGBJHHysSzF3kKLTbsivLM0xCdfh1FAVrbGwPEKd0uLLeEIXRwO3vSXkBTUSNF1yoos/EoxIikJ67DP0niCUYuiYJT95WGKglFCUYsku06m24Zd11FKsbEowJIdJXyx00dhMBo/NtVpcGbbVIa3TaVbprvGD5/BaKzmUciMBY/SnAZd0t14XTZS7A0vHm6GFdFyi2i5IlgQJVqmMIOxQIHu1LC5NfS02PldOTobZvpY9/zBDJeIT7Fueuxxt+u8x2RmUu/evfn4448pKipi06ZNWJZFp06dSE1N5eWXX+anP/0pGzZsqNO5AoFAwtS0SpVTzwKB6lcSBHA6nXTr1o2xY8dy6aWXYpomM2bM4KqrrmLhwoWcdtpp8bZffPFFwrHXXXcdAwcO5L777uPaa6+tEqg/1L333sudd94Zf+zz+WrMkjpaigMWlgUdf1QPyQwrQgW1F9VWSlGuFDv8ITYXByjyR3FpGrnJjiNeMU2h2KtZfF0e4POvDlBQGKvxdEqrFK4e0AKPs/5/AsOmxa7yMFkuO4OykmmdUnMGoRB14XA4WLBgAa+99hpz587FNE1CoVB8hcirr75a6msJIYQQ4pjSoECSZTXlx3ZxIgtHFNv2Rfl+c4Q5HwfZsieWsdNzkEaXn1oYdnCFdPYdiDKsdTIasCwQYL9p4tI0RiQlMTw5+ahPZ4uYFr6QSX4gwoHyCP6Iid2IZQdlVUxd214SZMmOEpbsLGFf+cGlrZLsOqe39jK8XSp9spNrXDUtEDUpC1uEzVhNpTSXQU6yG6/TRnIDg0dWRBH1xwJHoSKTiC9W60gpMBwahkvD7tWr1KrRDHCkGmx+vbTa825+rZQeN6XWuz9HSzgc5t1332Xz5s2kp6dz0UUX0apVKwYPHozf7+eZZ57hqaeeYu/evXTu3LnO53W73QnTxioFg8H4/prcdtttLFu2jG+++Qa9ombX5ZdfTu/evbn99tvjq1FWx+FwcNttt/HLX/6Sr7/+utbV4ZxOZ7XBrqZQWz2kqN8iXFJ7UW2/ZbEnGGZzSZADZWEMBS3c9noXu65OAMU6LczyLT5W/VCEaSpcdp0J/XIY2tbboN+nsojJfn+EDh4nA7NTSGtAIEqI6miaxlVXXdXsBfeFEEIIIepC3gWLZuPzW2zZY7L42yBvLA4SCIHTCQN/Bjn9LVDQstTOIJeLnEydnZEwp7rdDE9OJqQULk3DhKMWRLKUojRkUhSMsLcsQmk4iqZppNgNcivqteSVhfl8ZwlLdpSww3cw4OA0NIa08jCsXSqntEipdolypRRB00oIHmW4bWQn2RscPFJmrMZRtDz2IT5cbBINKlRUodt1DLeGK7tq4KhS8IBJ0Zow4WKTrtd4ifiqn9YU8SkipRbOjOavCbNnzx5GjBjB5s2b4wX4XS4X8+fPx+FwMH78eHbv3s2QIUN4+umnufTSS+t87tzcXHbv3l1le15eHgCtWrWq9rhwOMzMmTO555574kEkALvdzvnnn88zzzxDOByutbh/ZVZRYWFhnfvblGqqhwQQLjaxIgpvNzsp1RTVDloW+0IRtpYG2V0aQotCpsuOy37kASSFYrdm8UMwyBcr89m3Pxb065mTxLWntCQjyd6g80o9JHG0VE5hq2kl3B9++IE2bdqQnp7exD0TQgghhKjeEQWSli1bxuLFi9m/fz+33HILXbt2xe/3s27dOrp160ZKSv1rT4gTn1KKfUUW63dG+efiAJ//EMveyWkF/ScokjPAHtHo5XfTO8uOPQkCSuHVDVyahq3iH8DR+ChXHjYpDkbZVx6rexQxFckOg5wkB4auURCIsGhbEUt2+thYeHBqk03TODU3hWFtUxncyoOrmuXJlVKxaWsRk4ipcNliU+KykuykOm0k13NFqoSV1UotQkUWUb+Fiig0Wyxw5EzX0W1VA0dKxWrXFP8vTPHaMMVrwgQPxLINHek6fX6bht2rVRtMsns17J5jY1rbpEmT2Lp1K/fccw/Dhg1j69at/PGPf+Smm24iPz+f3r17M2fOHM4+++x6n3vAgAEsXrwYn8+XUHC7MptowIAB1R5XUFBANBrFNM0q+yKRCJZlVbvvUFu2bAFIWCzgWFFTPaTK15Th0Eirpqh2WCkKwhG2lIXYWRIkElGkO20kuxvnN9mPxVo9wqodZXz9bQGRqMJuaIzrk82IjmkNykKSekjiaLvjjjtYv349y5Ytq3b/zTffTM+ePRNWrxVCCCGEaE4NCiSFw2GuvPJK5s2bh1IKTdMYM2YMXbt2Rdd1zjvvPO644w4mTZrU2P0Vx7lIVLFzf5SV6yPM+jDA7orARc+zoOtohW6DzDIbpxgu2rQyCBkKpenk2mx4df2ofYALmRYlwSgH/BEK/BECUQu3TSfVacNh6PhCUT7eWsR/d5aw5oCfytCKDvTNSWZYu1ROa+0lpZpAkFKKQEXNo6ilcNt0stx2spIdpDoNkuqxWlTlympRvyLsi62sFvVbWGHQdDDcOo5Uo9ppREopgvtMiteGKaoIHoXyE6epagZ4OttJ6+kgsM+k8wRPvCbSoTpP8GBFVbXP09QWLlzIL37xCx599NH4tpYtWzJu3DguvPBC5s2bl5AVVB9jx47lL3/5CzNmzODuu+8GYqukzZo1i6FDh8azhnbs2IHf76dHjx4A5OTkkJaWxttvv80f//jHeOZRWVkZ8+fPp0ePHvFpcQcOHKgSLCotLeWpp54iKyuLgQMHNqjvR0tN9ZBqK6odVYriaJQt5WG2FwcIBE1SHTZyUo6sWHwlhWKnZrI2HGbZNwXs2uMHoFOGi+sG5tIipebMr9ocrIdkY2BWCm2kHpI4ChYtWlRrUf0xY8bw/PPPN2GPhBBCCCFq16BA0uTJk3nvvfeYPn06I0eOpHv37vF9LpeLcePGMW/ePAkkiQTlAYute03mLw3y1pIg4Qi4kuCUcYoWPcBmanQuctIvw4nbowih8BoGmYaBq4GBgNqYlqIkFKXAH2G/P1b3SNc0vA6DDLedQMTki10+luwo4bt9ZZiHJOb0yHQzrF0qZ7ZJJc1V9ddIKYU/alEeNokqhdvQyUlxkOW2461n8Ci+slq5RbjQJFJZIFsHm1PD7jEwHNUHjgL7KjKO1lQEjgqqDxyl93aQ1suBt5sdmyt2r62oott1sSycza8du6u27du3L6FwNRB/fN111zU4iAQwdOhQxo0bx7333sv+/fvp0qULr7zyCtu2bUvIDpg4cSKfffZZfGqdYRjcfffd3H///Zx22mlMnDgR0zSZOXMmu3btYs6cOfFjn332Wd555x3GjBlDu3btyMvL46WXXmLHjh3Mnj271ulvTam2ekjxoto5Bt4ujnhRbbNiJbZt/jDbioOUBqIkGVqjrMRWqRyLdUaU/+0pY8XXBYRCFoYGF/fMYnS3jAY/j9RDEk3lwIEDZGVl1bg/MzOT/fv3N2GPhBBCCCFq16B3xv/4xz/41a9+xU033URBQUGV/T179uRf//rXEXdOnDjySyzWbIsw6yM/X6+LrWLWoiMMuFLh9oK33KCf5aJDrg3ToUDXyTWMRs9CUkpRFrEoCkTYVx6hJBRFKUWKw6BFsoOIpfg6r4wlO0r4Oq+UsHUwetQpzcVZbVM5q62XnOSqH+4rg0dlYRNTKZJsBi1SHGS67aQ6Ddx1DB5ZEUWkrGJltcIo0VKFGVKgDq6s5kirWj9JKUUgz4wFjdaEY7WOiqoGjrxd7aT1cpDW00FqNzuGq/pAixWG8l1ROl6eQvcbU4mUWNhTdZSpjpkgEoBpmvFV1CpVPk5NPfKC4K+++iqTJ09m9uzZFBUV0a9fP9577z2GDx9e63GTJk2iY8eO/O1vf+Phhx+Or8I0d+5cLrvssni7M888k6VLl/L3v/+dgoICkpOTGTJkCC+99BKjRo064v43htrqIVUW1U5ua8fbKVZU21KKcstiRyDMtpIgReURHJpGy0ZYia2ShWKHZrLBjLDymwK2bi8HoLXXwfWDcmmb6jrMGWpWEIpQFrbonZHEAKmHJI6y3Nxcvv322xr3f/3118fkFFchhBBCnLwaFEjav38/ffv2rXG/YRj4/f4Gd0qcOExLsWu/yRerw8xcEOBAkYWmQY9Rim6jYjWO2hU56edx4E3XMDWFxzDIauQspGDUoigYZX95mKJglFDUIsmux1dc+25fGUt2lrB8dymB6MHgS6sUB8PapTKsbSptvFWntVhK4Y9YlEdMLKVw2wxapjjiNY+qq5NU5RzRWHHsaLkiVGwSKanbympKKfx7DgaOiteECRf/KHBkOxg4Su/lwNvVUacgkFIKK6QIFyvKd0bRDLB7dJKx4c4+9jIztm3bxjfffBN/XFJSAsDGjRtJS0ur0v7UU0+t87ldLhdTp05l6tSpNbb59NNPq90+fvx4xo8fX+v5zz33XM4999w696epBaMKs7xqPSSIFdU2DymqjQbllkVeKMKWkgD5ZRE0U5HltuOsw+9CXZVWZCFt2u9n2Vf5+AMmGvCTbhmM6ZFZbXH7urAq6yHpGkNzkuku9ZBEE7jkkkt49tlnOf/88/npT3+asG/evHnMmjWr1qlvQgghhBBNrUGfCNu2bcu6detq3P/FF1/QpUuXBndKnBgCIcWWvAhvfhpk/tIQUROSvHDqFYqsTpAc0OkdctMpx0Bzg67r5DRiFlLEtCgJmRQEIhwoj01dsxt6bOqay8aafD9Ldhxg6S4fpeGDhY+z3HbOaudleNtUOqa5qmT+VAaPysImiljwqFWKg8yK4NHhPjArM1bjKFJWsbJaiRkLHB1mZTWlFP7dPwoclSQGjnR7YsaRt5uj2mlvVfpkxabLmUGFFVYowHCCza3jzjWwp+jYknRsycfmh+rJkyczefLkKttvueWWhMeVNd0OV+haHGTXtSr1kJRShPItdDuk93TgbmEjYFnsD0XYWhpiry+EFVWkumwkJTVeNo+FYptussWM8O2qIjZsKgUgO9nOdQNz6ZLpbvC5w6bFLn+YLKfUQxJN66GHHuLjjz/mZz/7Gf3796dPnz4ArF69mu+++46ePXvy8MMPN3MvhRBCCCEOalAgafz48Tz55JNcdtlldOvWDSD+YfvFF1/kzTff5LHHHmu8XorjTmGpxQ9bIrwwv5zVW2Mf2nO7w4BxClcS5BY56Od2kNFaRxk0WhaSpRSlIZOiYIS9ZRFKw1E0TcPjMGjpsLO5OMT8DQV8vrOEwmA0flyq0+DMNqkMa5dK90x3lUBWPHgUMVFK4bYbtPE6yHAfPngUX1mt3CLiS1xZDUPDlqRXu7KaUgr/rihFa8KxOkfrIkSqCxx1i9U3SuvlwNvFXqfAkRU9GDhS0Yq6Pi4Nu0fDkWHDnqRjS9Iw3FqjFEM+mmbNmtXcXThhpSdp5Ng1WqYfDAZZpiK438SRpuPtYgevxu5QmO3lIXYVhwhHTLwOGymNVEi7UklFFtKOwiBfrjhAaVns93dExzQu65Ndp+y/mpRHTPb5I7T3OBkk9ZBEE0tNTWXZsmU88cQT/Pvf/2bu3LkAdO7cmcmTJ/O73/2O5OTkZu6lEEIIIcRBDXq3PGnSJJYtW8bw4cPp2bMnmqZxxx13UFhYyK5du7jgggu44447Gruv4jhgWYo9BSYffxNi1n8CFJUqdAN6/UTR+UxwR3S6FzrpkmXHnqKwGTpZjZCFVB42KQ5G2VsexheKEjEVyQ6DnCQHu0tDvLO1iCU7fOwtD8ePSbLrnN7ay7B2qfTNTo5nW1QylcIfMSmLWKBi7dt6nWS4bHhdNpw1TJ/58cpq4YrAkRmqfWU1ZSnKd0VjQaO1sYyjSKlKaKM7IPVHgaO6rJxmhisDRxZYCs2ITZlzZcf6YkuKBbSOpdpHdXXNNdc0dxdOWK3TDKLlB1+DZlgRyo/ibmHg7mynxGGx3RdmZ0kQf9Ak2W6QmdJ4hbQBTBRbdZNtKsrq/xWzZm0JCkhz2bjm1Jb0aXFkH7ALQhFKpR6SaGbJyck8/PDDknkkhBBCiONCgwJJDoeDBQsW8NprrzF37lxM04wXkv3Tn/7E1VdffcxnMYjGF4ootuyJ8MpHARYsD2MpSMmAgT9XpLeG7BI7fQ0n2a11dAd4DNsRZSGFTIuSYJQD/ggF/giBqIXbppPqtFEYiPLRliKW7Chhhy8UP8ZhaAxp5WFY21RObZlSpZaKqRTlYZPySCyLKtlh0N7rJMNtx+M0agweVbuyWkiBFltZzZZi4MyoJnC0I0rx2jBF/wtTsq6awJFTI7Wb/WDgqPPhA0eV9Y3M4MEi3ZodDJdOcmsbdk/FNLUkrU5BKCEgVlQ74rNwtbWhOhisD4fYsS9ImT+KQ9dpmeyoEow9UsUVWUh5vhBfrsinqDgWCB7a1sPP+7Ug2dHwoI9lWewJhLHpOqfnpNA11YVxFFaHFOJwotEofr8fr9db7X6fz0dSUhI2m2TKCSGEEOLY0OB3JZqmcdVVV3HVVVc1Zn/EccpXbvHNxghPv1POxp2xIEzrfooBP4NkQ6NjgYvu6XbcqQqnreFZSIGIiaFrlISieJ02QlELXyiKu2Jay+c7fSzZWcLGwkD8GJumcUrLFIa1S2VwqxTcP8o4MC1FeSQxeNQh1RUPHjmqCR4durJaqChKxJe4sppRzcpqylKU7ajIOFoTpnhdmGhZNYGj7nbSKwJHns72KlPefiyhvlEkdj7doWFL0hLrG7k1NEMCR6J+FMTqeIUUWkedvZkm2wv9FJdHMFSsplhDi1vXJIpiix5lp2ayboOP71cXYVmQ4jC4akALBrb2HNH5w6bFrvIwWS6phySa329+8xv++9//snr16mr3n3nmmYwaNYq//e1vTdwzIYQQQojqNdrXW0opFi9eTCgU4qyzzsLjObI3+uL4oJRiX5HFe18GeWlBgDK/wrBD3zGK9oMgo9RGz6iL3Fwdh/vIspCCUYsXvsnj1R/24QuZeJ0G1/RtwXUDWnLvoq18uKWIyrCMDvTNSeasdqmc3tpLiqP24FGKwxYPHnmdRpUPxrGV1WJ1jhJWVrPAcFa/spqyFKVbIxStCVOyNjZd7dBpQhCrS5Taw05az4rAUafDB44OrW9kRVRsupxLw+7VcKQfX/WNxLEvXGJiejQCHTV2uQIU5kdRpiLdZT+iukQ1KdQs1uuxAvnLvsrnQH4so7Bfy2QmntKSVNeR/dlKqIeUk0KaQ7I8RPNasGABEydOrHH/2LFjmTNnjgSShBBCCHHMaHCNpKVLl7J48WIgFkw477zzWLRoEUop2rVrxyeffELnzp0btbPi2BKJxqayPf+en0VfR1CAJ0cxeDxkZEHbfBfdU2x4Wmq4HQ3PQioNm4SiFnN+2MczK/fEt/tCJk+v3IMFXNw9iwVbiuiR6eastqmc2dZLusuecJ7oIcEjnVjwqFO6mzSXDa8jMXgUX1mt3CJcXLeV1SxTUbo5El9RrWRdmKj/R4EjdyzjqHKqmqfj4QNHCfWNTIVmq6hvlGPgTNUxKusb1aHIthD1EVKK4jRFYU6U/VaUSIlFqtNGsrvx6whFUWzSo+zRTDZtKePb7wqJmgqnTePKvi04s733iAOjlfWQ+mS46ZeZXCU7UYjmsGfPHlq3bl3j/latWrF79+4m7JEQQgghRO0aFEh66623uPjii+OP586dyyeffMIjjzxC//79ufnmm3nooYeYPXt2o3VUHFvKAhbL14SZ9u9yduyNrSTWYYiiz4WQETXoVuiiVbZOkge8toZlIZWFTfJKQ+T7I5ya6+HVH/ZV2272D/tYeu0pvHRRNzLcVYNHZWETf9RE1zRS7AZdKoNHThu2Q5Yzj5Rb9VpZzTIVpZvCFK+pyDpaF8YMVA0cpfV0xDOOUjra0GuZXqZUbIpcZX0jzQLNATa3jqvNIfWN3FLfSBx9e9NNNruCseL1hkFWiuOoZLnlaybr9SjFwSjLVxaQtzc2NbVblptfnJpLVrL9MGeondRDEseyzMxM1q9fX+P+tWvX1lg/SQghhBCiOTQokLR79266dOkSf/zvf/+bXr16ce+99wLwq1/9iunTpzdOD8UxRSnFgWKLf/03wCsfBgiGwOZUDLgU2vWBVoUOurnspLXVSXEZZBoGqfXMQioPm+wtD7PHFyJgWjgNnYJABF/IrLa9L2RSFjbJTXEQMhURy6I8bOGPmOi6hsdh0NXrJtV5MHhUubJa0G8RKTUJFR6yspoRK0z945XVrKiiZGP4YMbR+kiVwJEtSSO1InCU3ttBSgdbQtZSlft5SH0jM6zQiNVJsiVpJLcysKVUrKjm1mo9jxBHw14jTMCwaJ3cuCuxVYqg2KhH2adbbNtRxspvCglHLGy6xqW9szinc/oRP++h9ZAGZafQOlnqIYljy09+8hNeeOEFJkyYwCmnnJKw75tvvmHGjBmMGzeumXonhBBCCFFVgwJJNpuNUChWt0IpxSeffJIwv79Fixbk5+c3Tg/FMSNqKrbsiTLt3+V88X0EgLTWsalsOUk6nfNdtMk0SEmFNHv9s5D8EZO9ZWH2lIYpj5j4IxYLNhfy3b4yFl3dH6/TqDaY5HUaeJ0GmwoD+EJRjIrgURuvm1SXDa/DhqFrmCFFpMgiUNPKasmJK6tZUUXJhnCsOPbaMCXrIrH2h7Ala/Fso7Sehw8c1VjfKFXDk26vmKYWm7om9Y1Es1PgNOo/HbUu9msmG/QoZWGTr74pYMcuPwDt05xcNzCXVt4jD/hU1kNq53EyWOohiWPUlClTWLBgAUOGDOGnP/0pvXv3BmD16tXMnz+fnJwcpkyZ0sy9FEIIIYQ4qEHvqvv06cOcOXOYMGECb7/9NgUFBVx44YXx/du3bycrK6vROimanz+k+GJ1kL+86ScvPzaVrcswRe9zIbfUQZeAnYxWOmkp9c9CCkRM9pWH2eWLBZB8oSgfbCrki50+rIo23+8rZ2LfFgk1kipN7NuCA+URdF2jW6abNJcdj8NAMyFSbhHMNxNWVlOqokD2j1ZWs6KK4nWxoFFlxpFVXeCoImiU1ttBSrvaA0dS30iIRGEUG/QoB3SL3Xl+VqwsIBA00TW4sHsmF3TPjE85PRJSD0kcL1q1asXKlSv5wx/+wLx583j77bcB8Hq9TJgwgT//+c+0atWqmXsphBBCCHFQgwJJDzzwAGPGjIkHi84880xGjhwZ3//+++8zePDgxumhaHYFPotXF5bz2sIg4Qg4khSnjoOOnXTaFzhpnWqQnqWR4ahfFlIwarGvLMSu0jBlYZN8f5j3NxWxYk9pvM3gXA/jemaR6bZx86m5aMArP1q17aZTc4laihxXMioA0UILX3GYiM8i6o+trKY7YtPDDl1ZzYooStYdUhx7QxgrnNhHu0cjtUdsmlpaLwfJbWsOHClLHQwchRSaAt0BhlvHlXVIfaMk7bAFtoU40SgU+zSLjXqUQNTi228K2bS1DIBcj4PrBubSId11xM9zaD2k03JS6Cb1kMRxIDc3l1deeSU2ffzAAQCys7MlM1UIIYQQx6QGBZLOPfdcvvnmGxYuXEhaWhpXXHFFfF9RURHDhw9PKMYtjk+mpdi8O8Jj/yxn5dooAJkdFYMuh3aanY4lDjJbamSn2eqVhRSKWrEMpNIQpSGTPaUh3t9UyKp95QBowOltvIztmUWnNDcA5RGL1fvLubRHFr8c1Ap/2CTZYRAOW1CsMAssikpiNYusQ1ZWc2bp8eLWlYGjojWxrCPf+jBWJLFvdo8WX1EtrZeD5DY1B44sU2GFDqlvpMUynezJUt9IiEMFK7KQCnSL/QeCLP8qn9LyKBpwTud0ftY7C4dx5MGeynpImRX1kNpIPSRxnNE0jZycnObuhhBCCCFErRpcMKJXr1706tWryvb09HSmTZt2RJ0SzS8YVixaFeQv/ywnv1iBpugxCvoO12hX6KR1skFWW50sd92zkEKmxYHyCDt9IUqCEbaVhPhgUyFr8mO1UXQNzm6XymU9smlTUR8lbFoUB6OYSpGb4iRDt2EzNdzlOoahoRUpSrdFiIZiK5s5DllZzQwrStbFVlUrXhPGt7GawJFXrwga2Unv5SCpja3Gb4AT6htFY/WNbC4NR5qGM03qGwnxYwpFnmaxSY8Stiy+X1XM2o0+ADKTbPzi1Fy6Zyc1ynMdWg9pUE4K6VIPSRwn/vjHPx62jaZpTJ48uQl6I4QQQghxeEf0Tnv16tV88MEHbNu2DYAOHTpw/vnn07dv38bom2gmRaUmL7zv55+LgpgmuLyKQVdA12wbHQocZOXotEg3yLbb6pSFFD4kgFQcjLChMMAHmwrZVBQEwKZrnNMhjUt7ZNEi2QHE6iYVh6IYmkZWkp1WTgdpLhsb/u5j8+ulRHwKu1ej8wQP3a7zUr4rSqSsMnAUpmhNmNJNkSqBI0eqnpBxlNTaqDboo5TCCoMZtDCDsbpKui1WGNvdwsDh1bElx+obHbqymxAiJoBivR6hSFcUFoVYviKfIl/sF/Ks9qlc3jcbt71x6hZV1kPqneGmv9RDEseZhx56qMZ9mhZbZVQCSUIIIYQ4ljQokBQKhbj55puZPXs2Sin0imwUy7K49957mTBhAn//+99xOByN2llxdFmWYuPuCH+cU8YPm2Kro7XorhhymUZHv4PWERvZ7XRaptQtCyliWhzwR9jlC1EYiLD6gJ//bC5ke0lsxT+HoTG6UzqXdM8i021HKUVpOEpp2MRp6LTxOMnSbDh9YFM6G+b4WPe87+D5fYp1032gIGuQky9+eQAVTeyDI10/uKpaLwdJrWoIHFmxukbx+kYcrG/kbmFgT4nVNzLcUt9IiNooFLs1iy16lIhSrPlfCavXFmOp2AqLE09pSf/clEZ5rsp6SIamc3qLFLp6pR6SOP5YllXttu3bt/Pss8/y3//+l//85z/N0DMhhBBCiOo1KJD0+9//nldffZVbbrmFX//613Tu3BlN09i0aRP/7//9P6ZPn05GRgZPPfVUI3dXHC3hiOI/KwP85Z9+SkoVmqHoPRpOPVWnbbGLrAyN1jk2cuwGaYZRaxZS1FLxAFK+P8yqfWX8Z1MRe8pilazdNp0LumQwpmsmaS4bplIUB6OUR2J1jzp7XaRGbNjyFcF8k3ILck53sPn10mqfb/PrpXS7zovdE/sAmX5IxpE7t/rAkWUeMk0tckh9oxSN5LYG9mTj4DQ1qW8kRJ34sVhnRCnRFCW+MF+tyGd/Uez3fmCrFCYMaIHH2ThTzqQekjiR6bpOx44d+ctf/sKECRP49a9/zeuvv97c3RJCCCGEABoYSJozZw5XX301zzzzTML27t278+yzz+Lz+ZgzZ44Eko4TxaVRpr1Tzrz/hrEsSMpQDL0Serkd5JbbyGmj0ybNftgspKilyK8IIO0vD/HVnjIWbCnigD82ncXjMLioawYXdskkxWEQtRQFgQgh08LrsNHTm0RKQMPaoQgXRwnr4PDoGG6dcJFJxKeqfd6ITxEptRg8NRN7il594ChyaH0jC83QsLk0XBk6jrSKothJGoZLshmEqC8LxS7NZKtuYqLYuKGUVT8UEbUUSXad8f1bMKSNp9Fqh/kjJnv9Edp7nQzMlnpI4sQ2fPhwfv/73zd3N4QQQggh4hr07jsSiXDaaafVuP+MM85g/vz5De6UaBpKKdbujDD5lVI2boul1rfupzjzAo0OpU6yHQZtWtrIddWehWRaivxALICUVxpi6e5SFm4poigYm2eW5rRxSfdMRndKx203CJmxVdsspUh32ejkcJFUDuZORcBvYTg1HBk65TtMdswrpWh1mHPn5WL3atUGk+xeDUe6gbPIQpmHqW/U0sCRaq8IHEl9IyGOVFlFFlKppigvj7Lyq3x2H4jVP+uVk8Q1p7Ykw21vtOeTekjiZLNy5cp4CQEhhBBCiGNBgwJJo0eP5sMPP+RXv/pVtfsXLFjAeeedd0QdE0dXJKp458sAf/1XOf5y0G2KfmNgaBcbLcvt5OQadMi0kW2z1ZiFZFZkFO3yhdjlC/HfHSV8sq2Y0nCsvlKW286lPTI5p2M6TkPHHzHJKwth0zWyXTayInYchRqRApNglFgdomTY/3mQPYsClO84WPAo/5sQncd7EmokVeo8wUOoyCSYb2KFFajq6xvZkjQ0QwJHQjQGC8UOzWSbbmKh2L61jK9XFRKKKhyGxrg+2ZzdMa3RspCkHpI4Ub366qvVbi8uLua///0v//73v7nhhhuauFdCCCGEEDWrUyCpsLAw4fGUKVO4/PLLufTSS7n11lvp0qULABs3buTZZ59l+/bt/POf/2z83opGUVxm8uc3S1nwRcWUsxzFsMs1eionmZpOu4522iTXnIVkKUVhIMqu0hBbiwIs3l7Cp9uL8UdiWU0tkx1c1jOLEe1TsWkapWGTfH8Et02njdNJWtjAtlsR8VmENbB7dHwbI2x9o4wDXwXjBbN1O2QNcZE70o0rx6Db9V7QYPNrh6zaNt5Dt194KV4Xxp4Sy0yS+kZCHF2lFVlIZZoiEDRZtbKArXl+ADpnuLhuYC45KY232ELYtNhVFibTLfWQxInn2muvrXFfVlYWf/jDH3jggQearkNCCCGEEIdRp0BSVlZWlW+VlVL88MMPzJs3r8p2gN69exON/mgJLdHsvtsW4b5ZJezcFfs5tR+kOGeEjZZ+OznZOp1z7LRwVJ+FZClFUSDK7tIQGwv8fLytmP/uKCFkxs7Vzuvksp5ZnNUmFTTwhaL4IxbJdp1OdieecgOtSGH6TUy3jgL2fRZk72d+ggcOrlqT0sFG7kg3Lc5yY0+pWBEwrCjZGKH9Jcl0vyGVSKmF3asTLbewwor0Xg6pbyREI/PYDcLmwd9NC8U23WSHZqI02LOrnBVfF+APWxgaXNwri9FdM2otxl9fUg9JnOi2bt1aZZumaaSnp+PxeJqhR0IIIYQQtavTO/IHHnig0aYniOZhWoo5n5XzzNwAoSDYnIrBF2sMbekk3TRo18GgY6q92iwkpRSFwVgAaW2+n482F7F0l4+IFQsgdU53Ma5nNkNaeTAr2kZMi1SbQRvNRVKhjlViYZkWukPDty1K3qIART+EoaLkkS1Jo8VZbnJHuvF0OlhPJRqwiJYpVFRhJGlEHBqGy8KRGqtvZDikPooQjS2sFAbQLzuFJF2nIBRldamflSqCX1OEwyarvy1i3Y4yANp4nVw/qCVtUl2N2o9YPSST3hluBmQm45J6SOIE1L59++bughBCCCFEvdQpkPTQQw8d5W6Io6mw1OT+10v4fEWsdlFaa8W5Fxt0tBxkpeh0y7XTylU1C0kpRVFFAOmHfeUs2FLE8t0+KhKQ6JmZxLheWZzSIoWwqdjvj4BSpGEj23Lg2AuqHCxdEfYp9n0eYN+SAJHSgwWz03o7yB3pJnuoC8MRC2CZ4dgqbCpsobt0XNk6riwDR6ouWUdCHGURpfi8vJxlgQBBpXBpGkPdbk7P8rCmqIgdu8tYvrKAkkAUDTi/WwZjemZha8RppLF6SBFsmsZpLTx097qk2LA4aaxbt45//etf5OXl0b17d37xi1/g9Xqbu1tCCCGEEHFHNEegvLwcn8+Hx+MhJSWlsfokGtFXm4L8YVYZB/bGgjddz4TzTnWQjkG7Nja6ZthIt9kSspCUUpSETHaXhvh6TykfbC7k67yyyuQh+uckM65XNr2zkvBHLfLKw9g0yLHspPkN7CVgBRUWUPhDmL2fBSndFImf35mh0/JsN7kj3Lhbxl6CVlQRKjYx/Ra6U8ORquPOceJIjRXKFkIcfeGKINKnfj9JmkaOYVBmWXzm9xOKWGz9rpiP1hYAkJNs57qBuXTOdDduHyrqIWW5bZyanUJbqYckTkDPPPMM/+///T+WLl1KVlZWfPv8+fMZN24c4XA4vu3pp59m2bJlCe2EEEIIIZpTvQNJ27Zt44knnuD9999n165d8e2tW7dmzJgx3H333XTs2LFROynqL2paPP9JGS+9HSIaBkeSYvjFOv29DjLcOt1bOWiXXDULqaQiA2nZbh/vbyrku33l8X2Dcz2M65lFlww3ZWGTPWVh3Gi0jtrx+Axs5aBMRVmeyf6lQQ4sD2GFYuEnzYCsgU5yRyWR0d+BpmtYpiLss4j6LTQDHF4dT/uK4FGKJtMphWhiBrAxHObyZA9dXE6KglHSXTbWlAZ45NPtfLc7NpVtZKc0LuudjdPWuEHe8ojJPn+Edl4ng6QekjiBvfvuu3Tu3DkhOBSNRrnhhhswDINZs2YxaNAg3n//fSZNmsQjjzzCtGnTmrHHQgghhBAH1etd+rx587j66qspKyujQ4cOjBkzBo/HQ2lpKd9//z3Tp0/n1VdfZc6cOVx88cVHq8/iMPb7otz1agnffRsrkpvVQXHhaDstDYN2LWx0z7aT5UjMQvKFouwpDfPfHcXM31jA2vwAABpwRhsvY3tm087rpCQUJa8sTLKl0z5sJ7nEQA9AtFyx77sQe/8bIJBnxs+b1Nogd2QSLYe7cKQaKEsR9Ssi5SYaYE/R8Xa24Uw3sHt0WWVNiGYUUoqfe1J56Zs8Jv6wD1/IxOs0uLpvC14c3ZVbF2zk/7qk0ykzqdGfu7IeUq8MN6dIPSRxgluzZg033nhjwrbFixdz4MAB7rvvPq655hogtnDJd999xwcffCCBJCGEEEIcM+ocSFqzZg1XXHEFnTp14oUXXmDYsGFV2ixZsoRf/vKXXHnllXz99df06tWrUTsrDm/RugAPvlxGyQFAU/Q7W2NkdycZSQbdW9vplGpPyEIqDcUykD7ZVsz8DQVsKgoCoGtwdrs0LuuRRYtkO8WhKPvKwngjBq2CDpxlOlq5hW9LmAPLQxR+G0JVLO5kODVyznCRO8qNt2uscLYZUAT2R1EW2JM1PO0rgkdeHd0mwSMhjgmm4qVv8nhm5Z74Jl/I5NmKx89f0I0fivzxQvuNobIekiH1kMRJpKCggLZt2yZs++STT9A0jZ/97GcJ288880z+/e9/N2X3hBBCCCFqVedA0p///GeysrL4/PPPycjIqLbNsGHDWLJkCf369ePRRx9l9uzZjdZRUbuIafLoB6X8+70wVlTD5VGMvsigq9dO62yD3i0dZLtsGBVZSKVhk92+IAs2F/HuhgJ2+EIA2HSN/+uQxs96ZOF1GhSHTPJLI6SHbaSV67jKdUL7TfK+8bN/aZBw8cGlwb3d7LQa5Sb7dBc2l040aBEqsLAiCptbIynXhjOzomi2Q4JHQhxrbLrGqz/sq3bf7B/2ceugVo36fGHTYld5mEyXjYFSD0mcRFq0aMHevXsTti1ZsoSkpCT69++fsN3hcOBwOJqye0IIIYQQtapzIGnx4sXccMMNNQaRKmVkZHDdddcxc+bMI+6cqJvtxRF++1IJm/+nAI3crjBmuJOciiykbhk23EZsmkh5OFZE+931+by7sZA9ZbGCng5DY3SndC7ulonLZuALRSkrMckK6nhLDWylULw6wvYVQUrWHyycbffqtBzuIndkEsltbJhhRbTMIlwUwXBpODON+IprNrdkGQhxLCsNm/hCZrX7fCGTkpCJQ9caJSOpPGKyNxChvcfJwKxkMpz2Iz6nEMeLQYMG8corr/DrX/8aj8fD//73P1asWMHFF1+MzZb41mzdunW0adOmmXoqhBBCCFFVnQNJBQUFdOjQoU5tO3bsSEFBQUP7JOpIKcXcH8p58tUA5UWg6YqhIw1O62SjVYaNPq0c5CbHspD8EZNdvhBvrc1n/qYC9pfHgkFum84FXTK4sEsGhq5RGoyil0JLv0FKuU5kq8Xer/3kfxXCDFR8eNQgY4CTViPdZA6MZRBEyyz8eVF0OzhSdbydndhTdezJEjwS4njhdRh4nUa1wSSvM7Yv7DvyIFJlPaTe6VIPSZycHnzwQQYPHkzXrl3p3bs3X3/9NZqmce+991Zp+/bbbzNq1Khm6KUQQgghRPXqHEjKyspi69atdWq7detWWab2KPNHTe57p4TFH0ZRlkZyOlx0np2OmTa65trpmWMnxWYQiJjs9AV5438HeG9jIYXBKAAeh8FFXTMY3SkDSykCAZPkgEGbMjvOQvB9HWHDiiD+XQc/ULpaGOSOcJM7wo0jTSdSpgjlm6CD3aOT2taGI83A7pEV14Q4HkUtxTX9WvD0V3uq7LumbwvyA5EjykaSekhCxPTt25dFixbxyCOPsGXLFk477TTuvvtuBg4cmNDu008/JSkpiXHjxjVTT4UQQgghqqpzIGnEiBHMnDmT3/72t7VObyssLGTmzJmMHDmyUTooqvrmQID7XipjzwYAjY69NH4yxE6bTBt9W9tp67UTMRVr88t5bfV+/rOpkJKKDIM0p41LumcyqkMawaiF3xclxa/TptSGtcaiYGWAwu9CqFi8Cd0O2UNd5I50k9rDjhmEaLlF8ICJLVnD08mGI93A4dHRDAkeCXE8c9sNfnlqrA7SK98fXLXtmr4tuOHUXFYX+Rt87khFPaQMl41B2Sm0kXpI4iR3xhln8P7779faZsSIEfzwww9N1CMhhBBCiLqpcyDpvvvuY+7cuQwfPpwZM2ZwxhlnVGmzdOlSbr75ZgoKCqpNzxZHJmpZTP/Gx+zXwgR9GrpNMWKkjVM62ejS0k6fFg5chsa6A+W8+sN+FmwupDwSK4ad5bZzaY9MzmzjpTxk4s+Pkuo3cO02KF8WYdvKEKHCg4WzUzraaDUyiewznei6RqTMIpRvYUvSSG5TUTTbq6PbJXgkxInEadO58ZRcbhnYiqJQlFSHjQPBCKuL/Pij1uFPUI3yiMm+QIR2Ug9JCCGEEEKI416dA0m9evXi9ddfZ+LEiQwbNowOHTrQv39/PB4PpaWlfP/992zduhWXy8WcOXPo3bv30ez3SScvGObet0r4ZrECpZGaBReNdNC1lZ2+re209upsKQry0nd7+WhLEcGKD3y5KQ4u7Z7JoFYe/H6T0H6LjEId9Y2J76sAOzdEoGKmii1Zo8VZbnJHuUnKtREptYiWKWwuSGppVKy4ZmA4JXgkxIksyR6rWbS+JEBBMELqEQR+CoIRSiMmvdLdDMhMxi31kIQQQgghhDiu1as4xaWXXsr333/PjTfeSCgU4p133mH27Nm88847BAIBbrjhBr777jvGjh17tPp70rEsi/m7SpkwtZhvFgFKo1c/jasvcjC0q5MzOzkIRML8/pOtXP7vNby7oYBg1KKd18lvh7TmT2d3oGdSEtGdJunLNYy/R9gzuYydc/yx1dcUpPdx0Os3qZz+dDbtf5aM4YRouYkzQyO9l4PMU5yk9XTizrFJEEmcdEKhEL///e9p1aoVbreboUOHsnDhwjod+/HHHzNy5EiysrJIS0tjyJAhzJ49u9q2M2fOpGfPnrhcLrp27crTTz/dmJfRIKURk1ADayJZlsWu8hBhSzG0hYch2SkSRBJCCCGEEOIEUOeMpEqdOnXi+eefB8Dn81FaWorH48Hr9TZ65052JabJE0uL+fBNk7Bfw+aA/zvbYFB3Bz1a2AhYIR78704+3V5CtOLDXud0F5d2z6JrmotwsYVaa5K8zKJ8RZiinQcLZzszdVqOcNPiLDf2ZI1oQBENWDhSdTwdnThSdWzJUjRbiGuvvZa5c+fy29/+lq5du/Lyyy9zwQUXsHjxYs4666waj3v33Xe55JJLOP3003nooYfQNI0333yTiRMnkp+fzx133BFv+8ILL/DLX/6Syy67jDvvvJMlS5bwm9/8Br/fz+9///umuMxGdWg9pIHZKbSVekhCCCGEEEKcMOodSDqU1+uVAFIjCCuFAYSUwqlpmMAGf4iH/1nK+i8ANLJawpjhDnq2s4MtzN++3s2SnSVUJgv0zEziku6ZtHM5MEsstIUmti+jFH8fwYrE2mgGZA1y0nKEG29nO2YwdrBu1/C2seFMrVhxTZfgkRAAK1as4I033mDq1KncfffdAEycOJE+ffpwzz33sHTp0hqPfeaZZ8jNzWXRokU4nbFAys0330yPHj14+eWX44GkQCDApEmTuPDCC5k7dy4AN954I5ZlMWXKFG666SbS09OP8pU2nkPrIQ3KSiZd6iEJIYQQQghxQjmiQJI4chGlOBCIkumw4StXpCVr5JVFmfl6kPVfx9oMGKBzzmA72EPMXreHFXtKK8sa0T8nmTFdM8jV7LBTwWcRol9F8ecfLIqb1MZG7kgXWQNdaAagQNMhpb0NZ4aB3aOj2yR4JMSPzZ07F8MwuOmmm+LbXC4X119/Pffddx87d+6kbdu21R7r8/lIT0+PB5EAbDYbWVlZCe0WL15MQUEBt9xyS8L2W2+9lddee43333+fq666qhGv6uiprIfUM93NKVIPSQghhBBCiBOSBJIagWmamKZZZbumaei6ntDuUGGliEYUby/w88biID6/jidJ4+ejXNw3Ppm9BSV07AJpLcLM27aH1fnl8WMHt/Jyfsd0skI2WBQmusxPYL0JFfEjw62Rc7qLnNNdJLd0gAlGkoYtDVyZBo7UgyuuKSwqu2YYBz/4VXdNh2poW8uyUKrmuiv1aavrenz6nbStf1ulFJZV80pch76G69P2RPHtt9/SrVu3KpmXQ4YMAWDVqlU1BpJGjBjB448/zuTJk7nmmmvQNI3XX3+dlStX8uabbyY8B8CgQYMSjh84cCC6rvPtt9/WGkgKhUKEQqH4Y5/PV7+LbASWZbEnEMHQNIbmeOie6sI4wV4LQgghhBBCiBgJJDWCpUuXkpycXGV7RkYG/fr1iz/+4osvEj6IR6KKbzdF+HpDhHTNi48elPoVM94LENj/FVcOtHhvUwHrdsbmpvUF2niddM/JoD2dYKZJZGWQfbmrMe1h6A7OLJ2UdnYc2QaFmiJAMgNzBuHKigWPvv5+Jf79/mqvw+Vycdppp8Ufr1q1itLS0mrb2u12zjzzzPjjH374geLi4mrb6rrO8OHD449Xr15NYWFhtW0h9gG80tq1azlw4ECNbYcNGxYPPG3YsIG9e/fW2PaMM87A4XAAsGnTJvbs2VNj29NOOw2XywXA1q1b2blzZ41tBw8eHP/579ixg23bttXY9tRTT40HJXbt2sWWLVtqbDtgwADS0tIAyMvLY+PGjTW27du3L5mZmQDs37+fdevW1di2V69e5OTkAHDgwAHWrFlTY9sePXrQsmVLAAoLC/nhhx9qbNu1a1dat25d4/7jUV5eHrm5uVW2V26r7TU0efJktm7dyiOPPMKf/vQnAJKSknjrrbe4+OKLE57DMIz4z6SSw+EgMzOz1ucAePTRR3n44YfrfE2NrbIeUrrLxiCphySEEEIIIcQJTwJJzUjXYfXWaLX7Vm2OMKCLHX/URNOgvddJB5cL5w6FfblF6KtgvK3h0kjubCeplYHh0tEMMJwa9mSDlHQb6b3lg50QDREIBBKmplWqDDIGAoEaj3U6nXTr1o2xY8dy6aWXYpomM2bM4KqrrmLhwoXxoG0gEIgHOKt7ntqeA+Dee+/lzjvvjD/2+Xw1Zkk1Nn/EZK/UQxJCCCGEEOKkIoGkRnDGGWdUW3T8xyueHZrBA1Dgs/jutYJqz7m6oB99T8mgh76TLrscuD5VWN+ZqBBoaKBBxgAHOae7Se50BrpDw+HVcWfbYiuupVS/4trAgQPrfF0DBgyoc9u+ffvWuW2fPn1qnXp1qJ49e9KjR48a9x86lapbt2507dq1Tm27dOlC586d69S2Y8eOdOjQoU5t27VrV+uH+EPbtmnTptYMnkPb5ubmxjODDtc2JyeH7OzsOrXNzs5m2LBhNbY99DWUkZFR57YnCrfbnTBtrFIwGIzvr8ltt93GsmXL+Oabb+L3/PLLL6d3797cfvvtLF++PH6OcDhc7TmCwWCtzwGxgFV1wa6jrTAYoSRi0ivdzQCphySEEEIIIcRJQwJJjcAwjIS6PrW1O1S6RyclyUapv2pQJSXJRkaKnYHPuAiuN1GAhoG7hUGLM11knOKIrbKWouPK0XGmx4pmH27Ftbr082i3rU8dHWl7dNtqmlbnn1192p4ocnNz2b17d5XteXl5ALRq1ara48LhMDNnzuSee+5J+HnY7XbOP/98nnnmGcLhMA6Hg9zcXEzTZP/+/QnT28LhMAUFBTU+R3M5tB7SaVIPSQghhBBCiJOOBJKaUTismDDKzfPvVa1ZNGGUG3+BSXC9ie6ArEEusgY5Selkw5Gi48oyYiuueWXFNSGOlgEDBrB48WJ8Pl9C1mFlNlFNWXsFBQVEo9Fqi9BHIhEsy4rvqzzHypUrueCCC+LtVq5ciWVZ9coMPNqkHpIQQgghhBBCvkZuRm67xjWjXNx8oRtPUiwY5EnSuPlCN9eMcpGSrtP1Bg+nPJxB12s8tBzuJrO/i8xTXXi7OHBmGBJEEuIoGjt2bLy2UaVQKMSsWbMYOnRofBrjjh07Egqc5+TkkJaWxttvv50wba2srIz58+fTo0eP+JS1UaNGkZGRwfTp0xOee/r06SQlJXHhhRcezUusM3/EZGd5mDYeJ2fneiWIJIQQQgghxElKMpKaUdhnsfQX+7jozjRumJqBr8TCm6qT92WQpVfvY9isFrS7MBm7R4/VPXJL3E+IpjR06FDGjRvHvffey/79++nSpQuvvPIK27ZtY+bMmfF2EydO5LPPPovX/jIMg7vvvpv777+f0047jYkTJ2KaJjNnzmTXrl3MmTMnfqzb7WbKlCnceuutjBs3jtGjR7NkyRLmzJnDI488QkZGRpNf948VBiP4pB6SEEIIIYQQAgkkNSuHVyeYb/L1bfk40mPT1YL5JuEiC7s3VjzbmaGfkEWMhThevPrqq0yePJnZs2dTVFREv379eO+99xg+fHitx02aNImOHTvyt7/9jYcffphQKES/fv2YO3cul112WULbW265Bbvdzl//+lfeffdd2rZty7Rp07j99tuP5qXVSWEoSprTxpAcDz2kHpIQQgghhBAnPQkkNSMrqug8wcO66T7CRRbhIiu+r/MED1ZUYbPLhzYhmpPL5WLq1KlMnTq1xjaffvpptdvHjx/P+PHj6/Q8N954IzfeeGNDunjUaLpOyyQHA7NSaJciU9mEEEIIIYQQEkhqVja3TvcbUgHY/FopEZ/C7tXoPMFD9xtSMZySiSSEaD690lx47AYZTntzd0UIIYQQQghxjDjm0l1CoRC///3vadWqFW63m6FDh7Jw4cI6Hbt7924uv/xy0tLS8Hq9XHzxxWzZsqXatjNnzqRnz564XC66du3K008/3ZiXUWeGU6PbdV4u/KwNF/63NRd+1oZu13kliCSEaHbtU1wSRBLiONZU76mEEEIIcXI55gJJ1157LU8++SQTJkzgb3/7G4ZhcMEFF/D555/XelxZWRkjR47ks88+47777uPhhx/m22+/5eyzz6agoCCh7QsvvMANN9xA7969efrppzn99NP5zW9+w+OPP340L61GNreObtdiq7DZNSmqLYQQQogj1hTvqYQQQghx8jmmpratWLGCN954g6lTp3L33XcDsdWQ+vTpwz333MPSpUtrPPa5555j48aNrFixgsGDBwNw/vnn06dPH/7617/y5z//GYBAIMCkSZO48MILmTt3LhCrTWJZFlOmTOGmm24iPT39KF+pEEIIIcTR0xTvqYQQQghxcjqmUl/mzp2LYRjcdNNN8W0ul4vrr7+eL7/8kp07d9Z67ODBg+NveAB69OjBOeecw5tvvhnftnjxYgoKCrjlllsSjr/11lspLy/n/fffb8QrEkIIIYRoek3xnkoIIYQQJ6djKpD07bff0q1bN7xeb8L2IUOGALBq1apqj7Msi++//55BgwZV2TdkyBA2b95MaWlp/DmAKm0HDhyIruvx/UIIIYQQx6umeE8lhBBCiJPTMTW1LS8vj9zc3CrbK7ft2bOn2uMKCwsJhUKHPbZ79+7k5eVhGAY5OTkJ7RwOB5mZmTU+B8SKVoZCofjjkpISAHw+32GuTIiTi8fjQdOkYHxzUEoBMi4JcaiTcUxqivdU1anPe6VgAwNS0aC//seEy+p9TCAarPcxDRl7G3IfTrR7ACfXfTgZxyQhxInlmAokBQIBnE5nle0ulyu+v6bjgDodGwgEcDgc1Z7H5XLV+BwAjz76KA8//HCV7W3btq3xGCFORiUlJVW+BRdNozJTQMYlIQ46GcekpnhPVZ0T6b3S4gYcc1Nqo3ejWck9iGns+3AyjklCiBPLMRVIcrvdCd9iVQoGg/H9NR0H1OlYt9tNOByu9jzBYLDG5wC49957ufPOO+OPLcuisLCQzMzMI/5Wwefz0bZtW3bu3Cl/WA5D7lXTq+8993g8TdArUZ1WrVqxc+fOI/62U37P6k7uVfOoz30/GcekpnhPVZ2j+V6poeR3VO5BpWPlPpyMY5IQ4sRyTAWScnNz2b17d5XteXl5QOwDUnUyMjJwOp3xdrUdm5ubi2ma7N+/P2F6WzgcpqCgoMbngNi3cz/+hi4tLa32i6onr9d7Uv+Brw+5V01P7vmxT9d12rRp02jnk5953cm9ah5y36vXFO+pqtMU75UaSl4rcg8qyX0QQogjc0wV2x4wYAAbNmyoMqd4+fLl8f3V0XWdvn37snLlyir7li9fTqdOneKR/8pz/LjtypUrsSyrxucQQgghhDheNMV7KiGEEEKcnI6pQNLYsWMxTZMZM2bEt4VCIWbNmsXQoUPj8+t37NjBunXrqhz71VdfJbzxWb9+PYsWLWLcuHHxbaNGjSIjI4Pp06cnHD99+nSSkpK48MILj8alCSGEEEI0maZ4TyWEEEKIk9MxNbVt6NChjBs3jnvvvZf9+/fTpUsXXnnlFbZt28bMmTPj7SZOnMhnn30WX50I4JZbbuHFF1/kwgsv5O6778Zut/Pkk0/SokUL7rrrrng7t9vNlClTuPXWWxk3bhyjR49myZIlzJkzh0ceeYSMjIwmveZKTqeTBx98sNriliKR3KumJ/f85CM/87qTe9U85L7XrineUx0v5LUi96CS3AchhGgcmjr0ncMxIBgMMnnyZObMmUNRURH9+vVjypQpjB49Ot5mxIgRVd70AOzatYs77riDjz76CMuyGDFiBNOmTaNLly5VnufFF1/kr3/9K1u3bqVt27bcdttt3H777bIUpxBCCCFOCE31nkoIIYQQJ5djLpAkhBBCCCGEEEIIIY5Nx1SNJCGEEEIIIYQQQghx7JJAkhBCCCGEEEIIIYSoEwkkCSGEEEIIIYQQQog6kUBSIwqFQvz+97+nVatWuN1uhg4dysKFC+t07O7du7n88stJS0vD6/Vy8cUXs2XLlirtpk+fzrhx42jXrh2apnHttdc28lU0jaa4V5qmVfvvsccea+zLOS409J6vX7+eO+64gzPOOAOXy4WmaWzbtq3G9u+++y6nnnoqLpeLdu3a8eCDDxKNRhvxSkRdyZhUdzImNT0Zk0RdyVgWI+NUzZpqPBFCCFFBiUZz5ZVXKpvNpu6++271wgsvqNNPP13ZbDa1ZMmSWo8rLS1VXbt2VTk5Oerxxx9XTz75pGrbtq1q06aNys/PT2jbvn17lZGRoX7yk58om82mrrnmmqN4RUdPU9wrQJ177rlq9uzZCf9Wr159NC/tmNXQez5r1iyl67rq06ePGjBggALU1q1bq237wQcfKE3T1MiRI9WMGTPUr3/9a6XruvrlL395FK5IHI6MSXUnY1LTkzFJ1JWMZTEyTtWsKcYTIYQQB0kgqZEsX75cAWrq1KnxbYFAQHXu3FmdfvrptR77+OOPK0CtWLEivm3t2rXKMAx17733JrTdtm2bsixLKaVUcnLyMflG53Ca6l4B6tZbb23czh+njuSeFxQUKJ/Pp5RSaurUqbW+yerVq5fq37+/ikQi8W2TJk1SmqaptWvXHvmFiDqTManuZExqejImibqSsSxGxqmaNdV4IoQQ4iCZ2tZI5s6di2EY3HTTTfFtLpeL66+/ni+//JKdO3fWeuzgwYMZPHhwfFuPHj0455xzePPNNxPatm/fHk3TGv8CmlBT3atKgUCAYDDYeBdwHDqSe56RkYHH4znsc6xZs4Y1a9Zw0003YbPZ4ttvueUWlFLMnTv3yC5C1IuMSXUnY1LTkzFJ1JWMZTEyTtWsKcYTIYQQiSSQ1Ei+/fZbunXrhtfrTdg+ZMgQAFatWlXtcZZl8f333zNo0KAq+4YMGcLmzZspLS1t9P42p6a8Vy+//DLJycm43W569erF66+/3jgXcZxp6D2v73MAVX4+rVq1ok2bNvH9omnImFR3MiY1PRmTRF3JWBYj41TNmmI8EUIIkUgCSY0kLy+P3NzcKtsrt+3Zs6fa4woLCwmFQg069njVVPfqjDPO4JFHHuGdd95h+vTpGIbBhAkTmD59emNcxnGlofe8vs9x6Dl//Dwn2uv4WCdjUt3JmNT0ZEwSdSVjWYyMUzVrivFECCFEItvhm4i6CAQCOJ3OKttdLld8f03HAQ069njVVPfqiy++SGhz3XXXMXDgQO677z6uvfZa3G53wy7gONTQe17f54Cafz4+n++In0PUnYxJdSdjUtOTMUnUlYxlMTJO1awpxhMhhBCJJCOpkbjdbkKhUJXtlfPLa/rDW7m9Iccer5rrXjkcDm677TaKi4v5+uuv693v41lD73l9nwNq/vmcaK/jY52MSXUnY1LTkzFJ1JWMZTEyTtWsKcYTIYQQiSSQ1Ehyc3PjafSHqtzWqlWrao/LyMjA6XQ26NjjVXPeq7Zt2wKxVO+TSUPveX2f49Bz/vh5TrTX8bFOxqS6kzGp6cmYJOpKxrIYGadq1hTjiRBCiEQSSGokAwYMYMOGDVVS5ZcvXx7fXx1d1+nbty8rV66ssm/58uV06tTphFtNojnv1ZYtWwDIzs5uQM+PXw295/V9DqDKz2fPnj3s2rWrUZ5D1J2MSXUnY1LTkzFJ1JWMZTEyTtWsKcYTIYQQiSSQ1EjGjh2LaZrMmDEjvi0UCjFr1iyGDh0a/zZnx44drFu3rsqxX331VcIf+fXr17No0SLGjRvXNBfQhJriXh04cKDK85aWlvLUU0+RlZXFwIEDG/uyjmlHcs/rqnfv3vTo0YMZM2ZgmmZ8+/Tp09E0jbFjxx7ZRYh6kTGp7mRManoyJom6krEsRsapmjXFeCKEEOJHlGg048aNUzabTf3ud79TL7zwgjrjjDOUzWZTn332WbzN2WefrX58230+n+rcubPKyclRTzzxhJo2bZpq27atatWqldq/f39C23fffVdNmTJFTZkyRTkcDnXKKafEH3/33XdNcp2N4WjfqwcffFD1799f3X///WrGjBnq4YcfVu3bt1eapqk5c+Y02XUeSxp6z4uLi+OvsZ/85CcKUHfddZeaMmWKevrppxPazp8/X2mapkaNGqVmzJihfvOb3yhd19WNN97YJNcoEsmYVHcyJjU9GZNEXclYFiPjVM2aYjwRQghxkASSGlEgEFB33323atmypXI6nWrw4MFqwYIFCW2q+yOmlFI7d+5UY8eOVV6vV6WkpKiLLrpIbdy4sUq7a665RgHV/ps1a9bRurRGd7Tv1UcffaTOPfdc1bJlS2W321VaWpo677zz1CeffHJUr+tY1tB7vnXr1hpfc+3bt6/yPG+//bYaMGCAcjqdqk2bNur+++9X4XD4aF6aqIGMSXUnY1LTkzFJ1JWMZTEyTtWsqcYTIYQQMZpSSjVaepMQQgghhBBCCCGEOGFJjSQhhBBCCCGEEEIIUScSSBJCCCGEEEIIIYQQdSKBJCGEEEIIIYQQQghRJxJIEkIIIYQQQgghhBB1IoEkIYQQQgghhBBCCFEnEkgSQgghhBBCCCGEEHUigSQhhBBCCCGEEEIIUScSSBJCCCGEEEIIIYQQdSKBJCGEEEIIIYQQQghRJxJIEkIIIYQQQgghhBB1IoEkUYWmaXX69+mnnzZ3VxMsXbqUhx56iOLi4jq1v/baa2u8tgULFgCwbdu2hO12u52srCzOOOMM7rvvPnbs2FHlvJ9++mnCMYZhkJOTw9ixY1m7dm1jXrIQJwUZk2RMEuJYI+OSjEtCCHEyszV3B8SxZ/bs2QmPX331VRYuXFhle8+ePZuyW4e1dOlSHn74Ya699lrS0tLqdIzT6eTvf/97le39+/dPePzzn/+cCy64AMuyKCoq4quvvuKpp57ib3/7GzNnzuTKK6+sco7f/OY3DB48mEgkwvfff8/zzz/Pp59+yurVq2nZsmWDrlGIk5GMSTImCXGskXFJxiUhhDiZSSBJVHHVVVclPF62bBkLFy6ssr0hlFIEg0HcbvcRn6sx2Gy2Ol3XqaeeWqXd9u3bOe+887jmmmvo2bNnlTdUw4YNY+zYsfHH3bt351e/+hWvvvoq99xzT+NcgBAnARmTqpIxSYjmJeNSVTIuCSHEyUOmtokGmTVrFqNGjSInJwen00mvXr2YPn16lXYdOnTgoosu4sMPP2TQoEG43W5eeOEFIPbm4qc//SnJycnk5ORwxx138OGHH1abCr58+XJ+8pOfkJqaSlJSEmeffTZffPFFfP9DDz3E7373OwA6duwYT5Xetm3bUbsH7du35+WXXyYcDvPEE08ctv2wYcMA2Lx581HrkxAnKxmTZEwS4lgj45KMS0IIcaKSjCTRINOnT6d379789Kc/xWazMX/+fG655RYsy+LWW29NaLt+/Xp+/vOfc/PNN3PjjTfSvXt3ysvLGTVqFHl5edx+++20bNmS119/ncWLF1d5rkWLFnH++eczcOBAHnzwQXRdj785W7JkCUOGDOHSSy9lw4YN/OMf/2DatGlkZWUBkJ2dfdhryc/PT3hst9tJTU2t0304/fTT6dy5MwsXLjxs28o3aunp6XU6txCi7mRMipExSYhjh4xLMTIuCSHECUgJcRi33nqr+vFLxe/3V2k3evRo1alTp4Rt7du3V4BasGBBwva//vWvClDvvPNOfFsgEFA9evRQgFq8eLFSSinLslTXrl3V6NGjlWVZCc/fsWNHde6558a3TZ06VQFq69atdbqua665RgFV/p199tnxNlu3blWAmjp1ao3nufjiixWgSkpKlFJKLV68WAHqpZdeUgcOHFB79uxRCxYsUF26dFGapqkVK1bUqX9CiOrJmCRjkhDHGhmXZFwSQoiTiWQkiQY5dN5+SUkJkUiEs88+mw8//JCSkpKEb6k6duzI6NGjE45fsGABrVu35qc//Wl8m8vl4sYbb+Suu+6Kb1u1ahUbN27k/vvvp6CgIOEc55xzDrNnz8ayLHS9YbM0XS4X8+fPT9hW32/BUlJSACgtLcXr9ca3X3fddQntsrOzmT17NoMHD25QX4UQNZMx6SAZk4Q4Nsi4dJCMS0IIcWKRQJJokC+++IIHH3yQL7/8Er/fn7CvujdHP7Z9+3Y6d+6MpmkJ27t06ZLweOPGjQBcc801NfalpKSkwSnQhmHwf//3fw06tlJZWRkAHo8nYfsDDzzAsGHDKCsr4+233+aNN95o8Js4IUTtZEw6SMYkIY4NMi4dJOOSEEKcWCSQJOpt8+bNnHPOOfTo0YMnn3yStm3b4nA4+OCDD5g2bRqWZSW0P5JVRyrPNXXqVAYMGFBtm8pvuZrL6tWrycnJSfiGDaBv377xN16XXHIJfr+fG2+8kbPOOou2bds2R1eFOCHJmJRIxiQhmp+MS4lkXBJCiBOLBJJEvc2fP59QKMS7775Lu3bt4turK/5Yk/bt27NmzRqUUgnftG3atCmhXefOnQHwer2H/Tbsx9/YNYUvv/ySzZs312lZ3Mcee4y3336bRx55hOeff74JeifEyUHGpINkTBLi2CDj0kEyLgkhxIlHckdFvRmGAYBSKr6tpKSEWbNm1fkco0ePZvfu3bz77rvxbcFgkBdffDGh3cCBA+ncuTN/+ctf4mnRhzpw4ED8/8nJyQAUFxfXuR9HYvv27Vx77bU4HI74crq16dy5M5dddhkvv/wye/fubYIeCnFykDEpRsYkIY4dMi7FyLgkhBAnJslIEvV23nnn4XA4GDNmDDfffDNlZWW8+OKL5OTkkJeXV6dz3HzzzTzzzDP8/Oc/5/bbbyc3N5fXXnsNl8sFHPzGTNd1/v73v3P++efTu3dvfvGLX9C6dWt2797N4sWL8Xq98QKQAwcOBGDSpElceeWV2O12xowZE3/TdCS++eYb5syZg2VZFBcX89VXX/HWW2+haRqzZ8+mX79+dTrP7373O958802eeuopHnvssSPulxBCxiQZk4Q49si4JOOSEEKc0JpzyThxfKhuSdt3331X9evXT7lcLtWhQwf1+OOPq5deeqnKkrLt27dXF154YbXn3bJli7rwwguV2+1W2dnZ6q677lJvvfWWAtSyZcsS2n777bfq0ksvVZmZmcrpdKr27duryy+/XH3yyScJ7aZMmaJat26tdF0/7PK211xzjUpOTq712iuXtK38Z7PZVEZGhho6dKi699571fbt26scU7mk7b/+9a9qzzlixAjl9XpVcXFxrc8thKiejEkyJglxrJFxScYlIYQ4mWhKHZJzK0Qze+qpp7jjjjvYtWsXrVu3bu7uCCFOcjImCSGONTIuCSGEaG4SSBLNJhAIJKxSEgwGOeWUUzBNkw0bNjRjz4QQJyMZk4QQxxoZl4QQQhyLpEaSaDaXXnop7dq1Y8CAAZSUlDBnzhzWrVvHa6+91txdE0KchGRMEkIca2RcEkIIcSySQJJoNqNHj+bvf/87r732GqZp0qtXL9544w2uuOKK5u6aEOIkJGOSEOJYI+OSEEKIY5FMbRNCCCGEEEIIIYQQdaI3dweEEEIIIYQQQgghxPFBAklCCCGEEEIIIYQQok4kkCSEEEIIIYQQQggh6kQCSUIIIYQQQgghhBCiTiSQJIQQQgghhBBCCCHqRAJJQgghhBBCCCGEEKJOJJAkhBBCCCGEEEIIIepEAklCCCGEEEIIIYQQok7+P3xtBufK9TDVAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 900x250 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure 2h-j - FDR control h) Observed FDR, i) Recall, j) Success rate (proportion of runs where observed FDR is below target FDR)\n",
+    "palette_fdr = {'FDR-control': \"#7de9e9\", 'FDR-control+cred.': \"#2394c9\", 'FDR-control+clone': \"#2D62EA\", 'FDR-control+clone+cred.': \"#ad2be1\"}\n",
+    "hue_order_fdr = ['FDR=0.01', 'FDR=0.01,cred_intvl=0.5', 'FDR=0.05', 'FDR=0.05,cred_intvl=0.5', 'FDR=0.1', 'FDR=0.1,cred_intvl=0.5', 'median@FDR=0.01', \n",
+    "                 'median@FDR=0.01,cred_intvl=0.5', 'median@FDR=0.05', 'median@FDR=0.05,cred_intvl=0.5', 'median@FDR=0.1', 'median@FDR=0.1,cred_intvl=0.5', ]\n",
+    "\n",
+    "df_sub = df_fdr.copy()\n",
+    "df_sub = df_sub[~(df_sub['model_config'] == 'BEAM')]\n",
+    "df_sub = df_sub[df_sub['model_neg_ctrl_key'] == 'neg_control']\n",
+    "df_sub = df_sub[df_sub['posterior_config_clean'].isin(hue_order_fdr)]\n",
+    "# Remove sets where number of binders is lower than the inverse target FDR, as one false positive would already exceed target FDR\n",
+    "df_sub = df_sub[(df_sub['sim_num_binder'] > 1/df_sub['target_fdr'])]  \n",
+    "df_sub['fdr_config_clean'] = 'FDR-control' + df_sub['posterior_config_clean'].str.contains('median').map({True: '+clone', False: ''}) + df_sub['cred_intvl'].notna().map({True: f'+cred.', False: ''})\n",
+    "\n",
+    "####### Figure\n",
+    "fig = plt.figure(figsize=(9, 2.5))\n",
+    "\n",
+    "# Observed FDR\n",
+    "plt.subplot(1, 3, 1)\n",
+    "ax = sns.lineplot(df_sub, x='target_fdr', y='fdr', hue='fdr_config_clean', palette=palette_fdr, marker='o', legend=False)\n",
+    "plt.xlim(0, 0.105)\n",
+    "plt.ylim(0, 0.11)\n",
+    "plt.xlabel('Target FDR')\n",
+    "plt.ylabel('Observed FDR')\n",
+    "plt.axhline(0.01, color='grey', linestyle='dashed', label='FDR=0.01', alpha=0.5)\n",
+    "plt.axhline(0.05, color='grey', linestyle='dashed', label='FDR=0.05', alpha=0.5)\n",
+    "plt.axhline(0.1, color='grey', linestyle='dashed', label='FDR=0.1', alpha=0.5)\n",
+    "plt.xticks([0.01, 0.05, 0.1])\n",
+    "\n",
+    "# Recall\n",
+    "plt.subplot(1, 3, 2)\n",
+    "ax = sns.lineplot(df_sub, x='target_fdr', y='recall', hue='fdr_config_clean', palette=palette_fdr, marker='o', legend=False)\n",
+    "plt.xlim(0, 0.105)\n",
+    "plt.xlabel('Target FDR')\n",
+    "plt.ylabel('Recall')\n",
+    "plt.xticks([0.01, 0.05, 0.1])\n",
+    "\n",
+    "# Success rate\n",
+    "df_sub['is_below_target'] = df_sub['fdr'] <= df_sub['target_fdr']\n",
+    "df_sub_gb = df_sub.groupby(['target_fdr', 'fdr_config_clean'], observed=True)['is_below_target'].mean().reset_index(name='Success Rate')\n",
+    "plt.subplot(1, 3, 3)\n",
+    "ax = sns.barplot(data=df_sub_gb, x='target_fdr', y='Success Rate', hue='fdr_config_clean', hue_order=palette_fdr.keys(), palette=palette_fdr, legend=True )\n",
+    "ax.set_xlabel('Target FDR')\n",
+    "plt.ylim(0, 1.05)\n",
+    "for c in ax.containers:\n",
+    "    labels = [f'{v.get_height():.2f}' if pd.notna(v.get_height()) and v.get_height() > 0 else '' for v in c]\n",
+    "    ax.bar_label(c, labels=labels, label_type='edge', padding=4, rotation=90, fontsize=9)\n",
+    "\n",
+    "\n",
+    "# Legend on the right side\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "ax.get_legend().remove()\n",
+    "fig.legend(handles, labels, loc='upper left', bbox_to_anchor=(1.0, 1.0), ncol=1, frameon=False, title=\"\", fontsize='medium')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(f'{FIGURE_PATH}/Fig2h-j_synth_fdr_control.pdf', bbox_inches='tight')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf67e25f",
+   "metadata": {},
+   "source": [
+    "# Scaling analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "08beb62b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following CPU models were used: ['Intel(R) Xeon(R) Gold 6142M CPU @ 2.60GHz']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from matplotlib.lines import Line2D\n",
+    "from scipy.stats import linregress\n",
+    "\n",
+    "\n",
+    "df_scaling = aggregate_csv(rerun=RERUN, experiment_path='benchmarks/scaling', output_path=os.path.join(RESULTS_PATH, 'results_scaling.csv'), paths=['csv'])\n",
+    "df_scaling['model_config_clean'] = df_scaling['model_neg_ctrl_key'].fillna('base').map({'base': 'DextraDemixer(+clone)', 'neg_control': 'DextraDemixer+neg.(+clone)'})\n",
+    "print('The following CPU models were used:', df_scaling['cpu_model'].unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d8a02ac",
+   "metadata": {},
+   "source": [
+    "#### Runtime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d5892b22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_4032627/2311869785.py:1: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  fits = df_scaling.groupby(\"model_config_clean\").apply(lambda g: linregress(g[\"sim_total_cells\"], g[\"total_time\"]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "fits = df_scaling.groupby(\"model_config_clean\").apply(lambda g: linregress(g[\"sim_total_cells\"], g[\"total_time\"]))\n",
+    "fit_base, fit_neg = fits['DextraDemixer(+clone)'], fits['DextraDemixer+neg.(+clone)']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "893db35d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Hhf3ll1905/j4+IAQgpSUFMTHx+t9ni4uOe4FealMUW+FTjzUGOEIQDM/1nlnVbRe5QMWW38ITlEUnBqI4NPNFmYO+b+fZU25FLOqVauiXbt2GDZsGE6dOgWJRIJu3bqBEKILoyKXy/Odp11yLijUCp/Ph0gk0nsVhoWXABRFIfZ2hl55zIf3llWM33PGRJr9+Eiz7u7uenUB6L3PPYdaEC4uLli6dKneeTKZDLa2tnB0dNT70hc03whUnqi3snQlMmPkaL7ICzVHOmLg1TqoMczRqHM/NeVSzPLSp08f3L9/HyEhIbo/aO5fKS1xcXGwtrY22GsrCWYOPHh1tMKDtVF4tTcBKW+keH0gEfdXRcGjjRXMXYsWMybSbOlFmi0NRo4cid9++w3Pnz8HoPkctH6MX3/9NQ4dOoRnz54hKysLq1atKrCdihz1NiNSBpqmkZWgQHayEkInHszseQjaVhXOjctvHoMvQsy0w0axWAwXFxfY2dnhwYMH+erdu3cPderUKdVrN/nJA54drPHw12icGvwK91dHwa2VJb6a71noeUykWQ2lHWn2Y2natCmWLVuGwYMHQyQSoX79+rh79y4AoHbt2li8eDE6duwIf39/NGvWDDxeTp6HoKAg7NmzR/e+Ika9DT2RjK0B93Hj53AopWqYu5mAJ/xCIoV98lm6QkhISMhXplAoSGBgIBEIBCQzM5MQQsj48eOJQCDQTTYTQsilS5cIALJx40ajrycWi4tczdSSnaogSS8kRJqiKLIuQ8Xg2rVrxNPTs8DjGRkZpFq1aiQry/BCUHlDLBaTatWqGVwZpmma3FocTpZTHyb5qSsk9GTSZ7Cy5JSreGZff/01MjIy0KJFC7i4uCA+Ph579uxBcHAwVq9ejW+//RaAZpKzbt26sLS0xP/+9z9IJBKsXLkSrq6uuH//vtHDTCaeGUNeTp8+jXbt2kEikaBfv36oUaMGfv31189tVpmilKpxdlQwgg/kbAfkmbPRbX91eHf+NN77pUG5ErP9+/dj69ateP78OVJSUmBubo569ephypQp6N69u17dly9f4ttvv8WNGzfA4/HQpUsXrF69WrdSZgyMmDHkZciQITh58iQ4HA46duyIDRs26FZOKyIZUTIc7fkCCY9yVmYtvU3Q60RN2Fb/MrZpaSlXYvapYcSMoTITc1uMo1+/gDRBqSvzaGuJ7gcDILAuPPtYeeQLmdljYGAoTZ7viMOFcSFQK3L6MvWmuqD1am+wPtX2o1KGETMGhkoEraJx9Yf3eLA2WlfG4lJov8EXtb9x/oyWfTyMmDEwVCIIAeIf5ESGNbXjouc/AXBtZvn5jColvsz+JAMDQ4lQZKjRelUViDxNYF9HiGEP6lUIIQOYBQBmAYChUkBoAkm8ArSChtCZj4woOcwceeCZsYs++QuBGWYyMFRQCCG4vzoK9rXNYOVnBhYbELmbgGJRsPI2vH/5S4YRMwaGCohKpsb5sSF4uTsBfEsO+l2oBacGFXv0wcyZMTCUMp975iYzVo59LZ/g5e4EAIA8XYWbv4R/Vps+BUzPjIGhlNGGLqLVBCw2BUITUKxPk68g7n4GjvZ8AUlsTqw9t5YW6Ly96ie5/ueEETMGhjLg4fpoSJOUqDvRGUInzV7hsha1l3sScG50MNTynJ5hnfHOaLveB2xuxR+EVfw7ZGD4DLA4FNLfZ2N/6ye4/nMYxOHZoFhUmQxBaTXB1ZnvcHrIa52QsTgU2v/hiw4b/SqFkAGMawbjmsFQquTtfT36IwZh51ORGixF/0u1IXI3PjqxMcjFKpwc9Arvz6TqygQ2HHQ/FACP1laleq3yTuWQbAaGMoRW5/QHtEKmVmqS4wZOckGrFVUg8jDB/tZPkB5mOD9FXlJCsnCoy1PsavQQD9ZFgtCG+xzH+77UEzLbGmYYeq9epRMygOmZMT0zho9C2xOTi1UIv5QGQhN4trcCT8jW27AtTVbgcNBzcExZGHStbqFtHuv3AiGHkvXKKC4wMa4JzGxyYvURQhBxOQ3/dHsBlYyGTw8bdNldDXzzyjkVbpSY5Y0lVlwWL15sdAKFTwkjZgylQfyjTBzp/AxmjjyIw2UQ2PFQc4QjGnzvBg6fpVvVTH6VhX0tHqPhDHc0mmk4+cr788k43Cl/TgAAYJsB30laAdD0BrPi5ABFIfq/dCS/zEKzBV6fbNW0PGKUmLFYLNja2sLMrHjB2miaRnR0NC5evIg2bdqU2MiyghEzho9FlqbE0a9fwLG+OZrO9QTbhIVrM98h9k4GHOuZo/0ffgA0vShaRXBvZRTCL6Si885qsPDIP3+2UnAVRFbw9SbENYapNQ+ZMXLwzNkwteUVXLmSYXR/dN26dRg0aFCxGk9OTi40CwwDw5eOMkuNlNdSNPjODXwLzdep+SIvPN4Qi5e7E3BnaQQaz/YARVFgcym4NrfAm4OJUGSoAGhELndKvdxCZu7Jg8iFj7gHEtAfVikPtnuG7vurQ+RhUmmHkwVh1AJA7dq1YW1tXezGuVwuateuDXNz82Kfy8DwJUCxKZi78ZEVr3FSJYSAJ+Sg5khHeHW0wrvTKYi9I9bVd2tuCaELH8+3x2vOLyAfqn0DU4x72wSDb9RDt33VdOUpL6W4tSACPGHF2SBeWhgl7Y8fPy5R4xYWFiU+l4HhS0DoxIepLRev9iTAt6ctTO00wz5TOx6qD3ZA6NFkJL+SwrmxBdRKGmwuC/597KDILDj/KQAk3pdif+vHYJuwEHExXe8Yz5wNWkXA5lbe+TFDMK4ZDAwlROsu0eFPP8Tfz8Td5ZFQyWndcYe65rDwMkHYOY3rhNZ51a2VJRwbGB6t8GxyBCr6ekY+IWuz1hudtvhXGkfY4vBRg+7MzExEREQgLS3NoGdzixYtPqZ5BoZyDcWiQKsJLDwF6LStKk4OfAUTay5qj3WCqS0PhCZg81n5hMvSSwBLL8MheNqt88OZofmTAgMAW0ih/jS3Ur+PikKJxCwlJQWTJ0/GkSNHoFbn7y5rJzUNHWNg+BKgVbTOT0zrWmEIbXm1/vaQpSpxfU4YYm9nwK6WGbLiFYi5lYFmC72Mvq5LEwtwBCyosmm9cooLDLhUu4R3UzkokZiNGTMGJ0+exNSpU9G8eXNYWVU+b2OGiguhiU7I7i6PhG0NM3h1si5Q0LSOs3UnuEDozEfEpTTE3c0Az5yDYfcDYeVjmm/V0hCRV9NwvM9LPSFj8yl4drREi0XesKspLL2brICUSMwuXLiA6dOnY8WKFaVtDwPDZ4diUaBVNE4MeIWQf5JhV8MMQhceHOoYnufK7ajq28MWvj1soVbQYPOK7tnlJv2dDNkpKt37hj+4ocWSKkady1DCBQBTU1N4enqWsikMDOUDQhO83J2A7BQV+l+qDZWMxpXv3hW6r/LlngRc/zlM914rZIQ2TsjUShoe7axQZ5wT2HwKXf6uhlbLvRkhKwYlErMhQ4bg6NGjpW0LA0O5gGJRsPQWIGCoAzzaWGHA1TqIu5eJ2wsjIE1S5KuvlKqR/CILD3+NRsrrLL3FMGO2Fymz1ciMlsPEmoN2v/ti+MP6CBjsUKr3VBko0UbzW7duYcqUKbCzs8PYsWPh5uYGNju/E19gYGCpGFlWMNuZGApC6xOmJeJKGg60fYqmP3tovP0/eN9r58LS3kpBKwlsqhm35S/xmQTBBxLRcIYb5OkqmDnxweEz7hYfQ4nEjMXK+dANTWp+KauZjJgxFAUhBCCaHtaTTbG4MCEEnbb4o8ZwR8hSlQg7l4rqQxz0tyQVMdkfeiwJp4a8hjKLRtOfPdD0Z09mOFkKlGgBYPv27aVtBwNDuYSiKBBofu/rjHNGSrAUV79/B5VUjfuro2FfR4hqA+1BcSi9cwxBCMHtRRG48XO4ruzO0kjU+sYJIrfSDdpYGWHimTE9s0qPMauNuSPI7qj7AIlPJaja3x7d91U36hqKLDXOjgzGm0NJujKeiI1u+6rDu7NNyY1n0MEM0hkqNbSK1gnZs21xkKUrDdajWBTUShoP10cj6ZkErVZ664SMVtEGz9GSESnD3maP9YTMyleAoXcDGSErRYwaZo4aNQoURWHz5s1gs9kYNWpUkedQFIWtW7d+tIEMDGVFbi//EwNe4u3xFLh8ZQETS67B+vJ0FcIupKLjX/6oNcpJ04aa6EWUzUv0TTGOff0C0qQckfRsb4XuB6rDxMrwdRhKhlHDTE9PT7BYLLx58wZcLheenp5FejNTFIX379+XmqFlATPMrLxoh5ZysQoH2j0FKKD7geqw9BIgNVQKa19Tg+cppWpwTdl6CwMF8WxrHC5MCAGtzPmK1ZvmitYrqxQqgAwlg5kzY8SsUqF93CmKQuJTCY50fQ7H+ubotr86OHwW7q6MxPUfwzDyWX2j3SwMEXwoESf6vdK9Z/ModPjTDzVHOn30PTAYhglVyVBhMZR0VzuiCD6UiPNjQ1BzlCParPYBAJwf9wbvTqWgx6HqHyVkAODVyRqODcwRfz8TZg5c9PynBlyaWnxUmwyF89E9M5qmIRaLDYYAKkl02k8J0zOrWBCa4PWBRHAFLPj2tCuw3tMtsbg4IRRt1/ug7gQXEJrgYIenkMQp0H1/9Y/e0K2QqJCdogLFBi5PfYu2v/owrhefgBKJmVKpxPLly7Ft2zZERUWBpg2v5jBOswyfiswYOU70ewmb6mZIeJQJ12YWaPurb756tJrg5e54WHoL4NbcEuIIGQ51fAqRuwm67K4GM4eSJQjJjJHD3IWP7BQlVNlqmDnxGUfYT0yJhpnjxo3Dzp070bhxY/Ts2RMWFkz3meHzoVbQuDE3DB7trNDsFy/IM1XY2+wx3p1OgXcXfdcHFptCjeGOoCgKUf+l43DQMwQMc0Sbtd7gmBQ/rj4hBPdWROHGvDB021cdLl9ZQOjCL3KBjKH0KVHPzNzcHL1798aOHTvKwKRPB9Mzqzgkv8yC0IUHvogDikXhxrwwODUSFejHpZCocPOXCAideWgwvWTRW5XZapz75g1e700EAAhsOBj2sL7BFHIMZU+JemampqZo3LhxadvCwFBirPwEehvDpUlKiN8XHLKHJ+Sg6VwP8EUlWwPLjJHjaM8XiH+QmdOmiANlVvmeWqnIlMjZZeDAgTh16lRp28LAUGK0Qqb1xldJ1bCuqvEVuzgpxGAsspIKWezdDOxq8FBPyNxbW2LY/Xqwrf5xq6AMJadEw0yFQoFRo0YhPT0do0aNYkIAMXxyDLldADkRK+4si4BSSiP2lhi2NYVou9anVK77cnc8zo15A7U852tTd6Iz2qzzYTImfWZK9NMkl8tB0zTOnj2Ls2fP5jv+pYQAYvgyyb0NSZqsAEfABs9M82OqnXhPfpGFV3sT0X1/dVTtZw+g6NA8hV5TTXBt5jvcXx2tK2NxKLT73Rd1xjl/zO0wlBIlErNRo0bh6NGjGDBgABo1asSsZjJ8MnIL2fnxb5D8IguKDDVarfKGZztNYh2KRaHOBBfUneiic1QtqCdnDLJ0JU4OfK3LfwkAAlsueh4JgFsLy4+7IYZSo0TDTJFIhNGjR2Pt2rVlYdMngxlmflloBUkpVWPPV49BaIJao50Q8k8S0t/JMOJpfQis9Tdv596+VFIyY+XYVe8hsuI1IbPtapmh1/EasPA0nPuyMkLTNP48kYa9F8XIlNKo6s7HT8NsUdP7031GJeqZiUQi+PiUzhwEA4OxUCwKmbFy7P3qMSyqmGDA5ToAAMf65tjT7DFiborh081W/5xS8PfimrLQ8S8/HO/zEt5dbNB5Z1XwhMxOwNwM+iUKp25nQds1ehutwKUHmTiy0B2tAj9NirwSzViOGTMG+/btK/U5sfv372Py5MkICAiAmZkZ3N3d0a9fP4SEhOSr+/r1a3Tq1AlCoRDW1tYYOnQokpKSDLTK8KVC6PyDhpRXWciIkiFgaE7CD3GEDCwOBd6H1UlD55Xo+oQgK1EBWkng09UWQ+/WQ49DAYyQ5eH6UwlO3soRMi0yBTB8SbThk8qAEv1VqlevjuPHjyMwMBDDhw8vcDWzV69exWp3+fLluHnzJvr27YtatWohPj4ev//+OwIDA3Hnzh3UqFEDABAdHY0WLVrAwsICS5YsgUQiwapVq/D8+XPcu3cPPF7JtqQwlC8oFgVpkgL3VkWh1XJvAIBnO2vUGO6IO0siUXOEEyIup+HihBCw+SxEX0+HmSMPll4mYPOoEs2TqeQ0/pv9HvWmuYLFpsAzZ+tcOOxrM0l4DTFzY3yBx5LFNJLTVbC1LPsfgI9OaFJgwyVYzbx16xbq16+vJ0ahoaGoWbMm+vTpg7///hsAMHHiROzYsQPBwcFwd3cHAFy6dAnt27fHpk2bMHbsWKOux8yZlX8k8XLsa/EEvU7UgE1VjQ9XZqwc+1o8hok1F2khUjSc4Q6BLRcRl9MQcTkNtb5xQt2JLsX2xJfEy3Hs65eIvZMBu9pmGHClNgRWzA9jUQjbv0JhInJxjRua1jScQLk0KZFcXrlypbTtAAA0bdo0X5mvry8CAgLw+vVrXdmRI0fQtWtXnZABQLt27eDn54eDBw8aLWYM5RtCCAgNCF34kMQqYFPVDIQmMLXlovYYZzz4NRpN53nqtiPVGeeMO8siEHosGQ/XRWP064awrGLcBHTcgwwc7fkCkhjNJH/S0yw82RiHJj96lNn9VRRyCxmfCwj4FDKzCNQfDkQlygGUUzFr2bJladtRIIQQJCQkICAgAAAQExODxMRE1K9fP1/dhg0b4syZMwW2JZfLIZfLde8zMjJK32CGUoOiKJg78yFy4+Pm/HB4tLECxaLA5lHw7GiFtyeToZbTUMnUYPNYoFgUGs/ygEcbK8TcEhstZK/3J+DsyDdQyXKiv9Qa44SG35dsz2ZlITVDjdUHkvXKHKxY8HTi4e4rGdQfIoUrZIXnSCgtyr3L8p49exATE4P+/fsDAOLi4gAATk75I3Y6OTkhNTVVT7Bys3TpUlhYWOhebm7Mw1qe0U7kB052gSxNhWdb43THHOqYo0pnGzxYG42MKLku4QgAODUUof40N702Cmr/vznvcXLga52QUWyg3W8+6LjJD2xeuf96fBYk2TRW7ElCjWGhWHcwRe9YZCKN609lkOfKC+Pl+mncM4z6a02dOhUPHz4sduNSqRRTp05FaGhosc8FgODgYEyaNAlNmjTB8OHDAQDZ2Zo9dnw+P199ExMTvTp5mT17NsRise4VFRVVIrsYPg3ayXsLLxM4NTBHyD9JiL0j1h1v8qMHRB4m+Hf6WwAwuJ2ooAUAeYYKR79+gTtLInVlJlYc9DtfG4GTXZkQPgaQK2hsPJaKGsNC8cuOJIizDPe48v58eDh+mnlHo8Ts999/N+geURTZ2dn4448/SiQa8fHx6NKlCywsLHD48GHdaqlAoFF5Q70vmUymVycvfD4fIpFI78VQ/jG14yFguCPk6So83hiL5JdZumPNFnji/ZlUvNqXYPBcWkXjzZEknBz8Cv/0eI57qyIRezcDe5o+wtsTOb0Km+qmGHq/HjzaWpX5/XxpqNUEey+mo+7od/j+j3gkpecs7FkKWejSpOBVXhMu4GL3abJQGT1ntmnTJly8eLFYjRc03CsKsViMoKAgpKen4/r163B2ztn7ph1eaoebuYmLi4O1tbXBXhtD+SZvIt68+yjdW1qizkQXvNgeh8vT3qLdeh/YVDODXU0hWi6rAt8etvnaJDTB+QkhCN6fCGUWDRDg3dkUgFAgqpz+g3c3G3T9u1qJo2hUZM7eycTPWxPxKlz/uyzgU5j4tTWm97OFuYBCzeGhiEpU6/XKWBTww2A7o7wfSgOj/nru7u6IiIhAREREsS/g7u5eYE/JEDKZDN26dUNISAguXbqE6tX1M0a7uLjAzs4ODx48yHfuvXv3UKdOnWLbyPB50QqZWkEj8ZkETvVFekKmFbaAwQ4ws+fi2bZ47Gn2GO03+MG5sQiNfnDXa0dL2KVUPN+i7wNFlEDugVDj2e5ovsirxPs2KzqnbmXqCRmHDYwIssKswbZwss3pcV1ZXwXfrIjBvVfZUNOApZCNcT2s8MOggnMxlDblKtWcWq1Gr169cObMGRw/fhydO3c2WG/ChAnYuXMn3rx5o5vEv3z5Mtq1a4eNGzdi/PjxRl2P8TP7/GiFKvlVFk4Nfg1rfwFaLvfO5yOWt6d2e3EEEp9IkPYuG0Fb/WFX0yxfLsqN3jeR+d5whnIA6Lq3GqoPdCjweGUnI0uNt9FytJseAYWKoG9rEeYOt0cV54LnwLKy1cjIouFgzQHrE/9AlCsxmzZtGn799Vd069YN/fr1y3d8yJAhAICoqCjUrVsXlpaW+N///geJRIKVK1fC1dUV9+/fN3qYyYhZ+SDyahqO9nyBGsMdUWe8Myy9BUatJKrkNBKfSGBqz4WlV/7e/wrqqu7/VQfagW/BwbO/40AkmrIfSKtSuoMvn7fRcuw+n475o+whlRFkZKkhNGXB3JSN3efTUdvHBLW8y3c48HIlZq1atcK1a9cKPJ7b1JcvX+Lbb7/FjRs3wOPx0KVLF6xevRoODsb/0jJi9nkhhEAlo3F+bAhsqpqiyRwPXQ+ssK1IxsYl04qZbU1T9DlbC0IHHtLfZWNL1fsAGDEDgNhkJZb9nYwdZ9OgpoHfpjmidysLiExZX9yKbrma8bx69arRdQMCAnD+/PmyM4ah1Mg7lwXkhPOhKCAzWg7P9lagVTQir6Qj6roY2UlKNJzhZtDx1Zgv2ZsjOUEHkp9LcbTHc7i1tMLbE8mFnFV5SM1QY82BZGw8lgqZIqeTsP5wKoZ1svrihAwoZ2LGUPEgtEbI5GIVIv5NA60icGtpCTN7zbyLKpsG15SFV3sScOPnMFj7m0Ilo5HySgpaRdDpL/9iX+/mgnDc+kV/sSrhYRYSHmcBn8YZvdySlU1jw9EUrD2Yks9PzMuJi9lD7fClroUwYsZQplAsCnH3MnBy0CvwRBxkRsrAEbDR82gAnOqLYGLFRdO5Hnh7IgVVOtvAvbUl7GsJ8XRLLJ5vjYdKToPDN25pX5GlxpnhrxFypIDeV67vrqjKp/F9Ki8olATbz6Rh2Z4kJKbpB4BwsOZg1mBbjAiyAo/7hSoZGDFjKGMir6bh5KDXqDPeGYGTXMARsHC4y3NcnxOGfudrAwCcG1vAsYFIbygadzcTbq0sjRYycYQMR3s8R+LTHIdaa38BLLz5CDuTrleXbwsMvd3g42/uC+HWCynGLI9BeLz+yq6lkIVp/Wwxsac1zARf/tatjxKzO3fu4MqVK0hMTMTEiRPh6+sLqVSK4OBg+Pn5QShk4j9VZuSZKrzak4hqA+zReJY7WBwKFItC/emuuPLtW0iTFTCx4oLFpjRD0UwVxGEy3F8dhch/09H7dE2jrhN1PR3Her1EdnLOl9WrkzW67dM4woZdTMXrvYmQi1Xw7GCN6oPsYWJZeXpmjtZsRCflfDYCPoUJPTUOr9ai4mdxL6+USMwUCgUGDBiA48eP61aWunXrBl9fX7BYLHTo0AHTp0/HnDlzSttehnJM3ol+vjkH9rXNYGrP03O14JiwoJTS4JiwdPVpFY2XO+MRfCAJymw1htwNhLkzv8iVy6d/xeLixFDQuTz6G3zvhpbLqujartLRBlU6Gs5sXtHJyFLDTMDGsE6W2HUu3aDDa0WhRH3LuXPn4tSpU9i4cSPevHmj5zJhYmKCvn374vjx46VmJEP5RzvRr5KpIQ7P2egfONk1J9XbhwgWagUNoRMPPCFH9+ywOCz49LBF3UnOGP6gPsyd+aDVhQsZIQSxtzN0QsbmUei8sypar/TOt3paGXjyNhs9f4zAubuZkGTTSEhTgcOm4GDFwdzh9ni01Qe//s+pQgoZUMKe2b59+zBhwgSMHTsWKSkp+Y5Xq1YNhw4d+mjjGL4cKBaFzBg59jZ7DOcmIrRY4gULT4HOBSP3D54sRQWiJrpeV/ilVKS+yUadcU6oNkDjJ2jInSMvhAaazvNAymspxOEyfH00AM6NK1/aw7fRcizcmYTDVzXx+aISlbjyqxccrHK+3vZWHNhX8D30JeqZJSYmombNgucz2Gw2pFJpiY1i+DJJeJwJcYQM2clKPP0rDnKxSidkFEXpnGAlsXJwzdigKAoP10fjYIdnYPMpve1IRQmZSk5DmqSAuasJvj5WA8PuB1YoIRNL1Fh3MBk//RWP2y+yDNaJS1Zi6ro4BI5+pxMyAAiOUODWi8r3/StRz8zNzQ3BwcEFHr958yaTiq4SYuUjgE83G1j5meL92VSYWHNR/38uOpFSK2mwuSwoJGrYBJjh2uz3eLA2Cn3P1oJXR+si2w89kQx5ugp+vWyhyqZh5sADRVEwc6hYcfo3/JOMGRsTde/XHkyFhwMLj7b6wYTPKtDhFQACvPj4ZZQ9OjasfItvJRKzQYMGYc2aNejduzf8/PwA5Hhl//XXXzh48CCWLVtWelYyfBFIE5WQpanQclkVnB/7Bm+PJcOuhhnkYhWcGol0m8clsQq82BkPS28BRr1oACsfU818GmXYu58QgjtLI3H9pzCwOBRM7bmo0qliTuiHRGbrCZmWiAQajca8wZBOtgYdXj0dufhpuB36t7H45Bu8ywsl2pupUCjQrVs3/Pvvv6hWrRpevnyJmjVrIjU1FdHR0ejcuTOOHz9uMP1ceYLZm1m6ZKcocXLQK/Q5XRPKbBpnRwYj8YkE6e9l6HW8hi5B781fwhF2LhWDb9YFxaIKnR9TStU4OyoYwQdytidZ+gjwzesG+aJkVAQ8+7xCkrjoeloqisNraVCip4HH4+HcuXPYvn07qlSpgqpVq0Iul6NWrVrYsWMHTp48We6FjKH0UclopLyWQiFRg2/OgSxVhcwoOdxaWEDknhNx4at5nhhyO1AjZCq6QCHLiJJhb/PH+kLmbYJex2tUSCEDoCdkf37viPOr3dHAL38UGAszFuaPtMPzHT4Y29260gsZUM6iZnxqmJ5Z6UEIgVJK49zoYHz1ixdODngFUEDdCc54vj0ellVM8NUvXrDyztk4XlhkjJjbYhz7+gWyEnKcPT3aWaH7geoQWFdM1wIAMGv/CgAwd6gIs4a5ghCCdIkKrr00eTTMTSl809Ua3/avWA6vpQGznYmhVKAoCjwzNpKeZWFrtXvw7mKDTlv9YWbPg1pO49Xe/PNABQnZ8x1xuDAuBOpck9v1/ueC1qu8K2yPLC8Ld2cgIzsOVVx4WH8o57N7sNEDrs6mn9Gy8kuJe2Y3btzAtm3b8P79e6SlpSFvMxRF4enTp6ViZFnB9MxKD637RfChREhiFaj1jRN4Zjk9B1m6ssgtRLSKxtUf3uPB2mhdGYtLocNGP9QanT+1YEXi6VsZ5m9LxIX7kkLrSS5U+yLD83wKStQzW7NmDWbMmAETExP4+/vD2rroZXWGL5vUUCnYXErjCEsIQPR7VtovmG8PW72tS9qhpIklt8itSVdn6guZqR0XPf8JgGszy9K/oXLCuxgFFuxI1PMTK4jqnlxGyAqhRD0zJycn+Pr64uTJk7Cw+HIdFZmemXFI4uX4p9sL+HS3Qa1vnCB00kxIvz2ZDHmGGgGDSyeOfnpYNnY3eoTsJCXs6wjR63gNvYWDikRcshJL/07GznNpUOlH5IGvKxeh0foRLvzduLizyUc30U/TpNK6YBREiXpmUqkUgwcP/qKFjMF4hI581PufC55tiYOFpwkChjri8YYYXJwcinbrS8c5WilVgy/i4OtjAXiyIRYdNvnrDVMrCmmZGofXDUcNO7zOH2mPoMZCSLJp/PsgC2lZajSoaoKAPDkOtEKmUBJmJfMDJeqZ9ezZE66urvj999/LwqZPBtMzK5rcQ8MH66IQdj4NAJDwMBNddlczynO/oHaz4hUQOvEhF6sACpUib+WE1bHYdS5dr0zr8NqvtQXYRm6Qp2mCJbuTkC6hMb6HFXxcNb1lY/MjVERKtDT022+/4fLly1i1ahVSU1NL2yaGT4w2mgUAgws5KrnG27z+NDekvMpCzE0xWiytUmIhU8nUODM8GLsaPETyyyyweFSlEDKaJhjTzQqcDx1Oeys21kx2xONtPhjYzrJQIcv9dyFEM8Ts3NgcShXB0EUxuPZEs3+zsgoZ8BGrmevWrcP3338PQghMTEzyOclSFAWxuBiuzJ8BpmdmGEO/7unvs/FPjxcwc+CCa8aGwIaLwCkucKhrXqy2JXFyHP36BeLuZgIAnBqaY+C1OuCYVKwhJU0TXHwgQYcGmj2SGVIaNA2ITFmYty0RFmYsTPzapsgIr4QQ0DT0hC7v32f8qli8j1VgxkBbtG8grLS9sxL9HP78889YvHgxXFxcUL9+fWbu7AtHJafxT/fncG9licazPfJ9EVJDpTjQ5ik82lqi845qkMTJcW70G7zYEQ+OKRs2/sb5PcXdz8DRni8giVXoyjgCFlTZdIURM0IIzt2VYN62RLwMk2PvPFc0r2UGkRkLnA+CtGiM8QsmFEWBzQYypWr8cy0DNauYwM+dD6GA0i0CzB5ii1X7kzH11zg83+mju05lo0Q9M3t7ezRu3BjHjh0Di/XlOjEyPTMN2anKD2GslWiz1gfWvvrilPRCAnG4DD5dbXW/+jG3xLg0JRTt//A1KvTOyz0JODc6GGp5zuNWZ7wz2q73AZv75T5Dubn5PAs/b0nEnVc5wSlrVuHj1sYqJV55JITgj6OpmL8tEZ5OPKRnquHtzMOyCQ6o65uzKBCZoEDLKWH4pqsV5gyz/+h7+RIp0VOkUCjQpUuXL1rIKiu0Ov9vl8CaC78+dkh/m42oa+n5jtvVEMKnq2aTuDZBr0tTC/Q6WbNIIaPVBNdmvcPpIa91QsbiUGi/wRcdNvpVCCF7+laGXnMi0eHbCD0hAwB/dz4k2SXPb/c+Vom9F8X441tnXP/dC6eWeyBDSmPB9iSERMkBaIa07g48fDfAFhuPpSJbXjnz6ZXoSeratSuuX79e2rYwfAK0m7pjbomRESXTlft0tYVzEwvcXRYJpVTj+JR7YSA3WmdZc+ecFTRDyMUq/NPjOe4uj9KVCWw46HexFupOcPn4m/nMvItRYMSSaDSd8B7n7+l77ndoIMStjVWwc44rRB/hYhIaLUd4nAINqwkg4LNQ1YOPX0bbQ6ag8cc/msU3ba+vd0sR2GwK//ynccClC/j7VVRKJGbz5s3Dq1evMHHiRDx8+BBJSUlITU3N92Ion1yYGII9zR7j4qRQvD2Vk2Oy7TofKLNoXP8pDEDBeyfzYmiyOTVUit2NH+H96ZznwLaGGYberwf3Vl92/OaENBWmrY9D4Oi3OHRF33O/cXUBzq/2wNEl7qjt8/EOvwmpKng48qDO1aNuV88MLWqb4eYLKZ6+zflBMjNhIaiROf59qFnZrGxOtSVaAPD312SZfvLkCTZt2lRgPbVaXeAxhs+HWqYZhphYcXCs10s0/tEd3l1t4FRfhEYz3fDoj1h4d7WBR5uSi44kRo70tzlDLp8eNuiyuxr45l++C0aKWIWtp9NA5xrNVffURHgNaiwsciVRpSZFTtJr5ybb1BNi8ro4BEcqdL5kLBaFVnXNcO1JFi7cy9SJpsiMDUshC9lyUilXNEu8mlnZPqiKgHafZJtfffD+TApsqpli4LU6uDE3DG9PpKDJj+5wa2WJsPNpeHc6BQ51hTCxKlm4Hcf65mi5ogqufPsOTea4o9kCL6N7euUZmiZwtuWiT0sRDl7JgIcjF3OL6fCqFbJ7r6Tw9+DDwsAwlKI0q5Vu9ly0qyfExmOpaBNoBlMTzWCqSQ1T0ARIydB0GLQC6efGx9bTaZXy+1kiMZs/f34pm8HwKdBGdeWbc9DkJw9cmx0G/z526La/Op5vjceZ4cGoP90VKhmNmJtipL3LhlP94ouZLF0JjgkLDaa7wbW5BZzqf5krxQolwY6zaahZxQSNAwTIkGq6YhZmLMwfZY9G1U0xqkvxI7weuSbGDxsSkJCmwpmVHmhR28xgPe0wcc4wO7SaGoaTtzLRt5VIV85mA3EpKs3/P0wY2VmyEehngqxsukJkKS8OletuGXQLAJ4drOHazAJ3lkSCY8JCo5nu6H4wAFmJSkgTFIi7l4lXuxOMajMrQYF/v3sLlVyN7FQleEK2zm/sSxQymibYf1mMwNFvMf23eMz8Mx7pEjWEJixYfMgq5eHIw/iexY/wevuFFFtOpWFMNyt4OXGx50I6MqUFT8cQQlC/qgBDO1pi9f5kbD+TDoWS4MnbbKRmqNG/jf5qsoMVBz2aiSqdkAFG9swWLFgAiqIwZ84csFgsLFiwoMhzKIrC3LlzP9pAhrLB2s8U7m0s8Wp3AuLuZsKjrRW8u9jAoZ4QMTcz8GBNFBrOdC+ynfhHmTja8wUyo+SQp6nQaav/FzvEIYTg/D2Nw+uL93Jd+cM3Mtx8no2uTYu328EQluZsDGhjgSEdLFGjigmGLIzCkA6WaG6gd5Y7Msby8Q5YsjsJP25OwL5L6XgcKkPXpub4qpbGJ1D7mTesXnkDNxrlNMtisUBRFLKzs8Hj8YzyL6MoqtwvAFQWp9m8k8G532+reR821UzR42CA5lieUNa0itZFdyU0QfCRRNyYGwallIZzI3O8P50GVS4/qv6Xa3/UwsHn4tYLKX7ekoDbL/X9xNgsYESQFWYNsYVzKWQCzxu6p/6Ydwjw5GP9NCeDc2cyBY2MLBr2VhzIFTTeRCnwMkyGmt4mqOFVMcMjlRQmB0AFFzOtcOX9VytSUdfTcaDtU3TbVx3+ve30hE7v/zTBeqfrkCcadsikWEDrNT6oN9WlXPXMFEqCnefSsO+SGFIZjWa1TDGtry1c7TXC9OydJsJrXj8xAOjTSoS5w+10q4jFpbAVRW3onnN3M9F/XhSOL/VAq7r6vbNksQqz/kxAiliNo0vy95K1fmSVzQWjIL78dXKGQqEoCnKxChcmhsC/tx38etkBgK635dRQBN+vbXF7cQS8OlnrxRDL/UU8NuhpgUIGAH3P1YJn+/IVcZimCXr/FIErj6TQ/mI/fy/HoStiXFzriaV/J+Pgv/kjvLZvYIb5o+xRx0eQ75gxEEKgplGo+4V2rq1TI3NU9eBj66k01PUz0eudWQrZ8HDgQk0TZMtpCPi5IvgSJjhjXko0S8hms7F3794Cjx84cIBJNfeJybtNKXeHWxKvQFpoNh5vjAWtJnrHOHwWao50ROcdVQsNhhh6IL3Q65c3IQOA/ZfF+DeXkGlJFtMYvCASSpX+kUbVBTi3ygPHlniUSMgIIVCpNb0xDpuCSk2w4WgKpDLDPwKqD3+zJWMdcPxGBh6HyPA+VoG/TqYiLE4BDpvCt/1tsX22q56QAZU71E9BlKhnVtTIVK1WMx/2JyT3vFboiWQ4NTSH0DFnaGTjb4qgrf6wrWFmcO5Mmx28uI6WXkHWCDtbfnd6TF0Xq/eeywaUH6ZxX4WrsGWmHU7cyIS/u/EOr4bI3RPjsDXvF+1MwpZTabAWsdH9K5HOPyw32p5b23pCVPfkY+TSaCSlq/FVDVN0aqRZbNCuSqrVxGg/tspKiYeZBf3RMzIycP78edja2pbYKAbjITTRCdmxPi+QGixF/W/dUHOko94cmV1NTVyt3NnD8/4NC/qbJj7NP5/U8Ac31Jvugo1Od0rzdkqV7A+RhhysgMYB5tj9kwtuvchCp+81e0Vr+5jg0lpP1PMX5BOK1Aw1dpxNQ4AXH+3qCQsUEu2Evjbg4tLdSdh4LBVuDlz8Ns0J3Ztp5mIL+qFITFNh/eEUhEQpUM/fBJtm2OlioOWGEbKiMXqY+csvv4DNZoPN1vjZDBkyRPc+98vKygq7d+/GgAEDytJuhg9QLApKqRr7Wj1GZrQcPQ4FwLenre6Lk/cLVFD28MKwq6U/MS105SL+SUa5FrLcQ8iENOD4jUycvi2Bmz1Pr17D6qb5Ah/+diQFHb4NR0ySEhuOpmL25oL97XSuE3uSUKV/CI7dyMDqyY64uaEKujcT4X2sAl1nRmDPRcOBSt9EynHxgQRbZrrg4lovnZCpDUQ3YSgco3tmDRs2xMSJE0EIwYYNG9C+fXv4+fnp1aEoCmZmZqhXrx569epV6sYyGCbqWjooFoWBV3MitqqVdKmE1yGEQC5Wofkqd1z/PhIAIIlWQhKd8+UU+fAKOv2TQ9MEh65mYNHO/EmHJ6+NgyJvKqQ8UBQFkSkLG751QsPqpohKVKL3T5EQZ6kNuk5kZdOYvz0Rlx5IsOgbewxqbwlAE1/sp78ScfmhBM1qmaFJgGH/ryY1THF3k7fuvXZbEtMTKz5Gi1lQUBCCgoIAAFlZWRg/fjwaNWpUZoYxGMaQ60R2ihK0kkAuViP6hhiJTyR49XcCqg12QN2JLsXOciSJl0PoyAetoqGQqMG34KDJd1WQcFeKkEPJenXNnDkYG9yk1O6vpGgdXudvS8TzXA6vudHuYyyKHs1FsBSyoVBq9ka6O3DxLlqBQP/8iwJmAhYm97LGyomOADQp5OZsScTpW5loX98MZ1Z6Fho9Qztvpp0Tq6xRYkuDEs2Zbd++vbTtYDCC3BP9QM4QkiNgQylVY2fgAwhd+DBz5MGpkQjX54TBq4M17Gvnn4MpiNBjSTg1NBht1/nAv5+dXhbyngdrQBIvx+MNMZCLVag50gkOdT7eK/5jufVCinlbE3DrRX6HV3UB3iSHFzjr/p/XkdVSqBF/HpdCdKISMUkq+LsX7Gvm4chDplSNb3+Px4kbmWgdaIZzqzxQ18/4FVGmJ/bxlHgBQK1W4/z583j//j3S0tIMZvVhtjOVHoTkTPTfWhQOvogD56YiONUXwb+3HVhsIDNajiqdbcATsWFqy0PMTTEyY+RGiRkhBLcXReDGz+EAgIsTQ2AbYJovkqzQkY/mC6qU+v2VlBV7kvDLjqR85X1aifDTcDt4OXLQd24kLjzQCJ2tBXBupSeqeZlCrSagqMKdTp+8laFRdYFuVbGg5LtXH2eBz6VwYpk7GlXiLUWfkxLtAHjw4AF69+6N6OjoAt00mO1MpY9aSeNQx2cQh8ugktFwqCtEg+/d4NHaKt9qWdj5VFz/KQxd/q5WZMIRRZYaZ0cG482hHFHgidjocTCgxOnkPhWPQ7LRfHIYtI9hu/oah9fc8fENkdvVISRKju1n0uBsy0XHhkL4ufF1n+eGoykw5bNQv5oAy3Yn4buBtnpta+sZE6OMoWwpUc9s4sSJyM7OxrFjx9C8eXNYWlqWslkMQI4/n1akxGEyWPkJMODfOoh/lIlrP7xDxKU0ODcWgSvQDI0SHmci4nIabi2IQJO5HkUKWUakDP/0eIHEJznuF1a+AvQ6UQM2VQ2HpvlcpGaoYS3S3KdKTZAlo1HVg48BbS3wPlaBX0bZG9ywbcgtgs2mEBGvwPTf4nHlcRYaVxfg+I1M/HUiDceXucPLSbOocfDfDCSLNdFev25unk8kdUN9Rsg+OyUSs2fPnmHx4sXo1q1badvDkAvtFyXyWjqy4uRIC80G98NkvmOgOQKGOuLS1FBUG2APu5pCJD6T4MG6aMTfz0SnLf6o2k+TpacgH6foG+k41uslpElKXZlneyt0P1C9xEEZy4K0TDXWHUzGH0dT8c8id9TxNQGbRelWF9f/zwkCPlWgn5yh8nuvpOg6MwJ1fAV4/bcvHK05eBUuQ8Ox7/E4RAYvJx6UKoIG1QTwdeVhbPfy3UNlKKGYubq6FrkLgKHk5HZsvb82CtdmvgdfxAahgXrTXHX1agx3RMg/STg/NgRDbgfCvpYQtcc4ocViL5i7muTr2eXm2dY4XJgQAlqZ83esP90VrVZU0Vtk+JxIZTQ2HEvF2gPJSJdoZvJ/3pqIK+s99e7JkHe9FpomiE5S4sxtCcb3tNbNeVXz5IPLoTCwnQUcrTVfA7GEhgmPQtaH7UdcDqVbpQQYL/zyTome2pkzZ+Kvv/5CRkb+TboMH49WyMQRMiQ9y8KAK3XQ/3IdeLSzQvyDTL2sSl/94glllhq3l0QAAFybWWqEjNb0xvIKGa2icfl/oTj3zRudkLF5FIK2+aPNGp9yIWRKFcFfJ1NRc/hbzNuaqBMyAAiPVyA6SWV0WywWhbcxCmw5laYTMpWawNyUjdZ1zXDpgWZ4/SJMhoU7E2HCo9CoumYoqY1Kof2XEbLyTYl6ZpmZmRAKhfDx8cGAAQPg5uaWb2M5RVGYPn16sdqVSCRYuXIl7t69i3v37iEtLQ3bt2/HiBEj8tV9/fo1pk+fjhs3boDH46FLly5Ys2YN7OzsSnJL5YLcw8HrP73HnWWR8PvaDva1zcATclBjuCMuTQ5FwsNMiNw0vkuWVQRwaiQCJ09k0YLi7asVBFH/5Ti8mtpz8fXRGnBp+vmz0tM0weGrGVi4MxHvY5V6x0SmLEzrZ4NJvWwgLGYU1fA4JWr7mEChIuBzc+a3ujcTYeqvceg6MwIPgrNhwqNQ11eA60+l8HPj61YtmegUXwikBFAUVeSLxWIVu92wsDACgLi7u5NWrVoRAGT79u356kVFRRFbW1vi7e1Nfv31V7J48WJiZWVFateuTeRyudHXE4vFBAARi8XFtvVjodW07v+yDCWhaZqoVTllya8kZLPfHXKkxzO9885+E0w2+93RK5PEG3/PahVNEl9kkt/sbpAdgfeJODK7hHdQetA0Tc7eySCNxr4lpu1e6r2sO78iszfFk2SxskTtEkLIxfuZxK1XcL7yW8+zSIMxb0nQ92EkNklBEtOU5OL9TGLW/iVZtDOBJKRqrqnO9bdiKL+UqGcWFhZWqoKqxcnJCXFxcXB0dMSDBw/QoEEDg/WWLFmCrKwsPHz4EO7umqB1DRs2RPv27bFjxw6MHTu2TOwrLXJHc321LwEvdyVAkaGCa3ML1J3kApGbCYSufFTta4fQY8lIeJwJh7oa59SWy6tgz1ePcXn6W7Rd6wMAMHPg5WvXEGoFDbWChl2AEP3/rQ3LKgJwTT9/qKbXEXL0/ilKr4zNAoZ1ssTsIXZwsSvZYoS2l+vpyIWlkI1zdzPRqZH5hwgXgJ87DzYizXDTwZoDFotCu/pCrJnsiF3n0vE2RoHts12ZntkXQokmSDw8PIx6FRc+nw9HR8ci6x05cgRdu3bVCRkAtGvXDn5+fjh48GCxr/up0QrOjXlhuDA+BJ4drGBb0wyRV9Px/kNIHb45R+MAa85B1H9iqJWaeSO+BQcBQxyQ/i4b8gyVwXa1EEJwb1UkEh5nQpmtBq0m4Ak1v192NYTlQsgAwMeFjy5Nchx7e7cU4eFWb/w+3dloIStsYzabTcHPnYcrjzXJcTlsCmo1gY2IAy8nHv59lAWFiujaGNXFCqO6WOHkzUw8DskusF2G8sUXF2k2JiYGiYmJqF+/fr5jDRs2xJkzZwo8Vy6XQy7P2bf3ORcwYu9mIPRoMnqfqgm35pYAgP1tnkCVK1OPS1MLuLawwIO1Uag20B5m9jyw2BSqD3FAvf+56ITJEMpsNc598wav9ybC3JWPwbfrQuT6+WPGv49VQKUm8HPjQ6kikClo8DgUFn7jAKUK+HmkXZEOr7lRqwlYrJzJ+WSxCrYWHD1PfS8nHjwdeXgVJsejN9kI9BdATWtStY0IskS76eFITlfD1Z6rc34d2tESA9tZ5AuKyFB+KdFfysvLC1WqVCn05e3tXXRDJSAuLg6AZkiaFycnJ6SmpuoJVm6WLl0KCwsL3cvNza1MbDQGeboKWfEKmOZKkkEIkPhEgouTQ/BkkyawYKvl3uCasXHl27e6ehYeJuAJOSC04d5IZowc+1o8weu9msgRmdFyXJv5vgzvpmjiUpSYtj4OdUe9xbe/xSNTqoaa1qwq8nks+LvzcXSJe7GEDNCIGEVRuPxQgm4zIzBjQzyy5bROyLQrkb1aiiDJpnH4muYHTBu22tuVBw9HLjYc0/SItfknuRwKAj6LCcXzBVGinlnLli3zLfmr1WpERETg5s2bqFGjBurWrVsqBuYlO1vT7efz82/8NTEx0dUxdHz27Nn49ttvde8zMjLKXNBIAQ6rFAswd+fj7vJIBAxzxJXv30EhVsG9lSWSX2bh9qIIZCcr0WSOB5ov9ML5cSFIfCaBfS1hrjbytxt7NwNHe75AVrxCV+be2hLt1vuWzQ0WQW6H12y5RhiuPM7C/eBstAkses9oUduEUjJU+O73ePz7MAtDOlqiUTVNsl5tj0orak1rmKJzYyFO3MzErnPpGNbJUnOcovBVTTPYWmiG3IZ2CjB8GZRIzHbs2FHgsadPn6Jjx44YPHhwSW0qFIFA88ttqPclk8n06uSFz+cbFLmyIveEfGa0DCbWXN08lWd7ayQ8kSDuTgYe/RYDigUMvhUIMwce1Eoa//0YhrALqQic6gKnhuboe76WnpAZ4sWueJwf+wZqeU5vou4kZ7RZ61Mqsc2Kg1RGY+OxVKzJ5fCqxd2BC4XSuB6PVsiuPJKgqjtfN1Gv5d6rbCSkqnB3cxU42RieX9MOOUd01qTAm7kxHnX9TFDNgw8rczZWTXIstrsHQ/mj1OfMateujXHjxmHmzJl4+PBhaTevG15qh5u5iYuLg7W19ScVrMLQCtntJRF4+Gs0Om7yg3c3W51TbMPv3UBRFK7OfAeOKUu3KsnmssDiUADRLATwzTkw/zDfZainR6sJrs16j/urclYEWRwK7X73RZ1xzviUKFUEu86lY8nuJMSn6i9Q2FmyMXOwHUZ1tgSfZ5x47D6fjrlbEmBtzoaaBhpWF2DGQFv4ufGhUBLceC6FgzUHthYcRMQrIDRlITOLhqdTTsBIrfjZWXIwY5AdopNUmL0pAV/VMMXsoXYQClhM2rYKQJksADg4OODVq1dl0TRcXFxgZ2eHBw8e5Dt279491KlTp0yuW1Ie/BqNFzviYWLFwZUZ7yF05sOp4Ye48DRAsTX/cgQsKLLU4JmxkZ2iRPz9DDjWNwetJqBYOcOfvEImS1fi5MDXCDuXk1hEYMtFj8MBcG9p+cnuk5APDq87kvAuVqF3rKQOr/89zcKGo6lYMcER7eoL8eRtNuZtTcSczQn4/VtnOFhxwOdSCI6Qo+rgUNTyNsHbGDniUlT4e66rLimIFm0PbcUEBzx4k41DVzIQFqeAlxOPEbEKQKmLWUpKCrZu3QpXV9eiK5eQ3r17Y+fOnYiKitLNeV2+fBkhISHF3nVQlkiTFchOUqDmKEfUn+6Gvxs9xKWpoeiyqxqs/Ux1PTTX5hY42f8VsuIUsPIVIOJyOkztuWi92rvQbEGEEBzu/Byxt3NWZe1qmaHX8Rqw8CxZzseSQlEU9l0S6wkZn0thXA8rfD/QFjYiw4+aoTkxbS/p+PUMWJqz0L2ZOXgcCm0ChXgSKsPPWxOx+Xgq5o6wx5TeNqjmwUe2nMDWgg0vZx7mbE7AvktidGpkrteT1QoWj6uZJ6tZxQSiYkbhZSi/lEjM2rRpY7A8PT0dwcHBUCgU2L17d4kM+v3335Geno7YWM1q3smTJxEdHQ0AmDJlCiwsLPDjjz/i0KFDaN26Nf73v//ptkHVrFkTI0eOLNF1ywJTWx78etvB1J4HDp+F/v/WwRa/u7j+Uxja/uoDoZNmOOzb3RYdt/jj3akUSOIUqD7IHo1na/z0cm86zwtFUWj6sweOdH0Oogb8etmi886qhbpsfCwvw7Ixc0McMrJpfNffBt2bWWoSDSto/DDIBufvScBmAUM7WuLHoQU7vOZkNdLc28M32bC1YMPVjqubdI9IUMLekgOTXENSCzM2bC3Y2H9ZjGGdLOHhyEPf1vpbsSyELNhaaD4Dg4svH8oYIatYlCg4Y6tWrQymKbOysoK3tzdGjRqFqlWrlsggT09PREREGDwWFhYGT09PAMDLly/x7bff6u3NXL16NRwcHIy+1qcMzqhW0GDzWIh/lIndDR+i/nQ3NP3JA3wLjia6BdHMsankNDgfVuIKEzIAUMk0PmnPtsRBmqTEV/M8C90B8LH4D3yF6OT85VGHfWFuxgGXQ+HPY6loU88Mfm7GzVseuiLG4l1JoGmAJgQt65jpRPD0rUwM/CUKy8Y7YFB7S1y8L8GKvcloU88Md15KMaSDJcZ0s4ZKTRCXooJSRbD1dBouP5Bg8w8uqOX9+f3qGD4dJRIzYyjIJaE88akjzWrF6dW+BJwa/BrtfvOFX29b3F0WCb4lB1/N89SJmqHPTyWnocpWw8SSC6VUDRaHAtvIifSPZdTidzhw1bD/Ho8FpJ2vXqz2ksUqLNiehMsPJZjS2wadGglxPzgbU9fFYessF3Ruopnv+t+vcbj5QoqEFBXUNMHScQ4YHmSFwFFvMbKLFab0tsHR/zJw+KoYN55J4e3Cw8qJjqhnIPkIQ8Wm1McjCoUCO3bswKpVqxASElLazX/RsNgakao+0AFpodm49sM7PFgbBblYjaF3AzXi9UG/8gqZJF6OY71egmPKQo9DAeCLOCXKgVkS4lKUBoXMwoyCOItAUUDSkMJQqgiquHAxIshVl/XI04mHuVsSkZ2rwVWTHJGUrkJ4vBJNa+REzVXTObkxGwcIkCJW4dv+tjoR+xJ+TBlKl2L9rCsUChw+fBjLly/H5s2bdfNaACCVSrFixQp4enpi/PjxTPDGIqg+2AHKbBrWfqaYEN0EllUEBXr0xz/MxK76DxF7OwORl9Nxc174JxGydIka87YmoObwt/mOVfPgY9MMlxK37WTDxaB2lnrp29YdSoaNBRtsFoWUD/tOuRwKTjYcPSE7dj0DFAV0+8pc19Y33axRz18AQsiHRCWMkFU2jO6ZxcbGolWrVnj37p1OqAQCAU6cOAEej4dBgwYhJiYGDRs2xG+//cYkAS4AiqIQ/0gjTlX726P7Ps3wLG8aOS2v9yfg7Mg3UMlyeiuqbLrICBkfg9bhde3BZKRl6ne77C1ZWDTWEQPaWED1kVt97K00j19CmgodpodDnKVG60Az/PRXAtwcuBjT1Rq9WmqG/zFJSsQkKfHvoyxsOZWGPq1Eujj9WrS9MTYzr18pMXrObOTIkfj777/x/fffo3nz5ggLC8OCBQsgFAqRnJyMgIAALF26FC1btixrm0uNz5WdSRyejbj7majaVxOj35CQEZrg+tww3FkSqSuj2ECbtT4InOxSZj2Pc3czMWlNXD6H19y0b8CHnwsffxzLcQnJupgzZ1ZQOraC0Di/ZqG+vwAiMzYS0lQYvzIWViI2NnzrBBMeCw/fZGP94RS8Cpfjp2F26NG8/GfTYvi0GN0zu3jxIkaOHImlS5fqyhwdHdG3b1906dIFx48fB4vFbAkxBgtPgc4PjFaTfEImz1Th1ODXeHcyRVdmYsVBj0MB8GhrVaa22YjYekKmdXjt3ICLxpM00woX78tx8X7OHNqIDvoZoFgfhonpmTS8XfR7T4bgcSndPk2aJnCw4oDLAZLSVGB/EMVa3iZYMtZBz9WjuKLJULExWn0SEhLQuHFjvTLt+1GjRjFCVkLyzn2lvcvGniaP9ITMpropht4LLHMhy5ZrUrd1a2oOPpfClN7WeL7LBzMH26GmnyWu/55/U/7MQZb4Y4anXhkhBCMWx2D7mTSkSzTuI8YOSVksCmFxCkiyaXRuYg4uR/P5cDmUTsi0kSwYIWPIjdE9M7VarYtKoUX73sLi88eP/xJJfJmJM0NeQ60gaPubNyiwcbzvS8hy9Yy8u9qg655q4BfgQV9S7ryUYv62RCwd54BqnprYYgI+CwI+C8snOGDVJEe42us7vAb6m+sNJw2hzWDUv60FDl8V49k7GVrUNgOHTeHZOxloQlDHJ7/bxJtIOUx4FC4/zMLvR1Lg4chDj2bmBq7ARLJgMEyxviHh4eF49OiR7r1YrEmMERoaajARcGBg4MdZV4FZY3ENqoyc3srBti/y1Wn8ozuaL/Qq1Yn+F2EyLNieiNO3NVmJfvorAceWesDENKdn7eFoeGhojLuDVmgGtLXAyZuZeBIqgwmXwtqDKTh5KxPLxzsYFLMTNzNx5KoYkmwa0/vZYnTXsu2FMlQ8jF4AYLFYBh9kQw+4tkytVuerX574XAsA2xvcQdIDmeGDbIDDZaHTNn9UH2j8boaiCItTYNHOJBz4V4y8f/GbG70MCowhYpKUeP5epomlbyCPZO55rN+OpGD1/mRkyWj0bmmB6f1s4O/ON1g/LkWJkCgFWtbJyUjO5KlkKA5G98y2b99elnZUKrRCxrWgMCWxGdgcFrY1uIeUR9mAGhjyKLDI2GXGEp+qwoo9Sdh2Jg3KPAuUDaoK8Mtoe4NCRtMEFKXvvCtT0Fi5LxlXHmWhY0OhQaFhsSiExykwb1sizt2VQGTGwqB2llg0xh7mpux8P35a4XOy4erikWk3nzNCxlAcjBaz4cOHl6UdlZKvfvICi60J/TPqQUOsZF0DgFIRsnSJGusOpuCPoymQyvS7YtU8+Jg30g5dm5oXOGzUikzu3pEJjwUvJx7e2MrxOESm5/Cq5d9HEkxaE4da3iZ4uMUbD95k46+TaXgUIkPLOmZGuZQUFlmWoey4fv06pk2bViZxCD8FzBLkZ+TqjPd4sSsefHMOVDLjs3QXRVyKEjWGhWLlvmQ9IXOz5+LP751xd1MVdPtKVKiwZErV+N+vcZjzVwLC4nLC+jSvZYrQaAViUwzbW82dj9MrPHDgFze42nPRs7kIlkIWzt+TICGt9O7xYwkNDcXs2bMxcOBAzJ49G6GhoaXavqenJ0xNTSEUCmFjY4N27drhxIkTxTr/zp07pWpTUTRv3txoIWvVqhX2799fxhYVD0bMPiGEJrj5S7he2fkxb3C0/zOsNb1ZatdxtOboJQaxtWRjxQQHPN3ujaEdLY0avv37MAtbT6fh6H8Z+GFDvK480F8ASyEb155o0rblTfjhZMtFFWee3rFuX4kQGiU3OlR2WbN9+3ZUq1YNmzdvRkJCAjZv3oxq1aoVGg6+JPz777+QSCQIDg5G3759MWTIEPz555+leg2GXHyaXMPlk0+Z0VwuUZGjvZ+T5bhS6OvG0rfFaletpklapkr3XiJVE7FERR69kRKnHq/Jkl2JJCNLVUgL+mizfStVNHHrFUy+/yOO1P/mLRm/Koa8i9FkTl+yK5H4DnxjdFt5//85CQkJIWw2m4wZM4ZIpVJCCCFSqZR88803hM1mk9DQ0FK5joeHB7l9+7Ze2dq1a4mNjQ1RqTR/jwkTJhBHR0diYWFBOnXqRKKjowkhhIwbN45QFEUEAgExMzMjhw8fJsnJyaRDhw7E2tqa2NnZkYkTJxKFQmHw2sOHDyeTJ08mjRs3JiKRiPTv359IJBLd8XXr1hFPT09iZ2dHxo4dq/scrly5Qvz9/QkhhISFhRE+n082bNhA7OzsiIuLCzl+/DghhJDFixcTFotF+Hw+MTMzI6tXrybx8fGkffv2RCQSEVtbWzJlypRS+RyLA9Mz+wSII2TY+9UjhBzJCQYmdM3v/tB6nRe+mmVcij5CCC7cl+CriWEYtzIWUhmNjCw1THgURGZs1PE1Qcg+P8weagfzYiT7pSgKNK2ZgG9V1wwxSUqcWOqON5Fy/LI9ETIFjea1TcHjUDh3NxNATmRYQ21pbdW2+7nZtm0bLCwssH79el3iG4FAgPXr10MkEmHr1q1ldu0ePXogJSUFb968AQA0a9YMb9680eWu+O677wAAf/75J9zd3XU9u969e4OmaUyZMgWxsbF4/Pgx/vvvv0Jt3bt3L/744w/ExMQgLS0NCxYsAACcP38eq1atwoULFxAaGop3795h4cKFBttQKBR4//49oqOjsXTpUowfPx4A8OOPP6J58+bYsWMHJBIJvv32W6xduxa+vr5ISUlBZGRkmSU0KgxGzMqYqOvp2N3gIRKfZunKvDpZY9TzBviBtML3yhb4Tt4CP5BWaPA/47LA33kpRdD3Efj6x0g8eyfDqVuZeBKaDZEZWzeEpCiqxBmHtJP/beub4d7rbHA4FNZMccTbGAWmrtMkkvF04uHZW5le/YLIG7b6cxIeHo7atWvncwAXCASoU6cOwsPDy+za2mQ8aWlpAIBBgwZBJBJBIBDghx9+wI0bNwo8187ODl27dgWfz4eLiwu++eabQuv37t0bgYGBEAqFmDt3Lg4dOgQAOHDgAMaNGwdfX19YWFjg559/LnDuixCCuXPngsfjYeDAgUhISEBSUpLBulwuF3FxcYiJiYFAIECjRo2M+kxKE0bMypCnf8XiQJunkCYpdWUNvnNF71M1YWKpcUNgcVhGB1h8ESZD37mRaDstHNefSfWO5X1fHEgBroZu9lw423Jw4b4EdXwE2PGjC+6+ysapW5mIT1EhMV2NjKzy7UuYF09PTzx9+lSXf1VLdnY2njx5ootkXBZoM4pZWWkcghctWgQfHx+IRCI0b94cKSkpBZ6bmZmJYcOGwcXFBSKRCLNnzy60fu4cHG5ubrprx8bG6uWK9fDwMJjpDNCkZtT6X3I4HHC5XGRlZRmsO2PGDLi4uKBp06aoUaMGjhw5UqBtZQUjZmXEs21xOD82BPSHAIJsHoXOO6ui9SqfYsciC4tTYPSyGDQe9x5n7kj0jrUJNMP1370wc7BdiW3NvaqZW9gaVBVAoSQIi1UgW07D15WPxWMdkC6hERwpx6GrYnz+gWPxGDVqFMRiMaZOnaoTtOzsbEydOhUZGRkYPXp0mV37xIkTsLGxgb+/P65du4bNmzfj3LlzEIvFuH79ut5nn3elec2aNUhNTcWzZ8+QkZGBpUuXFhozUJs3AwCioqLg6OgIAHB2dkZUVE5KwsjISF2PsTjktU8kEuG3335DTEwM1q5di0GDBkEikRRwdtnAiFkZUbWfHWwCNNEkzBx5GHitDmoMcyxWGwlpKnz3exzqjnqL/Zf1PffrVzXB6RUeOLncw6C/V27UalLogy9X0Dj6nyacj/YhVasJzE3ZaFjNFBfuS8D7sOG7SxMhfhxqCy8nLjo0EELwicJ2lxa+vr7YsmULtm/fDldXV7Rp0wYuLi7Yvn07tmzZAh8fn1K/ZkpKCrZs2YKff/4Zv/zyC9hsNjIzM8HlcmFjYwOJRIIlS5bonWNvb6835M3MzISpqSksLCwQFhaGTZs2FXrNf/75B0+ePIFEIsHixYvRt29fAEDfvn2xefNmvH37FhkZGVi4cCH69etX7HvKa9+ZM2d07y0sLD7ElfvEgeU++ZJDOaKsVjNpNU1kYiVJey8lh4KekoxomcF6/z1OJdadXxLLji/J1pPx+Y73nB1BTNu91HsFjgolx6+LjVodpGlar97jUCn592Gm7piWI1fFxKz9S7L3YpreuYQQcvtFFhF1fEki4jUrmXKFplwmVxd5/fJMaGgomTVrFhkwYACZNWtWqa1iavHw8NCtRlpZWZE2bdqQf/75R3dcqVSSQYMGEaFQSLy8vMj69esJn8/XHT948CBxdnYmFhYW5MiRIyQyMpI0btyYmJmZkfr165Mff/yRdOzY0eC1865m9u3bl2RmZuqOr1mzhri7uxM7OzvyzTffkKysLEKI4dXM3PD5fBIWFkYIIeTatWvEy8uLWFhYkLVr15LVq1cTNzc3YmZmRnx8fMj+/ftL5XMsDmWW0ORLoDT3ZkqTFDC140GtoKGS0+CbF765wqy94STJmeergsVigRCCK4+y0G2WJjijmz0Xc4bZYVA7i2Jv83n4Jhs/bIhHRIIS3ZqaY+EYBwgFLN0qY0iUHPsuiTGwnUW+rErvYxUI+j4cQzpYYu4Ie71jTBbw8smIESNQtWpVzJo163Ob8kkpuwSLlQRCCO4ui8SdpZHod6k27GqYFSlkHr0KzvZu3jEY8cf9QQjQOtAMwztZIsCLj2+6WoFfxJDOULDC/55m4fs/4tGqrhm2zHIBiwLMTPSzo/u58TFvpH2+9gDA1oKNuSPs0a5+/i1WjIgxlCcYMfsIlFI1zo5+g+D9iQCAE/1eYtj9euAW4deVrHHPwqhOpmhZzwoLtifpZQI3M2HphGLDd85G2ZJbyEiuzdz/PsxCNQ8+Vkwoer7OkBiKzNgY0sEyX7sMDOUNRsxKSEaUDEd7vkDCo5wVGxaHgixNBVO7okNFA8DDtzS2nYvJV25sjye3+LBYFIIj5Fi5LxkO1hyM6WYFDwcuQqLkcLbl4sSNDIizaMgVNCiKwoig/NuaCrsuI2RfDqW9LetLgREzI4h9IEbc7QzY1xXCrZkVYm6LcezrF8hKyPEfc29jiR4HAyCw4RbSksbhVcvTtzkxzVgsgDYy/yQhBDStH3H1RZgMo5bGwNeVh8NXxYiIV+DP753RvZkIaw4k4/iNDNT3FyAyUYmnb2UwE7AwoK3xEYIZIWMo7zBiVgiSRBk2OOSPXMDiUqBzbZquN9UFrVd7G0wVp+VFmAy/bEvM5ycGAGZ8IMtwsnA9CCEgRNODYrOB8DgFDl0Ro10DIU7ezMSkr60xPMgKJ29m4NdDKbj5XIoBbS3QorYpLMzYEGep4WzLRafvwnH/tbRYYsbAUN5hxKwQDAkZAJ2QsbgUOmz0Q63RBTsdRiYosGBHUj4/sdzkFrLoQ14FZh2iKAoUpcnkffCKGDP+iIfQlIW/TqXBzpKDC6s9AWiiVCzZnYynbzURYZ1tNb1FMwELIVFyUCygezMmVRtDxeLL8nj8hNxa8073/4DRdnBuov/lZ/MpDPi3dqFCBgDvY5XYd0lfyOr5m+Dkcne0qJUzt+btxELyCR9YWQoKnbvafT4draaG4XW4HCeWeeDBX95oUdsMUhmNzOyccWq3r8yx75ImR8OD4Gwcv56B2Zvi0en7CLjYclHX16SgSzAwfJEwPbMCuPHDhy0fbKDJTC/sbqAftE4tJ3BtZlloG2o1QV0/E7SsY4prT6So6s7DvJH26PaVJsJrm0DD3uaXHkhw+GoGvJ25CPAyQecmOVmKPB25ePZOBl9XHur6moDNptCpkRAhUXLEJCnhaK35k3ZvZo6NR1PxOkKO1Ew1fv8nBRRFYdP3zmjfoHRCcjMwlCeYnllBaDtHasDa1xRd/64GigVQBch/tpzG2oPJCI6Qg6YJxFlqZMloiExZWDLOARu/c8bdzd7o3qzgCK+h0XL0mB2B0ctjwOUAlx5kof/8KF2oHQCwt+IgwJMPeyuObgGgW1NzhMUq8S4mx73D0oyNap58HLgsRocGQmz+wQUX1njqhCxvUEWGLxOpVIqgoCCIRCLMnDkTS5YswbRp0z63WZ8FRswKIGhXTlyxPR3vw9JHAI9OIpAPkZ9rTNBs7FaqCLafSUOtEW/x01+JmLslAZnZNMwFLIjM2KAoCnV8BBjWyVIX296QkGRl01h7IAUOVhxcW++F36Y54/waT/T4yhwbjqbq6rnaaXprUhkNyYdhJZ/HQrsGZth7KV1Xz9GGgy5NzNGzhaZX5+WkH/2VSRZS9qxbtw4+Pj4QCATw9fXFr7/+qne8oNDYqampGDJkCOzt7WFhYYFatWoVGIXi8OHDkMvlSE9Px/Lly/Hjjz9i3bp1AICrV6+iatWqpX5f5RVGzAqg5sCcMCnRF7Kwpdp9hJ3J0JV1/K0aDl8Vo/437zB5bRxikzUqd+aOBKFRCoPzXprVyJwEIVm55rjMBCzYW3HQt7UFPJ14OtFpHShERLxSlxHcTMCCjwsPbyLleuF3+rS0wKM3MkQmaHpnHDaF//W1yZd5iRGxT8P8+fOxZs0a7Nq1C5mZmdi5cyfWrFmDRYsWFXnu9OnTwePx8O7dO6SkpGDbtm2wtrY2WDcyMhJ+fn5gsZivMjNnVgj0796gJ79Dbn9+FYB7ExxxfEo4noTq577kcymM6W4FTyfDvmba4eWLMBlm/ZkALycufhnlAGuR5grzR+XfUvQoJBuB/ibgsCkoVQRcDoUhHS2x9O8kJKSpdCuVQlMWun1lni/OPuPs+ulJS0vD8uXLcejQITRt2hQA0LRpU/z+++/o378/Jk+ebDBptpYHDx7g119/hbm5plddv359g/VWrFiBBQsWgBCCv//+G4cPH8adO3cQHx+PP//8E0FBQZDL5RAKhTAxMUFycrLBdioKjJgVAE3TWHgsE2hrB6dMFSyz1Ug25SBJyAZCaQD6Dq9DO1jix6F2cLUv2GmWpglO3crE4t1JoGng/utsDGhrga9q5iS+1YoPm60JMx0cIceg9pYAAC5HU+Zmz0XjAFNsOZmGP77V9Lya1TTVS6CrpTIKWVxcXL6Ag1ZWVvDy8oJMJsOrV/n3xgYGBgIA3rx5ky8AoaenJ6ytrZGUlASVSlVk/K87d+5ApVIhKChIr7xz585QKpW4c+cOOnXqVOD5jRo1wsyZM/Hdd9+hefPmesEUc/PDDz9AKpXqxEt7bS1nz57F+PHjERwcXKi9FQWmb1oAXWaGaf5DUeD5CqAMNEeSOQfIIw49m5vjwV/e2PCdc6FCBgA0AWQKgs6NzXF1vRfc7LnYcipNb7iYW3yev5fjbYwCzWqZ6spYLAqEEHxV0xQJaSrd8FM7fCwPcfY/N5s2bUK9evX0XnPnzgWgCVqY91i9evV0544YMSLfsTNnzgAADh48WGQcMUATv8zW1jZfPC82mw0bG5tCI8QCwO+//44ePXpg+fLl8PT0ROPGjfH69evifgyVDqZnVgA3n2k8WVkU8HynDzp+F4G3MUq9Ov/97oV6RQRGzI02SUhQYyHMBCwsGmOP/vOiMCLISq9Xpc3ofeZ2JrxdePB314Tl+edaBjKy1BjWyRITv7aGnWX+Px8TyQIYN24cunfvrlemDVXt6upaaG7IHTt2GOyZAUC/fv2gUhWd+9Pa2hrJyclQq9V6gqZWq5GSkgIbG5tCzzc1NcXPP/+Mn3/+GSkpKfj+++8xfPhw3Lt3r8hrV2YYMSsAgQkgkWp6U2w2C/NH2qPj9xG64xRQLCHTYm+l+chpmqBTI3NU9eBjy8k01PE1gYWZ5sHXrno+eJON4Z0sce5uJn76KxHBkXJsmuEMFovSCVnujOMMGpycnAocCpqYmOiGlIbw9/cv8JidnXGhyZs0aQI2m42zZ8+ia9euuvIzZ86Aw+GgcePGRrUDADY2Nvjuu++KdY6WyjbFwAwzC2DVhJzJeGH7V1ixJwm5H42J3T/O8VQ7Glw8xgHHb2Tg4Rv9BBtP3mbj/D0Jpv8WjyELo9GnlQiSC9Ux+MP8mRZGyMofVlZWmDFjBiZNmoRbt25BpVLh9u3bmDx5MmbNmqU3+a9QKCCTyXQvmqaxaNEiPHr0CEqlEhKJBJs2bUKDBg2KbYe9vT0SExPzJW+pqDA9swIY1N4G41dr4pQRAJcf62c/+mWsq4GzjIfD1sx9tasvRE1vE/x1Ig0NqgrA57KQkKaCkw0XPA6Fhd/YY1KvnGGJdgjKUL5ZsGABLC0tMXToUMTGxsLZ2RnTpk3D9OnT9eq1bNlS7/3JkydB0zQGDx6M6OhoCAQCNGvWrERhfapVq4agoCC4uLiAx+MhPj6+6JO+YJiw2YWEzT59S4yBv8RAnSs0D4sC/pjuiGFBhv1+ioN2iHjjWRa6z4rExK+tce6uBC52HBxZ5K4nWio1AZtV+YYODAzGwvTMCqFLUws82mqCjUdT8ChUhqrufEzubYMAr9LZpM1mU8iW05ArCRQqgi2n0jAiyBKLxjjohEwrYkxvjIGhcJieWSklNCkJKjXBir3JWLI7CVP72GD+SHvwuDkuFszKJAOD8TBi9hnFDADeRsvhZMOFmUCzFsMMJxkYSgYzzPzM+LhqfMiY4SQDw8fBiFk5gRExBoaPo1KLmXaEnZGRUURNBgaGssDc3LzUplQqtZhlZmqCHha0kZeBgaFsKc356kq9AEDTNGJjY0v116G4ZGRkwM3NDVFRUZ9tEaK4MDZ/GiqDzUzPrJRgsVhwdf04T/7SQiQSfTEPrBbG5k8DY7NxMHszGRgYKgSMmDEwMFQIGDH7zPD5fMybNw98Pv9zm2I0jM2fBsbm4lGpFwAYGBgqDkzPjIGBoULAiBkDA0OFgBEzBgaGCgEjZuWYHTt2gKIoPHjw4HObYjTz588HRVFfVI5GxubPi6enp16uhJLCiFkpcv/+fUyePBkBAQEwMzODu7s7+vXrh5CQkHx1X79+jU6dOkEoFMLa2hpDhw5FUlLSZ7Ban8WLF4OiKNSoUSPfsVu3bqFZs2YwNTWFo6Mjpk6dColE8hms1PDo0SN0794d1tbWMDU1RY0aNbB+/Xq9OuXJ5tDQUAwYMACurq4wNTVF1apVsWDBAkil+iHZP4fNEokE8+bNQ6dOnWBtbQ2KogoM1W3ss0vTNFasWAEvLy+YmJigVq1a2LdvX5ndQ6XeAVDaLF++HDdv3kTfvn1Rq1YtxMfH4/fff0dgYCDu3LmjE4jo6Gi0aNECFhYWWLJkCSQSCVatWoXnz5/j3r174PF4n8X+6OhoLFmyBGZm+ZMJP3nyBG3btkW1atWwZs0aREdHY9WqVQgNDcXZs2c/ua0XLlxAt27dULduXcydOxdCoRDv3r1DdHR0ubQ5KioKDRs2hIWFBSZPngxra2vcvn0b8+bNw8OHD3H8+PHPanNycjIWLFgAd3d31K5dG1evXjVYrzjP7pw5c7Bs2TKMGTMGDRo0wPHjxzFo0CBQFIUBAwaU/k0QhlLj5s2bRC6X65WFhIQQPp9PBg8erCubMGECEQgEJCIiQld28eJFAoBs2rRJV7Z9+3YCgNy/f7/sjSeE9O/fn7Rp04a0bNmSBAQE6B0LCgoiTk5ORCwW68r++usvAoCcP39eVzZv3jwCgCQlJZWZnWKxmDg4OJCvv/6aqNXqAuuVJ5sXL15MAJAXL17olQ8bNowAIKmpqZ/VZplMRuLi4gghhNy/f58AINu3b89Xz9hnNzo6mnC5XDJp0iRdGU3TpHnz5sTV1ZWoVCpduYeHB+nSpctH3wMzzCxFmjZtmq9X5evri4CAAL2M1EeOHEHXrl3h7u6uK2vXrh38/Pxw8ODBQq+RlpaGhg0bwtXVFW/evCk12//77z8cPnwY69aty3csIyMDFy9exJAhQ/T22w0bNgxCobBImyMiIuDj44MaNWogISHho23du3cvEhISsHjxYrBYLGRlZYGmab065c1mbZgpBwcHvXInJyewWCzweLzPajOfz4ejo2OR9Yx9do8fPw6lUomJEyfqyiiKwoQJExAdHY3bt28Xep2dO3eCw+FgxowZRt8DI2ZlDCEECQkJsLW1BQDExMQgMTER9evXz1e3YcOGePz4cYFtJScno02bNkhISMC1a9cKTVhbHNRqNaZMmYJvvvkGNWvWzHf8+fPnUKlU+Wzm8XioU6dOoTa/e/cOLVq0gLm5Oa5evZrvy1wSLl26BJFIhJiYGPj7+0MoFEIkEmHChAmQyWTl0uZWrVoBAEaPHo0nT54gKioKBw4cwMaNGzF16lSYmZmVO5vzUpxn9/HjxzAzM0O1atXy1dMeL4jNmzdj5MiRmDVrFlauXGm0fYyYlTF79uxBTEwM+vfvDwCIi4sDAIMZt52cnJCamgq5XJ7vWHx8PFq1aoWMjAz8999/8Pb2LjUb//zzT0RERGDhwoUGjxdlc2xsrMHzgoOD0aJFCzg4OODff//VCfrHEhoaCpVKhR49eqBjx444cuQIRo0ahT///BMjR44slzZ36tQJCxcuxMWLF1G3bl24u7tjwIABmDJlCtauXVsubc5LcZ7duLg4ODg45Avvoz23oHtZv349xo8fjwULFmDRokXFso9ZAChDgoODMWnSJDRp0gTDhw8HAF12aUN710xMTHR1ch+Pjo7G4MGDAWiGgy4uLqVmY0pKCn7++WfMnTsXdnZ2BusUZbOhjNkvXrxA//794ePjg7Nnz5ZqOBiJRAKpVIrx48frVi979eoFhUKBTZs2YcGCBeXOZkDjgtCiRQv07t0bNjY2OH36NJYsWQJHR0dMnjy5XNqcm+I8u3mfYUP18rJixQrMnDkTK1asKNbwUgsjZmVEfHw8unTpAgsLCxw+fBhsNhsAIBAIAMBg70s7RNLW0TJ06FBwOBy8fv3aqHmN4vDTTz/B2toaU6ZMKbBOUTbntRcAunXrBgcHB5w/fx5CobD0DM5lz8CBA/XKBw0ahE2bNuH27dswNTUtVzbv378fY8eORUhIiC6GXq9evUDTNGbOnImBAweWu885L8V5dgUCQbGe8WvXruH06dOYOXNmiYQMYIaZZYJYLEZQUBDS09Nx7tw5ODs7645pu9naLntu4uLiYG1tne8XrVevXkhPT8evv/5aqnaGhoZi8+bNmDp1KmJjYxEeHo7w8HDIZDIolUqEh4cjNTW1SJtz35+W3r174927d9izZ0+p2gxAd72880L29vYANIsk5c3mDRs2oG7duvmCgXbv3h1SqRSPHz8udzbnpTjPrpOTE+Lj43V5NnLXA5DvXgICAuDv74/du3cjLCysRPYxYlbKyGQydOvWDSEhITh16hSqV6+ud9zFxQV2dnYGvfrv3buHOnXq5CufMmUKFixYgGXLlmHZsmWlZmtMTAxomsbUqVPh5eWle929exchISHw8vLCggULUKNGDXA4nHw2KxQKPHnyxKDNK1euxOjRozFx4kTs3bu31GwGgHr16unsz412HsbOzq7c2ZyQkAC1Wp2vXKlUAgBUKlW5szkvxXl269SpA6lUqreKDwB3797VHc+Nra0tLl26BC6Xi7Zt2xY4p1YoH+3cwaBDpVKR7t27Ew6HQ06fPl1gvfHjxxOBQEAiIyN1ZZcuXSIAyMaNG3Vlef3Mvv/+ewKAbNiwoVTsTUpKIkePHs33CggIIO7u7uTo0aPk2bNnhBBCOnXqRJycnEhGRobu/C1bthAA5OzZs7qy3P5ParWa9OvXj3A4HHL8+PFSsZkQQh49ekQAkEGDBumVDxw4kHA4HBITE1PubO7atSvh8XjkzZs3euU9e/YkLBarXNlcmJ+Zsc9uVFRUgX5mLi4uBfqZhYaGEkdHR1K9enWSnJxcLLsZMStF/ve//xEApFu3bmT37t35XloiIyOJjY0N8fb2JuvXrydLliwhVlZWpGbNmkQmk+nqGXKaHTNmDKEoSq+90saQ0+zDhw8Jn88ndevWJRs3biRz5swhJiYmpEOHDnr18jpzKhQK0rlzZ8Ln88nly5dLzcZRo0YRAKRfv37kjz/+IH379iUAyOzZs8ulzdeuXSNsNpvY29uTBQsWkD/++IMEBQURAOSbb74pFzb/9ttvZOHChWTChAkEAOnVqxdZuHAhWbhwIUlPTyeEGP/sEkLIjBkzCAAyduxY8tdff5EuXboQAGTPnj169fI6zT579oxYW1uTevXq6TkPFwUjZqVIy5YtCYACX7l58eIF6dChAzE1NSWWlpZk8ODBJD4+Xq+OITFTq9W6HsixY8fK7D7yihkhhFy/fp00bdqUmJiYEDs7OzJp0iS9HgQhhj3TpVIpadmyJREKheTOnTulYqNCoSDz588nHh4ehMvlEh8fH7J27dpybfPdu3dJUFAQcXR0JFwul/j5+ZHFixcTpVJZLmz28PAo8NkNCwvT1TPm2SVE86wuWbKEeHh4EB6PRwICAsjff/9t8Lp5dwDcvXuXmJubkxYtWhCpVGqU/UykWQYGhgoBswDAwMBQIWDEjIGBoULAiBkDA0OFgBEzBgaGCgEjZgwMDBUCRswYGBgqBIyYMTAwVAgYMWNgYKgQMGLGwMBQIWDEjKHUuXr1KiiKKjDDT1lBURTmz5+ve6/NOxoeHv5J7WD4PDBiVkGgKMqolzECs2TJEhw7dqzMbWYwjlevXmH+/PmMKBcBE2m2grB7926997t27cLFixfzledNMGGIJUuWoE+fPujZs2dpmshQQl69eoVffvkFrVq1gqen5+c2p9zCiFkFYciQIXrv79y5o0tbxsBQGWCGmZWIrKwsfPfdd3BzcwOfz4e/vz9WrVqlF9qYoihkZWVh586duqHpiBEjAGjyMk6cOBH+/v4QCASwsbFB3759P2r4ExMTg9GjR8PZ2Rl8Ph9eXl6YMGECFAqFrk56ejqmTZums9vHxwfLly/PlyvzYwgODka/fv1gZ2cHgUAAf39/zJkzR6/O48ePERQUBJFIBKFQiLZt2+LOnTt6debPn58vIxFgeP7O09MTXbt2xY0bN9CwYUOYmJigSpUq2LVrl955ffv2BQC0bt26WNMFlQ2mZ1ZJIISge/fuuHLlCkaPHo06derg/PnzmDFjBmJiYnTpznbv3o1vvvkGDRs2xNixYwFAl9bu/v37uHXrFgYMGABXV1eEh4dj48aNaNWqFV69eqVLImIssbGxaNiwIdLT0zF27FhUrVoVMTExOHz4MKRSKXg8HqRSKVq2bImYmBiMGzcO7u7uuHXrFmbPno24uDiDSYuLy7Nnz9C8eXNwuVyMHTsWnp6eePfuHU6ePInFixcDAF6+fInmzZtDJBLhhx9+AJfLxaZNm9CqVStcu3YNjRo1KtG13759iz59+mD06NEYPnw4tm3bhhEjRqBevXoICAhAixYtMHXqVKxfvx4//vijbprAmOmCSofxodsYviQmTZqkFxDy2LFjBABZtGiRXr0+ffoQiqLI27dvdWVmZmZk+PDh+do0FCTv9u3bBADZtWuXruzKlSsEALly5UqhNg4bNoywWCy94JNaaJomhBCycOFCYmZmRkJCQvSOz5o1i7DZbL3wzQDIvHnzdO+1wS1zBxY0RIsWLYi5uTmJiIgwaAMhmvDWPB6PvHv3TlcWGxurCyCoRRs0MS+GbNEGQ/zvv/90ZYmJiYTP55PvvvtOV3bo0CGjPs/KDjPMrCScOXMGbDYbU6dO1Sv/7rvvQAjB2bNni2wjd3owpVKJlJQU+Pj4wNLSEo8ePSqWPTRN49ixY+jWrZvBDNnaodqhQ4fQvHlzWFlZITk5Wfdq164d1Go1/vvvv2JdNy9JSUn477//MGrUKLi7uxu0Qa1W48KFC+jZsyeqVKmiO+7k5IRBgwbhxo0byMjIKNH1q1evjubNm+ve29nZwd/fH+/fvy9Re5UZZphZSYiIiICzszPMzc31yrXDlYiIiCLbyM7OxtKlS7F9+3bExMTozbWJxeJi2ZOUlISMjAzUqFGj0HqhoaF49uxZgQmKExMTi3XdvGhFozA7kpKSIJVK4e/vn+9YtWrVQNM0oqKiEBAQUOzr5xVQALCyskJaWlqx26rsMGLGYDRTpkzB9u3bMW3aNDRp0gQWFhagKAoDBgwo1cn43NA0jfbt2+OHH34weNzPz69MrltSDE3+AzCYZg6ALjl0XggTzb7YMGJWSfDw8MClS5eQmZmp1zsLDg7WHddS0Bfy8OHDGD58OFavXq0rk8lkSE9PL7Y9dnZ2EIlEePHiRaH1vL29IZFI0K5du2Jfwxi0w8bC7LCzs4OpqSnevHmT71hwcDBYLBbc3NwAaHpVgGYF1tLSUlfPmJ5vQRT092DQh5kzqyR07twZarUav//+u1752rVrQVEUgoKCdGVmZmYGBYrNZufrMfz2228F9joKg8VioWfPnjh58qTBpLLa6/Tr1w+3b9/G+fPn89VJT0+HSqUq9rVzY2dnhxYtWmDbtm2IjIw0aAObzUaHDh1w/PhxPdeKhIQE7N27F82aNYNIJAKQs/Kbey5P6+pSUszMzACgRD8alQmmZ1ZJ6NatG1q3bo05c+YgPDwctWvXxoULF3D8+HFMmzZN9yUENBnDL126hDVr1sDZ2RleXl5o1KgRunbtit27d8PCwgLVq1fH7du3cenSJdjY2JTIpiVLluDChQto2bIlxo4di2rVqiEuLg6HDh3CjRs3YGlpiRkzZuDEiRPo2rWrzmUhKysLz58/x+HDhxEeHg5bW9uP+mzWr1+PZs2aITAwEGPHjoWXlxfCw8Nx+vRpPHnyBACwaNEiXLx4Ec2aNcPEiRPB4XCwadMmyOVyrFixQtdWhw4d4O7ujtGjR2PGjBlgs9nYtm0b7Ozs8omlsdSpUwdsNhvLly+HWCwGn89HmzZtYG9v/1H3XeH4nEupDGVHXtcMQgjJzMwk06dPJ87OzoTL5RJfX1+ycuVKPRcEQggJDg4mLVq0IAKBgADQuWmkpaWRkSNHEltbWyIUCknHjh1JcHAw8fDw0HPlMNY1gxBCIiIiyLBhw4idnR3h8/mkSpUqZNKkSUQul+vZPXv2bOLj40N4PB6xtbUlTZs2JatWrSIKhUJXDyV0zSBEkwvy66+/JpaWlsTExIT4+/uTuXPn6tV59OgR6dixIxEKhcTU1JS0bt2a3Lp1K19bDx8+JI0aNSI8Ho+4u7uTNWvWFOiakTdfJCGavKUtW7bUK/vrr79IlSpVCJvNZtw0CoDJm8nAwFAhYObMGBgYKgSMmDEwMFQIGDFjYGCoEDBixsDAUCFgxIyBgaFCwIgZAwNDhYARMwYGhgoBI2YMDAwVAkbMGBgYKgSMmDEwMFQIGDFjYGCoEDBixsDAUCH4PwIbEwivOFkEAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 672.5x280 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure 2f - Runtime scaling with total cell count\n",
+    "df_sub = df_scaling.copy()\n",
+    "df_sub = df_sub[df_sub['sim_total_cells'].isin([10000, 20000, 40000, 60000, 80000, 100000])]\n",
+    "df_sub['total_time_min'] = df_sub['total_time'] / 60\n",
+    "g = sns.lmplot(df_sub, x='sim_total_cells', y='total_time_min', hue='model_config_clean', markers='o', palette=palette, \n",
+    "                height=2.8, aspect=1.3,\n",
+    "                line_kws={'linestyle': '--',},\n",
+    "                scatter_kws={'facecolors': 'none', 'edgecolors': None, 's': 20},\n",
+    "        )\n",
+    "sns.move_legend(g, \"upper right\", bbox_to_anchor=(0.5, 1), title='', fontsize='x-small', handletextpad=-0.3)\n",
+    "\n",
+    "sns.despine()\n",
+    "plt.text(x=16000, y=1.5, s=fr'${fit_base.slope*1000:.0f}ms \\cdot N + {fit_base.intercept:.0f}s\\ (R^{2}={fit_base.rvalue:.1f})$', fontsize=10, rotation=27, color=palette['DextraDemixer(+clone)'])\n",
+    "plt.text(x=14000, y=7.9, s=fr'${fit_neg.slope*1000:.0f}ms \\cdot N + {fit_neg.intercept:.0f}s\\ (R^{2}={fit_neg.rvalue:.1f})$', fontsize=10, rotation=40, color=palette['DextraDemixer+neg.(+clone)'])\n",
+    "\n",
+    "plt.xlabel('Total cell count')\n",
+    "plt.ylabel('Runtime [min]')\n",
+    "plt.xticks([20000, 40000, 60000, 80000, 100000], labels=['20k', '40k', '60k', '80k', '100k'])\n",
+    "\n",
+    "style_handles = [\n",
+    "    Line2D([0], [0], marker='o', linestyle='None', markerfacecolor='none', markeredgecolor='black', markersize=5, label='Data points'),\n",
+    "    Line2D([0], [0], linestyle='--', color='black', linewidth=1, label='OLS fit'),\n",
+    "]\n",
+    "g.ax.legend(handles=style_handles, loc=(0.5, 0.), fontsize='x-small', frameon=False)\n",
+    "\n",
+    "plt.savefig(f'{FIGURE_PATH}/Fig2f_scaling_time.pdf')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5fe5c20",
+   "metadata": {},
+   "source": [
+    "#### Memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6a640d96",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_4032627/1370476265.py:1: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  fits = df_scaling.groupby(\"model_config_clean\").apply(lambda g: linregress(g[\"sim_total_cells\"], g[\"peak_mem_resource\"]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "fits = df_scaling.groupby(\"model_config_clean\").apply(lambda g: linregress(g[\"sim_total_cells\"], g[\"peak_mem_resource\"]))\n",
+    "fit_base, fit_neg = fits['DextraDemixer(+clone)'], fits['DextraDemixer+neg.(+clone)']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fdb94a3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 644.5x280 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure 2g - Peak memory scaling with total cell count\n",
+    "df_sub = df_scaling.copy()\n",
+    "df_sub = df_sub[df_sub['sim_total_cells'].isin([10000, 20000, 40000, 60000, 80000, 100000])]\n",
+    "df_sub['peak_mem_resource'] = df_sub['peak_mem_resource'] / 1024\n",
+    "g = sns.lmplot(df_sub, x='sim_total_cells', y='peak_mem_resource', hue='model_config_clean', markers='o', height=2.8, aspect=1.2, palette=palette,\n",
+    "                line_kws={'linestyle': '--',}, scatter_kws={'facecolors': 'none', 'edgecolors': None, 's': 20},)\n",
+    "sns.move_legend(g, \"upper right\", bbox_to_anchor=(0.5, 1), title='', fontsize='x-small', handletextpad=-0.3)\n",
+    "\n",
+    "plt.text(x=16000, y=1.52, s=fr'${fit_base.slope*1000:.0f}kB \\cdot N + {fit_base.intercept/1000:.2f}GB\\ (R^{2}={fit_base.rvalue:.3f})$', fontsize=10, rotation=29, color=palette['DextraDemixer(+clone)'])\n",
+    "plt.text(x=8000, y=2.5, s=fr'${fit_neg.slope*1000:.0f}kB \\cdot N + {fit_neg.intercept/1000:.2f}GB\\ (R^{2}={fit_neg.rvalue:.1f})$', fontsize=10, rotation=38, color=palette['DextraDemixer+neg.(+clone)'])\n",
+    "plt.xlabel('Total cell count')\n",
+    "plt.ylabel('Peak memory [GB]')\n",
+    "plt.xticks([20000, 40000, 60000, 80000, 100000], labels=['20k', '40k', '60k', '80k', '100k'])\n",
+    "plt.ylim(1.5, 7)\n",
+    "\n",
+    "style_handles = [\n",
+    "    Line2D([0], [0], marker='o', linestyle='None', markerfacecolor='none', markeredgecolor='black', markersize=5, label='Data points'),\n",
+    "    Line2D([0], [0], linestyle='--', color='black', linewidth=1, label='OLS fit'),\n",
+    "]\n",
+    "g.ax.legend(handles=style_handles, loc=(0.5, 0.), fontsize='x-small', frameon=False)\n",
+    "plt.savefig(f'{FIGURE_PATH}/Fig2g_scaling_mem.pdf')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a8da2ed",
+   "metadata": {},
+   "source": [
+    "# Clone dropout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "0da1fe29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_dropout = aggregate_csv(rerun=RERUN, experiment_path='benchmarks/dropout', output_path=os.path.join(RESULTS_PATH, 'results_dropout.csv'), paths=['csv'])                                                              "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f59a0418",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 280x280 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure 2e - Sensitivity to binder dropout ratio\n",
+    "df_sub = df_dropout[df_dropout['threshold'] == 0.5].copy()\n",
+    "\n",
+    "plt.figure(figsize=(2.8, 2.8))\n",
+    "ax = sns.lineplot(data=df_sub, x='sim_p_binding_outlier', y='f1', hue='model_config', legend=False,            \n",
+    "                  marker='.', markerfacecolor='none', markeredgecolor=None, palette=palette)\n",
+    "plt.xticks([0, 0.25, 0.5, 0.75, 1.0])\n",
+    "plt.axvline(0.65, ls=':', c='grey')\n",
+    "plt.text(0.66, 0.6, '0.65', fontsize='medium', color='grey', rotation=90)\n",
+    "plt.xlabel('Binder dropout ratio')\n",
+    "plt.ylabel('F1')\n",
+    "sns.despine()\n",
+    "\n",
+    "plt.savefig(f'{FIGURE_PATH}/Fig2e_dropout_sensitivity.pdf')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27d752ea",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/synthetic_benchmark/SuppFig2_Estimate_sim_params.ipynb b/experiments/synthetic_benchmark/SuppFig2_Estimate_sim_params.ipynb
new file mode 100644
index 0000000..ea93598
--- /dev/null
+++ b/experiments/synthetic_benchmark/SuppFig2_Estimate_sim_params.ipynb
@@ -0,0 +1,1596 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dc225074",
+   "metadata": {},
+   "source": [
+    "# Manual data download\n",
+    "Download the following files from the links below: \n",
+    "- VDJ TCR - Filtered contig annotations (CSV), experiment name will end with \"filtered_contig_annotations.csv\"\n",
+    "- Gene Expression - Feature / cell matrix HDF5 (per-sample), experiment name will end with \"filtered_feature_bc_matrix.h5\"\n",
+    "\n",
+    "https://www.10xgenomics.com/datasets/10k-human-a0201-pbmcs-with-cmv-flu-and-sars-cov2-spike-in-beam-t-2-standard\n",
+    "\n",
+    "https://www.10xgenomics.com/datasets/5k-human-a0201-b0702-pbmcs-beam-t-2-standard\n",
+    "\n",
+    "https://www.10xgenomics.com/datasets/10k-human-a2402-pbmcs-with-ebv-and-cmv-spike-in-beam-t-2-standard\n",
+    "\n",
+    "https://www.10xgenomics.com/datasets/2k-mouse-h2kb-ot-1-splenocytes-beam-t-2-standard\n",
+    "\n",
+    "Put the files into `../../data/BEAM-T/` which is the `BASE_PATH`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "38a88b0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Changed directory to: /lustre/groups/imm01/workspace/yang/DextraDemixer/experiments/synthetic_benchmark\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Change cwd to the notebook's directory for VS Code \n",
+    "try:\n",
+    "    notebook_path = globals()['__vsc_ipynb_file__']\n",
+    "    notebook_dir = os.path.dirname(notebook_path)\n",
+    "    os.chdir(notebook_dir)\n",
+    "    print(f\"Changed directory to: {notebook_dir}\")\n",
+    "except:\n",
+    "    print(\"Could not find notebook path. Running from:\", os.getcwd())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a820175b-0368-4969-9eaf-8066fb5f142a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T15:46:33.412581Z",
+     "start_time": "2025-08-14T15:46:33.370004Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/dextrademixer/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "/opt/conda/envs/dextrademixer/lib/python3.13/site-packages/muon/_core/preproc.py:31: FutureWarning: `__version__` is deprecated, use `importlib.metadata.version('scanpy')` instead\n",
+      "  if Version(scanpy.__version__) < Version(\"1.10\"):\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import muon as mu\n",
+    "import scirpy as ir\n",
+    "import scanpy as sc\n",
+    "import numpyro.distributions as npd\n",
+    "import statsmodels.api as sm\n",
+    "import statsmodels.formula.api as smf\n",
+    "\n",
+    "from scipy import stats\n",
+    "from scipy.stats import truncnorm\n",
+    "\n",
+    "warnings.catch_warnings()\n",
+    "warnings.simplefilter(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c96e60db-9425-483a-9222-f14cdef98c17",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T12:42:11.737315Z",
+     "start_time": "2025-08-14T12:42:11.707443Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "BASE_PATH = '../../data/BEAM-T'\n",
+    "QUANTILE = 0.999\n",
+    "EXPERIMENTS = ['10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein', '5k_BEAM-T_Human_A0201_B0702_PBMC', '10k_BEAM-T_Human_A2402_EBV_CMV_spikein', '2k_BEAM-T_Mouse_H2Kb_OT-1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "afd46a09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_mapper = {'10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein': '10k Human A0201', \n",
+    "                  '5k_BEAM-T_Human_A0201_B0702_PBMC': '5k Human A0201 B0702', \n",
+    "                  '10k_BEAM-T_Human_A2402_EBV_CMV_spikein': '10k Human A2402', \n",
+    "                  '2k_BEAM-T_Mouse_H2Kb_OT-1': '2k Mouse H2Kb'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "81f76a0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Figure settings\n",
+    "sns.reset_defaults()\n",
+    "\n",
+    "global_settings = {\n",
+    "'font.size': 12, 'axes.titlesize': 'large', 'axes.labelsize': 'medium', 'xtick.labelsize': 'medium', 'ytick.labelsize': 'medium', 'legend.fontsize': 'medium', 'figure.titlesize': 'large',\n",
+    "    'figure.figsize': (3, 2.5), 'figure.dpi': 100, 'savefig.dpi': 300, 'savefig.bbox': 'tight', 'savefig.transparent': True,\n",
+    "}\n",
+    "plt.rcParams.update(global_settings)\n",
+    "os.makedirs('figures', exist_ok=True)\n",
+    "palette = {'signal': \"#003eda\", 'noise': \"#00C3FF\", 'negative control': \"#9e9e9e\", 'fit': \"#FF0000\",}\n",
+    "\n",
+    "plt.rcParams.update({ 'axes.spines.top': False, 'axes.spines.right': False, })"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6c468fc-85ba-4d58-b8b5-81b08d3eb1d7",
+   "metadata": {},
+   "source": [
+    "## Load and preprocess data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8e3a13b6796b87b3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T12:42:12.126649Z",
+     "start_time": "2025-08-14T12:42:11.831021Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create preprocessed mudata\n",
+    "\n",
+    "for experiment in EXPERIMENTS: \n",
+    "    if not os.path.exists(f'{BASE_PATH}/{experiment}_preprocessed.h5mu'):\n",
+    "        adata_tcr = ir.io.read_10x_vdj(f'{BASE_PATH}/{experiment}_5pv2_{experiment}_5pv2_vdj_t_filtered_contig_annotations.csv')\n",
+    "        adata = sc.read_10x_h5(f'{BASE_PATH}/{experiment}_5pv2_{experiment}_5pv2_count_sample_filtered_feature_bc_matrix.h5', gex_only=False)\n",
+    "        adata.var_names_make_unique()\n",
+    "        mdata = mu.MuData({\"gex\": adata, \"airr\": adata_tcr})\n",
+    "        ir.pp.index_chains(mdata)\n",
+    "        ir.tl.chain_qc(mdata)\n",
+    "\n",
+    "        # keep T cells only, reduce gex to contain antigen barcodes only\n",
+    "        mdata = mdata[mdata.obs[\"airr:receptor_type\"] == \"TCR\"]\n",
+    "        mdata = mdata[:, mdata.var[\"gex:feature_types\"] == \"Antigen Capture\"]\n",
+    "    \n",
+    "        # minimal pMHC QC filtering\n",
+    "        sc.pp.filter_cells(mdata[\"gex\"], min_genes=1)\n",
+    "        sc.pp.filter_genes(mdata[\"gex\"], min_cells=10)\n",
+    "    \n",
+    "        mdata.update()\n",
+    "    \n",
+    "        mu.pp.filter_obs(mdata, \"airr:chain_pairing\", lambda x: ~np.isin(x, [\"orphan VDJ\", \"orphan VJ\"]))\n",
+    "        mu.pp.intersect_obs(mdata)\n",
+    "    \n",
+    "        ir.pp.ir_dist(mdata)  # Adds clone_id\n",
+    "        ir.tl.define_clonotypes(mdata, receptor_arms=\"all\", dual_ir=\"primary_only\")\n",
+    "    \n",
+    "        mdata.write(f'{BASE_PATH}/{experiment}_preprocessed.h5mu', compression='gzip')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3ee6c4b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# SuppFig2a - Negative control and noise component overlap\n",
+    "\n",
+    "params = []\n",
+    "nrows = 2\n",
+    "ncols = 4\n",
+    "fig = plt.figure(figsize=(3 * ncols, 2 * nrows))\n",
+    "i = 1\n",
+    "\n",
+    "for experiment in EXPERIMENTS:\n",
+    "    mdata = mu.read(f'{BASE_PATH}/{experiment}_preprocessed.h5mu')\n",
+    "    genes = mdata.var['gex:gene_ids'].unique()\n",
+    "\n",
+    "    for gene in genes:\n",
+    "        if 'negative' in gene:\n",
+    "            continue\n",
+    "        # Get negative control key\n",
+    "        if experiment == '5k_BEAM-T_Human_A0201_B0702_PBMC':  # Has two negative controls\n",
+    "            mhc = gene.split('_')[-1]\n",
+    "            neg_ctrl_key = [gene for gene in mdata.var['gex:gene_ids'].unique() if 'negative' in gene and mhc in gene][0]  # Get the negative control for the same MHC\n",
+    "        else:\n",
+    "            neg_ctrl_key = [gene for gene in mdata.var['gex:gene_ids'].unique() if 'negative' in gene][0]\n",
+    "        \n",
+    "        # Fit NB on negative control with outlier filtering based on quantile threshold\n",
+    "        x_neg = mdata['gex'][:, neg_ctrl_key].X.toarray().reshape(-1, )\n",
+    "        thr = np.quantile(x_neg, QUANTILE) if experiment != '2k_BEAM-T_Mouse_H2Kb_OT-1' else np.quantile(x_neg, 0.99 )  # 2k only 1339 samples\n",
+    "        x_neg_in_threshold = x_neg[x_neg <= thr]\n",
+    "        x = mdata['gex'][:, gene].X.toarray().reshape(-1, )\n",
+    "\n",
+    "        if (x[x>thr].shape[0] < 20 or x[x<=thr].shape[0] < 20) and not 'negative' in gene:\n",
+    "            continue\n",
+    "        x_in_threshold = x[x <= thr]\n",
+    "        nbfit = smf.negativebinomial(\"nbdata ~ 1\", data=pd.DataFrame({\"nbdata\": x_in_threshold})).fit(disp=False)\n",
+    "        mean = np.exp(nbfit.params.iloc[0])\n",
+    "        concentration = 1 / nbfit.params.iloc[1]\n",
+    "\n",
+    "        # Fitted distribution for visualization\n",
+    "        x_range = np.arange(x_in_threshold.min(), x_in_threshold.max())\n",
+    "        prob = np.exp(npd.NegativeBinomial2(mean, concentration).log_prob(x_range))\n",
+    "        prob = prob / prob.sum()\n",
+    "\n",
+    "        plt.subplot(nrows, ncols, i)\n",
+    "        ax = sns.histplot(x_neg_in_threshold, discrete=False, stat=\"density\", bins=50, element='step', alpha=0.1, linewidth=1.5,\n",
+    "                          label='Observed (negative control)', color=palette['negative control'])\n",
+    "        ax = sns.histplot(x_in_threshold, discrete=False, stat=\"density\", bins=50, element='step', alpha=0.1, linewidth=1.5,\n",
+    "                          label='Observed (noise component)', color=palette['noise'])\n",
+    "        \n",
+    "        plt.title(f'{dataset_mapper[experiment]}\\n{gene.replace(\"negative_control\", \"neg. control\").replace(\"_\", \" \")}', y=0.75, fontsize='small')\n",
+    "        plt.ylim(0, ax.get_ylim()[1]*1.1)\n",
+    "        plt.xlabel('')\n",
+    "        plt.ylabel('')\n",
+    "        sns.despine()\n",
+    "        if i == 1:\n",
+    "            ax = plt.gca()\n",
+    "            handles, labels = ax.get_legend_handles_labels()\n",
+    "        i += 1\n",
+    "\n",
+    "fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.), ncol=2, frameon=False, fontsize='small')\n",
+    "fig.supxlabel('UMI count', fontsize='medium', y=-0.05)\n",
+    "fig.supylabel('Density', fontsize='medium', x=0.05)\n",
+    "os.makedirs('../../figures/SuppFig2', exist_ok=True)\n",
+    "plt.savefig('../../figures/SuppFig2/SuppFig2a_noise_negative_control_comparison.pdf')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cb2ab9b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x200 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# SuppFig2b - Fitted distribution of negative control\n",
+    "\n",
+    "params = []\n",
+    "nrows = 1\n",
+    "ncols = 5\n",
+    "fig = plt.figure(figsize=(3 * ncols, 2 * nrows))\n",
+    "i = 1\n",
+    "for experiment in EXPERIMENTS:\n",
+    "    mdata = mu.read(f'{BASE_PATH}/{experiment}_preprocessed.h5mu')\n",
+    "    genes = mdata.var['gex:gene_ids'].unique()\n",
+    "    for gene in [g for g in genes if 'negative' in g]:\n",
+    "        # Get negative control key\n",
+    "        if experiment == '5k_BEAM-T_Human_A0201_B0702_PBMC':  # Has two negative controls\n",
+    "            mhc = gene.split('_')[-1]\n",
+    "            neg_ctrl_key = [gene for gene in mdata.var['gex:gene_ids'].unique() if 'negative' in gene and mhc in gene][0]  # Get the negative control for the same MHC\n",
+    "        else:\n",
+    "            neg_ctrl_key = [gene for gene in mdata.var['gex:gene_ids'].unique() if 'negative' in gene][0]\n",
+    "        \n",
+    "        # Fit NB on negative control with outlier filtering based on quantile threshold\n",
+    "        x_neg = mdata['gex'][:, neg_ctrl_key].X.toarray().reshape(-1, )\n",
+    "        thr = np.quantile(x_neg, QUANTILE) if experiment != '2k_BEAM-T_Mouse_H2Kb_OT-1' else np.quantile(x_neg, 0.99 )  # 2k only 1339 samples\n",
+    "        x_in_threshold = x_neg[x_neg <= thr]\n",
+    "        nbfit = smf.negativebinomial(\"nbdata ~ 1\", data=pd.DataFrame({\"nbdata\": x_in_threshold})).fit(disp=False)\n",
+    "        mean = np.exp(nbfit.params.iloc[0])\n",
+    "        concentration = 1 / nbfit.params.iloc[1]\n",
+    "\n",
+    "        # Fitted distribution for visualization\n",
+    "        x_range = np.arange(x_in_threshold.min(), x_in_threshold.max())\n",
+    "        prob = np.exp(npd.NegativeBinomial2(mean, concentration).log_prob(x_range))\n",
+    "        prob = prob / prob.sum()\n",
+    "\n",
+    "        plt.subplot(1, 5, i)\n",
+    "        plt.plot(x_range, prob, color=palette['fit'], ls='--', label='Fitted NB')\n",
+    "        ax = sns.histplot(x_in_threshold, discrete=False, stat=\"density\", bins=50, label='Observed (negative control)', color=palette['negative control'], element='step', alpha=0.2, linewidth=1.5)\n",
+    "        plt.title(f'{dataset_mapper[experiment]}\\n{gene.replace(\"negative_control\", \"neg. control\").replace(\"_\", \" \")}', y=0.8, fontsize='small')\n",
+    "        plt.xlabel('')\n",
+    "        plt.ylabel('')\n",
+    "        sns.despine()\n",
+    "        if i == 1:\n",
+    "            ax = plt.gca()\n",
+    "            handles, labels = ax.get_legend_handles_labels()\n",
+    "        i += 1\n",
+    "\n",
+    "fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=2, frameon=False, fontsize='small')\n",
+    "fig.supxlabel('UMI count', fontsize='medium', y=-0.2)\n",
+    "fig.supylabel('Density', fontsize='medium', x=0.07)\n",
+    "os.makedirs('../../figures/SuppFig2', exist_ok=True)\n",
+    "plt.savefig('../../figures/SuppFig2/SuppFig2b_negative_control_fit.pdf')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "50d16c37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 14 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# SuppFig2c - Fitted distributions of pMHC signal and noise components\n",
+    "nrows = 4\n",
+    "ncols = 4\n",
+    "\n",
+    "fig = plt.figure(figsize=(3 * ncols, 2. * nrows))\n",
+    "gene_plot_idx = 0 \n",
+    "\n",
+    "# Initialize lists to track unique legend items\n",
+    "handles, labels = [], []\n",
+    "params = []\n",
+    "\n",
+    "for experiment in EXPERIMENTS:\n",
+    "    mdata = mu.read(f'{BASE_PATH}/{experiment}_preprocessed.h5mu')\n",
+    "    genes = mdata.var['gex:gene_ids'].unique()\n",
+    "\n",
+    "    for gene in genes:\n",
+    "        if 'negative' in gene:\n",
+    "            continue\n",
+    "            \n",
+    "        if experiment == '5k_BEAM-T_Human_A0201_B0702_PBMC': \n",
+    "            mhc = gene.split('_')[-1]\n",
+    "            neg_ctrl_key = [g for g in mdata.var['gex:gene_ids'].unique() if 'negative' in g and mhc in g][0] \n",
+    "        else:\n",
+    "            neg_ctrl_key = [g for g in mdata.var['gex:gene_ids'].unique() if 'negative' in g][0]\n",
+    "        \n",
+    "        x_neg = mdata['gex'][:, neg_ctrl_key].X.toarray().reshape(-1, )\n",
+    "        thr = np.quantile(x_neg, QUANTILE) if experiment != '2k_BEAM-T_Mouse_H2Kb_OT-1' else np.quantile(x_neg, 0.99 )  # 2k only 1339 samples\n",
+    "        x_neg_filtered = x_neg[x_neg <= thr]\n",
+    "        nbfit_neg = smf.negativebinomial(\"nbdata ~ 1\", data=pd.DataFrame({\"nbdata\": x_neg_filtered})).fit(disp=False)\n",
+    "        mean_neg = np.exp(nbfit_neg.params.iloc[0])\n",
+    "        concentration_neg = 1 / nbfit_neg.params.iloc[1]\n",
+    "\n",
+    "        x = mdata['gex'][:, gene].X.toarray().reshape(-1, )\n",
+    "        \n",
+    "        valid_noise = not ((x[x<=thr].shape[0] < 20) and not 'negative' in gene)\n",
+    "        valid_signal = not ((x[x>thr].shape[0] < 20) and not 'negative' in gene)\n",
+    "        if not valid_noise or not valid_signal:\n",
+    "            continue\n",
+    "\n",
+    "        row_base = (gene_plot_idx // ncols) * 2\n",
+    "        col = gene_plot_idx % ncols\n",
+    "\n",
+    "        for direction in ['-', '+']:\n",
+    "            x_in_threshold = x[x > thr] if direction == '+' else x[x <= thr]\n",
+    "            nbfit = smf.negativebinomial(\"nbdata ~ 1\", data=pd.DataFrame({\"nbdata\": x_in_threshold})).fit(disp=False)\n",
+    "            mean = np.exp(nbfit.params.iloc[0])\n",
+    "            concentration = 1 / nbfit.params.iloc[1]\n",
+    "            var_nb = mean + mean ** 2 / concentration\n",
+    "            \n",
+    "            x_range = np.arange(x_in_threshold.min(), x_in_threshold.max())\n",
+    "            prob = np.exp(npd.NegativeBinomial2(mean, concentration).log_prob(x_range))\n",
+    "            prob = prob / prob.sum()\n",
+    "\n",
+    "            row_offset = 0 if direction == '-' else 1\n",
+    "            plot_index = (row_base + row_offset) * ncols + col + 1\n",
+    "            \n",
+    "            ax = fig.add_subplot(nrows, ncols, plot_index)\n",
+    "            color_idx = 3 if direction == '-' else 1\n",
+    "            \n",
+    "            obs_label = 'Observed (Noise)' if direction == '-' else 'Observed (Signal)'\n",
+    "            \n",
+    "            ax.plot(x_range, prob, color=palette['fit'], ls='--', label='Fitted NB')\n",
+    "            sns.histplot(x_in_threshold, discrete=False, stat=\"density\", bins=50, label=obs_label, color=palette['noise'] if direction == '-' else palette['signal'], ax=ax, alpha=0.2, element='step', linewidth=1.5)\n",
+    "                                    \n",
+    "            ax.set_title(f'{dataset_mapper[experiment]}\\n{gene}', y=0.8, fontsize='small')\n",
+    "            ax.set_xlabel('')\n",
+    "            ax.set_ylabel('')\n",
+    "            ax.ticklabel_format(axis='y', style='sci', scilimits=(0,0), useMathText=True)\n",
+    "\n",
+    "            offset_text = ax.yaxis.get_offset_text()\n",
+    "            offset_text.set_fontsize('small')\n",
+    "            offset_text.set_position((-0.12, 0.5))\n",
+    "            sns.despine(ax=ax)\n",
+    "\n",
+    "            # Extract unique legend handles\n",
+    "            for h, l in zip(*ax.get_legend_handles_labels()):\n",
+    "                if l not in labels:\n",
+    "                    handles.append(h)\n",
+    "                    labels.append(l)\n",
+    "            \n",
+    "        gene_plot_idx += 1 \n",
+    "\n",
+    "if handles:\n",
+    "    fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 0.95), ncol=3, frameon=False, fontsize=10)\n",
+    "\n",
+    "fig.supxlabel('UMI count', fontsize='medium', y=0.05)\n",
+    "fig.supylabel('Density', fontsize='medium', x=0.07)\n",
+    "fig.subplots_adjust(hspace=0.35)\n",
+    "plt.savefig('../../figures/SuppFig2/SuppFig2c_signal_noise_fit.pdf', bbox_inches='tight')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fe4911be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>experiment</th>\n",
+       "      <th>gene</th>\n",
+       "      <th>threshold</th>\n",
+       "      <th>mean_neg</th>\n",
+       "      <th>concentration_neg</th>\n",
+       "      <th>var_nb_neg</th>\n",
+       "      <th>overdisp_nb_neg</th>\n",
+       "      <th>mean-</th>\n",
+       "      <th>concentration-</th>\n",
+       "      <th>var_nb-</th>\n",
+       "      <th>...</th>\n",
+       "      <th>N+</th>\n",
+       "      <th>mean_ratio</th>\n",
+       "      <th>concentration_ratio</th>\n",
+       "      <th>var_nb_ratio</th>\n",
+       "      <th>overdisp_nb_ratio</th>\n",
+       "      <th>N_ratio</th>\n",
+       "      <th>binder_ratio</th>\n",
+       "      <th>noise_to_neg_mean_ratio</th>\n",
+       "      <th>neg_inc</th>\n",
+       "      <th>noise_to_neg_overdisp_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>CMV</td>\n",
+       "      <td>197.938477</td>\n",
+       "      <td>3.867945</td>\n",
+       "      <td>0.489523</td>\n",
+       "      <td>34.430371</td>\n",
+       "      <td>8.901464</td>\n",
+       "      <td>25.003214</td>\n",
+       "      <td>2.897500</td>\n",
+       "      <td>240.761868</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3773</td>\n",
+       "      <td>49.212545</td>\n",
+       "      <td>1.354419</td>\n",
+       "      <td>1607.540828</td>\n",
+       "      <td>32.665265</td>\n",
+       "      <td>2.020889</td>\n",
+       "      <td>0.668972</td>\n",
+       "      <td>6.464212</td>\n",
+       "      <td>6.464212</td>\n",
+       "      <td>1.081759</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>EBV_BMLF-1_GLCT</td>\n",
+       "      <td>197.938477</td>\n",
+       "      <td>3.867945</td>\n",
+       "      <td>0.489523</td>\n",
+       "      <td>34.430371</td>\n",
+       "      <td>8.901464</td>\n",
+       "      <td>1.454062</td>\n",
+       "      <td>0.231539</td>\n",
+       "      <td>10.585553</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>172.963772</td>\n",
+       "      <td>107.313660</td>\n",
+       "      <td>264.241278</td>\n",
+       "      <td>1.527726</td>\n",
+       "      <td>0.000355</td>\n",
+       "      <td>0.000355</td>\n",
+       "      <td>0.375926</td>\n",
+       "      <td>0.375926</td>\n",
+       "      <td>0.817842</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>Flu</td>\n",
+       "      <td>197.938477</td>\n",
+       "      <td>3.867945</td>\n",
+       "      <td>0.489523</td>\n",
+       "      <td>34.430371</td>\n",
+       "      <td>8.901464</td>\n",
+       "      <td>11.730760</td>\n",
+       "      <td>1.432491</td>\n",
+       "      <td>107.794731</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1391</td>\n",
+       "      <td>78.636249</td>\n",
+       "      <td>2.942561</td>\n",
+       "      <td>1881.321785</td>\n",
+       "      <td>23.924358</td>\n",
+       "      <td>0.327371</td>\n",
+       "      <td>0.246631</td>\n",
+       "      <td>3.032815</td>\n",
+       "      <td>3.032815</td>\n",
+       "      <td>1.032309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>EBV_BRLF1_YVLD</td>\n",
+       "      <td>197.938477</td>\n",
+       "      <td>3.867945</td>\n",
+       "      <td>0.489523</td>\n",
+       "      <td>34.430371</td>\n",
+       "      <td>8.901464</td>\n",
+       "      <td>2.596270</td>\n",
+       "      <td>0.386810</td>\n",
+       "      <td>20.022444</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10</td>\n",
+       "      <td>312.294178</td>\n",
+       "      <td>7.326876</td>\n",
+       "      <td>11625.436849</td>\n",
+       "      <td>37.225916</td>\n",
+       "      <td>0.001776</td>\n",
+       "      <td>0.001773</td>\n",
+       "      <td>0.671227</td>\n",
+       "      <td>0.671227</td>\n",
+       "      <td>0.866375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>SARS_Cov2</td>\n",
+       "      <td>197.938477</td>\n",
+       "      <td>3.867945</td>\n",
+       "      <td>0.489523</td>\n",
+       "      <td>34.430371</td>\n",
+       "      <td>8.901464</td>\n",
+       "      <td>8.543229</td>\n",
+       "      <td>1.653138</td>\n",
+       "      <td>52.693679</td>\n",
+       "      <td>...</td>\n",
+       "      <td>574</td>\n",
+       "      <td>165.537081</td>\n",
+       "      <td>2.422136</td>\n",
+       "      <td>9505.974155</td>\n",
+       "      <td>57.425044</td>\n",
+       "      <td>0.113304</td>\n",
+       "      <td>0.101773</td>\n",
+       "      <td>2.208726</td>\n",
+       "      <td>2.208726</td>\n",
+       "      <td>0.692907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein</td>\n",
+       "      <td>negative_control</td>\n",
+       "      <td>197.938477</td>\n",
+       "      <td>3.867945</td>\n",
+       "      <td>0.489523</td>\n",
+       "      <td>34.430371</td>\n",
+       "      <td>8.901464</td>\n",
+       "      <td>3.867945</td>\n",
+       "      <td>0.489523</td>\n",
+       "      <td>34.430371</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6</td>\n",
+       "      <td>208.637941</td>\n",
+       "      <td>4.966156</td>\n",
+       "      <td>7804.024882</td>\n",
+       "      <td>37.404630</td>\n",
+       "      <td>0.001065</td>\n",
+       "      <td>0.001064</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
+       "      <td>Flu_A0201</td>\n",
+       "      <td>173.383301</td>\n",
+       "      <td>2.114713</td>\n",
+       "      <td>0.873756</td>\n",
+       "      <td>7.232860</td>\n",
+       "      <td>3.420256</td>\n",
+       "      <td>15.505495</td>\n",
+       "      <td>1.876203</td>\n",
+       "      <td>143.647475</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2227</td>\n",
+       "      <td>125.773976</td>\n",
+       "      <td>2.192459</td>\n",
+       "      <td>6449.982473</td>\n",
+       "      <td>51.282329</td>\n",
+       "      <td>12.236264</td>\n",
+       "      <td>0.924450</td>\n",
+       "      <td>7.332197</td>\n",
+       "      <td>7.332197</td>\n",
+       "      <td>2.708656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
+       "      <td>CMV_B0702</td>\n",
+       "      <td>87.184082</td>\n",
+       "      <td>1.782627</td>\n",
+       "      <td>0.662888</td>\n",
+       "      <td>6.576434</td>\n",
+       "      <td>3.689182</td>\n",
+       "      <td>3.732883</td>\n",
+       "      <td>1.053218</td>\n",
+       "      <td>16.963204</td>\n",
+       "      <td>...</td>\n",
+       "      <td>189</td>\n",
+       "      <td>174.126735</td>\n",
+       "      <td>3.431894</td>\n",
+       "      <td>6928.957342</td>\n",
+       "      <td>39.792610</td>\n",
+       "      <td>0.085135</td>\n",
+       "      <td>0.078456</td>\n",
+       "      <td>2.094035</td>\n",
+       "      <td>2.094035</td>\n",
+       "      <td>1.231781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
+       "      <td>negative_control_A0201</td>\n",
+       "      <td>173.383301</td>\n",
+       "      <td>2.114713</td>\n",
+       "      <td>0.873756</td>\n",
+       "      <td>7.232860</td>\n",
+       "      <td>3.420256</td>\n",
+       "      <td>2.114713</td>\n",
+       "      <td>0.873756</td>\n",
+       "      <td>7.232860</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>299.646619</td>\n",
+       "      <td>1.778455</td>\n",
+       "      <td>35813.138102</td>\n",
+       "      <td>119.517911</td>\n",
+       "      <td>0.001247</td>\n",
+       "      <td>0.001245</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>5k_BEAM-T_Human_A0201_B0702_PBMC</td>\n",
+       "      <td>negative_control_B0702</td>\n",
+       "      <td>87.184082</td>\n",
+       "      <td>1.782627</td>\n",
+       "      <td>0.662888</td>\n",
+       "      <td>6.576434</td>\n",
+       "      <td>3.689182</td>\n",
+       "      <td>1.782627</td>\n",
+       "      <td>0.662888</td>\n",
+       "      <td>6.576434</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>337.142924</td>\n",
+       "      <td>1.605272</td>\n",
+       "      <td>51705.636364</td>\n",
+       "      <td>153.364145</td>\n",
+       "      <td>0.001247</td>\n",
+       "      <td>0.001245</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10k_BEAM-T_Human_A2402_EBV_CMV_spikein</td>\n",
+       "      <td>EBV</td>\n",
+       "      <td>64.444336</td>\n",
+       "      <td>1.801178</td>\n",
+       "      <td>0.549719</td>\n",
+       "      <td>7.702813</td>\n",
+       "      <td>4.276543</td>\n",
+       "      <td>5.349569</td>\n",
+       "      <td>1.177876</td>\n",
+       "      <td>29.645753</td>\n",
+       "      <td>...</td>\n",
+       "      <td>570</td>\n",
+       "      <td>90.126625</td>\n",
+       "      <td>3.805148</td>\n",
+       "      <td>1765.748310</td>\n",
+       "      <td>19.591861</td>\n",
+       "      <td>0.245690</td>\n",
+       "      <td>0.197232</td>\n",
+       "      <td>2.970040</td>\n",
+       "      <td>2.970040</td>\n",
+       "      <td>1.295839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10k_BEAM-T_Human_A2402_EBV_CMV_spikein</td>\n",
+       "      <td>CMV</td>\n",
+       "      <td>64.444336</td>\n",
+       "      <td>1.801178</td>\n",
+       "      <td>0.549719</td>\n",
+       "      <td>7.702813</td>\n",
+       "      <td>4.276543</td>\n",
+       "      <td>3.266996</td>\n",
+       "      <td>0.645879</td>\n",
+       "      <td>19.792167</td>\n",
+       "      <td>...</td>\n",
+       "      <td>257</td>\n",
+       "      <td>424.225101</td>\n",
+       "      <td>4.467340</td>\n",
+       "      <td>33705.405280</td>\n",
+       "      <td>79.451699</td>\n",
+       "      <td>0.097607</td>\n",
+       "      <td>0.088927</td>\n",
+       "      <td>1.813811</td>\n",
+       "      <td>1.813811</td>\n",
+       "      <td>1.416615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10k_BEAM-T_Human_A2402_EBV_CMV_spikein</td>\n",
+       "      <td>negative_control</td>\n",
+       "      <td>64.444336</td>\n",
+       "      <td>1.801178</td>\n",
+       "      <td>0.549719</td>\n",
+       "      <td>7.702813</td>\n",
+       "      <td>4.276543</td>\n",
+       "      <td>1.801178</td>\n",
+       "      <td>0.549719</td>\n",
+       "      <td>7.702813</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>86.610000</td>\n",
+       "      <td>4.352510</td>\n",
+       "      <td>1340.694268</td>\n",
+       "      <td>15.479671</td>\n",
+       "      <td>0.001039</td>\n",
+       "      <td>0.001038</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2k_BEAM-T_Mouse_H2Kb_OT-1</td>\n",
+       "      <td>OT-1</td>\n",
+       "      <td>81.579956</td>\n",
+       "      <td>5.961509</td>\n",
+       "      <td>0.812680</td>\n",
+       "      <td>49.692846</td>\n",
+       "      <td>8.335615</td>\n",
+       "      <td>30.833333</td>\n",
+       "      <td>1.718346</td>\n",
+       "      <td>584.094782</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1333</td>\n",
+       "      <td>41.648426</td>\n",
+       "      <td>2.652040</td>\n",
+       "      <td>621.731210</td>\n",
+       "      <td>14.928084</td>\n",
+       "      <td>222.166667</td>\n",
+       "      <td>0.995519</td>\n",
+       "      <td>5.172068</td>\n",
+       "      <td>5.172068</td>\n",
+       "      <td>2.272612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2k_BEAM-T_Mouse_H2Kb_OT-1</td>\n",
+       "      <td>negative_control</td>\n",
+       "      <td>81.579956</td>\n",
+       "      <td>5.961509</td>\n",
+       "      <td>0.812680</td>\n",
+       "      <td>49.692846</td>\n",
+       "      <td>8.335615</td>\n",
+       "      <td>5.961509</td>\n",
+       "      <td>0.812680</td>\n",
+       "      <td>49.692846</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14</td>\n",
+       "      <td>91.551598</td>\n",
+       "      <td>0.919032</td>\n",
+       "      <td>8037.003192</td>\n",
+       "      <td>87.786597</td>\n",
+       "      <td>0.010566</td>\n",
+       "      <td>0.010456</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>15 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                      experiment                    gene  \\\n",
+       "0   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein                     CMV   \n",
+       "1   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein         EBV_BMLF-1_GLCT   \n",
+       "2   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein                     Flu   \n",
+       "3   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein          EBV_BRLF1_YVLD   \n",
+       "4   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein               SARS_Cov2   \n",
+       "5   10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein        negative_control   \n",
+       "6               5k_BEAM-T_Human_A0201_B0702_PBMC               Flu_A0201   \n",
+       "7               5k_BEAM-T_Human_A0201_B0702_PBMC               CMV_B0702   \n",
+       "8               5k_BEAM-T_Human_A0201_B0702_PBMC  negative_control_A0201   \n",
+       "9               5k_BEAM-T_Human_A0201_B0702_PBMC  negative_control_B0702   \n",
+       "10        10k_BEAM-T_Human_A2402_EBV_CMV_spikein                     EBV   \n",
+       "11        10k_BEAM-T_Human_A2402_EBV_CMV_spikein                     CMV   \n",
+       "12        10k_BEAM-T_Human_A2402_EBV_CMV_spikein        negative_control   \n",
+       "13                     2k_BEAM-T_Mouse_H2Kb_OT-1                    OT-1   \n",
+       "14                     2k_BEAM-T_Mouse_H2Kb_OT-1        negative_control   \n",
+       "\n",
+       "     threshold  mean_neg  concentration_neg  var_nb_neg  overdisp_nb_neg  \\\n",
+       "0   197.938477  3.867945           0.489523   34.430371         8.901464   \n",
+       "1   197.938477  3.867945           0.489523   34.430371         8.901464   \n",
+       "2   197.938477  3.867945           0.489523   34.430371         8.901464   \n",
+       "3   197.938477  3.867945           0.489523   34.430371         8.901464   \n",
+       "4   197.938477  3.867945           0.489523   34.430371         8.901464   \n",
+       "5   197.938477  3.867945           0.489523   34.430371         8.901464   \n",
+       "6   173.383301  2.114713           0.873756    7.232860         3.420256   \n",
+       "7    87.184082  1.782627           0.662888    6.576434         3.689182   \n",
+       "8   173.383301  2.114713           0.873756    7.232860         3.420256   \n",
+       "9    87.184082  1.782627           0.662888    6.576434         3.689182   \n",
+       "10   64.444336  1.801178           0.549719    7.702813         4.276543   \n",
+       "11   64.444336  1.801178           0.549719    7.702813         4.276543   \n",
+       "12   64.444336  1.801178           0.549719    7.702813         4.276543   \n",
+       "13   81.579956  5.961509           0.812680   49.692846         8.335615   \n",
+       "14   81.579956  5.961509           0.812680   49.692846         8.335615   \n",
+       "\n",
+       "        mean-  concentration-     var_nb-  ...    N+  mean_ratio  \\\n",
+       "0   25.003214        2.897500  240.761868  ...  3773   49.212545   \n",
+       "1    1.454062        0.231539   10.585553  ...     2  172.963772   \n",
+       "2   11.730760        1.432491  107.794731  ...  1391   78.636249   \n",
+       "3    2.596270        0.386810   20.022444  ...    10  312.294178   \n",
+       "4    8.543229        1.653138   52.693679  ...   574  165.537081   \n",
+       "5    3.867945        0.489523   34.430371  ...     6  208.637941   \n",
+       "6   15.505495        1.876203  143.647475  ...  2227  125.773976   \n",
+       "7    3.732883        1.053218   16.963204  ...   189  174.126735   \n",
+       "8    2.114713        0.873756    7.232860  ...     3  299.646619   \n",
+       "9    1.782627        0.662888    6.576434  ...     3  337.142924   \n",
+       "10   5.349569        1.177876   29.645753  ...   570   90.126625   \n",
+       "11   3.266996        0.645879   19.792167  ...   257  424.225101   \n",
+       "12   1.801178        0.549719    7.702813  ...     3   86.610000   \n",
+       "13  30.833333        1.718346  584.094782  ...  1333   41.648426   \n",
+       "14   5.961509        0.812680   49.692846  ...    14   91.551598   \n",
+       "\n",
+       "    concentration_ratio  var_nb_ratio  overdisp_nb_ratio     N_ratio  \\\n",
+       "0              1.354419   1607.540828          32.665265    2.020889   \n",
+       "1            107.313660    264.241278           1.527726    0.000355   \n",
+       "2              2.942561   1881.321785          23.924358    0.327371   \n",
+       "3              7.326876  11625.436849          37.225916    0.001776   \n",
+       "4              2.422136   9505.974155          57.425044    0.113304   \n",
+       "5              4.966156   7804.024882          37.404630    0.001065   \n",
+       "6              2.192459   6449.982473          51.282329   12.236264   \n",
+       "7              3.431894   6928.957342          39.792610    0.085135   \n",
+       "8              1.778455  35813.138102         119.517911    0.001247   \n",
+       "9              1.605272  51705.636364         153.364145    0.001247   \n",
+       "10             3.805148   1765.748310          19.591861    0.245690   \n",
+       "11             4.467340  33705.405280          79.451699    0.097607   \n",
+       "12             4.352510   1340.694268          15.479671    0.001039   \n",
+       "13             2.652040    621.731210          14.928084  222.166667   \n",
+       "14             0.919032   8037.003192          87.786597    0.010566   \n",
+       "\n",
+       "    binder_ratio  noise_to_neg_mean_ratio   neg_inc  \\\n",
+       "0       0.668972                 6.464212  6.464212   \n",
+       "1       0.000355                 0.375926  0.375926   \n",
+       "2       0.246631                 3.032815  3.032815   \n",
+       "3       0.001773                 0.671227  0.671227   \n",
+       "4       0.101773                 2.208726  2.208726   \n",
+       "5       0.001064                 1.000000  1.000000   \n",
+       "6       0.924450                 7.332197  7.332197   \n",
+       "7       0.078456                 2.094035  2.094035   \n",
+       "8       0.001245                 1.000000  1.000000   \n",
+       "9       0.001245                 1.000000  1.000000   \n",
+       "10      0.197232                 2.970040  2.970040   \n",
+       "11      0.088927                 1.813811  1.813811   \n",
+       "12      0.001038                 1.000000  1.000000   \n",
+       "13      0.995519                 5.172068  5.172068   \n",
+       "14      0.010456                 1.000000  1.000000   \n",
+       "\n",
+       "    noise_to_neg_overdisp_ratio  \n",
+       "0                      1.081759  \n",
+       "1                      0.817842  \n",
+       "2                      1.032309  \n",
+       "3                      0.866375  \n",
+       "4                      0.692907  \n",
+       "5                      1.000000  \n",
+       "6                      2.708656  \n",
+       "7                      1.231781  \n",
+       "8                      1.000000  \n",
+       "9                      1.000000  \n",
+       "10                     1.295839  \n",
+       "11                     1.416615  \n",
+       "12                     1.000000  \n",
+       "13                     2.272612  \n",
+       "14                     1.000000  \n",
+       "\n",
+       "[15 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Determine fitted distributions parameters\n",
+    "params = []\n",
+    "for experiment in EXPERIMENTS:\n",
+    "    mdata = mu.read(f'{BASE_PATH}/{experiment}_preprocessed.h5mu')\n",
+    "    genes = mdata.var['gex:gene_ids'].unique()\n",
+    "\n",
+    "    for gene in genes:\n",
+    "        # Get negative control key\n",
+    "        if experiment == '5k_BEAM-T_Human_A0201_B0702_PBMC':  # Has two negative controls\n",
+    "            mhc = gene.split('_')[-1]\n",
+    "            neg_ctrl_key = [gene for gene in mdata.var['gex:gene_ids'].unique() if 'negative' in gene and mhc in gene][0]  # Get the negative control for the same MHC\n",
+    "        else:\n",
+    "            neg_ctrl_key = [gene for gene in mdata.var['gex:gene_ids'].unique() if 'negative' in gene][0]\n",
+    "        \n",
+    "        # Fit NB on negative control with outlier filtering based on quantile threshold\n",
+    "        x_neg = mdata['gex'][:, neg_ctrl_key].X.toarray().reshape(-1, )\n",
+    "        thr = np.quantile(x_neg, QUANTILE) if experiment != '2k_BEAM-T_Mouse_H2Kb_OT-1' else np.quantile(x_neg, 0.99 )  # 2k only 1339 samples\n",
+    "        x_neg_filtered = x_neg[x_neg <= thr]\n",
+    "        nbfit_neg = smf.negativebinomial(\"nbdata ~ 1\", data=pd.DataFrame({\"nbdata\": x_neg_filtered})).fit(disp=False)\n",
+    "        mean_neg = np.exp(nbfit_neg.params.iloc[0])\n",
+    "        concentration_neg = 1 / nbfit_neg.params.iloc[1]\n",
+    "        var_nb_neg = mean_neg + mean_neg ** 2 / concentration_neg\n",
+    "        \n",
+    "        param = {'experiment': experiment, 'gene': gene, 'threshold': thr,\n",
+    "\t\t\t\t f'mean_neg': mean_neg, f'concentration_neg': concentration_neg,\n",
+    "\t\t\t\t f'var_nb_neg': var_nb_neg, 'overdisp_nb_neg': var_nb_neg / mean_neg,\n",
+    "                }\n",
+    "\n",
+    "        x = mdata['gex'][:, gene].X.toarray().reshape(-1, )\n",
+    "        for direction in ['-', '+']:\n",
+    "            x_in_threshold = x[x > thr] if direction == '+' else x[x <= thr]\n",
+    "            nbfit = smf.negativebinomial(\"nbdata ~ 1\", data=pd.DataFrame({\"nbdata\": x_in_threshold})).fit(disp=False)\n",
+    "            mean = np.exp(nbfit.params.iloc[0])\n",
+    "            concentration = 1 / nbfit.params.iloc[1]\n",
+    "            var_nb = mean + mean ** 2 / concentration\n",
+    "            \n",
+    "            param.update({f'mean{direction}': mean, f'concentration{direction}': concentration, \n",
+    "                          f'var_nb{direction}': var_nb, \n",
+    "                          f'overdisp_nb{direction}': var_nb / mean, \n",
+    "                          f'N{direction}': x_in_threshold.shape[0],})\n",
+    "\n",
+    "        \n",
+    "        params.append(param)\n",
+    "\n",
+    "params = pd.DataFrame(params)\n",
+    "for key in [f'mean', f'concentration', f'var_nb', f'overdisp_nb', f'N']:\n",
+    "    params[f'{key}_ratio'] = params[f'{key}+'] / params[f'{key}-']\n",
+    "params['binder_ratio'] = params['N+'] / (params['N-'] + params['N+'])\n",
+    "params['noise_to_neg_mean_ratio'] = params['mean-'] / params['mean_neg']\n",
+    "params['neg_inc'] = params['mean-'] / params['mean_neg']\n",
+    "params['noise_to_neg_overdisp_ratio'] = params['overdisp_nb-'] / params['overdisp_nb_neg']\n",
+    "params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8c729af-e292-49a7-b5f1-f4b871e4201c",
+   "metadata": {},
+   "source": [
+    "## Negative control distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1ce2c913-6426-43ae-a3d8-00f33f7930e2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T13:42:23.969837Z",
+     "start_time": "2025-08-14T13:42:23.850382Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean-</th>\n",
+       "      <th>concentration-</th>\n",
+       "      <th>var_nb-</th>\n",
+       "      <th>overdisp_nb-</th>\n",
+       "      <th>N-</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.867945</td>\n",
+       "      <td>0.489523</td>\n",
+       "      <td>34.430371</td>\n",
+       "      <td>8.901464</td>\n",
+       "      <td>5634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2.114713</td>\n",
+       "      <td>0.873756</td>\n",
+       "      <td>7.232860</td>\n",
+       "      <td>3.420256</td>\n",
+       "      <td>2406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.782627</td>\n",
+       "      <td>0.662888</td>\n",
+       "      <td>6.576434</td>\n",
+       "      <td>3.689182</td>\n",
+       "      <td>2406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1.801178</td>\n",
+       "      <td>0.549719</td>\n",
+       "      <td>7.702813</td>\n",
+       "      <td>4.276543</td>\n",
+       "      <td>2887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>5.961509</td>\n",
+       "      <td>0.812680</td>\n",
+       "      <td>49.692846</td>\n",
+       "      <td>8.335615</td>\n",
+       "      <td>1325</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       mean-  concentration-    var_nb-  overdisp_nb-    N-\n",
+       "5   3.867945        0.489523  34.430371      8.901464  5634\n",
+       "8   2.114713        0.873756   7.232860      3.420256  2406\n",
+       "9   1.782627        0.662888   6.576434      3.689182  2406\n",
+       "12  1.801178        0.549719   7.702813      4.276543  2887\n",
+       "14  5.961509        0.812680  49.692846      8.335615  1325"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "neg_ctrl_params = params[params['gene'].str.contains('negative')].copy()\n",
+    "neg_ctrl_params[['mean-', 'concentration-', 'var_nb-', 'overdisp_nb-', 'N-']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9100607f-13d1-456c-8e45-a3225e267b8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Truncated log-normal params:  -1.0539178917389453 1.8375518345106894 1.0181158793900795 0.4175162931163312\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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gwIGapPPknAkHDhzQOtWzsLAgKCiIoKAg3n33Xby9vTl+/LjWHA0lmcpuwIABrF27lueeew4TExPNFH8ALVu25NSpUwVOmuPt7c3Dhw85cuSI5hT1zJkzeW5ciJKTU1RRbi1dupSsrCy6du3KH3/8waVLl9i2bRtdunShTp06zJkzR9N27969zJs3j7Nnz/LFF18QERHB6NGjAfVd0JUrV3LixAkuXLjAmjVrsLCwwNXVtUhzNBRmwIABxMTEMGfOHIKDgzEzM9PUffDBB+zbt08zgXRcXBw//vij5iaDl5cXgYGBjBgxgujoaI4cOcKwYcOeydmyyis5gqvg0pISyu126tevz+HDhwkNDSUkJITbt2/j4OBA7969CQ0N1XoGbvz48Rw+fJgZM2Zga2vLwoUL6dq1KwBVq1blk08+Ydy4cWRnZ9OkSRO2bNmieVA4LCyM2bNnM378eK5cuUKNGjV4/vnn6dmz51NjrFevHr6+vhw8eJDFixdr1TVt2pTdu3czZcoUOnbsiKIoeHh48Nprr2nahIWFMWzYMPz8/KhduzazZ89m2rRpxf5difzJnAz5qAhzMjwrbzIIoQs5gqugXFxciI09bRTvogpREElwFZiLi4skHGHU5CaDEMJoSYITQhgtSXBCCKMlCU4IYbQkwQkhjJYkOCGE0ZIEJ4QwWpLghBBGSxKcEMJoSYITQhgtSXBCCKMlCU4IYbTKVYK7e/cuoaGhBAYGYm9vj0ql0hoC+mlSUlIYPnw4NWvWxMrKioCAAGJiYkovYCFEuVauElxycjIzZ87k9OnTWrMXFUVOTg49evRg3bp1jBw5knnz5nHjxg38/f21pn8TQlQc5Wq4JEdHR5KSknBwcODw4cOaceqLYsOGDezbt4+IiAiCg4MBCAkJwdPTk9DQUNatW1daYQshyqlydQRnZmaGg4NDidbdsGEDtWvXpk+fPpqymjVrEhISwo8//khWVpa+whRCPCPKVYLTxdGjR2nZsiUmJtq75OvrS0ZGRqFTtGVlZZGWlqa1CCGefUaT4JKSknB0dMxTnluWOzlvfubOnYudnZ1mcXZ2LrU4hRBlx2gSXGZmptaUbbnMzc019QWZPHkyqampmuXSpUulFqcQouyUq5sMurCwsMj3Otv9+/c19QUxMzPLNzkKIZ5tRnMEl3sH9km5ZU5OTmUdkhDCwHRKcN26dWPdunWFnv6VlebNmxMTE0NOTo5WeXR0NJaWlnh6ehooMiGEoeiU4C5cuMAbb7xB7dq1GTRoEJGRkZTFPNJJSUnExsby4MEDTVlwcDDXr19n06ZNmrLk5GQiIiIICgqSU1AhKiCdrsGdOXOGQ4cOsWbNGv773/+yZs0aHBwc6N+/PwMGDKB58+bF7nPp0qWkpKRo7npu2bKFy5cvA/Dee+9hZ2fH5MmTWbVqFfHx8bi5uQHqBPf8888zZMgQTp06RY0aNVi2bBnZ2dnMmDFDl90UQjyjdL7J0Lp1a1q3bs2iRYvYsWMHa9as4auvvmLhwoU0aNCAgQMH0r9/f5577rki9bdgwQIuXryo+bxp0ybNUdkbb7yBnZ1dvuuZmpry66+/8v777/PZZ5+RmZlJ69atCQ8Px8vLS9fdFEI8g1RKKZxTpqSkMGLECCIiIgAwMTHB39+fsWPH0qNHD31vTu/S0tKws7MjNTUVW1vbAtvFxMTg4+NDlylh2LvoJ4neTjzDjjlDOHLkCC1bttRLn0JUVHq9i7pnzx7efvtt6tWrR0REBI0bN2bevHl8+umn3Lx5k5dffpmPPvpIn5sUQogC6XyKeurUKdasWcN//vMfEhMTqVWrFoMGDeL//u//tK7BjR49muHDh/PFF18wc+ZMXTcrhBBPpVOCa968OcePH8fMzIxevXqxbNkyunbtmud90FwBAQF88803umxSCCGKTKcEV7VqVVasWEHfvn0LvVaVq1evXsTHx+uySSGEKDKdEtx3331HzZo1C3wNKjMzk5s3b+Li4gKApaUlrq6uumxSCCGKTKebDO7u7mzevLnA+p9++gl3d3ddNiGEECWmU4J72hMmDx48KPB6nBBClLZin6KmpaWRkpKi+Xzr1i0SExPztEtJSWH9+vX5jtEmhBBlodgJbtGiRZrHPFQqFWPGjGHMmDH5tlUUhdmzZ+sUoBBClFSxE9xLL72EtbU1iqIwceJE+vXrl+eJe5VKhZWVFT4+PrRq1UpvwQohRHEUO8G1bduWtm3bAnDv3j369OlDkyZN9B6YEELoSqfHREJDQ/UVhxBC6F2xEtzMmTNRqVRMmTIFExOTIr1ypVKpmDZtWokDFEKIkipWgps+fToqlYoPPviAKlWqMH369KeuIwlOCGEoxUpwTw4H/uRnIYQoT+QpXCGE0dL7tIEZGRmsX7+erKwsunfvLu+eCiEMRqcEN3ToUKKjozlx4gQA//zzD88//7zms52dHTt37qRFixa6RyqEEMWk0ynqrl276NOnj+bzunXrOHHiBGvXruXEiRM4ODjIhC9CCIPRKcFdu3ZNM6sVwA8//ECrVq3o168fDRs25K233iI6OlrXGIUQokR0SnBWVlaaF+8fPnxIVFQUXbt21dTb2NiQmpqqU4BCCFFSOl2Da9myJV9//TUBAQH89NNPpKenExQUpKk/f/48tWvX1jlIIYQoCZ0S3Jw5c+jatSutWrVCURSCg4Px9fXV1G/evJn27dvrHKQQQpSETgmuVatWxMbGsm/fPqpWrYqfn5+mLiUlhXfeeUerTAghypLOz8HVrFmTXr165SmvWrUqo0eP1rV7IYQoMb086Juens7Fixe5c+dOvsOYd+rUSR+bEUKIYtEpwd26dYuRI0eyceNGsrOz89QrioJKpcq3TgghSptOCe6tt95iy5YtjBo1io4dO1KtWjV9xSWEEDrTKcFt376dsWPHMm/ePH3FI4QQeqPTg76WlpZabzIIIUR5olOCe+ONNwqd+FkIIQxJp1PU4OBgdu/eTWBgIMOHD8fZ2RlTU9M87Z6cdUsIIcqCTgmuQ4cOmp937NiRp17uogohDEmnBBcWFqavOIQQQu90SnCDBg3SVxxCCKF3epuTISkpib/++ot79+7pq0shhNCJzgnuxx9/xNvbm+eee46WLVtqBrhMTk6mRYsWcpdVCGEwOiW4LVu20KdPH2rUqEFoaKjWe6g1atSgTp06hIeH6xqjEEKUiE4JbubMmXTq1Ik9e/bw7rvv5qlv27YtR48e1WUTQghRYjoluBMnThASElJgfe3atblx44YumxBCiBLT+VWtwm4qXLhwgerVq+uyCSGEKDGdElxAQACrVq3i4cOHeequXbvG119/zUsvvaTLJoQQosR0SnBz5szh8uXLtG7dmq+++gqVSsVvv/3G1KlTadKkCYqiEBoaqq9YhRCiWHRKcF5eXuzZs4fq1aszbdo0FEVh/vz5fPzxxzRp0oQ///xTRhsRQhiMzkOWN2rUiMjISO7cucO5c+fIycmhbt261KxZUx/xCSFEiZU4wWVlZbFmzRq2b9/O+fPnSU9Px8bGhnr16hEYGEj//v2pUqWKPmMVQohiKVGCO378OL169eLixYsoioKdnR3W1tbcuHGDmJgYIiIimDNnDj/99BMNGjTQd8xCCFEkxb4Gd/fuXV5++WWuX7/OnDlzuHTpEnfu3NH67+zZs7l69SpBQUHybqoQwmCKneDCwsJITEzkl19+YdKkSdSpU0ervk6dOkyePJktW7YQHx8vr2oJIQym2Anul19+4aWXXsLf37/Qdp07d6ZLly5s2bKlpLEJIYROip3gjh8//tTklqtz584cP368WP1nZWXxwQcf4OTkhIWFBW3atMl3tOAnTZ8+HZVKlWcxNzcv1vaFEMaj2DcZbt++jYODQ5Ha1q5dm9u3bxer/8GDB7NhwwbGjBlD/fr1CQ8Pp3v37uzatUtriPSCLF++HGtra83n/OaIEEJUDMVOcFlZWVSuXLlonVeqxD///FPkvg8ePMj69euZP38+EyZMAGDgwIE0btyYiRMnsm/fvqf2ERwcTI0aNYq8TSGE8SrRYyIJCQnExMQ8tV18fHyx+t2wYQOmpqYMHz5cU2Zubs7QoUP58MMPuXTpEs7OzoX2oSgKaWlp2NjYoFKpirV9IYRxKVGCmzZtGtOmTXtqu9xZtYrq6NGjeHp6Ymtrq1Xu6+sLwLFjx56a4OrWrcvdu3exsrKid+/efPrpp9SuXbvQdbKyssjKytJ8TktLK3LMQojyq9gJrjRn0kpKSsLR0TFPeW7Z1atXC1y3WrVqjBw5krZt22JmZsaff/7JF198wcGDBzl8+HCepPm4uXPnMmPGDN13QAhRrhQ7wZXmTFqZmZmYmZnlKc+9E5qZmVnguqNHj9b6/Oqrr+Lr68uAAQNYtmwZkyZNKnDdyZMnM27cOM3ntLS0px4pCiHKP51fttcnCwsLrVPFXPfv39fUF0f//v0ZP348kZGRhSY4MzOzfBOrMIzExESSk5P12meNGjVwcXHRa58VWWl8R6D/76lcJThHR0euXLmSpzwpKQkAJyenYvfp7Oxc7EdVhOEkJibi7d2AzMwMvfZrYWFJbOxpSXJ6UFrfEej/eypXCa558+bs2rWLtLQ0rWtmuVMRNm/evFj9KYpCQkICLVq00GeYpcryfgatgWq//QbbtsGNG5CWpl7efhtefFHdMD4ewsPBwQGcnMDbG+rWhSI+wlNeJScnk5mZQZs3Q7F1dNNLn2lJCUR/O4Pk5GRJcHpQGt8RlM73VK4SXHBwMAsWLGDFihWa5+CysrIICwujTZs2mutiiYmJZGRk4O3trVn35s2becagW758OTdv3iQwMLDsdkIHHfdv5V/hs9Wvl3z4Yd4GQUGPfj5+HGbO1K6vXBnq1YNWrWD4cCjCg9Hlla2jG/YuXoYOQxTiWfiOylWCa9OmDX379mXy5MncuHGDevXqsWrVKhISEli5cqWm3cCBA9m9e7fWPKyurq689tprNGnSBHNzc/bs2cP69etp3rw5I0aMMMTuFMj6biodo7fRJiaKrZ37Eu3TGYArjq6YoHAVsG3eHOtmzdRHaHZ2YGsL7do96uS559RHdNeuQWIinDkD9+7B6dPqpXv3R23PnYNduyAwEOTmiahAylWCA/juu++YNm0aq1ev5s6dOzRt2pSff/6ZTp06FbregAED2LdvHxs3buT+/fu4uroyceJEpkyZgqWlZRlFXzjPc3/TNWojvkejqPzwAQDJ9rU1CS7B2ZOQ0YuJWDKGIytX0rJly4I7a9kSli9/9DknBy5fhhMn4OBBePz3FRHx6IiwWTMICVEv9erpexeFKFfKXYIzNzdn/vz5zJ8/v8A2UVFRecq+/vrrUoxKB4pC49gj9Pk1jIZnH02CfcHFi93tenC4WUdNWY5pJVKs7Uq2HRMTcHFRL48fvYH6aK9tW4iOhr/+Ui9TpqiT5Ouvq48EbWxKtl0hyrFyl+CMjUpRGPT9IpyT4nlQqTJ/PB9IZKdXiHf1fvrK+vJ//6debt2CH36A77+HnTshJgbOnoV//avsYhGiDEmCKwU26XfIsLQh27QSiokJG4KG4h13jC1d3+B2tVqGC6x6dRg6VL3cvAmbNqnvzuaOvqIo6ut0vr7qpFeCx3KEKE90mjZQ5NXmyE4+De1Pl92bNGXRPp1Z9fo4wya3J9WsCSNGwPvvPyo7cgS2b4fZs8HVFfr1gwMH1IlPiGeQJDg9sb6byqivpzF2xVRs76Xie3T3s5cYmjZV35Do1AkePoT169XX7tq0gTVr4MEDQ0coRLFIgtODhpfimDfr/2h3+HeyTUzZ2GMIc0YvhmdtuKYqVSA4GHbvVl+fGzIEzMzg0CH1NbxffzV0hEIUi1yD04WiMBaYv/oTTJUcrji48sWQj7jgZgRTJbZoAd9+C//+N6xYATt2QM+ej+r37lW/PVG9uuFiFOIp5AhOB+bnzjEPMFVy2Nv6RT6cvNI4ktvjatZUP1ISFQW5w7/fvw+vvqp+JGXsWLh0yaAhClEQSXA6uF+/PhOBz7v+H58PnUGWefl4oLjUXbmivsOakQGLF6vfgR02DM6fN3RkQmiRBKejRcCWVp2fvettuvDwUN9x/e03CAhQ35BYuRK8vGDwYEhIMHSEQgCS4ERJqVTw0kvqB4b37lU/P5edDatWQUqKoaMTApAEJ/ShXTvYulX9KticOfD4sFYrV8LJkwYLTVRskuCE/vj6ag/zlJiofiOicWPo2xf+/ttwsYkKSRKcKD3Z2fDyy+qfN2xQj2Tyyitw9Gjh6wmhJ5LgROlxd1cntuPH4bXX1NftfvhBPYpJUJB6VGIhSpEkOFH6GjdWv/Z18iQMGKAe2mnXLhmiSZQ6SXCi7DRooH6n9fRp9VsSNWo8qhs7Vv22xLP2/q4o1+RVLVH2PD3VS64//lA/MLx4MV6NG9MD+EcSndADOYIThlevHoweDebmWJ04wc/A8m9CaXNkJ6qcHENHJ55hkuCE4Tk5qY/gEhK4NmgQ6YDHjUuMXTGV+TPfoGZykqEjFM8oSXCi/Khdm6ujRuEGrO7wMvcsrKn08AG3qj02HaScuopikGtwoty5Daz2e4WdwSOplXyVHFP1/6aVHvzDrH+/xcEW/mz3f5V7VraF9iOEJDhRbmVaWHPR+dHNiDYxu3C/FIf7pTh6bVvNrvY9+fXFftys4WjAKEV5JglOPDMOtHoBVCp6bl+L+6U4uu3awEu7N3PAJ4AtLw0goZzPsi7KnlyDE8+MbNNK7PV9iclTwpkzejF/N2iNaU427Q9F8smcIThcl4E3hTY5ghPPHpWK4w19Od7QF9dLZ+m5fR1WGelcq+2sadLyrz2c9WjC3ZJOpC2MgiQ48Uy76OzJF0Ono8rJ1pTZpd5i3FcfkmNiwh7frvy3QSsDRigMSU5RhVFQTEw1P1dLTeaSkztVHvxD571b+PKbUKKAqjt2wD//GCxGUfYkwQmjk+DixeQp4YS+v5z9Pi+QrTLBD6g7aRLUqQN//mnoEEUZkVNUYZxUKs7Ua8aZes0wObGfBp+P54MaNaiSng6NGj1qd+ECODqChYXhYhWlRo7ghNFLtrXnI+DEL7+o54+wt39U+cYb6lfF3ntPPdm1vClhVCTBiYqjUiXw8Xn0+c4dSEpST5KzdKm6rnFjmDtXPdy6eOZJghMVV7Vq6rlcf/sNQkLAzAxOnVLPK+Hqqp7wWjzTJMGJis3ERD394fffw/Xr6lnA/P3Vdc2aPWp38aJ6VOL0dIOEKUpGEpwQuezs4M031cOpX7z4aMIcgNWroV8/qFVLPXHOunWS7J4BkuCEyI+LC5ibP/psb68emPP+ffXEOQMGQM2a0KsXfPcdZGUZLFRRMElwQhTFO+/A2bPqKQ+nTFEPuZ6VBT/9pL4Dq1I9anvzptyNLSfkOTghikqlgubN1cusWXDiBEREwIMHUKWKuo2iQNu26jlhe/ZUT4/YqZP20aAoM5LghCgJlQqaNFEvj7t6FS5fVh/dLV2qXszN1UnuxRehRw9o2NAwMVdAcooqhD7VqQO3bqmv0w0bpn5L4v592L4dJk6EZcsetX3wQJ63K2VyBCeEvllZqW8+9OqlPmU9dUo952tkJHTv/qjd/v3g56d+5q5jx0eLt7f2NT1RYpLghChNKpX63ddGjWDMGO26U6fA1FT9SMrFi+pJsUE9IXaHDjBtGrRsWeYhGxM5RRXCUN5+W/2a2Pbt6mTm76++XpecrD7FfXxO2J9+glGj1Enw7FntOlEgOYITwpCsraFLF/UC6vHqjhxRD+nUvPmjdlu2wDffPPpctSq0bq1emjdX37ywtCzDwJ8NkuCEKE+qVFE/ZtK2rXb5q6+qk+HBg+pRT1JS1Nf1duxQ16ekPGobEaE+CmzWDJo2Va9XQUmCE+JZEBioXkB99/XECXWyO3xYnczsHpt7Yvly9etmoL4G6OoKDRqoF29v9d3dCnITQxKcEM+aypWhRQv1MmJE3vouXdTX8v76S/1cXkKCetm6FWrXhrfeetR24kRISwMvL/DwUC/u7kZzuisJTghjM3myegH10d2pU3D6tHrJfeMi1/ff5/8snqMjPP88bNr0qCw2Vn3t7xm6wSEJTghjVqOG+i2KTp3yr587F06ehLg49dh4589Daqp6INDr17Xbdu8O8fE0r1yZ80D66k9Ic3TjVrXaXHJyZ2+brqW+O8UlCU6Iiqx/f+3PigK3b6sTXfajqRjJyVHXqVSYPHhAXYDEM+oFOFu3sVaCmz9jAJUePiDVtjoptvbcsauu/tmuOtdr1uG0Z4vS3zckwQkhHqdSQfXq6uVxJiYQHw8PHnBi+3b+1bMn3XsNx9XElOp3bnDT3uFRFznZOF1LxDQnG8cbl/NsIrZeM6a/v1zzee7swZhmP2Rn/Wbs0PPulLsEl5WVxUcffcTq1au5c+cOTZs2Zfbs2XTJfU6oEFeuXGHs2LFs376dnJwcAgICWLRoEXXr1i2DyIWoACpX5h9HR/YAFo3bYu/ilU8jFeOnr6Nq2i2qpt76339vY5d2i6ppt7nk9OjvUZWTg/PVC1TKfohdHf3/nZa7BDd48GA2bNjAmDFjqF+/PuHh4XTv3p1du3bRoUOHAte7e/cuAQEBpKam8uGHH1K5cmUWLVqEn58fx44do/qT/yIJIUqFYmLCtdrOXKvtXKT2H05eiV36HS5mpMOhSL3GUq4S3MGDB1m/fj3z589nwoQJAAwcOJDGjRszceJE9u3bV+C6y5YtIy4ujoMHD9K6dWsAunXrRuPGjfn000/5+OOPy2QfhBBFp5iYkOhcH4Db/7uep0/l6l3UDRs2YGpqyvDhwzVl5ubmDB06lP3793Pp0qVC123durUmuQF4e3vzwgsv8N///rdU4xZClE/l6gju6NGjeHp6Ymtrq1Xu6+sLwLFjx3B2znvYm5OTw99//82bb76Zp87X15ft27eTnp6OjY1NvtvNysoi67Ex9VNTUwFIS0srNN67d+8CcPviGR5mZRbatqjSrqmfSTpy5Iimf30wMTEhpxSeX9J3v2fOqP8Vr6i/02ehz9L4juDR93T37t2n/u0B2NjYoHraGxlKOdKoUSOlc+fOecpPnjypAMqXX36Z73o3b95UAGXmzJl56r744gsFUGJjYwvcbmhoqALIIossz9CSmpr61JxSro7gMjMzMTMzy1Nu/r/x7DMz8//XIre8JOsCTJ48mXHjxmk+5+TkcPv2bapXr/70fyGKKC0tDWdnZy5dupTnCFUYhnwn5VNRv5eCzsgeV64SnIWFhdapYq779+9r6gtaDyjRuqBOjE8mx6pVqxYp5uKytbWVP6ZyRr6T8kkf30u5usng6OhIUlJSnvLcMicnp3zXs7e3x8zMrETrCiGMV7lKcM2bN+fs2bN5LjBGR0dr6vNjYmJCkyZNOHz4cJ666Oho6tatW6TDWSGEcSlXCS44OJjs7GxWrFihKcvKyiIsLIw2bdpo7qAmJiYSGxubZ91Dhw5pJbkzZ86wc+dO+vbtWzY7UAgzMzNCQ0PzvU4oDEO+k/JJn9+LSlHK1xTcISEhbN68mbFjx1KvXj1WrVrFwYMH+f333+n0vxER/P392b17N4+Hnp6eTosWLUhPT2fChAlUrlyZhQsXkp2dzbFjx6hZs6ahdkkIYShPvc9axjIzM5UJEyYoDg4OipmZmdK6dWtl27ZtWm38/PyU/EK/dOmSEhwcrNja2irW1tZKz549lbi4uLIKXQhRzpS7IzghhNCXcnUNTggh9EkSnBDCaEmCK0WHDh1i5MiRNGrUCCsrK1xcXAgJCeHs2bOGDk08Zs6cOahUKho3bmzoUCq0mJgYXn75Zezt7bG0tKRx48Z89tlnOvUp1+BKUXBwMHv37qVv3740bdqUa9eusXTpUu7evcuBAwfkD6ocuHz5Ml5eXqhUKtzc3Dhx4oShQ6qQtm/fTlBQEC1atOC1117D2tqa8+fPk5OTw7x580rcryS4UrRv3z5atWpFlcdmMoqLi6NJkyYEBwezZs0aA0YnAF5//XVu3rxJdnY2ycnJkuAMIC0tDU9PT9q1a8eGDRswMdHfiaWcopaidu3aaSU3gPr169OoUSNOnz5toKhErj/++IMNGzawePFiQ4dSoa1bt47r168zZ84cTExMuHfvnt6Gd5IEV8YUReH69evUqFHD0KFUaNnZ2bz33nsMGzaMJk2aGDqcCi0yMhJbW1uuXLmCl5cX1tbW2Nra8q9//UszWEZJSYIrY2vXruXKlSu89tprhg6lQvvyyy+5ePEis2bNMnQoFV5cXBwPHz6kV69edO3alY0bN/Lmm2/y5ZdfMmTIEN06N+BDxhXO6dOnFVtbW6Vt27bKw4cPDR1OhZWcnKzY29srCxYs0JT5+fkpjRo1MmBUFVfdunUVQHn77be1ykeMGKEAytmzZ0vctxzBlZFr167Ro0cP7OzsNHNPCMOYOnUq9vb2vPfee4YORfBorMZ+/fpplff/36TU+/fvL3Hf5WrAS2OVmppKt27dSElJ4c8//5Sx6QwoLi6OFStWsHjxYq5evaopv3//Pg8ePCAhIQFbW1vs7e0NGGXF4uTkxMmTJ6ldu7ZWea1atQC4c+dOifuWI7hSdv/+fYKCgjh79iw///wzDRs2NHRIFdqVK1fIyclh1KhRuLu7a5bo6GjOnj2Lu7s7M2fONHSYFYqPjw+g/m4el/sPkC4jAckRXCnKzs7mtddeY//+/fz444+0bdvW0CFVeI0bN2bz5s15yqdOnUp6ejpLlizBw8PDAJFVXCEhIXzyySesXLmSzp07a8q/+eYbKlWqhL+/f4n7lgd9S9GYMWNYsmQJQUFBhISE5Kl/4403DBCVyI+/v7886GtAQ4cO5dtvvyUkJAQ/Pz+ioqKIiIhg8uTJOk3aLgmuFOUOzFkQ+dWXH5LgDOvBgwd8/PHHhIWFcfXqVVxdXXn33XcZM2aMTv1KghNCGC25ySCEMFqS4IQQRksSnBDCaEmCE0IYLUlwQgijJQlOCGG0JMEJIYyWJDghhNGSBCeEMFqS4IQQRksSnJELDw9HpVKhUqnYs2dPnnpFUXB2dkalUtGzZ08DRFh0KpWKkSNH5lu3YcMGVCoVUVFRmrLBgwejUqmwtbUlMzMzzzpxcXGa382CBQs05VFRUahUKjZs2KD3fShrH3/8MT/88IOhwzAYSXAVhLm5OevWrctTvnv3bi5fvoyZmZkBoip9lSpVIiMjgy1btuSpW7t2Lebm5gaIquxIghMVQvfu3YmIiODhw4da5evWrcPHxwcHBwcDRVa6zMzMeOGFF/jPf/6Tp27dunX06NHDAFGJsiIJroLo168ft27dYseOHZqyf/75hw0bNmjGvn9STk4OixcvplGjRpibm1O7dm1GjBiRZwjpH3/8kR49euDk5ISZmRkeHh7MmjWL7OxsrXb+/v40btyYU6dOERAQgKWlJXXq1NFp5vKi6N+/P1u3biUlJUVTdujQIeLi4grc96K6f/8+06dPx9PTE3NzcxwdHenTpw/nz5/XtLl37x7jx4/H2dkZMzMzvLy8WLBggdZwWQkJCahUKsLDw/NsQ6VSMX36dM3n6dOno1KpOHfuHIMHD6Zq1arY2dkxZMgQMjIytNa7d+8eq1at0pyKDx48WKf9fdZIgqsg3NzcaNu2rdaRzNatW0lNTeX111/Pd50RI0bw/vvv0759e5YsWcKQIUNYu3YtXbt25cGDB5p24eHhWFtbM27cOJYsWYKPjw8fffQRkyZNytPnnTt3CAwMpFmzZnz66ad4e3vzwQcfsHXrVv3v9P/06dMHlUrFpk2bNGXr1q3D29ubli1blrjf7OxsevbsyYwZM/Dx8eHTTz9l9OjRpKamasaVUxSFl19+mUWLFhEYGMjChQvx8vLi/fffZ9y4cTrtV0hICOnp6cydO5eQkBDCw8OZMWOGpn716tWYmZnRsWNHVq9ezerVqxkxYoRO23zmlHg+LvFMCAsLUwDl0KFDytKlSxUbGxslIyNDURRF6du3rxIQEKAoiqK4uroqPXr00Kz3559/KoCydu1arf62bduWpzy3v8eNGDFCsbS0VO7fv68p8/PzUwDlu+++05RlZWUpDg4OyquvvvrUfQGUd999N9+6iIgIBVB27dqlKRs0aJBiZWWlKIqiBAcHKy+88IKiKIqSnZ2tODg4KDNmzFDi4+MVQJk/f75mvV27dimAEhERUWg83377rQIoCxcuzFOXk5OjKIqi/PDDDwqgzJ49W6s+ODhYUalUyrlz5xRFUTRxhIWF5bvfoaGhms+hoaEKoLz55pta7V555RWlevXqWmVWVlbKoEGDCt0PYyZHcBVISEgImZmZ/Pzzz6Snp/Pzzz8XeIoWERGBnZ0dXbp0ITk5WbP4+PhgbW3Nrl27NG1zp30DSE9PJzk5mY4dO5KRkUFsbKxWv9bW1lpDtVepUgVfX18uXLig573V1r9/f6Kiorh27Ro7d+7k2rVrOp+ebty4kRo1auQ7/aBKpQLg119/xdTUlFGjRmnVjx8/HkVRdDpyffvtt7U+d+zYkVu3bpGWllbiPo2NTDpTgdSsWZMXX3yRdevWkZGRQXZ2NsHBwfm2jYuLIzU1VTN125Nu3Lih+fnkyZNMnTqVnTt35vnjSk1N1fr83HPPaf74c1WrVo2///67JLuUx5N95+revTs2NjZ8//33HDt2jNatW1OvXj0SEhJKvK3z58/j5eVFpUoF/xldvHgRJycnbGxstMobNGigqS8pFxcXrc/VqlUD1JcBbG1tS9yvMZEEV8H079+ft956i2vXrtGtWzeqVq2ab7ucnBxq1arF2rVr863PncotJSUFPz8/bG1tmTlzJh4eHpibmxMTE8MHH3xATk6O1noFTXitFGHkfDMzs3yfZwM0F9cLeuzDzMyMPn36sGrVKi5cuKB10b48KCgxP3mj5nG6/C4rCklwFcwrr7zCiBEjOHDgAN9//32B7Tw8PIiMjKR9+/Zap6BPioqK4tatW2zatIlOnTppyuPj4/UaN4CrqytnzpzJty633NXVtcD1+/fvz7fffouJiUmBN1aKw8PDg+joaB48eEDlypULjDkyMpL09HSto7jcU/fceHOPvh6/0wu6HeFBwYmzopBrcBWMtbU1y5cvZ/r06QQFBRXYLiQkhOzsbGbNmpWn7uHDh5o/xNyjiMePGv755x+WLVum38BRn2YeOHCAI0eOaJWnpKSwdu1amjdvXujzfAEBAcyaNYulS5fq5bm/V199leTkZJYuXZqnLvf30b17d7Kzs/O0WbRoESqVim7dugFga2tLjRo1+OOPP7Ta6fp7tLKyypM0KxI5gquABg0a9NQ2fn5+jBgxgrlz53Ls2DFeeuklKleuTFxcHBERESxZsoTg4GDatWtHtWrVGDRoEKNGjUKlUrF69epSOU2aNGkSERERdOrUiREjRuDt7c3Vq1cJDw8nKSmJsLCwQtc3MTFh6tSpeotn4MCBfPfdd4wbN46DBw/SsWNH7t27R2RkJO+88w69evUiKCiIgIAApkyZQkJCAs2aNWP79u38+OOPjBkzRmuS6WHDhvHJJ58wbNgwWrVqxR9//MHZs2d1itHHx4fIyEgWLlyIk5MT7u7utGnTRtddf3YY8hauKH2PPyZSmCcfE8m1YsUKxcfHR7GwsFBsbGyUJk2aKBMnTlSuXr2qabN3717l+eefVywsLBQnJydl4sSJym+//ZbnsQ0/Pz+lUaNGebYxaNAgxdXVtUj7c/nyZWXYsGFKnTp1lEqVKin29vZKz549lQMHDuTbb+5jIgXR5TERRVE/IjNlyhTF3d1dqVy5suLg4KAEBwcr58+f17RJT09Xxo4dqzg5OSmVK1dW6tevr8yfP1/zKMnjfQ0dOlSxs7NTbGxslJCQEOXGjRsFPiZy8+ZNrfVzv+v4+HhNWWxsrNKpUyfFwsJCASrcIyMyL6oQwmjJNTghhNGSBCeEMFqS4IQQRksSnBDCaEmCE0IYLUlwQgijJQlOiHLM398ff39/Q4fxzJIEJ4SOKvq8B+WZJDghdCQJrvySBCdEGbp3756hQ6hQJMGJpyrqJCe51qxZg4+PDxYWFtjb2/P6669z6dKlPO2++OIL6tati4WFBb6+vvz55586X3OKjo6me/fuVKtWDSsrK5o2bcqSJUu02uzcuZOOHTtiZWVF1apV6dWrF6dPny7RPhc2sUtuH6dOnaJ///5Uq1aNDh06AOoRWWbNmoWHhwdmZma4ubnx4YcfkpWVVeJ9F3nJaCKiyEJCQnB3d2fu3LnExMTwzTffUKtWLf79739r2syZM4dp06YREhLCsGHDuHnzJp9//jmdOnXi6NGjmgE2ly9fzsiRI+nYsSNjx44lISGB3r17U61aNZ577rkSxbdjxw569uyJo6Mjo0ePxsHBgdOnT/Pzzz8zevRoACIjI+nWrRt169Zl+vTpZGZm8vnnn9O+fXtiYmJwc3Mr1j6vXr2aYcOG4evry/DhwwG0RggB6Nu3L/Xr1+fjjz/WjLIybNgwVq1aRXBwMOPHjyc6Opq5c+dy+vRpNm/eXKL9F/kw8Mv+4hlQ1ElOEhISFFNTU2XOnDla7Y4fP65UqlRJU56VlaVUr15dad26tfLgwQNNu/DwcAVQ/Pz8ih3jw4cPFXd3d8XV1VW5c+eOVt3jo3Y0b95cqVWrlnLr1i1N2V9//aWYmJgoAwcOLPY+K0rBE7vk9tGvXz+t8mPHjimAMmzYMK3yCRMmKICyc+dOTZmfn1+Jfh9CTU5RRZE9bZKTTZs2kZOTQ0hIiNZENQ4ODtSvX18zUc3hw4e5desWb731ltZ8BgMGDNCMbFtcR48eJT4+njFjxuQZhj13VNukpCSOHTvG4MGDsbe319Q3bdqULl268OuvvxZ7n4viyT5yt/PktIHjx48H4Jdffily36Jwcooqiuxpk5zExcWhKAr169fPd/3cYb1zh+GuV6+eVn2lSpXynCIWVe5Ey40bNy6wTe52vby88tQ1aNCA3377jXv37mFlZaUp18fELu7u7nniMDExybP/Dg4OVK1aVedhysUjkuBEkT1tkpOcnBxUKhVbt27Nt621tXWpxlca9DGxS0FzWlT0+RLKgiQ4oTceHh4oioK7uzuenp4FtsudaOXcuXMEBARoyh8+fEhCQgJNmzYt0bYBTpw4wYsvvljodvObuCY2NpYaNWpoHb0VVXETlaurKzk5OcTFxWmmDwS4fv06KSkphU6cI4pHrsEJvenTpw+mpqbMmDEjzxGOoijcunULgFatWlG9enW+/vprHj58qGmzdu1a7ty5k6ff2NhYEhMTC912y5YtcXd3Z/HixXkmWcmNxdHRkebNm7Nq1SqtNidOnGD79u107969OLurUdyJXXK3s3jxYq3yhQsXAtCjR48SxSHykiM4oTceHh7Mnj2byZMnax77sLGxIT4+ns2bNzN8+HAmTJhAlSpVmD59Ou+99x6dO3cmJCSEhIQEwsPD8fDwyHNE1KBBA/z8/IiKiipw2yYmJixfvpygoCCaN2/OkCFDcHR0JDY2lpMnT/Lbb78BMH/+fLp160bbtm0ZOnSo5jEROzu7Es+VWtyJXZo1a8agQYNYsWKFZl7ZgwcPsmrVKnr37q11VCt0ZMA7uOIZUZxJThRFUTZu3Kh06NBBsbKyUqysrBRvb2/l3XffVc6cOaPV7rPPPlNcXV0VMzMzxdfXV9m7d6/i4+OjBAYGarWjGI+O7NmzR+nSpYtiY2OjWFlZKU2bNlU+//xzrTaRkZFK+/btFQsLC8XW1lYJCgpSTp06VeJ9Lmhil4L6UBRFefDggTJjxgzNZDXOzs7K5MmTlfv372u1k8dEdCOTzohyIycnh5o1a9KnTx++/vprQ4cjjIBcgxMGcf/+/TzX6b777jtu374twwMJvZEjOGEQUVFRjB07lr59+1K9enViYmJYuXIlDRo04MiRI1SpUsXQIQojIDcZhEG4ubnh7OzMZ599xu3bt7G3t2fgwIF88sknktyE3sgRnBDCaMk1OCGE0ZIEJ4QwWpLghBBGSxKcEMJoSYITQhgtSXBCCKMlCU4IYbQkwQkhjNb/AyCyl0nMIFdKAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 300x250 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# SuppFig 2d - Fit negative control mean distribution using weighted MoM lognormal\n",
+    "ns, means = neg_ctrl_params['N-'].to_numpy(float), neg_ctrl_params['mean-'].to_numpy(float)\n",
+    "w = ns / ns.sum()\n",
+    "m = np.sum(w * means)\n",
+    "v = np.sum(w * (means - m)**2)\n",
+    "sigma2 = np.log(1 + v / m**2)\n",
+    "sigma  = np.sqrt(sigma2)\n",
+    "mu_ln  = np.log(m) - 0.5 * sigma2\n",
+    "low, high = means.min(), means.max()\n",
+    "a, b = (np.log(low) - mu_ln) / sigma, (np.log(high) - mu_ln) / sigma\n",
+    "samples = np.exp(truncnorm(a, b, loc=mu_ln, scale=sigma).rvs(1000, random_state=0))\n",
+    "print(f'Truncated log-normal params: ', a, b, mu_ln, sigma)\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(3, 2.5))\n",
+    "x_range = np.linspace(low, high, 500)\n",
+    "dist = stats.lognorm(s=sigma, scale=np.exp(mu_ln))\n",
+    "pdf_base = dist.pdf(x_range)\n",
+    "Z = dist.cdf(high) - dist.cdf(low)\n",
+    "pdf_truncated = pdf_base / Z\n",
+    "\n",
+    "sns.histplot(means, stat='density', bins=10, label='Observed', )\n",
+    "plt.plot(x_range, pdf_truncated, color=palette['fit'], ls='--', label='Fitted trunc. LogN')\n",
+    "sns.despine()\n",
+    "plt.legend(frameon=False, fontsize='small')\n",
+    "plt.xlabel('Mean UMI count\\nneg. control')\n",
+    "plt.ylabel('Density')\n",
+    "plt.savefig('../../figures/SuppFig2/SuppFig2d_negative_control_mean_fit.pdf')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4af0a035-0959-4cbc-8508-adcc2c68ae77",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Non-binder distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "fcb858ab-43d3-4467-b51b-a40223b56fa7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-14T15:47:43.631651Z",
+     "start_time": "2025-08-14T15:47:43.377680Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trunc log-normal params for noise:  -1.4325807532116341 1.9485510504360744 2.0461540382126118 0.6019089551720753\n",
+      "lowest sampled value 3.267036725146729\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x250 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# SuppFig 2e - Fit negative control mean distribution using weighted MoM lognormal\n",
+    "noise = params[~params['gene'].str.contains('negative', na=False)]\n",
+    "noise = noise[(noise[['N-', 'N+']] > 20).all(1)]\n",
+    "ns, means = noise['N-'].to_numpy(float), noise['mean-'].to_numpy(float)\n",
+    "w = ns / ns.sum()\n",
+    "m = np.sum(w * means)\n",
+    "v = np.sum(w * (means - m)**2)\n",
+    "sigma2 = np.log(1 + v / m**2)\n",
+    "sigma  = np.sqrt(sigma2)\n",
+    "mu_ln  = np.log(m) - 0.5 * sigma2\n",
+    "low, high = means.min(), means.max()\n",
+    "a, b = (np.log(low) - mu_ln) / sigma, (np.log(high) - mu_ln) / sigma\n",
+    "samples = np.exp(truncnorm(a, b, loc=mu_ln, scale=sigma).rvs(100000, random_state=0))\n",
+    "print('trunc log-normal params for noise: ', a, b, mu_ln, sigma)\n",
+    "print('lowest sampled value', samples.min())\n",
+    "\n",
+    "# Plot\n",
+    "fig = plt.figure(figsize=(3, 2.5))\n",
+    "x_range = np.linspace(low, high, 500)\n",
+    "dist = stats.lognorm(s=sigma, scale=np.exp(mu_ln))\n",
+    "pdf_base = dist.pdf(x_range)\n",
+    "Z = dist.cdf(high) - dist.cdf(low)\n",
+    "pdf_truncated = pdf_base / Z\n",
+    "\n",
+    "sns.histplot(means, stat='density', bins=10, label='Observed', )\n",
+    "plt.plot(x_range, pdf_truncated, color='red', ls='--', label='Fitted trunc. LogN')\n",
+    "sns.despine()\n",
+    "plt.legend(frameon=False, fontsize='small', ncols=1)\n",
+    "plt.xlabel('Mean UMI count\\nnoise component')\n",
+    "plt.ylabel('Density')\n",
+    "plt.savefig('../../figures/SuppFig2/SuppFig2e_noise_mean_fit.pdf')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c71ec5dc-b602-42e9-82aa-5499ec45679b",
+   "metadata": {},
+   "source": [
+    "## Log-linear relationship between mean and variance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "878b35ff-24bd-4955-a8e3-cfd24c59fe7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epitope_params = params[~params['gene'].str.contains('negative')].copy()\n",
+    "epitope_params = epitope_params[(epitope_params['N-'] > 20) & (epitope_params['N+'] > 20)]\n",
+    "neg_ctrl_params = params[params['gene'].str.contains('negative')].copy()\n",
+    "\n",
+    "signal_params = epitope_params[['mean+', 'var_nb+']].copy()\n",
+    "signal_params['set'] = 'signal'\n",
+    "signal_params.columns = signal_params.columns.str.replace('+', '')\n",
+    "\n",
+    "noise_params = epitope_params[['mean-', 'var_nb-']].copy()\n",
+    "noise_params['set'] = 'noise'\n",
+    "noise_params.columns = noise_params.columns.str.replace('-', '')\n",
+    "\n",
+    "control_params = neg_ctrl_params[['mean-', 'var_nb-']].copy()\n",
+    "control_params['set'] = 'control'\n",
+    "control_params.columns = control_params.columns.str.replace('-', '')\n",
+    "\n",
+    "all_params = pd.concat([signal_params, noise_params, control_params])\n",
+    "all_params['log_var'] = np.log(all_params['var_nb'])\n",
+    "all_params['log_mean'] = np.log(all_params['mean'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b136972e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x250 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                log_var   R-squared:                       0.996\n",
+      "Model:                            OLS   Adj. R-squared:                  0.995\n",
+      "Method:                 Least Squares   F-statistic:                     3774.\n",
+      "Date:                Thu, 18 Jun 2026   Prob (F-statistic):           2.09e-21\n",
+      "Time:                        21:02:01   Log-Likelihood:                -4.2241\n",
+      "No. Observations:                  19   AIC:                             12.45\n",
+      "Df Residuals:                      17   BIC:                             14.34\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const          0.7042      0.124      5.697      0.000       0.443       0.965\n",
+      "log_mean       1.6969      0.028     61.430      0.000       1.639       1.755\n",
+      "==============================================================================\n",
+      "Omnibus:                        0.472   Durbin-Watson:                   1.715\n",
+      "Prob(Omnibus):                  0.790   Jarque-Bera (JB):                0.417\n",
+      "Skew:                          -0.310   Prob(JB):                        0.812\n",
+      "Kurtosis:                       2.622   Cond. No.                         7.79\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# SuppFig 2f - Mean-variance log-linear relationship in negative control, noise and signal components\n",
+    "model = sm.OLS(all_params[\"log_var\"], sm.add_constant(all_params[\"log_mean\"])).fit()\n",
+    "\n",
+    "# visualize\n",
+    "x_grid = np.linspace(all_params[\"log_mean\"].min(), all_params[\"log_mean\"].max(), 200)\n",
+    "y_pred = model.params[0] + model.params[1] * x_grid\n",
+    "\n",
+    "all_params[\"log_mean\"] = np.log(all_params[\"mean\"])\n",
+    "all_params[\"log_var\"] = np.log(all_params[\"var_nb\"])\n",
+    "\n",
+    "sns.regplot(all_params, x='log_mean', y='log_var', line_kws={'color': palette['fit'], 'ls': '--'})\n",
+    "plt.legend(['Data Points', 'OLS fit', ], frameon=False, fontsize='small')\n",
+    "plt.xlabel(f'log Mean')\n",
+    "plt.ylabel(f'log Variance')\n",
+    "plt.text(0.05, 0.95, f'$R^2$ = {model.rsquared:.3f}\\np-value = {model.f_pvalue:.0e}', transform=plt.gca().transAxes, fontsize='medium', verticalalignment='top')\n",
+    "plt.savefig('../../figures/SuppFig2/SuppFig2f_mean_variance_relationship.pdf')\n",
+    "plt.show()\n",
+    "\n",
+    "print(model.summary())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3ac89cee-f463-46df-a166-123091633a83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.31049623532404286\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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vcXFxwdTUFF9fX968eZOvdeaXziS4rDRt2hRPT88cT25OmzaNL774QjEcFxf3wT0+QX1Kly5Nq1atWL16NRMmTFA6D/fvv/+yZcsW+vfvr5Rw3i8M8/fffysd6pmamtKxY0c6duzIqFGjcHNz4+rVq7kqRPMhffr0YcuWLZQvXx49PT3at2+vGFevXj1u3LiRbWUwNzc3UlNTOX/+vOIQ9fbt25kuXOTGzJkzadmyJSNGjMDBwQEHBwfu3bunOB+YFUtLS3r27EnPnj3x9fXFx8eHmJiYTFenq1Wrxo8//khycrJiL+797W1ra0t8fDyvX79WVAS7dOmS0jSnT5/Gz89P8Z9WQkICD9RYhS23tOoiQ0FwdHTMVEn8fcbGxlhaWiq9hMLz7bffkpKSQps2bfjjjz94+PAhBw8epFWrVpQrV4758+crTX/69GkWL17MnTt3WLVqFTt37mTcuHGA/Arfhg0buHbtGvfu3WPz5s2YmppSoUKFXBWi+ZA+ffpw4cIF5s+fj6+vL8bGxopxU6ZM4a+//mL06NFcunSJu3fv8ssvvyjON2UcHg4fPpzQ0FDOnz/PkCFDMl1YyY3GjRtTu3ZtxTnDgIAAAgMDWblyJXfu3OHq1asEBQUpztMtW7aMn376iVu3bnHnzh127tyJnZ1dlofHvXv3RiaTMXToUG7cuMGBAwf4+uuvlabx9PTEzMyM6dOnEx4eztatW5X2ZkF+DnL37t1cunSJy5cv07t3b0WJw8Kk03twIL/HKreHN7osLuqB1q4noyKUv78/PXr0ICYmBjs7Ozp37oy/v3+mvYwvv/ySc+fOERAQgKWlJcuWLaNNmzaA/J7HhQsX8sUXX5CWlkatWrXYt2+f4hxbUFAQ8+bN48svv+Tx48fY2NjQqFEjOnTokGOcLi4ueHh4EBYWpjjnl6F27dqcPHmSGTNm0KxZMyRJonLlyvTs2VMxTVBQEEOGDMHLy4uyZcsyb948Zs2aleftBTBhwgT8/PyYMmUKQ4YMwczMjCVLljBp0iTMzc2pVauW4oKHhYUFixcv5u7du+jr69OwYUMOHDiQ5b19JUqUYN++fXz++efUrVuX6tWrs2jRIsXFFJAX2dm8eTOTJk1i3bp1fPzxx8yZM4dhw4Ypplm2bBmDBg2iSZMm2NjYMGXKFM2c2/5w4XvNOXv2rARIQUFBmcY9efJEunnzpvTmzRtF27NnzzJNt3//fgmQxo4dm6d1x8bGSoAUGxub57i1TUREhGRqaiYBhfYyNTWTIiIiNP3WBUHSuj24b7/9llevXvHkyRMA9u3bx6NHjwAYM2YMVlZWTJs2jU2bNnH//n3F/TxNmjShbt26NGjQACsrKy5cuMDGjRtxdHRk+vTpmno7Gufk5MStWzd15llUQcgLrSsb6OzsTERERJbjMhKan59fpgQ3c+ZM9u/fz/3790lMTMTe3p727dvj7+9P2bJl8xSDKBsoCLpB6xKcNhAJThB0g85fRRUEofgSCU4QBJ0lEpwgCDpLJDhBEHSWSHCCIOgskeAEQdBZIsEJgqCzRIITBEFniQQnCILOEglOEASdJRKcIAg6SyQ4QRB0ltZ1lySoV2RkpNq7StL27pA+9J4Nnj/H7M4djP79F4MXL5ClpxP1+eeK8fqvXpFmbg6Ghoq2lJQUpd57VaXu5Wn756FJojeRLOhKbyKRkZG4uVUjKSlRrcs1NTXj1q2bWvmjyuo9twc6Az5A+femfwO8m2q2AF2B08CvQAjwSCYDdf5M1Lw8bf48NE3swemw6OhokpIS8Rzkn6+K81mJi3pA6MYAoqOjtfIHldV7nrVtOZ7hVwBIk8l4aOPAE2tbXpawItnQmFateinmr78xAJOoB3wMfAx8jYyDksQB7+7cbdJWnpxUEHX1DNf2fo977ynYVnRTaVmg/Z+HpokEVwxY2jtTyqmqpsMoFAYxMawATluURO+/93ysvR8xV//iYu2PuF25NinGyoVe3q34MGf2ZuyfRVLnWigNL52k+t1LtAfan9jJhehHLB6zVKX4MmpWlCjjVGw+E00SCU7QDamp8H//R41Zs6gN2IUdYk91eUX7KzUacaVGo1wtRtLT44mdM0/snPntk56kHd5Kk13fMsLAkNsudXJegKBVRIITir4bN8DPD86eRR84D4RVrp3DTLnz0Ko0XwC/D/qKUjUaKNpdw69SMfI2h726ImVRnUrQDuKTEYouSYI1a6BePTh7FqytiZg5k4bA1Qqqn9961yszC1JMzAAwfJPCiOB5DNy2jGkrJ2AZ9+G6u4LmiAQnFF1Ll8LIkZCSAm3bwrVrvOjShYK+LSDVwJADH/cg2ciE2jfPsnC+H67/XCngtQr5oVKCa9u2LVu3biUpKUld8QhC7g0YAM7O8PXXsH8/lCtXKKuV9PQ44t2NmdPW88jemVKvopm9bDTNzxwolPULuadSgrt37x59+/albNmyDBgwgKNHjyJuqxMK1IsX//vb1lZ+/u3LL1W+fSM/HjlUYsbU9YTW88YgLZWRwfPo8cv36r1nTlCJSgnu9u3bhIaGMnDgQA4fPkybNm0oX748kyZN4tKlS2oKURD+8+ef4OICQUH/azM1zX76QpBiYsaKofPY3XYAIL/4oJ+WqtGYhP9R+Rxcw4YN+eabb3j8+DEHDhygZcuWrF27lvr161OzZk0WL16sqEwvCPl2+DC0bg2vXsGPP0J6uqYjUpD09NjReTjLh83j65ELSTMwzHkmoVCo7SKDnp4ebdq04ccffyQyMhJfX19u3LjB1KlTcXZ25pNPPmH//v3qWp1QnBw6BJ9+CsnJ0KGD/HybFt6aEVq/Jckm5ophz/O/oyf25jRKrd+SU6dO8fnnn+Pi4sLOnTsVe3BLly7l+fPnfPrpp8yePVudqxR03bFj0KmT/Epp586wa5fGD0tzo8v+ICZ8P5NRQXORpadpOpxiS+UbfW/cuMHmzZv56aefiIyMpEyZMgwYMIB+/frh7u6umG7cuHEMGzaMVatWMXfuXFVXKxQHoaH/S26dOsH27WBkpOmociWifBVS9Q346OxREk1LsKH3JI1cCCnuVNqDc3d3p1atWqxYsYJGjRrx66+/8vjxY5YuXaqU3DK0aNGCly9fqrJKoTg5cABev4ZWrYpUcgO4UKcp/zd4DukyPVr98TOf/bxW0yEVSyrtwVlbW/P999/TvXv3XHUr1KlTJ+7fv6/KKoXiZM4cqFABevQANfafVlhC67dkXVICw39cSOeDP/CiVBnWvXOOTih4Ku3B/fDDD/Tu3Tvb5JaUlERkZKRi2MzMjAoVKqiySkHXvX4tf3Ae5Id0gwZBiRKajUkFx5t+yo5PhwIw8KdlNI28reGIiheVElzFihXZs2dPtuP37t1LxYoVVVmFUJykpUGvXtC+vfx2EB2xu50fx5p2BCQc4sUpmsKk0iFqTk8tvH37Fj0tvJwvaKnp02HfPvnh6D//QIMGOc9TFMhkbOw9iT8bteVgzFMIPaTpiIqNPCe4uLg4Xr3zv+uLFy+UDkMzvHr1im3btmFvb69SgEIxsWULLF4s/zsoSHeS23/S9A24VcVdkdxM3qagn/pW3BRcwPKc4JYvX664zUMmkzF+/HjGjx+f5bSSJDFv3jyVAhSKgYsXYcgQ+d8zZsgPU3VYRWDN9qXcq+HBxt6TNB2OTstzgmvdujUlSpRAkiQmT55Mr169qFevntI0MpkMc3Nz6tevTwMd+59YULMXL6BLF/lTCu3aQUCApiMqcG5ApegnuJzcQ3iFapz8qIOmQ9JZeU5wjRs3pnHjxgC8fv2arl27UqtWLbUHJhQTQ4dCRIT8IfotW0BfX9MRFbjfgI2N2zPkzK8M/ulrHji5EuHoqumwdJJKVwD8/f1FchNUM2sW1KwpfwTL2lrT0RSaHzzacKFWE4zevmHC2hmYJiVoOiSdlKc9uLlz5yKTyZgxYwZ6enq5euRKJpMxa9asXC0/ISGBJUuWEBoaSlhYGC9fviQoKAg/P79czf/q1SsmT57Mnj17SExMxMPDg6VLl2Y6hBa0SN26cPmyVj48X5AkmR6rBs5m4Xw/7J4/5vMfFrB82HzxOJea5SnBzZkzB5lMxpQpUzAyMmLOnDk5zpOXBBcdHc3cuXNxcnKiTp06nDhxItexpaen0759ey5fvsykSZOwsbFh9erVeHt7c/78eapUqZLrZQkF7NkzePRIXksBil1yy/Da3JLlw+Yxd/HneF44wcd//sKx5p01HZZOydM3Kz09nbS0NIz+eyYwPT09x1daWu57UrC3tycqKoqIiAiWLFmSpzcSEhLCX3/9RXBwMP7+/owaNYoTJ06gr6+Pv79/npYlFKD0dHlX440ayc+5FXP3nKuztetI7lSqyeUanpoOR+doVdlAY2Nj7Ozs8jVvSEgIZcuWpWvXroo2W1tbevTowebNm0lJScG4CD7PqHNWrICDB8HEBOqIOqMABz7uyaEWvqTpa9XPUSeo/dggMTGRjRs3smbNGiIiItS9+GxdvHiRevXqZXpywsPDg8TERO7cuZPtvCkpKcTFxSm9hAJw8SJMnSr/e8UK+cUFAWQypeTmEPVAc7HoGJUS3ODBg6n5zpf0zZs3NGrUiCFDhjBq1Cjc3d25ePGiykHmRlRUVJZPTWS0PXnyJNt5AwMDsbKyUrwcHR0LLM5iKzEReveGt2/lfbsNG6bpiLSPJNEn5FuWzelNg0snNR2NTlApwR0/flzpkHDr1q1cu3aNLVu2cO3aNezs7AgopBs3k5KSsjwENTExUYzPzrRp04iNjVW8Hj58WGBxFluTJsGtW2BvD+vXi6uFWZHJkP7bLsN+XIRV7IscZhByolKC+/fff3F2dlYM//zzzzRo0IBevXpRvXp1hg4dSmhoqKox5oqpqSkpKSmZ2pOTkxXjs2NsbIylpaXSS1Cj06dh9Wr535s2gY2NZuPRYjs+HcqD8lWwTHjF5z8sECUIVaRSgjM3N1c8eJ+amsqJEydo06aNYryFhQWxsbEqBZhbGVdg35fR5uDgUChxCFlo1AiWLIGJE+W98wrZSjU04tvB/rwxMKLutTO0+iP77siEnKmU4OrVq8e6deu4ePEi8+fPJz4+no4dOyrGh4eHU7ZsWZWDzA13d3cuXLhA+nvl5EJDQzEzM8PVVTwKozH6+vLklsdbf4qrRw6V+KnLCAD6hHxL2Wei7GZ+qZTg5s+fz7Nnz2jQoAEBAQF069YNDw8Pxfg9e/bw0UcfqRzk+6Kiorh16xZv375VtPn6+vL06VN2796taIuOjmbnzp107NhR3CKiCefOyS8uCHl2sGV3rlWth8mbZEYEz0OmRXVgixKVbrxp0KABt27d4q+//sLa2hovLy/FuFevXjFy5Eilttz49ttvefXqleKq5759+xSFo8eMGYOVlRXTpk1j06ZN3L9/X3EO0NfXl0aNGjFw4EBu3LiheJIhLS2t0C50CO+IipIXai5TRl7XVHRVnyeSnh5r+89g9tKRHPHqorj4IOSNyncW2tra0qlTp0zt1tbWjBs3Ls/L+/rrr5Xun9u9e7dir6xv375YWVllOZ++vj4HDhxg0qRJrFy5kqSkJBo2bEhwcDBVq1bNcxyCCiRJfhvIy5dQqRKI85/58tzGnvHzdoobgFWgli0XHx9PREQEL1++zLIb8+bNm+d6WQ8ePMhxmuDgYIKDgzO1lyxZkvXr17N+/fpcr08oAD/8AL/+Ki/zt2kTGIpea/Pr3eRm/jqOJBMz0kXCyzWVttSLFy8YPXo0u3btyvKZU0mSkMlkeXoeVSjiHj+GjD33gACoUUOz8egI92tnGL5pAQdbdueXtv01HU6RoVKCGzp0KPv27WPs2LE0a9aMkiVLqisuoSiSJBg+HGJjoWFD+ZVTQS0s419SMu4Fvr9u4FydZjx2ENXqckOlBHf48GEmTJjA4oxiIULxtnkz7N8vPzQNDgYDcSilLn80akujc8eod+0MIzbNZ/bk78Shai6odJuImZmZ0pMMQjHn5QVt2oC/P1SvrulodItMxvq+U0g0McflwQ3aHd2u6YiKBJX+C+jbty979uxh5MiR6opHKMqcnOC33+R9vuVBZGQk0dHRagnh5s2balmONoopWYYfu49l+I+B9Ni3jvPuzYgpgPWo8/PIYGNjg5OTk1qXmRsqJThfX19OnjyJj48Pw4YNw9HREf0sioaILsN13IsXULq0/G+ZLE+FYyIjI3Fzq0ZSknpvCH6b8katy9MWxz/qQONzR6l98yzDfwhkXI+xal1+QX0epqZm3Lp1s9CTnEoJrmnTpoq/jxw5kmm8uIpaDERHyw9HO3aEb76BEiXyOHs0SUmJeA7yx9LeWeVwoq6e4dre70lNTVV5WVpJJuP7flNZEtCPZzb2GL/zNI86qPvzAIiLekDoxgCio6OLVoILCgpSVxxCUTV+PDx/DqGh8osL+WRp70wpJ9VvyI4rBp1FRpe258uArcSULENy5O0CWYe6Pg9NUynBDRgwQF1xCEXR/v3yugp6erBxo0oJTsibmJJllBtEt0pZUluX5VFRUVy+fJnXr1+ra5GCFtNLSIDPP5cPTJgA73SyIBSe0nEv+RkotW+fpkPRSionuF9++QU3NzfKly9PvXr1FB1cRkdHU7duXfbsEf1Z6aJyK1fKS/9Vrgy5qI8rFIwWN/6mE1B++XL4919Nh6N1VEpw+/bto2vXrtjY2ODv76/0HKqNjQ3lypXL8plRoWhrDtju2iUfWL8ezMw0Gk9xttujNecBg7g4GDNG0+FoHZUS3Ny5c2nevDmnTp1i1KhRmcY3bty40IrOCIXHHHhbqpS8xxBvb02HU6yl6+kzGJD09SEkBN7pD1FQMcFdu3aNHj16ZDu+bNmyPHv2TJVVCFroN+DGzp2ih14tcRl4mnHBb9QoeTdVAqCGR7U+dFHh3r17lM64AVTQKWnW1iCK82iNqCFDoGpV+Xk40cmBgkoJrkWLFmzatCnLmyr//fdf1q1bR+vWrVVZhaAl9NNSmb5iHN7XC6dKmpA3krExbNggf5LkxAkQxcsBNdRkePToEQ0bNmTt2rXIZDIOHTrEzJkzqVWrFpIk4e/vr65YBQ3qeGgztW+eZdShzWTdp7KgcR99BLt2wZUrYu/6PyoluKpVq3Lq1ClKly7NrFmzkCSJJUuWsGDBAmrVqsWff/4pehvRAQ5RD+i2X/7UyprWvSmcQpBCvnTpAubmmo5Ca6jcoVSNGjU4evQoL1++5J9//iE9PZ1KlSpha2urjvgEDZOlp/H5DwswTH3LhVpN+L1GI/jle02HJeQkPV1ebLt+fWjcWNPRaEy+E1xKSgqbN2/m8OHDhIeHEx8fj4WFBS4uLvj4+NC7d2+MxKM7RZ7P8RBc710j0cSMDb0nQcIrTYck5EZgIMycCdWqwcWLUEzLZubrEPXq1atUq1aNYcOGsXPnTsLDw0lMTCQ8PJwdO3YwePBgatSoodN9cxUHZZ4/pufPawHY2nUUL0oVThFvQQ1GjICyZeHmTfjqK01HozF5TnAJCQl8+umnPH36lPnz5/Pw4UNevnyp9O+8efN48uQJHTt2FM+mFmF1r/6FyZtkrrvW41izzKUhBS1WqhSsWiX/e+FC+V5cMZTnQ9SgoCAiIyM5duwY3lncxV6uXDmmTZuGp6cnrVq1Ijg4OMunHATtd6hldx45VCS6tB2Sntr6ZRAKS7du8teuXTBoEISFFbsSjnn+1u7fv5/WrVtnmdze1bJlS1q1asU+0ctBkXbdrQFPbctrOgwhv1atku/NXboEixZpOppCl+cEd/Xq1RyTW4aWLVty9erVvK5C0CRJwnfvemyjozQdiaAOZcvCypXyvwMD5d3LFyN5TnAxMTHY2dnlatqyZcsSE1MQZTGEguJ15gC++zcyP3Awxsnq7Zdf0JDeveWPb5069b/aGcVEns/BpaSkYJjL43gDAwPevNHN4h+6qOTL5/Tf8Q0Av7b6jBQT0Q2STpDJim3HCPm6D+7BgwdcuHAhx+nu37+fn8ULmiBJDN28EPOkBP5xrs6vrXprOiKhoFy5Iu9e3s1N05EUuHwluFmzZjFr1qwcp8uoqiVoP68zB6h37QxvDQxZM2CGqJquq3bsgL59oU4dOHMGDHT7c87XbSKCbikd85QB21cAsLPjEB47VNRsQELBadpU/qzquXPyq6ozZmg6ogKV5wQnKmnpns6//YBZ8mvuVKzBvtbi0FSnOTjA//0f9OsHAQHQoYN8b05H6fb+qZArP/QYy2uzEpxs3A5JL/dV6YUiqk8f+c2/P/8M/fvLbwDW0WdVxe3pAm8NjdnWZQRRdhU0HYpQGGQyWLsWbG3lFxwCAjQdUYERCa6YkqWn0eLUPvTSMvfGLBQDZcrIkxzIz8VduqTRcAqKOEQtpjod3Mxnv6yl8bmjLBi3Qv6/ulC8dOkir4zm5ga1a2s6mgIhElwxVDHiFr771gNwyrONSG7FWcZenI4Sh6jFjNGbZEZvDMAgPY3Qet780aitpkMStMXr15jqWB+OWpfgUlJSmDJlCg4ODpiamuLp6cmRI0dynG/OnDnIZLJMLxMTk0KIuujoG/J/lPs3ghgrG9b1mSL23gS5+/ehbl1cRo8md0+aFw1ad4jq5+dHSEgI48ePp0qVKgQHB9OuXTuOHz9O06ZNc5x/zZo1lChRQjGsry9ue8hQ//KftD65B4A1fjNJKCHqYwn/cXAAc3MM795lE7BUStd0RGqhVQkuLCyMbdu2sWTJEib+V7y2f//+1KxZk8mTJ/PXX3/luAxfX19sbGwKOtQiRz8tFb/tywH4tVUvrlb30HBEglYxNoaffiLd3Z3WKSncDzvC8QrVNB2VyrTqEDUkJAR9fX2GDRumaDMxMWHw4MGcOXOGhw8f5rgMSZKIi4tDkqSCDLXISdM3IHDMMv70bMO2TsM1HY6gjdzcePTllwAM+n0nFSNuaTgg1WlVgrt48SKurq5Yvle01sNDvrdxKRf36lSqVAkrKyssLCzo27cvT58+zXGelJQU4uLilF666Im9M6sG+ZNqKKqdCVmL7tqVEMAwPY2x62djkly0a6poVYKLiorC3t4+U3tG25MnT7Kdt2TJkowePZq1a9cSEhLCkCFD2L59O82aNcsxYQUGBmJlZaV4OTo6qvZGtEgjoFbkbU2HIRQVMhlDgaeWpbF/9ohee77TdEQq0apzcElJSRhn8UxcxpXQpKSkbOcdN26c0nC3bt3w8PCgT58+rF69mqlTp2Y777Rp0/jiiy8Uw3FxcTqR5PRfvmQH4LB5EcssSnK+TjNNhyQUAa+AwM7D8Tt3jN3t/DQcjWq0ag/O1NSUlJSUTO3JycmK8XnRu3dv7OzsOHr06AenMzY2xtLSUulV5KWn4+zvjyPwpGRZrletp+mIhCLkhmMVFo5bTqxV0e7iXKsSnL29PVFRmYudZLQ5ODjkeZmOjo7Fsy7EwoVYnT5NEjCv60iSTcw1HZFQhDW8cALTpARNh5FnWpXg3N3duXPnTqZzZqGhoYrxeSFJEg8ePMDW1lZdIRYNx47Bfz0ujwHuly36h9uC5nT9dSNfrp3O55sWQBG7O0GrEpyvry9paWl8//33iraUlBSCgoLw9PRUnBeLjIzk1i3lS9jPnz/PtLw1a9bw/PlzfHx8CjZwbfLwIfTqBenpRHfqxAZNxyMUeZdrNCJV3wDPiyfocHirpsPJE626yODp6Un37t2ZNm0az549w8XFhU2bNvHgwQM2bPjfT7V///6cPHlS6V63ChUq0LNnT2rVqoWJiQmnTp1i27ZtuLu7M3x4wd33FRkZSXR0tNqWZ2Njg5OTU/4XsGkTPH8O7u48nDwZfvlFbbG966aanllU13KKu4L8PMIrVmdTz/EM3vo1vfes4YGTK9eqNVTL+gqaViU4gB9++IFZs2bx448/8vLlS2rXrs2vv/5K8+bNPzhfnz59+Ouvv9i1axfJyclUqFCByZMnM2PGDMzMCqb8XWRkJG5u1UhKUl/9UFNTM27dupn/JDdjhrySedu2SC9fqi2uDEmxLwAZffv2Vety36aI8pL5UVifx5HmXah8/wbeZw4wdt1sZkzfyHObzLd0aRutS3AmJiYsWbKEJR+o43jixIlMbevWrSvAqLIWHR1NUlIinoP8sbR3Vnl5cVEPCN0YQHR0dP4TnEwGI0fK/y6ABPc2MR6QcO89BduKqpedi7p6hmt7vyc1VXS8mR+F9nnIZGzoMwnHJ/eoHHGLL9dMxX/yd6QY5+3OhsKmdQmuKLK0d6aUU1XNBXDsGHz7rfzwtJBucSlRxkkt7zku6oHqwQiF8nm8NTRm2eeBzF8wCOdHd3G/dobQ+i1VXmdBEgmuqLt7F7p3l++tLVoE8+drOiJBh70oVZZlnwdiHfeCsHotNB1OjkSCK8piYqB9e3ly8/RU3BoiCAXpjst73ZtLktb2K6hVt4kIefDmDXTtKt+Dq1BBfrVUdO4pFLJSL5/x1aJhVP3nsqZDyZJIcEVRejoMHAgnT4KFBfz6K5Qtq+mohGKoy4Fgqty/zsTVU7D/N0LT4WQiElxRFBAAW7eCgYG8gG/NmpqOSCimfuw+lrsVa2DxOo5pK7/AKvaFpkNSIhJcUdSli7yL6Q0boFUrTUcjFGNvjExYPGoxUWXKU+ZFFNNWfqFVz6yKBFcUubvDrVvQv7+mIxEE4i1KsnDsMl5ZlsL50V0mrZqM4ZvMvQJpgkhwRcW+ffJzbhksLDQXiyC856lteQLHLiPRxJzqdy/RL2SlpkMCRIIrGg4fBl9f8PGBixc1HY0gZCnC0ZXFoxZzz6kqe9r6aTocQNwHp/2OH4fOneW3hXTrBrVqaToiQcjWLde6zJi2AUlPO/adRILTZsePy2/kTUqS/5tx5VQQtNi7ye2j0ENUPXeUYxqKRfxatNXhw/I9t6QkaNdOfjuIkaiGJRQdJV8+Z/gPgRilvuFHgLdvCz0G7diPFJSYXb8OHTv+b89t9255YV5BKEJelrTl/wbPIVVPn95ApalTIYuaKwVJJDgtlFi1KnToID/nJpKbUISdredNgO9okgHrEyfk3+v4+EJbv0hwWkQvPV3+h4EB/PQTbNsmDkuFIi+0ijvtgTRTUzh6FFq2lPc6XQhEgtMCsvQ0Bmxfzpe/vlNBwchIXFAQdMbvwN3vvoPSpeHcOfl/4IVA/II0zDg5kdEbA2h4+U8Ammg4HkEoKIk1a8Lp0/DDDzBmTKGsU+zBaZBtdBRzl3xOw8t/8sbAiHldRvCXpoMShIJUtaq8U9aM/uPi42HHjgJbnUhwGlL99nnmLRxMhUf/8MqyFPO++D/+qO6h6bAEofCkp0O/ftCzJ4waJb+ZXc1EgtOAT07uYebycVjFv+K+oyszpm3gTmXxhIJQzMhkUL++/O/Vq8HbG94r+q4qkeA04LG9M5JMxsnG7Zg9eS0vSonOKoViSCaTd7O/bx9YW4OTk9o7kRAXGTTgpmtdps0IIrJcZa3ty14QCk2HDnD+PJQpo/bfg0hwGhJZ3kXTIQiC9qhUqUAWKw5RBUHQWSLBCYKgs0SCEwRBZ4kEJwiCzhIJThAEnSUSnCAIOkskOEEQdJZIcIIg6CyR4ARB0FkiwQmCoLNEghMEQWeJBCcIgs4SCU4QBJ2ldQkuJSWFKVOm4ODggKmpKZ6enhw5ciRX8z5+/JgePXpgbW2NpaUlnTp14t69ewUcsSAI2krrEpyfnx/Lli2jT58+fPPNN+jr69OuXTtOnTr1wfkSEhJo0aIFJ0+eZPr06QQEBHDx4kW8vLx48eJFIUUvCII20ar+4MLCwti2bRtLlixh4sSJAPTv35+aNWsyefJk/vor+5Isq1ev5u7du4SFhdGwYUMA2rZtS82aNVm6dCkLFiwolPcgCIL20Ko9uJCQEPT19Rk2bJiizcTEhMGDB3PmzBkePnz4wXkbNmyoSG4Abm5ufPzxx+wowKo9giBoL63ag7t48SKurq5YWloqtXt4yKtNXbp0CUdHx0zzpaenc+XKFQYNGpRpnIeHB4cPHyY+Ph6LbPp7T0lJISUlRTEcGxsLQFwOBTASEhIAiIm4TWpK0genzY24fyMBOH/+vGLZqrh9+zagvvgA4qIiAIh9fBdDA9W7lxbL0+3lwf++1wkJCTn+pvLCwsICWU5dnEtapEaNGlLLli0ztV+/fl0CpO+++y7L+Z4/fy4B0ty5czONW7VqlQRIt27dyna9/v7+EiBe4iVeRegVGxubY07Rqj24pKQkjI2NM7WbmJgoxmc3H5CveQGmTZvGF198oRhOT08nJiaG0qVL5/w/hAbExcXh6OjIw4cPM+3tCnkjtqV6aGI7ZndE9i6tSnCmpqZKh4oZkpOTFeOzmw/I17wgT4zvJ0dra+tcxaxJlpaW4kepJmJbqoe2bUetushgb29PVFRUpvaMNgcHhyznK1WqFMbGxvmaVxAE3aVVCc7d3Z07d+5kOhEZGhqqGJ8VPT09atWqxblz5zKNCw0NpVKlSrnanRUEQbdoVYLz9fUlLS2N77//XtGWkpJCUFAQnp6eiiuokZGR3Lp1K9O8Z8+eVUpyt2/f5vfff6d79+6F8wYKibGxMf7+/lmecxTyRmxL9dDW7SiTJEnSdBDv6tGjB3v27GHChAm4uLiwadMmwsLCOHbsGM2bNwfA29ubkydP8m7o8fHx1K1bl/j4eCZOnIihoSHLli0jLS2NS5cuYWtrq6m3JAiCpuR4nbWQJSUlSRMnTpTs7OwkY2NjqWHDhtLBgweVpvHy8pKyCv3hw4eSr6+vZGlpKZUoUULq0KGDdPfu3cIKXRAELaN1e3CCIAjqolXn4ARBENRJJDhBEHSWSHBFxKtXrxg2bBi2traYm5vTokULLly4kKt5/fz8kMlkmV5ubm4FHLXmiH4F1SO/23HOnDlZfucyniwqLFr1JIOQtfT0dNq3b8/ly5eZNGkSNjY2rF69Gm9vb86fP0+VKlVyXIaxsTHr169XarOysiqokDXOz8+PkJAQxo8fT5UqVQgODqZdu3YcP36cpk2bZjtfRr+CsbGxTJ8+HUNDQ5YvX46XlxeXLl2idOnShfguNC+/2zHDmjVrKFGihGJYX1+/IMPNTNNXOYScbd++XQKknTt3KtqePXsmWVtbS7169cpx/gEDBkjm5uYFGaJWCQ0NlQBpyZIlirakpCSpcuXKUuPGjT8476JFiyRACgsLU7TdvHlT0tfXl6ZNm1ZgMWsjVbZjRgcWz58/L+gwP0gcohYBISEhlC1blq5duyrabG1t6dGjB7/88kuWz+BmJS0tTa3d1Wgr0a+geqiyHTNIkkRcXJzSPauFSSS4IuDixYvUq1cPPT3lj8vDw4PExETu3LmT4zISExOxtLTEysqKUqVKMWrUKLX0OaeNctOvYFYy+hVs0KBBpnEeHh6Eh4cTHx+v9ni1VX6347sqVaqElZUVFhYW9O3bl6dPnxZEqNkS5+CKgKioKMVTHO+yt7cH4MmTJ9SqVSvb+e3t7Zk8eTL16tUjPT2dgwcPsnr1ai5fvsyJEycwMNCtr0FUVJRi27zr3e2VlZiYGFJSUnKct2rVqmqMVnvldzsClCxZktGjR9O4cWOMjY35888/WbVqFWFhYZw7d67QehzRrW92EZCens6bN29yNa2xsTEymSzf/eRlCAwMVBr+7LPPcHV1ZcaMGYSEhPDZZ5/lMvqiQVP9CuoaVb5348aNUxru1q0bHh4e9OnTh9WrVzN16lT1BpsNcYhayP744w9MTU1z9crocjy//eR9yIQJE9DT0+Po0aOqvSEtpKl+BXWNur93vXv3xs7OrlC/c2IPrpC5ubkRFBSUq2kzDgXy20/eh5iamlK6dGliYmLyPK+2s7e35/Hjx5naRb+CeZPf7fghjo6OhfqdEwmukNnZ2eHn55enedzd3fnzzz9JT09XutAQGhqKmZkZrq6ueY4jPj6e6Ohonexlxd3dnePHjxMXF6d0rkf0K5g3+d2O2ZEkiQcPHlC3bl11hvlB4hC1CPD19eXp06fs3r1b0RYdHc3OnTvp2LGj0nmS8PBwwsPDFcPJyclZXvn76quvkCQJHx+fgg1eA0S/guqhynZ8/vx5puWtWbOG58+fF+53TqN34Qm5kpqaKjVq1EgqUaKEFBAQIK1atUqqUaOGZGFhkalaWIUKFaQKFSoohu/fvy9ZW1tLI0aMkL755hvpm2++kdq1aycBko+Pj5SWllbI76ZwdO/eXTIwMJAmTZokrV27VmrSpIlkYGAgnTx5UjFNVt1uxcXFSZUrV5bKlCkjLV68WFq+fLnk6OgoOTg4SM+ePSvst6Fx+d2Opqamkp+fn7R06VJp1apVUq9evSSZTCa5u7tLr1+/LrT4RYIrImJiYqTBgwdLpUuXlszMzCQvLy/p7NmzmaZ7P8G9fPlS6tu3r+Ti4iKZmZlJxsbGUo0aNaQFCxZIb968KcR3ULhEv4Lqkd/tOGTIEKl69eqShYWFZGhoKLm4uEhTpkyR4uLiCjN80R+cIAi6S5yDEwRBZ4kEJwiCzhIJThAEnSUSnCAIOkskOEEQdJZIcIIg6CyR4ARB0FkiwQmCoLNEghMEQWeJBCfonIySdbkhk8mYM2dOgcbj7e2Nt7d3ga5DyJpIcEKBCg4OVqqLaWBgQLly5fDz88uyrzFBUCfRH5xQKObOnUvFihVJTk7m77//Jjg4mFOnTnHt2jW1FwOeOXNmoXWJLWg3keCEQtG2bVtFtaohQ4ZgY2PDokWL2Lt3Lz169FDrugwMDHSukI6QP+IQVdCIZs2aASh1znnr1i18fX0pVaoUJiYmNGjQgL179yrN9/btWwICAqhSpQomJiaULl2apk2bcuTIEcU0WZ2DS0lJYcKECdja2mJhYcGnn37Ko0ePMsXl5+eHs7NzpvaslhkUFETLli0pU6YMxsbGVK9enTVr1uR5WwgFR/w3J2jEgwcPAHl5OYDr16/z0UcfUa5cOaZOnYq5uTk7duygc+fO7Nq1iy5dugDyRBMYGMiQIUPw8PAgLi6Oc+fOceHCBVq1apXt+oYMGcLmzZvp3bs3TZo04ffff6d9+/YqvYc1a9ZQo0YNPv30UwwMDNi3bx8jR44kPT2dUaNGqbRsQU0Ktfc5odgJCgqSAOno0aPS8+fPpYcPH0ohISGSra2tZGxsLD18+FCSJEn6+OOPpVq1aknJycmKedPT06UmTZpIVapUUbTVqVNHat++/QfX6e/vr9QB46VLlyRAGjlypNJ0vXv3lgDJ399f0TZgwAClDkOzW6YkSVJiYmKm6dq0aSNVqlRJqc3Ly0vy8vL6YMxCwRCHqEKh+OSTT7C1tcXR0RFfX1/Mzc3Zu3cv5cuXJyYmht9//50ePXooiuFER0fz4sUL2rRpw927dxVXXK2trbl+/Tp3797N9boPHDgAwNixY5Xax48fr9J7erdsXmxsLNHR0Xh5eXHv3j1iY2NVWragHuIQVSgUq1atwtXVldjYWDZu3Mgff/yhKJbzzz//IEkSs2bNYtasWVnO/+zZM8qVK8fcuXPp1KkTrq6u1KxZEx8fH/r160ft2rWzXXdERAR6enpUrlxZqV3VCvWnT5/G39+fM2fOkJiYqDQuNjYWKysrlZYvqE4kOKFQeHh4KK6idu7cmaZNm9K7d29u375Neno6ABMnTqRNmzZZzu/i4gJA8+bNCQ8P55dffuHw4cOsX7+e5cuX89133zFkyBCV48zuBuG0tDSl4fDwcD7++GPc3NxYtmwZjo6OGBkZceDAAZYvX654T4JmiQQnFDp9fX0CAwNp0aIF3377LYMGDQLA0NCQTz75JMf5S5UqxcCBAxk4cCAJCQk0b96cOXPmZJvgKlSoQHp6OuHh4Up7bbdv3840bcmSJXn16lWm9oiICKXhffv2kZKSwt69e3FyclK0Hz9+PMf4hcIjzsEJGuHt7Y2HhwcrVqzA0tISb29v1q5dm2VV+XdrbL548UJpXIkSJXBxcSElJSXbdbVt2xaAlStXKrWvWLEi07SVK1cmNjaWK1euKNqioqLYs2eP0nT6+vqAvJhxhtjYWIKCgrKNQyh8Yg9O0JhJkybRvXt3goODWbVqFU2bNqVWrVoMHTqUSpUq8fTpU86cOcOjR4+4fPkyANWrV8fb25v69etTqlQpzp07R0hICKNHj852Pe7u7vTq1YvVq1cTGxtLkyZNOHbsGP/880+maT/77DOmTJlCly5dGDt2LImJiaxZswZXV1cuXLigmK5169YYGRnRsWNHhg8fTkJCAuvWraNMmTJZJmlBQzR9GVfQbRm3iWRVwzUtLU2qXLmyVLlyZSk1NVUKDw+X+vfvL9nZ2UmGhoZSuXLlpA4dOkghISGKeebNmyd5eHhI1tbWkqmpqeTm5ibNnz9fqcZrVrd0JCUlSWPHjpVKly4tmZubSx07dpQePnyY6TYRSZKkw4cPSzVr1pSMjIykqlWrSps3b85ymXv37pVq164tmZiYSM7OztKiRYukjRs3SoB0//59xXTiNhHNEXVRBUHQWeIcnCAIOkskOEEQdJZIcIIg6CyR4ARB0FkiwQmCoLNEghMEQWeJBCcIgs4SCU4QBJ0lEpwgCDpLJDhBEHSWSHCCIOgskeAEQdBZ/w9+7n79YzR86wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 300x250 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# SuppFig 2g - Residual distribution of mean-variance fit\n",
+    "resid_std = model.resid.std(ddof=1)\n",
+    "print(resid_std)\n",
+    "plt.figure(figsize=(3, 2.5))\n",
+    "sns.histplot(model.resid, bins=10, stat='density', label='Observed Residual')\n",
+    "sns.lineplot(x=np.linspace(-0.6, 0.6, 100), y=stats.norm(0, resid_std).pdf(np.linspace(-0.6, 0.6, 100)), c='r', ls='--', label='Fitted Normal')\n",
+    "sns.despine()\n",
+    "plt.ylim(0, plt.ylim()[1] * 1.4)\n",
+    "plt.legend(frameon=False, fontsize='small')\n",
+    "plt.xlabel('Residual')\n",
+    "plt.savefig('../../figures/SuppFig2/SuppFig2g_mean_variance_residuals.pdf')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "513ee975-9723-43b8-88f2-9c0f730f50ee",
+   "metadata": {},
+   "source": [
+    "## Clone size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "43fbcad0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[np.float64(0.2550120234076684), np.float64(1151.0), np.float64(1.0)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x250 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# SuppFig 2h - Clone size distribution fit to Boltzmann\n",
+    "def remove_outliers(sr, iq_range=0.8):\n",
+    "    #  https://stackoverflow.com/a/39424972\n",
+    "    pcnt = (1 - iq_range) / 2\n",
+    "    qlow, median, qhigh = np.quantile(sr[~np.isnan(sr)], [pcnt, 0.50, 1 - pcnt])\n",
+    "    iqr = qhigh - qlow\n",
+    "    return sr[np.abs((sr - median)) <= iqr]\n",
+    "\n",
+    "\n",
+    "fp = f'{BASE_PATH}/10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein_preprocessed.h5mu'\n",
+    "mdata = mu.read(fp)\n",
+    "\n",
+    "# fit clonotype size distribution\n",
+    "clone_size = mdata.mod['airr'].obs.groupby(\"clone_id\", dropna=False).size()\n",
+    "rv = stats.boltzmann\n",
+    "bounds = [(0, 10000), (1, np.max(clone_size)), (1, 1)]\n",
+    "clone_size = remove_outliers(clone_size, 0.8)\n",
+    "res = stats.fit(rv, clone_size, bounds)\n",
+    "print(list(res.params))\n",
+    "\n",
+    "sns.histplot(clone_size, discrete=True, bins=100, stat='density', label='Observed')\n",
+    "x = np.arange(clone_size.min(), clone_size.max())\n",
+    "pdf = rv.pmf(x, *res.params)\n",
+    "sns.lineplot(x=x, y=pdf, color='r', ls='--', label='Fitted Boltzmann')\n",
+    "plt.legend(frameon=False)\n",
+    "plt.xlabel('Clone size')\n",
+    "plt.savefig(f'../../figures/SuppFig2/SuppFig2h_clone_size_fit.pdf')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bef513d2-a4bd-432b-aa04-67a481372226",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Relationship between noise and control (for negative control model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ae0d1844",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fitted distribution parameters are used for DextraDemixer negative control extension\n",
+    "epitope_params = params[~params['gene'].str.contains('negative')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "04d58df1-9ee9-4515-88e2-b4fd5170b54c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "log-normal params:  0.9692917285815053 0.629397707490648\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Mean ratio between noise and negative control\n",
+    "# Weighted MoM lognormal params\n",
+    "ns, means = epitope_params['N-'].to_numpy(float), epitope_params['noise_to_neg_mean_ratio'].to_numpy(float)\n",
+    "w = ns / ns.sum()\n",
+    "w = 1 / ns.shape[0]\n",
+    "m = np.sum(w * means)\n",
+    "v = np.sum(w * (means - m)**2)\n",
+    "sigma2 = np.log(1 + v / m**2)\n",
+    "sigma  = np.sqrt(sigma2)\n",
+    "mu_ln  = np.log(m) - 0.5 * sigma2\n",
+    "print(f'log-normal params: ', mu_ln, sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "3815806a-d8a8-4329-865b-bfb3605cb806",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "log-normal params for s:  0.19724303327974957 0.4397080632187905\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Overdispersion ratio between noise and negative control\n",
+    "# Weighted MoM lognormal params\n",
+    "ns, means = epitope_params['N-'].to_numpy(float), epitope_params['noise_to_neg_overdisp_ratio'].to_numpy(float)\n",
+    "w = ns / ns.sum()\n",
+    "w = 1 / ns.shape[0]\n",
+    "m = np.sum(w * means)\n",
+    "v = np.sum(w * (means - m)**2)\n",
+    "sigma2 = np.log(1 + v / m**2)\n",
+    "sigma  = np.sqrt(sigma2)\n",
+    "mu_ln  = np.log(m) - 0.5 * sigma2\n",
+    "print(f'log-normal params for s: ', mu_ln, sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e264f2e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/synthetic_benchmark/SuppFig3_Examples_of_simulated_datasets.ipynb b/experiments/synthetic_benchmark/SuppFig3_Examples_of_simulated_datasets.ipynb
new file mode 100644
index 0000000..97f0930
--- /dev/null
+++ b/experiments/synthetic_benchmark/SuppFig3_Examples_of_simulated_datasets.ipynb
@@ -0,0 +1,190 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "06f47777",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Changed directory to: /lustre/groups/imm01/workspace/yang/DextraDemixer/experiments/synthetic_benchmark\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Only needed for VS Code - change cwd to the notebook's directory for VS Code \n",
+    "import os\n",
+    "\n",
+    "try:\n",
+    "    notebook_path = globals()['__vsc_ipynb_file__']\n",
+    "    notebook_dir = os.path.dirname(notebook_path)\n",
+    "    os.chdir(notebook_dir)\n",
+    "    print(f\"Changed directory to: {notebook_dir}\")\n",
+    "except:\n",
+    "    print(\"ERROR. Could not find notebook path. Running from:\", os.getcwd())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ebe6d605",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import matplotlib.patches as mpatches\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c41a5490",
+   "metadata": {},
+   "source": [
+    "# Example of simulated datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4526e51f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "EXPERIMENT_PATH = 'benchmarks/synth_benchmark'\n",
+    "os.makedirs('../../figures/', exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ab9f45ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/dextrademixer/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "/opt/conda/envs/dextrademixer/lib/python3.13/site-packages/muon/_core/preproc.py:31: FutureWarning: `__version__` is deprecated, use `importlib.metadata.version('scanpy')` instead\n",
+      "  if Version(scanpy.__version__) < Version(\"1.10\"):\n"
+     ]
+    }
+   ],
+   "source": [
+    "import muon as mu\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"mudata\")\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"pandas\")\n",
+    "warnings.filterwarnings(\"ignore\", message=\".*Transforming to str index.*\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "003f0cc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/dextrademixer/lib/python3.13/site-packages/seaborn/axisgrid.py:123: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "  self._figure.tight_layout(*args, **kwargs)\n",
+      "/opt/conda/envs/dextrademixer/lib/python3.13/site-packages/seaborn/axisgrid.py:123: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "  self._figure.tight_layout(*args, **kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x1260 with 42 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "total_cells = 5000\n",
+    "binding_ratios = [0.001, 0.002, 0.01, 0.02, 0.1, 0.2,]\n",
+    "mean_incs = [10.0, 20.0, 30.0, 40.0, 50.0, 100.0, 200.0] \n",
+    "hue = 'is_binder'\n",
+    "log_scale = True\n",
+    "\n",
+    "dataframes = []\n",
+    "for binding_ratio in binding_ratios:\n",
+    "    for mean_inc in mean_incs:\n",
+    "        mdata = mu.read(os.path.join(EXPERIMENT_PATH, f'simulation/{total_cells},0.4,0.0,{binding_ratio},False,{mean_inc},None,0.h5mu'))\n",
+    "        df_ = mdata.mod['airr'].obs.copy()\n",
+    "        df_['x_umi'] = mdata['gex'][:, 'pmhc1'].X.toarray().flatten()\n",
+    "        df_['FoB'] = binding_ratio\n",
+    "        df_['SNR'] = mean_inc\n",
+    "        dataframes.append(df_)\n",
+    "combined_df = pd.concat(dataframes, ignore_index=True)\n",
+    "\n",
+    "custom_colors = {0: \"#00C3FF\", 1: \"#003eda\", }\n",
+    "hue_order = [0, 1,]\n",
+    "g = sns.FacetGrid( data=combined_df, row='SNR', col='FoB', sharex=False, sharey=False, height=1.8, aspect=1.0, gridspec_kws={'wspace': 0.15, 'hspace': 0.25} )\n",
+    "g.map_dataframe(sns.histplot, x='x_umi', hue=hue, hue_order=hue_order, stat='density', bins=50, palette=custom_colors, legend=False, element='step')\n",
+    "\n",
+    "g.set_titles(\"{row_var}={row_name}\\n{col_var}={col_name}\", y=0.6, fontsize=6)\n",
+    "g.set(yscale='log' if log_scale else 'linear')\n",
+    "g.fig.supxlabel(\"Fraction of Binder (FoB) →\", fontsize='medium', y=0.04)\n",
+    "g.fig.supylabel(\"← Signal-to-Noise Ratio (SNR)\", fontsize='medium', x=0.04)\n",
+    "\n",
+    "g.set(yticklabels=[])\n",
+    "g.set_axis_labels(\"\", \"\")\n",
+    "\n",
+    "for ax in g.axes.flat:\n",
+    "    title_text = ax.get_title()\n",
+    "    ax.set_title(title_text, fontsize=10, y=0.6)\n",
+    "    ax.tick_params(axis='both', which='major', labelsize=10)\n",
+    "    ax.tick_params(axis='y', which='minor', left=False, right=False)\n",
+    "\n",
+    "for ax in g.axes[6, :]:\n",
+    "    ax.set_xlabel(\"UMI count\", fontsize=10)\n",
+    "    \n",
+    "for ax in g.axes[:, 0]:\n",
+    "    ax.set_ylabel(f\"Density\\n({'log' if log_scale else 'linear'})\", fontsize=10)\n",
+    "\n",
+    "legend_handles = [mpatches.Patch(color=color, label={0: 'non-specific', 1: 'specific'}[label]) for label, color in custom_colors.items()]\n",
+    "g.fig.legend( handles=legend_handles, title=\"True binding status\" if hue == 'y_true' else \"Classification Type\", \n",
+    "                loc=\"upper center\", bbox_to_anchor=(0.5, 0.95), ncol=4, frameon=False, fontsize='small')\n",
+    "\n",
+    "g.fig.subplots_adjust(wspace=0, hspace=0)\n",
+    "plt.savefig(f'../../figures/SuppFig3_Sim_examples_{\"log\" if log_scale else \"linear\"}_{hue}.pdf', bbox_inches='tight')\n",
+    "plt.savefig(f'../../figures/SuppFig3_Sim_examples_{\"log\" if log_scale else \"linear\"}_{hue}.png', bbox_inches='tight', dpi=300)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/synthetic_benchmark/benchmarks/dropout/config.yaml b/experiments/synthetic_benchmark/benchmarks/dropout/config.yaml
new file mode 100644
index 0000000..099d588
--- /dev/null
+++ b/experiments/synthetic_benchmark/benchmarks/dropout/config.yaml
@@ -0,0 +1,7 @@
+total_cells: [1000, 10000]
+binding_ratio: [0.1,]
+p_binding_outlier: [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95]
+mean_inc: [20.0, 50.0]
+i: [0, 1, 2, 3, 4]
+
+model_config: ['DextraDemixer', 'DextraDemixer+neg.']
diff --git a/experiments/synthetic_benchmark/benchmarks/scaling/config.yaml b/experiments/synthetic_benchmark/benchmarks/scaling/config.yaml
new file mode 100644
index 0000000..a254193
--- /dev/null
+++ b/experiments/synthetic_benchmark/benchmarks/scaling/config.yaml
@@ -0,0 +1,7 @@
+total_cells: [10000, 20000, 40000, 60000, 80000, 100000, ]
+binding_ratio: [0.01, 0.1]
+p_binding_outlier: [0.0]
+mean_inc: [20.0, 50.0]
+i: [0, 1, 2, 3, 4]
+
+model_config: ['DextraDemixer', 'DextraDemixer+neg.']
diff --git a/experiments/synthetic_benchmark/benchmarks/synth_benchmark/config.yaml b/experiments/synthetic_benchmark/benchmarks/synth_benchmark/config.yaml
new file mode 100644
index 0000000..2c80257
--- /dev/null
+++ b/experiments/synthetic_benchmark/benchmarks/synth_benchmark/config.yaml
@@ -0,0 +1,7 @@
+total_cells: [100, 200, 500, 1000, 2000, 5000, 10000]
+binding_ratio: [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5]
+p_binding_outlier: [0.0]
+mean_inc: [10.0, 15.0, 20.0, 30.0, 40.0, 50.0, 100.0, 200.0,]
+i: [0, 1, 2, 3, 4]
+
+model_config: ['DextraDemixer', 'DextraDemixer+neg.', 'BEAM']
diff --git a/experiments/synthetic_benchmark/environment.yaml b/experiments/synthetic_benchmark/environment.yaml
deleted file mode 100644
index b5165c7..0000000
--- a/experiments/synthetic_benchmark/environment.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-name: dextrademixer
-channels:
-  - conda-forge
-  - bioconda
-dependencies:
-  - python=3.13
-  - pip
-  - snakemake
-  - jupyterlab
-  - pip:
-      - numpy>=1.25.2
-      - scipy>=1.11.4
-      - pandas>=2.1.4
-      - statsmodels>=0.14.1
-      - matplotlib>=3.8.3
-      - jax>=0.5.0
-      - jaxlib>=0.5.0
-      - numpyro>=0.16.1
-      - arviz>=0.17.1
-      - mudata>=0.2.3
-      - muon>=0.1.6
-      - optax>=0.2.2
-      - scanpy>=1.9.8
-      - scirpy>=0.13.0
-      - scikit-learn>=1.6.1  # TODO: fix scikit-learn version
-      - psutil
-