diff --git a/.gitignore b/.gitignore
index 5e99b95..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
diff --git a/dextrademixer/model/Dextrademixer.py b/dextrademixer/model/Dextrademixer.py
index facf550..d035727 100644
--- a/dextrademixer/model/Dextrademixer.py
+++ b/dextrademixer/model/Dextrademixer.py
@@ -3,11 +3,12 @@
import abc
import warnings
import os
+import pickle
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
@@ -26,10 +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
@@ -62,7 +63,8 @@ class DextraDemixer(ApMHCDeconvolution):
"""
- def __init__(self, model_type: str = "mixturemodel", mode: str = "H", alpha_model="overdispersion"):
+ def __init__(self, model_type: str = "mixturemodelkmeans", mode: str = "I", alpha_model="overdispersion",
+ model_config: Dict = None):
super().__init__()
if mode.upper() not in ("H", "I", "C"):
@@ -77,10 +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):
@@ -107,6 +113,8 @@ def preprocess_model_data(self,
ir_key: str = "airr",
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
@@ -120,6 +128,7 @@ 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]
@@ -136,9 +145,36 @@ 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:
+ 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,
- alpha_model=self.alpha_model, **kwargs)
+ 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, outlier_threshold=outlier_threshold, **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():
@@ -194,18 +230,21 @@ 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.AutoMultivariateNormal, 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) \
- -> az.InferenceData:
+ 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:
@@ -223,11 +262,12 @@ def fit_svi(self, guide=npy.infer.autoguide.AutoMultivariateNormal, svi_config:
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)):
@@ -258,11 +298,17 @@ 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)
@@ -273,16 +319,20 @@ 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,))
@@ -290,21 +340,22 @@ 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
def predict_posterior_class(self,
+ data: Dict = None,
threshold: float = None,
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,
+ clonotype_median_p: bool = False,
+ clone_id: Array = None,
) -> Tuple[Array, Array]:
"""
Returns the binder assignments based on the inferred posterior class probabilities.
@@ -322,25 +373,55 @@ 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 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)
+
+ if cred_intvl:
+ # 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_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_median_p:
+ mean_p = df.groupby("clone_id")["p"].quantile(0.5, 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 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:
+ # 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"])
@@ -348,11 +429,12 @@ 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:
- if clonotype_adherence and self.model.data["clone_continuous"] is not None:
+ # 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:
p = __return_p_summary(jnp.exp(self.sampler.get_samples()["log_p"][..., [0,1]]))
@@ -362,9 +444,16 @@ 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
@@ -388,7 +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).
@@ -420,186 +509,263 @@ 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, )
+ 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]
- # 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]
+ 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)
- # Expand shapes: (n_draws, n) vs (T,)
- # -> mask has shape (T, n_draws, n)
- mask = p_samples[None, :, :] >= candidate_thresh[:, None, None]
+ _, (valid_thr, n_discoveries) = jax.lax.scan(eval_threshold, None, candidate_thresh)
- # number of discoveries per draw: (T, n_draws)
- n_disc = mask.sum(axis=2)
+ 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)
- # local fdr = 1 - p
- lfdr = 1.0 - p_samples # (n_draws, n)
+ assignment = (p_mean >= threshold).astype(jnp.int32)
+ return p_mean, assignment, threshold
- # numerator = sum of lfdr among discoveries: (T, n_draws)
- num = jnp.einsum("tns,ns->tn", mask, lfdr)
+ def get_posterior_samples(self, num_samples: int = 1000, seed: int = 42) -> Dict:
+ """
+ Returns posterior samples of model parameters after fitting the model
- # avoid div-by-zero: set fdr_draws=0 where n_disc=0
- fdr_draws = jnp.where(n_disc > 0, num / n_disc, 0.0)
+ Args:
+ num_samples: number of posterior samples to draw
+ seed: random seed to initialize numpyros RNG-Key store
- # posterior probability that FDR ≤ target: (T,)
- # keep thresholds that pass cred_level
- valid = (fdr_draws <= target_fdr).mean(axis=1) >= cred_intvl
+ 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 valid.any():
- n_discoveries = n_disc.mean(axis=1)
- # select threshold that passes credibility interval on FDR with the largest number of discoveries
- threshold_idx = jnp.argmax(jnp.where(valid, n_discoveries, -1))
- threshold = candidate_thresh[threshold_idx]
- else:
- threshold = 1.0
+ 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)
- assignment = (p_mean >= threshold).astype(jnp.int32)
- return p_mean, assignment, threshold
+ # Extract mean from posterior samples
+ q = posterior_samples["delta_q"].mean(0).cumsum(0)
+ w = posterior_samples["w"].mean(0)
- def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
+ 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)
+ 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, ), mode=='I': (2, )
+ alpha = posterior_samples["alpha"].mean(0)
+ else:
+ overdispersion = posterior_samples["overdispersion"].mean(0) + 1
+ if self.mode == "C":
+ # alpha.shape = (C, )
+ q_weighted = (w * q).mean(1)
+ alpha = q_weighted ** 2 / (q_weighted * (overdispersion) - q_weighted)
+ 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
+ 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
+
+ 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, 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.")
+ if y_true is None:
+ 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)
- sns.histplot(x=self.model.data["x"], hue=assignment,
- discrete=True, element="step", alpha=0.7)
+ ax = sns.histplot(x=data["x"], hue=assignment,
+ discrete=True, element="step", alpha=0.3)
+ 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=data["x"], hue=assignment,
+ discrete=True, element="step", alpha=0.3)
+ 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=data["x"], y=p_pred, hue=assignment,
+ markers={0: ".", 1: "X"}, alpha=0.3)
+ 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=data["x"], hue=y_true,
+ discrete=True, element="step", alpha=0.3)
+ 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=data["x"], hue=y_true,
+ discrete=True, element="step", alpha=0.3)
+ 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=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)
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()
+ plt.title("Pred prob and true label")
- # Extract mean from posterior samples
- q = posterior_samples["delta_q"].mean(0).cumsum(0)
- w = posterior_samples["w"].mean(0)
- x = np.arange(0, self.model.data["x"].max())
+ 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')
- # 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)
+ # # 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, 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)
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')
+ 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")
# 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],
- 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")
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))
# 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])
+ 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,
- label=f"q={q[1]:.2f} alpha={alpha[1]:.2f}", color=sns.color_palette('tab10')[1])
+ 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]
+ ax1.legend(handles, labels, frameon=False, loc='best')
+ ax2.get_legend().remove()
sns.despine()
plt.title("Posterior NB without mixing weights")
plt.ylabel("Probability")
# 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]
@@ -615,7 +781,7 @@ 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]
@@ -628,6 +794,7 @@ 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")
@@ -636,11 +803,14 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
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)
- sns.histplot(x=self.model.data["x"], hue=labels, discrete=True, element="step", alpha=0.7, 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)
plt.axvline(cluster_means[0], color="red", linestyle="--")
plt.axvline(cluster_means[1], color="red", linestyle="--")
sns.despine()
@@ -648,7 +818,10 @@ 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=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)
plt.yscale("log")
plt.axvline(cluster_means[0], color="red", linestyle="--")
plt.axvline(cluster_means[1], color="red", linestyle="--")
@@ -659,12 +832,13 @@ def plot_results(self, assignment, p_pred, y_true=None, seed=42, config=''):
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))
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")
@@ -676,14 +850,53 @@ 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()
+ if return_plt:
+ return
plt.close()
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):
"""
@@ -710,19 +923,27 @@ 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
+ # 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),
+ "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),
+ }
+ # 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,
+ "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)],
}
- self.mode = mode
- self.alpha_model = alpha_model
-
- 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.
@@ -735,18 +956,7 @@ 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]
-
+ x_no_outliers = x
clone = self.data.get("clone_continuous", None)
sigma = self.data.get("sigma", None)
n_clusters = 2 # KMeans with 2 clusters
@@ -755,6 +965,11 @@ 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 = []
@@ -861,180 +1076,6 @@ 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
@@ -1053,7 +1094,8 @@ def __init__(self):
"sigma_q_var_prior": 10.0,
"alpha_var_prior": 10.0,
"var_hyperprior": 10.0,
- "overdispersion_scale_prior": 1.0,
+ "overdispersion_scale_prior": 1e-2,
+ "alpha_offset": 0.0,
}
@property
@@ -1073,32 +1115,34 @@ 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['outlier_threshold']
- if 'outlier_threshold' in kwargs else 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
@@ -1111,6 +1155,7 @@ 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"]
+ 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)
@@ -1142,7 +1187,7 @@ def model(self, **kwargs):
# 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
@@ -1164,7 +1209,12 @@ 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))
+ # 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),
@@ -1229,18 +1279,30 @@ 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]))
diff --git a/dextrademixer/utils/simulation.py b/dextrademixer/utils/simulation.py
index 549aa47..ccbbce3 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,
@@ -410,7 +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,
@@ -461,24 +498,47 @@ 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)
+
+ # 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
- binder_assignment = rng.binomial(1, binding_ratio, size=nof_clones)
K = None
cc_assignment = None
@@ -494,10 +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:
@@ -523,21 +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)
@@ -548,6 +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])
@@ -564,6 +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,
@@ -573,6 +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
@@ -766,6 +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"]]
diff --git a/dextrademixer/utils/utils.py b/dextrademixer/utils/utils.py
index 77d1e26..fb33cc3 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 +8,15 @@
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 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):
@@ -314,3 +319,151 @@ def str_to_bool(s):
setattr(args, key, str_to_bool(value))
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"):
+ 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, 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))
+ 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
+
+
+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"
diff --git a/env_full.def b/env_full.def
new file mode 100644
index 0000000..2e702e9
--- /dev/null
+++ b/env_full.def
@@ -0,0 +1,16 @@
+Bootstrap: docker
+From: condaforge/miniforge3
+
+%files
+ environment_full.yaml /environment_full.yaml
+
+%post
+ conda env create -f /environment_full.yaml
+ conda clean --all -y
+
+%environment
+ source /opt/conda/etc/profile.d/conda.sh
+ conda activate dextrademixer
+
+%runscript
+ python "$@"
\ No newline at end of file
diff --git a/env_minimal.def b/env_minimal.def
new file mode 100644
index 0000000..012891c
--- /dev/null
+++ b/env_minimal.def
@@ -0,0 +1,16 @@
+Bootstrap: docker
+From: condaforge/miniforge3
+
+%files
+ environment_minimal.yaml /environment_minimal.yaml
+
+%post
+ conda env create -f /environment_minimal.yaml
+ conda clean --all -y
+
+%environment
+ source /opt/conda/etc/profile.d/conda.sh
+ conda activate dextrademixer
+
+%runscript
+ python "$@"
\ 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/.slurm/config.yaml b/experiments/.slurm/config.yaml
index 73b2b13..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 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
+ --exclude=cpusrv101
+ --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}/$(date +%Y%m%d_%H%M%S)-{wildcards}.err
--parsable
default-resources:
- - c=16
- - mem_mb=64000
- - mem="64G"
- - disk_mb=250000
+ - c=1
+ - mem_mb=8000
+ - mem="8G"
+ - disk_mb=8000
set-resources:
- - dummy_rule:c=32
- - dummy_rule:mem=128000
- - dummy_rule:mem_mb=128000
+ - dummy_rule:c=1
+ - dummy_rule:mem=8000
+ - dummy_rule:mem_mb=8000
- run_simulation:c=1
set-threads:
- dummy_rule=20
-restart-times: 1
-max-jobs-per-second: 10
+restart-times: 0
+max-jobs-per-second: 500
max-status-checks-per-second: 1
latency-wait: 60
jobs: 1000
diff --git a/experiments/.slurm_one_node/config.yaml b/experiments/.slurm_one_node/config.yaml
new file mode 100644
index 0000000..48fa750
--- /dev/null
+++ b/experiments/.slurm_one_node/config.yaml
@@ -0,0 +1,45 @@
+cluster:
+ mkdir -p ./logs/{rule} &&
+
+ NODEFLAG="";
+ if [ "{resources.node}" != "" ]; then
+ NODEFLAG="--nodelist={resources.node}";
+ fi;
+
+ sbatch
+ --partition=cpu_p
+ --qos=cpu_preemptible
+ --nice=0
+ -t 1-00:00:00
+ --mem={resources.mem_mb}
+ -c {resources.c}
+ --job-name={rule}-$(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_one_node/status.py b/experiments/.slurm_one_node/status.py
new file mode 100755
index 0000000..aa8681d
--- /dev/null
+++ b/experiments/.slurm_one_node/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")
diff --git a/experiments/Ioanna_data/Benchmark_Ioanna.ipynb b/experiments/Ioanna_data/Benchmark_Ioanna.ipynb
new file mode 100644
index 0000000..28589e3
--- /dev/null
+++ b/experiments/Ioanna_data/Benchmark_Ioanna.ipynb
@@ -0,0 +1,964 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "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": 20,
+ "id": "d771bfc7-e30e-4d9f-ab47-1d80d0086f25",
+ "metadata": {},
+ "outputs": [],
+ "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": 21,
+ "id": "5cbbc2c8-b9fa-434c-8ac4-54af11f1c4e4",
+ "metadata": {},
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "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": [],
+ "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": 24,
+ "id": "7d171cd9-677e-476e-bf99-017f94d30ca1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "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'
"
+ ],
+ "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": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mdata = mu.read_h5mu(\"./mudata_scanpy_new.h5mu\")\n",
+ "mdata"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "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": "ba655f65-70ae-4f27-8e10-430fa11a06b1",
+ "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": 25,
+ "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": 26,
+ "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": 27,
+ "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": [
+ ""
+ ]
+ },
+ "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": [
+ ""
+ ]
+ },
+ "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": [
+ ""
+ ]
+ },
+ "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": [
+ ""
+ ]
+ },
+ "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": 28,
+ "id": "ccaea043-f350-4c32-8dee-722e7503a415",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# BEAMT\n",
+ "\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",
+ "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",
+ "\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",
+ " 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')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c9063fa1",
+ "metadata": {},
+ "source": [
+ "# DextraDemixer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "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, 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": 30,
+ "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": 31,
+ "id": "20892b4c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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",
+ " 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": 32,
+ "id": "f77605d3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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": 33,
+ "id": "d2a0e428",
+ "metadata": {},
+ "outputs": [
+ {
+ "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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O1/aGoG5CR5i+slY/tiQbmWUK/jbkbeTZFpAK6m+n58ZJuFT+iF71VhJ1Bmiut/T4+zi3YVm+cTs6OiIoKCjffcbExMDf3x9z5syBk5NTvus5OjrqHXd+SuI9qun9Cfz7HuW1ppmma60k3p+A5norTJ0BhnGtieVl9ku9642IVL0zybi9vb3Gr+TyyhwcHPLdViaT6Ty4065uY1g5VtMtyCIQE3kQNhlOsHhZvkSPm1Y2AbfwR6H3Y25fOf96q6Z/F5iCpMe//grZ9JV1/vWWlf/1ITYx6uxt5HK5Vi3aTk5ORd7yrS0x36O81rRX2t+fgDjXmnE5U8QcOyhqy7hCoRDn4ETvmHcmGa9fvz5+++035ObmqgziPHPmDMqVK4fq1asX6fHy/oCUpOdF0DJFRESln1xhDUiQ77dPBfUZj4+Px/r16zFq1KgCZ9xgn3GiklEqk/H4+HikpqbCxcVFObDF09MT4eHh2LNnj3Ke8cTEROzatQvdu3cvsmkNFQoFZHI5zm1YViT7IyKi0k2Mxpm8Y+b37dM///zz1rFQ69evL3B5cYzpICJ1BpeMf/vtt0hJScHDhw8BAD/99BMePHgA4PUjURUKBWbMmIHNmzfj7t27yhYBT09PNG/eHEOHDsXVq1eVT+DMycnRenCmNuzs7LAtLEzvWQcK0yKR1w+XiIjEJ3bjjEwuz7eryNvGdWijOMZ0EJE6g0vGly1bpjJl2p49e7Bnzx4AwKBBg/K98UilUhw8eBBTpkzBN998g4yMDDRp0gTBwcFF/snezs4u36/ntGmNAArXIvHcuPADRHUlxjGJiAzZ2xpnCqLtIOeCFNRVRNtxHYaooGk2S0phuuEEBwerzTZla2uL2rVrY+rUqejcubOyXCLJv9P/6NGj8f3336uV9+3bF7t27cLUqVOxZMkSteVRUVFo164dACAkJASDBg1SW6dly5Y4deoUateujcuXL2Pu3Lla5S5t27ZFVFTUW9cj3RhcMh4TE/PWdYKDgxEcHKxWbmVlhR9++AE//PBD0QempaJojcjbz38pFArIjGW4VP5IofevD5mxjAN23jPavB8L2k7f7Ut7f1R+YH5/FNQ4ow0xBzkbItGn2fx/BU3tqq2AgAA4OztDEAQ8fvwYwcHB6NKlC3766Sd069ZNuV6HDh0wZMgQte01jXVLS0vDTz/9BCcnJ2zbtg2LFy/ON6GXy+UICwtTS8ZjYmJw6tQplWc89O7dG9Wq/TsI+dmzZ/j888/Rq1cvlSebV6igPmsQFZ7BJeOlXXG2RtjZ2WHbjm16txgUtiWGCVLpOGZRyExNAgRJobtF6bt9YeZ+NgRifWAmKu3eNs1mSXjb1K7a6ty5Mxo3bqx8PXz4cFSoUAHbtm1TScarV6+usfVak927dyMnJwebNm3CRx99hBMnTqBt27Ya1+3SpQv279+PxMRE2NjYKMvDwsJQoUIFuLq6Ijk5GQBQr1491KtXT7lOYmIiPv/8c9SrV0/r2Eh/TMZLmcK2wgDvb0sMEyTtvXrxHJAI+c79XJwKO/ezIRCz3korflimNxU4zWYpZWlpCRMTE5Qpo3/qFRoaig4dOqBdu3aoWbMmQkND803Ge/TogcOHD2PXrl34/PPPleVhYWHo27cvLl68qHccVLSYjJegnJwcREdHKz+luru75/toeyp6TJB0V+Dcz5Qv1pvuSvP7JD+ZmZkqY6DepG1XLkdHR5XuBFR6pKamIjExEYIgICEhAYGBgXj27JlaS3NmZiYSExPVtrewsEDZsv8+JOvhw4eIjIzE5s2bAQADBgzAypUr8e2336qsl6dcuXLo0aMHtm3bpkzGo6OjceXKFfzwww9Mxg0Ik/ESEhUVhcDAQJWZVuzt7TF+/Hh4eHiIF9h7hAkSkeF6Fz8sx8bGqg3k+6+3deXi9IKlV/v27VVey2QybNq0CR06dFAp37hxIzZu3Ki2/bZt29C/f3+V1zKZDD169AAA9O/fH7Nnz8bBgwfRs2dPjTEMHDgQ3bt3x/3791G5cmWEhoaiatWqaN68eSHPjooSk/ESEBUVhZkzZ6Jly5bw9/dH1apVcefOHWzZsgUzZ87EggULmJAT0XvtXfywzOkF329r1qxRDsJ8/Pgxtm7dihEjRsDc3FxlUGSPHj0wbtw4te3r1q2r8jo0NBRdu3aFubk5AMDV1RWNGjVCaGhovsl4x44dYW1tje3bt8PX1xfbt2/XOFiUxMVkvJjl5OQgMDAQLVu2xOLFi5VPB61Tpw4WL16M6dOn49tvv0Xr1q3ZZYWI6B1SmqcXpMJr2rSpygDOAQMGoEGDBhg3bhy6deum7FpSqVIltVb0/7p27RrOnz+PIUOG4NatW8pyDw8PrFmzBmlpabCwsFDbztjYGF5eXggLC0PTpk1x//59DBw4sIjOkIqK0dtXocKIjo5GfHw8hgwZokzE8xgZGWHw4MF4+PAhoqOjRYqQiIiIipuRkRHatWuH+Ph43Lx5U6dtt27dCgD48ssv4erqqvxZvnw5MjMzsXv37ny3HThwIC5cuIC5c+fC3d0dtWrVKtR5UNFjy3gxyxuUUbVqVY3L88o1Dd4gIiKid0d2djaA1/N4a0sQBISFhaFdu3YYO3as2vJ58+YhNDQ03/EJrVq1QpUqVRAVFaXxIUEkPibjxSxvbs87d+6gTp06asvv3Lmjsl5hFDRyHzCM0fvp8feLZb8FeZ74uMSPSeLjtUZEhuTVq1c4cuQIypYti5o1a2q93cmTJxETE4OAgAB4enqqLb9x4wb8/Pzw8OFDODg4qC2XSCT45ptvcP78eQwePLhQ50DFg8l4MXN3d4e9vT22bNmi0mccAHJzcxESEgIHBwe4u7sX+ljajNwHxB29f27DsmLZL1EehUIBmVzOa42oFBPjw3RRH/vQoUO4fv06ACAhIQFhYWG4efMmpk+frtK/+8aNG8puKG+qUKECOnTogNDQUEilUnTt2lXjcT799FPMnDkT27dvx6RJkzSu06NHD+UsLGR4mIwXM6lUivHjx2PmzJmYPn06Bg8erJxNJSQkBCdPnsSCBQuKZPBmUYzcz9tPcRHjqWqPLp3Dtb0hJXrMd0VpfBCLnZ0dtoWF6fWk2sI+pTZveyLSj6F8mJbJ5VAoFIXax+zZs5X/l8vlqFGjBtauXYvRo0errBcREYGIiAi17du2bQsPDw/s2rULLVq0gLW15qk/69SpA2dnZ2zdujXfZJwMG5PxEuDh4YEFCxYgMDBQ5U3o4OBQpNMaloaR+2I8VU3MFpbSrrQ+iKWwT6p9X59SSyS2wnyYLkoKhULve4iPjw98fHy0WlcQhLeuo82Ysrwur8DrnEOb/UZFReW7zMbGRqt9UNFgMl5CPDw80Lp1az6Bk0qVd/FBLERk2Ar7YZqotGEyXoKkUikaNmwodhhEWnsXH8RCRERkSJiMU4niDBdERERE/2IyTiXCUAblEBERERkSJuNUIjjDBREREZE6JuNUYgoalPO2BxZpozgfVkRERERUHJiMk0HQ5oFFYj6siIiIiKg4MBkng1AUDywqzocV0bujoG9hYmJiVP7ND7+FISKiosJknAxCaXhgEb0bSuJbmNL45FIiIhIHk3F6bzBBIqB4v4VRKBSQGctEe2iRzFhW6Ed4ExFRyWIyTu88Jkj0puL8FsbOzg7bdmwTZdYgoHCP8CYiInEwGad3HhMkKkmFfZS3k5MTu2wREenBw8MDABAVFSVqHLpiMk7vBSZIRESlw6NHj/RqPClKhWlECQ4OVhmXIpPJYG1tjbp166Jr164YOnQozM3NiypUNWFhYUhISMDEiROLfN8xMTFwdnZWvi5TpgwUCgWqV68ODw8PjBkzBlWqVCny477rmIwTERGRQXj06BEG9BuArFdZosYhM5Zh245thWrECQgIgLOzM169eoVHjx4hKioKEydOxIoVK7B//37Uq1evCCP+V1hYGC5fvlwsyXieAQMGoEuXLsjNzUVycjLOnj2LVatWYfXq1di4cSP69+9fbMcuyJEj4nRHLSwm40RERGQQUlNTkfUqC3UTOsL0lbUoMTw3TsKl8keQmppaqGS8c+fOaNy4sfL1jBkzcOzYMXTr1g2ffvoprl27BhMTk6IIWW+ZmZkoW7YsjIyMdNquYcOGGDRokEpZbGwsOnbsCG9vb9SsWRPu7u5FGapWypYtW+LHfP78OUxNTQu1D91qn4hKXHr8fSTH3irRn+eJjwG8/qOUVjahRH84A837idcavcn0lTUsXpYX5ac4PwR89NFH8PPzQ2xsLLZu3aosv379Ojw9PWFtbQ25XI7GjRtj//79yuUJCQmwtbWFh4cHBEFQlt+6dQumpqbo168fgNd9pg8cOIDY2FhIJBJIJBLleKeoqChIJBJs374ds2bNQsWKFVGuXDmkpaUhKSkJvr6+qFu3LszMzGBhYYHOnTsjOjpa63NzdHREcHAwXr58ia+//lplWUpKCiZOnIjKlStDJpOhWrVqWLJkCXJzc5XrxMTEQCKRYNmyZVizZg2qVq2KcuXKoWPHjrh//z4EQcC8efNQqVIlmJiYoEePHkhKUn0Pe3h4KPuNA4C3tzfkcjmuXbumsl6nTp1gZWWFhw8fKssOHTqE1q1bw9TUFObm5ujatSuuXLmisp2Pjw/MzMxw+/ZtdOnSBebm5vjss8+0rqP8sGWcyMCd27BMnAML4Aw0VOyMy5kCgoTXGr03Bg8ejK+++gpHjhzByJEjceXKFbRs2RIVK1bE9OnTYWpqip07d6Jnz57YvXs3evXqhfLly2Pt2rXw8vJCYGAgvvjiC+Tm5sLHxwfm5ub47rvvAAAzZ85EamoqHjx4gJUrVwIAzMzMVI4/b948lC1bFr6+vsjKykLZsmVx9epV7Nu3D15eXnB2dsbjx4+xbt06tG3bFlevXoWDg4NW5/bhhx/CxcUFERERyrIXL16gbdu2iIuLw+jRo1GlShWcOnUKM2bMQHx8PFatWqWyj9DQULx8+RLjx49HUlISvv76a/Tt2xcfffQRoqKiMG3aNNy6dQuBgYHw9fXFpk2b8o1n9erVOHbsGLy9vXH69GlIpVKsW7cOR44cQUhIiPK8QkJC4O3tjU6dOmHJkiV48eIF1q5di1atWuH8+fMqEzhkZ2ejU6dOaNWqFZYtW4Zy5cppVTcFYTJOZOAaj/SFuX3lEj1mevx9nNuwTO9ZZAo7Cw1noHl/yBXWgETgtUbvjUqVKkGhUOD27dsAgAkTJqBKlSo4e/YsZDIZAGDs2LFo1aoVpk2bhl69egEAPD09MWDAAMyYMQOdO3fGjz/+iJMnT2Lfvn344IMPAAAdOnRAxYoVkZycrNaNJE9mZibOnTun0kWmbt26uHHjhkp3lcGDB6NGjRrYuHEj/Pz8tD6/OnXq4Mcff0RaWhosLCywYsUK3L59G+fPn4erqysAYPTo0XBwcMDSpUsxefJkVK7879+4uLg43Lx5U/khOScnB4sWLUJGRgbOnTuHMmVep65PnjxBaGgo1q5dq6y3/7K0tMTGjRvRqVMnLF68GAMHDoSvry969uyprJ9nz57hiy++wIgRI7B+/Xrltt7e3nBzc8PChQtVyrOysuDl5YVFixZpXSdvw2ScyMCZ21eGlWM1UY5d2FlkOAsNaYvXGr1PzMzMkJ6ejqSkJBw7dgwBAQFIT09Henq6cp1OnTphzpw5iIuLQ8WKFQEA3377LaKiouDp6YkbN25g8ODB6NGjh07H9vb2Vuur/mYym5OTg5SUFJiZmcHNzQ1///23zucGAOnp6bCwsMCuXbvQunVrWFlZITExUble+/btsXjxYpw4cUKlq4eXl5fKt1XNmjUDAAwaNEiZiOeVb9u2DXFxcahatWq+8XTs2BGjR49GQEAAwsPDIZfLsW7dOuXyiIgIpKSkYMCAASrxSaVSNGvWDJGRkWr7/Pzzz3WpkrdiMk5ERERUgp49e4by5cvj1q1bEAQBfn5++bY+JyQkKJNxa2trfPPNN/Dy8kKFChXwzTff6HzsN6cmzJObm4vVq1fju+++w927d5GTk6Ncltfqrq1nz54BgHL6xps3b+LixYuwtbXVuH5CQoLK6/9OjZiXmL/Zev5meXJy8ltjWrZsGX788UdcuHABYWFhKF++vHLZzZs3Abzuz6+JhYWFyusyZcqgUqVKbz2mLpiME5HOMjMzERsbm+/ymJgYlX/z4+joCLlcXoSREREZtgcPHiA1NRXVqlVTDmD09fVFp06dNK5frZrqN6OHDx8G8DoJffDgASwtLXU6vqYZXBYuXAg/Pz8MGzYM8+bNg7W1NYyMjDBx4kSVQZbauHz5MsqXL69MYnNzc9GhQwdMnTpV4/rVq1dXeS2VSjWul1/5mwNa83P+/Hll0n/p0iUMGDBAuSzv/EJCQjR2WXuzNR54/S2CrrPPvA2TcSLSWWxsrMpDLfLj7+9f4PKgoCB2LSCi90pISAiA191Q8rpXGBsbo3379m/d9pdffsEPP/yAqVOnIjQ0FN7e3jhz5oxKwiiRSHSOKTw8HO3atcPGjRtVylNSUmBjY6P1fk6fPo3bt2+r9Fd3cXHBs2fPtDq/4vD8+XMMHToUtWrVQosWLfD111+jV69eaNKkiTI+AChfvrxoMTIZJyKdOTo6IigoqEj2Q0T0vjh27BjmzZsHZ2dnfPbZZ5DL5fDw8MC6deswfvx42Nvbq6z/5MkTZfeOlJQUjBgxAk2bNsXChQvRrl07dO7cGQsXLsTs2bOV25iamur8BFOpVKrWwrxr1y7ExcWptcznJzY2Fj4+PihbtiymTJmiLO/bty/mzp2Lw4cPq7X+5/VN/2/rc1GaNm0a7t27hz/++ANubm749ddf4e3tjfPnz0Mmk6FTp06wsLBQ1qmxsbHK9m/+DooLk3Ei0plcLmeLNhFRAQ4dOoTr168jOzsbjx8/xrFjxxAREQFHR0fs379f2UVvzZo1aNWqFerWrYuRI0eiatWqePz4MU6fPo0HDx4o5/qeMGECnj59iqNHj0IqleKTTz7BiBEjMH/+fPTo0UP5kJ1GjRphx44dmDRpEpo0aQIzMzN07969wFi7deuGgIAADB06FC1atMClS5cQGhqa78DIv//+G1u3bkVubi5SUlJw9uxZ7N69GxKJBCEhISpPF50yZQr279+Pbt26wcfHB40aNcLz589x6dIlhIeHIyYmRqfWd10cO3YM3333HebMmYOGDRsCeP2NrIeHB/z8/PD111/DwsICa9euxeDBg9GwYUP0798ftra2uHfvHg4cOICWLVvi22+/LZb48jAZJyIiIoMi5gOZiurYea3VZcuWhbW1NerWrYtVq1Zh6NChysGNAFCrVi2cO3cO/v7+CA4OxtOnT1G+fHk0aNBAuY/9+/djy5YtWL58OWrUqKHcdsWKFYiIiIC3tzfOnj0LY2NjjB07FhcuXEBQUBBWrlwJR0fHtybjX331FZ4/f46wsDDs2LEDDRs2xIEDBzB9+nSN62/btg3btm1DmTJlYGFhAVdXV0ycOBFjxoxRG4BZrlw5HD9+HAsXLsSuXbuwZcsWWFhYoHr16vD39y+2ef7T09MxbNgwNGjQADNnzlSWt27dGhMmTMDy5cvRu3dvNG/eHAMHDoSDgwMWL16MpUuXIisrCxUrVkTr1q216pJZWBJBm57v75m0tDQoFAqkpqaqjaKl98s///yDoUOHitK3Oe/Y7WavLvGpDZNjbyEyYAL7dJcgXmv6nbeY9UZF79GjRxjQbwCyXmWJGofMWIZtO7ZxDnoqEWwZJyIiIoNgZ2eHbTu26dznuajxYVBUkpiMExERkcGws7NjIkzvlaKdKJGIiIiIiLTGZJyIiIiISCTspkJEVEIKenIpn1pKRPR+YjJORFRCtHlyKZ9aSkT0fmEyTkRUQoriyaV8aikR0buFyTgRUQnhk0uJiOi/OICTiIiIiEgkBpeMZ2VlYdq0aXBwcICJiQmaNWuGiIgIrbbdvn07GjZsCLlcDltbWwwfPhyJiYnFHDERERERkX4MLhn38fHBihUr8Nlnn2H16tWQSqXo0qULfv/99wK3W7t2LQYMGABra2usWLECI0eOxPbt2/Hxxx8jMzOzhKInIiIiItKeQfUZ//PPP7F9+3YsXboUvr6+AIAhQ4agTp06mDp1Kk6dOqVxu5cvX+Krr75CmzZtEBERAYlEAgBo0aIFunfvjg0bNmD8+PEldh5UdHJychAdHY3ExETY2NjA3d0dUqlU7LCIiIiIikShk/G4uDicOHECCQkJ6NOnDypVqoScnBykpqZCoVDolDiFh4dDKpVi1KhRyjK5XI7hw4fjq6++wv3791G5cmW17S5fvoyUlBT069dPmYgDQLdu3WBmZobt27czGS+FoqKiEBgYiPj4eGWZvb09xo8fDw8PD/ECIyIiIioiendTEQQBkyZNgrOzMz777DNMmjQJN27cAAA8e/YMTk5OCAwM1Gmf58+fR/Xq1WFhYaFS3rRpUwDAhQsXNG6XlZUFADAxMVFbZmJigvPnzyM3N1enWEhcUVFRmDlzJlxcXLB+/XocPXoU69evh4uLC2bOnImoqCixQyQiIiIqNL2T8aVLl2L16tXw9fVFREQEBEFQLlMoFOjduzd2796t0z7j4+Nhb2+vVp5X9vDhQ43bubq6QiKR4OTJkyrl//zzD548eYKMjAwkJyfne9ysrCykpaWp/JB4cnJyEBgYiJYtW2Lx4sWoU6cOypUrhzp16mDx4sVo2bIlvv32W+Tk5IgdKhEREVGh6N1NZcOGDRgyZAgWLlyIp0+fqi2vV68eDh06pNM+MzIyIJPJ1MrzHv2ckZGhcTsbGxv07dsXmzdvRs2aNdGrVy/ExcVh/PjxMDY2xqtXr/LdFgAWLVr01qfeUcmJjo5GfHw8/P39YWSk+nnRyMgIgwcPxujRoxEdHY2GDRuKFCXRuyc9/v57cUwiIkOidzJ+//59tGjRIt/lpqamOrcwm5iYKLucvClvNhRN3VDyrFu3DhkZGfD19VUO/hw0aBBcXFywZ88emJmZ5bvtjBkzMGnSJOXrtLQ0jX3TqWTkTUdZtWpVjcvzyt+XaSuZIFFJObdhmdghEBG9d/ROxsuXL4/79/P/g/3XX3+hSpUqOu3T3t4ecXFxauV5A/gcHBzy3VahUODHH3/EvXv3EBMTA0dHRzg6OqJFixawtbWFpaVlvtvKZDKNLfIkDhsbGwDAnTt3UKdOHbXld+7cUVnvXaVQKCCTy0VLkGRyORQKhSjHJnE0HukLc/uSbYhIj7/PDwFE9F7TOxnv3bs3vv/+e/j4+Cj/YOfNZHLkyBEEBwdj6tSpOu2zfv36iIyMRFpamsogzjNnziiXv02VKlWUHwJSUlLw119/oU+fPjrFQeJyd3eHvb09tmzZgsWLF6t0VcnNzUVISAgcHBzg7u4uYpTFz87ODtvCwpCamqrX9jExMfD398ecOXPg5OSk8/YKhQJ2dnZ6HZtKJ3P7yrByrCZ2GERE7xW9k3F/f39ERkaifv36aN26NSQSCZYsWQI/Pz+cPn0aDRo0wFdffaXTPj09PbFs2TKsX79e2dUkKysLQUFBaNasmbLryL179/DixQvUqFGjwP3NmDED2dnZ+PLLL/U7SRKFVCrF+PHjMXPmTEyfPh2DBw9G1apVcefOHYSEhODkyZNYsGDBezHfuJ2dXaETYicnJ7i5uRVRRERERFSU9E7GFQoF/vjjDyxfvhzh4eGQy+U4fvw4XFxcMGfOHEyZMqXAPt6aNGvWDF5eXpgxYwYSEhJQrVo1bN68GTExMdi4caNyvSFDhuD48eMqM7gsXrwYly9fRrNmzVCmTBns27cPR44cwfz589GkSRN9T5NE4uHhgQULFiAwMBCjR49Wljs4OGDBggWcZ5yIiIjeCYV66I+JiQlmzZqFWbNmFVU82LJlC/z8/BASEoLk5GTUq1cPP//8M9q0aVPgdnXr1sXevXuxf/9+5OTkoF69eti5cye8vLyKLDYqWR4eHmjdujWfwElERETvrEI/gbOoyeVyLF26FEuXLs13HU0PfOnatSu6du1ajJGRGKRSKacvJCIioneW3sn4sGHD3rqORCJR6V5CRERERET/0jsZP3bsmHL2lDw5OTmIj49HTk4ObG1tYWpqWugAiYpbZmYmYmNjNS6LiYlR+Tc/jo6OyodTEREREWlL72Q8v+Tk1atXWLduHVatWoWIiAh9d09UYmJjYzF06NAC13nbE1qDgoI4YwkRERHprMj7jBsbG2PcuHG4evUqxo0bhwMHDhT1IYiKlKOjI4KCggq9DyIiIiJdFdsATnd3d4SEhBTX7omKjFwuZ6s2ERERicLo7avoJyIiAuXKlSuu3RMRERERlXp6t4wHBARoLE9JScGJEyfw999/Y/r06XoHRkREVNAAa4CDrImo9NM7GZ87d67GcisrK7i4uOD777/HyJEj9d09ERGRVgOsAQ6yJqLSS+9kPDc3tyjjICIiUlMUA6zz9kNEZIgM7gmcREREeTjAmojedVon4/fu3dPrAFWqVNFrOyIiIiKid53WybiTk5PaEze1kZOTo/M2RERERETvA62T8U2bNumVjBMRERERkWZaJ+M+Pj7FGAYRERER0fun2B76Q0REREREBSv0bConT57E33//jdTUVLXpDiUSCfz8/Ap7CCIiIiKid5LeyXhSUhK6du2KP//8E4IgQCKRQBAEAFD+n8k4EREREVH+9O6mMmXKFFy8eBFhYWG4c+cOBEHA4cOHcePGDYwZMwb169fHw4cPizJWIiIiIqJ3it7J+MGDBzF69Gj069cP5ubmr3dmZIRq1aphzZo1cHJywsSJE4sqTiIiIiKid47eyXhKSgpq164NADAzMwMAPHv2TLm8Y8eOOHz4cCHDIyIiIiJ6d+mdjDs4OODRo0cAAJlMhvLlyyM6Olq5PC4ujvOSExEREREVQO8BnK1bt0ZERARmzpwJAOjXrx++/vprSKVS5ObmYtWqVejUqVORBUpERERE9K7ROxmfPHkyIiIikJWVBZlMhrlz5+LKlSvK2VPatGmDwMDAIguUiIiIiOhdo3cyLpVKMWnSJOVrKysrHD16FCkpKZBKpcpBnUREREREpJnefcbr1KkDd3d3LFy4ELdu3VKWW1paMhEnIiIiItKC3sn42rVrYWNjg9mzZ8PNzQ2NGjXC0qVLERsbW5TxERERERG9s/ROxkePHo1ff/0VcXFxWL16NUxNTTF9+nRUrVoVH374IVavXs2H/hARERERFUDvZDxPhQoVMG7cOJw4cQL37t3D8uXLIZFIMHnyZDg6OhZFjERERERE7yS9B3BqYm9vj9q1a6NmzZq4fPkynj9/XpS7JyKiYpQef/+9OCYRkSEpdDIuCAKioqKwY8cO7N27F4mJibCyskL//v3Rr1+/ooiRiIiKkUKhgEwux7kNy0Q5vkwuh0KhEOXYRERi0zsZ/+2337Bz506Eh4cjISEBFhYW6NmzJ/r164f27dujTJkibXQnIqJiYmdnh21hYUhNTdVr+5iYGPj7+2POnDlwcnLSeXuFQgE7Ozu9jk1EVNrpnTG3bdsWZmZm6N69O/r164dPPvkEZcuWLcrYiIiohNjZ2RU6IXZycoKbm1sRRURE9H7QOxnftWsXunbtCrlcXpTxEBERERG9N/ROxvv06VOUcRARERERvXcKPbUhERERERHph8k4EREREZFImIwTEREREYmEyTgRERERkUiYjBMRERERiYTJOBERERGRSJiMExERERGJhMk4EREREZFImIwTEREREYmEyTgRERERkUiYjBMRERERiYTJOBERERGRSMqIHQAR6SczMxOxsbH5Lo+JiVH5Nz+Ojo6Qy+VFGBkRERFpi8k4USkVGxuLoUOHvnU9f3//ApcHBQXBzc2tqMIiIiIiHRhcMp6VlYXZs2cjJCQEycnJqFevHubPn48OHTq8ddujR49iwYIFuHTpErKzs1G9enWMHz8egwcPLoHIiUqWo6MjgoKCimQ/REREJA6DS8Z9fHwQHh6OiRMnwtXVFcHBwejSpQsiIyPRqlWrfLfbv38/evbsiQ8//BBz586FRCLBzp07MWTIECQmJuLLL78swbMgKn5yuZwt2kRERKWcRBAEQewg8vz5559o1qwZli5dCl9fXwCv+8XWqVMH5cuXx6lTp/LdtmPHjrhy5Qru3LkDmUwGAMjOzkaNGjVgamqK6OhoreNIS0uDQqFAamoqLCwsCndSRETvuH/++QdDhw5llyciIj0Y1Gwq4eHhkEqlGDVqlLJMLpdj+PDhOH36NO7fv5/vtmlpabCyslIm4gBQpkwZ2NjYwMTEpFjjJiIiIiLSh0El4+fPn0f16tXVWqObNm0KALhw4UK+23p4eODKlSvw8/PDrVu3cPv2bcybNw/nzp3D1KlTizNsIiIiIiK9GFSf8fj4eNjb26uV55U9fPgw3239/Pxw9+5dLFiwAPPnzwcAlCtXDrt370aPHj0KPG5WVhaysrKUr9PS0vQJn4iIiIhIJwbVMp6RkaHSzSRP3hzIGRkZ+W4rk8lQvXp1eHp6Ytu2bdi6dSsaN26MQYMG4Y8//ijwuIsWLYJCoVD+VK5cuXAnQkRERESkBYNqGTcxMVFpoc6TmZmpXJ6fcePG4Y8//sDff/8NI6PXnzH69u2L2rVrY8KECThz5ky+286YMQOTJk1Svk5LS2NCTkRERETFzqBaxu3t7REfH69Wnlfm4OCgcbuXL19i48aN6Nq1qzIRBwBjY2N07twZ586dw8uXL/M9rkwmg4WFhcoPEREREVFxM6hkvH79+rhx44Zan+28Vu369etr3O7p06fIzs5GTk6O2rJXr14hNzdX4zIiIiIiIjEZVDLu6emJnJwcrF+/XlmWlZWFoKAgNGvWTNl15N69e7h+/bpynfLly8PS0hJ79+5VaQF/9uwZfvrpJ9SoUYPTGxIRERGRwTGoPuPNmjWDl5cXZsyYgYSEBFSrVg2bN29GTEwMNm7cqFxvyJAhOH78OPKeVySVSuHr64tZs2ahefPmGDJkCHJycrBx40Y8ePAAW7duFeuUiIiIiIjyZVDJOABs2bIFfn5+CAkJQXJyMurVq4eff/4Zbdq0KXC7mTNnwtnZGatXr4a/vz+ysrJQr149hIeHo0+fPiUUPRERERGR9iRCXvMyKaWlpUGhUCA1NZWDOYmI3uKff/7B0KFDERQUBDc3N7HDISIqVQyqzzgRERER0fuEyTgRERERkUiYjBMRERERiYTJOBERERGRSJiMExERERGJhMk4EREREZFImIwTEREREYmEyTgRERERkUiYjBMRERERiYTJOBERERGRSJiMExERERGJhMk4EREREZFImIwTEREREYmEyTgRERERkUiYjBMRERERiYTJOBERERGRSJiMExERERGJhMk4EREREZFImIwTEREREYmEyTgRERERkUiYjBMRERERiYTJOBERERGRSJiMExERERGJhMk4EREREZFImIwTEREREYmEyTgRERERkUiYjBMRERERiYTJOBERERGRSJiMExERERGJhMk4EREREZFImIwTEREREYmEyTgRERERkUiYjBMRERERiYTJOBERERGRSJiMExERERGJhMk4EREREZFImIwTEREREYmEyTgRERERkUiYjBMRERERiYTJOBERERGRSJiMExERERGJhMk4EREREZFImIwTEREREYmEyTgRERERkUiYjBMRERERiYTJOBERERGRSJiMExERERGJxOCS8aysLEybNg0ODg4wMTFBs2bNEBER8dbtnJycIJFINP64urqWQORERERERLopI3YA/+Xj44Pw8HBMnDgRrq6uCA4ORpcuXRAZGYlWrVrlu92qVavw7NkzlbLY2FjMmjULHTt2LO6wiYiIiIh0ZlDJ+J9//ont27dj6dKl8PX1BQAMGTIEderUwdSpU3Hq1Kl8t+3Zs6da2fz58wEAn332WbHES0RERERUGAbVTSU8PBxSqRSjRo1SlsnlcgwfPhynT5/G/fv3ddpfWFgYnJ2d0aJFi6IOlYiIiIio0AwqGT9//jyqV68OCwsLlfKmTZsCAC5cuKDTvq5du4aBAwcWZYhEREREREXGoLqpxMfHw97eXq08r+zhw4da7ys0NBSAdl1UsrKykJWVpXydlpam9XGIiN51mZmZiI2NzXd5TEyMyr/5cXR0hFwuL8LIiIhKP4NKxjMyMiCTydTK827eGRkZWu0nNzcX27dvR4MGDVCzZs23rr9o0SL4+/vrFiwR0XsiNjYWQ4cOfet6b7uPBgUFwc3NrajCIiJ6JxhUMm5iYqLSQp0nMzNTuVwbx48fR1xcHL788kut1p8xYwYmTZqkfJ2WlobKlStrtS0R0bvO0dERQUFBRbIfIiJSZVDJuL29PeLi4tTK4+PjAQAODg5a7Sc0NBRGRkYYMGCAVuvLZDKNLfJERPT620m2aBMRFQ+DGsBZv3593LhxQ63P9pkzZ5TL3yYrKwu7d++Gh4eH1sk7EREREZEYDCoZ9/T0RE5ODtavX68sy8rKQlBQEJo1a6bsOnLv3j1cv35d4z4OHjyIlJQUzi1ORERERAbPoLqpNGvWDF5eXpgxYwYSEhJQrVo1bN68GTExMdi4caNyvSFDhuD48eMQBEFtH6GhoZDJZOjTp09Jhk5EREREpDODSsYBYMuWLfDz80NISAiSk5NRr149/Pzzz2jTps1bt01LS8OBAwfQtWtXKBSKEoiWiIiIiEh/EkFT8/J7Li0tDQqFAqmpqWoPICIiIiIiKioG1WeciIiIiOh9YnDdVAxB3pcFfBInERGRfszNzSGRSMQOg8jgMRnXID09HQD44B8iIiI9sasnkXbYZ1yD3NxcPHz40OA+1ec9GfT+/fu8wemA9aY71pl+WG+6Y53ppzTUm6H9DSUyVGwZ18DIyAiVKlUSO4x8WVhYGOzN15Cx3nTHOtMP6013rDP9sN6ISj8O4CQiIiIiEgmTcSIiIiIikTAZL0VkMhnmzJkDmUwmdiilCutNd6wz/bDedMc60w/rjejdwQGcREREREQiYcs4EREREZFImIwTEREREYmEyTgRERERkUiYjBMRzp49ixYtWsDU1BQSiQQXLlwQO6RSSyKRYO7cuTpt4+PjAycnp2KJ510WHBwMiUSCmJgYZZmHhwc8PDxEi4mISFdMxonec69evYKXlxeSkpKwcuVKhISEoEKFCpg+fTratWunfIpeVFSU2KESERG9c/gETqL33O3btxEbG4sNGzZgxIgRAICoqCgsWbIErq6uqFu3Lk6fPi1ylKVHRkYGypTR7da6YcMG5ObmFlNERERkyNgyXoSeP38udgjvjdzcXGRmZoodxjshISEBAGBpaaksa9SoEZ4+fYobN25g0qRJIkVWvIrrGpLL5Ton48bGxqV6vmje+wwPfydEpQeTcT3NnTsXEokEV69excCBA2FlZYVWrVohOzsb8+bNg4uLC2QyGZycnPDVV18hKytLbR+HDh1C27ZtYW5uDgsLCzRp0gRhYWFax5CUlARfX1/UrVsXZmZmsLCwQOfOnREdHa2ynqZ+lcDr1k9N3Q/OnDmDLl26wMrKCqampqhXrx5Wr16tdVy6yKvH69evo2/fvrCwsMAHH3yACRMmqCRKEokE48aNQ2hoKGrXrg2ZTIZffvlFq2P89ttv8PLyQpUqVSCTyVC5cmV8+eWXyMjIUFkvv76mmvrz5ubmYvXq1ahbty7kcjlsbW3xySef4Ny5czrXgZh8fHzQtm1bAICXlxckEgk8PDxgbm4Oa2trkaPTTlFcQ3FxcRg2bBgqVKgAmUyG2rVrY9OmTWrHyszMxNy5c1G9enXI5XLY29ujd+/euH37tspx3uwznp6ejokTJ8LJyQkymQzly5dHhw4d8PfffyvX0XSNPX/+HJMnT0blypUhk8ng5uaGZcuW4b+Phsg7r3379qFOnTrK+LV9f+gqv3sfAGzduhWNGjWCiYkJrK2t0b9/f9y/f19tH2+7x1y8eBE+Pj6oWrUq5HI57OzsMGzYMDx9+rRYzkkbsbGxGDt2LNzc3GBiYoIPPvgAXl5eavdVAEhJScGXX36p/J1XqlQJQ4YMQWJionKdt11L+d2fY2JiIJFIEBwcrCzz8fGBmZkZbt++jS5dusDc3ByfffYZAO3vfwCU7yFbW1uYmJjAzc0NM2fOBABERkZCIpFg7969atuFhYVBIpHwGzQiPbGbSiF5eXnB1dUVCxcuhCAIGDFiBDZv3gxPT09MnjwZZ86cwaJFi3Dt2jWVm1hwcDCGDRuG2rVrY8aMGbC0tMT58+fxyy+/YODAgVod+86dO9i3bx+8vLzg7OyMx48fY926dWjbti2uXr0KBwcHnc8nIiIC3bp1g729PSZMmAA7Oztcu3YNP//8MyZMmKDz/rTVt29fODk5YdGiRfjjjz/wzTffIDk5GVu2bFGuc+zYMezcuRPjxo2DjY2N1gPedu3ahRcvXuDzzz/HBx98gD///BOBgYF48OABdu3apVe8w4cPR3BwMDp37owRI0YgOzsbv/32G/744w80btxYr32KYfTo0ahYsSIWLlyIL774Ak2aNEGFChXEDksv+l5Djx8/RvPmzZVJra2tLQ4dOoThw4cjLS0NEydOBADk5OSgW7du+PXXX9G/f39MmDAB6enpiIiIwOXLl+Hi4qIxrjFjxiA8PBzjxo1DrVq18PTpU/z++++4du0aGjZsqHEbQRDw6aefIjIyEsOHD0f9+vVx+PBhTJkyBXFxcVi5cqXK+r///jv27NmDsWPHwtzcHN988w369OmDe/fu4YMPPiiaCv6P/977FixYAD8/P/Tt2xcjRozAkydPEBgYiDZt2uD8+fPKb160ucdERETgzp07GDp0KOzs7HDlyhWsX78eV65cwR9//AGJRFIs51SQs2fP4tSpU+jfvz8qVaqEmJgYrF27Fh4eHrh69SrKlSsHAHj27Blat26Na9euYdiwYWjYsCESExOxf/9+PHjwADY2NnpfSwXJzs5Gp06d0KpVKyxbtkwZj7b3v4sXL6J169YwNjbGqFGj4OTkhNu3b+Onn37CggUL4OHhgcqVKyM0NBS9evVSOXZoaChcXFzw4YcfFqKGid5jAullzpw5AgBhwIAByrILFy4IAIQRI0aorOvr6ysAEI4dOyYIgiCkpKQI5ubmQrNmzYSMjAyVdXNzc7WOITMzU8jJyVEpu3v3riCTyYSAgABlWVBQkABAuHv3rsq6kZGRAgAhMjJSEARByM7OFpydnQVHR0chOTlZ77h0kVePn376qUr52LFjBQBCdHS0IAiCAEAwMjISrly5ovMxXrx4oVa2aNEiQSKRCLGxscqytm3bCm3btlVb19vbW3B0dFS+PnbsmABA+OKLL9TWLa56Kk5518GuXbs0Lt+1a5fKdWJoCnsNDR8+XLC3txcSExNVyvv37y8oFArl9bNp0yYBgLBixQq1GN78vQMQ5syZo3ytUCiE//3vfwWew3+vsX379gkAhPnz56us5+npKUgkEuHWrVsqxytbtqxKWXR0tABACAwMLPC4+tB074uJiRGkUqmwYMEClXUvXboklClTRlmu7T1G03t227ZtAgDhxIkTyjJN97b83seFpSmm06dPCwCELVu2KMtmz54tABD27Nmjtn7eOWpzLf33/pzn7t27AgAhKChIWebt7S0AEKZPn65V3Jruf23atBHMzc1Vyt6MRxAEYcaMGYJMJhNSUlKUZQkJCUKZMmVUrnki0g27qRTSmDFjlP8/ePAgAKj1sZ08eTIA4MCBAwBet/qkp6dj+vTpkMvlKuvq0uIjk8lgZPT6V5iTk4OnT5/CzMwMbm5uKl+Ba+v8+fO4e/cuJk6cqNJ/WNe49PG///1P5fX48eMB/FunANC2bVvUqlVL532bmJgo///8+XMkJiaiRYsWEAQB58+f13l/u3fvhkQiwZw5c9SWidFiR6/pcw0JgoDdu3eje/fuEAQBiYmJyp9OnTohNTVV+V7avXs3bGxslPt9U0G/d0tLS5w5cwYPHz7U+lwOHjwIqVSKL774QqV88uTJEAQBhw4dUilv3769SmtqvXr1YGFhgTt37mh9TF29ee/bs2cPcnNz0bdvX5U6tLOzg6urKyIjIwFof4958z2bmZmJxMRENG/eHAD0urcVhTdjevXqFZ4+fYpq1arB0tJSJabdu3fD3d1drfUY+Pcc9b2W3ubzzz8vMO787n9PnjzBiRMnMGzYMFSpUiXfeIYMGYKsrCyEh4cry3bs2IHs7GwMGjRI77iJ3ndMxgvJ2dlZ+f/Y2FgYGRmhWrVqKuvY2dnB0tISsbGxAKDsE1inTp1CHTs3NxcrV66Eq6srZDIZbGxsYGtri4sXLyI1NVXn/RVVXPpwdXVVee3i4gIjIyOV/phv1rUu7t27Bx8fH1hbW8PMzAy2trbKftL61pODg0Op6VP9vtDnGnry5AlSUlKwfv162NraqvwMHToUwL8DXG/fvg03NzedB2d+/fXXuHz5MipXroymTZti7ty5b02SY2Nj4eDgAHNzc5XymjVrKpe/6b8JFABYWVkhOTlZp1h18WZd3rx5E4IgwNXVVa0er127plKHwNvvMUlJSZgwYQIqVKgAExMT2NraKo+nz3u2KGRkZGD27NnKPvx599uUlBSVmG7fvv3W89P3WipImTJlUKlSJbVybe5/edfj2+KuUaMGmjRpgtDQUGVZaGgomjdvrvZ3j4i0xz7jhfRmq0OekmodXbhwIfz8/DBs2DDMmzcP1tbWMDIywsSJE1WmScsvnpycnBKJUx+aYtZU12+Tk5ODDh06ICkpCdOmTUONGjVgamqKuLg4+Pj4qNWT8J/BcXn7oNJHm2so7/c/aNAgeHt7a9xPvXr1ChVH37590bp1a+zduxdHjhzB0qVLsWTJEuzZswedO3cu1L7zSKVSjeWaruei8mZd5ubmQiKR4NChQxpjMTMz02nfffv2xalTpzBlyhTUr18fZmZmyM3NxSeffCLaFJDjx49HUFAQJk6ciA8//BAKhQISiQT9+/cvlph0vW+/+U3pm+tqe//T1pAhQzBhwgQ8ePAAWVlZ+OOPP/Dtt9/qvB8i+heT8SLk6OiI3Nxc3Lx5U9mCBQCPHz9GSkoKHB0dAUD5dfLly5cL1ZoQHh6Odu3aYePGjSrlKSkpsLGxUb62srJSlr/pv61rb8bVvn17vePSx82bN1Va2m7duoXc3NxCP5Xw0qVLuHHjBjZv3owhQ4YoyyMiItTWtbKy0thiqameDh8+jKSkJLaOGxB9riFbW1uYm5sjJyfnrde8i4sLzpw5g1evXsHY2Fin2Ozt7TF27FiMHTsWCQkJaNiwIRYsWJBvMu7o6IijR48iPT1dpXX8+vXryuWGxMXFBYIgwNnZGdWrVy9wPaDge0xycjJ+/fVX+Pv7Y/bs2crymzdvFm3QOgoPD4e3tzeWL1+uLMvMzFS7r7q4uODy5csF7kuba0nb+3ZBtL3/Va1aFQDeGjcA9O/fH5MmTcK2bduQkZEBY2Nj9OvXT+uYiEgdu6kUoS5dugAAVq1apVK+YsUKAEDXrl0BAB07doS5uTkWLVqkNs+xLi1ZUqlUbf1du3YhLi5OpSzvD+CJEyeUZTk5OVi/fr3Keg0bNoSzszNWrVql9gegOFvYAGDNmjUqrwMDAwGg0C2Hea10b8YvCILGqRpdXFxw/fp1PHnyRFkWHR2NkydPqqzXp08fCIIAf39/tX0Udz1R/vS5hqRSKfr06YPdu3drTETevBb69OmDxMREja2A+f3ec3Jy1LpVlC9fHg4ODhqnO83TpUsX5OTkqB1r5cqVkEgkRdaiXlR69+4NqVQKf39/tboQBEE5JaE29xhN71lA/b5a0jTdbwMDA9Vaqvv06YPo6GiNUwDmba/NteTo6AipVKpy3waA7777TqeY39xn3v//e/+ztbVFmzZtsGnTJty7d09jPHlsbGzQuXNnbN26FaGhofjkk09UGn+ISHdsGS9C7u7u8Pb2xvr165GSkoK2bdvizz//xObNm9GzZ0+0a9cOAGBhYYGVK1dixIgRaNKkiXKu3ujoaLx48QKbN2/W6njdunVDQEAAhg4dihYtWuDSpUsIDQ1VtnLkqV27Npo3b44ZM2YoW3O3b9+O7OxslfWMjIywdu1adO/eHfXr18fQoUNhb2+P69ev48qVKzh8+HDRVJQGd+/exaeffopPPvkEp0+fxtatWzFw4EC4u7sXar81atSAi4sLfH19ERcXBwsLC+zevVtjX9phw4ZhxYoV6NSpE4YPH46EhAR8//33qF27NtLS0pTrtWvXDoMHD8Y333yDmzdvKr86/+2339CuXTuMGzeuUDEbivnz5wMArly5AgAICQnB77//DgCYNWuWaHHlR99raPHixYiMjESzZs0wcuRI1KpVC0lJSfj7779x9OhRJCUlAXj99fyWLVswadIk/Pnnn2jdujWeP3+Oo0ePYuzYsejRo4favtPT01GpUiV4enrC3d0dZmZmOHr0KM6ePavSwvpf3bt3R7t27TBz5kzExMTA3d0dR44cwY8//oiJEyfqNfVdcXJxccH8+fMxY8YMxMTEoGfPnjA3N8fdu3exd+9ejBo1Cr6+vlrdYywsLNCmTRt8/fXXePXqFSpWrIgjR47g7t27op5jt27dEBISAoVCgVq1auH06dM4evSo2tSRU6ZMQXh4OLy8vDBs2DA0atQISUlJ2L9/P77//nu4u7trdS0pFAp4eXkhMDAQEokELi4u+Pnnn5X977Why/3vm2++QatWrdCwYUOMGjUKzs7OiImJwYEDB3DhwgWVdYcMGQJPT08AwLx583SvTCJSVUKztrxz8qb3evLkiUr5q1evBH9/f8HZ2VkwNjYWKleuLMyYMUPIzMxU28f+/fuFFi1aCCYmJoKFhYXQtGlTYdu2bVrHkJmZKUyePFmwt7cXTExMhJYtWwqnT5/WOLXX7du3hfbt2wsymUyoUKGC8NVXXwkREREap876/fffhQ4dOgjm5uaCqampUK9evWKZIk0Q/q3Hq1evCp6enoK5ublgZWUljBs3TmXaRwBvnR4uP1evXhXat28vmJmZCTY2NsLIkSOVU7+9OT2YIAjC1q1bhapVqwply5YV6tevLxw+fFht2jlBeD1F29KlS4UaNWoIZcuWFWxtbYXOnTsLf/31l14xiim/qQ0B5PtjSIriGnr8+LHwv//9T6hcubJgbGws2NnZCR9//LGwfv16lfVevHghzJw5U/n+trOzEzw9PYXbt2+rHCdvmresrCxhypQpgru7u/L95O7uLnz33Xcq+9V0jaWnpwtffvml4ODgIBgbGwuurq7C0qVL1abPzO+8HB0dBW9v77dVn87yu/cJgiDs3r1baNWqlWBqaiqYmpoKNWrUEP73v/8J//zzj8p6b7vHPHjwQOjVq5dgaWkpKBQKwcvLS3j48KHatJElObVhcnKyMHToUMHGxkYwMzMTOnXqJFy/fl1jPT99+lQYN26cULFiRaFs2bJCpUqVBG9vb5XpM7W5lp48eSL06dNHKFeunGBlZSWMHj1auHz5ssapDU1NTTXGrcv97/Lly8p6l8vlgpubm+Dn56e2z6ysLMHKykpQKBRq0/MSke4kgsDv1Uk8c+fOhb+/P548ecKvOkkvvIaISlZ2djYcHBzQvXt3tTFLRKQ79hknIiIire3btw9PnjxRGRRKRPpjn3EDlJGR8da5dK2trVG2bNkSisgwJSUl4eXLl/kul0qlsLW1LcGIiIjeXWfOnMHFixcxb948NGjQQDlfOREVDpNxA7Rjxw7lA0fyExkZCQ8Pj5IJyED17t0bx48fz3e5o6OjygNfiIhIf2vXrsXWrVtRv359BAcHix0O0TuDfcYNUHx8vHIGi/w0atRIOQ/t++qvv/4q8AmDJiYmaNmyZQlGRERERKQbJuNERERERCLhAE4iIiIiIpEwGSciIiIiEgmTcSIiIiIikTAZJ6L3xty5cyGRSMQOg4iISInJOBGVSgsXLsS+ffvEDoOIiKhQOJsKEZVKZmZm8PT01Gm+4+zsbGRnZ0MulxdfYERERDpgyzgRvfOeP38OAChTpgwTcSIiMihMxomoSOX1y75x4wYGDRoEhUIBW1tb+Pn5QRAE3L9/Hz169ICFhQXs7OywfPlyle2zsrIwZ84cVKtWDTKZDJUrV8bUqVORlZWlXEcikeD58+fYvHkzJBIJJBIJfHx8VI5/9epVDBw4EFZWVmjVqpXKsv/aunUrmjZtinLlysHKygpt2rTBkSNHiq+SiIiI/l8ZsQMgondTv379ULNmTSxevBgHDhzA/PnzYW1tjXXr1uGjjz7CkiVLEBoaCl9fXzRp0gRt2rRBbm4uPv30U/z+++8YNWoUatasiUuXLmHlypW4ceOGso94SEgIRowYgaZNm2LUqFEAABcXF5Xje3l5wdXVFQsXLkRBvfH8/f0xd+5ctGjRAgEBAShbtizOnDmDY8eOoWPHjsVWP0RERACTcSIqJk2bNsW6desAAKNGjYKTkxMmT56MRYsWYdq0aQCAAQMGwMHBAZs2bUKbNm0QFhaGo0eP4vjx48rWbACoU6cOxowZg1OnTqFFixYYNGgQxowZg6pVq2LQoEEaj+/u7o6wsLACY7x16xYCAgLQq1cvhIeHw8jo3y8LOZyGiIhKArupEFGxGDFihPL/UqkUjRs3hiAIGD58uLLc0tISbm5uuHPnDgBg165dqFmzJmrUqIHExETlz0cffQQAiIyM1Pr4Y8aMees6+/btQ25uLmbPnq2SiAPgFIhERFQi2DJORMWiSpUqKq8VCgXkcjlsbGzUyp8+fQoAuHnzJq5duwZbW1uN+0xISND6+M7Ozm9d5/bt2zAyMkKtWrW03i8REVFRYjJORMVCKpVqVQb82yUkNzcXdevWxYoVKzSuV7lyZa2Pb2JiovW6REREYmEyTkQGw8XFBdHR0fj444/f2k2kKLqRuLi4IDc3F1evXkX9+vULvT8iIiJdsc84ERmMvn37Ii4uDhs2bFBblpGRoZwvHABMTU2RkpJSqOP17NkTRkZGCAgIQG5ursoyDuAkIqKSwJZxIjIYgwcPxs6dOzFmzBhERkaiZcuWyMnJwfXr17Fz504cPnwYjRs3BgA0atQIR48exYoVK+Dg4ABnZ2c0a9ZMp+NVq1YNM2fOxLx589C6dWv07t0bMpkMZ8+ehYODAxYtWlQcp0lERKTEZJyIDIaRkRH27duHlStXYsuWLdi7dy/KlSuHqlWrYsKECahevbpy3RUrVmDUqFGYNWsWMjIy4O3trXMyDgABAQFwdnZGYGAgZs6ciXLlyqFevXoYPHhwUZ4aERGRRhKB38USEREREYmCfcaJiIiIiETCZJyIiIiISCRMxomIiIiIRMJknIiIiIhIJEzGiYiIiIhEwmSciIiIiEgkTMaJiIiIiETCZJyIiIiISCRMxomIiIiIRMJknIiIiIhIJEzGiYiIiIhEwmSciIiIiEgkTMaJiIiIiETyfzVHUGxODc+sAAAAAElFTkSuQmCC",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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 [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, 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=(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, 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",
+ " 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": 34,
+ "id": "883c4caa-c6af-403f-b88c-102fa8e7621a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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=(6, 2.5))\n",
+ " results_sub = results_sub[results_sub['model_config'].isin(['BEAMT', 'DextraDemixer'])]\n",
+ "\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='small', label_type='edge', padding=5, rotation=90)\n",
+ " \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": 35,
+ "id": "9ee1aec1-7a48-4974-b007-01d47a70ec74",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "results_sub_plot = results_sub[results_sub['model_config'].isin(['BEAMT', 'DextraDemixer'])].copy()\n",
+ "metrics = ['pr_auc', 'roc_auc', 'f1', 'precision', 'recall', 'accuracy', ]\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",
+ " 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='DextraDemixer', y='BEAMT', \n",
+ " # hue='dataset', \n",
+ " # hue='tcr_info_sequenced_only',\n",
+ " # style='tcr_info_sequenced_only', \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",
+ " 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(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": {
+ "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/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}"
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": [
+ ""
+ ]
+ },
+ "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": [
+ ""
+ ]
+ },
+ "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": [
+ ""
+ ]
+ },
+ "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": [
+ ""
+ ]
+ },
+ "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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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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": [
+ ""
+ ]
+ },
+ "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": [
+ ""
+ ]
+ },
+ "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": [
+ ""
+ ]
+ },
+ "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": [
+ ""
+ ]
+ },
+ "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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ubm5ISkrCH3/8gaZNm8LS0hIeHh4YOnQoRowYgSVLlqBZs2Z4/Pgx/vjjDzRp0gS9evXChAkT0LZtW3z11Vfo27cv9u/fj3379hU5phkzZuDHH3/E3bt3cwxgr1AoEBsbCwCIj4/H8uXLkZSUhN69e+vk9SiqAwcO6GW/hbF8+XK0adMG/v7+mD9/PurWrYsrV67go48+Qq1atbBgwQKt+idOnMDixYvRr18/HDx4ENu2bcOePXsAZMwuq1Kp4OvrC0tLS2zYsAEWFhaoU6cOqlWrVuB7Iz9Dhw7F3LlzsWDBAgwYMECrl9j06dPx8ssvY/z48Xj77bdhZWWFq1ev4uDBg1i+fDkaNmyIHj164J133sHKlSthbGyMSZMmFdijrFOnTkhKSsKVK1fg7e1d6Ne0oHg8PT3RtWtXjB07FitXroSJiQmmTJmi1QvR3d0d6enp+O6779C7d2+cOHEC//vf/7T2k1sbzKuH3pAhQ/C///0PN27cwOHDh6VyGxsbTJ06FZMnT4ZarUa7du2QkJCAEydOwNbWFiNHjsyxLTc3N4wcORJvvfUWvv32WzRt2hT37t1DXFwc3njjDYwbNw7Lli3DBx98gPHjx+P69euYM2cOPvzwQykhWRhubm74448/0LZtW5iZmeW43Vija9euaNy4MYYOHYply5ZBqVRi3Lhx6NChA1q2bJnn9o8dOybNIExERGQIOCYeERERFeizzz5DVFQU6tevL9161qZNG7z77rsYNGgQHBwcsHjxYgAZs0mOGDECU6ZMQcOGDdGvXz+cPXsWrq6uAICXX34ZP/74I7755hs0bdoUBw4cwKefflrkmKpWrYrp06fjRfaZfgHs27cPTk5OcHJygq+vL86ePYtt27ahY8eOxX8RKqgGDRrgr7/+Qr169fDGG2+gfv36GDt2LDp16oRTp06hatWqWvWnTJmCv/76C82aNcP8+fOxdOlS+Pv7AwDs7Ozw448/om3btmjSpAl+//13/Pbbb9KswAW9N/Lj7u6O1q1b4++//5bGc9No0qQJjhw5ghs3buCVV15Bs2bNMHv2bDg7O0t1goOD4ezsjA4dOuC1117D2LFj4ejomO8+q1Wrhv79+0vjyRVWYeJZt24datSogfbt26N///4YM2YMbGxspDH4mjZtiqVLl2LRokXw9vbGxo0bsXDhQq395NUGczN06FBcvXoVtWrVQtu2bbWWff7555g1axYWLlwILy8v9OjRA3v27EHdunXz3N7KlSsxYMAAjBs3Dp6enhgzZgySM2fKrlWrFsLCwnDmzBk0bdoU7777LgIDA4vczpcsWYKDBw/CxcUFzZo1y7OeTCbDr7/+Cnt7e7Rv3x5du3ZFvXr1sGXLlny3v3nzZowZM6ZIMREREemTTBRlJGMiIiIiKhQhhDSpQ1kzNjYu0u2flLe///4b3bp1w+3bt3U6rmN2Dx48gIuLizSZBZWuvXv3YsqUKfj7779hbMybk4iIyDAwiUdERERElI+QkBC0aNECjRs31tk2Dx06hKSkJDRu3BgxMTGYNm0aHj58iBs3buSY8IF0LzQ0FC4uLjlmyiYiIirPmMQjIiIiIipj+/fvx5QpU3Dnzh3Y2NigTZs2WLZsGerUqaPv0IiIiKicYhKPiIiIiIiIiIionOPEFkREREREREREROUck3hERERERERERETlHJN4RERERERERERE5RyTeEREREREREREROUck3hERERERERERETlHJN4RERERERERERE5RyTeEREREREREREROUck3hERERERERERETlHJN4RERERERERERE5RyTeEREREREREREROUck3hERERERERERETlHJN4pBNRUVGQyWSIiIjQdyhEFYJMJsMvv/yi7zCIqBjYfomoKHgdTVQx8PO/9PF8ySQeATh69Ch69+4NZ2fnPE88QgjMnj0bTk5OsLCwQNeuXXHz5s0S7Xfu3Lnw8fHJUV6RGqa/vz/kcjnOnj2b6/IVK1bAzc0N5ubm8PX1xZkzZ6RlT58+xQcffICGDRvCwsICrq6umDBhAhISErS2ER0djV69esHS0hKOjo746KOPoFQqpeUxMTEYMmQIPDw8YGRkhEmTJpXKsVLhzZ07FzKZTOvh6elZom2GhITAzs4u12UV5YLinXfegVwux7Zt23Jdvm3bNnh6esLc3ByNGzdGWFiYtCw9PR3Tp09H48aNYWVlBWdnZ4wYMQKPHj3S2sbTp08xdOhQ2Nraws7ODoGBgUhKSpKWv3jxAqNGjULjxo1hbGyMfv36FSr2rH9rY2NjuLq64sMPP4RCodCqFx4ejubNm8PMzAzu7u4ICQnRWu7m5pbjvSOTyfD+++9rxfj++++jWrVqsLa2xuuvv45//vlHWn7x4kUMHjwYLi4usLCwgJeXF7755ptCHQex/RYX2y/bb0XF6+jSk9919MKFC9GqVSvY2NjA0dER/fr1w/Xr13PdjhACPXv2zPXvU9B19I4dO9CtWzc4ODjA1tYWfn5+2L9/v06PkwxDUd5zhRUSEgKZTAYvL68cy7Zt2waZTAY3N7cS7aO05XfO6dixo/Tds7Cf5dnbaXp6OgYPHoxatWrh8uXLpXgkhoNJPEJycjKaNm2KFStW5Fln8eLF+Pbbb/G///0Pp0+fhpWVFfz9/fHixYsyjNSwREdH4+TJkxg/fjzWrFmTY/mWLVvw4YcfYs6cOTh//jyaNm0Kf39/xMXFAQAePXqER48e4auvvsLly5cREhKCffv2ITAwUNqGSqVCr169kJaWhpMnT2Lt2rUICQnB7NmzpToKhQIODg749NNP0bRp09I/cCqUl156CTExMdLj+PHj+g6pXEtJScHmzZsxbdq0XNvTyZMnMXjwYAQGBuLChQvo168f+vXrJ33Yp6Sk4Pz585g1axbOnz+PHTt24Pr16+jTp4/WdoYOHYorV67g4MGD2L17N44ePYqxY8dKy1UqFSwsLDBhwgR07dq1SMcQHByMmJgY3L17F99//z3Wr1+P+fPnS8vv3r2LXr16oVOnToiIiMCkSZPw9ttva31ZOHv2rNb75uDBgwCAgQMHSnUmT56M3377Ddu2bcORI0fw6NEjvPbaa9Lyc+fOwdHRERs2bMCVK1fwySefYMaMGVi+fHmRjqcyY/stGrbfDGy/FROvo0tHQdfRR44cwfvvv48///wTBw8eRHp6Orp3747k5OQcdZctWwaZTJajvDDX0UePHkW3bt0QFhaGc+fOoVOnTujduzcuXLig2wOmcq8o77misLKyQlxcHE6dOqVVvnr1ari6upZo2+VJYT/Ls6/Tp08fnD17FsePH4e3t3cZRlyOCaIsAIidO3dqlanValGzZk0RFBQklT179kyYmZmJn3/+WQghxN27dwUAceHCBSGEEEqlUowePVo0bNhQ3Lt3L9d9zZkzRzRt2jRHefZtBQcHiypVqmjV2blzp8j69tVsa/Xq1cLFxUVYWVmJ9957TyiVSrFo0SJRo0YN4eDgIObPn6+1nSVLlghvb29haWkpateuLd577z3x/Plzablm3/v27ROenp7CyspK+Pv7i0ePHuX3MgohhJg7d6548803RWRkpKhSpYpISUnRWt66dWvx/vvvS89VKpVwdnYWCxcuzHObW7duFaampiI9PV0IIURYWJgwMjISsbGxUp2VK1cKW1tboVAocqzfoUMHMXHixAJjp9KV13s/q+xtcfbs2aJmzZri4sWLudbPrZ3ktq3Dhw8LACI+Pl5afuHCBQFA3L17V2tbv/32m/Dw8BAWFhbi9ddfF8nJySIkJETUqVNH2NnZiQ8++EAolUppO+vWrRMtWrQQ1tbWokaNGmLw4MHin3/+kZZr9v3777+LFi1aCAsLC+Hn5yeuXbuW72shhBAhISHi5ZdfFs+ePROWlpYiOjpaa/kbb7whevXqpVXm6+sr3nnnnTy3eebMGQFAOkddvXpVABBnz56V6uzdu1fIZDLx8OHDHOuPHDlS9O3bt8DYhcj93BoYGCgCAgKk59OmTRMvvfSSVp1BgwYJf3//PLc7ceJEUb9+faFWq4UQGedmExMTsW3bNqlOZGSkACBOnTqV53bGjRsnOnXqVKhjqezYftl+hWD7pdzxOrrsrqOzi4uLEwDEkSNHtMovXLggatWqJWJiYnL8fYp6Ha3RqFEjMW/evAKPgSq23N5zxf38Hz9+vHj77bel8vv37wszMzPx8ccfizp16mit8/3334t69eoJExMT4eHhIdatWycty97+hRAiPj5eABCHDx8WQgjx9OlTMWTIEFG9enVhbm4u3N3dxZo1a6T60dHRYuDAgaJKlSrC3t5e9OnTR7rGyE1u+9Qo6Ltn9s9yIf57DePj40WbNm1EkyZNRExMTI79/fzzz8LPz0+YmZmJl156SYSHh+e5n4qGPfHKkBACiYmJEELoO5QiuXv3LmJjY7V+ta5SpQp8fX1z/GIAZPT8GjhwICIiInDs2LEy+wXh9u3b2Lt3L/bt24eff/4Zq1evRq9evfDgwQMcOXIEixYtwqefforTp09L6xgZGeHbb7/FlStXsHbtWhw6dAjTpk3T2m5KSgq++uorrF+/HkePHkV0dDSmTp2abyxCCAQHB2PYsGHw9PSEu7s7QkNDpeVpaWk4d+6c1mtqZGSErl275vqaaiQkJMDW1hbGxsYAgFOnTqFx48aoUaOGVMff3x+JiYm4cuVK4V44KjRdtuGbN2/C2dkZ9erVw9ChQxEdHZ3nPj/44AOsW7cOx44dQ5MmTUq878JISUnBt99+i82bN2Pfvn0IDw9H//79ERYWhrCwMKxfvx4//PCD1vs6PT0dn3/+OS5evIhffvkFUVFRGDVqVI5tf/LJJ1iyZAn++usvGBsb46233iowntWrV2PYsGGoUqUKevbsmeM2tVOnTuXoWePv719ge5LJZNJtjKdOnYKdnR1atmwp1enatSuMjIy0zhu6cOPGDRw6dAi+vr7FPoa0tDRs2LABb731ltTD4Ny5c0hPT9fajqenJ1xdXQt8LapWrVqSQzIIumrDbL9sv2y/ZY/X0aXLkK6jc6MZbiZrW0hJScGQIUOwYsUK1KxZM8c6xbmOVqvVeP78eaVoc7pmqG04L7m95zSK+vn/1ltvYevWrUhJSQGQcZttjx49tN6bALBz505MnDgRU6ZMweXLl/HOO+9g9OjROHz4cKHjnjVrFq5evYq9e/ciMjISK1euRPXq1QFkXAv4+/vDxsYGx44dw4kTJ2BtbY0ePXogLS2t0PsorOyf5RqxsbHo0KEDgIwekLm1348++ghTpkzBhQsX4Ofnh969e+PJkyc6j7Fc0kPisNJKSEgQAERCQoK+Q8kTcvkF8cSJEwJAjl/NBg4cKN544w0hxH8Z8WPHjokuXbqIdu3aiWfPnuW7rzlz5ggjIyNhZWWl9bC0tCzWL4iWlpYiMTFRKvP39xdubm5CpVJJZQ0bNsy3p9u2bdtEtWrVpOfBwcECgLh165ZUtmLFClGjRo18j+3AgQPCwcFB6jH39ddfiw4dOkjLHz58KACIkydPaq330UcfidatW+e6zcePHwtXV1cxc+ZMqWzMmDGie/fuWvWSk5MFABEWFpZjG+yJVzK6asNhYWFi69at4uLFi2Lfvn3Cz89PuLq6ar1/AYht27aJIUOGCC8vL/HgwYN8t6l5r2ZvT1ZWVsXqyZP9ff/OO+8IS0tLrV/Y/f398+0pc/bsWQFAWidrTx6NPXv2CAAiNTU1z+3cuHFDmJiYiMePHwshMtp/3bp1pd4rQghhYmIiNm3apLXeihUrhKOjY67bTE1NFc2bNxdDhgyRyhYsWCA8PDxy1HVwcBDff/99jvKi9uQxNzcXVlZWwszMTAAQr776qkhLS5PqNGjQQHzxxRda62len9x6IGzZskXI5XKtXkYbN24UpqamOeq2atVKTJs2LdfYTpw4IYyNjcX+/fsLdSyGTBdtmO2X7ZftVz94Ha2tMl9HZ6dSqUSvXr1E27ZttcrHjh0rAgMDpefZ/z5FvY4WQohFixYJe3t7rZ7KVDiG0IYLK6/3XHE+/zXt08fHR6xdu1ao1WpRv3598euvv4qvv/5aqydemzZtxJgxY7S2MXDgQKlneGF64vXu3VuMHj0613jWr18vGjZsqPUZrVAohIWFRZ6fM5p9WlhY5DgfGRkZ5fndM7fPciEyXkNTU1Ph6ekpkpOT89zfl19+KZWlp6eL2rVri0WLFuW6r4qGPfFIpwYPHozk5GQcOHAAVapUKbB+w4YNERERofXIOph1Ubi5ucHGxkZ6XqNGDTRq1AhGRkZaZZox5wDg999/R5cuXVCrVi3Y2Nhg+PDhePLkifQrCABYWlqifv360nMnJyetbeRmzZo1GDRokNRjbvDgwThx4gRu375drGNLTExEr1690KhRI8ydO7dY26Dyo2fPnhg4cCCaNGkCf39/hIWF4dmzZ9i6datWvcmTJ+P06dM4evQoatWqVeB2bWxscrSn4g5snf19X6NGDbi5ucHa2lqrLGtbOHfuHHr37g1XV1fY2NhIv6Bl76WU9ddIJycnAMi3Ta1Zswb+/v7Sr4QBAQFISEjAoUOHinVs6enpeOONNyCEwMqVK4u1jeL4+uuvERERgYsXL2L37t24ceMGhg8fXuztrV69Gj179oSzs3Oxt3H58mX07dsXc+bMQffu3Yu9ncqE7Zftl+2XSguvozMU9Tr6/fffx+XLl7F582apbNeuXTh06BCWLVtWuBegEDZt2oR58+Zh69atcHR01Nl2yfDk9p7TKOrnv8Zbb72F4OBgHDlyBMnJyQgICMhRJzIyEm3bttUqa9u2LSIjIwu9n/feew+bN2+Gj48Ppk2bhpMnT0rLLl68iFu3bsHGxgbW1tawtrZG1apV8eLFiwK/x27ZsiXH+Shr7/isCvosf/XVV3Hjxg388MMPee7Pz89P+r+xsTFatmxZpNfBkDGJRwXSdF/NOjua5nn2rq0BAQH4+++/873tIytTU1O4u7trPerUqaNVx8jIKEe36/T09BzbMjEx0Xouk8lyLVOr1QAyZtJ59dVX0aRJE2zfvh3nzp2TBiXO2l04t21kjyerp0+fYufOnfj+++9hbGwMY2Nj1KpVC0qlUhqYt3r16pDL5YV6TZ8/f44ePXrAxsYGO3fu1IqnZs2auW5Ds4wMg52dHTw8PHDr1i2t8m7duuHhw4eFngXNyMgoR3tyd3fPUQeA1ntYF+0pOTkZ/v7+sLW1xcaNG3H27Fns3LkTAHJ0v8+6Hc1tZJrtZKdSqbB27Vrs2bNHak+WlpZ4+vSp1kDXebWF7O1Ac9Fw7949HDx4ELa2tlrbyP7FQqlU4unTpzppTzVr1oS7uzsaNmyIXr16Yd68ediyZYv0d8/rGGxtbWFhYaFVfu/ePfz+++94++23c+wjLS0Nz549y7Gd7Mdw9epVdOnSBWPHjsWnn35a4uOrrNh+2X7zOwa2X+J1tO6vo7MaP348du/ejcOHD6N27dpS+aFDh3D79m3Y2dlJ2wGA119/HR07dgRQtOvozZs34+2338bWrVuLPDEOVSx5vec0ivr5rzF06FD8+eefmDt3LoYPHy69Z4uiMNcJPXv2xL179zB58mQ8evQIXbp0kW5xT0pKQosWLXIk427cuIEhQ4bku28XF5cc56Psn3+aePL6LNcYPnw41qxZg6lTp2Lp0qVFfh0qOibxqEB169ZFzZo18ccff0hliYmJOH36tFYGHMjI7H/55Zfo06cPjhw5opP9Ozg44Pnz51oz/xS3d0JW586dg1qtxpIlS/Dyyy/Dw8MjxxTXxbFx40bUrl0bFy9e1Dr5LVmyBCEhIVCpVDA1NUWLFi20XlO1Wo0//vhD6zVNTExE9+7dYWpqil27dsHc3FxrX35+frh06ZLWFxfNybBRo0YlPhYqG0lJSbh9+7bUq0WjT58+2LRpE95+++1cf+krDgcHBwBATEyMVKaL9nTt2jU8efIEX375JV555RV4enoW+Et7YYSFheH58+e4cOGCVnv6+eefsWPHDunLrp+fn1Z7AjLaQtb2pLlouHnzJn7//XdUq1ZNq76fnx+ePXuGc+fOSWWHDh2CWq3WGvtKV+RyOQAgNTW10MegERwcDEdHR/Tq1UurvEWLFjAxMdHazvXr1xEdHa21nStXrqBTp04YOXIkFixYoLNjqozYfvPG9sv2S7yOLqrCXEcDGUmK8ePHY+fOnTh06BDq1q2rtZ2PP/4Yf//9d46ezV9//TWCg4MBFP46+ueff8bo0aPx888/52i3VHkU9J7TKO7nf9WqVaW2n9d4s15eXjhx4oRW2YkTJ6T3a2GvExwcHDBy5Ehs2LABy5Ytw6pVqwAAzZs3x82bN+Ho6JgjIVeY3sEFKeizPKuRI0ciJCQE06ZNw1dffZVj+Z9//in9X6lU4ty5c/Dy8ipxjAZBP3fxVk7ldRyA58+fiwsXLkjj6ixdulRcuHBBa5aYL7/8UtjZ2Ylff/1V/P3336Jv376ibt260jg42e+///rrr4W1tbU4duxYnvst7KxaT548EVZWVmLChAni1q1bYuPGjcLZ2TnXWbWyym28m6xjwkVERAgAYtmyZeL27dti3bp1olatWlrjDRVmHJHsmjZtKqZPn56j/NmzZ8LU1FTs3r1bCCHE5s2bhZmZmQgJCRFXr14VY8eOFXZ2dtIMWQkJCcLX11c0btxY3Lp1S8TExEgPzYyCSqVSeHt7i+7du4uIiAixb98+4eDgIGbMmKG1b83ft0WLFmLIkCHiwoUL4sqVK3keA+VOV214ypQpIjw8XNy9e1ecOHFCdO3aVVSvXl3ExcVJdZBl3JZt27YJc3NzrRkLsyvs7JZpaWnCxcVFDBw4UNy4cUPs3r1bNGzYMNfZLbMqqI3FxcUJU1NT8dFHH4nbt2+LX3/9VXh4eGi15cKM55Vd3759xaBBg3KUq1QqUbNmTbF8+XIhxH/jQn311VciMjJSzJkzR5iYmIhLly5Jx92nTx9Ru3ZtERERodWess5A16NHD9GsWTNx+vRpcfz4cdGgQQMxePBgrX1fuXJFXLhwQfTu3Vt07NhRal/5ASCCg4NFTEyMePjwoQgPDxfe3t7Cw8NDGvPnzp07wtLSUnz00UciMjJSrFixQsjlcrFv374cx+7q6prreUYIId59913h6uoqDh06JP766y/h5+cn/Pz8pOWXLl0SDg4OYtiwYVqvQ9b3X0WlizbM9hsvbYPtl+23LPE6Wltlv45+7733RJUqVUR4eLhWW8hvFtus51MhCncdvXHjRmFsbCxWrFihtZ+CxiyknMprGy6swrznSvr5n5KSIv7991/pefYx8Xbu3ClMTEzE999/L27cuCGWLFki5HK5NN6dEEK8/PLL4pVXXhFXr14V4eHhonXr1lpj4s2aNUv88ssv4ubNm+Ly5cvi1VdflcZkT05OFg0aNBAdO3YUR48eFXfu3BGHDx8WH3zwgbh//36ux1DY2WkL+1mevZ1u2LBByOVysXjxYq39ubq6ih07dojIyEgxduxYYW1tLY2/W9ExiVeGyuuJS3Nhnv0xcuRIqY5arRazZs0SNWrUEGZmZqJLly7i+vXr0vLcGu+SJUuEjY2NOHHiRK77LezFhxAZJyx3d3dhYWEhXn31VbFq1aoSX3wIIcTSpUuFk5OTsLCwEP7+/mLdunUluvj466+/BABx5syZXJf37NlT9O/fX3r+3XffCVdXV2Fqaipat24t/vzzT2lZXn+X7F+YoqKiRM+ePYWFhYWoXr26mDJlivSlQiO3bWSfrpwKVpg2rFarRVpamtYj6+CwQggxaNAg4eTkJExNTUWtWrXEoEGDtAZ9FiLnB9iWLVuEubm52L59e677LWwSQAghjh8/Lho3bizMzc3FK6+8IrZt21biJIAQQmzatEm4ubkJMzMz4efnJ3bt2lWiJEBsbKwwNjYWW7duzfW43nvvPdGsWTPp+datW4WHh4cwNTUVL730ktizZ4+0THNeye2R9cLnyZMnYvDgwcLa2lrY2tqK0aNHa00GIIQQderUyXU7+claTyaTCScnJzFo0CBx+/ZtrXqHDx8WPj4+wtTUVNSrV08EBwfn2Nb+/fsFAK1zcFapqali3Lhxwt7eXlhaWor+/fuLmJgYafmcOXMq7TlBF5/DbL/x0jpsv2y/ZUlXn8G6xuto/VxH53VOyK3daWQ/nwpR8HV0hw4dCvz7UuEUpw2XdvstisK853T5+S9EziSeEEJ8//33ol69esLExER4eHiIdevWaS2/evWq8PPzExYWFsLHx0ccOHBA6/Py888/F15eXsLCwkJUrVpV9O3bV9y5c0daPyYmRowYMUJUr15dmJmZiXr16okxY8bk+XcrbBKvsJ/lubXTTZs2CblcLr788ktpO5s2bRKtW7cWpqamolGjRuLQoUN5vo4VjUyICjLHswFITExElSpVkJCQkOu930RUvhXUhoUQCAsLy3EbmqOjIwICAqTxo4hIP/g5TGS4+BlMZNiK04bZfoly4ph45c3evcCAAQAHcCQyOEqlMtdxpOLi4qBUKvUQEREVSXg48MYbAMcZIzI4/AwmMmy5tWG2X6KcmMQrb27fBrZvBwo5KxVRZaFQKDB9+nQ4OzvDwsICvr6+OHjwYJG3061bN8hkMowfP74UovxP9+7d0b1791LdBxHpWHQ0sG0bcOyYviMhohLgZzCRYevSpYu+QyAqt5jEK28yZztD5vTtRJRh1KhRWLp0KYYOHYpvvvkGcrkcAQEBOH78eKG3sWPHDpwqowS5XC6XZi8kIgNJxJuZZfz74oXut01EZYafwUSGje2XKG9M4pU3mhNW5vTpRAScOXMGmzdvxsKFCxEUFISxY8fi0KFDqFOnDqZNm1aobbx48QJTpkzB9OnTSzlaIsqNQSTiNUk8haL09kFEREREVExM4pU3TOIR5RAaGgq5XI6xY8dKZebm5ggMDMSpU6dw//79ArexePFiqNVqTJ06tTRDJaJcGEwi3tw8418m8YiIiIioHGISr7xhEo8yzZ07Fz4+PvoOo1y4cOECPDw8csxk1bp1awBAREREvutHR0fjyy+/xKJFi2BhYVHo/SoUCiQmJmo9qGJg+ypbBpOIZ088g8E2TET54TmCyHCx/eaPSbzyhkk8gyaTyfDLL7/oO4wKJyYmBk5OTjnKNWWPHj3Kd/0pU6agWbNmePPNN4u034ULF6JKlSrSw8XFpUjrk27pq335+/tDLpfj7NmzuS5fsWIF3NzcYG5uDl9fX5w5c0Za9vTpU3zwwQdo2LAhLCws4OrqigkTJiAhIUFrG9HR0ejVqxcsLS3h6OiIjz76SGs2tpiYGAwZMgQeHh4wMjLCpEmTSuVYS4u+EvFFxiReqSrLNhwVFQWZTCY9TE1N4e7ujvnz50MIoVV327Zt8PT0hLm5ORo3boywsLAccef2CAoKkuo8ffoUQ4cOha2tLezs7BAYGIikpCRpeXh4OPr27QsnJydYWVnBx8cHGzduLN0XgcjA8DqayHCx/ZYdJvHKG6PMPwmTeBVWWlqavkMwOKmpqTDTfLnOwjzz1rfU1NQ81z18+DC2b9+OZcuWFXm/M2bMQEJCgvQoTG8h0i9dt6/o6GicPHkS48ePx5o1a3Is37JlCz788EPMmTMH58+fR9OmTeHv74+4uDgAGQnmR48e4auvvsLly5cREhKCffv2ITAwUNqGSqVCr169kJaWhpMnT2Lt2rUICQnB7NmzpToKhQIODg749NNP0bRpU50eY1nQVyK+yL1pmcTTO1234d9//x0xMTG4efMm5s2bhwULFmi15ZMnT2Lw4MEIDAzEhQsX0K9fP/Tr1w+XL1+W6sTExGg91qxZA5lMhtdff12qM3ToUFy5cgUHDx7E7t27cfToUa2epydPnkSTJk2wfft2/P333xg9ejRGjBiB3bt36/R4iSo6XkcTGS62X91gEq+8eeMNICkJyPYrsKHq2LEjJkyYgGnTpqFq1aqoWbMm5s6dq1Xn2bNnePvtt+Hg4ABbW1t07twZFy9e1Kozf/58ODo6wsbGBm+//TY+/vjjInex1cyM6OLiAjMzM7i7u2P16tXS8iNHjqB169YwMzODk5MTPv74Y62eMAUdi5ubGwCgf//+kMlk0nNNd+CffvoJdevWlRJP0dHR6Nu3L6ytrWFra4s33ngD//zzT5GOqbKwsLCAIpcv1S8yZ5DMq2eOUqnEhAkTMHz4cLRq1arI+zUzM4Otra3Wozxh+yr99hUcHIxXX30V7733Hn7++eccCeOlS5dizJgxGD16NBo1aoT//e9/sLS0lJIE3t7e2L59O3r37o369eujc+fOWLBgAX777Tfp+A8cOICrV69iw4YN8PHxQc+ePfH5559jxYoV0sWOm5sbvvnmG4wYMQJVqlQp8nHom74S8UXuTevjAzx/Dty5U+R9FQfbcOm34WrVqqFmzZqoU6cOhg4dirZt2+L8+fPS8m+++QY9evTARx99BC8vL3z++edo3rw5li9fLtWpWbOm1uPXX39Fp06dUK9ePQBAZGQk9u3bh59++gm+vr5o164dvvvuO2zevFlKUM+cOROff/452rRpg/r162PixIno0aMHduzYUeRjosqD5wheR5PhYvtl+y0tTOKVNyYmgJUVYGqq70h0Zu3atbCyssLp06exePFifPbZZzh48KC0fODAgYiLi8PevXtx7tw5NG/eHF26dMHTp08BABs3bsSCBQuwaNEinDt3Dq6urli5cmWR4xgxYgR+/vlnfPvtt4iMjMQPP/wAa2trAMDDhw8REBCAVq1a4eLFi1i5ciVWr16N+fPnF/pYNLfaBQcHIyYmRuvWu1u3bmH79u3YsWMHIiIioFar0bdvXzx9+hRHjhzBwYMHcefOHQwaNKjIx1UZODk5ISYmJke5pszZ2TnX9datW4fr16/jnXfeQVRUlPQAgOfPnyMqKgopKSmlFndZYPsqvfYlhEBwcDCGDRsGT09PuLu7IzQ0VFqelpaGc+fOoWvXrlKZkZERunbtmu8MqgkJCbC1tYWxsTEA4NSpU2jcuDFq1Kgh1fH390diYiKuXLlSpJjLK30l4ovcm1YuB6ytMz6LywjbcNl9Rv711184d+4cfH19pbJTp05ptWEgo/3l1Yb/+ecf7NmzR6s37alTp2BnZ4eWLVtKZV27doWRkRFOnz6dZzwJCQmoWrVqcQ+HKgmeI3gdXVnI/voL7VetQvMK9OMG2y/bb6kQVGYSEhIEAJGQkKDvUMpMhw4dRLt27bTKWrVqJaZPny6EEOLYsWPC1tZWvHjxQqtO/fr1xQ8//CCEEMLX11e8//77Wsvbtm0rmjZtWug4rl+/LgCIgwcP5rp85syZomHDhkKtVktlK1asENbW1kKlUhXqWIQQAoDYuXOnVp05c+YIExMTERcXJ5UdOHBAyOVyER0dLZVduXJFABBnzpyR1ivKMVZkU6dOFXK5PEfbWbBggQCg9TpmNWfOHAEg30f2v1d+CmrDaWlpYs2aNWLNmjUiJiZGxMTESM/T0tIKvZ/CYvsq3fZ14MAB4eDgINLT04UQQnz99deiQ4cO0vKHDx8KAOLkyZNa63300UeidevWuW7z8ePHwtXVVcycOVMqGzNmjOjevbtWveTkZAFAhIWF5dhGhw4dxMSJE/ONvbzp2rWr8PLyylH++++/CwBi165dua63evVqYWJiIk6cOCHu3r0rPQCIESNGiLt374rk5ORCx1HePofZhkuvDWveJxYWFsLKykqYmJgIAGLs2LFa9UxMTMSmTZu0ylasWCEcHR1z3e6iRYuEvb29SE1NlcoWLFggPDw8ctR1cHAQ33//fa7b2bJlizA1NRWXL1/O8xhIW3n7DC4LPEfwOroiKagNp+/cKQQg4tzcxIMHD9h+Bdsv5Y498cqbiAhg1Chg3jx9R6IzTZo00Xru5OQkjRd18eJFJCUloVq1arC2tpYed+/exe3btwEA169flwY/18j+vCARERGQy+Xo0KFDrssjIyPh5+cHmUwmlbVt2xZJSUl48OBBoY4lP3Xq1IGDg4PW/lxcXLRu7WrUqBHs7OwQGRlZ6OOqLAYMGACVSoVVq1ZJZQqFAsHBwfD19ZVex+joaFy7dk2q8+abb2Lnzp05HgAQEBCAnTt3avXIMERsX6XXvtasWYNBgwZJPeYGDx6MEydOSK9dUSUmJqJXr15o1KhRjtspKjofHx/cuHEjx5h0ml5Ked0WEh0djfT0dLRt2xZ169aVHkBGT9u6deviwIEDugv0+XNg9GhgyBAg2+QHpYVtuHQ/I7ds2YKIiAhcvHgRW7duxa+//oqPP/64SNvIas2aNRg6dKh0S1BxHD58GKNHj8aPP/6Il156qdjbocqB5wheR1cadnYAANN8htgwNGy/bL+lwVjfAVA2Dx4Aa9cCLVsCc+boOxqdMMl2W5JMJoNarQYAJCUlwcnJCeHh4TnWs8s8keuCrmYzzO9Y8mNlZaWT/VdWvr6+GDhwIGbMmIG4uDi4u7tj7dq1iIqK0hrPYcSIEThy5Ig086Cnpyc8PT1z3WbdunXRr1+/sgi/VLF9lU77evr0KXbu3In09HSt2xZUKhXWrFmDBQsWoHr16pDL5TnG8Pjnn39Qs2ZNrbLnz5+jR48esLGxwc6dO7WOtWbNmloz2mq2oVlWEQwYMABfffUVVq1ahalTpwLIOxGfkpIitds333wz1wRf//79ERAQgDFjxug2Ea9WAyEhGf8PDv5vootSxDZcup+RLi4ucHd3BwB4eXnh9u3bmDVrFubOnQtzc3PUrFmzUG0YAI4dO4br169jy5YtWuU1a9bM8UVGqVTi6dOnObZz5MgR9O7dG19//TVGjBihi0OkCo7nCF5HVxYic+xpUwMf6iYrtl+239JQqXriaQZ0dHZ2hoWFBXx9fbXuSc/Lzp074e/vD2dnZ5iZmaF27doYMGCA1sxlOiOXZ/xbSWanbd68OWJjY2FsbAx3d3etR/Xq1QEADRs21LqvHkCO5wVp3Lgx1Go1jhw5kutyLy8vnDp1Skr+AMCJEydgY2OD2rVrF3o/JiYmUBXib+fl5YX79+9rjc909epVPHv2DI0aNSr0/iqTdevWYdKkSVi/fj0mTJiA9PR07N69G+3bt9d3aOUW21fx29fGjRtRu3ZtXLx4EREREdJjyZIlCAkJgUqlgqmpKVq0aIE//vhDWk+tVuOPP/6An5+fVJaYmIju3bvD1NQUu3btytGDx8/PD5cuXdJKAhw8eBC2trYV5nyQNRE/bdo0rFq1Cp07d0ZUVBQWL14s1RsxYgS8vLyk556entJsoVkfwH+J+NxmvS22rEm7cjBDLduw7j8j5XI5lEqlNGmMn5+fVhsGMtpf1jassXr1arRo0SLHDNF+fn549uwZzp07J5UdOnQIarVaK8kcHh6OXr16YdGiRVoz1xIVF88RvI6uUDQ98VJSyqw3vD6x/bL9FlelSuKNGjUKS5cuxdChQ/HNN99ALpcjICAAx48fz3e9S5cuwd7eHhMnTsT333+P9957DxcuXEDr1q1zzB5TYpUside1a1f4+fmhX79+OHDgAKKionDy5El88skn+OuvvwAAH3zwAVavXo21a9fi5s2bmD9/Pv7++2+tLr87d+7Ms8cVkDFjzsiRI/HWW2/hl19+wd27dxEeHo6tW7cCAMaNG4f79+/jgw8+wLVr1/Drr79izpw5+PDDD2FkVPhm4ubmhj/++AOxsbGIj4/P97gbN26MoUOH4vz58zhz5gxGjBiBDh06aA2MTf8xNzdHUFAQYmJi8OLFC5w5cwb+/v5adcLDw7U+gPIihNCaebCiYvsqfvtavXo1BgwYAG9vb61HYGAg/v33X+zbtw8A8OGHH+LHH3/E2rVrERkZiffeew/JyckYPXo0gP8SeMnJyVi9ejUSExMRGxuL2NhY6UKne/fuaNSoEYYPH46LFy9i//79+PTTT/H+++9rzeiqSSQmJSXh8ePHiIiIwNWrVwv9+umbQSTis0wqlZ6UhPT09EKdU0oL23DJPyOfPHmC2NhYPHjwAHv37sU333yDTp06SbONT5w4Efv27cOSJUtw7do1zJ07F3/99RfGjx+vtZ3ExERs27YNb7/9do59eHl5oUePHhgzZgzOnDmDEydOYPz48XjzzTeliZcOHz6MXr16YcKECXj99del84Bm8HLSDePUVFgkJJSLJHxZ4DmC19EVSmYST65SQZY58VVFxvbL9ltclSaJd+bMGWzevBkLFy5EUFAQxo4di0OHDqFOnTqYNm1avuvOnj0bmzdvxvTp0xEYGIhPPvkEJ0+ezHGblU5UsiSeTCZDWFgY2rdvj9GjR8PDwwNvvvkm7t27J83UOHToUMyYMQNTp05F8+bNcffuXYwaNUqrN0tCQgKuX7+e775WrlyJAQMGYNy4cfD09MSYMWOQnJwMAKhVqxbCwsJw5swZNG3aFO+++y4CAwPx6aefFul4lixZgoMHD8LFxQXNmjXL97h//fVX2Nvbo3379ujatSvq1auX4xYdopJg+ype+zp37hwuXryI119/PceyKlWqoEuXLtJt3IMGDcJXX32F2bNnw8fHBxEREdi3b5/0+p4/fx6nT5/GpUuX4O7uDicnJ+mh+QVSLpdj9+7dkMvl8PPzw7BhwzBixAh89tlnWvtu1qwZmjVrhnPnzmHTpk1o1qwZAgICCnVM5YEhJOKFTAZ15hiIO3/+GRs2bEBYWJjeEnlswyX/jOzatSucnJzg5uaGsWPHIiAgQGs7bdq0waZNm7Bq1So0bdoUoaGh+OWXX+Dt7a21nc2bN0MIgcGDB+e6n40bN8LT0xNdunRBQEAA2rVrpzWO69q1a5GSkoKFCxdqnQdee+21Ih8T5a3v3Ll4c/JkmPz9t75DKRM8R/A6ukKxtoY6MzklyzaGbkXE9sv2W1wyoc+fmMvQtGnTsHTpUjx9+lT69RUAFi5ciJkzZyI6OlprcMWCCCFgZ2eHnj17YvPmzYVaJzExEVWqVEFCQoJWDFrCw4FOnQBPT4ADO+apW7duqFmzJtavX6/vUKgSKagNp6enY8OGDQCAnj17AgD27t0LABg2bFiOsSTKK7YvqqgK04ZhYwMThQKhCxfieeZFtCG1X4BtmCqmwrTfpLp1Yf/wIZ5u3Yq0V14xyM/gssBzBOlDYdqw2t4eZsnJ+OfwYYTdvQuA7Tc7tl+qNBNbXLhwAR4eHjlOGJrZXSIiIgpM4j179gzp6emIjY3FsmXLkJiYiC5duug2UE1PvEIMEllZpKSk4H//+x/8/f0hl8vx888/4/fffy/UeIZElD+2LyJtKhMTmCgUaNuyJfZlGa+lvGIbJvqPMvOWeFkFmt2ypHiOIEMSunAhlObm6NqgAZCZxKvM2H4pN5UmiRcTE5Pr4NeaskePHhW4jZdfflnqqmptbY1PP/0UgYGBedZXKBRQZBmTI7Ew3YIr2e20haHparxgwQK8ePECDRs2xPbt29G1a1d9h0Zk8Ni+iLSpMn/tlyuVeo6kcNiGif4jJfEqwXhahcVzBBmSNGtrfYdQrrD9Um4qTRIvNTVVa4BwDc395KmF+MUuODgYiYmJuHPnDoKDg5GamgqVSpXngI8LFy7EvHnzihZoy5bAP/8A7DIssbCwwO+//67vMIgqJLYvIm2/zpkDIZejY6NGQEyMvsMpENsw0X80SXj2xPsPzxFEhovtl3JTaZJ4FhYWWr3iNF5k/lJnYWFR4Db8/Pyk/7/55pvw8vICAHz11Ve51p8xYwY+/PBD6XliYmLB4+6ZmgKOjgXGQkRERLqn0Ay7oekZT0QGQ6n5wZ498YgMkkd4OGreuAEz40qTpiAqskozO62TkxNicvlFXVPm7OxcpO3Z29ujc+fO2LhxY551zMzMYGtrq/UgIiIiIiLdY088IsPmePs26v/5J0w4wSNRnipNitvHxweHDx9GYmKiVjLt9OnT0vKiSk1NRUJCgq5CzPDoETB/PmBlBQQF6XbbRERElK+X9u9HlZgYmOQyji4RlW+xHh5Qy+Wo1qCBvkMhomJIy7w7zqgwY8kTVVKVpifegAEDoFKpsGrVKqlMoVAgODgYvr6+0m2u0dHRuHbtmta6cXFxObYXFRWFP/74Ay1bttRtoPHxwMqVQEiIbrdLREREBXKJiEDDo0dhHBWl71CIqIhudOyI44GBSOvSRd+hEFExpFlaAgBkTOIR5anSJPF8fX0xcOBAzJgxA9OmTcOqVavQuXNnREVFYfHixVK9ESNGSGPdaTRu3BhDhgzB4sWL8eOPP2LatGlo0aIF0tPT8eWXX+o2UM5OS0REpDdqzTg8uYyjS1RZKRQKTJ8+Hc7OzrCwsICvry8OHjxY5O1069YNMpkM48ePL4UoicjQaZJ47IlHlLdKk8QDgHXr1mHSpElYv349JkyYgPT0dOzevRvt27fPd7333nsPN2/exJdffolx48Zh48aN6N69O86cOYM2bdroNkgm8YiIiPRGGlMrLU3PkRCVH6NGjcLSpUsxdOhQfPPNN5DL5QgICMDx48cLvY0dO3bg1KlTpRglALUaxgoFZMnJpbsfonKguMn169evY/LkyWjTpg3Mzc0hk8kQlUfvczc3N8hkshyPd999V8dHk4G30xIVrNKMiQcA5ubmCAoKQlA+Y82Fh4fnKJs7dy7mzp1beoFlxSQekeFTqzMeRpXqdxKiCkGV2ROPSTyiDGfOnMHmzZsRFBSEqVOnAsi4c8Xb2xvTpk3DyZMnC9zGixcvMGXKFEyfPh2zZ88utVi9DxxAq61bkTpgABK++67U9kNUHowaNQqhoaGYNGkSGjRogJCQEAQEBODw4cNo165dnuudOnUK3377LRo1agQvLy9ERETkux8fHx9MmTJFq8zDw0MXh5CDdDvt8+elsn2iiqBSJfEMApN4RAat/yefoEpsLJ7s3Qtl06b6DoeIikjNJB6RltDQUMjlcowdO1YqMzc3R2BgIGbOnIn79+9LY0vnZfHixVCr1Zg6dWqpJvGUpqYAODstVXwlSa736dMHz549g42NDb766qsCk3i1atXCsGHDdBl+ntJ5Oy1RgdhNpLxhEo/I4MmEgBF/QSQySCqOiUek5cKFC/Dw8ICtra1WeevWrQGgwARAdHQ0vvzySyxatAgWmbfKlRal5nZ4JvGogssvuX7q1Cncv38/z3WrVq0KGxubIu0vLS0NyWVwm/o/7u7YtGwZ4vbtK/V9ERkqJvHKGybxiAwaZ9UiMmzSmHhM4hEBAGJiYuDk5JSjXFP26NGjfNefMmUKmjVrhjfffLNI+1UoFEhMTNR6FERlZpbxnxcvirQvIkNT0uR6URw6dAiWlpawtraGm5sbvvnmG51tOzu1iQkUtrZAZq9aIsqJt9OWNw4OwJ07/yXziMigSAPysicekUG60K8fLvXqhXavvgoUYqwvooouNTUVZprkWBbm5ubS8rwcPnwY27dvx+nTp4u834ULF2LevHlFWoe301JlUdLkemE1adIE7dq1Q8OGDfHkyROEhIRg0qRJePToERYtWpTvugqFAoosP4gVJhFPRAVjEq+8MTYG6tbVdxREVEzsiUdk2BQ2NlAAENbW+g6FqFywsLDQ+iKu8SKzt1tet8gqlUpMmDABw4cPR6tWrYq83xkzZuDDDz+UnicmJhY49p50Oy174lEFV5LkelHs2rVL6/no0aPRs2dPLF26FB988AFq166d57rFScRDCLy8cSPsf/sNJl26IN3SEkqlUlpsbGwMmUxWtG0SVTBM4hER6VB65pcZzqpFREQVgZOTEx4+fJijPCYmBgDg7Oyc63rr1q3D9evX8cMPPyAqKkpr2fPnzxEVFQVHR0dYZv74lZ2ZmVmuSYr8aG6nZU88quiKm1wvKZlMhsmTJ2P//v0IDw/Pd8KL4iTiIZPB/cQJmCgUMPf1RbqlJTZv3iwtdnR0REBAABN5VKkxiVfeKBTAp58CajWwcCHHAyAyMNLttOyJR2SQal67BpeICFg8fw4UceBvoorIx8cHhw8fRmJiotb4W5pbZH18fHJdLzo6Gunp6Wjbtm2OZevWrcO6deuwc+dO9OvXT2exptraIqpFCzjkERNRRVHc5LouaBJxT58+zbdecRLxQMZdLSYKBaoZGyP7T+JxcXFQKpUwyex1S1QZMYlX3qhUwFdfZfx/3jwm8YgMzDNnZzxs1AjWvC2eyCBVu3cP3gcOIMXSEujTR9/hEOndgAED8NVXX2HVqlWYOnUqgIyxroKDg+Hr6yt9oY+OjkZKSgo8PT0BAG+++WauCb7+/fsjICAAY8aMga+vr05jfe7oiMPvv4+ePXvqdLtE5U1xk+u6cOfOHQCAg4NDqWw/zdISVvHxaObmBq/MHwFUKhUOHDhQKvsjMjRM4pU3WSe04Ay1RAbnVrt2uNWuHb9AEBkolXHGpZEsLU3PkRCVD76+vhg4cCBmzJiBuLg4uLu7Y+3atYiKisLq1auleiNGjMCRI0cghAAAeHp6Sgm97OrWravTHnhElU1xk+tF8fTpU1SpUgXyLN9P09PT8eWXX8LU1BSdOnXSzcFkk3WSOGNjpiuIsmOrKG+YxCMiItIbleYWHSbxiCTr1q3DrFmzsH79esTHx6NJkybYvXs32rdvr+/QchICSE0FMgf4J6qIiptcB4CEhAR89913AIATJ04AAJYvXw47OzvY2dlh/PjxADImtZg/fz4GDBiAunXr4unTp9i0aRMuX76ML774AjVr1iyVY5MmiUtIKJXtExk6JvHKGybxiCoGIQAOuktkcNTsiUeUg7m5OYKCghAUFJRnnfDw8EJtK2syQdeMFQoMGzcOMiHwz+3bpbYfovKguMn1+Ph4zJo1S6tsyZIlAIA6depISbzGjRujUaNG2LBhAx4/fgxTU1P4+Phg69atGDhwYOkcFLIk8Ti+NFGumMQrb2SyjIcQTOIRGSCH27fR7euvYVS7Np4cOaLvcIioiDQ98WS5zPpHROWb0sQEMk2SMCVFv8EQlbLiJtfd3NwKlUxv0aIFdu3aVZIQi4WTxBHlj0m88kguB5RKJvGIDJDKxARmKSlQPXum71CIqBg4Jh6RATMygtLYGMZKJWQvXug7GiIqhgv9+6Pat9/CqGpVfYdCVC4Z6TsAyoXmllom8YgMjubXQ9nz53qOhIiKg2PiERk2lZkZAECWmqrnSIioOBTW1lBXrw6Ymuo7FKJyqURJvJ49e2LTpk1I5Yekbp0/D1y9Cjg56TsSIioi6RaA1FQgPV3P0RBRUcW5u2PHggV4Ehys71CIqBiUmlvi2ROPiIgqoBIl8e7cuYNhw4ahRo0aGDlyJH7//fdSHay20mjUCPDyAjS9AYjIYKRnJvEA9sYjMkRKc3MkODlB7eys71CIqBhUmb132BOPyDDZP3gAm5kzYZE52QYRaStREu/69es4ffo0Ro8ejQMHDsDf3x+1a9fGRx99hIiICB2FSERkOIRcjvTMW3mMmMQjIiIqU0om8YgMmmV8PKyCg2H222/6DoWoXCrxmHitWrXCN998g4cPHyIsLAydO3fGDz/8gBYtWsDb2xuLFy/GgwcPdBFr5bF4MTBrFhAXp+9IiKgY0s3NAQAyzqpFZHDMnj9Hsx07YMMeAEQGKcbTEy969IDazk7foRBRMaRZWgLgHS1EedHZxBZGRkbw9/fH+vXrER0djQEDBuDq1av4+OOP4ebmhq5du2LPnj262l3F9tVXwPz5wD//6DsSIiqGfzw8oGjfngPyEhkg05QU+OzeDetVq/QdChEVw5khQ/AsOBjKpk31HQoRFYOUxEtI0HMkROWTTmenPX78ON599124u7tj27ZtUk+8JUuW4PHjx+jTpw9mz56ty11WTJydlsighb/3HuK3bIGyYUN9h0JERaSZnVamUOg5EiIiospHqyeeWq3naIjKH+OSbuDq1avYsGEDfv75Z0RHR8PR0REjR47E8OHD4ePjI9WbOHEixo4dixUrVuCzzz4r6W4rNibxiIiI9EJlnHFpJFMqM748GOn0904iKiucbI/IIKVlThInU6shS06GsLHRc0RE5UuJrkx9fHzQuHFjLFu2DC+//DJ2796Nhw8fYsmSJVoJPI1OnTohPj6+JLusHDRJPP7yQEREVKbUWWaGlyuVeoyEiIqjTUgIatSuDcuVK/UdChEVg8rEBCJzkjgZcwdEOZQoiWdnZ4dVq1YhNjYWP//8M3r27AmjfH6x7tu3L+7evVuSXVYO7IlHZNBabNsGR09PWH37rb5DIaIi0vTEA5jEIzJEwsgIMpUKshcv9B0KERWHTAaVkxMAwOjhQz0HQ1T+lOh22nXr1sHBwQEWmV1es0tNTcXjx4/h6uoKALC0tESdOnVKssvKgUk8IoNmpFbDKCEBRs+e6TsUIioidZYknlF6uh4jIaLikMa1TE3VcyREVFzxmzbBqHp1CM4yTZRDiXri1a1bFzt37sxz+a5du1C3bt2S7KJyYhKPyKBJY3kkJuo5EiIqMpkMysxEHnviERkeZebM8EziERkuVd26EPb2gEym71CIyp0S9cQTBQwYm56enu/ttZSHrVuB9HTAw0PfkRBRMUhJvOfP9RwJERVH2CefwLddO6RyCBAig6NiEo+IiCqwIifxEhMT8SzLLWJPnjxBdHR0jnrPnj3D5s2b4ZR5PzsVQZMm+o6AiEog3dISAGDEnnhEBulJnTpQenlBff++vkMhoiKSeuJxTDwig2V87Rqs1q+HqFoVKdOn6zsconKlyEm8r7/+Gp999hkAQCaTYdKkSZg0aVKudYUQmD9/fokCJCIyNOyJR0REpB+8nZbI8Bk9fgyLNWugbNCASTyibIqcxOvevTusra0hhMC0adMwePBgNG/eXKuOTCaDlZUVWrRogZYtW+os2Epj/Xrg4UNgwADA3V3f0RBREWmSeOyJR2SYPI4cgc2VK7CqUQPJ1avrOxwiKoKkatWgaN8e6S+9pO9QiKiYVLVrAwDkDx4ABQzhRVTZFDmJ5+fnBz8/PwBAcnIyXnvtNTRu3FjngZUGhUKB2bNnY/369YiPj0eTJk0wf/58dOvWLd/1duzYgS1btuDs2bOIjY2Fi4sLXn31VcyaNQt2pTFjzvLlwJkzwEsvMYlHZIBSbW2R5uMDlZubvkMhomJodPAgbB89gu1HHzGJR2RgHjZpgnhNz529e/UbDBEVi8rZGUBGj1rZkycAZ6klkpRoYos5c+boKo4yMWrUKISGhmLSpElo0KABQkJCEBAQgMOHD6Ndu3Z5rjd27Fg4Oztj2LBhcHV1xaVLl7B8+XKEhYXh/PnzsMjsdaMznJ2WyKAl1KqFp5ovDvwCQWRw1Jmz0xqlp+s5EiIiokrIzAxqR0cYxcVB/uAB0pnEI5IUKYn32WefQSaT4ZNPPoGRkZE0Nl5+ZDIZZs2aVewAdeXMmTPYvHkzgoKCMHXqVADAiBEj4O3tjWnTpuHkyZN5rhsaGoqOHTtqlbVo0QIjR47Exo0b8fbbb+s2WCbxiIiI9EZlYgIAkCuVeo6EiIioclK5uMAoLg5G9+8D3t76Doeo3ChSEm/u3LmQyWSYPn06TE1NMXfu3ALXKS9JvNDQUMjlcowdO1YqMzc3R2BgIGbOnIn79+/DxcUl13WzJ/AAoH///hg5ciQiIyN1HyyTeERERHojJfHYE4/I4FSLioKjpyfUjo7AjBn6DoeIiklduzZw7hyMHjzQdyhE5YpRUSqr1WqoVCqYZs76pFarC3yoykki6sKFC/Dw8ICtra1WeevWrQEAERERRdpebGwsAKB6PmPlKBQKJCYmaj0KhUk8IoNXvX17OHp6wvrff/UdChEVkSrzdlr2xCMyPGojIxglJECWkKDvUIioBFSZHWzkMTF6joSofClSEs+QxcTEwMnJKUe5puzRo0dF2t6iRYsgl8sxYMCAPOssXLgQVapUkR559fTLgUk8IoMnS0iAUUICTFNS9B0KERURx8QjMlyqzM4Gshcv9BwJEZVE6gcf4MmNG0ieN0/foRCVKzpP4qWkpGDNmjVYuXIl7t27p+vNF1tqairMzMxylJubm0vLC2vTpk1YvXo1pkyZggYNGuRZb8aMGUhISJAe9+/fL9wOmMQjMnjCxgYAYFKEcwsRlQ8cE4/IcElJPH7+Ehk0UbUqhL09IJPpOxSicqVEs9MGBgbi9OnTuHz5MgAgLS0NL7/8svS8SpUqOHToEJo1a1bySEvIwsICCoUiR/mLzF/pCjvD7LFjxxAYGAh/f38sWLAg37pmZma5Jg4LtGhRxhgeDRsWfV0iKhfUmbfusycekeG50LcvrD/9FHfv3NF3KERUREpNEi89HTKVCkLz4zgREVEFUKKeeIcPH8Zrr70mPd+0aRMuX76MjRs34vLly6hZsybmlZPur05OTojJ5X56TZmzs3OB27h48SL69OkDb29vhIaGwti4RDnQvDVpArRvD9SoUTrbJ6JSp+mJZ8qeAEQGJ8HZGWktWuBFlSr6DoWIikiZ2ZMWAORpaXqMhKh0KRQKTJ8+Hc7OzrCwsICvry8OHjxY4HrXr1/H5MmT0aZNG5ibm0MmkyEqKirP+rt27ULz5s1hbm4OV1dXzJkzB8qy6KmuVMLq449hM2wYZMnJpb8/IgNRoiRebGws3NzcpOe//PILWrZsicGDB6NRo0YYM2YMTp8+XdIYdcLHxwc3btzIMbmEJj4fH5981799+zZ69OgBR0dHhIWFwdraurRCJaIKQGh64jGJR0REVGZUWZJ4xhzXkiqwUaNGYenSpRg6dCi++eYbyOVyBAQE4Pjx4/mud+rUKXz77bd4/vw5vLy88q27d+9e9OvXD3Z2dvjuu+/Qr18/zJ8/Hx988IEuDyV3xsYw27YNZvv3c4ZaoixKlMSzsrLCs2fPAABKpRLh4eHw9/eXltvY2CChnMwMNWDAAKhUKqxatUoqUygUCA4Ohq+vrzTpRHR0NK5du6a1bmxsLLp37w4jIyPs378fDg4OpRqr2LcPqmXLkP7XX0hPT4cQolT3R0S6p7md1oS30xIZHMebN2G9ahWcIiP1HQoRFZWREdJ8faFo0wbgNTRVUGfOnMHmzZuxcOFCBAUFYezYsTh06BDq1KmDadOm5btunz598OzZM1y6dAlDhw7Nt+7UqVPRpEkTHDhwAGPGjMG3336LGTNm4Icffsjxnbk0qDUz1DKJRyQp0f2gzZs3x48//ohOnTph165deP78OXr37i0tv337NmqUk1tCfX19MXDgQMyYMQNxcXFwd3fH2rVrERUVhdWrV0v1RowYgSNHjmglznr06IE7d+5g2rRpOH78uNavGzVq1EC3bt10FqcQAjFffAHnY8dwdvBgRHbrBkdHRwQEBEDGQT2JDIaqbl2kN22KF5m31RKR4aj999+osmcPXLt2RUwBvRSIqPx5+ssvAIAXe/fqNxCiUhIaGgq5XI6xY8dKZebm5ggMDMTMmTNx//59qZNKdlWrVi3UPq5evYqrV69ixYoVWsNIjRs3DgsWLEBoaCg+/fTTkh1IAVS1a8P4yhXIHz4ESrkjDZGhKFESb8GCBfD390fLli0hhMCAAQPQunVrafnOnTvRtm3bEgepK+vWrcOsWbOwfv16xMfHo0mTJti9ezfat2+f73oXL14EACxevDjHsg4dOug0iadUKpGa2fVflplIjIuLg1KphEmW2wOIqHxLHj8eyePH4wa/QBAZHHXmlxUjzk5LRETl0IULF+Dh4QHbzDs/NDTfxSMiIvJM4hVlHwDQsmVLrXJnZ2fUrl1bWl6apJ549+4xiUeUqURJvJYtW+LatWs4efIk7Ozs0KFDB2nZs2fPMG7cOK0yfTM3N0dQUBCCgoLyrBMeHp6jrKxvZxWZPe486tXD1TLdMxEREakyk3hyjqdFRETlUExMDJycnHKUa8oePXqkk31k3Wb2/RS0D4VCAYVCIT3PPjZ9YagaNgQAGF+/DmRLJhJVViUaEw8AHBwc0Ldv3xzJOjs7O0ycOLHACSMoJ2GU8Wcx4jgeREREZU4zMD574hEZJvvBg+HYqBGcrvLncKqYUlNTYWZmlqPc3NxcWq6LfQDIcz8F7WPhwoWoUqWK9ChOz0Bl5pAWJhyjlkhS4iQeADx//hyXL1/GsWPHcPTo0RwPKhpNEg9qtX4DISpHFAoFpk+fDmdnZ1hYWMDX1xcHDx4scL0dO3Zg0KBBqFevHiwtLdGwYUNMmTJFmpSntJgeP47qrVuj29KlpbofIkNhSG1Yk8STM4lHZJBkz5/DKD4eJll6ARFVJBYWFlq93DRevHghLdfFPgDkuZ+C9jFjxgwkJCRIj/v37xc5BpWnJwBAlpoKOdszEYAS3k775MkTjB8/Htu3b4dKpcqxXAgBmUyW6zLKmyaJJ+PrRiQZNWoUQkNDMWnSJDRo0AAhISEICAjA4cOH0a5duzzXGzt2LJydnTFs2DC4urri0qVLWL58OcLCwnD+/HmdXOTkxfj+fVgzCUAEwLDasJq30xIZNJF5XuCXfqqonJyc8PDhwxzlmltgnZ2ddbIPzTaz96KLiYnRGgs/N2ZmZrn24isKUaUKnly5gnR7e6j27SvRtogqihIl8caMGYPffvsNEyZMwCuvvAJ7e3tdxVWpqTU98ZjEIwIAnDlzBps3b0ZQUBCmTp0KIGMmaW9vb0ybNg0nT57Mc93Q0FB07NhRq6xFixYYOXIkNm7ciLfffrtUYlZXrw4AsCjG+B9EFY2htWGOiUdk2ETmLYXGbMNUQfn4+ODw4cNITEzUmtzi9OnT0nJd7AMA/vrrL62E3aNHj/DgwQOtmXFLk3B0BPijOJGkRLfTHjhwAJMnT8aSJUvQr18/dOjQIdcHFc21Tp1wYNIkpLz2mr5DISoXQkNDIZfLtS4WzM3NERgYiFOnTuXbPT/7l38A6N+/PwAgshTH11Bl/npplpzMngBU6RlaG47x8sK/mzbhzKBBpbJ9Iipdmp54xmlpeo6EqHQMGDAAKpUKq1atksoUCgWCg4Ph6+sr9ZyLjo7GtWvXirWPl156CZ6enli1apXWnXUrV66ETCbDgAEDSnYQRFQsJeqJZ2lpCTc3Nx2FQhrPatfGs9q18VLdusDNm/oOh0jvLly4AA8PD61fGgFIvwpGREQUabDc2NhYAED1zN5ypUHY2kJtYQGj1FRYlfL4e0TlnaG14VQ7Oyjat0d85thCRGRgMnviyZnEowrK19cXAwcOxIwZMxAXFwd3d3esXbsWUVFRWL16tVRvxIgROHLkCESWCRMTEhLw3XffAQBOnDgBAFi+fDns7OxgZ2eH8ePHS3WDgoLQp08fdO/eHW+++SYuX76M5cuX4+2334ZX5qQTpU1+6RKsv/wS7RMScPSdd8pkn0TlWYmSeMOGDcPOnTsxbtw4XcVDRJRDTExMntPbAyhwivvsFi1aBLlcXuAviAqFQmsw38Si3Bork0Ht5ASjO3dgGR9fpPiIKhqDbMNEZLDYE48qg3Xr1mHWrFlYv3494uPj0aRJE+zevRvt27fPd734+HjMmjVLq2zJkiUAgDp16mgl8V599VXs2LED8+bNwwcffAAHBwfMnDkTs2fP1v0B5UUmg/mBA6htZQVkSUYSVVYlSuINGDAAR44cQY8ePTB27Fi4uLhALpfnqNe8efOS7KbSqXb3Lqo+eACTUuwlRGRIUlNT85zeXrO8sDZt2oTVq1dj2rRpaNCgQb51Fy5ciHnz5hUt2CxUTk4wZhKPyODasElqKiw3b4bX+fOI7Nq1yOsTkX4p69RBetOmSM3W+5eoIjE3N0dQUBCCgoLyrBMeHp6jzM3NTatnXkH69euHfv36FSNC3VA1aAAhl8MsORmWvLuFqGRJvKyzyR08eDDHcs5OWzxuf/2FJnv3IsnYGPDz03c4RHpnYWGR5/T2muWFcezYMQQGBsLf3x8LFiwosP6MGTPw4YcfSs8TExOLdMufslEjJD56BKWpaaHXIaqIDK0Nm7x4AfupU9HayAiRXboUKjYiKj9Sxo1DyrhxuL53r75DIaKSMjODql49GN+8CfsHD/QdDZHelSiJFxwcrKs4KAvB2WmJtDg5OeHhw4c5ymNiYgAAzs7OBW7j4sWL6NOnD7y9vREaGgpj44JPf2ZmZrn2Hiqs5599hr1MxBMZXBtWWFsDAIzUapgWoZcgERER6V66lxeTeESZSpTEGzlypK7ioCyYxCPS5uPjg8OHDyMxMVFrYPzTp09Ly/Nz+/Zt9OjRA46OjggLC4N15hd0IiobhtaGVSYmUFtawiglBWbPn5fqvoiIiCh/yoYNAQD2ufwgSFTZGOlqQzExMbh48SKSk5N1tclKS0riqdX6DYSonBgwYABUKhVWrVollSkUCgQHB8PX11e6PS46OhrXrl3TWjc2Nhbdu3eHkZER9u/fDwcHhzKNHQAH4aVKzxDbsNreHgBglpRUJvsjIt0x//VXVG/dGm3XrNF3KESkA8rMmXDZE4+ohD3xAODXX3/F9OnTcfPmTQAZY+N17twZ//77L7p164bZs2ejf//+JQ60MtEk8WTsiUcEAPD19cXAgQMxY8YMxMXFwd3dHWvXrkVUVBRWr14t1RsxYgSOHDmiNVhvjx49cOfOHUybNg3Hjx/H8ePHpWU1atRAt27dSi1u+c2bGDBtGoRMBgwfXmr7ISrvDLENq6tWBR4+hDl/nCSCQqHA7NmztWbBnD9/foHtb8eOHdiyZQvOnj2L2NhYuLi44NVXX8WsWbNgZ2dXmgHD+P59WFapUnr7IKIyo3zpJShNTPDC2hq2/HGcKrkSJfF+++03vPbaa/Dz88OQIUMwd+5caVn16tVRq1YthISEMIlXRGrNDL9KpX4DISpH1q1bh1mzZml9gdi9ezfat2+f73oXL14EACxevDjHsg4dOpRqEk/Y2sLm33+hlsmgUioBE5NS2xdReWdobVjqicfbaYkwatQohIaGYtKkSWjQoAFCQkIQEBCAw4cPa010l93YsWPh7OyMYcOGwdXVFZcuXcLy5csRFhaG8+fPF3pSm6ISlpYAAOO0tFLZPhGVLZWLCzauWAG1sTGGyWT6DodIr0qUxPvss8/Qvn17HD58GE+ePNFK4gGAn58ffvjhh5LsolJSZg7CLUtJ0XMkROWHubk5goKCEBQUlGed8PDwHGVCj7/WqatXh9rICEZqNVT//AO4uektFiJ9M7Q2LCXx2BOPKrkzZ85g8+bNCAoKwtSpUwFk9Jr19vbGtGnTcPLkyTzXDQ0NRceOHbXKWrRogZEjR2Ljxo14++23Sydoc3MAgJxJPKKKQSaDuhATWhFVBiUaE+/y5ct444038lxeo0YNxMXFlWQXldIDb28cfu89JL37rr5DIaKSkMuRknm7kIwD8RIZlKR33sH+Dz9EVMuW+g6FSK9CQ0Mhl8sxduxYqczc3ByBgYE4deoU7t+/n+e62RN4AKQ7dCIjI3Ueq4bI7OHHnnhEFRBnjadKrkRJPEtLy3wnsrhz5w6qVatWkl1USs9r1EBUq1ZIL2C2PiIq/1Iye/OASTwig5LepAkeeXsjpWpVfYdCpFcXLlyAh4eH1szSANC6dWsAQERERJG2FxsbCyBj6J3SwiQeUcVj9e+/6DdrFowbNOCkcVSplSiJ16lTJ6xduxbKXMZui42NxY8//oju3buXZBdERAYtOTOJx554RERkiGJiYuDk5JSjXFP26NGjIm1v0aJFkMvlGDBgQL71FAoFEhMTtR6FJXg7LVGFk2pnB5u4OMji4oAbN/QdDpHelCiJt2DBAjx48ACtWrXCDz/8AJlMhv379+PTTz9F48aNIYTAnDlzdBVrpWGalAS3s2dhHham71CIqIQ0t9OyJx6RYTF69AgNjh5F3T//1HcoRHqVmpoKs8zxmrMyz0yUpRbh1rZNmzZh9erVmDJlCho0aJBv3YULF6JKlSrSw8XFpdD7ETY2UNavj8QaNQq9DhGVb2pjY/xbt27GkxMn9BsMkR6VKInXsGFDHD9+HNWqVcOsWbMghEBQUBC++OILNG7cGMeOHYMbB3IvMpt//0WnlStRhQlQIoP3rFYtxNWrBzg76zsUIioCk1u30C4kBE337NF3KER6ZWFhAYVCkaP8xYsX0vLCOHbsGAIDA+Hv748FCxYUWH/GjBlISEiQHvmNvZedysUF/x4/jr0zZhR6HSIq/+Lc3TP+c+IEhBBIT0+XHvqczI6oLJV4ipeXXnoJv//+O+Lj43Hr1i2o1WrUq1cPDg4OuoivUtLMTmvE2WmJDN6NDh1wo0MHDBs2DHJ9B0NEhSbNTpuUpOdIiPTLyckJD3PpTR4TEwMAcC7Ej1QXL15Enz594O3tjdDQUBgXYpZJMzOzXHsAElHl9U9mEk+cPImwsDCtSTQdHR0REBAAmUymr/CIykSxk3gKhQIbNmzAgQMHcPv2bTx//hw2NjZwd3dHjx49MGTIEJiamuoy1kojPfOCRcYkHhERkV6oMye0MEtKgpq/7lMl5uPjg8OHDyMxMVFrcovTp09Ly/Nz+/Zt9OjRA46OjggLC4O1tXVphktEFZimJ57s2jUk3LkDZDmfxMXFQalUwsTERF/hEZWJYt1Oe+nSJXh5eWHs2LHYtm0bbt++jZSUFNy+fRtbt25FYGAgXnrppVKdOr4i0/TEk6WnwyiXSUOIyAAJwZm0iAyIpieeXKUC2BuPKrEBAwZApVJh1apVUplCoUBwcDB8fX2lseqio6Nx7do1rXVjY2PRvXt3GBkZYf/+/WV6p47dqFEYNHkyqkVFldk+iah0pVlbQ3h6AgBqZE5u0aVLF32GRFTmitwTLykpCX369EFcXBwWLFiA4cOHo1atWtLyhw8fYt26dZg/fz569+6NixcvwsrKSqdBV3TKLLcOGCsUSCvELQdEVD7JlEq8PnMmjN99F4iJATJ79xBR+SYsLKA0MYFxejrw5AnbLlVavr6+GDhwIGbMmIG4uDi4u7tj7dq1iIqKwurVq6V6I0aMwJEjR7TGperRowfu3LmDadOm4fjx4zh+/Li0rEaNGujWrVupxW305AnMExJg/e+/pbYPIip76gEDILt3DymaH9vkHLCGKpciZ4eCg4MRHR2NP/74Ax07dsyxvFatWpgxYwZ8fX3RrVs3hISE4P3339dFrJWG2tgYKrkccpUqI4nHJCiRwRLGxjB58QKytDTgwQMmAogMhUwGhbU1jOPjIXvyBChgJk2iimzdunWYNWsW1q9fj/j4eDRp0gS7d+9G+/bt813v4sWLAIDFixfnWNahQ4dSTeKpXFyAv/6CDZN4RBWKevZsAMC/GzboORIi/Sjy7bR79uxB9+7dc03gZdW5c2d069YNv/32W3Fjq9Q0vfFMcpkNjIgMS3LmL4XIZWBwIiq/FJof0Z480W8gRHpmbm6OoKAgxMTE4MWLFzhz5gz8/f216oSHh+eYHVIIkecjPDy8VGNW1a4NAOyJR0REFUqRk3iXLl0qMIGn0blzZ1y6dKmouyAAp4YPx9Pvv0dKlSr6DoWISiiFSTwig3TmzTexf8oUiBYt9B0KERWRKnOsPibxiCogtRrV7t2Dw61b+o6EqMwVOYn39OlT1KxZs1B1a9SogadPnxY5KALu+voitU8fpFta6jsUIiqhFDu7jP/cv6/XOIioaGIaNcKjl14CqlXTdyhEVEQqV1cATOIRVUSyNWvQZ948tNixQ9+hEJW5IifxFApFoadtNjY2RlpaWpGDKi0KhQLTp0+Hs7MzLCws4Ovri4MHDxa43vXr1zF58mS0adMG5ubmkMlkiOJMV0RUSIk1amT85/p1/QZCRERUSWj1xOPs8EQViujcGQBQ4+ZNyBIT9RwNUdkq1rSnUVFROH/+fIH17t69W5zNl5pRo0YhNDQUkyZNQoMGDRASEoKAgAAcPnwY7dq1y3O9U6dO4dtvv0WjRo3g5eWFiIiIUo+1+p07MN+zB9YJCUiqXr3U90dEpeeZZgbvy5f1GwgRFUmVR4/geOsWZDVqAAEB+g6HiIpAVasWnlevjqTq1VE9JQUwNdV3SESkK/Xq4VnNmrCLjYXZ0aNs31SpFCuJN2vWLMyaNavAekIIyGSy4uxC586cOYPNmzcjKCgIU6dOBQCMGDEC3t7emDZtGk6ePJnnun369MGzZ89gY2ODr776qkySeM1+/RXVLl1CzdGjceuVV0p9f0RUeuJr1YK6aVOgWTPI1GrIjIrcCZqI9KDWlSvw/flnKJ8/R3rmLJrGxsbl5tqGiPJhZobQzFlxh2kmqSGiCuNBkyawi42F+aFDQI8e+g6HqMwUOYkXHBxcGnGUutDQUMjlcowdO1YqMzc3R2BgIGbOnIn79+/DJbPbfXZVq1YtqzAl6ZrZacvR7chEVDzJ1aph7cSJAADHvXsREBDAJACRAXhhbQ0AiLt6Ffs3bAAAODo6sg0TERHp2X0fH3gfOACL/fth1KUL1IUc8ovI0BU5iTdy5MjSiKPUXbhwAR4eHrC1tdUqb926NQAgIiIizySePigzk3jGCoWeIyGi4pLL5bC3t0d8fLxUFhcXB6VSWeixRYlIP+RyOUwyJ/IyS06WytmGiQwQx8SjCkihUGD27NlYv3494uPj0aRJE8yfPx/dMnuO5+fhw4eYPHkyDhw4ALVajU6dOuHrr79GvXr1tOrl9YPVwoUL8fHHH+vkOIrrHw8PJNvbwyo+HrUvXUJ08+Z6jYeorBTrdlpDFBMTAycnpxzlmrJHjx7pfJ8KhQKKLEm4xCIMuskkHpHhk8lkaNu2LVQqFVQqFQ7u3QuT1FR9h0VEheSVOV6unVKJ7t2748CBA3qOiIiKomF4OJru2gWjs2eB77/XdzhEOlXc8d6TkpLQqVMnJCQkYObMmTAxMcHXX3+NDh06ICIiAtWyzcjerVs3jBgxQqusWbNmpXJMRSGMjHC3dWt4798Pl4gIJvGo0qg0SbzU1FSYZSbGsjI3N5eW69rChQsxb968Yq3LJB5RxSCTyWBsbAzzrVsxfMoU3G/aFHj3XX2HRUSFIDJ74hnFxUHOnjxEBkdtZASrZ8+gLmeT7RGVVEnGe//+++9x8+ZNnDlzBq1atQIA9OzZE97e3liyZAm++OILrfoeHh4YNmxY6R1MCVzr1Ak1R43CySw95okqukozurqFhYVWrziNFy9eSMt1bcaMGUhISJAe9+/fL/S6iswBeM14QiKqENSOjpArlbArhV6/RFQ61DVrQpibQ6ZUQv7wob7DIaIiSsrsUSSLitJvIEQ6lt9476dOncr3e2doaChatWolJfAAwNPTE126dMHWrVtzXSc1NVX63lyePHd0hOKVVyA4aRxVIpXm3e7k5ISYmJgc5ZoyZ2dnne/TzMwMtra2Wo/CSrWxAQBYJCToPC4iKnvKhg0BALb//ANlcjLS09ORnp4Owd49ROWXkRFUbm4AADl78hAZnCQHh4z/3LvHcfGoQinMeO+5UavV+Pvvv9GyZcscy1q3bo3bt2/j+fPnWuUhISGwsrKChYUFGjVqhE2bNunmIIpJqVRCqVTmXMA2TpVEpbmd1sfHB4cPH0ZiYqLWye706dPS8vIkzsMD8V9+iUtPn+o7FCLSAXXNmlBYWsIsJQX7v/0W8ZkT6XCmS6LyLfmzzwBTU6R7eQEnTug7HCIqguSqVSFkMshSU4G4OKBGDX2HRKQTxR3v/enTp1AoFAWu2zDzx+c2bdrgjTfeQN26dfHo0SOsWLECQ4cORUJCAt577718YyzJ+PD52bx5s3aBSoVWmzej7tmzQKdOQN26OtkPUXlVaXriDRgwACqVCqtWrZLKFAoFgoOD4evrK81MGx0djWvXrukrTElijRpIGTYMsZ6e+g6FiHRAbmyMJFdXAIBdltvyNDNdElH5lN6pE9LbtoUoQm96Iiof1MbGSLGzy3jCW2qpAinueO+a8sKue+LECUycOBF9+vTBu+++i3PnzsHb2xszZ84scEz5hQsXokqVKtJD8327OORyOezt7XOU29vbQ25qiupRUbCKj4dRlu/6RBVVpemJ5+vri4EDB2LGjBmIi4uDu7s71q5di6ioKKxevVqqN2LECBw5ckTrFreEhAR89913ADJOZACwfPly2NnZwc7ODuPHjy/bgyEigyOTyWDZqhVw7RpampujAWe6JCIiKnXPHRxgFR+fkcTz9dV3OEQ6Udzx3jXlxR0r3tTUFOPHj5cSevnNgjtjxgx8+OGH0vPExMRiJ/JkMhnatm0LlUqlVS6Xy6FSqXC1a1fUvHEDRqtXA3PmAJkJSaKKqNIk8QBg3bp1mDVrFtavX4/4+Hg0adIEu3fvRvv27fNdLz4+HrNmzdIqW7JkCQCgTp06pZPEEwKmJ06g7pkziC5nt/oSUfGovLwAACY3b0Iul+s5GiIqDNmzZzDdswem8fFAnTr6DoeIiujfunXhWLMmjKyt9R0Kkc44OTnhYS4TLhU03nvVqlVhZmZWorHiNYm4pwUM+2RmZpZrj7/ikslkMDbOPX0R3awZku3tYfX4MbB1KzBihM72S1TeVJrbaYGMLsJBQUGIiYnBixcvcObMGfj7+2vVCQ8PzzHQvJubG4QQuT6iSqtrvkyGaoGB6Pi//2X8ekhEBk/ZvDkUffogvUMHfYdCRIUke/YMNpMmwWbhQsjUan2HQ0RFdHbQIKh+/x3o1UvfoRDpjI+PD27cuJFjnLmCxns3MjJC48aN8ddff+VYdvr0adSrVw82mRMs5uXOnTsAAAfNxDHlgJDLca1Tp4wny5frNxiiUlapkniGRl2tGgDAXEeDgBKRfilbtMDz1avxIjBQ36EQUSGpa9eGMDaGLC0NlvxRjYiIyoGSjPc+YMAAnD17ViuRd/36dRw6dAgDBw6Uyh4/fpxjv8+fP8eyZctQvXp1tGjRQteHVSI32reHMDUFzp6FOHUK6enpWo/sHXWIDFWlup3W0KiqV4fxvXuwYBKPiIhIP4yNoXZ1hfzOHdjExSE58wc2IjIw//4LVK+u7yiIdKIk472PGzcOP/74I3r16oWpU6fCxMQES5cuRY0aNTBlyhSp3ooVK/DLL7+gd+/ecHV1RUxMDNasWYPo6GisX78epqamZXrMBXlhawsxaBBk69fjnylTsHfMGK3ljo6OCAgIgEwm01OERLrBJF45ps680GBPPKIKRAgYRUXBOClJ35EQUSGp3Nwgv3MHtnFxiM0c25KIDINcoYCxtzdw82ZGIq9qVX2HRKQTxR3v3cbGBuHh4Zg8eTLmz58PtVqNjh074uuvv9a6RbZt27Y4efIkfvrpJzx58gRWVlZo3bo11qxZg86dO5f24RWL6qOPAGNjHPP0zLEsLi4OSqUSJiYmeoiMSHeYxCvHNEk89sQjqjjM166F9UcfQdG5MzBsmL7DIaJCUNWtCwCwjYvTcyREVFQqzcD6QgDHjgF9++o3ICId0Yz3HhQUlGed8PDwXMtr166Nbdu25bv9bt26oVu3biUJsex5ekL1ww9I2rABANC9e3cAwIEDB/QZFZFOcUy8ckxVsyYAwOrJEz1HQkS6kp45fojJ6dOQKZV6joaICkOTxLNhEo/IIKnatQMAiDwSGkRUMcnVasjlcn2HQaRTTOKVY8o6dQAANrkMKkpEhkn10ktQ29vDKDkZ1e/d03c4RFQITOIRGbbjxhk3HyX+9hsHtyeqBKwfP0bH77+H/fDhGb1wiSoQ3k5bjqW1aoWTw4fjWa1aMLCOzESUFyMjpLdpA7M9e+AUGYnH9evrOyIiKoCyVSs83boVfzDxTmQw5HI57O3tER8fj9iGDQEANnfuQPnkCUw4wQVRxSYEXC9cgFylgtn+/fqOhkin2BOvHFO5uOB6p074x8ND36EQkQ6lZ97WU/PaNT1HQkSFIeztkfbKK5yZlsiAyGQytG3bFj179sQrgwYh0dERRkJAduKEvkMjolKW5OiIyz16AABsZ82CXKHQc0REusMkHhFRGUt/5RUAQI1bt2CUnq7naIiIiCommUwGY2NjyOVyqTee7OhRPUdFRGXh71dfhcrZGfIHD9AkLEzf4RDpDJN45Zx9dDTqnzwJXL+u71CISEdUHh5QOTjAOC0NDnfu6DscIioE4+vX0Xz7djTiDHdEBules2a43r49ROfO+g6FiMqA0swMifPmAQC89+6FbWwsAEAIgfT0dK0Hx8okQ8Ix8co5n99+g9u5c1B5eQHe3voOh4h0QSZD0ief4K9bt/DU1VXf0RBRIchv30bTPXsQX6uWvkMhomJ44OODBz4+qNe9u75DIaIyoujVC4qOHWEWHo72P/4ITJyIsAMHEJdtoipHR0cEBARAJpPpKVKiwmNPvHLumZMTAEB2+bKeIyEiXUodNAjRzZsj3cJC36EQUSGkt2oFALB79Ah49ky/wRAREVHBZDIkLFkChZUVzJ8/hyoqKkcCDwDi4uKgVCr1ECBR0TGJV849qVMHACA7f17PkRAREVVeagcHJDo6QiYEZKdP6zscIioGmVIJ2ZkzwJEj+g6FiEqBUqnMkYxTOzvjwKRJ+GXePIi6daXy7t27ozt75pIB4u205ZwmiYcrVwCFAjAz029ARKQzNnFx8DhyBEapqcC4cfoOh4gKEOfuDtu4OMhOngRefVXf4RBREdU7fRrGY8cCXl4Z19a8dY6oQtm8eXOu5f/Wr6/1XKZWQy6Xl0VIRDrHnnjlXHK1anhhbQ2ZUglcuqTvcIhIh2pev44me/fCaNkygAPqEpV7/7i7AwBkp07pORIiKo7oZs0grKyAyEiII0c4uD1RBSCXy2Fvb5+j3N7ePmeiTgh4/fEHAr74AkhJKaMIiXSLPfHKO5kMT+rUQa0rV4C//gJattR3RESkI3dbtoTvxo0wuXkT+PNPwM9P3yERUT7iGjQAgIzb8ZRKwJiXUUSGJN3SEurBgyH/6SfEzJ6N/aNHay3n4PZEhkcmk6Ft27ZQqVRa5XK5PGdb/vdf+Pz6K8yTkpD64YdI+P77MoyUSDfYE88AxGV2/1UfOsRfCYkqEKWFBe5pEvPBwfoNhogK9MzJCQorK8DeHnjwQN/hEFExpAcGAgBqnjwJi2yT1HBweyLDJJPJYGxsrPXINRnv4IBD778PtVwOi19/he3MmbwbhgwOk3gG4I6fHw5OmICNHTtiw4YNCAsLYyKPqIK42a5dxn82b2a3fqLyzsgIe2bOxItr15BeqxZ/WCMyQD9HRuIfd3cYqVTwOHaMg9sTVTL/NGyIY4GBEDIZLNeuRastW5jII4PC+0DKMc39/fEAEmvUkMo1vxKamJjoLzgi0olYDw8INzfIoqKA7duB4cP1HRIR5SPByQmbt22TnvP2O6LyT7qmjo8HAFzr1Ak1bt2C59GjSDYygortl6hS0PS0vfPyy/Dx9ESVDz+E94EDMFKpgCFDAH6/JgPAJF45l/X+fpVKhQMHDug5IiLSKSMjqEeNgnzuXGDBAuDNN3kBQVQOZU8CyFQqmKakIA7gD2tE5VyOMbM6d4Z6+3aYWVrixf37ULm66jdAIioToaGh0v9TBw8G0tJQ5eOP0eiPP6A8dAgICNBjdESFwyReOae5v18WHw/rlSvR5dAh/DFhgr7DIiIdUr//PuSrVwP9+wPp6UziEZVDWZMAZvv3w3b6dETXqYPwceP0HRoRFYLmmhoAYGyMhD17oKpbF5DLMyaqIaIKKfuPcMB/M9emjhyJC1FRsI2NReNu3fQYJVHhMYlnKORyWH3/PawVClS9f1/f0RCRLlWpAty8CZiZ6TsSIsqHlARwdYX88WO4xMfDhGNZEhkklbu7vkMgojKQ2+y1WWeuvdu6NQCgsWbhw4fA9etA585lHClR4XBiCwMhbG2hyPx1oGF4uH6DISLdYwKPyGCovL2R3rAhjJVKeB06pO9wiKgk0tJgtWwZ7DnjNFGFlX322jzHsU1PB954A+jaFZgyhZPOUbnEJJ4BSX7rLQCA+4kTwL//6jkaIioVp04BXbpk9MwjovJJJkPyxIkAgMZ79/IzmciAWX3+OWwWLULb4GDI1Gp9h0NE+qRSAS+9lDFb7dKlgI8PcPiwvqMi0sIkngFJf/ll/FunDozT02EUFKTvcIioNHz6KXDoUMb4eM+f6zsaIsrDi7598cTVFaapqTD68kt9h0NExZQ6bhzUNjZwuHsXLbduzfjyTkSVkjAzQ/qKFVD++itErVoZP6p37gy89hpw65a+wyMCwCSeYZHJcP611wAARitWIP3qVaSnp0PwYoPIoCmVSqSnp2e053XrACcn4MoVYORIgL0CiMonIyP8NXBgxn9XrgRu39ZzQERUHGonJyQuXAgA8D5wAEbz5uk5IiLSB6VSiV27dmHDhg1Y/+QJNs2YgcjOnaE2MgJ27gS8vCBOnZKu2fk9nPSFE1sYmIeNG+OBtzdqX76M2MGD8fukSXB0dERAQEDe9/YTUbm2efNm6f+Ojo4I2L4dso4dMy4Y5s3LeBBRufPopZfwsFEjOF+7Bpw8CdSvr++QiKgYXrz+Ov48eRIvb9oE+RdfADY2wMcf6zssIipDWa/HASDN0hJ/DhuGa506oe/x45DdvYuwx48Rt2EDAMAyPh7WHh4I6NWL38OpTLEnngHRTI99csQI3G3ZEidGjgQAxMXFQalU6jk6IioKTXvOLi4uDi98fKD89tuMgs8+Az74AGAbJyqXTowaBdXu3cDw4foOhYhKILJrV/w1YEDGkxkzgLlz9RoPEZW+3K7HbW1t0bNnT3Tv3h0A8KxWLah++w3K48cR9+QJAMAoPR195s6F33vvQb1kCfDPP2UeO1Ve7IlnQKTpsV9+GRg+HK+oVDhw4EDG2B3syktkULJPd6/StGdk/hJoaorWI0fipbVrgeXLoXJygtGMGfylj6icSa5eHaJr1/8KHj0CatYEjPg7KZGhuRQQgKaNG8N4wQIgIAD8xCWq2LJfjwMZib1cr7ft7KT/+levDpMXL2Dx/Dnw0UcQ06dDtG8P0b8/1AEBQJ06AJD/TLhExVSprjAVCgWmT58OZ2dnWFhYwNfXFwcPHizUug8fPsQbb7wBOzs72Nraom/fvrhz504pR5xT1umx5XI5AKBxWBhkQ4ci/elT3pdPFVZFaL/ZZW3PpqamOX4JPNOhAw6NG4eYhg2xsWpVhIWFsY2TwaqIbTiHmBjg5ZeBIUOA1FR9R0OkM5Wi/Wba4OKCrV98gV0xMdK4V8pVqyCuX9d3aERayqpdrl69Gl5eXjA3N0eDBg3w3Xff6fIw9C7r9Xhhk24qX19sWboUJ4cPx+O6dSFTq2EUHg75xIkwadAAZ99+Gxs2bMi4duf41qRjlaon3qhRoxAaGopJkyahQYMGCAkJQUBAAA4fPox27drluV5SUhI6deqEhIQEzJw5EyYmJvj666/RoUMHREREoFq1amV4FNosnj2Dz65dME5PR+KRI7gZGIjmCxZAxh4AVMFUxPabVfZfAk+cOIHExETca9kS91q0AGSyjFvnU1NhMmFCxuy1PXoAmcl8ovKuIrdhzZAWspMnIY+JgWzLFohz54BlyyDr1UvP0RGVXEVuv8B/t9TFx8cDAFKqVkXK06fYsGEDbOLi8NrMmQAAMWAAZCNGAN27AyYm+gyZqEza5Q8//IB3330Xr7/+Oj788EMcO3YMEyZMQEpKCqZPn14Wh1kuZB+6Si6Xw6p2bVy3ssL1Tp1gHReHOufPo87583C4fRtxmWPkxsXFQfXjj5B/9RVEy5YQzZtDNG0K0bgxjGvWlBKGQgitfbAHH+VHJipJt44zZ87A19cXQUFBmDp1KgDgxYsX8Pb2hqOjI06ePJnnuosXL8b06dNx5swZtGrVCgBw7do1eHt7Y9q0afjiiy8KFUNiYiKqVKmChIQE2Nra5lonPT0dGzIHy+zZsyeMjfPOswohcOLECRifPYtOK1fCKvPCQ924MURgINSDBsG4Rg2eAMjglYf2CxTchovSfgsihMj1Vtuh//4L02nTMuq4uUE9bBhEr14QzZpp3b7HD38qTypiG1Yqldi7d2+O8pqRkeiwahUsExIAAOp27aB+5x2Ifv1gbGXFdkkGpyK239xk/dzV/JAGALb//INWW7bANSLiv7rVq0MEBEDdsSOEvz+MnZzYtqlMlUW7TE1NhYuLC15++WXs3r1bWn/YsGH45ZdfcP/+/VzHd85LWV5H60Jen/NARnxyuVzrNlwNWUIClFZWOJDZK3Lk8eMwWrMmRz1FlSow9fEBgoMRdvUq4uLiYJ6QAMhksK1fX5owI3uCD+B1fmVXabprhYaGQi6XY+zYsVKZubk5AgMDcerUKdy/fz/fdVu1aiWd5ADA09MTXbp0wdatW0s17vxoeu60/OADJJw6hQt9+yLdzAxGly5BPmkS5M7O+HPu3P+mwU5IQHpqKqfEJoNTEdtvQXK7dR4AfgFw2d8fCisryKKiIJ8/H8Z+flA6OuKRry92LVnyX/f9zDYuhPjvPJDLI+u5IHvdvM4TuW2T5xTKS0Vsw3lNThPr5YUdX3yBy/7+UMvlMDp+HMbDhyO1fn3s+vXX/9qLQqGHqImKriK239xk/dxt3749evbsiZ49e+Ll4cPxx4QJ+HXuXFzp1g0ptraQ/fsvjNatg/Fbb+HYF19g165dGW37zBkof/wRIjwciI4GMj8bi/NZyc9Zyk9ZtMvDhw/jyZMnGDdunNb677//PpKTk7Fnzx4dHlH5k9fnvL29vTRuXtbbcKXr9mrVIM+SgEyePRv7P/wQ5/v1w73mzZHo4AAAMEtIgOzIESitrBAXFwcAaLp7NwZPmoQe/ftD1KsHdZs2iGnTBrf8/XF5wABsWbUKGzZsyDjn3LmD9CtXkP7wIdKfP0d6WprWeaKg6/+C6pbkXGUo5y1DPc9WmttpL1y4AA8PjxxZ/9atWwMAIiIi4OLikmM9tVqNv//+G2+99VaOZa1bt8aBAwfw/Plz2NjY6Dzm3DL7eTGqUgV3hg/H1S5dUP/UKTQ4cQL29+/jbvXquJb5i0bz7dvROCwMKfb2SK5aFWmOjnDy9oawtQXs7KB8+20g8/WRRUZCFhMDmJlBmJoCpqaAmRlgbAzIZBB16mT8HwCePQOSkjJ6AclkGf9qHjIZUKXKf7f8vXgBpKXlfSBWVv/VVSgyHnmxtPwvhrS0/OtaWPxXNz09/7rm5oWva2b23+0USmX+dU1N/6urUmW8Frqoa2KSUR8A1Or8x2EqSl1j44zjK2pdIYCUFO3lMhlgaQlzc/Ni/WpU0dtvYdja2iIxMRHJ1avj7KBBON+/P9zOnoVrRAScr1yBRWIi6ly4gFPDhgHI6L6fNm0aTLZsQby5OZKsrfHCxgZplpZIs7REurk5brZrB6W5OWxtbdGrQQPI4uJw4uxZPFcoIORyqI2MYGVriw6dO0O4uv7XLhIT8cfu3XielAQhk2WcEwDY2NqiW7duGeeR3Np81r+95v8WFv/VTUvLaHN5MTf/r256ev7nEs35qqh1C2rHRalbFm1el+eH3Nq8XJ7xugPFbr9AxW3DL7/8cp71TlSvjivdusHj6FF4HD2Kfxo0wNP4+IxeBmo1hn3wAYzt7aF2c4NwdgZq1ICoWhXC3h7CywvqDh0yNqRWQ3buXMbfUvMwNs7428jlEJaWQNWq/+34yZOM9pX1AWT8a2yc8dmpkZSU+4FpPsstLP4rS07O+4XIXjclJe8JtzI/D4pVNzU1472ZFyur4tV98SKjLemirqXlf6+5QpH/zOJFqWth8V9v64LOlVnrqlTSeZOfwcUjl8tha2uLp66uOOPqirNvvIGa166h1tWrqHntGmIbNoQi89bbZjt3wue337TWV9jaItXGBi+srXFy1CigQQMEBATA6ORJGP35Z0YbtrQEzM0zrrnNzABTU+yLj8fTzPeEeUICLBITYWlri/YdO0rtX3O9LRwd/zt/JydntO3s5wHN397G5r/Pm7S0/z4Xsp4rNMzN/6tblOvnonw+6vKztCw/HwtTN7dr4qyyfMbmWjdLjNnbb1m0ywsXLgAAWrZsqVWvRYsWMDIywoULFzAs87pT13R9HV1cuX3O59UDL6usy0MPHQK8vfHI2xtdunTBMwC/7tmDKrGx6ObmBmWWz08XW1sImQxypRKIigKiouAMwDlz+dXMGXOfPn2KO6NHo+HRo//tUy5Hurk50iwsYOHggLCpU/E48z3jfuwYnCMjoTIxyXgYG0NlYgITKyu4N2qEfXXr4klmzNXv3kWVR49gZmMDn5Yt/zvfyOWAsTHUfn6AtTUAQPbgAU6FhiIpJQXCyAhCJvvvu4FMhueOjrCqXh0BAQHA06eQxcf/d42R9Zwjk+U8jyUm5ryO0bCz+6/9KhTa1yjZ61ta/lc36zkPyHi/Z7kWUpqYwKZq1Yx4lcrc27pmu9nzALmdbzR5FJTsOjq7SpPEi4mJgZOTU45yTdmjR49yXe/p06dQKBQFrtuwYcMcyxX/b+/Oo6Kq+z+Av5F9CRVEQXNBTDMQEXNfkNwQBc3EBbMsszLNtKLlqGlomGWpmQ+pdchjSk9Zpk8LLo/oUVHUFE2RcE3hQREXENnh8/tDZn4Os4iozJ3L+3XOPYf53s/c+X5n7mc+M1/uvVNcjOI7ilJu5ak1msPzDSktLUVh5c6yadMmo3FGWVvjcO/eONy7N+xv3kSxtbV257O5cAH5IsC1a3C8dg2Op0+j4I5DrX+wsUFJ5QeprvHxaLtnj9GH+XnBAhRU/mci8Kef8MSOHUZjN8+ZgzxPTwCA/+bN8N+yxWjs7++9h2uVBeeJLVsQuHmz0ditM2ciu00bAEC7xER02bDBaOz2qVNx6YknAAA+e/eix/r1RmN3TZ6MiwEBAICWBw+iz7ffGo3d+9xzONetGwCg2bFjCF650mhs8tixONWnDwCgSVoaBpq4KOyhkSOR1r8/AKDR2bMI+ewzo7EpQ4fieGgoAKBBRgaGLVxoNPb4wIFIGTECAOBy5QpGzJtnNDYtKAiHRo8GcPvD46jK68EYcrp7d+yfMAEAYFtYiDGVh/Vr5Lm7Y3N0NABg/Pjxd30De+SRR3RizJG/wL3n8H3n7z06HhiI44GBqFdWBvfz59EwMxPXHBy0OZ++fTtaXrwIWwCGTnY43rEjSkRQWFiI40uXou2ePXjSQFwJdHO+84YN6JuYaLBPpQA2zZmDm5U533HzZnQwkfO/vfcerlcz57fMnIkrlTn/eGIinnxAOb9z8mRkVDPn9zz/PM5XfkB+9Ngx9Ktuzp88iYFffmk09tAzzyDtqacAVCPnhw3D8SFDAAANLl7EsI8/Nhqrk/PZ2Rjx4YdGY9P69cOhiAgA/5/z5wMDsWfSJG1MTfIXqMM57OiIfYMHY9/AgbAtKkJp5bYfuXTp9uMUFt7+NdsqznTpgn0TJwIArEtKMG7mTKMPcb5TJ+x56aXbN0Tw7LRpRmMzfH2x844jKsbOmAEbI5NBlx57DNtnzNDeHvXOO3AwMpGX07IlEipP8weAEbNnw6XyEh9VXff0xG9z5mhvD4uORoPLlw3G3nRzw6b587W3QxYtQqMLFwzGFrq44KdFi7S3B37+OZqcOWMwttTWFv9eulR7O3jFCjRLTTUYCwDfrVih/bv311+jVeUXW0PiP/8c5ZVfQnqsWQOfAweMxv7w8cfaz11dvv8e7XbvNhr7c3Q0CiqvU9Vp40b4bt9uNHbzrFnIa3r7K1/j06e1n5UA1uAH5Wzr1jjbujUwbNjthsrHveTqirTHH4dLTg6cr12DdUUFkJcH67w8OAO4VVSE/MuXERcXh46bNqFD5aUyDCl47z0UVtbH1jt2oFPl5KChf0lteestXGndGgDw+I4dePKnn4xud9u0abjcvj2A21/su3//vdHYna+8ggx/fwBAqwMH0HvNGqOxeyZOxPnKI7seTUlBv9Wrjcbui4zEmV69AACeqakYcEeOVXVo1CikBQcDADxOn8bgJUuMxh4JC8OJkBAAQMOLFzHURH38a/BgHA0PBwA8cvkyhld+TjXkZHAw/hw1CgDgeP06npk922jsqZ49kTx+PADA9tYtjLnjvbGqs08+iaQXXgAA1CsrQ+Qbb+j2cdAgHB0+XHv7zvzNzMyEh4eHXj54Vn7+ehB5mZWVBWtrazRu3Fgnzs7ODu7u7kYfQ0PJOWwud56WfLNJE8QVFgJr12rb1g4YgHr9+sEhNxfOubnaCXyHvDzY37qFXED7fnOzogI5Dg6w00welZffnsy6dQv5OTnIunoVpZX/hHJKT4fH/v0G+1QE4Nr8+Sis/Iegx9692u/2hqbXN8+ejbzKfcX/P/9B54QEo+P9/d13cbm8HHFxcQ/0u/1/X3sNWb6+AO7xu/2hQ+gTF6ddF1wlVvPdPi4u7u7f7ceMwam+fQEY/25/5/d0oOafo/VIHdG6dWsZMmSIXvuZM2cEgCxZssTg/S5cuCAAZNGiRXrrvvnmGwEgR44cMXjfuXPnCgAuXLjc45Kbm2v2/GUOc+FSs6Vq/jKHuXCxrIU1mAsXy11SUlIEeDB5+eKLL4qjo6PB7TRv3lyGDx9uNH+Zw1y41Gwx9Dm6qjpzJJ6jo6POfwI0iipnrh3vPA2kyv0A1Oi+77//Pt58803t7YqKCly7dg3u7u4mZ1fz8vLQvHlzXLx40egPYNDDw+ff/KqeWmOO/AVqlsNq3X/UOC41jgkw/7gMnRrHHDYvjskyKGVMrMHKxjGqw/2OsXv37vDw8MB/qpy+rbkW3oPIS0dHR5QYuRxJUVGRyfwFmMPVwfGqV03HWp1LTNSZSTwvLy9kZmbqtWdlZQEAmjZtqrcOANzc3GBvb6+Nu5f72tvbw15zXnelBg0aVLvPrq6uqt+5lYzPv3KYI3+B+8thte4/ahyXGscEKGtczGFl4Jgsg9LGxPxVJo5RHWo6xmbNmiEzM1PvvpcuXQLwYPLSy8sL5eXlyM7O1jmltqSkBFevXjWZvwBz+F5wvOr1MMZaZ36dNiAgAOnp6Xrn4CcnJ2vXG1KvXj106NABhw4d0luXnJyM1q1bP5QL8hLR/2P+Elk25jCR5WL+EilPbeSlZhtVYw8dOoSKigqjj0FED1edmcQbNWoUysvLsWrVKm1bcXEx4uLi0K1bN+2v91y4cAFpaWl69z148KDOG9jff/+NHTt2IKLyIuBE9PAwf4ksG3OYyHIxf4mUpzby8qmnnoKbmxtiY2N17h8bGwsnJycMHTr0YQyNiO7mrlfNU5GIiAixsbGRqKgoWblypfTs2VNsbGxk165d2pigoCCp+rTk5eWJj4+PNG7cWD755BNZsmSJNG/eXJo2bSrZ2dkPvJ9FRUUyd+5cKSoqeuDbprvj869MzF/zUuO41DgmEeWOizlsPhyTZVDymJi/ysExqsODGGNt5OWKFSsEgIwaNUpWr14tzz33nACQjz76qMb9NqUuvPZ34njV62GOtU5N4hUWFsrbb78tnp6eYm9vL126dJGEhASdGENvdCIiFy9elFGjRomrq6u4uLjIsGHD5NSpU7XVdaI6j/lLZNmYw0SWi/lLpDy1lZerVq2Sdu3aiZ2dnfj4+MiSJUukoqLioYyJiO7OSkTEPMcAEhERERERERERUXXUmWviERERERERERERWSpO4hERERERERERESkcJ/GIiIiIiIiIiIgUjpN4ClFcXIx3330XTZs2haOjI7p164Zt27aZu1sWLz8/H3PnzkVISAjc3NxgZWWFb7/91mDsyZMnERISAhcXF7i5uWHChAm4cuWKXlxFRQU++eQTeHt7w8HBAf7+/oiPj3/IIyGls+QcPnjwIKZNmwZfX184OzujRYsWGD16NNLT0/Viq5snSvXRRx/BysoKfn5+euuSkpLQu3dvODk5wdPTE9OnT0d+fr4Zelk9hw8fRnh4ONzc3ODk5AQ/Pz988cUXOjGWNiZzstQcrm7+Tpw4EVZWVnrL448/bqaeG7dz506DfbWyssL+/ft1Yi1lHzf2/GuWzMxMAEC/fv0Mrg8JCTHzCJTNUvPXkLpUkzXUVJurYq2uHjXlsIYa67Mpaqzdppizrts8qEHQ/Zk4cSI2bNiAGTNm4LHHHsO3336L0NBQJCYmonfv3ubunsXKyclBdHQ0WrRogY4dO2Lnzp0G4zIyMtC3b1/Ur18fMTExyM/Px+LFi/HXX3/hwIEDsLOz08bOmjULH3/8MSZPnowuXbpg06ZNiIyMhJWVFcaOHVtLIyOlseQcXrRoEfbu3YuIiAj4+/vj0qVL+PLLLxEYGIj9+/drP1TfS54oUUZGBmJiYuDs7Ky3LiUlBf3790f79u3x+eefIyMjA4sXL8apU6fwxx9/mKG3pm3duhVhYWHo1KkT5syZAxcXF5w5cwYZGRnaGEsbk7lZag5XN38BwN7eHl9//bXO/evXr1/bXa626dOno0uXLjptbdq00f5tSfv4K6+8ggEDBui0iQheffVVtGrVCs2aNdO2P/roo1i4cKFObNOmTWuln5bKUvPXkLpSkzXUVJurYq2uPjXlsIaa67Mpaqrdppi1rpv3x3FJRCQ5OVkAyKeffqptKywsFB8fH+nRo4cZe2b5ioqKJCsrS0REDh48KAAkLi5OL27KlCni6Ogo//zzj7Zt27ZtAkBWrlypbcvIyBBbW1uZOnWqtq2iokL69Okjjz76qJSVlT28wZBiWXoO7927V4qLi3Xa0tPTxd7eXsaPH69tq26eKNWYMWPkqaeekqCgIPH19dVZN2TIEPHy8pLc3Fxt2+rVqwWAbNmypba7alJubq40adJEnn76aSkvLzcaZ0ljMjdLzuHq5u/zzz8vzs7Otd29GklMTBQA8uOPP5qMs/R9fPfu3QJAPvroI22bofcnMs2S89eQulKTNdRSm6tira4+teWwhhrrsyl1pXabUlt1nZN4ChAVFSXW1tY6O7KISExMjACQCxcumKln6mJqEq9x48YSERGh1962bVvp37+/9vaKFSsEgJw4cUInbv369QJAdu/e/cD7Tcqn1hwODAyUwMBA7e3q5okS7dq1S6ytreXYsWN6xTQ3N1dsbGwkKipK5z7FxcXi4uIikyZNqu3umhQbGysAJDU1VURE8vPz9b4gWNqYzE2NOVw1fzVfEsrKyvTGqTR3fhHIy8uT0tJSvRg17ONTpkwRKysrOXfunLZN8/5UWloqN2/eNF/nLIga89cQNdVkDTXV5qpYq6uvruSwhiXXZ1PqSu02pbbqOq+JpwBHjhxB27Zt4erqqtPetWtXALcPOaWHJzMzE9nZ2XjyySf11nXt2hVHjhzR3j5y5AicnZ3Rvn17vTjNeqp71JjDIoLLly+jUaNGAO4tT5SmvLwcr7/+Ol566SV06NBBb/1ff/2FsrIyvbHZ2dkhICBAcWPbvn07XF1dkZmZiXbt2sHFxQWurq6YMmUKioqKAFjemMxNbTlcNX81CgoK4Orqivr168PNzQ1Tp05V9DVoXnjhBbi6usLBwQHBwcE4dOiQdp2l7+OlpaX44Ycf0LNnT7Rq1UpnXXp6OpydnfHII4/A09MTc+bMQWlpqXk6agHUlr+GqKkma6itNlfFWl19dSGHNdRSn01Rc+02pTbrOq+JpwBZWVnw8vLSa9e0/e9//6vtLtUpWVlZAGD0Nbh27RqKi4thb2+PrKwsNGnSBFZWVnpxAF+rukqNObxu3TpkZmYiOjoawL3lidJ89dVX+Oeff7B9+3aD6+82tt27dz/U/t2rU6dOoaysDMOHD8ekSZOwcOFC7Ny5E8uXL8eNGzcQHx9vcWMyN7XlcNX8BW6P5Z133kFgYCAqKiqQkJCAf/3rXzh69Ch27twJGxvlfCS0s7PDM888g9DQUDRq1AipqalYvHgx+vTpg6SkJHTq1Mni9/EtW7bg6tWrGD9+vE67j48PgoOD0aFDB9y6dQsbNmzAggULkJ6ejn//+99m6q2yqS1/DVFTTdZQW22uirW6+upCDmtYen02pS7UblNqs65bxh6hcoWFhQaLrIODg3Y9PTya5/dur4G9vT1fKzJIbftFWloapk6dih49euD5558HcG95oiRXr17FBx98gDlz5sDDw8NgzN3GprTXLz8/HwUFBXj11Ve1v3A3cuRIlJSUYOXKlYiOjra4MZmbmnLYUP4C0Lug8tixY9G2bVvMmjULGzZsUNQPM/Xs2RM9e/bU3g4PD8eoUaPg7++P999/HwkJCRa/j69fvx62trYYPXq0Tvs333yjc3vChAl4+eWXsXr1asycORPdu3evzW5aBDXlryFqqskaaqzNVbFWV5/ac1hDDfXZlLpQu02pzbrO02kVwNHREcXFxXrtmkOtHR0da7tLdYrm+a3Oa8DXigxR035x6dIlDB06FPXr18eGDRtgbW0N4N7yRElmz54NNzc3vP7660Zj7jY2pY1L059x48bptEdGRgIA9u3bZ3FjMje15LCx/DVm5syZqFevntEjYZSkTZs2GD58OBITE1FeXm7R+3h+fj42bdqEwYMHw93d/a7xb731FgBYxOtkDmrJX0PUVpM11Fibq2Ktrj4157CGmuuzKWqq3abUdl3nkXgK4OXlhczMTL12zeGm9/Xzw3RXmsN5Nc/3nbKysuDm5qb9b4GXlxcSExMhIjqn1PK1qtvUksO5ubkYMmQIbty4gd27d+v0+17yRClOnTqFVatWYenSpTqnYhQVFaG0tBTnz5+Hq6vrXcemtNevadOmOHHiBJo0aaLT3rhxYwDA9evX4ePjA8ByxmRuashhU/lrjKOjI9zd3XHt2rVa6OH9a968OUpKSnDr1i2Ly9s7/fLLLygoKNA75caY5s2bA4DFvE61TQ35a4jaarKGWmtzVazV1afWHNaoC/XZFLXUblNqu67zSDwFCAgIQHp6OvLy8nTak5OTtevp4WnWrBk8PDx0LrqpceDAAZ3nPyAgAAUFBTh58qROHF+ruk0NOVxUVISwsDCkp6fj119/xRNPPKGz/l7yRCkyMzNRUVGB6dOnw9vbW7skJycjPT0d3t7eiI6Ohp+fH2xsbPTGVlJSgpSUFMWNrXPnzgCg94FX82XIw8PD4sZkbpaew3fLX2Nu3ryJnJwco6ezKc3Zs2fh4OAAFxcXi97H161bBxcXF4SHh1cr/uzZswBgMa9TbbP0/DVEjTVZQ621uSrW6upTYw5r1JX6bIpaarcptV7XH8hv3NJ92b9/vwCQTz/9VNtWVFQkbdq0kW7dupmxZ+py8OBBASBxcXF661599VVxdHTU+Qnz7du3CwCJjY3Vtl28eFFsbW1l6tSp2raKigrp06ePNGvWTMrKyh7qGEiZLD2Hy8rKJDw8XGxsbOS3334zGlfdPFGKK1euyMaNG/UWX19fadGihWzcuFGOHTsmIiIhISHi5eUleXl52vt//fXXAkD++OMPcw3BoMOHDwsAiYyM1GkfN26c2NjYSGZmpohY1pjMzZJzuDr5W1hYqLMfaERFRQkA+fnnnx92N+9Jdna2XltKSorY2tpKeHi4ts0S9/Hs7GyxsbGRCRMm6K3Lzc2VoqIinbaKigoZM2aMAJA///yztrppUSw5fw1Ra03WUGttroq1uvrUlsMaaqzPpqi5dptijrrOSTyFiIiIEBsbG4mKipKVK1dKz549xcbGRnbt2mXurlm85cuXy/z582XKlCkCQEaOHCnz58+X+fPny40bN0RE5MKFC+Lu7i4+Pj7yxRdfSExMjDRs2FA6dOigl3iaN9WXX35ZVq9eLUOHDhUAsm7dOnMMjxTCknP4jTfeEAASFhYma9eu1Vs07iVPlCwoKEh8fX112v7880+xt7eXTp06SWxsrMyaNUscHBxk0KBBZuqlaS+++KIAkNGjR8uKFSskIiJCAMj777+vjbG0MZmbpeZwdfL33Llz0qBBA5kyZYosW7ZMli1bJqGhoQJAQkJCpLy83Myj0BUcHCyhoaGyYMECWbVqlcyYMUOcnJykfv36kpqaqo2zxH18+fLlAkASEhL01iUmJoqnp6fMnDlTVqxYIYsXL5ZevXppP3OQcZaav4bUtZqsoYbaXBVrdfWpKYc11FifTVFz7TbFHHWdk3gKUVhYKG+//bZ4enqKvb29dOnSxeCOQPeuZcuWAsDgcu7cOW3c8ePHZdCgQeLk5CQNGjSQ8ePHy6VLl/S2V15eLjExMdKyZUuxs7MTX19f+e6772pxRKRElpzDQUFBRnOk6gHb1c0TJTP0RUFEZPfu3dKzZ09xcHAQDw8PmTp1qsH/jipBSUmJzJs3T1q2bCm2trbSpk0bWbJkiV6cJY3J3Cw1h6uTv9evX5dnn31W2rRpI05OTmJvby++vr4SExMjJSUlZh6BvmXLlknXrl3Fzc1NbGxsxMvLS5599lk5deqUXqyl7ePdu3eXxo0bGzxy/+zZsxIRESGtWrUSBwcHcXJyks6dO8tXX30lFRUVZuit5bDU/DWkrtVkDTXU5qpYq6tPTTmsocb6bIqaa7cp5qjrViIiNTsRl4iIiIiIiIiIiGoDf9iCiIiIiIiIiIhI4TiJR0REREREREREpHCcxCMiIiIiIiIiIlI4TuIREREREREREREpHCfxiIiIiIiIiIiIFI6TeERERERERERERArHSTwiIiIiIiIiIiKF4yQeERERERERERGRwnESj4iIiIiIiIiISOE4iUdERERERERERKRwnMQjIiIiIiIiIiJSOE7iERERERERERERKRwn8YiIiIiIiIiIiBSOk3hEREREREREREQKx0k8IiIiIiIiIiIiheMkHhERERERERERkcJxEo+IiIiIiIiIiEjhOIlHRERERERERESkcJzEIyIiIiIiIiIiUjhO4hERERERERERESkcJ/GIiIiIFGLevHmwsrJCTk6OwfV+fn7o16+f9vb58+dhZWUFKysrLFiwwOB9xo8fDysrK7i4uOi09+vXD35+fg+s7+aSlJSEefPm4caNG+buChEREdFDxUk8IiIiIgvn4OCA+Ph4vfZbt25h06ZNcHBwMEOvakdSUhI+/PBDTuIRERGR6nESj4iIiMjChYaGIjU1FUePHtVp37RpE0pKSjBw4EAz9YyIiIiIHhRO4hERERFZuB49esDb2xvr16/XaV+3bh1CQkLg5uZ2X9tPTk5GaGgoGjZsCGdnZ/j7+2PZsmU6MTt27ECfPn3g7OyMBg0aYPjw4Th58qROzMSJE9GqVSu97WtOI76TlZUVpk2bhl9++QV+fn6wt7eHr68vEhISdO4XFRUFAPD29taeWnz+/Pn7Gi8RERGREnESj4iIiEgFxo0bh++//x4iAgDIycnB1q1bERkZeV/b3bZtG/r27YvU1FS88cYb+OyzzxAcHIxff/1VG7N9+3YMHjwY2dnZmDdvHt58800kJSWhV69e9zWhtmfPHrz22msYO3YsPvnkExQVFeGZZ57B1atXAQAjR47EuHHjAABLlizB2rVrsXbtWnh4eNzXmImIiIiUyMbcHSAiIiKi+xcZGYmYmBjs3bsXvXv3xg8//AAHBweEh4frHL12L8rLy/HKK6/Ay8sLKSkpaNCggXadZrIQAKKiouDm5oZ9+/Zpj/obMWIEOnXqhLlz52LNmjU1evyTJ08iNTUVPj4+AIDg4GB07NgR8fHxmDZtGvz9/REYGIj4+HiMGDHC4FF+RERERGrBI/GIiIiIVMDX1xf+/v7aH7hYv349hg8fDicnpxpv88iRIzh37hxmzJihM4EHQHv6a1ZWFlJSUjBx4kSd03b9/f0xcOBA/P777zV+/AEDBmgn8DTbdHV1xdmzZ2u8TSIiIiJLxUk8IiIiIgtS9dpxd4qMjMSPP/6I06dPIykp6b5PpT1z5gwAwM/Pz2jMP//8AwBo166d3rr27dsjJycHt27dqtHjt2jRQq+tYcOGuH79eo22R0RERGTJOIlHREREpBAODg4AgMLCQoPrCwoKtDGGjBs3Djk5OZg8eTLc3d0xaNCgh9LPmjI2AVleXm6w3dra2mD7nafyEhEREdUVnMQjIiIiUoiWLVsCAP7++2+9dQUFBbh48aI2xpAWLVqgV69e2LlzJyIiImBjc3+XP9acynr8+PEa9TktLQ2NGjWCs7MzgNtH0d24cUMvTnM0X02YOjKRiIiISE04iUdERESkEP3794ednR1iY2NRUVGhs27VqlUoKyvDkCFDTG5jwYIFmDt3Ll5//fX77k9gYCC8vb2xdOlSvck3zdFwXl5eCAgIwJo1a3Rijh8/jq1btyI0NFTb5uPjg9zcXBw7dkzblpWVhY0bN9a4j5oJQkOTg0RERERqwl+nJSIiIlKIxo0b44MPPsDs2bPRt29fhIeHw8nJCUlJSYiPj8egQYMQFhZmchtBQUEICgp6IP2pV68eYmNjERYWhoCAALzwwgvw8vJCWloaTpw4gS1btgAAPv30UwwZMgQ9evTApEmTUFhYiOXLl6N+/fqYN2+edntjx47Fu+++i6effhrTp09HQUEBYmNj0bZtWxw+fLhGfezcuTMAYNasWRg7dixsbW0RFhamndwjIiIiUgseiUdERESkILNmzcJ3332H8vJyREdH4+2338aRI0fw4YcfYvPmzahXr3Y/vg0ePBiJiYlo27YtPvvsM7z55pv473//qzOZOGDAACQkJMDd3R0ffPABFi9ejO7du2Pv3r3w9vbWxrm7u2Pjxo1wcnLCO++8gzVr1mDhwoV3nZg0pUuXLpg/fz6OHj2KiRMnYty4cbhy5cp9jZmIiIhIiayEVwYmIiIiIiIiIiJSNB6JR0REREREREREpHCcxCMiIiIiIiIiIlI4TuIREREREREREREpHCfxiIiIiIiIiIiIFI6TeERERERERERERArHSTwiIiIiIiIiIiKF4yQeERERERERERGRwnESj4iIiIiIiIiISOE4iUdERERERERERKRwnMQjIiIiIiIiIiJSOE7iERERERERERERKRwn8YiIiIiIiIiIiBSOk3hEREREREREREQK9396jPpgdCZs8AAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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": [
+ ""
+ ]
+ },
+ "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": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " experiment | \n",
+ " gene | \n",
+ " threshold | \n",
+ " mean_neg | \n",
+ " concentration_neg | \n",
+ " var_nb_neg | \n",
+ " overdisp_nb_neg | \n",
+ " mean- | \n",
+ " concentration- | \n",
+ " var_nb- | \n",
+ " ... | \n",
+ " N+ | \n",
+ " mean_ratio | \n",
+ " concentration_ratio | \n",
+ " var_nb_ratio | \n",
+ " overdisp_nb_ratio | \n",
+ " N_ratio | \n",
+ " binder_ratio | \n",
+ " noise_to_neg_mean_ratio | \n",
+ " neg_inc | \n",
+ " noise_to_neg_overdisp_ratio | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein | \n",
+ " CMV | \n",
+ " 197.938477 | \n",
+ " 3.867945 | \n",
+ " 0.489523 | \n",
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+ " 8.901464 | \n",
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+ " 2.897500 | \n",
+ " 240.761868 | \n",
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+ " 6.464212 | \n",
+ " 6.464212 | \n",
+ " 1.081759 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein | \n",
+ " EBV_BMLF-1_GLCT | \n",
+ " 197.938477 | \n",
+ " 3.867945 | \n",
+ " 0.489523 | \n",
+ " 34.430371 | \n",
+ " 8.901464 | \n",
+ " 1.454062 | \n",
+ " 0.231539 | \n",
+ " 10.585553 | \n",
+ " ... | \n",
+ " 2 | \n",
+ " 172.963772 | \n",
+ " 107.313660 | \n",
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+ " 0.375926 | \n",
+ " 0.375926 | \n",
+ " 0.817842 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein | \n",
+ " Flu | \n",
+ " 197.938477 | \n",
+ " 3.867945 | \n",
+ " 0.489523 | \n",
+ " 34.430371 | \n",
+ " 8.901464 | \n",
+ " 11.730760 | \n",
+ " 1.432491 | \n",
+ " 107.794731 | \n",
+ " ... | \n",
+ " 1391 | \n",
+ " 78.636249 | \n",
+ " 2.942561 | \n",
+ " 1881.321785 | \n",
+ " 23.924358 | \n",
+ " 0.327371 | \n",
+ " 0.246631 | \n",
+ " 3.032815 | \n",
+ " 3.032815 | \n",
+ " 1.032309 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein | \n",
+ " EBV_BRLF1_YVLD | \n",
+ " 197.938477 | \n",
+ " 3.867945 | \n",
+ " 0.489523 | \n",
+ " 34.430371 | \n",
+ " 8.901464 | \n",
+ " 2.596270 | \n",
+ " 0.386810 | \n",
+ " 20.022444 | \n",
+ " ... | \n",
+ " 10 | \n",
+ " 312.294178 | \n",
+ " 7.326876 | \n",
+ " 11625.436849 | \n",
+ " 37.225916 | \n",
+ " 0.001776 | \n",
+ " 0.001773 | \n",
+ " 0.671227 | \n",
+ " 0.671227 | \n",
+ " 0.866375 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein | \n",
+ " SARS_Cov2 | \n",
+ " 197.938477 | \n",
+ " 3.867945 | \n",
+ " 0.489523 | \n",
+ " 34.430371 | \n",
+ " 8.901464 | \n",
+ " 8.543229 | \n",
+ " 1.653138 | \n",
+ " 52.693679 | \n",
+ " ... | \n",
+ " 574 | \n",
+ " 165.537081 | \n",
+ " 2.422136 | \n",
+ " 9505.974155 | \n",
+ " 57.425044 | \n",
+ " 0.113304 | \n",
+ " 0.101773 | \n",
+ " 2.208726 | \n",
+ " 2.208726 | \n",
+ " 0.692907 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 10k_BEAM-T_Human_A0201_CMV_Flu_Covid_spikein | \n",
+ " negative_control | \n",
+ " 197.938477 | \n",
+ " 3.867945 | \n",
+ " 0.489523 | \n",
+ " 34.430371 | \n",
+ " 8.901464 | \n",
+ " 3.867945 | \n",
+ " 0.489523 | \n",
+ " 34.430371 | \n",
+ " ... | \n",
+ " 6 | \n",
+ " 208.637941 | \n",
+ " 4.966156 | \n",
+ " 7804.024882 | \n",
+ " 37.404630 | \n",
+ " 0.001065 | \n",
+ " 0.001064 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 5k_BEAM-T_Human_A0201_B0702_PBMC | \n",
+ " Flu_A0201 | \n",
+ " 173.383301 | \n",
+ " 2.114713 | \n",
+ " 0.873756 | \n",
+ " 7.232860 | \n",
+ " 3.420256 | \n",
+ " 15.505495 | \n",
+ " 1.876203 | \n",
+ " 143.647475 | \n",
+ " ... | \n",
+ " 2227 | \n",
+ " 125.773976 | \n",
+ " 2.192459 | \n",
+ " 6449.982473 | \n",
+ " 51.282329 | \n",
+ " 12.236264 | \n",
+ " 0.924450 | \n",
+ " 7.332197 | \n",
+ " 7.332197 | \n",
+ " 2.708656 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 5k_BEAM-T_Human_A0201_B0702_PBMC | \n",
+ " CMV_B0702 | \n",
+ " 87.184082 | \n",
+ " 1.782627 | \n",
+ " 0.662888 | \n",
+ " 6.576434 | \n",
+ " 3.689182 | \n",
+ " 3.732883 | \n",
+ " 1.053218 | \n",
+ " 16.963204 | \n",
+ " ... | \n",
+ " 189 | \n",
+ " 174.126735 | \n",
+ " 3.431894 | \n",
+ " 6928.957342 | \n",
+ " 39.792610 | \n",
+ " 0.085135 | \n",
+ " 0.078456 | \n",
+ " 2.094035 | \n",
+ " 2.094035 | \n",
+ " 1.231781 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 5k_BEAM-T_Human_A0201_B0702_PBMC | \n",
+ " negative_control_A0201 | \n",
+ " 173.383301 | \n",
+ " 2.114713 | \n",
+ " 0.873756 | \n",
+ " 7.232860 | \n",
+ " 3.420256 | \n",
+ " 2.114713 | \n",
+ " 0.873756 | \n",
+ " 7.232860 | \n",
+ " ... | \n",
+ " 3 | \n",
+ " 299.646619 | \n",
+ " 1.778455 | \n",
+ " 35813.138102 | \n",
+ " 119.517911 | \n",
+ " 0.001247 | \n",
+ " 0.001245 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 5k_BEAM-T_Human_A0201_B0702_PBMC | \n",
+ " negative_control_B0702 | \n",
+ " 87.184082 | \n",
+ " 1.782627 | \n",
+ " 0.662888 | \n",
+ " 6.576434 | \n",
+ " 3.689182 | \n",
+ " 1.782627 | \n",
+ " 0.662888 | \n",
+ " 6.576434 | \n",
+ " ... | \n",
+ " 3 | \n",
+ " 337.142924 | \n",
+ " 1.605272 | \n",
+ " 51705.636364 | \n",
+ " 153.364145 | \n",
+ " 0.001247 | \n",
+ " 0.001245 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 10k_BEAM-T_Human_A2402_EBV_CMV_spikein | \n",
+ " EBV | \n",
+ " 64.444336 | \n",
+ " 1.801178 | \n",
+ " 0.549719 | \n",
+ " 7.702813 | \n",
+ " 4.276543 | \n",
+ " 5.349569 | \n",
+ " 1.177876 | \n",
+ " 29.645753 | \n",
+ " ... | \n",
+ " 570 | \n",
+ " 90.126625 | \n",
+ " 3.805148 | \n",
+ " 1765.748310 | \n",
+ " 19.591861 | \n",
+ " 0.245690 | \n",
+ " 0.197232 | \n",
+ " 2.970040 | \n",
+ " 2.970040 | \n",
+ " 1.295839 | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 10k_BEAM-T_Human_A2402_EBV_CMV_spikein | \n",
+ " CMV | \n",
+ " 64.444336 | \n",
+ " 1.801178 | \n",
+ " 0.549719 | \n",
+ " 7.702813 | \n",
+ " 4.276543 | \n",
+ " 3.266996 | \n",
+ " 0.645879 | \n",
+ " 19.792167 | \n",
+ " ... | \n",
+ " 257 | \n",
+ " 424.225101 | \n",
+ " 4.467340 | \n",
+ " 33705.405280 | \n",
+ " 79.451699 | \n",
+ " 0.097607 | \n",
+ " 0.088927 | \n",
+ " 1.813811 | \n",
+ " 1.813811 | \n",
+ " 1.416615 | \n",
+ "
\n",
+ " \n",
+ " | 12 | \n",
+ " 10k_BEAM-T_Human_A2402_EBV_CMV_spikein | \n",
+ " negative_control | \n",
+ " 64.444336 | \n",
+ " 1.801178 | \n",
+ " 0.549719 | \n",
+ " 7.702813 | \n",
+ " 4.276543 | \n",
+ " 1.801178 | \n",
+ " 0.549719 | \n",
+ " 7.702813 | \n",
+ " ... | \n",
+ " 3 | \n",
+ " 86.610000 | \n",
+ " 4.352510 | \n",
+ " 1340.694268 | \n",
+ " 15.479671 | \n",
+ " 0.001039 | \n",
+ " 0.001038 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " | 13 | \n",
+ " 2k_BEAM-T_Mouse_H2Kb_OT-1 | \n",
+ " OT-1 | \n",
+ " 81.579956 | \n",
+ " 5.961509 | \n",
+ " 0.812680 | \n",
+ " 49.692846 | \n",
+ " 8.335615 | \n",
+ " 30.833333 | \n",
+ " 1.718346 | \n",
+ " 584.094782 | \n",
+ " ... | \n",
+ " 1333 | \n",
+ " 41.648426 | \n",
+ " 2.652040 | \n",
+ " 621.731210 | \n",
+ " 14.928084 | \n",
+ " 222.166667 | \n",
+ " 0.995519 | \n",
+ " 5.172068 | \n",
+ " 5.172068 | \n",
+ " 2.272612 | \n",
+ "
\n",
+ " \n",
+ " | 14 | \n",
+ " 2k_BEAM-T_Mouse_H2Kb_OT-1 | \n",
+ " negative_control | \n",
+ " 81.579956 | \n",
+ " 5.961509 | \n",
+ " 0.812680 | \n",
+ " 49.692846 | \n",
+ " 8.335615 | \n",
+ " 5.961509 | \n",
+ " 0.812680 | \n",
+ " 49.692846 | \n",
+ " ... | \n",
+ " 14 | \n",
+ " 91.551598 | \n",
+ " 0.919032 | \n",
+ " 8037.003192 | \n",
+ " 87.786597 | \n",
+ " 0.010566 | \n",
+ " 0.010456 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
15 rows × 26 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " mean- | \n",
+ " concentration- | \n",
+ " var_nb- | \n",
+ " overdisp_nb- | \n",
+ " N- | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 5 | \n",
+ " 3.867945 | \n",
+ " 0.489523 | \n",
+ " 34.430371 | \n",
+ " 8.901464 | \n",
+ " 5634 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 2.114713 | \n",
+ " 0.873756 | \n",
+ " 7.232860 | \n",
+ " 3.420256 | \n",
+ " 2406 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 1.782627 | \n",
+ " 0.662888 | \n",
+ " 6.576434 | \n",
+ " 3.689182 | \n",
+ " 2406 | \n",
+ "
\n",
+ " \n",
+ " | 12 | \n",
+ " 1.801178 | \n",
+ " 0.549719 | \n",
+ " 7.702813 | \n",
+ " 4.276543 | \n",
+ " 2887 | \n",
+ "
\n",
+ " \n",
+ " | 14 | \n",
+ " 5.961509 | \n",
+ " 0.812680 | \n",
+ " 49.692846 | \n",
+ " 8.335615 | \n",
+ " 1325 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "