diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000..c9e275f
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,34 @@
+name: CI
+
+on:
+  push:
+    paths:
+      # Changes to source code
+      - hobj/**
+      - test/**.py
+      # Changes to workflows
+      - .github/workflows/ci.yml
+      # Changes to project/dependency metadata
+      - 'pyproject.toml'
+      - 'uv.lock'
+jobs:
+  unit_tests:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.12"]
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Set up uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+      - name: Install dependencies
+        run: uv sync --locked --dev
+      - name: Run pytests
+        run: uv run pytest -s
diff --git a/.gitignore b/.gitignore
index 1f1025f..ac631e5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,7 @@
 .idea
-.DS_Store
\ No newline at end of file
+.DS_Store
+.venv
+__pycache__/
+*.egg-info/
+dist/
+/data/
diff --git a/.idea/hobj.iml b/.idea/hobj.iml
deleted file mode 100644
index f45935a..0000000
--- a/.idea/hobj.iml
+++ /dev/null
@@ -1,13 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$">
-      <excludeFolder url="file://$MODULE_DIR$/hobj.egg-info" />
-    </content>
-    <orderEntry type="jdk" jdkName="paper2" jdkType="Python SDK" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-  <component name="PyDocumentationSettings">
-    <option name="renderExternalDocumentation" value="true" />
-  </component>
-</module>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
deleted file mode 100644
index 0c6e339..0000000
--- a/.idea/inspectionProfiles/Project_Default.xml
+++ /dev/null
@@ -1,14 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <profile version="1.0">
-    <option name="myName" value="Project Default" />
-    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
-      <option name="ignoredPackages">
-        <value>
-          <list size="1">
-            <item index="0" class="java.lang.String" itemvalue="opencv-python" />
-          </list>
-        </value>
-      </option>
-    </inspection_tool>
-  </profile>
-</component>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
deleted file mode 100644
index 105ce2d..0000000
--- a/.idea/inspectionProfiles/profiles_settings.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="USE_PROJECT_PROFILE" value="false" />
-    <version value="1.0" />
-  </settings>
-</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
deleted file mode 100644
index 32f1555..0000000
--- a/.idea/misc.xml
+++ /dev/null
@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (hobj)" project-jdk-type="Python SDK" />
-</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
deleted file mode 100644
index d52717c..0000000
--- a/.idea/modules.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/hobj.iml" filepath="$PROJECT_DIR$/.idea/hobj.iml" />
-    </modules>
-  </component>
-</project>
\ No newline at end of file
diff --git a/.idea/other.xml b/.idea/other.xml
deleted file mode 100644
index 68993fb..0000000
--- a/.idea/other.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="PySciProjectComponent">
-    <option name="PY_SCI_VIEW" value="true" />
-  </component>
-</project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
deleted file mode 100644
index 94a25f7..0000000
--- a/.idea/vcs.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$" vcs="Git" />
-  </component>
-</project>
\ No newline at end of file
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..e4fba21
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..ef16cf7
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,28 @@
+lint:
+	uv run ruff check --fix && \
+	uv run ruff format
+
+check:
+	uv run ty check && \
+	uv run ruff check && \
+	uv run ruff format --check
+
+test:
+	uv run pytest tests
+
+
+publish:
+	@version="$$(uv version --short)"; \
+	case "$$version" in \
+		*.dev*) ;; \
+		*) \
+			printf "Version %s does not include .dev. Continue? [y/N] " "$$version"; \
+			read -r answer; \
+			case "$$answer" in \
+				y|Y) ;; \
+				*) echo "Aborted."; exit 1 ;; \
+			esac ;; \
+	esac; \
+	rm -rf dist && \
+	uv build && \
+	uv publish
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..85c27db
--- /dev/null
+++ b/README.md
@@ -0,0 +1,99 @@
+# `hobj`: human object learning benchmarks
+
+[![CI](https://github.com/himjl/hobj/actions/workflows/ci.yml/badge.svg)](https://github.com/himjl/hobj/actions/workflows/ci.yml)
+
+This repository contains benchmarks for comparing models of object learning against measurements of human behavior, from Lee and DiCarlo 2023 (["How well do rudimentary plasticity rules predict adult visual object learning?"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011713)). It also lets you download the raw data and images from the experiments in the paper.
+
+<div style="text-align: center;">
+  <img src="site/readme_images/human_learning_curves.svg" alt="Alt text" >
+</div>
+
+If you just want to download the raw data and images without using the `hobj` library, you can do so at the [OSF repository](https://osf.io/pj6wm/files/osfstorage) for this project.
+
+## Quickstart
+
+### Install
+
+The `hobj` package works for Python >=3.12.
+
+Install it from [PyPI](https://pypi.org/project/hobj/):
+
+```bash
+pip install hobj
+```
+
+If you prefer to use [uv](https://docs.astral.sh/uv/getting-started/installation/),
+you can install it with:
+
+```bash
+uv pip install hobj
+```
+
+
+### Using `hobj` to comparing a linear learner against human learning data 
+
+The template script below shows you how you can run a benchmark on a linear learning model based on your image encoding model. 
+
+All you need to do is have a way to process a `PIL.Image` into a vector of image features (as an `np.ndarray`). There are ~18,000 images that you'd need to compute image features for.    
+
+```python
+import hobj
+import numpy as np 
+
+# Compute your features for the images 
+my_image_features: dict[str, np.ndarray] = {}
+for image_id in hobj.list_image_ids():
+    image = hobj.load_image(image_id=image_id) # PIL.Image
+    
+    # Compute your features here:  
+    my_image_features[image_id] = ... # replace right hand side with your image-computable model
+
+# Assemble the learning model:
+model = hobj.create_linear_learner(
+    image_id_to_features=my_image_features,
+    update_rule_name='Square', # "Square", "Perceptron", "Hinge", "MAE", "Exponential", "CE", "REINFORCE",
+    alpha=1, # learning rate between [0, 1]
+)
+
+# Load the benchmark:
+benchmark = hobj.MutatorHighVarBenchmark()  # or hobj.MutatorOneshotBenchmark()
+
+# Score the model:
+result = benchmark.score_model(model)
+
+# Print its score and its CI:
+print(result.msen, result.msen_CI95)
+
+# You can also check out more granular statistics of the model's behavior, like its learning curves: 
+# print(result.model_statistics)
+```
+
+
+For more details (e.g., how to load the raw behavioral data or images in Python), check out the Jupyter notebooks in `examples/`.
+
+To use a different location, pass `cachedir=...` to a data loader or benchmark
+constructor, or prefetch manually with `hobj-download-data --cachedir /path/to/data`.
+
+## Contact 
+If you have any questions, need help, or experience a bug, please don't hesitate to email me ([mil@mit.edu](mailto:name@example.com)), or open an issue on this repo!
+
+
+
+## Changes to codebase since publication
+This codebase was overhauled in 2026 to improve its accessibility, performance, and quality. Along the way, minor changes to the statistical analysis procedure were introduced, along with changes to the names of the original filenames (see [changelist](site/changelist.md)). To see the codebase at the time of publication, check out the repo with the `v1` tag [here](https://github.com/himjl/hobj/releases/tag/v1).
+
+
+## Citation
+
+```
+@article{lee2023well,
+  title={How well do rudimentary plasticity rules predict adult visual object learning?},
+  author={Lee, Michael J and DiCarlo, James J},
+  journal={PLOS Computational Biology},
+  volume={19},
+  number={12},
+  pages={e1011713},
+  year={2023},
+  publisher={Public Library of Science San Francisco, CA USA}
+}
+```
diff --git a/examples/example_features.npz b/examples/example_features.npz
new file mode 100644
index 0000000..fe64bbb
Binary files /dev/null and b/examples/example_features.npz differ
diff --git a/examples/loading_raw_data_and_metadata.ipynb b/examples/loading_raw_data_and_metadata.ipynb
new file mode 100644
index 0000000..a144c4a
--- /dev/null
+++ b/examples/loading_raw_data_and_metadata.ipynb
@@ -0,0 +1,1321 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:25:10.359485Z",
+     "start_time": "2026-03-27T03:25:09.984598Z"
+    }
+   },
+   "source": "import hobj",
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Loading image meta\n",
+    "\n",
+    "The cells below show you how to load the image metadata for the two experiments. This is not necessary for scoring a model, but you might have another reason for doing this."
+   ],
+   "id": "3c595fba1f12f519"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:25:10.486574Z",
+     "start_time": "2026-03-27T03:25:10.360160Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "df_meta_highvar = hobj.load_imageset_meta_highvar()\n",
+    "df_meta_highvar"
+   ],
+   "id": "15d6b734f6956ddc",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                           image_id          category  \\\n",
+       "0      MutatorObject000-Image00.png  MutatorObject000   \n",
+       "1      MutatorObject000-Image01.png  MutatorObject000   \n",
+       "2      MutatorObject000-Image02.png  MutatorObject000   \n",
+       "3      MutatorObject000-Image03.png  MutatorObject000   \n",
+       "4      MutatorObject000-Image04.png  MutatorObject000   \n",
+       "...                             ...               ...   \n",
+       "12795  MutatorObject127-Image95.png  MutatorObject127   \n",
+       "12796  MutatorObject127-Image96.png  MutatorObject127   \n",
+       "12797  MutatorObject127-Image97.png  MutatorObject127   \n",
+       "12798  MutatorObject127-Image98.png  MutatorObject127   \n",
+       "12799  MutatorObject127-Image99.png  MutatorObject127   \n",
+       "\n",
+       "                                                  sha256      rx      ry  \\\n",
+       "0      b96530de7f7dcaaa76caba7073af79bccaab6d84162435...  100.12  117.05   \n",
+       "1      0ac6e0901cc2748588194365aa989cbfe70796f0c41380...  101.80   31.50   \n",
+       "2      111427384671e7d08e075c980fbb70f90fc9d2f8e6e95c...  102.11  345.85   \n",
+       "3      382bd29bd25edf6374f095bc9a69940381772a01701b21...  102.14   19.54   \n",
+       "4      abddf8cb648ee7ab4c29b563e16fe3b23145b11e07779d...  108.26   43.83   \n",
+       "...                                                  ...     ...     ...   \n",
+       "12795  f1dfd9515fa8075df7aa08f4652c7a6f28b64211ae6167...   92.38  309.66   \n",
+       "12796  6dff558c524bd58fc54d40374b11912575e69c7e70425d...   94.04  274.02   \n",
+       "12797  a74d53268aae7222df6e933ba92cd364eb7d29e15e7edc...   95.11  208.91   \n",
+       "12798  4a53f1cada4db5cea540368bdaad2054cc8051a711e3a0...   96.62  270.95   \n",
+       "12799  3373a6a5da7d78d5e30958976514d7d6e785bbefd3977d...   97.26  348.00   \n",
+       "\n",
+       "           rz     s     u     v  \\\n",
+       "0      282.98  0.91 -0.17 -0.04   \n",
+       "1      334.00  0.97 -0.13  0.14   \n",
+       "2      293.62  0.72 -0.24  0.20   \n",
+       "3      305.56  0.67 -0.08 -0.30   \n",
+       "4      273.33  0.78  0.30  0.37   \n",
+       "...       ...   ...   ...   ...   \n",
+       "12795   19.61  0.78  0.10 -0.35   \n",
+       "12796   11.67  0.96 -0.09  0.35   \n",
+       "12797  346.27  0.51  0.09  0.09   \n",
+       "12798  214.11  0.67 -0.26 -0.18   \n",
+       "12799  267.09  0.98  0.13 -0.29   \n",
+       "\n",
+       "                                                 relpath  \\\n",
+       "0      images/MutatorHighVarImageset/MutatorObject000...   \n",
+       "1      images/MutatorHighVarImageset/MutatorObject000...   \n",
+       "2      images/MutatorHighVarImageset/MutatorObject000...   \n",
+       "3      images/MutatorHighVarImageset/MutatorObject000...   \n",
+       "4      images/MutatorHighVarImageset/MutatorObject000...   \n",
+       "...                                                  ...   \n",
+       "12795  images/MutatorHighVarImageset/MutatorObject127...   \n",
+       "12796  images/MutatorHighVarImageset/MutatorObject127...   \n",
+       "12797  images/MutatorHighVarImageset/MutatorObject127...   \n",
+       "12798  images/MutatorHighVarImageset/MutatorObject127...   \n",
+       "12799  images/MutatorHighVarImageset/MutatorObject127...   \n",
+       "\n",
+       "      original_category_name  \\\n",
+       "0             MutatorB2000_0   \n",
+       "1             MutatorB2000_0   \n",
+       "2             MutatorB2000_0   \n",
+       "3             MutatorB2000_0   \n",
+       "4             MutatorB2000_0   \n",
+       "...                      ...   \n",
+       "12795      MutatorB2000_4998   \n",
+       "12796      MutatorB2000_4998   \n",
+       "12797      MutatorB2000_4998   \n",
+       "12798      MutatorB2000_4998   \n",
+       "12799      MutatorB2000_4998   \n",
+       "\n",
+       "                                       original_filename  \n",
+       "0      MutatorB2000_0_rx100.12_ry117.05_rz282.98_s0.9...  \n",
+       "1      MutatorB2000_0_rx101.80_ry31.50_rz334.00_s0.97...  \n",
+       "2      MutatorB2000_0_rx102.11_ry345.85_rz293.62_s0.7...  \n",
+       "3      MutatorB2000_0_rx102.14_ry19.54_rz305.56_s0.67...  \n",
+       "4      MutatorB2000_0_rx108.26_ry43.83_rz273.33_s0.78...  \n",
+       "...                                                  ...  \n",
+       "12795  MutatorB2000_4998_rx92.38_ry309.66_rz19.61_s0....  \n",
+       "12796  MutatorB2000_4998_rx94.04_ry274.02_rz11.67_s0....  \n",
+       "12797  MutatorB2000_4998_rx95.11_ry208.91_rz346.27_s0...  \n",
+       "12798  MutatorB2000_4998_rx96.62_ry270.95_rz214.11_s0...  \n",
+       "12799  MutatorB2000_4998_rx97.26_ry348.00_rz267.09_s0...  \n",
+       "\n",
+       "[12800 rows x 12 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>image_id</th>\n",
+       "      <th>category</th>\n",
+       "      <th>sha256</th>\n",
+       "      <th>rx</th>\n",
+       "      <th>ry</th>\n",
+       "      <th>rz</th>\n",
+       "      <th>s</th>\n",
+       "      <th>u</th>\n",
+       "      <th>v</th>\n",
+       "      <th>relpath</th>\n",
+       "      <th>original_category_name</th>\n",
+       "      <th>original_filename</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MutatorObject000-Image00.png</td>\n",
+       "      <td>MutatorObject000</td>\n",
+       "      <td>b96530de7f7dcaaa76caba7073af79bccaab6d84162435...</td>\n",
+       "      <td>100.12</td>\n",
+       "      <td>117.05</td>\n",
+       "      <td>282.98</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>-0.17</td>\n",
+       "      <td>-0.04</td>\n",
+       "      <td>images/MutatorHighVarImageset/MutatorObject000...</td>\n",
+       "      <td>MutatorB2000_0</td>\n",
+       "      <td>MutatorB2000_0_rx100.12_ry117.05_rz282.98_s0.9...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MutatorObject000-Image01.png</td>\n",
+       "      <td>MutatorObject000</td>\n",
+       "      <td>0ac6e0901cc2748588194365aa989cbfe70796f0c41380...</td>\n",
+       "      <td>101.80</td>\n",
+       "      <td>31.50</td>\n",
+       "      <td>334.00</td>\n",
+       "      <td>0.97</td>\n",
+       "      <td>-0.13</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>images/MutatorHighVarImageset/MutatorObject000...</td>\n",
+       "      <td>MutatorB2000_0</td>\n",
+       "      <td>MutatorB2000_0_rx101.80_ry31.50_rz334.00_s0.97...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>MutatorObject000-Image02.png</td>\n",
+       "      <td>MutatorObject000</td>\n",
+       "      <td>111427384671e7d08e075c980fbb70f90fc9d2f8e6e95c...</td>\n",
+       "      <td>102.11</td>\n",
+       "      <td>345.85</td>\n",
+       "      <td>293.62</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>-0.24</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>images/MutatorHighVarImageset/MutatorObject000...</td>\n",
+       "      <td>MutatorB2000_0</td>\n",
+       "      <td>MutatorB2000_0_rx102.11_ry345.85_rz293.62_s0.7...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>MutatorObject000-Image03.png</td>\n",
+       "      <td>MutatorObject000</td>\n",
+       "      <td>382bd29bd25edf6374f095bc9a69940381772a01701b21...</td>\n",
+       "      <td>102.14</td>\n",
+       "      <td>19.54</td>\n",
+       "      <td>305.56</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>-0.08</td>\n",
+       "      <td>-0.30</td>\n",
+       "      <td>images/MutatorHighVarImageset/MutatorObject000...</td>\n",
+       "      <td>MutatorB2000_0</td>\n",
+       "      <td>MutatorB2000_0_rx102.14_ry19.54_rz305.56_s0.67...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>MutatorObject000-Image04.png</td>\n",
+       "      <td>MutatorObject000</td>\n",
+       "      <td>abddf8cb648ee7ab4c29b563e16fe3b23145b11e07779d...</td>\n",
+       "      <td>108.26</td>\n",
+       "      <td>43.83</td>\n",
+       "      <td>273.33</td>\n",
+       "      <td>0.78</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>images/MutatorHighVarImageset/MutatorObject000...</td>\n",
+       "      <td>MutatorB2000_0</td>\n",
+       "      <td>MutatorB2000_0_rx108.26_ry43.83_rz273.33_s0.78...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12795</th>\n",
+       "      <td>MutatorObject127-Image95.png</td>\n",
+       "      <td>MutatorObject127</td>\n",
+       "      <td>f1dfd9515fa8075df7aa08f4652c7a6f28b64211ae6167...</td>\n",
+       "      <td>92.38</td>\n",
+       "      <td>309.66</td>\n",
+       "      <td>19.61</td>\n",
+       "      <td>0.78</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>-0.35</td>\n",
+       "      <td>images/MutatorHighVarImageset/MutatorObject127...</td>\n",
+       "      <td>MutatorB2000_4998</td>\n",
+       "      <td>MutatorB2000_4998_rx92.38_ry309.66_rz19.61_s0....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12796</th>\n",
+       "      <td>MutatorObject127-Image96.png</td>\n",
+       "      <td>MutatorObject127</td>\n",
+       "      <td>6dff558c524bd58fc54d40374b11912575e69c7e70425d...</td>\n",
+       "      <td>94.04</td>\n",
+       "      <td>274.02</td>\n",
+       "      <td>11.67</td>\n",
+       "      <td>0.96</td>\n",
+       "      <td>-0.09</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>images/MutatorHighVarImageset/MutatorObject127...</td>\n",
+       "      <td>MutatorB2000_4998</td>\n",
+       "      <td>MutatorB2000_4998_rx94.04_ry274.02_rz11.67_s0....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12797</th>\n",
+       "      <td>MutatorObject127-Image97.png</td>\n",
+       "      <td>MutatorObject127</td>\n",
+       "      <td>a74d53268aae7222df6e933ba92cd364eb7d29e15e7edc...</td>\n",
+       "      <td>95.11</td>\n",
+       "      <td>208.91</td>\n",
+       "      <td>346.27</td>\n",
+       "      <td>0.51</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>images/MutatorHighVarImageset/MutatorObject127...</td>\n",
+       "      <td>MutatorB2000_4998</td>\n",
+       "      <td>MutatorB2000_4998_rx95.11_ry208.91_rz346.27_s0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12798</th>\n",
+       "      <td>MutatorObject127-Image98.png</td>\n",
+       "      <td>MutatorObject127</td>\n",
+       "      <td>4a53f1cada4db5cea540368bdaad2054cc8051a711e3a0...</td>\n",
+       "      <td>96.62</td>\n",
+       "      <td>270.95</td>\n",
+       "      <td>214.11</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>-0.26</td>\n",
+       "      <td>-0.18</td>\n",
+       "      <td>images/MutatorHighVarImageset/MutatorObject127...</td>\n",
+       "      <td>MutatorB2000_4998</td>\n",
+       "      <td>MutatorB2000_4998_rx96.62_ry270.95_rz214.11_s0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12799</th>\n",
+       "      <td>MutatorObject127-Image99.png</td>\n",
+       "      <td>MutatorObject127</td>\n",
+       "      <td>3373a6a5da7d78d5e30958976514d7d6e785bbefd3977d...</td>\n",
+       "      <td>97.26</td>\n",
+       "      <td>348.00</td>\n",
+       "      <td>267.09</td>\n",
+       "      <td>0.98</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>-0.29</td>\n",
+       "      <td>images/MutatorHighVarImageset/MutatorObject127...</td>\n",
+       "      <td>MutatorB2000_4998</td>\n",
+       "      <td>MutatorB2000_4998_rx97.26_ry348.00_rz267.09_s0...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>12800 rows × 12 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:25:10.596806Z",
+     "start_time": "2026-03-27T03:25:10.507768Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "df_meta_oneshot = hobj.load_imageset_meta_oneshot()\n",
+    "df_meta_oneshot"
+   ],
+   "id": "8037e3111fd3bc97",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                                               image_id  \\\n",
+       "0          MutatorOneshotObject00-backgrounds-0.1-0.png   \n",
+       "1          MutatorOneshotObject00-backgrounds-0.1-1.png   \n",
+       "2     MutatorOneshotObject00-backgrounds-0.215443-0.png   \n",
+       "3     MutatorOneshotObject00-backgrounds-0.215443-1.png   \n",
+       "4     MutatorOneshotObject00-backgrounds-0.464159-0.png   \n",
+       "...                                                 ...   \n",
+       "3899  MutatorOneshotObject63-outplanerotation-90.0-3...   \n",
+       "3900           MutatorOneshotObject63-scale-0.125-0.png   \n",
+       "3901            MutatorOneshotObject63-scale-0.25-0.png   \n",
+       "3902             MutatorOneshotObject63-scale-0.5-0.png   \n",
+       "3903             MutatorOneshotObject63-scale-1.5-0.png   \n",
+       "\n",
+       "                    category  \\\n",
+       "0     MutatorOneshotObject00   \n",
+       "1     MutatorOneshotObject00   \n",
+       "2     MutatorOneshotObject00   \n",
+       "3     MutatorOneshotObject00   \n",
+       "4     MutatorOneshotObject00   \n",
+       "...                      ...   \n",
+       "3899  MutatorOneshotObject63   \n",
+       "3900  MutatorOneshotObject63   \n",
+       "3901  MutatorOneshotObject63   \n",
+       "3902  MutatorOneshotObject63   \n",
+       "3903  MutatorOneshotObject63   \n",
+       "\n",
+       "                                                 sha256  \\\n",
+       "0     1577bf576db2b88cc5801d3f02eb375b6e8fcc161b0dd4...   \n",
+       "1     db76eef9cb6580ba9b689506852eef20a31df9371ee29a...   \n",
+       "2     ce14b235f218f6ab9789e8681000545317449339d579ac...   \n",
+       "3     f99d0949a742bf9240a87226735bc122e21a49c4c464e1...   \n",
+       "4     f290df90e4d32f42a3008d2696afa5e8c2e7b5391215c0...   \n",
+       "...                                                 ...   \n",
+       "3899  1cbb3ed038c52b1b64aa3ee8c4b6948d53d8339e872e12...   \n",
+       "3900  c928c3e293faf146fea3452d583021edc01928f6d91dff...   \n",
+       "3901  929a7df3077bf2af726320c6fef57f04f2c43541c04830...   \n",
+       "3902  e4fcf112d188822385ad433a1f390b5fd8095883bbf418...   \n",
+       "3903  68b34b82ba92d25a8f09185a888a0605b01ca09bf26362...   \n",
+       "\n",
+       "                            base_image_id    transformation  \\\n",
+       "0     MutatorOneshotObject00-original.png       backgrounds   \n",
+       "1     MutatorOneshotObject00-original.png       backgrounds   \n",
+       "2     MutatorOneshotObject00-original.png       backgrounds   \n",
+       "3     MutatorOneshotObject00-original.png       backgrounds   \n",
+       "4     MutatorOneshotObject00-original.png       backgrounds   \n",
+       "...                                   ...               ...   \n",
+       "3899  MutatorOneshotObject63-original.png  outplanerotation   \n",
+       "3900  MutatorOneshotObject63-original.png             scale   \n",
+       "3901  MutatorOneshotObject63-original.png             scale   \n",
+       "3902  MutatorOneshotObject63-original.png             scale   \n",
+       "3903  MutatorOneshotObject63-original.png             scale   \n",
+       "\n",
+       "      transformation_level original_category_name  \\\n",
+       "0                 0.100000        MutatorB2000_46   \n",
+       "1                 0.100000        MutatorB2000_46   \n",
+       "2                 0.215443        MutatorB2000_46   \n",
+       "3                 0.215443        MutatorB2000_46   \n",
+       "4                 0.464159        MutatorB2000_46   \n",
+       "...                    ...                    ...   \n",
+       "3899             90.000000      MutatorB2000_4792   \n",
+       "3900              0.125000      MutatorB2000_4792   \n",
+       "3901              0.250000      MutatorB2000_4792   \n",
+       "3902              0.500000      MutatorB2000_4792   \n",
+       "3903              1.500000      MutatorB2000_4792   \n",
+       "\n",
+       "                                      original_filename  \\\n",
+       "0     MutatorB2000_46_rx0.00000_ry0.00000_rz0.00000_...   \n",
+       "1     MutatorB2000_46_rx0.00000_ry0.00000_rz0.00000_...   \n",
+       "2     MutatorB2000_46_rx0.00000_ry0.00000_rz0.00000_...   \n",
+       "3     MutatorB2000_46_rx0.00000_ry0.00000_rz0.00000_...   \n",
+       "4     MutatorB2000_46_rx0.00000_ry0.00000_rz0.00000_...   \n",
+       "...                                                 ...   \n",
+       "3899  MutatorB2000_4792_rx90.00000_ry0.00000_rz0.000...   \n",
+       "3900  MutatorB2000_4792_rx0.00000_ry0.00000_rz0.0000...   \n",
+       "3901  MutatorB2000_4792_rx0.00000_ry0.00000_rz0.0000...   \n",
+       "3902  MutatorB2000_4792_rx0.00000_ry0.00000_rz0.0000...   \n",
+       "3903  MutatorB2000_4792_rx0.00000_ry0.00000_rz0.0000...   \n",
+       "\n",
+       "                                                relpath  \n",
+       "0     images/MutatorOneShotImageset/MutatorOneshotOb...  \n",
+       "1     images/MutatorOneShotImageset/MutatorOneshotOb...  \n",
+       "2     images/MutatorOneShotImageset/MutatorOneshotOb...  \n",
+       "3     images/MutatorOneShotImageset/MutatorOneshotOb...  \n",
+       "4     images/MutatorOneShotImageset/MutatorOneshotOb...  \n",
+       "...                                                 ...  \n",
+       "3899  images/MutatorOneShotImageset/MutatorOneshotOb...  \n",
+       "3900  images/MutatorOneShotImageset/MutatorOneshotOb...  \n",
+       "3901  images/MutatorOneShotImageset/MutatorOneshotOb...  \n",
+       "3902  images/MutatorOneShotImageset/MutatorOneshotOb...  \n",
+       "3903  images/MutatorOneShotImageset/MutatorOneshotOb...  \n",
+       "\n",
+       "[3904 rows x 9 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>image_id</th>\n",
+       "      <th>category</th>\n",
+       "      <th>sha256</th>\n",
+       "      <th>base_image_id</th>\n",
+       "      <th>transformation</th>\n",
+       "      <th>transformation_level</th>\n",
+       "      <th>original_category_name</th>\n",
+       "      <th>original_filename</th>\n",
+       "      <th>relpath</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MutatorOneshotObject00-backgrounds-0.1-0.png</td>\n",
+       "      <td>MutatorOneshotObject00</td>\n",
+       "      <td>1577bf576db2b88cc5801d3f02eb375b6e8fcc161b0dd4...</td>\n",
+       "      <td>MutatorOneshotObject00-original.png</td>\n",
+       "      <td>backgrounds</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>MutatorB2000_46</td>\n",
+       "      <td>MutatorB2000_46_rx0.00000_ry0.00000_rz0.00000_...</td>\n",
+       "      <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MutatorOneshotObject00-backgrounds-0.1-1.png</td>\n",
+       "      <td>MutatorOneshotObject00</td>\n",
+       "      <td>db76eef9cb6580ba9b689506852eef20a31df9371ee29a...</td>\n",
+       "      <td>MutatorOneshotObject00-original.png</td>\n",
+       "      <td>backgrounds</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>MutatorB2000_46</td>\n",
+       "      <td>MutatorB2000_46_rx0.00000_ry0.00000_rz0.00000_...</td>\n",
+       "      <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>MutatorOneshotObject00-backgrounds-0.215443-0.png</td>\n",
+       "      <td>MutatorOneshotObject00</td>\n",
+       "      <td>ce14b235f218f6ab9789e8681000545317449339d579ac...</td>\n",
+       "      <td>MutatorOneshotObject00-original.png</td>\n",
+       "      <td>backgrounds</td>\n",
+       "      <td>0.215443</td>\n",
+       "      <td>MutatorB2000_46</td>\n",
+       "      <td>MutatorB2000_46_rx0.00000_ry0.00000_rz0.00000_...</td>\n",
+       "      <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>MutatorOneshotObject00-backgrounds-0.215443-1.png</td>\n",
+       "      <td>MutatorOneshotObject00</td>\n",
+       "      <td>f99d0949a742bf9240a87226735bc122e21a49c4c464e1...</td>\n",
+       "      <td>MutatorOneshotObject00-original.png</td>\n",
+       "      <td>backgrounds</td>\n",
+       "      <td>0.215443</td>\n",
+       "      <td>MutatorB2000_46</td>\n",
+       "      <td>MutatorB2000_46_rx0.00000_ry0.00000_rz0.00000_...</td>\n",
+       "      <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>MutatorOneshotObject00-backgrounds-0.464159-0.png</td>\n",
+       "      <td>MutatorOneshotObject00</td>\n",
+       "      <td>f290df90e4d32f42a3008d2696afa5e8c2e7b5391215c0...</td>\n",
+       "      <td>MutatorOneshotObject00-original.png</td>\n",
+       "      <td>backgrounds</td>\n",
+       "      <td>0.464159</td>\n",
+       "      <td>MutatorB2000_46</td>\n",
+       "      <td>MutatorB2000_46_rx0.00000_ry0.00000_rz0.00000_...</td>\n",
+       "      <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3899</th>\n",
+       "      <td>MutatorOneshotObject63-outplanerotation-90.0-3...</td>\n",
+       "      <td>MutatorOneshotObject63</td>\n",
+       "      <td>1cbb3ed038c52b1b64aa3ee8c4b6948d53d8339e872e12...</td>\n",
+       "      <td>MutatorOneshotObject63-original.png</td>\n",
+       "      <td>outplanerotation</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>MutatorB2000_4792</td>\n",
+       "      <td>MutatorB2000_4792_rx90.00000_ry0.00000_rz0.000...</td>\n",
+       "      <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3900</th>\n",
+       "      <td>MutatorOneshotObject63-scale-0.125-0.png</td>\n",
+       "      <td>MutatorOneshotObject63</td>\n",
+       "      <td>c928c3e293faf146fea3452d583021edc01928f6d91dff...</td>\n",
+       "      <td>MutatorOneshotObject63-original.png</td>\n",
+       "      <td>scale</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>MutatorB2000_4792</td>\n",
+       "      <td>MutatorB2000_4792_rx0.00000_ry0.00000_rz0.0000...</td>\n",
+       "      <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3901</th>\n",
+       "      <td>MutatorOneshotObject63-scale-0.25-0.png</td>\n",
+       "      <td>MutatorOneshotObject63</td>\n",
+       "      <td>929a7df3077bf2af726320c6fef57f04f2c43541c04830...</td>\n",
+       "      <td>MutatorOneshotObject63-original.png</td>\n",
+       "      <td>scale</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>MutatorB2000_4792</td>\n",
+       "      <td>MutatorB2000_4792_rx0.00000_ry0.00000_rz0.0000...</td>\n",
+       "      <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3902</th>\n",
+       "      <td>MutatorOneshotObject63-scale-0.5-0.png</td>\n",
+       "      <td>MutatorOneshotObject63</td>\n",
+       "      <td>e4fcf112d188822385ad433a1f390b5fd8095883bbf418...</td>\n",
+       "      <td>MutatorOneshotObject63-original.png</td>\n",
+       "      <td>scale</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>MutatorB2000_4792</td>\n",
+       "      <td>MutatorB2000_4792_rx0.00000_ry0.00000_rz0.0000...</td>\n",
+       "      <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3903</th>\n",
+       "      <td>MutatorOneshotObject63-scale-1.5-0.png</td>\n",
+       "      <td>MutatorOneshotObject63</td>\n",
+       "      <td>68b34b82ba92d25a8f09185a888a0605b01ca09bf26362...</td>\n",
+       "      <td>MutatorOneshotObject63-original.png</td>\n",
+       "      <td>scale</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>MutatorB2000_4792</td>\n",
+       "      <td>MutatorB2000_4792_rx0.00000_ry0.00000_rz0.0000...</td>\n",
+       "      <td>images/MutatorOneShotImageset/MutatorOneshotOb...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3904 rows × 9 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:25:10.975037Z",
+     "start_time": "2026-03-27T03:25:10.964839Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "df_meta_catch = hobj.load_imageset_meta_catch() # These are used for catch trials in Experiment 1.\n",
+    "df_meta_warmup = hobj.load_imageset_meta_warmup() # These were used to 'warm up' the human participants, and also to normalize image features prior to benchmarking.\n"
+   ],
+   "id": "353a0c85e5abca6a",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Loading images\n",
+    "\n",
+    "`hobj` offers the `load_image` function for loading images by `image_id` as `PIL.Images`, and the `get_image_path` function as an alternative. See below!"
+   ],
+   "id": "41f2abd21b6a50f6"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:25:11.263172Z",
+     "start_time": "2026-03-27T03:25:10.995997Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "image_id = df_meta_highvar['image_id'].iloc[3]\n",
+    "image = hobj.load_image(image_id = image_id)\n",
+    "image"
+   ],
+   "id": "511d8b0d0f7fdd52",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=L size=256x256>"
+      ],
+      "image/png": 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J6b8A0z+2//++cTB9+c/68zMfrBqA2gaiQJbDXQISwIFB6tClwPrAgpBuPziBY1OIPwWATcBL/P/LwsT9i5Hj38/v3379/wjyMKi0Y2RkY/nBxM4Oov9x/vvN8g+0LuA/IxvDbwamPwz/f3MKcfzh/s/7g+H/9z8/f71lpdQduB0JSsTYfA7VgdNiSIWJCEioelSKkYGBmYODgZ2H4Scjp+Afpn+gZWf/WZkYGFn+/WdgZfzP+u8f8+//DP9+/Pv25w8DJy/L199c3/5KmD3jVuH9/5eB/SXjzydf/375x8cBcyPCRkgZhOCjWk2Ah9AGZkF8g00PzgDAphib2NcfQixcTP/Z2cHjUOBdHQxM/5gY/jL/Y2L4zcDw+9/vr4y/fvz+wSAkxPDr+z/Ov3/YmaVevTDlZfr979lHHfb/vxilREVg/sdmBWVi/8F9HkZoFxjDLBaMcguqBOIiCAkVglFIgv8ZGF7/4/rHxcrIysDA+I8JtDYWHOT/GBj+/QEtjfr19/f3nz8//JORYGf++/cHH/M39p8MgqJ3r8ny/Xz7Vl76GRfzx0+q7DCzUWkkmyAS8GYunAGujEGeBKkFjSQygpoczH+Zmf7+ZGRlYfjN8ucP2/+/TCygnPmbken/PwZmhv//Wf7++8/IxPz/D1IhCLECjQQZiyaExP3PyMD77+MLHiE+VlYmZqa/TIwM//4z/Pv/79+/fyx/fv/98/P3z28///7+wy4vxfPn57cfPBzfGBiZ/7Mof/jy/c0vPkO+p2/Yvv1/8UwRnhIRFoIDEkvZC4lRJFcwgpr4IH3/GVgYGf/+B60oZfz/n5GNkYnxP+O/f4wc/3//Z/z7/+//v8z///1h/vfnL+v//+CVqwx/4BYjGYidCbIBIgNxGYTNwMjP++rLJz5WThYWFoa/zP9Zfv1j+Pvv908GBqZ/n0ErAhj5hVh5+Tn//fz3/QMX99/vnP/+sf3gln/Fa876k+PZsz//mdl5//4i3h1Qe1EoUA0P6rz8Z2H6+/cfMxPDXyaG/0ws///+ZmT6848Z5F3QekpGdk6mP79Ane3/f//9Z2BiZWH6S7zFiABAtKtBYoxMkn9/f3z3nYmN4f8fJkYGlh+g0uvnfw7Of3/4uTmYmf8z8PCyMf36/vUdr9T/T//YP/5n+sMr9emCuOjrH9/Yfr9jYPr5/S+Kf/BwoA1UkMVwVf9B+RvsLQZGhr+//zEz/fvLzsgAmqxhYGX/x/T/799f/1mZWFg5uITYfn3+zsT4++MfBiZWXl52JuQNE0gRi2I+1CJs0lB1zGzcUl9+/f/97f//f0wcnCwcLJwQg0E54jcLtxDDnx+/v7z+q8b87Qsj418mJgZGQbG7x3Tf/xBg+vSRiYGZmRlLUofai0rBXAG1GST5H7x4DFT8MrOx/P/HxsDO8o+Rmen/v7/MLIxM/1mZ//3/8vkfLy+/CC8DD+dfnm8c3MxfP/36zyEsyPSbhBQAsgsDw93xn5v7P2hFK2hvADSWQIr//Wbg5WX+x/D7y4eXf3U5f337Ls307y8f8zdhIfl7vGLvGP5zsn3Q4hfG0hIGacfEoNTFAIlxmCRYiIGBiZGRmfXv339M///8Zfj35y8DM+u/37//s7L8+/vz23fG/yxsbKx/fv7++5udl5uJ6f9/JjYB3n+M1BkWhzgF7CxIhQsNl/+/f3MIsLP9+/H9+8fHH/TE//3+yszx8wczC9MXUQ7557c4/71mZGX+zSsDVQ8xh1QS1NsBTaQwMjL++8fAzPT3zz8m5v8MTBycnz5+5ZTg/P2DkZuXV5id+es3Hpbf33/zMv//9fsfByvjnx+/f7Mi1wKw5IU9OSJtEoW7F87A1PL//98/f1nFONj//vr97dOrt5w6yv9+ff0gxvL9Lycj8+8fPH8lbzzg/yjw96/Ev39El0QgJ0IshZCgwAIFALjr+o+BgZWN9f/37/+Y2Zn+snKxffnymUOM4/P//7wiUsKf39z7pCHy99e3378Zfn75yfT35x+230y8RFuNdR4Z5B6QGzDx/98/mbnYedn+/v/z/cuHFz/FhcU4/3z/9FPo/78fHIxsfz9I/ZJ4elv8J+f/n8KYunGKgJZyQqpBZKtBQfD/779/TNxCwlx/P35jYOX4/5+F9f+Xr7yiQh8/fWZh+ffn26snj37KsHL/+/nrO8O/n+8/fX/zmf0fmyjxAYAIcmS7sTv1749f3AI8LCxM///8/P7xzTtmFTYenn+/vr7iYPv7/QcTCyvTNwZmCeW3L9g+8PKysKFMkGM3EY8oaFMGI8Pfv7//MPyXFOD99ZeNiZ317c9v/1mEhH5+vffzP7/gz9cvGb9+Z+R+c4ef4d9vNhYOse+f37/7w8XJI8ICT7yI7jcey8BSiKAAc8HEf3ADDNys/PvjLyOrMDsH43+mP7+/f3734r+I5H8mIcafX989NWP5+RtUVzK+/8HOICl0k0lG9Q+7MKgaB5uChQCFNsJCiCNB/P8MTKCt1AxMoDbh3/8/P35mZ+NgE/jyiZnhNwfzl1d/WVgZePl+PnrDIiTM9ejDtz//WRl/Pf0txf71268vjL8+/vjBwMTExvAOnAKgI6Qgc9GdALIfXQwL/8/P7z+//eUQYPr39xcLDxs/ByuoBfLn2+f3H75xyXH8/SXE+vfnl1cCYv/////y+x8j79OPUv94FB78/y/CBo8CNHOhpTu4rQuTAjkH4kxQtQfigYr/f7/+fH3/hZ+ZkZflxRcBgR8sf77+ZmZmYuZk+/2PhYmZ6dOH/yyf//Iz/f/+jEXo7983v3//YxHg5v7058/fF+AAgBlPLv3/599vv1m5WJg+/2fmEOVhZWNjZPgDCpOvb5/zqHIx/vshyP/v56cXvzQ5/v/59puFmZ3l530JJh4puVf/cHkf5hZQBMPYyDSo1f3vPxMTwz8mZqbvP3+zc/IwMHG9e/ub5x+oqSvJ+usrE8uPj38kX7//+v6PJPPX95zcfFwPOfgkft94ySTGpSHw6vqLP8zvQLUAuOr6j8smZFuxs3//+c/K/4OFh/ELPys7CyczE6jt9ePbp0+fv7Mq83MwMPziFGT4/e3TGxFO5j8/v7Ny/GdkYL+kLMQlIPFND2WRILr5oKTJCGrEwiQgsQ/igSs+ZmaWv7///OfiZuX8z8nx5R/3LxZWlh+/v///8Yvr16cP39n+szx7w8DFxP6bQVzh/U8G1m+veHlF+b4xCQtLsb39845TgB+cAkD2gIdMQCaTjP/95f37l5Obi+GnECsjGzMT47+/v39+//r++S8RVfBagd9M4ix/f3x8zSPExfjv709ehn//WfjePxRhZJOTkgJbh/AXmAsjoNNXSLII5j8GJiZQL+jfn7/MHBx//zDwcn/9+IaJg5P9Jws768+vf5hY/zxiERb48YmRmfPzG1Yx8Y+fGAWFGF9/5v/w7v9/rjeMH9n/v/oiBA4AmIXk0X942f7942Bn/s3OBKor//779f3rt49v/wkL8oKL9z//Rdj/fvv06qciE9v/398/yvxhYGVl+XlaiY9N6PVPHB1hgk4BLXhi+PeP8T8DGxc7w79/zPxCf9gfsQlxM39nYWFi5Pr/n4Xt8XdGSbl/v3mYP//+/4uR5/MvFslvH558ZWNk+S/MxPyTlffLDyZQLQDOAogeDkG7URX8Z2H9x/mHnfUPExMTw3/Q+Mf3ry8+cPBLcIHrBIY/vwS4///69u6tHPc/9n+/fvxg/vWPkYFRSuA7Hwffh49ikOSHaiaUB8qVIAzlolBMjAz//v37z8jMycXN+ouJmY1bgJnhyd8//xmZmBj//uX8+/s/r8iLH7+Vub8y/v3H+ff/D2GmT9wsLL/e3+EX5fjx+isPzz+hv/+FQGUAxGBE6oLwiST/M/xiY/rLyPD3H+O/33/+/Pz0/dv731yq/LC8/funMC/jzy/vXkkI/eBi/vX/FxfHv9+s/3nVtX7+Y2Hh+CjAzgDqQ2CzDdTiAWEkOYQjGf/9+c/EzMjCxcvO9O8PMzsXDwcjB/+Hj4wCrMyMf3+94Odg5rN++eo1ozD3byHhZ28YmflYWJj/8/FJPPvDLcbw4fN/7q98fKwCoBQA3o2PK6SRbMfKZGT8/4z35382NsYfjD9+f/76+Su/kCg7vGb/yyjOyfL3x6dnrFL/mfj+//nxmYX1DwMTqM7+9VaUiffTFzaEp9AsADkJhJEqCoRaxn9/mDhB0y2cTH/ZmJlYmf/9/Pv1P/Ovz5w8XOy/PjFz/Xl8XktM6jffm6e/OD49esbKwPL3LysbLy+//PPX37m/8Qrw8jF8/vyMCmUA2993v34xsPB+//r3NyObJAsvIlX9//lHnJv5z7d3j3+pMv5hYP//+/sPIabf/xkZ/3z/y/X1Hzs353shXAkAMp0PChSEt0E8CGZiYmDm5Pz//QcTKwv7/z8/v/5iYvgvIPDlH+homO+v3nP8e3Plq6YU07ef3z89f/n555ePHHycf9l/fvnJx/Lh25/frH+5lD/dffmVggCANlQYWaX/ffv++y8DA7MgPwfKKkyGP99FuZn+/vz69q0yFwMjN8u/v3+YBZmYGf79Y+H8x87wk4uJ7+sXPmz+Q2n+QLwMqqf+MYCWeYD5TMx/mFl//WD6ysz8l/nXHybGn0xMfIxM3///+fznzYuvf3+/eMvM+oub/cfP319Y5cXf3/30j+33h28cf9gFWJ79YGf89UeE++vn3+DYAreowIkNbDT45BKw95BzIFgRRB7iOpC7IYMxTDxc4OgCiUCXeIAU/v/BKsbP/PvX55dPJEWZ/v8CjYu8Y2T/z/CXmYXl5zd2ri9cbDxsb/EEAMRAhn/wFjCYAa4d/zIwf/39++d3fsYvX/9yMXOLsoFGAb/9+vn/75e3777/efLhA9u/Z1/5BZj+cHIrCbK+O3KFgZP/NSsn+28WNrZ/Qv+/vHgtqsp5j8wUAHUZyJtgDOKDMJgDJf5/+SXNx/L358cXr0Vlmf7//svO8OfnV0FGJqa//xj+MjD8Z/7Mx8nC/voHfE4AqhGDYmQCJax///4ycP//9esfEwsTwz9mBgbuH38YOFjYfnz8LCgrJfT5Gws727+3z9+x/P/+k+Hfz3+8XD+ePxA1VOBl5WP6zyxuxP2VV+IvB9evhy9+cMnzvnp79p+RjJIwUgCgeADEwZk5wZJgZ4IHQcDFOCKBgGQZGBj+fmIX4WX5+/3j02dKQswMDP+FmP79/vaTl4mRkZWRgQkUYczvxBi5mH+iDAchzAFbACZAYqDtLgz/f/z/x8jCxMzwn/Hbzz8MH9j5hHn5BHmeMXCwfHv3lZXzD+ObN6/ZWf78Yfr5/cfP7///szJ8+CnA+Pr9X0FpWc67r14wsDH+5v799RM70+8fv27xsUtSoyEEdiM68f/HbzZhbtZ/vz4/fyAGWgz7j52X+cf39/85mf7//cXDxPrn939Gvt+M7Hx8P9H1YvCZ/v//B2qngHp+LEyMoD0/zGx/f/38zs76n+k/G/+3L78+/v/xi+H/B87vP34y/vzxk4WBjYnpCyMbJ9/fe+94f335+41JlpGL4/3XX8z/xVg/fOPj4xVn4Pnx4g8nuAzAsBGUyyCZGRqdGCpA4uBiAlRHgWIIJABW9f/v3/9M33+w8ApyMYEawO8lFUDx/0OE7f+fH195WRkZ///+8//P738MDOwsLP+Z2d+JQVpMYN1IBNxEBqZ/oLKdEdTSYvgPHtFnYeJm/f/53/+/X1m+sjCx/nr9noGPhYOZhYmFjZ3p58/fLPy8/z+//MPOzfn/8TN+RhaGH78YGTgkmf98/s0h/YuXXZmDjY2V4deLp7imxsD1OL42GqQcZIBstUdW+Pfb5x//+P6xC/CzMf/99fnVQwkp0Mz3PwZupr+/f/6WB0UgO+Pff1x/GRgYf/7kZGH/+5UfFIJIXkdjMjKBy35QOcD249v3/0wsTP//s7Cz/+HkZfvG+IlHlOHLu+/MKuIc3Lx//n7985eJnUNU7v+HN2x/fv/6zML679VX7v//uTileMXUn958+uPZD3EJxc8M7NzMH54/fIU9BcBcAMniMB4KDYofEIYIwlm/PjEw8/D9Z+dm4WD6+/3Di0diMuBi5i8f8/9fX1+J8DMy/GflZGJhY2H9z8D4+xM7CyfTe+z1ANzM/78ZQYc3gQrB75+//uMX4WL8/uMHG5ewmCDbby7G3x++/fvPzMUjKs784g+7MOv3P5xCUmLvvn7/yvjv17e/bP/+88szf/r0nPEjlzi/lvDzX1zycszvfv5iFuX5/gh5PABuIbwaRKoZIf5EkCDFEAzKCHCFv/9z/uPgYmNgZ2di+vvj04uXIgqg+Gf49UOA8e+Pjz9lGf8zgBYO/P/3XYiJgYH1+09GRva/4PQGMRtrWoAOGoAOPOFk45AWZ/n2/CunMJc4LxfzX2aGp68YWdj+M3649+A/KzcPL//zz/9/vvnzj43z55c///8z/GDgklFmefPs56dfH18yi4jw//jJ/efPt/8cbOz/f7+jRksQ4nIw+ZOB6/c/Np6fzBzM//58+/D0tZw4uEP4/xsP97/f314zgUPj72/m/39+gVZEcHz9y8bE8w20hwqsHzvByPL3/z8GJmYWVhbWf8x8krzfv3x+w84uyPHnPzMXg+DHT5zsv/6y/v/+6QuXvJTI1++/fn14956fkZv908//DP/ZWDgEOP6zsH35xfCP4cMvOcFf7/5+/f2Zl1eQ4/27t5/A6RPDXlDcggThMQvioGOYIoT4n598DEwsfIzsbMz//3x///y9ONT/vxl4Gf9///hJGRQA////ZvsPWi3FwMDM8JeJk+vlN1BgwIyBGAtJCaDU9Z+RkZnx73/QQgR2Nj42Fg6u32+fv3rH+5mb68d3Fh4GVqH/zD/f/mMT4RF4/uo3r+TPdz8+/PjOysnGK8X8/vPXX4JsrP/e/P7y6w8DI4fIz49vOH995WB895mDienHs+c/wb1BcJkPsRfiCmipDLIeaTYAIgclQcpBGFJXQOZq/jPzM7GwsLP9YmP4++vr66c/1PjB8c/w7xcPB+PvX59ZhZgY/v//94/p758/jIxM/xkYWb5yMzGz/QBVJRCDmcBzu6CDIxkZ/zEyMf37/Y+J+Q8jCysLBwcLEwObKNOHx69ffxMT//nq+0eu/18/cvBxfbzH/OYDCxO7sPDlvz9+sch8/MUhycfBxiTC8+jfnz8/OFi+fWVlYGHg4OFW5Hv+75cIy7evX/88esrFce+luAO0EASVdpBAh7gDRiLlTpgQqEmOwHBRUGj84frLzgDq3f399fn1M35l2Arg30xczP9+fn4hxgLS+IeRjenvX1bQoUoMHD9+sXBwvxcH6QYbBVrIAOIwQgZ9//1nZGFmZnr/k1NSkf/Ppw9P7377wS6myvD9+8ff///8Y2Vh4OL984PlKwMn8/v3jL9eP/76QZlH4NtfUbbPnxg5Rf4wcv5h+P/9u5Q428cnrH+/Pvvxm/ErH8evv79efmPm5hYQ4RQEBQBoXgE0xQ52AYgAuQBEgw5BgNLQmAbzwKECUoOWQ/6ysv1j/M/4l+HHj/fPvwrLwBL27+98XP//fP/ALgXSxPCP6Q8rM9N/8G4pxp8///9n/oDc5wBbAa5emf79//8fNMT25+dP5r8Mf7++evrwFzMLK6cYz8uv3799Z+TlYfnDxsH4W+YVww/G71++fPv28cuPn9L/GP7+Yf3xiYWL9+cvDvbfH7/95hTgZvv6l4HpCxvfv/8MvEw/P/7/z8jCI84GmpeHjAdBrCWSBPkDhBEpFxw+//7/YWb++fPvr49v3vxThq16+f/xqwAXC2iCUgp0oBAD43921v9/frGAEh0DEwMb0z92TqQ5WlA6/A8ZB2ViAq3iYPj35zsDG/P358zvnzx6xiPM8pqdh+HHt58/frEKcYNMYeNXZBP88v7H9/dfGRk+PmD7y8H08x33D0Z2zr/sXKyC3//++PXtMyMj72cmpl/cXD9/fuFgZmMT5GATkuX79v4LJAWAFxeAvURMEIAUYgs2pu//mX5///Xt56cf0mKQc5L+//n99TsnNzvD3y8vPsmDk87//6Ap+z8c4ILm16/vnGwcrB/FYfb+AzsFHJ6gMW9GRqa/f35+52Fj+vHg1/uXz7mEhf58vv//I8M3Dvb//5995OcX+Pvrv+BP0c//vv75x8rP/P3/h8+CvJ8+fOQQEhH8xMzIygPqKL/8zi/A/f/7t3/c/76++/Ce+ccf0T9swopfn71kAY8IgRrXoJCHuQIrDXYwVhmYIDPDiz/MP/8y88hzQ7zP8PfH++/CbDxsLH//fH8jC95VyfD9uwDDH3Apx8Dw7x8PaK8o0w+YEeAmH2SKHRQIoG4gw38mpt//mRl+fv7yl0eJ++d7bu4fj5i/cwrw/Xn1/pcAx8+vn34+52H8/f0vKwsX0+8/Xz/9+P33HxevgLAY94+ff/i5WLn/8jD9/c0LOtbsz68fvz+/Z2Fh4WVjZv786QcrH2iRFLijBU6RcHdgYYDiHUkYxEUuA0BJgofhLyMDGwek6GdgYPj35Q+jEM9fLpZ/vz+/ZJOABOEvNnbG3z++cDMy/v/zhpWD8T8jK+M3eGZiBHd1QPaAhhmZGMAz3iy//7EyfX/1iYGfl+fbN7b/b36y/eZnYGBlZvjx+g/Trx+/Pr5j+qfI/vXjH/4f7z8/4+UUZuL8+f7Hb9b/TMxcXJxsf8UYf/5jZuHj+//tNwfzx6+/GVn+szN9efn3+4/vLMxg74MLHZC1RGGw57GoZOJj+M+IdFTxL0YWTiEGRnbGPz+/fJOGDID/+MIBigoGAdB60r88TMxM//4w/vkHCzQmxv/gmhfU8P8DOjzv969/f3/+Y2L8++ntDx5+HkEepndv3/7gZmP485mNkeHz9zd87MwcAu9/8yiI/3x0m1+I+fPblxL8XKwvXv7+KvL1179/TKxsf/lZv37+/4+R8ffP30wsbN//M/z7zv7/wyuWP9+/sTD/+wda28bIyATKfhBfgTwIYSGRSIIgJggjycKYjEhB+ZeB+xcD528W5n+/PjzlAff5/n//y8v2n+Hfn19soGaABBPDz38MrOyf4aUgqBoEmQVa6fWPgYn1z48vv3+952Nm/v7+OyMnHz8/M8fvd6AF9mwMv/6z/Pn+6+sfbk4Bed7PrOw8/D9u/eDl4fvx6zcz+5+/v7985PrD8O8Hy8+/P7////ub4cefPz+//WNi+snIzPj7979/P9hY/vz9y8IEWlvAyAhqc4AsRseQZAsSRRQSUEeC2klIgiA1YAwX+8PKwMLM8of1369fH3/KghLA329vhLmY///7/4ednYGR4Q/3D/b/f5kYmUBtB7BeBlAFAFr1B1rz9A80dvXj/bdPtxXE2T6/YxMQ4+L6wyjGLfnsyQuG51xifH9ZeNlY/r38I6yt8Pn5qVtiDGxPBYT1ub+zsrN9F+D+LyD0jesLCw836+uvn79/Z/8C2qvG9Of3P04Ohq9/P/7n02R4842b5RMXC8M/UNucHbw6G+R65FoZxIc4DZThQV4HRT2o1ASVXqCJif///v9nYv73ByYHWov4H7Tj5+8v7n/sLEwsDH9+vnkjIsz679+f77+4uP6xsfz484OBjYmBieUXwy/27//+s/z9wwraLg6qJRiZGP8xMLH8+/mfmYOF6eOz9/+/vmL9++Tnf2EBPi6Gv5wsTNJSfP++/+BmffdMgJmN5z8bO+OfXz/ffZNjYWWW5+UQFmb7xszFJ/D+BxPnt1/sf5794WHk+PLtx28mpn8/3v3/z8n95/f/v19//fj3k5mLRYSb5bUQL/Pf//+ZQJ1u0KgLyLcgP4JoBlDDDcqAFFOQKVSQ/8HxD0o4DIz/QBkfxAd7AlSHg3IwAwMrEzMjMyPj319f3rNJM/8DLR1n+sH26y8j048v7Kz///9h+M30i4315/+/P/78BetnYPjHxMIM6jH+Y/j/j+3f53dv/vH+4WHn+fGXTZiHk4ODnZXh58dv7xjFv7IxfP/15QeThMDPX3//fWFhFhJnZGX9/fgPFy/zX0YOrn9/f7OzfPv0+cub7wJsf77++PP/H/PfH7+ZmTl4fnGCikBGZuZ/H5kFBFieM7DysP4CLZkFpTzwqCvMyyD/I8ICIgVafA4qqEFTc/9A7XbQ/DwowkFrUEHLhUF5BlSj/Gf49Y8RNH/198+X538Uef79/c/4/z/rfxZGJsa/DH/5GP7/Y2D5xsX6k+P337/M4F1joFUu/5hAmeAfAyMz4z+WH1/ef+Fg4//7nZ/7L7cIAyMjM8PPv7+/PvvKIsLI+IeJ7efbv+zsP38xM3/hZOEWYmBn//Pm4+8/DL8Zfv39+f3bP7bvHz/++PfxNxfD97+MzKCT5Lm4OHi5f3CyMfxm42T//+bTL25WlpdsfPwsv/4zsYIOPkLyOoQJOREUzAaN4IAZ/0FVFRMoxf//z/AXtCYOtCT1N6gFw8DA+BfS5f33/x8oVTEx/Pn76eVXBT5QF4j5PwMzOwPzf4Z/P/9Isvxj5mT885fx708WJgYpKWaGf3///Przl+H/P9Buov8soPm2v79///35g5WbgZuVQ5Dn0/d/vz7+/fP74xNmMe7HLIwM/75+//+JnYWdk/XnX1a27/8YWH5/fvudjeMzAyPHjz//fn35+ouRm/vL7x9/fzKyMP35zcTLzw+aSGViYvrL+O/ff1au/zycLH++ffvD++PPv3/gYQlQhEOSOdivkHQPYYLyPFgaVGqCRilBsfT/P+jcDFAAMDCD4htUIPwFhxojaL0maLH414/flIT+/WFiY+P88/of01+OHww/v3OpMP9j5GLmfMTA8IX1H6OCJhPDvz9/fv/5++v71x///v/9/fs3M/PPb9/+cnH8+/T5q5i8lCjT8zdf/7z7+vn7f1Yxsf/v/3L8+/CFifPXK3lJ3v+M3798/8DAL8Tw/jXLL+4/7Mzs37l4WP99Y2H4+vkf0/f/LFzs///9Y2Bi52T79+3dPxbIbjZmIWauvyzsvz595WVnAKVQUJKHehZGgXwMZUPyNahQ/AfqqYFKKhZmUPf277+/oPV5oHIbVDj8/80ETh5MbD///mX+9u0DuxLPX0Y2Ni4urp+vWZn+Mvz79+eLhjTIPYycL74xsP36wq3JwcTABGqQ/P/14/tP0MKyb3+Zmb6+f/uDS/DH+ze/mQRlxP6zsL18+5/p35d/olzsf8Xf/Pj78ysb25///359ZWFhYWH69o+Zh1/gy49PP9hZGX79+M/858fnL5+/fWFmZWHlZPnx+RsbByfTf8Z/v//++c/CysXOwsLw4/fPTyycfz++5WNh/f0fstkFSyBAAwCUucElAAMDIzPzf2ZGZnZW0KQk478/P//8+c/w9+/fv6C5aVDTlYEBNIz7+9/vf+8+Soiz/Wfg4OLk5WL7+I/l79+/TN+//FVkAy1w/c+oduHPX6bvzCw/GEFnK4FWPDBzMvz//f3bL1DL5/ufvz8+/PzNyy/K9usbtzDD92d/eLk5PzF9/s+jxv36G+uPH1+ZOX68+MctKMjD/OXnvz9MQn8//frHyPjn69efv//8/vrtx38uJhZWVubvH978FeLhZGVgYmLn/c3Kzc3GwcX+783ff4ws7F8/v+IXZv73hxm0t+Q/OMrBBNjfIDEwgwGUd0GOZmRkYGEG7YdkZmFj+c/Iwcn8/+/PP39BqfYXaMKamfEXEyOI95eB4dvHv7/kREFVAR8/Fw8b4zcmxv9Mv/79+SonDGp1Mf9jEJZ+/P37H6W/35iYmMGj3v8YWFkZ/nEKMLN+vvv9P8e/jy9ZBZU0ef8//yQlyMf545OwuMTT1y/fymtzsr7j/P70Ky/Xz/ffeP4J8rExsDD//sYlzvPpC8v/H19/fPryk5nh918mHkZmFoYf799+4uTiB/WFmTnZ/rDw8Pxi5eBj+P6HkZeFieH7W35uLua/oB1OUJ9DKajP4RRoOwLInYxMTKysrCzMTAy/QVH+/z8bOxML858/fyAB8JuJ8d/Pb99+/Gb+xsrKzsTIzMbOws/Hycn0l4X1z0/mX/+/8JrxgGpclv8MbMa8t7/IqbAyMTExg1Y8/P8NWmjAzMbC9P/Xh/csvH8/fRAT0zP6+vT1ByZWBqZ/z4XE+L9/+vPr+x92rh9C/G9/MIKy85ePP/4zszL8//GGk4uN4cff7z9+//j28x8H28+/X79zczL8/viFUVhUTpDp21dmtv+cv1k4uf///fmbg5uJmQ+0r/XbO3EOxv+gOAGVfyi+R+KAbjdhBA1MMv8FLU1kZWX88+cP068/oFVK7Oxc3KClYaAeESMbKxPDr29fv/368+3X3z9/mNk4ONlY////z8TAz/PpL/Pvr+8dpEBTHQzgVT5Kwq8EWP8wsYDLDQZQzfGfmYXx24tnTx6/FP7/iVFNUZ75Cscvll/PvzO/+f35zlcOZk55Bq7H7159/feNne3/dympny8Yf3xjYuRi+gdaKf/z0282TqZf/xl/v+dhYfv1jYWV6c9fdl4u7j8M3H8+/2Nk4/rLwsEixMjE+JOBifE3y19Wjj8/fvxjZfnDxPjvDyfDP0b270j9GVCTDjRMDlr4zsDCys7K+O8vqMRmY+Fg/P7r799fjKDi9e+/f7+ZWUCDN//+/f/9B7RWmZXn31+Rn99//mFkYmH6+Y+Zi/nPPyapNwxf/nzR02IDz/eCVzoycsuCAhS03AdUcIBSGO//t3efPHv9/suvP3+E1QW/3/ouJCfy9vFzPiZ2GbZ/P//y8rD++vr569ffvziYGH78YPr2+RMn588/3KD5sH//mDl+fvvHzfrv/19QAuVi4mL9x8jKxfTr7x9WFk52RlZedkYmNm6O3z9/s/z7/pOZ5R8zy6+Pr9kFWFj/MfwD7Xn5y8D0H3YZA3g5JrhcBHVymNjYQctRfrMxgYpB0P6g///+MjAxMjOAVgf9+weqH//9/f/35z9Gpn+MzCx/2FmZ2RkY/v399eP/3///f/5nFGL79PmrshXPn7/gVSmgaa4/f/8xMIL0g/dMMDIxM7MwfHp468O7z9+//WTiERdl+vzx9TcRfsYPf39yfhfg5GT68Pc3w/f/v//+/sDIxsrw9/27719/sTEJMghzszGDFgyzM/z4A5p8YAcNSrCzM/77y8DO9e/HH8ZP7OKCzLx8oF1tf5gY//xg+vjmGzMLB8M/hi/PGP+I8H/+z/LnF8u/P585GcE9UtBSC0ZQbxnUPP/7HzQuz/zv798//xmZmVjYGX///fUX2vVjZGJiZvn/9x8TaPUCI8u3b39Z2EBz4d//M7CwM/z69vs/C8ufnz8ZmJjlHn7RMxX69/8PaG37X9Cmhr+gAAANkP4FhTZo6pjt7Z1br0Etlx+CwkKCv1h4WT99e/b/Lwvjr69PBXn4OP9/+fDvN+d/pv9fmRhYmP99+PmP+89Hhs9sjKxs38E93n9///z/y8rKxfr3128Gtm8//nEws4E6GP9+MotxsjL/Ymf68Z6J8e+P7y/e/mRnkeRm/fXlL8Pbjxx/OLj//eLj4fwIKvVAy7JBM3/gopEZtFiRmZWdlenPH1BIsLKygdbc//oHWRYBKsEZwfUFaPr2/19mtv+snCx/f/38AzpLg+Hv7x9MrKx/f/5mZmXlV1KTEmb99fcvCzMDw+////+BljgzMf5jAnWB/v9j+s/EzMr67slrUH39i4lPlI/pIys/t8DV54pacv++v2Nl/fSd/e+Pz1/+SHIy/XvD8PsTAz8rC6f05++MjP9//GD7/e/f9y/fGViZ2P4y/P33C1Tpsv758YeRjYGDg+vfH3YmNubfXxlA2/WZmVhZv/z4/I2FRVREhJfhy5tXbz/++s/075uYgtjPX4zg4Qlwi/4PqAXOxMDM+J8NVLj9ByV+UMXK8Pf3738gX4OaBkyMjOAygIXx/z/G/384OBlAXaBf/1hZ2dmZf//49osXlGD+c3Cz/2YDrWKDTKaCGj5///z+y8DM+IeZkYnl/3/Q8rn/Pz8//cjE/Ortdy5xJb6/P1g52Thkf/4W4Xn/4xcjn+Dv339Yhfi//xL9/f7PTz62X18FuX9+//SfmfEvw6ff7P85/v38wcDMzMTE8e8fw4//Pz795mX49w88W8H7lZufl5/h8082Tg52BjYmdj4pkefvfrMw/mPiEfgnLv7649sXb37/+M8nzPUdNhcHinhQ+czAwMDMxMoEKqQYmFlY2FkY///98+cfqNsCTi3MzAz//4Gq8X9/QT0qNqa/v/7++vHzPwcHGxvjnx+/GDlY/v5kZmVm5/7+5w94qhvcbGT4B0oAf0HJhoGBkQm09YeJmeHLkzsfmP+9+cgjJ6nA8PEzLzcnq5DoD4a/r5lYmX9/4+Bi+cnOz/GH9TvzN1FZvj+fpNnfv/rNzfPvx59vXxjZOf7/Bq0ZYwRtXf7/henLN3YR5p9/+Jk5Wf9/fsH04x83048PnLwcDL9/MLNxsP37/fczy6eff/4x84kKSH5+9P3FT5ZfHz4KcEA6Nv8Z/jEwMLOyMv7/+w9UODH8BdVlLKysHIx/f//5A9p4BsoDIClmRpAH/v7++wdUnoGyF+Ovnz8YeZhB+3Z+/WPnYPz5g42F4T8DM2h8/88/BsZ/DP9BO3t+//7zn4H5P2jNCygMGJlY/3y8dY9J8P8/PlkNEYnv/xkFOVmYuXk+v3z2Toib+ccPcX7WT6xc0iy/f3KzcKkIs/wU+f362Vshjh8vfzH+/cPx7+9vUML6/+/nL8Z/L9h/MggrMP/8x/2b6e/3Ly++v/4ixAOqwv4x/P3N9B/kzG+fWd7/+/jtrxgfGycD6BQgUYa/r0EZHFQAgtYiMLOB6ntwE+A/qKXAxMzKxsIGauj9A81xgTtITMwsoM3CjL//gJahszAz/fzz/TcPy7+vv/j+/mf59+cPMzf735+/2Bh+f/3/8w8D098/f/8z/gW1lcH9P1DHmg1UmTIyg+rA3x8eveNnYVOWlBUCNZtZfv/9z8r099WzN4ycfBzcrz6ysXz494ORh+U3J6eEhAAXp+CnL+/vCrG8+/OD9f8/9n9ff//+xcDMxPTpw5//T8WZWBjZuf7/Z/3MzMjKJPjzPe8fHh5eZpa//1j/MjP9+vDy1fuvLKDxuW9vvrAw/fjAJMwgw/jz39dvbKyg6UJQDgcld0ZQI42J6S8DIxMjMysbG+v/P3/+gtI6qNxjYmJiYWFl+s/w7/fv37//g+qcX1wMv5m4mBi+/2dlYmL9/fMHA/v/P/9YuNn+ffn1j4mVA+x5cN3/59evP4zMLKA25X8GUBb6+/vLm8fv2Hl5+dQVuT7c+/Hnz5/fX3+zCPz5+oeZ7S2zgDzjv6+sHNyMX/4LvX36/s/vD7xCvz9/fPvyJ8vXf+ysLAy//vz88fvv9/9MrL++/mH6z/v/8312RX6mb5/5BcUYpe48Y//zj5ud8cOXT18Z2Fn/fXj39Q8rC8tfhr/f//7k5uX9JSf4U4T59y+ub/9+gzahgQblmUEVACNoWx/jHyZmFpBrWVh//vr9F5T4QcOJoGhjYWH89//3759/QR0q0BEq/38z83EwcbJyM7Gy/P7+nZX5z19Gdh7mH9//sXKwsf0Dxf6ff0xM///8/vOPmYmZmfkfw38mVhaGvz9/vXv04AM/FyuHpOC/H8/fMbAz/3z75R/7t18cYvz/mNnYvvwDlQRvvzJw/3vxlPkfKx/jpw/Pnz5/z/zvP/s3BtD4AjcTy/ev33+y/Pnx//NPFlZ2ET4epp/grZO/OIVZ/n5nYPj/+dvPb39/sPx79+k3EwsL23/m/79+/WLk5ODl/PObnZGJhf3th2//fjGxgfo6oFG6f+DVif+YmVhYWZiZmf+DovovaFCEgZmJkYGJhYUZtEr4/78/DEwsf3/9/v2NjeHnv89MTByMnEzsHL/+/Wb4zfifkekvGxcbqNgDjaeAw+DfH9DmPhZQCPz7x8LB+u/v78+fXz5+8kWCm/HTO6a/7xhZPn/hYvjz889fZqZ/jPwfvrKz/v3/58evHz+Zv//g+vWV8z8Tw+/fnz68//KbhYnp/78/f36xsnEy/vn19Rf7t5//GVn+8svIazIxMv39+/sD0/+Pv7n///n4lZkTVO1w/f/+5es/JkYmFlYGNtb/P378Y+DkZGXn/PuHQ5iH7/m7n18YWTmZfzMw/AN1URhAG6I5GZmZWUEF/C+GP3//soLmdljZmH6DMsnvP9//MLIwMTIz/fj6E1Qf/P7z5z8LqMnJwc7MxPDnFzMz458PzEIcf959ZGQEbScBVaF/f/8BVVDMjAxM/xiY2f98/fH99Zv3bz785+L+9/yjGPtfWenbTxg42PkZ+OS+f+MSe/32m7LID4YvX5l52b8+5hES5udn+vKF4f03BhYW1v+//rP9+fyTl4vl+/u3H0Hn+3HycAnJaejyP/v8j0vo6XtGjk+M7Ax/PjFy8HD9/8Ys9uPru3+s//8zsTCwc/Fy/Pz59w87OwvHP5Z/v74ysYoL/fv249evr7+ZWJi+g5b0fP/LxM7GxMzM9Pc3KNkyMLEz/Pn9l+nfT0Ymdi6G799BLWJWZtCgzj9GRi52Zg4mSfZfvxh//P7P+hdUcnCw//36hY2TjZnpHzMr419oGcL07w8DIysT4+8/rBzMnx59+v3z+SumvzycoKW/fxnFpCS4+LhefmISFRNW/v/xCw/79w+MKi++cLG++c7044u4JD8f1/dnH/5/+8XAzs7wl4X9x6uPotxcvz++/8P06/3/X0yC8hpSAh8ePfrExPv33Xc2ZvaP7wX/gZLKtw8/JXgY2Fk/vv3LLsDyh4OdjeH3N8Y/7P+YOL/ysrL8/sTwl5Gdm+vfz4/fGRn+MP9n+f/nDyMbF7jDChq1+vePiZnhz+8/jCwsoCbwvx9fvnOCDAGdls7C84+BnZuFmVOI8Tsj688/P3+CThD6z83558/33z9/s7JwMjL8/vXj5x+Gf38ZWBhBQwWg9hYDy793D74y/nnz7T+LOLus6H/u76xMvxnYefi//2bl5/74hpuJXUDo0at7LL/ZOdl+v//OqK4p+P3t6y/vPvz7yifB9Oc3IzsX629Wtr9fvn34wcjw+xursJyG2o9H574y/mFk/8f0+x8jD4/Ah99P/7DwfPzxm4OT4de3b6Ch4j8srBxsDD++fwevEfj3j1GA+f83UCJm52T+A8pvv1jZmP/9YWJlZwVNWf7/8xfU/mFk+PfrNyMrG6j4/vX1y3dmNh4OBtCyCzZ+ll+/mP79+f/7O+MfJnZQff8P1ChiYWMCbRf9/peJ8//vX99//gENNv79958VtHCaiYWZ+c+XVx9ZWP7842Ln5Wfk5WDg/Pb502dWVk5Z4Td/OVh/vPvCwPSXi+/nOx5uxp//pXjefRcVYXn77P/vn4xsf7i4fn/7zcL+j5GH8dOn/z8+/2BhYOWTEZVVU7n38s1LVkFQpcTMJSDIJSD67c3r7x/fM7D+fcPy+tlbBm7Gf19ZuLhY/nz5/ofpzx8Wxr/f2XiYWf7//v//D/M/NmZ2hh9ff/IyM/z7x8bO+h+U6UFDQqDpvz9/fjOwsIPmuP79+fztvyAX+79vHz/9YuLgYPnz899/JqZff1mYWRlAY/xMDL9/fP3z9z8bI6i2YWb9+/3H19//mUFrJ/8yMzGDDjpgZGL8/PIVowDLX0YeaUn2d3+/Mv759eXvn9dszMx8338y/Of995WJ6ze72P/PDIL/X/yWB51F9fvX1+/MjGxM7Bzsv79//8/C+PPff4YPr/8x/P7Bxi8ury3IK8LFLfab468Y55//v5l4hNh/sgtK8TE8/fiBif3TC7b3L78I8jF8fMfCyc787fOv/4x/fzEz/f3FyMHKy8r4l/H/r/9/mf4wsDCDduCxgioE0JAZqJ0Lasf/+ffnPxsnO8Ov3/8Y//74zyHA+fvD668MLJwcbAx/vv9lYvv37ys7G/PP3wyMLKysjL+/fGMGnbb79zcDK9MfUAYATY+CTvf5+5+JlZnx368/L59/4QMdRMkvp/Dj3cdP7P+4mFl5/j/+8fP3AyYBCanf/9gFuHgkGJ5/4GV98/kv4x/u/28ZGARYf375/vfftzfMDBygkvznp7fv2Dg5/nFLaerI8TD8evZNWunHR7b/H19+ZmZi+fLyv5SuMsvfu69B4c/x5ycrJ/vPb29Yvv5n+PLxHxsrKFv//fP7/++fXAx//jH9Y2Bj+ccj9P/ji/d/uXgZ/7CAOjrgXPDvL2iKgY2NjQE0+cDNycHMzv/3w9uvv7l5eThYWPh+/2JmZWViYmH+++07MxMDBx//rz9fOVj//GFiY2X/++PX7z9/QeeL/Gdg4Pjz9y8zK9vfH19fffgnwvP9xWsmvpcffj75ycnDJsz24+0PRh5hxhd/WRiesbCy8wlzfeO+8eQJ9++Pj5llFbm+/2ZgZ/z98TXTb1YePh52dnbmX+9efvwjIC7AJi4qxPhQivPdhfuCiuK/3/789erxv7ffOD/9/f2D4x8Ly7svzFy8jEJcrMx/P33+w/KN6d/nz+zcXCyM/77/ZOHkZGNh/8/4j/HP359//rMwcbB///b99/ffLKygM4FAnaP/jMz/GZlYWEGDwX9ZmLn4mf/9+frj4y92Bg5eHhY2Nsbfn378ZmVn/s3I9O8v6+8/bHwif1j+/2H8/ZeFkZnpz4+ff0FjAf9BowqgeTNmht8/P39895dP6OuTd2wS4t++MfPyM/5+/5+PlZvhLzsPm9APtv//WP9+esrGI8jxRvDbx6/ffgkLMn749peJgZ2Zl5n5Jzs/OydoNf6P9+/+cQjLSfJLcvz88PXj/5fnX/G/lfz9D5QU//1jl1T88uLSBwFuRi42Ng5OUXHuP++/gEbCmEGrt9lZOdmZ33/9yscnys/H+esXKAD+M/z+8+PvXxZelr//fv1iZOFnZQVt0P8PGr9kZmX+/f3HXxZudg4ehq+fv/z9zcjCwMjKxQya9OZ+95WJm40JtKKX4d9vVk6BX39ZQU3Hf6DO36+foBvoQOUJ0//vrKxM/3/++vrpMxsrN/OrR2xySn9+MnHys7x7/lhAhJ+dn5GHl1Xw87/vvIJMX55xSPCwqXy98fbjZy5R3jfffzOxsoA2C7N/Z2AFLYX49e/Hz//CnMrqElwiTF9+vXn44cM7Hs6/X3+zgka+GP8xsAkwPX92m1+SX5SD4Q+bqATLW9DMETMLG/e/75ygg0A53375wsEuKMzN/pXlH2hlIsPvj98/fWXkZP32hYHhz3+2/wysrL9By9ZYmRiZ//768Zudi5eTienHp7fvmDi4QCcnMjIx/f/1h+/z11/MfxlYuDgYWJn//2bk5P/D/vsvEwsjw5+/P379Ac0ogiZQQYHD+Ofrz+/ff/wTZfv79sF7RTn5O0wcnFy/Pz2+I/zrvygHOzcnC9+fr395JVhffnzL9PH3738f3vz6LyjC9pyRgRW0NJyJgZ31x4937Fygc7o5JST51JWE/rKyMn798ewdi7IUOyvn37//QFtJv3/9+O3Di8+ff7NxifJ8+8LByfT93ds/zOy/WD6J83Jy8/z8+FeemZWV9fs/pv9sTJz//v3+95+Z9/a3/7++cjB8ecQpxPb9228mTrHv/9k4OVh+/fjx7dM/fglONjam5y8//Wb6xcouyC3I9J5RkpWHmUGI/ee/318YBAQ4uDgYv3z+x/P/378/jCxcnAxfv/0CTSv9B40J/GMW4vj05B4LOyO79G+mzzee84mwff/wlfH/91cPvkmrqkizPvwoxMHM/4edU0b076+fzH8/vH7869s/ESExUQZOJnamf/9/MTD9+czAyMT+/wMjFwe7Ah+vqBAn07/XHD+v3fynqm785jMD179Hf3lEXr7/xMB09cozZnEBSQmOr985xH4++PjuOz/nXx4WSR5uXin251+e/1BV+vP5/+9vX3+I8P/5+vfnd0YBkbffv75i42UX/s8rwffnx89/X66/+8erKMLF8OkLLz83Myvbnw+vvv3794tVUIL/1c9/oH1IXJyf2X79/vmPm4PnDyvz799ff/1j/P+XEbRb5M//Hz/+gEZ9GBhAK8BYWH98+/6XWYCXhZnh2bNf2sI8Hz7zS/96+YlPU/TRW74/fz7+/fkV1Ehl4Hz94Se3PMuLF79e/uUVFGP//JuBlZ2FkYGF9T/jP+Z/vzn//WYSUfv95dNHSVHWp/e/cPx/KMghIcX65BW7OOjc/y+Mvx++F/74W0SG/9/vl4ws7Ixv2Jj+M/7/xfj3N8u/Dz+4BDi4ebj+MUvwv/3A+efTdw7mPx9/fXn/l5ed+/1v0J0QLIzc/AJ/GX5+ef3s++9nrxWlOH/+Yefl/sfO8O3d5z9/fvzm4uBg+fWX+f+Pfyyg1ZHMLMx/OLi4mSGV3n8mRkYWhj9/f/4F7ZRg+Avd9szE8OnTdyZhDlGe318+vPgmrCj07/MXcTYG9jd/eGUYX7z+xcXJwsrwj4P1x+/H/34xM/98dufex9d/2Vg4GX/+5mAFbYcBDSUwsjIw/vvNzi0q8YPh84+33L9vPWDiZfmtysvy+f67b3+/cLP9+faRU+7Hp6e/GFlZWL99+83O/O/3fwamnz++/mNg5WB5/JeFV4LvFwPv398/f/349YXxy993n768Y/z29heXxD8mVm6O/z9+svz8wsgtzPTm2R+BPw+u/P0rxApa7szy7+u7L/8Z/vzl5Gf/9+fXP3bWb6DjTP/8ZmBn+PmbgY2D6d+/v39+/2NhZf37H7SRhAE0Gg6aEgAtrGD8++4rIxsLK/OPDy8/fOWWFuL4w/Lrww9WHrZv/CIcPz5/YBTlZfj9l5mR8dsbUU7mz29v33nL/o2Z9d9Php//2f///AvqRYG6UYx//zALCItzM7Cz/Ln/6sfz7/wMHBxaDE8esjLwsjGw8jD++ickzvj43p+/Pz/9/cPAL8T44zfL/98/vn9l4ODiYnnw8TenMA8HO+v/H49u3GMQ5GVj+PXqJSPX3y9/7v7k+C/A9fH11/+Mvz+JyYpz/2Lll///8fH3D0xinD/f/eP98O7TP/afjBxiYv//MrD85+P4BBpe+fObiZPl7U8uFsHvoOmAv/8YWVlAsc7wn4GRiQHUt2ZmAu0b+f2dnfXPp6/fvjx9wikqJvjz67/v399wSQrxMfAIsvP8+vaSi+vvx09MbMy/3oiw/Xz+5M7L/2IsrGyMb//+Y2Fg+MvOwvSfgenPn3//vv/jEREXYuXk4/n16Ot/NnEeYTEe9Yev7wrLiLD9+sohwPxRSImL8+Ofv+w//nCIKUp+ev6eDTShzQAatWfxvHbv3ZevLELSAm9ev77ByMPGwMn+jUmO5TczF8OnX+yiv58/+CrynUFGkfsX4z8BKaHfYmoKfCy//v799v3tF9As1F82AS2hl7/5hf7ysDH9/M0I2tHC8Ps7qIv/5cefn3+ZGJn///31+x8DqCz49/8/MyNoJvj/n7+/GAQZXz9n4vj4/I08y9eHvEx/frP/+vbozQculi+3brJwcTD/+/bywRc+ET6uT6/fvvzNJs7A+YOL6//Lz4wcnzm4mRkZ//9h/PP3+9+/PKJCnD+/MjH/ffH5K4eY4G8GPqEXN98IqXz/zvz7tJQUuxDzu1cPv3LyCrIzcYmLcn5jZvn9l4GLiY2R5ccvFnuW3/++vXvz9oOgiDCPEofA3yc3OfhkZf++/s/z/xcLJxuj7N/HL5hkFGSEGH7951cT/vmDjZeP9et7BlZWJo7fP37+ZOLi4udg/PkVtLKZ6eeP/z//s/75+vorN+u/D9+///r5j4mV8c93ULoHDSL+Bc0AMDKB5gp+fn//k+k7B++Pbz/4+P48+yUr8P8vO9cXRhb2/9+YeBVY+XmZWRjF/71i+P/n7+NPn36xs7OAV04w/GHh5PjPzsHKAOpQsP1j+PvrIyvL75+/uBh/qyh+/sP28/G3P3xffoorqN37xczA+uc7O+O39++fMYjxsbL8///91duPn378/83EwsUMmtNmEZP5wPNX6vXHX0+//hJi5xVm/nPzn4Ci/MfPP/984mVh/P9fnOn7bV5haX6Ov3+YOAUF/zD8+vKP5fdrRi4uNm7WP0ysjMxM///8+/HnJ/Pffxz/fv///g90TAQTB/P3739+/foN6u/9/gmaLfz3HzwJxgjaY//n25evXz49YuLg+f329S8Jrh+fvvMy///J+IdRgJ/l3Qcmcb5fzIxv/3KI8Al8/Pf/56evf5hBg3D//v/79Y+Dj4OVkZ2L+T8z87//LAzff3348vGv0I+PAtx8Skyf3r1/+f35P843onzcXIKffzNJcbH/+vbh3ZuvolzsjAx/f/z7yfDjz99/35nY2VnBo56/+BQk2X5+en778vu/P4VA69AEeRTVZHj+ffj2VJiP6S9oOAs0ns30l4n1LwMvN9O/p+++svz99o+Vi5/1HzM/14//DL9YmBm/gwb2uRn///nBxMzIxMHKzfDlL8P//6yMDKAlGaCNlH9B216YwHOFv358/PDx6483jLyC/95+55H9++svB9PvXx8+MYmxsjF+/MYuwvv1y/snf8SlBRkYvv1h/MfI8u8bExvLP2Y2pj+C3H/+cXFy/f3FwvLnLwvz1x9fXn0XFOBiFOUWUGP4+fYZx4ev/xgYf7+5x/mXiZFFkfH7hzdv3nzl4GP8C5q2/v2H6T8T08/voEkoRiZGFpY/PKLf/0vzfpb+cf83k9SbU19FWKSUFSUlZd7cucDFyv3n1+2HHyS+PxKT/c/GzPDrKwMnmxD7D8bfTH84pEUf/eZXEXr7gZmJQ+DfdyYGFibOv6BJfCZmXvb/f/98Y/3JwsH+7/v3P0xsDH///fkDGtBkAR348+Pb57dv339n+fr3w42PQuqGesc/M4hLsHz6/0mA4987TuEfP+5ws/36+u3v929/P379zcz98efXbz8Eudh+cAgyfRP9++yzNDf33/+sLH/+/WP89ptDmItHSIRP9PeXl8xCskKyou85xL/eevpBgJ+di4Xh+fsXT199ZBMT+v2Xi4Pt258/LCysv7//YGZlBM1mMLIckWD7foGZ8/+bX6yKdsZ/zl568f4+w09NOSGOn+a/OFgEhfi1mHgfvOFiZPn94bfIv1/svKIc3/7//fj72+cv71g4+Xm+g/rRTByMoH4t2/9/DGzffzH9///n928GNl5wD4uJ4/+nH/9BPYk/v3+yMjIL/3z+6fN/JoZfH3/9+/OLS1ZdgV3h12e2H/+YJDm+MXMLCf/9+Obz+3/cMko/P7z+zsTO8oeV878g258ff3+xC3z/y8X0/hcrE+t/RhaGL+9/cjI//8xn8p6LW4Lz3++/P0CHdAtzfWVhkhd7+Pgd1/8fnOxcQn8Y2MT4xNkF2Zh+c/Ex/fv19R8zz08GNk62n3/+M7D8Y+SS/MLw8eXLT6LKskKcrBIPb7x/e/GRtq6QNM97TlHBr1x/WHmEXjMwcL569IaVgUdKRlj0w0dW7t9sTEzsv9/dlfr57/+HD0ygY/wY/oGz9/9f////+cPEyczG/Pvnn3//mP6B6gJQL/L/byZGdpaPH1+9fPPjx/dvX1gYGFj4JAQZ3rCJMX75LSDC9+P6749/v7P+Y/z7+S87r+iPP2++8LAygAadWEDTNUyMHL9///vHxsPA9vf7n+/MPz/9/M3y9Rfj/y9MfNy8bIyMoCkZJm52htdvlPj5vzN+ZOXmZuYS5uRn/8Uv/Jvp948fjEysLEy//jNw8v1n/f3jNxMHOwvjDzZGIU4+xs+8Avwc/5ikhBVFn377dPPNFw1xBYl/LMw/P3xn5P3P+Pvd/7cvbr9gk3z/XezPl0/iMr/FJXhePXny6hs/C9P3T6ys4JUUoEM6//77/h+UvliY///99uUPGwvo0AAOJtCM+/9/jKwcf+++ePfmxQ8Whp8fOdnYBOSVhf9/YudgfM0jKiDwi/3/l9e/BMS4uL/++/P9+49foLV7v79++fST5T8HOwcb43+W339/swkwsP3/9+P9799f//5n/cPO/e39L24OYV5OJg6Grz///+Vk/vyZ4/c/pu8fOaTY/jMys/MI/mT79wd0zNyfH78Z/337zcLBx/jvx4d/3GzMLC+/sv14LSckz8Hw9g8L8y82QWG+rz8e7L76+bmKgRTj1ze3H3/8z8v4m12IheXv9+uMbz+9Ur73gpGP75uIhCjTk6efGRVFuNn/ff/DCj7RhJ3h/7/vPxg42Bj/gGbAvv8ErcZmZOHgZPjyE3S6DiPnr/dXHn39/v4fN8v3938F+BX15dm+fgetpv3759f7D7/5/rx5Lsknyf//468n739//MXC8OPj+69/QSdkMLH+Z/jH+vffDw52ZmZW1r9v3/5j5ORmZOeXYORgZ/z55f8/bn5WLh5GZk5u7v9/Pv58//EDx18h5h+fPn77+fvL0z9C3OzMbL+//vr19hunHAfbr8+fGFn//WW5y/z381MxEX6m3xz8/BzfmZnZhQW+/ZS/9vD50b1Bxsx31/wRYjvFIifz/rWpLt+Hu/eeSf7/zMzO+vXh/Rcy7x8+e/XrvZKCkMz3779BJ3uBTlT9+5/v+7uXnCxM//4yM/H8Bw15MrH9//rjB+u/nx8/fPzz5MaLf2xszIzfvvLwSyprqTB//y3w9usfjnfXn/58LiLMycknwMPF9v3n2zeglRiMHz5/+cXCz8P2+zMLaP0hD/vvX78ZmFi4Pj65+1pAQlKW6xOrMK8CH/u3r99///3C8efPv7esHLI/v7x78/LT2593HooK/vn4i4Pp19u/3FzMoFnjf79fP2f4Ii7MwsLHJcTLwiL48guTouiPp5+/sj+/dvetoIwkB+cPRkGZr9/ef9/7UfI7NxM/A9v31384/z5581aY58WHXx/5/n979vPBp4eqMmK/WD/f//75rxIv80/m/6A4Z2Bg+M/2+MxjVRVxLoY/nOy/vzJysv/7/f7dV0Z+1h+fXv/+/vb9b1Z2tu9ffrHJKqmqyrI/efKBg4mPlfPi9T9//gv8ZBaTE+J4y/Tty6fvjCyMPz+9+8XEzg3aEv6XGTTMyvr/5xcGZmYmhu8/2UQV1FXl2L9+/MXDKcj6+dOH/79/v/v6/fcbab5/P2RYv7z99O7lt++Mn/6zcnIzcbL9Ag1Is7JwsP39/fvzz5//WHg5uTkZf7J8+/GHi1foLwfbrx+Pvv54I6P2RYjz2xdWmce/RWW+XH7JpfLj93+jX98Y+f/zcrKL/752+/srgZ/ff/z7x/jzK7MoG9udny++/+ASApV7P3/9Bi3LZPnz4u4bhb8/GTjYuDm+fmZkZv7x8f2nH2x/GH5+evEZNK7A9O/3lz88EopKMjw/Prx+/faPhDgfy7vvb/9KCzP94xVl/vyNheHHz99Mv75/+vSTmYOJ8denP19/CoD2dPxjZGZjYGNn/v2LWZBNSVlegvP3h4+MDF//g7q2DGwfP/8DHSj87y+noBDfRynxN3/Yfv5jB9X27KBjNP////XzJzOnpCAXHzs74x/mn98+sTxh4mH/xCkmIsf96uezDx+YeRle/Pr395vAKzYxiye/fglKvHrNasr18ivvDzkBFobPDN8fvvvy/w8DOw8nw583PAw8XD++f/8u85/z398f33785QCtnnn1kVFJnevzBx5hZtAxw3+/ffjw4z/D3+//33/49PnXHxYmhp+/vvPJKfFxfPn4+dvfnz8+c/Gzsqv8/vdTUYjpL/PPbx9+crP8+ff3z5ePn/9xcbIxM3z9++MHMx8z879fv5gYmRlZWRm+M/Bw8koL87JzcrGw/Hz/5vM/rnfcnJzMLGyCwky//rB/42T894dLkuv9P2bGP99ZWX98Y2D9z8D89/27b+ysPOJsrBws//58//bpA8tLOdG/lx6r6EozSX98z8EvzfX+zsP//ELc/L9+/9USEOR9+o1FUEHiz9cfP3j4OBn+cLAzvnj/j5mVjZXh24PrXHyglgXDzzcCbNx/mJnZGdk52Jj/f2PRMNZ+dO89GzsHy08Wpm9f3zGzMTH8/vzl1etv/5iYWZh/ffnCI6cq9vnt05cPv4hJ8vN/fcjFwS3N9Fv06x/WXy/f/+dh+/b9759v774x8Amz/v0Fmnpn4uDkYP//6zdo2yloeyIXFyOvqCAvNwfTt18sjO8ffed7I/Sfm52LhVPu52sG9ndf/n19z8XFyPSRl/XrB2b2b19B69X+/3x5+zmbrJoYCyjFfvr45ft3FsFnD/5+Yv32VOi7MBuj4L837xgEldnZWP4afri6zUVO+O2FB9w6jN9//uP+dhN0QNfHl5/ZmT5+ZhNg+/OHiePLVwHG33+5RRh/fvnJwsrN/e/Xu8/f/7/5IyoFWtn79ecHdiYO5o9vvkn9+c3E+OPTmzdf/jP8Zxf4w/hfTpH7zXNJUQGu61/esXJ/esvG/+Mbt8iXK4ISXK/fcbN+ePX9x7fP3xk4/r4GnZTMwsbGzAEecP3NwPb/w49fHD85WP7///3j0/fvX1+/Zfj0nkld6tz7x3Jq7+/cNRRj/fhFjJGd7f+XP8wMoAX1bD8Z2ETZ/zKx/gK1WTlEhfgZ/jIz/Pry4tOvfyxyv/7+/cL48c2r/8+5uJm/vv/GocPx8xvnFwlN6cc/n7GyKP94c19a7e/HD++vvvjOwPLn40c+JjaW7594QCfJczH+Z2Fl4RJgfPOZh5ONlZX5w9OXv0UZhQUZP/1mZWL78uUPz99XT95ySHP+/vrhx4cPH/8z/WNgZ/jxk1VM6Nf9uz85RMSffvvxkYuJleHLN24exk+sLAyMwiw/P7P8+/XlGzMveGKV7d8rDg4mJhH+f59ZvzOw/X/1mpmXX4iPk5Hp08NfvOLsv389EJTk5pX9eOHuG7YfLz/eY/j+j4GH9funr6CRH/YvTJx8DIwMzKCtSsycopz/OJhYPv//zsTy+/u3r38YWSTY2f5//Pf82Tfmt5+5ub69/sb1+MOPH9z/ZRRlxU8/4FDT//j0oRYD0+/PLx/efsvIxfSfgZ2djePXr78MzAxMHEx/2Rn/MjH8/fqLhek/IyvD98/fWYUYmDh//v7zl5Hl97cvf1hePv0o+IXr97cP399/+M7BwMj879tPJi5ujn8MP1/yMrPxs/1j5vnz4w8bB+e/H38FOVh4+d+9+MjI8fXndyEBTtBkFdvfzxwsDEycrJ8/cf1m+vvn7XcOJg4hEV6mb6+ffxbk4ef8ySMh9P/7T7b/b97+YfzL9oabnV9EmPH3959s//7+Zfj8g4Od6e+vn/+Z//9lYeXhYmZ5/+XNx+8sbMxfGXhZWFnYubmY2RjZ+P6y3Hz1m/EfOxvbw/+/fjFKvXrNwvr5vbAOD8v3L/9Ae89//f7PxMzO/PfPbzZmNk42lv+gbd0MLJzMP35/AZVF7Oyc7H//c4oLqHz7/geU1xl+/Pj09wfH5z8Mf1+z/3j/6ev7z6DdcSyfGdkF2f/+4VX69fUduwgPaALq04df0sKfP/7jYPvzl1eM4fMP0PJTBk5RYY6/r959ZRBgZGBi/f/98/svPMzfvjII8IpI8rAx/H79XUiCl4f7P78B18+37/89/8b26w6LoPivd6Jiwsx/2EHH7fz99eU1O4cgD+OXT794WcBHjv/6+OLbx0cMHBxszILCAuwsrzi52H4JCPKzMDGwf+TgUeVhuP7oD9Nf7ndXXkh9+PH4KvMHXubXX/h+P37zk4WbmZ2RAbRSmoXp32/Q5RF//jAxsf3+/YNZQICPjeHfhy+fWXhFOX/8+Q46xujf5/+cDMyfPrGI87xm+vn67Y8PP9l5fn/58puXn//3cxYpScFXfzgYf335/ef18w88Auy33nJLff/wnZ/3PyvTz78/+SQlOP/9+P//9zdGvr9sHFys3z68ZeAXZOXi5uQWVWC+eefpbSkrbcb7N7h5OJ59/8t07zGzyqe/YoIMb18ys/9gZfj04+ffX18+vGHg/vv7/7+fH9iY+Ni//WNg+PPu0bufP4WEuBgZxcQEmFlkGBj/s/JwMDF+45WV/MXJwcmh++v9Fw6x50/uKb/4wSzw6w///1uSMq+uvfwKWsn/5w8DaJnsvx+MbEwcbL9/MTKCxqZ5xLj+MXx7+eIjMx/zJ4ZPP37/YOL5+/UFO2hv69P/Imyv/v1885WZmZOZ/R/DP05hKeFf3zhBa+3+/v33/9NHps9v/3C8+fnkB9OPP9+//f/C/f3H65//eQU5P3/4zc3By/b7DyuPINfvH99+//8qJMrDzcLBz/fvzVs2BYHvV1/cZ1NV///py++/zEJ/GXj55Xk+MrGLsL3nBG2U+v3jL7sAFx8HuNpmZWP89ZkBVE39/v5fQFqa9983buaf/1gY//xn+APa8PDp8x+mv7//fuPk4+dhEWX68v0l88fPX77ysn3/xHjh/tubv0DrD36CFjkzMzEz/AY3R/7//fOV6ccvUA/lG+unV8++CPEx/f3+n4OdkZmL8fsHSabv375/+Prt3/dvX78w83B+/v7rz38WPh4uHvZ/LL8////KyCsqwPHo7bO/7N/vfvvF8u8jF9uP9/8F/3399IeXm4MRdG4AKxvrn9/s3BzM337+Y2UUERAQYfn15/VbRmZ1Ke53Lx6+YhXneP35NzvTTznhny/ecDL8ZpHj4mf49e/n1x9///xl4Rb8z8YOOmTmHyv7r48fWRk5Wbn4/rGIiolx/2X99+3TD5bX/1jZ/339wfDn3cefTL9/Mf57945VVI7lw3dG1i//fn14q/rv/Tfxu9/ffeZnYQLtBGX+8+8PK2jLGDML899//358Zf79m+HzB5ZPDN8//WViZmVl/sfJxcjCxPnj61culj//mf58/M7L+P0rs6Do7/8fGRjY+aV4udgEuH4/f8PwnZNDTEiE79oNDk7G119FOf++EWZj/vv1D8N3Bm4RIY7/LJxMDL9Y2TgZ2Bh/fPv6h42VVZmbi+v/1zdvP/GJiosJMDz/wSUj9ffFI25pbnZpxZ+cjIy//rCLszP9BLWMP4PGDLgYP/7hZmT6x8D8//und7/4GP4zcYoLcYsxc7IwcXz/+uETy0NWQTbm77/+MHz6/+sfNyMH86cz31T+fHvwikXqOwcv41/W92++C315/fKfIAtoqIvlz7d/3//9A+21BY11/v75mQ00T/zx16vfTExcoLMx/v/k5Gb4/P/Xp3dfuQU4/38DHfzF+p9JWEzo67uff7mEJZXZGZnZOJlYfr/i4+Zg4ONg/nyZTYCX9Z0o07u33yUFGb+9/8csKCzO++8TKw/Lt6/cXLzMrB/ef2Ni4Gdnk/3DzMjy79PLTzy/bx3+x/mPj1/495dvX78wSQgyPPvHLMP6+xcj04/PH79/+vjlFx8LM+OPzx/5+Dn+fvv46fevX/85uNj+/WIW4RTk//TrLyPHv09f37D8/MPxg/HHhx9M37/9YWX7/ZPxG7MoHzPT9x9s/L+ZBTVVmB4yPv8qKMnxFrRG6j8jMyPLrx8MDMygKQ/Gn8ysHBw8bExsfMxfn/9mY/v/iemdIOc/GYb/z/+zvr734Tnj93cvPv5m+/uFkZ///8evv5jZ+ISFBFl+fXvx7M+Xv4wcHIxfvnBIGn56ysnHzM/6g5XjIxcn2/d3TCKigux/fnz4/ff3X14hjj8fRX5//sLLw8/LxsrCzCHAwyv1R+rp/VvfJEUEWT+9fvaB5cf7x8qiLz8zSQgy//ry9sfHDz+//2Xl5GVk/Pfz7R8+Rqaf3969/v2LW4iXl/EXIysvL/fPn78YmJhY+X7+ZmH4+YWV+deP36wM/1mY3rGw/fnMzvTtgyjTz78cP38LC/z9y8jC+J2R89vvb0wc///8/guaN2ZkhmQE5v+gJR4cjCz/v/4ArVB4/4z521cu3r+MDH/+//3y5f+j958ffPz8g+HTL1Yupjfffv1g4eYXFGL7x8j888Ofv1w8X559/c/7h4nTkOPj498irAz8XC+Yfvz++IWbjevfh3//fzIyMHGIsb958pftDyMjO6+gAIcQy39mTn5e4c+fv/Dq/frJxCbJK8Tx6IM008/nj1h+MP/6xPr/3zvQivm/HJxsjL+Y2JlZWdkYfv9lZGFj5eDi4mT89ZeNk4WTg4Hpx4+fP1g5RdlZ/v79Djqrg52V4TcTy18mBmYOtp9f3wiAGqd8LEL/bvz48okZtCSC8Rvbb4bfX39xsjP+Z2ZhYmZjYfjD+A+0WI6F4dfbPz85OBneff3M8ePLbzYGFka2tz8+/v//4P/393++/2Ni5GVm+PziIxMnLxcnB8uXv6CdRCxMLPx3X/1g/fWDW0xA4OUbNqY/fzkEGH8ysXEKc3P/+f3lL/MXTg5OVr5f7x/+E2H6/ZeRkYmNj4/l9z8WLi7uNw8Z1WV+3volrs4rzvSdV5rp5cNbImxMz9+wszN++f6LgQlUFv35w8jELsrExfjv/z9mbhYWVtAsOiMLJy8rE8PfP0wMDP+ZudlYfjL+/szIxMXF/I+VgUXgNyMLz4+P39/8f/FajE/gy78319+Bzipm+M/M8p2D7f//n3+5+L9/B90kws325xcz079f/0CDk89/M/Kx/2dk4BAU5ARtsPz399n7X///vgLtPPjPISzE++Ptyzc/uHj5OHnYfnwG3YDAw8nEwMXN+vPd23+C/1i+/GTgYvj6m5NT9CsLDzczG+P3/9/+/v/Az8nLwfLly9c/n1m/ff3xj4mZjfEPaFsZB9PXb+zS6l9+MMrI/fjJyinH8/sb+1MGEdZnDJzcrD9+/mf8z/z3+78/zMz/WMVYQEvnGZi5OdgYmTkZmP6zcIgwff/+8/tPBlaGfwysLN+YQXtN2Jj/gxaaM/1j/MP4h/HHu/efOZV0WG48eMUmxM7y/x8rG9PPz8wszCzsTKxsjH/+//7HxsH2+y9o4Q8LExvLjy8//37/z/yDQ0ZWio/hK+e3Dw8efGLkZGbk4mT6xMwrKcb+/N3zr7y8bCxiSmyv/n/99p+PneX3p9vfGLjYGNnZnt9gYxTi+Q2KN05+FvafHMw/3v3n+/nl37fP7Gx/v337y8/379c7Xj1Vnk9vINfX/Hj36+O3u99YFbne33r0hef3RwZm3l+sPHxcvxj//fgOWuX2m52dBbT8moGR4dc3Bg4ejj+gbsu/nyycvHw8bMyffnz9yQAatPrHwgZaZQs64u/Hj3+/WBgZGRj+MgnxglYOPeFkkmB/9pHp38/vIjy8Qv/ZWFk5vv/8wgpa7vAbFKB/2Jj//GRiZGTjePODU4jj8w9WCUV+ZkbZX0/u3HjFyPH7lyAXw5ffHIJc796+fvWFg5uViVtSgJWd7fXzr4wMf37//s3388cPAZ6/375/5/j35SurEDc/168/DOxc/z5/ZuRgZeMD3Vj5+9tvHg3hD///MooqML9lYGN4e+onIxs7C+fXp2/E2f9/u/NFQJLxN4uQ4EsObk62H79+//rDwPzv/08m0AIC5v9//v0DXezCwc7+/y8L8+//nAJcf17//fLu1z82ZhbQviAWTob/oL2//3/+/vvn7z/QJUzMbD//sopx8v1k5OLj+f3jH+jMtz8s3B///fnPyvLj1x8Wxj+//rKwsf5m+MfAwvnn9w829v//BA2437//JyH058s/od/fvv/8/ucbO6sA3//fLGzMPz8++8YgIsb18x8729ufTKLM7L8YPv/++ZeX9+3bH+y/v37gZfn//dNPER4+1r+/foBHoj+xsrIz8/1nY2P49o2Rl/vr5788PKAh7F/M//58efuVSUxW4MvfFx8ef/n5iUNQjO0rE4uE0C/QgV6M//78ZWEBNdRBi9uZGP/+/v3zH9NvRhZWpn/ff4FuFWH59P37b2Z2Tg6mP38Z2VhYWEAlNiPonGZGxn9/QdsDWTl/M/KLC/OwMTH8ZWADLSZl+f2ThYX5L2hx//9//1n//v/HCFpH/+8HIyPHr88MzBwsLFySUn++/OT+9w50wRi32E+W9z///Wdj+fn73/9ff99/+cUqIMP16Tsvx5tXDMxM7AxM3z/94hRn+8L05+uPT38lmN5//cUrIsL19+P33wwcv798+M3OwsHG/PP//58fvzBwMf5gFGUV+/Oe9ffbv///iYm8evuNWYjv57vPL39w8gqLcnP++8/Ozv7565eP//7+ARV9f8H7cJhYmUHbspg4WBj/MLAw/vn46f+XN2z/3n/7xijAL8jN9OMnEycXC6g0ZGJj+QXaDQY64uAfIzM7L5eIohTX/39vHr/8/uv3b8bfP3+wMXD8/AvaIs3wF3QtEGjTF8P/b0zsrH++fWdm42T4+URSmvnn74//eVkYOTmF5N++evv5lyDD13ffmJkZvv7/8/8vszD/V14ubpZv33kFmP6/+vubk+s3lyjLt88/OTk/v//MqyzBy/j93R9G5v/fPv3kFuDlYv/7/TsD4/uPv9hZeWV4OH9+usXy5s57ZllrxZfnb73l4uaXevDzD7+4ABfDnx//GNiYmZl+vWECle9/f/39zgBKz4zMDH+ZmNhZQVUH6/9fnz+xvf/659+P/wzMv75/4/j39TsjaJ0saJCdk/H7t9/gve///jH++cPC9P/LZxZ+uX9vHt7hefn8+btfHELcvxhB1waAlsyxcDD/Yv7LyML8++9/xl8//3Lyc/z/epP3D8/f36AtKf8+f/7Hxvn36/tP/96y/33/+/3XH2wS3CxMgowvHn/9+5vlP4+UEOONr59eMX3hYPpzl0VY8Mu3/7zS8ux/fv5hYWZk4fzxh1lIiJX5z4eX3xjZPnz6zcosLyvw6/7j7/9+fPrPwfrl8z/Wnw/+KEkKsr9h4xPjYfj97Q8ry5+vX79+/8HExgTavPDrOxsj2x/mn3+ZQLc6MbEwMrFy/vrPwMrN+fUPEwc7279v334y/P31m/kzB2j14n/QAsavf0HD7n///mf++5OVje3vN4a/YvzSIprvHly5dOvf97/sf38xsv1hZGFmY2VmYgGtlGfmYPj17++Xf0x/f4D60r8+//v/m5Hj7y8mdlbQrh0hRZZXHzW+vLp+9T0jPzcXNysTG8f/n8zsDG8/Pednffub6++b/yyMn36JKQh9FZdh4GYDHbTKwfifgen3j/9M/7//+fP+0z+2v6CNdZyiojx/GNk/g3baszI+fMf8k+sXv7QyB9+D7/xsjIysrKBtqv+Y/v9j+fePmZnl9+8fP1k5eHkZQc09UO5m/P/r+5c/337+YP/1j5nlP2h04d+ff39/Q7QwMPz7ByoHGUAnt/37/5/x/+//zP///vj9nf8PD6eApKSoIO8Lpn/soCurQDvl/v5mYWX+++cnCycz45/fv78ysjP95AKt9/zOyfKXkfHfLx525v//fvxlZOGSEvzN9FWQheUT239OWQ4WJm4hjt8/f376946b8du3X//+CjJ/fschKszNzsjy99s7Dhb2XyxMf//8//Xj/68v337+/vqfkwt0mhwD078PX1l5pb7+5fz37fOHd7/52EFzXX/ZpVm/MnP8Z2Bj5/j94xszz59PrD/+MjODpj/+cglJ8P98/f0vOwfrn///fn/8/IOJ6et3ps/fmdj+/gEdhAy6EfLfX9ACA9BZpb9AhcQ/Rsb/jKBtQf9//QPV7r9f/BXi/ccjwS3Ae+L1738s7P/YWZn+/vrCyMIEOgnoDyM7K9O/fz/ZWFn+MzP9es8vLsTO8J+VlYWDhfn/9x/f/rOyS7D84xZm4pK4/+y/oALDz7/s4j9/fv/6nZP1z9fP334x8gv+//ZVQoD5J+cvxn8fP4oKcTAyMDH8+vnj+7/3X378ZmDkF+D8+YGBme3nhx+MHPI8DN/ZuX58ePOPhY1Z+D/r+6d/mIV5//79/o+V9f/Xj9/5OX+z/vv/+z8bEwMDE4+4hCg7A+O/P6xMoD7rr4/PPzJzgXZg/WT99///X8b/oCGn36CdDyzs/0Bz7P/+sYE20/1l4eRkZ/zz4+uvfxxcnL9+/Gdh+vePW+nXnVfvf39k4ODm/v3/K5Pgt09/GfiZ/v3l4P7HyPL5z392Dq7//xi+veNj+fmPm4f13y82hh9PXzNwc4GOGWIT4dd5dvy9sPD7p4yinPc+/f3PKs/9+fN/Nh4+DrF3f7kkOH7+Zfr359v337//MbH+ZGJi+PL994/HzNw8XIKC3MyfH3zhFvj6Q5zzJzP3pxfskl8+f+X/9o1bkv/v/x/fuf79/vP3238Ghl8fP/zh+Pf910e2H3/ZeP/+5pBT5/vz9NPX3wxMf7/9YmT9x8j8/8v7f8xcLByM/1j/MzKzfP3NzMrN8Os3I8sXJiZ2Hu7foD4uAwNoVyELGzfX79+/v7KzcjD9+f73/99/v9g0WF985Pr+68dfTmFOTk7Q/O8/ZhamP39+sbCx/AVtxP7DxCwkI8f/4cO/v6xMHCz/eCW/f2ZS+PKZmYXp8w8mGd/XH79/fPPpIcc3Vq5/3z5+/cUm9Z+RVYCBRYjxzztOXp7/X34xCPAw/vjDxPrr+7t3736w8gpJCT5/xyXILckmosLPz/LtMyP3y7vP5RnevX7ByS4iLvP/FwMbaGP6948sbN9+/fr9/S/Dl7e/2ARBPubk4/r46vNXDnYuRvb/vz6D5veZmV98Z+bh+/f9DxPr/9+/GfgYfnz+xMTDz83ym4mVmYPrOxPo8C5GBgbQLh52DsYf/5n//2P48+0vK2hHPY+G0NO7174ycfxlYGfk4Pz57d+/34z/GUAnVLCw/WZm+vfj77+/v79942RkZmHnYWb4y8Ah/uv17zcsrGxcXKCVEnw8L+6ziTL9YhFk//eZ4SfotJz/f////vCHW+DPfw5OBobv35mEeP59+wk6MPsvI2gPKCsH47cfnJwinOICCsI/P718xij65+f7L0IMv39ysDD+Zf3/+9cX1r+gdae/3//7C9ph+P8HowATK+Ovj//4eBi+fPv6m/0vEzPz919fPv0V5vwPqqtBy9P//Gdi/AtavPOfmZ+fnxe03wq8e+EfI/vvPwygywl//2fh5gBt42Jn+ff9B8NfLg5mZiF+CZHfj78zfQPtnmH7x8LI8PPPH0bm3/8YWNn+MTP/+cHE8PfL03/vmH4x87MyfvzLzskizPblCwfowh1WbkZ2VoE/7zkk3n348+PbpzffvrAy/GH495/16wcuSfGfv5gYP3P++cvKzfL1+y/QznfwRk1Wfh5mfkFBYX7+P7ziQh8+ff/8k1VA4tdTdgGuH+I8nJygUuj3b9Chm0x/fvwB7dH7z8DG+fvTvz9/mbj4OX7++sfCxvKLmY3lz7fP775+5WX6wSL0++8/UPf1LxsrA8P3v0z8QkIcDL9YuP8y/P/z/dsvJp4fP3+DTgBgYWZi4eJkZwGt5vv7+/tfAQbWz/8FxKTFLz7+8PPLb7Z/oMntv78YWZj+/QENjTMyMv/9zczE9PPJGy5WBlkWrp+PGIQFPjCLij99/v/Xv68cnNzs/5j4FTgYPr15f+/965c/GJgY//xiYGVj+i/JxMrI+PvbFxlWNubfP77/AVXKrAy//zCzC0lJMAtyMDP+5eAV4GPk4hdj/iskxCTCriD59wcvCzMXAzNoyozh3+9f/1hAHdl/v5mY//36/ufjHzZJbrZf375y87H//srKzP7v08dvX358ZGNmF/j9jRF0Gepvdpb/f74z8IrKcP3++ouF4/e//z9+fvvFws3wj+EvCysrGwfohBzQghPQeibQgSUsH///YeBQ49L7+fzCk2//2bgZmZj/snHyg87TAZ35xcD4+z/jL85f7xn+/nzD+P/OfQYJWQZhNnbG939/fucQ+Pfv638GRn6u74y8isIicu++PX39EXSo7B9GGXnhP99//f718x8b64/HP9n4hJnZfv/4+PIbt7CkpCQzA8u391+/cfxj4ODg51Jm//H14x8ZKQmWL19+M3H8ZmFlZGb4/4eRmZOd6z9oYSYT4//fX3//ZeYV+Pf541dOfj72718Zfv/88voTMxf4WAJWXoEvX9l+//r9l/XfTz5ubrZ3b0DV4F+G/wy/QadtgOq2/0xsXJwsDAwMP/+zsjIx/vr1+Qvoqk6Of98efHj6T1FFnl3w6acfP7/9Zv/z8xM7C2gNNCMTwx/QrtFfP7/8Y2P9cp/hG6von2/3JVj/vX1x9AM7K5+kIOsfdhZ29r8v/okq8WvIfGW9den2e2amHz94+bgZf/xm+Pftw+0/3378YBISYGX88uXzb05+ESG2D7x8f5h/szH//vbnn9i/758///z46Tentjg3Mxvzfx6RH9/+MYK2G/9nYWf+//fv359f/jL8/sXAzfKf5efnj19Bg9f/GH68f8H95fNvFtb/XFygXSvsH3//+//v979/DGwSvH/ev2fh5GRh+QM61Qy0pekP+AwLVk5uRtABXowsrMxMf/58/8H77y8j77/fH9/c/PjqiyizHN/bn+/+/mf5+/3nX46/TP//sLKDTtn4z8zF9PMfO+/bV1++6YozfnjHzMz+9e1f5n/vXr3k+P+Hl41LiPcnJ+u/968+MYuCzqJhZvr9/++PL6DrMb+8fQnaRMTA+//nu/9vv/7nlBMSZPjwmoeX8c8/Ft7/31h5Wb59ffP4xW8mXiEBll9M7LzMnKyffjOwgLa3/2fkYPrxF7QD9R8D6FbVn79+fPj8i/n/m5/f2H6/efmLi/En6JQwFjbGX39///728zeoK/P/HzM7y6+foBYvA8t/BtAdwYxM/37+Y2H+z8LKzgm6ngJUVTIyMYMu0mX4+5cZ1Jl8/uzSU0EeaUnJf3f/v//799ff/xws7P//cTAygVb/s7Iy/f7LxMvx8guLGBsv179f9378+CMlxPHq7uf3P/4Ic3H+4hQXF+Z8fP05q9Sbz5xczEw/Pn39+omR4fuvz29fgbafc3Iwf3779/1fASFBUbY3oK0E3P++MXBz/ubg+fr5978fH3mFxaRYfv8B7ellBjVRmP+Cls4yMvz/9Y2ZlZfpO2hv81smhq/v/7Izff7w+zvD7/eff3xl+MH0+w8rw79/P1l+/vn55x8DCxf77z/MoE6JMD8nw0/QUf1/QOvu//9kZmX8z8jGzvHv59c/rOyg24B4ef58Y/z95c8/dkFpLaF/N59fZ9I1UpcTZrr+5RczC9PPP8wMjOzM/xn/szD+/8n0k+E/Czvnf7bbt6R1Db9cf/D6n76R6o/jN59++C0kyvT5Baswh6jChz+vn71jVpViZf777gILJxfD2w9fvjMIMX7/w83D8OENw29ONgEh7j8/GfiYP4myMXz/z8nBwfL5LbcQs5C0COhQSmYmZpZ/P5nYfjMwgC5YBfUCvn1k5+dkZmblYXr78fuvb1+42P985OX9//HLf17Wz2+ZOZlBJ2z8+MnF8J8Z1Nbk5fry99drLk4BXra/3/6xMH/9y8754w8DK9M/Btb/zCz/QWc2/2FiZ2HiEBLi/c/y/R/T75+iTAw/2Tk5uJi/Pz/9XEXIQPbelT+/v30X/fuT4ycjB9cvJoY/P5jYf/78x8Ty7QPX39tPHzo6mL74xifw86eWwuvnPzlYmX4/vvD6Lze7nurrfT+42BhFuUX5DS5/ZvzLzvWTSQB0VYog+5f335kYmJg4Pr1h4Nbk4eVh+ffuzaffDD9+/X//kJFXUoSLCXRKDtO//6CzC//9Z+T4+/M3KxfT378/fzJz/frJ+vvT5ftf2AXEX7/4ySwr8v39a2E+9h98XMzMfH9+M/BzibL//vbh2R/OPz+ZuH/+YOLm+PHoL684CwsrIwPogi6QFzhYWEH7PH8xMP38w872++dPTk6Rl1///P3z9RMz4zdOgX+Mf789+fxLmp9VjuX96yev2X7/Z2NgBRUgv39+52Zn5uYR+fbzAx/nzxfPGfTFxDjZhTh+/GeU/fL52QcWcfEPfCI/Xnxm5+X4+5uB5+87Bm7d37dffGFgYWZi4/j3+9dPxo/f2Xn/crAx//rLwc/P/OPRT+anbz9/+vXpB+f3X6xM3778Z2FgZmVj+fsTdFbZPyYGRtAmjB/fvn3+9o/p569f7KysIm++M7Bw//zxh/nvz18sguJcv1j/Mv/6zfLlKysLNwsz6G50ZuZfDP+ZhJjYWL58+sLOxMDy7e//v6B70/8x/mH6zcD8+++fX0zs/77/ZWFk+PGVnYXxLwMLG/O/r4wM7zglON6xgxY5vX3HxCUm+0WM5eefHyzsLH9+/v336w8rG8e3n18YeT9++czP++HBoVfy4jKS379+5xASEPv8/+9/PqF/7Fz//nxgYOfhY+GT+vzq/T9Thc/v/nD8+P2D7Q/ozJdvX/4JSvz+y8bCzsnN+e/b8+e8vB+/ffj7/ScTHyc3F+ffH6zMrAzgbTiga0oYQedQgVYlfvv+7fe/b79+MfxlYhcS/cfAzSXwj/M/08+/3FxiHL+4Gf5++/bvM2ib2h+Gn19/MLCy/fnLwCHB8vfLp/dMrGwMLF8YQWumf//6z8XGwPCH6RvjPxY+jp8f/jAwMfz4xPzz91dWTl42Zqbf/77x8397/eX9R2bBby8/cKlrqSjx3n775ysT8/+fLAwMbDwcv9+9+8nBxv/jBzv397dPP92RVNNiePtJRkuCjUmG8f3v78ws/9j5P718wS3CJKL07PWLH6LcgvxfOT///PaLgY2Z6++n3wLSkt8+s3BwcbL//Pj+yeO/fP/+sX/5x8nPyCvKx8bEwszO/Q1U2/9hYgGVu6DWOxPjnz+g5aR///xg/v379xdmPgZOZnY+0LFijOxcvEy/OZj/gtYwcjEL8oCWyv5mZOX4yfifnffP719/WdnZGH6xSP78+gO0Q/4/CxvomMh//xnYBNg/sf/9+/3f9zfs7KxsArxsPFzsrP+YuBm/irL+f/vo1auXnzl+fVUQkvjF+OoHB8N/Bh42DkYuVva/r1/zy/Mxvf71j1X4///fLJJ3b97+oKguJyknxfHj7rdf7KIK3z9+/snz8+d3UYFfL8881Pr5kYGFQ4Dx3xsWHrYff0WU1dke/WQR4Ob49fU/t4rk049vfvAJiIny/hHg52f79e/3XxYO0OJ7BtDxNqBDZxhA1yuDDoH78fPH5///Xnx5/I8FdLzYPxY2tp9/mLmY/jKwMrGy8fzn+8XCw/r7yw/QguP/TAx/P95mYWGT5mT8/PYui9uHh/ffMDCz///7n5EBtBz/98/vv7/+ZfwPGl3++pVF8Mdn0Ejrl88/xUUFBNm4fkide8fMy/3z2j0+5p+/Gf/8+/PrPxcjy683XEzCf78xMbDxMf77+IWZk5FPkP+bgBT78/vcMkqc3968ZvzNKKTC/PKnACtosT+XpNS9mz/+fmZmFpNn/3fxzbdv37nFxRlfPvsrq8j4/Y+QkADnn5fPb74XUxf48+XH/7+/2Jh+/mPlYfnx/Tcr27/ff/6zM//78+vbDxZOVl62zz///Pjxh/HPp09MHEwsLKBr50HHtTD8YmBk/M3EwMDIw8DMxvDz13/2P/++MzIw/vr9U5idkYGdh/HlUxaFX3zMP74zsDD8ZGP6z8DC8O/Pj6+M3/4xMzIxc/z/+ZuB4dvPjz8Ef7998/fTDyWZP78ZZTn+ML5nZPz95dd/UP8ZNJ78/x/ozAI2TiH2bwxMHP9//mHk4vj/5+VZIS2DDxeff33xiIXj3xcBJobvXzkZf75iZGX9e5/rp5gWO/c3xp/vWMUlWBjvP3nzj5eb8eMnZlF1hbdf/oqI8zD8kBAT+cgvy/aRmfcjw39Q4cXA9PPjx798DAy/fzOz/P7D9P3rV6a/7MzM//78Y+Vm4PjOzMLOxcH8989/ZqYfDKBz7xiZ/v9kAM2p/f/x8cfbr4zsv3/9ZOVgZ/nPw8v85+cXpr/sgix/eBX+f370l4nhOzPj33/MoEPOfjCCzr34x8jG8I+Jnf3nF8a/bGw8/37/eM0n8/vlXxGNjz9vfWbiYWb4wcDIwsL65z8j419mFrb/oO2xf3//ZWX8w8DBwvj717MnBiraDJIfX906w8HOyMTBwcjGyMT5++cvlv+fbwtwCOoJ/HrL9Oo9IxsLn4Dgp9+/BNh+/WGTUtQUev/9Fx/b94+f2EUlOP5//fOf8//3vyxsTD/+MjF9+fCZgZ0VdBb537+gMw/+/mZlZP717SczDx8T65ffP0HjMV9+MHKwfQUdUv4PdHzKX2ZmJtYf7159fv2Hh4/h17//LLwszLz/fzP+//vxD7cSy+v/nDKf/r//A+or/mdgZGNh+8PADKpg/vz9zvyHQ4D914+fHNJiIpw/7715/4bt29f/nJKKn/7+YWFnZWf98/kPO+j0YNCZsT+/fv328yMHH8fv7wwcXD/+M/z/fF1EWNmI88MlVo7vr77/Yv7//Q83A7/ir3fffrHx/2HjF/ny5wvzl1cfH4sxf/zP95+Pk4lFREBR7O+Pz39Zfn3/+OUH69vH77hkRNk/MbFwsv/5/x/UOWVlZmVkYGJm+/vjBxNoVJ+N+d+3b384+Vj///0GOt+O4e+Pr8ygQ6kZ//3//+cf6MALBoZ/318/+vbx/+//HKBDHTh5WRi+MvJy/3//j0OIReD3bwbJ3y8/fGT99ZeNQ0iQ9een33///f7L8O8/AzsX0zceYbYvLO/Z/gp+FWL98VZKiYmdjZeRifPRTwbQyRI8f9l+gI6UY/7/mQm0N/IHaLnfN0Y+7p/ff/39/XT7U1eJL785jG/+/f+XgfXX10/cvAw/uX79+v/pzZMPrHyfP3/8K8nx88/vF7+ZOf9ysDN9+8T1+9PfT1/+MwgLMXxn/3n8+Vs1Huavv1i5+Ti+/f/7+xcXOyML6/8/zEysX79/ZePjZvrDwPSf7f8vPu6/Hz4+f8386ysHGz/HfxZGdoZ/f/4z/fv7jxXUW/v59etvXp5f/8Fz5Z/YGTkY/rEwMbII/GViZeH++fMvpyQL6983fxmZ2X9/Z2BmZPgLCu1/f/+zcDL9/sgrz/Hp9Z+vb1nZP375J8TG+u/Fr3/KvH8e/mJmYGZl4GBm/P3nB9fP778EGHkUOW5//vmNk/3fn59sv39ySr58e/GvALOo2J+Pv9h5/v9jZ/7JyvLnOw8b4//3b7/++PecgY3j9y9mhn/vwQfrffrNzvT1Lfvf73/kBPj+vv76ne/lS05lWZ7///n+c7Cz/fjygYeHi5Xh759/jMxMLKDp9J8czMz/mRi5hH4wfPv+9u1nRk4W0EECjAz/foNuswEfdA26l/bff05Z0f+/vv5kYuL58/f/n98cbH84mD9/FuH48pDlIzvbn398rFwcv97+BV3L9If9L+gacQaG///+/WRiZPzOxSHI/OXft//8zP/+/377nf3fw29cghIarG9/fPnLzczGzgYqOn7+ZGBmEZHi/X33x7e/DIx/fjIzMnKKcb/78+qboMCv32zMTOw/fnFxsv77/Yft938Whl+f//398o1DgPXvl/8s/76CD2T/8fc/97/PL/+z8vOwPv369Jeg+Ns/YkKCLL9Z2BnZWP59ffVWQIqL+f8f0PVRfxn+M/77//MHuPxhZv73/fuX95/+sHGwMDP+/Q3avPIfvEEddMkDC+jWQA7O/79+cn379Y+N4S8DJysLaCgBdO7q++csdxSF/n9h5RYW4bj05s8vhh////wCnR0OOuby729GBvY//398ZZX7+Yedh5PzF+unj2x/b35mk1TVkHj64Panv5yMHBzsvxn+M7CyMYLuO/z1+/dP0MGkDKAjCXmVf31j5uNm+fSB7S9oPQbLf4b/Pxj+g3cMg877Ax0l/e0bCycDaO0hIxPoSF9Wpj8/Bbk+PH/z5SaPKRcTHwvXH9B6HGbG/z/ePH376z3HX9AR0P/+/mVi+P2X5f+v/6zMv1hYfnz98e3rt7+gFVrM/0H9BVDD7d+//6A1EKCOEOikS9BELiPDL2Y2ViYWVqZfoNEhjm/fP/5jeSIuwvSfmZ2N8/+Tdz+Z2EFlyG8WFlAFxwSaPfnF8o+JTZT9409OLtDygDff/3x//fDvWx4DBY1H/NfffWfnZ2f7D7pgTeDv9+evv9z+wc3Gx/rlL9+/X6++K6vJsDP8fXTp4rP/vP8/cLL8+cnExfHrGzvn319f//EIcjCxc/1lZgJlpO8MDAwsbKzszExiQtwsj27c/yPAxM0FSuWfv7NycIKO7/z17tXzD2z/uJlZWUDH2f778/UfBwczIwvL998sf9mYWJj+Mv9jBh1MCVoP8e8f6NhCBtCwyP9/oNnMf79//mH8//fHD3Z2bo7/f399+s7EwsT08dNvbtBJnUz/GX98/88FOiaWm5sNVLH9//efEXyv4+9vXD8YuPl+MbDyMH/9wSLCL/CLiV+KUfDbeTZBYWfl83e+f2XkBh2Hw87x7+/3Lx8ZuVn/6Sp/evzxtwzD+ys8v8QYXl28+VFVlPHN739/mVg52T795eXm+v/5KwMHBxM3Cysz17fv/7g4WP/9+cXMDmqPcPIwPb3/hpn7/78vzzhAB7f8+MzAxAGa2r//4t2fd19ZOFjZQZcv/v3xj5WDEXRZwq8f/zg5fn///5fpF2iQ+D8Tyx/QcZr/QPdzMDEygedlWf79/v4LdFzJtz+snMz/fv34+YMNVHixi0iw8DL+ZGRk+fOLgUPxz7MvIHf8hRz3CBpDZv7zj+3bOy7mb7/Yvv9///obH+icaG4mVu7/D97xaWjKf/35/Me/3+zs//+Awvfvf1YBpl8/QCebfmUV+vPj9alnoizvb3yWMJZ+z/TuKyMDC+hgQTZQHcvI/O8XAxszGyv7n/c/WDi4/oPux/z7/z/b6z/ibNyff3z4+f3PazH2v7/+fP/z+z/PP2aGT3df/ePi5+bm5uP4AdoD9p+ZifPvd0Y2Dqb/f1j+fAVNgvz/y/CfmZGJlfHvH9A6cfBBuKAFH6BFO6Bd6yyM7L+Z/v/58+fXr++fOBn+/2LlExJhEeZk/MfC/JeRgVFF6Nnzp9/+/voFGiT6z8gEOijwPwfvr+c/3//j4f3089v7F//+svD84/z///vHN68/sAlxi6qyv333k4ETdKw4aGkKIyMDw8/rX7+++ifKxsn988Ezfv7/r/4xMH//9JWBjZWZmYXzHwcb6/+fPxkYv/9i+sPwn43j49ufTGxcLIz//oAOOhH6/e2thPC3959+/Pr//d+fz59+MzL9+f+LgeHvtx8cfMrKgtygNtx/ZpafzL+ZWb7++f+N+cfnH+w/33/4AfY3KBswMvz9CTqqigl8KCIj6Kiuf//+MzOxM//mYQQ1EP8zMvz+DDodmZ+fj5FFlI/5N2hvLuMfcRmFhyyvfoI2ev8DnyjJyMLGxsr18v37l8IcjD8+s/Jwvf3wmZWJ99vL118FhLlZ333hVRF6cfPhV1ZWdrZ/P7///ff5PSsHw5WrP5hkeH//YBf4/pWRV+Dju4e7/vz+9U+QB9S+YuLiZvrx/dMf5p+gZjsTC/vfH9/+/WfjYgCdKcjMJcX77j4DDxsbJ8v7/8zs/399/cvN8f8PaHXUP1lZCVXFf8wMf35+Z+Lm+fEfdHcJ299fr75++Mbx59PXf1zMf/+C1nEw/f316+cfdtA1ReBjcZkY/v8DHUvJzPb/Lyfzf1DeZ/7L9O/XT3YuQR6mrywC7P/+Mf7+zcD87w0vMxcbHzMD6/9v3/+DzocHqWZl4Pn37Rvf7x///zGL8H9iY2TgkeFhfPv8v6iipPgfHg4xOV7O+z85hLn/snP/+f/n49+/7PzMzKycf/5zMX4W+sEGWmD+7gMrFyc3rzAXw5/Pb5jFmd4/ec/O/IVJVJ75788fX379+//jJRfrH0b2/z8ZvzB//cnIws7NzMT79/9fNj4uLn6Gz+++sHGxc/HySUnzPvz6hen7dwFOlv+/fzP++MbA8uf9h4/fWZkZ2BgY/oJax/8Yfv369Rt0pyPoTEjQnS2gbf1///5j+Pfrz+//zP8ZQTmcmVfsPweXmBDogCi2f3/+s/8ADQf8YGJ+//oPHzvTjz//GEDhBpoX+MPAx/35/Y8XX7jYPnLx8X74yiEqLCElK/mZjZlH9C/L/+/MYl9+PP75j5OV+eM3Ri7p////cX1j5+P+94WJhYvn1x+Gzwx8bD/4WXlllAT+ffnAyv/vJ/M/fikt0Q9/hOT+vf/wiYOfmef/Y9ClyVx/v7x/8vYvBzerINv/bz9YxSS+/2MWE/7F/JOVm5fp8w/mzx9//Pz++9eHn794ub9++/3tzScOLgbQDUqga+f+fv/7nxHU3f3z+ycD6IAuUB0AOsL9338m0CnBoCPQQZXE3z8//v39/4/rz7/fDD9+M/CxsP39z8zN+u75XwnGv9y8vM++c//5+Y/xFwfTPyZONhbQYZAsnD/+fP79h5fhz0/GH39Aow2CXAIvPv98zsrHCrpbgImD4+/3jwKCP35/ZeYE7R/9y87B/vU3AwOX2NdPf38xsHDycjIw/WT8+esnlwTn749/xVkl9YV+/2Hj//npw2elT9++f3j77S8L679/rEyffrKyfmfn5fzyS0hYUuj9X0ZWJtAWX36+Px8+Pf/CLMb49+fPv7//MnN9Yvj94907Vm72nwyg4fv/jH9+MLKyM/3//ecf+P4a0EV5rKCVrCyg+y5AE59/fjCxgo7x/P4TdMwzqOXy89tfLl4WZlZ2Bi72n7eeyIuJiIrIPn/Kzs/B/PMtEwMjKz8TM6gA+83I8e3Pr+9s3Exfv/3h+vnp/y8OXlWhe49fvpRmY+X6/vYTs+Tfrx+ZlJmZXoEOjGP7Dz6BnPHfXy7OH6BBhl8M4qDF6wIM/7gVdUR/vvjNwcIjBtqz+OntbzYRge8vH35iA93/8p2Bne0DE/Ofjzzc/7985+LjZ2Nn/fvz45tX75h5OBmYWT+xf2fg+vuPgRk0GcTGzPDn76+v3zhB2wFBO2P+MbBysv/7/u0XGx/Tv39MoBteWFl+geYyGf7+Bd1/8p/x3x+G/6CjYjg42H7+Z+bh/fOJhf03yy9e9n//vv/9/eSGoqi4+j/2fx8FNNlf//v18z8zExNoLpGBkenfP4Z/oAPUGZg4uDn+fWRk/fPvy9dvn94/+cUmw/3lv5gY6N7ff9zykgyPP39gBV07CZorYOX8CDrJ4M9vZm6GT99E/3xmEVHU4PrGz8P67OEjBU7mL8cv/hKR+s/B+Psb17/vzEysDIy/f/xj4gbdGwRqsP9m/A1Kr98/vf7xUk5eXFCOU+CLguC7t1+5GT+/+PbrHyv39x///zAy/Wdm+g9au8/I/Pcb6KxWJtBCFc5ff/6Bzj5m/Pf73x/Qya2sDOzff/77/+cPKxcL06+/3GycHAyM/xh/sjD9/fT+ycvnb1n/v315+Trf61f/hJkk5dS/Pnz67Tvb/x9fGNg5WH99Y2T4A1n2ysny4/MvATHu/xxCHH/+fGf8L8j3h5nzDZsIG8sPJjExcb7nP/79+ff7HwMbI/O/77///WICHUn149cvpk+/eIUY7r549EVW7PPzzwzv3ry9/YyXgfUDk6i41Y1voKP5//5jZv8Jqqa5mP5xsPwCLaxh5ODlZPp/+w0jD6+gENOvN0/5WNk/PP9yXU70289/oAECFgYmBoZfoDEJhl/f//5lZgKdTQxaywe6VAx0e9C/P79+/AGll//MzCyg1TfsHP9//GIXZGdmYmH48QvUnv71+cmVd3+4WVnevf3C/es3x8+3vJIKv749/8zE/e8X6NgzRtCBkJwsf34xsjL///7t3T/W30ysbGz/GBh4mfmk/nz5+vn1b06Wnx8ZmMTFGQQ/Pv/86y8L6OZN0GjxJ3YGdm7en//+/gDN8935+fgD2+fHX9/9/PP21Yd33OIi3F/f/vgv++X7f9b/oPF6Hg4G5p/vf/BwcnIwff3PzcHEwsPLycb8QojtH4sQ6GZNhl+/QMd2fvv44+fvj9+Y2ZkZmECT/X9BQ3O//rKAluv8B23E+fmTiYX9D2jNCcNf0OFw/34xsrOw/QbdXsvyj4GRnY/l7x8Ghp8/mFi+s7HwcHx5z8zPzsr9/e+Xf6zsv54xMnL8/fHjBzvoCOZ/oDBlZOXh4fjxm5Ht37efnz4xcX3jAd2EwsQBOhL3++ePv97//fntz8e/vzm4/3H+e8vw+xcbG2ghLgPDH1BbjZvnLePfLz94BV99/PeHU4j105u3v34y/efiEmL7/fb7p3c/f73+xsrG+B909hI3098fvxhYeblZ///+x8ry58d3Li6RXyJcXIwsvEx/+Ti/f//Nxcb/l+3nt5+/vv1kZfnL8P8vqGL7C269MzGDbjECrX36/pMdtM4CtCaMEXSK9++fTEygU2eYmFmZmbn+8XD9+/OH4fvX7ywsnwT4eN4Lvfv97SsfmwjjD9ABa29AV309/878H9SvBI17sLCwc7Dz/Pv1n+H318+ffzGxgfYzf//wn//bn2+sb97+4v756+MPlr+Mv38w/AN1xEDTtaygnb7gw6iZ2ZiZGP9+/fiN7ed7SRUpDran39//faMoysLx9cHTnyy/mH69+cHACDpg5NcPdjZmRra/f3+CzuUCHT/4+eMnXqZv/2XYf/348+/Hhx8sP34y8/5i+8/0+vuv/8zM/3/+ZmRl+MsIuhyCgY39/x9QhQda9cXw8zfzn18/frGCFgqCNpuDvQ867JeVlYmVk5Gd9eef33+/fvnJzvL394//30HHfH1mZgOdN80E2gsryPyTlf//dyYOZtCmBWYmDlYm0OLmf4y/fnz9+f/b7z//hNl4vv38/+vj+1dffzJyfQDdNMLIyfTuHegm4D8sLEy/GZk5OT7/+8fI+u/3j48//v3//evXJx5GRva/H6T4hP/++CUv/Onp6xdfOBi5uRgeCjH//8PE/f/z7y+f/7NxMv798ZkbdGnDX9BM9xfQSTbvH74QVWH58vc5Oxcvw/NX/wR+fvrHxc3y++cfFvDWANBBxqzgs47/M/7985eJlZmB+c+3H7/+Mv0HLQcEH97E8R9UKYEu3mNhYfz58/cf8PpoFr4/b39/+v0bdAI6aGsRBwvjdw5eMTFBHo7/n77+ZWdmY2Xj4OFlY/zz5ee/37/Ag0XM/3//+AOKn+/MbJ+//Wf49+Xrf3Z20NUizKBED9pe8e83AxsDC9tvdjaG739+vfvyn+k/KwMHD9OzL5zMUl/evP/DzP702/+/rNwcf5l5eVl//vr78ycbK8d/3r+/GBhAx16wsLGwcrH8Yef7/+83mwLvq3s32d7J8XH8Ymb59fUXI2hzPQNoQouJ4f9/RiaWv39B5S4j6HIr0BzHbyZmRhZG8MDJd9A1MKD5A9D514ygo96ZmH//YeBiYQCNRjD8+87C++EPi4T029ffWP6ArvcA3bTy9x+HpDAPyz/QAnJmZhYWVnZu1t+//7CAhh0ZGRh+/WX5+f3L/y+gwwF/M/7+z/D/N8vff6DLdX4wM3OAVhsygEYs/oKW7f9nZ/j/9/+PX/8Z/7IzsrP9ePmMR5jh97ff37jfMjEICP77yML14xvDf9AA3vf//1g5Jdm/ffjD/I/x/++f7MKC//7zcP789PMvaOsM75fP37m/f2dn+vzxP+ff/wxsDP9/gs7lZPjDDLp6FnTHFwPoEG0GBoY/v5hYQUsX/jEy//3B8OcfI7hbBLq/hxXkKOZfn39wsLIz//v1j+HvdxZmFiYuIY0PPz79/83KxcIC6iszffv6V4CZmUPgw/tv/0EnloEW1fz6zcrEzPQHdHLVLxbmTwz/Pnz4zg7aOfoXvCr8108Wxr8/GVlZ//1jAp06D/LC359//v5mZf//+y8LK8MvblZWBqafX5lZmf9z/P/HyczABLqh+y/H788f/oGm+n8wMjKx8gt+/fsJdFvJpx8cbEw/wVNBH/9ziXCr8t7lYPz99TfHj7dv+LlZfjGy/f/7DXSzzZ/fDP+Z/4Ea/6Bhzt+gmf5/v/4z/wZddMf07+9vhj+ghb2M4FEnJhbGfwwsbP8Y//38z8L4+/u//3++snxhZPv/R0qP9e6bn9//cAn+fveVmenHUyYNPgXp70/evn399dsfFm6Gb1+/v+NkYeJm+fzz/98//778/cLw9dMvZoa/P0C3qzCCdpMx/P37j/EP+E4SsFN+/Of9ysz+iZuT/f9//h9vvvLwsP4XEPrLxsP/ETRRJfTnw/dXn37++87M+vvfL2YWxl+szH/eCPz5+fUv4++f3/6wM7OyffnPI/Trw8vf/39Jq/E/ZwAdRv6N8dunv/+4/vwB3ab5lY2LBTR0A7ozlAV0AwYbC2jLByjemUH9H9CcCMtv0DpW0BAn6EKy379YWJhYhUB7r37/+sLw7+8vll9//jJxCrMLij948/bL01+czHx/fz398vK3khDzPyZuLonHz/6ycDN9efOe59+3Pyw/v/5h/POT6e+//+AVqMygabq/fxmZ/v4DH03NCJoqZ/nxh1eE7cvnnz+4//1k+s3BwfSVgV2Y+d+LP9xCAj85WX7//MvCwsrGxPKNm/vXL9BZ5Ezf/zP8/Qo6dPjZ5/9fQcdv//z+4/tzBlZ+ST5uSe7fvzm4mH7/fPNNXuDXv3/MktxMv3+ygibAv37+xsvOwvT7D2jp619Qc5D5H+iyJXYGdtCdEAzgsQAmZtDpMwyMDP9YOf6AVon9+fefhRt8VSIjC9dfll/////5xsXHIaTw6sWLD38//2NkZ/3x+e3vT0ri7LxsTCz/fn3+8+Hv+x//GX7++MMOWmcLGuNnZGYFrY0ELb/59/cf6KZY0GpD8NAL6A7C/2wCHMzvvnH9/ccJOqr4Mzcr7+9v//78+PyLlYvt319GFiaGv4wcjCx/GZjYWP7/+cf89++fb384uP7++srwG7RIgvEv44cfTEoCvAzf3r//+ePrd463zF9YpQRB+xdYGf98+8r5/99v0N4MNrZ/IAuZGf+Dzpb49+vv7z9sjMygS+0YmUCNRAZQB+D/77+M4NM8fv5iYGVh/sfMzMj8H3SoIwsrM8tfFsa/v/5xckqJyb1/+/rp03d/QEtQft3//kldmYuViePP35efn3//xSTM9P3PH3YWlr+g40BBJ/mBDon8/+8P6KhdZvBmFfB9G/9BQcTw5ysPFyPDf4Y//1j+gwaK2UDXETD9+Qw6fRV05SQz48/vrKycnz/9YudgBZ3fCr5zgZGT8etvhn/gKwwZGP//ZGZj+fr6w+P3v/6yvRViEnn/+8Pnrz9/fGP4yfzr2y8mxn9sHH8ZGFj+g66F+svw9xcj6Oabv7/+cHIwgY7LAR1fzvgfVAr8Zfj9B3Sd31/QGeSc7KCePmg2gomdiZmBmYWBkYnh97dfv3m4OIQVv9+7fvfN/+9/WFh/3nv3lZOfkYlNnIXt0aN3zCKi/xhBG9dYQDdnglbogfIY6E4D0NmQkIMiQSkONB/xh5XhzztWcQFunnd/fn3/x8T0D3Tc4Pf//7+/Z+H4z8rEyPyP6f/v/+xsHK8//mFlZ/r1l/HP91/MDH9+M7D9+PMf1G4D3bjDyv33/9vPz358+PaflZXhj6jYl9dP/rAxfn3zRViI/e93VjZGNu6//1jZ/v5nYP375/efP0z//v/4/w+0yhJ0pSFoNOg/KEz+ghqHf0FnmP//8/M/GwfLX9AAJqijzwm6UAu0k4qBjfUv6AZiNm5BbRH5Rw8efmHnZX35+gODPK8gDzcbB8vvXz9//hQF9W1AzWxQLmP8++8P5DIecGuRkZHhH2TR2r9//xhYGf5+f8/DLy7/9OnjpyxcLBxC3998/c/+69MfCYmvLKCFPv/Z//z5+e/jP3bwkDcTOxPoYPJ/f38wM/36w8oCCrOfjGzcP799+fBBQP4foyjfty9f/7Azvfqgxvbz0xtm0PYwJvA1dv/YWFl//mb99+vbXxbG339+gnI9yDH//oGWT4DmGv4xsbEysIGKQ0ZWFtANEb9/fv/Dxsf46y8DJ9O/X6CK4Q8jKwsj6NTnn9+/sQtwiEmK33z9meXbt5cXH/GKvpUU5lNk/v/0y9+/338xsrH8/f37+z82RkbIBhsmZnC1///fX1AQ//vHADo3lAXULP/2iplVQoWf/Q9oDJuX5dtvXpF/oMlVFtCg8B8Wbs5PX799ZeUBTVWw/PvBwsrAyfqP6e8/Ntb/TH9+MIMmtf78/vue6S+3ENfX39KqH24/fC8oxgaa2PnPycXOyMjB+JeZgY3r959P/3+DVsr/AC1gBpVsoOUzv37/ZwFd/8HM/P//bwYWRgaWf6ArRplYWBhYGf/8Zmbi4vn7+99/dubfoBOxmVhA2Y6JieXvn99ffrOyivNL8l798PUP4/9371kF3ikoSXPLfP7+89NPUJOD7e8f0P1CoOTKCGq/g9bXgM7P+veP6R/o2jkmRkYm0LnhTH8+sXHz8/CI/f756SvDHwbOf0zCguL3Hv6V5GNgY/vHyMr89c83Rmbmv7+YOBn+/WAFDVmAFv6ysvxn+v0PNH7MzMLM+Ps/MxsbH9vHdz/Z+Xj/Cf1m/cn0h5GNiZsFdNr4P8bfTKx/vrz7z8D4i+HHb6Z/DIxMLKCVu39+gRoeLEyM/1lYQKtgGf4zsvz/85eB+f8f0CGUv8GbKP78+snADLpDhOUfC+P/P6BRYwbQ0Nnv36xcQsLsnDce/2TmYPn+89f3L9//KAjIfPn+8x0LE/vf/8xs/3//Ad00xQwq/UDpHtT0AA09g2YTQPcN/v/FyMwIujX35+df3xhlfr15/+3rbwa2v98kpP88fifEwMD5l5HhL+iYdU7QMvf//5n//v/1D3TXGMP/P0ygHPWfmfX/b0YmVvb3/1kYfrJy/H3zmJMRNIL7k/HffxbOv2yMoK1B//99Z/r/491HJla2n8z/Qbe0g0v6f/9A5+6ws7IwMf5jZfn/j+Xv/7/MLIyM/5nY/n//zcb49+cvBubvf/9/+cEK6ib+Z/nPxPwfVEn8/sHExMLB+OfPD2ZWOV4OFuYvLFygqeX3D1m4RKW+ffnxkxG0fwB0r8Z30O5SRtAKhD8MoAuTGUETMJCT5xkYGf+D52xZ+AW4/j77yCLOycz94clnxv/f3rHLCIu9YfjPwMn09/uPX1+//2H9/+vnH8Y/rKzcvxiY2P6yMbIxMfz8w87Eys72/9vv7/8Ev7Mw/P/0U0Dg3zumf4x8H76ATrJk4/zxH3Q1Adv/b18Y/n17/5GN8+9v0Eau/3+ZGZn/gHLhf2ZWzv9MoAtMmRiYmJn+/2NmZWFiYOYCbar4//vbz1+MDP+YQXcigfbWgY6BAB34+f8fM+iGtP///v/+ysLBbSx66darb1xsoqrs7z/cFBaX/MH88wsjJydo6AV0gBA7y5ffjMzgawP/MbOy/mdi+M385y8z559foFtUQAkDtDCXkYvpBwu7gLz0jdef/ghIMj5+LsbDxv7hy0+G3z9//P7zl4mN5R8r6LpWUN+B/e9fBubfTMx/WBl//GDgBCUGuW+fGbieveP69+3nb8Zv/79/By3W52BjBiXlv2/fsIn++8vPBZpj+MnOzsTBBjpnipUFdJsF008G0LQa+9+/P/6w/vvxm53h739mdubfnMy/f4BW9vz+zs7E9PcTBwPrXxYGRtCF2QygAx8h987++/3/PyuXIiPjj0esAgr6HPcff3zKySbFwfjw7dd/oMOyvzEx/GL8C1qj9P8fEzsXG8PfH6C7dFlYGf//Aq0sANU4DAy/P337JcLNzsPOwcnC+p2F5cOvH7w8XKC08vobG8ev1x//crBwgdbZ/vrx+wfooibQKi1WRtDVlaBShI3776//jExsbDyg/uavH38YQekcdN8bA6hvC7644wcHMwMnB8MfJlCaYQFlSdBVT0zgS7BYQSt1QF1FcIvtL+N/hv9/GNh+//sLSqP//jExsTD8/fEf1A74ywDKvKDrhEEXx4E4//78/f2fU/bX18/f/vznEv3//8OfXxzcTD+/f3r/g5flz8/fLMyMDKBT+b/+//33928Whl9f/4Ot+wvaZvsPtByTAXSW7O9PTGxsvKAbsNiluUVeP/rCxs7LyPz/+9dfjEx//rKysv4Ft9iYmP+ChnNAFQmoY/v7PwPzv78//rOxgA6MZALNYf5i+fn7P9NPFtB53n8hw/ygeQT2X984WNj/cf/6z8DOBprxBxUE/5mYQdPsTKzsDIy/QZsQmdl+MfwBVQd/QZNnoITwl+kfIwcbaIADtIiO5TfoRk3QDW2gGowRfAX6P9Dp+mwy/9mffWd4LybO9Jab+f9/ZiHR1+++MbP8/8fJyMXByMjL9uv97y+ff375zfjnJwMjaFwedNA969+foAXooMWsPz5/4BX6///v779/+QWk3n3/+u8/5x+Gn7//s/z9+k+Q+dvn75z///4AXbXD/P0P6HZX8Hzcf1Ba/PPtLzPosiwGlv+gCwmYQMvhfzCycPz7/Ad0hQuoWccm+O8jIxML408m5v8sXKDl5gyMTMyg/gCoBQDGoFU+jKygzbTMDAygVtD/7/9YWZj+gq4RYmX49e/PX1Bn/xc4AECpALSZnAnUnGP8//fXd05uTaVHD/59+cEjBGpoM7AIKfz89YyJkYmdF3RE7RcBEd5ff18/e/7qJ+gS9U9MHD9YQbdlgRZi/AFtwgYV19/fsf7i4WH8+ZuHi5uZ79ZfNhbGLz8YWVl+/WSR4nzx7jfr1x9fmLi42Vn//AVt9v//7/fPnz9BNdnf3//+cbGBljl9//UT1Eph5uJgYmBj/vPr17//jKD7KplZOVhYfrOx/v75+z8zCxsraEEAA2h8nJWF4R9oCT0jqI/8n4EJdJIoqHX2j+HXn9+fmEDmgM44B3sWdOgNaHsIpBIDtZ1BPXlQY4aJ9d/fH0y8suw/+Rn/MXP9//uPgY1VWPrLP2YuLi4eVn7mZ6/efJAUFOITlnjx5S/D7xfPP/34ycMBKoCY2P7+BZcrrJwc378+fiEqy8f07+tX5n/s3N9YuP9++cn2nYud5e+XH9+ZmTmYGX4zMP35/xN03ytoyTKoMGRh/c/AIcT4l5UbtC4cdFrLr5/fWJkZQRf1fQOtewCNfTAyfWEEdUT+/fnLysgIGu35Ber7MIInv/4xMP79+4ftP+geSybm/0xsTGyMoI0xf3+BKuC/P//+A53JC7r7m4mJkYUVOocGKgXA648Z/jOA9of/+/31z3+BP0w/GJhYGUCn4/5nFpL5/YNTQEiQkfH7n18///xj5OPhEhT/9oeJ4fXz5y8+/P3+n5mFhQk0c8XMBLKDhfk3qHcvxMnK8f0Hq8TfV9/+s3L8ZmBg4fz75S3jT2ZubkZQ6cz0j4Hl/7/f/3+xMDEzcbKygg5Z4Wf8yvD332/QbgCG3/9BBS7Db9DOFFB/8z/okPL/X/8z/f71h+kfAwfTX1Cs/2YELZ1iZmb8+/c/09/fP///ZfzHwAzKFqyMoFYa6EpIjv+s7AygC6JBS/sYQC3A339BFyKAMz64GACVg6BrJRn/M4LOomABbaL+BbouAbTmhJNH8teLP7/+Mvz/9YNZ6Bd4+x07J9+Pv4ws0sqvHr14/uEnGysjI9Pv/6BTZkAXCXOyMPz//uK7sATP/+/sXAx///xmZmf8xQba1v/j/z/QcgkWNtCGE0YmBtAVzSyg9dCcbMzf/7FyM/77/gM0tMXAwPjnH0jHL9AiTlDPEXT7JRMDqNPz5+8fhv+srMx/waPj/0CVIzMzw98/oHVuvyC+Bs0bsYFnDv4zMLCwMrIw/f0P6pmCcgAj43/QkXcsDAygOgLUjQMXQqCeLaijzcwIGmhgAc23gO6zYGH4/49V6M+nV1++vmdhZOYRYGRg5vz/mZGN+e+ff3/ZucVV39+4+eTXvz+gDioLM/M/hn9/GNlYQQdpfOYCbRLiZOHk/fnr92+G/ywMoOWkoH27//+CNnszMDH/A227YmRmYgSv7QLdXcwITkggf4IuN+Jm+v8btOyKhQUUv2xsTKBlwP8YWf7+/sfCwAw6Zp6Z+R8T4/+/oKW+f5gYQUuYwTeGgs6+YvgPPtcbdNHHvz8Mf/4wM4KGz5mYmJj+/2VkAJUBoMtT/4Nu1ANNBIBWVP0H3WDAwMLyD7SNGrS5HBSu7N/YOPglf3/89u07K78QJ+P/7y/e/2Ri5wSdFMXNzS+sISp46fEfZnbW339AG/ZBFeq3f4xcfEycAizvf7EwfP8jKsd27+EXhq/f2LkF/vxmYP4FihR2hn9/Wf4xgxMr05+fn5k5OP78ZvzD+JlBgAsUYL+Y2Bh/cLP9+QPqYjCAPAO6+h1UGIKnIP7++/uHCXTuIui+zn8/2P///fETnA3+MfwFLXthBN+Y9u/v37/Mf398/c3IyAA+YuAfE3j5ABMjaEQVlO5BGLR1GzSkxcD4D9Qd+/8PdDMqaLiPieH3P1Az8RcLmwDDh7ePvwiIC/36zcj4/tp3FkZ2ht9/2Dn/s/AJsX/69vUn+2+Gn5yg/cQM/xmZ/vxh5ZH88/Xdb1FOpn9/2XiFmf5//fGX+9vPb2wsoGscOZggo6q/uBj+ff3//9dXzn9MzKCzyVgYf/7+8+8H6z/QxY9MjDyg9ScsbMwM/1jYGX/9+8PM+PMzCzMTKzP7r5+gJMrx69+PX2ygFA1aJsEAOsefDdTX///vL8Nf0GV5TFxc7Fw8TN+/fP/+5ctnJnbO32ws7Bw8oD4LKAVAfM8A6lGAqmMGBqb/4Lun/4EGFUA9bNCoF9Pf//9+M7AIMH/7+oWLhev/b4b/v778Yf7/+8/3X+w/fv9lY+P48/E9aDDkFzczAyszIzsvy7+/oK2FDL++/2Rj/PaJ6d+vb+wi378r/Xj2+h33/3+gLsjfXwxM///9YwNtAWT49wt0fj7zr1/MrAz/mVgYf//9/5+ZlfnfX6Yff7///MfKxPCbjY3h369/oC2Cf1lAKyBBy7t+gNT9+ssO6tywsDKAbj7/x8DAxMT6989vBnYWBmae/xx8fJwc3BwM377/+PDq+S9WNgY2FlZOPtBoEgtoJRG4IgAtCgHtRAA1hkACjKAjNUClLui6YdCAxz+G37//sXJzsTH+/cXA8efH9z/cjNzMjD9YfjODlkn+/fb1yz8m0AaOP/8Z2P8z8jCx/AZdBMLyn5Pj75+/X1/yMLx/IcUt8Fv4z+cX776CpjTeg2YhQGsq/zL/ZWZm+P0f1Mph+Pf7H2joloPv19ff/9mZQBcc/2T4wwgai/nFDqroQOXVv/////1hAiXYP39AS7T+/gMVCf/+go68Y2RgYgUdf8nMxMDKzcvIxs3GwcvFwsDMzsb9/99XIa4Pf0FXhbGwczH9/8cICgBQUwh07Cio5QQKD5DXmcAFAygjM4BWnYJ2GfwDTRGwsIorc//78UOQ4fOr9/x/GJj//Pz9j4GBhYf/31fQ0p6f338z/vr9n/nrP4Z/P559Y+biFxCTEmZj+P3vB8vv93f/Cf77ev3b648/Pv5kYGX/xMDOAdpJ/kOMi4WJGXRYJWgY498/xn//mVhEFX68ePft949fTBxM/5g5/v9l5/j/C7TJ8T9o/h3cJQfNBjP9YWJlZvoLGhJk/vnpKwvjfwZ2Tm6Wj99/MvOIcHNycnDxcoGGQf+BVtyBJoS/gA7O+MLKxMoI2hTBBAoAUB8cNGYEOlkCNBYDChBQzQCqGf+BOaDxTlABz/bvGyufnsxnVk6Wfz8+fWKVlWB6+/LDt2/fGVmY+RhB20dZ2ED11v+/jH+5WD5wsDKx/nn37RXbL1kpNl6mXz+YvjH/ZbvzmolP4tO738wMfxiZuZiY/3/98oqThw90ny/r339/mP8zMYM6PZ+e/Hv35S/7/3+gWVZ2NqafoBIQtHjvPyMTAwvotHjQEWEMf399+/sPNNfPxsrMyAK65Y4ZdCQk89sPv/hEeVj+/v/+6Q8DqMH95++n97/+/Pv3B7Lv+9/PX38Yfv/5Dy4DQFs/GEE3wzIwgTwN6m+DygUQhgz3g2Z5QQua//7+/4eDm4GHif3tq0//fr/68pr165c/oHYTaEzjH2jd0R9G0KztPyY2XvZPjMygjbVM/xmYX33hYfz//Svbl7////0BrcZn5fn15/Mv5t/fmblZ2Zm+/WXmYmEFlZ2gBXqgS3gYmb59Y/r8n1uI88Off8yMDMwskEE3RtDmeiZGFo7f/xkZQLfGgvaDga7IZGZmZmbiBG15Z2T685MFNGr/492n//+YPjFx/v0GXkj04yeot8j4n+kvIyvj/3+/voAvjAP5EoL/g1IBqJUFagqAWrOgwADNMIGG9RhAQxYMoJmxn3/+szH///iZgY/7+at73IzM7KALX5iYOH79/PUdtF2GhZPl/8+/nJzs7L/BaxcYWFm53r5i4/338y/bay7QAsEfH7+xglZjczExfvvHwMMGur/g23fQakZQWwBcCDGy/PrJ9I+FgxO0ihd0+TEDI2gYD3Q+0H/wmh1QrwU0Gv2fgYWREXTX/J/foElA0Aqwf8wf2Vh+/Pj7/d2//8zs3xiYf3wGVUn/mFmZQDchMoAqVNBt6X9///oF2h4IavuCggCU+sHziCAGE2il+f//DGCFoGHA/wwsLL9/gw6e+soEuiqTm/v/3x+33v/h4gR1Ov+z8f3+8gm09P0PAw/zn98/QDsBWTl+//v94zsj51/O75//fGb8/Y/rMw/H338Mvz/9ZOfhYBH89+/X5+8MTNwCrL/+fmZhZWLlBU2v/P/HCN7m8p+FmeHPR1BF+QfcumVg+vfrO8gp4LQIGukBXW4KvpT4N+g6P0YmUEf6D2gZFQvoyBtGlv+gO36Y/31/9x5UNTBxgEZTQUct/f37H3TjLgMTC8d/0N5hUPfnPzTKGRiYGP+DcgMjw39Qg54ZNEwPaisy/GdmYGBmY/r7n5X9718WiX9Mf3kkOO+wcjL/+fqXmYmR7eu333//c3Aw/PwG2kfJzMjI/Zedk4n575eff9/+YGNl/MX2//eXr39/sf36w8zxj4mJje3X/z8/fvz89/cbCwfTj1/fGBjZQGmP8edvJtDldL++sjGAmoOsHH8///rLzMoIGugAXfkNqplAPRfQICzI1QyM/9hYWJg5OUCtX4Z//1jZ2dlYQFuAGFgZGf5+4WD49f03Exs7KyfDf1Zmhr//QTvBQa2Df6BgY4aUAaDqH9QQ/g86LBQ03Avalwka6mf4D1tyDLpFh4mZ8dtPBr7ff1mYfvxhY+X5q8D2h+n7uy+/udl+f/nJwMz45Tf7/x/f2P7+52Bj+P2X68Vv0LlAjKz/fr1n5PrN/e8n899PH5nY2P78Bjcg/v79/ZeLH3Qxw89/LOz8/3/8+fLrLyPLH9CoGuhkme+gw+BY/nz9ywQavmb5++8/KwMTKKOCBrH+/wfNofwDDRqx/GdnZ2ViZQBtjWdk+Q86YOD373+gc8EYGf7/+fXvFwMXGzsTaN6SlRVUp4PWEDL9Aw3gsrCysoDCE1SQgNoO4BIP1CYBdWhBmfQ/A8NfUJPkzx/Q5CcjI8s/8CJ6FtBo+F9GZm7hn5/Zmb+yc3L8//PzM3hQ6jdoNw4rOy/4Nrtv35mYv4NWroBmrxhBJRAHaJXS3/+gG2lANzL8Y2QC7WwHHV3A+JOJCbzY4M9/UJcA1Fb9/5eBiQk0p8cMypmgGWgWFshldaD8+Y8ZFBhMzGzs7KBIZQTNS4P6/0ygix3/gUZYGP/+Y/j779u/f5yMrKAGO6iMA/WEGECX3TKBbsBgZWVhAU1FghqQoEEMZibQnBRo6SFogASUG0BtbxZWFtA6CNCsw///bEz/frOwMoAaKL8ZuQU+gda1s/Ezga5g5GZjZeNkBV1jxMbD/fU/F893HlA7nZ317z8eXnBBwwgaUwbv3WH78/sXaD4CtMKJ8R8D45//bP/ApSYjI+RGRgbQCSWgQ6xB3RXW/2yg9M7EyMT2HzQQC9oJwgAafGdiBi2PZwW5HzQxxfj/HyNo9OYP6GIzBlCogY7H+M/KAR7tBPUAQUPgoGr2308GRtB+EyZQQwg03cnCCJodYQSlMNDCOHDLAFQWgsZKQBOgLIygCXDwFAAjqAPxnwF0ixOr+O8Xj17/YONgAZ32yvv/4w8eXjYmtj9MoNUVstI/b977zM7C+efrf97/v/+Dsi2o4/2f89+3L//Z/v39/YsRdDEx6A5bpm9MIiLcXKzfnoFuPwKVQgxMv7+ALudkZgbFAxMTqMcI2pb2H7RTFjR2w8wIOtWMEcT/BcrBoFTMwPwP3GL/A9opxQhuNDIycTCysIBu/WVkBN0Zygha88nw6zPo4J8fTBycrKBCEHSP6L8/zL9Ae4fBUzugwXXQTDpoywBoeBm0ZOUXK8j1oKlUhn//QOXz3/////OoCr5/9uTpSwZpAXYWIYbPQjxMPz5/ZmFn/fGBU8SA79uzD7////38keEz6GA/JtBwCRMjK8d/Bj4+vg9PX7IxgBZ4gg7rZmKVcXDjeP/o+oO/LLAU8Bc0sweaYgbF7T9QBIEKasiQ3X/G/z//sf77C+rZ/gON0YKarKC1OqDhXlC2Ai+KAQ0Mgpo3oBYCqCXz7w/D7/+gi1AY//748efvnx+MoPUSoBXloDEg0K4b0LgouBUCag38+/fvDwMzqMXA+I+F4e9vUFQwMPxnAo03MTEzs/3/w/DvPzf/X/XXd44//gYagmfj4hX4/vL7E27QstE3TyR43396z8rI+PMny2cWJoZ/jMxsjEyMrDz8vPxMwrzPfnz4B4lNFia2PwwcAtL/3756+QOS1EElBcRPoAqI8Q9oCwd4IIQZ1C4HpSUGRqbfP0ANZ9AmEVA6/Q/yCAP4AgsGJlDHFlR9gUrw/4x/IG0G8FphkDLQaer/2Rn+/QRNirKAyjvwgUAszH9B605AuR80rQ5Ksv/+/frJzAjqNTMxM4Om2EFdQ0bGv/9AlwiCthr/+8vGzCfK++r1+19s39+zcwn///P9+79/DFzc/9j+f/rxl4mZ6R8jOzvbL1Cx8w80+PPv7++/DIx/v/79DJpB+/8PJAhax/f72dWf1y+9ZmFmgqWA/6AhMNDxdQz//4Dnu5hYWFhAGwz/gXppDCwgadAB16AZIHBtCBrr/Ae6muc/AxMz6MKjf+CSHpw/wHvNQcmJkRmcIRhYmJkZOUHrlUCn6zH+//2PAbR549fP3+BTppiY/oAKYCbI/D8jM2i/CejaBtDIA2j1+b/foH1SzAz/GH7+/CHAJyDO+eoj77vfzLyyf35+YZBnFhSVVmAWlWQUl/jP/JeZgZH1x38m0K2joBVW/778+PXp61/G719/cYO88p/x/99/nExfL3z8+egFpygHohAE5TGwX0EFyD+Q15iZQVXff1C+ZwQdlglOQaCYB1VpoJEjUL+Q4R9oZyUHI2i9KCjTgK43B1fmDMygbgYbOygeQOvLmFjZ2Jn/sTCDB6JAK+lYQdeu//n77+9/VhbwsmNQYfsfdOYXaB0Y6zdQoQjqiDCChqCZWEAr+Rj//fj6/9cPVuFX73+DVoZ//ckiJCjGxCepwMHEK8z86ulflv+MP38wcP9lZGf+8+fXz29f/rJ9/fr52z9GJhb29+ysoH7f/z+/v3Iyv3v/9+MvLiYudlgKAI1LghrjoK2voO4RqHsKmsQCxeN/UDsFEjj/wTPVjKCsAtor9Pf/v7+gigI0EwBKxAygsXLQchhQGQiqB5jZWJjBC4xBa7pYQPulQINj//6DTlED1bGg+x1BhSEjaKbq/3/Q1OG/f/++/2YFpU0G0D75/4y//4AH5/+yMrH9/fLhNyO78u8/l99wcDBx8olyMYKOk/39geHD118/n7z5w/OHlYGREXQkDRMrCyvr768/v/3+8ouBHbSqhkFInJ+NhY3xz9e7f5iZxb7+4ZAQZgV3zMAEaJAP1C9jAnUBQfEI7SiBpt7AJTy4/Q4axgO3ZUBtA9CyYdBIM8i3oG4sqI0LOiiXkQnUxAEPOIICAzRoCFpFArpriAXS3/3/989P0AIF8ETvf9CCK9AWVNAhm4wsoFmeX/9YQf31//9A8fIHtEwINNLI9Ovvtz8/P//hkJa4946L4+fXt2+/M/z8wPb/Hxfnt0+fvvznFGD7/o+ZFdRt+/eXgYmFiY35/5//TKA1Sv8ZmNlFVSQ4QHdnfHz/gVVI/fnXz6yg1U0gf4FLwH+gdP6f6T/jX6Z/4BEuRoa/jEygJT6QKg6UwBlA50nBAgA8bQgKKkZQQw/Uqfr/H3TeEqigAQ12gObQwXUraPad4f8fNlbwKZygrRDgo4WZQd0aFiZQ2gBlTNDuXAZwrf/7N6hCADkMPGYOarmw/P0LGqL594eF4ftPjv+C0j85f3z5ChrC//eL+fsPZgHQDSvcHKBQAx1wwMT4H3QqMTPjnz+geQZQzfb/PyfL31+gCfFvH//+/MvIAhq5BLeXQIkdsscD1HD995/xD9gvoAE60JFH//+DWq2g5hq4WQwpmMF1NANolS7oMloGSLkPDkYG0MWa//7/+/+PCTQCzgTaEPgXdFI56Mauv4ws///+A50++4+B+QczK9N/RjaGvz+ZWH79+M3MxvkXdOIiuPXz/w9oqwroWNn/TCwsv3+w8YCc852BmYXp/58fH38LG/B8uvvt968Hv5i42NnZuRnZOVn+gpZifwR1SP6D5uiZ/zExM/77+pXlFygMWBj+MXA8f8nMxgxa//j78/8/79995wCNvYKSLGguF+RpUO0NWgUJqtFBVTOoQwbJ+qD5XlCFBcrnoHoK3G0HFQ3gxA5uPDGDJn4YGH6BFq2Ajs0EnQoCakKBalJmUHkO6hczs7AwMIL60P9BJQOoQP3z9/cvVkZm1v//QAt8mdmZQY1AJtBRDKDdCP8ZmFlAhzj9/vyXmZ2D+d//P6DT2ZhY/4sLfhe//4aZ7f8/Zh7wodZszP9/g1wAmm0C1UtMf34zgsb1/v5n5GDn5mYDhQnIKaDWIPMbVlYB1k9/f/34CVrEBhqSAzVJQEkOnCRA/gcNaIN8C/Y1RBZU/YPkQXUASBo0VgheQg3q7YBrSFAZABojAXkMtKADPNYJqjPBvT5Q+gKNCP3/C1qcy/gftPoH1DUDrddhAS2V/s3KzMzEDEqZoCFa0OKr/6D7ihl/gRpp/3+DFp4z//vz+w8j6CJETiYR0fsfudgZQQcOgyYT//8HTXOAEjvo1Nr/oJFUlv/vv/8BdVZ+Mv76//c/C6h6Z2Tj5GThZGXnZ+RjYOUGNW1AZ1UygJpy4AAAEeAEDXI8JCODPA0SBkUnGIMLQtB+b3AQgHjg/eigrAKq3v6BugKgMT7QMDeoKQkqzEEJDFQNs/z+B17yyQg6MfnXXxZQs4IFdOQY6JQ8RmbQSC3Ih2x//jMw/f8Laqf9Ax3BAzpu5sfXP8ygQ1cZmVlBd6pyS/z78fknC9v/76Cg+vUbtFodNPYNWvgIqrn//mZgZf36n5UFNO32G1S9gvI6qL/HwsLzj5WDUYiHTZANFKugyg8S2aAYBNUIoMwM8irI1eCAAKUDKAN0ChRYjgG00R7UCQblTtAoFsgsBlAHCbTimxG5VQQa44TUEgwsP0GG/wMtovr38+cfVlD/iAlUKvxjAI3A/v3779+v38xsTKA9GX9BIfj/399f334w8bMy/PrF+OcXG+juWtBmtPc8Alxc316zcTJ8B3W3/nz/BepRgQofUGHNyMj45w8j238WXkY20JAvKysTaIUnIwMjaG6bFdzxFWRmZ/sFWXwIzgSgCh+UdkEeBYUVKKWDBq0hvgWVkuBkAPIuJLhA9SNoAQgo2EADGaDIZmT8C6q6wKqZQKuAwH4HVxvgnvF/ll+gZM4E8jw3qAUOGmn6w8D46x8DA+jGbGbQHP0/hr/MjEygw3uY2EG7nH99e/P7mwDzHyb+79//gdeY/fr1h+HLf1YuhiffmNlY/7Nw8bIwMf0HdZ9AowrMoL1coJY4aJEgKxdojQcLC6jKAa1oBpUR/0CT+Azs7KBagRnkX5DPQDU3yCsgP4Pm7cDNPkhiBrVuIa00UACBlmeBWkgMDKBD5cE5HBwgjJDRHTAbFICg1igoEEHFBQsoHEAphJHlHyjbgY7e+PYHdAYBaETsH2hyEbSi9c8vVh4Whv//QW0E0Fwa6GRKxn8MrEx/PzP9Z/7578/v3z+Y//0GNYxY2D+/5+OUfAy67esPI6sgP+NfBlZQ4gIV5KCmNbh2Yvj/n4UDVIKBFmb+B18bDFpq+5eViZnx7x9QZQEuAED+B2GQR0HxDQkD0AgVJFRA4QIKClBGALdP/4Nqwf8MoD4KyFeQ1A0qAhjBJQMokYASFWg5EGjUjYHxDzgAQAmB5Q9oHBJ0/P7/r/8ZmH7/5mBj+M/0/y8TaKjkz68/bKDlZn/+MLOygsajQQfw//vPyMbx9c8Pxm/ffzKBJmb/fP/ByMr29wvDnz+swsy//339/ecfKw/z3/+sEAeCvAKuB0BHmjIw/oY4B3TIzV9wTmVkYPoDSnggv4PSBVg9ON5ALDAG+wo03QsKkv8gheDuP0gRqHADZTZQ8Q0aswHFMWSCA1RWgpM+qJcE0ggSAIUdzEhIAPwENfsZWdg5fn359e/fFw5uHpb/zIx/mEEjE39//2ECLUv8/YuZAzQNBbpkHDSlxcrzHXRYw9/vrKCWx/8/30F+YPj16SdoWvDP6z+gvMT09y94sg3U//wPOrH8L6hPy8DEAOrjgAMdtOYD5HwG0BEB///9Y2b4D1r7DZ6uB6UdULzD8gOoHgCNUoDdDiZALVgwAxwckKwPWhAB6SuDBUGnp0ByBBNoRS9UBzhMIDoBA6eAn6D7u9mYWTl+//z798ev738YOBhYmEDr3EF6f//5ywU6rR3UVwbF5x9GJiamv0zs7N//s3KDppZBJy+Ctp6wsfIy/frylZGL9d/v36BjWP78/AFaoASyg5EBtDABvBaMGdSsAN3sxczCBDoei/Hf37//QTU16MgT0FQdKFJBMQUaogXHGjg5M4I69qDkD6kGQM4HKQQrAI2QghI0SBA8rwMu+0D6wQmC8Q8oxEE1D7hBDCoMQBPXoBAGFZSMAHM9K3ZEK6jIAAAAAElFTkSuQmCC",
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"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:25:11.289879Z",
+     "start_time": "2026-03-27T03:25:11.264772Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "hobj.get_image_path(image_id=image_id)",
+   "id": "aa94e3b9654c93d5",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/Users/mjl/PycharmProjects/hobj/data/images/MutatorHighVarImageset/MutatorObject000/MutatorObject000-Image03.png')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Loading raw human learning behavior\n",
+    "\n",
+    "It's recommended you use the `hobj.MutatorHighVarBenchmark().target_statistics` and `hobj.MutatorOneshotBenchmark().target_statistics` if you just want to see learning curves (and associated uncertainty estimates and bootstrap resamples), but if you'd like to see the raw behavioral data, see below!"
+   ],
+   "id": "911fd7cf3ac548f8"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:25:11.682894Z",
+     "start_time": "2026-03-27T03:25:11.291361Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "hobj.load_highvar_behavior()",
+   "id": "443a8b78aff3e604",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "        trial                   assignment_id  \\\n",
+       "156000      0  3018Q3ZVOJ00HY15URPTWYSDSTCARE   \n",
+       "156001      1  3018Q3ZVOJ00HY15URPTWYSDSTCARE   \n",
+       "156002      2  3018Q3ZVOJ00HY15URPTWYSDSTCARE   \n",
+       "156003      3  3018Q3ZVOJ00HY15URPTWYSDSTCARE   \n",
+       "156004      4  3018Q3ZVOJ00HY15URPTWYSDSTCARE   \n",
+       "...       ...                             ...   \n",
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+       "<p>319900 rows × 11 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:25:11.784589Z",
+     "start_time": "2026-03-27T03:25:11.702409Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "hobj.load_oneshot_behavior()",
+   "id": "6d656ccac57e527d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "       trial                   assignment_id  slot  \\\n",
+       "0          0  3018Q3ZVOJ00HY15URPTWYSD0NIRAX     1   \n",
+       "1          1  3018Q3ZVOJ00HY15URPTWYSD0NIRAX     1   \n",
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+       "4          4  3018Q3ZVOJ00HY15URPTWYSD0NIRAX     1   \n",
+       "...      ...                             ...   ...   \n",
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+       "50939     19  3ZWFC4W1UVHVDPID57BLAZ34X3VRFA     4   \n",
+       "\n",
+       "                                               worker_id  \\\n",
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+       "50939  e43cc225dbf11ef57616e082e51622e8f1e7d9c9b27b2c...   \n",
+       "\n",
+       "                                             subtask  \\\n",
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+       "2      MutatorOneshotObject24,MutatorOneshotObject29   \n",
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+       "4      MutatorOneshotObject24,MutatorOneshotObject29   \n",
+       "...                                              ...   \n",
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+       "50937  MutatorOneshotObject09,MutatorOneshotObject58   \n",
+       "50938  MutatorOneshotObject09,MutatorOneshotObject58   \n",
+       "50939  MutatorOneshotObject09,MutatorOneshotObject58   \n",
+       "\n",
+       "                                                image_id      trial_type  \\\n",
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+       "1                    MutatorOneshotObject24-original.png         support   \n",
+       "2                    MutatorOneshotObject29-original.png         support   \n",
+       "3                    MutatorOneshotObject29-original.png         support   \n",
+       "4                    MutatorOneshotObject29-original.png         support   \n",
+       "...                                                  ...             ...   \n",
+       "50935  MutatorOneshotObject58-outplanerotation-135.0-...  generalization   \n",
+       "50936          MutatorOneshotObject09-contrast-0.8-0.png  generalization   \n",
+       "50937           MutatorOneshotObject09-noise-0.375-0.png  generalization   \n",
+       "50938           MutatorOneshotObject58-blur-0.0625-0.png  generalization   \n",
+       "50939                MutatorOneshotObject09-original.png           catch   \n",
+       "\n",
+       "       stimulus_duration_msec  reaction_time_msec  timed_out   perf  \\\n",
+       "0                         200               728.0      False   True   \n",
+       "1                         200               663.0      False   True   \n",
+       "2                         200               211.0      False   True   \n",
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+       "\n",
+       "       timestamp_start  \n",
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+       "[50940 rows x 12 columns]"
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+       "      <td>True</td>\n",
+       "      <td>1623768770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3018Q3ZVOJ00HY15URPTWYSD0NIRAX</td>\n",
+       "      <td>1</td>\n",
+       "      <td>009362951d33e90bceaaf84f4d4373c58ffc464f60ec34...</td>\n",
+       "      <td>MutatorOneshotObject24,MutatorOneshotObject29</td>\n",
+       "      <td>MutatorOneshotObject29-original.png</td>\n",
+       "      <td>support</td>\n",
+       "      <td>200</td>\n",
+       "      <td>211.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1623768772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3018Q3ZVOJ00HY15URPTWYSD0NIRAX</td>\n",
+       "      <td>1</td>\n",
+       "      <td>009362951d33e90bceaaf84f4d4373c58ffc464f60ec34...</td>\n",
+       "      <td>MutatorOneshotObject24,MutatorOneshotObject29</td>\n",
+       "      <td>MutatorOneshotObject29-original.png</td>\n",
+       "      <td>support</td>\n",
+       "      <td>200</td>\n",
+       "      <td>298.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1623768773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>3018Q3ZVOJ00HY15URPTWYSD0NIRAX</td>\n",
+       "      <td>1</td>\n",
+       "      <td>009362951d33e90bceaaf84f4d4373c58ffc464f60ec34...</td>\n",
+       "      <td>MutatorOneshotObject24,MutatorOneshotObject29</td>\n",
+       "      <td>MutatorOneshotObject29-original.png</td>\n",
+       "      <td>support</td>\n",
+       "      <td>200</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1623768775</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50935</th>\n",
+       "      <td>15</td>\n",
+       "      <td>3ZWFC4W1UVHVDPID57BLAZ34X3VRFA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>e43cc225dbf11ef57616e082e51622e8f1e7d9c9b27b2c...</td>\n",
+       "      <td>MutatorOneshotObject09,MutatorOneshotObject58</td>\n",
+       "      <td>MutatorOneshotObject58-outplanerotation-135.0-...</td>\n",
+       "      <td>generalization</td>\n",
+       "      <td>200</td>\n",
+       "      <td>653.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1623769458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50936</th>\n",
+       "      <td>16</td>\n",
+       "      <td>3ZWFC4W1UVHVDPID57BLAZ34X3VRFA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>e43cc225dbf11ef57616e082e51622e8f1e7d9c9b27b2c...</td>\n",
+       "      <td>MutatorOneshotObject09,MutatorOneshotObject58</td>\n",
+       "      <td>MutatorOneshotObject09-contrast-0.8-0.png</td>\n",
+       "      <td>generalization</td>\n",
+       "      <td>200</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1623769461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50937</th>\n",
+       "      <td>17</td>\n",
+       "      <td>3ZWFC4W1UVHVDPID57BLAZ34X3VRFA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>e43cc225dbf11ef57616e082e51622e8f1e7d9c9b27b2c...</td>\n",
+       "      <td>MutatorOneshotObject09,MutatorOneshotObject58</td>\n",
+       "      <td>MutatorOneshotObject09-noise-0.375-0.png</td>\n",
+       "      <td>generalization</td>\n",
+       "      <td>200</td>\n",
+       "      <td>601.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1623769463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50938</th>\n",
+       "      <td>18</td>\n",
+       "      <td>3ZWFC4W1UVHVDPID57BLAZ34X3VRFA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>e43cc225dbf11ef57616e082e51622e8f1e7d9c9b27b2c...</td>\n",
+       "      <td>MutatorOneshotObject09,MutatorOneshotObject58</td>\n",
+       "      <td>MutatorOneshotObject58-blur-0.0625-0.png</td>\n",
+       "      <td>generalization</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1535.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1623769464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50939</th>\n",
+       "      <td>19</td>\n",
+       "      <td>3ZWFC4W1UVHVDPID57BLAZ34X3VRFA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>e43cc225dbf11ef57616e082e51622e8f1e7d9c9b27b2c...</td>\n",
+       "      <td>MutatorOneshotObject09,MutatorOneshotObject58</td>\n",
+       "      <td>MutatorOneshotObject09-original.png</td>\n",
+       "      <td>catch</td>\n",
+       "      <td>200</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1623769468</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>50940 rows × 12 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 8
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/score_example_model.py b/examples/score_example_model.py
deleted file mode 100644
index ad53ef5..0000000
--- a/examples/score_example_model.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import hobj.learning_models.learning_model as lm
-import hobj.benchmarks.leedicarlo_benchmarks.MutatorHighVar.mut_highvar_benchmark as highvar_benchmark
-import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_benchmark as oneshot_benchmark
-
-dummy_model = lm.LearningModel(learner_id='dummylearner')
-benchmarks = [
-    highvar_benchmark.MutatorHighVarBenchmark(),
-    oneshot_benchmark.MutatorOneshotBenchmark()
-]
-
-force_recompute = False
-for benchmark in benchmarks:
-    result = benchmark.evaluate_model(
-        learner=dummy_model,
-        force_recompute=force_recompute
-    )
-    print(result)
diff --git a/examples/score_model.ipynb b/examples/score_model.ipynb
new file mode 100644
index 0000000..deb69bf
--- /dev/null
+++ b/examples/score_model.ipynb
@@ -0,0 +1,321 @@
+{
+ "cells": [
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:53:14.780221Z",
+     "start_time": "2026-03-27T03:53:14.450467Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import hobj"
+   ],
+   "id": "accb05e90bcf1ce9",
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:53:18.055705Z",
+     "start_time": "2026-03-27T03:53:14.783113Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Load benchmarks\n",
+    "from hobj import MutatorHighVarBenchmark, MutatorOneshotBenchmark\n",
+    "\n",
+    "benchmark1 = MutatorHighVarBenchmark()\n",
+    "benchmark2 = MutatorOneshotBenchmark()"
+   ],
+   "id": "714700a9832048e8",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Load model",
+   "id": "82e3c177a4b43961"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:53:18.102448Z",
+     "start_time": "2026-03-27T03:53:18.084732Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Loading the exmaple precomputed image features. You can replace this with your own features!\n",
+    "data = np.load('example_features.npz') # ResNet50/avgpool features, randomly downprojected to 100 dimensions\n",
+    "features = data['features'] # N x 100 feature matrix\n",
+    "image_ids = data['image_ids'] # N image ids\n",
+    "image_id_to_features = {image_id:f for (image_id, f) in zip(image_ids, features)}"
+   ],
+   "id": "bdc5bcb78d79e755",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:53:18.142566Z",
+     "start_time": "2026-03-27T03:53:18.104374Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "learner = hobj.create_linear_learner(\n",
+    "    image_id_to_features=image_id_to_features,\n",
+    "    update_rule_name='Square',\n",
+    "    alpha=1,\n",
+    ")\n",
+    "\n",
+    "# Alternatively, you can use a random guesser as a baseline:\n",
+    "# learner = hobj.RandomGuesser()"
+   ],
+   "id": "65327b245c715ea2",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:53:30.344682Z",
+     "start_time": "2026-03-27T03:53:18.143795Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Benchmark on Experiment 1\n",
+    "result1 = benchmark1.score_model(learner=learner, show_pbar=True)\n",
+    "result1"
+   ],
+   "id": "a5710bce79c8e484",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Subtask simulations:: 100%|██████████| 64/64 [00:09<00:00,  6.49it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "MutatorHighVarBenchmark.LearningCurveBenchmarkResult(msen=0.0055892710374308065, msen_sigma=0.0002920322833183841, msen_CI95=(0.005035330830028786, 0.006137235513358789), lapse_rate=<xarray.DataArray 'phat' ()> Size: 8B\n",
+       "array(0.07971531), model_statistics=<xarray.Dataset> Size: 103MB\n",
+       "Dimensions:           (subtask: 64, trial: 100, boot_iter: 1000)\n",
+       "Coordinates:\n",
+       "  * subtask           (subtask) <U33 8kB 'MutatorObject000,MutatorObject095' ...\n",
+       "Dimensions without coordinates: trial, boot_iter\n",
+       "Data variables:\n",
+       "    phat              (subtask, trial) float64 51kB 0.496 0.684 ... 0.928 0.918\n",
+       "    varhat_phat       (subtask, trial) float64 51kB 0.000501 ... 0.0001509\n",
+       "    boot_phat         (boot_iter, subtask, trial) float64 51MB 0.47 ... 0.918\n",
+       "    boot_varhat_phat  (boot_iter, subtask, trial) float64 51MB 0.0004992 ... ...)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:53:30.445355Z",
+     "start_time": "2026-03-27T03:53:30.353255Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Plot model behavior\n",
+    "print(f\"MSEn 95% confidence interval: [{result1.msen_CI95[0]:.3f}, {result1.msen_CI95[1]:.3f}]\")\n",
+    "plt.plot(result1.model_statistics.phat.mean('subtask'), label = 'model')\n",
+    "plt.plot(benchmark1.target_statistics.phat.mean('subtask'), label = 'human')\n",
+    "plt.ylim([0.4, 1])\n",
+    "plt.axhline(0.5, ls = ':', color = 'gray')\n",
+    "plt.legend()\n",
+    "plt.xlabel('trial')\n",
+    "plt.ylabel('p(correct)')\n",
+    "plt.show()\n"
+   ],
+   "id": "346a1c6cb7ffb288",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSEn 95% confidence interval: [0.005, 0.006]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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XDx/Ti0eP6eXDJ+L4n99/QKG375K+am4Q3AAAACjxqFqJBi6eQ7YFC1Dky3B6ducexUfHUNzbaLKyySdqPdgt/9O0cewkRU1LyRpVqf2oLxSBCNewnN62m85s200RYSm5pP815bhT9Y9aUp2PO1A+h5SR8xPj48nCykrtd8H5p/fOB9HVw/+SY7EiVKVFEyrgqjqUCr8HBymPrlynfPZ2VKJaZSpeqTxZ2dgotuGanuvH/enivoNkbmVJNdu1oXI+3iIIUsa1Oo+v3RDNS1xjw72ZiZf3n+sFBzMPHtHLR49FueQEBDdaKhwAAMg51vZ2oqaDAwlNFCpWlOr37kaVmzcWF3AzC3MyMzcnMwsLSk5MpLevXotaBXH78pUIRq4eOS6eS0/lFk2oz4zJZGFtJS7uq74YI/ahjIOS7pPHi0DnVUgo7Z63hGq1b0MVmzYUz8fHxIogwrZAfvH4XXIy3T17UQQTXBNkX8hRZX8cJJzc9Ced3bmPTExNqXjF8uReqQK5V6kggp3r/56kSwePUtTLcJXX8XZVWjahcj51RHMTf3Z1YiOj6MHlq3Tl0FG67HdMPFbGx1Pto5ZUqmY1en7vAd09d4EeBF2hhFjV6Y70DcGNlgoHACAvs7azpcIlPOjVk6dq80C0gWsEytatRT6+H1PFJg3EBZprBZ7evE1Pb9yikNt3xEU95k2kWBJiYqlM7ZqiGad84/pkapq54do40Dm3ez+d/nsXPb//UKzL5+BABVwLU/mG9emjLz8X+7x27CT9/u336V7gXcuWpv4LZojkWVlyUpKoqTm4fJUIIDjoqtO1I5WtUyvN67lG6Mm1m3Rqy99062RgSl5LNnAuULEKXuRRpSIVr1RBBIgPgi7Tg0tXRcCS3f0bAgQ3WiocAIC8hJs7yvnUFhdI98oVybV0ScVz4U9C6Mn1m4ol5NYdEShkNpDh2hlu7rErUEA0m9Tt3lklQPgQrgVRbkK5cTKA/DdvE80j3JTCtTIcZJhbWpJdIUdRK2HnWJAKlyguck4KuDgrXsu1LrYFCogaGGWnNv9NO2YuEO/1oeCv57TvRRBz+dBR2rfoF1ELkxr3IvKsV0eUFx/ny0dPNK6dgv8guMkAghsAgLR/9bccOoAaf9pLNO0o4yaZ1M0oyrUPobeCKeT2XZGXwvkZYfceUEJsrHiec0LK1qlJZWrXEk0e+V0Kq206iY16S+d27aOArdvF+xUt70nFypcTt9yDyDZ/frIp4KDIR4mPiaGzO/bSyU1/qQ0m0sNBkWf9ulS3W0cq37CeyrFw4MEJuae376ZTm/7K1CnCOTQIVnQPwY2WCgcAQFtMzc3ExTQxLl79b1MhR6rfqxvZFSpIt/3P0K1Tp8VFPMN9mpmJWhBeuOkooxwJbrLgwCX09h2V7fgi//F3Y0TXX3b3/EW6dy6IHl6+Ro+uXKPo1xGihqKoVzmR48EJqkU8y4raiPSahLhGhHNnOCdGHU5Affv6NUU8e04X9h4Qya2a5HdwHgwHOtFv3qRbjpqydyok8l8in7+kN89fZLpLNeQ8BDdaKhwAgOziAISDltbDB5GFlSVd//cUnd9zgG6c8BdNKNxM0nTAJ1Tn447i4i3ji23w6XNie+4pk9+5sGJxKFxIXJw5qJEDjJjISAr8c4eozZC7BHMzECe5NhvYVzQ1MW5qef7gkchn4RqHSk0bKQKSbT/NoxvHT2n0ufhYXcuUpiKeZahIuTLkUqokuZQpqTLuCTcPcVLundPn6c7ZC6JWh4OljBJ6AdKD4CYDCG4AIKeUqlWdPp4wWu3YIpwc++DSFZHjYm5hIdbx40eXr1P5RvU0zkPhYIVrPbh2hXHeycV/DoneLg16dSPXMqXEeq7piH37Ns2gaxyAHN+whQ4uX6mV3jE2+R3EeC0W1tb08PJVNNeA1iC40VLhAACkxt17eeC116HP0i2cYhU8qXG/3lSjbSvxmGsr9v68nB5fvSHWcWIr55/IuIaGB4XjQd9kHCBUbNpI5KxwYMJNJ/LCTSmc7xIVHk7Rr9/w0LFUoXF98Z48gFzqfBZOkD2xcQu9DX8tanyKli9Hxcp7ikRb7t3DTVUAhg7BjZYKBwDyVlKtW7kyVKh4Ucpnby8GQePB1WwcHCi/sxMVcHMVCbJyzxoOLuRh+HlwNW4uqtSskejOLA+uxs1JnCT7z5IVoqZGxmOZcBBSonoV0WTDXXa1hXNrGvftSc4lS9Clg4fJf8u2HBtkDUCXENxoqXAAwHhwQMG1FvaOBUWNha1jAXJwchKDn3EthnNJjzSjtKaHm35S9ypSxonAN08G0uGV6+jpjdta/BQAhm/MmC60detJevQo7XQQ2YGJMwHA6MgT+XEeC/fC4UTb09t2pRltNXX+h1eDuqJHEHcBlkeMTQ/XxoTduS9648RGRVFc5FvRrBP54gW9Dg0Ty5v3w+i7V65Apb1rUOma1cV4LZzPcu3YCbp25IRoZkLvG8iLpk37lCZ850vDhrejypW+oJiY7PVqyyrMLQUABou7RbceNkjkk6SeR0ce5p7HRzn5x59ivJPCJdxFvkvKSK2VRACiXBvDybOc/8Jjmrx9f8s9eJ5ev0VPbtxKM7y9prhXkjGMAAuQHZMn96YfJvUS978c8SstWbKHtAnNUloqHADQDw4Wan/cQUxCyDkvyhP5cY5L5IuXYlh77oKs3BSkPEGgjIfv55mTues1Tyr4oVFnASDzvv++J02Z2kfcH/X1Slq4cCdpG5qlACBHcS4LNwHxCLWa4MHsyvp4U4VG9cVMyM+C74ogJOzeQypUrAh1/2GsmGGZPb5+k/Yv+pXuXwhS6arMtTVijqFPfKlC4wYisOHnQ24FK6YICD59XsxgDGBoatcuR/nz29KhQxcptxs/vrsisPlmzGqdBDaZlX5GHACABnheHZ5fh8dZuXr0OO1buFw09aTGMzXzBInVWjcXA8vJNTLKuFaFm3c4+OGamP2LV4ih8NOrbeGu07xwt2prOzvRNIWaGcgqa2tLio9P1HkTY/nyxenYvzPE+9Wv9w0FBNyk3MjS0lzU2Hw3sYd4PG7sWpo3bzsZAuTcAOQRPr5dyKd7Z7qw5wAF/Lkjw6H9uVmIZz3m2YxL16omclt4ckIeZE7GuSxtvxwqRtdVxsEFz/vzz7KVYvRcr4Y+VL5BXSpb11ul2YjHa7ly+F+xPQ9yx01MPOIuu3b0BG2bPg+1LpBjqlYtSSdOzqK///anz/ov1Nn7mJub0Sn/OeTtXVY89ve/QQ3qf0u5TevWNejnRUOoXLmUKTYmfreBpk/fqtP3RM6NlgoHwFhwTUn/hTP/G6r/TSSd+H0LnfjjL4qNjEyZO6i8JxWv4EXuVSpSGe8aikBDGQc3/67fTA8uXqY+MyeLZiF2bO0fdHbXPmrzxWBRk5Ned2kR0Pgdo0sHj9D9i5fF/EOpm7d4HBmeNRnynlq1ytLLl5H04EHONyXu3PU9dehQW4xN5FluKN29G6qT95k4sQdN/fETev36raj5sLW1pm5dZ9C2bf5kSExMTGjmzH5UpWpJun7tEV258oCuXn1Eb9/G0vQZ/ahz57piu9DQVzRm9GratOlfnR8TghstFQ6AMeBxXEZsWCFqTa4dO0nOJdxFryIWFx0tRq3lCQRT45odHpzuzpkL4jU1O7Qhc0tL8RzXtnDNDb9+y/c/0eVDRxWvK1G1skgE5pwZ3o4nYORk3psnAyjkZnCe7VU0eHBr0eSxfv2RLL2+TBk38vVtSN19G1CBArbUvt0UunZN8xmxDV316qXpzNl5dPfuM/LyHJrj733+wn+1NQsX7KRRo1bqpHbozNn5ZGFhTn16zyUvr2L0/Q89KTg4hCpV/IISE5PIUCxfPpw+H/pRus/zsS76eTdNnbqJoqJSZoHXNQQ3WiocgNyOB6v7atMqciziRrcDz9Jvw74m6Z1EVVo2peaD+oqZnmXhT0LeJ+LeonvnLtKja9fpXVKySrfs+j27Uf0eH4tanWd379O6r8fT8/sP1b43D4oXFf5a1AzldZ991oJWrf5K3K9YYTjduPFY4yaMESPaU59PmlKNGqrzU3HtRt06Y+j584gP7sfKyoLGju0qAoeNG4+RIVq16kv6bEBLcd+9+Gf05MlLre7fzMyUkpNVawpl27Z/J2oi+HvhfJg3b6KpeLHPRC2FtnAtzekz80WAw01f3bvNIDu7fBR851dycSmok67TWTV79mc05puPKTk5mab/9Cc5OOSjSpVLUOXKHuTsXIAOH74kjlfT81hf128pLy329vYS41t9HwsWlIG6c8DUzEwr54aZhYX0v3W/SPOuBEjjdm+R8jmkPedL1awmlfPxlmzyO2i8X3MrK6lsXW/JMp81zmENyqtKlRJSdMxf0jtpt1gWL/5c43LjbeXXJSTukPbtnywNHtxaunX7V7HulP8cycrKIsN9FChgKx09NkOxn2nTPjW4761gQTuVMvr443rpbmtqaiqZmZlmav9duvhIryM2qy37qlVLivdMSt4peXkVk67fWC4eDx/eVqN9W1tbSvXrV/jgMXG5836fhW2QChfOr1j/+edtxPrnLzZKDg422S5LPh/mzh0g/fBDT8nJSfP/1/Ly3Xe+iu/hs89aqP28ueT6rZ+DzCWFgwVlkGPnQKla1aX+C2dKsy+ekPrMnCzlc/jwD5O1na1UoloVyce3i9Tmf0OklkMHSE0HfCI1/KSH1G/BDBHYTDt1UHIu6YFz+X2ZmZiYSA0aVJB+/nmIdOnyYmn9hlFSnTqeOikfvljJgci168vE7ZvILZK9fb4PvpYv8PJF5uuvO0mFCv13PpQrV1QKf7VJPPf7xjHp7qN48cLSlatLxXZvo/8LHpYtGyaCBG19zqZNq0h2dh/+TOkt/PnkY+Nl1qz+areztDQXwcer15uk6dP7Si4uBT647woV3KXIqK2KffNFX/n5rX+OUylHDmr48Y2by8W5ktG+uXyDLi0S2587v1CqUaN0mm24nPv2bSYlJu0Q23Ggpfw8B0XyucGfKbvfxZq1IxWflQNGDuhKlHDR6LUjRnRQvParrzoa3G8GghvtFQ4WlIFOzwGuXanZ4SPp6y1rRSCivPxweJfk1TDVD6GFhVSlVTMRuHz3z7Y0r0m9cKBUzqc2zmMiqXz54iKgefxkrcqFVF7OnJ0v9e/fXKt/mcoXzvsPVongRL6IffFFuwxfxxcjvoDztjNm9FO7TbNmVaT4hO1im++/V71gyzVG8md99HiNVKmSh6j14RoKXrfxjzGShYV5tj/jqFGdxf4CAudmaX8cQMgB4MlTs8XtkaPT1W7bqFElle8sJvZv6ZdfvpDKlHFLN7i8eesXsW3wnRWK1/Xr11w8z2Ui19pwEMTrOEjjWh5e37p1jXSP29u7rBQSul7leDiA4VoTW1tr8bl8fRtIV6+lBJe8cCCtbl8dOtRWBCNNmlSWqlcvrViUg9oPLSNHdlLU8p2/sFCl1o+Dt7Jli6Rb2yPXLPEycWKPXH/9RldwAD3xqFpJ9DjieZIYD0B3bvd+uu1/mtp+NUzkrLDAv3aKOZSqf9SKarZvnaYXEw9SFxp8l149DRWTQ5pbWojEX+4ZdW73P3Tj+CnK6zjf4tHjNeTm5igeR0S8pR07TtPBAxeoZctq1Kt3YzHmCIuKiqGzZ4Pp3NlgOnMmWNx//Dj9CQALFrSjoUM/Eq8/c+a2WF68eENfftmBFv48hBISEqlRw3Fi/fDhbWnJ0mF08+YTqlB+mNr9cbLp8RMzqU4dT9FNuEnj8ZSklPukbNCgVrTitxHiPufSxMUmiPsmJkRdu9UXg8RdvfqQ2n40WZHD0r17A9rw+yiytLSgffvOUe9ecygyMv1hATJStGghunFzucgdYYsX7aavvlqRqX20bFmdDhycKvJcWrf6gQJPzxO5LgXy9xQ9l5RNmtSLJk3uTadP36J37yTy8fES63m71asO0YQJ60Vvq5QyMKFt2ydQp051xQSOtWqOpJEjO4l5jzgZtn27qTRgYEvq0aOhmOSxZ49ZiveZP38Qjfy6kygfTtxOrWvXerR+wyjKl8+KLl++TwMHLBLb9+nTRJEPxUm2lSuXEI9fvYqi+fN2iDFgOKlcnaPHZlDjxpXSrI+NjaePu0ynAwcufLAc9+2fRGZmZvTVlyto8eLd1KRJZfp2bFdq0yalVyOfRyt+/YemTNkkzlHG7/nriv8punTPmf03jR27lgwRcm60FPlhQRno4hwwMTWVWnz+mahVkWtomg3sq5L3YmFtJXX89iu1tTHf++2UPhrxuciXUZdHY+wL55BwDcuHck2UF/5rmP8ifRn+h9S2bS3RvKH8PP91/M03H0v37q9UW6vDTQ9c62FjY6V4Db//6NFdFM1DygvvR65V+d//2itew7UCEW+2iPXNm1dVe6yzZ38mnuf9ursX/uBnmzNngNpjlmtA8ue3TfMarpGQm6nCnv8ujtHcXH2uV758/33m1MuWrWPT1Ip0794gU9/ntu3fiddxrRo34XCzHT/mWpXU2/Ln4eeGDGkjHjdsWFHatfsHxXtzmXGzEjf1yLkjXLtTs2YZxT649kRuHpRrsVK/V+nSbornlGs7uCy4lkx+P35v5eY4Lte79/47h7j2jbfXJJeGj4GbEB8+Wi1q2njhPBz5WKtVK5Xua/l45fNw1aov1eYV7dg5UXFcfA5OmOAr/fbbCMW6J0/XSp0719X7/++MFjRLaa9wsKAMtHoOFHBxloatXqoIVDi3hvNm0v3RqlVdmrD/b2nWheNS33k/SV4N6orgyFi+F/7RVXfxzWjhpFr+MV6yZKjGr1m0aEi6P/zKC19cK1cuIQ0Y0FJavny4yKPgKn3li+e8eQOlgQNbqQRCHPysXjNSXJzkiyIvmzZ/m+6x8EU99XMffVRT8dpOnTS70PAx9+jRUBo/vrvKwseYOohTXjjPSG4m44WbbzgfhJNQOUDhzy83Fy1cODhN/kmrVtUVTR5cZpwvIl+IOSdIk2PnnBU5F4WTeXmd3+Fp4jEfv/K23FwYG7ctTcDBCyf0Xrj4s+Kz8OeSv4fUSbHcdCa/By9//jVe7bHt3PW9IugqWdJFBJEcHMuvW7BgkNq8JQ6AOaDh4CGz53bqhY/1kN80RfDB5ZV6G87fkpu+OME8o++8ceNKovk1dRC8dOkwrSQz63pBcKO9wsGCMsjSOVCsgpfUaexIkSA8cOlcacivC6Vhq5ZIP546IIKanwL9RK6NJvsyNTeTrGyy/8PDtR18AZAvIvpcOPFS/tF+8HC1VKqUq0av69ixjuIHmWtG+KLzodfwRZn/CubXtGvnnaWaIk54vR2ccqFXXni/nL+hfJHjiw3XFH36aVO1tUtc/nJ+hoeHs2J9nz5NpKi3fyouqDnxPXANB/fWCX2mmjuibuEaD7l2hz+XHPhwsCfvS65Z4UTtjGp85OXHHz8R2/O5IK/jHCNe9+uvX6itfeOLvLp98XcwbFhblQCEAzR123LQcTFokajV4cBM3TYtWlRTnGfKASvXUsk5OzmxcNDB5cnvffnKEkXAxEEMB+Gc+Cyfi66uBTX6/9CrV2PxObgM6tUrn2OfJbsLghvtFQ4WlIHG54BtgfxSwz6+0ui/N2SY5PvVplVSoeKaBxicOKquS2Zm/wKUL2Dbd6StMciphRNlObEx9YWTE18/9Nc+/+UuV/nLQcDadV9/8D058VOuUchMU5a6i0KbNjWl3Xt+EBeUsWO7ZTn5+OChH8Ux8YWcj4kvwnJZ7P9nSoZ/feti4aaVqVP7KJqquCZq/vxBUvv23iIolmuvuDaDP7PcNMOBhnLPL764ykm2XJOl6Tmp3PWbm0Z4HV94lbefPLm3WL/h99EZ7pebGDng4tqIjMqRy93ZOePeVnJPM1727pssmjS12ctM06VYMSdFcjjXOo0Z00UlMf7Fy41SrVplc/y4KIcXBDfaKxwsKAONxqVp+9Uw0XQkBzAzzx0TTU4+3btItTu3l2q0by1Va91c8qxXRzIzN8/Uj5o8/gdfaLL6fXTrVl8lmMio/f5DS+3a5cQFnv/iz8wPPfeqiYtPaVbgv4TXrR8lAg/+a5TX8UVR7rGibpEvqPwXqtxrhvdTsWL6r1GuCfhj0zcG8/+Zm5zki9LZcwsUn4Uv4Pq4eMoL9/JR1zuHa7zk85B7NHGNB9/n3kDqmj7kpia+EKfXQ4drtuTAVjnfp0gRR0XNlnKO07F/U8bqGTRItblKlwvXsnEgkV5vrJxuwpXzkZRrDvn/VXa64VMuWhDcaK9wsOi4DHj8B849UB7UypAWzodpNvBTqev330rulSukPZ8KOark0Izcskaq1+NjrSX6Ko9ZEXh6Xpb3c+DgVJUaj7/+Vp9nwEmzPMgY10qoe56DCuUxQ7iZi8eM+dD7c06I/Jp/DkxVCa44x0POl+DkVu7CnPr13HwjX1zlC6rczVpd7oryIncFVnch1tfCTTjcPVwuE04c5RwWfR9XRgt/93IyNC98TqW3LTeXyLVAHAhxci/X1PDn5poZueZK3bgzvMjNiJwwzI+5iUvOtzGEQENfS8uW1cX/P84p4ho1bXTlp1y0ILjRXuFg0XEZyFXYK1b8z6DKmoOTVsMGKnJk5GXA4jlSUa9yYpuS1auInk5yDk3V1s21/pea3NYv/6Wc0bgbGfWkkGsGuFlF3mfqXAPOg5FrVnjhnjD8l7z8PPfukS9Yp8/MV+klxE0F/Be3uvfnsTrk13FPoPRGqOV98jacM8GDiSn/1c5Jn/zc4SM/qfxVLdcQcG2Suv1yTRA/zxdGQ/vrlvND5CRQrqHT9/FosvB3ycEvjwPzoWZEzofiJjblJF/Or5If83fH55i670X+vrk3mjxIoFxToe8y0PeS002WZEALghvtFQ4WHZYBX1zlHzq+4Kr7i13XC08hwCMDV2zaUCT4NujdTXTB/inATxHQfLPjD6nPrCnSnKCTinVDf1sszb6Q0pX7m+0bdTICsFzbwoOt8cBg8oUws/uZObOfImeAH3PzDD/evGWsYhsOYuQaDu4hJAc5nMjIwRH34pEDLG6S4nwFrnHhAdTkYImrzL/8soNKswrnNHDXVn5+z95JGTa5cOKkPIib3GTDzTRyrQ/nfaTusrtq9VdpElKVFx6MTM4VMcT/z1y2mZ1KQN8L16JkZlj/3r0bi4BI/l65du6nnz7NsJv7t992VQTY/JjzgeSkZn1/fiyktzJAcKO9wsGiwzLgv8qU24/Tu0DpanFyLyZNPLg93cRfTgzm0YDlLrBOHsWl3jMmST9fOSldCj8oHX56RAQ9lvk0qxHg7qqcZ8Ij5X5oW66hkWscOAmXh5mXm2XSGx9F3cLV1vKFRe5azDkqckAp57isXJky3gUHIlyLUreup0gWlcfqkIMdbgJK/Zcj1/hw0KXcfMa1TrzdiZOzxDoeMl+TbrEcNHEuj7qeSdwdOfX2fIGUmyvUlYs8SitX4eP/s/5+zxwd7UXOFPcI06TmgfN2+Hvjpjt+/O/xmeIxN3fhe8y71yV7zC2ltcLBosMykKus+aIlX6Cy0lU3KwvXtMhNSlP+3Sd9uXGl6K7NY8l0mzRWqtSsUbrzyvy27r/ePjwIliZ/efv4eCnyXbiGRLm5J/XCtRty10+usZHX87gavI5/6FNvz2Nq/L1tQpq/hnm8ErlXi/JxyvkqXCskb8PBDudVyNu4uTmq1KRwTU96A71xWXFQIg9bz7Us8ngaHBxpOu6J8mfq2rWeoqnqaci6dIMjPn94Gw5klHvucFAoN39kZgh7LPovA26qkmsEuXlLDq41HTIACxllGSC40V7hYNFRGfBf6HJNBNdkyE0n/Bd+ehfQ7C78Vx83dXBwMvXfPSKwGbPtd8mu0IfHhpAX7m4p/+jK+R7cTKOcH6IuT0GeJ0heuKYkve252zdvwzktXIuiHGzITUM85ofc7COPFyPXvHh6/tfNXE7c5Gp95ffgJkA5oJGTRHnMkdTHwn9lT5rUSyyaBHF8jPKotcp5Ptn53jj5OL18HrkM5KCKBzOTE07lyRi5xw7+L+e+3zI5wOcxf+RzW9/HhIX0WgYIbrRXOFh0VAbchCB3A5XzLeTmkw9NKpiVRZ7pV9GF8u1+6Yed68XYNJrugy/unI8it/3zZHdyoiw3xajr8cWBmzyEOndlVU7o5ZqJ1NtzkCSPX8FdPFM/zzP8yom13EtJbjriHhRyUw7nNHBTkXIisbr8Bq7pkcuDm5W0GVRyt/Wjx2Zke3yezASdcg8bDgq5We/4iVk6O5+w6L4M5KZS+f+YJmMaYTHuMrBHs5TWCgeLjspg1qz+4geLE0Llddyswes4GOBRYbOzf66NKVO7phhjZumW/2o2rr3YK8Um7Xnf82KtCAIyO+MuXzzlwb84N0UOXu7c/U30BuLRS3n8Fk5+lYMPbl6Rm0zk2Xd5P8q9ZLiGQm7K4cHq1OUm8PZyFb1cc8QDjXHPIW56kZtxuDaGE3jlRN70apR4H7ytJiP9GvrCeUlyMxp/LjmILFq0kN6PDUvmy4CHJVD+gySnAmUsZLBlgFnBtTirKOjGhYs/U7VqpcSMxJs3H1fM3Bx0aTFVrOhOFy/epXv3wigmJp5iY+LFrMyLF+9Jd/Zi1zKlyKd7Z3IrV4ZcS5dUzJxdyymGGrqmvObMi3x0KsyGYu7foO5eSVSubBGKi0ugwYMWixmVM1KsmBNdv7FMzH48ZPBiWrnyoOI5nk13/z9TqGRJF7WvvXLlATVtMkHMDMzMzc3oxMlZYtbnY8euUIf2U+mHH3rS16M6i+dev35L3bvNoCNHLqvd3/Llw+nzoR+J+7//fpSGfr5UlBOzt89HO3ZOpKZNqyi279xpGu3adVrtvvgY+P1u335KxsDS0pyWLh1GAwe1Eo959mifumP0fViQBVWrlqSLQYsUj0uXGkT374ehLPMw+0xev/UejRlq5IdFN2XAtR7yX2Opm3LkyfjULdzzJnVui5WtjdTxmy8VM2ynLP7S2lv/SifvpczKzMuGffOk+r26SRWbNJDMLCxEM5g8mzD/lf+hgcHkJhxu6lCXaMy5MTxTMU+KyDke8vg9nEOkbr4XToyURxtVzsfhiRa5BiKjY+FuuDy4X3p/yfLw+PIMwNxUk9u6GmtrDBnO0cjsDNVYDKcM+LyVm6TkXlNY8nYZ2KNZSmuFg0UHZcDjXvAPFuevpNdlmkcuHjr0I5F3wsms8oBxnCArzw9UvW0radKR3SKgWXDFX1q4a4n01/6Z0pOQ/8bU4IV7Eql7Hw5S5C6m//tf+3SPl3Nr5An0PjTUv/LCTWsZdXuVh5+XkyWzM71C6oXzZ7iZLzPNblhQBoZ2Dsj/PzmY1/exYKFcdf02z5G6JAAlLVpWF7d+hy6qLZdTp66LRdk//1ygQ34/UosW1cjPfxFtu21JxSpVEOdx4binVN/5LZXs4KHYnptpDh++ROvXHaa///ZX+z6SJNHePWepYcOK1LxFVVqyZI/a7SZP6S1u58/bQdeuPdL4u4yIiM7w+Q0bjpKjoz0VKGBL8+btoLdvY0lbkpKS6ddf/9Ha/gD0Yf26I+TtXZbWrvHDFwCZYvI+yskzkHOjf0+erqUiRQpRi+bfqc0rsbC2IscibmTnWJDsCzmSXSFHci7pQW071ae+Nc3J3JToRoQVnQ81pWoWj6lSGSfxuufPI2jrlhO0d+85kcsSH5/4wWOpUaM0nTu/UOTyFHLsRcnJ71Sed3cvTA8erqbk5GRycf5UkTcDAACGe/1GzQ3kKE4W5sCGa1ZOnbqR5vliFbxoyC8LFAnByt4Q0c670dS5VAyVLxBP5cUmTmJf8+dtp9mzt2W69iMo6D6Fh0dSoUIO4i/EwMBbKs+3a+ctbv39byKwAQDIJRDcQI5q+b5J6vjxq2lqVrimpv/CGSKwiXsbTZEvXlJU+CuxvHn+goIDz9GdM+fo4061aeMfKT1guLr6hx82UkjIqywdz7t370TtUffuDcSxpQ5u2neoLW737D6TxU8MAAA5DcEN5KiWrVKCm0MHVfNtTM3M6JPZU6mgmys9v/+Qfu49UAQ46mzZcoKuX39MsbHxdOdOaLaPye9QkAhuOO/mxx83K9bb2lpTs2YpXar37Dmb7fcBAICcgeAGcnQMksaNK4n7B1MFN+1GDqeydWpRfEwMrR05Lt3ARnnsGG3x8wsStz4+XiKgiY6OE485ednKyoLu3g2lGzcea+39AABAt0x1vH8Akc8yeHBrOnDwR7KxsaKQkHCVXkfVWjenJv1TeiRtnjiNwu5pL3DRBA8Mdu/eM7KwMKdGjSoq1rdvn5Jvwz2qAAAg90DNDWiFiYkJtWpVnYoXdxK1HxzE8G3NWmWoefOqYuRd2QqlLsquZUuT79TvxP0jq9bT5UNH9fKNHPYLolJD2ojamv37z4vP0+59cLMb+TYAALmOXgflGT58uHT//n0pNjZWCgwMlLy9Mx7I7KuvvpJu3rwpxcTESI8ePZLmz58vWVlZ6WQQoLy48IjBPEHdkiVD1U4Emd4iT+iY3nL23ALpm28+lkqU+G8OI+9ObaVp/ofEIHyfr/hZMjXTzWzgmiw8ki0fZ9ClRYqJGOU5miws0h+IDwvKAOcAzgGcA5QjZZBrBvHz9fWl+fPn09ChQ+n06dM0cuRIOnDgAHl6etKLFy/SbN+rVy+aOXMmDRgwgPz9/alcuXK0du1aMRjb6NGj9fIZjEn9+hVo85ZvqWjRQuJx7z6N6YfvN9Ly5fvSjP+irGfPRvTF/9orEm95zBieD4pzV54+Daft2wNUEn/tnQpR9x/GUsWmDcXjB5eu0O/f/kDvkpNJX+TxdqpUKUkuLgWow/teUgcOXKDExCS9HRcAAGSN3qJOrqlZvHixynD4T548kcaOHZtO7cBiyc/PT2Xd3LlzpRMnTugk8stLy+jRXaSExJRZpq9dXyZqWuRaF67NaNiwotrX8WzUkVFbxXY//vhJhu/B32/1j1pKU0/8I2prZp3/V2o28FO91tgoLzwdBH+OXr0aK+7zNBD6Pi4sKAOcAzgHcA5Q7phbysLCQkpMTJQ6deqksn7t2rXSjh071L6mV69e0uvXrxVNVyVLlpSuX78ujR8/Pt33sbS0FAUhL0WKFEFwo1Q++fPbStu2f/ffBJO/j5Zsba0lU1NTMRHki5cbVZ5zc3NUvJYnsbxydal4jieL5Neo+w7yuxSWWgzpL03Y/5dicsuvt6yVXMuUMqj/sLNm9Ref5Z8DU8VtUvJOMUmlvo8LC8oA5wDOAZwDlDuCGzc3N3GQdevWTXWBmSVqdNJ73YgRI6T4+HgpISFBvH7ZsmUZvs+kSZMkdVBzQyIYOXJ0uriQx8ZtExMtpi4/R0d7afny4eJCz9vxTNZjxnQReSicm8PrnoasUzuTdckaVaWBS+dKc4JOKoIazrFpOXSAZGpuGLU1ykvLlqozkp88NVvvx4QFZYBzAOcAzgEy7uCmcePGUmhoqDRw4ECpUqVKUufOnaWHDx9KEydOTPd9UHOT/nfAs23LAQsn0Gb0fVWvXlo65T9HceF/8nStuE1M2iE1alQpzfaVmjWWZl84oQhqhq9ZJtVs30aysNY8+Tunl3z5rESQJ3/GceO66f2YsKAMcA7gHMA5QMbdLHX8+HFp9mzVv6b79OkjRUdHi3wOTd4XOTcp5dC4cSURmPBFvHfvxhqVHZcx56CEPluvCADGjk0bAFRp1UyafTElsOk3f7rk5FE81/zn5OY1+bNVrOiu9+PBgjLAOYBzAOcAZfr6rbdB/BITE+n8+fPUvHlzxToeW4QfBwQEqH2NjY2NmAtIGc/WLL8WNOPk5EC/bxxDZmZmtGb1Ifrjj381eh33Slu//gh5lhtKP07dTJMnbaTZs/9W2ab6Ry3pk1lTyMzcnM7t3k8bvvmeXj7MPaP7Hva7JG4fPAhTGWgQAAByF71Fhb6+vmJ8m759+0peXl7SL7/8Ir169UpydnYWz69bt06aPn26Sv7MmzdvpB49ekglSpSQWrRoIQUHB0ubN2/W+D3zes0N177s3vODolcUJwVra9/c7CTn1/SY+p1kkk6CsSEvnDt0yG+a6DGl72PBgjLAOYBzAOcA5b5xbrZu3UqFCxemqVOnkqurKwUFBVGbNm3o+fPn4nl3d3eVmppp06aJ2gO+LVq0qBgLZ/fu3fTddykj3IKq2bM/o4+71qPbt5/StauP6OrVh1S2bBFq186b4uISqGeP2RQTE6+VYvPu1FaMNGxqakoBf+6gv3+cLb6r3CYsLIJatpio78MAAIBs4Lac3HcFygZ7e3uKjIwkBwcHioqKImO1aNEQ+t+IDuk+P2zoUvpVaRqE7KjduT11nzJeBDanNv9N26fPy5WBDQAAGMf1G3NLGaEFCwaJwIZrvcZ+u5bevImmSpU8qGIlD/L0LComgtRFYHNy018isAEAANAnBDdGZu7cAfTVyE7i/pDBS2j16kM6ey+VwOaPP2n7jPk6ey8AAABNIbgxEhxgzJzZj0aN7iIeDxm8WGeBjWU+a6rbvTN1GD1CvO+JjVtpx8wFOnkvAACAzEJwk8sVLGhHgwa1omHD21KJEi6KfJqVKw9q9X1MTE2pTO2aVLN9G6rcojFZ29qK9QhsAADA0CC4yaUqVy5BI0a0pz6fNKF8+azEuvDwSPr2mzW0Zo2fVt+rbF1v6jltIhVwcVase/n4iUgePr5+s1bfCwAAILsQ3OQi5uZm1KWLD33xv3bUqFElxfqLF+/SksV7aNOm46KLtza5lCpB/RfMIGs7W4p5E0lBBw7T+d3/0IOgy1p9HwAAAG1BcJNLgpqxY7uKpqciRQqJdYmJSbRtW4AIak6duq6T97XJ70ADlswRgc3dcxdpxecjKSlBu8ETAACAtiG4yQUmTOhOk6f0EfdDQ1/Ril//oRUrDoj7umJqbkZ95/1ETsWLUfiTEFr39XgENgAAkCsguDFwnE8jD8Y39ts1tHDhLlFro2udx35NZevUorjoaFr95bcUHfFG5+8JAACgDQhuDFz//s3FRJd374bSvHk70kwcqgs+3btQ/Z5dxXv9MW4yPQu+q/P3BAAA0BYENwaMx5D5elTKgHwL5us+sHEo7ETtvh5OtTp8JB7vX/QrXTt2UqfvCQAAoG0IbgxY5851qUyZIqKL99q1h3X2PmYWFtS4b09qMaQ/WdnYiCCKx685smq9zt4TAABAVxDcGLAx36SMNrx82T6tzd6dWqma1ajH1O/Iyb2YePwg6IqYRuHJ9Zs6eT8AAABdQ3BjoOrXr0B163qJcWuWLNmrk/eo1qYF9frpezK3tKQ3z1/Q3gXL6MLeA5jRGwAAcjUENwZq9JiUWpsN64/Q8+cRWt9/4769qOM3X4r7lw4eoS3f/0TxMTFafx8AAICchuDGAJUrV5Q6dqwt7s+fv1Or+zYxMaGO33xFjT7tIR4f37CFds35GbU1AABgNBDcGKBRozqLnlI7dwbSrVtPtLZfUzMz6jNzsmiOYrvmLKJ/12/S2v4BAAAMAYIbA2NlZUGf9m0q7s+ft0Or+67Xo4sIbJISE2nThKkU9I92J9gEAAAwBKb6PgBQVaNGaTEqcVjYazpx4prWise2QH5q/cVgcX/HzAUIbAAAwGghuDEw9eqVF7f+/trtis2BjY2DA4XcCqbAv7SbxwMAAGBIENwYmLo+XuI2MEB7wY1buTLk072zuL995gKScmAKBwAAAH1BcGNg6tXz0nrNTeexI0UycdCBw3Tv3EWt7RcAAMAQIbgxICVKuJCbmyMlJCTS+fN3tLLPKi2bUpnaNSkxLp72zFuilX0CAAAYMgQ3BsTnfZPUxYv3xMjE2WVuZUUdRo8Q94+u+Z1ehz7L9j4BAAAMHYIbA2ySCtBSk1Tr4QPJsagbRTwLE8ENAABAXpDlcW6KFy9OHh4eZGNjQy9evKBr165RQkL2axvyMjmZ2N//RrZn+e763Riq07WjeLx73hJKiI3TyjECAAAYVXDDwcywYcOoZ8+eVKxYMTGUv4wDmxMnTtCKFSvo77//xnD+mWRra01Vq5YU9wOy0VPKrlBB6j9/BpWsUZXeJSfTnvlLMaYNAADkKRo3S/3888906dIlKlmyJE2cOJEqVKhA+fPnJ0tLS3J1daW2bdvSyZMnaerUqXT58mWqVauWbo/cyHh7lyVzczN69OgFPX0anqV9FPUqRyM3rRaBTWxkFK38YgymVwAAgDxH45qb6OhoKlWqFL169SrNc9wsdfToUbFwcNO6dWvRbHXu3DltH6/RJxNntUnKvXIFGrZqKVnms6bn9x/S6i+/pRcPHmn5KAEAAIwouJkwYYLGOz1w4EBWjyfP8nk/MnFWBu+zsrGhPrOmiMDmduBZWjdqAsVFvdXBUQIAABhpb6nDhw+LJqnU7O3txXOQeT4+nlkevK/LhFHkVLwYvXoaisAGAADyvCwFN02aNBG5NqlZW1tTw4YN83yhZla5ckWpUCEHiomJp6Cge5l6bbXWzcm7UzuRPPzHhCmosQEAgDwvU72lKleurLjPCcXK+TdmZmbUpk0bevr0aZ4v1KyOb3PuXDAlJSVr/LoCri7U9Ydvxf3DK9fT/QuXUPYAAJDnZSq4CQoKEl28eTly5Eia52NjY2nEiJQRcSHzM4FnZvA+E1NT6j1jkpjp++Glq3Twl1UocgAAgMyOc8PdwHlsm3v37lHt2rVFLynlcW6eP39O7zDjdI4M3tf0s0+odK3qFBcdTRvHTaZ3majxAQAAMGaZCm4ePXqkaIIC7cif35YqVfIQ9wMDb2n0Gmt7O2oxpL+4v2PmAgp/gqZAAACAbCUUjxs3jj777LM063ndt9+m5ICAZurWTeklFRwcQi9evNHoNbU7tycrm3wUGnyXzu7Yi6IGAADIbnDz+eef082bafNDeH6poUOHZmWXeVbTppUz1STFzYL1e3YV909u+kunxwYAAJBnghuebiE0NDTNes7BcXNz08Zx5Qn58lnRgIGtxP3du85o9BrP+nXIyb2YmF7hwh4MlggAAKCV4Obx48dUv379NOt5XUhISFZ2mSd99llzcnJyoLt3Q2nHjkCNXtOgd3dxe2bHHkqIjdXxEQIAABh5QrHst99+o4ULF5KFhYWiS3jz5s1p9uzZNG/ePG0fo1EyNTWlr0d1FvcXzN+hUS+zQsWLUfmG9cS2pzZvy4GjBAAAyCPBzZw5c6hQoUK0bNkyxUjFcXFxNGvWLJo5c6a2j9EodelSl0qXdqOXLyNpzRrNpqyo3/NjcXvzZACFP36i4yMEAADIQ8GN3GPqxx9/pPLly4vB+4KDg8VYN6CZMd+kBCrLl+2j2Nj4D25vmS+f6CXFTiGRGAAAQLs5N8qJxY6OjnT37l0ENpnQoEEFqlPHk+LiEmjJkj0avaZG+9aUz8GeXjx8TLdOnc7qVwYAAGD0shTccEDj5+dHt2/fpn379il6SK1atYrmzp2r7WM0OqPHdBG369cd0Xhsmwa9uonbU5v/FtNfAAAAgBaDmwULFlBiYiK5u7tTTEyMYv2WLVvE5JmQPk/PYtSpU12RFDx//g6NioqnWXArW5riY2Lp7E4M2gcAAKD1nJtWrVpR69at08wAznk3Hh4pUwmAeqNHp/SQ2rXrDN2+rdm0CU0HfCJuz+/eT3FRb1G0AAAA2q65sbW1VamxUW6uio//cHJsXmVvn48++bSpuD93jmZdud3KlUnp/p2cTMfW/qHjIwQAAMijwc2JEyeob9++isecA8LTAvC8UkePHtXm8RmVsmWLkLW1JYWGvtJ4uoXmAz8Vt5cOHMYEmQAAALpqluIg5vDhw1SrVi0xzg0P3lexYkVRc6Nu5GJI4eHhLG4fPHiuUZHwoH1VWzcX9w+v2oBiBAAA0FXNDU+QWa5cOTp58iTt3LlTNFNt27aNqlevTvfu3cvKLvOEEiVSgpuHDzULbpp+1odMzczo+vFTFHr7jo6PDgAAII8GN+bm5qIbuLOzM02fPp169OhB7dq1o++//56ePXuWpYMYPnw43b9/XwwGGBgYSN7e3uluy81e3AyWetmzR7PxYgyh5uaRBsGNQ2En8u7UVtw/globAAAA3QU3SUlJVKVKFdIWX19fmj9/Pk2ZMoVq1KhBly5dogMHDlDhwoXVbv/xxx+LwQPlhZvD+Jj+/PNPMnTu74Obhw9ffHDbRp/2JHNLS7p/4ZJYAAAAQIfNUr///jsNHDiQtGHUqFFiIs61a9fSjRs3aOjQoaIn1oABA9Ru//r1awoLC1MsLVu2FNvnhuDGwyMlYHvwICzD7XgkYh/flC7jh1euz5FjAwAAyNMJxdw0xcFHixYt6Pz58xQdHa3y/OjRozXaD88qXrNmTZoxY4ZiHTcxcbOXj4+PRvvgIGvz5s1qu6YzTni2srJSPLa3tyd9KVHCRaOam/o9u5K1rS2F3AqmGyf8c+joAAAA8nBwU6lSJbpw4YK4z4nFyjIzNYCTk5MIlLgGRhk/9vLy+uDrOTencuXKGdYijR8/niZPnkyGMMZNwYJ2H0woNjM3pwa9u4v7yLUBAADIgeDG1NSUJk2aRFeuXKGIiAjSJw5qLl++TGfPnk13G64V4pwe5Zqb1CMr52QycXh4JEVHx6W7nVdDH7Iv5Ehvwl7QpYNHcvAIAQAA8mjODc+JdPDgQSpQoEC23/zly5ciGdjFJaW5RsaPP9TzysbGhnr27Ckm68xIQkICRUVFqSz6IAc3H2qSqtk+ZW6uC/sOilGJAQAAIAcSiq9evUqlSpWi7OLJNzlnp3nzlIHqGI90zI8DAgIyfG337t1FLg0nN+cGmiQTW9vbUYXGKYMgnt/zT44dGwAAAOX14GbixIk0d+5cMb4Nd8fmph7lJTO4yWjw4MFiOgfOs1m+fLkYFHDNmjXi+XXr1onxdNQ1Se3YsYNevXpFuYGcTPwog5qbKi2akoWVFYUG38WgfQAAADmZULxv3z5xu2vXLpUEYq514cecJKyprVu3ijFtpk6dKgKloKAgatOmDT1/npJ06+7uLprClHESc8OGDUU38NzivzFu0k8mrtm+tbhFrQ0AAEAOBzdNm6bMbK0tS5cuFYum73X79m0RSOUmcrNUesFNAVcXKlO7prh/ce/BHD02AAAAyuvBzfHjx7V/JEbuQwnFNdq1Erd3zpyniDDN5p4CAAAALQU3LH/+/CLvpXz58orJNFevXk2RkZFZ3aXRsra2JFfXghkmFMu9pM7vOZCjxwYAAGBsspRQzKMK3717l77++mtydHQUC0+jwOt4ZnBQ5e6e0iQVFRVDr1+/TVM8RTzLkmuZUpQYH0+X/Y6i+AAAAHK65mbBggUimZh7OSW/H4vFzMyMVq5cSQsXLqTGjRtn55jyXJOUXGtz/d9TFBeVNvgBAAAAHQc3tWrVUglsGN+fPXs2nTt3Liu7NGoZJRObmJpS9bYpvb7QSwoAAEBPzVKcV8NdtFMrXry43kYAzg01N+rGuOEeUvmdC1N0xBu6eSLjgQsBAABAR8HNli1bxLQHvr6+VKxYMbH06NFDNEtt2rQpK7s0ahmNcVO1VTNxy/NIJScl5fixAQAAGJssNUuNGTNGDNa3fv16xYB9PJUCjy48btw4bR9jrleihHO6PaU869URt9eOncjx4wIAADBGWQpuOJAZOXIkjR8/nkqXLi3WcU+p2NhYbR+fUScUO7kXI8eibpSUmEj3zl3U09EBAAAYlywFNw4ODqJ31OvXr8UkmrKCBQuKWb6Rd6NUwOZmVLSoo9pmKbnW5v6FS5QQG5e1bxAAAACyn3OzefNm6tmzZ5r1nIPDz8F/ihYtJALB+PhECguLUBvc3A44gyIDAADQZ3BTp04dOno07WBzx44dE89B2nybR49eqEwyamZuTqVr1xD3b506jSIDAADQZ3BjZWWlduZvCwsLypcvnzaOy+jybVInE3tUrUTWtrYUFf6KQm4F6+noAAAAjE+WgpszZ87QkCFD0qwfOnQonT9/XhvHZfRj3JSrV1vcBgeeVanRAQAAAD0kFE+cOJH8/PyoatWqdPjwYbGuefPm5O3tTa1apcxuDRmPTuzpk9J8d8sf+TYAAAB6r7nx9/cnHx8fevz4sUgi7tChA925c4eqVKlCJ0+e1OoBGuMAfjb5HahYRS9xH8nEAAAABlBzwy5dukSffPKJdo/GiBOKlce4KVvXm0xNTSk0+C5Fvnipx6MDAADIwzU3NjY2mdpxZrc3RiYmJlS8eOE0CcVyF3D0kgIAANBjcMPNTmPHjiVXV9cMt2vRogXt27ePvvzyS8rrXF0LkpWVBSUlJdPTp+GK9Z7vk4lv+aMLOAAAgN6apZo0aULTp0+nyZMniyapc+fOUUhICMXFxYmRiStUqCDycHiE4hkzZtCvv/5KeZ2cTMyBTXLyO3HfuaQHFXB1ocT4eLp34ZKejxAAACAPBze3b9+mbt26UfHixal79+7UsGFDqlevnhjX5uXLl3Tx4kUaPHgw7d+/n969S7mQ53UeapKJ5Sape+eDKCk+Xm/HBgAAYKwynVDMPaTmz58vFshYiRIuaZKJ5fFtbqMLOAAAgOF0BWcDBgygK1euiGYpXvj+wIEDtXt0RtIs9eh9zY2ZhQWVrvV+yoUA5NsAAAAYTFfwKVOm0KhRo2jx4sUUEBAg1nG+zYIFC8jd3Z0mTZqk7ePMlVxcC4rbkJBX4ta1dEmysslHMW8iKfT2XT0fHQAAgHHKUnAzbNgwkV+jPAP47t276fLlyyLgQXCTws7OWtxGRsaIW5fSJcRt6B0ENgAAAAbVLMUTZHJvqdR4Xil1E2rmVfb2KZOIRkXFiluX0qXEbdid+3o9LgAAAGOWpeBmw4YNovYmNZ5Mc+PGjdo4LqNgZ5cS3Lx9mxLcuL6vuQm7h+AGAABAV7JczcLJwzxJZmBgoHhcp04dkW+zfv16mjdvnmK70aNHU16Vbs3N3Qd6PS4AAABjlqXgplKlSnThwgVxv3Tp0uKWx7rhhZ+TSZJEeZmcc/P2bRyZW1lRoWJFxONnd+7p+cgAAACMV5aCm2bNmmn/SIy85sa5hDuZmpmJnlJR4Sm9pwAAAMCAxrmBjFlYmJOlpYUi58aldElxH7U2AAAAuoXgRse1NnKzlNwNPOwe8m0AAAB0CcGNjvNt4uISxKzgru+TiVFzAwAAoFsIbnTcDVzRU6oUam4AAAByAoIbHTdLiZ5Slpbk5F5MPEbNDQAAgG4huNFxs1RUVAwVLlE8padUZCRFvQzX1VsCAAAAgpucqbmR820w7QIAAIDuoeYmB3JuFN3AMe0CAACAziG4yYGaGzm4Qc0NAACA7iG40XnOTSy5ysENam4AAAB0DsGNjmtuYmLiqVDxouL+szuYDRwAAEDXENzoOOcm2cySzMzNKTYyiiJfvNTV2wEAAMB7CG50XHNjam0jbp/dRa0NAABATkBwoyO273NuTG3sxW0YghsAAIAcgeBGxzU3VvkLilvU3AAAAOQMBDc6zrmxKlhI3KLmBgAAIGcguNFxzY21o5O4Rc0NAABAzkBwo+NxbpJMzCk26i1FPn+hq7cCAAAAJQhudFxzk/jOBE1SAAAAOQjBjY5rbhIQ3AAAAOSt4Gb48OF0//59io2NpcDAQPL29s5w+/z589OSJUsoJCSE4uLi6NatW/TRRx+RITExMSF7+5TxbRKTTSjs3gN9HxIAAECeYa7PN/f19aX58+fT0KFD6fTp0zRy5Eg6cOAAeXp60osXaXNULCws6NChQ/T8+XPq1q0bPX36lDw8PCgiIoIMiY2NleI+19xEhYfr9XgAAADyGklfS2BgoLR48WLFYxMTE+nJkyfS2LFj1W7/+eefS3fu3JHMzc2z/J729vYS41tdfS5X14LSO2m3lJS8S5p3xV/yalBXb2WMBWWAcwDnAM4BnAPGcA5k5vqtt2YproWpWbMm+fn5/RdlSZJ47OPjo/Y1HTt2pICAAFq6dCk9e/aMrly5QuPHjydTU723rqnPt0nmf00oOiJS34cEAACQZ+itWcrJyYnMzc0pLCxMZT0/9vLyUvuaUqVKUbNmzWjjxo3Utm1bKlOmDC1btkwESlOnTlX7GktLS7Ky+q+ZyN4+ZToEXVLk20gm4jbmDYIbAACAnGJYVR4fwDU0nG8zZMgQunDhAm3dupV++uknkbOTHq7ZiYyMVCycp5NTNTeJkpm4RXADAACQB4Kbly9fUlJSErm4uKis58fc5KROaGgo3b59m969e6dYd+PGDXJzcxO1N+rMmDGDHBwcFEvRokUpJ8e44WONe/tW5+8JAAAAeg5uEhMT6fz589S8eXOVLtT8mPNq1Dl16pRoiuLtZOXKlRPdwnl/6iQkJFBUVJTKknNj3BDFRb0lSSkYAwAAACNuluJu4IMHD6a+ffuKPJvly5eTra0trVmzRjy/bt06mj59umJ7ft7R0ZF+/vlnKlu2rMi7mTBhgkgwNiTKY9ygSQoAACAPjXPDOTOFCxcWycCurq4UFBREbdq0EXk1zN3dXaUJ6smTJ9S6dWtasGABXb58WeTPcKAza9YsMtTRiRHcAAAA5KHghnGtS3o1L02bNk2zjkcxTq+ruKFQzrmJfvNG34cDAACQp+Sq3lK5hXLNTSy6gQMAAOQoBDe6zLlBsxQAAECOQ3CjA7bIuQEAANAbBDe6zLlBbykAAIAch+BGB9BbCgAAQH8Q3Oiw5gZdwQEAAHIeghsdsLNDV3AAAAB9QXCjA6i5AQAA0B8EN7qcFRzj3AAAAOQ4BDc6HOdGDOIXhRnBAQAAchKCGy2zsDAnS8uUWS2iIqLpXXKytt8CAAAAMoDgRkf5NiwiIkrbuwcAAIAPQHCjo3ybpHdEbyMitb17AAAA+AAENzrqBi7GuInAjOAAAAA5DcGNrqZewKSZAAAAeoHgRpdTL0Qi5wYAACCnIbjRMtTcAAAA6BeCG13l3GBGcAAAAL1AcKNlqLkBAADQLwQ3usy5eYOu4AAAADkNwY0ua27QFRwAACDHIbjR5Tg3kai5AQAAyGkIbrTM3iFl0kyMcwMAAKAfCG60LH8Bu/96S6HmBgAAIMchuNGy/AVTgpuY2AR6l4QZwQEAAHIaghstc3CwFbdRUbHa3jUAAABoAMGNjnJuoiJjtL1rAAAA0ACCGy2ztbUSt28iMK8UAACAPiC40TI7m/fBzeu32t41AAAAaADBjZbly2chbl+Hv9H2rgEAAEADCG60yMTEhGys5eAmQpu7BgAAAA0huNEim/dNUiz8+Wtt7hoAAAA0hOBGB/NKvZOIXr9EcAMAAKAPCG50MCM4pl4AAADQHwQ3WmRvrzSvFGYEBwAA0AsENzqouREzgr/BjOAAAAD6gOBGixwwIzgAAIDeIbjRooKFC4jbhHdEMZEYoRgAAEAfENxoUSGnguI2PuEdJScmanPXAAAAoCEEN1pUoFB+cRsTn6TN3QIAAEAmILjRooKFHMRtTEyCNncLAAAAmYDgRoscCtiJ2+joeG3uFgAAADIBwY0W5X8f3ES9jdPmbgEAACATENzoYBC/qKgYbe4WAAAAMgHBjQ4G8YuMiNbmbgEAACATENxoka1tyqzgbyIwxg0AAIC+ILjRItt8luL2dTimXgAAANAXBDdalM/aXNxGhL/R5m4BAAAgExDcaJG1pZm4DX/xSpu7BQAAgExAcKNFVhYpxRn+HMENAACAviC40SKrlIobehkars3dAgAAQG4LboYPH07379+n2NhYCgwMJG9v73S37devH0mSpLLw6/TNvqADmb0vzRfPXur7cAAAAPIsvQc3vr6+NH/+fJoyZQrVqFGDLl26RAcOHKDChQun+5o3b96Qq6urYvHw8CB9cy7irLiPhGIAAAD9Seneo0ejRo2i3377jdauXSseDx06lNq1a0cDBgygWbNmqX0N19aEhYVl630tLCxUHpuZmZGpqSm9e/eOkpOT02yXlJQk3pfxdrw9P+b1zMnViRISkikxWaLk5HeK16vbNjP7zey25ubmZGJiIj4DfxbGj3l9ZrZliYmJOt9WXblnZlvl8tHVtsrlnt3zJCe+++yeJzn13Wf3PMmJ7z6r50lOf/f4jTCc796QzxNzI/iNyBXBDX8pNWvWpBkzZijWcQH7+fmRj49Puq+zs7OjBw8eiC/lwoULNGHCBLp+/brabS0tLcnKKmVwPWZvby9ux4wZQ9OmTaOYmJSpEurVq0fNmzen8+fP0+7duxXbf/PNN2IfCxcupIiICLGudu3a1KZNG7p8+TJt27Yt5Zjs89Gin69STEwSOTk50YsXL8T6atWqUceOHenmzZu0efNmxX6/+OILKlCgAK1YsYJCQkLEuooVK1LXrl3p7t27tGHDBsW2gwcPJmdnZxEA8udm5cqVo549e9KjR49o9erVim0/++wzKlq0KG3cuJGCg4PFupIlS1Lfvn3p2bNn9Msvvyi2/eSTT6hEiRK0detWRfkVK1aMBg4cSOHh4bR48WKVGjZ+zx07dlBQUJBY5+LiIoLRyMhIUfsm69Kli/gse/fupbNnz4p1BQsWpC+//JLi4uJo5syZim07dOggyujgwYPk7++v+H5Hjx4tTugff/xRsW3r1q1F2R87dkwszNramsaNGyfuT506VfEfoFmzZlS/fn06deoUHTp0SKzj8+W7774T9/kY+FhYw4YNqUmTJnTmzBnat2+f4v14v/yfft68eRQVlTIwY506dahVq1aiDLgsZHy8fCyLFi2iV69SEsr53OZA/dq1a/Tnn38qth0xYgQ5ODiI74K/E1alShXq3Lkz3b59m/744w/Ftly+hQoVolWrVtHjx4/FOi8vL/F98Lkg/1HABg0aJGoy169fT/fu3RPrSpcuTX369KGnT5+KPyJkfD64u7uLc5LPTcY1oP3796fnz5/TsmXLFNv26tVL7Ofvv/+mK1euiHVubm40ZMgQ8X+C/2/IunXrJo5v165d4v8m4/8PfL5HR0fTnDlzFNvy/wv+3P/8849ojmb58+enkSNHUkJCAk2fPl2xbdu2bUV5Hj58mE6cOCHW2djY0LfffivuT548WbFty5YtqW7dunT8+HE6cuSI4rdG/u5/+uknxQ9o48aNqVGjRuL9+Thk8razZ8/W2m8E489ma2tLS5cuxW8EfiPwG3Eh878RuaJZij8QR2apa2H4Mf9Iq3Pr1i1Rq9OpUydxceYLFl8U+YKuzvjx48XFV174R14X3oamBCgAAACgf5K+Fjc3N4nVrVtXZf2sWbOkwMBAjfZhbm4uBQcHS1OnTlX7vKWlpWRvb69YihQpIt7T0dFRZTszMzPJwsJC3Cqv53W8mJiYKNaZmpqKdfze8jpPz2LSmrWjpAULhnxw28zsN7Pb8mNez8/L6/h1md2Wl5zYVl25Z2Zb5fLR1bbK5Z7d8yQnvvvsnic59d1n9zzJie9em78RhrItfiPwG2GSS38j+BrO+FZ5O3WLyfs7esFVxVzly1XZO3fuVKznqnZusuFqek1wswq3Ffbu3fuD23KzFNfgcLOA3NQAAAAAhi0z12+9Nktxuze3X3M7toyTh/hxQECARvvgZqnKlStTaGioDo8UAAAAchNJn4uvr68UGxsr9e3bV/Ly8pJ++eUX6dWrV5Kzs7N4ft26ddL06dMV23///fdSy5YtpZIlS0rVq1eX/vjjDykmJkYqX768Ru+XmWotLCgDnAM4B3AO4BzAOUAGUQaZuX7rvSs4NynxmDbc04WTiLkXCvcy4B4bjHt0KHcB41433OuDt339+rWo+eFeDDdu3NDjpwAAAABDodecG31Azg0AAEDuk2tybgAAAAC0DcENAAAAGBUENwAAAGBUENwAAACAUUFwAwAAAEYFwQ0AAAAYFQQ3AAAAYFQQ3AAAAIBRQXADAAAARgXBDQAAABgVBDcAAABgVBDcAAAAgFFBcAMAAABGBcENAAAAGBUENwAAAGBUENwAAACAUUFwAwAAAEYFwQ0AAAAYFQQ3AAAAYFQQ3AAAAIBRQXADAAAARgXBDQAAABgVBDcAAABgVBDcAAAAgFFBcAMAAABGBcENAAAAGBUENwAAAGBUENwAAACAUUFwAwAAAEYFwQ0AAAAYFQQ3AAAAYFQQ3AAAAIBRQXADAAAARgXBDQAAABgVBDcAAABgVBDcAAAAgFFBcAMAAABGBcENAAAAGBUENwAAAGBUENwAAACAUUFwAwAAAEYFwQ0AAAAYFQQ3AAAAYFQQ3AAAAIBRQXADAAAARgXBDQAAABgVBDcAAABgVBDcAAAAgFFBcAMAAABGBcENAAAAGBUENwAAAGBUDCK4GT58ON2/f59iY2MpMDCQvL29NXpdjx49SJIk2r59u86PEQAAAHIHvQc3vr6+NH/+fJoyZQrVqFGDLl26RAcOHKDChQtn+DoPDw+aO3cuHT9+PMeOFQAAAAyf3oObUaNG0W+//UZr166lGzdu0NChQykmJoYGDBiQ7mtMTU1p48aNNGnSJLp3716OHi8AAAAYNnN9vrmFhQXVrFmTZsyYoVjHzUx+fn7k4+OT7ut++OEHev78Oa1evZoaNmyY4XtYWlqSlZWV4rG9vb3KLQAAABi+zFy39RrcODk5kbm5OYWFhams58deXl5qX1O/fn0aOHAgVatWTaP3GD9+PE2ePDnN+qdPn2bxqAEAAECfQU5UVJThBjeZZWdnRxs2bKDBgwdTeHi4Rq/hWiHO6VHm6OhIr1690kmBc9BUtGjRDxY8oKxzC5zXKGtjhPM6d5Y17yskJOSD2+k1uHn58iUlJSWRi4uLynp+/OzZszTbly5dmkqWLEm7d+9Wyb9hiYmJ5OnpmSYHJyEhQSzKdB148P4R3OQMlHXOQVmjrI0RzuvcVdaavl6vCcUckJw/f56aN2+uWGdiYiIeBwQEpNn+5s2bVKlSJdEkJS+7du2io0ePivuPHz/O4U8AAAAAhkbvzVLcZLRu3To6d+4cnTlzhkaOHEm2tra0Zs0a8Tw/x9VZEyZMoPj4eLp27ZrK6yMiIsRt6vUAAACQN+k9uNm6dasY02bq1Knk6upKQUFB1KZNG9Ebirm7u9O7d+8oN+Dgi5OX+RZQ1sYC5zXK2hjhvDbusjbh3tc59m4AAAAAxj6IHwAAAIA2IbgBAAAAo4LgBgAAAIwKghsAAAAwKghutGT48OF0//59io2NpcDAQPL29tbWrvOscePGieEBIiMjxZQc27dvp3Llyqlsw/OGLVmyRAwIyYM7/fXXX+Ts7Ky3YzYWY8eOFfO8LViwQLEOZa09RYoUEaOt83nLEwVfvnxZzLOnbMqUKWIkVn7+0KFDVKZMGS0eQd7Ag7xyT1we3JXL8c6dOzRx4sQ026Gss4bnduSx5ni4Fv696NSpU6bLtmDBgvT777/Tmzdv6PXr17Ry5UoxHIw2cG8pLNkoA19fXykuLk7q37+/VL58eenXX3+VXr16JRUuXBjlmo1y3b9/v9SvXz+pQoUKUpUqVaQ9e/ZIDx48kGxsbBTbLFu2THr48KHUtGlTqUaNGpK/v7908uRJlHs2yr1WrVrSvXv3pKCgIGnBggUoay3/PhYoUEC6f/++tHr1asnb21sqUaKE1LJlS6lUqVKKbb799lvp9evXUseOHaXKlStLO3bskO7evStZWVnh3M5EWY8fP1568eKF1LZtW8nDw0Pq2rWrFBkZKY0YMQJlTdk/l9u0aSP9+OOPUufOnSXWqVMnlec1OY/37dsnXbx4Uapdu7ZUv3596fbt29LGjRu1cZ4jsMluGQQGBkqLFy9WPDYxMZGePHkijR07Fj9EWjy/nJycxH+ghg0biscODg5SfHy8+MGSt/H09BTb1KlTB2WfhTK2tbWVbt26JTVv3lw6evSoIrhBWWvvPJ4xY4Z0/PjxDLcJCQmRRo8erXjM5R8bGyv16NED53Umynr37t3SypUrVdb99ddf0oYNG1DWpN1rv7rg5kPnsZeXl3hdzZo1Fdu0bt1aSk5Oltzc3LJ1PGiWyiYLCwtRnezn5/dfVZgkicc+Pj7Z3T0oyZ8/v7iVJz3lcre0tFQp+1u3btHDhw9R9lm0dOlS2rt3Lx0+fFhlPcpaezp27ChGZOcBTLm59cKFCzRo0CDF8zx/npubm8p5zU2zp0+fxnmdSf7+/mI6n7Jly4rHVapUoQYNGtD+/ftR1jqmyXnMt9wUxdMwyXh7Hri3Tp06uXuE4tzOycmJzM3NxY+UMn7s5eWlt+MyNjzn2MKFC+nkyZOKqTZ4RGse8ZLbalOXPT8HmdOjRw+qUaOG2nwxlLX2lCpVioYNGyamnpk+fboo70WLFokJftevX684d9X9puC8zpyZM2eSg4ODmJcwOTmZzMzM6LvvvqM//vhDPI+y1h1NypZv5dkIZPw98R+w2T3XEdxArqlR4ElT+a8u0L5ixYrRzz//TC1btsT0ITmQ5Mo1N3yRZTzlDJ/bQ4cOFcENaI+vry/16dOHevfuLf4o4gmW+Y8kTnBFWRs3NEtlE/d2SEpKIhcXF5X1/PjZs2fZ3T0Q0eLFi6l9+/bUtGlTkZUv4/LlHjxycxXKPuu42YnPWW4iSUxMFEuTJk3oyy+/FPf5ry2UtXaEhobS9evXVdbduHFDzKPH5N8N/KZk35w5c0TtzZYtW+jq1auiVw73ABw/fjzKWsc0OY/5NnXvVq5dc3R0zPb1E8FNNvEPP7cXcruuchMKPw4ICMju7vM8Dmy6dOlCzZo1owcPHqiUB5c7V+Urlz13Fffw8EDZZxLn2HDtAf9lKy9nz56ljRs3ivtc04Cy1o5Tp06Rp6enyjo+bzlXjPGQEhwAKZ/X9vb2IgcBvymZY2Njk2biZW724NozlLVuaXIe8y13BefmcBn/1vP3w7k52YXsey10BecM8L59+4rs719++UV0BXd2dkbZZqNcly5dKroRNmrUSHJxcVEs1tbWKl3BuXt4kyZNRFfwU6dOiQXndfb/Xyv3lkJZa7erfUJCguimXLp0aalXr17S27dvpd69e6t0oeXfkA4dOkiVKlWStm/fjq7gWSjrNWvWSI8fP1Z0Becuy8+fP5dmzpyJsibt9K6sWrWqWNjIkSPF/eLFi2t8HnNX8PPnz4thEerVqyd6a6IruAEFZl988YW4yPJ4N9w1nPvs6/uYcvuSHh77Rt6G/5MsWbJECg8PFxeIv//+WwRA+j52YwxuUNbaK9t27dpJly9fFn8UXb9+XRo0aFCabaZMmSKFhoaKbQ4dOiSVLVtW7+dEblvs7OzEOcy/zTExMdKdO3fEuCwWFhYoa8p++TZu3FjtbzQHlZqexwULFhTBDI8/FBERIa1atUoETdk9NpP3dwAAAACMAnJuAAAAwKgguAEAAACjguAGAAAAjAqCGwAAADAqCG4AAADAqCC4AQAAAKOC4AYAAACMCoIbADAKkyZNoosXL2bqNZIkUadOnXR2TACgHwhuAMCgHT16VEx2+CFz585VmccGAPIuc30fAABAdvFMwtHR0WIBAEDNDQAYrDVr1lCTJk1o5MiRogmJl379+onbNm3aiNnK4+PjqUGDBmmapWrVqkUHDx6kFy9eUEREBB07doyqV6+u188DADkDwQ0AGKyvvvqK/P39acWKFeTq6iqWx48fi+dmzpxJ48aNo/Lly9Ply5fTvNbe3p7WrVsnAp+6detScHAw7du3j+zs7PTwSQAgJ6FZCgAMVmRkJCUkJFBMTAyFhYWJdV5eXuL2hx9+ID8/vwxzdZQNGTJE1OA0btyY9u7dq+MjBwB9Qs0NAORK3CSVEWdnZ1Hjc/v2bRHUcKDEtTbu7u45dowAoB+ouQGAXOlDycPcJFWoUCHRtPXw4UORmxMQEECWlpY5dowAoB8IbgDAoHGzFPeGyqz69evT8OHDaf/+/eJxsWLFqHDhwjo4QgAwNAhuAMCgPXjwgOrUqUMeHh709u1bMjXVrDWdE4g//fRT0Xzl4OBAc+bMEbk7AGD8kHMDAAaNB+dLTk6m69ev08uXLzXOmRk4cCAVLFiQLly4QBs2bKBFixbR8+fPdX68AKB/JjwCub4PAgAAAEBbUHMDAAAARgXBDQAAABgVBDcAAABgVBDcAAAAgFFBcAMAAABGBcENAAAAGBUENwAAAGBUENwAAACAUUFwAwAAAEYFwQ0AAAAYFQQ3AAAAYFQQ3AAAAAAZk/8Dy1VdBmUh6AMAAAAASUVORK5CYII="
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:53:30.519863Z",
+     "start_time": "2026-03-27T03:53:30.453561Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.figure(figsize = (5, 5))\n",
+    "plt.errorbar(\n",
+    "    result1.model_statistics.phat.mean('trial'),\n",
+    "    benchmark1.target_statistics.phat.mean('trial'),\n",
+    "    xerr=result1.model_statistics.boot_phat.mean('trial').std(dim='boot_iter', ddof=1),\n",
+    "    yerr=benchmark1.target_statistics.boot_phat.mean('trial').std(dim='boot_iter', ddof=1),\n",
+    "    marker='.',\n",
+    "    lw =0,\n",
+    "    elinewidth=1\n",
+    ")\n",
+    "plt.xlabel('model')\n",
+    "plt.ylabel('human')\n",
+    "plt.title('Subtask difficulty pattern')\n",
+    "plt.plot([0.5, 1], [0.5, 1], ls = ':', color = 'gray')\n",
+    "plt.show()\n"
+   ],
+   "id": "b8cef9e268b1508e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:53:33.098646Z",
+     "start_time": "2026-03-27T03:53:30.525642Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "result2 = benchmark2.score_model(learner = learner)\n",
+    "result2"
+   ],
+   "id": "545e5142766c2141",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MutatorOneshotBenchmark.GeneralizationBenchmarkResult(msen=0.018940506085930897, msen_sigma=0.002367913012416273, msen_CI95=(0.013982943571075011, 0.02329907055826775), model_statistics=<xarray.Dataset> Size: 583kB\n",
+       "Dimensions:               (transformation: 36, boot_iter: 1000)\n",
+       "Coordinates:\n",
+       "  * transformation        (transformation) <U26 4kB 'backgrounds | 0.1' ... '...\n",
+       "    transformation_type   (transformation) <U18 3kB 'backgrounds' ... 'scale'\n",
+       "    transformation_level  (transformation) float64 288B 0.1 0.2154 ... 0.5 1.5\n",
+       "Dimensions without coordinates: boot_iter\n",
+       "Data variables:\n",
+       "    phat                  (transformation) float64 288B 0.9751 0.9745 ... 0.9563\n",
+       "    varhat_phat           (transformation) float64 288B 5.639e-06 ... 1.944e-05\n",
+       "    boot_phat             (boot_iter, transformation) float64 288kB 0.9747 .....\n",
+       "    boot_varhat_phat      (boot_iter, transformation) float64 288kB 5.855e-06...)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-27T03:53:33.146922Z",
+     "start_time": "2026-03-27T03:53:33.106147Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.figure(figsize = (5, 5))\n",
+    "x = result2.model_statistics.phat \n",
+    "xerr = np.sqrt(result2.model_statistics.varhat_phat)\n",
+    "\n",
+    "y = benchmark2.target_statistics.phat \n",
+    "yerr = np.sqrt(benchmark2.target_statistics.varhat_phat)\n",
+    "plt.errorbar(x, y, xerr=xerr, yerr = yerr, lw = 0, elinewidth=1, marker = '.')\n",
+    "plt.plot([0, 1], [0, 1], ls = ':', color = 'gray')\n",
+    "plt.axis([0.4, 1.05, 0.4, 1.05])\n",
+    "plt.xlabel('model')\n",
+    "plt.ylabel('human')\n",
+    "plt.title('One-shot generalization patterns')\n",
+    "plt.show()"
+   ],
+   "id": "26ae4f65589f6075",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 9
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/view_behavioral_data.py b/examples/view_behavioral_data.py
deleted file mode 100644
index 9fc5dd8..0000000
--- a/examples/view_behavioral_data.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import hobj.benchmarks.leedicarlo_benchmarks.MutatorHighVar.mut_highvar_benchmark as highvar_benchmark
-import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_benchmark as oneshot_benchmark
-import matplotlib.pyplot as plt
-import numpy as np
-
-# %% View processed data for Experiment 1
-experiment1_benchmark = highvar_benchmark.MutatorHighVarBenchmark()
-ds_experiment1 = experiment1_benchmark.ds_behavioral_statistics
-
-plt.figure()
-plt.errorbar(x=np.arange(100) + 1, y=ds_experiment1.glc, yerr=ds_experiment1.glc_std)
-plt.xlabel('trial')
-plt.ylabel('subtask-and-subject averaged accuracy')
-plt.show()
-
-
-# %% View processed data for Experiment 2
-experiment2_benchmark = oneshot_benchmark.MutatorOneshotBenchmark()
-ds_experiment2 = experiment2_benchmark.ds_behavioral_statistics
-
-plt.figure(figsize=(12, 4))
-xx = np.arange(len(ds_experiment2.transformation_id))
-xlabels = ds_experiment2.transformation_id.values
-plt.errorbar(x=xx, y=ds_experiment2.perf, yerr=np.sqrt(ds_experiment2.hat_var_perf))
-plt.xlabel('trial')
-plt.ylabel('generalization accuracy')
-plt.xticks(xx, xlabels, rotation=45, ha='right')
-plt.tight_layout()
-plt.show()
\ No newline at end of file
diff --git a/examples/view_experiment1_behavior.ipynb b/examples/view_experiment1_behavior.ipynb
new file mode 100644
index 0000000..0dca904
--- /dev/null
+++ b/examples/view_experiment1_behavior.ipynb
@@ -0,0 +1,160 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## View human learning curves from Experiment 1",
+   "id": "be362495b3302922"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-26T22:23:26.063761Z",
+     "start_time": "2026-03-26T22:23:26.059240Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ],
+   "id": "initial_id",
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-26T22:23:27.770785Z",
+     "start_time": "2026-03-26T22:23:26.064934Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from hobj import MutatorHighVarBenchmark\n",
+    "benchmark1 = MutatorHighVarBenchmark()\n",
+    "target_statistics = benchmark1.target_statistics\n",
+    "print(target_statistics)"
+   ],
+   "id": "40343652efc6724e",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.LearningCurveStatistics> Size: 103MB\n",
+      "Dimensions:           (subtask: 64, trial: 100, boot_iter: 1000)\n",
+      "Coordinates:\n",
+      "  * subtask           (subtask) <U33 8kB 'MutatorObject000,MutatorObject095' ...\n",
+      "Dimensions without coordinates: trial, boot_iter\n",
+      "Data variables:\n",
+      "    phat              (subtask, trial) float64 51kB 0.44 0.68 0.62 ... 0.9 0.94\n",
+      "    varhat_phat       (subtask, trial) float64 51kB 0.005029 ... 0.001151\n",
+      "    boot_phat         (boot_iter, subtask, trial) float64 51MB 0.46 ... 0.92\n",
+      "    boot_varhat_phat  (boot_iter, subtask, trial) float64 51MB 0.005069 ... 0...\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-26T22:23:27.905063Z",
+     "start_time": "2026-03-26T22:23:27.805804Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Plot the [subtask, trial] performance data\n",
+    "plt.figure()\n",
+    "plt.imshow(target_statistics.phat, aspect='auto', cmap = 'viridis', vmin = 0, vmax = 1)\n",
+    "plt.xlabel('trial')\n",
+    "plt.ylabel('subtask')\n",
+    "plt.title(\"Performance data\")\n",
+    "plt.colorbar(label='p(correct)')\n",
+    "plt.show()\n"
+   ],
+   "id": "648668fd741654e3",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-26T22:23:28.055608Z",
+     "start_time": "2026-03-26T22:23:27.905949Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Calculate the subtask-averaged learning curve\n",
+    "glc = target_statistics.phat.mean('subtask')\n",
+    "\n",
+    "# Compute standard error using the bootstrap samples:\n",
+    "glc_se = target_statistics.boot_phat.mean('subtask').std('boot_iter', ddof = 1) \n",
+    "\n",
+    "plt.figure()\n",
+    "plt.errorbar(np.arange(len(glc))+1, glc, yerr = glc_se,  lw = 1, elinewidth = 1, marker = '.')\n",
+    "plt.axhline(0.5, ls = ':', color = 'gray')\n",
+    "plt.xscale('log')\n",
+    "plt.xlabel('trial')\n",
+    "plt.ylabel('p(correct)')\n",
+    "plt.title(\"Human learning curve\\n(averaged over all subtasks)\")\n",
+    "plt.show()"
+   ],
+   "id": "e0d81a639257d99e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 4
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/view_experiment2_behavior.ipynb b/examples/view_experiment2_behavior.ipynb
new file mode 100644
index 0000000..20ac169
--- /dev/null
+++ b/examples/view_experiment2_behavior.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## View human one-shot behavior from Experiment 2",
+   "id": "bb5fb3d6fa3feb0"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-26T22:23:36.412Z",
+     "start_time": "2026-03-26T22:23:36.407827Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ],
+   "id": "initial_id",
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-26T22:23:39.053698Z",
+     "start_time": "2026-03-26T22:23:36.412905Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from hobj import MutatorOneshotBenchmark\n",
+    "benchmark2 = MutatorOneshotBenchmark()\n",
+    "target_statistics = benchmark2.target_statistics\n",
+    "print(target_statistics)"
+   ],
+   "id": "40343652efc6724e",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.GeneralizationStatistics> Size: 583kB\n",
+      "Dimensions:               (transformation: 36, boot_iter: 1000)\n",
+      "Coordinates:\n",
+      "  * transformation        (transformation) <U26 4kB 'backgrounds | 0.1' ... '...\n",
+      "    transformation_type   (transformation) <U18 3kB 'backgrounds' ... 'scale'\n",
+      "    transformation_level  (transformation) float64 288B 0.1 0.2154 ... 0.5 1.5\n",
+      "Dimensions without coordinates: boot_iter\n",
+      "Data variables:\n",
+      "    phat                  (transformation) float64 288B 0.9969 1.0 ... 0.945 1.0\n",
+      "    varhat_phat           (transformation) float64 288B 0.0003446 ... 0.0003748\n",
+      "    boot_phat             (boot_iter, transformation) float64 288kB 0.9797 .....\n",
+      "    boot_varhat_phat      (boot_iter, transformation) float64 288kB 0.0004078...\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-26T22:23:39.471935Z",
+     "start_time": "2026-03-26T22:23:39.087799Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Plot by transformation\n",
+    "fig, ax = plt.subplots(3, 3, figsize=(20, 15))\n",
+    "c=0\n",
+    "for transformation, ds in target_statistics.groupby('transformation_type'):\n",
+    "    ds = ds.sortby('transformation_level')\n",
+    "    x = ds.transformation_level.values\n",
+    "    y = ds.phat.values\n",
+    "    yerr = np.sqrt(ds.varhat_phat.values)\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    # Plot\n",
+    "    i, j = c // 3, c % 3\n",
+    "    c+=1\n",
+    "    \n",
+    "    plt.sca(ax[i, j])\n",
+    "    plt.title(transformation)\n",
+    "    plt.ylim([0.45, 1.05])\n",
+    "    plt.axhline(0.5, color='gray', linestyle='--')\n",
+    "\n",
+    "    plt.errorbar(x, y, yerr=yerr, )\n",
+    "    plt.xlabel('Transformation level')\n",
+    "\n",
+    "plt.tight_layout()\n"
+   ],
+   "id": "4bec37a335eebb04",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2000x1500 with 9 Axes>"
+      ],
+      "image/png": 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xrFm/XHOZMWNGMmjQoAqP6ZfVi2XcXi5dujT5/ve/n+6Hzj///GT58uVJ3759y2Jpn169WMZcaOLEiUm/fv2SXXfdNTn99NPTfPLcc88Vy4wvG2PcrRiXmsaxOuMCxbbUNIY777xz8sUXXyS/+MUvko4dO6a59OzZs5Mrrrgi5++lUGJ4zTXXpDE8+eST021/HNObPn168oc//CHn7yVf95cxfjGOpevHuMVj4Jtvvjnp3Llzct5556W5yMCBA3P+XgopjnEsIY4txPiV37c0a9Ys5++lUGK45hI/32+88UbO30fI3yXnDSjKJR6wxQHXmEyOHj062Xfffct+Fw9IYkJUej8OkFUmrle6zuOPP54WzePrxYG1eL9du3ZFF6s4qFCZuCEo/5rXXntt8vHHH6fJwvPPP58mULl+n/kWq8033zx59tlnk08++SQdbIjrP/DAA2XJVLHE6brrrkumTZuWDgp89tlnaWEkJpprvqY+tf5Y6VM1OwDWp9YfK31qdRxuv/32sm1a3Lf99a9/TXr06FE0fWpjFMZj0eyee+5Jt2XxIPf//u//0oOyXLczH5ejjz46efPNN9N+NXny5LQ4seY6+t66Y7jVVlul/S9+juM+9O233073qeULjuJYeexiAacy5XO59fW/Fi1apIXwkpKSZP78+clvfvObohzcX1csqzoejeLzxLLm/XJ9hXH9svqxPPPMM8uOQeLg37HHHlshlvbp1YtlzHMeeuih5KOPPkpjOWXKlOTCCy8UyyJZNsa4WzEuNYnjmovCeO1iuN9++yWjRo1K87yYQ8cv7NavXz/nfaFQ+mGDBg2Sq6++Oi2Gx23/+++/nx4HN2/ePOfvI1/3l/Fn+ZpM6XPGjx+fxjz2w/jlsly/j0KLY7xdk/y5GJba9MXyi8J4WGd86/3nBgAAAAAAAABkkmuMAwAAAAAAAJBpCuMAAAAAAAAAZJrCOAAAAAAAAACZpjAOAAAAAAAAQKYpjAMAAAAAAACQaQrjAAAAAAAAAGSawjgAAAAAAAAAmaYwDgAAAAAAAECmKYwDRaNVq1Zh2LBhYeHChWHevHmhUJx++umbpL0zZswIgwYNCpvS8OHDwx133LFJ/yYAwIaSV66bvBIAQF5ZF+SVQF1TGAfqXJIk61yuueaanET9wgsvDDvssEPo0aNH6NSpU8hHlSV7f/zjH/O2vQAAG5O8svbklQAA8sq6IK8EsqRhrhsAZE/r1q3Lbp988slh8ODBoXPnzmWPxTO2y2vQoEFYuXLlRm9X+/btw7hx48Lbb79d69do1KhRWL58ediUlixZki4AAMVGXlm35JUAQLGSV9YteSVQqJwxDtS5Tz75pGz54osv0jN9Su936dIlLYwfccQR4fXXXw9Lly4NBxxwQGjXrl0YOnRomD17dliwYEEYM2ZMOOSQQ9b6duLll18efvOb34SSkpLw/vvvh7POOqtC0fruu+8Os2bNCosXLw7vvfde+PGPf1z23G9+85vptOSxPUOGDEkfb9OmTfp349+MbY1nZ7ds2bLsNePZ7W+88Ub43ve+F959992yAnV8jbPPPjv85S9/CV9++WWYPHly2G+//dLie5wePL7HESNGpO+r1PreY3zerrvuGu68886ys6Oqmkr93HPPTQv8MX7//ve/w2mnnVbh9/G5sc1PPvlk2r5p06aFY445pkb/x+bNm4df//rXYc6cOWls/vGPf4Tu3bunv+vYsWP6N8p/4SG64IILKnzxoFu3buGZZ55J329834888kjYdttta9QOAKB4ySvllaXklQCAvHI145XGK4ENEysvFjHQB/SBjdIHTj/99GTevHll9/v165dEEyZMSA499NCkXbt2SYsWLZLu3bsnZ599dtKtW7ekQ4cOyeDBg5NFixYlbdq0KXvujBkzkrlz5ybnnXde0r59++Syyy5LVqxYkXTq1Cn9/UUXXZS8//77yQEHHJDssssuSd++fZNvfetb6e+222675Jlnnkn+8Ic/JK1atUqaNWuW1KtXLxk/fnzy8ssvJ3vvvXey7777JmPHjk2GDx9e9jevueaaZMGCBelze/Tokey5557p49GHH36YnHjiiUnHjh2TJ598Mnn33XeTF154IRk4cGDSpUuXZOTIkenzSl9rfe8xxuGDDz5IrrzyyrSNcakshscff3yydOnSNA7xb1944YXJ8uXLk/79+5etE8XXiu8/xurOO+9MSkpK0r9R1f8qxnfQoEFl94cNG5Y8/fTTSc+ePdP23nrrrcmnn35a9hpjxoxJ30P514jxK32sefPmySeffJJcf/31SefOndP4Pffcc8k//vGPsvVjrO+44w7bH9sffUAf0Af0AX1AH5BXyivllbYD9gX6gD6gD+gD+sAm6QPGK41XGq+0vbW9DcUcg5w3wCIG+kARJprHHnvsep87ceLE5Pzzz69QuH3kkUcqrDN79uzknHPOSW/fddddaWG6qtd76qmnkiFDhpTdj4X5WFDeeeedyx7r2rVr2r599tmnrDAei9CxsF7+taLyReHevXunj5155pllj5188slp4bum77F8cbqyGL766qvJAw88UGGdP/7xj8lf//rXKtu35ZZbpo8dfvjhVbal/N+OXyqYP39+0rhx4wrrTJ8+PTnrrLPS23HdeL/0d7FIH8UieLz/k5/8JHn22WcrPH+nnXZK14nrxvsK47n/jFrEQB/QB/QBfaBQ+oC8Ul4pr8z959AiBvqAPqAP6ANZ6APySnmlvDL3n0OLGIQcxcBU6kBOxGnUy2vSpEm49dZb0ynJ47Thcertrl27hl122aXCem+++WaF+3F67tKpz3/729+GHj16hKlTp4a77rorHHbYYetsQ3z9Dz/8MHz00Udlj02ZMiX9+/F3peKU7XPnzl3r+eXbEqf5jCZOnFjhsS222CI0bdq0Ru9xfeJz4jTt5cX75du8ZvsWLVqUTodefpr4dfna174Wttpqq/DZZ5+l7Sxddtttt3S6+OgPf/hDOvV779690/unnnpqeg33GP/S1xgwYECF58dp36PS1wAA2FDySnklAEBdkFfKK4Hsa5jrBgDFKV73uryf//znaSH74osvTq9RHa8R/qc//Sk0bty4wnrLly+vcD9e57p+/dXf8YnXAo+F2yOPPDIceuih4YknnggvvPBCOPHEE+u0rZW1pfR64JU9Vtq+6r7HurKuWK1PLIp//PHHoX///mv9bv78+WWF/3/+85/h29/+dnjttdfSn/fff3+F14jXYL/sssvWeo342gAAdUFeKa8EAJBXrma80nglsG4K40Be6Nu3b3rG99ChQ8vOro5nI9dUPCs5FsTjEovOzz33XGjRokV6hvaa4tnhbdq0CTvvvHPZWePxrOu4fjyrOxfvcdmyZaFBgwbrfJ3Y7vhajzzySIXXrss2jx8/PrRu3TqsWLEiPWO+Ko8++mi45ZZbwuOPPx7atWuXnkVe/jVOOOGE8N5774WVK1fWWdsAANZFXrmavBIAYMPIK+WVQPaYSh3IC9OnTw/f+MY30um3u3fvHh577LFqn91c6sILLwzf+ta3QufOnUPHjh3TM8XjmcmlZzivKZ5NHqc+j8XdvfbaK/Tq1SstNr/44ovplOC5eI+xiHzQQQeFHXfcMWy77baVvk6cjv2MM84I5557bujQoUP6vuPrxjPS60qMzahRo9IifjzLvW3btqFPnz7hZz/7WejZs2fZek8++WQ6VXw8U3z48OEVzgS/9957wzbbbJMWzffZZ5+0cD5w4MDw0EMP1fh/CwBQXfLK1eSVAAAbRl4prwSyR2UCyAs/+tGP0rO6R44cmU6/Hc/0jmcc1/Rs8UsvvTS9HtDYsWPTs7GPOuqosinNK3Pcccelf/fll19Oi8HvvvtuOPnkk0Ou3uPVV1+dtvudd96p9Lrm0dNPPx0GDRqUTsk+adKkcM4554QzzzwzvPTSS3Xa3hi7GJchQ4aEadOmpWeDxwJ56fXUo4ULF6bvJV7bPX7BoLxYJI/frI1nwA8bNiz9EsKdd96ZflFh1apVddpWAIBS8srV5JUAABtGXimvBLKnXrzsbK4bAQAAAAAAAAAbizPGAQAAAAAAAMg0hXEAAAAAAAAAMk1hHAAAAAAAAIBMUxgHAAAAAAAAINMUxgEAAAAAAADINIVxAAAAAAAAADJNYRwAAAAAAACATFMYBwAAAAAAACDTFMYBAAAAAAAAyDSFcQAAAAAAAAAyTWEcAAAAAAAAgExTGAcAAAAAAAAgZNn/BzkJF79yS17UAAAAAElFTkSuQmCC"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 3
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/hobj/__init__.py b/hobj/__init__.py
index e69de29..0c4ae45 100644
--- a/hobj/__init__.py
+++ b/hobj/__init__.py
@@ -0,0 +1,41 @@
+from hobj.benchmarks.mut_oneshot_benchmark import MutatorOneshotBenchmark
+from hobj.benchmarks.mut_highvar_benchmark import MutatorHighVarBenchmark
+
+from hobj.data.behavior import load_highvar_behavior, load_oneshot_behavior
+
+from hobj.data.images import (
+    load_image,
+    list_image_ids,
+    get_image_path,
+    load_imageset_meta_highvar,
+    load_imageset_meta_oneshot,
+    load_imageset_meta_warmup,
+    load_imageset_meta_catch,
+)
+
+from hobj.learning_models.linear_learning_model.make_model import (
+    create_linear_learner,
+)
+
+from hobj.learning_models.random_guesser import RandomGuesser
+
+__all__ = [
+    # Image loader:
+    "list_image_ids",
+    "get_image_path",
+    "load_image",
+    # Learning Models:
+    "create_linear_learner",
+    "RandomGuesser",
+    # Benchmarks:
+    "MutatorHighVarBenchmark",
+    "MutatorOneshotBenchmark",
+    # Raw behavior loaders
+    "load_highvar_behavior",
+    "load_oneshot_behavior",
+    # Image meta loaders:
+    "load_imageset_meta_highvar",
+    "load_imageset_meta_oneshot",
+    "load_imageset_meta_warmup",
+    "load_imageset_meta_catch",
+]
diff --git a/hobj/behavioral_data/human_data.py b/hobj/behavioral_data/human_data.py
deleted file mode 100644
index daa64f3..0000000
--- a/hobj/behavioral_data/human_data.py
+++ /dev/null
@@ -1,150 +0,0 @@
-import xarray as xr
-import hobj.behavioral_data.template.sr_data as template
-import os
-import numpy as np
-import collections
-from tqdm import trange
-
-_loc = os.path.dirname(__file__)
-
-class MutatorHighVarDataset(template.SR_Dataset):
-    """
-    "Experiment 1" from the paper.
-    """
-    data_url = 'https://hlbdatasets.s3.amazonaws.com/behavioral_data/ds_human_raw_MutatorHighVar.nc'
-    probe_trials = [25, 51, 77, 103]
-
-    def apply_experiment_specific_filtering(self, ds_flat):
-        # Remove data variables
-        ds_flat = ds_flat[['perf', 'reaction_time_msec']]
-
-        # Assign subtask names
-        import hobj.images.imagesets as imagesets
-        ds_meta = imagesets.MutatorHighVarImageset().ds_meta
-        urls = ds_flat.stimulus_url.transpose('session', 'trial').values
-        url_to_obj = {url: obj for url, obj in zip(ds_meta.image_url.values, ds_meta.obj.values)}
-        sseq = []
-        for i in range(urls.shape[0]):
-            urlseq = urls[i]
-            urlseq = [u for u in urlseq if 'orange' not in u and 'blue' not in u]
-            objects = [url_to_obj[url] for url in urlseq]
-            objects = sorted(set(objects))
-            assert len(objects) == 2
-            subtask = ','.join(objects)
-
-            sseq.append(subtask)
-        ds_flat = ds_flat.assign_coords(subtask = (['session'], sseq))
-        return ds_flat
-
-    def load_ds_worker_table(self):
-        """
-        A worker-wise table of the human data.
-
-        :return:
-            ds_data:
-                k: (worker_id, subtask, trial)
-                n:  (worker_id, subtask, trial)
-        """
-
-        ds_data = self.load_ds_data()
-
-        all_subtasks = sorted(np.unique(ds_data.subtask.values))
-        assert len(all_subtasks) == 64
-
-        ntrials = len(ds_data.trial.values)
-        subtask_to_workers = collections.defaultdict(list)
-        for sub, worker in zip(ds_data.subtask.values, ds_data.worker_id.values):
-            subtask_to_workers[sub].append(worker)
-        max_reps_per_subtask = max(len(v) for v in subtask_to_workers.values())
-        assert max_reps_per_subtask == 50
-
-        all_workers = sorted(list(set(ds_data.worker_id.values)))
-        nworkers = len(all_workers)
-
-        k = np.zeros((nworkers, len(all_subtasks), ntrials), dtype=int)
-        n = np.zeros((nworkers, len(all_subtasks), ntrials), dtype=int)
-
-        ds_data = ds_data.transpose('session', 'trial')
-        for i_session in trange(len(ds_data.session.values), desc='unpacking data', disable = True):
-            subtask = ds_data.subtask.values[i_session]
-            worker_id = ds_data.worker_id.values[i_session]
-
-            i_subtask = all_subtasks.index(subtask)
-            i_worker = all_workers.index(worker_id)
-            perf_seq = ds_data.perf.values[i_session]
-            k[i_worker, i_subtask, :] = perf_seq
-            n[i_worker, i_subtask, :] += 1
-
-        ds_table = xr.Dataset(
-            data_vars=dict(
-                k=(['worker_id', 'subtask', 'trial'], k),
-                n=(['worker_id', 'subtask', 'trial'], n),
-            ),
-            coords=dict(
-                worker_id=all_workers,
-                subtask=all_subtasks,
-                trial=np.arange(ntrials),
-            )
-        )
-
-        assert np.max(ds_table.n == 1)
-        assert np.max(ds_table.k == 1)
-
-        return ds_table
-
-
-class MutatorOneShotGeneralizationDataset(template.SR_Dataset):
-
-    """
-    "Experiment 2" from the paper.
-    """
-
-    data_url = 'https://hlbdatasets.s3.amazonaws.com/behavioral_data/ds_human_raw_MutatorOneShotGeneralization.nc'
-
-    def apply_experiment_specific_filtering(self, ds_flat):
-
-        import numpy as np
-        import hobj.images.imagesets as imagesets
-        ds_meta = imagesets.MutatorOneshotImageset().ds_meta
-        image_url_to_transformation = {url:trans for url, trans in zip(ds_meta.image_url.values, ds_meta.transformation.values)}
-        image_url_to_transformation_level = {url:trans_level for url, trans_level in zip(ds_meta.image_url.values, ds_meta.transformation_level.values)}
-
-        valid_sess = (((ds_flat.stimulus_url == '').sum('trial') == 0))
-        ds_flat = ds_flat.sel(session=valid_sess)
-
-
-
-        get_trans_level = np.vectorize(lambda url: float(image_url_to_transformation_level[url]))
-        get_trans = np.vectorize(lambda url: str(image_url_to_transformation[url]))
-
-        trans = get_trans(ds_flat.stimulus_url.values)
-        trans_level = get_trans_level(ds_flat.stimulus_url.values)
-
-        trans_ids = []
-        for i in range((trans.shape[0])):
-            cur = []
-            for j in range((trans.shape[1])):
-                t = trans[i, j]
-                l = trans_level[i, j]
-                cur.append(' | '.join([str(t), str(l)]))
-            trans_ids.append(cur)
-
-
-        ds_flat = ds_flat.assign_coords(
-            transformation_type=(ds_flat.stimulus_url.dims, trans),
-            transformation_level=(ds_flat.stimulus_url.dims, trans_level),
-            transformation=(ds_flat.stimulus_url.dims, trans_ids)
-        )
-
-        ds_flat = ds_flat.transpose('trial', 'session')
-        ds_flat.transformation.values[9] = 'probe | 10'
-        ds_flat.transformation.values[14] = 'probe | 15'
-        ds_flat.transformation.values[19] = 'probe | 20'
-
-        # Remove data variables
-        ds_flat = ds_flat[['perf', 'reaction_time_msec']]
-        return ds_flat
-
-if __name__ == '__main__':
-    dataset = MutatorHighVarDataset()
-    ds_data_example = dataset.load_ds_data()
\ No newline at end of file
diff --git a/hobj/behavioral_data/readme.md b/hobj/behavioral_data/readme.md
deleted file mode 100644
index 82d0201..0000000
--- a/hobj/behavioral_data/readme.md
+++ /dev/null
@@ -1,3 +0,0 @@
-#
-
-Worker IDs have been obfuscated. 
\ No newline at end of file
diff --git a/hobj/behavioral_data/template/sr_data.py b/hobj/behavioral_data/template/sr_data.py
deleted file mode 100644
index 3d524e7..0000000
--- a/hobj/behavioral_data/template/sr_data.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import xarray as xr
-import hobj.utils.hash
-import hobj.utils.file_io as io
-import hobj.config as config
-import os
-
-class SR_Dataset(object):
-    """
-        - Wraps data on disk
-        - Ensures data is valid
-
-    """
-
-    """
-    xr.DataArray
-
-    mandatory coords: 
-        stimulus_url: (trial, session)
-        worker_id: (trial, session) 
-        timestamp_start: (trial, session)
-
-    mandatory data variables: 
-        perf: (trial, session) 0s or 1s
-    """
-
-    data_url = None
-    probe_trials = None
-
-    def __init__(self, cachedir = config.behavior_cachedir):
-        self.cachedir = cachedir
-        self.savepath = io.get_local_save_location(url = self.data_url, cachedir = self.cachedir)
-
-    @property
-    def name(self):
-        return str(type(self).__name__)
-
-
-    def load_raw_ds(self):
-
-        if not os.path.exists(self.savepath):
-            io.download_file(url = self.data_url, cachedir=self.cachedir)
-
-        ds_data = xr.load_dataset(self.savepath)
-        return ds_data
-
-    def load_ds_data(self):
-        ds_data = self.load_raw_ds()
-        mandatory_coords = [
-            'stimulus_url',
-            'worker_id',
-            'timestamp_start',
-        ]
-        mandatory_vars = [
-            'perf',
-        ]
-
-        mandatory_dims = ['trial', 'session']
-
-        for d in mandatory_dims:
-            assert d in ds_data.dims, d
-
-        for v in mandatory_vars:
-            assert v in ds_data.data_vars, v
-        for c in mandatory_coords:
-            assert c in ds_data.coords, c
-
-        # ds_flat = ds_data.reset_coords()[mandatory_coords + mandatory_vars]
-        ds_flat = ds_data.reset_coords().set_coords(mandatory_coords)
-        ds_flat = ds_flat.assign_coords(trial = range(len(ds_flat.trial.values)))
-
-        hash = ''
-        for v in mandatory_vars + mandatory_coords:
-            hash += hobj.utils.hash.hash_ndarray(ds_flat[v].values)
-        ds_flat = ds_flat.assign_coords(behavioral_hash=hash)
-
-        if self.probe_trials is not None:
-            ds_flat = ds_flat.assign_coords(
-                probe_perf=ds_flat.perf.sel(trial=self.probe_trials).mean('trial')
-            )
-            ds_flat = ds_flat.sel(trial=[v for v in ds_flat.trial.values if v not in self.probe_trials])
-            ds_flat = ds_flat.assign_coords(trial=range(len(ds_flat.trial.values)))
-
-        ds_flat = self.apply_experiment_specific_filtering(ds_flat = ds_flat)
-        return ds_flat
-
-    def apply_experiment_specific_filtering(self, ds_flat):
-        return ds_flat
diff --git a/hobj/benchmarks/benchmark_utils/data_resampling.py b/hobj/benchmarks/benchmark_utils/data_resampling.py
deleted file mode 100644
index fb2f867..0000000
--- a/hobj/benchmarks/benchmark_utils/data_resampling.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import xarray as xr
-import numpy as np
-from tqdm import tqdm, trange
-
-
-def get_subtask_grain_splithalf_and_bootstrap_resampled_data(ds_raw, nsplits):
-    """
-    Performs split-half and bootstrap resampling of the data, by worker.
-
-    ds_raw: xr.Dataset with mandatory variables:
-        stimulus_url: (trial, session)
-        worker_id: (trial, session)
-        perf: (trial, session)
-        subtask: (session)
-
-    ds_splits:
-        perf: (worker_split, split_iter, trial, subtask)
-    ds_boot:
-        perf: (boot_iter, trial, subtask)
-    """
-
-    all_subtasks = sorted(set(ds_raw.subtask.values))
-    subtask_to_i_subtask = {sub: i_subtask for (i_subtask, sub) in enumerate(all_subtasks)}
-    ntrials = len(ds_raw.trial.values)
-    nsubtasks = len(all_subtasks)
-
-    # %% Get workerwise data
-    make_subtask_kn_table = lambda: np.zeros((nsubtasks, ntrials, 2)).astype(int)
-    worker_to_kn = {}
-    nworkers = len(set(ds_raw.worker_id.values))
-    for worker_id, ws in tqdm(ds_raw.groupby('worker_id'), total=nworkers):
-
-        worker_to_kn[worker_id] = make_subtask_kn_table()
-
-        perfs = ws.perf.transpose('session', 'trial').values
-        subtask_seq = ws.subtask.values
-
-        for i_sess in range(perfs.shape[0]):
-            subtask_cur = subtask_seq[i_sess]
-            i_subtask = subtask_to_i_subtask[subtask_cur]
-            perfs_cur = perfs[i_sess]
-
-            worker_to_kn[worker_id][i_subtask, :, 0] += perfs_cur.astype(int)
-            worker_to_kn[worker_id][i_subtask, :, 1] += 1
-    all_workers = sorted(worker_to_kn.keys())
-
-    # %% Perform splitting and bootstrapping
-    i_split_to_k = []
-    i_split_to_n = []
-
-    i_boot_to_k = []
-    i_boot_to_n = []
-
-    for i_split in trange(nsplits):
-        RS = np.random.RandomState(i_split)
-        wcur = RS.permutation(all_workers)
-        workers0 = wcur[:len(wcur) // 2]
-        workers1 = wcur[len(wcur) // 2:]
-        workers_boot = RS.choice(all_workers, replace=True, size=nworkers)
-
-        dat0 = make_subtask_kn_table()
-        dat1 = make_subtask_kn_table()
-        datboot = make_subtask_kn_table()
-
-        for workers, dat in zip([workers0, workers1, workers_boot], [dat0, dat1, datboot]):
-            for w in workers:
-                dat += worker_to_kn[w]
-
-        i_split_to_k.append((dat0[..., 0], dat1[..., 0]))
-        i_split_to_n.append((dat0[..., 1], dat1[..., 1]))
-        i_boot_to_k.append(datboot[..., 0])
-        i_boot_to_n.append(datboot[..., 1])
-
-    ds_splits = xr.Dataset(
-        data_vars={
-            'k': (['split_iter', 'worker_split', 'subtask', 'trial'], i_split_to_k),
-            'n': (['split_iter', 'worker_split', 'subtask', 'trial'], i_split_to_n)
-        },
-        coords={
-            'subtask': all_subtasks,
-        }
-    )
-    ds_splits['perf'] = ds_splits.k / ds_splits.n
-
-    ds_splits = ds_splits.transpose('worker_split', 'split_iter', 'trial', 'subtask')
-
-    ds_boot = xr.Dataset(
-        data_vars={
-            'k': (['boot_iter', 'subtask', 'trial'], i_boot_to_k),
-            'n': (['boot_iter', 'subtask', 'trial'], i_boot_to_n)
-        },
-        coords={
-            'subtask': all_subtasks,
-        }
-    )
-    ds_boot['perf'] = ds_boot.k / ds_boot.n
-    ds_boot = ds_boot.transpose('boot_iter', 'trial', 'subtask')
-
-
-    rvalues = {
-        'ds_boot':ds_boot,
-        'ds_splits':ds_splits,
-    }
-    return rvalues
-
-
-def get_policy_grain_splithalf_and_bootstrap_resampled_data(ds_raw, nsplits):
-    """
-    Performs split-half and bootstrap resampling of the data, by worker.
-
-    ds_raw: xr.Dataset with mandatory variables:
-        stimulus_url: (trial, session)
-        worker_id: (trial, session)
-        perf: (trial, session)
-        subtask: (session)
-
-    ds_splits:
-        perf: (worker_split, split_iter, trial, subtask, stimulus_category)
-    """
-
-    all_subtasks = sorted(set(ds_raw.subtask.values))
-    subtask_to_i_subtask = {sub: i_subtask for (i_subtask, sub) in enumerate(all_subtasks)}
-    ntrials = len(ds_raw.trial.values)
-    nsubtasks = len(all_subtasks)
-
-    # %% Get workerwise data
-    nobjects_per_subtask = 2
-    make_subtask_kn_table = lambda: np.zeros((nsubtasks, nobjects_per_subtask, ntrials, 2)).astype(int)
-
-    # k: the number of times that action 1 was chosen
-    # n: the number of observations
-
-    worker_to_kn = {}
-    nworkers = len(set(ds_raw.worker_id.values))
-    import hobj.images.imagesets as imagesets
-    ds_meta = imagesets.MutatorHighVarImageset().ds_meta
-
-    for worker_id, ws in tqdm(ds_raw.groupby('worker_id'), total=nworkers):
-
-        worker_to_kn[worker_id] = make_subtask_kn_table()
-
-        perfs = ws.perf.transpose('session', 'trial').values
-        subtask_seq = ws.subtask.values
-
-        for i_sess in range(perfs.shape[0]):
-            subtask_cur = subtask_seq[i_sess]
-            i_subtask = subtask_to_i_subtask[subtask_cur]
-            perfs_cur = perfs[i_sess]
-            stimseq_cur = ws.stimulus_url.values[i_sess]
-            objseq_cur = ds_meta.sel(image_url = stimseq_cur).obj.values
-            subtask_objects = sorted(set(objseq_cur))
-            assert len(subtask_objects) == nobjects_per_subtask
-            i_objseq_cur = [subtask_objects.index(obj) for obj in objseq_cur]
-            for t, (i_obj, p) in enumerate(zip(i_objseq_cur, perfs_cur)):
-                if p > 0:
-                    performed_action1 = i_obj
-                else:
-                    performed_action1 = 1 - i_obj
-                worker_to_kn[worker_id][i_subtask, i_obj, t, 0] += int(performed_action1)
-                worker_to_kn[worker_id][i_subtask, i_obj, t, 1] += 1
-    all_workers = sorted(worker_to_kn.keys())
-
-
-
-    # %% Perform splitting and bootstrapping
-    i_split_to_k = []
-    i_split_to_n = []
-
-    i_boot_to_k = []
-    i_boot_to_n = []
-
-    for i_split in trange(nsplits):
-        RS = np.random.RandomState(i_split)
-        wcur = RS.permutation(all_workers)
-        workers0 = wcur[:len(wcur) // 2]
-        workers1 = wcur[len(wcur) // 2:]
-        workers_boot = RS.choice(all_workers, replace=True, size=nworkers)
-
-        dat0 = make_subtask_kn_table()
-        dat1 = make_subtask_kn_table()
-        datboot = make_subtask_kn_table()
-
-        for workers, dat in zip([workers0, workers1, workers_boot], [dat0, dat1, datboot]):
-            for w in workers:
-                dat += worker_to_kn[w]
-
-        i_split_to_k.append((dat0[..., 0], dat1[..., 0]))
-        i_split_to_n.append((dat0[..., 1], dat1[..., 1]))
-        i_boot_to_k.append(datboot[..., 0])
-        i_boot_to_n.append(datboot[..., 1])
-
-    ds_splits = xr.Dataset(
-        data_vars={
-            'k': (['split_iter', 'worker_split', 'subtask', 'stimulus_category', 'trial'], i_split_to_k),
-            'n': (['split_iter', 'worker_split', 'subtask',  'stimulus_category','trial'], i_split_to_n)
-        },
-        coords={
-            'subtask': all_subtasks,
-        }
-    )
-
-    ds_splits['action'] = ds_splits.k / ds_splits.n
-    ds_splits = ds_splits.transpose('worker_split', 'split_iter', 'trial', 'subtask',  'stimulus_category',)
-
-    ds_boot = xr.Dataset(
-        data_vars={
-            'k': (['boot_iter', 'subtask',  'stimulus_category', 'trial'], i_boot_to_k),
-            'n': (['boot_iter', 'subtask',  'stimulus_category', 'trial'], i_boot_to_n)
-        },
-        coords={
-            'subtask': all_subtasks,
-        }
-    )
-    ds_boot['action'] = ds_boot.k / ds_boot.n
-    ds_boot = ds_boot.transpose('boot_iter', 'trial', 'subtask', 'stimulus_category')
-
-
-    rvalues = {
-        'ds_boot':ds_boot,
-        'ds_splits':ds_splits,
-    }
-    return rvalues
-
-
-if __name__ == '__main__':
-    import hobj.behavioral_data.human_data as human_data
-    ds_raw = human_data.MutatorHighVarDataset().load_ds_data()
-    result = get_policy_grain_splithalf_and_bootstrap_resampled_data(ds_raw = ds_raw, nsplits = 1000)
diff --git a/hobj/benchmarks/benchmarks.py b/hobj/benchmarks/benchmarks.py
deleted file mode 100644
index 74dccc9..0000000
--- a/hobj/benchmarks/benchmarks.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import xarray as xr
-import hobj.learning_models.learning_model as lm
-
-class Benchmark(object):
-
-    def __init__(self):
-        return
-
-    def evaluate_model(self, learner: lm.LearningModel, force_recompute=False):
-        return xr.Dataset()
-
-
diff --git a/hobj/behavioral_data/__init__.py b/hobj/benchmarks/binary_classification/__init__.py
similarity index 100%
rename from hobj/behavioral_data/__init__.py
rename to hobj/benchmarks/binary_classification/__init__.py
diff --git a/hobj/benchmarks/binary_classification/estimator.py b/hobj/benchmarks/binary_classification/estimator.py
new file mode 100644
index 0000000..13c0267
--- /dev/null
+++ b/hobj/benchmarks/binary_classification/estimator.py
@@ -0,0 +1,211 @@
+from dataclasses import dataclass
+from typing import Dict, List, Tuple
+
+import numpy as np
+import xarray as xr
+
+from hobj.benchmarks.binary_classification.simulation import (
+    BinaryClassificationSubtaskResult,
+)
+from hobj.stats import binomial as binomial_funcs
+
+
+@dataclass(frozen=True)
+class _BootstrapSamples:
+    """Bootstrap resamples for binary-classification benchmark statistics."""
+
+    boot_k: np.ndarray
+    boot_n: np.ndarray
+
+
+def build_learning_curve_statistics(
+    subtask_name_to_results: Dict[str, List[BinaryClassificationSubtaskResult]],
+    nbootstrap_samples: int,
+    bootstrap_by_worker: bool,
+) -> xr.Dataset:
+    """Build learning-curve summary statistics as a plain xarray dataset.
+
+    Args:
+        subtask_name_to_results: Mapping from subtask name to session results.
+        nbootstrap_samples: Number of bootstrap replicates to generate.
+        bootstrap_by_worker: Whether to resample workers instead of sessions.
+
+    Returns:
+        A dataset containing point estimates and bootstrap replicates.
+    """
+
+    subtask_names = sorted(subtask_name_to_results.keys())
+    subtask_name_to_i = {name: i for i, name in enumerate(subtask_names)}
+    nsubtasks = len(subtask_names)
+
+    ntrials_observed = set()
+    all_workers = set()
+    nsessions = 0
+    for results in subtask_name_to_results.values():
+        nsessions += len(results)
+        for result in results:
+            ntrials_observed.add(len(result.perf_seq))
+            all_workers.add(result.worker_id)
+
+    if len(ntrials_observed) != 1:
+        raise ValueError(
+            f"Expected all subtasks to have the same number of trials, but got {ntrials_observed}"
+        )
+
+    ntrials = ntrials_observed.pop()
+    all_workers = sorted(all_workers)
+    worker_to_i = {worker: i for i, worker in enumerate(all_workers)}
+
+    i_subtasks: List[int] = []
+    i_workers: List[int] = []
+    i_session = 0
+    perf_mat = np.zeros((nsessions, ntrials), dtype=bool)
+    for subtask_name, results in subtask_name_to_results.items():
+        i_subtask = subtask_name_to_i[subtask_name]
+        for result in results:
+            i_worker = worker_to_i[result.worker_id]
+            perf_mat[i_session] = result.perf_seq
+            i_session += 1
+            i_subtasks.append(i_subtask)
+            i_workers.append(i_worker)
+
+    k_mat = np.zeros(shape=(nsubtasks, ntrials), dtype=int)
+    n_mat = np.zeros(shape=nsubtasks, dtype=int)
+    for i_subtask, perf in zip(i_subtasks, perf_mat):
+        k_mat[i_subtask] += perf
+        n_mat[i_subtask] += 1
+
+    phat, varhat_phat = _get_point_estimates(k=k_mat, n=n_mat[:, None])
+
+    if bootstrap_by_worker:
+        bootstrap_resamples = _get_bootstrap_resamples_by_worker(
+            perf_mat=perf_mat,
+            i_subtasks=i_subtasks,
+            i_workers=i_workers,
+            nbootstrap_samples=nbootstrap_samples,
+        )
+    else:
+        bootstrap_resamples = _get_bootstrap_resamples_by_session(
+            perf_mat=perf_mat,
+            i_subtasks=i_subtasks,
+            nbootstrap_samples=nbootstrap_samples,
+        )
+
+    boot_phat, boot_varhat_phat = _get_point_estimates(
+        k=bootstrap_resamples.boot_k,
+        n=bootstrap_resamples.boot_n[..., None],
+    )
+
+    return xr.Dataset(
+        data_vars=dict(
+            phat=(["subtask", "trial"], phat),
+            varhat_phat=(["subtask", "trial"], varhat_phat),
+            boot_phat=(["boot_iter", "subtask", "trial"], boot_phat),
+            boot_varhat_phat=(["boot_iter", "subtask", "trial"], boot_varhat_phat),
+        ),
+        coords=dict(subtask=subtask_names),
+    )
+
+
+def _get_point_estimates(
+    k: np.ndarray,
+    n: np.ndarray,
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Estimate binomial means and variances from count matrices."""
+
+    phat = k / n
+    varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion(
+        kvec=k, nvec=n
+    )
+    return phat, varhat_phat
+
+
+def _get_bootstrap_resamples_by_worker(
+    perf_mat: np.ndarray,
+    i_subtasks: List[int],
+    i_workers: List[int],
+    nbootstrap_samples: int,
+) -> _BootstrapSamples:
+    """Bootstrap binary-classification sessions by worker."""
+
+    if perf_mat.dtype != np.dtype(bool):
+        raise ValueError(
+            f"Expected perf_mat to have dtype bool, but got {perf_mat.dtype}"
+        )
+
+    ntrials = perf_mat.shape[1]
+    nworkers = max(i_workers) + 1
+    nsubtasks = max(i_subtasks) + 1
+
+    if len(set(i_subtasks)) != nsubtasks:
+        raise ValueError(
+            f"Expected i_subtasks to be a contiguous range, but got {set(i_subtasks)}"
+        )
+
+    if len(set(i_workers)) != nworkers:
+        raise ValueError(
+            f"Expected i_workers to be a contiguous range, but got {set(i_workers)}"
+        )
+
+    perf_table = np.zeros(shape=(nworkers, nsubtasks, ntrials), dtype=bool)
+    was_recorded_table = np.zeros(shape=(nworkers, nsubtasks), dtype=bool)
+    for i_subtask, i_worker, perf in zip(i_subtasks, i_workers, perf_mat):
+        perf_table[i_worker, i_subtask] = perf
+        was_recorded_table[i_worker, i_subtask] = True
+
+    gen = np.random.default_rng()
+    boot_k = np.zeros(shape=(nbootstrap_samples, nsubtasks, ntrials), dtype=int)
+    boot_n = np.zeros(shape=(nbootstrap_samples, nsubtasks), dtype=int)
+    for i_bootstrap in range(nbootstrap_samples):
+        i_workers_boot = gen.integers(low=0, high=nworkers, size=nworkers)
+        boot_k[i_bootstrap] = perf_table[i_workers_boot].sum(axis=0)
+        boot_n[i_bootstrap] = was_recorded_table[i_workers_boot].sum(axis=0)
+
+    return _BootstrapSamples(
+        boot_k=boot_k,
+        boot_n=boot_n,
+    )
+
+
+def _get_bootstrap_resamples_by_session(
+    perf_mat: np.ndarray,
+    i_subtasks: List[int],
+    nbootstrap_samples: int,
+) -> _BootstrapSamples:
+    """Bootstrap binary-classification sessions within each subtask."""
+
+    ntrials = perf_mat.shape[1]
+    nsubtasks = max(i_subtasks) + 1
+
+    if len(set(i_subtasks)) != nsubtasks:
+        raise ValueError(
+            f"Expected i_subtasks to be a contiguous range, but got {set(i_subtasks)}"
+        )
+
+    i_subtask_to_perf_data: Dict[int, np.ndarray] = {}
+    for i_subtask, perf in zip(i_subtasks, perf_mat):
+        if i_subtask not in i_subtask_to_perf_data:
+            i_subtask_to_perf_data[i_subtask] = np.empty((0, ntrials), dtype=bool)
+        i_subtask_to_perf_data[i_subtask] = np.vstack(
+            [i_subtask_to_perf_data[i_subtask], perf]
+        )
+
+    gen = np.random.default_rng()
+    boot_k = np.zeros(shape=(nbootstrap_samples, nsubtasks, ntrials), dtype=int)
+    boot_n = np.zeros(shape=(nbootstrap_samples, nsubtasks), dtype=int)
+    for i_bootstrap in range(nbootstrap_samples):
+        for i_subtask in range(nsubtasks):
+            perf_data = i_subtask_to_perf_data[i_subtask]
+            nsessions_for_subtask = len(perf_data)
+            i_sessions = gen.integers(
+                low=0,
+                high=nsessions_for_subtask,
+                size=nsessions_for_subtask,
+            )
+            boot_k[i_bootstrap, i_subtask] = perf_data[i_sessions].sum(axis=0)
+            boot_n[i_bootstrap, i_subtask] = len(i_sessions)
+
+    return _BootstrapSamples(
+        boot_k=boot_k,
+        boot_n=boot_n,
+    )
diff --git a/hobj/benchmarks/binary_classification/simulation.py b/hobj/benchmarks/binary_classification/simulation.py
new file mode 100644
index 0000000..39b3300
--- /dev/null
+++ b/hobj/benchmarks/binary_classification/simulation.py
@@ -0,0 +1,106 @@
+from typing import List, Optional, Union
+
+import numpy as np
+import pydantic
+from hobj.types import ImageId
+from hobj.learning_models import BinaryLearningModel
+
+
+# %%
+class BinaryClassificationSubtaskResult(pydantic.BaseModel):
+    model_config = dict(arbitrary_types_allowed=True)
+
+    perf_seq: np.ndarray
+    worker_id: Optional[str] = pydantic.Field(default="NO_WORKER")
+
+
+class BinaryClassificationSubtask(pydantic.BaseModel):
+    """
+    A representation of a simple binary classification task which samples uniformly (with or without replacement) from a pool of images.
+    Feedback consists of +1 or -1 rewards, depending on the class of the image.
+    """
+
+    model_config = dict(frozen=True)
+
+    classA: List[ImageId]
+    classB: List[ImageId]
+    ntrials: int = pydantic.Field(
+        description="The number of trials in the subtask.", gt=0
+    )
+    replace: bool = pydantic.Field(
+        description="Whether to show stimulus images with replacement or not."
+    )
+
+    @pydantic.field_validator("classA", "classB", mode="after")
+    @classmethod
+    def sort_image_refs(cls, value: List[ImageId]) -> List[ImageId]:
+        return sorted(value)
+
+    @pydantic.model_validator(mode="after")
+    def validate_image_refs(self) -> "BinaryClassificationSubtask":
+        if not self.replace and len(self.classA) + len(self.classB) < self.ntrials:
+            raise ValueError(
+                f"Specified replace=False, but only {len(self.classA) + len(self.classB)} images available. (len(classA)={len(self.classA)}; len(classB)={len(self.classB)}"
+            )
+        return self
+
+    def simulate_session(
+        self,
+        learner: BinaryLearningModel,
+        seed: Union[int, None],
+    ) -> BinaryClassificationSubtaskResult:
+        """
+        Convenience method to simulate a session of the binary classification task on a given BinaryLearningModel.
+        :param learner:
+        :param seed:
+        :return: a [ntrials] boolean np.ndarray vector where True indicates a correct response.
+        """
+
+        perf_seq = np.zeros(self.ntrials, dtype=bool)
+
+        # Initialize random state of environment
+        gen = np.random.default_rng(seed=seed)
+
+        # Sample the stimulus image sequence
+        num_classA = len(self.classA)
+        num_classB = len(self.classB)
+        n_all_images = num_classA + num_classB
+        if self.replace:
+            i_image_seq = gen.integers(n_all_images, size=self.ntrials)
+        else:
+            i_image_seq = gen.permutation(n_all_images)[: self.ntrials]
+
+        # Randomly sample the reward contingency
+        classA_correct_action = 0 if gen.random() < 0.5 else 1
+        classB_correct_action = 1 - classA_correct_action
+
+        # Iterate over trials
+        i_trial = 0
+        for i_image in i_image_seq:
+            # Retrieve trial information
+            image_ref_cur = (
+                self.classA[i_image]
+                if i_image < num_classA
+                else self.classB[i_image - num_classA]
+            )
+            correct_action_cur = (
+                classA_correct_action if i_image < num_classA else classB_correct_action
+            )
+
+            # Get response from learner
+            a = learner.get_response(image=image_ref_cur)
+
+            # Calculate feedback based on response
+            feedback = 1.0 if a == correct_action_cur else -1.0
+
+            # Deliver feedback to learner
+            learner.give_feedback(feedback)
+
+            # Record results of trial
+            perf_seq[i_trial] = feedback > 0
+
+            i_trial += 1
+
+        return BinaryClassificationSubtaskResult(
+            perf_seq=perf_seq,
+        )
diff --git a/hobj/benchmarks/common.py b/hobj/benchmarks/common.py
new file mode 100644
index 0000000..d271d23
--- /dev/null
+++ b/hobj/benchmarks/common.py
@@ -0,0 +1,116 @@
+from dataclasses import dataclass
+from typing import Any, Protocol, TypeVar
+
+import numpy as np
+import xarray as xr
+
+from hobj.learning_models import BinaryLearningModel
+from hobj.stats.ci import estimate_basic_bootstrap_CI
+
+ResultT = TypeVar("ResultT")
+
+
+class SessionSubtask(Protocol[ResultT]):
+    """Protocol for benchmark subtasks that can simulate one learner session."""
+
+    def simulate_session(
+        self,
+        learner: BinaryLearningModel,
+        seed: int | None,
+    ) -> ResultT:
+        """Simulate a single session of learner behavior."""
+
+
+@dataclass(frozen=True)
+class BootstrapSummary:
+    """Summary of a bootstrap distribution for a scalar benchmark score.
+
+    Attributes:
+        sigma: Sample standard deviation of the bootstrap distribution.
+        ci95: Basic bootstrap 95% confidence interval.
+    """
+
+    sigma: float
+    ci95: tuple[float, float]
+
+
+def simulate_sessions(
+    subtask: SessionSubtask[ResultT],
+    learner: BinaryLearningModel,
+    nsimulations: int,
+) -> list[ResultT]:
+    """Simulate repeated learner sessions for a single benchmark subtask.
+
+    Args:
+        subtask: Benchmark subtask to simulate.
+        learner: Learning model to evaluate.
+        nsimulations: Number of independent sessions to run.
+
+    Returns:
+        A list of session results in simulation order.
+    """
+
+    results = []
+    for _ in range(nsimulations):
+        learner.reset_state(seed=None)
+        results.append(
+            subtask.simulate_session(
+                learner=learner,
+                seed=None,
+            )
+        )
+
+    return results
+
+
+def compare_msen(
+    model_phat: xr.DataArray,
+    model_varhat_phat: xr.DataArray,
+    target_phat: xr.DataArray,
+    target_varhat_phat: xr.DataArray,
+    condition_dims: tuple[str, ...],
+) -> xr.DataArray:
+    """Compute the variance-corrected mean squared error normalized over dims.
+
+    Args:
+        model_phat: Mean model performance estimate.
+        model_varhat_phat: Estimated variance of the model mean.
+        target_phat: Mean target performance estimate.
+        target_varhat_phat: Estimated variance of the target mean.
+        condition_dims: Dimensions across which to average the squared error.
+
+    Returns:
+        The normalized mean squared error with variance penalties removed.
+    """
+
+    msen = (
+        np.square(model_phat - target_phat).mean(condition_dims)
+        - model_varhat_phat.mean(condition_dims)
+        - target_varhat_phat.mean(condition_dims)
+    )
+    return msen
+
+
+def summarize_bootstrap_score(
+    point_estimate: Any,
+    bootstrapped_point_estimates: Any,
+) -> BootstrapSummary:
+    """Summarize a bootstrap distribution for a benchmark score.
+
+    Args:
+        point_estimate: Point estimate corresponding to the bootstrap samples.
+        bootstrapped_point_estimates: Bootstrap replicates of the score.
+
+    Returns:
+        The bootstrap standard deviation and 95% confidence interval.
+    """
+
+    boot_array = np.asarray(bootstrapped_point_estimates)
+    return BootstrapSummary(
+        sigma=float(np.std(boot_array, ddof=1)),
+        ci95=estimate_basic_bootstrap_CI(
+            alpha=0.05,
+            point_estimate=point_estimate,
+            bootstrapped_point_estimates=boot_array,
+        ),
+    )
diff --git a/hobj/benchmarks/experiment_simulation/environment.py b/hobj/benchmarks/experiment_simulation/environment.py
deleted file mode 100644
index d4dea50..0000000
--- a/hobj/benchmarks/experiment_simulation/environment.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import numpy as np
-from typing import Union, List
-import xarray as xr
-
-class Environment(object):
-
-    def __init__(self):
-        self._meta = {}
-        return
-
-    def initialize(self, RS: np.random.RandomState):
-        raise NotImplementedError
-
-    def sample_image(self):
-        image_url = ''
-        return image_url
-
-    @property
-    def meta(self):
-        return self._meta
-
-    @meta.setter
-    def meta(self, value):
-        assert isinstance(value, dict) or isinstance(value, xr.Dataset)
-        self._meta = value
-
-
-    @property
-    def current_state_meta(self):
-        # Returns meta information for the current environmental state, that will be logged in a simulation. Not allowed to be used by the learner.
-        return {}
-
-    def provide_feedback(self, action: int):
-        reward = 0.
-        return reward
-
diff --git a/hobj/benchmarks/experiment_simulation/experiment_simulation.py b/hobj/benchmarks/experiment_simulation/experiment_simulation.py
deleted file mode 100644
index 6be39d8..0000000
--- a/hobj/benchmarks/experiment_simulation/experiment_simulation.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import hobj.config as config
-import hobj.learning_models.learning_model as lm
-import hobj.benchmarks.experiment_simulation.environment as env
-import hobj.utils.file_io as io
-import os
-import xarray as xr
-import numpy as np
-from tqdm import tqdm
-from typing import Union
-
-class ExperimentSimulation(object):
-    """
-    Wraps a set of environments, and simulates them self.nreps times each
-    """
-    nreps = None
-    ntrials = None
-    environment_name_dim = 'environment'
-
-    def __init__(self, cachedir=config.simulation_cachedir):
-        self.cachedir = cachedir
-        return
-
-    @property
-    def experiment_name(self):
-        raise NotImplementedError
-        return 'write_experiment_name_here'
-
-    @property
-    def environments(self):
-        raise NotImplementedError
-        return [env.Environment()]
-
-    def run(self, learner: lm.LearningModel, seed: Union[type(None), int], force_recompute=False, show_pbar=True):
-
-        savepath = os.path.join(self.cachedir, self.experiment_name, 'seed_'+str(seed), f'ds_{learner.learner_id}.nc')
-        if os.path.exists(savepath) and not force_recompute:
-            ds = xr.load_dataset(savepath)
-            return ds
-
-        dlist = []
-        RS = np.random.RandomState(seed=seed)
-        if show_pbar:
-            pbar = tqdm(total=self.nreps * self.ntrials * len(self.environments), desc='simulation trials')
-        else:
-            pbar = None
-
-        for environment in self.environments:
-            ds = self.simulate_environment(learner=learner, environment=environment, RS=RS, nreps=self.nreps, ntrials=self.ntrials, pbar=pbar)
-            ds = self.postprocess_behavioral_data(ds=ds, environment=environment)
-            dlist.append(ds)
-
-        ds = xr.concat(dlist, dim = self.environment_name_dim, )
-        io.prepare_savepath(savepath)
-        ds.to_netcdf(savepath)
-        return ds
-
-    def postprocess_behavioral_data(self, ds:xr.Dataset, environment:env.Environment):
-        """
-        Called following the simulation of a single environment. Allows for postprocessing of the behavioral data.
-        :param ds:
-        :param environment:
-        :return:
-        """
-        return ds
-
-    @staticmethod
-    def simulate_environment(
-            learner: lm.LearningModel,
-            environment: env.Environment,
-            RS: np.random.RandomState,
-            nreps: int,
-            ntrials: int,
-            pbar: tqdm = None,
-    ):
-
-        d = {
-            'image_url': (['trial', 'rep', ], [['' for _ in range(nreps)] for _ in range(ntrials)]),
-            'action': (['trial', 'rep', ], np.zeros((ntrials, nreps))),
-            'reward': (['trial', 'rep', ], np.zeros((ntrials, nreps))),
-        }
-
-        for i_rep in range(nreps):
-            learner.reset()
-            environment.initialize(RS=RS)
-            for i_trial in range(ntrials):
-                if pbar is not None:
-                    pbar.update(1)
-
-                image_url = environment.sample_image()
-                action = learner.respond(image_url=image_url)
-                action = int(action)
-                reward = environment.provide_feedback(action=action)
-                reward = float(reward)
-                learner.learn(reward=reward)
-
-                d['image_url'][1][i_trial][i_rep] = image_url
-                d['action'][1][i_trial, i_rep] = action
-                d['reward'][1][i_trial, i_rep] = reward
-
-                for k in environment.current_state_meta:
-                    d[k][1][i_trial, i_rep] = (environment.current_state_meta[k])
-
-
-        ds = xr.Dataset(d)
-        ds = ds.assign_coords(environment.meta)
-        ds = ds.assign_coords(image_url = ds['image_url'])
-        return ds
diff --git a/hobj/behavioral_data/template/__init__.py b/hobj/benchmarks/generalization/__init__.py
similarity index 100%
rename from hobj/behavioral_data/template/__init__.py
rename to hobj/benchmarks/generalization/__init__.py
diff --git a/hobj/benchmarks/generalization/estimator.py b/hobj/benchmarks/generalization/estimator.py
new file mode 100644
index 0000000..775842c
--- /dev/null
+++ b/hobj/benchmarks/generalization/estimator.py
@@ -0,0 +1,146 @@
+import warnings
+from typing import List, Tuple
+
+import numpy as np
+import xarray as xr
+
+from hobj.benchmarks.generalization.simulator import GeneralizationSessionResult
+from hobj.stats import binomial as binomial_funcs
+
+
+def build_generalization_statistics(
+    results: List[GeneralizationSessionResult],
+    perform_lapse_rate_correction: bool,
+    n_bootstrap_iterations: int,
+    bootstrap_by_worker: bool,
+) -> xr.Dataset:
+    """Build one-shot generalization statistics as a plain xarray dataset.
+
+    Args:
+        results: Session-level generalization results.
+        perform_lapse_rate_correction: Whether to correct point estimates using
+            catch-trial lapse rates.
+        n_bootstrap_iterations: Number of bootstrap replicates to generate.
+        bootstrap_by_worker: Whether to resample workers instead of sessions.
+
+    Returns:
+        A dataset containing point estimates and bootstrap replicates.
+    """
+
+    all_transformations = sorted(
+        {
+            transformation
+            for result in results
+            for transformation in result.transformation_to_kn.keys()
+        }
+    )
+    transformation_to_i = {
+        transformation: i for i, transformation in enumerate(all_transformations)
+    }
+
+    all_workers = sorted({result.worker_id for result in results})
+    nworkers = len(all_workers)
+    worker_to_i = {worker: i for i, worker in enumerate(all_workers)}
+
+    nsessions = len(results)
+    ntransformations = len(all_transformations)
+
+    if bootstrap_by_worker:
+        if nworkers == 1:
+            raise ValueError(
+                f"Only one worker {all_workers}, cannot perform valid bootstrap by worker."
+            )
+        if nworkers < 20:
+            warnings.warn(
+                (
+                    f"Only {nworkers} unique workers, which is less than 20. "
+                    "Bootstrapping by worker may not be reliable."
+                ),
+                stacklevel=2,
+            )
+
+        kmat = np.zeros(shape=(nworkers, ntransformations))
+        nmat = np.zeros(shape=(nworkers, ntransformations))
+        kcatch = np.zeros(shape=nworkers)
+        ncatch = np.zeros(shape=nworkers)
+    else:
+        kmat = np.zeros(shape=(nsessions, ntransformations))
+        nmat = np.zeros(shape=(nsessions, ntransformations))
+        kcatch = np.zeros(shape=nsessions)
+        ncatch = np.zeros(shape=nsessions)
+
+    for i_session, result in enumerate(results):
+        i_row = worker_to_i[result.worker_id] if bootstrap_by_worker else i_session
+        kcatch[i_row] = result.kcatch
+        ncatch[i_row] = result.ncatch
+
+        for transformation, (k, n) in result.transformation_to_kn.items():
+            i_transformation = transformation_to_i[transformation]
+            kmat[i_row, i_transformation] += k
+            nmat[i_row, i_transformation] += n
+
+    phat, varhat_phat = _get_point_estimates(
+        k=kmat.sum(0),
+        n=nmat.sum(0),
+        kcatch=kcatch.sum(),
+        ncatch=ncatch.sum(),
+        perform_lapse_rate_correction=perform_lapse_rate_correction,
+    )
+
+    gen = np.random.default_rng()
+    boot_phat = np.zeros(shape=(n_bootstrap_iterations, ntransformations))
+    boot_varhat_phat = np.zeros(shape=(n_bootstrap_iterations, ntransformations))
+    for i_boot_iter in range(n_bootstrap_iterations):
+        if bootstrap_by_worker:
+            i_boot = gen.integers(low=0, high=nworkers, size=nworkers)
+        else:
+            i_boot = gen.integers(low=0, high=nsessions, size=nsessions)
+
+        kboot = kmat[i_boot].sum(0)
+        nboot = nmat[i_boot].sum(0)
+        kcatch_boot = kcatch[i_boot].sum()
+        ncatch_boot = ncatch[i_boot].sum()
+
+        phat_cur, varhat_phat_cur = _get_point_estimates(
+            k=kboot,
+            n=nboot,
+            kcatch=kcatch_boot,
+            ncatch=ncatch_boot,
+            perform_lapse_rate_correction=perform_lapse_rate_correction,
+        )
+
+        boot_phat[i_boot_iter] = phat_cur
+        boot_varhat_phat[i_boot_iter] = varhat_phat_cur
+
+    return xr.Dataset(
+        data_vars=dict(
+            phat=(["transformation"], phat),
+            varhat_phat=(["transformation"], varhat_phat),
+            boot_phat=(["boot_iter", "transformation"], boot_phat),
+            boot_varhat_phat=(["boot_iter", "transformation"], boot_varhat_phat),
+        ),
+        coords=dict(transformation=all_transformations),
+    )
+
+
+def _get_point_estimates(
+    k: np.ndarray,
+    n: np.ndarray,
+    kcatch: np.ndarray,
+    ncatch: np.ndarray,
+    perform_lapse_rate_correction: bool,
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Estimate generalization means and variances from binomial counts."""
+
+    phat = k / n
+    varhat_phat = binomial_funcs.estimate_variance_of_binomial_proportion(
+        kvec=k, nvec=n
+    )
+
+    if perform_lapse_rate_correction:
+        hat_lapse_rate = 2 - 2 * (kcatch / ncatch)
+        phat = (phat - hat_lapse_rate / 2) / (1 - hat_lapse_rate)
+        varhat_phat = varhat_phat / (1 - hat_lapse_rate) ** 2
+
+    phat = np.clip(phat, 0, 1)
+    return phat, varhat_phat
diff --git a/hobj/benchmarks/generalization/simulator.py b/hobj/benchmarks/generalization/simulator.py
new file mode 100644
index 0000000..8b9c2ae
--- /dev/null
+++ b/hobj/benchmarks/generalization/simulator.py
@@ -0,0 +1,158 @@
+import collections
+from typing import Dict, List, Optional, Union
+
+import numpy as np
+import pydantic
+
+from hobj.learning_models import BinaryLearningModel
+from hobj.types import ImageId
+
+
+# %%
+class GeneralizationSessionResult(pydantic.BaseModel):
+    transformation_to_kn: Dict[str, List[int]]
+    kcatch: int
+    ncatch: int
+    worker_id: Optional[str] = pydantic.Field(default="NO_WORKER")
+
+    @pydantic.model_validator(mode="after")
+    def validate_results(self) -> "GeneralizationSessionResult":
+        for transformation in self.transformation_to_kn:
+            k, n = self.transformation_to_kn[transformation]
+            if k < 0 or n < 0 or k > n:
+                raise ValueError(
+                    f"Got invalid Binomial parameters k={k}, n={n} for transformation {transformation}"
+                )
+
+        if self.kcatch < 0 or self.ncatch < 0 or self.kcatch > self.ncatch:
+            raise ValueError(
+                f"Got invalid Binomial parameters k={self.kcatch}, n={self.ncatch} for catch trials"
+            )
+
+        return self
+
+
+class GeneralizationSubtask(pydantic.BaseModel):
+    """
+    A simulator for a single session of the specific "one-shot" task used in Lee and DiCarlo 2023.
+    """
+
+    model_config = dict(frozen=True)
+
+    support_imageA: ImageId
+    support_imageB: ImageId
+    test_imagesA: List[ImageId]
+    test_imagesB: List[ImageId]
+    image_ref_to_transformation: Dict[ImageId, str]
+
+    @pydantic.field_validator("test_imagesA", "test_imagesB", mode="after")
+    @classmethod
+    def sort_image_refs(cls, value: List[ImageId]) -> List[ImageId]:
+        return sorted(value)
+
+    @pydantic.model_validator(mode="after")
+    def validate_model(self) -> "GeneralizationSubtask":
+        for ref in self.test_imagesA + self.test_imagesB:
+            if ref not in self.image_ref_to_transformation:
+                raise ValueError(
+                    f"Expected all test images to have an associated transformation, but {ref} was missing."
+                )
+
+        return self
+
+    def simulate_session(
+        self,
+        learner: BinaryLearningModel,
+        seed: Union[int, None],
+    ) -> GeneralizationSessionResult:
+        """
+        Convenience method to simulate a session of the one-shot task on a given BinaryLearningModel.
+        :param learner:
+        :param seed:
+        :return: a [ntrials] boolean np.ndarray vector where True indicates a correct response.
+        """
+
+        # Initialize random state of environment
+        gen = np.random.default_rng(seed=seed)
+
+        # Allocate results
+        transformation_to_kn = collections.defaultdict(lambda: [0, 0])
+        kcatch = 0
+        ncatch = 0
+
+        # Sample the sequence of trials using the procedure described in Lee and DiCarlo 2023
+        support_trials = set(range(10))
+        catch_trials = {9, 14, 19}
+        ntrials = 20
+        stimulus_category_is_A_seq = []
+
+        # An initial phase of 10 support trials, with classes sampled in equal number (5 each)
+        support_sequence = [True] * 5 + [False] * 5
+        support_sequence = [
+            support_sequence[i] for i in gen.permutation(len(support_sequence))
+        ]
+        stimulus_category_is_A_seq.extend(support_sequence)
+
+        # A testing phase of 8 test trials with 2 catch trials, where classes are sampled i.i.d.
+        # with 50-50 probability, and test images for each class are sampled without replacement
+        test_sequence = [bool(v) for v in gen.random(size=10) < 0.5]
+        stimulus_category_is_A_seq.extend(test_sequence)
+        assert len(stimulus_category_is_A_seq) == ntrials
+
+        # Randomly sample the reward contingency
+        classA_correct_action = 0 if gen.random() < 0.5 else 1
+        classB_correct_action = 1 - classA_correct_action
+
+        i_test_seq_A = list(gen.permutation(len(self.test_imagesA)))
+        i_test_seq_B = list(gen.permutation(len(self.test_imagesB)))
+
+        # Iterate over trials
+        for i_trial in range(ntrials):
+            # Retrieve trial information
+            stimulus_category_is_A = stimulus_category_is_A_seq[i_trial]
+            correct_action_cur = (
+                classA_correct_action
+                if stimulus_category_is_A
+                else classB_correct_action
+            )
+
+            if i_trial in support_trials or i_trial in catch_trials:
+                # Supply a support image
+                image_ref_cur = (
+                    self.support_imageA
+                    if stimulus_category_is_A
+                    else self.support_imageB
+                )
+
+            else:
+                # Sample a test image without replacement
+                if stimulus_category_is_A:
+                    image_ref_cur = self.test_imagesA[i_test_seq_A.pop()]
+
+                else:
+                    image_ref_cur = self.test_imagesB[i_test_seq_B.pop()]
+
+            # Get response from learner
+            a = learner.get_response(image=image_ref_cur)
+
+            # Calculate feedback based on response
+            correct = a == correct_action_cur
+            feedback = 1.0 if correct else -1.0
+
+            # Deliver feedback to learner
+            learner.give_feedback(feedback)
+
+            # Record results of trial if catch or test:
+            if i_trial in catch_trials:
+                kcatch += correct
+                ncatch += 1
+            elif i_trial in support_trials:
+                pass
+            else:
+                transformation_cur = self.image_ref_to_transformation[image_ref_cur]
+                transformation_to_kn[transformation_cur][0] += correct
+                transformation_to_kn[transformation_cur][1] += 1
+
+        return GeneralizationSessionResult(
+            transformation_to_kn=transformation_to_kn, kcatch=kcatch, ncatch=ncatch
+        )
diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/MutatorHighVarSubtasks.json b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/MutatorHighVarSubtasks.json
deleted file mode 100644
index 118e37f..0000000
--- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/MutatorHighVarSubtasks.json
+++ /dev/null
@@ -1,13186 +0,0 @@
-{
-    "MutatorB2000_0,MutatorB2000_4718": {
-        "MutatorB2000_0": [
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx62.58_ry188.16_rz222.01_s0.91_u0.28_v0.03_meanpx113.5.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx117.95_ry98.32_rz149.17_s0.92_u-0.15_v0.23_meanpx113.1.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx152.50_ry257.99_rz187.21_s0.77_u0.23_v-0.35_meanpx112.0.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx159.02_ry144.52_rz179.12_s0.96_u0.19_v-0.08_meanpx112.8.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx62.81_ry237.86_rz73.69_s0.50_u-0.01_v-0.35_meanpx113.0.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx64.43_ry102.62_rz83.02_s0.71_u-0.23_v0.30_meanpx112.3.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx108.26_ry43.83_rz273.33_s0.78_u0.30_v0.37_meanpx112.8.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx65.65_ry305.66_rz147.62_s0.91_u-0.26_v-0.33_meanpx112.0.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx38.33_ry13.89_rz271.69_s0.93_u0.33_v-0.29_meanpx113.1.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx123.38_ry156.67_rz336.13_s0.78_u-0.15_v0.02_meanpx113.5.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx114.49_ry28.81_rz65.00_s0.88_u-0.06_v-0.31_meanpx112.9.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx40.48_ry228.27_rz2.57_s0.53_u0.09_v-0.04_meanpx112.4.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx17.60_ry189.16_rz12.65_s0.82_u-0.04_v0.16_meanpx112.2.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx117.52_ry337.96_rz123.54_s0.87_u0.16_v0.07_meanpx113.0.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx120.83_ry153.41_rz31.93_s0.54_u0.09_v0.28_meanpx113.0.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx122.98_ry49.02_rz59.58_s0.69_u-0.29_v0.32_meanpx112.8.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx123.52_ry300.54_rz288.02_s0.91_u0.37_v0.25_meanpx112.1.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx83.22_ry58.10_rz8.28_s0.61_u0.34_v-0.31_meanpx113.1.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx116.78_ry247.03_rz326.58_s0.71_u-0.40_v-0.09_meanpx113.4.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx122.82_ry10.88_rz47.49_s0.74_u0.23_v0.05_meanpx113.5.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx169.05_ry296.87_rz240.06_s0.74_u0.30_v-0.06_meanpx113.1.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx68.13_ry262.31_rz240.38_s0.97_u0.28_v0.21_meanpx112.2.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx85.30_ry135.74_rz200.99_s0.59_u-0.28_v0.14_meanpx112.9.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx95.48_ry139.95_rz56.04_s0.61_u0.12_v0.11_meanpx113.2.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx141.63_ry43.21_rz130.98_s0.88_u0.12_v-0.12_meanpx112.9.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx32.44_ry265.27_rz224.71_s0.83_u0.26_v-0.29_meanpx113.2.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx81.35_ry93.94_rz227.90_s0.54_u-0.20_v0.15_meanpx112.2.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx76.51_ry249.39_rz96.08_s0.99_u0.21_v-0.21_meanpx112.9.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx139.28_ry150.68_rz86.64_s0.96_u0.14_v-0.11_meanpx112.4.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx22.59_ry110.41_rz211.97_s0.98_u-0.27_v-0.16_meanpx112.7.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx88.39_ry256.32_rz332.94_s0.93_u-0.17_v0.25_meanpx112.5.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx57.91_ry143.08_rz166.85_s0.68_u-0.17_v0.17_meanpx113.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx28.97_ry191.89_rz255.73_s0.63_u-0.27_v0.37_meanpx112.2.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx138.81_ry91.77_rz168.34_s0.97_u0.33_v0.35_meanpx112.4.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx7.06_ry160.46_rz99.73_s0.70_u0.13_v-0.38_meanpx112.9.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx113.35_ry112.56_rz297.35_s0.88_u0.08_v0.25_meanpx112.3.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx118.67_ry210.22_rz264.77_s0.55_u-0.03_v-0.30_meanpx113.7.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx35.51_ry106.52_rz180.93_s0.93_u-0.10_v0.36_meanpx112.4.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx115.38_ry347.21_rz142.29_s0.77_u0.25_v0.31_meanpx113.0.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx52.24_ry148.52_rz330.88_s0.87_u-0.11_v0.28_meanpx113.0.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx119.53_ry51.34_rz254.61_s1.00_u0.26_v-0.22_meanpx112.4.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx146.65_ry281.28_rz31.88_s0.57_u0.25_v-0.14_meanpx113.1.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx125.01_ry96.98_rz65.48_s0.60_u0.36_v0.33_meanpx112.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx156.72_ry244.28_rz358.22_s0.98_u-0.07_v-0.33_meanpx112.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx81.02_ry190.67_rz273.50_s0.71_u-0.35_v-0.24_meanpx113.4.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx164.89_ry265.47_rz82.36_s0.84_u0.40_v0.23_meanpx112.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx175.53_ry231.96_rz270.38_s0.95_u0.31_v0.07_meanpx112.7.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx73.39_ry105.50_rz32.64_s0.66_u-0.34_v-0.34_meanpx113.4.png",
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-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx102.11_ry345.85_rz293.62_s0.72_u-0.24_v0.20_meanpx113.6.png",
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-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx121.53_ry252.59_rz344.02_s0.61_u-0.02_v0.13_meanpx112.7.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx90.55_ry259.58_rz119.73_s0.81_u-0.15_v0.09_meanpx112.2.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx93.55_ry29.59_rz171.85_s0.96_u-0.03_v0.26_meanpx112.9.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx155.60_ry287.80_rz281.58_s0.78_u0.06_v0.17_meanpx112.7.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx100.12_ry117.05_rz282.98_s0.91_u-0.17_v-0.04_meanpx112.2.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx132.23_ry340.02_rz248.71_s0.91_u-0.22_v0.11_meanpx112.7.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx102.14_ry19.54_rz305.56_s0.67_u-0.08_v-0.30_meanpx114.0.png",
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-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx99.39_ry49.42_rz246.51_s0.60_u-0.17_v-0.00_meanpx113.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx144.27_ry340.97_rz288.89_s0.52_u-0.02_v0.38_meanpx112.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_0/MutatorB2000_0_rx40.86_ry50.30_rz42.44_s0.80_u0.03_v-0.16_meanpx113.5.png",
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-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx99.78_ry254.54_rz212.39_s0.97_u-0.33_v-0.21_meanpx110.7.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx163.69_ry312.29_rz158.93_s0.60_u0.01_v-0.17_meanpx111.2.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx100.09_ry293.65_rz164.41_s0.69_u-0.22_v-0.24_meanpx110.7.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx64.86_ry12.70_rz89.65_s0.80_u-0.15_v-0.34_meanpx111.4.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx112.85_ry36.47_rz335.44_s0.58_u0.23_v0.20_meanpx111.3.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx57.37_ry201.65_rz123.64_s0.58_u-0.28_v0.30_meanpx111.1.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx150.50_ry22.22_rz310.48_s0.70_u-0.10_v0.36_meanpx111.1.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx143.92_ry221.57_rz154.93_s0.84_u-0.23_v0.33_meanpx110.7.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx104.10_ry265.73_rz213.97_s0.77_u-0.18_v-0.16_meanpx110.7.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx126.98_ry189.20_rz245.82_s0.86_u-0.22_v0.35_meanpx111.2.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx80.70_ry307.19_rz200.52_s0.70_u-0.22_v-0.14_meanpx110.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx122.58_ry321.18_rz358.71_s0.73_u0.35_v-0.19_meanpx110.5.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx26.81_ry14.28_rz52.12_s0.76_u0.30_v0.39_meanpx110.8.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx104.77_ry222.00_rz237.60_s0.74_u-0.12_v-0.28_meanpx111.3.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx127.19_ry345.79_rz135.45_s0.83_u-0.35_v-0.23_meanpx110.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx74.45_ry352.81_rz163.95_s0.54_u0.14_v0.14_meanpx112.3.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx58.71_ry213.04_rz282.99_s0.72_u-0.29_v-0.06_meanpx110.9.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx59.18_ry37.19_rz272.78_s0.58_u0.25_v0.15_meanpx110.9.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx112.59_ry39.02_rz109.90_s0.81_u0.05_v-0.39_meanpx111.0.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx125.87_ry135.67_rz334.72_s0.95_u-0.09_v0.04_meanpx111.0.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx77.37_ry306.42_rz339.83_s0.64_u0.31_v-0.21_meanpx110.5.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx17.41_ry37.03_rz68.87_s0.76_u-0.25_v-0.28_meanpx110.8.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx115.21_ry310.23_rz200.04_s0.82_u0.31_v-0.29_meanpx110.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx161.65_ry8.74_rz49.46_s0.69_u-0.23_v0.11_meanpx110.8.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx71.86_ry71.22_rz151.64_s0.68_u-0.25_v0.05_meanpx110.9.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx58.01_ry168.51_rz33.88_s0.88_u0.29_v0.02_meanpx111.1.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx93.75_ry114.14_rz201.82_s0.62_u0.05_v0.21_meanpx110.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx103.78_ry226.63_rz307.44_s0.55_u0.07_v0.10_meanpx111.4.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx105.85_ry88.35_rz113.10_s0.68_u0.26_v0.06_meanpx110.7.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx103.50_ry65.57_rz174.67_s0.86_u-0.35_v0.29_meanpx110.8.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx62.10_ry330.33_rz338.07_s0.51_u-0.35_v0.38_meanpx111.2.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx113.52_ry114.69_rz243.50_s0.87_u-0.24_v0.20_meanpx110.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx50.40_ry197.83_rz213.38_s0.63_u-0.20_v-0.22_meanpx111.5.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx85.53_ry353.78_rz222.44_s0.94_u0.40_v-0.20_meanpx111.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx26.12_ry335.17_rz273.15_s0.50_u-0.26_v0.18_meanpx110.9.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx68.85_ry147.71_rz91.27_s0.91_u-0.00_v0.12_meanpx111.1.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx22.85_ry290.31_rz269.95_s0.72_u-0.15_v0.27_meanpx110.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx12.10_ry27.01_rz90.31_s0.69_u0.36_v0.17_meanpx110.4.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx45.85_ry348.23_rz50.12_s0.77_u-0.24_v-0.09_meanpx111.1.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx38.53_ry292.43_rz222.14_s0.82_u0.22_v-0.25_meanpx111.1.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx114.01_ry36.31_rz292.39_s0.62_u0.32_v0.12_meanpx111.2.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx81.63_ry309.65_rz270.78_s0.93_u-0.11_v0.09_meanpx110.6.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx101.57_ry223.36_rz358.65_s0.68_u0.07_v0.10_meanpx111.8.png",
-            "https://s3.amazonaws.com/mildatasets/imagesets/MutatorB2000_Subset128_FullVar_Train/images/MutatorB2000_93/MutatorB2000_93_rx66.18_ry105.34_rz200.41_s0.68_u-0.18_v-0.29_meanpx110.5.png"
-        ]
-    }
-}
\ No newline at end of file
diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py
deleted file mode 100644
index 38cd8e1..0000000
--- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_benchmark.py
+++ /dev/null
@@ -1,350 +0,0 @@
-from typing import Union
-
-import numpy as np
-import xarray as xr
-
-from hobj.behavioral_data import human_data as human_data
-from hobj.benchmarks.benchmarks import Benchmark
-from hobj.learning_models import learning_model as lm
-from hobj.utils import stats as stats
-import hobj.benchmarks.leedicarlo_benchmarks.MutatorHighVar.mut_highvar_experiment as mutator
-
-import hobj.statistics.lapse_rate as lapse_rate_funcs
-from hobj.statistics.resamplers.resamplers import LearningCurveResampler, WorkerResampler
-import hobj.statistics.score_statistics.MSEn as R2n_funcs
-import hobj.statistics.variance_estimates.binomial as binomial_funcs
-import hobj.statistics.hypothesis_testing.bootstrapped_confidence_intervals as bootstrapped_confidence_intervals
-import hobj.statistics.hypothesis_testing.approximated_null_distribution as approximated_null_distribution
-
-
-class MutatorHighVarBenchmark(Benchmark):
-
-    def __init__(self):
-        super().__init__()
-        self.nboot = 1000
-        self.tearly = slice(1, 6)
-        self.tlate = slice(94, 100)
-        self.dataset = human_data.MutatorHighVarDataset()
-        self.experiment = mutator.MutatorHighVarExperiment()
-        self.resampler = LearningCurveResampler(
-            ds=self.ds_worker_table,
-            condition_dim='subtask',
-            rep_dim='worker_id'
-        )
-
-        self.worker_resampler = WorkerResampler(
-            ds_worker_table=self.ds_worker_table,
-            condition_dim='subtask',
-            worker_dim='worker_id'
-        )
-
-    def evaluate_model(self, learner: lm.LearningModel, force_recompute: bool = False, seed = None) -> xr.Dataset:
-        """
-        :param learner: LearningModel
-        :param force_recompute: bool. If True, recompute the model behavior, even if it is already cached.
-        :return:
-        """
-
-        # Get behavior of this model
-        ds_behavior = self.experiment.run(learner=learner, seed=seed, force_recompute=force_recompute)
-
-        k = ds_behavior.k.isel(stimulus_category=1) + (ds_behavior.n.isel(stimulus_category=0) - ds_behavior.k.isel(stimulus_category=0))
-        n = ds_behavior.n.isel(stimulus_category=1) + ds_behavior.n.isel(stimulus_category=0)
-
-        # Fit optimal lapse rate
-        lapse_rate = lapse_rate_funcs.fit_optimal_lapse_rate(
-            phat=k / n,
-            p=(self.ds_behavioral_statistics.perf),
-            nway=2,
-            condition_dims=('subtask', 'trial')
-        )
-
-        # Compute scores
-        target_point = self.ds_worker_table.sum('worker_id')
-        ds_scores = self.score_predictions(
-            k=k,
-            n=n,
-            ktarget=target_point.k,
-            ntarget=target_point.n,
-            lapse_rate=lapse_rate,
-        ).assign_coords(
-            lapse_rate=lapse_rate
-        )
-
-        return ds_scores
-
-    @property
-    def ds_ceilings(self):
-        """
-        - The point estimate for the R2n noise ceiling, and associated CI.
-        - The approximate null (where the model is equal to humans) distribution of R2n scores, given the number of model simulations.
-        - The point estimates for the grand-human learning curve, and associated CI.
-
-        - The expected range of SpearmanRs between the subtask difficulty vector between two repetitions of this experiment
-
-        :return:
-        """
-        if not hasattr(self, '_ds_ceilings'):
-            alpha = 0.05
-            ds_ceilings = xr.Dataset()
-
-            # %% Get point estimate of R2 ceiling, and confidence intervals
-            point = self.ds_worker_table.sum('worker_id')
-            ds_ceilings['MSEn'] = binomial_funcs.estimate_variance_of_binomial_proportion(
-                kvec = point.k,
-                nvec = point.n
-            ).mean(['subtask', 'trial'])
-
-            # Approximate the null distribution of R2n,
-            # The null hypothesis is that null samples are drawn from the same distribution as the human data.
-
-            def get_null_MSEn_functional(ds_data: xr.Dataset):
-                pvec = ds_data.k / ds_data.n
-                pvec_null = ds_data.knull / ds_data.nnull
-                var_pvec_null = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=ds_data.knull, nvec=ds_data.nnull)
-
-                MSEn = R2n_funcs.estimate_MSEn(
-                    hat_theta_model=pvec_null,
-                    hat_var_hat_theta_model=var_pvec_null,
-                    hat_theta_target=pvec,
-                    condition_dims=('subtask', 'trial')
-                )
-                return MSEn
-
-            nsessions = self.resampler.nsessions.copy(deep=True)
-            n_null_sessions = nsessions - nsessions + 500  # Because 500 simulations per subtask were performed, for the models.
-
-            ds_boot = self.resampler.get_ds_boot(self.nboot, seed=0)
-            ds_null = self.resampler.get_ds_resamples(nresamples=self.nboot, nsessions=n_null_sessions, seed=1)
-            ds_null = ds_null.rename({'k': 'knull', 'n': 'nnull'})
-
-            ds_data = xr.merge([ds_boot, ds_null])
-
-            ds_ceilings['MSEn_null'] = approximated_null_distribution.estimate_null_distribution(
-                statistical_functional=get_null_MSEn_functional,
-                ds_null_samples=ds_data,
-                hat_mu=ds_ceilings['MSEn'],
-                resample_dim='boot_iter'
-            )
-
-            # %% Approximate the distribution of SpearmanRs between the subtask difficulty vectors from two independent repetitions of this experiment
-            ds_boot1 = self.worker_resampler.get_ds_boot(nboot=self.nboot, seed=1)
-            ds_boot2 = self.worker_resampler.get_ds_boot(nboot=self.nboot, seed=2)
-            vec1 = ds_boot1.k.sum('trial') / ds_boot1.n.sum('trial')
-            vec2 = ds_boot2.k.sum('trial') / ds_boot2.n.sum('trial')
-            ds_ceilings['SpearmanR_repeat'] = stats.calc_correlation(obs_dim='subtask', y_pred=vec1, y_actual=vec2, spearman=True)
-
-            # %% Approximate the distribution of overall performances over repetitions of this experiment
-            ds_ceilings['overall_perf'] = (self.ds_human.k / self.ds_human.n).mean(['subtask', 'trial'])
-            ds_ceilings['overall_perf_repeat'] = (ds_boot1.k / ds_boot1.n).mean(['subtask', 'trial'])
-
-            # %% Get point estimate of the grand-human learning curve, and confidence intervals
-            ds_ceilings['glc'] = (self.ds_human.k / self.ds_human.n).mean('subtask')
-            ds_ceilings['glc_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval(
-                alpha=alpha,
-                ds_data=self.ds_human,
-                ds_boot=self.ds_boot,
-                statistic_functional=lambda ds_data: (ds_data.k / ds_data.n).mean('subtask'),
-                parameter_functional=None,
-            )
-
-            # %% Get point estimates of early and late performances
-            ds_ceilings['early_perf'] = ds_ceilings.glc.isel(trial=self.tearly).mean('trial')
-            ds_ceilings['early_perf_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval(
-                alpha=alpha,
-                ds_data=self.ds_human,
-                ds_boot=self.ds_boot,
-                statistic_functional=lambda ds_data: (ds_data.k / ds_data.n).mean('subtask').isel(trial=self.tearly).mean('trial'),
-            )
-            ds_ceilings['late_perf'] = ds_ceilings.glc.isel(trial=self.tlate).mean('trial')
-            ds_ceilings['late_perf_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval(
-                alpha=alpha,
-                ds_data=self.ds_human,
-                ds_boot=self.ds_boot,
-                statistic_functional=lambda ds_data: (ds_data.k / ds_data.n).mean('subtask').isel(trial=self.tlate).mean('trial'),
-            )
-
-            # %% Save
-            self._ds_ceilings = ds_ceilings
-
-        return self._ds_ceilings
-
-    def score_predictions(
-            self,
-            k: xr.DataArray,
-            n: xr.DataArray,
-            ktarget: xr.DataArray,
-            ntarget: xr.DataArray,
-            lapse_rate: Union[float, int, xr.DataArray] = 0.,
-    ):
-
-        """
-        k: (subtask, trial). The number of correct classifications in that condition.
-        n: (subtask, trial). The number of trials for each condition.
-        ktarget: (subtask, trial). The number of correct classifications in that condition, for the system aiming to be modeled.
-        ntarget: (subtask, trial). The number of trials for each condition, for the system aiming to be modeled
-        lapse_rate: a scalar. Adjusts the predictions of the model by simulating a random guess rate. Can only drive the behavior of the model to randomness.
-
-        returns ds_scores, which contains:
-
-            Point estimates, 95% CIs, and standard errors of:
-                R2n: () the noise-corrected R^2 score
-                glc: (trial) (g)rand (l)earning (c)urve over all subtasks
-                overall_perf_raw: () the average performance over all subtasks and trials (before lapse rate correction)
-                overall_perf: () the average performance over all subtasks and trials  (after lapse rate correction)
-                SpearmanR: () between subtask difficulty vectors
-        """
-
-        assert np.all(lapse_rate >= 0) and np.all(lapse_rate <= 1), lapse_rate
-
-        def check_kn(k, n):
-            assert isinstance(k, xr.DataArray)
-            assert isinstance(n, xr.DataArray)
-            assert np.all(k <= n)
-            assert np.all(n > 0)
-            assert 'worker_id' not in k.dims
-            assert 'subtask' in k.dims
-            assert 'trial' in k.dims
-
-            # assert set(k.dims) == {'subtask', 'trial'}, (k.dims)
-
-            assert set(k.dims) == set(n.dims), (k.dims, n.dims)
-
-        check_kn(k, n)
-        check_kn(ktarget, ntarget)
-
-        alpha = 0.05
-        # Get estimates of performance
-        hat_prob = k / n
-        hat_var_hat_prob = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=k, nvec=n)
-
-        # %% Get MSEn point estimate on the lapse-rate corrected predictions
-        hat_prob_lapse_rate = hat_prob * (1 - lapse_rate) + 0.5 * (lapse_rate)
-        hat_var_hat_prob_lapse_rate = hat_var_hat_prob * (1 - lapse_rate) ** 2
-
-        hat_prob_target = ktarget / ntarget
-
-        MSEn_point = R2n_funcs.estimate_MSEn(
-            hat_theta_model=hat_prob_lapse_rate,
-            hat_var_hat_theta_model=hat_var_hat_prob_lapse_rate,
-            hat_theta_target=hat_prob_target,
-            condition_dims=('subtask', 'trial'),
-        )
-
-
-        # %% Get SpearmanR between the subtask difficulty patterns
-        predicted_subtask_vector = hat_prob.mean("trial")
-        actual_subtask_vector = hat_prob_target.mean('trial')
-
-        SpearmanR_point = stats.calc_correlation(
-            obs_dim='subtask',
-            y_pred=predicted_subtask_vector,
-            y_actual=actual_subtask_vector,
-            spearman=True
-        )
-
-        # %% Get the (g)rand (l)earning (c)urve: the average over all subtasks, and the confidence intervals
-        glc = hat_prob.mean('subtask')
-        glc_lapsed = glc * (1 - lapse_rate) + 0.5 * (lapse_rate)
-        overall_perf = glc_lapsed.mean('trial')  # Prediction of lapse-rate adjusted model
-
-        # %% Get early and late performances
-        perf_early = glc.isel(trial=self.tearly).mean('trial')
-        perf_late = glc.isel(trial=self.tlate).mean('trial')
-
-        # %% Assemble scores
-        ds_scores = xr.Dataset(
-            data_vars=dict(
-                overall_perf=overall_perf,
-                overall_perf_raw=glc.mean('trial'),
-                SpearmanR=SpearmanR_point,
-                MSEn=MSEn_point,
-                glc=glc,
-                early_perf=perf_early,
-                late_perf=perf_late,
-            ),
-            coords = dict(
-                model_var_correction_term = float(hat_var_hat_prob_lapse_rate.mean())
-            )
-        )
-
-        return ds_scores
-
-    @property
-    def ds_worker_table(self):
-        if not hasattr(self, '_ds_worker_table'):
-            self._ds_worker_table = self.dataset.load_ds_worker_table()
-        return self._ds_worker_table
-
-    @property
-    def ds_human(self):
-        if not hasattr(self, '_ds_human'):
-            self._ds_human = self.ds_worker_table.sum('worker_id')
-        return self._ds_human
-
-    @property
-    def ds_behavioral_statistics(self):
-        """
-
-        perf: (subtask, trial)
-        perf_CI: (subtask, trial)
-        perf_std: (subtask, trial)
-
-        glc: (trial)
-        glc_CI: (trial)
-        glc_std: (subtask, trial)
-
-        subtask_vector: (subtask)
-        subtask_vector_CI: (subtask)
-        subtask_vector_std: (subtask)
-
-        :return:
-        """
-        if not hasattr(self, '_ds_behavioral_statistics'):
-            alpha = 0.05
-            ds_stats = xr.Dataset()
-
-            # Performance point statistics
-            ds_stats['perf'] = self.ds_human['k'] / self.ds_human['n']
-            ds_stats['perf_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval(
-                alpha=alpha,
-                ds_data=self.ds_human,
-                ds_boot=self.ds_boot,
-                statistic_functional=lambda ds: ds['k'] / ds['n'],
-                parameter_functional=None,
-            )
-            ds_stats['perf_std'] = (self.ds_boot['k'] / self.ds_boot['n']).std('boot_iter')
-
-            # Overall learning curve
-            ds_stats['glc'] = ds_stats['perf'].mean('subtask')
-            ds_stats['glc_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval(
-                alpha=alpha,
-                ds_data=self.ds_human,
-                ds_boot=self.ds_boot,
-                statistic_functional=lambda ds: (ds['k'] / ds['n']).mean('subtask'),
-                parameter_functional=None,
-            )
-            ds_stats['glc_std'] = (self.ds_boot['k'] / self.ds_boot['n']).mean('subtask').std('boot_iter')
-
-            # Subtask difficulty vector
-            ds_stats['subtask_vector'] = ds_stats['perf'].mean('trial')
-            ds_stats['subtask_vector_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval(
-                alpha=alpha,
-                ds_data=self.ds_human,
-                ds_boot=self.ds_boot,
-                statistic_functional=lambda ds: (ds['k'] / ds['n']).mean('trial'),
-                parameter_functional=None,
-            )
-            ds_stats['subtask_vector_std'] = (self.ds_boot['k'] / self.ds_boot['n']).mean('trial').std('boot_iter')
-            self._ds_behavioral_statistics = ds_stats
-
-        return self._ds_behavioral_statistics
-
-    @property
-    def ds_boot(self):
-
-        if not hasattr(self, '_ds_boot'):
-            self._ds_boot = self.resampler.get_ds_boot(nboot=self.nboot, seed=0)
-
-        return self._ds_boot
-
diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py
deleted file mode 100644
index e632486..0000000
--- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/mut_highvar_experiment.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import os
-
-import xarray as xr
-import numpy as np
-
-import hobj.benchmarks.experiment_simulation.environment as env_template
-from hobj.benchmarks.experiment_simulation.experiment_simulation import ExperimentSimulation
-import hobj.utils.file_io as io
-import hobj.images.imagesets as imagesets
-
-_loc = os.path.dirname(__file__)
-
-
-class BinaryClassificationEnvironment(env_template.Environment):
-    """
-    Implements a binary classification subtask
-    """
-
-    def __init__(
-            self,
-            category0_urls: list,
-            category1_urls: list,
-            replace=False,  # sample with replacement
-    ):
-        super().__init__()
-
-        assert isinstance(category0_urls, list)
-        assert isinstance(category1_urls, list)
-
-        assert len(category0_urls) == len(category1_urls)
-        self.labels = [0 for _ in category0_urls] + [1 for _ in category1_urls]
-        self.image_urls = category0_urls + category1_urls
-        self.i_seq = None
-        self.step = None
-        self.replace = replace
-        return
-
-    @property
-    def current_state_meta(self):
-        # Returns meta information for the current environmental state, that will be logged in a simulation. Not allowed to be used by the learner.
-        return {}
-
-    def initialize(self, RS: np.random.RandomState):
-        if not self.replace:
-            self.i_seq = RS.permutation(len(self.image_urls))
-        else:
-            self.i_seq = RS.choice(len(self.image_urls), replace=True, size=len(self.image_urls))
-        self.step = 0
-
-    def sample_image(self):
-        icur = self.i_seq[self.step]
-        image_url = self.image_urls[icur]
-        return image_url
-
-    def provide_feedback(self, action: int):
-        assert action in [0, 1]
-        icur = self.i_seq[self.step]
-
-        if action == self.labels[icur]:
-            reward = 1.
-        else:
-            reward = -1.
-
-        self.step += 1
-
-        return reward
-
-
-class MutatorHighVarExperiment(ExperimentSimulation):
-    nreps = 500
-    ntrials = 100
-    imageset = imagesets.MutatorHighVarImageset()
-    environment_name_dim = 'subtask'
-
-    @property
-    def experiment_name(self):
-        return 'MutatorHighVarExperiment'
-
-    def postprocess_behavioral_data(self, ds: xr.Dataset, environment: env_template.Environment):
-
-        """
-
-        :param ds:
-        :param environment:
-        :return:
-        action: (trial, stimulus_category). Probability of taking action 1, given this trial, and this stimulus category.
-        """
-        ds = ds.transpose('rep', 'trial')
-
-        ds_meta = self.imageset.ds_meta
-        nreps = len(ds.rep)
-        ntrials = len(ds.trial)
-        k_action = np.zeros((nreps, ntrials, 2))
-        n_action = np.zeros((nreps, ntrials, 2))
-        for i_rep in range(len(ds.rep)):
-            objseq = ds_meta.sel(image_url=ds.image_url.values[i_rep]).obj.values
-            objects = sorted(np.unique(objseq))
-            assert len(objects) == 2
-
-            for i_trial in range(len(ds.trial)):
-                i_obj = objects.index(objseq[i_trial])
-                reward = ds.reward.values[i_rep, i_trial]
-
-                if i_obj == 1:
-                    if reward > 0:
-                        took_action1 = True
-                    else:
-                        took_action1 = False
-                elif i_obj == 0:
-                    if reward > 0:
-                        took_action1 = False
-                    else:
-                        took_action1 = True
-                else:
-                    raise Exception
-
-                k_action[i_rep, i_trial, i_obj] = int(took_action1)
-                n_action[i_rep, i_trial, i_obj] = 1
-
-        assert np.all(k_action <= n_action)
-
-        k_action = k_action.sum(0)
-        n_action = n_action.sum(0)
-        prob_action = k_action / n_action
-        del ds['action']
-        del ds['reward']
-        del ds['image_url']
-        ds['action'] = (('trial', 'stimulus_category'), prob_action)
-        ds['k'] =  (('trial', 'stimulus_category'), k_action)
-        ds['n'] = (('trial', 'stimulus_category'), n_action)
-
-        return ds
-
-    @property
-    def environments(self):
-        if not hasattr(self, '_environments'):
-            subtasks = io.load_json(json_path=os.path.join(_loc, 'MutatorHighVarSubtasks.json'))
-            """
-            subtasks:
-                {subtask:obj:[image urls]}
-            """
-
-            environments = []
-            for subtask in subtasks:
-                obj0, obj1 = subtask.split(',')
-                urls0, urls1 = subtasks[subtask][obj0], subtasks[subtask][obj1]
-                env_cur = BinaryClassificationEnvironment(category0_urls=urls0, category1_urls=urls1)
-                env_cur.meta = {'subtask': subtask}
-                environments.append(env_cur)
-            self._environments = environments
-        return self._environments
-
-
-
diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/__init__.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py
deleted file mode 100644
index da6ba14..0000000
--- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_benchmark.py
+++ /dev/null
@@ -1,618 +0,0 @@
-import collections
-
-import xarray as xr
-import numpy as np
-from tqdm import trange
-
-import hobj.learning_models.learning_model as lm
-import hobj.images.imagesets as imagesets
-import hobj.benchmarks.leedicarlo_benchmarks.MutatorOneshot.mut_oneshot_experiment as mutator
-
-
-import hobj.benchmarks.benchmarks as benchmarks
-import hobj.behavioral_data.human_data as human_data
-from hobj.statistics.resamplers.resamplers import Resampler
-import hobj.statistics.score_statistics.MSEn as R2n_funcs
-import hobj.statistics.variance_estimates.binomial as binomial_funcs
-import hobj.statistics.hypothesis_testing.bootstrapped_confidence_intervals as bootstrapped_confidence_intervals
-import hobj.statistics.hypothesis_testing.approximated_null_distribution as approximated_null_distribution
-
-
-class MutatorOneshotBenchmark(benchmarks.Benchmark):
-    environment_name_dim = 'session'
-
-    def __init__(self):
-        super().__init__()
-        self.nboot = 1000
-        self.dataset = human_data.MutatorOneShotGeneralizationDataset()
-        self.ds_raw = self.dataset.load_ds_data()
-
-        # Remove sessions with less than 100% catch performance
-        self.ds_meta = imagesets.MutatorOneshotImageset().ds_meta
-        self.url_to_trans_id = {url: trans for (url, trans) in zip(self.ds_meta.image_url.values, self.ds_meta.transformation_id.values)}
-        self.url_to_obj = {url: obj for (url, obj) in zip(self.ds_meta.image_url.values, self.ds_meta.obj.values)}
-        self.experiment = mutator.MutatorOneShotExperiment()
-
-        self.transformation_ids = ['backgrounds | 0.1',
-                                   'backgrounds | 0.215443',
-                                   'backgrounds | 0.464159',
-                                   'backgrounds | 1.0',
-                                   'blur | 0.007812',
-                                   'blur | 0.015625',
-                                   'blur | 0.03125',
-                                   'blur | 0.0625',
-                                   'contrast | -0.4',
-                                   'contrast | -0.8',
-                                   'contrast | 0.4',
-                                   'contrast | 0.8',
-                                   'delpixels | 0.25',
-                                   'delpixels | 0.5',
-                                   'delpixels | 0.75',
-                                   'delpixels | 0.95',
-                                   'inplanerotation | 135.0',
-                                   'inplanerotation | 180.0',
-                                   'inplanerotation | 45.0',
-                                   'inplanerotation | 90.0',
-                                   'inplanetranslation | 0.125',
-                                   'inplanetranslation | 0.25',
-                                   'inplanetranslation | 0.5',
-                                   'inplanetranslation | 0.75',
-                                   'noise | 0.125',
-                                   'noise | 0.25',
-                                   'noise | 0.375',
-                                   'noise | 0.5',
-                                   'outplanerotation | 135.0',
-                                   'outplanerotation | 180.0',
-                                   'outplanerotation | 45.0',
-                                   'outplanerotation | 90.0',
-                                   'scale | 0.125',
-                                   'scale | 0.25',
-                                   'scale | 0.5',
-                                   'scale | 1.5']
-        self.subtasks = [
-            'MutatorB2000_2292,MutatorB2000_2444',
-            'MutatorB2000_138,MutatorB2000_2344',
-            'MutatorB2000_1251,MutatorB2000_953',
-            'MutatorB2000_3043,MutatorB2000_694',
-            'MutatorB2000_3496,MutatorB2000_496',
-            'MutatorB2000_1219,MutatorB2000_296',
-            'MutatorB2000_1825,MutatorB2000_2757',
-            'MutatorB2000_3077,MutatorB2000_4703',
-            'MutatorB2000_270,MutatorB2000_3615',
-            'MutatorB2000_3066,MutatorB2000_3585',
-            'MutatorB2000_2139,MutatorB2000_746',
-            'MutatorB2000_116,MutatorB2000_2365',
-            'MutatorB2000_2130,MutatorB2000_4628',
-            'MutatorB2000_462,MutatorB2000_926',
-            'MutatorB2000_2304,MutatorB2000_3733',
-            'MutatorB2000_1363,MutatorB2000_3278',
-            'MutatorB2000_4049,MutatorB2000_663',
-            'MutatorB2000_2722,MutatorB2000_3527',
-            'MutatorB2000_2832,MutatorB2000_801',
-            'MutatorB2000_1258,MutatorB2000_3123',
-            'MutatorB2000_1865,MutatorB2000_613',
-            'MutatorB2000_1164,MutatorB2000_2106',
-            'MutatorB2000_1229,MutatorB2000_1280',
-            'MutatorB2000_1767,MutatorB2000_2122',
-            'MutatorB2000_2198,MutatorB2000_701',
-            'MutatorB2000_3636,MutatorB2000_4305',
-            'MutatorB2000_3035,MutatorB2000_46',
-            'MutatorB2000_3601,MutatorB2000_4792',
-            'MutatorB2000_2092,MutatorB2000_288',
-            'MutatorB2000_1424,MutatorB2000_2314',
-            'MutatorB2000_3308,MutatorB2000_3525',
-            'MutatorB2000_2909,MutatorB2000_4256'
-        ]
-
-    def evaluate_model(self, learner: lm.LearningModel, force_recompute: bool = False):
-        """
-        :param learner: LearningModel
-        :param force_recompute: bool. If True, recompute the results even if they are already cached.
-        :return:
-        """
-        ds_model_behavior = self.experiment.run(learner=learner, seed=0, force_recompute=force_recompute)
-        ds_model_behavior = ds_model_behavior.sel(transformation_id=self.ds_table.transformation_id)
-        ds_model_behavior = ds_model_behavior.sum('subtask')
-
-        # Compute scores
-        ds_score = self.score_predictions(
-            k=ds_model_behavior.k,
-            n=ds_model_behavior.n,
-        )
-
-        return ds_score
-
-    def get_lapse_rate_corrected_performance(
-            self,
-            k,
-            n,
-            kcatch,
-            ncatch,
-    ):
-        """
-
-        Returns an estimate of the performance that would be expected if the system had a lapse rate of 0.
-        The values kcatch and ncatch are assumed to be drawn from a Bernoulli distribution with parameter p = (1 - lapse rate).
-        This function also returns an estimate of the variance associated with that estimate, using bootstrapping.
-
-        We assume
-        kcatch ~ Binomial(ncatch, (1 - lapse_rate) * 1 + lapse_rate * 0.5)
-        k ~ Binomial(n, (1 - lapse_rate) * p + (lapse_rate) * 0.5)
-
-        Denote
-        pcatch = (1 - lapse_rate) * 1 + lapse_rate * 0.5)
-
-        Then,
-        lapse_rate = 2 - 2 * pcatch
-        praw =  (1 - lapse_rate) * p + (lapse_rate) * 0.5 (the empirically observed probability of a correct response)
-
-        We have the relation:
-        p = (praw - lapse_rate / 2) / (1 - lapse_rate)
-
-        Here, we estimate p using the plug-in estimator for p.
-
-        :param k:
-        :param n:
-        :param kcatch:
-        :param ncatch:
-        :return:
-        """
-
-        assert np.all(kcatch <= ncatch)
-        assert np.all(kcatch >= 0)
-
-        hat_praw = k / n
-        hat_pcatch = kcatch / ncatch
-        hat_lapse_rate = 2 - 2 * hat_pcatch
-        hat_p_adjusted = (hat_praw - hat_lapse_rate / 2) / (1 - hat_lapse_rate)
-
-        return hat_p_adjusted
-
-    def score_predictions(
-            self,
-            k: xr.DataArray,
-            n: xr.DataArray,
-    ):
-
-        """
-        k: (transformation_id)
-        n: (transformation_id)
-        kcatch: int
-        ncatch: int
-        returns ds_scores
-        """
-
-        # Compute R^2 score
-        assert isinstance(k, xr.DataArray)
-        assert isinstance(n, xr.DataArray)
-        assert set(k.dims) == {'transformation_id'}, (k.dims)
-        assert set(k.dims) == set(n.dims), (k.dims, n.dims)
-
-        hat_prob = k / n
-        hat_var_hat_prob = binomial_funcs.estimate_variance_of_binomial_proportion(kvec=k, nvec=n)
-
-        # %% Get MSEn point estimate
-        MSEn_point = R2n_funcs.estimate_MSEn(
-            hat_theta_model=hat_prob,
-            hat_var_hat_theta_model=hat_var_hat_prob,
-            hat_theta_target=self.ds_behavioral_statistics['perf'],
-            condition_dims=('transformation_id',),
-        )
-
-        # %% Assemble scores
-        ds_scores = xr.Dataset(
-            data_vars=dict(
-                MSEn = MSEn_point,
-                perf=hat_prob,
-                perf_by_type=hat_prob.groupby('transformation').mean(),
-            ),
-        )
-
-        return ds_scores
-
-    @property
-    def ds_behavioral_statistics(self):
-        if not hasattr(self, '_ds_behavioral_statistics'):
-            p = self.ds_table.k.sum('session') / self.ds_table.n.sum('session')
-            pvec_boot = self.get_lapse_rate_corrected_performance(
-                k=self.ds_boot.k,
-                n=self.ds_boot.n,
-                kcatch=self.ds_boot.k_catch.sum(['catch_trial']),
-                ncatch=self.ds_boot.n_catch.sum(['catch_trial']),
-            )
-
-            var_pvec = pvec_boot.var('boot_iter', ddof=1)
-
-            ds_behavioral_statistics = xr.Dataset(
-                {
-                    'perf_raw': p,
-                    'perf': self.get_lapse_rate_corrected_performance(
-                        k=self.ds_table.k.sum('session'),
-                        n=self.ds_table.n.sum('session'),
-                        kcatch=int(self.ds_table.k_catch.sum(['session', 'catch_trial'])),
-                        ncatch=int(self.ds_table.n_catch.sum(['session', 'catch_trial'])),
-                    ),
-                    'hat_var_perf': var_pvec,
-                }
-            )
-
-            self._ds_behavioral_statistics = ds_behavioral_statistics
-        return self._ds_behavioral_statistics
-
-    def _process_raw_experiment(self, da_perf: xr.DataArray):
-        """
-
-        :param da_perf:  (session, trial)
-        :return: ds_oneshot
-            perf: (transformation_id, stimulus_category)
-            catch_perf: ()
-        """
-
-        """
-        ktraining: (session, trial)
-        ntraining: (session, trial)
-        k: (session, condition)
-        n: (session, condition)
-        kprobe: (session, condition)
-        nprobe: (session, condition)
-        :return:
-        """
-
-        train_trials = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-        catch_trials = [14, 19]  # trials used to estimate the lapse rate (each of these trials presented one of the two support images)
-        test_trials = [10, 11, 12, 13, 15, 16, 17, 18, ]  # trials containing a test trial (defined as a randomly sampled, transformed version of one of the two support images)
-        assert np.all(np.mod(da_perf, 1) == 0)
-        da_perf = da_perf.astype(int)
-        k_catch = da_perf.isel(trial=catch_trials)
-        n_catch = k_catch - k_catch + 1
-
-        k_training = da_perf.isel(trial=train_trials)
-        n_training = k_training - k_training + 1
-
-        da_train = da_perf.isel(trial=train_trials).transpose('session', 'trial')
-        da_test = da_perf.isel(trial=test_trials).transpose('session', 'trial')
-
-        nsessions = len(da_perf.session)
-        ntransformations = len(self.transformation_ids)
-        k = np.zeros((nsessions, ntransformations))
-        n = np.zeros((nsessions, ntransformations))
-
-        trans_id_to_i = collections.defaultdict(lambda: np.nan)
-        for i, t in enumerate(self.transformation_ids):
-            trans_id_to_i[t] = i
-
-        worker_ids = []
-        for i_session in range(da_test.session.values.size):
-            worker_ids.append(str(da_train.isel(session=i_session).worker_id.values))
-            support_ids = sorted(set(da_train.stimulus_url.values[i_session]))
-            support_objects = sorted([self.url_to_obj[u] for u in support_ids])
-            assert len(support_objects) == 2, support_objects
-
-            url_seq = da_test.stimulus_url.values[i_session]
-            perf_seq = da_test.values[i_session]
-            trans_id_seq = [self.url_to_trans_id[u] for u in url_seq]
-
-            for i_trial in range(len(trans_id_seq)):
-                trans = trans_id_seq[i_trial]
-                i_trans = trans_id_to_i[trans]
-                if np.isnan(i_trans):
-                    continue
-
-                perf = int(perf_seq[i_trial])
-
-                k[i_session, i_trans] += perf
-                n[i_session, i_trans] += 1
-
-        ds = xr.Dataset(
-            {
-                'k': (['session', 'transformation_id'], k),
-                'n': (['session', 'transformation_id'], n),
-                'k_catch': (['session', 'catch_trial'], k_catch.transpose('session', 'trial').values),
-                'n_catch': (['session', 'catch_trial'], n_catch.transpose('session', 'trial').values),
-                'k_train': (['session', 'train_trial'], k_training.transpose('session', 'trial').values),
-                'n_train': (['session', 'train_trial'], n_training.transpose('session', 'trial').values),
-
-            },
-            coords={
-                'transformation_id': self.transformation_ids,
-                'transformation': (['transformation_id'], [t.split(" | ")[0] for t in self.transformation_ids]),
-                'transformation_level': (['transformation_id'], [float(t.split(" | ")[1]) for t in self.transformation_ids]),
-                'session': da_perf.session.values,
-                'catch_trial': catch_trials,
-                'train_trial': train_trials,
-                'worker_id': (['session'], worker_ids)
-            }
-        )
-
-        return ds
-
-
-    @property
-    def ds_table(self):
-
-        """
-        ktraining: (session, trial)
-        ntraining: (session, trial)
-        k: (session, condition)
-        n: (session, condition)
-        kprobe: (session, condition)
-        nprobe: (session, condition)
-        :return:
-        """
-        if not hasattr(self, '_ds_table'):
-            self._ds_table = self._process_raw_experiment(da_perf = self.ds_raw.perf)
-        return self._ds_table
-
-
-    @property
-    def ds_ceilings(self):
-        """
-        - The point estimate for the R2n noise ceiling, and associated CI.
-        - The approximate null (where the model is equal to humans) distribution of R2n scores, given the number of model simulations.
-        - The point estimates for each transformation, associated CIs, and standard errors.
-        - The point estimates for each transformation type, and its associate CI, and standard errors.
-
-        :return:
-        """
-        if not hasattr(self, '_ds_ceilings'):
-            alpha = 0.05
-            ds_ceilings = xr.Dataset()
-
-            # %% Get point estimate of MSE floor
-            pvec_boot = self.get_lapse_rate_corrected_performance(
-                k=self.ds_boot.k,
-                n=self.ds_boot.n,
-                kcatch=self.ds_boot.k_catch.sum(['catch_trial']),
-                ncatch=self.ds_boot.n_catch.sum(['catch_trial']),
-            )
-
-            var_pvec = pvec_boot.var('boot_iter', ddof=1)
-
-            ds_ceilings['MSEn'] = var_pvec.mean('transformation_id')
-
-            # %% Approximate the null distribution of MSEn, using the null hypothesis that the model is equal to the (lapse-corrected) human performance
-
-            var_pvec_null = self.get_lapse_rate_corrected_performance(
-                k=self.ds_null.k,
-                n=self.ds_null.n,
-                kcatch=self.ds_null.k_catch.sum(['catch_trial']),
-                ncatch=self.ds_null.n_catch.sum(['catch_trial']),
-            ).var('boot_iter', ddof=1)
-
-            def get_null_MSE_functional(ds_data: xr.Dataset):
-                pvec = self.get_lapse_rate_corrected_performance(
-                    k=ds_data.k,
-                    n=ds_data.n,
-                    kcatch=(ds_data.k_catch.sum('catch_trial')),
-                    ncatch=(ds_data.n_catch.sum('catch_trial')),
-                )
-
-                pvec_null = self.get_lapse_rate_corrected_performance(
-                    k=ds_data.knull,
-                    n=ds_data.nnull,
-                    kcatch=(ds_data.k_catch_null.sum('catch_trial')),
-                    ncatch=(ds_data.n_catch_null.sum('catch_trial')),
-                )
-
-                MSEn = R2n_funcs.estimate_MSEn(
-                    hat_theta_model=pvec_null,
-                    hat_var_hat_theta_model=var_pvec_null,
-                    hat_theta_target = pvec,
-                    condition_dims=('transformation_id',),
-                )
-
-                return  MSEn
-
-            ds_boot = self.ds_boot
-            ds_null = self.ds_null
-            ds_null = ds_null.rename({
-                'k': 'knull',
-                'n': 'nnull',
-                'k_catch': 'k_catch_null',
-                'n_catch': 'n_catch_null',
-                'k_train': 'k_train_null',
-                'n_train': 'n_train_null',
-            })
-            ds_data = xr.merge([ds_boot, ds_null])
-
-            ds_ceilings['MSEn_null'] = approximated_null_distribution.estimate_null_distribution(
-                statistical_functional=get_null_MSE_functional,
-                ds_null_samples=ds_data,
-                hat_mu=ds_ceilings['MSEn'],
-                resample_dim='boot_iter'
-            )
-
-            # %% Get point estimate of per-transformation performances, confidence intervals, and standard errors (lapse-rate adjusted)
-            perf_lapsed_functional = lambda ds_data: self.get_lapse_rate_corrected_performance(
-                k=ds_data.k,
-                n=ds_data.n,
-                kcatch=ds_data.k_catch.sum('catch_trial'),
-                ncatch=ds_data.n_catch.sum('catch_trial'),
-            )
-
-            ds_data = self.ds_table.sum('session')
-            perf_lapse = perf_lapsed_functional(ds_data)
-
-            perf_lapsed_boot = perf_lapsed_functional(self.ds_boot)
-            ds_ceilings['perf'] = perf_lapse
-            ds_ceilings['perf_std'] = perf_lapsed_boot.std('boot_iter', ddof=1)  # Get via bootstrapping
-            ds_ceilings['perf_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval(
-                alpha=alpha,
-                ds_data=ds_data,
-                ds_boot=self.ds_boot,
-                statistic_functional=perf_lapsed_functional,
-                parameter_functional=None,
-            )
-
-            # %% Get point estimate of per-transformation performances, confidence intervals, and standard errors
-            point = self.ds_table.sum('session')
-            ds_ceilings['perf_raw'] = point.k / point.n
-            ds_ceilings['perf_raw_std'] = (self.ds_boot.k / self.ds_boot.n).std('boot_iter', ddof=1)
-            ds_ceilings['perf_raw_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval(
-                alpha=alpha,
-                ds_data=self.ds_table.sum('session')[['k', 'n']],
-                ds_boot=self.ds_boot,
-                statistic_functional=lambda ds_data: ds_data.k / ds_data.n,
-                parameter_functional=None,
-            )
-
-            # %% Get point estimate of per-transformation-type performances, confidence intervals, and standard errors
-
-            def perf_by_type_functional(ds_data):
-                pvec = self.get_lapse_rate_corrected_performance(
-                    k=ds_data.k,
-                    n=ds_data.n,
-                    kcatch=ds_data.k_catch.sum('catch_trial'),
-                    ncatch=ds_data.n_catch.sum('catch_trial'),
-                )
-                perf_by_type = pvec.groupby("transformation").mean()
-                perf_by_type = perf_by_type.rename({'transformation': 'transformation_type'})
-                return perf_by_type
-
-            perf_by_type = perf_by_type_functional(self.ds_table.sum('session'))
-            perf_by_type_boot = perf_by_type_functional(self.ds_boot)
-
-            ds_ceilings['perf_by_type'] = perf_by_type
-            ds_ceilings['perf_by_type_std'] = perf_by_type_boot.std('boot_iter', ddof=1)
-            ds_ceilings['perf_by_type_CI'] = bootstrapped_confidence_intervals.estimate_basic_confidence_interval(
-                alpha=alpha,
-                ds_data=self.ds_table.sum('session'),
-                ds_boot=self.ds_boot,
-                statistic_functional=perf_by_type_functional,
-                parameter_functional=None,
-            )
-
-            self._ds_ceilings = ds_ceilings
-
-        return self._ds_ceilings
-
-    @property
-    def worker_resampler(self):
-        if not hasattr(self, '_worker_resampler'):
-            self._worker_resampler = ExperimentResampler(
-                ds_session=self.ds_table,
-            )
-        return self._worker_resampler
-
-    @property
-    def ds_boot(self):
-        if not hasattr(self, '_ds_boot'):
-            dlist = []
-            RS = np.random.RandomState(seed=0)
-            for i_boot in trange(self.nboot, desc='resampling experiment'):
-                ds_resampled = self.worker_resampler.get_ds_resampled(RS=RS).sum('session')
-                dlist.append(ds_resampled)
-
-            self._ds_boot = xr.concat(dlist, dim='boot_iter').assign_coords(
-                transformation=(['transformation_id'], self.ds_table.transformation.values),
-                transformation_level=(['transformation_id'], self.ds_table.transformation_level.values),
-            )
-
-        return self._ds_boot
-
-    @property
-    def ds_null(self):
-        if not hasattr(self, '_ds_null'):
-            RS = np.random.RandomState(seed=1)
-            dlist = []
-            for i_boot in trange(self.nboot, desc='resampling experiment'):
-                ds_resampled = self.worker_resampler.get_ds_resampled(RS=RS, nsessions=16000).sum('session')
-                dlist.append(ds_resampled)
-
-            self._ds_null = xr.concat(dlist, dim='boot_iter').assign_coords(
-                transformation=(['transformation_id'], self.ds_table.transformation.values),
-                transformation_level=(['transformation_id'], self.ds_table.transformation_level),
-            )
-
-        return self._ds_null
-
-
-class ExperimentResampler(Resampler):
-    """
-    Resamples the experiment, simulating the data-generating process:
-        1) First, re-samples a subject randomly
-         2) Then for that re-sampled subject, resamples a session associated with that subject.
-
-    The probability of re-sampling a particular session is then given by:
-
-    Prob(session) = Prob(worker) * Prob(session | worker)
-    Prob(worker) = 1 /nworkers
-    Prob(session | worker) = 1 / nsessions[i_worker]
-    Prob(session) = 1 / (nworkers * nsessions[i_worker])
-
-    """
-
-    def __init__(
-            self,
-            ds_session: xr.Dataset,
-    ):
-
-        super().__init__()
-        mandatory_vars = ['k_catch', 'n_catch', 'k_train', 'n_train', 'k', 'n']
-        for var in mandatory_vars:
-            assert var in ds_session.data_vars, ds_session.data_vars
-
-        all_dims = ds_session.dims
-        assert 'session' in all_dims
-        assert 'worker_id' in ds_session.coords
-
-        prob_session = []
-        nworkers = len(set(ds_session['worker_id'].values))
-        worker_to_nsessions = collections.defaultdict(lambda: 0)
-        for worker_id in ds_session['worker_id'].values:
-            worker_to_nsessions[worker_id] += 1
-
-        for worker_id in ds_session['worker_id'].values:
-            prob_session.append(1 / (nworkers * worker_to_nsessions[worker_id]))
-
-        assert np.isclose(np.sum(prob_session), 1)
-
-        self.ds_session = ds_session.transpose('session', 'transformation_id', 'catch_trial', 'train_trial')
-        self.transformation_ids = self.ds_session['transformation_id'].values
-        self.catch_trial = self.ds_session['catch_trial'].values
-        self.train_trial = self.ds_session['train_trial'].values
-
-        self.prob_session = np.array(prob_session)
-        self.nsessions_empirical = len(self.prob_session)
-        self.worker_id_seq = self.ds_session['worker_id'].values
-
-    def get_ds_resampled(self, RS: np.random.RandomState, nsessions=None, ):
-        """
-        Returns a single bootstrap re-sample
-        :param seed:
-        :return:
-        """
-
-        if nsessions is None:
-            # Take a bootstrap resample
-            nsessions = len(self.ds_session['session'])
-
-        i_sessions = RS.choice(self.nsessions_empirical, size=nsessions, p=self.prob_session, replace=True)
-
-        kbootstrapped_data = self.ds_session['k'].values[i_sessions]
-        nbootstrapped_data = self.ds_session['n'].values[i_sessions]
-        kcatch_resampled_data = self.ds_session['k_catch'].values[i_sessions]
-        ncatch_resampled_data = self.ds_session['n_catch'].values[i_sessions]
-        ktrain_resampled_data = self.ds_session['k_train'].values[i_sessions]
-        ntrain_resampled_data = self.ds_session['n_train'].values[i_sessions]
-        worker_ids = self.worker_id_seq[i_sessions]
-
-        ds_resampled = xr.Dataset(
-            data_vars={
-                'k': (['session', 'transformation_id'], kbootstrapped_data),
-                'n': (['session', 'transformation_id'], nbootstrapped_data),
-                'k_catch': (['session', 'catch_trial'], kcatch_resampled_data),
-                'n_catch': (['session', 'catch_trial'], ncatch_resampled_data),
-                'k_train': (['session', 'train_trial'], ktrain_resampled_data),
-                'n_train': (['session', 'train_trial'], ntrain_resampled_data),
-            },
-            coords={
-                'transformation_id': self.transformation_ids,
-                'catch_trial': self.catch_trial,
-                'train_trial': self.train_trial,
-                'session': np.arange(nsessions),
-                'worker_id': (['session'], worker_ids),
-            }
-        )
-
-        return ds_resampled
diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py b/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py
deleted file mode 100644
index 07aa448..0000000
--- a/hobj/benchmarks/leedicarlo_benchmarks/MutatorOneshot/mut_oneshot_experiment.py
+++ /dev/null
@@ -1,280 +0,0 @@
-import os
-from typing import List
-
-import xarray as xr
-import numpy as np
-
-import hobj.benchmarks.experiment_simulation.environment as env_template
-from hobj.benchmarks.experiment_simulation.experiment_simulation import ExperimentSimulation
-import hobj.images.imagesets as imagesets
-
-_loc = os.path.dirname(__file__)
-
-
-class OneshotTestEnvironment(env_template.Environment):
-    """
-    Trials 0-9 are training trials involving two support images. They are shown an equal amount of times.
-    Trials 10-13 are test trials involving transformed versions of the support images.
-    Trial 14 is a catch trial.
-    Trials 15-18 are test trials involving transformed versions of the support images.
-    Trial 19 is a catch trial.
-
-    In all of the test trials, the test images are shown *without replacement*.
-
-    """
-
-    def __init__(
-            self,
-            support_imageA_url: str,
-            support_imageB_url: str,
-            test_imageA_urls: List[str],
-            test_imageB_urls: List[str],
-    ):
-        super().__init__()
-
-        assert isinstance(support_imageA_url, str)
-        assert isinstance(support_imageB_url, str)
-        assert isinstance(test_imageA_urls, list)
-        assert isinstance(test_imageB_urls, list)
-
-        assert len(test_imageA_urls) == len(test_imageB_urls), 'Imbalanced test images'
-        assert np.all([isinstance(url, str) for url in test_imageA_urls])
-        assert np.all([isinstance(url, str) for url in test_imageB_urls])
-        assert support_imageA_url not in test_imageA_urls
-        assert support_imageB_url not in test_imageB_urls
-        assert support_imageA_url != support_imageB_url
-
-        self.support_image_urls = [support_imageA_url, support_imageB_url]
-        self.test_imageA_urls = test_imageA_urls
-        self.test_imageB_urls = test_imageB_urls
-        self.step = None
-        return
-
-    def initialize(self, RS: np.random.RandomState):
-        self.step = 0
-
-        url_sequence = []
-        correct_action_sequence = []
-
-        # Sample the order of the support images during the training phase, balancing the times they appear
-        i_training_sequence = RS.permutation([0] * 5 + [1] * 5)
-        for i in i_training_sequence:
-            url_sequence.append(self.support_image_urls[i])
-            correct_action_sequence.append(i)
-
-        # Assemble the test sequence
-        test_class_sequence = RS.choice([0, 1], size=10, replace=True)
-        testA_seq = RS.permutation(self.test_imageA_urls)
-        testB_seq = RS.permutation(self.test_imageB_urls)
-
-        for i_test_trial in np.arange(10):
-            class_cur = test_class_sequence[i_test_trial]
-
-            if i_test_trial in [4, 9]:
-                # Catch trial
-                url_sequence.append(self.support_image_urls[class_cur])
-                correct_action_sequence.append(class_cur)
-                continue
-
-            if class_cur == 0:
-                url_sequence.append(testA_seq[i_test_trial])
-            else:
-                url_sequence.append(testB_seq[i_test_trial])
-
-            correct_action_sequence.append(class_cur)
-
-        self.url_seq = url_sequence
-        self.correct_action_seq = correct_action_sequence
-
-
-    def sample_image(self):
-
-        image_url = self.url_seq[self.step]
-        return image_url
-
-    def provide_feedback(self, action: int):
-
-        correct_action = self.correct_action_seq[self.step]
-        assert action in [0, 1]
-        assert correct_action in [0, 1]
-
-        if action == correct_action:
-            reward = 1.
-        else:
-            reward = -1.
-
-        self.step += 1
-
-        return reward
-
-
-class MutatorOneShotExperiment(ExperimentSimulation):
-    nreps = 500  # per subtask (n=32)
-    ntrials = 20
-    imageset = imagesets.MutatorOneshotImageset()
-    environment_name_dim = 'subtask'
-
-    @property
-    def experiment_name(self):
-        return 'MutatorOneShotExperiment'
-
-    @property
-    def ds_meta(self):
-        if not hasattr(self, '_ds_meta'):
-            self._ds_meta = self.imageset.ds_meta
-        return self._ds_meta
-
-    @property
-    def url_to_transformation_id(self):
-        if not hasattr(self, '_url_to_transformation_id'):
-            self._url_to_transformation_id = {url: trans for (url, trans) in zip(self.ds_meta.image_url.values, self.ds_meta.transformation_id.values)}
-        return self._url_to_transformation_id
-
-    @property
-    def transformation_ids(self):
-        if not hasattr(self, '_transformation_ids'):
-            self._transformation_ids = sorted(np.unique(self.ds_meta.transformation_id.values))
-        return self._transformation_ids
-
-    def get_i_transformation_id_from_url(self, url: str):
-        transformation_id = self.url_to_transformation_id[url]
-        if not hasattr(self, '_transformation_id_to_i'):
-            self._transformation_id_to_i = {transformation_id: i for (i, transformation_id) in enumerate(self.transformation_ids)}
-        i_transformation_id = self._transformation_id_to_i[transformation_id]
-        return i_transformation_id
-
-    def postprocess_behavioral_data(self, ds: xr.Dataset, environment: env_template.Environment):
-
-        """
-
-        :param ds: [rep, trial]
-        :param environment:
-        :return:
-
-        k: (transformation_id)
-        n: (transformation_id)
-
-        k_train: (train_trial)
-        n_train: (train_trial)
-
-        k_test: (test_trial)
-        n_test: (test_trial)
-
-        k_catch: (catch_trial)
-        n_catch: (catch_trial)
-
-        """
-        assert set(ds.dims) == {'rep', 'trial'}
-        assert 'reward' in ds.data_vars
-        ds['perf'] = ds.reward > 0
-        ds_table = xr.Dataset(
-            dict(
-                k=(['transformation_id'], np.zeros(len(self.transformation_ids), dtype=int)),
-                n=(['transformation_id'], np.zeros(len(self.transformation_ids), dtype=int)),
-                k_train=(['train_trial'], np.zeros(10, dtype=int)),
-                n_train=(['train_trial'], np.zeros(10, dtype=int)),
-                k_catch=(['catch_trial'], np.zeros(3, dtype=int)),
-                n_catch=(['catch_trial'], np.zeros(3, dtype=int)),
-            ),
-            coords={
-                'transformation_id': self.transformation_ids,
-                'train_trial': np.arange(10),
-                'catch_trial': [9, 14, 19],
-            }
-        )
-
-        perf_dat = ds.transpose('rep', 'trial').perf.values
-        url_dat = ds.transpose('rep', 'trial').image_url.values
-        for i_rep in ds.rep.values:
-            for i_trial in range(self.ntrials):
-                url = url_dat[i_rep, i_trial]
-                perf = int(perf_dat[i_rep, i_trial])
-
-                if i_trial < 10:
-                    ds_table.k_train.values[i_trial] += perf
-                    ds_table.n_train.values[i_trial] += 1
-                if i_trial == 9:
-                    ds_table.k_catch.values[0] += perf
-                    ds_table.n_catch.values[0] += 1
-                if i_trial == 14:
-                    ds_table.k_catch.values[1] += perf
-                    ds_table.n_catch.values[1] += 1
-                if i_trial == 19:
-                    ds_table.k_catch.values[2] += perf
-                    ds_table.n_catch.values[2] += 1
-
-                i_transformation_id = self.get_i_transformation_id_from_url(url)
-
-                ds_table.k.values[i_transformation_id] += perf
-                ds_table.n.values[i_transformation_id] += 1
-
-        return ds_table
-
-    @property
-    def environments(self):
-        subtasks = ['MutatorB2000_2292,MutatorB2000_2444',
-                    'MutatorB2000_138,MutatorB2000_2344',
-                    'MutatorB2000_1251,MutatorB2000_953',
-                    'MutatorB2000_3043,MutatorB2000_694',
-                    'MutatorB2000_3496,MutatorB2000_496',
-                    'MutatorB2000_1219,MutatorB2000_296',
-                    'MutatorB2000_1825,MutatorB2000_2757',
-                    'MutatorB2000_3077,MutatorB2000_4703',
-                    'MutatorB2000_270,MutatorB2000_3615',
-                    'MutatorB2000_3066,MutatorB2000_3585',
-                    'MutatorB2000_2139,MutatorB2000_746',
-                    'MutatorB2000_116,MutatorB2000_2365',
-                    'MutatorB2000_2130,MutatorB2000_4628',
-                    'MutatorB2000_462,MutatorB2000_926',
-                    'MutatorB2000_2304,MutatorB2000_3733',
-                    'MutatorB2000_1363,MutatorB2000_3278',
-                    'MutatorB2000_4049,MutatorB2000_663',
-                    'MutatorB2000_2722,MutatorB2000_3527',
-                    'MutatorB2000_2832,MutatorB2000_801',
-                    'MutatorB2000_1258,MutatorB2000_3123',
-                    'MutatorB2000_1865,MutatorB2000_613',
-                    'MutatorB2000_1164,MutatorB2000_2106',
-                    'MutatorB2000_1229,MutatorB2000_1280',
-                    'MutatorB2000_1767,MutatorB2000_2122',
-                    'MutatorB2000_2198,MutatorB2000_701',
-                    'MutatorB2000_3636,MutatorB2000_4305',
-                    'MutatorB2000_3035,MutatorB2000_46',
-                    'MutatorB2000_3601,MutatorB2000_4792',
-                    'MutatorB2000_2092,MutatorB2000_288',
-                    'MutatorB2000_1424,MutatorB2000_2314',
-                    'MutatorB2000_3308,MutatorB2000_3525',
-                    'MutatorB2000_2909,MutatorB2000_4256']
-
-        # Replicate the same kinds of trials done in humans
-        if not hasattr(self, '_environments'):
-            ds_meta = imagesets.MutatorOneshotImageset().ds_meta
-
-            obj_to_support_url = {}
-            obj_to_test_urls = {}
-            for obj, ds_obj in ds_meta.groupby('obj'):
-                ds_support = ds_obj.sel(image_url=ds_obj.transformation == 'original')
-                ds_test = ds_obj.sel(image_url=ds_obj.transformation != 'original')
-                assert len(ds_support.image_url) + len(ds_test.image_url) == len(ds_obj.image_url)
-                assert len(ds_support.image_url) == 1
-                support_url = ds_support.image_url.values[0]
-                test_urls = ds_test.image_url.values
-                obj_to_support_url[obj] = str(np.array(support_url))
-                obj_to_test_urls[obj] = list(np.array(test_urls))
-
-            environments = []
-            for subtask in subtasks:
-                objA, objB = subtask.split(',')
-                support_imageA_url = obj_to_support_url[objA]
-                support_imageB_url = obj_to_support_url[objB]
-                test_imageA_urls = obj_to_test_urls[objA]
-                test_imageB_urls = obj_to_test_urls[objB]
-
-                env_cur = OneshotTestEnvironment(
-                    support_imageA_url=support_imageA_url,
-                    support_imageB_url=support_imageB_url,
-                    test_imageA_urls=test_imageA_urls,
-                    test_imageB_urls=test_imageB_urls,
-                )
-                environments.append(env_cur)
-            self._environments = environments
-        return self._environments
diff --git a/hobj/benchmarks/leedicarlo_benchmarks/__init__.py b/hobj/benchmarks/leedicarlo_benchmarks/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hobj/benchmarks/mut_highvar_benchmark.py b/hobj/benchmarks/mut_highvar_benchmark.py
new file mode 100644
index 0000000..ca213ce
--- /dev/null
+++ b/hobj/benchmarks/mut_highvar_benchmark.py
@@ -0,0 +1,243 @@
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import numpy as np
+import xarray as xr
+from tqdm import tqdm
+
+from hobj.benchmarks.common import (
+    compare_msen,
+    simulate_sessions,
+    summarize_bootstrap_score,
+)
+from hobj.benchmarks.binary_classification.estimator import (
+    build_learning_curve_statistics,
+)
+from hobj.benchmarks.binary_classification.simulation import (
+    BinaryClassificationSubtask,
+    BinaryClassificationSubtaskResult,
+)
+from hobj.data.behavior import load_highvar_behavior
+from hobj.data.images import load_imageset_meta_highvar
+from hobj.learning_models import BinaryLearningModel
+
+
+class MutatorHighVarBenchmark:
+    num_simulations_per_subtask = 500
+    num_bootstrap_samples = 1000
+    bootstrap_by_worker = False
+    ntrials = 100
+
+    def __init__(self, cachedir: Path | None = None):
+        images_df = load_imageset_meta_highvar(cachedir=cachedir)
+        df_behavior = load_highvar_behavior(
+            remove_probe_trials=True,
+            cachedir=cachedir,
+        )
+
+        image_id_to_category = dict(
+            zip(images_df["image_id"], images_df["category"], strict=True)
+        )
+        category_to_image_ids = (
+            images_df.groupby("category", sort=False)["image_id"]
+            .apply(lambda image_ids: sorted(image_ids.tolist()))
+            .to_dict()
+        )
+
+        subtask_name_to_results: Dict[str, List[BinaryClassificationSubtaskResult]] = {}
+        subtask_name_to_subtask: Dict[str, BinaryClassificationSubtask] = {}
+
+        for _, session in df_behavior.groupby("assignment_id", sort=False):
+            categories = set()
+            perf_seq: List[bool] = []
+
+            session = session.sort_values("trial")
+            for perf, image_id in zip(session.perf, session.image_id):
+                categories.add(image_id_to_category[image_id])
+                perf_seq.append(bool(perf))
+
+            if len(categories) != 2:
+                raise ValueError(f"Expected two categories, but got {categories}")
+
+            cat0, cat1 = sorted(categories)
+            subtask_name = ",".join((cat0, cat1))
+
+            if subtask_name not in subtask_name_to_subtask:
+                subtask_name_to_subtask[subtask_name] = BinaryClassificationSubtask(
+                    classA=category_to_image_ids[cat0],
+                    classB=category_to_image_ids[cat1],
+                    ntrials=self.ntrials,
+                    replace=False,
+                )
+                subtask_name_to_results[subtask_name] = []
+
+            subtask_name_to_results[subtask_name].append(
+                BinaryClassificationSubtaskResult(
+                    perf_seq=np.array(perf_seq),
+                    worker_id=session.worker_id.iloc[0],
+                )
+            )
+
+        self.subtask_names = sorted(subtask_name_to_subtask)
+        self.subtask_name_to_subtask = {
+            name: subtask_name_to_subtask[name] for name in self.subtask_names
+        }
+        self.subtask_name_to_results = {
+            name: subtask_name_to_results[name] for name in self.subtask_names
+        }
+
+        observed_ntrials = {
+            subtask.ntrials for subtask in self.subtask_name_to_subtask.values()
+        }
+        if len(observed_ntrials) != 1:
+            raise ValueError(
+                f"Expected all subtasks to have the same number of trials, but got {observed_ntrials}"
+            )
+
+        observed_ntrials_value = next(iter(observed_ntrials))
+        if observed_ntrials_value != self.ntrials:
+            raise ValueError(
+                f"Expected ntrials to be {observed_ntrials_value}, but got {self.ntrials}"
+            )
+
+        self._target_data: Dict[str, Dict[str, List[bool]]] = {}
+        for name in self.subtask_names:
+            self._target_data[name] = {}
+            for result in self.subtask_name_to_results[name]:
+                worker_id = result.worker_id
+                if worker_id in self._target_data[name]:
+                    raise ValueError(
+                        f"Worker {worker_id} has already been seen for subtask {name}"
+                    )
+                self._target_data[name][worker_id] = [bool(v) for v in result.perf_seq]
+
+        self._target_statistics = build_learning_curve_statistics(
+            subtask_name_to_results=self.subtask_name_to_results,
+            nbootstrap_samples=self.num_bootstrap_samples,
+            bootstrap_by_worker=self.bootstrap_by_worker,
+        )
+
+    @property
+    def target_data(self) -> Dict[str, Dict[str, List[bool]]]:
+        return self._target_data
+
+    @property
+    def target_statistics(self) -> xr.Dataset:
+        return self._target_statistics
+
+    @dataclass
+    class LearningCurveBenchmarkResult:
+        msen: float
+        msen_sigma: float
+        msen_CI95: Tuple[float, float]
+        lapse_rate: xr.DataArray | None
+        model_statistics: xr.Dataset
+
+    def score_model(
+        self, learner: BinaryLearningModel, show_pbar: bool = False
+    ) -> "MutatorHighVarBenchmark.LearningCurveBenchmarkResult":
+        subtask_name_to_model_results: Dict[
+            str, List[BinaryClassificationSubtaskResult]
+        ] = {}
+        for subtask_name in tqdm(
+            self.subtask_names,
+            desc="Subtask simulations:",
+            disable=not show_pbar,
+        ):
+            subtask_name_to_model_results[subtask_name] = simulate_sessions(
+                subtask=self.subtask_name_to_subtask[subtask_name],
+                learner=learner,
+                nsimulations=self.num_simulations_per_subtask,
+            )
+
+        model_statistics = build_learning_curve_statistics(
+            subtask_name_to_results=subtask_name_to_model_results,
+            nbootstrap_samples=self.num_bootstrap_samples,
+            bootstrap_by_worker=False,
+        )
+
+        msen_point, lapse_rate = self._compare_learning_curves(
+            model_phat=model_statistics.phat,
+            model_varhat_phat=model_statistics.varhat_phat,
+            target_phat=self.target_statistics.phat,
+            target_varhat_phat=self.target_statistics.varhat_phat,
+            condition_dims=("subtask", "trial"),
+            fit_lapse_rate=True,
+        )
+
+        msen_boot, _ = self._compare_learning_curves(
+            model_phat=model_statistics.boot_phat,
+            model_varhat_phat=model_statistics.boot_varhat_phat,
+            target_phat=self.target_statistics.boot_phat,
+            target_varhat_phat=self.target_statistics.boot_varhat_phat,
+            condition_dims=("subtask", "trial"),
+            fit_lapse_rate=True,
+        )
+
+        summary = summarize_bootstrap_score(
+            point_estimate=msen_point,
+            bootstrapped_point_estimates=msen_boot,
+        )
+
+        return self.LearningCurveBenchmarkResult(
+            msen=float(msen_point),
+            msen_sigma=summary.sigma,
+            msen_CI95=summary.ci95,
+            lapse_rate=lapse_rate,
+            model_statistics=model_statistics,
+        )
+
+    @classmethod
+    def _compare_learning_curves(
+        cls,
+        model_phat: xr.DataArray,
+        model_varhat_phat: xr.DataArray,
+        target_phat: xr.DataArray,
+        target_varhat_phat: xr.DataArray,
+        condition_dims: Tuple[str, ...],
+        fit_lapse_rate: bool,
+    ) -> Tuple[xr.DataArray, xr.DataArray | None]:
+        if fit_lapse_rate:
+            lapse_rate = cls._fit_lapse_rate(
+                pmodel=model_phat,
+                ptarget=target_phat,
+                condition_dims=condition_dims,
+            )
+            model_phat = model_phat * (1 - lapse_rate) + 0.5 * lapse_rate
+            model_varhat_phat = model_varhat_phat * (1 - lapse_rate) ** 2
+        else:
+            lapse_rate = None
+
+        msen = compare_msen(
+            model_phat=model_phat,
+            model_varhat_phat=model_varhat_phat,
+            target_phat=target_phat,
+            target_varhat_phat=target_varhat_phat,
+            condition_dims=condition_dims,
+        )
+        return msen, lapse_rate
+
+    @staticmethod
+    def _fit_lapse_rate(
+        pmodel: xr.DataArray,
+        ptarget: xr.DataArray,
+        condition_dims: Tuple[str, ...],
+    ) -> xr.DataArray:
+        nway = 2
+        numerator = -(
+            2 * pmodel / nway
+            - 2 * np.square(pmodel)
+            + 2 * pmodel * ptarget
+            - 2 * ptarget / nway
+        ).sum(dim=condition_dims)
+        denominator = (2 / (nway**2) - 4 * pmodel / nway + 2 * (pmodel**2)).sum(
+            dim=condition_dims
+        )
+        gamma_star = numerator / denominator
+        gamma_star = np.clip(gamma_star, 0, 1)
+        return gamma_star
+
+
+if __name__ == "__main__":
+    experiment = MutatorHighVarBenchmark()
diff --git a/hobj/benchmarks/mut_oneshot_benchmark.py b/hobj/benchmarks/mut_oneshot_benchmark.py
new file mode 100644
index 0000000..0240a56
--- /dev/null
+++ b/hobj/benchmarks/mut_oneshot_benchmark.py
@@ -0,0 +1,321 @@
+import collections
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import xarray as xr
+from tqdm import tqdm
+
+from hobj.benchmarks.common import (
+    compare_msen,
+    simulate_sessions,
+    summarize_bootstrap_score,
+)
+from hobj.benchmarks.generalization.estimator import (
+    build_generalization_statistics,
+)
+from hobj.benchmarks.generalization.simulator import (
+    GeneralizationSessionResult,
+    GeneralizationSubtask,
+)
+from hobj.data.behavior import load_oneshot_behavior
+from hobj.data.images import load_imageset_meta_oneshot
+from hobj.learning_models import BinaryLearningModel
+from hobj.types import ImageId
+
+
+class MutatorOneshotBenchmark:
+    num_simulations_per_subtask = 500
+    num_bootstrap_samples = 1000
+    bootstrap_target_by_worker = True
+
+    subtask_names = [
+        "MutatorOneshotObject00,MutatorOneshotObject43",
+        "MutatorOneshotObject01,MutatorOneshotObject37",
+        "MutatorOneshotObject02,MutatorOneshotObject36",
+        "MutatorOneshotObject03,MutatorOneshotObject55",
+        "MutatorOneshotObject04,MutatorOneshotObject27",
+        "MutatorOneshotObject05,MutatorOneshotObject17",
+        "MutatorOneshotObject06,MutatorOneshotObject14",
+        "MutatorOneshotObject07,MutatorOneshotObject50",
+        "MutatorOneshotObject08,MutatorOneshotObject26",
+        "MutatorOneshotObject09,MutatorOneshotObject58",
+        "MutatorOneshotObject10,MutatorOneshotObject44",
+        "MutatorOneshotObject11,MutatorOneshotObject32",
+        "MutatorOneshotObject12,MutatorOneshotObject31",
+        "MutatorOneshotObject13,MutatorOneshotObject41",
+        "MutatorOneshotObject15,MutatorOneshotObject19",
+        "MutatorOneshotObject16,MutatorOneshotObject28",
+        "MutatorOneshotObject18,MutatorOneshotObject21",
+        "MutatorOneshotObject20,MutatorOneshotObject47",
+        "MutatorOneshotObject22,MutatorOneshotObject48",
+        "MutatorOneshotObject23,MutatorOneshotObject35",
+        "MutatorOneshotObject24,MutatorOneshotObject29",
+        "MutatorOneshotObject25,MutatorOneshotObject40",
+        "MutatorOneshotObject30,MutatorOneshotObject61",
+        "MutatorOneshotObject33,MutatorOneshotObject38",
+        "MutatorOneshotObject34,MutatorOneshotObject57",
+        "MutatorOneshotObject39,MutatorOneshotObject52",
+        "MutatorOneshotObject42,MutatorOneshotObject59",
+        "MutatorOneshotObject45,MutatorOneshotObject53",
+        "MutatorOneshotObject46,MutatorOneshotObject62",
+        "MutatorOneshotObject49,MutatorOneshotObject51",
+        "MutatorOneshotObject54,MutatorOneshotObject63",
+        "MutatorOneshotObject56,MutatorOneshotObject60",
+    ]
+    transformation_ids = [
+        "backgrounds | 0.1",
+        "backgrounds | 0.215443",
+        "backgrounds | 0.464159",
+        "backgrounds | 1.0",
+        "blur | 0.007812",
+        "blur | 0.015625",
+        "blur | 0.03125",
+        "blur | 0.0625",
+        "contrast | -0.4",
+        "contrast | -0.8",
+        "contrast | 0.4",
+        "contrast | 0.8",
+        "delpixels | 0.25",
+        "delpixels | 0.5",
+        "delpixels | 0.75",
+        "delpixels | 0.95",
+        "inplanerotation | 135.0",
+        "inplanerotation | 180.0",
+        "inplanerotation | 45.0",
+        "inplanerotation | 90.0",
+        "inplanetranslation | 0.125",
+        "inplanetranslation | 0.25",
+        "inplanetranslation | 0.5",
+        "inplanetranslation | 0.75",
+        "noise | 0.125",
+        "noise | 0.25",
+        "noise | 0.375",
+        "noise | 0.5",
+        "outplanerotation | 135.0",
+        "outplanerotation | 180.0",
+        "outplanerotation | 45.0",
+        "outplanerotation | 90.0",
+        "scale | 0.125",
+        "scale | 0.25",
+        "scale | 0.5",
+        "scale | 1.5",
+    ]
+
+    def __init__(self, cachedir: Path | None = None):
+        support_trials = set(range(9))
+        catch_trials = {9, 14, 19}
+
+        images_df = load_imageset_meta_oneshot(cachedir=cachedir)
+        image_id_to_row = images_df.set_index("image_id")
+
+        image_ref_to_transformation_id: Dict[ImageId, str] = {}
+        cat_to_support_image: Dict[str, ImageId] = {}
+        cat_to_test_images: Dict[str, List[ImageId]] = {}
+
+        for row in images_df.to_dict(orient="records"):
+            image_id = row["image_id"]
+            transformation_id = (
+                f"{row['transformation']} | {row['transformation_level']}"
+            )
+            image_ref_to_transformation_id[image_id] = transformation_id
+
+            if row["transformation"] == "original":
+                if row["category"] in cat_to_support_image:
+                    raise ValueError(
+                        f"Multiple support images for category {row['category']}"
+                    )
+                cat_to_support_image[row["category"]] = image_id
+            else:
+                if row["category"] not in cat_to_test_images:
+                    cat_to_test_images[row["category"]] = []
+
+                cat_to_test_images[row["category"]].append(image_id)
+
+        self.subtasks = []
+        for subtask_name in self.subtask_names:
+            cat_a, cat_b = sorted(subtask_name.split(","))
+            test_images_a = cat_to_test_images[cat_a]
+            test_images_b = cat_to_test_images[cat_b]
+
+            if len(test_images_a) != 60 or len(test_images_b) != 60:
+                raise ValueError(
+                    f"Expected 60 test images per category for {subtask_name}, "
+                    f"but got {len(test_images_a)} and {len(test_images_b)}."
+                )
+
+            self.subtasks.append(
+                GeneralizationSubtask(
+                    support_imageA=cat_to_support_image[cat_a],
+                    support_imageB=cat_to_support_image[cat_b],
+                    test_imagesA=test_images_a,
+                    test_imagesB=test_images_b,
+                    image_ref_to_transformation=image_ref_to_transformation_id,
+                )
+            )
+
+        oneshot_df = load_oneshot_behavior(cachedir=cachedir)
+        self.results: List[GeneralizationSessionResult] = []
+
+        for _, session_df in oneshot_df.groupby(["assignment_id", "slot"], sort=False):
+            session_df = session_df.sort_values("trial")
+            transformation_to_kn = collections.defaultdict(lambda: [0, 0])
+            kcatch = 0
+            ncatch = 0
+            observed_categories = set(session_df["subtask"].iloc[0].split(","))
+
+            for _, row in session_df.iterrows():
+                i_trial = int(row["trial"])
+                image_id = row["image_id"]
+                annotation = image_id_to_row.loc[image_id]
+                perf = bool(row["perf"])
+
+                if i_trial in support_trials:
+                    if annotation["transformation"] != "original":
+                        raise ValueError(
+                            f"Expected support trial {i_trial} to use an original image."
+                        )
+                elif i_trial in catch_trials:
+                    if annotation["transformation"] != "original":
+                        raise ValueError(
+                            f"Expected catch trial {i_trial} to use an original image."
+                        )
+                    kcatch += perf
+                    ncatch += 1
+                else:
+                    if annotation["transformation"] == "original":
+                        raise ValueError(
+                            f"Expected generalization trial {i_trial} to use a transformed image."
+                        )
+
+                    transformation_id = image_ref_to_transformation_id[image_id]
+                    if transformation_id in self.transformation_ids:
+                        transformation_to_kn[transformation_id][0] += perf
+                        transformation_to_kn[transformation_id][1] += 1
+
+            if len(observed_categories) != 2:
+                raise ValueError(
+                    f"Expected two categories for a one-shot session, got {observed_categories}."
+                )
+
+            subtask_name = ",".join(sorted(observed_categories))
+            if subtask_name not in self.subtask_names:
+                raise ValueError(f"Unexpected subtask name: {subtask_name}")
+
+            self.results.append(
+                GeneralizationSessionResult(
+                    transformation_to_kn=transformation_to_kn,
+                    kcatch=kcatch,
+                    ncatch=ncatch,
+                    worker_id=session_df["worker_id"].iloc[0],
+                )
+            )
+
+        self._target_statistics = build_generalization_statistics(
+            results=self.results,
+            perform_lapse_rate_correction=True,
+            n_bootstrap_iterations=self.num_bootstrap_samples,
+            bootstrap_by_worker=self.bootstrap_target_by_worker,
+        )
+
+    @property
+    def target_statistics(self) -> xr.Dataset:
+        gen_statistics = self._target_statistics
+        return gen_statistics.assign_coords(
+            transformation_type=(
+                ["transformation"],
+                [
+                    transformation.split(" | ")[0]
+                    for transformation in gen_statistics.transformation.values
+                ],
+            ),
+            transformation_level=(
+                ["transformation"],
+                [
+                    float(transformation.split(" | ")[1])
+                    for transformation in gen_statistics.transformation.values
+                ],
+            ),
+        )
+
+    @dataclass
+    class GeneralizationBenchmarkResult:
+        msen: float
+        msen_sigma: float
+        msen_CI95: Tuple[float, float]
+        model_statistics: xr.Dataset
+
+    def score_model(
+        self, learner: BinaryLearningModel, show_pbar: bool = False
+    ) -> "MutatorOneshotBenchmark.GeneralizationBenchmarkResult":
+        results: List[GeneralizationSessionResult] = []
+        for subtask in tqdm(
+            self.subtasks,
+            desc="Subtask simulations:",
+            disable=not show_pbar,
+        ):
+            results.extend(
+                simulate_sessions(
+                    subtask=subtask,
+                    learner=learner,
+                    nsimulations=self.num_simulations_per_subtask,
+                )
+            )
+
+        model_statistics = build_generalization_statistics(
+            results=results,
+            perform_lapse_rate_correction=False,
+            n_bootstrap_iterations=self.num_bootstrap_samples,
+            bootstrap_by_worker=False,
+        )
+        model_statistics = model_statistics.sel(
+            transformation=self.target_statistics.transformation
+        )
+
+        msen_point = self._compare_generalization_patterns(
+            model_phat=model_statistics.phat,
+            model_varhat_phat=model_statistics.varhat_phat,
+            target_phat=self.target_statistics.phat,
+            target_varhat_phat=self.target_statistics.varhat_phat,
+            condition_dims=("transformation",),
+        )
+        msen_boot = self._compare_generalization_patterns(
+            model_phat=model_statistics.boot_phat,
+            model_varhat_phat=model_statistics.boot_varhat_phat,
+            target_phat=self.target_statistics.boot_phat,
+            target_varhat_phat=self.target_statistics.boot_varhat_phat,
+            condition_dims=("transformation",),
+        )
+
+        summary = summarize_bootstrap_score(
+            point_estimate=msen_point,
+            bootstrapped_point_estimates=msen_boot,
+        )
+
+        return self.GeneralizationBenchmarkResult(
+            msen=float(msen_point),
+            msen_sigma=summary.sigma,
+            msen_CI95=summary.ci95,
+            model_statistics=model_statistics,
+        )
+
+    @staticmethod
+    def _compare_generalization_patterns(
+        model_phat: xr.DataArray,
+        model_varhat_phat: xr.DataArray,
+        target_phat: xr.DataArray,
+        target_varhat_phat: xr.DataArray,
+        condition_dims: Tuple[str, ...],
+    ) -> xr.DataArray:
+        msen = compare_msen(
+            model_phat=model_phat,
+            model_varhat_phat=model_varhat_phat,
+            target_phat=target_phat,
+            target_varhat_phat=target_varhat_phat,
+            condition_dims=condition_dims,
+        )
+        return msen
+
+
+if __name__ == "__main__":
+    benchmark = MutatorOneshotBenchmark()
diff --git a/hobj/config.py b/hobj/config.py
deleted file mode 100644
index 8179c15..0000000
--- a/hobj/config.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import os
-import torch
-
-hlb_cachedir = os.path.join(os.path.expanduser('~'), 'hlb_cache')
-
-image_cachedir = os.path.join(hlb_cachedir, 'images')
-behavior_cachedir = os.path.join(hlb_cachedir, 'behavior')
-simulation_cachedir = os.path.join(hlb_cachedir, 'model_simulations')
-features_cachedir = os.path.join(hlb_cachedir, 'image_features')
-
-device = 'cpu'
-
-
-if torch.cuda.is_available():
-    print("Using GPU")
-    device = 'cuda'
diff --git a/hobj/benchmarks/benchmark_utils/__init__.py b/hobj/data/__init__.py
similarity index 100%
rename from hobj/benchmarks/benchmark_utils/__init__.py
rename to hobj/data/__init__.py
diff --git a/hobj/data/behavior.py b/hobj/data/behavior.py
new file mode 100644
index 0000000..15d6875
--- /dev/null
+++ b/hobj/data/behavior.py
@@ -0,0 +1,128 @@
+from pathlib import Path
+from typing import Literal
+
+import pandas as pd
+
+from hobj.data.download import resolve_data_root
+
+
+# %%
+def load_human_behavior(
+    experiment: Literal["highvar", "oneshot"] = "highvar",
+    cachedir: Path | None = None,
+) -> pd.DataFrame:
+    """Load trial-level human behavior for one of the packaged experiments.
+
+    Args:
+        experiment: Name of the experiment to load.
+        cachedir: Optional directory containing the packaged ``data`` tree.
+
+    Returns:
+        The requested behavior dataset.
+    """
+    if experiment not in {"highvar", "oneshot"}:
+        raise ValueError(
+            f"Invalid experiment name: {experiment}. Provide either 'highvar' or 'oneshot'."
+        )
+
+    if experiment == "highvar":
+        return load_highvar_behavior(cachedir=cachedir)
+    return load_oneshot_behavior(cachedir=cachedir)
+
+
+# %% Data loaders
+def load_highvar_behavior(
+    remove_probe_trials: bool = True,
+    cachedir: Path | None = None,
+) -> pd.DataFrame:
+    """Load trial-level human learning data from Experiment 1.
+
+    Args:
+        remove_probe_trials: Whether to exclude probe trials and renumber the
+            remaining trial index within each assignment.
+        cachedir: Optional root directory containing the packaged ``data`` tree.
+
+    Returns:
+        A DataFrame containing the high-variance behavior dataset with
+        ``stimulus_id`` normalized to ``image_id``.
+    """
+    cache_root = resolve_data_root(cachedir=cachedir)
+    dataset_path = cache_root / "behavior" / "human-behavior-highvar-subtasks.csv"
+    if not dataset_path.exists():
+        raise FileNotFoundError(
+            "Expected highvar behavior CSV to exist after resolving packaged data "
+            f"at:\n  - {dataset_path}"
+        )
+
+    df = pd.read_csv(dataset_path)
+
+    required_columns = {
+        "trial",
+        "assignment_id",
+        "worker_id",
+        "subtask",
+        "image_id",
+        "trial_type",
+        "stimulus_duration_msec",
+        "reaction_time_msec",
+        "timed_out",
+        "perf",
+        "timestamp_start",
+    }
+    missing_columns = required_columns - set(df.columns)
+    if missing_columns:
+        raise ValueError(
+            f"Highvar behavior CSV missing required columns: {sorted(missing_columns)}"
+        )
+
+    if remove_probe_trials:
+        df = df.loc[df["trial_type"] != "probe"].copy()
+        df = df.sort_values(["assignment_id", "trial"]).copy()
+        df["trial"] = df.groupby("assignment_id").cumcount()
+
+    return df
+
+
+def load_oneshot_behavior(
+    cachedir: Path | None = None,
+) -> pd.DataFrame:
+    """Load trial-level human learning data from Experiment 2.
+
+    Args:
+        cachedir: Optional root directory containing the packaged ``data`` tree.
+
+    Returns:
+        A DataFrame containing the one-shot behavior dataset with
+        ``stimulus_id`` normalized to ``image_id``.
+    """
+    cache_root = resolve_data_root(cachedir=cachedir)
+    dataset_path = cache_root / "behavior" / "human-behavior-oneshot-subtasks.csv"
+    if not dataset_path.exists():
+        raise FileNotFoundError(
+            "Expected oneshot behavior CSV to exist after resolving packaged data "
+            f"at:\n  - {dataset_path}"
+        )
+
+    df = pd.read_csv(dataset_path)
+
+    required_columns = {
+        "trial",
+        "assignment_id",
+        "slot",
+        "worker_id",
+        "subtask",
+        "image_id",
+        "trial_type",
+        "stimulus_duration_msec",
+        "reaction_time_msec",
+        "timed_out",
+        "perf",
+        "timestamp_start",
+    }
+    missing_columns = required_columns - set(df.columns)
+    if missing_columns:
+        raise ValueError(
+            f"Oneshot behavior CSV missing required columns: {sorted(missing_columns)}"
+        )
+
+    return df
diff --git a/hobj/data/download.py b/hobj/data/download.py
new file mode 100644
index 0000000..62a797e
--- /dev/null
+++ b/hobj/data/download.py
@@ -0,0 +1,297 @@
+"""Download packaged HOBJ datasets into a local ``data`` directory."""
+
+from __future__ import annotations
+
+import argparse
+import shutil
+import tarfile
+import zipfile
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from urllib.parse import urlparse
+
+import requests
+from tqdm import tqdm
+
+
+# Public OSF node that hosts the packaged HOBJ dataset files.
+OSF_NODE_ID = "pj6wm"
+# Bump this to invalidate the local cache when the packaged OSF dataset changes.
+DATASET_CACHE_VERSION = "v2"
+DATA_ARCHIVE_URL = (
+    f"https://files.osf.io/v1/resources/{OSF_NODE_ID}/providers/osfstorage/?zip="
+)
+EXPECTED_DATA_RELATIVE_PATHS = (
+    Path("meta-MutatorHighVarImageset.csv"),
+    Path("meta-MutatorOneShotImageset.csv"),
+    Path("meta-MutatorWarmupImageset.csv"),
+    Path("meta-CatchImageset.csv"),
+    Path("behavior/human-behavior-highvar-subtasks.csv"),
+    Path("behavior/human-behavior-oneshot-subtasks.csv"),
+)
+_DOWNLOAD_CHUNK_SIZE_BYTES = 1024 * 1024
+
+
+def _get_default_data_root(repo_root: Path | None = None) -> Path:
+    """Return the default packaged data directory.
+
+    Args:
+        repo_root: Unused legacy parameter kept for compatibility with the
+            public function signature.
+
+    Returns:
+        A persistent per-user cache directory outside the installed package.
+    """
+    del repo_root
+    return (
+        Path.home().resolve()
+        / ".hobj_cache"
+        / f"{OSF_NODE_ID}-{DATASET_CACHE_VERSION}"
+        / "data"
+    )
+
+
+def _get_missing_expected_paths(data_root: Path) -> list[Path]:
+    """Return expected packaged data paths that are currently missing."""
+    return [
+        relpath
+        for relpath in EXPECTED_DATA_RELATIVE_PATHS
+        if not (data_root / relpath).exists()
+    ]
+
+
+def _derive_archive_path(repo_root: Path, url: str) -> Path:
+    """Return the on-disk path for the downloaded archive."""
+    parsed_url = urlparse(url)
+    path_segments = [segment for segment in parsed_url.path.split("/") if segment]
+    if (
+        parsed_url.query == "zip="
+        and len(path_segments) >= 4
+        and path_segments[:3] == ["v1", "resources", path_segments[2]]
+    ):
+        archive_name = f"{path_segments[2]}.zip"
+    else:
+        archive_name = Path(parsed_url.path).name
+    if not archive_name:
+        raise ValueError(f"Could not derive archive filename from URL: {url}")
+    return repo_root / archive_name
+
+
+def _download_archive(url: str, archive_path: Path) -> None:
+    """Download an archive to disk atomically."""
+    archive_path.parent.mkdir(parents=True, exist_ok=True)
+    temp_path = archive_path.with_suffix(f"{archive_path.suffix}.part")
+
+    with requests.get(url, stream=True, timeout=(10, 300)) as response:
+        response.raise_for_status()
+        total_bytes = int(response.headers.get("content-length", 0))
+        with temp_path.open("wb") as handle:
+            with tqdm(
+                total=total_bytes or None,
+                unit="B",
+                unit_scale=True,
+                unit_divisor=1024,
+                desc=f"Downloading {archive_path.name}",
+            ) as progress_bar:
+                for chunk in response.iter_content(
+                    chunk_size=_DOWNLOAD_CHUNK_SIZE_BYTES
+                ):
+                    if chunk:
+                        handle.write(chunk)
+                        progress_bar.update(len(chunk))
+
+    temp_path.replace(archive_path)
+
+
+def _safe_extract_tarball(archive_path: Path, destination: Path) -> None:
+    """Extract a tarball while preventing path traversal."""
+    destination = destination.resolve()
+
+    with tarfile.open(archive_path, mode="r:gz") as tar:
+        for member in tar.getmembers():
+            member_path = (destination / member.name).resolve()
+            member_path.relative_to(destination)
+        tar.extractall(destination, filter="data")
+
+
+def _safe_extract_zipfile(archive_path: Path, destination: Path) -> None:
+    """Extract a zipfile while preventing path traversal.
+
+    Args:
+        archive_path: Zipfile to extract.
+        destination: Destination directory for extracted contents.
+
+    Raises:
+        ValueError: If a zip entry would escape the extraction directory.
+    """
+    destination = destination.resolve()
+
+    with zipfile.ZipFile(archive_path) as archive:
+        for member in archive.infolist():
+            member_path = (destination / member.filename).resolve()
+            member_path.relative_to(destination)
+        archive.extractall(destination)
+
+
+def _find_extracted_data_root(extraction_root: Path) -> Path:
+    """Return the packaged data directory from an extracted archive tree.
+
+    Args:
+        extraction_root: Temporary directory containing the extracted archive.
+
+    Returns:
+        The directory that contains the packaged data files.
+
+    Raises:
+        FileNotFoundError: If the extracted archive does not contain the
+            expected packaged dataset layout.
+    """
+    nested_data_root = extraction_root / "data"
+    if not _get_missing_expected_paths(nested_data_root):
+        return nested_data_root
+
+    if not _get_missing_expected_paths(extraction_root):
+        return extraction_root
+
+    raise FileNotFoundError(
+        "Packaged dataset archive did not contain the expected data directory."
+    )
+
+
+def _extract_nested_images_archive(data_root: Path) -> None:
+    """Extract ``images.tar.gz`` into ``data/images`` when present.
+
+    Args:
+        data_root: Extracted packaged data directory.
+    """
+    images_archive_path = data_root / "images.tar.gz"
+    images_root = data_root / "images"
+    if images_root.exists():
+        if images_archive_path.exists():
+            images_archive_path.unlink()
+        return
+
+    if not images_archive_path.exists():
+        return
+
+    _safe_extract_tarball(archive_path=images_archive_path, destination=data_root)
+    images_archive_path.unlink()
+
+
+def resolve_data_root(
+    *,
+    cachedir: Path | None = None,
+    repo_root: Path | None = None,
+) -> Path:
+    """Return a populated packaged data directory, downloading it on demand.
+
+    Args:
+        cachedir: Optional directory to use instead of the repository
+            ``data`` directory.
+        repo_root: Optional repository root used only when ``cachedir`` is not
+            provided.
+
+    Returns:
+        The absolute path to the packaged data directory.
+    """
+    data_root = (
+        cachedir if cachedir is not None else _get_default_data_root(repo_root)
+    ).resolve()
+    if _get_missing_expected_paths(data_root):
+        return download_data(cachedir=data_root, url=DATA_ARCHIVE_URL)
+    _extract_nested_images_archive(data_root)
+    return data_root
+
+
+def download_data(
+    *,
+    url: str = DATA_ARCHIVE_URL,
+    repo_root: Path | None = None,
+    cachedir: Path | None = None,
+    force_download: bool = False,
+) -> Path:
+    """Download and extract the packaged learning dataset into a cache root.
+
+    The function is idempotent: if the expected packaged files already exist
+    under the resolved data directory, it returns immediately without
+    downloading or extracting. Otherwise it downloads the archive if needed and
+    extracts it into the target cache.
+
+    Args:
+        url: Archive URL containing the packaged ``data`` tree.
+        repo_root: Repository root used when ``cachedir`` is not provided.
+        cachedir: Optional directory to use instead of the repository
+            ``data`` directory.
+        force_download: Whether to redownload the archive even if it is already
+            present on disk.
+
+    Returns:
+        The absolute path to the extracted ``data`` directory.
+
+    Raises:
+        requests.HTTPError: If the archive download fails.
+        tarfile.TarError: If a tar archive cannot be unpacked.
+        zipfile.BadZipFile: If the zip archive cannot be unpacked.
+        ValueError: If the archive path is invalid or extraction would escape
+            the extraction directory.
+        FileNotFoundError: If extraction completes but the expected dataset
+            files are still missing.
+    """
+    data_root = (
+        cachedir if cachedir is not None else _get_default_data_root(repo_root)
+    ).resolve()
+    missing_paths = _get_missing_expected_paths(data_root)
+    if not missing_paths:
+        return data_root
+
+    archive_root = data_root.parent
+    archive_root.mkdir(parents=True, exist_ok=True)
+    archive_path = _derive_archive_path(archive_root, url)
+    if force_download or not archive_path.exists():
+        _download_archive(url=url, archive_path=archive_path)
+
+    with TemporaryDirectory(dir=archive_root) as extraction_root_str:
+        extraction_root = Path(extraction_root_str)
+        _safe_extract_zipfile(archive_path=archive_path, destination=extraction_root)
+        extracted_data_root = _find_extracted_data_root(extraction_root)
+        _extract_nested_images_archive(extracted_data_root)
+        shutil.copytree(extracted_data_root, data_root, dirs_exist_ok=True)
+
+    missing_paths = _get_missing_expected_paths(data_root)
+    if missing_paths:
+        missing_str = "\n".join(f"  - {path}" for path in missing_paths)
+        raise FileNotFoundError(
+            "Packaged dataset archive was extracted, but expected files are still "
+            f"missing:\n{missing_str}"
+        )
+
+    return data_root
+
+
+def main() -> None:
+    """Download packaged dataset files into a local data directory."""
+    parser = argparse.ArgumentParser(
+        description="Download and extract the packaged HOBJ dataset archive."
+    )
+    parser.add_argument(
+        "--cachedir",
+        type=Path,
+        default=None,
+        help="Optional directory to use instead of the repository data directory.",
+    )
+    parser.add_argument(
+        "--force-download",
+        action="store_true",
+        help="Redownload the archive even if it already exists locally.",
+    )
+    args = parser.parse_args()
+
+    data_root = download_data(
+        cachedir=args.cachedir,
+        force_download=args.force_download,
+    )
+    print(data_root)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/hobj/data/images.py b/hobj/data/images.py
new file mode 100644
index 0000000..810d0a8
--- /dev/null
+++ b/hobj/data/images.py
@@ -0,0 +1,206 @@
+"""Load packaged image manifests and images."""
+
+from functools import lru_cache
+from pathlib import Path
+
+import pandas as pd
+from PIL import Image
+
+from hobj.data.download import resolve_data_root
+from hobj.types import ImageId
+
+
+def _load_image_manifest(
+    *,
+    dataset_name: str,
+    required_columns: set[str],
+    cachedir: Path | None = None,
+) -> pd.DataFrame:
+    """Load a packaged image manifest after validating packaged image files.
+
+    Args:
+        dataset_name: Name of the packaged manifest.
+        required_columns: Columns that must exist in the manifest.
+        cachedir: Optional root directory containing the packaged ``data`` tree.
+
+    Returns:
+        A copy of the manifest as a DataFrame.
+
+    Raises:
+        ValueError: If required columns are missing.
+        FileNotFoundError: If the packaged manifest or images are missing.
+    """
+    cache_root = resolve_data_root(cachedir=cachedir)
+    manifest_path = cache_root / f"meta-{dataset_name}.csv"
+
+    if not manifest_path.exists():
+        raise FileNotFoundError(
+            "Expected image manifest to exist after resolving packaged data at: "
+            f"{manifest_path}"
+        )
+
+    manifest_df = pd.read_csv(manifest_path)
+    missing_columns = required_columns - set(manifest_df.columns)
+    if missing_columns:
+        raise ValueError(
+            f"{dataset_name} manifest.csv missing required columns: "
+            f"{sorted(missing_columns)}"
+        )
+
+    manifest_df = manifest_df.copy()
+    missing_paths = manifest_df.loc[
+        ~manifest_df["relpath"].map(lambda p: (cache_root / p).exists()), "relpath"
+    ]
+    if not missing_paths.empty:
+        raise FileNotFoundError(
+            "Expected packaged images to exist after resolving packaged data "
+            "under:\n"
+            f"First missing path: {missing_paths.iloc[0]}"
+        )
+
+    return manifest_df
+
+
+def load_imageset_meta_highvar(
+    cachedir: Path | None = None,
+) -> pd.DataFrame:
+    """Load the high-variance image manifest."""
+    manifest_df = _load_image_manifest(
+        dataset_name="MutatorHighVarImageset",
+        required_columns={"image_id", "category", "sha256", "relpath"},
+        cachedir=cachedir,
+    )
+    return manifest_df.sort_values("image_id").reset_index(drop=True)
+
+
+def load_imageset_meta_oneshot(
+    cachedir: Path | None = None,
+) -> pd.DataFrame:
+    """Load the one-shot image manifest."""
+    manifest_df = _load_image_manifest(
+        dataset_name="MutatorOneShotImageset",
+        required_columns={
+            "image_id",
+            "category",
+            "transformation",
+            "transformation_level",
+            "base_image_id",
+            "sha256",
+            "relpath",
+        },
+        cachedir=cachedir,
+    )
+    return manifest_df.sort_values("image_id").reset_index(drop=True)
+
+
+def load_imageset_meta_warmup(
+    cachedir: Path | None = None,
+) -> pd.DataFrame:
+    """Load the warmup image manifest."""
+    manifest_df = _load_image_manifest(
+        dataset_name="MutatorWarmupImageset",
+        required_columns={"image_id", "category", "sha256", "relpath"},
+        cachedir=cachedir,
+    )
+    return manifest_df.sort_values("image_id").reset_index(drop=True)
+
+
+def load_imageset_meta_catch(
+    cachedir: Path | None = None,
+) -> pd.DataFrame:
+    """Load the probe image manifest."""
+    manifest_df = _load_image_manifest(
+        dataset_name="CatchImageset",
+        required_columns={"image_id", "sha256", "relpath"},
+        cachedir=cachedir,
+    )
+    return manifest_df.sort_values("image_id").reset_index(drop=True)
+
+
+# %% Image loaders:
+
+
+@lru_cache(maxsize=None)
+def _image_id_to_local_path_table(cache_root: Path) -> dict[ImageId, Path]:
+    """Build a mapping from image ID to absolute local path.
+
+    Args:
+        cache_root: Directory containing the packaged image manifests.
+
+    Returns:
+        A mapping from image ID to absolute image path.
+    """
+    manifest_paths = sorted(cache_root.glob("meta-*.csv"))
+    table = {}
+    for manifest_path in manifest_paths:
+        manifest_df = pd.read_csv(manifest_path)
+        for _, row in manifest_df.iterrows():
+            image_id = row["image_id"]
+            relpath = row["relpath"]
+            abs_path = cache_root / relpath
+            table[image_id] = abs_path
+    return table
+
+
+def list_image_ids(cachedir: Path | None = None) -> list[ImageId]:
+    """Iterate over all packaged image IDs.
+
+    Args:
+        cachedir: Optional directory containing the packaged ``data`` tree.
+
+    Yields:
+        Image IDs from the packaged dataset.
+    """
+    cache_root = resolve_data_root(cachedir=cachedir)
+    table = _image_id_to_local_path_table(cache_root)
+    return sorted(table.keys())
+
+
+def load_image(
+    image_id: ImageId,
+    cachedir: Path | None = None,
+) -> Image.Image:
+    """Load an image by ``image_id`` from the packaged dataset.
+
+    Args:
+        image_id: ID of the image to load.
+        cachedir: Optional directory containing the packaged ``data`` tree.
+
+    Returns:
+        The requested image.
+    """
+    path = get_image_path(image_id=image_id, cachedir=cachedir)
+    return Image.open(path)
+
+
+def get_image_path(
+    image_id: ImageId,
+    cachedir: Path | None = None,
+) -> Path:
+    """Return the local path for an image in the packaged dataset.
+
+    Args:
+        image_id: ID of the image to resolve.
+        cachedir: Optional directory containing the packaged ``data`` tree.
+
+    Returns:
+        The absolute path to the requested image.
+
+    Raises:
+        ValueError: If the image ID is not present in any packaged manifest.
+        FileNotFoundError: If the resolved image file is missing on disk.
+    """
+    cache_root = resolve_data_root(cachedir=cachedir)
+    path = _image_id_to_local_path_table(cache_root).get(image_id)
+    if path is None:
+        raise ValueError(f"Image ID not found in any manifest: {image_id}")
+    if not path.exists():
+        raise FileNotFoundError(
+            f"Expected image file to exist after resolving packaged data at: {path}"
+        )
+    return path
+
+
+# %%
+if __name__ == "__main__":
+    df = list_image_ids()
diff --git a/hobj/images/__init__.py b/hobj/images/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hobj/images/image_meta/ds_MutatorB0_BurnIn.nc b/hobj/images/image_meta/ds_MutatorB0_BurnIn.nc
deleted file mode 100644
index d03abed..0000000
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diff --git a/hobj/images/image_meta/ds_MutatorB2000_Oneshot64.nc b/hobj/images/image_meta/ds_MutatorB2000_Oneshot64.nc
deleted file mode 100644
index c84a227..0000000
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diff --git a/hobj/images/image_meta/ds_MutatorB2000_Subset128_FullVar_Train.nc b/hobj/images/image_meta/ds_MutatorB2000_Subset128_FullVar_Train.nc
deleted file mode 100644
index c999e7b..0000000
Binary files a/hobj/images/image_meta/ds_MutatorB2000_Subset128_FullVar_Train.nc and /dev/null differ
diff --git a/hobj/images/imagesets.py b/hobj/images/imagesets.py
deleted file mode 100644
index db20faf..0000000
--- a/hobj/images/imagesets.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import hobj.config as config
-import os
-import xarray as xr
-import hobj.images.load_image as load_image
-import PIL.Image
-
-image_meta_loc = os.path.join(os.path.dirname(__file__), 'image_meta')
-
-
-class Imageset(object):
-    meta_path: str = None
-
-    def __init__(self, cachedir=config.image_cachedir):
-        self.cachedir = cachedir
-        return
-
-    def load_image(self, image_url: str) -> PIL.Image:
-        return load_image.get_image(image_url=image_url, cachedir=self.cachedir)
-
-    @property
-    def ds_meta(self):
-        if not hasattr(self, '_ds_meta'):
-            ds_meta = xr.load_dataset(self.meta_path)
-            self._ds_meta = ds_meta
-
-        return self._ds_meta
-
-    @property
-    def image_urls(self):
-        return list(self.ds_meta.image_url.values)
-
-
-class WarmupImageset(Imageset):
-    meta_path = os.path.join(image_meta_loc, 'ds_MutatorB0_BurnIn.nc')
-
-
-class MutatorHighVarImageset(Imageset):
-    meta_path = os.path.join(image_meta_loc, 'ds_MutatorB2000_Subset128_FullVar_Train.nc')
-
-
-class MutatorOneshotImageset(Imageset):
-    meta_path = os.path.join(image_meta_loc, 'ds_MutatorB2000_Oneshot64.nc')
-
-    @property
-    def ds_meta(self):
-        if not hasattr(self, '_ds_meta'):
-            ds_meta = xr.load_dataset(self.meta_path)
-            ds_meta['transformation_id'] = (['image_url'], [f'{trans} | {tlevel}' for (trans, tlevel) in zip(ds_meta.transformation.values, ds_meta.transformation_level.values)])
-            self._ds_meta = ds_meta
-
-        return self._ds_meta
diff --git a/hobj/images/load_image.py b/hobj/images/load_image.py
deleted file mode 100644
index 72f5a2e..0000000
--- a/hobj/images/load_image.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import os
-
-import PIL.Image
-
-import hobj.utils.file_io as io
-import hobj.config as config
-
-def get_image_location(image_url: str, cachedir=config.image_cachedir):
-    """
-    Return the location of the image at the specified image_url on disk. If it is not present on disk yet, download it.
-    :param image_url: URL of the image to download.
-    :param cachedir: Directory to cache the image in.
-    :return:
-    """
-    save_location = io.get_local_save_location(url=image_url, cachedir=cachedir)
-
-    if not os.path.exists(save_location):
-        io.prepare_savepath(save_location)
-        image = io.get_image_from_url(image_url=image_url)
-        image.save(save_location)
-        print('Downloaded image to {}'.format(save_location))
-    return save_location
-
-
-def get_image(image_url: str, cachedir=config.image_cachedir) -> PIL.Image:
-    """
-    Download an image from a URL and return it as a PIL.Image.
-    If cached, return the cached image.
-    :param image_url: URL of the image to retrieve.
-    :param cachedir: Directory to cache the image in.
-    :return:
-    """
-
-    save_location = get_image_location(image_url=image_url, cachedir=cachedir)
-    image = PIL.Image.open(save_location)
-    return image
diff --git a/hobj/images/readme.md b/hobj/images/readme.md
deleted file mode 100644
index 069c162..0000000
--- a/hobj/images/readme.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# images 
-
-A centralized location for all the images used in this study. 
-
-Example:
-
-```python
-
-import hobj
-
-imageset = hobj.images.imagesets.MutatorImageset()
-ds_meta = imageset.ds_meta
-```
\ No newline at end of file
diff --git a/hobj/learning_models/__init__.py b/hobj/learning_models/__init__.py
index e69de29..895d5c6 100644
--- a/hobj/learning_models/__init__.py
+++ b/hobj/learning_models/__init__.py
@@ -0,0 +1,115 @@
+import typing
+from abc import ABC, abstractmethod
+
+import numpy as np
+
+from hobj.learning_models.linear_learning_model.representation import (
+    RepresentationalModel,
+)
+from hobj.learning_models.linear_learning_model.update_rules import UpdateRule
+from hobj.types import ImageId
+
+
+# %%
+class BinaryLearningModel(ABC):
+    @abstractmethod
+    def reset_state(self, seed: typing.Union[int, None]) -> None:
+        """
+        This function resets the LearningModel to some initial state.
+        :param seed: if the LearningModel has any stochastic components, this seed should be used to seed them.
+        :return:
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_response(
+        self,
+        image: ImageId,
+    ) -> typing.Literal[0, 1]:
+        """
+        This function takes the current stimulus image (given either as a PIL.Image or a ImageRef) and returns one of two possible actions (parameterized by an integer).
+        :param image: An image (or a reference to an image).
+        :return:
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def give_feedback(self, reward: float) -> None:
+        """
+        This function takes as input a scalar reward from the environment. The LearningModel may use this reward to update its parameters.
+        :return:
+        """
+        raise NotImplementedError
+
+
+# %%
+class LinearLearner(BinaryLearningModel):
+    def __init__(
+        self,
+        representational_model: RepresentationalModel,
+        update_rule: UpdateRule,
+    ):
+        self.representational_model = representational_model
+        self.update_rule = update_rule
+
+        # State variables
+        self.w = None
+        self.b = None
+        self._f_last = None
+        self._logits_last = None
+        self._action_last = None
+        self._generator: np.random.Generator = np.random.default_rng()
+
+        # Initialize state
+        self.reset_state(seed=0)
+        return
+
+    def reset_state(self, seed: int) -> None:
+        """
+        :param seed:
+        :return:
+        """
+        self.update_rule.reset()
+        self.w = np.zeros((self.representational_model.d, 2))
+        self.b = np.zeros((2,))
+        self._f_last = None
+        self._logits_last = None
+        self._action_last = None
+        self._generator = np.random.default_rng(seed=seed)
+
+        return
+
+    def get_response(
+        self,
+        image: ImageId,
+    ) -> typing.Literal[0, 1]:
+        f = self.representational_model.get_features(image=image)
+        logits = f @ self.w + self.b
+        action = self._random_tiebreaking_argmax(logits[0], logits[1])
+
+        # Update internal state with traces
+        self._f_last = f
+        self._logits_last = logits
+        self._action_last = action
+        return action
+
+    def give_feedback(self, reward: float) -> None:
+        delta_w, delta_b = self.update_rule.get_update(
+            x=self._f_last,
+            w=self.w,
+            b=self.b,
+            logits=self._logits_last,
+            action=self._action_last,
+            reward=reward,
+        )  # [action]
+        self.w += delta_w
+        self.b += delta_b
+        return
+
+    def _random_tiebreaking_argmax(self, logit0, logit1) -> typing.Literal[0, 1]:
+        if logit0 > logit1:
+            return 0
+        elif logit0 < logit1:
+            return 1
+        else:
+            return 0 if self._generator.random() < 0.5 else 1
diff --git a/hobj/learning_models/learning_model.py b/hobj/learning_models/learning_model.py
deleted file mode 100644
index 9c20b22..0000000
--- a/hobj/learning_models/learning_model.py
+++ /dev/null
@@ -1,39 +0,0 @@
-class LearningModel(object):
-    """
-    This class is meant to be subclassed. As long as the functions below are implemented (and returns the correct dtype),
-    the LearningModel can be scored on any benchmark in this repo.
-    """
-
-    def __init__(self, learner_id: str):
-        """
-        :param learner_id:  a string that identifies the learning model.
-        """
-        self.learner_id = learner_id
-        return
-
-    def reset(self) -> None:
-        """
-        This function is called at the beginning of each simulated behavioral session.
-        It should reset the learning model to an initial state.
-        :return:
-        """
-        return
-
-    def respond(self, image_url: str) -> int:
-        """
-        This function takes the current stimulus image (given by its image_url) and returns an action (parameterized by an integer).
-        :param image_url: A public URL to the image.
-        :return:
-        """
-
-        action = 0
-        return action
-
-    def learn(self, reward: float) -> None:
-        """
-        This function takes a reward from the environment. One may use this reward to update the learning model.
-        This function is called by the environment, after the call to respond().
-        :return:
-        """
-
-        return
diff --git a/hobj/learning_models/linear_learners/__init__.py b/hobj/learning_models/linear_learners/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hobj/learning_models/linear_learners/linear_learner.py b/hobj/learning_models/linear_learners/linear_learner.py
deleted file mode 100644
index a990a69..0000000
--- a/hobj/learning_models/linear_learners/linear_learner.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import hobj.learning_models.learning_model as lm
-import os
-import hobj.learning_models.linear_learners.representational_models.representational_model as rms
-import hobj.learning_models.linear_learners.update_rules.update_rules as urs
-import numpy as np
-
-
-class LinearLearner(lm.LearningModel):
-    """
-    A learning model based on a standard cognitive theory of learning.
-    """
-    def __init__(
-            self,
-            representational_model: rms.RepresentationalModel,
-            update_rule: urs.UpdateRule,
-            nactions=2,
-    ):
-        learner_id = representational_model.representational_model_id + '+' + update_rule.update_rule_id
-        super().__init__(learner_id)
-
-        self.representational_model = representational_model
-        self.update_rule = update_rule
-        self.nactions = nactions
-        self.w = np.zeros((self.representational_model.d, self.nactions))
-        self.b = np.zeros((self.nactions,))
-        self.reset()
-        return
-
-    def reset(self):
-        self.representational_model.reset()
-        self.update_rule.reset()
-        self.w = np.zeros((self.representational_model.d, self.nactions))
-        self.b = np.zeros((self.nactions,))
-        return
-
-    def respond(self, image_url: str):
-        f = self.representational_model.get_features(image_url=image_url)
-        self.f = f
-        self.logits = self.f @ self.w + self.b # [action]
-        self.action = int(_random_tiebreaking_argmax(self.logits))
-        return self.action
-
-    def learn(self, reward: float):
-        delta_w, delta_b = self.update_rule.get_update(x=self.f, w=self.w, b=self.b, logits=self.logits, action=self.action, reward=reward)  # [action]
-        self.w += delta_w
-        self.b += delta_b
-        return
-
-
-def _random_tiebreaking_argmax(x):
-    return np.random.choice(np.flatnonzero(x == x.max()))
diff --git a/hobj/learning_models/linear_learners/representational_models/__init__.py b/hobj/learning_models/linear_learners/representational_models/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hobj/learning_models/linear_learners/representational_models/representational_model.py b/hobj/learning_models/linear_learners/representational_models/representational_model.py
deleted file mode 100644
index 9dbba85..0000000
--- a/hobj/learning_models/linear_learners/representational_models/representational_model.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import numpy as np
-
-
-class RepresentationalModel:
-    """
-    Model which represents image_urls as feature vectors of shape (d,).
-    Meant to be subclassed (see PrecachedRepresentationalModel below for an example).
-    """
-
-    def __init__(self, representational_model_id: str):
-        self.representational_model_id = representational_model_id
-        self._d = 1
-        return
-
-    def reset(self):
-        """
-        Resets the model to its initial state. Not used by all representational models (e.g., fixed intermediate layers of DCNNs), but
-        included here for completeness.
-        :return: None
-        """
-        return
-
-    @property
-    def d(self):
-        """
-        Returns the dimension of the feature vectors.
-        :return:
-        """
-        return self._d
-
-    @d.setter
-    def d(self, value):
-        assert isinstance(value, int)
-        assert value > 0
-        self._d = value
-
-    def get_features(self, image_url: str) -> np.ndarray:
-        """
-        Returns a feature vector for the image_url.
-        To work successfully with UpdateRule, the feature vector must be of shape (d,) and have norm 1.
-        :param image_url: str, url of the image
-        :return: np.ndarray, shape=(d,)
-        """
-        return np.zeros(self.d)
-
-
-class PrecachedRepresentationalModel(RepresentationalModel):
-
-    def __init__(self, url_to_features: dict, representational_model_id: str):
-        self.url_to_features = url_to_features
-        super().__init__(representational_model_id=representational_model_id)
-        self.d = len(url_to_features[list(url_to_features.keys())[0]])
-
-    def get_features(self, image_url: str) -> np.ndarray:
-        return self.url_to_features[image_url]
diff --git a/hobj/learning_models/linear_learners/update_rules/__init__.py b/hobj/learning_models/linear_learners/update_rules/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hobj/learning_models/linear_learners/update_rules/update_rules.py b/hobj/learning_models/linear_learners/update_rules/update_rules.py
deleted file mode 100644
index 9de1b5e..0000000
--- a/hobj/learning_models/linear_learners/update_rules/update_rules.py
+++ /dev/null
@@ -1,310 +0,0 @@
-import numpy as np
-import scipy.special
-
-
-class UpdateRule(object):
-    def __init__(self, alpha: float):
-        """
-        Implements an update rule for a linear learner. Assumes that all features have at most norm 1.
-        :param alpha: the normalized learning rate. Between 0 and 1.
-        """
-        assert 0 <= alpha <= 1
-        self.alpha = alpha
-        self._update_rule_id = None
-        self.reset()
-        return
-
-    def reset(self):
-        """
-        Resets the update rule to its initial state. Not used by all update rules.
-        :return:
-        """
-        return
-
-    @property
-    def update_rule_id(self):
-        return str(self.__class__.__name__) + '_%0.3e' % (self.alpha)
-
-    def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float):
-        """
-        Returns delta_w and delta_b, where
-        w{t+1} = w{t} + delta_w
-        b{t+1} = b{t} + delta_b
-
-        :param x: np.ndarray, shape=(d)
-        :param w: np.ndarray, shape=(d, actions)
-        :param b: np.ndarray, shape=(actions,)
-        :param logits: np.ndarray, shape=(action,)
-        :param action: int, action taken by the learner
-        :param reward: float, reward received by the learner
-        :return: delta, np.ndarray, shape=(actions)
-        """
-        delta_w = np.zeros(w.shape)
-        delta_b = np.zeros(b.shape)
-        return delta_w, delta_b
-
-
-class Prototype(UpdateRule):
-    """
-    Simulates the decision boundary implemented by a prototype learner.
-    """
-
-    def reset(self):
-        self.ncounts = None
-
-    def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float):
-        """
-        Returns delta_w and delta_b, where
-        w{t+1} = w{t} + delta_w
-        b{t+1} = b{t} + delta_b
-
-        :param x: np.ndarray, shape=(d)
-        :param w: np.ndarray, shape=(d, actions)
-        :param b: np.ndarray, shape=(actions,)
-        :param logits: np.ndarray, shape=(action,)
-        :param action: int, action taken by the learner
-        :param reward: float, reward received by the learner
-        :return: delta, np.ndarray, shape=(actions)
-        """
-        if self.ncounts is None:
-            self.ncounts = np.zeros(w.shape[1])
-
-        if reward > 0:
-            # Received a positive example of the class associated with the action
-            i_updated_class = action
-        else:
-            # Unknown which class this is associated with. In the two-way case, it could be inferred, but in general, not.
-            if w.shape[1] == 2:
-                i_updated_class = 1 - action
-            else:
-                return 0, 0
-
-        mu_cur = w[:, i_updated_class]
-        n = self.ncounts[i_updated_class]
-        mu_next = (n / (n + 1)) * mu_cur + (1 / (n + 1)) * x
-        norm_next = np.square(np.linalg.norm(mu_next))
-
-        delta_w = np.zeros(w.shape)
-        delta_w[:, i_updated_class] = mu_next - mu_cur
-
-        delta_b = np.zeros(b.shape)
-        delta_b[i_updated_class] = (-norm_next) - b[i_updated_class]
-
-        return delta_w, delta_b
-
-
-class Square(UpdateRule):
-
-    def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float):
-        """
-        Returns delta_w and delta_b, where
-        w{t+1} = w{t} + delta_w
-        b{t+1} = b{t} + delta_b
-
-        :param x: np.ndarray, shape=(d)
-        :param w: np.ndarray, shape=(d, actions)
-        :param b: np.ndarray, shape=(actions,)
-        :param logits: np.ndarray, shape=(action,)
-        :param action: int, action taken by the learner
-        :param reward: float, reward received by the learner
-        :return: delta, np.ndarray, shape=(actions)
-        """
-        delta = np.zeros(w.shape)
-
-        reward_prediction = logits[action]
-        rpe = float(reward) - reward_prediction
-
-        delta[:, action] = (self.alpha) * rpe * x
-        return delta, 0
-
-
-class Perceptron(UpdateRule):
-
-    def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float):
-        """
-        Returns delta_w and delta_b, where
-        w{t+1} = w{t} + delta_w
-        b{t+1} = b{t} + delta_b
-
-        :param x: np.ndarray, shape=(d)
-        :param w: np.ndarray, shape=(d, actions)
-        :param b: np.ndarray, shape=(actions,)
-        :param logits: np.ndarray, shape=(action,)
-        :param action: int, action taken by the learner
-        :param reward: float, reward received by the learner
-        :return: delta, np.ndarray, shape=(actions)
-        """
-
-        nactions = len(logits)
-        gts = np.zeros(nactions)
-
-        yhat = logits[action]
-        z = reward * yhat
-        if z <= 0:
-            gts[action] = self.alpha * reward
-        delta = x[:, None] * gts[None, :]
-        return delta, 0
-
-
-class Hinge(UpdateRule):
-
-    def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float):
-        """
-        Returns delta_w and delta_b, where
-        w{t+1} = w{t} + delta_w
-        b{t+1} = b{t} + delta_b
-
-        :param x: np.ndarray, shape=(d)
-        :param w: np.ndarray, shape=(d, actions)
-        :param b: np.ndarray, shape=(actions,)
-        :param logits: np.ndarray, shape=(action,)
-        :param action: int, action taken by the learner
-        :param reward: float, reward received by the learner
-        :return: delta, np.ndarray, shape=(actions)
-        """
-
-        nactions = len(logits)
-        gts = np.zeros(nactions)
-
-        yhat = logits[action]
-        z = reward * yhat
-        if z <= 1:  # and yhat >= 0:
-            gts[action] = self.alpha * reward
-        delta = x[:, None] * gts[None, :]
-        return delta, 0
-
-
-class MAE(UpdateRule):
-
-    def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float):
-        """
-        Returns delta_w and delta_b, where
-        w{t+1} = w{t} + delta_w
-        b{t+1} = b{t} + delta_b
-
-        :param x: np.ndarray, shape=(d)
-        :param w: np.ndarray, shape=(d, actions)
-        :param b: np.ndarray, shape=(actions,)
-        :param logits: np.ndarray, shape=(action,)
-        :param action: int, action taken by the learner
-        :param reward: float, reward received by the learner
-        :return: delta, np.ndarray, shape=(actions)
-        """
-
-        nactions = len(logits)
-        reward_prediction = logits[action]
-
-        gts = np.zeros(nactions)
-        if reward_prediction < reward:
-            gts[action] = self.alpha
-        elif reward_prediction > reward:
-            gts[action] = -self.alpha
-
-        delta = x[:, None] * gts[None, :]
-        return delta, 0
-
-
-class Exponential(UpdateRule):
-    max_weight_norm = 10.0
-
-    def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float):
-        """
-        Returns delta_w and delta_b, where
-        w{t+1} = w{t} + delta_w
-        b{t+1} = b{t} + delta_b
-
-        :param x: np.ndarray, shape=(d)
-        :param w: np.ndarray, shape=(d, actions)
-        :param b: np.ndarray, shape=(actions,)
-        :param logits: np.ndarray, shape=(action,)
-        :param action: int, action taken by the learner
-        :param reward: float, reward received by the learner
-        :return: delta, np.ndarray, shape=(actions)
-        """
-        nactions = len(logits)
-
-        gts = np.zeros(nactions)
-        gts[action] = self.alpha * reward * np.exp(-reward * logits[action])
-        delta = x[:, None] * gts[None, :]
-
-        # Bound norm of weights
-        wnext = w + delta
-        if np.linalg.norm(wnext) > self.max_weight_norm:
-            wnext = wnext / np.linalg.norm(wnext) * self.max_weight_norm
-            delta = wnext - w
-        return delta, 0
-
-
-class CE(UpdateRule):
-
-    def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float):
-        """
-        Returns delta_w and delta_b, where
-        w{t+1} = w{t} + delta_w
-        b{t+1} = b{t} + delta_b
-
-        :param x: np.ndarray, shape=(d)
-        :param w: np.ndarray, shape=(d, actions)
-        :param b: np.ndarray, shape=(actions,)
-        :param logits: np.ndarray, shape=(action,)
-        :param action: int, action taken by the learner
-        :param reward: float, reward received by the learner
-        :return: delta, np.ndarray, shape=(actions)
-        """
-        nactions = len(logits)
-        gts = np.zeros(nactions)
-        reward_prediction = logits[action]
-
-        gts[action] = 4 * self.alpha * (reward / (1 + np.exp(reward * reward_prediction)))
-
-        delta = x[:, None] * gts[None, :]
-        return delta, 0
-
-
-class REINFORCE(UpdateRule):
-
-    def _compute_trace(
-            self,
-            action_taken: int,
-            logits: np.ndarray,
-    ):
-        """
-        Computes the negative derivative of the probability of taking each action with respect to logit[i]
-        exps = np.exp(XW)
-        denominator = exps.sum()
-        probs = exps / denominator
-        expression = -probs
-        expression[action_taken] = 1 + expression[action_taken]
-        return expression
-        """
-
-        log_numerator = logits
-        log_denominator = scipy.special.logsumexp(logits)
-        log_expression = log_numerator - log_denominator
-        """
-        expression[i] = -exp(XW[i]) / (np.sum(np.exp(XW)) for i!=action_taken
-        expression[action_taken] = 1 - -exp(XW[action_taken]) / (np.sum(np.exp(XW))
-        """
-        expression = np.exp(log_expression)
-        expression = -expression
-        expression[action_taken] = 1 + expression[action_taken]
-        return expression
-
-    def get_update(self, x: np.ndarray, w: np.ndarray, b: np.ndarray, logits: np.ndarray, action: int, reward: float):
-        """
-        Returns delta_w and delta_b, where
-        w{t+1} = w{t} + delta_w
-        b{t+1} = b{t} + delta_b
-
-        :param x: np.ndarray, shape=(d)
-        :param w: np.ndarray, shape=(d, actions)
-        :param b: np.ndarray, shape=(actions,)
-        :param logits: np.ndarray, shape=(action,)
-        :param action: int, action taken by the learner
-        :param reward: float, reward received by the learner
-        :return: delta, np.ndarray, shape=(actions)
-        """
-        gt = self.alpha * reward * self._compute_trace(action, logits)
-
-        delta = x[:, None] * gt[None, :]
-        return delta, 0
diff --git a/hobj/benchmarks/experiment_simulation/__init__.py b/hobj/learning_models/linear_learning_model/__init__.py
similarity index 100%
rename from hobj/benchmarks/experiment_simulation/__init__.py
rename to hobj/learning_models/linear_learning_model/__init__.py
diff --git a/hobj/learning_models/linear_learning_model/make_model.py b/hobj/learning_models/linear_learning_model/make_model.py
new file mode 100644
index 0000000..ebe0dd5
--- /dev/null
+++ b/hobj/learning_models/linear_learning_model/make_model.py
@@ -0,0 +1,91 @@
+"""
+This module provides an alternative interface for instantiating a linear learning model.
+"""
+
+from functools import lru_cache
+from pathlib import Path
+from typing import Literal
+
+import numpy as np
+
+import hobj.learning_models.linear_learning_model.update_rules as update_rules
+from hobj.learning_models import LinearLearner, RepresentationalModel
+from hobj.types import ImageId
+
+
+# %%
+@lru_cache(maxsize=None)
+def _get_calibration_image_ids(cachedir: Path | None = None) -> list[ImageId]:
+    """Return the warmup image IDs used for feature calibration.
+
+    Args:
+        cachedir: Optional directory containing the packaged ``data`` tree.
+
+    Returns:
+        Sorted image IDs used for calibration.
+    """
+    from hobj.data.images import load_imageset_meta_warmup
+
+    images_df = load_imageset_meta_warmup(cachedir=cachedir)
+    return sorted(images_df["image_id"].tolist())
+
+
+# %%
+def create_linear_learner(
+    image_id_to_features: dict[ImageId, np.ndarray],
+    update_rule_name: Literal[
+        "Square",
+        "Perceptron",
+        "Hinge",
+        "MAE",
+        "Exponential",
+        "CE",
+        "REINFORCE",
+    ] = "Square",
+    alpha: float = 1,
+    cachedir: Path | None = None,
+) -> LinearLearner:
+    """
+    Instantiate a linear learner from precomputed features, using the procedure
+    described in the paper to calibrate the features based on the warmup images.
+
+    Args:
+        image_id_to_features: Precomputed features keyed by image ID.
+        update_rule_name: Name of the update rule to use.
+        alpha: Learning-rate parameter for the update rule.
+        cachedir: Optional directory containing the packaged ``data`` tree.
+
+    Returns:
+        A calibrated linear learner.
+    """
+
+    if alpha < 0 or alpha > 1:
+        raise ValueError("alpha must be in the interval [0, 1]")
+
+    f_calibration = np.array(
+        [
+            image_id_to_features[ref]
+            for ref in _get_calibration_image_ids(cachedir=cachedir)
+        ]
+    )
+    mu_calibration = np.mean(f_calibration, axis=0)
+    norms_calibration = np.linalg.norm(f_calibration - mu_calibration, axis=1)
+    norm_cutoff = np.quantile(norms_calibration, 0.999)  # Will clip the rest
+
+    ref_to_calibrated_features = {}
+    for ref in image_id_to_features:
+        f = image_id_to_features[ref]
+        fc = f - mu_calibration
+        fcn = fc / norm_cutoff
+        norm_cur = np.linalg.norm(fcn)
+        if norm_cur > 1:
+            fcn = fcn / norm_cur
+        ref_to_calibrated_features[ref] = np.array(fcn)
+
+    update_rule_name = getattr(update_rules, update_rule_name)
+    return LinearLearner(
+        representational_model=RepresentationalModel.from_precomputed_features(
+            image_ref_to_features=ref_to_calibrated_features
+        ),
+        update_rule=update_rule_name(alpha=alpha),
+    )
diff --git a/hobj/learning_models/linear_learning_model/representation.py b/hobj/learning_models/linear_learning_model/representation.py
new file mode 100644
index 0000000..7a3dd69
--- /dev/null
+++ b/hobj/learning_models/linear_learning_model/representation.py
@@ -0,0 +1,82 @@
+from typing import Callable, Dict
+
+import numpy as np
+
+from hobj.types import ImageId
+
+
+# %%
+class RepresentationalModel:
+    """
+    Class which maps images to feature vectors of shape (d,).
+    """
+
+    def __init__(
+        self,
+        d: int,
+        image_to_features_func: Callable[[ImageId], np.ndarray],
+    ):
+        if not isinstance(d, int):
+            raise ValueError(f"Expected d to be an int, but got {d} of type {type(d)}")
+        if d <= 0:
+            raise ValueError(f"Expected d to be positive, but got {d}")
+
+        self._d = d
+        self._image_to_features_func = image_to_features_func
+        return
+
+    @property
+    def d(self) -> int:
+        return self._d
+
+    def get_features(self, image: ImageId) -> np.ndarray:
+        """
+        Returns a feature vector for the image_url.
+        To work successfully with UpdateRule, the feature vector must be of shape (d,) and have norm 1.
+
+        :param image: schema.ImageRef or PIL.Image
+        :return: np.ndarray, shape=(d,)
+        """
+
+        f = self._image_to_features_func(image)
+
+        if f.shape != (self.d,):
+            raise ValueError(
+                f"Expected feature vector of shape {(self.d,)}, but got {f.shape}"
+            )
+
+        return f
+
+    @classmethod
+    def from_precomputed_features(
+        cls, image_ref_to_features: Dict[ImageId, np.ndarray]
+    ) -> "RepresentationalModel":
+        """
+        Convenience method for creating a RepresentationalModel, when one has a precomputed dict which maps ImageRefs to np.ndarray feature vectors.
+
+        If get_features is called with an ImageRef (or PIL.Image with an ImageRef) not in image_ref_to_features, a KeyError will be raised.
+        """
+
+        def image_to_features_func(image: ImageId) -> np.ndarray:
+            return image_ref_to_features[image]
+
+        # Ensure all feature vectors are the same shape
+        d = None
+        for ref in image_ref_to_features:
+            f = image_ref_to_features[ref]
+            if not isinstance(f, np.ndarray):
+                raise ValueError(
+                    f"Expected feature vector to be a np.ndarray, but got {f} of type {type(f)}"
+                )
+            if not len(f.shape) == 1:
+                raise ValueError(f"Expected feature vector to be 1D, but got {f.shape}")
+
+            if d is None:
+                d = f.shape[0]
+
+            if not f.shape[0] == d:
+                raise ValueError(
+                    f"Expected feature vector to be of shape ({d},), but got {f.shape}"
+                )
+
+        return cls(d=d, image_to_features_func=image_to_features_func)
diff --git a/hobj/learning_models/linear_learning_model/update_rules.py b/hobj/learning_models/linear_learning_model/update_rules.py
new file mode 100644
index 0000000..85e8492
--- /dev/null
+++ b/hobj/learning_models/linear_learning_model/update_rules.py
@@ -0,0 +1,229 @@
+from abc import ABC, abstractmethod
+from typing import Tuple, Union
+
+import numpy as np
+import scipy.special
+
+
+class UpdateRule(ABC):
+    def __init__(self, alpha: float):
+        """
+        Implements an update rule for a linear learner. Assumes that all features have at most norm 1.
+        :param alpha: the normalized learning rate. Between 0 and 1.
+        """
+        if not 0 <= alpha <= 1:
+            raise ValueError("alpha must be in the interval [0, 1]")
+
+        self.alpha = alpha
+        self.reset()
+        return
+
+    def reset(self) -> None:
+        """
+        Called to reset the update rule to some initial state. Not used by all update rules.
+        :return:
+        """
+        return
+
+    @abstractmethod
+    def get_update(
+        self,
+        x: np.ndarray,
+        w: np.ndarray,
+        b: np.ndarray,
+        logits: np.ndarray,
+        action: int,
+        reward: float,
+    ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]:
+        """
+        Returns delta_w and delta_b, where
+        w{t+1} = w{t} + delta_w
+        b{t+1} = b{t} + delta_b
+
+        :param x: np.ndarray, shape=(d)
+        :param w: np.ndarray, shape=(d, actions)
+        :param b: np.ndarray, shape=(actions,)
+        :param logits: np.ndarray, shape=(action,)
+        :param action: int, action taken by the learner
+        :param reward: float, reward received by the learner
+        :return: delta_w, delta_b, which are np.ndarrays of the same shape as w and b, respectively.
+        """
+        delta_w = np.zeros(w.shape)
+        delta_b = np.zeros(b.shape)
+        return delta_w, delta_b
+
+
+# %%
+class Square(UpdateRule):
+    def get_update(
+        self,
+        x: np.ndarray,
+        w: np.ndarray,
+        b: np.ndarray,
+        logits: np.ndarray,
+        action: int,
+        reward: float,
+    ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]:
+        delta = np.zeros(w.shape)
+
+        reward_prediction = logits[action]
+        rpe = float(reward) - reward_prediction
+
+        delta[:, action] = self.alpha * rpe * x
+        return delta, 0
+
+
+class Perceptron(UpdateRule):
+    def get_update(
+        self,
+        x: np.ndarray,
+        w: np.ndarray,
+        b: np.ndarray,
+        logits: np.ndarray,
+        action: int,
+        reward: float,
+    ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]:
+        nactions = len(logits)
+        gts = np.zeros(nactions)
+
+        yhat = logits[action]
+        z = reward * yhat
+        if z <= 0:
+            gts[action] = self.alpha * reward
+        delta = x[:, None] * gts[None, :]
+        return delta, 0
+
+
+class Hinge(UpdateRule):
+    def get_update(
+        self,
+        x: np.ndarray,
+        w: np.ndarray,
+        b: np.ndarray,
+        logits: np.ndarray,
+        action: int,
+        reward: float,
+    ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]:
+        nactions = len(logits)
+        gts = np.zeros(nactions)
+
+        yhat = logits[action]
+        z = reward * yhat
+        if z <= 1:  # and yhat >= 0:
+            gts[action] = self.alpha * reward
+        delta = x[:, None] * gts[None, :]
+        return delta, 0
+
+
+class MAE(UpdateRule):
+    def get_update(
+        self,
+        x: np.ndarray,
+        w: np.ndarray,
+        b: np.ndarray,
+        logits: np.ndarray,
+        action: int,
+        reward: float,
+    ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]:
+        nactions = len(logits)
+        reward_prediction = logits[action]
+
+        gts = np.zeros(nactions)
+        if reward_prediction < reward:
+            gts[action] = self.alpha
+        elif reward_prediction > reward:
+            gts[action] = -self.alpha
+
+        delta = x[:, None] * gts[None, :]
+        return delta, 0
+
+
+class Exponential(UpdateRule):
+    max_weight_norm = 10.0
+
+    def get_update(
+        self,
+        x: np.ndarray,
+        w: np.ndarray,
+        b: np.ndarray,
+        logits: np.ndarray,
+        action: int,
+        reward: float,
+    ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]:
+        nactions = len(logits)
+
+        gts = np.zeros(nactions)
+        gts[action] = self.alpha * reward * np.exp(-reward * logits[action])
+        delta = x[:, None] * gts[None, :]
+
+        # Bound norm of weights
+        wnext = w + delta
+        if np.linalg.norm(wnext) > self.max_weight_norm:
+            wnext = wnext / np.linalg.norm(wnext) * self.max_weight_norm
+            delta = wnext - w
+        return delta, 0
+
+
+class CE(UpdateRule):
+    def get_update(
+        self,
+        x: np.ndarray,
+        w: np.ndarray,
+        b: np.ndarray,
+        logits: np.ndarray,
+        action: int,
+        reward: float,
+    ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]:
+        nactions = len(logits)
+        gts = np.zeros(nactions)
+        reward_prediction = logits[action]
+
+        gts[action] = (
+            4 * self.alpha * (reward / (1 + np.exp(reward * reward_prediction)))
+        )
+
+        delta = x[:, None] * gts[None, :]
+        return delta, 0
+
+
+class REINFORCE(UpdateRule):
+    @staticmethod
+    def _compute_trace(
+        action_taken: int,
+        logits: np.ndarray,
+    ):
+        """
+        Computes the negative derivative of the probability of taking each action with respect to logit[i]
+        exps = np.exp(XW)
+        denominator = exps.sum()
+        probs = exps / denominator
+        expression = -probs
+        expression[action_taken] = 1 + expression[action_taken]
+        return expression
+        """
+
+        log_numerator = logits
+        log_denominator = scipy.special.logsumexp(logits)
+        log_expression = log_numerator - log_denominator
+        """
+        expression[i] = -exp(XW[i]) / (np.sum(np.exp(XW)) for i!=action_taken
+        expression[action_taken] = 1 - -exp(XW[action_taken]) / (np.sum(np.exp(XW))
+        """
+        expression = np.exp(log_expression)
+        expression = -expression
+        expression[action_taken] = 1 + expression[action_taken]
+        return expression
+
+    def get_update(
+        self,
+        x: np.ndarray,
+        w: np.ndarray,
+        b: Union[np.ndarray, np.generic, float],
+        logits: np.ndarray,
+        action: int,
+        reward: float,
+    ) -> Tuple[np.ndarray, Union[np.ndarray, np.generic, float]]:
+        gt = self.alpha * reward * self._compute_trace(action, logits)
+
+        delta = x[:, None] * gt[None, :]
+        return delta, 0
diff --git a/hobj/learning_models/random_guesser.py b/hobj/learning_models/random_guesser.py
new file mode 100644
index 0000000..476ce04
--- /dev/null
+++ b/hobj/learning_models/random_guesser.py
@@ -0,0 +1,31 @@
+from typing import Literal
+
+import numpy as np
+
+from hobj.learning_models import BinaryLearningModel
+from hobj.types import ImageId
+from typing import cast
+
+
+class RandomGuesser(BinaryLearningModel):
+    """
+    A dummy model which simply returns random outputs, independent of the given image.
+    """
+
+    def __init__(self, seed: int | None = None):
+        self.random_generator = np.random.default_rng(seed=seed)
+
+    def reset_state(self, seed: int | None) -> None:
+        self.random_generator = np.random.default_rng(seed=seed)
+
+    def get_response(
+        self,
+        image: ImageId,
+    ) -> Literal[0, 1]:
+        action = self.random_generator.integers(2)
+        action = int(action)
+        action = cast(Literal[0, 1], action)
+        return action
+
+    def give_feedback(self, reward: float) -> None:
+        return
diff --git a/hobj/statistics/__init__.py b/hobj/statistics/__init__.py
deleted file mode 100644
index 3cbba05..0000000
--- a/hobj/statistics/__init__.py
+++ /dev/null
@@ -1,30 +0,0 @@
-"""
-
-Benchmark 1:
-- CI for the raw R^2 ceiling
-- Approximate null distribution for R^2_n, given # of model simulations
-- CI for overall/early/late performance
-- Approximate null distribution for SpearmanR upon experimental repeat
-
- Scoring model:
- - R^2_n point estimate
- - Overall/early/late point estimates
- - Raw SpearmanR point estimate
-
-Scoring heldout human split-half
-- R^2_n point estimate, and CI
-- Overall/early/late point estimates, and CIs
-- Raw SpearmanR point estimate
-
-Benchmark 2:
-- CI for the raw R^2 ceiling
-- Approximate null distribution, given the null hypothesis
-- Bootstrapped standard errors for the performances
-
-Scoring model on Benchmark 2:
-- R^2_n point estimate
-
-Scoring heldout human split-half
-- R^2_n point estimate, and CI
-
-"""
diff --git a/hobj/statistics/hypothesis_testing/__init__.py b/hobj/statistics/hypothesis_testing/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hobj/statistics/hypothesis_testing/approximated_null_distribution.py b/hobj/statistics/hypothesis_testing/approximated_null_distribution.py
deleted file mode 100644
index 1ad6e3e..0000000
--- a/hobj/statistics/hypothesis_testing/approximated_null_distribution.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import xarray as xr
-from typing import Union, List
-
-
-def estimate_null_distribution(
-        statistical_functional,
-        ds_null_samples: xr.Dataset,
-        hat_mu: Union[xr.Dataset, xr.DataArray] = None,
-        hat_sigma: Union[xr.Dataset, xr.DataArray] = None,
-        resample_dim='boot_iter'
-):
-    null_stats = statistical_functional(ds_null_samples)
-
-    if hat_mu is not None:
-        null_stats = null_stats - null_stats.mean(resample_dim) + hat_mu
-    if hat_sigma is not None:
-        null_stats = (null_stats - null_stats.mean(resample_dim)) * hat_sigma / null_stats.std(resample_dim, ddof=1) + null_stats.mean(resample_dim)
-    return null_stats
diff --git a/hobj/statistics/hypothesis_testing/bootstrapped_confidence_intervals.py b/hobj/statistics/hypothesis_testing/bootstrapped_confidence_intervals.py
deleted file mode 100644
index 68d222a..0000000
--- a/hobj/statistics/hypothesis_testing/bootstrapped_confidence_intervals.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import xarray as xr
-
-def estimate_basic_confidence_interval(
-        alpha: float,
-        ds_data: xr.Dataset,
-        ds_boot: xr.Dataset,
-        statistic_functional,
-        parameter_functional = None,
-
-):
-    assert 'boot_iter' in ds_boot.dims
-    for dim in ds_data.dims:
-        assert dim in ds_boot.dims, dim
-
-    assert alpha <= 0.1
-    assert alpha > 0
-
-    point_estimate = statistic_functional(ds_data)
-    ds_boot_stats = statistic_functional(ds_boot)
-
-    if parameter_functional is not None:
-        empirical_parameter = parameter_functional(ds_data)
-    else:
-        empirical_parameter = ds_boot_stats.mean('boot_iter')
-
-    low = ds_boot_stats.quantile(alpha/2,'boot_iter', )
-    high = ds_boot_stats.quantile( 1 - alpha / 2,'boot_iter',)
-
-    delta1 = empirical_parameter - low
-    delta2 = high - empirical_parameter
-    CI = xr.concat([point_estimate - delta2, point_estimate + delta1], 'CI')
-    return CI
diff --git a/hobj/statistics/lapse_rate.py b/hobj/statistics/lapse_rate.py
deleted file mode 100644
index d31f548..0000000
--- a/hobj/statistics/lapse_rate.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import xarray as xr
-import numpy as np
-from typing import Union, Tuple, List
-
-
-def fit_optimal_lapse_rate(
-        phat: Union[xr.DataArray, np.ndarray, float, int],
-        p: Union[xr.DataArray, np.ndarray, float, int],
-        nway: int,
-        condition_dims: Union[float, int, tuple, Tuple[str], List[str]] = -1):
-    """
-    Fits a lapse rate (gamma, which is the probability of a uniform random guess) that minimizes the empirical MSE loss between the phat and p:
-
-        z = phat * (1 - gamma) + 1/nway * (gamma)
-        Loss = ((z - p)**2).sum()
-
-    :param phat:
-    :param p:
-    :param nway:
-    :return:
-    """
-
-    assert nway >= 2
-    assert np.all(phat <= 1)
-    assert np.all(phat >= 0)
-    assert np.all(p <= 1)
-    assert np.all(p >= 0)
-
-    if not isinstance(phat, (np.ndarray, xr.DataArray)):
-        phat = np.array(phat)
-
-    if not isinstance(p, (np.ndarray, xr.DataArray)):
-        p = np.array(p)
-
-    assert p.shape == phat.shape
-
-    numerator = -(2 * phat / nway - 2 * np.square(phat) + 2 * phat * p - 2 * p / nway).sum(condition_dims)
-    denominator = (2 / (nway ** 2) - 4 * phat / nway + 2 * (phat ** 2)).sum(condition_dims)
-    gamma_star = numerator / denominator
-    gamma_star = np.clip(gamma_star, 0, 1)
-
-    if isinstance(gamma_star, np.ndarray):
-        if gamma_star.shape == ():
-            gamma_star = float(gamma_star)
-    return gamma_star
-
-
-def _test():
-    import matplotlib.pyplot as plt
-
-    np.random.seed(0)
-    gamma_true = np.random.rand() * 0.3
-    phat = np.clip(np.random.rand(500) + gamma_true, 0, 1)
-    p = phat - gamma_true
-
-    # Numerical solution
-    nway = 8
-    gamma_seq = np.linspace(0.01, 1, 1000)
-    gamma_star = fit_optimal_lapse_rate(phat, p, nway=nway)
-    phat_gamma = phat[None, :] * (1 - gamma_seq[:, None]) + gamma_seq[:, None] * (1 / nway)  # [gamma, cond]
-    mse = np.square(phat_gamma - p).mean(1)
-    plt.plot(gamma_seq, mse)
-    plt.axvline(gamma_star, color='black', ls='-', label='true')
-    plt.axvline(gamma_seq[np.argmin(mse)], color='red', ls='-', label='numerical')
-    plt.axvline(gamma_star, color='blue', ls=':', label='function')
-    plt.legend()
-    plt.show()
-
-
-if __name__ == '__main__':
-    _test()
diff --git a/hobj/statistics/resamplers/__init__.py b/hobj/statistics/resamplers/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hobj/statistics/resamplers/resamplers.py b/hobj/statistics/resamplers/resamplers.py
deleted file mode 100644
index fdb0593..0000000
--- a/hobj/statistics/resamplers/resamplers.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import numpy as np
-import xarray as xr
-from tqdm import trange
-
-
-class Resampler(object):
-    def __init__(self):
-        return
-
-
-class WorkerResampler(Resampler):
-    def __init__(self, ds_worker_table: xr.Dataset, condition_dim='subtask', worker_dim='worker_id'):
-        """
-        Bootstrap resamples workers.
-        ds:
-        :param k, n: (worker_id, subtask, trial)
-        """
-        super().__init__()
-        assert 'k' in ds_worker_table.data_vars
-        assert 'n' in ds_worker_table.data_vars
-        assert 'trial' in ds_worker_table.dims
-        assert worker_dim in ds_worker_table.dims, ds_worker_table.dims
-        assert condition_dim in ds_worker_table.dims, ds_worker_table.dims
-        assert np.all(ds_worker_table.n <= 1)
-        assert np.all(ds_worker_table.k <= ds_worker_table.n), ds_worker_table
-        assert np.all(ds_worker_table.k >= 0)
-
-        self.ds_worker_table = ds_worker_table.transpose('worker_id', 'trial', 'subtask')
-        self.worker_dim = worker_dim
-        self.condition_dim = condition_dim
-
-        return
-
-    def get_ds_boot(self, nboot: int, seed: int):
-        RS = np.random.RandomState(seed=seed)
-        kbootstrapped = np.zeros((nboot, len(self.ds_worker_table[self.condition_dim]), len(self.ds_worker_table.trial),))
-        nbootstrapped = np.zeros((nboot, len(self.ds_worker_table[self.condition_dim]), len(self.ds_worker_table.trial),))
-
-        kdat = self.ds_worker_table.k.transpose('worker_id', 'subtask', 'trial').values
-        ndat = self.ds_worker_table.n.transpose('worker_id', 'subtask', 'trial').values
-        for i_resample in trange(nboot, desc='bootstrap resampling workers'):
-            i_workers = RS.choice(len(self.ds_worker_table[self.worker_dim]), size=len(self.ds_worker_table[self.worker_dim]), replace=True).astype(int)
-            kbootstrapped[i_resample] = kdat[i_workers].sum(0)
-            nbootstrapped[i_resample] = ndat[i_workers].sum(0)
-
-        ds_boot = xr.Dataset(
-            data_vars={
-                'k': (('boot_iter', self.condition_dim, 'trial',), kbootstrapped),
-                'n': (('boot_iter', self.condition_dim, 'trial',), nbootstrapped),
-            },
-            coords={
-                'boot_iter': np.arange(nboot),
-                'trial': self.ds_worker_table.trial,
-                self.condition_dim: self.ds_worker_table[self.condition_dim],
-            }
-        )
-        return ds_boot
-
-
-class LearningCurveResampler(Resampler):
-
-    def __init__(self, ds: xr.Dataset, condition_dim='subtask', rep_dim='worker_id'):
-        """
-        For each condition, bootstraps sequences of Bernoulli trials.
-        ds:
-        :param ds: An xr.Dataset with data variables 'k' and 'n' with dims ('trial', condition_dim, rep_dim)
-        """
-        super().__init__()
-        assert 'k' in ds.data_vars
-        assert 'n' in ds.data_vars
-        assert 'trial' in ds.dims
-        assert rep_dim in ds.dims, ds.dims
-        assert condition_dim in ds.dims, ds.dims
-        assert np.all(ds.n <= 1)
-        assert np.all(ds.k <= ds.n), ds
-        assert np.all(ds.k >= 0)
-
-        self.ds = ds
-        self.rep_dim = rep_dim
-
-        condition_to_kn_dat = {}
-        ntrials = len(ds.trial)
-        for subtask, ds_subtask in ds.groupby(condition_dim):
-            ds_subtask = ds_subtask.sel({condition_dim: subtask})
-
-            n = ds_subtask.n
-            nsessions_with_data = ((n > 0).sum('trial') > 0).sum(rep_dim)
-            valid_sessions = (n > 0).sum('trial') == ntrials
-            assert valid_sessions.sum() == nsessions_with_data
-            ds_valid = ds_subtask.sel({rep_dim: valid_sessions})
-            ds_valid = ds_valid.transpose(rep_dim, 'trial')
-            condition_to_kn_dat[subtask] = ds_valid['k'].values, ds_valid['n'].values  # [session, trial]
-
-        self.condition_dim = condition_dim
-        self.condition_to_kn_dat = condition_to_kn_dat
-        self.conditions = sorted(condition_to_kn_dat.keys())
-
-        # Number of sessions for each condition
-        self.nsessions = xr.DataArray([self.condition_to_kn_dat[condition][0].shape[0] for condition in self.conditions],
-                                      dims=self.condition_dim,
-                                      coords={self.condition_dim: self.conditions}
-                                      )
-
-        return
-
-    def get_ds_resamples(self, nresamples: int, nsessions: xr.DataArray, seed: int):
-        """
-        Simulate taking nsessions samples for each condition.
-        :param nsessions:
-        :return:
-        """
-
-        assert nresamples >= 1
-        assert nsessions.dims == (self.condition_dim,)
-        nsessions = nsessions.sel({self.condition_dim: self.conditions})
-        nsessions = np.array(nsessions.values)
-        RS = np.random.RandomState(seed=seed)
-        kresamp = np.zeros((nresamples, len(self.ds.trial), len(self.ds[self.condition_dim])))
-        nresamp = np.zeros((nresamples, len(self.ds.trial), len(self.ds[self.condition_dim])))
-
-        for i_resample in trange(nresamples, desc='resampling learning curves'):
-
-            for i_condition, condition in enumerate(self.conditions):
-                kdat, ndat = self.condition_to_kn_dat[condition]  # [session, trial]
-                nsessions_cur = nsessions[i_condition]
-                navailable_lcs = kdat.shape[0]
-
-                i_sessions = RS.choice(navailable_lcs, size=nsessions_cur, replace=True)
-                kboot_samp = kdat[i_sessions].sum(0)
-                nboot_samp = ndat[i_sessions].sum(0)
-
-                kresamp[i_resample, :, i_condition] = kboot_samp
-                nresamp[i_resample, :, i_condition] = nboot_samp
-
-        ds_resamp = xr.Dataset(
-            data_vars={
-                'k': (('boot_iter', 'trial', self.condition_dim), kresamp),
-                'n': (('boot_iter', 'trial', self.condition_dim), nresamp),
-            },
-            coords={
-                'boot_iter': np.arange(nresamples),
-                'trial': self.ds.trial,
-                self.condition_dim: self.conditions,
-            }
-        )
-
-        return ds_resamp
-
-    def get_ds_resamples_path2(self, nresamples: int, nsessions: xr.DataArray, seed: int):
-        """
-        Simulate taking nsessions samples for each condition.
-        :param nsessions:
-        :return:
-        """
-
-        assert nresamples >= 1
-        assert nsessions.dims == (self.condition_dim,)
-        nsessions = nsessions.sel({self.condition_dim: self.conditions})
-        nsessions = np.array(nsessions.values)
-        RS = np.random.RandomState(seed=seed)
-        kresamp = np.zeros((nresamples, len(self.ds.trial), len(self.ds[self.condition_dim])))
-        nresamp = np.zeros((nresamples, len(self.ds.trial), len(self.ds[self.condition_dim])))
-
-        for i_resample in trange(nresamples, desc='resampling learning curves'):
-
-            for i_condition, condition in enumerate(self.conditions):
-                kdat, ndat = self.condition_to_kn_dat[condition]  # [session, trial]
-                nsessions_cur = nsessions[i_condition]
-                navailable_lcs = kdat.shape[0]
-
-                ntimes_resampled = RS.multinomial(n=nsessions_cur, pvals=1 / navailable_lcs * np.ones(navailable_lcs), )  # [navailable_lcs]
-                kboot_samp = (kdat * ntimes_resampled[:, None]).sum(0)
-                nboot_samp = (ndat * ntimes_resampled[:, None]).sum(0)
-
-                kresamp[i_resample, :, i_condition] = kboot_samp
-                nresamp[i_resample, :, i_condition] = nboot_samp
-
-        ds_resamp = xr.Dataset(
-            data_vars={
-                'k': (('boot_iter', 'trial', self.condition_dim), kresamp),
-                'n': (('boot_iter', 'trial', self.condition_dim), nresamp),
-            },
-            coords={
-                'boot_iter': np.arange(nresamples),
-                'trial': self.ds.trial,
-                self.condition_dim: self.conditions,
-            }
-        )
-
-        return ds_resamp
-
-    def get_ds_boot(self, nboot: int, seed: int):
-        ds_boot = self.get_ds_resamples_path2(nresamples=nboot, nsessions=self.nsessions, seed=seed)
-        return ds_boot
diff --git a/hobj/statistics/score_statistics/MSEn.py b/hobj/statistics/score_statistics/MSEn.py
deleted file mode 100644
index 7dc8f78..0000000
--- a/hobj/statistics/score_statistics/MSEn.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import xarray as xr
-import numpy as np
-from typing import Union, List
-
-
-
-def estimate_MSEn(
-        hat_theta_model: Union[xr.DataArray, np.ndarray],
-        hat_var_hat_theta_model: Union[xr.DataArray, np.ndarray],
-        hat_theta_target: Union[xr.DataArray, np.ndarray],
-        condition_dims: tuple,
-):
-    """
-    The expected value of the raw MSE is:
-
-    (E[x] - E[y])^2 + var(x) + var(y)
-
-    The MSEn performs a bias correction using the estimated variance of the model.
-
-    :param hat_theta_model:
-    :param hat_var_hat_theta_model:
-    :param hat_theta_target:
-    :param hat_var_hat_theta_target:
-    :param condition_dims:
-    :return:
-    """
-    hat_MSE_raw = np.square(hat_theta_model - hat_theta_target).mean(condition_dims)
-    hat_MSE_n = hat_MSE_raw - hat_var_hat_theta_model.mean(condition_dims)
-    return hat_MSE_n
-
diff --git a/hobj/statistics/score_statistics/__init__.py b/hobj/statistics/score_statistics/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hobj/statistics/variance_estimates/__init__.py b/hobj/statistics/variance_estimates/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hobj/statistics/variance_estimates/binomial.py b/hobj/statistics/variance_estimates/binomial.py
deleted file mode 100644
index 3f36a1d..0000000
--- a/hobj/statistics/variance_estimates/binomial.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from scipy.stats import beta
-import numpy as np
-import xarray as xr
-
-
-def estimate_variance_of_binomial_proportion(kvec, nvec):
-
-    """
-    Returns the *unbiased* estimate of the variance associated with the proportion estimator, phat = k/n:
-
-    phat = kvec / nvec
-    \hat var(phat) = (phat * (1 - phat)) / (nvec - 1)
-    :param kvec:
-    :param nvec:
-    :return:
-    """
-    phat = kvec / nvec
-    return (phat * (1 - phat)) / (nvec - 1)
-
-
-def get_variance_of_binomial_proportion(pvec, nvec):
-
-    """
-    Returns the ground-truth variance associated with the proportion estimator associated with this distribution, phat = k/n:
-
-    kvec ~ Binomial(nvec, pvec)
-    phat = kvec / nvec
-    \hat var(phat) = (phat * (1 - phat)) / (nvec - 1)
-    :param kvec:
-    :param nvec:
-    :return:
-    """
-
-    return (pvec * (1 - pvec)) / (nvec)
-
-
-def get_CI_of_binomial_proportion(kvec, nvec, alpha:float):
-
-        """
-        Returns the exact (Clopper-Pearson) confidence interval for the Binomial proportion estimator, phat:
-
-        kvec ~ Binomial(nvec, pvec)
-        phat = kvec / nvec
-
-        These confidence intervals may be conservative (wide), due to the discrete nature of the Binomial distribution: they may have higher coverage than alpha.
-        :param kvec:
-        :param nvec:
-        :return:
-        """
-
-        assert np.all(kvec <= nvec)
-        assert np.all(kvec >= 0)
-        assert np.all(nvec >= 2), np.min(nvec)
-
-        template = kvec - kvec
-        alpha_lower = (template + 1) * alpha / 2
-        alpha_upper = (template + 1) * (1 - alpha / 2)
-
-        p_lower, p_upper = beta.ppf([alpha_lower, alpha_upper], [kvec, kvec + 1], [nvec - kvec + 1, nvec - kvec])
-
-        p_lower = (template) + p_lower
-        p_upper = (template) + p_upper
-
-        if isinstance(template, xr.DataArray):
-            CI = xr.concat([p_lower, p_upper], dim='CI')
-        else:
-            CI = p_lower, p_upper
-        return CI
-
diff --git a/hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/__init__.py b/hobj/stats/__init__.py
similarity index 100%
rename from hobj/benchmarks/leedicarlo_benchmarks/MutatorHighVar/__init__.py
rename to hobj/stats/__init__.py
diff --git a/hobj/stats/binomial.py b/hobj/stats/binomial.py
new file mode 100644
index 0000000..465d9e1
--- /dev/null
+++ b/hobj/stats/binomial.py
@@ -0,0 +1,10 @@
+def estimate_variance_of_binomial_proportion(kvec, nvec):
+    """
+    Returns the unbiased estimate of the variance associated with the proportion estimator, phat = k/n:
+    :param kvec:
+    :param nvec:
+    :return:
+    """
+    # todo: add checking on argument
+    phat = kvec / nvec
+    return (phat * (1 - phat)) / (nvec - 1)
diff --git a/hobj/stats/ci.py b/hobj/stats/ci.py
new file mode 100644
index 0000000..83af88a
--- /dev/null
+++ b/hobj/stats/ci.py
@@ -0,0 +1,43 @@
+from typing import Tuple, Union
+
+import numpy as np
+
+
+def estimate_basic_bootstrap_CI(
+    alpha: float,
+    point_estimate: Union[np.ndarray, np.generic, float, int],  # [*]
+    bootstrapped_point_estimates: np.ndarray,  # [n_bootstraps, *]
+) -> Tuple[float, float]:
+    """
+    Estimates the basic confidence interval for a given point estimate(s) using the bootstrap method.
+    :param alpha: Sets the width of the confidence interval to be 1 - alpha. Must be in the range (0, 1).
+    :param point_estimate: The point estimate(s) for which the confidence interval is to be estimated.
+    :param bootstrapped_point_estimates: Bootstrap resamples of the point estimate in question.
+    :return: A tuple containing the lower and upper bounds of the confidence interval.
+    """
+
+    if not 0 < alpha < 1:
+        raise ValueError(f"alpha must be in the range (0, 1); got {alpha:.2f}")
+
+    # Coerce point_estimate to a numpy array
+    point_estimate = np.array(point_estimate)  # [*]
+
+    # Ensure the dimensions are as expected
+    if not len(point_estimate.shape) == len(bootstrapped_point_estimates.shape) - 1:
+        raise ValueError(
+            f"The dimensions of the point estimate and bootstrapped point estimates do not match: {point_estimate.shape} vs {bootstrapped_point_estimates.shape}"
+        )
+
+    # Calculate confidence interval using the basic bootstrap method
+    low = np.quantile(bootstrapped_point_estimates, alpha / 2, axis=0)  # [*]
+    high = np.quantile(bootstrapped_point_estimates, 1 - alpha / 2, axis=0)  # [*]
+
+    empirical_parameter = bootstrapped_point_estimates.mean(0)  # [*]
+
+    delta1 = empirical_parameter - low
+    delta2 = high - empirical_parameter
+
+    CI_low = point_estimate - delta2
+    CI_high = point_estimate + delta1
+
+    return float(CI_low), float(CI_high)
diff --git a/hobj/types.py b/hobj/types.py
new file mode 100644
index 0000000..0f0952f
--- /dev/null
+++ b/hobj/types.py
@@ -0,0 +1 @@
+type ImageId = str
diff --git a/hobj/utils/__init__.py b/hobj/utils/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hobj/utils/file_io.py b/hobj/utils/file_io.py
deleted file mode 100644
index 9b0e48a..0000000
--- a/hobj/utils/file_io.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import urllib.request
-import requests
-import PIL.Image
-from io import BytesIO
-import os
-import url_normalize
-from urllib.parse import urlparse
-
-from tqdm import tqdm
-
-import json
-
-
-def get_canonical_url(url:str):
-    return url_normalize.url_normalize(url = url)
-
-def get_local_save_location(url:str, cachedir:str):
-    canonical_url = get_canonical_url(url)
-    parsed = urlparse(canonical_url)
-
-    path = parsed.path
-    if path.startswith('/'):
-        path = path[1:]
-
-    domain = parsed.netloc # domain is the hostname
-    save_location = os.path.join(cachedir, domain, path)
-    return save_location
-
-def prepare_savepath(savepath):
-    savedir = os.path.dirname(savepath)
-    if not os.path.exists(savedir):
-        os.makedirs(savedir, exist_ok=True)
-
-def get_image_from_url(image_url:str):
-    response = requests.get(image_url)
-    img = PIL.Image.open(BytesIO(response.content))
-    return img
-
-def download_file(url:str, savepath=None,cachedir:str=None):
-    if savepath is None:
-        assert cachedir is not None
-        savepath = get_local_save_location(url = url, cachedir=cachedir)
-
-    prepare_savepath(savepath)
-
-    def my_hook(t):
-
-        last_b = [0]
-
-        def update_to(b=1, bsize=1, tsize=None):
-            """
-            b  : int, optional
-                Number of blocks transferred so far [default: 1].
-            bsize  : int, optional
-                Size of each block (in tqdm units) [default: 1].
-            tsize  : int, optional
-                Total size (in tqdm units). If [default: None] remains unchanged.
-            """
-            if tsize is not None:
-                t.total = tsize
-            t.update((b - last_b[0]) * bsize)
-            last_b[0] = b
-
-        return update_to
-
-    with tqdm(desc = f'downloading {url}') as pbar:
-        urllib.request.urlretrieve(url = url, filename=savepath, reporthook=my_hook(pbar))
-    return savepath
-
-
-def load_json(json_path):
-    with open(json_path, 'r') as fb:
-        val = json.load(fb)
-    return val
-
diff --git a/hobj/utils/hash.py b/hobj/utils/hash.py
deleted file mode 100644
index 7b33a8b..0000000
--- a/hobj/utils/hash.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import numpy as np
-import hashlib
-
-
-def hash_ndarray(x:np.ndarray):
-    assert isinstance(x, np.ndarray)
-    bytes = x.tobytes(order = "C")
-    m = hashlib.sha256(string=bytes)
-    digest = m.hexdigest()
-    return digest
-
-def hash_image(image:np.ndarray):
-    assert isinstance(image, np.ndarray)
-    assert 2<= len(image.shape) <= 3
-    image = image.astype(np.uint8)
-    return hash_ndarray(image)
-
-def hash_string(x:str):
-    return hash_ndarray(np.fromstring(str(x), dtype=np.uint8))
\ No newline at end of file
diff --git a/hobj/utils/state_space/__init__.py b/hobj/utils/state_space/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hobj/utils/state_space/fit_bernoulli.py b/hobj/utils/state_space/fit_bernoulli.py
deleted file mode 100644
index e5ee6d3..0000000
--- a/hobj/utils/state_space/fit_bernoulli.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# Adapted from code written by Anne Smith in 2003 from the Brown Lab at MIT
-import numpy as np
-from tqdm import tqdm
-from PySS.src.forwardfilter import forwardfilter
-from PySS.src.backwardfilter import backwardfilter
-from PySS.src.em_bino import em_bino
-from PySS.src.pdistnv2 import pdistnv2
-
-def fit(Responses,
-                  MaxResponse = 1,
-                  chance_prior_type = 'fixed',
-                  disable = True
-                  ):
-    '''
-
-    :param Responses: sequence of 0s or 1s. dtype = list, np.array. shape = (1, N)
-    :param MaxResponse: number of possible correct per trial. Can be a vector.
-    :param chance_prior_type one of 'fixed', 'update', 'none', : dtype: str
-    :return: pmid, p025, p075
-    '''
-
-    if chance_prior_type not in ['fixed', 'none', 'update']:
-        raise ValueError('Specify chance_prior_type of "fixed", "update", or "none". Gave: %s'%(str(chance_prior_type)))
-
-    UpdaterFlagDict = {'fixed': 0, 'update': 1, 'none': 2, }
-
-
-    BackgroundProb = 0.5
-    UpdaterFlag = UpdaterFlagDict[chance_prior_type]
-
-    SigE = 0.5  # default variance of learning state process is sqrt(0.5)
-
-    Responses, MaxResponse = _check_input(Responses, MaxResponse)
-    I = np.concatenate([Responses, MaxResponse * np.ones(Responses.shape)], axis=0)
-
-    SigsqGuess = np.square(SigE)
-
-    # set the value of mu from the chance of correct
-    mu = np.log(np.true_divide(BackgroundProb, 1 - BackgroundProb))
-
-    # convergence criterion for SIG_EPSILON^2
-    CvgceCrit = 1e-5
-
-    ''' Start '''
-    xguess = 0.
-    NumberSteps = 3000
-
-    newsigsq = []
-    xnew1save = []
-    for i in tqdm(range(NumberSteps), disable = disable):
-        p, x, s, xold, sold = forwardfilter(I, SigE, xguess, SigsqGuess, mu)
-
-        # Compute the backward (smoothing algorithm) estimates of the learning
-        # state and its variance: x{k|K} and sigsq{k|K}
-        xnew, signewsq, A = backwardfilter(x, xold, s, sold)
-        # xnew: (1, ntrials+1)
-        # signewsq: (1, ntrials+1)
-        # A: (1, ntrials)
-
-        if chance_prior_type == 'fixed':
-            # UpdaterFlag == 0
-            xnew[0, 0] = 0 # fixes initial value to 50%
-            signewsq[0, 0] = np.square(SigE)
-        elif chance_prior_type == 'update':
-            # Dampens the initial value toward chance
-            xnew[0, 0] = 0.5 * xnew[0, 1]  # updates the initial value of the latent process
-            signewsq[0, 0] = np.square(SigE)
-        elif chance_prior_type == 'none':
-            # UpdaterFlag == 2
-            # Unconstrained initial value
-            xnew[0, 0] = xnew[0, 1]  # x[0] = x[1] means no prior chance probability
-            signewsq[0, 0] = signewsq[0, 1]
-
-        # Compute the EM estimate of the learning state process variance
-        newsigsq.append(em_bino(I, xnew, signewsq, A, UpdaterFlag))
-
-        xnew1save.append(xnew[0, 0])
-
-        # Check for convergence
-        if i > 0:
-            a1 = np.abs(newsigsq[i] - newsigsq[i-1])
-            a2 = np.abs(xnew1save[i] - xnew1save[i-1])
-
-            if (a1 < CvgceCrit) and (a2 < CvgceCrit) and (UpdaterFlag >= 1):
-                if not disable:
-                    print ('EM estimates of learning state process variance and start point converged after %d steps'%(i+1))
-                break
-            elif (a1 < CvgceCrit) and (UpdaterFlag == 0):
-                if not disable:
-                    print ('EM estimates of learning state process variance converged after %d steps'%(i+1))
-                break
-
-        SigE = np.sqrt(newsigsq[i])
-        xguess = xnew[0, 0]
-        SigsqGuess = signewsq[0, 0]
-
-    if i == (NumberSteps-1):
-        print( 'Failed to converge after %d steps; convergence criterion was %f'%(i+1, CvgceCrit))
-
-    # Use sampling to convert from state to a probability
-
-    # Can used smoothed or filtered estimates - here used smoothed
-
-    if chance_prior_type == 'fixed':
-        # UpdaterFlag == 0
-        xnew[0, 0] = 0  # fixes initial value to 50%
-
-    p025, p975, pmid, _, pcert = pdistnv2(xnew, signewsq, mu, BackgroundProb);
-
-    # Remove the prior
-    p025 = p025[0, :-1]
-    p975 = p975[0, :-1]
-    pmid = pmid[0, :-1]
-
-    return pmid, p025, p975
-
-# Helper functions
-def _check_input(Responses, MaxResponse):
-
-    Responses = np.array(Responses)
-    if isinstance(MaxResponse, (np.ndarray, list)):
-        MaxResponse = np.array(MaxResponse)
-        if (MaxResponse.shape) == 1:
-            MaxResponse = MaxResponse[None, :]
-        if MaxResponse.shape[0] !=1:
-            MaxResponse = np.transpose(MaxResponse)
-        assert MaxResponse.shape[0] == 1
-
-
-
-    if len(Responses.shape) == 1:
-        Responses = Responses[None, :]
-
-    if Responses.shape[0] != 1:
-        Responses = np.transpose(Responses)
-
-    assert Responses.shape[0] == 1
-    return Responses, MaxResponse
diff --git a/hobj/utils/state_space/readme.md b/hobj/utils/state_space/readme.md
deleted file mode 100644
index 0f09fcf..0000000
--- a/hobj/utils/state_space/readme.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# PySS
-Functions for computing the state space trajectory of a Bernoulli process. 
-
-# References
-
-Smith, Anne C., and Emery N. Brown. "Estimating a state-space model from point process observations." Neural computation 15.5 (2003): 965-991.
-
-Smith, Anne C., et al. "Dynamic analysis of learning in behavioral experiments." Journal of Neuroscience 24.2 (2004): 447-461.
diff --git a/hobj/utils/state_space/src/__init__.py b/hobj/utils/state_space/src/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/hobj/utils/state_space/src/backwardfilter.py b/hobj/utils/state_space/src/backwardfilter.py
deleted file mode 100644
index 9e74f4c..0000000
--- a/hobj/utils/state_space/src/backwardfilter.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import numpy as np
-
-def backwardfilter(x, xold, sigsq, sigsqold):
-    '''
-    backwardfilter is a helper function that implements the backward filter
-    smoothing algorithm to estimate the learning state at trial k, given all
-    the data, as the Gaussian random variable with mean x{k|K} (xnew) and
-    SIG^2{k|K} (signewsq).
-
-    :param x: (1 x (N(+1?))
-    :param xold: (N+1). dtype = list
-    :param sigsq: (N+1). dtype = list
-    :param sigsqold: (N+1). dtype = list
-    :return:
-    '''
-
-    T = x.shape[1] # total number of posterior mode estimates (K + 1)
-
-    # Initial conditions: use values of posterior mode and posterior variance
-    xnew = list(np.zeros(T-1)) + [x[0, -1]]
-    signewsq = list(np.zeros(T-1)) + [sigsq[-1]]
-    xnew = np.array(xnew)[None, :]
-    signewsq = np.array(signewsq)[None, :]
-
-    A = np.zeros((1, T-1))
-    for i in range(T-1, 1, -1):
-        A[0, i - 1] = np.true_divide(sigsq[i - 1], sigsqold[i])
-
-        xnew_update = x[0, i - 1] + A[0, i - 1] * (xnew[0, i] - xold[i])
-        xnew[0, i - 1] = xnew_update
-        signewsq[0, i - 1] = sigsq[i - 1] + A[0, i - 1] * A[0, i - 1] * (signewsq[0, i] - sigsqold[i])
-
-    return xnew, signewsq, A
diff --git a/hobj/utils/state_space/src/em_bino.py b/hobj/utils/state_space/src/em_bino.py
deleted file mode 100644
index ced7526..0000000
--- a/hobj/utils/state_space/src/em_bino.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import numpy as np
-
-def em_bino(I, xnew, signewsq, A, startflag):
-    '''
-    em_bino is a helper function that computes sigma_eps squared (estimated
-    learning process variance).
-    :param I: (2 x ntrials)
-    :param xnew: (1 x ntrials(+1?))
-    :param signewsq: (1 x ntrials(+1?))
-    :param A: (1 x ntrials)
-    :param startflag: ()
-    :return:
-    '''
-
-    '''
-    print I.shape
-    print xnew.shape
-    print signewsq.shape
-    print A.shape
-    print startflag
-    raise Exception
-    '''
-
-    M = xnew.shape[1]
-    xnewt = xnew[0, 2:M]
-    xnewtm1 = xnew[0, 1:M-1]
-    signewsqt = signewsq[0, 2:M]
-    A = A[0, 1:]
-
-    covcalc = signewsqt * A
-
-    term1 = np.sum(np.square(xnewt)) + np.sum(signewsqt)
-    term2 = np.sum(covcalc) + np.sum(xnewt * xnewtm1)
-
-    if startflag == 1:
-        term3 = 1.5 * xnew[0, 1] * xnew[0, 1] + 2 * signewsq[0, 1]
-        term4 = np.square(xnew[0, -1]) + signewsq[0, -1]
-    elif startflag == 0:
-        term3 = 2 * xnew[0, 1] * xnew[0, 1] + 2 * signewsq[0, 1]
-        term4 = np.square(xnew[0, -1]) + signewsq[0, -1]
-    elif startflag == 2:
-        term3 = 1 * xnew[0, 1]*xnew[0, 1] + 2*signewsq[0, 1]
-        term4 = np.square(xnew[0, -1]) + signewsq[0, -1]
-        M = M - 1
-    else:
-        raise ValueError('startflag of %d not supported'%startflag)
-    newsigsq = (2 * (term1 - term2) + term3 - term4) / float(M)
-
-    return newsigsq
\ No newline at end of file
diff --git a/hobj/utils/state_space/src/forwardfilter.py b/hobj/utils/state_space/src/forwardfilter.py
deleted file mode 100644
index 47afe88..0000000
--- a/hobj/utils/state_space/src/forwardfilter.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import numpy as np
-from PySS.src.newtonsolve import newtonsolve
-
-
-def forwardfilter(I, sigE, xguess, sigsqguess, mu):
-    '''
-    forwardfilter is a helper function that implements the forward recursive
-    filtering algorithm to estimate the learning state (hidden process) at
-    trial k as the Gaussian random variable with mean x{k|k} (xhat) and
-    SIG^2{k|k} (sigsq).
-
-    :param I: [2xN]. dtype = int. [num_successes_v_time, total_possible]
-    :param sigE: (). dtype = float. default variance of learning state process
-    :param xguess: ().
-    :param sigsqguess: ().
-    :param mu: ().
-    '''
-
-    '''
-    :param xhatold: x{k|k-1}, one-step prediction (equation A.6)*
-    :param sigsqold: SIG^2{k|k-1}, one-step prediction variance (equation A.7)*
-    :param xhat         x{k|k}, posterior mode (equation A.8)*
-    :param sigsq        SIG^2{k|k}, posterior variance (equation A.9)*
-    :param p            p{k|k}, observation model probability (equation 2.2)*
-    :param N            vector of number correct at each trial
-    :param Nmax         total number that could be correct at each trial
-    :param K            total number of trials
-    :param number_fail  saves the time steps if Newton's Method fails
-    '''
-
-    K = I.shape[1] # total number of trials
-    N = I[0, :] # vector of number correct at each trial
-    Nmax = I[1, :] # total number that could be correct at each trial
-
-    # Initial conditions: use values from previous iteration
-    xhat = [xguess]
-    sigsq = [sigsqguess]
-    number_fail = []
-
-    xhatold = [0]
-    sigsqold = [0]
-
-    for k in range(2, K+1+1):
-        # for each trial, compute estimates of the one-step prediction, the
-        # posterior mode (using Newton's Method), and the posterior variance
-        # (estimates from subject's POV)
-
-        # Compute the one-step prediction estimate of mean and variance
-        xhatold.append(xhat[k-1-1])
-        sigsqold.append(sigsq[k-1-1] + np.square(sigE))
-
-        # Use Newton's Method to compute the nonlinear posterior mode estimate
-        xhat_next, flagfail = newtonsolve(mu, xhatold[k-1], sigsqold[k-1], N[k-1-1], Nmax[k-1-1])
-
-        xhat.append(xhat_next)
-
-        # if Newton's Method fails, number_fail saves the time step
-        if flagfail > 0:
-            number_fail.append(k-1)
-
-        # Compute the posterior variance estimate
-        denom = np.true_divide(-1, sigsqold[k-1]) - Nmax[k-1-1]*np.exp(mu)*np.exp(xhat[k-1])/np.square(1 + np.exp(mu) * np.exp(xhat[k-1]))
-        sigsq.append(np.true_divide(-1, denom))
-
-    if len(number_fail) > 0:
-        print ('Newton convergence failed at times', number_fail)
-
-    # Compute the observation model probability estimate
-    p = np.true_divide(np.exp(mu) * np.exp(xhat), 1 + np.exp(mu) * np.exp(xhat))
-
-    xhat = np.array(xhat)[None, :]
-    return p, xhat, sigsq, xhatold, sigsqold
diff --git a/hobj/utils/state_space/src/newtonsolve.py b/hobj/utils/state_space/src/newtonsolve.py
deleted file mode 100644
index ac65e3e..0000000
--- a/hobj/utils/state_space/src/newtonsolve.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import numpy as np
-
-
-def newtonsolve(mu,  xold, sigoldsq, N, Nmax):
-
-    '''
-
-    newtonsolve is a helper function that implements Newton's Method in order
-    to recursively estimate the posterior mode (x).  Once the subsequent estimates
-    sufficiently converge, the function returns the last estimate.  If, having
-    never met this convergence condition, the function goes through all of the
-    recursions, then a special flag (timefail) - indicating the convergence
-    failure - is returned along with the last posterior mode estimate.
-
-    :param mu: float
-    :param xold: float
-    :param sigoldsq: float
-    :param N: float
-    :param Nmax: float
-    :return:
-    '''
-
-    it = xold + sigoldsq * (N - Nmax * np.exp(mu) * np.exp(xold) / (1 + np.exp(mu) * np.exp(xold)))
-
-    it = [it]
-
-    g = []
-    gprime = []
-    niter = 100
-
-    for i in range(niter):
-        gupdate = xold + sigoldsq*(N - Nmax*np.exp(mu)*np.exp(it[i])/(1+np.exp(mu)*np.exp(it[i]))) - it[i]
-        g.append(gupdate)
-
-        gprime_update = -Nmax*sigoldsq*np.exp(mu)*np.exp(it[i])/np.square(1+np.exp(mu)*np.exp(it[i])) - 1
-        gprime.append(gprime_update)
-
-        it_update = it[i] - g[i]/gprime[i]
-        it.append(it_update)
-
-        x = it[-1]
-
-        if np.abs(x - it[-2]) < 1e-8:
-            timefail = 0
-            return x, timefail
-
-    # This tries a new initial condition if first Newtons doesn't work
-    if i == (niter - 1):
-        it = [-1]
-        g = []
-        gprime = []
-
-        for i in range(niter):
-            gupdate = xold + sigoldsq*(N - Nmax*np.exp(mu)*np.exp(it[i])/(1+np.exp(mu)*np.exp(it[i]))) - it[i]
-            g.append(gupdate)
-
-            gprime_update = -Nmax*sigoldsq*np.exp(mu)*np.exp(it[i])/np.square(1+np.exp(mu)*np.exp(it[i]))- 1
-            gprime.append(gprime_update)
-
-            it_update = it[i] - g[i] / gprime[i]
-            it.append(it_update)
-
-            x = it[-1]
-
-            if np.abs(x - it[i]) < 1e-8:
-                timefail = 0
-                return x, timefail
-
-    # This tries a new initial condition if second Newtons doesn't work
-    if i == (niter - 1):
-        it = [1]
-        g = []
-        gprime = []
-
-        for i in range(niter):
-            gupdate = xold + sigoldsq*(N - Nmax*np.exp(mu)*np.exp(it[i])/(1+np.exp(mu)*np.exp(it[i]))) - it[i]
-            g.append(gupdate)
-
-            gprime_update = -Nmax*sigoldsq*np.exp(mu)*np.exp(it[i])/np.square(1+np.exp(mu)*np.exp(it[i]))- 1
-            gprime.append(gprime_update)
-
-            it_update = it[i] - g[i] / gprime[i]
-            it.append(it_update)
-
-            x = it[-1]
-
-            if np.abs(x - it[i]) < 1e-8:
-                timefail = 0
-                return x, timefail
-
-    timefail = 1
-    return x, timefail
diff --git a/hobj/utils/state_space/src/pdistnv2.py b/hobj/utils/state_space/src/pdistnv2.py
deleted file mode 100644
index 5f85625..0000000
--- a/hobj/utils/state_space/src/pdistnv2.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import numpy as np
-
-def pdistnv2(x, s, mu, background_prob):
-
-    '''
-    pdist is a helper function that calculates the confidence limits of the EM
-    estimate of the learning state process.  For each trial, the function
-    constructs the probability density for a correct response.  It then builds
-    the cumulative density function from this and computes the p values of
-    the confidence limits
-
-    :param x: (1 x ntrials)
-    :param s:
-    :param mu: ()
-    :param background_prob: ()
-    :return:
-    '''
-
-    num_samps = 10000
-    p025 = np.zeros(x.shape)
-    p975 = np.zeros(x.shape)
-    pmid = np.zeros(x.shape)
-    pmodnew = np.zeros(x.shape) # not computed
-    pcert = np.zeros(x.shape)
-
-    for ov in range(1, x.shape[1]+1):
-
-        xx = x[0, ov-1]
-        ss = s[0, ov-1]
-        samps = xx + np.sqrt(ss)*np.random.randn(num_samps)
-        pr_samps = np.true_divide(np.exp(mu + samps), 1 + np.exp(mu + samps))
-
-        order_pr_samps = np.sort(pr_samps)
-
-        p025[0, ov - 1] = float(order_pr_samps[int(np.floor(0.025 * num_samps))])
-        p975[0, ov - 1] = float(order_pr_samps[int(np.ceil(0.975 * num_samps))])
-        pmid[0, ov - 1] = float(order_pr_samps[int(np.round(0.5 * num_samps))])
-        pcert[0, ov - 1] = np.mean(pr_samps > background_prob)
-
-    return p025, p975, pmid, pmodnew, pcert
diff --git a/hobj/utils/stats.py b/hobj/utils/stats.py
deleted file mode 100644
index d3f8c3f..0000000
--- a/hobj/utils/stats.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import xarray as xr
-from typing import Union, List, Tuple
-import numpy as np
-
-
-def calc_correlation(
-        obs_dim:Union[str, List[str]],
-        y_pred: xr.DataArray,
-        y_actual: Union[xr.DataArray, type(None)] = None,
-        spearman=False,
-):
-    """
-    This function returns a DataArray populated by Pearson correlation coefficient values with the same dimensions as x, but reduced along obs_dim.
-    """
-
-    if spearman:
-        if isinstance(obs_dim, list):
-            y_pred = y_pred.stack(spearman_rank_dim = obs_dim)
-            y_actual = y_actual.stack(spearman_rank_dim=obs_dim)
-            y_actual = y_actual.rank('spearman_rank_dim')
-            y_pred = y_pred.rank('spearman_rank_dim')
-            obs_dim = 'spearman_rank_dim'
-        else:
-            y_actual = y_actual.rank(obs_dim)
-            y_pred = y_pred.rank(obs_dim)
-    assert y_actual is not None, 'If supplying pre-computed ysigma and yresids, do not pass y'
-
-    ymean = y_actual.mean(obs_dim)
-    ysigma = np.sqrt(np.square(y_actual - ymean).sum(obs_dim))
-    yresids = y_actual - ymean
-
-    xmean = y_pred.mean(obs_dim)
-    xsigma = np.sqrt(np.square(y_pred - xmean).sum(obs_dim))
-    xresids = y_pred - xmean
-
-    r = (xresids * yresids).sum(obs_dim) / (xsigma * ysigma)
-    return r
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..14eef81
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,44 @@
+[build-system]
+requires = ["uv_build>=0.10.9,<0.11.0"]
+build-backend = "uv_build"
+
+[project]
+name = "hobj"
+version = "2.0.0"
+description = "Human object learning benchmarks"
+authors = [
+    { name = "Michael J. Lee", email = "mil@mit.edu" }
+]
+license = "MIT"
+license-files = ["license.md"]
+readme = "README.md"
+requires-python = ">=3.12"
+
+dependencies = [
+    "numpy>=2.2",
+    "pandas>=2.2",
+    "matplotlib>=3.9",
+    "requests>=2.32",
+    "tqdm>=4.67",
+    "pydantic>=2.10",
+    "xarray>=2025.1",
+    "scipy>=1.17.1",
+]
+
+[project.scripts]
+hobj-download-data = "hobj.data.download:main"
+
+[dependency-groups]
+dev = [
+    "enczoo==0.1.3.dev1",
+    "netcdf4>=1.7.4",
+    "pytest>=8.3",
+    "ipykernel>=7.2.0",
+    "scipy-stubs~=1.17.1",
+]
+
+[project.urls]
+repository = "https://github.com/himjl/hobj"
+
+[tool.uv.build-backend]
+module-root = ""
diff --git a/readme.md b/readme.md
deleted file mode 100644
index a67044d..0000000
--- a/readme.md
+++ /dev/null
@@ -1,36 +0,0 @@
-# Human object learning benchmarks
-
-This repository contains code for comparing models of human object learning against measurements of human behavior (n=371k trials). 
-
-# Installation
-
-We developed this repository using Python 3.9. Follow the steps below to use it: 
-
-1. Begin by cloning the `hobj` repository to your local machine. 
-2. Make and activate a new conda environment. 
-3. Install the `hobj` package itself. To do so, `cd` to the `hobj` directory (the top-level one), then run:
-
-```bash
-pip install -e .
-```
-4. Then, install the following dependencies:
-```bash
-conda install -c conda-forge xarray dask netCDF4 bottleneck
-```
-5. Install [PyTorch](https://pytorch.org). If you are using a computer without a GPU, you can run the command:
-```bash
-conda install pytorch torchvision torchaudio -c pytorch
-```
-
-### Download all images (recommended)
-
-The code in this repository works without this step. But to save time, it is recommended to download the images in a batch.
-They are located [here](https://hlbdatasets.s3.amazonaws.com/images/LeeDiCarlo_hobj_Images.zip).
-
-Once it is downloaded, unzip it (it should turn into an `images` folder). Move that `images` folder to `~/hlb_cache/`.
-
-# Usage
-
-To see how to view the raw behavioral data and/or score an example learning model, check out the examples in
-`examples/`.
-
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index cbd745b..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-pillow
-url-normalize
-matplotlib
-requests
-tqdm
-scipy
\ No newline at end of file
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 830fcfe..0000000
--- a/setup.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from setuptools import setup, find_packages
-import os
-
-if os.path.exists('requirements.txt'):
-    with open('requirements.txt', 'r') as fb:
-        requirements = fb.readlines()
-else:
-    requirements = []
-
-print(find_packages())
-setup(
-    name="hobj",
-    version="0.1",
-    packages=find_packages(),
-    install_requires=requirements,
-    author="Michael J. Lee",
-    author_email="mil@mit.edu",
-    description="Human object learning benchmarks",
-    keywords="",
-)
diff --git a/site/changelist.md b/site/changelist.md
new file mode 100644
index 0000000..4c5fe6d
--- /dev/null
+++ b/site/changelist.md
@@ -0,0 +1,253 @@
+
+We note the following changes since this codebase was released alongisde the paper:
+
+
+## 1/27/2025: 
+* Benchmarks now report MSEn scores which subtract the sampling variance of the human target, meaning that a "correct model" will have an expected score of 0. Before, these benchmarks did not perform this subtraction, meaning a "correct model" would have an expected score equal to the human sampling variance.
+* Statistical analyses other than the calculation of MSEn scores, their 95% confidence intervals, and generation of bootstrap resamples have been removed from this codebase.  
+* Experiment 2: The lapse-rate corrected performance is now clipped between [0, 1].
+* Experiment 2: The estimation of the variance of a lapse-rate corrected performance estimate from using bootstrapping to using the formula $\frac{1}{1-\hat p_{l}} \hat \sigma^2$, which treats the estimated lapse rate as a constant. Because a high number (~1000s) of Binomial observations are used to estimate $\hat p_{l}$, we consider this a decent approximation that is less computationally expensive.
+
+## 3/25/2026 - cleaner data packaging
+
+
+
+### Object renaming
+I renamed the objects to follow a cleaner naming scheme. See below for the map from old names to new names:
+
+<details>
+<summary>High-variation object renames</summary>
+
+```json
+{
+  "MutatorB2000_0": "MutatorObject000",
+  "MutatorB2000_19": "MutatorObject001",
+  "MutatorB2000_84": "MutatorObject002",
+  "MutatorB2000_91": "MutatorObject003",
+  "MutatorB2000_93": "MutatorObject004",
+  "MutatorB2000_98": "MutatorObject005",
+  "MutatorB2000_104": "MutatorObject006",
+  "MutatorB2000_133": "MutatorObject007",
+  "MutatorB2000_149": "MutatorObject008",
+  "MutatorB2000_160": "MutatorObject009",
+  "MutatorB2000_172": "MutatorObject010",
+  "MutatorB2000_174": "MutatorObject011",
+  "MutatorB2000_185": "MutatorObject012",
+  "MutatorB2000_188": "MutatorObject013",
+  "MutatorB2000_190": "MutatorObject014",
+  "MutatorB2000_196": "MutatorObject015",
+  "MutatorB2000_200": "MutatorObject016",
+  "MutatorB2000_206": "MutatorObject017",
+  "MutatorB2000_214": "MutatorObject018",
+  "MutatorB2000_218": "MutatorObject019",
+  "MutatorB2000_229": "MutatorObject020",
+  "MutatorB2000_247": "MutatorObject021",
+  "MutatorB2000_348": "MutatorObject022",
+  "MutatorB2000_367": "MutatorObject023",
+  "MutatorB2000_376": "MutatorObject024",
+  "MutatorB2000_385": "MutatorObject025",
+  "MutatorB2000_417": "MutatorObject026",
+  "MutatorB2000_419": "MutatorObject027",
+  "MutatorB2000_450": "MutatorObject028",
+  "MutatorB2000_455": "MutatorObject029",
+  "MutatorB2000_470": "MutatorObject030",
+  "MutatorB2000_475": "MutatorObject031",
+  "MutatorB2000_487": "MutatorObject032",
+  "MutatorB2000_527": "MutatorObject033",
+  "MutatorB2000_561": "MutatorObject034",
+  "MutatorB2000_577": "MutatorObject035",
+  "MutatorB2000_578": "MutatorObject036",
+  "MutatorB2000_609": "MutatorObject037",
+  "MutatorB2000_631": "MutatorObject038",
+  "MutatorB2000_645": "MutatorObject039",
+  "MutatorB2000_656": "MutatorObject040",
+  "MutatorB2000_671": "MutatorObject041",
+  "MutatorB2000_693": "MutatorObject042",
+  "MutatorB2000_695": "MutatorObject043",
+  "MutatorB2000_697": "MutatorObject044",
+  "MutatorB2000_700": "MutatorObject045",
+  "MutatorB2000_722": "MutatorObject046",
+  "MutatorB2000_770": "MutatorObject047",
+  "MutatorB2000_772": "MutatorObject048",
+  "MutatorB2000_774": "MutatorObject049",
+  "MutatorB2000_797": "MutatorObject050",
+  "MutatorB2000_798": "MutatorObject051",
+  "MutatorB2000_812": "MutatorObject052",
+  "MutatorB2000_819": "MutatorObject053",
+  "MutatorB2000_820": "MutatorObject054",
+  "MutatorB2000_823": "MutatorObject055",
+  "MutatorB2000_856": "MutatorObject056",
+  "MutatorB2000_876": "MutatorObject057",
+  "MutatorB2000_899": "MutatorObject058",
+  "MutatorB2000_902": "MutatorObject059",
+  "MutatorB2000_938": "MutatorObject060",
+  "MutatorB2000_963": "MutatorObject061",
+  "MutatorB2000_972": "MutatorObject062",
+  "MutatorB2000_1012": "MutatorObject063",
+  "MutatorB2000_1018": "MutatorObject064",
+  "MutatorB2000_1024": "MutatorObject065",
+  "MutatorB2000_1030": "MutatorObject066",
+  "MutatorB2000_1034": "MutatorObject067",
+  "MutatorB2000_4193": "MutatorObject068",
+  "MutatorB2000_4235": "MutatorObject069",
+  "MutatorB2000_4255": "MutatorObject070",
+  "MutatorB2000_4259": "MutatorObject071",
+  "MutatorB2000_4267": "MutatorObject072",
+  "MutatorB2000_4287": "MutatorObject073",
+  "MutatorB2000_4370": "MutatorObject074",
+  "MutatorB2000_4448": "MutatorObject075",
+  "MutatorB2000_4451": "MutatorObject076",
+  "MutatorB2000_4454": "MutatorObject077",
+  "MutatorB2000_4482": "MutatorObject078",
+  "MutatorB2000_4489": "MutatorObject079",
+  "MutatorB2000_4491": "MutatorObject080",
+  "MutatorB2000_4521": "MutatorObject081",
+  "MutatorB2000_4530": "MutatorObject082",
+  "MutatorB2000_4567": "MutatorObject083",
+  "MutatorB2000_4568": "MutatorObject084",
+  "MutatorB2000_4569": "MutatorObject085",
+  "MutatorB2000_4578": "MutatorObject086",
+  "MutatorB2000_4614": "MutatorObject087",
+  "MutatorB2000_4622": "MutatorObject088",
+  "MutatorB2000_4630": "MutatorObject089",
+  "MutatorB2000_4634": "MutatorObject090",
+  "MutatorB2000_4635": "MutatorObject091",
+  "MutatorB2000_4678": "MutatorObject092",
+  "MutatorB2000_4710": "MutatorObject093",
+  "MutatorB2000_4715": "MutatorObject094",
+  "MutatorB2000_4718": "MutatorObject095",
+  "MutatorB2000_4720": "MutatorObject096",
+  "MutatorB2000_4723": "MutatorObject097",
+  "MutatorB2000_4724": "MutatorObject098",
+  "MutatorB2000_4732": "MutatorObject099",
+  "MutatorB2000_4750": "MutatorObject100",
+  "MutatorB2000_4752": "MutatorObject101",
+  "MutatorB2000_4754": "MutatorObject102",
+  "MutatorB2000_4772": "MutatorObject103",
+  "MutatorB2000_4805": "MutatorObject104",
+  "MutatorB2000_4821": "MutatorObject105",
+  "MutatorB2000_4829": "MutatorObject106",
+  "MutatorB2000_4832": "MutatorObject107",
+  "MutatorB2000_4835": "MutatorObject108",
+  "MutatorB2000_4845": "MutatorObject109",
+  "MutatorB2000_4847": "MutatorObject110",
+  "MutatorB2000_4872": "MutatorObject111",
+  "MutatorB2000_4878": "MutatorObject112",
+  "MutatorB2000_4908": "MutatorObject113",
+  "MutatorB2000_4910": "MutatorObject114",
+  "MutatorB2000_4911": "MutatorObject115",
+  "MutatorB2000_4932": "MutatorObject116",
+  "MutatorB2000_4939": "MutatorObject117",
+  "MutatorB2000_4940": "MutatorObject118",
+  "MutatorB2000_4945": "MutatorObject119",
+  "MutatorB2000_4946": "MutatorObject120",
+  "MutatorB2000_4950": "MutatorObject121",
+  "MutatorB2000_4951": "MutatorObject122",
+  "MutatorB2000_4953": "MutatorObject123",
+  "MutatorB2000_4968": "MutatorObject124",
+  "MutatorB2000_4974": "MutatorObject125",
+  "MutatorB2000_4986": "MutatorObject126",
+  "MutatorB2000_4998": "MutatorObject127"
+}
+```
+
+</details>
+
+<details>
+<summary>One-shot object renames</summary>
+
+```json
+{
+  "MutatorB2000_46": "MutatorOneshotObject00",
+  "MutatorB2000_116": "MutatorOneshotObject01",
+  "MutatorB2000_138": "MutatorOneshotObject02",
+  "MutatorB2000_270": "MutatorOneshotObject03",
+  "MutatorB2000_288": "MutatorOneshotObject04",
+  "MutatorB2000_296": "MutatorOneshotObject05",
+  "MutatorB2000_462": "MutatorOneshotObject06",
+  "MutatorB2000_496": "MutatorOneshotObject07",
+  "MutatorB2000_613": "MutatorOneshotObject08",
+  "MutatorB2000_663": "MutatorOneshotObject09",
+  "MutatorB2000_694": "MutatorOneshotObject10",
+  "MutatorB2000_701": "MutatorOneshotObject11",
+  "MutatorB2000_746": "MutatorOneshotObject12",
+  "MutatorB2000_801": "MutatorOneshotObject13",
+  "MutatorB2000_926": "MutatorOneshotObject14",
+  "MutatorB2000_953": "MutatorOneshotObject15",
+  "MutatorB2000_1164": "MutatorOneshotObject16",
+  "MutatorB2000_1219": "MutatorOneshotObject17",
+  "MutatorB2000_1229": "MutatorOneshotObject18",
+  "MutatorB2000_1251": "MutatorOneshotObject19",
+  "MutatorB2000_1258": "MutatorOneshotObject20",
+  "MutatorB2000_1280": "MutatorOneshotObject21",
+  "MutatorB2000_1363": "MutatorOneshotObject22",
+  "MutatorB2000_1424": "MutatorOneshotObject23",
+  "MutatorB2000_1767": "MutatorOneshotObject24",
+  "MutatorB2000_1825": "MutatorOneshotObject25",
+  "MutatorB2000_1865": "MutatorOneshotObject26",
+  "MutatorB2000_2092": "MutatorOneshotObject27",
+  "MutatorB2000_2106": "MutatorOneshotObject28",
+  "MutatorB2000_2122": "MutatorOneshotObject29",
+  "MutatorB2000_2130": "MutatorOneshotObject30",
+  "MutatorB2000_2139": "MutatorOneshotObject31",
+  "MutatorB2000_2198": "MutatorOneshotObject32",
+  "MutatorB2000_2292": "MutatorOneshotObject33",
+  "MutatorB2000_2304": "MutatorOneshotObject34",
+  "MutatorB2000_2314": "MutatorOneshotObject35",
+  "MutatorB2000_2344": "MutatorOneshotObject36",
+  "MutatorB2000_2365": "MutatorOneshotObject37",
+  "MutatorB2000_2444": "MutatorOneshotObject38",
+  "MutatorB2000_2722": "MutatorOneshotObject39",
+  "MutatorB2000_2757": "MutatorOneshotObject40",
+  "MutatorB2000_2832": "MutatorOneshotObject41",
+  "MutatorB2000_2909": "MutatorOneshotObject42",
+  "MutatorB2000_3035": "MutatorOneshotObject43",
+  "MutatorB2000_3043": "MutatorOneshotObject44",
+  "MutatorB2000_3066": "MutatorOneshotObject45",
+  "MutatorB2000_3077": "MutatorOneshotObject46",
+  "MutatorB2000_3123": "MutatorOneshotObject47",
+  "MutatorB2000_3278": "MutatorOneshotObject48",
+  "MutatorB2000_3308": "MutatorOneshotObject49",
+  "MutatorB2000_3496": "MutatorOneshotObject50",
+  "MutatorB2000_3525": "MutatorOneshotObject51",
+  "MutatorB2000_3527": "MutatorOneshotObject52",
+  "MutatorB2000_3585": "MutatorOneshotObject53",
+  "MutatorB2000_3601": "MutatorOneshotObject54",
+  "MutatorB2000_3615": "MutatorOneshotObject55",
+  "MutatorB2000_3636": "MutatorOneshotObject56",
+  "MutatorB2000_3733": "MutatorOneshotObject57",
+  "MutatorB2000_4049": "MutatorOneshotObject58",
+  "MutatorB2000_4256": "MutatorOneshotObject59",
+  "MutatorB2000_4305": "MutatorOneshotObject60",
+  "MutatorB2000_4628": "MutatorOneshotObject61",
+  "MutatorB2000_4703": "MutatorOneshotObject62",
+  "MutatorB2000_4792": "MutatorOneshotObject63"
+}
+```
+
+</details>
+
+
+<details>
+<summary>Warmup object renames</summary>
+
+```json
+{
+  "Mutator19": "MutatorWarmupObject0",
+  "Mutator20": "MutatorWarmupObject1",
+  "Mutator21": "MutatorWarmupObject2",
+  "Mutator22": "MutatorWarmupObject3",
+  "Mutator25": "MutatorWarmupObject4",
+  "Mutator26": "MutatorWarmupObject5",
+  "Mutator29": "MutatorWarmupObject6",
+  "Mutator30": "MutatorWarmupObject7"
+}
+```
+
+</details>
+
+
+
+### Image renaming
+
+Following the renames of the object categories, the images were renamed as well. The old image names are available in the `original_filename` columns of each `meta-*.csv` file for each imageset.
diff --git a/site/readme_images/human_learning_curves.svg b/site/readme_images/human_learning_curves.svg
new file mode 100644
index 0000000..8b2d032
--- /dev/null
+++ b/site/readme_images/human_learning_curves.svg
@@ -0,0 +1,8132 @@
+<?xml version="1.0" encoding="utf-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+  "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg xmlns:xlink="http://www.w3.org/1999/xlink" width="432pt" height="288pt" viewBox="0 0 432 288" xmlns="http://www.w3.org/2000/svg" version="1.1">
+ <metadata>
+  <rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
+   <cc:Work>
+    <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
+    <dc:date>2026-03-26T18:22:59.609218</dc:date>
+    <dc:format>image/svg+xml</dc:format>
+    <dc:creator>
+     <cc:Agent>
+      <dc:title>Matplotlib v3.9.0, https://matplotlib.org/</dc:title>
+     </cc:Agent>
+    </dc:creator>
+   </cc:Work>
+  </rdf:RDF>
+ </metadata>
+ <defs>
+  <style type="text/css">*{stroke-linejoin: round; stroke-linecap: butt}</style>
+ </defs>
+ <g id="figure_1">
+  <g id="patch_1">
+   <path d="M 0 288 
+L 432 288 
+L 432 0 
+L 0 0 
+L 0 288 
+z
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+  </g>
+  <g id="axes_1">
+   <g id="patch_2">
+    <path d="M 47.72 242.728 
+L 289.35 242.728 
+L 289.35 26.88 
+L 47.72 26.88 
+L 47.72 242.728 
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+   <g id="matplotlib.axis_1">
+    <g id="xtick_1">
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+      <use xlink:href="#DejaVuSans-65" x="728.466797"/>
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+    </g>
+   </g>
+  </g>
+ </g>
+ <defs>
+  <clipPath id="pee8cec0876">
+   <rect x="47.72" y="26.88" width="241.63" height="215.848"/>
+  </clipPath>
+ </defs>
+</svg>
diff --git a/site/readme_images/run_make_images.py b/site/readme_images/run_make_images.py
new file mode 100644
index 0000000..f827a3b
--- /dev/null
+++ b/site/readme_images/run_make_images.py
@@ -0,0 +1,44 @@
+if __name__ == "__main__":
+    # %%
+    import matplotlib.pyplot as plt
+    from hobj import MutatorHighVarBenchmark
+    import numpy as np
+
+    benchmark = MutatorHighVarBenchmark()
+
+    target_stats = benchmark.target_statistics
+    x = np.arange(100) + 1
+
+    # %%
+    plt.figure(figsize=(6, 4))
+
+    for subtask in target_stats.subtask.values:
+        y = target_stats.phat.sel(subtask=subtask)
+        yerr = np.sqrt(target_stats.varhat_phat.sel(subtask=subtask))
+        plt.plot(
+            x,
+            y,
+            alpha=0.05,
+            color="gray",
+            lw=1,
+        )
+
+    glc = target_stats.phat.mean(dim="subtask")
+    glc_se = target_stats.boot_phat.mean("subtask").std("boot_iter", ddof=1)
+
+    plt.errorbar(x, glc, yerr=glc_se, color="blue", label="human average")
+    plt.axhline(0.5, ls=":", color="black", label="chance", zorder=0)
+    plt.ylabel("test accuracy")
+    plt.xscale("log")
+    plt.xlabel("# examples")
+    plt.title("Human learning curves")
+    plt.xlim([None, 100])
+    plt.legend(loc=(1.05, 0.5))
+
+    # Remove top and right spines
+    plt.gca().spines["top"].set_visible(False)
+    plt.gca().spines["right"].set_visible(False)
+
+    plt.tight_layout()
+    plt.savefig("./human_learning_curves.svg", transparent=True)
+    plt.show()
diff --git a/tests/test_data_loader_cache.py b/tests/test_data_loader_cache.py
new file mode 100644
index 0000000..4ef9035
--- /dev/null
+++ b/tests/test_data_loader_cache.py
@@ -0,0 +1,262 @@
+import tarfile
+import zipfile
+from pathlib import Path
+
+import pandas as pd
+from PIL import Image
+
+from hobj.data import behavior as behavior_loaders
+from hobj.data import download as download_module
+from hobj.data.images import get_image_path, load_image
+
+
+def _write_minimal_packaged_dataset(data_root: Path) -> None:
+    data_root.mkdir(parents=True, exist_ok=True)
+
+    pd.DataFrame(
+        [
+            {
+                "image_id": "img-highvar",
+                "category": "cat-a",
+                "sha256": "unused",
+                "relpath": "images/highvar.png",
+            }
+        ]
+    ).to_csv(data_root / "meta-MutatorHighVarImageset.csv", index=False)
+
+    pd.DataFrame(
+        [
+            {
+                "image_id": "img-oneshot",
+                "category": "cat-b",
+                "transformation": "original",
+                "transformation_level": 0.0,
+                "base_image_id": "img-oneshot",
+                "sha256": "unused",
+                "relpath": "images/oneshot.png",
+            }
+        ]
+    ).to_csv(data_root / "meta-MutatorOneShotImageset.csv", index=False)
+
+    pd.DataFrame(
+        [
+            {
+                "image_id": "img-warmup",
+                "category": "cat-c",
+                "sha256": "unused",
+                "relpath": "images/warmup.png",
+            }
+        ]
+    ).to_csv(data_root / "meta-MutatorWarmupImageset.csv", index=False)
+
+    pd.DataFrame(
+        [
+            {
+                "image_id": "img-catch",
+                "sha256": "unused",
+                "relpath": "images/catch.png",
+            }
+        ]
+    ).to_csv(data_root / "meta-CatchImageset.csv", index=False)
+
+    behavior_root = data_root / "behavior"
+    behavior_root.mkdir(parents=True, exist_ok=True)
+
+    pd.DataFrame(
+        [
+            {
+                "trial": 0,
+                "assignment_id": "assignment-1",
+                "worker_id": "worker-1",
+                "subtask": "cat-a,cat-d",
+                "image_id": "img-highvar",
+                "trial_type": "train",
+                "stimulus_duration_msec": 100,
+                "reaction_time_msec": 200,
+                "timed_out": False,
+                "perf": True,
+                "timestamp_start": "2025-01-01T00:00:00",
+            },
+            {
+                "trial": 1,
+                "assignment_id": "assignment-1",
+                "worker_id": "worker-1",
+                "subtask": "cat-a,cat-d",
+                "image_id": "img-highvar",
+                "trial_type": "probe",
+                "stimulus_duration_msec": 100,
+                "reaction_time_msec": 200,
+                "timed_out": False,
+                "perf": False,
+                "timestamp_start": "2025-01-01T00:00:01",
+            },
+            {
+                "trial": 2,
+                "assignment_id": "assignment-1",
+                "worker_id": "worker-1",
+                "subtask": "cat-a,cat-d",
+                "image_id": "img-highvar",
+                "trial_type": "train",
+                "stimulus_duration_msec": 100,
+                "reaction_time_msec": 200,
+                "timed_out": False,
+                "perf": True,
+                "timestamp_start": "2025-01-01T00:00:02",
+            },
+        ]
+    ).to_csv(behavior_root / "human-behavior-highvar-subtasks.csv", index=False)
+
+    pd.DataFrame(
+        [
+            {
+                "trial": 0,
+                "assignment_id": "assignment-2",
+                "slot": 0,
+                "worker_id": "worker-2",
+                "subtask": "cat-b,cat-e",
+                "image_id": "img-oneshot",
+                "trial_type": "support",
+                "stimulus_duration_msec": 100,
+                "reaction_time_msec": 200,
+                "timed_out": False,
+                "perf": True,
+                "timestamp_start": "2025-01-01T00:00:03",
+            }
+        ]
+    ).to_csv(behavior_root / "human-behavior-oneshot-subtasks.csv", index=False)
+
+
+def _write_packaged_images_archive(data_root: Path) -> None:
+    """Write the nested ``images.tar.gz`` archive expected by OSF downloads.
+
+    Args:
+        data_root: Packaged data directory containing metadata and behavior CSVs.
+    """
+    staging_root = data_root.parent / "images-staging"
+    images_root = staging_root / "images"
+    images_root.mkdir(parents=True, exist_ok=True)
+
+    Image.new("RGB", (2, 3), color=(255, 0, 0)).save(images_root / "highvar.png")
+    Image.new("RGB", (2, 3), color=(0, 255, 0)).save(images_root / "oneshot.png")
+    Image.new("RGB", (2, 3), color=(0, 0, 255)).save(images_root / "warmup.png")
+    Image.new("RGB", (2, 3), color=(255, 255, 0)).save(images_root / "catch.png")
+
+    with tarfile.open(data_root / "images.tar.gz", mode="w:gz") as archive:
+        archive.add(images_root, arcname="images")
+
+
+def test_download_data_extracts_into_custom_cachedir(tmp_path: Path) -> None:
+    staging_root = tmp_path / "staging" / "data"
+    _write_minimal_packaged_dataset(staging_root)
+    _write_packaged_images_archive(staging_root)
+
+    archive_path = tmp_path / "download"
+    with zipfile.ZipFile(archive_path, mode="w") as archive:
+        for path in sorted(staging_root.rglob("*")):
+            archive.write(path, arcname=Path("data") / path.relative_to(staging_root))
+
+    custom_cache = tmp_path / "custom-cache"
+    resolved_cache = download_module.download_data(
+        url="https://example.com/download?zip=",
+        cachedir=custom_cache,
+    )
+
+    assert resolved_cache == custom_cache.resolve()
+    assert (custom_cache / "meta-MutatorHighVarImageset.csv").exists()
+    assert (custom_cache / "behavior" / "human-behavior-highvar-subtasks.csv").exists()
+    assert (custom_cache / "images" / "highvar.png").exists()
+    assert not (custom_cache / "images.tar.gz").exists()
+    assert not (tmp_path / "data").exists()
+
+
+def test_resolve_data_root_downloads_missing_data_once(
+    monkeypatch,
+    tmp_path: Path,
+) -> None:
+    custom_cache = tmp_path / "resolved-cache"
+    calls: list[Path] = []
+
+    def fake_download_data(
+        *,
+        url: str = download_module.DATA_ARCHIVE_URL,
+        repo_root: Path | None = None,
+        cachedir: Path | None = None,
+        force_download: bool = False,
+    ) -> Path:
+        assert url == download_module.DATA_ARCHIVE_URL
+        assert repo_root is None
+        assert force_download is False
+        assert cachedir is not None
+        calls.append(cachedir)
+        _write_minimal_packaged_dataset(cachedir)
+        return cachedir.resolve()
+
+    monkeypatch.setattr(download_module, "download_data", fake_download_data)
+
+    resolved_cache = download_module.resolve_data_root(cachedir=custom_cache)
+    assert resolved_cache == custom_cache.resolve()
+    assert calls == [custom_cache.resolve()]
+
+    resolved_cache = download_module.resolve_data_root(cachedir=custom_cache)
+    assert resolved_cache == custom_cache.resolve()
+    assert calls == [custom_cache.resolve()]
+
+
+def test_load_highvar_behavior_uses_custom_cachedir(tmp_path: Path) -> None:
+    custom_cache = tmp_path / "behavior-cache"
+    _write_minimal_packaged_dataset(custom_cache)
+
+    df = behavior_loaders.load_highvar_behavior(cachedir=custom_cache)
+
+    assert df["trial"].tolist() == [0, 1]
+    assert set(df["trial_type"]) == {"train"}
+    assert df["image_id"].tolist() == ["img-highvar", "img-highvar"]
+
+
+def test_load_image_uses_custom_cachedir(tmp_path: Path) -> None:
+    custom_cache = tmp_path / "image-cache"
+    _write_minimal_packaged_dataset(custom_cache)
+    _write_packaged_images_archive(custom_cache)
+
+    image = load_image("img-highvar", cachedir=custom_cache)
+
+    assert image.size == (2, 3)
+    image.close()
+
+
+def test_get_image_path_uses_custom_cachedir(tmp_path: Path) -> None:
+    custom_cache = tmp_path / "image-path-cache"
+    _write_minimal_packaged_dataset(custom_cache)
+    _write_packaged_images_archive(custom_cache)
+
+    resolved_path = get_image_path("img-highvar", cachedir=custom_cache)
+
+    assert resolved_path == custom_cache / "images" / "highvar.png"
+
+
+def test_resolve_data_root_extracts_nested_images_archive_without_redownload(
+    tmp_path: Path,
+) -> None:
+    custom_cache = tmp_path / "resolved-cache"
+    _write_minimal_packaged_dataset(custom_cache)
+    _write_packaged_images_archive(custom_cache)
+
+    resolved_cache = download_module.resolve_data_root(cachedir=custom_cache)
+
+    assert resolved_cache == custom_cache.resolve()
+    assert (custom_cache / "images" / "highvar.png").exists()
+    assert not (custom_cache / "images.tar.gz").exists()
+
+
+def test_get_default_data_root_uses_versioned_home_cache(monkeypatch) -> None:
+    monkeypatch.setattr(download_module.Path, "home", lambda: Path("/tmp/fake-home"))
+
+    resolved_path = download_module._get_default_data_root()
+
+    assert (
+        resolved_path
+        == Path(
+            f"/tmp/fake-home/.hobj_cache/{download_module.OSF_NODE_ID}-"
+            f"{download_module.DATASET_CACHE_VERSION}/data"
+        ).resolve()
+    )
diff --git a/tests/test_dummy_learner.py b/tests/test_dummy_learner.py
new file mode 100644
index 0000000..38937b9
--- /dev/null
+++ b/tests/test_dummy_learner.py
@@ -0,0 +1,19 @@
+import pytest
+
+from hobj.learning_models.random_guesser import RandomGuesser
+
+
+@pytest.fixture
+def dummy_learner() -> RandomGuesser:
+    return RandomGuesser(seed=0)
+
+
+def test_dummy_learner_deterministic(dummy_learner):
+    ntests = 10
+    actions = []
+    expected_actions = [1, 1, 1, 0, 0, 0, 0, 0, 0, 1]
+    for i in range(ntests):
+        a = dummy_learner.get_response(image="hi")
+        actions.append(a)
+
+    assert actions == expected_actions
diff --git a/tests/test_load_behavior.py b/tests/test_load_behavior.py
new file mode 100644
index 0000000..c9ea350
--- /dev/null
+++ b/tests/test_load_behavior.py
@@ -0,0 +1,70 @@
+import os
+
+import pytest
+
+from hobj.data.behavior import load_highvar_behavior, load_oneshot_behavior
+from hobj.data.images import (
+    load_image,
+    load_imageset_meta_catch,
+    load_imageset_meta_highvar,
+    load_imageset_meta_oneshot,
+    load_imageset_meta_warmup,
+)
+
+pytestmark = pytest.mark.skipif(
+    os.environ.get("GITHUB_ACTIONS") == "true",
+    reason="Packaged dataset is not available on GitHub Actions runners.",
+)
+
+
+def test_load_highvar():
+    df = load_highvar_behavior()
+    assert len(df) == 3199 * 100
+    assert "image_id" in df.columns
+    assert "stimulus_id" not in df.columns
+
+
+def test_load_oneshot():
+    df = load_oneshot_behavior()
+    assert len(df) == 2547 * 20
+    assert "image_id" in df.columns
+    assert "stimulus_id" not in df.columns
+
+
+def test_load_highvar_images():
+    df = load_imageset_meta_highvar()
+    assert len(df) == 12800
+    assert {"image_id", "category", "sha256", "relpath"} <= set(df.columns)
+
+
+def test_load_oneshot_images():
+    df = load_imageset_meta_oneshot()
+    assert len(df) == 3904
+    assert {
+        "image_id",
+        "category",
+        "transformation",
+        "transformation_level",
+        "base_image_id",
+        "sha256",
+        "relpath",
+    } <= set(df.columns)
+
+
+def test_load_warmup_images():
+    df = load_imageset_meta_warmup()
+    assert len(df) == 400
+    assert {"image_id", "category", "sha256", "relpath"} <= set(df.columns)
+
+
+def test_load_probe_images():
+    df = load_imageset_meta_catch()
+    assert len(df) == 2
+    assert {"image_id", "sha256", "relpath"} <= set(df.columns)
+
+
+def test_load_image():
+    df = load_imageset_meta_highvar()
+    image = load_image(df["image_id"].iloc[0])
+    assert image.size[0] > 0
+    assert image.size[1] > 0
diff --git a/tests/test_simulate_subtask.py b/tests/test_simulate_subtask.py
new file mode 100644
index 0000000..711a7ef
--- /dev/null
+++ b/tests/test_simulate_subtask.py
@@ -0,0 +1,34 @@
+from typing import List
+
+import numpy as np
+
+from hobj.benchmarks.binary_classification.simulation import BinaryClassificationSubtask
+from hobj.learning_models.random_guesser import RandomGuesser
+from hobj.types import ImageId
+
+
+def create_image_refs(nimages_per_class: int, seed: int) -> List[ImageId]:
+    images = []
+    np.random.seed(seed)
+    for i in range(nimages_per_class):
+        images.append(f"seed{seed}_image{i}")
+
+    return images
+
+
+def test_simulate_subtask():
+    nimages_per_class = 10
+    ntrials = nimages_per_class * 2
+
+    subtask = BinaryClassificationSubtask(
+        classA=create_image_refs(nimages_per_class=10, seed=0),
+        classB=create_image_refs(nimages_per_class=10, seed=1),
+        ntrials=ntrials,
+        replace=False,
+    )
+
+    learner = RandomGuesser(seed=0)
+
+    result = subtask.simulate_session(learner=learner, seed=0)
+
+    assert len(result.perf_seq) == ntrials
diff --git a/tests/test_statistics_builders.py b/tests/test_statistics_builders.py
new file mode 100644
index 0000000..b5ac802
--- /dev/null
+++ b/tests/test_statistics_builders.py
@@ -0,0 +1,102 @@
+import numpy as np
+import xarray as xr
+
+from hobj.benchmarks.binary_classification.estimator import (
+    build_learning_curve_statistics,
+)
+from hobj.benchmarks.binary_classification.simulation import (
+    BinaryClassificationSubtaskResult,
+)
+from hobj.benchmarks.generalization.estimator import build_generalization_statistics
+from hobj.benchmarks.generalization.simulator import GeneralizationSessionResult
+
+
+def test_build_learning_curve_statistics_returns_expected_dataset_shape():
+    stats = build_learning_curve_statistics(
+        subtask_name_to_results={
+            "task-b": [
+                BinaryClassificationSubtaskResult(
+                    perf_seq=np.array([True, False, True]),
+                    worker_id="worker-1",
+                ),
+                BinaryClassificationSubtaskResult(
+                    perf_seq=np.array([False, False, True]),
+                    worker_id="worker-2",
+                ),
+            ],
+            "task-a": [
+                BinaryClassificationSubtaskResult(
+                    perf_seq=np.array([True, True, False]),
+                    worker_id="worker-3",
+                ),
+                BinaryClassificationSubtaskResult(
+                    perf_seq=np.array([True, False, False]),
+                    worker_id="worker-4",
+                ),
+            ],
+        },
+        nbootstrap_samples=4,
+        bootstrap_by_worker=False,
+    )
+
+    assert isinstance(stats, xr.Dataset)
+    assert set(stats.data_vars) == {
+        "phat",
+        "varhat_phat",
+        "boot_phat",
+        "boot_varhat_phat",
+    }
+    assert dict(stats.sizes) == {"subtask": 2, "trial": 3, "boot_iter": 4}
+    assert list(stats.subtask.values) == ["task-a", "task-b"]
+    np.testing.assert_allclose(
+        stats.phat.values,
+        np.array(
+            [
+                [1.0, 0.5, 0.0],
+                [0.5, 0.0, 1.0],
+            ]
+        ),
+    )
+    assert stats.boot_phat.shape == (4, 2, 3)
+    assert stats.boot_varhat_phat.shape == (4, 2, 3)
+
+
+def test_build_generalization_statistics_returns_expected_dataset_shape():
+    stats = build_generalization_statistics(
+        results=[
+            GeneralizationSessionResult(
+                transformation_to_kn={
+                    "blur | 0.1": [1, 2],
+                    "noise | 0.2": [0, 1],
+                },
+                kcatch=1,
+                ncatch=2,
+                worker_id="worker-1",
+            ),
+            GeneralizationSessionResult(
+                transformation_to_kn={
+                    "blur | 0.1": [1, 1],
+                    "noise | 0.2": [1, 2],
+                },
+                kcatch=2,
+                ncatch=2,
+                worker_id="worker-2",
+            ),
+        ],
+        perform_lapse_rate_correction=False,
+        n_bootstrap_iterations=5,
+        bootstrap_by_worker=False,
+    )
+
+    assert isinstance(stats, xr.Dataset)
+    assert set(stats.data_vars) == {
+        "phat",
+        "varhat_phat",
+        "boot_phat",
+        "boot_varhat_phat",
+    }
+    assert dict(stats.sizes) == {"transformation": 2, "boot_iter": 5}
+    assert list(stats.transformation.values) == ["blur | 0.1", "noise | 0.2"]
+    np.testing.assert_allclose(stats.phat.values, np.array([2 / 3, 1 / 3]))
+    assert stats.boot_phat.shape == (5, 2)
+    assert stats.boot_varhat_phat.shape == (5, 2)
diff --git a/tests/test_statistics_dataset_usage.py b/tests/test_statistics_dataset_usage.py
new file mode 100644
index 0000000..e532a75
--- /dev/null
+++ b/tests/test_statistics_dataset_usage.py
@@ -0,0 +1,220 @@
+import pandas as pd
+import xarray as xr
+
+import hobj.benchmarks.mut_highvar_benchmark as highvar_module
+import hobj.benchmarks.mut_oneshot_benchmark as oneshot_module
+from hobj.learning_models.random_guesser import RandomGuesser
+
+
+def _make_highvar_images_df() -> pd.DataFrame:
+    rows = []
+    for category in ["CatA", "CatB", "CatC", "CatD"]:
+        for index in range(50):
+            rows.append(
+                {
+                    "image_id": f"{category}_{index:03d}",
+                    "category": category,
+                }
+            )
+    return pd.DataFrame(rows)
+
+
+def _make_highvar_behavior_df() -> pd.DataFrame:
+    rows = []
+    for assignment_id, worker_id, class_a, class_b in [
+        ("assignment-1", "worker-1", "CatA", "CatB"),
+        ("assignment-2", "worker-2", "CatC", "CatD"),
+    ]:
+        for trial in range(100):
+            category = class_a if trial % 2 == 0 else class_b
+            rows.append(
+                {
+                    "assignment_id": assignment_id,
+                    "worker_id": worker_id,
+                    "trial": trial,
+                    "image_id": f"{category}_{trial // 2:03d}",
+                    "perf": trial % 3 == 0,
+                }
+            )
+    return pd.DataFrame(rows)
+
+
+def _make_oneshot_images_df() -> pd.DataFrame:
+    rows = []
+    for category in ["CatA", "CatB"]:
+        rows.append(
+            {
+                "image_id": f"{category}_original",
+                "category": category,
+                "transformation": "original",
+                "transformation_level": 0.0,
+            }
+        )
+        for index in range(30):
+            rows.append(
+                {
+                    "image_id": f"{category}_blur_{index:02d}",
+                    "category": category,
+                    "transformation": "blur",
+                    "transformation_level": 0.1,
+                }
+            )
+        for index in range(30):
+            rows.append(
+                {
+                    "image_id": f"{category}_noise_{index:02d}",
+                    "category": category,
+                    "transformation": "noise",
+                    "transformation_level": 0.2,
+                }
+            )
+    return pd.DataFrame(rows)
+
+
+def _make_oneshot_behavior_df() -> pd.DataFrame:
+    transformed_a = [
+        "CatA_blur_00",
+        "CatB_noise_00",
+        "CatA_noise_01",
+        "CatB_blur_01",
+        "CatA_blur_02",
+        "CatB_noise_02",
+        "CatA_noise_03",
+        "CatB_blur_03",
+    ]
+    transformed_b = [
+        "CatB_blur_04",
+        "CatA_noise_04",
+        "CatB_noise_05",
+        "CatA_blur_05",
+        "CatB_blur_06",
+        "CatA_noise_06",
+        "CatB_noise_07",
+        "CatA_blur_07",
+    ]
+    rows = []
+    for assignment_id, slot, worker_id, transformed_images in [
+        ("assignment-1", 0, "worker-1", transformed_a),
+        ("assignment-2", 0, "worker-2", transformed_b),
+    ]:
+        transformed_iter = iter(transformed_images)
+        for trial in range(20):
+            if trial in {9, 14, 19} or trial < 9:
+                image_id = "CatA_original" if trial % 2 == 0 else "CatB_original"
+            else:
+                image_id = next(transformed_iter)
+
+            rows.append(
+                {
+                    "assignment_id": assignment_id,
+                    "slot": slot,
+                    "worker_id": worker_id,
+                    "trial": trial,
+                    "image_id": image_id,
+                    "perf": trial % 2 == 0,
+                    "subtask": "CatA,CatB",
+                }
+            )
+    return pd.DataFrame(rows)
+
+
+def test_highvar_target_and_model_statistics_behave_like_datasets(monkeypatch):
+    monkeypatch.setattr(
+        highvar_module,
+        "load_imageset_meta_highvar",
+        lambda cachedir=None: _make_highvar_images_df(),
+    )
+    monkeypatch.setattr(
+        highvar_module,
+        "load_highvar_behavior",
+        lambda remove_probe_trials=True, cachedir=None: _make_highvar_behavior_df(),
+    )
+    monkeypatch.setattr(
+        highvar_module.MutatorHighVarBenchmark,
+        "num_bootstrap_samples",
+        4,
+    )
+    monkeypatch.setattr(
+        highvar_module.MutatorHighVarBenchmark,
+        "num_simulations_per_subtask",
+        3,
+    )
+
+    benchmark = highvar_module.MutatorHighVarBenchmark()
+    target_stats = benchmark.target_statistics
+
+    assert isinstance(target_stats, xr.Dataset)
+    assert target_stats.phat.mean("subtask").sizes == {"trial": 100}
+    assert target_stats.boot_phat.mean("subtask").sizes == {
+        "boot_iter": 4,
+        "trial": 100,
+    }
+    assert target_stats.sel(subtask=benchmark.subtask_names[0]).phat.sizes == {
+        "trial": 100
+    }
+
+    result = benchmark.score_model(RandomGuesser(seed=0))
+    assert isinstance(result.model_statistics, xr.Dataset)
+    assert result.model_statistics.boot_phat.sizes == {
+        "boot_iter": 4,
+        "subtask": 2,
+        "trial": 100,
+    }
+
+
+def test_oneshot_target_and_model_statistics_behave_like_datasets(monkeypatch):
+    monkeypatch.setattr(
+        oneshot_module,
+        "load_imageset_meta_oneshot",
+        lambda cachedir=None: _make_oneshot_images_df(),
+    )
+    monkeypatch.setattr(
+        oneshot_module,
+        "load_oneshot_behavior",
+        lambda cachedir=None: _make_oneshot_behavior_df(),
+    )
+    monkeypatch.setattr(
+        oneshot_module.MutatorOneshotBenchmark,
+        "subtask_names",
+        ["CatA,CatB"],
+    )
+    monkeypatch.setattr(
+        oneshot_module.MutatorOneshotBenchmark,
+        "transformation_ids",
+        ["blur | 0.1", "noise | 0.2"],
+    )
+    monkeypatch.setattr(
+        oneshot_module.MutatorOneshotBenchmark,
+        "num_bootstrap_samples",
+        4,
+    )
+    monkeypatch.setattr(
+        oneshot_module.MutatorOneshotBenchmark,
+        "num_simulations_per_subtask",
+        3,
+    )
+    monkeypatch.setattr(
+        oneshot_module.MutatorOneshotBenchmark,
+        "bootstrap_target_by_worker",
+        False,
+    )
+
+    benchmark = oneshot_module.MutatorOneshotBenchmark()
+    target_stats = benchmark.target_statistics
+
+    assert isinstance(target_stats, xr.Dataset)
+    assert [name for name, _ in target_stats.groupby("transformation_type")] == [
+        "blur",
+        "noise",
+    ]
+    assert target_stats.sel(transformation="blur | 0.1").phat.sizes == {}
+    assert target_stats.boot_phat.sizes == {
+        "boot_iter": 4,
+        "transformation": 2,
+    }
+
+    result = benchmark.score_model(RandomGuesser(seed=0))
+    assert isinstance(result.model_statistics, xr.Dataset)
+    assert result.model_statistics.sel(
+        transformation=target_stats.transformation
+    ).phat.sizes == {"transformation": 2}
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..84c8fc8
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,2688 @@
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