From dda345e16e98352b9e32a3f707ade12faa07a7fe Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Mon, 26 Apr 2021 12:41:11 -0400
Subject: [PATCH 01/24] Add list input to enrichment.py

---
 cytominer_eval/evaluate.py              |  5 +-
 cytominer_eval/operations/enrichment.py | 67 +++++++++++++------------
 2 files changed, 39 insertions(+), 33 deletions(-)

diff --git a/cytominer_eval/evaluate.py b/cytominer_eval/evaluate.py
index 59a3272..f9b721c 100644
--- a/cytominer_eval/evaluate.py
+++ b/cytominer_eval/evaluate.py
@@ -26,11 +26,11 @@ def evaluate(
     similarity_metric: str = "pearson",
     replicate_reproducibility_quantile: np.float = 0.95,
     replicate_reproducibility_return_median_cor: bool = False,
-    precision_recall_k: int = 10,
+    precision_recall_k: List[int] = [5,10],
     grit_control_perts: List[str] = ["None"],
     grit_replicate_summary_method: str = "mean",
     mp_value_params: dict = {},
-    enrichment_percentile: float = 0.5,
+    enrichment_percentile: List[float] = [0.99, 0.98],
 ):
     r"""Evaluate profile quality and strength.
 
@@ -148,6 +148,7 @@ def evaluate(
             params=mp_value_params,
         )
     elif operation == "enrichment":
+        print(enrichment_percentile)
         metric_result = enrichment(
             similarity_melted_df=similarity_melted_df,
             replicate_groups=replicate_groups,
diff --git a/cytominer_eval/operations/enrichment.py b/cytominer_eval/operations/enrichment.py
index f8cb34d..8a04f88 100644
--- a/cytominer_eval/operations/enrichment.py
+++ b/cytominer_eval/operations/enrichment.py
@@ -14,7 +14,7 @@
 
 
 def enrichment(
-    similarity_melted_df: pd.DataFrame, replicate_groups: List[str], percentile: 0.9,
+    similarity_melted_df: pd.DataFrame, replicate_groups: List[str], percentile: List[float],
 ) -> dict:
     """Calculate the enrichment score. This score is based on the fisher exact odds score. Similar to the other functions, the closest connections are determined and checked with the replicates.
     This score effectively calculates how much better the distribution of correct connections is compared to random.
@@ -28,7 +28,7 @@ def enrichment(
     replicate_groups : List
         a list of metadata column names in the original profile dataframe to use as
         replicate columns.
-    percentile :  float
+    enrichment_percentile :  List of floats
         Determines what percentage of top connections used for the enrichment calculation.
 
     Returns
@@ -36,40 +36,45 @@ def enrichment(
     dict
         percentile, threshold, odds ratio and p value
     """
-    # threshold based on percentile of top connections
-    threshold = similarity_melted_df.similarity_metric.quantile(percentile)
-
+    print('here')
+    result = []
     replicate_truth_df = assign_replicates(
         similarity_melted_df=similarity_melted_df, replicate_groups=replicate_groups
     )
-    # calculate the individual components of the contingency tables
-    v11 = len(
-        replicate_truth_df.query(
-            "group_replicate==True and similarity_metric>@threshold"
+    # loop over all percentiles
+    for p in percentile:
+        # threshold based on percentile of top connections
+        threshold = similarity_melted_df.similarity_metric.quantile(p)
+
+        # calculate the individual components of the contingency tables
+        v11 = len(
+            replicate_truth_df.query(
+                "group_replicate==True and similarity_metric>@threshold"
+            )
         )
-    )
-    v12 = len(
-        replicate_truth_df.query(
-            "group_replicate==False and similarity_metric>@threshold"
+        v12 = len(
+            replicate_truth_df.query(
+                "group_replicate==False and similarity_metric>@threshold"
+            )
         )
-    )
-    v21 = len(
-        replicate_truth_df.query(
-            "group_replicate==True and similarity_metric<=@threshold"
+        v21 = len(
+            replicate_truth_df.query(
+                "group_replicate==True and similarity_metric<=@threshold"
+            )
         )
-    )
-    v22 = len(
-        replicate_truth_df.query(
-            "group_replicate==False and similarity_metric<=@threshold"
+        v22 = len(
+            replicate_truth_df.query(
+                "group_replicate==False and similarity_metric<=@threshold"
+            )
         )
-    )
 
-    v = np.asarray([[v11, v12], [v21, v22]])
-    r = scipy.stats.fisher_exact(v, alternative="greater")
-    result = {
-        "percentile": percentile,
-        "threshold": threshold,
-        "ods_ratio": r[0],
-        "p-value": r[1],
-    }
-    return result
+        v = np.asarray([[v11, v12], [v21, v22]])
+        r = scipy.stats.fisher_exact(v, alternative="greater")
+        result.append({
+            "enrichment_percentile": p,
+            "threshold": threshold,
+            "ods_ratio": r[0],
+            "p-value": r[1],
+        })
+    result_df = pd.DataFrame(result)
+    return result_df

From 470ef91dbdab762f8ea7b84bf01e51424c82757f Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Mon, 26 Apr 2021 16:09:43 -0400
Subject: [PATCH 02/24] delete prints

---
 cytominer_eval/evaluate.py              | 1 -
 cytominer_eval/operations/enrichment.py | 1 -
 2 files changed, 2 deletions(-)

diff --git a/cytominer_eval/evaluate.py b/cytominer_eval/evaluate.py
index f9b721c..60c815b 100644
--- a/cytominer_eval/evaluate.py
+++ b/cytominer_eval/evaluate.py
@@ -148,7 +148,6 @@ def evaluate(
             params=mp_value_params,
         )
     elif operation == "enrichment":
-        print(enrichment_percentile)
         metric_result = enrichment(
             similarity_melted_df=similarity_melted_df,
             replicate_groups=replicate_groups,
diff --git a/cytominer_eval/operations/enrichment.py b/cytominer_eval/operations/enrichment.py
index 8a04f88..133d054 100644
--- a/cytominer_eval/operations/enrichment.py
+++ b/cytominer_eval/operations/enrichment.py
@@ -36,7 +36,6 @@ def enrichment(
     dict
         percentile, threshold, odds ratio and p value
     """
-    print('here')
     result = []
     replicate_truth_df = assign_replicates(
         similarity_melted_df=similarity_melted_df, replicate_groups=replicate_groups

From a0f7030323911fa3ebdd95d39f81ec51429ca703 Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Mon, 26 Apr 2021 16:12:04 -0400
Subject: [PATCH 03/24] Apply Black

---
 cytominer_eval/evaluate.py              |  3 ++-
 cytominer_eval/operations/enrichment.py | 18 +++++++++++-------
 2 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/cytominer_eval/evaluate.py b/cytominer_eval/evaluate.py
index 60c815b..431c6e6 100644
--- a/cytominer_eval/evaluate.py
+++ b/cytominer_eval/evaluate.py
@@ -17,6 +17,7 @@
     enrichment,
 )
 
+
 def evaluate(
     profiles: pd.DataFrame,
     features: List[str],
@@ -26,7 +27,7 @@ def evaluate(
     similarity_metric: str = "pearson",
     replicate_reproducibility_quantile: np.float = 0.95,
     replicate_reproducibility_return_median_cor: bool = False,
-    precision_recall_k: List[int] = [5,10],
+    precision_recall_k: List[int] = [5, 10],
     grit_control_perts: List[str] = ["None"],
     grit_replicate_summary_method: str = "mean",
     mp_value_params: dict = {},
diff --git a/cytominer_eval/operations/enrichment.py b/cytominer_eval/operations/enrichment.py
index 133d054..5017048 100644
--- a/cytominer_eval/operations/enrichment.py
+++ b/cytominer_eval/operations/enrichment.py
@@ -14,7 +14,9 @@
 
 
 def enrichment(
-    similarity_melted_df: pd.DataFrame, replicate_groups: List[str], percentile: List[float],
+    similarity_melted_df: pd.DataFrame,
+    replicate_groups: List[str],
+    percentile: List[float],
 ) -> dict:
     """Calculate the enrichment score. This score is based on the fisher exact odds score. Similar to the other functions, the closest connections are determined and checked with the replicates.
     This score effectively calculates how much better the distribution of correct connections is compared to random.
@@ -69,11 +71,13 @@ def enrichment(
 
         v = np.asarray([[v11, v12], [v21, v22]])
         r = scipy.stats.fisher_exact(v, alternative="greater")
-        result.append({
-            "enrichment_percentile": p,
-            "threshold": threshold,
-            "ods_ratio": r[0],
-            "p-value": r[1],
-        })
+        result.append(
+            {
+                "enrichment_percentile": p,
+                "threshold": threshold,
+                "ods_ratio": r[0],
+                "p-value": r[1],
+            }
+        )
     result_df = pd.DataFrame(result)
     return result_df

From 9d0eb6f0537a7a6e049c6ae28446ba2c0feb5e15 Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Mon, 26 Apr 2021 16:55:35 -0400
Subject: [PATCH 04/24] modify test two new input

---
 .../tests/test_operations/test_enrichment.py  | 29 +++++++++----------
 1 file changed, 13 insertions(+), 16 deletions(-)

diff --git a/cytominer_eval/tests/test_operations/test_enrichment.py b/cytominer_eval/tests/test_operations/test_enrichment.py
index 8f2ff08..88b5eda 100644
--- a/cytominer_eval/tests/test_operations/test_enrichment.py
+++ b/cytominer_eval/tests/test_operations/test_enrichment.py
@@ -42,25 +42,22 @@
 
 
 def test_enrichment():
-    result = []
-    for p in np.arange(1, 0.97, -0.005):
-        r = enrichment(
-            similarity_melted_df=similarity_melted_df,
-            replicate_groups=replicate_groups,
-            percentile=p,
-        )
-        result.append(r)
-    result_df = pd.DataFrame(result)
+    percent_list = np.arange(1, 0.97, -0.005)
+    result = enrichment(
+        similarity_melted_df=similarity_melted_df,
+        replicate_groups=replicate_groups,
+        percentile=percent_list,
+    )
 
     # check for correct shape and starts with 1.0
-    assert result_df.shape == (7, 4)
-    assert result_df.percentile[0] == 1.0
+    assert result.shape == (7, 4)
+    assert result.enrichment_percentile[0] == 1.0
     # check if the higher percentiles are larger than the small one
-    assert result_df.percentile[1] > result_df.percentile.iloc[-1]
+    assert result.enrichment_percentile[1] > result.enrichment_percentile.iloc[-1]
 
 
 def test_compare_functions():
-    percentile = 0.9
+    percent_list = [0.95, 0.9]
     eval_res = evaluate(
         profiles=df,
         features=features,
@@ -68,11 +65,11 @@ def test_compare_functions():
         replicate_groups=replicate_groups,
         operation="enrichment",
         similarity_metric="pearson",
-        enrichment_percentile=percentile,
+        enrichment_percentile=percent_list,
     )
     enr_res = enrichment(
         similarity_melted_df=similarity_melted_df,
         replicate_groups=replicate_groups,
-        percentile=percentile,
+        percentile=percent_list,
     )
-    assert enr_res == eval_res
+    assert enr_res.equals(eval_res)

From 4b123b66f985252e7cd9e8206a804b54e28ce320 Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Mon, 26 Apr 2021 17:47:09 -0400
Subject: [PATCH 05/24] Change the input of precision_recall.py

---
 cytominer_eval/evaluate.py                      |  2 +-
 cytominer_eval/operations/enrichment.py         |  2 +-
 cytominer_eval/operations/precision_recall.py   | 17 ++++++++++-------
 .../test_operations/test_precision_recall.py    |  6 +++---
 4 files changed, 15 insertions(+), 12 deletions(-)

diff --git a/cytominer_eval/evaluate.py b/cytominer_eval/evaluate.py
index 431c6e6..1fa01c4 100644
--- a/cytominer_eval/evaluate.py
+++ b/cytominer_eval/evaluate.py
@@ -130,7 +130,7 @@ def evaluate(
         metric_result = precision_recall(
             similarity_melted_df=similarity_melted_df,
             replicate_groups=replicate_groups,
-            k=precision_recall_k,
+            k_list=precision_recall_k,
         )
     elif operation == "grit":
         metric_result = grit(
diff --git a/cytominer_eval/operations/enrichment.py b/cytominer_eval/operations/enrichment.py
index 5017048..803591e 100644
--- a/cytominer_eval/operations/enrichment.py
+++ b/cytominer_eval/operations/enrichment.py
@@ -17,7 +17,7 @@ def enrichment(
     similarity_melted_df: pd.DataFrame,
     replicate_groups: List[str],
     percentile: List[float],
-) -> dict:
+) -> pd.DataFrame:
     """Calculate the enrichment score. This score is based on the fisher exact odds score. Similar to the other functions, the closest connections are determined and checked with the replicates.
     This score effectively calculates how much better the distribution of correct connections is compared to random.
 
diff --git a/cytominer_eval/operations/precision_recall.py b/cytominer_eval/operations/precision_recall.py
index 4edfee2..6d22df3 100644
--- a/cytominer_eval/operations/precision_recall.py
+++ b/cytominer_eval/operations/precision_recall.py
@@ -13,7 +13,7 @@
 def precision_recall(
     similarity_melted_df: pd.DataFrame,
     replicate_groups: List[str],
-    k: int,
+    k_list: List[int],
 ) -> pd.DataFrame:
     """Determine the precision and recall at k for all unique replicate groups
     based on a predefined similarity metric (see cytominer_eval.transform.metric_melt)
@@ -27,7 +27,7 @@ def precision_recall(
     replicate_groups : List
         a list of metadata column names in the original profile dataframe to use as
         replicate columns.
-    k : int
+    k_list : List of ints
         an integer indicating how many pairwise comparisons to threshold.
 
     Returns
@@ -49,11 +49,14 @@ def precision_recall(
         "{x}{suf}".format(x=x, suf=pair_ids[list(pair_ids)[0]]["suffix"])
         for x in replicate_groups
     ]
-
-    # Calculate precision and recall for all groups
-    precision_recall_df = similarity_melted_df.groupby(replicate_group_cols).apply(
-        lambda x: calculate_precision_recall(x, k=k)
-    )
+    # iterate over all k
+    precision_recall_df = pd.DataFrame()
+    for k in k_list:
+        # Calculate precision and recall for all groups
+        precision_recall_df_at_k = similarity_melted_df.groupby(replicate_group_cols).apply(
+            lambda x: calculate_precision_recall(x, k=k)
+            )
+        precision_recall_df = precision_recall_df.append(precision_recall_df_at_k)
 
     # Rename the columns back to the replicate groups provided
     rename_cols = dict(zip(replicate_group_cols, replicate_groups))
diff --git a/cytominer_eval/tests/test_operations/test_precision_recall.py b/cytominer_eval/tests/test_operations/test_precision_recall.py
index 7e23b55..410025a 100644
--- a/cytominer_eval/tests/test_operations/test_precision_recall.py
+++ b/cytominer_eval/tests/test_operations/test_precision_recall.py
@@ -42,11 +42,11 @@ def test_precision_recall():
     result = precision_recall(
         similarity_melted_df=similarity_melted_df,
         replicate_groups=replicate_groups,
-        k=10,
+        k_list=[5, 10]
     )
 
-    assert len(result.k.unique()) == 1
-    assert result.k.unique()[0] == 10
+    assert len(result.k.unique()) == 2
+    assert result.k.unique()[0] == 5
 
     # ITGAV has a really strong profile
     assert (

From 1584d13890af1fcf67240da9693836b3227cd65f Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Mon, 26 Apr 2021 17:49:35 -0400
Subject: [PATCH 06/24] Black changes

---
 cytominer_eval/operations/precision_recall.py          | 10 ++++------
 .../tests/test_operations/test_precision_recall.py     |  2 +-
 cytominer_eval/transform/util.py                       |  8 +++++++-
 3 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/cytominer_eval/operations/precision_recall.py b/cytominer_eval/operations/precision_recall.py
index 6d22df3..3834f48 100644
--- a/cytominer_eval/operations/precision_recall.py
+++ b/cytominer_eval/operations/precision_recall.py
@@ -11,9 +11,7 @@
 
 
 def precision_recall(
-    similarity_melted_df: pd.DataFrame,
-    replicate_groups: List[str],
-    k_list: List[int],
+    similarity_melted_df: pd.DataFrame, replicate_groups: List[str], k_list: List[int],
 ) -> pd.DataFrame:
     """Determine the precision and recall at k for all unique replicate groups
     based on a predefined similarity metric (see cytominer_eval.transform.metric_melt)
@@ -53,9 +51,9 @@ def precision_recall(
     precision_recall_df = pd.DataFrame()
     for k in k_list:
         # Calculate precision and recall for all groups
-        precision_recall_df_at_k = similarity_melted_df.groupby(replicate_group_cols).apply(
-            lambda x: calculate_precision_recall(x, k=k)
-            )
+        precision_recall_df_at_k = similarity_melted_df.groupby(
+            replicate_group_cols
+        ).apply(lambda x: calculate_precision_recall(x, k=k))
         precision_recall_df = precision_recall_df.append(precision_recall_df_at_k)
 
     # Rename the columns back to the replicate groups provided
diff --git a/cytominer_eval/tests/test_operations/test_precision_recall.py b/cytominer_eval/tests/test_operations/test_precision_recall.py
index 410025a..cdfd9b7 100644
--- a/cytominer_eval/tests/test_operations/test_precision_recall.py
+++ b/cytominer_eval/tests/test_operations/test_precision_recall.py
@@ -42,7 +42,7 @@ def test_precision_recall():
     result = precision_recall(
         similarity_melted_df=similarity_melted_df,
         replicate_groups=replicate_groups,
-        k_list=[5, 10]
+        k_list=[5, 10],
     )
 
     assert len(result.k.unique()) == 2
diff --git a/cytominer_eval/transform/util.py b/cytominer_eval/transform/util.py
index db5d782..b957248 100644
--- a/cytominer_eval/transform/util.py
+++ b/cytominer_eval/transform/util.py
@@ -7,7 +7,13 @@
 
 def get_available_eval_metrics():
     r"""Output the available eval metrics in the cytominer_eval library"""
-    return ["replicate_reproducibility", "precision_recall", "grit", "mp_value", "enrichment"]
+    return [
+        "replicate_reproducibility",
+        "precision_recall",
+        "grit",
+        "mp_value",
+        "enrichment",
+    ]
 
 
 def get_available_similarity_metrics():

From d732dce4dec8556a99bc34f32c0bfb360453061e Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Tue, 27 Apr 2021 09:21:08 -0400
Subject: [PATCH 07/24] fix tests

---
 cytominer_eval/tests/test_evaluate.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/cytominer_eval/tests/test_evaluate.py b/cytominer_eval/tests/test_evaluate.py
index 34af0b3..e21543d 100644
--- a/cytominer_eval/tests/test_evaluate.py
+++ b/cytominer_eval/tests/test_evaluate.py
@@ -141,7 +141,7 @@ def test_evaluate_precision_recall():
             replicate_groups=gene_groups,
             operation="precision_recall",
             similarity_metric="pearson",
-            precision_recall_k=k,
+            precision_recall_k=[k],
         )
 
         assert (
@@ -160,7 +160,7 @@ def test_evaluate_precision_recall():
             replicate_groups=["Metadata_broad_sample"],
             operation="precision_recall",
             similarity_metric="pearson",
-            precision_recall_k=k,
+            precision_recall_k=[k],
         )
 
         assert (

From 86e53147f9b0fd3f46572b617d9fee038b5cf026 Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Tue, 27 Apr 2021 11:19:23 -0400
Subject: [PATCH 08/24] rerun the demo

---
 demos/CellPainting_Demo.ipynb | 965 ----------------------------------
 1 file changed, 965 deletions(-)
 delete mode 100644 demos/CellPainting_Demo.ipynb

diff --git a/demos/CellPainting_Demo.ipynb b/demos/CellPainting_Demo.ipynb
deleted file mode 100644
index 790cdfb..0000000
--- a/demos/CellPainting_Demo.ipynb
+++ /dev/null
@@ -1,965 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "offensive-courage",
-   "metadata": {},
-   "source": [
-    "# Demo for a Cell Painting dataset\n",
-    "\n",
-    "To reproduce this notebook, first activate the `demo_environment.yml` using conda:\n",
-    "\n",
-    "```bash\n",
-    "conda env create --force --file demo_environment.yml\n",
-    "conda activate cytominer-eval-demo\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "sophisticated-creek",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import preprocessing\n",
-    "import plotnine as gg\n",
-    "\n",
-    "from cytominer_eval import evaluate\n",
-    "from pycytominer.cyto_utils import infer_cp_features\n",
-    "\n",
-    "import utils"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "mental-beach",
-   "metadata": {},
-   "source": [
-    "### Load the Cell Health profile datasets\n",
-    "\n",
-    "The dataset measures CRISPR perturbations in a Cell Painting experiment.\n",
-    "\n",
-    "> Predicting cell health phenotypes using image-based morphology profiling.\n",
-    "> Gregory P. Way, Maria Kost-Alimova, Tsukasa Shibue, William F. Harrington, Stanley Gill, Federica Piccioni, Tim Becker, Hamdah Shafqat-Abbasi, William C. Hahn, Anne E. Carpenter, Francisca Vazquez, Shantanu Singh\n",
-    "> bioRxiv 2020.07.08.193938; doi: https://doi.org/10.1101/2020.07.08.193938"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "growing-blond",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(3456, 956)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "  Metadata_Plate Metadata_Well  Metadata_WellCol Metadata_WellRow  \\\n0     SQ00014618           A01                 1                A   \n1     SQ00014618           A02                 2                A   \n2     SQ00014618           A03                 3                A   \n3     SQ00014618           A04                 4                A   \n4     SQ00014618           A05                 5                A   \n\n  Metadata_cell_line Metadata_gene_name Metadata_pert_name  \\\n0              HCC44              EMPTY              EMPTY   \n1              HCC44               MCL1             MCL1-5   \n2              HCC44               AKT1             AKT1-1   \n3              HCC44               KRAS            KRAS-2B   \n4              HCC44               AKT1             AKT1-2   \n\n   Cells_AreaShape_Center_Y  Cells_AreaShape_Compactness  \\\n0                 -0.894997                    -1.515696   \n1                 -0.479926                     0.246423   \n2                 -0.635578                     0.416772   \n3                  1.024707                     0.645336   \n4                 -2.036443                     0.159822   \n\n   Cells_AreaShape_Eccentricity  ...  Nuclei_Texture_SumEntropy_RNA_5_0  \\\n0                     -1.787667  ...                           0.107581   \n1                      0.629901  ...                           0.165935   \n2                      0.039795  ...                          -1.358338   \n3                      0.714847  ...                          -0.975661   \n4                      0.736176  ...                          -0.575835   \n\n   Nuclei_Texture_SumVariance_AGP_20_0  Nuclei_Texture_SumVariance_AGP_5_0  \\\n0                            -0.659049                           -0.676846   \n1                             1.999006                            1.204036   \n2                             0.155556                           -0.112177   \n3                            -0.931362                           -0.809894   \n4                             0.093271                           -0.298606   \n\n   Nuclei_Texture_SumVariance_DNA_10_0  Nuclei_Texture_SumVariance_DNA_20_0  \\\n0                            -1.229791                            -1.336051   \n1                             0.560228                             0.686189   \n2                            -1.258864                            -1.394609   \n3                            -1.526434                            -1.541661   \n4                            -0.564712                            -0.512144   \n\n   Nuclei_Texture_SumVariance_DNA_5_0  Nuclei_Texture_Variance_AGP_5_0  \\\n0                           -1.125138                        -0.972360   \n1                            0.601634                         1.154001   \n2                           -1.277509                        -0.128419   \n3                           -1.482596                        -1.004789   \n4                           -0.513650                        -0.438356   \n\n   Nuclei_Texture_Variance_DNA_10_0  Nuclei_Texture_Variance_DNA_20_0  \\\n0                         -1.393856                         -1.244227   \n1                          0.596441                          0.680359   \n2                         -1.359460                         -1.299859   \n3                         -1.817028                         -1.887102   \n4                         -0.562306                         -0.276279   \n\n   Nuclei_Texture_Variance_DNA_5_0  \n0                        -1.308729  \n1                         0.715469  \n2                        -1.396879  \n3                        -1.681831  \n4                        -0.547497  \n\n[5 rows x 956 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metadata_Plate</th>\n      <th>Metadata_Well</th>\n      <th>Metadata_WellCol</th>\n      <th>Metadata_WellRow</th>\n      <th>Metadata_cell_line</th>\n      <th>Metadata_gene_name</th>\n      <th>Metadata_pert_name</th>\n      <th>Cells_AreaShape_Center_Y</th>\n      <th>Cells_AreaShape_Compactness</th>\n      <th>Cells_AreaShape_Eccentricity</th>\n      <th>...</th>\n      <th>Nuclei_Texture_SumEntropy_RNA_5_0</th>\n      <th>Nuclei_Texture_SumVariance_AGP_20_0</th>\n      <th>Nuclei_Texture_SumVariance_AGP_5_0</th>\n      <th>Nuclei_Texture_SumVariance_DNA_10_0</th>\n      <th>Nuclei_Texture_SumVariance_DNA_20_0</th>\n      <th>Nuclei_Texture_SumVariance_DNA_5_0</th>\n      <th>Nuclei_Texture_Variance_AGP_5_0</th>\n      <th>Nuclei_Texture_Variance_DNA_10_0</th>\n      <th>Nuclei_Texture_Variance_DNA_20_0</th>\n      <th>Nuclei_Texture_Variance_DNA_5_0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>SQ00014618</td>\n      <td>A01</td>\n      <td>1</td>\n      <td>A</td>\n      <td>HCC44</td>\n      <td>EMPTY</td>\n      <td>EMPTY</td>\n      <td>-0.894997</td>\n      <td>-1.515696</td>\n      <td>-1.787667</td>\n      <td>...</td>\n      <td>0.107581</td>\n      <td>-0.659049</td>\n      <td>-0.676846</td>\n      <td>-1.229791</td>\n      <td>-1.336051</td>\n      <td>-1.125138</td>\n      <td>-0.972360</td>\n      <td>-1.393856</td>\n      <td>-1.244227</td>\n      <td>-1.308729</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>SQ00014618</td>\n      <td>A02</td>\n      <td>2</td>\n      <td>A</td>\n      <td>HCC44</td>\n      <td>MCL1</td>\n      <td>MCL1-5</td>\n      <td>-0.479926</td>\n      <td>0.246423</td>\n      <td>0.629901</td>\n      <td>...</td>\n      <td>0.165935</td>\n      <td>1.999006</td>\n      <td>1.204036</td>\n      <td>0.560228</td>\n      <td>0.686189</td>\n      <td>0.601634</td>\n      <td>1.154001</td>\n      <td>0.596441</td>\n      <td>0.680359</td>\n      <td>0.715469</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>SQ00014618</td>\n      <td>A03</td>\n      <td>3</td>\n      <td>A</td>\n      <td>HCC44</td>\n      <td>AKT1</td>\n      <td>AKT1-1</td>\n      <td>-0.635578</td>\n      <td>0.416772</td>\n      <td>0.039795</td>\n      <td>...</td>\n      <td>-1.358338</td>\n      <td>0.155556</td>\n      <td>-0.112177</td>\n      <td>-1.258864</td>\n      <td>-1.394609</td>\n      <td>-1.277509</td>\n      <td>-0.128419</td>\n      <td>-1.359460</td>\n      <td>-1.299859</td>\n      <td>-1.396879</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>SQ00014618</td>\n      <td>A04</td>\n      <td>4</td>\n      <td>A</td>\n      <td>HCC44</td>\n      <td>KRAS</td>\n      <td>KRAS-2B</td>\n      <td>1.024707</td>\n      <td>0.645336</td>\n      <td>0.714847</td>\n      <td>...</td>\n      <td>-0.975661</td>\n      <td>-0.931362</td>\n      <td>-0.809894</td>\n      <td>-1.526434</td>\n      <td>-1.541661</td>\n      <td>-1.482596</td>\n      <td>-1.004789</td>\n      <td>-1.817028</td>\n      <td>-1.887102</td>\n      <td>-1.681831</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>SQ00014618</td>\n      <td>A05</td>\n      <td>5</td>\n      <td>A</td>\n      <td>HCC44</td>\n      <td>AKT1</td>\n      <td>AKT1-2</td>\n      <td>-2.036443</td>\n      <td>0.159822</td>\n      <td>0.736176</td>\n      <td>...</td>\n      <td>-0.575835</td>\n      <td>0.093271</td>\n      <td>-0.298606</td>\n      <td>-0.564712</td>\n      <td>-0.512144</td>\n      <td>-0.513650</td>\n      <td>-0.438356</td>\n      <td>-0.562306</td>\n      <td>-0.276279</td>\n      <td>-0.547497</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 956 columns</p>\n</div>"
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = utils.load_cell_health()\n",
-    "\n",
-    "print(df.shape)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "local-offense",
-   "metadata": {},
-   "source": [
-    "#### Set feature information\n",
-    "\n",
-    "Note that the profiling dataset has columns representing metadata and profiling feature measurements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "alpine-pattern",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "['Metadata_Plate',\n 'Metadata_Well',\n 'Metadata_WellCol',\n 'Metadata_WellRow',\n 'Metadata_cell_line',\n 'Metadata_gene_name',\n 'Metadata_pert_name']"
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features = infer_cp_features(df)\n",
-    "meta_features = infer_cp_features(df, metadata=True)\n",
-    "\n",
-    "meta_features"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "actual-holocaust",
-   "metadata": {},
-   "source": [
-    "### Collect metrics per cell line\n",
-    "\n",
-    "We profiled three cell lines in this dataset: A549, ES2, and HCC44.\n",
-    "\n",
-    "Use gene and perturbation (CRISPR guide) to denote replicates."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fifth-light",
-   "metadata": {},
-   "source": [
-    "### Part 1: Replicate reproducibility\n",
-    "\n",
-    "`operation=\"replicate_reproducibility\"`\n",
-    "\n",
-    "Cytominer-eval can quickly calculate median pairwise correlations for all perturbations in a dataset.\n",
-    "\n",
-    "We can also use replicate reproducibility to calculate a \"percent matching\" score.\n",
-    "Percent matching is the fraction of replicate pairs that are more similar to each other than 95% of non-replicate pairs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "super-witness",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "percent_matching_scores = []\n",
-    "pairwise_corrs = []\n",
-    "\n",
-    "for cell_line in df.Metadata_cell_line.unique():\n",
-    "    cell_line_df = df.query(\"Metadata_cell_line == @cell_line\")\n",
-    "\n",
-    "    percent_matching, corr_df = evaluate(\n",
-    "        profiles=cell_line_df,\n",
-    "        features=features,\n",
-    "        meta_features=meta_features,\n",
-    "        replicate_groups=[\"Metadata_gene_name\", \"Metadata_pert_name\"],\n",
-    "        operation=\"replicate_reproducibility\",\n",
-    "        similarity_metric=\"pearson\",\n",
-    "        replicate_reproducibility_quantile=0.95,\n",
-    "        replicate_reproducibility_return_median_cor=True,\n",
-    "    )\n",
-    "    \n",
-    "    percent_matching_bundle = [cell_line, percent_matching]\n",
-    "    \n",
-    "    percent_matching_scores.append(percent_matching_bundle)\n",
-    "    pairwise_corrs.append(corr_df.assign(cell_line=cell_line))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "handed-rating",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  cell_line  percent_matching\n0     HCC44          0.109077\n1      A549          0.065802\n2       ES2          0.114925",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cell_line</th>\n      <th>percent_matching</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>HCC44</td>\n      <td>0.109077</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A549</td>\n      <td>0.065802</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ES2</td>\n      <td>0.114925</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "percent_matching_scores = pd.DataFrame(percent_matching_scores, columns=[\"cell_line\", \"percent_matching\"])\n",
-    "percent_matching_scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "employed-oracle",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(357, 4)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "  Metadata_gene_name Metadata_pert_name  similarity_metric cell_line\n0               AKT1             AKT1-1           0.240928     HCC44\n1               AKT1             AKT1-2           0.178413     HCC44\n2             ARID1B           ARID1B-1           0.220200     HCC44\n3             ARID1B           ARID1B-2           0.133993     HCC44\n4               ATF4             ATF4-1           0.797046     HCC44",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metadata_gene_name</th>\n      <th>Metadata_pert_name</th>\n      <th>similarity_metric</th>\n      <th>cell_line</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AKT1</td>\n      <td>AKT1-1</td>\n      <td>0.240928</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AKT1</td>\n      <td>AKT1-2</td>\n      <td>0.178413</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ARID1B</td>\n      <td>ARID1B-1</td>\n      <td>0.220200</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ARID1B</td>\n      <td>ARID1B-2</td>\n      <td>0.133993</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ATF4</td>\n      <td>ATF4-1</td>\n      <td>0.797046</td>\n      <td>HCC44</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairwise_corr_df = pd.concat(pairwise_corrs).reset_index(drop=True)\n",
-    "\n",
-    "print(pairwise_corr_df.shape)\n",
-    "pairwise_corr_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "welsh-attempt",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<ggplot: (8781651364172)>"
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(\n",
-    "    gg.ggplot(pairwise_corr_df, gg.aes(x=\"similarity_metric\"))\n",
-    "    + gg.geom_density(gg.aes(fill=\"cell_line\"), alpha=0.6)\n",
-    "    + gg.theme_bw()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "veterinary-cable",
-   "metadata": {},
-   "source": [
-    "### Part 2: Replicate reproducibility\n",
-    "\n",
-    "`operation=\"precision_recall\"`\n",
-    "\n",
-    "Given a profiling dataframe, cytominer-eval can quickly calculate precision and recall of all replicate perturbations.\n",
-    "\n",
-    "To calculate precision recall for every perturbation, we first rank order all perturbations by pairwise correlations.\n",
-    "We use a user-defined `k` as a threshold for calling hits.\n",
-    "\n",
-    "### Precision\n",
-    "\n",
-    "precision = number of replicates selected in top k ranking / k\n",
-    "\n",
-    "### Recall\n",
-    "\n",
-    "recall = number of replicates selected in top k ranking / number of total replicates\n",
-    "\n",
-    "Here, calculate precision and recall for a range of k."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "under-western",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "precision_recall_at_k = []\n",
-    "\n",
-    "for cell_line in df.Metadata_cell_line.unique():\n",
-    "    cell_line_df = df.query(\"Metadata_cell_line == @cell_line\")\n",
-    "    \n",
-    "    for k in [2, 3, 5, 10, 15, 25]:\n",
-    "    \n",
-    "        precision_recall_results = evaluate(\n",
-    "            profiles=cell_line_df,\n",
-    "            features=features,\n",
-    "            meta_features=meta_features,\n",
-    "            replicate_groups=[\"Metadata_gene_name\", \"Metadata_pert_name\"],\n",
-    "            operation=\"precision_recall\",\n",
-    "            similarity_metric=\"pearson\",\n",
-    "            precision_recall_k=k\n",
-    "        ).assign(cell_line=cell_line)\n",
-    "        \n",
-    "        precision_recall_at_k.append(precision_recall_results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "mighty-experiment",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2142, 6)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "  Metadata_gene_name Metadata_pert_name    k  precision  recall cell_line\n0               AKT1             AKT1-1  2.0        0.0     0.0     HCC44\n1               AKT1             AKT1-2  2.0        0.0     0.0     HCC44\n2             ARID1B           ARID1B-1  2.0        0.0     0.0     HCC44\n3             ARID1B           ARID1B-2  2.0        0.0     0.0     HCC44\n4               ATF4             ATF4-1  2.0        0.0     0.0     HCC44",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metadata_gene_name</th>\n      <th>Metadata_pert_name</th>\n      <th>k</th>\n      <th>precision</th>\n      <th>recall</th>\n      <th>cell_line</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AKT1</td>\n      <td>AKT1-1</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AKT1</td>\n      <td>AKT1-2</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ARID1B</td>\n      <td>ARID1B-1</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ARID1B</td>\n      <td>ARID1B-2</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ATF4</td>\n      <td>ATF4-1</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>HCC44</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "precision_recall_at_k = pd.concat(precision_recall_at_k).reset_index(drop=True)\n",
-    "\n",
-    "print(precision_recall_at_k.shape)\n",
-    "precision_recall_at_k.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ranking-valentine",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 18 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<ggplot: (8781569597850)>"
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(\n",
-    "    gg.ggplot(precision_recall_at_k, gg.aes(x=\"precision\", y=\"recall\"))\n",
-    "    + gg.geom_point(alpha=0.6)\n",
-    "    + gg.facet_grid(\"cell_line~k\")\n",
-    "    + gg.theme_bw()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "damaged-cleanup",
-   "metadata": {},
-   "source": [
-    "### Part 3: mp-value\n",
-    "\n",
-    "`operation=\"mp-value\"`\n",
-    "\n",
-    "Calculate the \"multidimensional perturbation value\" (mp-value).\n",
-    "\n",
-    "> Hutz, J. et al. “The Multidimensional Perturbation Value: A Single Metric to Measure Similarity and Activity of Treatments in High-Throughput Multidimensional Screens” Journal of Biomolecular Screening, Volume: 18 issue: 4, page(s): 367-377. doi: 10.1177/1087057112469257\n",
-    "\n",
-    "The mp-value is the mahalanobis distance between a perturbation and control, as compared to a randomly permuted null distribution."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aerial-studio",
-   "metadata": {},
-   "source": [
-    "**NOTE: Parts 3 and 4 (mp-value and grit) introduce the concept of _control perturbations_**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "juvenile-scholar",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "['Chr2-1', 'Chr2-4', 'Chr2-5', 'Chr2-2', 'Chr2-3', 'Chr2-6']"
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Chr2 is an intergenic CRISPR cutting control\n",
-    "control_perts = df.query(\"Metadata_gene_name == 'Chr2'\").Metadata_pert_name.unique().tolist()\n",
-    "control_perts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "suitable-medicine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mp_values = []\n",
-    "\n",
-    "for cell_line in df.Metadata_cell_line.unique():\n",
-    "    cell_line_df = df.query(\"Metadata_cell_line == @cell_line\")\n",
-    "    \n",
-    "    for num_perm in [10, 100, 1000]:\n",
-    "    \n",
-    "        mp_results = evaluate(\n",
-    "            profiles=cell_line_df,\n",
-    "            features=features,\n",
-    "            meta_features=meta_features,\n",
-    "            replicate_groups=\"Metadata_pert_name\",\n",
-    "            operation=\"mp_value\",\n",
-    "            similarity_metric=\"pearson\",\n",
-    "            mp_value_params={\"rescale_pca\": True, \"nb_permutations\": num_perm},\n",
-    "            grit_control_perts=control_perts\n",
-    "        ).assign(cell_line=cell_line, permutations=num_perm)\n",
-    "        \n",
-    "        mp_values.append(mp_results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "interior-brave",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1071, 4)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "  Metadata_pert_name  mp_value cell_line  permutations\n0             AKT1-1       0.1     HCC44            10\n1             AKT1-2       0.1     HCC44            10\n2           ARID1B-1       0.1     HCC44            10\n3           ARID1B-2       0.2     HCC44            10\n4             ATF4-1       0.0     HCC44            10",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metadata_pert_name</th>\n      <th>mp_value</th>\n      <th>cell_line</th>\n      <th>permutations</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AKT1-1</td>\n      <td>0.1</td>\n      <td>HCC44</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AKT1-2</td>\n      <td>0.1</td>\n      <td>HCC44</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ARID1B-1</td>\n      <td>0.1</td>\n      <td>HCC44</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ARID1B-2</td>\n      <td>0.2</td>\n      <td>HCC44</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ATF4-1</td>\n      <td>0.0</td>\n      <td>HCC44</td>\n      <td>10</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mp_values = pd.concat(mp_values).reset_index(drop=True)\n",
-    "\n",
-    "print(mp_values.shape)\n",
-    "mp_values.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "verbal-beatles",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mbornhol/miniconda3/envs/noncyto/lib/python3.8/site-packages/pandas/core/arraylike.py:358: RuntimeWarning: divide by zero encountered in log10\n",
-      "/Users/mbornhol/miniconda3/envs/noncyto/lib/python3.8/site-packages/plotnine/layer.py:324: PlotnineWarning: stat_density : Removed 434 rows containing non-finite values.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 3 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<ggplot: (8781519474595)>"
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(\n",
-    "    gg.ggplot(mp_values, gg.aes(x=\"-np.log10(mp_value)\"))\n",
-    "    + gg.geom_density(gg.aes(fill=\"cell_line\"), alpha=0.6)\n",
-    "    + gg.facet_wrap(\"~permutations\", nrow=3)\n",
-    "    + gg.theme_bw()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "opposite-flood",
-   "metadata": {},
-   "source": [
-    "### Part 4: Grit\n",
-    "\n",
-    "`operation=\"grit\"`\n",
-    "\n",
-    "Grit is a metric that represents the strength and reproducibility of a given perturbation.\n",
-    "The strength of the perturbation is measured relative to a reference panel of controls.\n",
-    "The reproducibility of the pertubation is measured relative to perturbation replicates. \n",
-    "\n",
-    "For more details about the metric and to observe its behavior, see https://github.com/broadinstitute/grit-benchmark."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "major-arthritis",
-   "metadata": {},
-   "source": [
-    "#### The replicate_groups variable\n",
-    "\n",
-    "Of all metrics, `grit` requires a slightly more complicated `replicate_groups` variable.\n",
-    "\n",
-    "In `grit`, the variable must be a dictionary with two keys: \"profile_col\" and \"replicate_group_col\".\n",
-    "\n",
-    "- \"profile_col\" - this column stores profile identifiers.\n",
-    "    - It can include unique entries, but it is not required.\n",
-    "    - `cytominer_eval` will calculate grit for each perturbation in the \"profile_col\"\n",
-    "- \"replicate_group_col\" - this column stores replicate and group identifiers.\n",
-    "    - Typically one higher level \"above\" the profile_col, the replicate_group_col indicates how the profiles should be grouped together\n",
-    "  \n",
-    "In a CRISPR experiment, for example, the \"profile_col\" stores guide profiles while the \"replicate_group_col\" stores the guide targets.\n",
-    "If multiple guides target the same gene, `grit` will determine the reproducibility and strength of the guide."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "respective-constant",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grit_replicate_groups = {\n",
-    "    \"profile_col\": \"Metadata_pert_name\",\n",
-    "    \"replicate_group_col\": \"Metadata_gene_name\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "accepting-basement",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grit_scores = []\n",
-    "\n",
-    "for cell_line in df.Metadata_cell_line.unique():\n",
-    "    cell_line_df = df.query(\"Metadata_cell_line == @cell_line\")\n",
-    "    \n",
-    "    grit_results = evaluate(\n",
-    "        profiles=cell_line_df,\n",
-    "        features=features,\n",
-    "        meta_features=meta_features,\n",
-    "        replicate_groups=grit_replicate_groups,\n",
-    "        operation=\"grit\",\n",
-    "        similarity_metric=\"pearson\",\n",
-    "        grit_replicate_summary_method=\"median\",\n",
-    "        grit_control_perts=control_perts\n",
-    "    ).assign(cell_line=cell_line)\n",
-    "\n",
-    "    grit_scores.append(grit_results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "complex-settle",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(357, 4)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "  perturbation   group      grit cell_line\n0       AKT1-1    AKT1  0.918037     HCC44\n1       AKT1-2    AKT1  0.858680     HCC44\n2     ARID1B-1  ARID1B  0.304972     HCC44\n3     ARID1B-2  ARID1B  0.096940     HCC44\n4       ATF4-1    ATF4 -0.122386     HCC44",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>perturbation</th>\n      <th>group</th>\n      <th>grit</th>\n      <th>cell_line</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AKT1-1</td>\n      <td>AKT1</td>\n      <td>0.918037</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AKT1-2</td>\n      <td>AKT1</td>\n      <td>0.858680</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ARID1B-1</td>\n      <td>ARID1B</td>\n      <td>0.304972</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ARID1B-2</td>\n      <td>ARID1B</td>\n      <td>0.096940</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ATF4-1</td>\n      <td>ATF4</td>\n      <td>-0.122386</td>\n      <td>HCC44</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grit_scores = pd.concat(grit_scores).reset_index(drop=True)\n",
-    "\n",
-    "print(grit_scores.shape)\n",
-    "grit_scores.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "ceramic-teens",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mbornhol/miniconda3/envs/noncyto/lib/python3.8/site-packages/plotnine/layer.py:324: PlotnineWarning: stat_density : Removed 18 rows containing non-finite values.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<ggplot: (8781651834640)>"
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(\n",
-    "    gg.ggplot(grit_scores, gg.aes(x=\"grit\"))\n",
-    "    + gg.geom_density(gg.aes(fill=\"cell_line\"), alpha=0.6)\n",
-    "    + gg.theme_bw()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Part 5: enrichment\n",
-    "Use the enrichment analysis developed by Shantanu and Juan. It looks at the shortest connections within the dataset and evaluates based score is based on the fisher exact odds score\n",
-    "`operation=\"enichment\"`"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "   percentile  ods_ratio\n0        0.99   3.685582\n1        0.98   2.597346\n2        0.97   2.116626\n3        0.96   1.875379",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>percentile</th>\n      <th>ods_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.99</td>\n      <td>3.685582</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.98</td>\n      <td>2.597346</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.97</td>\n      <td>2.116626</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.96</td>\n      <td>1.875379</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = []\n",
-    "for p in np.arange(0.99, 0.96, -0.01):\n",
-    "    r = evaluate(\n",
-    "        profiles=df,\n",
-    "        features=features,\n",
-    "        meta_features=meta_features,\n",
-    "        replicate_groups=[\"Metadata_gene_name\"],\n",
-    "        operation=\"enrichment\",\n",
-    "        similarity_metric=\"pearson\",\n",
-    "        enrichment_percentile = p,\n",
-    "    )\n",
-    "    result.append(r)\n",
-    "\n",
-    "result_df = pd.DataFrame(result)\n",
-    "result_df[[\"percentile\",\"ods_ratio\"]]"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ordinary-breakdown",
-   "metadata": {},
-   "source": [
-    "## Combine metrics and generate a combined prioritization rank"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "cathedral-familiar",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(357, 12)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "  Metadata_gene_name Metadata_pert_name  median_correlation cell_line  \\\n0               AKT1             AKT1-1            0.240928     HCC44   \n1               AKT1             AKT1-2            0.178413     HCC44   \n2             ARID1B           ARID1B-1            0.220200     HCC44   \n3             ARID1B           ARID1B-2            0.133993     HCC44   \n4               ATF4             ATF4-1            0.797046     HCC44   \n\n   precision_recall_k  precision    recall  mp_value  mp_value_permutations  \\\n0                25.0       0.16  0.133333     0.118                   1000   \n1                25.0       0.08  0.066667     0.154                   1000   \n2                25.0       0.00  0.000000     0.091                   1000   \n3                25.0       0.00  0.000000     0.114                   1000   \n4                25.0       0.00  0.000000     0.002                   1000   \n\n  perturbation   group      grit  \n0       AKT1-1    AKT1  0.918037  \n1       AKT1-2    AKT1  0.858680  \n2     ARID1B-1  ARID1B  0.304972  \n3     ARID1B-2  ARID1B  0.096940  \n4       ATF4-1    ATF4 -0.122386  ",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metadata_gene_name</th>\n      <th>Metadata_pert_name</th>\n      <th>median_correlation</th>\n      <th>cell_line</th>\n      <th>precision_recall_k</th>\n      <th>precision</th>\n      <th>recall</th>\n      <th>mp_value</th>\n      <th>mp_value_permutations</th>\n      <th>perturbation</th>\n      <th>group</th>\n      <th>grit</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AKT1</td>\n      <td>AKT1-1</td>\n      <td>0.240928</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.16</td>\n      <td>0.133333</td>\n      <td>0.118</td>\n      <td>1000</td>\n      <td>AKT1-1</td>\n      <td>AKT1</td>\n      <td>0.918037</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AKT1</td>\n      <td>AKT1-2</td>\n      <td>0.178413</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.08</td>\n      <td>0.066667</td>\n      <td>0.154</td>\n      <td>1000</td>\n      <td>AKT1-2</td>\n      <td>AKT1</td>\n      <td>0.858680</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ARID1B</td>\n      <td>ARID1B-1</td>\n      <td>0.220200</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.091</td>\n      <td>1000</td>\n      <td>ARID1B-1</td>\n      <td>ARID1B</td>\n      <td>0.304972</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ARID1B</td>\n      <td>ARID1B-2</td>\n      <td>0.133993</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.114</td>\n      <td>1000</td>\n      <td>ARID1B-2</td>\n      <td>ARID1B</td>\n      <td>0.096940</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ATF4</td>\n      <td>ATF4-1</td>\n      <td>0.797046</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.002</td>\n      <td>1000</td>\n      <td>ATF4-1</td>\n      <td>ATF4</td>\n      <td>-0.122386</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merge_cols = [\"Metadata_gene_name\", \"Metadata_pert_name\", \"cell_line\"]\n",
-    "\n",
-    "metric_df = (\n",
-    "    pairwise_corr_df.merge(\n",
-    "        precision_recall_at_k.query(\"k == 25\"),\n",
-    "        on=merge_cols\n",
-    "    )\n",
-    "    .merge(\n",
-    "        mp_values.query(\"permutations == 1000\"),\n",
-    "        on=[\"Metadata_pert_name\", \"cell_line\"]\n",
-    "    )\n",
-    "    .merge(\n",
-    "        grit_scores,\n",
-    "        left_on=merge_cols,\n",
-    "        right_on=[\"group\", \"perturbation\", \"cell_line\"],\n",
-    "    )\n",
-    "    .rename(\n",
-    "        {\n",
-    "            \"similarity_metric\": \"median_correlation\",\n",
-    "            \"k\": \"precision_recall_k\",\n",
-    "            \"permutations\": \"mp_value_permutations\"\n",
-    "        },\n",
-    "        axis=\"columns\"\n",
-    "    )\n",
-    ")\n",
-    "    \n",
-    "print(metric_df.shape)\n",
-    "metric_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "retained-companion",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mbornhol/miniconda3/envs/noncyto/lib/python3.8/site-packages/plotnine/layer.py:401: PlotnineWarning: geom_point : Removed 18 rows containing missing values.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<ggplot: (8781651557524)>"
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(\n",
-    "    gg.ggplot(metric_df, gg.aes(x=\"median_correlation\", y=\"grit\"))\n",
-    "    + gg.geom_point(gg.aes(fill=\"precision\", shape=\"cell_line\"), size=4, alpha=0.6)\n",
-    "    + gg.theme_bw()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "graphic-actor",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mbornhol/miniconda3/envs/noncyto/lib/python3.8/site-packages/pandas/core/arraylike.py:358: RuntimeWarning: divide by zero encountered in log10\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<ggplot: (8781651990959)>"
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(\n",
-    "    gg.ggplot(metric_df, gg.aes(x=\"median_correlation\", y=\"recall\"))\n",
-    "    + gg.geom_point(gg.aes(fill=\"-np.log10(mp_value)\", shape=\"cell_line\"), size=4, alpha=0.6)\n",
-    "    + gg.theme_bw()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "ceramic-drunk",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  Metadata_gene_name Metadata_pert_name  median_correlation cell_line  \\\n0                MYC              MYC-1            0.896095       ES2   \n1               YAP1             YAP1-2            0.855538       ES2   \n2              CCND1            CCND1-2            0.912482       ES2   \n3              EGLN1            EGLN1-9            0.799812       ES2   \n4               CDK2             CDK2-2            0.891436     HCC44   \n5              ITGAV            ITGAV-2            0.895882      A549   \n6               CDK4             CDK4-2            0.913841       ES2   \n7              ITGAV            ITGAV-2            0.919061     HCC44   \n8              ITGAV            ITGAV-1            0.925652     HCC44   \n9               GPX4             GPX4-1            0.844319       ES2   \n\n   precision_recall_k  precision    recall  mp_value  mp_value_permutations  \\\n0                25.0       0.72  0.600000       0.0                   1000   \n1                25.0       0.80  0.666667       0.0                   1000   \n2                25.0       0.64  0.533333       0.0                   1000   \n3                25.0       0.72  0.600000       0.0                   1000   \n4                25.0       0.60  0.500000       0.0                   1000   \n5                25.0       0.56  0.466667       0.0                   1000   \n6                25.0       0.40  0.333333       0.0                   1000   \n7                25.0       0.40  0.333333       0.0                   1000   \n8                25.0       0.40  0.333333       0.0                   1000   \n9                25.0       0.40  0.333333       0.0                   1000   \n\n  perturbation  group      grit  metric_sum_scaled  \n0        MYC-1    MYC  3.087105           3.741246  \n1       YAP1-2   YAP1  1.888577           3.609051  \n2      CCND1-2  CCND1  2.555242           3.419675  \n3      EGLN1-9  EGLN1  1.972640           3.388326  \n4       CDK2-2   CDK2  1.582908           3.059086  \n5      ITGAV-2  ITGAV  1.886879           3.038947  \n6       CDK4-2   CDK4  2.829412           2.889714  \n7      ITGAV-2  ITGAV  2.783719           2.882135  \n8      ITGAV-1  ITGAV  2.689741           2.863432  \n9       GPX4-1   GPX4  2.836964           2.839359  ",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metadata_gene_name</th>\n      <th>Metadata_pert_name</th>\n      <th>median_correlation</th>\n      <th>cell_line</th>\n      <th>precision_recall_k</th>\n      <th>precision</th>\n      <th>recall</th>\n      <th>mp_value</th>\n      <th>mp_value_permutations</th>\n      <th>perturbation</th>\n      <th>group</th>\n      <th>grit</th>\n      <th>metric_sum_scaled</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>MYC</td>\n      <td>MYC-1</td>\n      <td>0.896095</td>\n      <td>ES2</td>\n      <td>25.0</td>\n      <td>0.72</td>\n      <td>0.600000</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>MYC-1</td>\n      <td>MYC</td>\n      <td>3.087105</td>\n      <td>3.741246</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>YAP1</td>\n      <td>YAP1-2</td>\n      <td>0.855538</td>\n      <td>ES2</td>\n      <td>25.0</td>\n      <td>0.80</td>\n      <td>0.666667</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>YAP1-2</td>\n      <td>YAP1</td>\n      <td>1.888577</td>\n      <td>3.609051</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CCND1</td>\n      <td>CCND1-2</td>\n      <td>0.912482</td>\n      <td>ES2</td>\n      <td>25.0</td>\n      <td>0.64</td>\n      <td>0.533333</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>CCND1-2</td>\n      <td>CCND1</td>\n      <td>2.555242</td>\n      <td>3.419675</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>EGLN1</td>\n      <td>EGLN1-9</td>\n      <td>0.799812</td>\n      <td>ES2</td>\n      <td>25.0</td>\n      <td>0.72</td>\n      <td>0.600000</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>EGLN1-9</td>\n      <td>EGLN1</td>\n      <td>1.972640</td>\n      <td>3.388326</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CDK2</td>\n      <td>CDK2-2</td>\n      <td>0.891436</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.60</td>\n      <td>0.500000</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>CDK2-2</td>\n      <td>CDK2</td>\n      <td>1.582908</td>\n      <td>3.059086</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>ITGAV</td>\n      <td>ITGAV-2</td>\n      <td>0.895882</td>\n      <td>A549</td>\n      <td>25.0</td>\n      <td>0.56</td>\n      <td>0.466667</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>ITGAV-2</td>\n      <td>ITGAV</td>\n      <td>1.886879</td>\n      <td>3.038947</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CDK4</td>\n      <td>CDK4-2</td>\n      <td>0.913841</td>\n      <td>ES2</td>\n      <td>25.0</td>\n      <td>0.40</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>CDK4-2</td>\n      <td>CDK4</td>\n      <td>2.829412</td>\n      <td>2.889714</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>ITGAV</td>\n      <td>ITGAV-2</td>\n      <td>0.919061</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.40</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>ITGAV-2</td>\n      <td>ITGAV</td>\n      <td>2.783719</td>\n      <td>2.882135</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>ITGAV</td>\n      <td>ITGAV-1</td>\n      <td>0.925652</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.40</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>ITGAV-1</td>\n      <td>ITGAV</td>\n      <td>2.689741</td>\n      <td>2.863432</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>GPX4</td>\n      <td>GPX4-1</td>\n      <td>0.844319</td>\n      <td>ES2</td>\n      <td>25.0</td>\n      <td>0.40</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>GPX4-1</td>\n      <td>GPX4</td>\n      <td>2.836964</td>\n      <td>2.839359</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min_max_scaler = preprocessing.MinMaxScaler()\n",
-    "\n",
-    "metric_summary_df = (\n",
-    "    metric_df\n",
-    "    .assign(\n",
-    "        metric_sum_scaled=(\n",
-    "            min_max_scaler.fit_transform(metric_df.grit.values.reshape(-1, 1)) + \n",
-    "            min_max_scaler.fit_transform(metric_df.median_correlation.values.reshape(-1, 1)) +\n",
-    "            min_max_scaler.fit_transform(metric_df.precision.values.reshape(-1, 1)) +\n",
-    "            min_max_scaler.fit_transform(metric_df.recall.values.reshape(-1, 1)) -\n",
-    "            min_max_scaler.fit_transform(metric_df.mp_value.values.reshape(-1, 1))\n",
-    "        )\n",
-    "    )\n",
-    "    .sort_values(by=\"metric_sum_scaled\", ascending=False)\n",
-    "    .reset_index(drop=True)\n",
-    "    .dropna()\n",
-    ")\n",
-    "\n",
-    "metric_summary_df.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "cathedral-bailey",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<ggplot: (8781651513890)>"
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(\n",
-    "    gg.ggplot(metric_summary_df, gg.aes(x=\"metric_sum_scaled\", y=\"grit\"))\n",
-    "    + gg.geom_point(gg.aes(fill=\"median_correlation\", shape=\"cell_line\"), size=4, alpha=0.6)\n",
-    "    + gg.theme_bw()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "choice-background",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<ggplot: (8781651698770)>"
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(\n",
-    "    gg.ggplot(metric_summary_df, gg.aes(x=\"metric_sum_scaled\", y=\"recall\"))\n",
-    "    + gg.geom_point(gg.aes(fill=\"precision\", shape=\"cell_line\"), size=4, alpha=0.6)\n",
-    "    + gg.theme_bw()\n",
-    ")\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "name": "python3",
-   "language": "python",
-   "display_name": "Python 3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file

From a9087301b48399b580ba92c7b781c1861115f8cf Mon Sep 17 00:00:00 2001
From: Michael Bornholdt <56402523+michaelbornholdt@users.noreply.github.com>
Date: Fri, 30 Apr 2021 11:08:22 -0400
Subject: [PATCH 09/24] also accept ints

Co-authored-by: Greg Way <gregory.way@gmail.com>
---
 cytominer_eval/evaluate.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cytominer_eval/evaluate.py b/cytominer_eval/evaluate.py
index 1fa01c4..2c61ff4 100644
--- a/cytominer_eval/evaluate.py
+++ b/cytominer_eval/evaluate.py
@@ -27,7 +27,7 @@ def evaluate(
     similarity_metric: str = "pearson",
     replicate_reproducibility_quantile: np.float = 0.95,
     replicate_reproducibility_return_median_cor: bool = False,
-    precision_recall_k: List[int] = [5, 10],
+    precision_recall_k: Union[int, List[int]] = 10,
     grit_control_perts: List[str] = ["None"],
     grit_replicate_summary_method: str = "mean",
     mp_value_params: dict = {},

From 03cb138686411e4f042daac4d52bb0373a021407 Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Fri, 30 Apr 2021 11:36:25 -0400
Subject: [PATCH 10/24] ints and floats also allowed

---
 cytominer_eval/evaluate.py                    |  8 ++++----
 cytominer_eval/operations/enrichment.py       |  6 ++++--
 cytominer_eval/operations/precision_recall.py | 12 +++++++-----
 3 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/cytominer_eval/evaluate.py b/cytominer_eval/evaluate.py
index 2c61ff4..cf723f1 100644
--- a/cytominer_eval/evaluate.py
+++ b/cytominer_eval/evaluate.py
@@ -31,7 +31,7 @@ def evaluate(
     grit_control_perts: List[str] = ["None"],
     grit_replicate_summary_method: str = "mean",
     mp_value_params: dict = {},
-    enrichment_percentile: List[float] = [0.99, 0.98],
+    enrichment_percentile: Union[int, List[int]] = 0.99,
 ):
     r"""Evaluate profile quality and strength.
 
@@ -86,7 +86,7 @@ def evaluate(
         Only used when `operation='replicate_reproducibility'`. If True, then also
         return pairwise correlations as defined by replicate_groups and
         similarity metric
-    precision_recall_k : {10, ...}, optional
+    precision_recall_k : int or list of ints {10, ...}, optional
         Only used when `operation='precision_recall'`. Used to calculate precision and
         recall considering the top k profiles according to pairwise similarity.
     grit_control_perts : {None, ...}, optional
@@ -101,7 +101,7 @@ def evaluate(
         Only used when `operation='mp_value'`. A key, item pair of optional parameters
         for calculating mp value. See also
         :py:func:`cytominer_eval.operations.util.default_mp_value_parameters`
-    percentile : float, optional
+    percentile : float or list of floats, optional
         Only used when `operation='enrichment'`. Determines the percentage of top connections
         used for the enrichment calculation.
     """
@@ -130,7 +130,7 @@ def evaluate(
         metric_result = precision_recall(
             similarity_melted_df=similarity_melted_df,
             replicate_groups=replicate_groups,
-            k_list=precision_recall_k,
+            k=precision_recall_k,
         )
     elif operation == "grit":
         metric_result = grit(
diff --git a/cytominer_eval/operations/enrichment.py b/cytominer_eval/operations/enrichment.py
index 803591e..bf54f76 100644
--- a/cytominer_eval/operations/enrichment.py
+++ b/cytominer_eval/operations/enrichment.py
@@ -2,7 +2,7 @@
 """
 import numpy as np
 import pandas as pd
-from typing import List
+from typing import List, Union
 import scipy
 
 from .util import assign_replicates, calculate_grit, check_grit_replicate_summary_method
@@ -16,7 +16,7 @@
 def enrichment(
     similarity_melted_df: pd.DataFrame,
     replicate_groups: List[str],
-    percentile: List[float],
+    percentile: Union[int, List[int]],
 ) -> pd.DataFrame:
     """Calculate the enrichment score. This score is based on the fisher exact odds score. Similar to the other functions, the closest connections are determined and checked with the replicates.
     This score effectively calculates how much better the distribution of correct connections is compared to random.
@@ -43,6 +43,8 @@ def enrichment(
         similarity_melted_df=similarity_melted_df, replicate_groups=replicate_groups
     )
     # loop over all percentiles
+    if type(percentile) == float:
+        percentile = [percentile]
     for p in percentile:
         # threshold based on percentile of top connections
         threshold = similarity_melted_df.similarity_metric.quantile(p)
diff --git a/cytominer_eval/operations/precision_recall.py b/cytominer_eval/operations/precision_recall.py
index 3834f48..6f66c1e 100644
--- a/cytominer_eval/operations/precision_recall.py
+++ b/cytominer_eval/operations/precision_recall.py
@@ -4,14 +4,14 @@
 
 import numpy as np
 import pandas as pd
-from typing import List
+from typing import List, Union
 
 from .util import assign_replicates, calculate_precision_recall
 from cytominer_eval.transform.util import set_pair_ids, assert_melt
 
 
 def precision_recall(
-    similarity_melted_df: pd.DataFrame, replicate_groups: List[str], k_list: List[int],
+    similarity_melted_df: pd.DataFrame, replicate_groups: List[str], k: Union[int, List[int]],
 ) -> pd.DataFrame:
     """Determine the precision and recall at k for all unique replicate groups
     based on a predefined similarity metric (see cytominer_eval.transform.metric_melt)
@@ -25,7 +25,7 @@ def precision_recall(
     replicate_groups : List
         a list of metadata column names in the original profile dataframe to use as
         replicate columns.
-    k_list : List of ints
+    k : List of ints or int
         an integer indicating how many pairwise comparisons to threshold.
 
     Returns
@@ -49,11 +49,13 @@ def precision_recall(
     ]
     # iterate over all k
     precision_recall_df = pd.DataFrame()
-    for k in k_list:
+    if type(k) == int:
+        k = [k]
+    for k_ in k:
         # Calculate precision and recall for all groups
         precision_recall_df_at_k = similarity_melted_df.groupby(
             replicate_group_cols
-        ).apply(lambda x: calculate_precision_recall(x, k=k))
+        ).apply(lambda x: calculate_precision_recall(x, k=k_))
         precision_recall_df = precision_recall_df.append(precision_recall_df_at_k)
 
     # Rename the columns back to the replicate groups provided

From 2b38adb24fe89ff7219741c1132f10109510b1ed Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Fri, 30 Apr 2021 11:42:59 -0400
Subject: [PATCH 11/24] add further tests

---
 cytominer_eval/tests/test_operations/test_enrichment.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/cytominer_eval/tests/test_operations/test_enrichment.py b/cytominer_eval/tests/test_operations/test_enrichment.py
index 88b5eda..b1b544d 100644
--- a/cytominer_eval/tests/test_operations/test_enrichment.py
+++ b/cytominer_eval/tests/test_operations/test_enrichment.py
@@ -52,6 +52,7 @@ def test_enrichment():
     # check for correct shape and starts with 1.0
     assert result.shape == (7, 4)
     assert result.enrichment_percentile[0] == 1.0
+    assert result.enrichment_percentile[0] == 0.995
     # check if the higher percentiles are larger than the small one
     assert result.enrichment_percentile[1] > result.enrichment_percentile.iloc[-1]
 

From d6a24f438634eed9ed508eff6b0f8dea2c2f01d6 Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Fri, 30 Apr 2021 11:45:34 -0400
Subject: [PATCH 12/24] Black

---
 cytominer_eval/operations/precision_recall.py |  4 +++-
 cytominer_eval/tests/test_evaluate.py         | 14 +++-----------
 2 files changed, 6 insertions(+), 12 deletions(-)

diff --git a/cytominer_eval/operations/precision_recall.py b/cytominer_eval/operations/precision_recall.py
index 6f66c1e..5bd015d 100644
--- a/cytominer_eval/operations/precision_recall.py
+++ b/cytominer_eval/operations/precision_recall.py
@@ -11,7 +11,9 @@
 
 
 def precision_recall(
-    similarity_melted_df: pd.DataFrame, replicate_groups: List[str], k: Union[int, List[int]],
+    similarity_melted_df: pd.DataFrame,
+    replicate_groups: List[str],
+    k: Union[int, List[int]],
 ) -> pd.DataFrame:
     """Determine the precision and recall at k for all unique replicate groups
     based on a predefined similarity metric (see cytominer_eval.transform.metric_melt)
diff --git a/cytominer_eval/tests/test_evaluate.py b/cytominer_eval/tests/test_evaluate.py
index e21543d..dafefb9 100644
--- a/cytominer_eval/tests/test_evaluate.py
+++ b/cytominer_eval/tests/test_evaluate.py
@@ -111,11 +111,7 @@ def test_evaluate_replicate_reprod_return_cor_true():
 
     assert np.round(med_cor_df.similarity_metric.max(), 3) == 0.949
     assert sorted(med_cor_df.columns.tolist()) == sorted(
-        [
-            "Metadata_gene_name",
-            "Metadata_pert_name",
-            "similarity_metric",
-        ]
+        ["Metadata_gene_name", "Metadata_pert_name", "similarity_metric",]
     )
 
 
@@ -205,9 +201,7 @@ def test_evaluate_grit():
     top_result = (
         grit_results_df.sort_values(by="grit", ascending=False)
         .reset_index(drop=True)
-        .iloc[
-            0,
-        ]
+        .iloc[0,]
     )
     assert np.round(top_result.grit, 4) == 2.3352
     assert top_result.group == "PTK2"
@@ -233,9 +227,7 @@ def test_evaluate_grit():
     top_result = (
         grit_results_df.sort_values(by="grit", ascending=False)
         .reset_index(drop=True)
-        .iloc[
-            0,
-        ]
+        .iloc[0,]
     )
 
     assert np.round(top_result.grit, 4) == 0.9990

From 60d8868b318276d40adb8318ded65223525b59cc Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Fri, 30 Apr 2021 11:46:56 -0400
Subject: [PATCH 13/24] fix test

---
 cytominer_eval/tests/test_operations/test_enrichment.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cytominer_eval/tests/test_operations/test_enrichment.py b/cytominer_eval/tests/test_operations/test_enrichment.py
index b1b544d..ee00b48 100644
--- a/cytominer_eval/tests/test_operations/test_enrichment.py
+++ b/cytominer_eval/tests/test_operations/test_enrichment.py
@@ -52,7 +52,7 @@ def test_enrichment():
     # check for correct shape and starts with 1.0
     assert result.shape == (7, 4)
     assert result.enrichment_percentile[0] == 1.0
-    assert result.enrichment_percentile[0] == 0.995
+    assert result.enrichment_percentile[1] == 0.995
     # check if the higher percentiles are larger than the small one
     assert result.enrichment_percentile[1] > result.enrichment_percentile.iloc[-1]
 

From 2f631dad1d246404684e86c91ff5d68d0393bf8e Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Fri, 30 Apr 2021 11:48:55 -0400
Subject: [PATCH 14/24] Add Demo

---
 demos/CellPainting_Demo.ipynb | 999 ++++++++++++++++++++++++++++++++++
 1 file changed, 999 insertions(+)
 create mode 100644 demos/CellPainting_Demo.ipynb

diff --git a/demos/CellPainting_Demo.ipynb b/demos/CellPainting_Demo.ipynb
new file mode 100644
index 0000000..adaf1cf
--- /dev/null
+++ b/demos/CellPainting_Demo.ipynb
@@ -0,0 +1,999 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "offensive-courage",
+   "metadata": {},
+   "source": [
+    "# Demo for a Cell Painting dataset\n",
+    "\n",
+    "To reproduce this notebook, first activate the `demo_environment.yml` using conda:\n",
+    "\n",
+    "```bash\n",
+    "conda env create --force --file demo_environment.yml\n",
+    "conda activate cytominer-eval-demo\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "sophisticated-creek",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/mbornhol/git/mycyto/cytominer-eval/demos\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import preprocessing\n",
+    "import plotnine as gg\n",
+    "\n",
+    "from cytominer_eval import evaluate\n",
+    "from pycytominer.cyto_utils import infer_cp_features\n",
+    "\n",
+    "import utils"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mental-beach",
+   "metadata": {},
+   "source": [
+    "### Load the Cell Health profile datasets\n",
+    "\n",
+    "The dataset measures CRISPR perturbations in a Cell Painting experiment.\n",
+    "\n",
+    "> Predicting cell health phenotypes using image-based morphology profiling.\n",
+    "> Gregory P. Way, Maria Kost-Alimova, Tsukasa Shibue, William F. Harrington, Stanley Gill, Federica Piccioni, Tim Becker, Hamdah Shafqat-Abbasi, William C. Hahn, Anne E. Carpenter, Francisca Vazquez, Shantanu Singh\n",
+    "> bioRxiv 2020.07.08.193938; doi: https://doi.org/10.1101/2020.07.08.193938"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "growing-blond",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3456, 956)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "  Metadata_Plate Metadata_Well  Metadata_WellCol Metadata_WellRow  \\\n0     SQ00014618           A01                 1                A   \n1     SQ00014618           A02                 2                A   \n2     SQ00014618           A03                 3                A   \n3     SQ00014618           A04                 4                A   \n4     SQ00014618           A05                 5                A   \n\n  Metadata_cell_line Metadata_gene_name Metadata_pert_name  \\\n0              HCC44              EMPTY              EMPTY   \n1              HCC44               MCL1             MCL1-5   \n2              HCC44               AKT1             AKT1-1   \n3              HCC44               KRAS            KRAS-2B   \n4              HCC44               AKT1             AKT1-2   \n\n   Cells_AreaShape_Center_Y  Cells_AreaShape_Compactness  \\\n0                 -0.894997                    -1.515696   \n1                 -0.479926                     0.246423   \n2                 -0.635578                     0.416772   \n3                  1.024707                     0.645336   \n4                 -2.036443                     0.159822   \n\n   Cells_AreaShape_Eccentricity  ...  Nuclei_Texture_SumEntropy_RNA_5_0  \\\n0                     -1.787667  ...                           0.107581   \n1                      0.629901  ...                           0.165935   \n2                      0.039795  ...                          -1.358338   \n3                      0.714847  ...                          -0.975661   \n4                      0.736176  ...                          -0.575835   \n\n   Nuclei_Texture_SumVariance_AGP_20_0  Nuclei_Texture_SumVariance_AGP_5_0  \\\n0                            -0.659049                           -0.676846   \n1                             1.999006                            1.204036   \n2                             0.155556                           -0.112177   \n3                            -0.931362                           -0.809894   \n4                             0.093271                           -0.298606   \n\n   Nuclei_Texture_SumVariance_DNA_10_0  Nuclei_Texture_SumVariance_DNA_20_0  \\\n0                            -1.229791                            -1.336051   \n1                             0.560228                             0.686189   \n2                            -1.258864                            -1.394609   \n3                            -1.526434                            -1.541661   \n4                            -0.564712                            -0.512144   \n\n   Nuclei_Texture_SumVariance_DNA_5_0  Nuclei_Texture_Variance_AGP_5_0  \\\n0                           -1.125138                        -0.972360   \n1                            0.601634                         1.154001   \n2                           -1.277509                        -0.128419   \n3                           -1.482596                        -1.004789   \n4                           -0.513650                        -0.438356   \n\n   Nuclei_Texture_Variance_DNA_10_0  Nuclei_Texture_Variance_DNA_20_0  \\\n0                         -1.393856                         -1.244227   \n1                          0.596441                          0.680359   \n2                         -1.359460                         -1.299859   \n3                         -1.817028                         -1.887102   \n4                         -0.562306                         -0.276279   \n\n   Nuclei_Texture_Variance_DNA_5_0  \n0                        -1.308729  \n1                         0.715469  \n2                        -1.396879  \n3                        -1.681831  \n4                        -0.547497  \n\n[5 rows x 956 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metadata_Plate</th>\n      <th>Metadata_Well</th>\n      <th>Metadata_WellCol</th>\n      <th>Metadata_WellRow</th>\n      <th>Metadata_cell_line</th>\n      <th>Metadata_gene_name</th>\n      <th>Metadata_pert_name</th>\n      <th>Cells_AreaShape_Center_Y</th>\n      <th>Cells_AreaShape_Compactness</th>\n      <th>Cells_AreaShape_Eccentricity</th>\n      <th>...</th>\n      <th>Nuclei_Texture_SumEntropy_RNA_5_0</th>\n      <th>Nuclei_Texture_SumVariance_AGP_20_0</th>\n      <th>Nuclei_Texture_SumVariance_AGP_5_0</th>\n      <th>Nuclei_Texture_SumVariance_DNA_10_0</th>\n      <th>Nuclei_Texture_SumVariance_DNA_20_0</th>\n      <th>Nuclei_Texture_SumVariance_DNA_5_0</th>\n      <th>Nuclei_Texture_Variance_AGP_5_0</th>\n      <th>Nuclei_Texture_Variance_DNA_10_0</th>\n      <th>Nuclei_Texture_Variance_DNA_20_0</th>\n      <th>Nuclei_Texture_Variance_DNA_5_0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>SQ00014618</td>\n      <td>A01</td>\n      <td>1</td>\n      <td>A</td>\n      <td>HCC44</td>\n      <td>EMPTY</td>\n      <td>EMPTY</td>\n      <td>-0.894997</td>\n      <td>-1.515696</td>\n      <td>-1.787667</td>\n      <td>...</td>\n      <td>0.107581</td>\n      <td>-0.659049</td>\n      <td>-0.676846</td>\n      <td>-1.229791</td>\n      <td>-1.336051</td>\n      <td>-1.125138</td>\n      <td>-0.972360</td>\n      <td>-1.393856</td>\n      <td>-1.244227</td>\n      <td>-1.308729</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>SQ00014618</td>\n      <td>A02</td>\n      <td>2</td>\n      <td>A</td>\n      <td>HCC44</td>\n      <td>MCL1</td>\n      <td>MCL1-5</td>\n      <td>-0.479926</td>\n      <td>0.246423</td>\n      <td>0.629901</td>\n      <td>...</td>\n      <td>0.165935</td>\n      <td>1.999006</td>\n      <td>1.204036</td>\n      <td>0.560228</td>\n      <td>0.686189</td>\n      <td>0.601634</td>\n      <td>1.154001</td>\n      <td>0.596441</td>\n      <td>0.680359</td>\n      <td>0.715469</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>SQ00014618</td>\n      <td>A03</td>\n      <td>3</td>\n      <td>A</td>\n      <td>HCC44</td>\n      <td>AKT1</td>\n      <td>AKT1-1</td>\n      <td>-0.635578</td>\n      <td>0.416772</td>\n      <td>0.039795</td>\n      <td>...</td>\n      <td>-1.358338</td>\n      <td>0.155556</td>\n      <td>-0.112177</td>\n      <td>-1.258864</td>\n      <td>-1.394609</td>\n      <td>-1.277509</td>\n      <td>-0.128419</td>\n      <td>-1.359460</td>\n      <td>-1.299859</td>\n      <td>-1.396879</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>SQ00014618</td>\n      <td>A04</td>\n      <td>4</td>\n      <td>A</td>\n      <td>HCC44</td>\n      <td>KRAS</td>\n      <td>KRAS-2B</td>\n      <td>1.024707</td>\n      <td>0.645336</td>\n      <td>0.714847</td>\n      <td>...</td>\n      <td>-0.975661</td>\n      <td>-0.931362</td>\n      <td>-0.809894</td>\n      <td>-1.526434</td>\n      <td>-1.541661</td>\n      <td>-1.482596</td>\n      <td>-1.004789</td>\n      <td>-1.817028</td>\n      <td>-1.887102</td>\n      <td>-1.681831</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>SQ00014618</td>\n      <td>A05</td>\n      <td>5</td>\n      <td>A</td>\n      <td>HCC44</td>\n      <td>AKT1</td>\n      <td>AKT1-2</td>\n      <td>-2.036443</td>\n      <td>0.159822</td>\n      <td>0.736176</td>\n      <td>...</td>\n      <td>-0.575835</td>\n      <td>0.093271</td>\n      <td>-0.298606</td>\n      <td>-0.564712</td>\n      <td>-0.512144</td>\n      <td>-0.513650</td>\n      <td>-0.438356</td>\n      <td>-0.562306</td>\n      <td>-0.276279</td>\n      <td>-0.547497</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 956 columns</p>\n</div>"
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = utils.load_cell_health()\n",
+    "\n",
+    "print(df.shape)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "local-offense",
+   "metadata": {},
+   "source": [
+    "#### Set feature information\n",
+    "\n",
+    "Note that the profiling dataset has columns representing metadata and profiling feature measurements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "alpine-pattern",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "['Metadata_Plate',\n 'Metadata_Well',\n 'Metadata_WellCol',\n 'Metadata_WellRow',\n 'Metadata_cell_line',\n 'Metadata_gene_name',\n 'Metadata_pert_name']"
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features = infer_cp_features(df)\n",
+    "meta_features = infer_cp_features(df, metadata=True)\n",
+    "\n",
+    "meta_features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "actual-holocaust",
+   "metadata": {},
+   "source": [
+    "### Collect metrics per cell line\n",
+    "\n",
+    "We profiled three cell lines in this dataset: A549, ES2, and HCC44.\n",
+    "\n",
+    "Use gene and perturbation (CRISPR guide) to denote replicates."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fifth-light",
+   "metadata": {},
+   "source": [
+    "### Part 1: Replicate reproducibility\n",
+    "\n",
+    "`operation=\"replicate_reproducibility\"`\n",
+    "\n",
+    "Cytominer-eval can quickly calculate median pairwise correlations for all perturbations in a dataset.\n",
+    "\n",
+    "We can also use replicate reproducibility to calculate a \"percent matching\" score.\n",
+    "Percent matching is the fraction of replicate pairs that are more similar to each other than 95% of non-replicate pairs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "super-witness",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "percent_matching_scores = []\n",
+    "pairwise_corrs = []\n",
+    "\n",
+    "for cell_line in df.Metadata_cell_line.unique():\n",
+    "    cell_line_df = df.query(\"Metadata_cell_line == @cell_line\")\n",
+    "\n",
+    "    percent_matching, corr_df = evaluate(\n",
+    "        profiles=cell_line_df,\n",
+    "        features=features,\n",
+    "        meta_features=meta_features,\n",
+    "        replicate_groups=[\"Metadata_gene_name\", \"Metadata_pert_name\"],\n",
+    "        operation=\"replicate_reproducibility\",\n",
+    "        similarity_metric=\"pearson\",\n",
+    "        replicate_reproducibility_quantile=0.95,\n",
+    "        replicate_reproducibility_return_median_cor=True,\n",
+    "    )\n",
+    "    \n",
+    "    percent_matching_bundle = [cell_line, percent_matching]\n",
+    "    \n",
+    "    percent_matching_scores.append(percent_matching_bundle)\n",
+    "    pairwise_corrs.append(corr_df.assign(cell_line=cell_line))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "handed-rating",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  cell_line  percent_matching\n0     HCC44          0.109077\n1      A549          0.065802\n2       ES2          0.114925",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cell_line</th>\n      <th>percent_matching</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>HCC44</td>\n      <td>0.109077</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A549</td>\n      <td>0.065802</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ES2</td>\n      <td>0.114925</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "percent_matching_scores = pd.DataFrame(percent_matching_scores, columns=[\"cell_line\", \"percent_matching\"])\n",
+    "percent_matching_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "employed-oracle",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(357, 4)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "  Metadata_gene_name Metadata_pert_name  similarity_metric cell_line\n0               AKT1             AKT1-1           0.240928     HCC44\n1               AKT1             AKT1-2           0.178413     HCC44\n2             ARID1B           ARID1B-1           0.220200     HCC44\n3             ARID1B           ARID1B-2           0.133993     HCC44\n4               ATF4             ATF4-1           0.797046     HCC44",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metadata_gene_name</th>\n      <th>Metadata_pert_name</th>\n      <th>similarity_metric</th>\n      <th>cell_line</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AKT1</td>\n      <td>AKT1-1</td>\n      <td>0.240928</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AKT1</td>\n      <td>AKT1-2</td>\n      <td>0.178413</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ARID1B</td>\n      <td>ARID1B-1</td>\n      <td>0.220200</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ARID1B</td>\n      <td>ARID1B-2</td>\n      <td>0.133993</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ATF4</td>\n      <td>ATF4-1</td>\n      <td>0.797046</td>\n      <td>HCC44</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pairwise_corr_df = pd.concat(pairwise_corrs).reset_index(drop=True)\n",
+    "\n",
+    "print(pairwise_corr_df.shape)\n",
+    "pairwise_corr_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "welsh-attempt",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<ggplot: (8790176578443)>"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    gg.ggplot(pairwise_corr_df, gg.aes(x=\"similarity_metric\"))\n",
+    "    + gg.geom_density(gg.aes(fill=\"cell_line\"), alpha=0.6)\n",
+    "    + gg.theme_bw()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "veterinary-cable",
+   "metadata": {},
+   "source": [
+    "### Part 2: Replicate reproducibility\n",
+    "\n",
+    "`operation=\"precision_recall\"`\n",
+    "\n",
+    "Given a profiling dataframe, cytominer-eval can quickly calculate precision and recall of all replicate perturbations.\n",
+    "\n",
+    "To calculate precision recall for every perturbation, we first rank order all perturbations by pairwise correlations.\n",
+    "We use a user-defined `k` as a threshold for calling hits.\n",
+    "\n",
+    "### Precision\n",
+    "\n",
+    "precision = number of replicates selected in top k ranking / k\n",
+    "\n",
+    "### Recall\n",
+    "\n",
+    "recall = number of replicates selected in top k ranking / number of total replicates\n",
+    "\n",
+    "Here, calculate precision and recall for a range of k."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [],
+   "source": [
+    "precision_recall_at_k = []\n",
+    "\n",
+    "for cell_line in df.Metadata_cell_line.unique():\n",
+    "    cell_line_df = df.query(\"Metadata_cell_line == @cell_line\")\n",
+    "    \n",
+    "    k_list = [2, 3, 5, 10, 15, 25]\n",
+    "    \n",
+    "    precision_recall_results = evaluate(\n",
+    "        profiles=cell_line_df,\n",
+    "        features=features,\n",
+    "        meta_features=meta_features,\n",
+    "        replicate_groups=[\"Metadata_gene_name\", \"Metadata_pert_name\"],\n",
+    "        operation=\"precision_recall\",\n",
+    "        similarity_metric=\"pearson\",\n",
+    "        precision_recall_k=k_list\n",
+    "    ).assign(cell_line=cell_line)\n",
+    "\n",
+    "    precision_recall_at_k.append(precision_recall_results)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "mighty-experiment",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2142, 6)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "  Metadata_gene_name Metadata_pert_name    k  precision  recall cell_line\n0               AKT1             AKT1-1  2.0        0.0     0.0     HCC44\n1               AKT1             AKT1-2  2.0        0.0     0.0     HCC44\n2             ARID1B           ARID1B-1  2.0        0.0     0.0     HCC44\n3             ARID1B           ARID1B-2  2.0        0.0     0.0     HCC44\n4               ATF4             ATF4-1  2.0        0.0     0.0     HCC44",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metadata_gene_name</th>\n      <th>Metadata_pert_name</th>\n      <th>k</th>\n      <th>precision</th>\n      <th>recall</th>\n      <th>cell_line</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AKT1</td>\n      <td>AKT1-1</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AKT1</td>\n      <td>AKT1-2</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ARID1B</td>\n      <td>ARID1B-1</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ARID1B</td>\n      <td>ARID1B-2</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ATF4</td>\n      <td>ATF4-1</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>HCC44</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_recall_at_k = pd.concat(precision_recall_at_k).reset_index(drop=True)\n",
+    "\n",
+    "print(precision_recall_at_k.shape)\n",
+    "precision_recall_at_k.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ranking-valentine",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 18 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<ggplot: (8790124303948)>"
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    gg.ggplot(precision_recall_at_k, gg.aes(x=\"precision\", y=\"recall\"))\n",
+    "    + gg.geom_point(alpha=0.6)\n",
+    "    + gg.facet_grid(\"cell_line~k\")\n",
+    "    + gg.theme_bw()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "damaged-cleanup",
+   "metadata": {},
+   "source": [
+    "### Part 3: mp-value\n",
+    "\n",
+    "`operation=\"mp-value\"`\n",
+    "\n",
+    "Calculate the \"multidimensional perturbation value\" (mp-value).\n",
+    "\n",
+    "> Hutz, J. et al. “The Multidimensional Perturbation Value: A Single Metric to Measure Similarity and Activity of Treatments in High-Throughput Multidimensional Screens” Journal of Biomolecular Screening, Volume: 18 issue: 4, page(s): 367-377. doi: 10.1177/1087057112469257\n",
+    "\n",
+    "The mp-value is the mahalanobis distance between a perturbation and control, as compared to a randomly permuted null distribution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aerial-studio",
+   "metadata": {},
+   "source": [
+    "**NOTE: Parts 3 and 4 (mp-value and grit) introduce the concept of _control perturbations_**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "juvenile-scholar",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "['Chr2-1', 'Chr2-4', 'Chr2-5', 'Chr2-2', 'Chr2-3', 'Chr2-6']"
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Chr2 is an intergenic CRISPR cutting control\n",
+    "control_perts = df.query(\"Metadata_gene_name == 'Chr2'\").Metadata_pert_name.unique().tolist()\n",
+    "control_perts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "suitable-medicine",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mp_values = []\n",
+    "\n",
+    "for cell_line in df.Metadata_cell_line.unique():\n",
+    "    cell_line_df = df.query(\"Metadata_cell_line == @cell_line\")\n",
+    "    \n",
+    "    for num_perm in [10, 100, 1000]:\n",
+    "    \n",
+    "        mp_results = evaluate(\n",
+    "            profiles=cell_line_df,\n",
+    "            features=features,\n",
+    "            meta_features=meta_features,\n",
+    "            replicate_groups=\"Metadata_pert_name\",\n",
+    "            operation=\"mp_value\",\n",
+    "            similarity_metric=\"pearson\",\n",
+    "            mp_value_params={\"rescale_pca\": True, \"nb_permutations\": num_perm},\n",
+    "            grit_control_perts=control_perts\n",
+    "        ).assign(cell_line=cell_line, permutations=num_perm)\n",
+    "        \n",
+    "        mp_values.append(mp_results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "interior-brave",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1071, 4)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "  Metadata_pert_name  mp_value cell_line  permutations\n0             AKT1-1       0.2     HCC44            10\n1             AKT1-2       0.2     HCC44            10\n2           ARID1B-1       0.1     HCC44            10\n3           ARID1B-2       0.1     HCC44            10\n4             ATF4-1       0.0     HCC44            10",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metadata_pert_name</th>\n      <th>mp_value</th>\n      <th>cell_line</th>\n      <th>permutations</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AKT1-1</td>\n      <td>0.2</td>\n      <td>HCC44</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AKT1-2</td>\n      <td>0.2</td>\n      <td>HCC44</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ARID1B-1</td>\n      <td>0.1</td>\n      <td>HCC44</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ARID1B-2</td>\n      <td>0.1</td>\n      <td>HCC44</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ATF4-1</td>\n      <td>0.0</td>\n      <td>HCC44</td>\n      <td>10</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mp_values = pd.concat(mp_values).reset_index(drop=True)\n",
+    "\n",
+    "print(mp_values.shape)\n",
+    "mp_values.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "verbal-beatles",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mbornhol/miniconda3/envs/noncyto/lib/python3.8/site-packages/pandas/core/arraylike.py:358: RuntimeWarning: divide by zero encountered in log10\n",
+      "/Users/mbornhol/miniconda3/envs/noncyto/lib/python3.8/site-packages/plotnine/layer.py:324: PlotnineWarning: stat_density : Removed 444 rows containing non-finite values.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 3 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<ggplot: (8790125296104)>"
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    gg.ggplot(mp_values, gg.aes(x=\"-np.log10(mp_value)\"))\n",
+    "    + gg.geom_density(gg.aes(fill=\"cell_line\"), alpha=0.6)\n",
+    "    + gg.facet_wrap(\"~permutations\", nrow=3)\n",
+    "    + gg.theme_bw()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opposite-flood",
+   "metadata": {},
+   "source": [
+    "### Part 4: Grit\n",
+    "\n",
+    "`operation=\"grit\"`\n",
+    "\n",
+    "Grit is a metric that represents the strength and reproducibility of a given perturbation.\n",
+    "The strength of the perturbation is measured relative to a reference panel of controls.\n",
+    "The reproducibility of the pertubation is measured relative to perturbation replicates. \n",
+    "\n",
+    "For more details about the metric and to observe its behavior, see https://github.com/broadinstitute/grit-benchmark."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "major-arthritis",
+   "metadata": {},
+   "source": [
+    "#### The replicate_groups variable\n",
+    "\n",
+    "Of all metrics, `grit` requires a slightly more complicated `replicate_groups` variable.\n",
+    "\n",
+    "In `grit`, the variable must be a dictionary with two keys: \"profile_col\" and \"replicate_group_col\".\n",
+    "\n",
+    "- \"profile_col\" - this column stores profile identifiers.\n",
+    "    - It can include unique entries, but it is not required.\n",
+    "    - `cytominer_eval` will calculate grit for each perturbation in the \"profile_col\"\n",
+    "- \"replicate_group_col\" - this column stores replicate and group identifiers.\n",
+    "    - Typically one higher level \"above\" the profile_col, the replicate_group_col indicates how the profiles should be grouped together\n",
+    "  \n",
+    "In a CRISPR experiment, for example, the \"profile_col\" stores guide profiles while the \"replicate_group_col\" stores the guide targets.\n",
+    "If multiple guides target the same gene, `grit` will determine the reproducibility and strength of the guide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "respective-constant",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grit_replicate_groups = {\n",
+    "    \"profile_col\": \"Metadata_pert_name\",\n",
+    "    \"replicate_group_col\": \"Metadata_gene_name\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "accepting-basement",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grit_scores = []\n",
+    "\n",
+    "for cell_line in df.Metadata_cell_line.unique():\n",
+    "    cell_line_df = df.query(\"Metadata_cell_line == @cell_line\")\n",
+    "    \n",
+    "    grit_results = evaluate(\n",
+    "        profiles=cell_line_df,\n",
+    "        features=features,\n",
+    "        meta_features=meta_features,\n",
+    "        replicate_groups=grit_replicate_groups,\n",
+    "        operation=\"grit\",\n",
+    "        similarity_metric=\"pearson\",\n",
+    "        grit_replicate_summary_method=\"median\",\n",
+    "        grit_control_perts=control_perts\n",
+    "    ).assign(cell_line=cell_line)\n",
+    "\n",
+    "    grit_scores.append(grit_results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "complex-settle",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(357, 4)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "  perturbation   group      grit cell_line\n0       AKT1-1    AKT1  0.918037     HCC44\n1       AKT1-2    AKT1  0.858680     HCC44\n2     ARID1B-1  ARID1B  0.304972     HCC44\n3     ARID1B-2  ARID1B  0.096940     HCC44\n4       ATF4-1    ATF4 -0.122386     HCC44",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>perturbation</th>\n      <th>group</th>\n      <th>grit</th>\n      <th>cell_line</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AKT1-1</td>\n      <td>AKT1</td>\n      <td>0.918037</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AKT1-2</td>\n      <td>AKT1</td>\n      <td>0.858680</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ARID1B-1</td>\n      <td>ARID1B</td>\n      <td>0.304972</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ARID1B-2</td>\n      <td>ARID1B</td>\n      <td>0.096940</td>\n      <td>HCC44</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ATF4-1</td>\n      <td>ATF4</td>\n      <td>-0.122386</td>\n      <td>HCC44</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grit_scores = pd.concat(grit_scores).reset_index(drop=True)\n",
+    "\n",
+    "print(grit_scores.shape)\n",
+    "grit_scores.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ceramic-teens",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mbornhol/miniconda3/envs/noncyto/lib/python3.8/site-packages/plotnine/layer.py:324: PlotnineWarning: stat_density : Removed 18 rows containing non-finite values.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<ggplot: (8790176556358)>"
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    gg.ggplot(grit_scores, gg.aes(x=\"grit\"))\n",
+    "    + gg.geom_density(gg.aes(fill=\"cell_line\"), alpha=0.6)\n",
+    "    + gg.theme_bw()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Part 5: enrichment\n",
+    "Use the enrichment analysis developed by Shantanu and Juan. It looks at the shortest connections within the dataset and evaluates based score is based on the fisher exact odds score\n",
+    "`operation=\"enichment\"`"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "outputs": [],
+   "source": [
+    "percent_list = [0.99, 0.98, 0.97, 0.96]\n",
+    "\n",
+    "enrichment_result = evaluate(\n",
+    "    profiles=df,\n",
+    "    features=features,\n",
+    "    meta_features=meta_features,\n",
+    "    replicate_groups=[\"Metadata_gene_name\"],\n",
+    "    operation=\"enrichment\",\n",
+    "    similarity_metric=\"pearson\",\n",
+    "    enrichment_percentile= percent_list,\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   enrichment_percentile  ods_ratio\n0                   0.99   3.685582\n1                   0.98   2.597346\n2                   0.97   2.116626\n3                   0.96   1.875379",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>enrichment_percentile</th>\n      <th>ods_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.99</td>\n      <td>3.685582</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.98</td>\n      <td>2.597346</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.97</td>\n      <td>2.116626</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.96</td>\n      <td>1.875379</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enrichment_result[[\"enrichment_percentile\",\"ods_ratio\"]]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ordinary-breakdown",
+   "metadata": {},
+   "source": [
+    "## Combine metrics and generate a combined prioritization rank"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "cathedral-familiar",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(357, 12)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "  Metadata_gene_name Metadata_pert_name  median_correlation cell_line  \\\n0               AKT1             AKT1-1            0.240928     HCC44   \n1               AKT1             AKT1-2            0.178413     HCC44   \n2             ARID1B           ARID1B-1            0.220200     HCC44   \n3             ARID1B           ARID1B-2            0.133993     HCC44   \n4               ATF4             ATF4-1            0.797046     HCC44   \n\n   precision_recall_k  precision    recall  mp_value  mp_value_permutations  \\\n0                25.0       0.16  0.133333     0.115                   1000   \n1                25.0       0.08  0.066667     0.161                   1000   \n2                25.0       0.00  0.000000     0.094                   1000   \n3                25.0       0.00  0.000000     0.138                   1000   \n4                25.0       0.00  0.000000     0.002                   1000   \n\n  perturbation   group      grit  \n0       AKT1-1    AKT1  0.918037  \n1       AKT1-2    AKT1  0.858680  \n2     ARID1B-1  ARID1B  0.304972  \n3     ARID1B-2  ARID1B  0.096940  \n4       ATF4-1    ATF4 -0.122386  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metadata_gene_name</th>\n      <th>Metadata_pert_name</th>\n      <th>median_correlation</th>\n      <th>cell_line</th>\n      <th>precision_recall_k</th>\n      <th>precision</th>\n      <th>recall</th>\n      <th>mp_value</th>\n      <th>mp_value_permutations</th>\n      <th>perturbation</th>\n      <th>group</th>\n      <th>grit</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AKT1</td>\n      <td>AKT1-1</td>\n      <td>0.240928</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.16</td>\n      <td>0.133333</td>\n      <td>0.115</td>\n      <td>1000</td>\n      <td>AKT1-1</td>\n      <td>AKT1</td>\n      <td>0.918037</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AKT1</td>\n      <td>AKT1-2</td>\n      <td>0.178413</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.08</td>\n      <td>0.066667</td>\n      <td>0.161</td>\n      <td>1000</td>\n      <td>AKT1-2</td>\n      <td>AKT1</td>\n      <td>0.858680</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ARID1B</td>\n      <td>ARID1B-1</td>\n      <td>0.220200</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.094</td>\n      <td>1000</td>\n      <td>ARID1B-1</td>\n      <td>ARID1B</td>\n      <td>0.304972</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ARID1B</td>\n      <td>ARID1B-2</td>\n      <td>0.133993</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.138</td>\n      <td>1000</td>\n      <td>ARID1B-2</td>\n      <td>ARID1B</td>\n      <td>0.096940</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ATF4</td>\n      <td>ATF4-1</td>\n      <td>0.797046</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.002</td>\n      <td>1000</td>\n      <td>ATF4-1</td>\n      <td>ATF4</td>\n      <td>-0.122386</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merge_cols = [\"Metadata_gene_name\", \"Metadata_pert_name\", \"cell_line\"]\n",
+    "\n",
+    "metric_df = (\n",
+    "    pairwise_corr_df.merge(\n",
+    "        precision_recall_at_k.query(\"k == 25\"),\n",
+    "        on=merge_cols\n",
+    "    )\n",
+    "    .merge(\n",
+    "        mp_values.query(\"permutations == 1000\"),\n",
+    "        on=[\"Metadata_pert_name\", \"cell_line\"]\n",
+    "    )\n",
+    "    .merge(\n",
+    "        grit_scores,\n",
+    "        left_on=merge_cols,\n",
+    "        right_on=[\"group\", \"perturbation\", \"cell_line\"],\n",
+    "    )\n",
+    "    .rename(\n",
+    "        {\n",
+    "            \"similarity_metric\": \"median_correlation\",\n",
+    "            \"k\": \"precision_recall_k\",\n",
+    "            \"permutations\": \"mp_value_permutations\"\n",
+    "        },\n",
+    "        axis=\"columns\"\n",
+    "    )\n",
+    ")\n",
+    "    \n",
+    "print(metric_df.shape)\n",
+    "metric_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "retained-companion",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mbornhol/miniconda3/envs/noncyto/lib/python3.8/site-packages/plotnine/layer.py:401: PlotnineWarning: geom_point : Removed 18 rows containing missing values.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApkAAAGvCAYAAAAQZM5/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3wc1dXw8d9sb+rdXS5y7w1sXDEdTBLqS4A4hBIIeRICgSSEh0CAQMhDQgg1CRAggC13XHDBDePee5Vl2SpWL9vLzPuHvLLK7molr/r95jMx2rk7c3c10h6dufdcSVEUBUEQBEEQBEGIIFVbd0AQBEEQBEHofESQKQiCIAiCIEScCDIFQRAEQRCEiBNBpiAIgiAIghBxIsgUBEEQBEEQIk4EmYIgCIIgCELEiSBTEARBEARBiDhNW3egucrLy7Hb7W3dDUEQBEEQmsBkMhEbG9vW3RBaQYcMMsvLy3n77bfxeDxt3ZV2zWKxMHr0aPbu3YvVam3r7nQKKpWq5j2VZbmtu9MpiOs0ssQ1GnniGo0srVbLz372MxFodgEdMsi02+14PB5+8IMfkJiY2Nbdabc0Gg1xcXEMHz4cr9fb1t3pVMaOHdvWXeg0xHXaMsQ1GjniGo2c4uJiFi5ciN1uF0FmF9Ahg0y/xMREunXr1tbdaLcURcHr9ZKUlIQkSW3dnU5BlmUKCgpITU1FpRJDmiNBXKeRJa7RyBPXqCA0j/gNJAiCIAiCIEScCDIFQRAEQRCEiBNBpiAIgiAIghBxIsgUBEEQBEEQIk4EmYIgCIIgCELEiSBTEARBEARBiDgRZAqCIAiCIAgR16HrZAqCIAhCJFmtVtxud53H/HUyNRpNnTqZOp0Oi8XS2l0UhA5DBJmCIAiCQHWA+chjP6fK5qy3R0FRoDq+vBRkRpkNvP/OWyLQFIQgRJApCIIgCIDb7abK5mTgxBswmMw1jytKdTZTkiT8iUyn3cbx7SsbZD0FQbhEBJmCIAiCUIvBZMZkia75ujrIlJEkFWJVSUEIn5j4IwiCIAiCIEScCDIFQRAEQRCEiBNBpiAIgiAIghBxIsgUBEEQBEEQIk4EmYIgCIIgCELEiSBTEARBEARBiDgRZAqCIAiCIAgRJ4JMQRAEQRAEIeJEMXZBEARBqMVpt9X5uv6KP26XA1tVJW63i7KyspDHak/rm+fn55Oamlpn/XVBaEkiyBQEQRAEqgPCKLOB49tX1ttzae1yr9fLwcMHkVVqNGoVP3/6t6jV6qDHjDYaeP/ttl/f/PTp07zy6nM8/OAvmDhxYpv2Reg6RJApCIIgCIDFYuH9d95qsB65oih4vV40Gg3l5eX87KlnSJ92A8boKLSa4B+jTpuNrPUr2sX65ku+WoBHOsOSr+Yyfvx4VCoxWk5oeSLIFARBEISLAmUcaweZkiSh1WqJjo/DGBUd4Ajtz+nTp9l/cCP3PDyGeR8dYefOnSKbKbQK8aeMIAiCIHRiS75aQMZwLX36JTBmUjRLvpqLLMtt3S2hCxBBpiAIgiB0Uv4s5pRZ6QBMmtaXguLqbKYgtDQRZAqCIAhCGykqKmLhwoUoitIix/dnMZNTowAwmXUimym0GhFkCoIgCEIbmTd/Pv+aO48DBw5E/Nj1s5h+IpsptBYRZAqCIAhCG8jLy2PVpm/xRscyd0Hks5n1s5h+IpsptJZ2EWT+4x//YM6cOdx11108+OCDzJs3r627JAiCIITB5XK1dRc6rEVLliDHJTLymuvZe+JERLOZwbKYfiKbKbSGdhFkzp49m/fff5+5c+fypz/9iY0bN7J58+a27pYgCIIQgsPh4Fe/+jXr169v6660O7IsU2W1Bt3vz2L2GTseg8WCuU+/iGYzg2Ux/UQ2U2gN7aJOZq9evep8LUkSeXl5bdQbQRAEIRzr1q3j2LEcvpy7gMmTJ6PT6dq6S63GabOF3H/u3DlOHjtOXJBMrz+LGZeWBkD6mHHsXTiXAwcOMHLkyMvq2+nTp9l3YCPXfC+R7NMlQdt16xHD9o0HRN1MocW0iyAT4D//+Q/Lli3D5XKRnJzMjBkz2rpLgiAIQhAOh4P5C5YycNCVnD93kE2bNjFr1qy27laL0+l0RBsNZK1fEbSNT/Zx5MgxnA4Pqu6JDYLvmizmrBtqHqudzRwxYsRlrS+ek5ODVm1m/Vc2IHQwrFXFkp2dLYJMoUW0myDzRz/6Effffz+nTp1i+/btmM3mOvvz8/PJz88Hqks+2C7+FSnS/MEpioIsy8iyfFm/sIRL/NebuO4iR1ynkdVa1+jatWspKXYwZdpQZFkmM3MRV111VafMZta+Rk0mE+++9WbIpSJXrVrFZzaJjEGTOHN6MxUVFZhMppr9CxcvRo5LIDY1tc7t8T6jx7Jn4Vz27dt3WdnMadOmMW3atCY9R/xOE1pCuwkyofo2+YABA9i9ezdffPEFP/nJT2r2vf/++7zwwgs1X999990AFBQUtHo/BaGwsLCtuyAIIbXkNepwOPjvfzNJTh2I0+kiNa0vW7fsYfHixUydOrXFztsROBwOvvpqFd17jiAxsQfZZ2L57LP/ct999wLVn1lfrf2GHjOvxWG3132ySoWmW08++vRTnklObvYfXYqiiD/YhHahXQWZfrIs12Qt/R555BFmz54NVGcy165dC0Bqamqr96+jqL/ernD5ZFmmsLCQ5ORkVKp2MW+uwxPXaWS1xjW6fPly7HYYO34kKpUagPT0Maxf/y3f+973Ol02synX6LJly3A4Lr03Q4Zeyfbta7n33h+SlpbGwsWL0aZ2Iy29b8DnD7piEocWzqWwsLBZ2UxZlnnxjy9z7TVXM2nSpCY/v6WJxFDX0uZBps1mY8eOHUycOBGDwcCxY8dYuXIld911V512aWlppF0cIJ2Xl8fWrVsBxAd9CIqioFKpUKlU4sM7wvzvq3D5xHXaMlrqGnU4HCxctIzefUaiVl/6COnXfzjfbjzM5s2bO93YzHCvUbvdzqLFy+nV+9J7k5TUjVMn4/jqq2XMnn0Lq7/dTJ9ZNwQ9jjEqCkt6fzIXLWbUqFFN/pnYtm0bW3cfpLi0lIkTJ6LVapv0fEGIpDYPMiVJ4ptvvuGDDz5AlmXi4+P53ve+x0033dTWXRMEQRDqWbduHSUlDoYOG1TncbVaQ7ceQ8mcX33LvLNlM8Oxbt06SkudDd6bARljWb16LVXWKoodTqRDBzh3KHhNTJfDwe6SQg4fPsywYcPCPr8sy2QuWERynyGczcti8+bNYhJtOzF9+nQsFgvLli0Lq/3HH3/Mj3/8Y4qKikhMTGzh3rWcNg8yTSYTL730Ult3QxAEQWiEf0Z5796XbpPX1q9fdTazq8w0r81ut7Ng4Vf06jWiwXuTmNgNtSaOosIiHrv9B2EdT5IkEhISmtSHbdu2cSI7jwnX3s1Zg5HMhYu56qqrRDazHXjnnXdQqxv+zARz0003sXXrVmJjY1uuU62gzYNMQRAEoWMIlsX068rZzGBZTL8BGWM5dmQtTzzxy5qhX5Hkz2Im9ByITm8gfeAItn19WGQzm8ntdqPRaCI25GTIkCFNap+UlERSUlJEzt2WxKAyQRAEoVH+LGZq2kDcbidOpy3g1r17P86fK2HTpk1t3eVWEyqL6efPZi5ZsrRF+uDPYvYbPBoAjVZHSvpQMhcuxuPxtMg5O4o5c+YwbNgwVq5cybBhwzAYDIwdO5Zt27bVtOnTpw+PP/44r7/+Or1798ZoNFJSUl3I/uOPP2bEiBEYDAa6d+/Os88+i9frrXOO3Nxc7r//flJSUjAajQwaNIg333yzZv/06dO5+eaba74+f/48d955JykpKRgMBtLT03niiSdq9n/88cdIkkRxcXHNY6WlpTz44IMkJSVhNBqZMGECq1evrtMP/3kyMzMZOHAgFouFmTNncvr06ci8mU0kMpmCIAhCo44dO4bNasPlOsiF/IMh22o0sH37jja/ZW61WkPWs6xNp9NhsViadZ5169aRm1vCsGF68nKzgraLikpk9eqN3Hrr7CZlM30+H4qioNEE/siun8X0E9nMS/Lz83nsscf4wx/+QFxcHK+++irXXXcdJ0+eJDk5GYAFCxaQkZHBm2++iVqtxmQy8cYbb/D000/zxBNP8H//938cPXqUZ599Fp/Px6uvvgpASUkJV155JQAvv/wyffv25eTJkyEDu/vvv5+8vDz+/ve/k5KSQk5ODrt27Qra3ufzccMNN3Dq1Cn+9Kc/0aNHD959911uvPFG1qxZU+f7u2/fPoqKinj11Vfx+Xz88pe/5N57762ZMN2aRJApCIIgNGrUqFG8887fwi7aHRUVeM3s1mK1WnnksZ9TZXOG1T7KbOD9d95qVqBZWFhMj+6JlJcdarRtQkIshYWFTQoy337vPVwuF7/+1a8C7q89FrO22tnMYGMzFUVh+/btjBkzplMPbygtLSUzM5OZM2cCMHXqVHr27Mnf/vY3XnnlFQC8Xi8rV66sKZxfVVXF888/z9NPP13T5pprrkGj0fDUU0/x61//moSEBN544w0KCws5duwYffr0Aag5TzA7duzgT3/6U51KOvfff3/Q9suXL2fHjh0sX76cG2+8EYDrr7+eYcOG8cILL9QJMsvLy9m7d2/N7fby8nIeeughzp8/T48ePZrytl02EWQKgiAIjZIkqUPNcnW73VTZnAyceAMGkzlkW6fdxvHtK8POetb3wANzeOCBOc16bmNycnJY8913+BSZ7586Rf/+/evsD5bF9Gssm7l//35e+8c/ePiHP+SGG25osL+ziImJqRP4xcbGMnPmzDq3zKdPn15nZaYtW7ZgtVq544476twenzlzJg6Hg0OHDjFt2jS++eYbZs6cWRNghmPMmDH85S9/QaPRcM011zT4vtb37bffEhUVVRNgQnWZsjvvvJNXXnkFn89XM7Fo1KhRdcZz+seDtkWQKcZkCoIgCJ2WwWTGZIkOuTUWhLalhUuWoKQmo+nRnQWLFzfYX38sZn2hxmYqisK8RYsoQmHu0qU4neFlfTuiQJNokpOT6yz84r9t7ucfDzlmzBi0Wm3NNnjwYADOnTsHVN8u79atW5P6M3fuXK6++mqeffZZBgwYwKBBg1i4cGHQ9mVlZaSkpDR4PDU1FY/Hg9VqrXms/ox0f4a6Lb6/IsgUBEEQhHYoJyeHb7ZuJX3CeNInjGfj7l2cOnWqZn9jWUy/9IEjOJtXwubNm+s8vn//fvacPMH4235Avt3G+vXrW+y1tLWioqIGj9UftlC/8H18fDwACxcuZOfOnQ02f+Y3ISGBvLy8JvUnLS2NDz/8kOLiYnbs2MHAgQO56667yMoKPKY3Pj6eCxcuNHi8oKAArVbb7PHELU3cLhcEQRCEdsifxYy+mGHzZzOfeeopoHoyVs75fOyuc5TkHA15LK/Xyzfr1tfcMq+qquI/n3+Opns3JAliMgbwybx5jBw5EoOhbsB6OZOi2ouKigrWrVtXc8vc//Xjjz8e9DmTJk3CZDJx/vx5vv/97wdtN2vWLP7yl7+Qk5NDr169mtQvlUrF+PHjeemll1i6dCmnTp2ib9+GS45eddVVvP7663z99ddcf/31wMU/MjIzmTRpUpNqcLYmEWQKgiAIQjtTk8WcfWn1u/QJ49m4cDG3XRybOXDgQF5+4fdhT8byF3e3Wq3cdd997Dl5AktaKicPHERRFCrz8vj+j+4nKbHureVog4EP3mrepKj2Ij4+np/85Ce88MILxMbG1swM/+Uvfxn0OTExMbz44os8/fTTnD9/nhkzZqBSqcjKymLJkiUsWLAAk8nEE088wSeffMLUqVN57rnn6Nu3L1lZWZw4cYLXXnutwXErKiq47rrruO+++xg4cCAej4e///3vxMbGMmbMmIB9uemmm5gwYQL33Xcfr7zyCj169OC9997j+PHjvP322xF5j1qCCDIFQRCELkWWFdxuNwaDvq27ElT9LCaAJSGhTjZTrVYzaFDg4u+huFwuTuXmknzTjcSn9655PKasHLmikr7jx6PRVGfGnFYbOcubPymqvUhLS+O1117j17/+NadPn2bo0KGsWrUq4DjH2p588km6d+/OG2+8wVtvvYVWq6Vfv37cfPPNNWMdExIS+O677/jtb3/L008/jd1up0+fPjz22GMBj2kwGBg+fDhvvfUWOTk5GI1Gxo0bx+rVq4NOrlOr1axcuZJf//rX/Pa3v8VqtTJixAiWL1/O9OnTL+u9aUkiyBQEQRC6lNNZWeQV5DP5iiuC1p5sS4GymH71s5nNcejQISrtNlJ6dEdbKzsZYzZT7HBQZq1q8m3fjuCmm27ippsavqcA2dnZQZ939913c/fddwfdD9CzZ08+++yzoPs3bNhQ8996vZ5//vOfIY83Z84c5syZU+ex+Ph4/v3vf/Pvf/87rPP4jRs3DkVRQp6vpYiJP4IgCEKX4XK5yT5/jkqnk9zcpk3WaC2Bsph+tbOZzaEoCouXL0dtMqHW1g2wJUnCmJjA6bNnG6xoIwjNIYJMQRAEocvIOXcOj0qFJSWFrJz2F0zVnlEO4HW7Ka9VZgcIONM8XPv372f/6VMYY6ID7jfHxWH3eps8W1oQAhFBpiAIgtBpOe027NZK7NZKyktLOHn6BFqLCY1eQ4XdyumTJ3DabW3dzRoLlyzBmxCH3mLBZbdzYNUaNr3/HyoKC3HZ7bjsdrRGIyQnNzmb6a+LaeqbjlodeJhAZ8xmfvzxxxw61PhqTELktb/BKIIgCIJwmXQ6HVFmA8e3r6x5LD8/n7zSEqJTU0GScFZVUbhvLUMHDiIm2tzmyyq6XC727NuHZLNx5LMvcLtcnNhxEJVTzeY3/0G33r3rtD9YVYXNZsNsDq+Y/IEDB9h19Ci6Pr2wV1ZgLSlFE6BAtyIr2J0O8vLySKpX2FsQmkIEmYIgCEKnY7FYeP+dt2pmRVdUVPDL3/2OQXfeQtLF5f9kn8yBBQv54S2zueWWW9q8RI9er+etN96oWZll3tx5rC6U6B3Xj1wli9defJGYmJg67cMNMP0mDB2K3W7npFfG4vWidrkDTgoxR8fg9flwulx4PB7Kyso6Rb3M9qC8vJyHH36YlStXEh0dzbPPPht0Jvq8efP4wx/+wLlz50hLS+O5557jvvvua+UeN58IMgVBEIROqXZA9PWqVbiiLPQcPrzOyi49rpjI1xs3cuutt7ZFFxuIiYkhJiaGkpISvv1mM0O7DyclNo3c49ns2rWLe+65p9nHHjlyJCNHjqS0tJQTOTmkDxvGzgMHcPt8AduXW614HQ6Kjh7h0V//msSYmA5VL9Nb+iMU9w4ktFSHO+oGq/pEng/woiheUMWgTlqHJNUtbv/444/jvTju9dSpU8yaNYvBgwc3WFv+3Llz3HvvvSxYsICbb76ZzZs3c9111zF27Nia9cjbOxFkCoIgCJ1aeXk5i1atosfkKxoEGd0HD2LP3n2sW7eOm2++uY162NDyZcvxVUikZKQhSRL9kwaxdP4ybrzxxgZrUzeXz+fD7fNh6dUTjTZwOOCx2rCnptLj2lkUbvy2Q9XL9PkKURQPUGvNdgX8V0BLhJtK7X+9F1DLNlBfCjJtNhuZmZns3buXqKgoRo8ezZw5c/jwww8bBJk5OTnExsZyyy23ADBlyhT69evHkSNHOkyQKSb+CIIgCJ3aipUrqdJpSQ6wXJ9KrSZl9Cgyly3Dbre3Qe8aKikpYdnC5QxIHlgTFHeL746rxMOKFSsidh6X1YbX4UBxuZBd7sCbx4Nao8FgNkXsvK3Fh4QPpcHmvbh5Lm7+rwO1bWwLeawAUeyJEydQFKVOkDhq1KiAE5MmTpxIRkYGixYtQpZl1q9fz4ULF5g8eXJLvm0RJTKZgiAIQqcVKovp196ymbWzmH6RzGbqdDqiDQbOr1xF0dEj2FNTUYcoSq9Vq1G1w6L1jZEVGVkJb8nNQAJdL9XDV8MvbK5QN2NqtVqJjq5bPio2NpaqqqoGz9VoNPz4xz/m/vvvx+FwoFKp+Ne//kVaWlqDtu1Vx7tqBEEQBCFMK1aupNjrIclopCwvP2g7U4/uZC5bxsyZMzGZ2i5rdymLOahBkNMtvjunjh9jxYoVlzU202Kx8MFbb3HhwgUe/fWv6f/92RiiooK2V2u1+DyeoPvbKx9wKn9+0P3pabeFPoCiICHhDyqDhZZn8hcEP0Z+NmPHJtR8abFYqKysrNOkoqKCqADv/6pVq3jqqadYvXo1EydO5OjRo9x8880kJCQEXbmovRFBpiAIgtBpnTh1ilgk8r9e02hbr05Lfn4+/fr1a4WeBRYoi+kXyWymxWLB7Xaj0+sxREVhjA5cnN3P0QGDTLmRjGOw/f7Z9nWykJIUkTGcGRkZSJLE0aNHGTx4MAD79u1j2LBhDdoePHiQyZMnc+WVVwIwdOhQbrzxRlauXCmCTEEQBEFoa8///vdNat/ys4+DC5XF9ItUNrMr8DVyq9x7cX9YN7/rlXlq7lViNpu5/fbbee655/joo4/Iysri448/Zt68eQ3aTpgwgVdeeYWdO3cyfvx4jh8/zooVK/jd737XzLO3PhFkCoIgCJ1WWwaNTbV82XIqCqy4u3s4V3Q2aDuLOirsbGZlZWWDMYBdhQ+F1NSGpan8ecpI5GYlICV1NsHynKmp+xs89vbbb/PQQw+RlpZGdHQ0L774IjNnzgSqM8wrV65kypQpTJ06lVdeeYUf/vCH5OfnExcXx7333stPfvKTCPS8dYggUxAEQRDagYqKSvoM7YmN0pDtTLE6dAYLZWVlIYPMrKws/vDHl3jmqScZOnRohHvb/nl8BcjKpZvi4U/XaY7qo9cvjxSo0H1sbCyZmZkBj2K1Wut8/dOf/pSf/vSnkepkqxNBpiAIgiC0Az//n8cjeryFixdzqqCMzAULGTJkSIfK6kaCW1YhK0rg4LKl3wr/SbvYe16fCDIFQRAEoZPJyspi45adjJl5Izu3r+PIkSMBs5my7KOiJHTmFMBptbVEN1uUrEnB5SkLvFNR6sSZlxsKNsyWdu3g0k8EmYIgCILQySxcvBhdYjdSeqZTmJMeMJup0+koys3l+Nv/YGDGIFRS6PVZog0GdDpdS3c9YmRFwqcEDvaUBkFg9SOSFF54qCiXbpDXr4UpXCKCTEEQBEHoRPxZzEEzqye99Bsxjp2rMhtkM8vLy+mVnkg3KYqf3P5DrrrqqpDH1el0HWbdcgAvKrxNXdiwVrF1iVpjK/3/NvE2eMuOA23/RJApCIIgCJ2IP4sZHZ8IgCkqGnNaw2zmkqWLSR4cRXKvXqzdtJobbrgBrVbbll2PKFkhaCYzNH92MkSOsqtHj2ESa5cLgiAIQifhz2L2HTG+zuP9Roxj54EjHDlyBIDz58/z7a71jJ45iGFXDqDQdp7vvvuuLbrcYmRU+MLc/FlPDyrcNf9qcKOu+drfJtxj+kSIJd4BQRAEQegs6mcx/WpnMxVFYcnSxcT1N5LcIwGtXsvAyT1ZuGw+ng64sk8wXkUVcvNc3Ny1/turqPDVbNLFrWH7+s8JtnX1hKcIMgVBEAShEwiWxfTzZzPXrVtXk8X064zZTI/iwYtUs3nqbc3JTDbMftY/Zt2tq08JEkGmIAiCIHQCwbKYfv5s5htvvklsPwPJPRJq9nXGbKbNU4JXVuG5uHnl+plGKQJb4OzopUxm185liiBTEARBENqJ4uJiZDn0mtuBZGVlsWHLDpJ79aOqrCToFpWYyoGTp0hNj29wjM6WzVRre+FEi6v2pmjqbO4IbrWP61S0OBUtXT2TKWaXC4IgCEI7UFZWxq9+8xv+3223ccMNNzTpuUeOHEGn0ZC/bzP5IdqdPXsWvQlsFY4G+2pnMydPntzhZ5r7FDVeRd1Iq8DLQYajTgF2scJPQB02yLRYLGg0moDrggrV/O+NeI8iR1GUmutOvK+RIa7TyBLXaOS11jW6fMUKTpeXMXfpEqZPn47BYAj7uTfeeCPTp08P2SY3N5dnX3qaqQ9cTfd+KQFv5A69cgALv1vH5s2bGz1ec2g0rRd2+C6Ou2ya0EFn0JV9RGwZUIcNMkePHk1cXBxer7etu9Lu+Xy+tu5CpxIXF4csy826pSUEJ67TyBHXaMtoyWu0rKyMRau+JmPWTHK3bmPt2rVcf/31TTqGXq8PuX/Vmq/RJVevbJN3+kLQdvHpZhZ8NY+JEydGPJsZFxcX0eOF4lPU+BrNZIaiXKyVWXuFoKZFkw1XFupaOmyQuXfvXoYPH05SUlJbd6XdUhQFn8+HWq2us5SY0HyyLFNSUkJCQgIqlRjSHAniOo2srniN5ufn4/V66dmzZ4scvzWu0dVr1mA3Gsno3x+Pw8mCFcuZNWtWk7KZjcnNy0VdaWHH56cabWvSe6mqqiI5OTli5wcoKiqK6PFC8Zceaoq6mcrqYuzNCy/rHbCL6rBBptVqxev1ig+lMEiSJN6nCJEkqea6E+9pZIn3NDK62jUqyzJ/fesf2B0O3vzL6y16O7al3tOysjIWr1lNj6smo1Kp6DlsKHv2HWDDhg1NHpsZymuv/Dlix2qu1rz7GM7tcv8IiPAylbVXKxfDL8PRNf7MFQRBEDqlPXv2cPBMNqcuFLJly5a27k6zrFixApteT1J6HwBUajXJo0cwb+lSnE5ns487LzOTw4cPR6iXHY/34sSf+pun1uZFjRcNPtQXt1C1MavbeNHgRVPnOJ4g5+riiUwRZAqCIAgdkyzLzF2wkKh+A4gfPJS5Cxd1uHH6ZWVlLFqzmh7jx9XJkvYYOpQ8u53169c367hZWVn8Z+EC/v3pp112bG65p7CmbqWnTg3L+sFg6FV7gm91j9HgPKjEmMy27oAgCILQcqxWK263O6y2Op0Oi8XSwj2KHH8Wc8Ttd6PWaNg773O2bNnC1KlT27prYfNnMTMuZjH9amczZ8yY0eSxmQuXLEFOSuTwuRx27tzJxIkTI9jrjkGS4nErLi7dAq+bV2yp8M8/nlMUdxBBpiAIQqdltVp5+BePU+kK75ZrtN7AB2/+o0MEmrWzmHqTCYC4QUOYu3ARkyZNatVSOc1Vk8W8anLAsZ49hg5lz94DrF+/vkljM7Oysli/cwcDvncreceOMXfRIsaPH99lJoL5qVQWPEqo66C5c8brH6Xuv6Ke0SXt/6dQEARBaBa3202ly0mv26/B0Ejg6LRayZm/JmjW0+Fw8M477/LDH95DampqS3S3SWpnMf36jBjVobKZwbKYfs3NZi5csgR1925EJSaQPnYMBz7/sktmM2VFwhf27PJ69TFDxImXJgv5iaAyGBFkCoIgdHIGiwVjTNRlHWPdunWsWrUZk8nEo4/+NEI9a55AWUwAjU7XYbKZ/ixmdMYAKgoKgrazxCdwvGxH2NlMfxaz3/duBUBnNBI1MKNLZjN9iqoJQWY9SnWWU5ICBZUQfmDZtQPQ9vsTKAiCILQLDoeD+QuWkpjUjzVrNnHrrbPp1q1bm/UnUBbTr6NkM8+ePYtKUXAfOUbekWMh28ZotJw4eTKsILN2FtOvq2Yz5YuzwptD8a8VeTGybG7pqq4+LFMEmYIgCEJI69ato6TEwZSps9m5fSVLlixts2xmsCymX0fJZo4aNYovP/o47PbhBDn1s5h+XTWb6WvC7fLQt8BrBZuI+phN0TWuNEEQBKFZ/FnMXr1HoFarGZAxljVrNpGXl9cm/fFnMdNHjw3aps+IUZwuLGr3dTNVKlXYWzhBZqAspl/62DE1M827ChlVzS3z+pu/DNGlckPVNTMD18tU16qRqa5VCql6C3aOZt+q70Ta5594giAIQrvgz2IOGToYgITENDSa+DbJZiqKwtwFC3EbjBRkhV4a0Wcyt3k20+l08t133zFjxowWzx4Gy2L6dcVsptVrr1lWsuFt60imI4OP1hS3ywVBEAQhgPpZTL/qbOaaVh+b6fP5iImKYhQKlIVeAzsxKZ4Ysxm3291mQeaqVav46NO5REdHM378+BY9V6gspl9XG5tp9XjwyKoGt8Bb/m63/4wSKF373roIMgVBEISA6mcx/doqm6nRaPj9737baue7HDabjQWLl2H1aZmbuZCxY8e2WPbw/PnzfLtrJw6Dgf2Ll4Rs6/D5WLJ8eZcIMs3aZCqdrgB76pUritD5lAZHFkSQKQiCIDQQLIvp11bZzI5i7dq1lNq8TL72++xeM4/du3eHlc202WwsWrSI22+/Pey6mHFxcfz03vvw+XxhtU9MTAyrXUcnhz0usnlBZzhF2MXtckEQBKFTc1qtTW4TLIvp15ZjM9s7fxazZ8YoDEYzsd0zws5mrlq1ik+WLiUhISHsVX7MZnOTVgTqKvwTf5ou8EpAkVrZp7y8nIcffpiVK1cSHR3Ns88+y2OPPRawrdPp5JlnnuGLL77A6XQyYMAANmzYQFTU5dW9bS0iyBQEQeikdDod0XoDOfPXhNU+Wm9Ap9M1msX0E9nMwPxZzIF9BwHQf+gYdq3+stFsptVqZf6KFZCcyNxmrlkuXNKUEkYBKUqdYuyRql30+OOP4/V6ycvL49SpU8yaNYvBgwczY8aMBm1/+tOfYrPZOHjwIElJSRw8eBCdTheRfrQGEWQKgiB0UhaLhQ/e/EfQpSLr0+l0WCwW1q1bR+GFUvTlRynIOx7yOVabi7Vrv+H++++LRJc7vNpZTH/WUm8whpXNXL16NSWKjzGzb2Hv5182ec1yoa7wb5dXC1grU6l1K11pepxZ/3a5zWYjMzOTvXv3EhUVxejRo5kzZw4ffvhhgyDzxIkTLFy4kJycHGJjYwEYOXJk0zrQxkSQKQiC0IlZGlmzPJARI0bw1FOBb98F0rdv3yafo7Oqn8X0ayyb6c9idhs7BrVGQ/KokSKbeZnCqVWpKKEKEAV5Qq1WjQeddRucOHECRVEYMmRIzWOjRo3ijTfeaPDM7du307t3b1544QU+/fRTkpKSeOKJJ3j44YcbO2m7IYJMQRAEoY7ExMSAt+6E0AJlMf0ay2b6s5hjMzIA6DF0CHv27RfZzMvgU+Dn6uB/LL3heZemj6tsuBLQr7SPBm198sAJxo69tHCA1WolOjq6TpvY2FiqqqoaPPfcuXMcOnSI2bNnk5uby4EDB7jmmmsYMGBAh/n57PzVWAVBEAShFfizmD3qZTH9+g8dw7FTZ9m9e3edx2tnMaWLwadKra7JZjqdzhbve2eU7ywNud+nqEOu1tP4psanBB+zHIjFYqGysrLOYxUVFQEn8phMJtRqNc8//zx6vZ7x48dzxx13sGLFiiadsy2JIFMQBEEQLlOoLKZf7WymLMs1j/uzmGkXs5h+PYYOId9hZ/369S3a984qRpMUcr9/WcjL3ZoiIyMDSZI4evRozWP79u1j2LBhDdqOGDGiScduj8TtckEQBEG4TN988w05+cV0N5dyZE/wNdM9bicHs0/VjM0MlMX0q53NFGMzm0PNHx0fhthf9/1uzuRxRSHkOf4z8dU6X5vNZm6//Xaee+45PvroI7Kysvj444+ZN29eg+dOnTqVvn378vLLL/Pcc89x8OBB5s+fz5IloQvutyciyBQEQRCEyxQfH8+NV18VRksLUkYyJpMJaDgWsz4xNrP5ZEWF3JRMo1JvClCgoFNp4qo+Aaqxv/322zz00EOkpaURHR3Niy++yMyZM4Hq2+krV65kypQpaDQali5dykMPPcRf/vIXunXrxhtvvMHUqVPDf01tTASZgiAIgnCZrrrqKq66Kpwg85JQWUy/2tnMxMREoqKiGDQo8JhPoS4ZCV+T1g6v11apznU2LMJ+eWJjY8nMzAy4z1pvUYRBgwbx7bffRujMrU8EmYIgCEKnVVRUhEqlIiEhoa270sC6des4V1pCzKksirPOBG0n+3xU5ufxf395jl690nnlT2+h0YiP78bIitS0TOZFysX/r6nBjtTs1ciVLr6OubhKBUEQhE5JlmX+/KfX0el0vPSnPyJFYMUWj8fDex98wA++9z26d+9+WcfKyMjg8bvuDqvtXr0Re+VWPM4zbN68menTp1/WubsCWQkvk9kwQxnkPnkjLYSGRJApCIIgdEq7d+/m+P6TgML+/fsZNWrUZR9z8+bNLN6wHp8s88uf//yyjjVo0KCwbn07nU62bf2a227pg93hYcXyuVx11VUim9kIX5AgM7ygsrE2IugMhyhhJAiCIHQ6siwz74tMEtUpxKuSyfxyPlarFUVp/sg6j8fD3MWLMab34ZutW8nJyYlgj4Nbv349enU+V07owfSretdkM4XQipyVNdnM2pv/NvqlTWrGpqqzBT6HFLmBnB2UCDIFQRCETsefxczoPpiBPYawd+te5sz5CRs2bGj2MTdv3syZslKGzpyBkprMwlYoJeN0Olm9ah43zEpBo1Gh12u4dkY8K5bPxev1tvj5OzJF0eDxqfAG2mQpwpuqzubxqXD7VDRp3lEnJIJMQRAEoVOpncU06AwYdUbs5W4OHsziy7kL8Hg8TT6mx+Nh7pLFxA8bgkanI33C+FbJZtbOYvqJbGZ4orUxNavyNNhkVZ2tfmayqVv94/kUNXITVwPqjESQKQiCIHQqtbOYAE63E6vTSUxUX06eOM+mTZuafMzvvvuO7NJSel1chSU6ObnFs5n1s5h+IpsZHqUpt7tlqc4mN7LVtPWp8PkCBZ4Xb5d3cSLIFARBEDqN+llMgOPnj4DaTEpsX2QllnmZi5qUzfR4PGR+tZS4YUPR6HQ1j7d0NjNQFtNPZDMbJyuNB4uXNlWtrVaweTFYvBSAXsx8+jd/0BrkuGJMpiAIgiB0EoGymEfOHyctZRCxlng0UnSTs5mbN2++mMUcXufxlsxmBsti+kUym2m1WiktLQ1rq18svD2TFUJkMsPLcnob3E5v2rG6+rxzUf9AEAShE1mxYgU6nY7U1NS27kqrC5bF9EkGEmJ7IEkqTGoLld7qbObUqVPRarUhj3lpLGbdLKZf+oTxfLN0OT+49VZ69eoVsddyKYs5Nmib6Vf1ZvX6vZdVN7OgoIDHn/wVVS53o20llUS82cIHb72FxWJp1vlak9LEW9YNCw/4nxvGcpPBjhl+005JBJmCIAidRGFhIR9+8gUWs5Fp06ah1+vbukutyp/FnNRrOlAri5k6CkmqzgbGmOOwV1o5eSKHTZs2cfXVV4c8pj+LOeK6awLur53NvNy6mX4ul4vVq+aRGO1i0bJjIdvGWBysWD6XqVOnogqyNGUwVquVx3/5K7Yc2Ef8tKmoG7leJK8HCotwuxsPSNuDmlvWQYRfL7P240qdJ3btPGXjRJApCILQSSxd+hVeTRT5xaVs3ry50QCqM/FnMVUOLcWVhQCcOH8MuxcMhlhszku3edWyFq/UeDbTXxez/ljM+loimzl8xGRcrtGU2kO3S+kFJlPzsoput5sqpxNjfBIJ/QeiNZuDtvV6PZSdOoEid5zcnNPnq5PJbF4R9saeI4qyh9LmQabH4+G9995j//79VFVVkZiYyB133CGWzBIEQWiCwsJCVqxeR//RV1OQm0PmgiVh3Q7uLFwuFwoKiQNiKacIt8fNibJTJKeNxq2rm3kzxOiJ0vYkN/d0yGzmrl27yC7IR3LYOZB9tnpZyiBRRJXHzarVq3nowQcD7vcXgQ9naUu9Xs8DDwQ+TktQqdWoNRrUnexaueCw4ZOlWrfBWyIEDBx0SoHvtHc5bR5k+nw+4uPjeemll0hOTubYsWO8+OKLpKamhrXcliAIglCdxZR1MSSm9kBnjObgt4vZvHkzM2bMaOuutQqj0chf3ni95usFCxZQ6ZC4asodQW8jnzhuIjNzcdBgPCMjg1/95EEURcHn86FWq0MGienp6UH3vfPO30lOTuOOO+5swqsSLkeaIY4TF7PawUQy7FRqHbEmsO3iqc02DzINBgM//OEPa74eMmQIgwcP5ujRoyLIFARBCIM/i9lvzCwANFotyX2GMnf+Iq666qouk830q6qqYvHiFSSn9MdurwraLiW1Fzu37wuazUxISODaa69FURS8Xi8ajSasTGR9p0+fZvvu1Wi1Rq6+ehbx8fFNPkZLsFqteNxufB4PHpstZFv5MpbjbCuKIqGEGJMJdRONUs3/hXuCLp+obFSbB5n1OZ1OTp06xS233NLWXREEQegQamcx/bdl0wcOZ/vqI10qm+l34sSJ6vGG1oMUXjgYsq1Or2LPnr0tOn518dIFZAzXUl5mZ8WK5dx7730tdq5wWa1WfvW733Dk7EkcHi+2lUuQ1MFDApUC8UOHtWIPI0CRUJoyu7zm/2rFmvWe3tRYu6sHoe0qyFQUhTfffJMBAwYwevToOvvy8/PJz88HoKioCNvFv7pkWW71fnYUiqIgyzKyLDfrr2+hIf/1Jq67yBHX6eUpLCxk+ep1pI+aidVqxWSqnryh1uhI7j2EuZmLmDRpUpfKZo4aNYp3330z7PYmkynkz/TlXKOnT5/mwKGN/OSJAZSXOVj86WKuv/6GNs9mOp1OKt1O4q+fhF0lYe7VC5U2cEjgszspWbIeWfahcOm9aPcUmh3lKfX+o7l3v6UuHmW2myBTURTeeecdSkpKePHFFxv8IL///vu88MILNV/ffffdQHWNL0FobYWFocf5CEJr+fyLL3BhpMLuZt+BrYwZNwaj0YjD4SCl5wB2f7OXpUuXMnny5LbuarvldDpb7Nj//fw/9M6QsUSrMUeZMced5osvPue2225vsXOGo6ysDLfbjaTVotaqUJmNqDSBQwJZVmoCbbfLzYULFzpEGSOliZnM6ucE3yc1YwGfLh5jto8gU1EU3nvvPbKysvjjH/+IwWBo0OaRRx5h9uzZQHUmc+3atQBdsuBwuC53HJHQkCzLFBYWkpyc3OSadEJg4jptvsLCQr7bvocBI2Zw/ORZXDYPhYWF9O7dG6PRiGQy0iNjNN+s/5bZs2d3qWxmJDX3Gj19+jRncvbykycGYjSZALjm5n4s/nQj/+//3dOm2UydTodKpUJ2u6vX3rY5IEgmU3Y4UGQZlUqFTq8jJSWl2X1vzcRQOEFmU25/128rfl01rl0Eme+//z7Hjx/npZdewnTxB7G+tLQ00tLSAMjLy2Pr1q0A4oM+BEVRUKlUqFQq8eEdYf73Vbh84jptvmXLlqPoYvAoapxWJ8kxKZw/e57k5GRMJiOSJNF30Ai2rTrCli1butzYzEhp7jW6dNkiMoZrSU6NrnlswKBk4lPP8fXXK9t0bKbdbif7+AF8RVpkCaz7DUFfm+Lz4c0rxtunHxJSh/n9pygEnPgTqexig6AzQsftTNo8yCwsLGTFihVotVoeeOCBmsdvv/127rxTlHoQBEEIpKysjFXfbMDuktjw1ReoPGrsejMV9jKqCk/Qq3evmg89p9vLgkVLRZDZii6Nxcyo87gkSUy9phdLPlvCjTfe1OSMYPWdv7cZM2Y8EydObLS90+lkx44dbNu1g0pbFdHmKK4YN4HExES0BoWpP+qFxhK80DyAs9LFmtfzKN+2naiJE9CFKEzfnpyvqgySqWyZcFAJFL428XZ9Z9PmQWZycjJLly5t624IgiB0KCaTiR/f9//YsWMHK06cYGi3kagkFRVaHdmlJ7nl/lvrBDDR0dEhjiZEmn9GeXJqVIN9AwYlEZeS06yZ5ocPH2bvrmWcyTrEmDFjQg6B2Lt3L3//59uUyFbiB6dh7G7mfFUum//7Nlpb9RAAg1lHdFoikip4MGQzWNHqdKSn9eWNP73aIdYtB4jVGkOvyR7x+O/iARvUReq62jzIFARBiDSr1Rr2xASdTtdhPjRr0+v1XH311Sycu4gJ6ZPp3606Y6ag4Dnoxu32cOutt7ZxL7umYFlMv3CymYqicOrUKdZ+8w1bt+/G6XQSExODtaKA8SMhNz87ZHmq/fv38/LfXyNhcl+mXXk1ao26Zp9vlo+dizfi2OkARcHjCF0j0+twIHu9xEVFtfms+KYwafShM4mRnpUjCrA3IIJMQRA6FavVyk8f/TnWKkdY7S1RRt57960OGWhu2LCBorMlTB1Qt+Rb36QMVi1dza23zhaTI9tAqCymX6hspizL/Oc/n7Bw2Sp0Mal06zOGJL2B/Nxz7D+5B5vNydSJ8axcMTdgsX2fz8cHn/yb2Am9GDxlVINzqzVq+o0fwuYv1Ki0arqlpoYMjKo0VURZLLz0wvMd6+dElqq3pmrsKY0Fp0qQ/+6CRJApCEKn4na7sVY5GDrsOozG0B+IDoeVw4dWdYhyLPW5XC4yP59Pz6h01Cp1nX2J0UmcP5fNV0u/4qGHH2qjHnZ8Z8+eZcuWrdx1V/jzA0pLSzl8eA8exc6ffrM9ZFtZUdhUuJYf/vDeOpNuvvjySzKXrWX4pJuJTUgGqieZnMsvYOKsyeixsnH7TmLM+wNmM/fv38+58nyumhQ6k63WaJBlH16vB72+YVWXmnaq6uU0zWZzYy+/fWluncz6t7u7eKB4OUSQKQhCp2Q0WjCZg2eSOrpgWUy/AamD+HrJam6ZfYvIZoaQnZ1Nr169GsyWVhSFT//7OVt272P48GEMHDgwrOPFxcXxv79/FZfLFVZ7s9lcJ8AsKSlhweLlDBw3sybAhOqJXtaqIgb1i8NgiMdWZcOau48Vy79skM08fPgwpvQEdIZGJuhI1ZvdakMjqYM283q9Yb2WdqcZdTIvPbfe1+IWeLOIIFMQBKGDCZXF9EuKSebkBW2nzWZGYtxtdnY2v/vd//LQQz9ukA08fvw4W/ccQB2dTOaCRTz722fCOpckSfTp0yestoFs2rQJr9pEUmrPmscUBc6ePU1KshqDofpju+/Avuw4e5TCgsOsWbOGK664oqZ9SUkJsuzDVm5tcHyNVoPebECj1aCSJFy5RVRVWpFDZCkdVg8alarj1VlVgCYuTCQFiyaVILPHhZBEkCkIgtDBbNiwgXMncxnZLZULZfl19ikoOJwujC49sfq4TpnNtFqtPPyLx6l0Va/U4/V5UeTgAUCUVs8br1TPiq4dcC5atIT8Aivz5i2qkw1UFIX5CxdhSulF/1ET2fn1PI4cOcLIkSNb/LUdO3GS2OSedbKbtbOYfnqDHmN0It3Tqnjm6adJ7zcU1cXn5BcUcKGqmBPfnmxwfJ1JzW3P/widyUBq3+4kJEQzuE86vUMExpXldnI3mzrWeEy4eLu8iSv+hNwr0plNJYJMQRCEDubcuXMk9kggl+yGOxUFp+LEoBggWiJKayEvL69TBZlut5tKl5Net1+D2qhn25ZteN0yiiLj8rhxOd3IiowEqGUZ6/qd/PTB/0Gn0xEVbeTdD96muLiYDRu3MmnSLRw4sLbO2EZ/FnP4tbdhiorGkpbOgkVLGDFiRIsvGOD1+lDVyk4HymLWkFQMSI/H47lAcvIY+vTpC0CV1cqOfTtJHNgdrf5S9tHtdHDqyHq8nurb37LsA7dM9+7dMej1Qfvk1vlQq4PfTm+vyh3OS1Fjg+gxkt/HplVlLy8v5+GHH2blypVER0fz7LPP8thjj4V8zscff8yPf/xj3n33XX76059eZn9bjwgyBUEQOpgHH3yQBx98MOA+WZYpKCggNTW1Q6zKcjkMFgsqgw40OmLNCVRWVqFTgdGiRaVWoygKrspKqrw64tT96NurLwdzNuJ2u1m0aAlGQwoJiWl06z60Jpup0Whqspjm6FgA+o0Yx66L2cxhw4a16GvqlprC/lMHar4OlMWE6hnkbnsFKck9SYzXUFx8gUGDh6NSqTCZo+iW3J2yIivJ/bsHDIxlnw9rcQVGdXdkj4rKMnvQPlkrw6vU0N44XF7wtUb2McQt9gDV4B9//HG8Xi95eXmcOnWKWbNmMXjw4KDlqEpKSnj11Vdb/NprCSLIFAQhYlwuFzqdTiwPKbQqr89LeXkVar0Bs8FY5/pTKxI6g5mC4nIM+uqhBTk5OWzYuJWRo24CoF+/YXy76TCbN28mLS2tJovpZ4qKxpyWTuaCRQwdOrRFr+9p06aycNlqHLYqDKaooFnM/HP5xJk9jByeQlycEUW2UpBfQLfu3QAYOngoO/fs4sLJ88R2S0RvMdY8t/T8BU5vOYzaoePYOh+nv93TaL8M6qgOs9KPX5o5ikpnGBOwIvXtDHCvvf61YrPZyMzMZO/evURFRTF69GjmzJnDhx9+GDTIfPLJJ3nqqaf47LPPItTR1iOCTEEQIkKWZZ7//fOMGjuKu+++u627I3Qhdrsdk96Crl6A6adSq4lO7c7Z8+fRxjlZtmwFRkMKsbFJAGg0Orp1H8rceQtJ65ZSJ4vp12/4OHaumc+RI0cYOnRoi72Wfv36MWHMMPZuXUPGmOlUVZbidHgpLy+uaeNy2Mk+sJVBvdT85a3DnD5TRd/+Mrl5OTVBpsFgYPyYcRw5eoj8I2dQtCp8sofiM/kc/nwzU8ZP5sV/P12nLJFWqw067rJDLlqgSOGNyWzuTPJmzAM6ceIEiqIwZMiQmsdGjRrFG2+8EbD9hg0bOHHiBB999JEIMgVB6Lp27tzJgR2HyT6dw/XXX09sbGxbd6lD6gqrFUWS3WHH5fYQHW0ImWHUG4xUafUUFFzguy07GT/hB3X29+s3jFVf72Tf4cNMv7fh+Dj/2MzMBQsZMmRIi2UzJUniFz9/nJdeeZXD274mOrE3yT3SUas1uF0O8rJPUJWXy/UzZ3HbD76P1Wpl76G/0rffaOJiE+odS+Ho6W9xyB7cHjc+nxeXyodbUbP10AG2HjpQp3203sAHb/6j01xTkgLzJ00Puv+O7zYEfFypXyezZke941/8N3Ny8HNkHzlKwtixNV9brdYGS7zGxsZSVVXV4Lkul4uf/exnfPbZZx327pAIMgVBuGyyLJP5RSZphp6UlRazYsUK7rnnnjbtk8PRsHxLc9q0pq60WlGkVFVWgSSFNTFFb7ZQeKaYPv1H1WQx/dRqLUhxlJbnYzAFfj/7jRjHjlWZLZ7NjI2N5YXnn2PFihWsXL2O0zuPIanUIHsZ2K83P//hL5gxYwYqlYrS0lJiY2NJTkpuUBfW5/Xiknyk3joTrdGE1+ehtPgsgydfgV5Xd6KP02olZ/6aDrkwQVBNWZmnFqn2/trF2CMQ51ksFiorK+s8VlFRQVRUw5q+r732GrNmzWL06MC1cDsCEWQKgnDZdu7cyfEDp7iqz0xKKotYOn8ZN954Y5tkM3U6HZYoI4cPrQqrvSXK2G7GmnWV1YoiyT+LPKy2sozT6aR/xqgG+yoqKkAdg1YXw9njh+g7tGEbU1Q0lm4tn82E6iLtd9xxB9/73vc4c+YMbrcbi8VC7969m3xerdGEzmJG5fWgsRswRkehDzGbvMuoH2QGyFTWyWr6g87LkJGRgSRJHD16lMGDBwOwb9++gJN61q1bx759+/jvf/8LVF+ju3btYsuWLXzyySeX15FWIoJMQRAuiz+Lmazthl6rJy2+O6dOHG+zbKbFYuG9d9/q0LecO/tqRZGk1+tRFCXgLN76qiorUKs1xEQnNth39mwOPlQYDUl8u3guZfk5tUoJKSiygqSScDkc7Mo/w8mTJ8nIyIjwq2lIq9WGPE/18AoXdnvDrLzdbsXn9eL1eVB5Pcg+X0t2td2RZLhz44bg+8N4tEGbANdZqHPs+Omjdb42m83cfvvtPPfcc3z00UdkZWXx8ccfM2/evAbPzczMrLNy1B133MGtt97KQw91nMUVRJApCMJlqZ3FhOoxZf0TB7ZpNrO9BY3CJf6AMFLllWJjY1GrVHjdbjSa4Blpl9uFvbwUo8FCwYUcjMa6K9xoNF66JZrwxfXktCuX0T0TGD58eE2ffb7qWpGSJKFSqUhKSgp0mlZltVp56lfPcHj/Ec5nl6BW1/1I93o9XHDm4T1zHLXRgCSBydw+svatItyJP01y+cd7++23eeihh0hLSyM6OpoXX3yRmTOrf39aLBZWrlzJlClTGlxjer2e2NhYEhISAh22XRJBpiAIzVY/i+nX1tnMYPyBgtB2Pv3kM0pKSnniV7+47GM5rVYktxYd4KwoR+VTkOp9f32O6vGt5QX5aCUJjUahqHBfwNvFGnX1NmRIPwYMGMAPflA9OUhRFLxeLxqNpl1NwHC73dirnPRNGEdSYnr1uNJaXB475QXfEGNMQWXQUW4rCJSI67TOl1cgNXFZyYgKcqnExsaSmZkZcJ/VGnyc+IYNGyLQqdYlgkxBEJqtfhbTrz1kM+s7duwYb735D1586YV2mwn49ttvKSgoaOtutJjCwkK+WrwSt8vD7Ftvpl+/fs06jk6nI1pvIGf+GjweD5UHD6OS9RR7fKg0WtQ1waCC7JNRbC4MRi/jplzJyUI1777zJvHx8ZF9cW1EUkkYDSZs1pIG+9xeJ16PC4/bgaTy4fE60WotqNSdu0i/X3dzFCedpW1z8i4UzIcigkxBEJolWBbTrz1lMxVFIXPefI7sy2L5suXc/6P727Q/gdhsNr74ZC75uQXYHfZOOSbzq6+WIbmNRGnjWTB/IU8/8+tmHcdisfDBm/+onihltfLUE09jrXJQWlZKcVExDqsHBRWgoEYiPi4WKaaEk4U7iIpuPxO9IkGt1jDhyokYdCZcbic+n7dmn91p49zeEyQkJqAy6JAqHYwZMwrZJ+PBg1ajDXHkjk+NqmmZzOYkqUUwGZIIMgVBaJZgWUy/9pTNPHbsGLu27md4/ytYtmQVN918U7vLZq5Zs4bKAiv4pOq1yRNT2qwvLVGrs7CwkK+XrWVgryvR64xs2bSa07efbnY203/O+Ph4PvzPv2r6K8syp06dorKyEq1WS3p6ep26hO1lolck32ONRgOSzJJvP8elXIqqvF4PF2wFeAvPoDLqcHmcbNu1B5VKhU6jZspVkzt1oCkp1VvY6rSVAgedTRxv0KTzd0IiyBQEocn8WcxYKRFFkXG6A9d1jItK4Nipw22azVQUhfmZC4g1dKNfryHk7W1/2UybzcaieUvoFdOHbH0+uefyGDjQjslkavW+WK1WHn3oZ1RV2MJqHxVj5t1/vt1o4ObPYiYnVK9IY9EkX1Y2s7b6505MbDh7vD1piXqoXp8XlyLT7YpZ6AzV143baaN851dYUnuiMhrQuG3EpqWALFOZl4Pskzt/FNDsIE+5WLJIanJgGZHTdxKd/fISBKEF5OXlkZebj01jZ2vexpBtJRNs/W5bmwWZ/izmxME3IkkSA3qOanfZzDVr1lCRV8WAXkPRaXYjeVWcPXu2po5ea3K73VRV2BieeiVGQ+gg1+G0c7Bga6MZudpZTL+BfUZddjazo2rJeqg6gwldrZnzKrUG2eMBlQrZ40ZxuVFkH167A0dlFbKu+rjOEBNOOixZQpIjMVGr+cdoP9PE2oYIMgVBaLIePXrw9gf/wOv1Nt4Y2iQjB3WzmBZT9S3T1MQenDy3r91kM/1ZzD6x/ZCk6gkZRo2e0ydPkpQUj8kY+L1r6dWKjAYTZkNkbivXz2ICxETFRTSb2RG1dD1UlVqN2idRunwTsizj9jqQ4+NRFBlbUQGnTlxAq710uzxab+hU41VRaHIqsbGgsKtnJptKBJmCIDRLXFxcW3ehUbWzmH7tLZvpz2KOzJiAx+tGp1Jx/tw2yu2lVFQcoGfPHkGf255WKwomUBbTrytnM1uDRmdg9PQ7kX0+PF43xRW5TJ5yJbLHxfHVS3jnlT/X+TluL+NVIyXomMzLiBSDBqFdPWUZhAgyBUHolAJlMf3aSzazdhZTJanQaw3cNvl2vD4fJZWFnLQe5U9/ep6UlMCTgDpCUBAoi+knspktT6MzACB5tWhcRoyWKGSPDp1OT1xcXKcp5RRIYYUVAswub4l4MOiwzS6e+uwaxbIEQehy/FnMgemjGuyrnc0sKWlYX7C1+LOYvZL71Dym1xowG8z0Sk7H4DWz+dvNxMfHB9zae4Dpz2Jm9BoVtE11NnMnp0+fbr2OdTIOpx27w4pP9uJ2u3C5nA02t9uNIsu43C5cLjcej4eysrKQxb87Oh0qJB8NNnxUB58R3KT6m/9cXTzDKTKZgiB0OqGymH5tnc2sn8UMJCNlEF8vWc0ts28hNTW1lXt4+b76ahm4DCTGpyIrAVJKQJQlBrM6SWQzm0Gn0xEVbeTA2Q243W7KrYW4zh9GpQ0yhEIFW3duxed2kXfsML/8/RPEmWN552/vtPs/WJoj3mSiuMoeeGf9DOPlBoNdPGMZjAgyBUHodPxZzPEDr8Mn+4K269d9eJuNzaw9FjOYpJgUTlw4xldLv+Khhx9qxd5dPlmW2bRxMw5fBWt3fdlo+/37qigvL2/z1aGys7NZu3YtDzzwQMTWV28pFouFdz94G7fbTVlZGQ//4ldUJEWROqwXGm3Dj3dJJaHWqHHbbDjPxTPmnnEcWnAg7JnrHY1EE+pUNjdI9D8vWJDaxYNPEWQKgtDpfPfddyiSwvZjKxttq9Vp2LVrF9ddd10r9KyaoigsW7Icl8fFltOhS0C5vE7WrV7PXXffVaeoeHunUqn48+t/wm4PkkmqR6/Xt3mAqSgKn3/2OVs27GDUqFGMGzeuTfsTjtoZSK1Wi0qlxmA2oNUFL7Iuu7VoNBqMUcbW6GLbacbs8rCOCQ2Dyi4eTAYjgkxBEDqde+65h2uuuSbs9t26NZyU0tIeeexhbLbwCp7rdLo2KwN1OYJNWGqvTpw4wfbNu7FI8WR+OZ8xY8a0+2xmfT6XC7fNhuwOHmR67OEVge/wmrriT73n1gi48k8zj9vFiCBTEIROx2Qy0bt377buRlA2my3skj1tNYPc4Ww8AxlOm45CURQWZC4gWp3EsP5j+G7/Kvbs2dOi2cxwap2GWw9Vp9NhMeg4tGMTnsL46qUmQ9DrVWi06rCO3WH5J+Y0kz+2vIwFf7o8EWQKgiBcpCgKktSy00GtViuPPvgoVeVhLtsYa+bdf73baoGmTqcjKsbMwYKtYbWPijG3+1qd4fBnMcf3mYlBZyTR0KMmmxnpa0Kn02GJMnL40Kqw2odTD9VisfDGa6/ys2ceZ/IjUzDHmEO212g1eD3hLabQUTW6dnmYwWPI734Xnz3eGBFkCoIgAE6nk5f++DI/vPeeFl3O0e12U1VuY1jcGIy6RpZtdNs5VLanVSdmWCwW3v3n2+EvY9gBanU2pnYWM+piNYKBvYfWZDPHjh0b0fNZLBbee/etiL/HFosFnV6HOcaMJbbx9tbyzlu+CMDp9tQNJFsiIxnpWeqdjAgyBUEQgPXr17Ptmx2oJBUv/PEPLZ7RNOpMmA2hs01tpaMHjU1VO4vpVzubOXr06Iifs6u9x22hrNKBJNM24ycv/vro6rfaO9aIZkEQhBbgdDqZ/8UCesf0Ze/W/Rw6dKituyS0kkBZTL+BvYdydP9J9uzZ00a9Ey5H95jo6qLogYql+zclgluAYuwt/LdquyeCTEEQurz169dTnFPKkF7DiZMSyZw7H6WrpyC6CH8Wc2DP4Q321WQz5y5Ali9jBonQJvx1MkNtEVntxx9QBjp+F/81Im6XC4LQpfmzmL2i+6JWqRnUYwhbt27i0KFDDB/eMPAQOo9QWUw//9jMvXv3MnHixJDHs1qt7WIsq70yvFn/4bbrsMKYXd5oorEp9TC7eEAZSIcNMi0WCxqNRmQbQvC/N+I9ihxFUWquO/G+RkZbX6fr1q2jKKeUqQPGoABGvbk6m/llJkOHDo342Mya10vjn0n+/U253sQ1Gr7jx4+z/dvdjE+fGbRNdTazOwvmLWTcuHGo1YHL/litVh75n/+h0hleDcpog5H3//73RgNNr9eLw+FAr9c3OsNcq9Vi1pjY8VF4lQEAzBozWq221a6VxkorRVKjs8vDIX6ELkuHDTJHjx5NXFwcXm/nLsEQCT5f8GX1hKaLi4tDlmVx+yzC2uI6dTqdZH6+gJ6WPqiQUC5+TzO6DWbb1k3s27cv4tlMr9dbfR5ZrjlfUBfbeL3eJv2uE9doeJZ9tQx7lZMDZ7aHbOf2uCk+7ObAgQNBrwe73U65w07vm25Abw4dOLpsVs4uX4ndbsdgMARsc/LkSdauW8e3m7fj9nhQq1WMHTWCa2bNZOTIkQGLxBsMBt58/c0mVSPQ6XQYDIZW+yyNi4trlfMA1bNumho8K7X/U7qYyLz4YDP+3uziQzI7bpC5d+9ehg8fTlJSUlt3pd1SFAWfz4darW7xmbJdhSzLlJSUkJCQ0OFWAmmv2vI6/fbbbyk5X8bQAWORan0/zUYL8aokFi9YzKhRoyLaL41GU30ularOOQO62Eaj0YSdARLXaPiuve5ahgwd0mg7f1a4T58+Qb8PGo0GlSRhsFgwNrL8pySBSpICfl8VRWHx4sX85/P5aKJT6DFsKmZLDC6nnYNnjrP11b9x3czJ/PSRhwP2pa2X5mxMUVFR653s4mScxtoEIzWWxhQfq43qsEGm1WrF6/WK4CkMkiSJ9ylCJEmque7EexpZl/ueKoqC0+lEURSMRmOjx3I6nSz4ciG9o/uiUTW8BTqoxxC2btvE4cOHI5rN9PdLovHPKP/+prw34hoN34gRIxgxYkSj7RRFwev1Vv+BEOQ9rX5cCut9D9V2w4YN/PuzTDLGzyI5rVfN41HEkZjanaqKMr7esAyL2cyPfzyn0b63N6159zHo7fJI3QIXNTIb1WGDTEEQBKheonHz5s0sX76K3NwCFCApKZ4bb7iGadOmBc3s+GeUTx0wJuB+k39s5tz5DBs2LOIBm8MdxrKNYbQROg+v18vnc+eTNmBMnQCztqiYOAaNm8lXK9dwyy03k5iY2Mq97DjyiyoDTvxpqVhQDIFuSASZgiB0WPn5+bzyyp/JOVdKamoGQ4ePRpJUFBfn8fF/lrJk6Up+99un6N+/f53n1Z9RHkxLzDTX6XRExZo5VBZe7cWo2M6xbGMokZqV3V5mdzfX3r17yS0s48qxN4Zsl5DSjZNqE5s2beIHP/hBK/Wu44k3GbHbWm+1rAbBqyhhJIJMQRA6JpvNxiuvvEZRscJVU+6sM+s3JiaBvn2HsX/fJl5++c+89tpLJCcn1+zfvXs3pUVlFLlKyDmVFfI8Xq+X9evWRyzItFgsvPuvdzt0MBRJVquVnz76c6xV4c3KtkQZee/dtxq8J1arlUd+9nMqHc6wjhNtNPD+2w2P05bOnTuHMToJjTb0HxWSJBGd2I2s7LOt1LOOyaTVogoxJrNB/NfcFGe9A4m75peIIFMQhA5p8+bN5JwraxBg+kmSxMhRU9myeTGrV6/h3nt/WLNv7NixvPDa82GXbUlLS4tYv0EsKVib2+3GWuVg6LDrMBpDvy8Oh5XDh1YFDNDdbjeVDid9Z9yIwRx6uU6nzUbW+hWtuiZ8bV6vj9KyUl5+5TXKyyvRarQMHz4In89LuKkvSZJQ5C6eJmtMI5nEgJnHkA2CtBOCEkGmIAgdjqIoLF++itTUjKB1C6H6g7hXn2GsXr2eO+64Hb1eD1SXeglnwofQeoxGCyZz1GUfx2A2Y4wKPbu7LVVWVrJz5y5yzpwnPXEkKalDkX0+vt18nLzc45RUljH8ilnoDcaQx6kqK6TnpMGt1OuOSQpndnkQNXGkRMQzleXl5Tz88MOsXLmS6Ohonn32WR577LEG7bZt28bzzz/Prl27ALjiiiv429/+xoABAy6zB61H1LcQBKHDcTqd5OYWkJIaeHJEbakpvSgrr2zd0ilhKCsra3d9ElqW3W5nz559+Hwa4uLTGDx4PD169KdX74FMmHgDM2beh8epYu2iT0Jm2StKi5AdZUydOqUVe98B+etkNmOT5Iub7+K/yqWtSccK4PHHH8fr9ZKXl8eyZct47rnnWL9+fYN2ZWVlPPDAA2RlZZGfn8+wYcOYPXt2S79rEdWsIDMnJwePxxNwn9frJScn57I6JQiCEIqiKCiAJDX+K0xSSaC0r5WvFEXhb2+8yWuv/LlFC6bLssyRI0fYsmULO3fupLy8vMXOJTTuTHY2Ho+KuLiUgNUKYmITmDr1+5w9fowzxw4EPIbTYePQ9rVcPXUS3bp1a+kud2yNrFteZ5P9m1S9KfU2WbrUpgnHrc9ms5GZmclLL71EVFQUo0ePZs6cOXz44YcN2t5www3cddddxMTEoNPpePLJJzl27BglJSWt8OZFRrNul6enp7N161YmTJjQYN/+/fuZMGGCWGVGEIQWYzQaSUqKp7g4j5iYhJBti4vyMVuMJCSEbldbS89SPnz4MHu37kORYefOnY2uid1Usizz9ddfs2zZ15w7X4hWa8Tn9aDTwfTpk/jBD74f8XGmQjWn1Vb3a6eTggsXKC+vIOfsOWJju+GkKujzBw8ezskTw9i6eh7Wqgr6DBiKKSoat9PBudPHKDp3jCtGDeaRhx+q87xz585x/PhxPB4PcXFxjBo1KuhqQl1GGGuX+10K+Rv/Y/Ry/l49ceIEiqIwZMilRQBGjRrFG2+80ehzN27cSGpqapN+l7W1ZgWZoTICLperZtyTIAhCS5AkiRtvuIaPP15C376ha1hmZx/kmllTMJlMYR3barXy6MM/o6oyvNnOUdFG3v3g7bADTUVRyJw7n1gS0Wp1ZH6Ryfjx4yO2Oo8sy/znP5+wbdthevYawVVXXY1Wp0dRFIqL81i/YTe7du3nD3/4Hb17947IOf06egmhy6HT6Yg2GMhZvhIAWZHJzc2juLAYCTWKDFa7FZspAQUZvaTm6NFjSJIGnU5LUnISSUlJqFQqRo+5kuwsmYGpevZ9txSP14daLdG/b2/ue3QOU6dORavVAnD69Gk+++S/7Nm6F41Xh0alwSk7iEmO4sbv3cBtt91W07arqaxy8PZT04Puf/zPG5p13Pq/bf7xdPBzZJ08ytixY2u+tlqtRNdbESo2NpaqquB/eABkZWXx+OOP89ZbbzW1u20q7CDz2LFjHDlypObrDRs2cP78+TptnE4nX3zxBX379o1cDwVBEAKYNm0aS5auZP++TYwcNTVgoHn8+B5UUhXXX39d2Md1u91UVToY0Xs6RkPowNThtHPg7IYmzVL2ZzGv6DEVtUrD5gPrIprNXLFiBZs27eXKyd8nOjq+5nFJkkhK6k5iYjf27F7Ha6/9H2+++X8RC0CaW4qos7BYLHzw1lu43W5kWeaD9//Jt4Verr1qNvGxSeReyObbPZtI7zuVggvncHs85GaXEhsTi0+2k3uuAKNZz7DhQ9FodMTGxvCH//09paWlWK1WDAYDSUlJda7zo0eP8uLvX0JVpmNc90lEmaqDF1mWyS05x+fvzuPM6TM89fRTXTLQ9DgCD+vza+6koMthsViorKys81hFRQVRUcEnvZ0/f55rrrmG3/zmN9x5550t3cWICjvInDt3Li+88AJQ/cvqN7/5TcB2sbGxfPTRR5HpnSAInZ7VasVut4dcss+vdvYrNjaW3/32KV5++c9s+W4xvXoPIzWlF5JKorgon+zsg6ikKp555pf07Nmzyf0yGkyYjZc/27m22llMk766zE6ytlvEspler5evvvqaHj1HEBUVF7CNJEmMGj2NTRs/Z9euXVx55ZWXdU6/SJUi6sj81+bOnTvZvnkvU0behMUcA0BCbDIej4OCwvOYDYkkx8TgcNnQa82YzSZkWaa8qoQ9u/YSEyPRs1cSAPHx8cTHxzc4l8Ph4PVX/oLJGsPwASPr7FOpVPRM6k1idBJbV29i6aCl3HbbbS386tuf5LjQP79SG5SAysjIQJIkjh49yuDB1dUB9u3bx7BhwwK2z83NZcaMGTz00EM88cQTrdnViAg7yPzlL3/JnDlzUBSFvn37snDhQkaPHl2njU6nIzU1VayXKwhCWKxWK48+9BhVpVVIYQRYUbFm3v3XuzUf5v379+e1115i9eo1rF69nlMnvwUFzBYj18yawvXXX9esALOl1M5i+mV0HxSxbObhw4cpKChjzLipIdup1Rri49NZv35jxIJMv0iVIurIVq9aS5yxR02ACRAbnYjdXo5WW0ZqTHUJGo1ah9VqxWw2oVKpiI9JorhMZv/+Nfz4x38IeY5t27ZRcr6caf1nBW1j1JtIjx3AiiUrmT17dpfLZkoK/OKlhrO2a/Y3eUc9F2PUUOf46uO6pYnMZjO33347zz33HB999BFZWVl8/PHHzJs3r8Fz8/LymD59Ovfee2/QxF57F3aQGRMTQ0xM9Q/MmTNnSEtL6/RLnQmC0LLcbjfWchvD4sZiNJhD/m53uO0cKtvTIPuVnJzMvff+kDvuuJ2ioiIURSEhISHsMZitJVAWE0Cv1Ucsm1lWVoZWZ0KjaTyYiI5J4MKFvGafqzX5vF6KioupqCiluLiYM2fOEBcXFzCh4bTZAhyh6W2ay+l0snf3AYZ2r1teqKDoHFqVAXtVPnZHKSZjPFqNDofbitfrQ6NRoygKFRXncNrKGozbk2WZgwcPsm7TBnIL8tixbQeUqKjoVk6cpWGm0y81vhsbTh5k69atTJkypWslgfwlh5r8vFr/Xfv9ilCFirfffpuHHnqItLQ0oqOjefHFF5k5cyZQnQ1fuXIlU6ZM4Z///CenTp3i9ddf5/XXX695/pEjR+jVq/Hybe1B2EFmaWkpsbGxqFQqoqKisFqtIdsHSu8LgiAEYtQZMTcSZDZGr9fTo0ePiPUp0gJlMf382cw//vGPjB49utm18LRaLbLsDautz+vBYGo/mS2Ho+FniqzInD17lvM5uXjcPmSfj/Nn8/nNU//LsJGDuPOu2xk4cCBQXeTcbbeybd5/kKTqgvsGvQGVShWwYH+00dAiiRKXy4Xsk9Fp687szi/KISUqHa3aQHbOZuITMoiP7Q1KdQBZUVlCXsFR7NZcYi3xfPTRR+Tm5jJ27FjMZjN/+fsbHD1/iujBqcQNTgBnHNbTZSw5sYQBpn5ckTEZterS68wvzePEuaPk5J+lxFrM/z79PBOvmsgNN1/P5MmTu0RW019y6PJcXmAZ6HdabGwsmZmZAdvXjq2ef/55nn/++cs6f1sLO8hMSkqqKVuUmJjY6F9DooSRIAhCtWBZTD+9Vk+MnMDnH3/JkX3HmDp1KrGxsU0+T//+/UFxUVFejMkUOtNRWJjNrbMnN/kckabT6bBEGTl8aFWdx2VF4Wx2NqXFlWhVekCFIitoJB3nzxSQdSKHxZnLSO+Xjs/r5cKFQmxWO5Kkqq6DLfmIirbQJ70nb//znQYz2VtqdrvJZEKn02JzVBFV63a5x+NBq9bTP200FmMs54pPcKzoCApQVBqF12PD53Oj1hiRJQvHT1RRWvYNH374BcXl+SRP6M1V/3MLelN18FroqcTXqzcal8TJNbvxHd/ElEHTATiYvY+9R3YTpcSRbhxEvK6cQXEDKTlYyV93/51vp3/LU08/hdEYelWhTqEpMWKtTGXtCKfOIZqYCW4/1XnbRthB5ocffki/fv1q/rtLpdwFQRAuQ6gspp/WbcBT7qMg+wIrVqzgnnvuafJ5UlJSuPLKsWzbvp/UtJ5Bf0+XlBTg9ZQxc+aMJp8j0iwWC++9+1aDYRCrVq3iH//3LxITB6DyqjGoTajVKpwuB0cKttM/aSIGkwFbUTkajZaB8UOJ6hmDSlX9sebxuCipKODYoc2cPn2aGTNa57VqtVqmTJ/EltWHSU28lFk36I24fXlIkkRaXF9SY9M5e+EYWqMPU7Sew6cPEBObQZ/eo6i0lZOe0YN+/fpx8OA+si98g5RTgtPqqAkyk1OSyCrNJjW+GwNvmMCRed8xoCwPm9PGnsN76GcYTKw+HofbgU6vo0dqD/Q6PU53BtvWbeYd0zs8+esnW+U9aSvVmczww7xgUU2dgLOJt8y7eqQUdpD5ox/9CKh+g2+55RZMJlPX+CtIEAThMvizmJJNTZm1jDJrWYM2TqeDk2eOE6dJwml1sSTzK2688cZmZTPvvPN2tu/4LYcObmHY8CtRqereKi4tvcCBfWu4/fYb6N69e3NfVkT5M4pVVVVs3bqV/Px83nnrfbwlUXRPSCEmNhb/x7XTY0dfbECLEZ9DhduhEJsUT0p83ddi0JrQavWcLd/LP958jxEjRrRaEetrrp3F2q83cqE4l5TE6n716taPfYd34XTbMOjMON0OPG4PkqRhT/ZOTDG9iDKkU1xYil0uJypqMD6fj+LyCgZdMZ2CnENsz9zAtT/7PgBp3bpx+kQWdpcdU7SF6IHJHDt9lLKSElI1PYjVx6OgUGEvJ61vGnpddf1qg87I2F4T2bjmW2674zb69OnTKu9Jm5CVhqvuXOa4ykaDRpGAq6PJI8y9Xi+pqamsW7euJfojCEIX4/V6sbvs2Jy2kJvdZcfrDW+8YXvi8/mw2+0k9I+jxJgfcNtbtIMC5Tz6WDUmvQl7kZMVK1Y063y9e/fml794DJWqmE0bv+TQoW2cyzlBVtYhtm75ioMHVnLbbddw7733RviVNp/H4+GT/3zCg/c/zDt/+oAv/7GAs8fy0XmjKa4optxWTv0bj3qNHp9XRqcy4XTZgx7bZLTgtkps3LixZV9ELRkZGdz3wF0czN7MyexDeL0e4mOSSU1OI6twP+VVJWTnHkf2+lD5QKXW0Tt1FCa9GZfbgeL1cfTwUYqKinB5XUTFx9Bz4EjyjuRTmlt08XUZ6T+wH2XOEmwuG/F90siqyKKq0kqKMQ2f7KOoohBdlKZB7eooUzQGj5kN6ze02nvSFvLzysCn1NkuLR8Z+Q2ZuueTFZQ2KJPUnjR5xR+tVkv37t3FmEtBEC6b1Wrl0OGDnJXOoVZrQmYJvLKPKrkMq9Xa7ImFVVVVGAyGsCY9OJzBA5emtNFoNLz+f38Ouj8/P5+fPfhzpve9lsTo6tqIuSXnWTp/WbOzmf379+etv/8fO3bs4Jt1GykqPIFOp+OmG8czc+bMdjUz1efz8fe//Z2Ny75jcPJwUgeksefoLszqaFKNPXB6HVwoLkCWfcRHJV56niyDAmpJh8vtDHEGiW4J6az5eh0/+MEPWv4FXfT973+fuLg45n4+nw37DmPWxmIwajhRdYDCsvMkGnqS0X0EJwv3Y4lKQwHK7YXIkhuLPooLuUUUFhahsmhwez0YzFEY9PHkHs0mvnv1dZLeNx1Jkjh14jRWZwXl1jLinPGUSWW4cREdF8WIUSMwBbjrGG9M4Mzp7FZ7P9pC9+QYzpwpCt0ogplHCRpkSrv60MJmLSv5+OOP89e//pXrrrtOLCEpCEKzeTweFA/0jxqKSWcJGWTa3Fb2VH2HxxN6FY9gfD4fz//+DwwaMpCHH3k4aDudTkdUtJEDZzeEddyoaONlzVJesngpGruexB5JNY91i+/OqePHmj02E6rXd58xY0arjUX0CzRLPFSbDRs2sGnld0zsMxmzofq2udVqQ1ZkJCRMWjMan4bismJMekvN/UpFkan5opFsUZQ5lpzisyiK0mof+pIkMWPGDKZMmcK+ffvIzc1FURTc7tm89PzLVFizOZxXSbH1Atro7pQ58vHJXiRUeJwyFk0cDrcNp9VBwfl8zNEWVGoDbselgFpCIj09nbRu3di1ZgsXYkxoZTWJ3eNJS0sjISEBVYjX29nDH0mh8Zk39W+fN/VN6dqJykY1K8g8e/Ysx48fp1evXkyfPp3k5OQ6P7iSJPHmm29GrJOC0NqCrcEsyzJlZWXodLqaeoadbQ3mtqBT6dGrDSF/v3vUzQsu/bZt28bxfSc5eyqH2bfOJjU1NWA7i8XCux+83SprcOfn57N62RqGpI6q87gkSfRPGnRZ2czWFmyWeDCWKCNarZYVS1eSou9WE2ACWLRRoHipdJcQo09Ep9aj8WqptJcTba6uH1n9kaPg9tmxNFIT1evzotW1TckejUbDuHHjGDduHABr166lX2oGo7qPI680l8NnPdgUNTqNFp9HS6w+oWYcrQo1kgSKXcapcVJRUYJa1/C61et0yHk2Hr7/ATYs38SgPgPRaUL/4VPmLOXK/mNDtunwFKVJE38atAz0C6nJMWnXjkKbFWQuW7asJoO5Y8eOBvtFkCl0ZNWr0PyMqorABZtdLledDH5UjJl3//m2CDQvg1f24vWFDiJ9vuaPx/T5fGR+OZ9uxp5U2MtZumRpyGxma30vA2Ux/SKRzWxNwWaJB6PT6SgvL+fk0VNc2X163WOZo4jSRHHBdoYoXTwqSYVBbaTSVkn0xdWEVCo1suLC7bMTZe4W8lz5xWcZN214u7h1WVFRgUFlJMYcS4w5lihjFEt3fY3emEayqTuSdGmqhEpSYVSbsZdaUWmhoiIPfVLDVaFO7ziCrkLh3mfuJev4Gc6cP8XAHkOC9qHSXoFDY2P6jOkt8ArbEYUmTfRpcHV07fgwIpoVZJ45cybS/RCEdsPtdlNVYWN46pUYDfUyJEr1msFGoxGk6jF5Bwu2dro1mFuLVqtFrVVT7i7B7gt9m9Xtc6PWqptVRHrbtm1kHc7mqvSZVNjKWbV0TchsZmsIlsX0a41sZrCMfSDhZGybGpzn5eXh88gY6hUuT0lJIcmYTIEzn+yKA/SKHoJaUiPLck3Q4Pa5cHgrQFKwWKIDHR6o/uOkwlHItdf9rEl9aylarRafcukPprT47jgdFdgqLyCZ6y2BqiiY9GZ0koZje7egjoaSqnIUFCQkrKWVnNh6EPvBCzzz2JMkJydz5w/v4C8vvEFsWRwpcWkNzu9wO9iTs4Ppt0yhd+/eLf1y25Z/Mk6TKTX/XzfwbMYfKV08UG1WkPnJJ58E3adSqYiJiWHkyJHtanC5IDSV0WCqcwsPAAUkRYXJYOr8A5pagcViYcjQIYxKmIjFGHq9a7vLhqlY1eRAxp/FTNF1Q6fRkRSTzMkLukazmS0tVBbTryWzmY1l7OtriYy90WhErVbh9rrQXww0bU4rZ0uyKFWKcLudWD1nKXcWEKVLBo0KdZmLKmcpVc5i9AYdGr2B0soCkuK6oZLqlmvyeF1U2Eq4Y/oNDB8+PGL9vhyDBw/GqbJXX896Mw63A7PGhL0ihzyNkeT4AWjUOkDBhw+VGirLzqL32Il2Wdj9/hp8R0pRSSq8pQ5G9h/CXU89zNChQwGYMmUKpT8v5eN3P+Fs8Rn6JPUjyhiF2+smp+gsRe4CJs4ay2OPPxa6o52AFO6ykkGaNPwVr4TaKQTQrCBzzpw5Nbcdahcmrf2YJEl873vf49NPP213awgLgtB+aDQaDDo9er0+5O9tn+INuDxgY2pnMf0GpAxq02xmVVUVm9Z/i122sunU2pBt3YqL1SvWcOedd6LRNOtXduDjhsrY19NSGfvu3bvTJ6M32WezGNB9EHtO7eTI6UNoPHripETU6LG6qrCqyshzHSE5KRW1wYBBr0OrV5GQEsvAQQM5fuw4Zy8cQ6c2otcaURQFp8eG01NFfGIMDz70k8taEz6S+vfvz5DRgzi29whj+o3HJ/vQa40MjB3C6bLjHC8/g8WSCpIat9eGtsxJUkwcd864B41ay7rDS5gz4w5iYmLo06dPgzqX/s/eQYMGserr1Xy3/jvc5R5UahWDRg3kRzfdzaRJkyJ6LbVXUhPHZDZJF89QhqtZV9m2bdu45557uO+++7j11ltJTk6msLCQRYsW8dlnn/Huu++Sk5PDk08+yW9+8xv+/ve/R7rfgiAEEelboB1Z/SymX6SzmU15z6E6sH7m2V/jcrnCam82m5sVYIcjYMa+lahUKm6cfQNv/+l9io4UkpWVRV/jYKJNsUiShDfKR2VFBSVVxWS5j+Hy2lFbJNRGBZvxLNFmN0fyCiFKwUcV50tKcJa7kCQJi8VEQkoCQ1MGERUVOkvemiRJ4oGHHuB/n3meQ2cP0De1P2qVCp1az4Tu0yizF5FbmY3VY6Vveh9GDBpNclwakiSRX3yelNQUbrjhhkYrGgwaNIhBgwbxyE8fxmq1VldNiIpqF+NSW0vxhcomj8uMmC70PofSrCDz2Wef5eGHH+bpp5+ueaxbt26MGjUKs9nMa6+9xjfffENJSQlvvfWWCDIFoZVYrVYe/PHDVFY0Xr8RIDrGxL8++qDTBpqBsph+kcpmWq1WHn3wUarKw7vtDBAVa+bdf73bad/3prj66qv5esXXLPp0KaNjJhGjj6vZp1ar0Jl0GCQ9V6VMJU/OYfJ1E/nowX+GXT6vPf4hlZGRwXMv/Z43/vxXtp/9Fq1OzZmSE/SKzsDlc5MS25PpwwbRrVvdCU1nC08x4/tTm1Qyy2AwYDAYGm/YCenVElZvsJreLRkEKnX+6cqaFWRu2bKlToBZ2+jRo3nhhRcAmDBhAkVFjRRCFYROoj1kEEtLS9n23XbMmjjUqtCZL5/sw+Yto7S0tN19CEdCsCymX6SymW63m6pyG8PixmDUNT40yOG2c6hsT4eYLKYoCpWVlVTZKrDbW2bFJa1WS3rfdHolp+N2OMkvz0Wr0qEoCh7FjcagZvCIQfTp04czBUmcPZVDYmJisyaAtSdDhw7lnQ/eZseOHSxcsIhVS9fiNndjcN8hpKamNnh95y5k49ZWMeuaWW3U444nJspE6YWqgPuUBrWILjPorF+E/fKO1mk0K8hMSkpiwYIFXHPNNQ32ZWZmkpRUPZC9qqqKuLi4Bm0EobOxWq08+5vnqKp0hNU+KtrIux9EvuxRdXFzhYHRIzHpQh/b7rayu2R9s4ubt3ehsph+kRybadSZMBvMl3WMppBlGVmWW2RsneyTyTmXw7nsc9itdtw+Nycqj/H0r57mtrtu47rrrrusAvT17d6+lyuGXUm8JYmCggLsNjuSSiIqKqo64Lr4Gnsl9WFD1iqys7MZMGBAxM7fVvR6PVOmTGHKlCksm7WMf771ERXuYhLlBLRUB5l2p41T549R4jnPz596tMESkUIIihL0VnnDckVNDDrb4hZ8B9Ss306//e1vefTRR8nKyuKWW24hKSmJoqIilixZwrp163jvvfcAWLduHePHj49ohwWhPfJ4PFRVOhjRe3pYkygOnN3QopksnVqPXtNwKbnaGqtL2ZocbgeoVCH/+ne4wxsCAJeymGY5GpvTSu0b2W6PC5986RaarcjBF59/wY/m/KjBcdrjrVa/f/3735SUlvGbp38d0XF2Xq+XfXv3U1JQikUXRVp0D9w+F0WebDRFJv71fx+zZ9cenvntMxG5DasoCk6HA51Bh9FgIL3eRJbaNGoNKlQdIgvcVDfffDOJiYlkzl3AlsOr0Kmqf4+4FTuDRwzkthkPYLFY2L59O926daNnz56NHFFAURpdDSrEk6v/8f9sNTeo7OKxaLOCzEceeYS0tDRefvllnnzySbxeLxqNhtGjR7NkyRJuueUWAJ5//vkOf0tDEJrCaDBhbqQUj3CJTqfDEmvmUOlupDBm/0bFmsPKoBUXF1NaUorbYudQxZ6ax71eLwcOHUSR654r++2zbFy3teH5WijjfLny8vJY/s16PD6Z7x8/zqBBgyJ27OPHjlNaUEZqdFrNyjMAKkkiPbU//VUD2bbhWz5O/ZifPvrTyz6fJEnExsViLbWSFJMSsq3T7QB16xXLb21XXHEFEydO5OTJk+Tl5SFJEjabjb37tvHl/HewxFRHLNYKyOg3ltm33M6IESPauNftmHwZQaa/SKaiiHvfl6HZ91lmz57N7NmzkWWZoqIikpKSGpSIELfKhY7M4QyQOVPA4XSgSHJNMXah+SwWC+/+8x3sdjsajabRjFy4mcWUlBT+/Z9/VRfvrqW0tJRHfvJzRvSehqHWrW1Jkhqs8dwaGedAKioq2Lx5M/v3HsDldJGcmsSUqVMYNmxYze/YJUuXokQnYjQamb9oEc/+5jcRyWY6HQ7yzuURb06sE2DWZtAZGNZtFGuXf8Mdd95BQkLCZZ935nUzmP/+YtJT+4Vsl1VwigFD+3fqGsySJJGRkUFGRgabNm0ic9F7DJug4ZHvDyQhqfqaLSmyseO7w7zx1l7m/PBJpk+fDlQPoaioqMDn8xETE9PkJI+iKPh8vs5T3qipQWaQMphhLTcZzkG7oMu+klQqFSkpof/6FISORKfTERVj5mBBw8wWBF5WUmTsm89isWAwGMIKMpsi0PdEr9ejUqkwm6JaLeN8JjubxMREohoJjhVFYdmyZXz67//iLveSoE9Cq9ZyynWWrxeuIWNEf5565kkUReHrDZtIn3YDOqORrSsWcDxC2cyCCxfAJ9UURg8mISoROV9i69at3HzzzZd93hkzZrDg84WcLTxD7+T0gG2q7JVccOXx/279eZOvE4/Hw65du9i0fhOFF4rQ6XSMmTCaadOmkZycfNn9bwnZ2dl89Olfuf72JEaO7V5nX0KSmRu+N5QevfP4z3//Rnx8PGfPnmXFslUU5BaiANExFq65fgazZs0iLa3hyj9+siyzf/9+1qz5ht279+PzyURHR3HNNdOZOXNGh/98D1mMPcxb4I0uNynKFQXVSf5cEYTIqc6uvR0wgyXLMhcuXCAlJaUmq6TT6TrlGDGhcVarlbKyMjweDy63C7VU91dqZWUFxw4fJyGpiFGjR6EL8cfI0qVL+fffPyYjdgjdM3rWCaRcHhf7DuzmD79/gX6D+qFEJxKXWh04GNJ6XVY2s3Y2vryiDNkn4/LWncDm8jnrfC1JEgbJTHFxcZPPF0hKSgo/f+px3njlb1SdraJ/WgYGXXWg65N95Baf42TpMa67/WqmTZvWpGOfOXOGP7/yOrmn8onTJBFtjMbus/Hlrvl88fFc/t+cu7j99tvbXf3Itd+sptcAX4MAs7bho7uxb+d5fvnLXxKr7073xP6MzxiLSlJRUn6Br77cwMpla3jmd08ycuTIBs93uVy8/fa7bNiwnZiYXvTpOxWtVkdVZSnzF6xn0aLlPPHEY1x55ZUt+VJbjMvmqpvJbIEZ4Eqt/68+aPu6jtqaCDIFIYBgt2RlWcbtdhMfH19neEhpaWlrda1d8/l8nDhxgqqqKvR6PQMGDOi0K35ZrVYeeeznlJRVcOTQERwGNTp13fGilZWVeNxeSiorKC0v5+qZ0wMeq7CwkE/++SmD44eTFt8wqNBr9YzvfwXrD65h677dzHjwf2r29R09vlnZzEAZ+9ziXEoqS4nxxjZor9Vp0NQqCK8okb2tOnXqVEwmE599/F+2HNuAQTEhIeFU7MSmxvCjn/+QH/zgB2Gv3GO1WsnOzubl519BLlEzrueVqFWX+tuD3hRVXOBfb36Ew+Hg/vvvj9hruVwul4vtO7/hlh92C9nO6/ViiC6nuLiE669/AIPu0mQ/k9FCj9S+HD29l1df/guv/eXlBsMM/vnPf7Fh417GTfgeFktMzePx8Sn07jOYrNOHeP0v/+CFP1jazbKcTVFZXInk87XoHeugIaXk/z9xu7xNLVu2jHXr1pGdnc2VV17Jr3/967bukiAITeT1evn6669ZunwVuQVFqHUGfB430WY9182azvduvZXY2Ni27mZEud1uqmxO+o+dxblTF4iP7o1ea6yz36mUEKM14XTbqay6gM8nBzzWxo0bwaYmrXvwrJVKUmH0WKgotmG8uIKN2+GgOPccZU43r/75z7z4hz/Qo0ePsPofKGO/ZcsW3n71PSalT2swJlOjVtfcRvfJPmxSVcTL6YwbN46xY8dy5MgRzpw5gyzLJCYmMnbs2LCLr0N1gPnwLx7nwPGjlBdWEWuO40hFFgrV16rL6cTj9YKioEgyz/z+OSZOnMjAgQMj+nqaq6qqCpfHRmq33iHbXbhQgE6rEBcbjcfjrhNkQnXGeUj/MWzdX8zyZSt49LFLE7Wys7NZs+ZbxoyZXSfArK1vv2FUVZby5ZeZDBs2rN1lexuTnBpL9vH8RlpF8jXVK8IuAUrHes8irc2DzPj4eO6880727dtHVVXgoqmCIDSN1+drtESR1xdsJYym8Xg8vPHXv7Fpx0G69R/BxDHXo9XqkGWZgvNnWPD1d+zavY8//O+zJCYmRuSc4apfIL+srAyvx4Pb5Uajqruko0qtQqtp+thag9GEWqNBlnz4uFSsvLyqHFCQVQoqtQq7y05BUT6W6Ia1NHdu3UmKOXTWyma3U1nmxGKI40J2FqUF+ZzdtRfFJqOWNXy3/QQPnHiQKVdP5vobrw+r7mf9jP2sWbPI/HwBxZVFISfhZF/IIrFHPOPGjWv0HE0lSRJDhw5l6NChzT6G2+2muKoS1bihZMSkodMaUBSZ8vIK7DY7elRoVGpAwu2wk/P11zz26GN8+tmnDVbZqU1RFC5cuIDdbsdoNJKamtoigZdGo0FChdsVuvh9Ts55tCozElUhF19I7z6EDd98y733/bBmic1169ajNyQSHRMf8hz9M0axc/tCsrOzSU8PPF623Qpr4k/9+phNOL5S/z+kIPu7rjYPMidNmgRAVlaWCDIF4TJptVrUGjVFVTmUWUOX+vEqbtQaddiTloKtaJQ5fz5rN+9m1FU3YDBZ8LicyD4feoORbr36kdK9D7s3Luevb77FSy/+IWIfyjt37mTgwIFER0cH7W/95R7dbjeHjx7HVRCNTlN3cotOp+GqaVOaHGiqNRq0Jh2nbAdrHvN4PVSUV6DTGJB8KmTZR6UvjzVHyxiQMaBBKSa73YFOE3pYQfbZbNAb0amN7F27Coq89IgZQExaEpIkUao6j9nu49imLA7seZ1X/vJykzNzBoOBe398D2+9+jaGUiNp8Q0DrryS85y1n+aJJ/4nogXZI81ms6HWp2C5GESVlpbg8SpEWeoOddGotRg1JnKPF/Di//6RV/78MvHxdQMvWZbZtGkTq1eu4djB43g9PtQaNf0H9+Wm2Tcybdq0iA4diImJIS0lnaMHC5hydf+AbRRFxmq1cSFXwqBOwhhiIYDkhG4cyHJQWFhYE2QeP3GKxMTGa22azdFotRbOnj3b8YJMRQY58N2DsNX/fRVyspCIKutr8yAzXPn5+eTnV6e9i4qKsNmqPzjqlygRLlEUpWZVkI52m6O98l9v9a+76q8V7E5bw+XK6qmeaHHpexNJsbGxTJg8lspSKxA6U6lHzYT4scTGxjbaD6vVymMP/wxrWd31uX2yj0NHjqIzRnPhzL9rHteY9Nwy53H0BiMqlYrhE2eye+1cjh492mDcYFOuU1mWUalUnDt3jtf++Do3zL6OHz/w44BtnU4nVWU2hsWPxnBxuUe7y87504UkGlLq3Nr2+byUuorxeS+NM6z+Pgb/Pvm/51qdgZvnPI7PeylzfOjgYcqL7STEVWcT3S4bJ3ctIyU5jqf/9ymGDRuGyWSqOW58Qjx52UVBrx2bzc65vALMKWmUn92D1mZkTPoMNDXLZSpEJSZRnnOGCf1GcarwOP/36hu89d7fm1z5YObMmdhsNv7z/idkFZ+kW0wPjDojDreDvIrz+IwuHvrFA0ybNq3d/v6tuZ4ujolzuVzYrHaMWlPAMZ0SKhIsSRSeKGXp0qV1xmd6vV7+8dbbrP9qI8n6boxPnYxRb8LldpJ9Ios3//gPtm/dwRNP/rJJt/QbM2PajSxe+VfGTXJjMDb8HioKeFwyB3fb6JM6HaQASyX6217cU/ta9nq8IEko4cywliS8Xm+7/X4HJUcwyGz2Cj9dO/DsMEHm+++/X7MmOsDdd98NQEFBQVt1SejCCgsL63xts9nQ6CR2nViNoigXx3wHD5jMUXrKyspaZFb6H//0xyatoW61WrFarTWPeTweFi5cyLXXXltT67asrIySC6UMiRldM+sXqoueV6kkEhL61Lxcj8/FqaqDWKuqcDgc+C6ud+1Vm8nMnM/tt98WtC9mc/BsjNfr5fXX/sKMq6dXB3HnKlk8bykTJk4IWKuxrKwMl9uF4pOQLhZfl+TqTkqKhFR7rJQCsuzD4XAgX1wNyOl04HK5uXDhQsD3s6ysDJfLjcPhxGjWgqo6EKisrKSktIqkuB6oLwaBaq8XrVpHtC6ZJYuX0rt37zrv+dARQ9i5/lN62/qhkhoGQSdPncSnUlNqvYDVXsHIpFHIXgW3t+4tf0Wr5+SpU6T16saOfZv5z3/+ww033IBaHXod+/omTJhAjx492LplK9u/24HNZsdiMTNt1iQmTZpEampqu/7dW1ZWVr32uc+Dx+OhsrIKlVL9vgb6A9En+9BpdKRZevDVguVMmTIFo7H6j5AF8xeyav5axva4ArPBAjI4HS5AondiP5Ld3fh2+RZUahX3/+i+iL2GAQMGYFzdj0/f28Vt9w3BaKobaDrsHr5bW0JhTgxXTuuDwx68Zm9haR5qbXVA6f++xcREcfbgOdLSQtcmdbtdVFWVoFKp2vX3PCCFpgeHodqLZE2TdZgg85FHHmH27NlAdSZz7dq1AJe93nBnpihKzWpMIpMZGbIsU1hYSHJycoOMyIcf/wu32817776HyWTi/h8Fn63anpcr/Oabb1iz5BtiYmKYM2cOUN1fvU5PXExcnfW5rRU2TKZoLKZLNSddHg1qpwq1SmHt3H/jtjpQZBm7tZKcPTvYtmF7g+tRURQssWbefPtvQX+mv/32W47vO0lZcRmVZVVMGjKVw7kH2LF9R8Bspr/PRqMRk6E6YFBUMmqVGh9uvMql759X8eGVPSj4kKXqIFORFPR6HSkpKQ1un9YcX6/DaDRgMl3Kip44fgKD1oKh1vKiPo0GlVrN0AFjOHhwMxUVFXUyujfddBMrl37N2ZIshvaqO4tXkRXKyyuRPQ5OFx5Ekr2oKpxUVGbXaScrMiWOC5wuKMGQp8XlcfHWa2+zd+c+brjleq699tom3d5OTU1lzJgx/Ozxn4X9nMsRbDhGII39/Oh0OuLj45EtRpxeBx63G61aGziLKYFXcjOgVwZ9e/Qj+8QJXC4X6enpVFVV8d367xjRfQwJsQlIkqpBnGEyGRmrmsCOzTt54Cc/jui4498/+0f+/tZf+Ndf9zB4lIk+/av/6Ms+VcrRfQ4s2jEkJXoxW6JCjsnMP5HN9TfNon//S7feZ8++mZ07X0WtltDrgy9Be/78cQb078nUqVPDntkfSqsGquHcLm9SDNqM8ZtdO5HZcYLMtLS0moKyeXl5bN1aXXYjEhd9Z6UoCiqVCpVKJYLMCPO/r7VFR0dz+vRpdm07gEqt4u7/d3fISQTtkcfjYcHchZi8UaxcvIrZs2eTmJhY/VolkC7+z0+lUgW53SYh+3y4rQ6MF4xIqNG7DUj4KCq2Ex0TXeea9PhcHPbu5he/fIqPPvygQQDh9XpZMG8hPS192LtnNzHmaOLTE8hIGczXS1cx+9bZJCUl1XlO/T57fT5UkoRWq+ZE0bY6bX2yjwp3GfpTlWhq3V6OijZhMBgC/p6pfkxCkqSa11JeXkHhhWKSYi6NdfN4nLhdNrxeLypJjVEVx2effs6TTz1R5z148NGf8MZrf2PPKScDewzBYqh+DxRJIX1gT44XHebKsWNx5Hu4ot/kuu+Pz8t3hzcg2130tWSQaEjB7XWhMimoCgz8+43/sHf33oitNx5pVquVnz38szrjZ0OJijXz7r/eDRpo+n8+e/buwdkzefhkBa0qQICtgM1rRa1W0yOxF2pJjUqlrhmWsWPHDpzlHtIGdAdFqf5eBzhfQlQSUoGarVu3cuuttzbhlYcWHx/P/z73Evv372f9xjVsW3UaUOje/Qr+56fX0r9/f377zLPsPrSRccOmBww0j57eh2K0cdNNN9a5jkeMGMGwYf3Zs3stEybegCbAWOSSkgLyzh/kiSce6pirAAWc+BPBqK+ZSwGVl5fz8MMPs3LlSqKjo3n22Wd57LHHItevdqTNrxqfz4fP56sZK+J2u1GpVB3zgha6vEULFmPRJOOVPSxetITHfvZoW3epSTZt2kTeqQKmDprF1lPfsuyrZcz58Zyg7WNiopE9rpqMeX2KrCChJiEuHbfDjiJ7QYa4mHjMF+tnKoDLa6ekogSb1REwm7V161ayj+QwOnUiB/cexibb8ck+kmNSOHnhGF8t/YoHfvJA0H4qwP59+1Gr1dx21R0NZtbbXXb2Fm7jzXf+UjNEwH872+12B6yDWj3cwYXDVoVao0VvMHLmzBm0KiNabXVA4/E42bNxHj6XG3tFEUvXfIGiKGzbU8zObXsbDg9QyaSOTGDHwc3ofHrUkgan7MCSaGTM9BH069+PZZ99jcloxOVx1ryOvVk7uZB/gQFRw9CpdXi9PpweOyaVgW7xPUiOSWXvhh38K/FfPP7zx4O+T23F7XZTVW5jWNwYjLrQE6AcbjuHyvaElfXs1aMnLq+P44dOIvtkLKpLf9x4ZA92TxU+lZv46ATUKg02pxVJI9WU2yosLMSEGamRsYuSJGFWRZGX21i5nKZTqVSMHj2a0aNHB9z/u9//hpde/BOb9nxFz6QBpCX3QpJUlJRdIOfCcdQmN8/87lf07t27wXGfeuoJXnrpT3y3eSG9eg2jZ68BqNVarNZysk4foqw0i7vvvpkZM2ZE/HW1hsKzRZc/JrNJAl0jDR97/PHH8Xq95OXlcerUKWbNmsXgwYM77PscSptHcnPnzuXLL7+s+fq7775j5syZ/PKXv2y7TgldjqIo/Pe//2XSpEnNrv13+vRpNm/cztj+1+L1uVm7aiPf+/6t7SKbWVVVxRv/9wY/efAnQesoejweMr+YT3dzbzRqDRnJg1i+aCU333Jz0DsGMTGxxMZEUVlWTHxS8KEriiyjoKCSNGi1Ouw2B9FR1R/4CuBTtKhUGmRFITc3l/LycmJjY4mPj8fr9ZL55XxSDd05fzaXRE0qBY4cTuefJKP7IAakDGLlkq+5ZfYtdbKZe/fu5eTJk4xLmkRZWRlFBUVIkkR63z7ERDesC6jT6YiLiyM+Ph6r1cpvnn+eSqezQbva79fBU8c4U1qOUavn6pvupbCgGGQNhSW51W1cDlx2O73jxuGimARjAiq1hgSdjWRtCsMGXirT418r/TfPPkNFRQVHjx6luLgYi8XC0KFD+d9n/kBpcRmK3suFsnzWHvwGt6LgU3xcKC3AiJndVfsABUVRcPvcmCtMnNiehU6SuHrYDNat3MBdd9/VIOvbXhg0BmxVdhwOB5IkERUVRWxsbLMrGUqSihEjh+Nyuzh9PAuX24laUqOgIEs+LOYokrRxFGlOAXC64BTDxwyl+8V6pdXXZ3iZLwUFlbr17xh169aNV//8MuvXr2flstVsP34IgKhoMzfdMYNZ18wK+jsoISGBF198ntWrV7Ni5Ro2bdiOrCjotGpGjRrKzx57gvHjx3fYO2HRcWZsobLjLfW6lNrrANU9h81mIzMzk7179xIVFcXo0aOZM2cOH374oQgyW8I999zDPffc09bdELq4w4cPM/fjTLJOZfHc888165eqP4sZE1WdCdOdjQk7mxnJ8WiBLFmyhHVfbUSr1fHTRx8J2Gbjxo1kH81hcr8ZHMk5SN/UAciFEsu+WsbsW2cHfI4EZAzoz+69+6gs0xId13ACjuLz4XY5kACdWodWrcPutmG322syebIs43A6OH70OE89/jQatRa9QceEq8aRkpZSk8XccXgXCeYkFLeXA6f20S9tQMBspizLzJ+7gIrSSgoKCykvLkePEVmROZOVzahRDZfYq83tdlPpdNLrphswWAJPRnK6XBR0747ZEk/Jho14XHZ69kqpk7hwObScN2iIiragU9z0H5iORlv9a9dsNgdcP91qtXLgwAFWLFlJQe4FkKGwpBB7pYOECwkMGzuYo/sO41YUeg+fic3jQltaQpw2oebzzO314JO8pKSmIHtdnD24jihTLFKRmi1btkT0lm4k+Mc6bz25DZ9TQSNpqv/4wENMXAz9M/qRlNi8wFiSVIwaNZKqCis+u4xJb0KSVBh1RrQaHe6LE2ZKq4op8RXw8Pd+VPPz36tXL+xY8ck+VCFCXUVRqPKV06dPn2b18XJFR0dz6623cvPNN1NeXo4sy8TExIQ1BjcqKorbbruN2bNnk5ubi9vtJiYmpsOvWQ6g12ualslsbtAZJMsd6GgnTpxAURSGDBlS89ioUaN44403mnfudq7Ng0xBaGuKopA5dz4Gt5ldm/dw5MiRJheCrp3F9BvYe2RY2cxA9RxDaWw8Wn35+fn84XcvoNhUvP/XD9i05tsG4/IUWebIkaMoTonsM9moVHqsjip6xvZm0dzFDB02tMH63OqLxcsT4uMZNWI4Bw8d5oK1HLVBj8tlJ/fsSSrLi9B5U9HrtHhdPrRqHZIEWpWWysoqTCYTPlmmuLgEh9WB0aTlyp7TiDbFUGmr4PA3J/j4xCf0SurDeVcuap8GvVZPqqY7FyrygmYzt2/fzpkj2RjUBk4eP4HkVZFsTqsupp13gYq+FQGzmfUZLGaMQepwajwejNEWbJWlVJUWcGL7SnS6uiVs3G4XXkcFPkc5MdFR9O3XN2QdTrfbzcsvvELh6RJ6xvRhUvfBeLw+vjm6BsWr5XxlLpYEE+ZUCxXnKlBr9UiyD73Ogk5nQqG6RidqmaTERExGCy7XpSy0SW1pdzOEFUXhs08/43x2LknmXiRHJ6G6+GHvlb1UVlSwZ8deho8eRre0pt0VcNaawT84ox8H9x3EXuUhyhiDghe324vLVoXVaeVw8X7ue/geJk6cWPOc8ePHE5sWQ05hNn2Sg9eIzCvNxZhgYPLkyUHbtAa1Wh2w0kI4tFptmwXJLUaRUS7jdnmwZENYZZ8uta7zldVqbVDbNzY2ttPWCRdBptDlHT58mL1b9zExfSrHco+wIHMBQ4YMaVI2s34WEyAhNiWsbGZLjUfzW7VqFR6rjwnxk6mwVZBoS2Rw78F12uQX5FPl8VDlsnGy4hA6g4l1ezeQEptKhaOchx/4GaUFxTj1qpr1ubU6DVOmTkar0ZKclMRVkydRUFDAmZxsvI5KpKrzJMVFE62KRvb60Kq1NYkCraY6m2mz27FZbfjcPnQaPQa9Hq1ah0pSEWuJo5ulN4muc+Tl5VOlOOgXXT0ju9JdgdPpZP/JvQ2ymXN+PId5X2SSpEsj33CB0pIyEoxJaKOrgzuNogsrm+nxeigqLqZXkCBTq9Uy9arJWMvLOZ2by9uvvVYzntOvrKyMXzz2FOMHXIHFEh0ywJRlhTNZZ9DZtEwZMrNmEkdO9glMSjR9EwZSaM9n/7adPPKrB/8/e+cdJ1dZ9+3rtOkzO9trstlkN72HEBIC0jsKiqAitudRsKG8Kj42iiIgCooI2LEr0ktICBDSe8+mZ3vvu9NPf/+Y7CabLdlAEtpcfPajmXPPOfecmTPzPd/7V9iyZw8tkSYsQ8QwLRK6imkZCJJAWnoAr3fgTYhlWSdczuhUs2bNGl597jUCrjT8nkCfwASQRZkMXybhuIPdO/cQTAvCCHI9HQ4HAaeL2qdf6/d4IBajpbmZxq4esAAEbCzS3G6+8b2vcdlll/W77p1OJ5/8zA089sBv8Tq95KQPDAnpDHewv6Ocz3z1xndtxYgPLJbFvW8OHYP8/Q/9etin2wCieMSpHEJc3rvi1iH3UVFzkDlZc/r+7fP5CIVC/cb09PT0Fcl/v5ESmSk+0PS6mEGy8Lq8TCycxIbVq07IzRzMxexlpG4mgNvh6Vce6GQQDod55fnFuBUPTslFjs9NR0sr9gQb32ERYlkWTbXNBJR0wlYUp+xnRsElxNQwQb+f8dkeOtQWdLdNtr8Qp+zGMA06w42YpsXh1V+cDgfFo0eTlZMJLXF+ctcd3Pqlb9LToSNZJq6jXluvm9nd1YVtgktxwTEd9CzbprKikhxXPkbYoF1vZVLGdGzbpjJ0ENUyae9oH+Bm5uXnUbGrktmFZ7F5zyYETUcTdBJGMs7P7XRTV19HbmEOAX9SQMa1gTUGm5uaadtRTmZu7pD1OxVFSQpjRemL5zwWWVFwOB3H7STU0dFOLBxj9vwz+gRmQlWpqaolzR1EQCDXU0BupIgXn3uJiZMmMmrmDGrrGmmsbkJxSQQ8Ptxuz6BFs23LIkIP48YNXxfxdGLbNq+8tJhMOZdmqXXIcX63n2hPmIbGRgqK8o+7X5/Px+8f/s2QN2MtLS3U1tZiWRYZGRlMnjx5yB/5Sy+9lFAoxD//+G+q2/2MzhyD2+Ehocep66ghIvbwsc9ew0c/+tGRvegUp4/jOY7DuJx9buXh5LqTFZc6fvx4BEFg7969TJqUvNnfvn07U6dOPSn7f7eREpkpPtD0uZiF5wLgdflII+uE3MzBXMxeRupmniqWLl1KtC2By5Gsg+dUnEgxharKaqZNS36pNTU3Ee2JkeXLQW/TkSUFl+xFkdxEE11kp+UTinejaTomFqZtYNo6mqkRU6OYdn91eLRgsyyLeDyO168MCHdSZAfRaDTpjB6VMFFVXcnY0ePoCYUId0XIcufg1v2oeh0N3bXolka3GiLNmU9XrIaV25ehGxqiKNHVGuLhBx9mlLMUl+IiooboEbrp0Jpo6q7CdbgjS0+im/byekrGHlkCPbrVY3NzM+1tHWQmLKqrqpky9a330R4pjQ1NKIIDSTwiRmtrarA1AXfwiMM9Njie1RWvIXq8zMzIoKBwNKvUVSiaA5/H19fZ5VgaOuvwZ/s466yzTvlrGSlNTU3s3r6HqTmz2X1w17BjvQ4/DXUjE5kwsC/70WRkZPT9wB+Nqqq0tbVhWRaZmZl4vcnM8o9//ONMnTqV5ctXsPKNVeg9OrIicfbVC7jokmRm8Hs1Oeb9zPGWyk9kKf3ElsiHxuv1ct111/GjH/2IJ554gsrKSv7yl7/w3//+96Ts/91GSmSm+MByrIvZy4m4mcO5mL2ciJt5MgmHwzz/3xcoDpbQWNvY93jQk05jfSMlY8fgcXuoPFSFV/ERT8SwLBtRSbpoLsVDTO2hJ9pF0JNJoknlQGw7oihhmgY9ahuuVovm5mbcbne/ItT+oBdFUejq6sIyJByDuHjC4RqWlmnD4c2GrlN5sBpTs4mEI3ik5I+8y+kCw2ZPbAcJQ0URPYiygOCQSZgJtKwIY0vGYtWPYvvGnVx41kQ6Qu0E3RlMyJiGmlARfDZnnjkXQRQJx0PsaNvED37yvT5n7+iEqsWvLEEwRTL9WdTXNpCdk91Xe9Hv959wq8aREA1HUOQjiRrHupi9+Bx+3HhobmrGtm0kSaRsQim7tu1GVmXczoGuq2aoVIUO8vWvfLWvk827gVAohKGZeAaZ87EokkJUC520H/ujaW9v59VXX2Xpy6/R1daNZdt4/G7Ov+Q8Lr/iMoqLiykrK2PSpEnccsvNqKqK0+lM1Wl+t2NZfG/+L075YYY7xn8bfjfgsUcffZQvfvGL5OfnEwgE+PGPf8wFF1xwKqf4jpESmSk+sBzrYvZyIm7mc888j1vIwOP2ohuDL80FfOnIpu+0u5lLly4l0hJnwuj+JYuOdjOzsrMI90RQUOgIdQACtm0R1+OYlo0kOmnuaiYzLYPS3LmUTiyhZGwJsXiELYde59vf+wY/+/EvCAT9PPjrn/ctKzscDsLhMK0tbWSIg5dMApKFrw0Lw0y6obF4HFGSqa2uS9bdNG1aw82oqo6BTdCbTwYBXIqXnkQzMTOBYhkUjiriR3f/iG9989vMGHUGiqSws2IbmWIuac50LIdFU6iBUE+YgvwCvC4vdR05LHv9TebOndtvTk1NTbz56nI8Dg+aodET7WHVm6vxeX3Yto3ilCkYXciY4uIRCzbbtjFN8zjxkP0/Z4O5mL24nR7aoj10dHTg9adRUFCIrhsc2HuAUCKES3LjdDgxLYPucBsRI8wNX/gKV1xxxYjme7pwOp0IIpiWftyxlm0eLrB/ch3Dqqoq7rnzp3TXRijOGMvU4jMQBIGOcDsrnl7LsiVv8q3v38acOcm4OlEU31VCPcXQqDG1b0n8dDbeOd4nNBgM8tRTT52WubzTpERmig8kQ7mYvYzEzYxEImzfvoOoGWPFjmeOe8wN6zfyxS/97ylxwY6l18UsSS9FGKQXdq+bmZuXy6RpE+nu7iZcEcGwbSRZxh/04VTc2Laf9h6dQI6f0txxBIPBvrI7DoeD1StXky5kEu+MsXnzZq699tq+YyxZsgQtrmELFjEjimQOFFiGlUA3DATbxrB1TMMgKzeLSCxCWrYfl9tFbVUDGRl+EkaU4vxJSLqf9LRkOZvW7hp21S8jLz+XDRs2ULGrkoVjL6S5q4mm1iameGYDIAoiXslH5aEq8nLzEEWRCQWTWPvmOg5ed5CysrK+Ob3w/ItIMYWEFqe1owWXIw1JF8n25CHLMnE1Sv2BelqbWpgzdw4jSaOpqKhAjRnMnXcGg/eMgUDQj1aT7EU+lIsJENHDINm4RDdVldWMKi5BEATGjCkmKyuLhoZ66mrqSZhRZFmmYGw+TnMiH/nIR951S7qjRo2iYHQ+dU21xx0bUSPkjM55yzUzByMcDvPTu+9Da7RZOP68fucnN5hHbjCPQ437uf/uB/jeXd+ltLT0uOfw3dwy9oPGVTdfxMZFW8kelYXbd/o6XemqTntjF8HsAIHMD/ZnISUyU3wgGcrF7GUkbqbP5+OXDz+IqqojOqbb7T4tAhOOuJizxo8mrsYHbO91M1uaW5g6bQrr1zUT9Gejak3IsozP58elJB00QbKJRropml3Ub/7xeIy1KzZwVsE5dEe7ePbJ57nkkkv63Mw1q9YgOKAytBs69gw6T8u2sUwTSZCxRZuAIx2H5CDoSaettRlEkXR/Li1qHdk5Bdi6QlZGLpKY/Ooqyi6jJ9bBX//8TyoOVJLjLOxzMd2mD9M2iOrJ0iCSQ6K1s4mD1QfIzUnWAJRiDp59+jm++73bgaSLufTl13CILiLRCFliEI8zjZgWIxwOk5Gejsfpxe300h5qZdvW7cyaNnzAvqZpVDVXI9gSdXU1ZGYNXmImIyuIgY5mqLQ1NgzpYjbH6ynMHUVDtJ1QV5i2tjZycnIA8Pm8jB8/nqKiUbg9bkRBIBYJEavfPuwcj8a2bQ4dOsSyN96kfEc5IFBcMpoLLjqfGTNmnNTsdFmWufzDl/GHnz+BZVuo5uDXkmZoRK0IGTmlgyZpvVVWrVpFe3Un546/cEjxOCq7mFdeXcwtt3yT0cXFxxW5Pr+b3z7+SEpovgu47rarue62q9/paXygSYnMFB84judi9jISN/Pd2DnlaBdTHMTF7KXXzfQHfPR0R8gOFNLSMbAtns8TINzeSX19PSUlRxJlWppbGZcxljRvEL8nQNX+QyxdupRrr72W9vZ22jq6+Pmvf8a4cePQ9cGXQ23b5h9/+web39yOo8tDcfq4ZI9pEfROA8O26YlW0qm0MFE5A8X09QnMXkoLpvPGrp2semMVn1jwOTRDoyvSheZQOaD1TyaJWGGaDlQzRZmMIIoIQTiw/wCapuFwOJIuZtRBc1sTDsGJaJroiRiiZRHq6MApSyhyUmT5ZQ8tLU00Bod24RwOBz2hThrbDiBLCk1rdzB+QhmDLqjZNunZfjZXrMdslLBNgdbuFsJGNz1aNxYmcTNGXI4yiSmE4yEc0Rj7du/G53EdtRubeDwBlo4gCCRiI6u/CpBIJHjsN4+x/NWVSDEH7XVdjB9fxo6De1j56momzCjl9u/dflI/95deeikrlq1g76Hd7Ld3DOi/rRkaYTVEVl4WBxO7IdE/SevtsPilJRT4Rg17nRimicvhI+CfzIyZF+BQhj5uPB5hd/mrJ1RiLEWK9zMpkZniA8fBgwfZsWknaBLrD60edmwslGDRS4tOuDj7O8m6devo7ughYseoDh1C0zU6Yx00m004xP4/kKZpsmf3PryOdMQhHCoBAb8ng6rKGoqKkm5mOBKmq6OL0skTgORydElGaZ+b+fLLL1Pf0M3adZu47rrrBu1r3suMGTO48rKraGxvwhFzkuXKQRQkImaIlkQrlsOiIFiGGhXIzhnoAjoVD14pk6a2WlZVvo4gCGQVpA+aIGLZObh8Dm6/4zvMnDkTSBawVhSlz8XM8RayL7QPr+GgfdWKvueqhkrI7eznUIXjIXZt3sDsqVMHFT2xWIyc7FzmTbqcgDedDXuX8LVv3szs2bMHPRfxeJxfPfgrVi1Zi8Ploj7UjqqpuC0fpmmBCMH0AFKmRZYSgPghuqtq2BGpOmovNqqq4XQ66BWzfq/ruKLMsix+/atfs+aVDcwdczbVh2rpUTWMkM3sWWeiGxqbt23gJ3fdwz33/WRAQem3isfj4e577sbr97Jl7Xbcpo80ZxqWbdGtdmC6DC69+mI+8clP9H2OTsaStK7rNDU0MzkwfL1USFZCEFAQRQWP9/1ZzzBFilNBSmSm+MCRk5PD/37tC4OWeRmM05URPpJlwJGMmTdvHsF7g33/7unp4d477qcsMDZZj/Iourt7qDxUjUCEWEeEhBFFE1Q6e1pwyEclN9g2sUSC+oZ6SsaUUFddh2TLBDxHuuaMyi6mav8hnn76aRYtep3Zsy/i4MG1rF+/noULFw453wMHDuCV/Vy0YBY1TZVUtu/FxqZdbEdXRMZlzMI2RGLxKLqpoZsDXaJAIB2HpHHjzZ9k2rRpw54fQRAoKysbELrwwvMvIsecuNJcuAQ3szLOwrKPfEYSeoJutZN5Z5yJx51cxm7oqCOa1sWjjwy+PPryS4tQrADFBWUIgkBhRhlLl7zOBRdcMGRm8j333cOTM5/kgR//AofpodBXgtvlpqi4kMKCQgQJdtXuIKs4k+/+6Lt9fbZ7sSyLlpYWcnNz+44xElG2bds2Vr+2jrPGnoNl2DQ2NpOTXkBrcxs9h3vJzytdwOryN3n11Vf5+Mc/Puz+ToS0tDTu+ek9HDp0iBUrVlBfXY8ky4yfdDHnnXfeKWlx2Ls8bo0oW/10po2kSPH+ISUyU3zgCAaDXHXVVe/0NPpwOBz4g17Ku7aOaPzxlgrT0tI488wz+/4diUQoKi6gqbtmQMHzmBAlnN5BmFZMy6S5pwWfO51AlhPlGPfRTyaCaNPc1kh9fR1eZ3/R0utm/vGxP5GRV0ph0Tg6Ohr52QMP8uQ//0tbSzsOh8KMOdO5+NKLmTp1KoIg8PSTz5At5zG+cCITCidiWiZxNc7jLz6MmtCobtmBDdiCRWPH7kGXNnvUdmbNnMeWTdu4/vrrT7i0TK+LOSl3BnE1hoWVXLY/CqfkIqHGaW1s7XO2e7p7MByJQQVca2sri19+jfGjz+oTNOPHzGBN+Yts2bJlQFZ7L7Iss2HNRs4ct4DJRdOwbBtZlvt1wplbehYbD67l6Sef5u577u73fMuy0DSNjIyMEzoPSxcvJShk4nF62XWgHBknXk+AuBqlqqqambNmIokSxeljWfziEq655pqTGmPcK/6PTsI6lciyzNiyElp2N5EVGH75X9NVFIecyipPkeIESYnMFCneYXw+H4//8fERx3Gd6FLhSPcfiUT49m23E4tqxOT6Qcf0dFZSXVVNqD1MVlom8jFL7Nn+XFoqO8gqnEJLcy17tm+iq74R7/gAY0dNwDAMti3ZzYolqzjn0rM559xzKN+8m3lFRxKwJFHCti0MzaCQEtKEXCRBwhRMBB2UY/qDx/UQHXoDSAYVB6vZs2fPCXfPePGFlwi1RGi3WkloccJ6N/XdtThFZz83UzM1KquqySnIQVEUtu3fhl910djYSCAQ6Pe+vPzSIgTNTU7GESfc5XST7SvmqSefYc6cOYOKwB07dlB3qIGFYy4YMsxAEASmjJrOpo1rqK6ufts9p23bZtvmHYzLnEQkGqGxsZksf9IhTfNn0tpc1+dmFmWNZnnlXhoaGt7zva4vv+oyfrX1ESaYk5Glwc+1bdvEtRj5hfnDhn2kSJFiIKkrJkWKdwGnOhP12P23t7cTCoUYO3Zs32MZGRn8+a9/HFaMVldX84Nv38Glky8nw5+B85jl97q6erIco6jcV07NgX1kmDkUZpdghnWy0nIRBYGCzCKiiQirF63l1cVLKZTG4Hb0d4iqWiqxTfDJXkxLxakE8Tgc6LZGRno60uEfe8PUae4KU5Cdx63f+SJpaWn9XtNIychI50NXHVnSb5Ua6djfQme0C9s+JklHh7b1zcQTMdqjHYSqPVx/7acpGTeKx3//KD6fb1AXs5fjuZlbt2zFZ6fhkIePofS5/Th0F9u3b3/bYs80TSzLQpFkqiqrkXHiOPzeKrIDh+Tp52baNu+L5Jb58+fz4tSX2LhrLWeWLhggNG3bZk/dLkSnSEHh6WukkCLF+4WUyEyR4h0gEomcMufyeNi2zWO/eYza6noe/d0jOJ1HnMHjHeeJPz1BOpnkZwz8wU0kEtTW1jM6ewJr6xaT4chl1JhSbNuiubmSlpYW8vPygGSJqLLgJP676p8IY2XUCvVwjW0By7LYsncjoi3hEjzEzRhhtQOfmIZDdmIYBh6vh3Ckm5gWYvSYAjIynMyfP3/Q3uFDcfR7cOFFF3LhRRf2bfvwtVfzo/+7g46tXcwsuhCX4kY8KutZ01UqGysoGj2btEAmCauLnu4j+xvMxezleG5mOBzGJY+spp+MQjw+sETViSLLMsH0NJqbm/u5mL0c7WaKDgHFKRMMBt/2cd9pXC4X37/je9z74/tYtesN8tyF5GUUJIuxh9ppCNXizJIZK4/F4xlYTipFihTDkxKZKVKcZiKRCLd8+etEwiMTBye77t7evXvZvHortg7Lly/n0ksvHdHz2tra2LR+MxErxvJDrw3Y3tTUSHu4B49dRzwexSV4EAQBQZBwe4JUVlYnk1EOO3uZwWzGF0xk9PRCLrr4QiRJQhAEdu3aRWXTAURdJuD0kyFkEE6EiOtREmqIzlaDNC1AekaQKRMnEsxMY3PLmhM6B5FIhC//75cJdw9d3qenK0R3tIua5mqcipvROWMQBIGoGiGU6MHt9TFu1BQEUaC6uYfOjk5g8FjMYxnOzQwEAsT1I58NwzAQJalfTGbfNkE/aeLnkisv5qEfPEI2xX0uZi9Hu5lCmsmchbP6anP2Eo/HCYVCpyRJ51SSnZ3NT392D2vXruWVlxZTXrEVsMnMyeKmz3+SGTNmcNv/+/47Pc0UKd6TpERmihSnGU3TCIfjTJ16KW738MLxZNfds22bp//7NGlkkRZI46l/Pc15553Xz808mqPdPkEQ+NptXx205mV3dzcPP/w4C+ddgK6pWKtMSrIm921PC2QMcDPdLhfTymaQEEN9ZY4Mw+CNJcuYOmoW26Nb8Hv8OCUXwbQglm2j6gnaQ63k5GUz54w5CEA0MfI6kL1omka4O8rU9Nm4HYOLtFgwRmdThPxAIV2xTiKEcMgKmdkZ6E0aQU9en7vpdQapbm4hkUiwZPGr6FERr9tPJBYacg5+JXdQN3POGXN44d8voRkaiqywadNmMjIzmDB+fL/nh2MhNCUxZDmkE6WsrIzmcBN+9+AiMc2fSVXdHhyyzdeu/NKA7X954q9s37KdXz/28JCfp3crHo+Hiy66iAsvvBBVVbEsC7fbjSAIdHZ2vtPTS5HiPUtKZKZIcRqwLIvt27ez9NXX2LxxOzu278S2RzN2XCl5eXknpbD0SOh1MecVnoPL4WblwdeHdDMjkQhf/tJXCYeO77g2NDTQ3tnNJZfNpqW5BpfLi/uoQveiOLibKQoihmH2jVu5ciXV+2opy5yEZmgYlonz8Cq1KAi4HW6y03LpaGsjGo3g8749d9ft8AxbkF+WZPxeP5bT4twPnYPT6aSiooK2Jgdez5F6iX5fAK3OYPXq1WzZvAXBqbHp4OLjHr+p0U1bW1s/92/69OmMmVDM7oM7yfMU0d3eTSQUoXj0aBAEWltb0TSVvS27mHzueIqKhu4NfyKsXLGK8aXT6WhpQe9SKfCNwa0kz41uqjRH62kwajlz1IwBwraxsZHXFr1OIqSdkDv+bkMQBFyuwUMV4vEIAsKwbSXj8cipmlqKFO9JUiIzRYpTTCwW45cP/YqNa7YTdBdQFJjKXkc1VkJmX/khKiuqmDlrximPcbMsq8/F9LqS4myUv2RIN1PTNMKhONOLz8PtGnpJVk2ohOpXEZL209PVjm2DasSIxEM4pCP7dLu8tLd39nMzOyOdFE9Jxi3u2rWLH/3fj2iqbKVSqKIz1IEndIh0TxC/Jw2HfKRcjqXaVFdVjziL/NgY2K6uLjRNI6YOrDsqS9KAhKa+c6Jr1FTXEvBm9OspLiDicvh46flFPPLYwyOaE3C4hecxpaBEkVv/39e54//uZMWGN8gS8zFVk7Vr12GYNppp0JpoJOaOIh5w8J3bv8f//s/nmDhx4oiPeyx1dXW8+fpqZk06G71Mp3zfJsqbNqLYDgQENDSCGemcPfViunrqOXDgQL/jJWuMuhgbHM3T/35mWHf8vYbD4cDvc7G7fGky/OM4431+92m7aUyR4t1OSmSmSHEKsSyLX/3yYbas2ceCKVfhdnmJxsM4FBdp/nQcDi+dPa1s3bKdefPOwHuKssxbW1v57ne/S+OhZs4de1Hf4yV544Z1MwHcLg9e99BdTsKhKA7FiaqG2L37VRTFgS5GqGzaRo6nf+KLLIt0dnaSn5eHaZl06C38z6WfZuXKlfzyvofRGmGcbzKSIJGI7cQtuIlGY4QSPQTTgzgOly9y+p3EYiOLaR0s/lLXdXbv3E2dsxH5mDaGikPhYx+6YdB91dXWYWrgCQw8Hx6Xl47mMOvWrePyyy8f0dyGorS0lGuu/wg/3HAHETNMIqRh90ikZWVguU3yx49j/sIrUBxO9u/bzB13/pQ7fvTdEy7d1Mv27duRJJH9TRuSryVTIt+TTSwew7Zt0pxBfF4fIbMJxSGzbdu2PpHZ62JOzptJui+DFcf5PL3X8Pl8PP74I8RiMWRZHtbJhJOfqJcixXuZlMhMkeIUsmvXLtav2saCqVf1Wz7uRRAgIy2Hlo56qqprmDr11LSvfOGFF1j56mqKA+PwTj7yAyiJ0rBu5kjIy81l4YIFOA508+jjv6Krq4u/PPEXXn76FRyySEF2IePyy8gKZCMIAqIkYdkWWyo2MnpCEcFgkB99505K/ZO56Kpkb/RoIkrrsnpGe0bjkJx0hjuwnSYLFi7AeYIu0WDxl6qmknBCpisbAQHlsEuqmiqH1HIM0xy4nyFczCMIFGVP4Jn/Ps/5558/5LLrSLAsi3Wr13PO5Ato6mxgX1MtHl82maMLmXfWeaSn5/SJnZmzPsSe8vU89NAjPP74r99SgfQrr7ySBQsWjHh8WtqRTk+9LmZWUbKg+Wj/mPedm+nz+XC5XCMSmSlSpDjCibXFSJEixQnx+tI3CLrz8AwT9ycIkObLoLmxGVVVT/ocWltbeeofTyNqMmJEIRrrv0RckjeOtpoOli9f/haPICDJSTfwD7/7Az/8f3fQuKOdydkzMbpsDu2t4JU1L7Ji1zJ0S6eps4E1+1fgLBD51Gc+yfPPv4AQVsgK5BBNRIkmosTUGIZlENZChLUeHC4HkXCMqprKvjG9f8drtRmJRNB1HREJSZCRBJne/+KRBN3tPQi2gIiELAx9393Q0JB0MYdxdUcXlNHeFOLNN998a6fyMOvXr6eivIoxuSW0hruYPvFipo47F0XKxONJGyB0JkyaS1t7hA0bNryl44miSGZm5oj/eouS97qY4/OOLJ2X5JXSUt3+Nj5PKVKkeL+QcjJTpDiF7C7fS17m+OOOczk9dIQtIpHISXd/XnjhBRqrmyl0FeO0PVRXVzNl8pHM75PhZlqWRVVVFWaPwYKyc/G6fNhAa3ErtdU1NLc0c+Dgfg617WPWnJlc/InzeOn5l7nxozehqzpprnQqayr6/EHLtmjtaqXFagbAFi2C3nRqD+xjimOg2ztUq81IJMK3v/FtyneUk3CCQ3L0zbejo/Nwm02BnrpuZEnGFA2swMCe9rZlU1/bgGnatHY2DNiuGQl6wu1s27sKNWHw3DMvcOmll55we8veuT31n6fJcRTQ1tOKLTgI+nNBEOjp6aampoYJEyb0e44kSWRmlrB27fB94k82x7qYkPw8vR/dzBQpUpw4KZGZIsUpxDQtRKd03HGCAIIgYtv2ST1+r4tpJaAwrRgsaKhNtgP0HlVfcSSxmcc7TqgrzJzZ8xAEgd21uwhHQwiiQHphBqUTS0kkzmBL43o++8XPcOaZZ/K7h/9AVqwILCjLnoAgiP0WoSe7TSzbQjNVDml7+NDs86nVKvjtXx4bICiHioPTNI1ITwyfEiDLnYNTSi5hm6aFioGIjCRKWKaFS3HSbrZgHfMeaGYC3UxQVFiCJA7+Xqp6nG45i6s/8SH8fj9er/ctL6v2upgLSy6gsukgiuzu21eaK0hddR3FxcUDluM9Hj9d3T1v6ZhvhaNjMY+lJK/0fRebmSJFihMnJTJTpDiFFBbl017VRkFO8bDjdEMHrLcVxwfJepUbNmygvb0dWZbZuXMnDVVNuGwv3Vqy3l9XtJN1O9ZQckwrQkl3vEU306ahvgFFcLCzage1jdVIuoxH8GPbFgc4gOQSmF42k+LgWJa99iZ5eXl0tncy3V9Gc0cztg4ut6t/pONhPaeaCrIh4VScOAQHWVlZJ9xDWhQlFElBkZLxirapYZk2TlFBFCUMU8ey+juYsiShKBIH2tbTo3XhzA8jDxPvWFo2huuuu+5tJX0c7WI6ZAeyJGOYGrZtJ8vrOD2Dupm2bdPaWk9LcxO/eeRRdENj4cKFzJkz55T12x7Mxewl5WamSJECUiIzRYpTysWXXsjDP/sDljWrX1vCYwlFusjMSsfrHTp2czhUVeWvf/kbr7/yOvFODcEQUbUE5RXlWJZBZkCgU0kuPSd8CfZ01OKf6MDn9aE4FCRRoqSkCK/fSzwePyFRoGkaPd0hVC1BVWUlZb7JBDzBPvfNtm3aEy1s2bWZcSXj6NjTzL//9W8wRdwuN6Io0trZSmFe4aD7N61kEk5LdxPj55adFNEUiUbBBkSwbQtBENA0DUu2sSyTmBrD4/Rw2RlXEk2E2du9k4cf+wXp6elD7vNkZBUf7WIC5GcUYu3fSCTWid+bicBANzPU08nqFS9ycNcWJhSMZ0vPTiLxCMtfXkXB2Dxu+drNzJo1623N61iGczF7SbmZKVKkSInMFClOIWeddRZPjn6abXvXMHvyOf2WULXDrQNjaoxwvJWSCdOIx/oXcx5JcWdN0/jZfQ+w9c0dlGaOZ1n9G9S1VGPpNpZpggB10VpkSelb7tVNnWWvvInX72XK1CkEMwM8/sfH35JIsiyLhJpAVTXGZ0wh4Aj22y4IAtnuPJySm/3VO/DmuNm0eSNazKBGq8G2bDoi7cTrooMKcVM0MPwm7VorN1/1hROeXy82yRaNumEQCUcQRRHNPpJopZs6cTNKSOtmW+v6fkvymTnp5ObmntLSNL0upt8OouoJVD0BQF4wi5rGnYwvWYAgiMiSghGzqKmpoaAgh6WL/onaFGOSfzoXnnE+siwTi8VxOGUONOznnh/dyw9+/L2T1hkIki5mokND9+g0dTYOOc6LP+VmpkjxASYlMlOkOIW43W7+7/vf4Sd338vqba8wtnAKGcEcFFli3/6lxOJRdCtBYVE+1ZWdVFcO3MfxijsvWbKELcu3M3/sOZiWha5peGw/hYzDIbkQBQHTNtBtHa/Dh4SIaZskrDgyApMCMyhv3cqKFSu48sorT/g1yoqCpqooOPpiHgcj4EjDFfZR11SFqMt4JTfpUiaSrBA2exANCb8n0G/J3LRN2o0WemJdnD/jHM4888wTnp9tW6iqSlusFXQR3dAxDROH7MChOFGk5NdgOBbGNmzyCvJ45A8Pk56eTnNzM+Xl5WiaxqpVq5g1axZ5hwvJn2yam5tpa20j5o6xtX39kQ1+g862CrYfipGXNxFFcSI6oKO9nfqaHcQbwuQqucyYNgVFVrBJxpTKksKU0dPYWyfym18+yuN/fOykCb3mxiYyx6TTSPWw4+Q8EKXDtVHz80/KsVOkeKe46667+MUvfkEkkrz5r66upqSkhKeeeorrrrvuuM8fbPx5552Hz+fj5ZdfPqVzf6dIicwUKU4xY8aM4b6f3cPLLy3ijaXL2VMbJSvbD6KP6bPO4dLLLmHGjBlDPn+4ZVjDMFj0wiuMDpTgcrj7+nhblo3DduGWjyT3qGYc2ZJxOQ4LQRUSagyXw01TUxO/ffj3TJ8+nVGjRvU7RjwxfImgeDyGjY0oHD+TWjGchGMR3KIHQ7OImXE8soDP5SeSiBDXYjgkJ5IoYVomqq6SMOPIAYlbb/v6CYukRCJBdU0N0XAUS7HxSG5sDRTRgWWZxNUYpuJIdiayRQREmhqbSCQS/P63v2f9yo1ImgNFUNBtHcv5B846dx6f/5/Pk5OTc0JzObbr0LG4XC7u/8V9mKY54D1vaWnhscd+x959VXi8uXjcAaKRdnZtXslE3zRmTp9KXu7g4nd84URWHnqdjRs3cs4555zQnIfi7nvuPin7SZHig85jjz2GJB0/OfS9SkpkpkhxGsjJyeEL//N5bvjE9dTV1WGaJhkZGW/b3Tl06BBN1c2cW3Kki49pWViGiSj1z26WBAXN0PpEpiI56I530tHRSWdbJzm+PJ595jm+8c1bgcPt9AJudtYsH3YOsViMhvBuRCQSWgKne3A3szPcQU+4G0wBQU7WpRQtCU3XsbGQJRlZkTBtg1g8mkzEEZOZ91pC44F7f85Nn//0CQmlv/3174Q7IzhkJ6IkEDOiGJbZ96Uu2AJRNUJciCf7UosCkXCUb379NoJWNjMLzyToOxKH2R3pYvOrO6g8dAc/vvfufj3Hh2OwrkPD4Q96+4Uv+P1+HnzwAfbv38+6devo6OimqipMWVEpl865eMisd0gm4QSlTDZt3Nzv3G3atIlJkyalutOkSPEOMvmocnLvR1IiM0WK04jX631bPaZ76XXFmpqaMHQTVddQ9WQv7ng8hmWbqFYCy7YwLRMBAcu20NFIWHEs28ayLeJ2jHXr16HZGuNyynh90Rt89GPXMmrUqGQ7vd8/Oqz7BtDU1MS3vnY7RXYJDYeasEwLvyetr12jYZl0hNpo7mxCshUyXFl9j0uihENwYtkmmqkSj8dRZAcepwdFdiSXuvUYQXcGWq3Nz+9+iM6vd/KRj3zkuOeoqamJVa+vwu8KoJsGFcZeVFVFFEQE64gAN20T3dJxSA6ihNFNnbrtTVz6sWsGtJwM+tKZP/4cNhxcw2O/eZy7f3LXiN6vwboODUVci1HetXXAeRcEgYkTJ/Z9fp5//nk69nUPKzB7cSkuwqEj8b3V1dU88JOf8+Hrruamz9w0oteQIsV7nXXr1nHnnXeyfv16bNtm8uTJ3HPPPVx88cWoqsrdd9/NP//5T5qbmxk7diw/+tGP+NSnPnVK53TscnnvkvzatWv5yle+wtatWxk7diwPPvjggAS6v/zlLzz00EMcOHCAzMxMPve5z3H33XefsooSb4V3z0xSpEgxIiKRCF/+0lcJh+JEoxH27z9AY10bAgKmZRIxYpiKRa1QiWVZhK1O/CTbAFpYCIaQrEkpChi2jh0XiFtxnl/5LE6Xwr//9R9u/+53AEbkcqWnpzNlxiTC+xNMmz2VykNVtIQakQ7XIDJJCl6P7KXDbiHbm0dbtLnfPkRBQhYcxI0oigAOlxMBMA+XrBQEkbLCiUTiBTzx2F8ZP348kyZNGnZeq1atQlIVXIqL6XnTiUXihLvDOOT+S+6aqtJjduFwuKizDiJbCp5EGk2NTYwqKhqwX1EQmTZqJps2rKG6upoxx5SCGg63w4N3mO5PJ4LX60Wz9b7yRsMR02IE04+0gnz2meeINMd5+dlFXHHlFWRmZp6UOaVI8W5lzZo1XHDBBZx11ln88Y9/JBgMsnnzZmprawG4/vrrWb16NXfeeSeTJk3ilVde4dOf/jTp6elcfvnlp3Wuuq7z6U9/mltvvZUf/ehH3HfffXzsYx+jpqam71p96KGHuP3227ntttt48MEH2bt3Lz/4wQ8wTZP777//tM53OFIiM0WKEVJdXc1zzz3Hxz72sVN2DF3X2bRpE2tXr6OrswuP18PceWdw9tln95U30jSNcCjO9OLzcDpcuKLrkQ0nPpcf0zTwWBUYuonfChA3ouzvXkkx4xFtGVGUkEQJGxvLttEFFVl2EiWEJMrUJ/bx5qvL+eSnPjEgNnMoBEHgyg9fwcM/+Q3R9gSiJHLmgrnE47HD20W2bd5OwowSEyOMdhbTEmkE28a27b5EFWwbEQnDNDFNE3mQOKXc9Hxq2qt5dcnS44rMupp6/EoaUI9lWxiGgaRImMk2P0Ayo1u1VERBQrXjqHaCgJSOS/ZScaiSwsJCxEEEnM/tx6l72LRp04hEZjgcpr2tnV2tu1AEBZfbRV5+HhkZQ/VBPz6zZs1CcFt0RTrI8GcNOc4wdXrsTs6aPw9Ifo5XvraKeWVns6thG68seiXlZqZ433P77bdTWlrKsmXL+sJlLrnkEgDefPNNXnzxRV599dW+xy6++GIaGhq48847T7vI1DSN+++/nyuuuAKAcePGUVZWxuLFi/n0pz9NOBzmzjvv5Pbbb+fee+/tm68sy3z729/mO9/5zrvmxjHVuzxFiiGIRCJ0dnb2/T3xpyd46clFLFu2rN/jnZ2dfdmGb4d9+/bx1Zu/zv0/+Dk7l+6jc1eEytV1PHrv7/jS525m5cqV/ca7XR783jSKS0pI6AlEQUISFWRJOdyn24kiJl07wRZRRAdOyY0iOHEILhyCE9mWcYouFEEh6MvDtGxi7XGefea5E5r7ueeey9T5k1hfsYrGlgY0TaWwoJDCgkJsLDqj7TTa1aR5g2iWjmHpWLaFasdJWLHknxlPLptbKgkjhmapaJaKaRv9jlWSM5Y1b64hdlQP9q6uLh759SP93gdREhAQURwKh9Ryml1VdKY19P11pDVQI+6nwVFFo1JFJNBNMCODoqwSJoydTiKu0dLSMuRrdghOQqHQsOfFtm2WLFnCN79yG3VV9YRbokTbVJqr29i0bjPr124gGhtZnOaxZGVlcfYFC9jTsKuvluhg7KnbTX5Jbl8Jo2efeQ635iPoS6c0ewIvP7uIjo6OtzSH43HsNTTc38m4hlKkGIxYLMb69ev57Gc/O2iSzdKlS8nIyOCCCy7AMIy+vwsvvJBt27ZhmkNfX6cCURS56KIjcfalpaU4HA7q6+sBWLt2LZFIhI9//OP95nvBBRcQj8cpLy8/rfMdjpSTmSLFIBybqBGLxdi3ez+SLfOT79/Ls/96vl9f6mMTNbZt28bEiRNxu90jOl5FRQV3/+AnuCN+PlTaP5HDsi2qWyp58J5fIfxIYMqUZO9uG5u6ujpqq2sJx8J0dXchiwpxNY5t2eioqHYCi2QspmRLmKaBU/Ic5Z4JvT4ioiCiKG4MA5a/uqIvNvN4WdG9uN1u0rOCtHbXs2jz80wbPxNsi/quWrpdrVx45oVMGjWVuBajZVk98XCcZqum77VoCRUEsEUbt+AGI/naI1aIIseoPmfT7w6gdeqEw2E8h1tjLlq0iJf/u5iiUUVce+21AIwrHccqex0fPfd6TGtgP/Ku7i62bt5JZiCP/ZFtLDznUjp72qnfXY0oSngcASorqsjNzR3UzTRs/bjv7+LFi/ntg7+nyDWGdG8d6Z6MI60tLZOOznY2b9zC3Hlz8Yzws3I0n//C56g8WMn6fauZWjSDNG+wb1tCi7Ovfg+qL8Kd374DRVH6XMyZ+clSUHnpBRw6sP+UuJlHh3WMBH/AzeO/fzSViJTipNPV1YVlWRQUFAy6vb29nc7OTpQhOno1NTVRNEjozKnC7R5Ytk5RFBKJZO3c9vZ2gCFr39bV1Z3aCZ4AKZF5mJaWFg4cOICu6wSDQaZNmzbkBy7F+59jEzXKy3cxyekhw5tJU08Do4Xx5B8uGXNsosbBgwf56Z33YUkmX7/1a5x//vnDHsu2bf70hz8jdzuZXjqwM4soiIzNK8VusvnDo3/k/ofuA6C9rZ19uw6gaRrF2WOJhCPEozEEWyRBHAUHhq0gIKDgRBGc6Gj9jisIySXjXs0pS06iWgxHvIDnnn2eL/zP50eUFZ1IJNhbvpd0bybXnf9J3ti7mJL5BYwfPx5N03j+Hy8xuXgqsqSgmTKKJDMl72wUUcEGTNOktbkFzdJweh3kZyR/DFQzwb74Dq6afy1OJSnOdENHlMS+L+Guri5eemYRGXI2zz75PBdffDE+n4+FCxfytz/8g55YD7nB/uV9bGBP3T78znSiRphAejplxVNpbq9n/+6dGJZOmi+Dps5qWlpayD+mNqaqq0TFMNOmTRvynHR2dvLEb//C+OAU0n2ZCKzpt10SJbLTcmjpbqbiUAXTpk0d9hwPRnp6Onf/9C5+//jv2bB6A3KjE8V2ENdimC6d8dNK+dKXv93XgvJoFxOSoQ69bubJjs08OqzD7TpOslMixs6a5SO6mUmR4kQJBoOIokhj4+CNAzIyMsjOzuaVV14ZdPuJlis71WRkZADw7LPPDhrWVFJScrqnNCTvWZHp8/mQZRnbto8/eBjq6ur4z7/+w7oVGxA0EREJzVbJKszgqmuv5Oqrr35XZWqdCL3n5u2eow8ivefM5fCgqwZdLT3k+PJQZIWgI4PmumbGji5BEMU+J9A+HGP47NPP0ri/mfqOWv7m/Tvz588ftr5jRUUF5Vt2c2bROQz3To3JHceKgxVs2rQJbJvqymqMhEksHkcOyuRk5ZDwJ6irq0PQBQRBOOy2Cn3/YYONhW0LR2Ihjz6qkHQVxxdMZtniNznv/A/1iW3XUVnRoZ5u6uvrmTx5CggCe/fsYTQmdaEKVu1YTiKmsXblWq6//nomTJjAhtUbqWmrSYrlw0e0bOvwkW1EUURSZBKxbrLcWUfNSEASZRyKs++xmrZqSieNJS0tDdu2efnlRRhdFgvGn8Wq/W+wdOlSrr32WtLT07n6uit5+s/P43LMJ+A5kvjS2dlBR3sXLrePJrWaBXMvQpIk8nNGkZaZTkNXJcXB8UfczJwchKOc6331uymZWMzkyZOHvL5WrlyJGYKC8aOIqbG+191/tECaJ0hzQzNlZaU4Xa4Bn6fjkZGRwf/94P9obGxk06ZNhEIhNE3jrLPOYvLkyQiCgG3bSRdz6Spm5J/Zbw656QUcPLCfRS8vOqluZu/c3S43Xvfx3Mkj31Xvxu+r1HfpyeOd+D31er3Mnz+fv/3tb3zrW98asGR+0UUX8cADD+BwOJg+ffppn9+JsmDBAjweD/X19X0rN+9W3pvqiWTQe3p6OoZhHH/wEFRUVHDPnfeiNVtMLZhNui95d2BaJnVtNTzx8N84uP8gt37z1ves0AROezzJ+wHDMLAtCyyLqspKFNuJIilgJ8vYNHU30NTcRH5ePlgWtpVMLNm7dy+r31hLupVDl9VN+dbdLFu2jIsvvnjIY+3atQun4cbj8CSPOQQCkCZmsHP7Trq7u5G73eQHR9ET2Uc8FkPxKyhK8s8yLEzBwMDAxsZER7clDHQsywbbTu5RAJHkMS3bQtdVnHIGTskBYYn//OfJpLtkCYj2EZF1cF8FbS3tFBWMxu12UXWwGl01UC0VZ8RHvphOfXk1X/vCN1h40QI+dNG5PPmHZ8jyZ+OQFRSHzEH1SNyQbdt0mu1EiaIaYaTYketNcchIgoBtWSS0BC3xBm644quYppl0MZ9+ibGZ4xFsGBMcxzP/eY7zzz8fn8/HDTfcQCQUYcnzrxGw0xmdVYxTcbFz3w5qwoeIxyPkjS6gqbuS5m1VyeN5BQ7Wl9MVbifLlYuoC7R3tJOVmYVh6uyr30vY1cVtN/8Qy7KSTvAgrF+zgSx3bvJc946xe8/9EZyKCytu09nVmSyoftTn6US+33JycgZ0bDr62n/mqWdwaV7SPGkDPmfjMsfz0rOLuOTSS06am2kYBrZtkbyMhhdnlpXszHSir/l0k/ouffukp6cff9Ap4P777+eCCy7goosu4itf+Qrp6els3bqVrKwsvvCFL3D11Vdz2WWXcfvttzN9+nSi0Si7d+/m0KFD/PGPf3xH5jwUaWlp/PjHP+b222+nvr6e888/H1EUqays5IUXXuCZZ57pCyV6p3nPKqdt27Yxbdo0srOz39LzVVXloQd+idTp5KwJc/qVAJFFkZL8UnLS81nz6irKJpS96+8WBsO2bUwzWXj6eCVOUvRHlmUEUSQcidDS2Eq2Ny9ZFRwb27Lxyj6qKqqTIlMUEUQRWZZ56YWXsLoEzJjFWP8Eqnr289S/nuaCCy4Y0s00TRNJkPs5Zcdi2zYd4XYaOuroXN9CRUUlk9KLkEQJSZCJRCL4/QEkUSI/P59QTTcxO4IqRrExiQlhNEHFxESWHNh20t+0sBDtOAgCbV31RPRO6tpNnnjzrximwabqbYiqieqRcUjJ5WlN0+hob8chKOzfu5+EGseKQ4Yzmy6rhVxvAU7JhRsPxCwOrq9EUzXmX3Imq5asIN8zmgvPuDzZIci26Qi3Ut1WScnMUfjSvNTuaWRq4QyCvqTYkSUR07ZpC7ezs3YzUxZMZPbs2ciyzKuvLsXsgfzxhQiCwKjcMVTvr+DNN9/k0ksvZe3atdz85Zs5+5yzWfrqa2xaswkzYmEVaFx5yYXMOWPOoMtNlZWVPPPks3Q3dRIQM2gKN1DXU0W33Ul+cS7f/J9b6ejoYOnSpTidTsaPH9+X/d9LT3cPti2gmRr0vreCcPhzdASBZEiEZdnJz8BRn6e3cnNrWRa1tbX4fL6+uOHa2lqWLVnO1MzZxLT+MZKyJJGfWUjFgQMsfXXpSXMzZVlGEESSL2no7x/Ltunq7KSqqppvfv3bKIrC6DFFXHzJhcybN+9d0fM89V168mhra3tHjrtw4UKWL1/OD3/4Qz73uc8hSRJTpkzhnnvuAeDpp5/m/vvv57HHHqOmpoa0tDSmTp3K5z//+XdkvsfjW9/6FoWFhTz00EM88sgjKIrCuHHjuOqqq4ZtQ3y6ec+KzEgkgmEYb/mC37x5M81VrXyo9OJBg/oBfC4vY9JKWfT8K1x99dXv2RhNQRBSX4wnSO/5qq2tQcGJQ05etDZJIyrgSaO5p5HmlhbS0gNAUpisWbYOMeLAIToRJLAiFhvWbWLJkiVcc801gx4rEAig2SoMUe+wJ9bDuvKVNLe1YEQt3B0uEj0qYSVMnVmLKAgYmkk8FsPr9eF0OCnKGk24J4KJhxpJIdOZg6GZxIQYGRmjwBYxLANTMPD7fViWRUPzQdzedOadcy1u9+FySXqCfVvfJCttNE7ZjQ00NzWC3YFDdNHY0IhtQq6zEEQLDod3CpBcBg41MK1sCnu37CIqRtA0g8qGVagx9fAoG1EWSUsL0Lm5HUxIxBPs2rOTgCcN5+Hzrls6JibpmUHEHfDNr36Te39+Ly8/u4jS7Al917AkiJRklPLcf18gkUjw1D+eIT09nTPOOIOZM2diGAaqquJyuYZt5bZw4UI++clPsnXrVjZt3ERPV4i09AAzZs7g0KFDfOGmL6LGDAREbCwkRSIzK4PcvLw+YVhZUUGsK8GOyh1cPvcKBEAz1QEFi2zbJmHGMTGIJaIktN7ST2/tuo1Go3z7O9/DMPpCbamurqanLUyjp2vAeIco8rGzr6MsewKLnnuFK6+68qS4mUfm3vuJGIima+zYvpPm5mZiPRrZzvF43D6aDzTz0JbHGT3uOb73g9uHTNg43aS+S98+76RTvWDBApYtWzboNofDwR133MEdd9wx5PPvuusu7rrrrr5/jxkz5oRCKAYbv3z58mGP0ctg1Rc+8YlP8IlPfGLEx38neM+KzLfLimUryFRyj9stY3T2GFZWHqK8vJxZswYmZaR4/xKLxQg16RT6BjpdoijikXxUHqpk5hnJvuMvv7gIo8Ompb2BhBlD1VVsyyYSCfGdr3+XhoZGbrjherKy+tc0nDt3Lr/3/ZH2UBvZaf0DzMOxEEs3LkKIyEz0zKDb7gSHTavcQYYjG1VTSWgqPkcaoVAy21oQRIJpQQxdpysUBsAhOnA4RCJqDwktjEPxIjklstIzME2NUKgdSRJID6Yzbdp0PB7/4XMQ5uCO1YcdKQlN00gkNCRBJqCk0RDtwS14UCQF3Vb7zV0SJVyil327DxAR4uxq2c2MMeczIXs8wYwggWAAj8uD3+9HNRK8tPgflLqmIiLRHmqgqKiAeDyZTenz+8jPy8Pn95M4nGi1aFEyFjNvfH8BMiq7mEN79/P7x36PWw3w338/xezZsxFP0B1UFIV58+Yxb16yvmQikeDee+5j07KtiJqTeaMvxal4AJu4FicU70EOu5k5awZOp5MxwRZ2bNlOV6IK2xb6SikdS0KLo4kqXk2iqiXpPPqD3rfsRmiaRjSqMnvO1XjcfsKRCKHuTeQEFBS5/42yrieoql7F3ro9uBQnHa1dp61upmVZ7Ni+k+72CLnpo2hPVJKfMwqv209BzmhMczabd6/gJ3ffy30/u4dgMHjK55QiRYqTywdWZLa2tBNwB447TpaSdQR7enpOw6xSvJtoaW4hj5I+F/NY0jxpNPU00NLaSjgUYtUbq2isaSUeUclTihglZyAi06m1Eu+J8Z9Hn2bjuo388M4fUFZW1ref9PR0zr/0Q7zx1ArSfeciS0eEwIa9ayEsU5Y2hfZQG4pbIRqJokgKpqXhEB1YlkFMjyBbMl0hGY8n6UL607yE9U5s2yash1AEB5ZgEtc7iOmduNwu2isOISKgKAq6qREOh1mzeh2Kkiyno+txOjs7aY624lRcCAjYJkiClEwlskVEZHRTG1B117RMYoko3W1deDxuREskqGThwEV3c5hQZ4RpM6cRTEsnHA0jSRIehw9JkIkqHqZOnoZrkKVSgWTR+iUvLWVC9tQBzpIoiHj0AOWVu/jsJV9ky7Z1bN26lTPOOGPY91tVVRwOx5BO1d/++nd2rtrNmSULaG56EafiwaUk455cipegJ4PGzjr27tnLzFkzyMrKxuFRCHV2Y1oml591FYZ5JBZSEiUEwWZj1Tqu/dzVXHfddX3bHA7H2y7l43b58Hj9GKZNflHhoI6LqsZwdCrkTA/idrvJZf5pKyHU0tpCZ3s3eRlj0E11wHZJkpk79TxWbVvE0qVLuf7660/LvFKkeCv0hlQMhSiK/crefVD4wIpMl8tB2Dx+uQzbtjFs4z27VJ7irVFRUUFXRzcThnFPet3MqopKypt3Yxk2hmrik/w0q3U0k6xVZtkmhqbTVeOgvrmOazZey9pNa0hLO5Lt/NnPfZbqymrWbFnBhNzJ5Abz6Yl109BSz1jnJFp7WlB8EoIo45V9SAJUdG3Esi26E10oOJFEEalHJiMzPSkGbYvuUAjJKdLmqseyLCJyiAmTxuPz+YjH4tTsryPgziAhQGtXK35nHjn+USiHWy+qWoxqyYVTcmHoBrquI4syuiCi6RoSEoItEI1HcbqdWJZFQotjSTat3c0kYippYgaWZSIIIoqk4HcH8LsDhGLd7Ni2kzPmzUFWjt9/+2ja2trI9eeRVzhwGVU3DOJtKj4rQG1rFVlyXj83czBM0+QH3/shZ5+zYND4656eHl5/5XUm5U0b0JayF8PSOdiymURtjD0V65BlmYSaoLung3+++hf8nkBfL3QbMNEpLh7DvIvm8NnPfvaUxR4GAgHmnDFn0G2xaBjTqOT/fvDdvrIop4v6ugbcih9JlNCH+G0WRYnivIksWfQ611xzzbsq1ixFiqP561//Omz85p133jnoMvj7nQ+syJw1dxZPb32B8UwcdlxXpAPRRV+duRTvPCMtDg5v3RF6c9mbWKZFZ6yDqJqMhTEsAwurLwEAkkkLkUiIeDxOvj2GNCEDl+BBQEQWjlxempkAA3xpXnZ3bGbr1q396mf6fD7u/PGd/Ptf/+b1xcvYd2g3HZ3txCIJYv4YuaNySM8IsnfnPvL8hczznZPsmGMm2Ny0lmyzCM1KoNgKc0vnUlhQQG1dLbsrKigcm8X0KdOIqTG2ta7nkT88THp6Ol1dXXz95tuYPfpcAF5Y9QwTJ1yK+6je2qYpIYjJODRd18ECRVSIA7ZlJ5N3LBvd1LF0Ax2dHqMbQRMIRXtIkzIQRZGYmVy2l8Qj5yTgCaKFNCorqhg/sXTE742mq7S1tHFWyTmDuo719fXocYMx/jJ2V5RzxfwPs3nb2mHdzPXr11O+cQ/N9c1cdNFF+P3+fts3bdqEFjLJyc0lpsYG3YdpmWBalEiTCArpBLwBDJdOs5lHMD2NcE8Y0ZQQSNb/rOUQl15/IV/80hffFcktpxPLsuju6iHdm3fcsYW5Yzi4fQstLS0jbnWaIsXp5uqrr06WlxuCd0tc8enmAysyzz//fP77t6do7W4mJzj4F51t2+xr3MvZV80fEEeX4p3h2E48x+PYTjzHwzAMtmzZQmVlNbZoEzMjWJJFMMNPRctubDP5AymaRxyxSDyKbdp00kpMCIMJIDBRnoksJB1wSUzWX9UiOrIis2b1mgFF2n0+H1/80he54RM3sH37dhYvXsy2JeWcM20hbreLdWvX45a8yYxyjjh/kiBhWxaCLWDrNgcPHCQrK5Pq+nrcGUHaOrsIRyLIjmQB8/T09D7XyuFw4DksKmVJ6XMwj8U0TSzTQhEdfd2C3G4PuqqRUBM4SD4vMyuTD11wLjUVNThxkZ2WC8C+7g7cqntAsk3AE6S9pYWi0fkjen8Ampqa0TWDmq5KmiIN/bZZlsm+PftRLAdOwUXMitHYUTekmxmJRIjH4/ztz38n25FHR30bTz/9NB/5yEf67be2thbZVEaU9OEQXUjIOGU3kiDjdng468yz0DSN7u7uZNkjwSRgOLnx0zficrlG/Nrfi8QTA0W5aVpoRhzNSIAAmj50VyBRlJIrSu/i0kYpUmRmZr5r+oW/m/jAiszc3Fyu/8zH+dfvnmQKMwd0BDEtkx3VW/EUKNzwyRveoVmmOJZjO/EMx7GdeI5HNBrlFz9/iK3ry/E7c8jKKCCkh+gw2jnQECYWCTPJPx2H04l4eNnTNA06jU4CZBIgHTcedFujhgPoltY/qdYGTVWxRJuWptYh5xEIBDj33HMJhUIcXF+Fx+2mpbWV7vZu3JKXrkhn31jD0tFMjQpjD6IkIpkSVS0aTZuq6E7ESUvLJ2y107mnjjHFxW85oSSmRjFMHUWU0S0Vw9bRLBUsUO04NjaWapJIJBAQ6O7qwaW4k/9f7SRMN163d8B+HbIjudx+Av27MzOzGDOumJtu/SSBQP+46jVr1tDU3si0UTMRhKSYzE8vwKm4WLPtzX5uZu8NS11VA5WHqkh3ZxBX49x5+4955p/P9UsQamlpoaWxlbmlZx13fsnQx4FiNOD3EzjskEYTUWpbDo74Nb8XcTgc+ANudtYsH3R7ZcdupA4Xbpfv8HgZWRr4k9Qdasfldpz25fwU7w+2bNlyyvY9Z87gYSgpjvCBFZkAN9xwA4Ig8ORfn+Jg6z6yPbnIkkwoHqLLaKOwNJ/bv/8dCgsL3+mppjhMR0cH4XCYhFPF5/CfNBfIsix++dCv2LHhIGdPuxqX082MCfMwzKR7Eo2H+fczj+Ny+Jg5fTrZWVkgCLS2trBqxWpsU0VBQRQkRFvCwkJDPaqrDoe76YCuaSOK8Z01axZ/dP6ZnlgPkiSSVzS44z7WnsC+jp1MK56FV/GyL7QTd46P6dd+iryyMiIdnVS9spg7f/gjysrKTih8QBRlEEUq1XJMW8eJG2yLqNlOm+bDNExM0SRhJdtZOkIOEloC1UhgGjbtPc20Wy1MnzidPdX7Bz1GMn505GVAfF4vWVlZXHDBBf2ERzQa5R9//ifzSs+mOGdgW7Vj3UxN0wh1RXB3BZnmOpN0bzqNiQa6o13kaqMZWzi277nNYjMv1D5FNBHta285GMmOPiYu1wdr+XswfD4fj//+0SFv8l544QWe+9drnDXtYgRBSCZZOgb2b6+s38uCc87sF8OcIkWK9wYfaJEpCAI33HAD559/PsuXL2fbpu0kEgkm5Y/jQ+d/njPOOCOV8PMu4cCBA7zw3IuseG0FB/cdwna4cClOcgtyKR5TTPBt/gDt2rWLjWt2cPbUq3A5kz90Toebo6VCwJeOrDlpbWmlqKgQAYF4PEGmP4t2vQ0RCY/Lg4GCy3RRlFuIU+ovSFQzQV3XfqbOmHLcORUWFjL37DPY8cZOzhp/NlmZA0M2wpEIu6q2M7qomMvOvRxJlIivj1IVb6J45kxEScIdCNA0pphlK1YMWYZL01QMy8QwTaSjliUFQaJkzLn0dHWRsCIEHEFMQ2NPzavEdRVREhEECdl2kbDiaGaMPyz+LaZhYZoGTqeLgNdHQcYodlXuwTQNTMnoi800LQMTE5droLg4EVpaWli+fDmhpigzJxQPOmZC4aQBbmZPdw9i2MvotEI0Q0NL6LjwUHWgmgmlE3Ecvv5zs/Pw+NxUNh9k0qih+5WbpoHgEHC7397reb8w3A3N1VdfzeuvrqC28RCTS2cPOqauqZKo2cYVV371VE0xxQeE8ePHn9T9HThw4KTu7/3KB1pk9pKTk8P111+fKpHxLmX9+vX84qcP4Yx5mRScQYOnmQJPIbYF7XWdtDa3Mn3WdHJzco6/syF4benrBN15/ZJeBsPvDdLa2k4ikSyqXVtVS8CdRkgJgQ66qSErEqIl4FAcfV1yeokZUSzJZPLkyXR2dg5+kKP45I2foKrifjYeXMe00TPwuo78aKt6gjc3v06P0MkNsz+VzNI1dIg5MASTjrp6ssckBVfJmWey7IUX+WhVFSUl/V0+VU/w0vInaeloRnHtRjpmzrqmY1omBgYhoxvTUkGAXP8E0lyZWEBUD2FbNvbhPuSiS8CybBRJoq5nJy+vfZ7OSDc+uxKnw01J/jgkUSYU6yEtI61vGfmtoKoq3//uD9lbvpd5oxYmk5EGweVw9XMzTdOkqamJUnE6kijRGerAgQNFcdIVaqempoay0mRCkgAUFhXSI3VS2ZRc5taPqU7RHe1AtzR8Pk8yCQgTw9CxLBND1+EDltxzPLKysrj9e7dx3z2/YMPOLsaNmkpmMAdBEOgJd1FRt4cevYGv3Pq/TJw4fIJmihQp3p2kRGaKdzV1dXU8dN8vyRdGM660jGgiGbunaRpqVCMrM4dwPMTObTuZv3A+Pu/wInEo9u05SG7GuOOOczlchMLJeL7Ozk5sTcDlcyMKEooko1v6kAkKXaEu6kPVaKLKT37w0xG55P6glzvvuZO//vmvbNi0GpfhwSE4MWydDq2NungVV8y7itz0ZOJMfX0Dui3h0P1sX/IaF938PwiCgD87CzE/j2dfeIFvffOb/Y5hmiaGZuLFQ0vV1gFzsGzAtjBsHVESidtRdC1OO1WMKSglHIugmU4CYkayPaYAbreLSCKCqidwmi5KHZPZI5bjEjwktBgJLYZu6fRonUwtmEIsEcU0TWJaBBEZzdSIqVFMe+C5jGv9E0mWL1/O9i27aa9rodXbQig2dE1b3dDYtXk327Zto7W1lXg0QSAjQFyLE4lFSJczkQSZsK6wd/deiouL+9xMv9/P1779FX77yO9oj7TR0b0IQZSxTAvLNrFsA02P0NxRT0dXMubWsmx0U2Xdug2cd/65A4qhn0riichxE5Xi8YFdRE4nM2bM4L4HfswLz7/I6hWr0CrM5E2CZDHnzOl85JrPMX369Hd0jilSpHjrpERminc1S5cuRQgpjBtf1u/xcDiCETNwud2kedOI98RoaGhgwltcErEsE1EeWaFcQRBQ1QS1VbWkudP7cjxcihvVVLEEE1VT6Y504XX6sSyTmBalvacNS7ZQRCdTgrNI8wWHPU5v4lJGRgY//umPqaqqYvPmzYRCITweDzu27yRjUzalhcnyWrqhU1lbg+V2EdDyady6n/aa2hG5mZIoMSd7AZ0tHTgkV1/SzJHzY9CltZGbnsOeyHYUrQBN1rBtG9sAh+jEJboQkTAsHUO38Dp9WLaBYRn0dIeQBJHq7i2EzR6aEuVYoknBqAKqumLorTohrZ0t0TeT85ElPO0M2fqxN4FJVVWefvI5ivMnYBk2akaEWQumDvocVVUxTROBsSQSCf7zzydRULBsi86edmRbTvZzt028sp+eUGc/NxNg9uzZPPLbX3P9dZ9k9479FPqmIToURFHEsg2aO/ch48UluY8IPMHGNHRM00I5Dd+4DocDr9fJ7vKlQzRz7I/P735H60+WlJTwzdu+wWc+exP19fXYtk1OTg75+SOvOJAixVvFtm0OHjzIjh07iEajeL1eZsyYQVlZWaqF6EkgJTJTvGtRVZU3Fi+jOHNsv8d1wyART+AS3YR6koLL7wxQX1tPaWkp0lvoqlA0upDGfS0U5o4ZdpxuGNhYdHQkXUx30N2XHCSJEpIlIbnA7XCC3yJidCMpEopHxK270dFIxBJ9iS6yJA2bSNKLIAiMHTuWsWOT56Kmpob//u1pZhac2Temvr6BqG5gKk7SvBl0t4/MzUyocUzLRLAFJEFGEZQBItMURRyiA4/Di6mZ+EQ3Gho9kW78UgY9eheWbWFaFrZtYaoakiEhWiKiIBLWQhQGRmOgU2WE+eiNV/Hha65m2rRpfeVpEolEnwusKMqw8Xy99U9fffVVWht6OG/ONUwtO4Pd9au48dOfGiBQIpEIX/7SVwmHkqVy3nhtJYf2H0KNJahMVGHqBrLgIGE09z1HEuU+N/NosrKymL9gHtWHGhg3ahLBQHKJV9VihHbV4xScKIqDzMxMFEXGMA3aQ/XHfY9PFj6fj/vv+wnp6ekj6jByMroLnQwyMjJSGeQpTis1NTX89re/pby8HNM0kzeLloUkSUydOpVbbrllwPWf4sRIicwU71pCoRDRcIz0gv61x+KxGAIiTsVFTIsSi8VwuRz0xA10TUN6CxnnF19yIT9b/2tyW8cgCiIOp4O0QIBjS9GEo934A15am1pJc6f31YwEMG0TBJtdPdvQFY2K8CEsywLbJhKJIgsyFhaqbvDyhpdxOVwoisTHFn58RELzaJ579nlcmo+gLx3o72JKSEiiTLZ/DDVbdw3pZmZnZ+NP87KnZRM98Q46jCbCWgSH6Bz0Dl6URFRVw9ANZFnCtExsExyKsy9mU0ZBFORkbCYikigjIiHbCnkFeYwuGU2gx8kPfvT9PkHRKzJlWT4h56DXxRydMxFJkslKz4MKF//657/5/Bc+129sV1cXHe09TBl1Nk6ni21t2xifkU21vh2PlYYkOFAOx6JatklIbcUn+Qj1dFFTU0NB0RHR+vTTT7NryyEyArkE/Jl9HYBM2UCWZNxOH5Zu0d3dTW5uDqKdFHqRSISamhoioTCqqdJkNdLQ0NBPWNXU1LBp0yZCoRBut5spU6YwderUE25H5/V6ycjI+EC2sUuRYiTU1NRw11130d7eTlFRUT83X9M0du3axV133cVdd931loTmRz7yEZYsWUJTU1PfNb58+XIuuOACPJ4j5fe+//3v8/3vf7/fc1VVZcaMGXR3d9PcfOTGd9OmTXzjG99g165dFBQU8Itf/IKrr776hOd2OkmJzBTvWiRJQhCEw0kUSUKhEJqqHe4xDYqkEOoJ4XAmL2JBPPHljZaWFrZv28HBit3U7m9hVMZEbAHSggGKS4opKEgKDFWLo1pR/HJ6n4sJyV7ZoiTSY3ah2xoGFjnuiThx990ZOz0GkighSzLpukahfxSBgI8DbeswTBPnCYTq1dfXs/zVFdhxkQ371wLQ1d1FTVs98YSKJMp94lc3unjtwV8z6cwj2buxhMoPfvADHn30UR7/w6O0tLRw65e+ybS0M9i2YTuZ7pxB6xWKgkhTayO2ZWOYBrqtY5kWsUQUy7YQEJNlmg67tJZlgSgk41VtBw11DYyfVIYSOzlxicuXL0+6mLOTRe1VLU5FxT42b13F64ve6NdFR9d1du/cTf2BJmw72SbStC0wLXRMHKLcN2/btjFtE9sGwRaprKjsE5mJRILFLy9lXMF0mhuWDTovw9KQZYVYIkJP1IkkCnT2dLBmzWoUHDhEF7qt0pJo47vf+B5XXHMZ13z0Gv78xz+zee02nLobB05MDP4lPsnYiWO45Ws3M2nSpJNy3lKk+KBj2za//e1vaW9vp6SkZMDNrcPhYOzYsVRWVvLb3/6We++994RugFtbW1m8eDE+n49//etffO1rX+vblpOT0084DsZ9991Hfn4+3d3dfY91d3dz5ZVX8uCDD/KpT32K1157jY9+9KPs3LmT0tKRd0w73aREZop3LcFgkKLiQhob6hlfmMwurampQUDEEgw0S8UWIKHFae5qwpPpSibeJJJLrscmiAxGbW0td//wx4RqYlw0+TJ2VO6gPVxDjn80Wo/Ozi27aGyuQxNDRBPd5JfNprGmmaAjHc1U+/ZTmFOIZVuE4z1Ut1cgiw7G5o7H6/bR2tyCYImYmETNMAIyZsJGDLy1y8/r9fLxmz6WFHEkxVxtbS0dLzZhNJkUZ4/uE4ktXTI5BVnc/ZWv9n1JvvHGGyx+cRmvvPIKN954I9rhup0OpwNREpElGUUaXAi63G7cthuH7UAWZBxuB4KZXM4XRRGRY2I5D/+vLMlYukFrSyucBI15rIsJYJgGgiUxTplKdjyfCaOPxOeqmkrCKZLtLUSWFOKeGPui24hGo7hsF17nUa00LYMETvJyc0loAXRZxTwcErFr1y56OuJMmDSwvaEkSIiyTKW+GwDNUKntkjFNg+5YG/nufJxHdVTKSc9lZtZcXvnPUv71j39T6Clm7ugF+N1HCsyrusreg7u58//u5o6f/pCpUwePN02RIsXIOXjwIOXl5RQVFQ0pHgVBoKioiPLycg4dOkRZWdmg4wbjH//4B2VlZXziE5/giSee6Ccyj8eBAwd46qmnePDBB/nc5z7X9/iaNWsIBoPcdNNNAFx22WXMmzePv//979x9990j3v/pJiUyU5x2qqur2bZtG5FIhKysLBYuXDigVzSAKIpc8ZHL+f0Df2acVUY4HKGjpROH4qDS3Ns3Trd1oqEw40eVsbmlf1mZ4TrcaJrG/T/9GWqDxYIJ5yIIAgWZReyv382B+gPEVRXDMChv7uacC+cz/Yyp7K/bSUusjQxfFsIxGtayLZq6GtGwkAUFS7fRBA1MIdmdSACHqdAUacQSLSLht5bZm56ezo033ohlWbzxxhu89NzL7N62h46KHrwOH6GeHsaNKmPqmBlIosja2uXIssysWbMIh8M8/NCjFGSU8tLzS7jiiitO6Nh+nw8PHty2B6/gRXKI2HE7GY8qikjHxnIeVpm6rRFwBmhsaEQY85Zedj+OdTGPJsOfS3tzJxPLhL5lKUmQcUgOnLIb+bCAtiwLQzMQnQK6feSGwbRNdHQMdFxuF909XVTVVoEn6YC6Fd+gy9CK7GTBlGuSYRNAKNyJSph4OIrHfZBrFl6Lx3lkmaw3HjdYmc2+in3MPffsfgITwKk4mVkym101O/j1Q4/w6O9+k6rdmyLF22THjh2YpnnchDen04lpmuzYseOEROYTTzzBjTfeyCc/+UnuuOMOdu7c2VcloaOjg7y8PJxOJ5dddhn33Xdfv5CZW265hV/84hcDGo1YljWgaYVt2+zcuXPE83onSInMFKeNQ4cO8fOf/YL1KzaiduoItoguqHiz3Fz10Sv4wQ9/0C9WBeBDH/oQry1+nQ271uCIeHELHhYUno9lJ9WLaRkcCu+jQ4Cv/L9bBoim4ZIaNm3aRP2BRj5UenHf3azX5WV26ZlML5lFdzTZZ3rr3i3s27WXRa+9zK1f+QZetw+3yw3H3AG3d7XhDLlQBJGAkkYilkCNJ5BFpS+0UxIUJCG5hB6LRfsEyYliWRaPPfoYS599gwLPaHK10Xg9WWT5c+hS26k8WEV9ay0Xn3E5mVIuTz35NDNnzmTp0qVEuwzOnX0eq7ct4pVXXuHyyy9/S3OQFQXFrZCIaP1iUwfM1bZAtgl60+mOd+Dm7cUJDuZiHo3L4SYRj1JdXcPkyUMvMauJBLYAFXo56P3nG9E66exuQBQlVD1B9cG9nLlwLm63G8PSh9ynIjv7jNqYFCUc6SLgTKdHdeBxevAeU4c1Fo/T1dpDoWsMB+v3UZI3eBmtSUVTWFn5Olu2bOGss47f2jJFihRDE41GRxyvLIoi0ejI295u3ryZ3bt3c+ONN1JcXMzZZ5/NE088wS9/+UsmTpzI9u3bmTRpEo2Njdx888189rOf5aWXXgLgL3/5C+np6Vx++eUsX768334XLFhAW1sbTzzxBJ/+9Kd59dVXWbNmDRdccMGI5/ZOkBKZKU4Le/bs4UufvZlQRZx8ZwlZwSxkScEwDZq663nysWfZvGELTz//VD9X0+v18v07vsd3v/1/vPnSMka7xiHrMtg2PVoXnVY76dlBphdczIrXV/Lxj3+8XyzecCx7/U0ylBwkcWCZHFlSyApko2oaYkimrbuDpUuX8uN776auro6srKwBX1KP/Oo3FLlDHGysIDsvB0uDSCiC65i7ZVGQkv2tzWQP65g6+LJ+TI2haRpdXV1Af8H88ssvs/SZN5g7agGmblGzcyNZnlwEQSDDlU3QmcnB0G6Wb3+Di+ZcyvpNK1mzZg3PP/MyJflTEQWR0qLpvPT8EubPnz/g2KZlYhgGtg2yLCMPUkrIskxy8rLY3bwP0RLQSCAJ/cepZgLD1kn3ZySX1KW3n4gynIvZS5o/k/q6RsaMKR5w49KLKMrMmboQ+Zji85qe4EDzOhZ+6Kw+N8HtdnPrbV9HFEX+xN8IR4euxQlgYxNVkwXqvV4/hAYf19HRgWAKFPiKONhejmZoOOSB7oosyaQJGWxYvzElMlOkeJt4vd6+cKPjYVkW3hOov/zEE0+wcOHCvmShm266iR/+8Ic88MAD5OXlkZeXbA9cVFTEb37zG0pLS4nFYiQSCe666y5WrVo16H4zMzN58cUX+c53vsO3vvUt5s+fzw033DDi37t3ipTITHHKUVWV/3frt4lWaEzLOAOX48gygCzJlGSUkqsXsGnLKr7xjdv485//2O/52dnZlJSMoWFsM6Il0tBTBQJk5GRw7qgPMSq7GEEQWHHwdZYvX86ll146onk1NzST7k0fdkxdXS1oIj4pjReff4lPfOITBAIB8vLyBohMwRbIzy2kuq0Wh6zQ1RNCER3HJqgjHPb9REGkM9zBs8v/i0sZ+EVhWCYhtYtbv/RNFEXBH/Ty+B8fx+l08uIzLzEmrRSfJ8D2bdtQbEc/cSIKIuP8E9nRuZGucCeZUi4PPfhL7LiHOXOSZZDys0dzqH4nr732GpCMbY1rcVpjLWiqzmGzGEEEl8uJz+fDlmwEAWRZIhLrpE3dR5O2k2g4hgdfshg7yR7elm1iY6PIIj6vn65oJ4E8HzpvvQB4r4vpdWTQ1tnUb1s8EUXTEsTlKE7Jhanbw7qZfr+f+fMX4HX3D9WIxsPI+0P84pc/H7Skzuy50ynfuHvYecbiEQTRxKk4kIZJGDAMI5l9LyjYlp2M/RxEZAK4HW7CPUOo1fcAqqqyadMm1q/dQE93D/40P/POOpN58+YNWBpMkeJUMmPGDCRJQtO0YZfMVVVFkiRmzJgxov2qqsq///1vVFXtE5OGYdDR0cHLL7/Mtdde22+8KIrJWsOHl70bGxv7Wv/quk44HCYrK4vly5czdepUzj33XDZs2ND3/Pnz5/OFL3zhRF/+aSUlMlOcclavXk3lrmqm+c/sJzCPxqN4KPNPZvni5dTW1jJ69Oi+bQcOHGDz2q0snHzegJi1oxntH8PT/36G8847b0R3d4pD6atxORiqplFTWUvAFcSFi5bqVtatWzdkbI7T5Uy2dSS5DGrpFi7H4D2sLds6vERvk60Wku8rGDDGNA266GROzgIsTMq7tqJpGgcOHKC1rp1zx86kp6eblsZW/HKQuBYfsA+34WdH5TYmjJrEsvXLuHjBdX1tFwVBoLRoOkuXvInD42B93Up2dZXjEfwokqNPRFuWhRk2sCMWPp8Xj8/DzJnTcYcEfv37X/HC8y/w4D2/RLIFEloE27IRBAGn04XX5UGRneyNbaNH7Wa8p4z8YO5bLv7d0dGBKAlI/hh14R39tqmqStwIEdU70S0nTo9MNDbyZa6RcuNNn+L27d8nEutG1QfuP56I0h1tpWTsaGqqaohqQ4tqWZaxMImbMSRZwjHIzUYvCT2BL/DW22++k+zcuZOHH/w1bbUdpMvZuB1u6rRmVi9eR0ZhGl+77at9/eRTpDjVlJWVMXXqVHbt2sXYsWMHTf6xbZv6+nqmT58+4uzt559/PhnHX16O233ku/+b3/wmTzzxBMFgkJKSEoqLi5NVPW69lUsuuQSv18v8+fOprKzse866dev46le/ytatW8nNzQVg27ZtTJkyBU3T+PWvf01bWxuf/exn3+bZOLWkRGaKU87TTz6NRw8QyEgbdly+r4j9zeX885//5Hvf+17f46+99hpqTGNb48Zhn29ZFrZmsXnzZs4+++zjzmv6rGm8unsZ4/IHF411dbWoMQ3JE0UXNPK94/jHX//J17/5NRwOxwAnc/K0SSwtT3as6enpQbadxI4RIZZtY9g6CTsOhoVhG8k4xYRr0Bghr9uL3+NHM44kNHV0dOAUXMiSTDgcQXbIxKwwDKKXbcGkPlRDuKkblxzA5+zv3Pa6mWddNJ0Vy1aTnZ2HUx58eTmuRokaEQoL86mM7yMjO0hubi6f/dxnKd+xm56qKDPGzEIUpH7F3EPxEDvrt/DJKz7OzV++GafT+ZaLfxcUFPC7Pzw+6LbOzk6+8Kn/ZW7uwgGxjyeTcePG8b0ffocbb/gM6w89j0NyIUnJEkiqHgfRIr8gj1Y9SrNWRaQjSmZaxqAhB5mZmdiSTWO0lpLScYOGbkAyfKHb7ODMeXNP2es6VezevZuf/PCnBPUcPlR6cb/XaFkWBxr3cd9dP+OHP/l+n4uTIsWpRBAEbrnlFu666y4qKysZNWrUgDqZdXV1ZGdnc8stt4y4fNETTzzBTTfdNKCj2m233cbChQs5++yz+cxnPkNnZyfBYJDLLruM+++/H0gmGRUVFfU9Jzs7G1EU+z320EMP8eKLL2JZFueffz6vvfbaO9qtaySkRGaKU05jQxMeyYN4nAtVEAQ8gofq6up+j3/kIx85IZdjypQpIxp3wYUX8MJ/X6Y70tVX1LwXVdOoOFTBofAe4l0xHG6F/VX76NrdSfm23eTm5A5cBlcgJsaIJWKkyxa+oAeX61gn08YOGwTzAqRnpNG0YhdnTJtLWXHZgC47AJIkoshKP5Epy8li5wCjiooYddSX0LHsq99DYIKTpuY2cqZNpLigf2JJr5v51JPPkh8cxxc/9X8D3F3D0Glqr0fXEhys2cmFl5/DLV++pS9G1Ofzcc/9P+H+n/6M5Vtex2E48bn8uBxudFslocS48pOX8qWbv/SOfyH2vjbD1DFNg9ggvbvjieOXvjrjjDNYs2Ela9euZe2a9XS0d+F2O5k1ZwYLFiwgOzsbgPLycu678wGmZc4atOC+x+3GkSbR0tjI+YVDB/Dvq99DTnE2c+e+t0SmZVn8/rE/4E+kM6VkYPklURSZWDQZs9bkd7/5Pb/53SPIcupnKcWpp7i4mLvuumvIjj/Tpk3jy1/+cr9VteOxZMmSQR+fN28eup5c5frud787on2dd955A+pp/v3vfx/xXN4tpK7mFKcch9Mxoixq27YxMQcUAi8qKup3N3eyGDNmDJdfcymv/OdV5oyaR5o32Letrq4WLW6gmgmKlHEUZ4zBpbjosrsgbDJn6ln9nMfePuPnXXYuv9r2GJm+OJFYCI/P1ZeFbtsWnZEOnAGJSZMnoOoJHA4H+Xn5yWz1ETJ27Fg0SSUcDw0bPgDQHmvBbecT7TIYNXvsoGNyMgtpbehiVJofp8NN74Ktbmjs2LueA4fK0aMaiuAgqkX446NP4PF4uOkzN/U5ks3NzbjdbkzLoL0rTIPagC6ojJlQzJdu+SLXXnst4XCY9vZ2CgoGhgacaiRJRHHIdIYbAdBMjR61jS2HXh9U+PoDx+/nnZWVxYc//GE+/OEPDznmnHPOofHmRv71+/8QVaOMyRmLIifzzzVDo7L5EGp6hPG5Y6lqPYTP5cPrOuLyaobG/vq9hJzt/Oj//fA9V76ovLycyn3VLCwePgN2fMFEVla9zvbt21PL5ilOG8XFxdx7770cOnRoQO/y0tLSVO/yk0BKZKY45cw96wy2LXsC07KG7Sse0UPEhShnzD19PzL/+8X/wbZtFj+3BI/hJz9YiGXBxvINhI1uTMmgMLMI27BpCbWgGzrx7hgH9h1kyuTJAxIWZs+ejS0bHAyvw+628fcEcDgdGKaBYWu4PC5KckvYUbsMQ9cRRAtJlNANHdMcOttR1VR0Xaerq4vc3FxmzZvBgbV7mVM6b8jntIfaUJU4ddVNlOTP6ovFPBZNV3EpXkIdMVRNxelwoukqr69+jva6Vgo9JWSk5yAKIqZpUNlazhv/Xcm+3fu5+6d3UV5eziMPPEqGncOlk6/Gc7iweVyLU9F0kL8+/necTif79uzj0IEKHn70V6fU0RyqCP+ceTP7znFCi+Pqtvj14w+Snj4w+etk9fMWBIEbbriBrKwsnv7306yqfgO3kAxHiNtRRpeO4oc3fY+ysjIe/fWjbNi4GpfhwSkmGwtEhRCjy4q47et3vCcLse/ZsweP5cM5TKwpgCIr+Ow0du3alRKZKU4rgiBQVlZ2QnUwU4yclMg8zXR1dSW7jLhcZGZmfiDulD7zmc/wh1//ifpQNcXBwd0027apCh0kkO3nqquuOi3zqqmpYffu3eTkZvOxT19Le3sHB3cf4MCBg4SVDuZNXci6natp6WpGMR04RRcuwQ2WQOXeKjpaO5k4ZTyjRh1ZTln2+ptcNONSRmcXs27varqNDubNn0deQR4Lzz2bmTNn9rlRXV1dfOPm25BEkVUrVqNrQ9df1EyNQ2o5X//iN8jMSee222/j/p0/Y2/dbiYWTUYzVAwz6RZrRoLOcCe7m3cwZuooDu2tJda9ib3Vm4Fk5rl82E3rdWNjaphEXKWxoZGSkhI271pFR30bUzLOSNb57MPG6/BzVul8dlZs5a4f3U1zfQtjvRMZnd2/v6/b4WZq8XSaOjP59QO/QTcNPJKXlStXctFFF73t9+9YHA4H/qCX8q6tIxrfG1N6MsTkcAiCwEUXXcR5553Hzp07+5bAioqK+vUlv/ueu6mqqmLjxo3Jou9uN9OmTWPGjBnv2R7kqqoijbDFkyIqxGOJUzyjFClSnE5SIvM0YNs2mzZtYvGiJezYtANDMxFlkQlTx3PF1Zdz9tlnv6/jkPLy8rjpSzfyp1/+BUmSKPSN7ieuDUvnYM9eOoRmvvyFW/ri2Y7Gtm06OzuJx+O43W4yMjLeskCvrq7miT8+wbaNO3CaHiQkVCuB7BeZd86ZtLd3MidvAaqeoCvcRYE0ljRHBoKQLM3jEFzEjRiK7mTPzn0IgkhGVjqRSISdm3ZxzpiL8Lq8fGT+daw4+Do33Hh9P1EViUTQNK3vdcVjCdSESrojC0ns/zkQRRAEEdVM0Eo6k4LTqejex5gxY/j+3d/jF/c9xLK9SzhYcTDZutIyaetqwbQsBFFg2/ZtIBxV61IAJJsxxSXJY+sxcnNyMa0E4Xgbba1tKA6JQ4fKKfZOPEZgQjQewe1x4XZ7mDnmDJ5e9U9yAwWMntlfYB5NfkYha7etJi5EmThhKk//+2nOPffck+5m+nw+Hv/j433n9nicLLdypMiyzOzZs4fcLggCY8eOZezYwW/E3osEAgF0RiYcE2acjMzhS4qlSJHivcX7V9m8S7Asiz/98U+8/N/FpAtZTM+di8fpRdUT1Oyq4sEtD7Ph0g1847ZvvOuLqr4dfvjDHxIJR/jv356muvkguc5CHLKDmBGlVWtE8Njc+IVP8s3bvtHveZZlsX79epYsWsKurbsxdANZkZg8YzKXXXkpZ5999gm5PIcOHeLO79+N3S4ztyjZxi+SiHCoYT979+9l7apHSBgJ9CkWdS01yJaMS3Fj2xa9Hb1sbGRBxkiY+NPSOLDvAGfMn0NzUzNTimb0ZTZLokSRr7ifqIpEInz5S18lHIqjaRq7d+4m7rAJd0YwJQHxmGLmoiSQn5eHIinIooTL4YHDq8EzZ87ksT/8hsWLF3Pn7XdTYBcTC8dw2n6CSiYO2YkgHC5BZJtIiHgDHqqtA8zKnce+PftpijSTRzF5ShEJM0K4NcKKqteJdscJFCV/8HVT66t52RltpXRCMXE1hm1ZxNt0VEeCaCKZRd/bKvFoQuEQQkRBk3WKskazvmLVCbmZ4XCYtWvXsnPHLtSESm5+DgsXLmTixIkDbjROp2hMcXzmzp3LX51/pyfWQ5pn6OoS0USEhBLjzDPPPI2zS5EixakmJTJPMc8//zwv/msRc4rOInhU4W+H7GDqmBnE1FLWLF5DIO0JbvnyLe/gTE8toijyswd+xjXXXsOf/vhnNq3djKEbOAIOLlhwLp/53GdYsGBBP8FomiaPP/Y4S597gyw5l1m583A7PCS0ONXbqvj5xl+y+aotfP3Wr43ICTYMg4ce+CVyl4uZpXMQBIGKpoOs27MOyfaQ7RuPK62IrlgrWw6UE4m047bdJMwEpn0kXtK2LQRBxDYtXLKLSCxEZWUVoe4wpXMn9DvmuPwyVhx8vU9UaZpGOBRnevF52LZF/YEmMlwFqOFaXLK3n5NpWRaqHsM6rG4t2yJ+TBcgXddxuVwE04MoYQW35EVwSqQpQZSjXEgbULUEsZ4Eptvk4O4KvLafgCuIGRIImJmI3k7a4zW4JBehhEh3dxf+NB8bGt7EtGw0XUWUBdorG9lYtQHLsuiIdRKui9AZegoARZH42MKP9xOaVZXVeIUAITrQTX2A8B4K27ZZvHgxf//TP4l3qmQoWUiSzB71AC89uYjJsyfx/759Gzk5Ocd971O8MxQVFTFv4Vy2vr6Ns8rOGbREk2Vb7KzdxswF0waUfkmR4mRx4MCBd3oKH0hSIvMUkkgkeO6/L1CWMbmfwDwaj9PL9ILZLH3pdT523ccGXSp+vyAIAgsWLGDBggVEo9G+2NRAYPAM6Sf/82Rf60S/58gYzVCxbYuupi5+88CjvLb4NW7+2pc455xzBu3Q0sumTZvYv+MgZ+QvoKu7ix6tmzXlaylKm0qOfxSCIJCbZhFLFHOwQ0ZT0ujsacWf5iMjkAmAbSeLpEuSjCAISKKIaItUVB5EtJICudfV6yXLkcs//vIPpk6d2reU63Ylkz8kKVmM28DAsHXsXrsUsLDQbQ3VTKBbKs2dzSxe/zIxPczXv/gNuru7aW1pIx6LE41EKTINnKIL0zYxtGbyfYVHCq8DTocLNZEgHAohBSXSAkESrXFEr4zXHcCnuAkr3VQ370W0FXp6urEknYSmMSZtFi6nh8yMDCRZwrJMTMvCbVTh9rrITy9AMxMcaFtPKBbG40zGh4YjYerr6kn3ZNCsCUii1E94L1iwoF/R4qN5+eWX+cPDf6Y0MIlRZf1DLBJanG2bNnPnD+7invt/QmZmZr/nHh2ScDzezrJ5OBxm9erVvL7kddpbO3A4HcyaO4uLLr6QsrKyD0TM9fG4+Ss3c2fDXazds5JJBVPJ9GchCAK2bdMV6WBvw24ySgN87RtfS52vFCedOXPmvNNT+ECTEpmnkI0bNxJqCTOrdNSw4zIDWQjNEitXruRjH/vYaZrdO4vX6x22H2w4HObFZ15mUva0fgLzYMM+1pevRdIcZCrZlCpBOnZ28Kef/5V/PvFvvvLNWzjvvPP67UvXdRYtWsQD9/6CyCGVnQ3lWJZJZfwAeekTyPYV9f24iYKIz+3FNm0kQUGRnEQiMTICWUiiiI2NbUtIkoiqJWiJdLC9cxOmYGOIOs+veW5A72kbm67tHVx/7Y3kFeZgGMlajbIko7gcHIqW02zU02YH+i2X27aFZiboiKdjY2NhMM4xiShhPO3pJFoFpiijkTNk1kdXEhSzcIseYkYE1YxjmEa/uQgkRa0W0/G6/BiGQSKhEsjNwO9Ko6mzmjmzzqQgq4Dlm5bRo7aQ6HEgyZCdlU2aPyOZYW4ZVLVWYJoWhmUQi8Vp0BvQTZWWriZeXPUMsihjmCY9PT3omk6etwgzYCIi9IUR/PMv/+Rff/83//OlLwxYJm1ra+Ovv/87E4NTKcgcWL7K5XAzr/Rs1hxYwVP/fZpbvnxz37ajQxKGwzAMLMvC53fxi18+MKzQ7BWiR8cG19TU8PtH/0BPY4Q8byF5ntHoqsGqZ9ax5PmlXHbNJXzxS//7nis7dLLJyMjgx/fezd//9g9Wvr4Kq8VGEZ3olobgtjn76vl85nOfISsr652eaooUKU4yKZF5CmlqasIt+EYUM+iX06irrT8Ns3pvsG7dOhJdGnllR2oqVjYfYu321YxSxpGVlpt0Q7ARu2XG+ctAsPjlvb/G4XCwYMECIJnd+sD9P2fzm9uwuiHPW0CBv4j2eCtmRMRtpNPW1tbXXSGJgFN20WV1IkkStmUTj8fxHRbFpmXS0t5INBY77DxKlPhno4px3IKLdG8Gmf6svvqYAOFYN6oRJxLqpLeKu9Ph5uNX/g+hSDfPv/I3Jnpm4ZRdgE3icM/c9lAD02ZMprG5gc7OLsLtUXrUHno6w4zNLyXgCaCZKrZtowgKkiAlhaotoCZUZK8D8ShzSEQEBGzbIhKJACIuhxtRlHArfmqqapl31pk0tjUQbUqQlZFJXIvidnv6XFHbtrFMCy9+nE4Xgi3ilj3ogopPDDDeOY14OEEsGsOjplPHIdpjzbglN8+seJKp46YztWQG/17zN0RD4Enff5kzZ06/kIcVK1ZgRwQKCoaujyqKIuNzJ/Lm0uV86sZP9jniR4ck9DrGx6JqCV567R8kYgki8U6+/PmvDisGfUEvN33+06xYtpI9O/YSCoWoqqii2DeOeVPPJi8v93BHehidXUxPrIcl/30NURT7CeC3QywWo6OjA0EQyM7OPmUx3KfCBU5PT+fWb3ydT990I9u3bycajeLxeJgxY0ZKXKZI8T4mJTJPIYIgHNsUZljeq2VKTgVNTU148PU5jKZlsnnPBvKl0WS78/rGCQgogkIsFmfS6Ikk6hM88fu/cOaZZyLLMn//2z/Ysmw788eew9rYaqKxpLsV0UMEXFn4XGnEEzE6Ozv7/dgF3Nl0xuvQYwaiIKGpKni9WJZFU2cDpmqSrmShmgkEW8AjexAEAb8YJBSKYJgW6b4jS/cOxUMoEiJON16fF13TwJ0Umh63gSwpOGUXTtlNIpEg0hXDn+bDUA327tgPgoxDciGLDkQkFDw0tjehBhIE/AGwQThaTQqHl/YNA1E5+jJPLsebhkksFu8rDA4Q9GfS1FlNW1s7Z048i+fbnsWKmHBM2KRtg2GZmIJJVlY28VgcNaYiigKCIBLpiWEnBJy2B1sQ0G2NTG8Bs7LOImKE2LV3J5FEmFhnlHzXKCp3V7NhwwbOPffcvmNsWLORXO/xi7bnBPPYc2gXe/fuZd68/jVD3S4PXvfQvb4t3WaCZwY9Vjtzcs7C6RhctMUSURZtf45f3vNrCj3FzMmZz7q21eQJowlqOezYspOOMYVMnjy5r6tVmieNWUVzWfzcEi6/4jKKi4fOvj+WlpaW5CpIwFXf3wAAU3FJREFUKITT6SQ9PZ1DBw/x5qvLiYZjCAgEs9K45KqLufTSS09qiE0kEuHmr3ydcHRkGeF+r4vfPfbIiMMNMjIyuOCC4Quzp0hxMtmyZcsp23dqKf74pETmKaSoqIioHca0zCF7EkPSGerRuygZO+b0Te5dTlJwH4lPrG2tJh5VmZA2uPDoNQ3H5ZeysvINtmzZwsSJE1n68lIm5U3D5XBTlDOKDfXrsW076YAKEoIg4FLcJGIxdF1HURQ0Q0VyyJSVTmbjxhXoUjIuUjUTdITbicajZDgyMW2DqB4GEWzRBgEUh0LADtKjduLL8PQTLkFnGrF4DbVV1ewR97Jg/tkM6E2JTTj0/9u77/ioqvTx4587vSaT3klooYROaIKCoNi7rgULuv5cse3qfnXdVVbQtazrunbUXSurrogKKFZQRJHee4B00vv0du/vj5iRIYUACQnkvPfFvsydM/eeO7mZeea555yngWAgSG1NLS63m6SYdDQ6LQfqdxL0y0iosOqjkSSFOkc9QSmAJEnIihy2O0mSCAQCaA8JMpVf/t/ldoMioTrkumzKZuYeyGX8hHH0S+9Hsb2AmqoG6hy1mPR+ZDmIw+cgqASwRduwmC2YjCZqpBrq7bV4fB7scgNmjQWH34kXNyq1ikExw9CqtUSpY2hwJLB222pshhh0PhM6p46P/rcwbCkvl9OFXnvkwEWSJLQqDV6v94htW6JT69GpdZj0ZgytZAb37d9PTUUt54+4jPjIeOpd9ZRWlDDAOgyztrGufHHuQQwGA/36/lq2M8oSjb7UzPfffc/Mm2cesS91dXX85/U3WLXiZ9QeLVpFT72jnt0FOzDojEzMOoPsjInIikJ5bSmf/udzln/1HQ/PfYi+ffsecf/t4fP5sDs9DBh3HgZT2/XfPS4ne9d+2e6spyAIPY8IMjtRdnY2MSnRFFTk0SexX6vtKurLUVslJk2adAJ7172lpqbiwoGsyKgkFZX1FVikiGZL/Cgo+BUfZnNjQKJRazErEezbt69xPGCDTHxCAgAZCX3YYFxHubsEvVqP212FoiiNAa2iwu12odVGUOesJT45jv4Dx7Nl1xp21q3HorZic0ZSUV+ORtFS4S8mEPSDBgKSH5/GjVFnJCEhHrVKg7ZeTVx8XFgddafbztJVu3E6PVRVVFNbW9es2ozH48Xr8WLSmal1VKOS1Oj1RoJK4/jBYDAIkoRapUGRZUwaG7X2SjRqDcFAeM1xCQlZDq8iFJQDqNUqHC4HEfoYOKzIUFM2s6S0FMko89jDc3n68ecwGw2gyOg1WhKj+7A/R8FsbrwVrVKpiI2NRWfSsLdaRtYGcAWdKCqFCK0No2zG5w/gkB34Az4cdieSV0NACqDRaAnWQ96uAtasWRP6G4iKiaK8qPaI10lQDuKTfVitrWcsj4fX5+Vg4UGs+gjMv1Qyqm6oRC1rMWsbj6nT6LAZoinILSA9vVdYdjjenMC2zdvh5raP09DQwJzZcyneXsqwlGyiLNE4HA7W/LyWofox2JV6Nu/dSExEDEnRKaTFpZMa24vNuRt4Yu6T/POFZ7DZbB123gaTGZOl7ZKlgnAyyczM7ND9idnq7dMt7s86HA7+/ve/c/XVVzNz5ky++OKLru5Sh9DpdPxmxpXk2fdR1VDZYpsGVz07y7dw0RUXtFjerqcaN24c5lgjxVVFwC/BEc2zwS6PC41BTWJiQmibChU+n4+amhqMkil0y12n0TEuawIlwXyCcpCg7KbBV934HEmFz++jsqECtUkic0AmFlMEN15+D2kD0xh+WhaRSRGotRr6WAfQP3YQU0efw7XnziAxJomMpD70TuobWoLIrLdQUVYR1lcFhYryCvQqHXoM5Obmcmi2timLqZY0BOUAEioUWfll3KdCIBBEOmS2uCSpkBQNKFLjBpWEXw6vGhS26H0wgIyMRqvFq3iQCQ9KoTGbqdeY+X7zt/TJ6s3YsWOJjolm+MjhnDbxNMaOG0uvXr1aHNqhQoVKUtE7sS8x5lhspihUahWKSsavdePTuqnxVEFQwqpEUeerRmtVo9PpiVElsOCDjxqDaOCMM0+nwl3amJ1tQ3FVITFJUWHBfEcqKytH9oP2kAlUiqI0K9FpMpgIeIOUl4f/ztUqNX5/89f5cPPfnU/h1hIm9D8jNMyisKgIfBJx1gT6RGQSHYznx60/NH65ofF3O7JPNjWF9fz444/He6qCIAgdrlsEma+99hrBYJC33nqL2bNn895777Ft27au7laHOPfcc5lx2zXsqNnE+v2rKa8rw+G2U9VQyaYD69lYuoZzrjybGdfP6Oqudismk4krr7uC/bW7qbFXYzZY8CjhNam9fi91nmoy+maEZY88uIiJiUGtViMflqrrm9Sf00dNoV5XhU9xc6B2M7XuKhyBBmq8VRhsOrLHjsZsNhMI+NlbuIUrf3M5Hy1awN0P3EFG73RGZY/mrGnTGDl0BJGWKNQqNWq1JmyNS7VKTSAQ5NAgsqqiCrfDjVFnJtJko7KsitrautDjdpcdh6sBVArugAtZCuKX/dQ7amlw1RMMBkLjLoNK4xAMOSijknTIsoJWr8GLG5/swS/7cAed+CUfzoCDem8dDf461EYVwWCQiCgbtYEq3AEnbn/jBCZZkal0llLiz6eWas4+76yjqkSl/PJaq1Vq4uLiSE5OIikpkejoKE6beBqjskeh1xmw6KxE6aNR1DD2tGxOm3gaWRlDyN9dyJo1awCYOHEikUkWcop3t3o8j8/DgeocxkzM7rSKWU6nE61KEzaowaQ34VN8BA4J6CUkNGhxu8Ov0TpXLSlpbY8traur44dvVzIwKSs0rMYfCFBSVILV8Gs2MdWSjsfhobCy4NfjShIp1jSWLvoibPkrQRCE7qDLb5d7PB5WrVrFc889h8lkom/fvkydOpVly5YxbNiwru7ecZMkiauvvprhw4fzzdffsmrFz/gb/Kg1arKnjmL6udMZOXKkWB+uBZdeein2Bjsfv7cIjUuLXamnzlODXmXE4bHjx0t6/3T69Pl1AedaRw2yMcD48eMpKSnBiR1fwBe2lE+fxH6kxvYit2w/P25fQYF7E2ZdPOOyJjFkyFBkWabg4D5yS3bSb0gyd99zJ3q9nvHjx/NuzH+xRJjR6hqDWpe7cSynz+dDpzaEMnz+oB+dXkfTIEkFhfz8fHQqfeMyQEoAKQC79+xk4OBMVFqJrRVrCPpltIoWv99PIBjAIzuorilDkiRcOHAq9ajUaoJKAC36xkXh/QqSCgqVHIJaGafswCU7QAZ1QI0qKKFWq9HqdATcATyyhxKpgIDZh+wLsKNhLVK9GpDRm4z0zhxAurYv3y1b0WwyTVskSQVSY8ZU38IQ5Py8fAKuIKqgGk/Ai6KW2Xcwh2F9RqLV6EjQp7Dg/Y8YP348ZrOZex/4A0888iSbcz0MTBmEUd94e15RFMpqS9hbsYvM0X1Y+f0qMjMzO6UeutRUS/QQCVFJRERYqHCVkmzudfgzQv8VCPqpDVZx5rRb2zzGli1b8Ntl4hJ+XVTe4/Hg9wcwWH9dQ1QlqYlUxVBYlh82/CY2Ip7t5RvxeDytrjkqCILQFbo8yDx48CAAvXr9+mbdp08fFi1a1EU96niSJDFo0CAGDRrErDtux+VyYTQaT+kykh1BkiRuuPEGRo4aybdff8uBt3LYUbqJ3tZMklKTSE1LITo6JvSx7g/42V68mTMuOZ2EhARiY2NJ65fCgaIcBvUaErZvnUbHwNTB9E8ewMod35FTsYcS13aqNu0jKPuJjovkNzPP5+KLLw6t55mamkr/rH4UbMtDDsgU5BdSWlZKfX09lVIlDVonZosJq9WK0+ugd59fZxRXlFfQUGvHZDCj1ajIqVxNUA5SV1FLVbA/5hgD5dVeIk1RqFQqXN4gXpefaEsSibZ+KCiopRzi9L3QSjpcbie+oBetSossBxgUMYK4qMZZxjXOGn6uWI4p0sTFl1/I5VdcxisvvIKzwc2B/QfQ+/R4/HYAtBoNLq8d5Zfb0u6Ana17Kxk2YgSVlTJVVVWN2z2/Zuh8Xh++gAev3x2WzZMJoNFosLsbMOvCJ+04nE6KCw8i+VXIMtQHq7FqbezK3c6AtEHo1Dr6JQ9g1e7vWLNmDRMnTmTkyJHMfWoOb7/xDqu3/oBBMaNChUd2Y4jScclNF1Bf38DmdTl89L9POq0euh9/WJypklQM7jOUtZtXE+mPwqy1oigKAfyhcaqyIrMpdz3pA9OOOAPV6XSiU+nCvmhKUvMpYQB6tQG3N3z9TwUFSRKrUwjC8SguLuaxxx5j9uzZpKa2vnSacHS6PMhs6du32WzG7Q5/Iy0tLaW0tBRoXKjZ6WysqnL4xIbuTqPRhNbz6+y+K4qCLMvIsnxSZ0qzsrLIysrihptu4G9zHydvSyFR8RG/THRQCMhBDlYVkVu9j0Hj+nPr//tt6JxvuPl6nprzDwzlRtLjezd7HSrqy5GNAZ569nEyMzNxOByNs4T79Qt9CTj093T+Refxl29mU7ynBKvaRowhDr0zH7PejAotzgYXdfZatJEqkpKTflm8XeHA/gMY1WY0Kg0Xjr8Qnaax7OK2/M30HpOCx+0hRZdCVq/G7L3X7+az1UtIt42k3u4iqIBapSFIEJUURGvQ4nY7cfrrcQXsaNwSOp0Wd8CDM9DQOB546kwKi/eQmJgIfhXjUiZxRvrZoYCyJW6vmx11m3j51eeJjY3F5/NhiTCyreD7UBu/z09O+U7K9VHNVk2wWIw4PU7q7PVo1RpkKYgCFBYUEHAFUAU11Adr8KrcJCgpeOwu9hTtYmjGcLRqLQn6ZD58bwFjx45FrVYzcOBAnnz6Cfbt20dOTg5+vx+bzUZ2djYNDQ38/s7/Y/yws9iR+zMrVqzgrLPO+uX3pYT+15LG7Yc8prTcNiE+HrW2ceH9Q/eXmTKQWnsNe/dvJ1aThEWKQGvUEBMby8GaYg6U5xDTJ5IH/nw/KpWqzb91nU6HX/aHHd+gN6Az6HB5XZgNv87y9gW9mLX6sLZltaWk9E5Bo9GE/t7h2N5fQq+dohzx9nvTWOFDj3mqOlXeS4XWLV68mJ07d7J48WLuvPPOY97PlClTWLNmTdgQngEDBrBx40befvtt/vGPf1BYWIher2f48OG88cYbZGRk8M477/DSSy+Rk5OD2Wzmkksu4R//+McxVyPrLro8yDQYDM0CSqfT2SzwfO2115g7d27o52uuuQaAsrKyzu+k0G3c/Ye7+Oqrr/jh25Xs2r0VtaQlIPuJjLcy/eqpnHvuudjtduz2xkxdeno6N/3uet5+7R0O7MwhwZSCSW/C6/dS7ijBr/dw5fWXk52djSRJREZGAoRqgx8uEAjg8Nux+1wYzCZ0ih5FAW/Ag1oK4gm6cMgNWPxmGux1uD0OqqqqqSgrJ9Jga5xw5FeQfsk69U8YyPJlX6CVtEzpfy7SL6UpDWoz/ZL6syt/MxGaXpj00cgo5Pp2hPoSlII45BrQyRT7A0QGIrBF2airrSMyIhqbJZbCCh0LPlyA1+dFCUqoNGqQWl9OSyX5CAQC1NfXhzJjj8x9OGyZmvr6emY/8AjDY8dg1P262HlNdTUHi0qorK+mqKEAk8qMIinUSDXkOfPQekzU+8uplEtI0WegDerxNLjZsmcjcdZ4dBo90aZYNm5awxdffMGYMWNC+46IiCA7Ozv0s9PpZP67/0XxGLEabcRbM5j/9vtkZmbidDrxen3U1Vbjcbdc9cftceLz+/CqvASDQVxuN8FWAqXE5AR27d1MeXU5iVFJoe1De43Eoo9ge+5m9lRvJSk5ie/21mOyGZl08STOOXc6arX6iO9RiYmJeCQXBysOhq2tGpcQx8EDJeg1ehoX0FeoCVSSYcvG9ct6r0E5SGFdHjdOuo7y8vKw/VZUhE9Cao/a2lq8Xh9utwdUbVcqcrs9eL0+ysvLxTJGwkmtuLiY5cuXk5qayvLly7nkkkuOK5v53HPPcfvtt4dtW7lyJffffz9Lly5lzJgx1NXV8c0334S+tLhcLp555hnGjRuHw+Hgmmuu4f7772fevHnHdW5drcuDzJSUFACKiopIS2ssv5iXl9ds8eLf/e53XHzxxUBjJnPZsmVA4xu00DJFUQgEGm9hnkrfvu+44w5uueUWdu/ejdvtxmQyMXDgQAwGQ4vtL730UiZNmsTKlSv5YflK6usqMVvMXDX5UqZMmUJy8pEX/W7y+rzXGZY8CnWahj35O/E4vdj9NWyt/JYgMlqNGovJSmm5G9emImKio9m/fz+KR6LWYMWg12K1WNBrG/tqNBpw1bixacxERUYdWiSIcYMnsL1gC8UNO4g19SE5cggxsXEEgj6qHSXUOotJyshi2MCxFJbsp++QBM4+Zxqv/PM/nD7wHCymCIb0Hs2mdT+gKApGoxGToeUxe7LceGu8orqCqtoqtm3bxvTp01tc8aCmpgar2YrJaMSga9xfcVEROXsOYJSM9IpPp8FVT52jDr/fh9ProMBzAFktgyyRrOtFpCoat9+NWtZRW1fG9we+JjUpBZCw2Mzk5+Vz0UUXtfp7OHjwIBvWbmFo5hkYTSYG9R/BD5sWkZOTw/jx44mJs5FTsbbV5/t8PhzuGmqCJRh0Bhqc9cQaYlu8R52WkUqCPZ599p3UuqvoFZeBQWfA7XPjll0k9onnqtsvY8qUKeh0Onr37n1U2YfExESmnjOFtV9sIikuKTRzvW/fvlRVVFHvqiMmIpZSZxE6s5YBvQai0+gIykHW719Nv+G9ueiiizCZTKHfZUVFBfHx8Ud9C12n06HX6zAaDZhMRxjfKfvR63UkJCQQHR3ddtuT3Kn6XtoVumNiaPHixXi9XtLS0ti/f/9xZzNbsm7dOsaOHRsqoRsVFcXVV18denzWrFmh/zYYDNx222089thjHdqHrtDlQabBYGDixIm899573HPPPZSXl7N8+XIeeOCBsHZJSUkkJTVmEUpKSli9ejUgxiG1pWkNSJVKdcq9MZpMpqOqthAfH8+VV17JlVdeeczHLC0tZcu6bfSNHsSPu1YQVCCoUbBaG7OfGo32l6xTY1WcuppatFotbpeLG6bMxGywolGrQwEmQHF1EUGvgsNhZ0vOxrD1HoPBABajlYBcR5lrO4X1AawuK3qDHkuEkf79UkhKTAZVHb0GxGIwGvhq6TekR2diMTcOyYixxaHNt1BSvhcpXaKlGlSlZWXk7MnB7fCgyDJV3hree/lDPn7/U86+YBo3zbwpLIA3GAxYo8zsqN0MNC5Btm/PPkwaKwatHryAGuQIGafbiUqWiLHEUeOtoJ8xi4yYvjQ0NCA51BAAvUFNekYS/3zxH6Hzt1jaLse6ZPFn6BQbsbbGL5katZa0uIF8vGARU6ZM4dXXX24zu+ZwOPi/P9yPs96FvaGWxRsW0Ltv79BQlsMNGjqQO39/Bz+tXMXaVesINgTR6jSMP3880885m6ysrOP6G7tx5o3s27uf9fvXMLzXSIx6EyajkVHZI9m4YTPbyzfRoKnh9JFTcHtc5NXup9R1kPTBqTz0yF9aDGqb/vaPRmN7Ca/bdcTz8bpdgHRMxznZnMrvpT1dUxazKWGVmJjYIdnMw40fP56HHnqIhx9+mOnTp5OdnR36YtiSH374gSFDhrT6+Mmiy4NMaMxSvvTSS8ycOROTycSMGTMYPnx4V3dLEMJUV1cT9AUxRBrx+2UGxE8Mja08nMfvxiHXo5FUFMsHaXA14A/4m7UrryklIsJKjaOafF8OEzInHBIG6jh70BSg8UPO5XYz9rQxnH322SQkJDT7sPvxxx955tHnOX3QeWHb+yUPYtP21Thd4eP7AIoPFrNzyy7MaitJETH4ZR8VrmLG9zkdt8/Jlx98S1lpOX9+6MHQpBqLxcK8/8wLBXGvvPQKNlUsyeY0oqKi0Gl/nXyze9duqopriDUnsKHoZ7xmN4oCTqcbnc6IVqen3O6kLLeC9evXc8UVVxzx93Dw4EGWf7OSoelnhG3vmzaIFZv3snLlyiPONI+OjubNd9/A4/Hw14ceQXFLDB01hL/Ond1iENFUo3vChAl4vd7QWPKOmmgUHx/Po0/M5fl/vcDqjSsxBsyN9eCDfgKJDqLTzKSa46hVyqmtryCtTyp3Xvw7Jk6cGJqY1hF0Oh1Ws4G9a79sV3ur2XDMr0EwGBRBm9DlmrKYTV/ULBYLZWVlx5XNvO+++3jwwQdDP19xxRW88cYbLFq0iFdffZV58+bhcrm47rrreOGFF5r9DS9evJgPPviAdevWHfuJdRPdIsi0WCxhvxBB6I7UavUvE3kaf9ZpDBi0LX8TlWUFreRBo1GhNWmoN1Xg0TcPSA1mNVlpAwGIiY/mib8/jlp9WFUjReHDDz9k1KhRrVatCAQCLPjfQhItGWELhwNERcSgUjQUFRYRH/1rnWuX283uHXuI0EVhMYRnwiRJIi4ygfHG01n9w0q+HPUll1xySejxpjdku93OlvXbSLX1IWfrAdIyUhk0sPF8HC4nFRWVBJUg+RUH0Cl6chv2UuetwePzocOIBNiVWtTFEl989iWXX375EYOORZ8uRqdEEmNLCNuuVmtIix3Q7pnmFouFHTt2ULyvhLNGnsfG3T9TUFDAqFGj2nyeXq/vlJUhEhISePzJv7F//35Wr15NbU0dJpORocOGMnr0aDQaDW63G5VKhV6v75TgzGKx8NorL7Z7jGVT8N1etbW1rFy5kq8++5qqiipUahVDRmQx/dzpoXM82TkcjqMao3q0r6HQcQ7PYjY53mzms88+22xMJsB5553Heeedh6IorFmzhmuvvZbHH3+cJ554ItRm+fLl3HrrrSxZsoR+/VqvFHiyOPn/ogXhKHk8HjZs2EBpaSmSJJGamsro0aNpaGigrq6u1TrQaWlpWGxmqhoaJ1TIchBfwNdYP1ut4dABfS6vk9jkWPoP7IscWcnz854/6nFrBw4cYPmy5Sxd9CVbNm0l0hbBb2fdwtnTz2LQoEFhQcbq1aspzDnYLIvZxGSwUHqwDEd/J5ZfvjWXlJSAX8IS2foHnEFnoFdEb75Y8iUXXHBBsyDAbrfj8/iorqsh6AlSnF+MOcpIQWUe+Qfz8Hg9BIIBfAE/iREpRGAjQhdFanwChl/WvXR6GthZuYaxE8YcMXBqLYvZ5GiymbIs89EHC4nTJRFpiiRG3Vh1qCvXrZUkif79+9O/f/8WH2/r9lpH6ayAZ8eOHTz9t3/gKHOTbE1jgGUYshzkwE9FPP7DU2SfMYo/3n9fh2ZmTzSHw8GsW2dhr3O2+zlWm5l5/5knAs0ucHgWs0lHZDPbIkkSEyZM4Morr2T79u2h7d9//z1XX301H3300SlTZloEmUKPIcsyixcvZtGCxdSW1WOSrEgSOGU7sanRKGoZKajipddebPHD3GKxMPXcM5n/0gfU2KvJl3NRq3RIgE6nx2axEWGyoSDjw0tqWtM3YIWdO3eyedMWSopL0Gq1ZA0bzJlnnklCQkKz4yiKwqeffsq7r/8Xg8+CUqmlr3oovgY3y99fyXdLv+ey6y7hxptuRKVStZrFtDvr2Ve8mwP5e6ipq8REJJs3bWbcuLHodDrKSsowaY/8gd4rLoOVed+yf/9+Bv6SpWyi0WjweD24SkpJikxld9V29q3cRYIxmQxDJsZIExVVFVTK5VS4SrFTR7p5MKmx/UMheaw1hRp7OYs+XsRtt93WZoDXWhazydFkM9etW8e+7QeYmHEmAANSBvHzphVs3rz5iNlM4ejk5+fz+CNPYnFFMTJzQtjvON6WiMfnYe33P/Ev1b/4y8N/OWnHePp8Pux1ToZEjQpbeaE1bp+LHbWbxOz8LtBaFrNJR4/NXLx4MU6nk3POOYeYmBh27drF4sWLufnmmwFYsWIFV1xxBR988AFnnnnmcR+vuzg5/5IF4SgpisKbb77JWy+8S7Qnkcn9zmZ8/4mM6zeRyf3ORinR8f2SlWxZv43vvvuu1f0YTUYKyvLxOD1YVJHEaOOJ0sSi9muprK6ksCKfsroS4pJiiYmJwePxkLM3h8cfepJ1SzZh3+2hcksdC19bxO0z7+Ddd94N1etusmzZMt5++V0GRQ5ncMJQ/A0KKTF9iDWmYA1EMSxuDB+/s4iPP/4YaMxi7t+ZT5Q1hsq6cipqy1i17Ts++OJNdmzagqbOgCaoRS8bOJhbwvffraC8ogKfz4dGfeTvmSqVitz9eaxd23y2dmxsbOP4U6+DWl81NZ4q4jwp9DEPwKaPhqAKvBIpugyS6Y3ap6PMU4zbZ8d1yL/k6L7s3JzD119/3Wo/GrOYP2Azx1JWWdTqP7PRSsGBUlauXNnqvg7NYuq1jbe+DTpjKJspSjR2rEWfLkapUTM4bUiLXyIMOgNj+0xk3Q8bwjI7JyujzoTZYD7iv/YEokLnaC2L2cRiseD1elm8ePFR7/sPf/gDFosl9C81NZWoqCjefPNNBgwYgMVi4cILL+Sqq64KTXKeO3cuDQ0NXHHFFaHnZWVlHdc5dgcikyn0CBs2bGDJ/5YyKnkcNkv4sjxqlRrZDr11gyipyeON195i6tSpzbKZa9eu5aN3P+GMgVP5cdsqAoEAfsmPWqVGLxmQUFHvrMViaBxH5/f72Lp5G+4GL+PGnh62BiJkUdVQycK3PsXn9/Hb3/4WSZLw+Xx88O6HpJv7kWBLZMvWrWglIzqtnkhrNOVVhfTxw6DYoSx8/xPOOeccDhw4gDXKRK59KwAlZSWU7C8lVdcHqyESX7AxSyKpwKS2IHsU1q9fj6SSkBQ1as2vY0C9QW+z166ouJiGBjvLvvmOGTNmhN0yz8/Px+8L4FZ5yaurI1GdikWJxO5wEGWz4fV6kVQqvLIbd8BJrDqJcmcx2+sVdIfMslcUBY3OwHvvvc+5557b4u+wtLSUiEgLjkAJjoaSNn/fsYk2iosOtvr44VnMJsebzVQUhZKSktCi/mlpaSdtVq6jVFdX89PynxiYOLzNLLVRZySCaL79epmY+Cl0qiNlMZscSzZzxYoVrT7WtPRiS77//vtWHzuZiSBT6BG++uIroqTYZgEmQF19PeUlFaRG9CLg9HJg9wG+++47LrzwwlAbRVFY+L+FxKuTSEhKZnvRDmw2GwFPEP8v4zKNFgNRxgzqAjU47A4qqypx1buJNNnQaZpPFImNiGOkaixLPlzKlClT6NevHxs2bKCquJqsfqNoaGigvLSS2IjGNzeNWotBYyH3QB6js0eTk7ObVatWMXPmTGbOnAk0LlJ+6423MTZxIr3iMwDw+j3U/1CBw1cVOrbd3YBXcaNWtEQRPlZUq9Oi+WXyUVAOsmnXRiJtMRTsL+Xbb7/lvPN+Hff56cefkmbtTVFUAfn5BaQbBqJGjdPuxGqxYLVa0Rt0VDaUo9PqyEgdgtkVSeqIAYzOnhp6bVEU7I5admz/hn379rU4JjE7O5u357/Rnl93m5qymPG65FAWs8mh2cyjGZspyzIrV65k6ZIv2LtjH0F/EJVaIq1PKudffB5nn312jy0jW1RUhM8VJCY59ohtE21J7Nmx5wT0SujJmm5dFxYWHrFtTExMp43N7AlEkCmc8urq6ti8divD4rNbfDwvNw8tOnQaHTG6eApr9/OPp/6J3W6nf//+DB8+nM2bN7N3+z7Gp56B0+VCJakwmUzorIZmmSpnvZOiwiKqKquxGiKodLUeqERZojGVWlm+bDn9+vWjqKgIs2RFrVKTm5cXymI2acpm1tbWEKG2kXsgN2x/q1atwlXtITWzV2ibXmvgislXEzjktrwv4OGH/ctAUhiWMIYE26/f6A9dy7OgqJASexHZ46eQuy+fZ57+F2effTYajYbc3Fx+XLaKkanjMOssVJXU0BCsRYcOOahQXlOOVq/GL/nQWNREBGOxWqOIDMZgr6tB/8ts+6YShrGGZKzWVD75ZBF/+tP9R/q1HrPWsphNjjabGQwGefnFl1m+ZAXxuiQmJJ+BUW/CF/BRUJrHv595i7Wr1/Hnhx48IRN3uhtZllFJUrsCdpXUOMZYEDqLoigUFBRQXV3drvbV1dUUFhaiKIpYbusYiCBTOOU5HA4CvgAmQ/NJLk1ZzFhjPPkN+ymsz8Pvl6nYUcMbT72LKcaAogtSXllGgikFo96E0+UCwOvzUlNfR3x8fNgtZIPWQHV1DT6PjxhjzBH7lxyZwvrVG/jd7b9rrAGt0CyL2eTQbKYmWkKWw8cO7t61B5smOlQ1polea0B/SJVAE2YSzMn0Hd+LHWt3oZIkesVlhAXM1fYqvtv6NVFJcZjU0di0HvbvXcvnn3/OpZdeyqcff4oxYCXSbEOv0RETEUOaIY16Zz1Oj5N6Xw0D+g0gIyOdLVu2EfB7KTm4lyp3GQ5vHcu+/fXWvCIrSCoJFIWtWyuprq4mJubIr93RaiuL2eRos5kLFy5k2aLvGdPrNKzGXxdz12l09E8eQC9/Bmt++pFXX3mV+/7vvg49n5NBXFwcslrG4XE0WyrrcNX2KtJGd9wC2IJwOEmSmDNnDl5v86FBrXnkkUdEgHmMRJApnPKMRiMqjQqv34PusDUk83Lz0Cha8u05VNgriJdSkDSQEJlM0O1jbGo2X634ksKqgzREO5k6+JzQc2trqwl4glTXQqTNFtruDbjx4cYfDOBrYYzj4bQaHV5PY7v4+HjcuDiQm9ssi9mkKZuJ2k1ySvj4RZ/Xe8TJPBWVFeTnFSDZJEaMGMHkyZP571vvk7dvHxYpErVKTb2vljJ7CUFTkMnjLmLrxu1Em2OJcqbw7DPPk5yczPIvv2dozGi8fg8Rpkg8uNFpdSRFN5bpLKsvRa/XY4u0MWZMNoFfFqPfkLeGiRdfwBVXNS68fnjJPp1O12llCtetW8fOTbvpaxtAQUVeq+0MWiPb1m8/YjbT5XKxZOFnZMYMDgswD6XX6hmRls3KZT9x9bVXh0rp9hSpqamMGDOUA+v3Mbz3yFbbBeUglb4ybjr32hPYO6EnMplMPP/8813djR5BBJnCKS86OpoBQzIp2J7HkIxfJxQ0ZTFRKVTUldNXOwhvwIfWpCHSFElp/UH27MnBFIggK3IUmytWk3twP1GWWGQ5wJ7KVWhUWgINAaKcNtSqxnGM9a46zFEmGmobsBmi0et/HePYEoe7gehejUHV+PHjeV7/Agf27Kd3bMslxTRqLbICB2sKm62lFp8Yz0b/tlaPJSsK+3IOUFtdTwA7MTExTJs2jcmTJ7NmzRr25ezD7nCwaOEiKssr0UomFi99F6/bh16jJyAHyCur5NrLrkf2KJSaytHqtFx2xlVERERQ7iolxdx4qz7SaKM4v5j09HTMv9wmdrjtqCIUrr72anr37g2c2LrQTqeTfll9QPHjp3kFpiZqoI/UB7vd3ub+1q9fj7PaTXK/trNvkWYb2mI9K1eu5Npre1YQJUkSl1x+CY+u/xulNQdJim4eZMuKzMYD6+g1IJVx48Z1QS8FQegMIsgUTnmSJHHehefy7MYXcHn7YdI33jZvymIe9OQTJcWiQYcdO3GWWCRJwqyzsH/vfhIjk7GZU8mtzWH5pm+ZMe0mMiMH4/A7ibHGUt5QRmJSAgMHDMDtc7G+5GfmPDWbd954F09+kEG9ssLqlR9KURRKHMXcct6NAJjNZsw2M3v8ufRiUIvP8QW9VFOGWq+lrKwsbK3NSZMm8fF/P8XpcTYrIQlQUVGOo8EJGomyulJGjBgBNNYjnzJlClOmTKGmpoZP3v+UNDJJiMygqqISveHXsadV/jK8VQ3ERSUgBSDXvYddBduxWqzkVOwm4A1g1TbWc/d6fBQUFDBo4EA8Pg8bC9Yy8bzTyMjIOKbf5fGaNm0a06ZN67D9VVRUYMTcbHhCS6y6SA4Wtz0r/lQ1evRobvv9rbz+/BuU1pbQJ7EfkSYbsiJTUl1Mfk0usX1tPPjwn3rsBCmhc+Xk5HR1F3okEWQKPcKkSZNYffYa1ny9iuEpo1HJGspLKrDqrdjr68mQkqjz1xBhjcD8y7gxRVYIeGTUEVokSSIjsh+bq9ewLWcL9hoHCdZkNGoN8eYEqkuq8WZ42Vm6jclnT2L8+PE4nU5eePxlfH5fq0FmzsE9mOL0oYxkYWEhbocfQ4yBdaXLiVbFEaWPQ6vSEVAC1HorqQlWYIoxExeVzOJFS8KWe+nTpw8jx49gy48bGN9/Uii7Co1ZzAP789BojFQG8jGYzGzfvp0pU6aE9cnr9VJeVs5gUwYBTwC9ZMCg+XXCSrQxnrIGF6YIIyarkXK7kT4Te5FlHEDUDivb1+/EIzuJtySSoI1DVsnsLNhGubeUUZOHc9c9d54y45tUKhUK7VtTU1bkU6Js4rG64IILSEtLY+mSpaz7eT2+g36QICY+istuvZDzzjuvU8bhdgW3z9Wh7YRjN3r06K7uQo/Wc9/xhB5Fo9Fw3x/v5U3bm3zz+TKKd5fgs8uUqyXqvXU4NA1YzFZMehN2VwOyolBeW4ZRMuGwO7CYzcRHJGJ0G/lp/3fEKEkYdSZMipmgFKDCUcYXmxZx4TXn8ft7f48kSUydOpW9e/by9cfLyIjoR6+4jNB4SYfHQc7BPbiM9fz5wT8RFdW4tJLNZmPmrTMIBALs3r2bzRu2UFpQQjAgo1JLxCfHcs6YSQwZMgSNRtOsYpAkSfzhvt8zp3YuP21fQb/YTJJiUlBJKkrLDlJUWYBP7ycpI42UxAw++t8nTJw4Ea3211lBq1atwusJoLdaqK9qwKAJD5ANOhMRhlicTidjxmfjrqjj9/feExpHuXPnTr7+8mt+XrkGu8eJS11D/8H9ue7Cu5k0aVLYsU526enpuHDgD/jRalo/L0VRqPfX0q9/yyVLe4phw4YxbNgwqqqqqKmpQa1Wk5KSgsHQ8pewk41Op8NqM7OjdlO7n2O1mdusTCUIJzMRZAo9hl6vZ9Yds7j8ist54P4HKMwrwufzYszVEx8dG1Z9w+V0EpSDRJhsqNUaZEUhKAeIMERS760ltX8yxdW5BNx+kCTMCSZUkWpu/u3NodrLKpWKWXfMIq1XGosXLuHHvAPoJQOyouBXexg6JotBQ6ZQUlIS+rYdERHBpZdeGuqHoiiUlZXhdDoxGo0kJycfMQsYFRXFY088yicff8K3Xyxj7/6daCQNu3J2ozbqGTd6KkMyR4MCKzZ/yqpVq0LZTK/Xy+JFn6PXGmloqEcJKijqxnM/lFlvpbK2mNy8XIKG8IpFWVlZZGVl8btZTux2e+PkH5sNSZJwOBzNxjm2NSZTp9N165rOI0aMILl3AnnlB8hMGdhqu7LaUrSRqlOmHvHxio2NJTb2yOtmnmwsFgvz/jPvqMpEdvdrXBCOhwgyhR4nISGBd959B2hc0ubeu+/Dm68wMHVwaNuqH1dhVJvR6w1E/5JlrHSWYdZYUfm0RFpsTBt5Dl6/B7VKjU6jZ/W+H/li6ZfMuuP20LFUKhUXX3wx5513Hlu3bqW6uhqNRkOfPn1ISEjgrt/djb3ewYQJE1r80JUkiaSkpKM+x4iICGbePJPfXP0b9u7dy7p166h9t4FzJ8zAeMhYzZTozLBs5o4dO6iprKGyuhR90IRW0gEtB7WKIrN5wxbqbeU4HI5mM8LNZnMo4IbGpaRm3ToLe52z+b5kGamFyjhWm5l5/5nXbT+ENRoN1910Hf987F9Yqi0kxzSfAFTrqGFX5Vaun3UttkNWITgZORyOdgdQPTV46onn3J1t3Lix0/YtbsUfmQgyhR5NpVJx/sXn8fKTr5Hh64NBZ6C0rJTa6nrUQS0uhxOr1YJf8VLtryCSaJIj0snJ30NW+rCwdf8yEwfx7efLuOTSi0lOTg47jlarJTs7fDH4pUuXUl1UhwYtn3/2OTNvntnh52cymRg2bBhvvzmfzNTRYQEmQP/0rLBs5siRI3ngL3/kyh/XYlPFEKmztbrvoCzjlBuo8hbj97c+U7uJz+fDXudkSNSosKyxAiDLoFKFhbNun4sdtZuOKivUFc444wwc9zr4z0tvkl+TR5qtF2aDGY/fS3F1AQ51A5fdcDFXXXVVV3f1uLT1JaEl3f0LgiAInU8EmUKPd+aZZ7Lm5zWsWfEjw1NGk7svF7wSGpWOoBykpPYgNdpSzFoLMUoCkfooKp2l7CrYxtgBp4X2ExsRh6ZMz+JFS8KymS1xu918/L9PSI/sg1lvZumnX3LhRRd2yi3ENWvWkL/vIKcPv6TZYxqNtlk2U61WN96+VquRldYntUiSBEHpqINAo84UNvNd4ddM5sk6Hej8889nyJAhfLf8O77/dgUFDhc6nZbTLpvAWWefxYABA076yU6tfUloycnyBUHoOTIzMzt0f2K2evuIIFPo8XQ6HQ88+ACvRr7GJx98SklxBUa/Bb1aj12uw2m30793JsFqFZIRGry1WFWR7Dqwk4yEvmEBU6qtV6vZzEN99913VBfVMaR/NpIkkZOzp1OymcFgkAX/+5jEyL4tLuwOzbOZABqtFp1Bh07d9iQdo86A5D/y8j09Qa9evZh580xumnkTwWAQtVp90geWLTn8S4IgCEJrRJApCDRWBbr3vj/gcjt5JX8edqUWvU6PVqNDHVRRZM8nQIBC9y+1mFUqkGBVwXdE2cLHImq0GrZv395qkHloFrNp7cnM+IGdks1sK4sZ6u9h2cykpCRMRiNRtigsOmub+3f4dOjrxMzYQ0mS1GlLFbVnTKQsy9TW1mKxWIiIaLkKkSAIwokggkxBOMSUKVP4+Zu1TOozNVSCssZezfaajTz69BySkpIoLy8nISEBlUrV6nI8kZGRrR7j0Cxmk3hbYqdkM79bvoJA0M/KrZ+22U5RFPQeLTt37sRqbQwsfUEv3mDbmcz2lM0UOobD4eD2WXfjsLvbbKfQuEpAbEwkr776khgTeYorLi5m/fr1NDQ0oNfrGTJkCFlZWadkFr2juFwu/vznP7e7/ZNPPonJ1PYQEaFlIsgUhF/IsszCDz8myZiGUWcMbY+LjMdUYeWnH3/ijjvvIBgMEhcXF8pCHo2WspjQmP3qjGzm7bNu48qrqtrVVpIk+vXrR0lJCZIW9vt2oQm0Xg4TICAHkbScUmtfdlc+nw+H3U3WkHMwGlsPHBVFoaa2itwDK8WYyFNYdXU1r817nXU/rkfr06NFT0AJ8J76f/Qd1Jvf3Xkbgwa1XDWsJ3O5XNx3331s2LCh3c+57777ePbZZ0WgeQxEkCl0G4qikJeXx8aNG3E4HBiNRoYPH87AgQNPyLfyNWvWsH97Lqf3aV52MDNxEN998T0XX3IxGo2G77//nkGDBrU57rIlLWUxm3RGNjMuLo64uLijeo7FYmFI1lCGx47FpG/7TdXldbG1ap3Ilp1ARqMFk7n1YQyKouByi0oyp7La2loeeWgO5XuqGZk2nkjTr3dOPD4Pe3J28ciDc/jr47MZMmRIF/a0+/F4PJSXlzNs2DC2bdt2xPbDhg2jvLwcj8fTriBzypQpXHPNNdx++6+TP/fs2cOgQYNQfplIuWnTJubMmcNPP/2ELMtkZGRw0003cc8996BWq/H7/Tz99NPMnz+foqIioqOjmTBhArNnz2bo0KFhxzvzzDNZsWIFbre7WVGD6upqBgwYQL9+/VizZk17Xp4OJ4JMoVsoKSnhlRdfYduGHegDJnToCeDnPdUHDBiWyay7bqdv386rliLLMh/9byEJ+pTQbfJDRVtj0JWZWLxoMRNOm8DLz85jzGmj+fND7b/l0loWs0lnZTOPhUajwaRv3wSP7l4qUaztKJxq3nn7Xcp2VzIh84yw0rEABp2BEb1HsaNgG8898wKvvP6SqCjUAp1Ox8SJE4/Yzuvt2CFBa9euZdq0aTz44IP8+9//JiEhgV27dvHoo49it9ux2WxcffXVHDhwgLfeeovs7Gx8Ph8ff/wxn3/+eViQ+fbbb7d5rD/+8Y8MHToUt7vtITadqXt/Ogg9QklJCbMf/CuuIh/jep0eqh0O4PG52bV9B3998BHmPjmHfv36dUof2spiNslMHMR3X66guKiYYB2sXrGWnKty2r00RltZzCadNTazp3I4HMy67U7sDe17k7VGGJn3+ssi0BS6raqqKn5c/hODk0c0CzAPNSgtix/2f8uGDRs47bTTWm0nnFj3338/N9xwAw8//HBo2+DBg/nf//4HNH5OfPHFF+zdu5f09HSgcTjSjTfeGLaf6upqnnjiCd577z3Gjh3b7DgrVqzgwIED3HLLLbz22mudeEZtE0Gm0OVem/c6rmIfY/uf1uy2uEFnZGSfbDbnbuDF517iXy88e0xjIdvSlMU0y1Yc7oY229orXHy5+Gsuyb6K3PL9fPzRx+3KZsqyzOKPl+Dz+lib91Obbb1+D98s/ZYrrrwiNAmnK7h9R77l2p42R3pOW4uxHy+fz4e9wc2w9CkYDUdY29HjYlvBCjGOUejWtmzZguKSiE6OabOdWqXGpo5lzc9rRJDZTbhcLlatWsWcOXNabfPNN98wduzYUIDZmj/+8Y/ce++9LQ6H8nq93HXXXXzwwQedWvGoPUSQKXSp/Px8Nq/dwtjUSa2Ou5Qkiaxew1i1+zt27NjBsGHDOrQPNTU11NbWEoj0ssuxtc221d4KJLum8fa5RsfqFavalc2UJIkbbr4ep7N91VL0ej1Go/HIDTuBTqfDajOzo3ZTu9pbbeZ23Y5ra79tlZU8mlt9h98ar62txefzoShys7YatQa9rmte45NZZ335ENrH5XKhk3TtGqdu0hlpqGv7i7PQ8e677z4efPDB0M+y3Pj+U1tbiyzLbY7lr66uPuJY/xUrVrBr1y7efPNNCgsLmz3+5JNPcs455zB06FARZAo92+bNm9H7TViMbWfs9Fo9ZjmSDRs2dHiQGRsby5vvvBEalN2a4uJifv+7e+nbr7HGudUYgVWOalc2U5IkTj/99A7rc2eyWCzM+8+8Dh/H2Np+FUVprDCk0TT74DyaMZItlT30+/3s3LaT4pxS1OrwtzutQcdVF/xWBJrt1FlfPoSjYzQa8SlHLuMK4PZ7sER03d2QnurZZ59tceJPVFQUKpWKkpISBg4c2OJzY2Ji2Lt3b6v79vl83Hnnnbzzzjst3tXbt28f8+fPZ+vWthMmJ4oIMoUu5XK50NC+5W/0Gj0N9fZO6Ud7bsF//tnn6LwmouN+vU01MGVwu7OZbfH5fHi93i69PX6ozhqT2NJ+2woyj0ZLZQ+9Pi8evYo4cwqaQ6oXeYNe9nu2EwgGaLkOknC4zvryIRyd4cOHgyFAjb2aaGvrt8yDcpDaQCXjJtxwAnsntMVkMjFx4kQWLlzI1KlTW2wzffp0nnvuOQoLC+nVq1ezx0tKStizZw/nnnsu8GuWNDU1lfnz51NeXs7BgwfJyMgAGm+dezweYmNjyc3NPeEFGkSQKXQpo9FIkPZ9K/cGvFgjuuZDq7i4mOVffE9W0siw7UeTzWzL66++TmFBIU/946kOH3Pa0xxa9lAtadCpdeg1xrAgUzg2ImjsevHx8Zw2ZQJrv9jEhMzTUUktv1/sO7iHmJSoFieFCF3n6aef5uyzzyYtLY1bb72VuLg49u7dy9y5c3nllVeYOnUq5513Hpdeeinz5s1j1KhR+P1+Fi1aRGFhIffffz8FBQWh/RUXFzNhwgTWrl1LSkoKsixz1llnhR7/6KOPmD9/PkuWLOmSJIb4NBO61MiRI/Fo3Dg8jjbb+QI+HFI9o0ePPkE9C7d40WK0bkOLmYPGbOZacnJyjmnfxcXFLP/ye3Zt2ntUCwQLPZPb7cDltLf5z+MRYyJPZTfdfBPRfSNYu28VzsPeO/0BPzsLtlGlKuPu++5Crxe5+u5k/PjxrFixgp9//pn+/fuHliwaN25cKAhcsGABl19+OTfeeCM2m43MzEyWLFnCRRddhFqtJjU1NfQvMTERgJSUFAwGAyaTKezxqKgodDodqampXVIFSmQyhS6VkZHBiLFD2bl6G2P7TWj1j2B30Q4yBqR1+HjM9mjKYg45LIvZ5HizmYs+WYTeayJKZ2HBBx+RnZ0tsplCMzqdDovVyM4dX7fZ7tCykmJM5KkpLi6OR5+Yy4vPvcTaDT9iCFgwqAz4gz4cqgZS+iTx8F1/YcSIEV3d1R5nxYoVzbYNHDgwbMz/6NGj+eyzz1rdh1ar5eGHHw5b5qg1GRkZbc4nmDlzJjNnzjzifjqLCDKFLiVJErfNuo2H82ezfv8ahvYajvGQKjNev5ddRdvxRbj4872PdEnwFcpipsWi0PIf87GOzWzKYg5NHI3ZYGbV1u/ZsGGDuMUlNGOxWHh13otHHBMpyzLl5eWkpaWJ29unsISEBB574lFyc3NZu3YtdbX1GE0Ghg4dyogRI7p9kYSu5vP52nXnqCsSG6cScRUKXS41NZXHnnqUl55/idVbfmj8Vq424At6cart9BmUwZ333H9cE2uOVWlpKcuWfgdeNZv2r0dBwev1odfrkAjPujpr3Hz68af86c9/avf+m7KYTbfhY7VJIpsptKo9QaMsy/h8PhFg9gCSJNG3b99OrYZ2qjEYDCQkJLR7aNK2bdvIzs5uVrJRaB8RZArdQlpaGk/94yn27dvHxo0bqa+vx2w2M2LECLKysros4NLpdFxw+XkEg0EAZEWhob6eiMhIVM1u7Y8hJTWl3fs+NIvZZEDKQJHN7ASBYOCwn/0EgwFc7l/Hs7nFOEZBOOWZTCaeffZZ/vzn9g9tevLJJ9tVt1xoTgSZQrchSRKZmZldkrFsTUxMDLf+v1tDP8uyTFlZGYmJiccd+B6exQTQaw0im9mB1GoVWp2GGntJ2HZf0Ee9t5KN+5eFjVu0RhjFOEZBOMWZTCaef/75ru5GjyCCTEHoAi1lMZuIbGbH0Wq0nH7GRILB8Io/Lq8TQ4XMC/P+SVRUVGi7WNtREASh44ggUxC6QEtZzCYim3l82lPSUCaIVqslKiqK6OjoE9ArQRC60rEuMSccn5M2yLRYLGg0miOWAuzJml4b8Rp1HEVRQtfdsb6uTVnMIYmjW5mrDpm/ZDPXr19/ymczO+o61Wq1WI6i7KHFZkar1Xb434eiKOzZs4cVK36gKL8ItVrNgMEDOPPMKaSktH/M7vEc/3ivUSGceC/tOCd61ntXra0sNDppg8yRI0cSFRVFIBA4cuMermnSitAxoqKikGU5VM7raH2y8BOC9YBNoaahqtV2xoCFD99fwIgRI44pm+lwOE6qEoDHe50aDAZenPfCUZ2zwWDo0PeQuro6nn/2ebau245FjiTSGIWiyOxZvZSF8xdyzsXTmXnLTLTazq0+dLzXqNAy8V56/A4dniKc+k7aIHPz5s0MHTqUuLi4ru5Kt6UoCsFgELVa3SUr/Z+KZFmmurqamJiYY76NvXdvDtpoFbvsW9puGAHVVdXY7XZiYlqvUdwSh8PB3bPuwVHnbFd7i83MvH+/0iWBZkdepzabrWM6dQycTid/f+Jp8jcXMaH3mRh1xrDHax01fPXRMgKBAHfdc1en/U12xDUqhBPvpR2nsrKyq7sgnEAnbZDpcDgIBALiD74dJEkSr1MHkSQpdN0d62v64ssvtDsjolKpjun2kt/vx1HnZEjUKIy6tpfecPtc7KjdhN/v79Lr5GS/Tr/99lv2b8pjUv8pLdZJj7ZEMyZ9Ass+/57JZ05m+PDhndKPjrhGhZaJ1/T4ibuPPctJG2QKwslKrVajVqtPyLGMOhNmg/mEHKsnCwQCfLnkK9IiM1oMMJtYjRFEKFEs+2ZZpwWZgiAI3YW4lyIIgnCciouLKSkso1dcxhHbpsX0Yv3qjWISiSAIpzwRZAqCIBwnn8+HIito1Ee+OaTVaAkEAmISiSAIpzwRZAqCIByniIgItDoNTo/jiG3tbjvWSOsJX8pFEAThRBNBpiAIwnFKSEhgaPYQcsv2H7FtcV0BZ5837QT0ShAEoWuJIFMQBOE4SZLEBRedT7VcQa2jptV2+eW5SBEyZ5555gnsnSAIQtcQQaYgCEIHGDduHJdedxGbStayvyQHf8AfesztdbEtbzMF3v3c/X93kZSU1IU9FQRBODHEoCBBEIQOIEkSM2+eSXJKMp8s+JQf85djkIwoioJXcjNw+ADuvv53jBw5squ7KgiCcEKIIFMQTmFun6tD2gjtI0kS55xzDtOmTWP79u1UVFSgVqvp1asX/fv3Fwt5C4LQo4ggUxBOQTqdDqvNzI7aTe1qb7WZ0el0ndyrRoqikJeXx4YNG6ivq8doMjJo0CBGjBhxwhap72wajUZkLAVB6PFEkCkIpyCLxcK8/8zD5/O1q71OpzshdcvLysp45aVX2bppJwaVDb3GhC/g5UP/Inr3T2PWnbcxaNCgTu+HIAiC0PlEkCkIp6gTETQejfLycmb/ZQ6OSonxAy/AbLL+8oiC2+Nmb/5W5jz8N/766F/Iysrq0r52N263G6fTiV6vx2q1HvkJgiAI3YAIMgVBOCHe+M9b2CsUxg+fhkoKX9hCrzMwYuB4tues58XnXualeS+IxcqBnTt38vVX37D6h9X4fQFUahVDRgzm3AvOZdy4cafM8AJBEE5NYgkjQRA6XUlJCetWbyKrb3azAPNQg/qMpKSwik2b2jeW9FSlKAoLFizgoXtns/GLbWSahjA2cRLDbNkUra/gqYef4dl/Ptvu4RCCIAhdQaQKBEHodFu2bEGLmUhrdJvtNBotkcZE1q/bwNixY09Q77qfZcuWMf/V9xkWP4q4yITQdpPejM0ShdPj5Kelq7BYLMy6Y1YX9lQQBKF1IpMpCEKnc7lcaFT6drU16sw0NNg7uUfdVyAQ4KP3F5Ju7hsWYB7KbDAzIjWbbz77ltLS0hPcQ0EQhPYRQaYgCJ3OZDIRCHrb1dbtcxIR0XMnt2zevJmyggoyEnq32S7KEo3ao+OHH344QT0TBEE4OiLIFASh040YMQK/5KTe3npdb4BAwE+9p4yx48acoJ51PyUlJZgkCxq19ohtbbpo8g7kd36nBEEQjoEIMgVB6HTJycmMO20UOw+sR1bkVtvtzt1Mcq84sZB5e4kKQoIgdGMiyBQE4YS45bc3Y01QsXbbchyuhrDHvD4PW/aswS4f5J4/3Nmjly9KTk7GpTgJBANHbFvvrSGjT/oJ6JUgCMLR67nv5IIgnFAJCQk89vgcXnnpVdZu+gKDKhKd2kwg6MXhr6J3/zTuv/OhHl/xZ8SIEcT3iqWgIo++Sf1bbVfnqMWv9zJlypQT1zlBEISjIIJMQRBOmMTEROY+9gj5+fmsX7+ehoYGjEYjgwcPZtiwYWJxcUCr1XLVtVfw0t/nEdlgIzYirlkbl9fJluINnHXlVJKSkrqgl4IgCEcmgkxBEE4oSZLo3bs3vXs3zp5WFIVAIIBKJUbvNJk+fTrV1dUseHshlgobGfF9sBiseANeCivyqfSVMuGccfy/2/5fV3dVEAShVSLIFARB6GYkSeLaa69lyJAhfP3lN6z9aS2+6gBqjYpBIwZy0wXXcNppp/XosauCIHR/4h1KEAShG5IkiWHDhjFs2DCcdzlxOBzo9XoiIyORxKxyQRBOAiLIFARB6ObMZjNms7mruyEIgnBUxCAoQRAEQRAEocOJIFMQBEEQBEHocCLIFARBEARBEDqcCDIFQRAEQRCEDieCTEEQBEEQBKHDiSBTEARBEARB6HAiyBQEQRAEQRA6nAgyBUEQBEEQhA53Ui/GXlVV1dVd6NY0Gg1RUVFUVlYSCAS6ujunlLKysq7uwilDXKedQ1yjHUdcox1HfG73LCdlkGkymdBqtXzyySdd3RWhh7Hb7WzcuJHRo0djtVq7ujuC0Iy4RoXuTqvVYjKZurobwgkgKYqidHUnjkVdXR0ul6uruyH0MNu3b+fcc8/lq6++YujQoV3dHUFoRlyjQndnMpmw2Wxd3Q3hBDgpM5kANptNXKTCCdd0CzIuLo7k5OQu7o0gNCeuUUEQugsx8UcQBEEQBEHocCLIFISjkJSUxCOPPEJSUlJXd0UQWiSuUUEQuouTdkymIAiCIAiC0H2JTKYgCIIgCILQ4USQKQiCIAiCIHS4k3Z2uSCcaA6Hg5dffplNmzZhNBr5zW9+w/nnn9/V3RJ6oKO5Fi+++GL0ej2SJAEwePBg5syZcwJ7KwhCTyWCTEFop9dee41gMMhbb71FaWkpf/3rX0lNTWXYsGFd3TWhhznaa/Ff//oXqampJ7iXgiD0dOJ2uSC0g8fjYdWqVVx//fWYTCb69u3L1KlTWbZsWVd3TehhxLUoCMLJQgSZgtAOBw8eBKBXr16hbX369KGgoKCruiT0UMdyLT788MPccMMNPProoxQWFnZ6HwVBEEDcLheEdvF4PBiNxrBtZrMZt9vdRT0SeqqjvRafeOIJBgwYgN/v55NPPuGvf/0rr7zyiqgdLQhCpxOZTEFoB4PB0OxD3Ol0NvuwF4TOdrTX4pAhQ9BqtZhMJq6//nrUajW7d+8+EV0VBKGHE0GmILRDSkoKAEVFRaFteXl5pKend1WXhB7qeK/FplnmgiAInU0EmYLQDgaDgYkTJ/Lee+/hcrnIy8tj+fLlTJs2rau7JvQwR3MtFhYWcuDAAYLBIF6vl/fffx+fz8eAAQO6oOeCIPQ0oqykILSTw+HgpZdeYtOmTZhMJrFOptBl2roWf/Ob3/DII4+QlZXFtm3bmDdvHlVVVeh0Ovr168fMmTPp3bt3F5+BIAg9gQgyBUEQBEEQhA4nbpcLgiAIgiAIHU4EmYIgCIIgCEKHE0GmIAiCIAiC0OFEkCkIgiAIgiB0OBFkCoIgCIIgCB1OBJmCIAiCIAhChxNBpiAIgiAIgtDhRJApCIIgCIIgdDgRZAqCIAiCIAgdTgSZgnAKuPDCC5kyZUro57fffhtJkqiqquq6TnVTGRkZ3HXXXUf1nPz8fObMmUNJSUmz7ZIksXDhwo7soiAIwilBBJmCcAq64IILWL16NTabrau7ckrIz89n7ty5zYLMpKQkVq9ezdSpU7uoZ4IgCN2Xpqs7IAhCx4uLiyMuLq6ru9GhFEXB5/Oh1+ubPebxeDAYDCe8T3q9nvHjx5/w4wqCIJwMRCZTEDrYzJkzGTJkCF9//TVDhw7FaDRy+umnk5eXR01NDVdffTURERH07duXDz/8MOy5S5cuZdy4cRiNRuLi4pg1axZOpzOsze7du5k8eTIGg4G+ffvy7rvvNutDS7fLH3zwQYYOHYrFYiElJYVrr72W0tLSsOdNmTKFCy+8kI8++ogBAwZgsViYOnUqBw4cOKrXYPfu3Vx++eVER0djMpkYPnw4H3zwQehxj8fDH//4R1JSUtDr9QwdOpT333+/xdfxiy++YPjw4ej1epYsWcKcOXOwWCysW7eOCRMmYDAYePHFF0PHveSSS4iMjMRsNnPBBRccse+rV6/m4osvJjk5GbPZzIgRI5g/f37o8RUrVnDmmWcCMGbMGCRJQpIkoOXb5bIs88QTT9C7d2/0ej39+/fnueeeCztm0zls27aNSZMmYTKZQteMIAjCqUIEmYLQCUpLS/nTn/7E7Nmzee+998jLy2PGjBlcc801DBkyhI8//pjRo0dz/fXXU1BQAMDChQu5+OKLGTp0KJ9++ilPP/00n3zyCb/97W9D+/V4PEyfPp3y8nLmz5/PU089xeOPP86mTZuO2KeKigr+8pe/sHTpUp5//nny8/OZPHkygUAgrN2WLVt45plneOqpp3j77bfJycnh+uuvb/e579u3jwkTJrBv3z5eeOEFlixZws0330xhYWGozYwZM3jllVe47777WLJkCdnZ2cyYMSMsuAMoKSnh97//Pffddx9fffUVI0aMAMDn8zFjxgxuuOEGvvrqK6ZPn05ubi6nnXYaNTU1vP3227z//vtUVlYybdo0vF5vq/0tKChg4sSJ/Oc//+Gzzz7jiiuu4Le//W0oeB81ahQvv/wyAG+99RarV69m9erVre7v/vvvZ/bs2Vx//fV89tlnXHrppdx777089thjYe38fj/XX389M2fO5NNPPyU2NpYrrriC6urqdr/WgiAI3ZoiCEKHuummmxRJkpRdu3aFtr344osKoPzpT38KbautrVXUarXy3HPPKbIsK+np6cq1114btq+lS5cqkiQpO3bsUBRFUebNm6eoVColJycn1GbPnj2KJEnK5MmTQ9veeustBVAqKytb7GMgEFCKi4sVQPn6669D2ydPnqyYzWaloqIitO3f//63AihFRUXtOv/rrrtOiYuLU+rr61t8fOvWrQqgvPzyy2Hbp0+frqSnp4d+vummmxRAWbt2bVi7Rx55RAGUBQsWhG2/8cYbld69eytutzu0raKiQjGbzWHHSk9PV+68884W+ybLsuL3+5XbbrtNmTBhQmj7999/rwDK+vXrw9rn5eUpgPLRRx8piqIolZWVilarVe6///6wdrfddptiNpsVu90edg5Lly4Ntdm3b58CKPPnz2+xb4IgCCcbkckUhE6QnJzMoEGDQj9nZmYCcNZZZ4W22Ww24uPjKSoqIicnh4KCAn7zm98QCARC/yZPnowkSWzYsAGAtWvXMmTIEPr37x/az4ABAxgyZMgR+/Tll19y2mmnERkZiUajITU1FYCcnJywdiNGjAgbzzl48GAAiouL23Xuy5cv58orryQiIqLFx3/88UcArr766rDt1157LQUFBRQVFYW2xcbGMnbs2Bb3c/7554f9/M0333DJJZeg0WhCr19UVBTDhw9n/fr1rfa3traWe+65h/T0dLRaLVqtltdff73Z69Iea9euxe/3t3huTqeTzZs3h7apVKqw66Ffv37odLp2v86CIAjdnQgyBaETHD6rW6fTtbrd4/GExk5edtlloUBHq9VisViQZTkUeJWWlhIfH9/seAkJCW32Z/369aFxh/Pnz2f16tWsWbMGaLwF356+H96uNdXV1SQnJ7f6eG1tLRqNhpiYmLDtiYmJANTU1IS2tXSuACaTCbPZHLatqqqK5557Luz102q1/Pzzz2GB6+FmzpzJBx98wP/93//xzTffsH79em655ZZ2n+/h53boubR1bkajMfTaNtFqtcd0XEEQhO5IzC4XhG4gOjoagJdeeolx48Y1e7wpaEtKSmpx/GV5eXloHy359NNPiYyMZMGCBahUjd8tm8aCdrSYmJhmS/0cKjo6mkAgQE1NTVify8rKQo83aZpgc7iWtkdHR3PBBRdwxx13NHvMarW2uB+Px8PSpUv55z//yd133x3aLstyq/1vS1Pfy8vLSUlJCW1v6dwEQRBOdSLIFIRuYODAgaSmppKbm8udd97ZaruxY8fy7rvvsm/fvtAt871797Jjxw7OOOOMVp/ndrvRarVhwdl7773XcSdwiLPOOouFCxfy97//vcXgbtKkSQAsWLCA22+/PbT9ww8/JD09nbS0tGM+7o4dOxg5ciRqtbpdz/F6vQSDwbCMot1uZ8mSJWHt2pvNHTt2LFqtlgULFjBq1KjQ9g8//BCz2Ry2TRAE4VQngkxB6AYkSeLZZ5/luuuuw+l0csEFF2A2mykoKGDp0qU88cQTZGZmMnPmTP72t79x0UUX8be//Q1FUZg9e3az27OHO/vss3nuuee4++67ueyyy1i9enWzmdwd5ZFHHuHzzz9n0qRJPPDAAyQlJbFr1y5cLhcPPPAAw4YN44orruC+++7D5XKRlZXFggUL+Oqrr1pcjqm95s6dy5gxYzjnnHO47bbbSEhIoKysjB9++IHTTz+da6+9ttlzIiMjGTNmDE899RRxcXFoNBqeeuopIiMjqaioCLXLzMxErVbz5ptvolar0Wq1ZGdnN9tfbGws99xzD8888wx6vZ6JEyeyfPlyXnvtNebOndvsFr8gCMKpTASZgtBNXHXVVdhsNh5//HH++9//Ao0lEM8999zQmEuj0cg333zDrFmzmDFjBikpKcyePZuPP/4Yh8PR6r7PP/98/v73v/Piiy/y1ltvMXHiRD7//PPQhKSO1L9/f37++Wf+/Oc/c8cddxAIBMjMzOTBBx8Mtfnvf//LQw89xDPPPENVVRWZmZn897//ZcaMGcd83H79+rFu3Toefvhh7rjjDhwOB0lJSZxxxhkMGzas1ee9//773Hbbbdx0003ExMRwzz334HA4eOaZZ0JtYmNjefnll3n66aeZP38+gUAARVFa3N/TTz9NVFQU//73v3nyySfp1asX//znP7n33nuP+dwEQRBORpLS2julIAiCIAiCIBwjMbtcEARBEARB6HDidrkgCO0my3KbM6/VanWrM8IFQRCEnkVkMgVBaLdbbrml2TqUh/774YcfurqLgiAIQjchxmQKgtBu+fn5oYXjWzJgwIBW16QUBEEQehYRZAqCIAiCIAgdTtwuFwRBEARBEDqcCDIFQRAEQRCEDieCTEEQBEEQBKHDiSBTEARBEARB6HAiyBQEQRAEQRA6nAgyBUEQBEEQhA4ngkxBEARBEAShw/1/WXadhbI4l2kAAAAASUVORK5CYII=\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<ggplot: (8790074077323)>"
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    gg.ggplot(metric_df, gg.aes(x=\"median_correlation\", y=\"grit\"))\n",
+    "    + gg.geom_point(gg.aes(fill=\"precision\", shape=\"cell_line\"), size=4, alpha=0.6)\n",
+    "    + gg.theme_bw()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "graphic-actor",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mbornhol/miniconda3/envs/noncyto/lib/python3.8/site-packages/pandas/core/arraylike.py:358: RuntimeWarning: divide by zero encountered in log10\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<ggplot: (8790075732505)>"
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    gg.ggplot(metric_df, gg.aes(x=\"median_correlation\", y=\"recall\"))\n",
+    "    + gg.geom_point(gg.aes(fill=\"-np.log10(mp_value)\", shape=\"cell_line\"), size=4, alpha=0.6)\n",
+    "    + gg.theme_bw()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ceramic-drunk",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  Metadata_gene_name Metadata_pert_name  median_correlation cell_line  \\\n0                MYC              MYC-1            0.896095       ES2   \n1               YAP1             YAP1-2            0.855538       ES2   \n2              CCND1            CCND1-2            0.912482       ES2   \n3              EGLN1            EGLN1-9            0.799812       ES2   \n4               CDK2             CDK2-2            0.891436     HCC44   \n5              ITGAV            ITGAV-2            0.895882      A549   \n6               CDK4             CDK4-2            0.913841       ES2   \n7              ITGAV            ITGAV-2            0.919061     HCC44   \n8              ITGAV            ITGAV-1            0.925652     HCC44   \n9               GPX4             GPX4-1            0.844319       ES2   \n\n   precision_recall_k  precision    recall  mp_value  mp_value_permutations  \\\n0                25.0       0.72  0.600000       0.0                   1000   \n1                25.0       0.80  0.666667       0.0                   1000   \n2                25.0       0.64  0.533333       0.0                   1000   \n3                25.0       0.72  0.600000       0.0                   1000   \n4                25.0       0.60  0.500000       0.0                   1000   \n5                25.0       0.56  0.466667       0.0                   1000   \n6                25.0       0.40  0.333333       0.0                   1000   \n7                25.0       0.40  0.333333       0.0                   1000   \n8                25.0       0.40  0.333333       0.0                   1000   \n9                25.0       0.40  0.333333       0.0                   1000   \n\n  perturbation  group      grit  metric_sum_scaled  \n0        MYC-1    MYC  3.087105           3.741246  \n1       YAP1-2   YAP1  1.888577           3.609051  \n2      CCND1-2  CCND1  2.555242           3.419675  \n3      EGLN1-9  EGLN1  1.972640           3.388326  \n4       CDK2-2   CDK2  1.582908           3.059086  \n5      ITGAV-2  ITGAV  1.886879           3.038947  \n6       CDK4-2   CDK4  2.829412           2.889714  \n7      ITGAV-2  ITGAV  2.783719           2.882135  \n8      ITGAV-1  ITGAV  2.689741           2.863432  \n9       GPX4-1   GPX4  2.836964           2.839359  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Metadata_gene_name</th>\n      <th>Metadata_pert_name</th>\n      <th>median_correlation</th>\n      <th>cell_line</th>\n      <th>precision_recall_k</th>\n      <th>precision</th>\n      <th>recall</th>\n      <th>mp_value</th>\n      <th>mp_value_permutations</th>\n      <th>perturbation</th>\n      <th>group</th>\n      <th>grit</th>\n      <th>metric_sum_scaled</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>MYC</td>\n      <td>MYC-1</td>\n      <td>0.896095</td>\n      <td>ES2</td>\n      <td>25.0</td>\n      <td>0.72</td>\n      <td>0.600000</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>MYC-1</td>\n      <td>MYC</td>\n      <td>3.087105</td>\n      <td>3.741246</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>YAP1</td>\n      <td>YAP1-2</td>\n      <td>0.855538</td>\n      <td>ES2</td>\n      <td>25.0</td>\n      <td>0.80</td>\n      <td>0.666667</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>YAP1-2</td>\n      <td>YAP1</td>\n      <td>1.888577</td>\n      <td>3.609051</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CCND1</td>\n      <td>CCND1-2</td>\n      <td>0.912482</td>\n      <td>ES2</td>\n      <td>25.0</td>\n      <td>0.64</td>\n      <td>0.533333</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>CCND1-2</td>\n      <td>CCND1</td>\n      <td>2.555242</td>\n      <td>3.419675</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>EGLN1</td>\n      <td>EGLN1-9</td>\n      <td>0.799812</td>\n      <td>ES2</td>\n      <td>25.0</td>\n      <td>0.72</td>\n      <td>0.600000</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>EGLN1-9</td>\n      <td>EGLN1</td>\n      <td>1.972640</td>\n      <td>3.388326</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CDK2</td>\n      <td>CDK2-2</td>\n      <td>0.891436</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.60</td>\n      <td>0.500000</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>CDK2-2</td>\n      <td>CDK2</td>\n      <td>1.582908</td>\n      <td>3.059086</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>ITGAV</td>\n      <td>ITGAV-2</td>\n      <td>0.895882</td>\n      <td>A549</td>\n      <td>25.0</td>\n      <td>0.56</td>\n      <td>0.466667</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>ITGAV-2</td>\n      <td>ITGAV</td>\n      <td>1.886879</td>\n      <td>3.038947</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CDK4</td>\n      <td>CDK4-2</td>\n      <td>0.913841</td>\n      <td>ES2</td>\n      <td>25.0</td>\n      <td>0.40</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>CDK4-2</td>\n      <td>CDK4</td>\n      <td>2.829412</td>\n      <td>2.889714</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>ITGAV</td>\n      <td>ITGAV-2</td>\n      <td>0.919061</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.40</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>ITGAV-2</td>\n      <td>ITGAV</td>\n      <td>2.783719</td>\n      <td>2.882135</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>ITGAV</td>\n      <td>ITGAV-1</td>\n      <td>0.925652</td>\n      <td>HCC44</td>\n      <td>25.0</td>\n      <td>0.40</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>ITGAV-1</td>\n      <td>ITGAV</td>\n      <td>2.689741</td>\n      <td>2.863432</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>GPX4</td>\n      <td>GPX4-1</td>\n      <td>0.844319</td>\n      <td>ES2</td>\n      <td>25.0</td>\n      <td>0.40</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>1000</td>\n      <td>GPX4-1</td>\n      <td>GPX4</td>\n      <td>2.836964</td>\n      <td>2.839359</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min_max_scaler = preprocessing.MinMaxScaler()\n",
+    "\n",
+    "metric_summary_df = (\n",
+    "    metric_df\n",
+    "    .assign(\n",
+    "        metric_sum_scaled=(\n",
+    "            min_max_scaler.fit_transform(metric_df.grit.values.reshape(-1, 1)) + \n",
+    "            min_max_scaler.fit_transform(metric_df.median_correlation.values.reshape(-1, 1)) +\n",
+    "            min_max_scaler.fit_transform(metric_df.precision.values.reshape(-1, 1)) +\n",
+    "            min_max_scaler.fit_transform(metric_df.recall.values.reshape(-1, 1)) -\n",
+    "            min_max_scaler.fit_transform(metric_df.mp_value.values.reshape(-1, 1))\n",
+    "        )\n",
+    "    )\n",
+    "    .sort_values(by=\"metric_sum_scaled\", ascending=False)\n",
+    "    .reset_index(drop=True)\n",
+    "    .dropna()\n",
+    ")\n",
+    "\n",
+    "metric_summary_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "cathedral-bailey",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<ggplot: (8790073997343)>"
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    gg.ggplot(metric_summary_df, gg.aes(x=\"metric_sum_scaled\", y=\"grit\"))\n",
+    "    + gg.geom_point(gg.aes(fill=\"median_correlation\", shape=\"cell_line\"), size=4, alpha=0.6)\n",
+    "    + gg.theme_bw()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "choice-background",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<ggplot: (8790074061942)>"
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    gg.ggplot(metric_summary_df, gg.aes(x=\"metric_sum_scaled\", y=\"recall\"))\n",
+    "    + gg.geom_point(gg.aes(fill=\"precision\", shape=\"cell_line\"), size=4, alpha=0.6)\n",
+    "    + gg.theme_bw()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "pycharm-f57c4e3b",
+   "language": "python",
+   "display_name": "PyCharm (git)"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file

From 25f58bcd61ff91fd185edfe1b51eb09c1de20fdc Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Fri, 30 Apr 2021 13:10:41 -0400
Subject: [PATCH 15/24] Fix test

---
 cytominer_eval/tests/test_operations/test_precision_recall.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cytominer_eval/tests/test_operations/test_precision_recall.py b/cytominer_eval/tests/test_operations/test_precision_recall.py
index cdfd9b7..0afce85 100644
--- a/cytominer_eval/tests/test_operations/test_precision_recall.py
+++ b/cytominer_eval/tests/test_operations/test_precision_recall.py
@@ -42,7 +42,7 @@ def test_precision_recall():
     result = precision_recall(
         similarity_melted_df=similarity_melted_df,
         replicate_groups=replicate_groups,
-        k_list=[5, 10],
+        k=[5, 10],
     )
 
     assert len(result.k.unique()) == 2

From 6d42cd2203d6777907a666855c600b355f19e300 Mon Sep 17 00:00:00 2001
From: Michael Bornholdt <56402523+michaelbornholdt@users.noreply.github.com>
Date: Tue, 4 May 2021 11:27:47 -0400
Subject: [PATCH 16/24] change input to floats

Co-authored-by: Greg Way <gregory.way@gmail.com>
---
 cytominer_eval/evaluate.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cytominer_eval/evaluate.py b/cytominer_eval/evaluate.py
index cf723f1..d52ca3c 100644
--- a/cytominer_eval/evaluate.py
+++ b/cytominer_eval/evaluate.py
@@ -31,7 +31,7 @@ def evaluate(
     grit_control_perts: List[str] = ["None"],
     grit_replicate_summary_method: str = "mean",
     mp_value_params: dict = {},
-    enrichment_percentile: Union[int, List[int]] = 0.99,
+    enrichment_percentile: Union[float, List[float]] = 0.99,
 ):
     r"""Evaluate profile quality and strength.
 

From bbdfe743b3b6ca8953c4dbd73ab3944aff1d24e0 Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Tue, 4 May 2021 11:29:17 -0400
Subject: [PATCH 17/24] correct doc

---
 cytominer_eval/operations/enrichment.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cytominer_eval/operations/enrichment.py b/cytominer_eval/operations/enrichment.py
index bf54f76..2466984 100644
--- a/cytominer_eval/operations/enrichment.py
+++ b/cytominer_eval/operations/enrichment.py
@@ -30,7 +30,7 @@ def enrichment(
     replicate_groups : List
         a list of metadata column names in the original profile dataframe to use as
         replicate columns.
-    enrichment_percentile :  List of floats
+    percentile :  List of floats
         Determines what percentage of top connections used for the enrichment calculation.
 
     Returns

From fc15e74ab0c6f4fb908b0600cda6e76115946364 Mon Sep 17 00:00:00 2001
From: Michael Bornholdt <56402523+michaelbornholdt@users.noreply.github.com>
Date: Tue, 4 May 2021 13:49:59 -0400
Subject: [PATCH 18/24] change input to floats

Co-authored-by: Greg Way <gregory.way@gmail.com>
---
 cytominer_eval/operations/enrichment.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cytominer_eval/operations/enrichment.py b/cytominer_eval/operations/enrichment.py
index bf54f76..3e9f727 100644
--- a/cytominer_eval/operations/enrichment.py
+++ b/cytominer_eval/operations/enrichment.py
@@ -16,7 +16,7 @@
 def enrichment(
     similarity_melted_df: pd.DataFrame,
     replicate_groups: List[str],
-    percentile: Union[int, List[int]],
+    enrichment_percentile: Union[float, List[float]],
 ) -> pd.DataFrame:
     """Calculate the enrichment score. This score is based on the fisher exact odds score. Similar to the other functions, the closest connections are determined and checked with the replicates.
     This score effectively calculates how much better the distribution of correct connections is compared to random.

From c353179db0a5f8d1184959416c6d7388a1185674 Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Tue, 4 May 2021 14:00:16 -0400
Subject: [PATCH 19/24] More tests

---
 cytominer_eval/tests/test_evaluate.py          |  2 +-
 .../test_operations/test_precision_recall.py   | 18 +++++++++++++-----
 2 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/cytominer_eval/tests/test_evaluate.py b/cytominer_eval/tests/test_evaluate.py
index dafefb9..68eb41e 100644
--- a/cytominer_eval/tests/test_evaluate.py
+++ b/cytominer_eval/tests/test_evaluate.py
@@ -137,7 +137,7 @@ def test_evaluate_precision_recall():
             replicate_groups=gene_groups,
             operation="precision_recall",
             similarity_metric="pearson",
-            precision_recall_k=[k],
+            precision_recall_k=k,
         )
 
         assert (
diff --git a/cytominer_eval/tests/test_operations/test_precision_recall.py b/cytominer_eval/tests/test_operations/test_precision_recall.py
index 0afce85..75f0bc7 100644
--- a/cytominer_eval/tests/test_operations/test_precision_recall.py
+++ b/cytominer_eval/tests/test_operations/test_precision_recall.py
@@ -39,22 +39,30 @@
 
 
 def test_precision_recall():
-    result = precision_recall(
+    result_list = precision_recall(
         similarity_melted_df=similarity_melted_df,
         replicate_groups=replicate_groups,
         k=[5, 10],
     )
 
-    assert len(result.k.unique()) == 2
-    assert result.k.unique()[0] == 5
+    result_int = precision_recall(
+        similarity_melted_df=similarity_melted_df,
+        replicate_groups=replicate_groups,
+        k=5,
+    )
+
+    assert len(result_list.k.unique()) == 2
+    assert result_list.k.unique()[0] == 5
 
     # ITGAV has a really strong profile
     assert (
-        result.sort_values(by="recall", ascending=False)
+        result_list.sort_values(by="recall", ascending=False)
         .reset_index(drop=True)
         .iloc[0, :]
         .Metadata_gene_name
         == "ITGAV"
     )
 
-    assert all(x in result.columns for x in replicate_groups)
+    assert all(x in result_list.columns for x in replicate_groups)
+
+    assert result_int.equals(result_list.query("k == 5"))

From 9123f51fab3b18602220bc6037c73053b154700a Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Tue, 4 May 2021 14:01:37 -0400
Subject: [PATCH 20/24] named percentile in enrichment.py

---
 cytominer_eval/operations/enrichment.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cytominer_eval/operations/enrichment.py b/cytominer_eval/operations/enrichment.py
index cfd84b6..37ab2ff 100644
--- a/cytominer_eval/operations/enrichment.py
+++ b/cytominer_eval/operations/enrichment.py
@@ -16,7 +16,7 @@
 def enrichment(
     similarity_melted_df: pd.DataFrame,
     replicate_groups: List[str],
-    enrichment_percentile: Union[float, List[float]],
+    percentile: Union[float, List[float]],
 ) -> pd.DataFrame:
     """Calculate the enrichment score. This score is based on the fisher exact odds score. Similar to the other functions, the closest connections are determined and checked with the replicates.
     This score effectively calculates how much better the distribution of correct connections is compared to random.

From 5fb4d24beae819ddf5f4faf5385bbb42479b8e49 Mon Sep 17 00:00:00 2001
From: Michael Bornholdt <56402523+michaelbornholdt@users.noreply.github.com>
Date: Thu, 6 May 2021 14:44:39 -0400
Subject: [PATCH 21/24] update docstring

Co-authored-by: Greg Way <gregory.way@gmail.com>
---
 cytominer_eval/evaluate.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cytominer_eval/evaluate.py b/cytominer_eval/evaluate.py
index d52ca3c..744f094 100644
--- a/cytominer_eval/evaluate.py
+++ b/cytominer_eval/evaluate.py
@@ -101,7 +101,7 @@ def evaluate(
         Only used when `operation='mp_value'`. A key, item pair of optional parameters
         for calculating mp value. See also
         :py:func:`cytominer_eval.operations.util.default_mp_value_parameters`
-    percentile : float or list of floats, optional
+    enrichment_percentile : float or list of floats, optional
         Only used when `operation='enrichment'`. Determines the percentage of top connections
         used for the enrichment calculation.
     """

From bc679fe30807d0ffc0c078da5f61da98509f82b6 Mon Sep 17 00:00:00 2001
From: Michael Bornholdt <56402523+michaelbornholdt@users.noreply.github.com>
Date: Thu, 6 May 2021 14:50:16 -0400
Subject: [PATCH 22/24] Update
 cytominer_eval/tests/test_operations/test_enrichment.py

Co-authored-by: Greg Way <gregory.way@gmail.com>
---
 cytominer_eval/tests/test_operations/test_enrichment.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/cytominer_eval/tests/test_operations/test_enrichment.py b/cytominer_eval/tests/test_operations/test_enrichment.py
index ee00b48..40ab8f7 100644
--- a/cytominer_eval/tests/test_operations/test_enrichment.py
+++ b/cytominer_eval/tests/test_operations/test_enrichment.py
@@ -55,6 +55,14 @@ def test_enrichment():
     assert result.enrichment_percentile[1] == 0.995
     # check if the higher percentiles are larger than the small one
     assert result.enrichment_percentile[1] > result.enrichment_percentile.iloc[-1]
+    
+    result_int = enrichment(
+        similarity_melted_df=similarity_melted_df,
+        replicate_groups=replicate_groups,
+        percentile=0.97,
+    )
+    
+    assert result_int.enrichment_percentile[0] == result.enrichment_percentile[1]
 
 
 def test_compare_functions():

From 990e14ed187d8093d7b2059453eb7a2206579496 Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Thu, 6 May 2021 14:50:52 -0400
Subject: [PATCH 23/24] add comment for test

---
 cytominer_eval/tests/test_evaluate.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/cytominer_eval/tests/test_evaluate.py b/cytominer_eval/tests/test_evaluate.py
index 68eb41e..ed50551 100644
--- a/cytominer_eval/tests/test_evaluate.py
+++ b/cytominer_eval/tests/test_evaluate.py
@@ -130,6 +130,7 @@ def test_evaluate_precision_recall():
 
     for k in ks:
 
+        # first test the function with k = float, later we test with k = list of floats
         result = evaluate(
             profiles=gene_profiles,
             features=gene_features,
@@ -148,7 +149,7 @@ def test_evaluate_precision_recall():
             result.query("recall == 1").shape[0]
             == expected_result["gene"]["recall"][str(k)]
         )
-
+        # test function with argument k = list of floats, should give same result as above
         result = evaluate(
             profiles=compound_profiles,
             features=compound_features,

From 04be2104bf4e9b9d961fc98caebeeb66835b6109 Mon Sep 17 00:00:00 2001
From: michaelbornholdt <michael.bornholdt@outlook.com>
Date: Thu, 6 May 2021 14:53:04 -0400
Subject: [PATCH 24/24] finalize test enrichment

---
 cytominer_eval/tests/test_operations/test_enrichment.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cytominer_eval/tests/test_operations/test_enrichment.py b/cytominer_eval/tests/test_operations/test_enrichment.py
index 40ab8f7..b49d70a 100644
--- a/cytominer_eval/tests/test_operations/test_enrichment.py
+++ b/cytominer_eval/tests/test_operations/test_enrichment.py
@@ -62,7 +62,7 @@ def test_enrichment():
         percentile=0.97,
     )
     
-    assert result_int.enrichment_percentile[0] == result.enrichment_percentile[1]
+    assert result_int.enrichment_percentile[0] == result.enrichment_percentile.iloc[-1]
 
 
 def test_compare_functions():