diff --git a/docs/apidocs/mod_calibration_management.rst b/docs/apidocs/calibration_management.rst
similarity index 100%
rename from docs/apidocs/mod_calibration_management.rst
rename to docs/apidocs/calibration_management.rst
diff --git a/docs/apidocs/curve_analysis.rst b/docs/apidocs/curve_analysis.rst
new file mode 100644
index 0000000000..60a3bf5475
--- /dev/null
+++ b/docs/apidocs/curve_analysis.rst
@@ -0,0 +1,6 @@
+.. _qiskit-experiments-curve-analysis:
+
+.. automodule:: qiskit_experiments.curve_analysis
+   :no-members:
+   :no-inherited-members:
+   :no-special-members:
\ No newline at end of file
diff --git a/docs/apidocs/mod_data_processing.rst b/docs/apidocs/data_processing.rst
similarity index 100%
rename from docs/apidocs/mod_data_processing.rst
rename to docs/apidocs/data_processing.rst
diff --git a/docs/apidocs/mod_database_service.rst b/docs/apidocs/database_service.rst
similarity index 100%
rename from docs/apidocs/mod_database_service.rst
rename to docs/apidocs/database_service.rst
diff --git a/docs/apidocs/framework.rst b/docs/apidocs/framework.rst
new file mode 100644
index 0000000000..d7b8255231
--- /dev/null
+++ b/docs/apidocs/framework.rst
@@ -0,0 +1,6 @@
+.. _qiskit-experiments-framework:
+
+.. automodule:: qiskit_experiments.framework
+   :no-members:
+   :no-inherited-members:
+   :no-special-members:
\ No newline at end of file
diff --git a/docs/apidocs/index.rst b/docs/apidocs/index.rst
index 0aff5e6963..31921c8b33 100644
--- a/docs/apidocs/index.rst
+++ b/docs/apidocs/index.rst
@@ -4,26 +4,22 @@
 Qiskit Experiments API Reference
 ================================
 
-.. toctree::
-    :maxdepth: 1
-
-    main
-    library
-
-Utility Modules
+Package Modules
 ===============
 
 .. toctree::
     :maxdepth: 1
 
-    mod_analysis
-    mod_calibration_management
-    mod_composite
-    mod_data_processing
-    mod_database_service
+    main
+    framework
+    library
+    data_processing
+    curve_analysis
+    calibration_management
+    database_service
 
-Experiments Modules
-===================
+Experiment Modules
+==================
 
 .. toctree::
     :maxdepth: 1
diff --git a/docs/apidocs/mod_analysis.rst b/docs/apidocs/mod_analysis.rst
deleted file mode 100644
index 130427c3ae..0000000000
--- a/docs/apidocs/mod_analysis.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-.. _qiskit-experiments-analysis:
-
-.. automodule:: qiskit_experiments.analysis
-   :no-members:
-   :no-inherited-members:
-   :no-special-members:
\ No newline at end of file
diff --git a/docs/apidocs/mod_composite.rst b/docs/apidocs/mod_composite.rst
deleted file mode 100644
index 63972ed91e..0000000000
--- a/docs/apidocs/mod_composite.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-.. _qiskit-experiments-composite:
-
-.. automodule:: qiskit_experiments.composite
-   :no-members:
-   :no-inherited-members:
-   :no-special-members:
\ No newline at end of file
diff --git a/docs/tutorials/qst_example.ipynb b/docs/tutorials/qst_example.ipynb
index 22513353f4..631b00028f 100644
--- a/docs/tutorials/qst_example.ipynb
+++ b/docs/tutorials/qst_example.ipynb
@@ -13,7 +13,7 @@
    "source": [
     "from pprint import pprint\n",
     "import qiskit\n",
-    "from qiskit_experiments import ParallelExperiment\n",
+    "from qiskit_experiments.framework import ParallelExperiment\n",
     "from qiskit_experiments.library import StateTomography\n",
     "\n",
     "# For simulation\n",
@@ -405,15 +405,15 @@
  ],
  "metadata": {
   "interpreter": {
-   "hash": "42e50baa22dbe56fc7f4f6bf32ac20952839a6a23dcf2ef84eded7e8cac03444"
+   "hash": "c45f46a7fd077198472649b02925a2e599779de14e258f4f9ba8eb1d4e684fd2"
   },
   "kernelspec": {
-   "display_name": "Python 3.8.5 64-bit ('py38': conda)",
-   "name": "python3"
+   "name": "python3",
+   "display_name": "Python 3.7.7 64-bit ('qiskit-dev': conda)"
   },
   "language_info": {
    "name": "python",
-   "version": ""
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/qv_example.ipynb b/docs/tutorials/qv_example.ipynb
index 57d5fba942..69c1929769 100644
--- a/docs/tutorials/qv_example.ipynb
+++ b/docs/tutorials/qv_example.ipynb
@@ -20,7 +20,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit_experiments import BatchExperiment\n",
+    "from qiskit_experiments.framework import BatchExperiment\n",
     "from qiskit_experiments.library import QuantumVolume\n",
     "from qiskit import Aer\n",
     "from qiskit.providers.aer import AerSimulator\n",
diff --git a/docs/tutorials/rb_example.ipynb b/docs/tutorials/rb_example.ipynb
index e26639f8fa..a0bf8558e2 100644
--- a/docs/tutorials/rb_example.ipynb
+++ b/docs/tutorials/rb_example.ipynb
@@ -2,23 +2,21 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "# Randomized Benchmarking\n",
     "\n",
     "A **randomized benchmarking (RB)** experiment consists of the generation of random Clifford circuits on the given qubits such that the unitary computed by the circuits is the identity. After running the circuits, the number of shots resulting in an error (i.e. an output different than the ground state) are counted, and from this data one can infer error estimates for the quantum device, by calculating the Error Per Clifford. \n",
     "See [Qiskit Textbook](https://qiskit.org/textbook/ch-quantum-hardware/randomized-benchmarking.html) for an explanation on the RB method, which is based on Ref. [1, 2]."
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from qiskit_experiments.library import StandardRB, InterleavedRB\n",
-    "from qiskit_experiments.composite import ParallelExperiment\n",
+    "from qiskit_experiments.framework import ParallelExperiment\n",
     "from qiskit_experiments.library.randomized_benchmarking import RBUtils\n",
     "import qiskit.circuit.library as circuits\n",
     "\n",
@@ -27,11 +25,12 @@
     "from qiskit.test.mock import FakeParis\n",
     "\n",
     "backend = AerSimulator.from_backend(FakeParis())"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "# 1. RB experiment\n",
     "\n",
@@ -57,23 +56,37 @@
     "- `alpha`: The depolarizing parameter. The fitting function is $a \\cdot \\alpha^m + b$, where $m$ is the Clifford length.\n",
     "\n",
     "- `EPG`: The Error Per Gate calculated from the EPC, only for 1-qubit or 2-qubit quantum gates (see Ref. [3])."
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Running 1-qubit RB experiment"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {},
+   "source": [
+    "lengths = np.arange(1, 1000, 100)\n",
+    "num_samples = 10\n",
+    "seed = 1010\n",
+    "qubits = [0]\n",
+    "# Run an RB experiment on qubit 0\n",
+    "exp1 = StandardRB(qubits, lengths, num_samples=num_samples, seed=seed)\n",
+    "expdata1 = exp1.run(backend)\n",
+    "expdata1.block_for_results()\n",
+    "result = expdata1.analysis_results(0)\n",
+    "# View result data\n",
+    "print(result)\n",
+    "display(expdata1.figure(0))"
+   ],
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "\n",
       "Analysis Result: StandardRB\n",
@@ -89,83 +102,30 @@
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
-      ]
+      ],
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf0AAAFGCAYAAABgwUY+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABwIklEQVR4nO3dd3hUVfrA8e+ZSSYdQug9gIA0VwnSFAQEVNQVXVdELGDD7i6KBUWxrr2sZRFcRdEVFLGg8BMsFOlEUJqISBEIvSakzry/P04mmZlMGiSThLyf57lPMveee+fMTXnv6UZEUEoppdTJz1HRGVBKKaVUaGjQV0oppaoJDfpKKaVUNaFBXymllKomNOgrpZRS1YQGfaWUUqqaCKvoDJSnOnXqSGJiYpldLy0tjZiYmDK7ngpO73Po6L0ODb3PoaH32UpOTt4nInWDHTupg35iYiIrVqwos+vNnTuXPn36lNn1VHB6n0NH73Vo6H0ODb3PljFma2HHtHpfKaWUqiY06CullFLVhAZ9pZRSqprQoK+UUkpVExr0lVJKqWpCg75SSilVTWjQV0oppaqJk3qcvlIV5eKLLyYlJaWis1GsjIwMIiMjKzobJz29z+WnYcOGzJgxo6KzUWVo0FeqHKSkpJTpxFDl5ejRo8TFxVV0Nk56ep/LT5cuXSo6C1WKVu8rpZRS1YQGfaWUUqqa0KCvlFJKVRMhD/rGmN7GmC+NMTuMMWKMGV6CczoZY+YZY9Jzz3vEGGNCkF2llFLqpFERJf1YYA1wN5BeXGJjTA1gDrAbODP3vNHAqHLMo1LV3nfffUfPnj2Ji4ujQYMG3H///eTk5BR5zu7duxk+fDiNGjUiOjqa888/n40bN+YdP3DgAHfeeSennnoqUVFRNG3alFtvvZX9+/fnpfF4PPz1r3+lWbNmREZG0rBhQ66++mp27NhRJp/rzTffpEWLFkRGRpKUlMSCBQv8jk+YMIG+ffsSHx+PMYYtW7aUyfsWZuTIkRhjeOGFF4pMl5KSwlVXXcWpp56K0+lk+PDhBdL06dMHY0yBrUOHDkGv+dFHH2GM4aKLLiqLj8K2bdu4+OKLiYmJoU6dOtx1111kZWUFTfvjjz8SFhZGx44dy+S9VcmEPOiLyEwRGSMi0wBPCU4ZBkQD14nImtzzngVGaWlfqfLx888/M2jQIAYMGMDKlSuZOnUqX375JQ888ECh54gIgwcPZuPGjXz++eesXLmS5s2b079/f9LS0gDYuXMnO3bs4LnnnmP16tV88MEHzJ8/n6FDh/pdq1+/fnz88cds2LCBTz/9lD/++INLL730hD/X1KlTufvuuxkzZgwrV66kZ8+eXHDBBWzbti0vzbFjxxg4cCDjxo0r8XWP9+Fg2rRpLFu2jEaNGhWbNjMzkzp16vDAAw/QrVu3oGmmT59OSkpK3rZlyxbi4uK44oorCqT9448/GD16NL169Sp1voNxu91ceOGFHD16lAULFvDRRx8xbdo07rnnngJpDx48yLXXXsu5555bJu+tSkFEKmwDUoHhxaR5H/g6YN+ZgAAtijo3KSlJytIPP/xQptdTwZ0M9/l4f/dSU1Plpptukho1akjt2rVlzJgxcvToUYmOjpYtW7aUcS5Fjhw5EnT/gw8+KKeffrrfvi+//FIiIyMLPWfDhg0CyKpVq/L2ud1uqVu3rkycOLHQPHz99ddijJHDhw8XmuaLL74QQNLT0/P2rV27VgYNGiSxsbFSt25dufLKKyUlJaXQa4iIdO3aVW688Ua/faeccoo88MADBdIuX75cANm8eXOR1xSRYtMFu2dbtmyRRo0aybp166R58+by/PPPF/s+XhdeeKFcd911xab74IMPxOl0yrZt2/z2Z2VlSdeuXWXSpEly3XXXyYUXXuh33OPxyLPPPistW7aUyMhI6dixo0yePLnI95o5c6YYY/zea/LkyRIREVHgZ3vppZfKuHHj5NFHH5UOHToU+zmK4vu3djL87ygLwAopJC5WhXH6DYDtAft2+xzb7HvAGHMzcDNA/fr1mTt3bpllJDU1tUyvp4I7Ge5zRkYGR48eLfV5w4cPZ+XKlXzxxRccPnyYYcOGsWXLFtq0aUNCQkKh17zssstYvHhxkdcONlmQ2+0Oes2jR48SHh7ud0xEyMjIYMGCBUFLhwcOHAh6TZfLxQ8//MCQIUOC5mv37t1EREQUmpcDBw7w3nvv0aVLF7Kzs8nOzmbXrl306tWLa6+9lscee4zs7Gwef/xxLrroIr777jscjoKVmFlZWSQnJ3P77bf7vU+fPn1YsGBBgff21k6kpqaW6GdZVLrAz5aTk8MVV1zBvffeS5MmTRARMjMzS/w7k5OTQ3Z2drHpx48fT//+/YmPj/dLO3bsWJo0acJll13G7NmzycnJ8Tv++OOP8/nnn/P888/TunVrli1bxsiRI4mIiOD8888P+l5z586lbdu2fu911llnkZmZyYIFC+jduzcAEydOZOfOnbzzzjs8++yzeDye4/pb8crIyMj7f3Ey/O8od4U9DYRio2Ql/dnAOwH7mmFL+j2KOrcsS/oej8fvKdLj8ZTZtZW/k+Fp/Xh+9/bu3SvGGHn//ffz9o0YMUIAeeKJJ4o8d/v27bJx48Yit2AKK7V/8803YoyRyZMnS3Z2tmzfvl169eolgPzvf/8Lek5WVpY0a9ZMLrvsMtm/f79kZmbKM888I4AMHDgw6DkHDx6UU045Re68884Cx+677z6Jjo4WQLp37y579+7NOzZ27Fjp16+fX/oDBw4IIEuXLg36Xjt27BBA5s2b57f/sccekzZt2hRIX1RJv3379hITE5O3ARIdHZ33un379n7pA+/zmDFj5OKLL857XR4lfW/Ny+eff+63/5tvvpHmzZvLwYMHRUQKlPRTU1MlMjJS5s+f73fe3XffLRdccEGh73fTTTdJ3759/fZ5PB5xOp15vzO//PKL1KtXT/744w8RES3plxOqeEl/F1A/YF99n2PlLjk5mfT0dETg0CGoWVNYuHAhUVFRJCUlhSILqhr4/fffERF69OiRt69bt268++67XHbZZUWe27hx4zLNy8CBA3nhhRe4/fbbGT58OBEREYwdO5YFCxYELUUDhIeHM336dG644QZq166N0+mkf//+XHDBBd6HdT+pqalcfPHFNG7cmOeee67A8dGjR3PDDTewdetWHnvsMa6++mpmzZqFMYbk5GTmz59PbGxsgfM2bdpEZmYmF1xwQd6+t956i759+57AHfE3c+ZMsrOz8163bt2amTNn5v0cwsPDCz137ty5TJo0iVWrVpVZfoKZOHEiDRs25MILL8zbt3fvXoYPH85HH31EfHx80PPWrVtHRkYG559/Pr7dprKzs0lMTATgggsuyOsA2bx5c9auXVtsfjIzMxkyZAgvvPACLVq0OP4Ppk5IVQj6i4FnjTGRIpKRu28AsBPYUt5vLiKsWLGCnTuP0bp1e2bMEMLCficqajmNGkXTuXNntD+hKgsRERGArQ73ql+/PrVq1aJ9+/ZFnuv7T7gwqamppcrPqFGj+Oc//0lKSgq1atViy5YtPPjgg7Rs2bLQc5KSkli1ahWHDx8mKyuLunXr0q1btwJTpaampjJo0CAAvvrqq6Dz0tepU4c6derQpk0b2rVrR9OmTfnxxx/p1asXHo+HCy+8MGiP9/r16xMWFuYXVOvXr09ERAROp5Pdu3f7pd+9ezcNGjQoza2hefPmQfd5g2JR5s6dS0pKCg0bNszb53a7uf/++3nllVfYvj2wNbP0srKyeO+997jpppsIC8v/N7927VpSUlL8OtB5PLY/dVhYGGvXrs17PWPGDJo1a+Z3Xe/DzNtvv016errfvgYNGrBw4UK/9Pv27cPtdtOgQQNSUlJYv349I0aMYMSIEXnvLSKEhYUxc+ZMBg4ceMKfXRUt5EHfGBMLnJL70gE0M8acDhwQkW3GmH8BXUXE+1v5P+BRYJIx5kmgDfAA8JgEKz6UMY9H+OKLJObM+QvPPvsjL78M4eHNEbmDAQN+5sYbBadTg746cS1atMDhcLBx48a8f7ZffvklBw8e5PDhw9SsWbPQc33/CZclY0xez/KPPvqIpk2b0rlz52LP8+Z148aNrFixgieeeCLv2NGjR/NK///3f/8XtLQeyBuIMjMzAejcuTMff/wxzZs3L7RUfcoppxTYl5SUxJw5c/j73/+et2/OnDn87W9/KzYPZeW2227j8ssv99t33nnnMXToUG666aYyeY/PP/+cffv2ccMNN/jtP/PMM1m9erXfvocffpiDBw/yxhtv0KJFCzIzM4mIiGDr1q3069cv6PWD1Sz16NGDJ598ku3bt9OkSRPA3tuIiAiSkpKIiooq8N5vvvkmc+bM4bPPPivRA5MqA4XV+5fXBvTBtscHbpNyj08CtgSc0wmYD2QAKdiHAFPce5VFm/7DD3skMjJHQOSFF34QkLwtMjJHHn5Y2/bL2snQLne8v3uXX3659OvXT9LS0uTXX3+VuLg4adSoUbE9p49XYW36IiLPPfec/PLLL7JmzRp5/PHHJTw8XD777LO849u3b5e2bdvK9OnT8/Z9/PHH8v3338umTZvk888/l+bNm8tll13m937du3eX9u3by2+//SYpKSl5W2ZmpoiILFq0SF5//XVZtWqVbNmyRb777jvp2bOnJCYm5vXe37Fjh9StW1cuvfRSWbJkiWzatEnmzJkjN910U5GfacqUKRIeHi4TJ06UdevWyV133SUxMTF+IyNSUlJk5cqV8uGHHwogX3/9taxcuVL279+fl2bPnj1+eQ/c9uzZU+L7LBK8Tf+aa66Ra665xm/fypUrZeXKldKrVy+5+OKLZeXKlbJ27doC1zv33HOlf//+Rb6nV7De+w899JAkJCTIf//7X9m4caOsXLlS/vOf/8hbb71V6HVycnKkY8eO0rdvX/npp59kzpw50qhRI7njjjsKPUfb9MsHRbTpV2hHvvLeTjToHzggEhmZH+QDg74N/CK5/WFUGTkZ/nCP93dv9+7dMnjwYElISJCEhAR58cUXZebMmdKoUSN59NFHyzaTUnQw6tu3r9SsWVMiIyOlW7duMnPmTL/jmzdvFkDefffdvH2vvvqqNGnSRMLDw6VZs2by8MMP5wVzEfuzLeShP+/nvnLlSunTp48kJCRIRESEJCYmyi233CJ//vmn3/v/9ttv8re//U3i4+MlMjJS2rRpI3fccYff+wXzxhtvSPPmzcXlcknnzp0LdOx79NFHg+bP93M2b9680M8BSPPmzUt8n73XCwz655xzjpxzzjl++0ryXps2bRJjjEydOrXI9/QqbMjev//9b2nXrp24XC6pU6eO9O/fX2bPnl3ktbZu3SoXXnihREVFSUJCgtx5552SkZFRaHoN+uWjqKBvpPxryCtMly5d5ESWN504Ef75TyEtzVbfN2lyhO3ba/iliYkRXn7ZUEa1cgrb5tmnT5+KzsYJ6dKliy6tq/LofS4/vn9rJ8P/jrJgjEkWkaBrDuuCO0XYtQuOHct/HRjwwR7fFZIxBEoppdSJ0aBfhAYNIDo6/3VMTME5pKOjbTqllFKqstOgX4TLLwe3O79nftu2BwukcbsNPh2BlVJKqUpLg34RatWCe+7x4HLZSThOPXW/3/Hw8GzuucdDIXNcKKWUUpWKBv1iPP64oVevFYSFZdO27QG8nWYdjhy6d1/F44/rGH2llFJVgwb9Ygl/+9sqRo9+hWbNjlC/PoDh/PO/4dxzNwSdXlSpYIYPHx50rfPu3bvnpUlMTMzbHx0dTceOHZkwYYLfdbKysnj++ec544wziI6OJiEhge7du/PWW2/lTV4TCvPmzaNnz57Url2bqKgoTj311GLXhAf47bffGDx4MHXq1CEuLo7u3bvzf//3f3nHJ02aFPQ+GWNYvnx5gevt27ePxo0bY4xh3759J/y5RIRx48bRqFEjoqKi6NOnj980sx6Ph7/+9a80a9aMyMhIGjZsyNVXX82OHTtO+L29Dhw4wJ133smpp55KVFQUTZs25dZbb2X//v1Fnrd27Vouv/xyWrZsiTGm0OWB33zzTVq0aEFkZCRJSUmFzuYoIlxwwQUYY5g2bdqJfiwAPv30U9q3b09ERATt27fns88+KzTtyJEjMcaU6PdKlYwG/WI4HA7q169Px46Nad48miuusCX7I0fakZBQl+xsvYWq5Pr37++33nlKSgozZ870S/PII4+QkpLCL7/8wuDBgxk5ciRTp04FbMA/77zzeOqppxgxYgQLFy4kOTmZUaNG8e677xa70l5Zio2N5a677mL+/PmsW7eOhx9+mEcffZQ333yzyPMuuugiMjIy+O6771i5ciVnn302l1xyCZs2bQJgyJAhBe7R1VdfTcuWLQtM5wswYsQITj/99DL7XM899xwvvvgir732GsuXL6devXoMGDDAbyW4fv368fHHH7NhwwY+/fRT/vjjDy699NJCr7l169ZSTde9c+dOduzYwXPPPcfq1av54IMPmD9/PkOHDi3yvGPHjpGYmMiTTz5Z6Pz2U6dO5e6772bMmDGsXLmSnj17csEFF7Bt27YCaV988cVC11o4HosXL2bIkCEMGzaMVatWMWzYMP7+97+zdOnSAmmnTZvGsmXL8maEVGWksAH8J8NWlqvsud1u+eGHH+STT+ykPE2aeGTDBjuBjypbJ8MEG8F+94JNghIo2CQtrVu3liuvvFJERJ599lkxxsjy5csLnOt2u4tckz6Y4iaNKa1LL700L6/B7N27VwD5/vvv8/ZlZ2eLw+GQTz75JOg5aWlpUrNmTXnqqacKHHvllVekX79+8t133wngtxKfiMjChQuld+/eEhUVJY0aNZJbbrmlyHvk8XikQYMG8uSTT+btO3bsmMTGxsr48eMLPe+LL74QIG/GwECrV68W++/2+H399ddijCnxz7hDhw5BJ3Tq2rWr3HjjjX77TjnlFHnggQf89i1btkyaNGkiu3fvFqDAz2f79u0yZMgQiY+Pl/j4eBk0aJD89ttvRebpiiuuKDBT4Lnnnlvgd2bLli3SqFEjWbduXbErEOrkPAVRxOQ8WkwtIe/TbteuULs2bN9u+PNPu+qeUuUpMjIyb0W3Dz/8kP79+wct8TocDmrUKDiXhFdsbGyBrWHDhnnf+65KdzxWrlzJokWLOOeccwpNU7t2bdq1a8fkyZNJTU3F7XYzYcIE4uLiOOuss4Ke8/HHH5OWlsb1119f4P2effZZ3n///aCl0dWrVzNw4ED++te/8vPPPzN9+nRWrVpV4Dq+Nm/ezK5du/wWfomKiqJ3794sWrQo6DkHDhzgww8/pFu3bkEXDiorR44cISIigmjfccSllJWVRXJycoGFbQYOHOj3+Y4ePcpVV13FhAkTqFevXoHrHDt2jL59+xIZGcm8efNYvHgxDRs2pH///hzzndwkwOLFiwu893nnnef33jk5OQwdOpSHH36Ydu3aHe9HVYWoCqvsVSo1a8JZZ8GXX8L8+dCkCWRnQxEraSqVJ9gCM7fffjvPPvtsgbQ5OTl88MEHrF69mltvvRWwC9gc74xjwZZyTU1NzctPVFTUcV23SZMm7N27l5ycHB599FFuueWWQtMaY5gzZw6XXnopNWrUwOFwkJCQwKxZs/xWnfM1YcIELrroIr+V8NLS0rjyyit57bXXaNy4MRs3bixw3vPPP8+QIUO455578vb95z//4YwzzmDPnj1Bg9mu3Jm26tf3X827fv36Bdrs77//fl5//XWOHTtG9+7d+eqrr/yO+/6cJbfvj+++Xr16MWvWrKCfOdChQ4cYO3ZsgVXzSsu76l2wz/ftt9/mvb7llls4//zzC30QnDJlCiLCu+++m9ds8dZbb1GvXj2++uorrrjiiqDn7dq1K+h77/KZ4ezRRx+lTp06eb/zqmxp0C+lqCjo3dsG/e+/h2HD7Kx8RSyAplSe3r17F+iYF7iu+UMPPcS4cePIzMzE5XIxevRoRo4cCeQHj+MRbNW5spgedsGCBaSmprJkyRLuv/9+WrRowTXXXBM0rYhw2223Ubt2bRYsWEBUVBRvv/02f/vb31i+fHmB1dvWrl3L4sWL+frrr/3233XXXZx99tlFro6XnJzM77//ntcfwvv+AJs2bWLOnDl59xVg1qxZOJ3OEn/u0aNHc8MNN7B161Yee+wxrr76ambNmpUXBH0fsjZu3MigQYP89pX0ISs1NZWLL76Yxo0b89xzz5U4f8dr8uTJ/Pzzz0VOI52cnMzmzZsL/O4cO3aMTZs2sW3bNr/loMeMGcOYMWOKfe+5c+cyadKkoA+oqmxo0C+l8HAb9B0OWLYMcnLg8GEN+qpkoqOjgwZfX6NGjeKGG24gOjqahg0b+nUAa9OmDevXrz+u9y5uCdvSlDx9eTuMderUid27dzNu3LhCg/7333/PjBkzOHDgQN7Djnd51XfffZeHH37YL/2ECRNo2rQp559/vt/+7777jj///JP33nsPyA/mDRo04P777+epp57C4/Fw44038s9//rNAPho3bkzHjh3p1q2b376UlBQAdu/e7beW/O7du/1qGgDq1KlDnTp1aNOmDe3ataNp06b8+OOP9OrVC/B/yMrIyCiwryRSU1MZNGgQAF999dUJNx/UqVMHp9PJ7t27/fb7fr7vvvuOdevWFfh9GTJkCD169ODHH3/E4/Fw+umnM2XKlALvkZCQQI0aNfwCd0JCAmB/PkW999y5c0lJSfGr9XG73dx///288sorbN++/fg/vAI06JeaMdC8OZx2GqxaZQN/jx42+J9ArZtSeWrXrl1ocLjqqqt48MEHWbFiRYF2fY/HQ2pqaqHt+uVVvR+Yh6KGDXrbewPb4B0OBx6Px29fRkYGkydP5q677iqQfvbs2WRl5U+LvXz5cq6//nrmzp1L69atAejcuTNr164tMtAGllRbtGhBgwYNmDNnDmeeeWZePhYsWMDzzz9f6HW8eS/LIZNHjx7lggsuQESCNgsdD5fLRVJSEnPmzOHvPlOJzpkzJ6/W5KmnnuLee+/1O69Tp0688MILXHLJJYC9tx999BF16tQpUFPlFey+9+jRgzlz5jB69Gi/9+7ZsycAt912G5dffrnfOeeddx5Dhw7lJl3VrGwU1sPvZNjKsve+SH7P0NRUkTvvtL34r75aZMMGkVJ2mlZFOBl64BbWe79///5Frr1eXE/ljIwM6dWrl8THx8urr74qK1eulD/++EM+/fRT6dGjR6nv3Yn03v/3v/8tM2bMkN9++01+++03efvttyUuLk7uv//+vDTTp0+Xtm3byvbt20XE9t6vXbu2XHbZZbJq1SrZsGGD3HvvvRIWFibJycl+1588ebI4HA7ZunVrsXnxLtnr23v/559/lqioKBk5cqT89NNPsnHjRpkxY4bcfPPNRV7rmWeekRo1asinn34qq1evliFDhkjDhg3z7tWiRYvk9ddfl1WrVsmWLVvku+++k549e0piYqJf733fn/HGjRsL/Nz3799faB6OHDki3bt3l/bt28tvv/3md57v0sH9+vXz63WfmZkpK1eulJUrV0qrVq1k5MiRsnLlStm4cWNemilTpkh4eLhMnDhR1q1bJ3fddZfExMTIli1bCs0PAb3309LSpE2bNtK7d2+ZO3eu/PHHHzJv3jwZNWpUkT34Fy5cKE6nU/71r3/J+vXr5emnn5awsDBZsmRJoedo7/3So4je+xUemMtzK6+gn50tMm2avXuNGols2iQSsNS3OgEnwx9uYUGfIOuhN27cOC9Ncf/gRGzgf+aZZ+S0006TyMhIiY+Pl27dusn48eOLXUs+0IkE/Zdfflnat28v0dHRUqNGDTnjjDPkjTfeELfbnZfm3XffFUA2b96ct2/58uUycOBASUhIkLi4OOnatat89dVXBa7fu3dvueCCC0qUl2BB3/te5513nsTFxUl0dLR07NhRxo4dW+S1PB6PPProo9KgQQOJiIiQ3r17y+rVq/OOr1y5Uvr06SMJCQkSEREhiYmJcsstt8ifAf8Egv2sfbdzzjmn2M8TbPP9+2jevLlcd911ea83b95covd64403pHnz5uJyuaRz584yb968Iu9JYNAXEdm1a5cMHz5c6tatKy6XSxITE2XEiBEFfgaBPvnkE2nbtq2Eh4fLqaeeKp9++mmR6TXol15RQd/Y4yenLl26SFmuae67VvMff9hq/T174JtvIDERWrWCUvQDUoU4GdbE9l3juzLTdd5DQ+9z+fH9WzsZ/neUBWNMsogUHNeLzsh33GrVgr597fezZ9uvuX11lFJKqUpJg/5xioqCfv3s9998YzvxHT5csXlSSimliqJB/zhFREDPnhAdDWvWwP79kJYGAR2QlVJKqUpDg/5xMgYSEiB3SC5z5oCIVvErpZSqvDTon4C4uPx2/W++sZ34fBbiUqpSGD58OBdddFFFZ6PK6tixY6FL1CpV1WjQPwGRkXDOOXZ2vsWLISsLjhzRKn4VesOHDw+6/vyqVat49dVX+eCDD/LS9unThzvuuKMCc6tKoqRr3nulpKRw1VVXceqpp+J0Ohk+fHiBNJ988gldunQhPj6emJgYTj/99LxZDYP517/+hTGmwO9LsN81Ywy33377cX1WFToa9E9AeDjUqwdnnmkX3Zk71wZ8reJXFaF///4F1qHv2LEjNWvWLHTWtOrCd/a+qqA0a957ZWZmUqdOHR544AG/6YV91a5dm4cffpglS5bwyy+/MGLECG644QZmzpxZIO2SJUuYMGECp512WoFjgb9nM2bMACh0oR1VeWjQP0Hx8flV/HPm2F78WsWvKkJERAQNGjTw28LCwvyq94cPH868efN44403MMZQo0YNtmzZUuBaI0aMoG7dujz99NN5+zZv3ozL5fKrNQg0ffp0TjvtNKKiokhISOCcc87xm2v9ueeeo0GDBsTGxnLttdcybtw4EhMT844Ha4oYN24cHTt2zHu9fPlyBg4cSJ06dahRowZnn302ixcv9jvHGMMbb7zBZZddRkxMTN5iLzNmzCApKYnIyEhatGjBQw895PdAsGfPHi655BKioqJo3rw577zzThF3vPy89NJLDB8+nJtuuol27drx2muv0bBhQ/7zn/8Uek5iYiL//ve/GT58eN5c94H69evH4MGDOfXUU2nVqhV33303p512WoFahMOHDzNs2DDeeecdatWqVeA6gb9nX3zxBW3atClyWWVVOWjQP0HR0eCdC+L77227vlbxq8rq1VdfpUePHowYMYKUlBQ2btxI06ZNC6R76aWX+Ne//sXYsWPZsGEDAI888ght27blqquuCnrtXbt2ceWVV3Ldddexfv165s+f77fwzscff8zDDz/MY489xk8//UTbtm156aWXSv0Zjh49yjXXXMOCBQtYtmwZp59+OoMGDWL//v1+6R577DEGDRrE6tWruf322/nmm28YNmwYd9xxB2vXruWdd95h2rRpfqu/DR8+nN9//51vv/2Wzz//nPfffz/oQ5GvBQsWEBsbW+T2wgsvlPjzlXTN+xMlInz33Xds2LCB3r17+x27+eabufzyy+nrLdEUITU1lSlTpujc+FWELhFzgiIioGVLaN0aNm60C/D85S+2ij86uqJzp6qTwEVZgq2aV7NmTVwuF9HR0TRo0ICjR48GXU62Vq1a3HjjjUybNo3333+foUOH8r///Y/p06cXWPzGa+fOnWRnZ3P55ZfTvHlzAL8S+iuvvMJ1112Xt5ztQw89xA8//MDvv/9eqs/ZzztBRq7XXnuNTz/9lFmzZnH11Vfn7R8yZAg33nhj3uvrrruO0aNHM2LECABatWrFs88+y9VXX83zzz/Pxo0bmTVrFj/++CNnnXUWAO+99x4tW7YsMj9dunQpdinY8PDwEn++kq55f7wOHz5M48aNyczMxOl08sYbb3DBBRfkHZ84cSK///57kTU6vv73v/+RlZXFddddd8J5U+VPg/4JMsb24u/Xzwb92bMhKcmW9jXoq1Dq3bs3EyZMyHtdFqvmXXvttTz44IOsXr2aM888M2+VtQ8//LDAWvQ9e/akf//+dOzYkYEDB9K/f38uv/xy6tatC8D69ev9gjDYVddKG/T37NnD2LFj+eGHH9i9ezdut5v09PQC7d2BqxAmJyezbNkynn322bx9Ho+H9PR0du3axfr163E4HHTt2jXvePPmzWnUqFGR+YmKiip2ydyjlajNLy4ujlWrVpGamsp3333HqFGjSExM5Nxzz2XDhg2MGTOGH3/8scQPKhMnTuSSSy7J+zmryk2DfhnwDt176y07dG/cONuuX6+e7dmvVChER0eXer324gwePJhbbrmFGTNm+JUy//rXvxZYi97pdDJ79myWLFnC7Nmz+e9//8uDDz7IvHnz+Mtf/lKi93M4HASuB5Kdne33+rrrrmP37t28/PLLJCYmEhERwbnnnlugs15MTIzfa4/Hw6OPPuq3pKyXb8AyxpQor14LFizwKykHc8899/DYY4+V6HolWfP+RDgcjrzfk9NPP53169fz9NNPc+6557J48WL27dtHhw4d8tK73W7mz5/P+PHjSUtLIyIiIu/YqlWrWLFihV/fD1W5adAvA5GRcNppUL8+bN9uZ+hr0QLS0yHg/45SFc7lcuF2u0uUNjo6mtatW2OM4dxzz83bHxcXF3QBGWMMPXr0oEePHjzyyCN06NCBqVOn8pe//IV27dqxZMkSrr/++rz0S5Ys8Tu/bt26BarKA1//+OOP/Pvf/+bCCy8EbDBMSUkp9rN07tyZX3/9tdAHo1NPPRWPx8OyZcvy1nfftm0bO3fuLPK6ZV29X5I178uSx+MhMzMTsA95gTUkI0aMoHXr1owZMwaXy+V3bMKECbRo0YL+/fuXeb5U+dCgXwacThvcBw6EyZPhq69g1Chbxa9BX1U2iYmJLFu2LK+DWkxMTKHt9HPmzOGnn34iNjaWY8eOEV1Em9WSJUv49ttvOe+886hfvz4rV67kzz//pH379gDcfffdXHvttZx55pn06dOHadOmsXTpUr+e5v369eO5557jnXfeoXfv3kyfPp2FCxfSpEmTvDRt2rThgw8+oFu3bqSlpXHfffcVCEbBPPLII1x00UU0b96cK664grCwMNasWcOyZct47rnnaNu2Leeffz4jR45kwoQJREVFMWrUqGKbScqjen/UqFFcc801dO3albPOOovx48ezc+dObrnllrw01157LQDvv/9+3j7vw8eRI0dwOBysWrUKl8uV9zN46qmn6NatGy1btiQzM5OZM2cyefJkXnvtNQDi4+MLDO+MiYkhISHBr38GwLFjx/jwww+57777Sl07oiqOVj6XkZo1bdAH+PprcLkgNRVKWKBSKmTuvffevEDQokWLIsd+P/TQQ1xyySUkJCQwffr0Iq9bs2ZNFi5cyEUXXUTr1q255557GDt2bF7nuiFDhjBu3DgeeughzjjjDFavXs2oUaP8rnHeeefx6KOP8tBDD5GUlMSWLVu47bbb/NK88847pKamkpSUxJVXXsn111/vN+yvMOeddx5ff/01P/zwA127dqVr164888wzNGvWLC/NpEmTaNGiBf369ePiiy/mqquuKtG1y9qQIUN45ZVXePLJJzn99NP58ccfmTlzZl4HSbC1EIE/uzPOOIMzzjiDBQsWMGPGDM444wwGDRqUdzw1NZVbb72VDh06cNZZZ/Hpp5/y/vvv+z1MlNTUqVNJS0vL6xipqggROWm3pKQkKUs//PBDoccyM0XWrROpW1cERL75RuTXX0VSU8s0C9VCUfe5qijr373ycuTIkUKPTZ8+XRwOh6xZs0bGjBkjAwYMKPP3f/7556V58+Zlft3Kpqj7rE6M79/ayfC/oywAK6SQuFghJX1jzG3GmM3GmAxjTLIxplcx6W83xqw3xqQbYzYYY64NVV5LyuWyw/fOP9++/uorO2OfLrerqiKPx8PYsWMZOnQoHTp04Nprr+W7777jP//5D/v27avo7CmljlPIg74xZgjwKvA0cAawCJhljGlWSPpbgWeBx4EOwKPAG8aYi0OT45KrUQMGDLDff/WVVvGrquvDDz9kw4YNeT3O27ZtyxNPPMHYsWMZPXp0BedOKXW8KqKkPwqYJCITRWS9iNwJpAC3FpL+GmCiiHwkIn+IyBRgAnB/iPJbYjEx0Lkz1K4NmzfDr7/a/ceOVWy+lCqta665huzsbFq1apW3b8yYMezbt4933323zN7n3nvvLXbGO6VU2Qlp0DfGuIAkYHbAodlAz0JOiwACl7BJB7oaY0o+DiYEIiJs6d5bxe/t0HfwYMXmSymllILQl/TrAE5gd8D+3UBhs058A1xvjDnTWF2AG4Hw3OtVGg6HnajH24vf266fnm5X4VNKKaUqUlUYp/8E9oFgEWCwDwjvAfcBBZa1McbcDNwMdq7quXPnlllGUlNTi72exwO1axtq1OjJ77+HM3PmMpo1O8b27XY8vypeSe5zZZeRkVGppl4tjNvtrhL5rOr0PpefjIyMvP8XJ8P/jvIW6qC/D3AD9QP21wd2BTtBRNKxJf2RuelSsEH9KLA3SPoJ2DZ/unTpIn28S+CVgblz51Lc9Twe2LQJBg2CKVNgw4aunHce5OTYhXlU8Upynyu7Fi1alGiFsoqWkZFBZGRkRWfjpKf3ufy0aNEi7//FyfC/o7yFNOiLSJYxJhkYAHzic2gA8Gkx52YD2wGMMVcCX4lIpVvA1uGwHfrOO88Gfe/sfOnpduU9/buvHmbMmFHRWSgR/ScZGnqfVWVREdX7LwGTjTHLgIXALUAjYDyAMeZ9ABG5Nvd1G6AbsASohe393xGotOs41qwJZ54J8fGwYYPtxd+smR2+p0FfKaVURQn5kD0RmQr8A3gYWAWcDQwSka25SZrlbl5ObKD/GZgDRAI9RWRLaHJcepGRtgNf7nogfPaZ3XfoEAQsIKaUUkqFTIXMyCcib4pIoohEiEiSiMz3OdZHRPr4vF4vImeISLSI1BSRwSKyoSLyXVJOJ0RHw8W50wd9/rn96nbban6llFKqIuiCO+UkPh7+8hdo1Mgut5ucDGFhduU9pZRSqiJo0C8nkZG2U9/gwfb19Ol235EjOi2vUkqpiqFBv5yEhdkg723XnzHDDtsDSEuruHwppZSqvjTol6P4eGjVCtq2tVPxzptnp+o9cKCic6aUUqo60qBfjqKi7FdvFf9nn9le/ZmZdlNKKaVCSYN+OQoPt1X8F11kX3/zja3adzpBZ+RUSikVahr0y1mtWlCvHnTpYofrffNN/ph9T6WbT1AppdTJTIN+OYuOthPyXHqpff3ZZ7ZXv47ZV0opFWoa9MtZWJht2z/vPFutP28e7NsHLpd26FNKKRVaGvRDID4e4uKgb19bwp8+3fbiP3YMsrIqOndKKaWqCw36IeCt4h8yxL7++GP72um0i/AopZRSoaBBPwTCwuxyu716QUICrF8Pv/xiS/sHD+oiPEoppUJDg36IxMeDMfkd+qZOtSX9nBzt0KeUUio0NOiHiHeiHm8V/+efQ0aGduhTSikVOhr0Q8TphNhYOy1vx45w+LAdsx8RYSfs0Q59SimlypsG/RCqWROys/079IEdt69L7iqllCpvGvRDKCrKtusPHmyr9efNg5077X6doU8ppVR506AfQg6H7dAXFQUDB9pe+9Om2f0ejy65q5RSqnxp0A+xuDjbY99bxT91qg3+ERGwf3/F5k0ppdTJTYN+iEVG2tX3zj4bGjSALVtgyRK7LyPDbkoppVR50KAfYsbYlfd8O/R98IH9Gh5ue/UrpZRS5UGDfgWIibFt+MOG2fb8r7+2i/BERtqgn5NT0TlUSil1MtKgXwFcLtuZr25d6NfPlvqnTrW1AABHj1Zs/pRSSp2cNOhXkFq1IDMTrr3Wvv7gA1v6j4qyM/Tp8D2llFJlTYN+BYmOtiX7c86BJk1g2zaYPz9/Pv5jxyo6h0oppU42GvQriNMJNWrYqv1hw+y+99+3XyMibBu/UkopVZY06FegmjVtqX7oULv87pw5doY+l8tW/evqe0oppcqSBv0KFBlpg31CAlxwgW3H/+gjeyw8HA4erNj8KaWUOrlo0K9A3jH7GRlwzTV23//+Z6v8IyNtL35dfU8ppVRZ0aBfweLibAm/Z0+77O6uXTB7tj3mdOrqe0oppcqOBv0KFh5uJ+vJyoLhw+2+//7Xfo2KslX8bneFZU8ppdRJRIN+JZCQYIP+FVfYkv/SpfDLL7b6X0RL+0oppcqGBv1KICrKduiLirI9+QHefjv/2P79OlmPUkqpE6dBvxIwxpb2MzJgxAg7H/+XX8Lu3bZd3+2G1NSKzqVSSqmqrkKCvjHmNmPMZmNMhjEm2RjTq5j0VxljVhljjhljdhljPjDGNAhVfkMhNtaW5ps1g/PPtz34vZP1REXZyXpEKjaPSimlqraQB31jzBDgVeBp4AxgETDLGNOskPRnAZOB94AOwGCgPfBhKPIbKuHhNvBnZMCNN9p9779vX4eF2YcALe0rpZQ6ERVR0h8FTBKRiSKyXkTuBFKAWwtJ3wPYLiIvi8hmEVkCvAZ0C1F+Q6ZWLRvcu3aFTp3swjuff26PRUbC3r1a2ldKKXX8Qhr0jTEuIAmYHXBoNtCzkNMWAg2NMRcbqw5wJTCz/HJaMbwd+jye/NL+22/bQB8ebh8IdCEepZRSx8tICIuOxphGwA7gHBGZ77P/EWCYiLQt5LzLgElAFBAGzAEuEZECs9MbY24GbgaoX79+0pQpU8os/6mpqcTGxpbZ9YJxu+18/Dk5hmuu6cHBgy6efXYVZ5xxKK+U73KVaxYqXCjus7L0XoeG3ufQ0Pts9e3bN1lEugQ7FhbqzJSWMaY9tjr/CeAboCHwPPAWcG1gehGZAEwA6NKli/Tp06fM8jJ37lzK8nrB5OTApk22ff+GG+CFF2DWrNO5+mp7/MgRaN7c1gqcrEJxn5Wl9zo09D6Hht7n4oW6TX8f4AbqB+yvD+wq5JwHgWUi8ryI/CIi3wC3AdcYY5qUX1YrRlgYxMfbDnzDh0N0NMydC2vW2OMREbZtXymllCqtkAZ9EckCkoEBAYcGYHvxBxONfVDw5X19Us4zEB9vS/y1apFXwn/9dfs1IsK26+uyu0oppUqrIoLmS8BwY8yNxph2xphXgUbAeABjzPvGmPd90s8ALjHG3GqMaZk7hO/fwE8isi3kuQ+BiAhbfZ+VBTfdZDvxff01/PFH/nEt7SullCqtkAd9EZkK/AN4GFgFnA0MEpGtuUma5W7e9JOww/zuANYA04DfgEtCleeKULu2reJv1Aj+9jfbo3/8eHssIsKW9LUnv1JKqdKokOpxEXlTRBJFJEJEknx78otIHxHpE5D+NRHpICLRItJQRIaJyPaQZzyEoqNtCd/thltvtVP1fvKJnZoX8kv7Om5fKaVUSZ2UbeInA2NsaT89HU45BS64wFb3exficbnsMW3bV0opVVIa9CuxuDj7VQRuv91+//77cPiw/T4yEvbs0dK+UkqpktGgX4k5nbYnf3o6nH46nH22nX//3XftcZfLtvtr275SSqmS0KBfycXH23Z9gDvvtF8nToSjR+33UVFa2ldKKVUyGvQrOZfLVvOnp8NZZ0H37nDoEPz3v/Z4eLht6/c+BCillFKF0aBfBSQk2MV2jIFRo+y+CRPslLxgS/t799phfUoppVRhNOhXAZGRdghfZqYt7ffoYTvzeUv7YWG2CcDbwU8ppZQKRoN+FVGnjq3GB7jnHvt1woT8QB8dDfv25bf/K6WUUoE06FcRUVF2Qp7sbFvS79nTVu97x+07cn+SBw9WXB6VUkpVbhr0qwhjbGk/I8O+vvde+3XiRNuxD2xp/8AB+2CglFJKBSpV0DfGdDfGjDPG/J8x5hdjzEZjzGJjzCRjzAhjTK3yyqiCmBjbWz8nB7p1s+P2jx611fxgHwwcDti/v2LzqZRSqnIqUdA3xlxnjFmNXf72n9jlbjcCS4GDQDfgbWBH7gNAi3LKb7XmLe17p971Le3v2WO/j462JX9vjYBSSinlVWzQN8b8AjwDzASSgHgR6S0ifxORq0VkkIi0AxKAm4B6wDpjzJDyzHh1FRub31v/zDPhvPPsjHwvv5yfJiLCLszj8fjP2CM6g49SSlVrJSnp/xdoISL3i8hKKSRyiMhhEflQRAYB3YFDZZhPlcvhgLp186fefeABu+/DD2HTJrsvIgKWL1/Dt98uzQv0IsKiRYtITk6uoJwrpZSqaMUGfRF5VURKVVksIj+LyDfHny1VFN/Sfps2cOWV9vtnn7XHRYQ9e/7ghx/WsmDBoryAv3TpUjZt2qQlfqWUqqa0934VFFjaHzXKTuDz9dfw0092X+PGDQHD3Lk/M2HCBJYuXQpAkyZNKibTSimlKlyJg74xZrAx5l1jzNLcXvsbc79/1xgzuBzzqILwLe03bAg33mj3P/UUgKFLly5069aJgwcd7N59kIyMDLp160aPHj0wxlRk1pVSSlWQknTkq2WM+RGYDvQF9gFLcrd9QB9gujFmoQ7ZC53A0v5tt9kV+ZYsge++g19++YXt2//EmBzS0qIQEbZs2aJt+kopVY2VpKT/ItAMOEdEEkXkQhG5Jne7UERaAL2BxsAL5ZlZ5c+3tF+zJtx1l93/5JPCihWr2LJlS+68/fVxuyPZtGkTy5Ytw6Mr8yilVLVUkqD/V+BeEVlQWAIR+RG4HxhcRvlSJRBY2h8+HBITYeNGw9KlpwLwwQeX8t57fThyJBqPR6v1lVKqOitJ0I/ATsBTnEOA64Ryo0otNjZ/lr6ICHj0Ubt/3rwkYmI6cPRoNPv3R7Fq1anExXWia9euOBzaf1Mppaqjkvz3Xww8ZIyJKyxB7rEHsTP2qRByOKB+/fxZ+gYMgHPOgdTUMJ54YgCHD9fkyJEazJ9/Fg88cDFvvNEIHbGnlFLVU1gJ0vwDmAtsNcZ8Dawhv+RfC+gAXAi4sR39VIhFR9tSflYWuFzQpIkHMIjkP9NlZ9tKmA8/rEfjxh6efFJL+0opVd0UG/RFZJ0x5i/AfcDFwFWAt3FYgC3A+8DzIrKznPKpimCMLe1v22bb96dNM+T/iPxlZ4fx4ovCvffa3v5KKaWqjxIV90QkRUT+KSKnADHYnvqNgVgRaZV7TAN+BYqKsu37n38OTmfRaY2BKVNCki2llFKVSEmq9/3kTsmbUg55USeoTh270I63fb8wGRl2nn4R+wCglFKqeijJ5DyXlfaixpiGxpjux5cldbwiIqBpUzslb1GioiAuDo4eDU2+lFJKVQ4lqd5/zRizyhhzizEmoaiExphexpgJwO/AaWWSQ1Uq115LsePx3W7DZZfZWoHs7BBlTCmlVIUrSfV+a+Be4HHsA8B64GdgL5CJ7cHfEugC1ATmAwNERIfvVYB69eD22z289pqH7OyCP97w8BxGjnSQkOAgPR327IFGjbSaXymlqoOSLK17TEQeB5oAVwPJQBJwPfBPbI9+J/Aq0EFE+mrAr1jPPmvo2XM9TmcOxniwgyzs4PwWLXYzerSN8FFRtoo/NbXi8qqUUip0StyRT0SyjDHfAV/kduZTlZTTCYMHL6N9+2T+/LMFmZk1OXAgnOTkDmzdWpfdu+3KfAAxMbBrl+0HEB5esflWSilVvkrSkc9pjBlnjDkI7AaOGGM+NcbEl3vu1HFLSmpLjRppnHLKz3TuPJ8+fWZzyimbycx0MW5cfjqn087qt2sXOlOfUkqd5ErSke8W4BFgJXYVvS+AS4CXyzFf6gQ5nQ5q1crG7Xbi8QjGwAUXLCQy0s1XXxm+/jo/bVSUndTn0KEKy65SSqkQKEnQvwmYKCL9ROR+Efk7cDtwtTFGF9iphIwxuFwumjatR3R0Jjk54TgcDtq2jWHECDvFwoMPwv79+efExNhOfZmZFZRppZRS5a4kQb8l8EnAvqnYznvNj+dNjTG3GWM2G2MyjDHJxpheRaSdZIyRIFva8bx3dSAiZGVlsWvXLmrXNsTH18LlimLPnj0MGJBCjx7C/v3w0EP55zgcdpz/zp3g8VRc3pVSSpWfkgT9WOBIwD7vtC6FrrxXGGPMEGxP/6eBM7Ar880yxjQr5JS7gYYB2x/Ax6V97+pk+/btAPTs2ZW7776Sdu2SANi9O4UXX7SL9MyYAV99lX+Oy2XH7fvWACillDp5lLT3fmNjTEuf106f/Yd8E4rIH8VcaxQwSUQm5r6+0xhzPnArdnlePyJyGDjsfW2MOQtb+3BNCfNe7RhjaNWqFY0bN6Znz56A4eyzOxMeLsTFuWje3PDwwzBmjN169IDate25MTE26Hvn8ldKKXXyKGnQn1bI/s+D7Ct0uZfcPgBJ2A6BvmYDPUuYl5uAtToXQNGSkpIQEUzurDsNGhg6dUoiLs6+vuYa+PprWLjQtu+/9ZadoMcYG/h37oTERFv6V0opdXIwUsw4LWPMdaW5oIi8V8S1GgE7gHNEZL7P/keAYSLStpi81MQu9vOgiLxaSJqbgZsB6tevnzSlDJeTS01NJbYKF3/dbsjJse33ALt2RTJyZBfS08MYPXo9Awbszkvr/bWoiKBf1e9zVaL3OjT0PoeG3merb9++ySLSJdixYoN+WSqDoH878CLQSEQOFPd+Xbp0kRUrVpxgrvPNnTuXPn36lNn1Qs3jgS1bbND3BvOpU2HUKFu6/+YbaNEiP31aGtSsaaf2DaWqfp+rEr3XoaH3OTT0PlvGmEKDfkk68pWlfYAbqB+wvz6wqwTn3wR8WpKArwpyOOxMfBkZ+SX5K66Av/7VBvg77vBfgCc6Gg4c0NX4lFLqZBHSoC8iWdi5+wcEHBqA7cVfKGNMV+AvwMSi0qmiRUVBQoKdjAdsG/4zz0DjxrBqFbzg09vCt31fx+8rpVTVF+qSPsBLwHBjzI3GmHbGmFeBRsB4AGPM+8aY94OcdzOwUUTmhi6rJ6fate30u95Sfc2a8NprtibgjTdgkc/jl9NpmwJ27LB9ApRSSlVdIQ/6IjIV+AfwMLAKOBsYJCJbc5M0y93yGGPigCuBt0OW0ZOY02mr+dPT86v5u3WDu++2r++801bre0VE2P06P79SSlVtFVHSR0TeFJFEEYkQkSTfTn0i0kdE+gSkPyoisSLyXMgze5KKirIl/jSfeQ3/8Q/o0sUG9zvu8C/ZR0XZJXh14h6llKq6KiToq8qhdm0IC4OsLPs6LAz+8x/b5j9vHrzyin/62FjYt0879imlVFWlQb8aC9abv1Ej265vDLz8MvzwQ3563459GRkVk2ellFLHT4N+NRcVZcfhp6bm7+vdG+65xz4I3HGH7cTn5XTaNv7t2/2H9ymllKr8NOgratWyY/LT0/P33X039OsHhw7ByJH+Q/ZcLltLoD36lVKqatGgrzAGGjSwM/Z5g7jDAa++asfvr1wJDzzg33M/MtKmTUnRpXiVUqqq0KCvAAgPt4E/LS0/uCckwDvv2AD/8ccwYYL/OVFRdpKfvXt1KJ9SSlUFGvRVnrg4W9XvO4yvY8f8XvxPPgnff+9/TmwsHDzoP65fKaVU5aRBX/mpW9e22fu24V98sV2Ux+OB226DjRv9z4mLs6X9gwdDm1ellFKlo0Ff+XE47LC97Gz/Tnr//CdceKEdoz98uH/J3hhb4t+9G44cCXmWlVJKlZAGfVWAy2XH7/u27zsctpq/Y0e7PO+IEf69/R0OG/h37vQf/qeUUqry0KCvgoqLgzp1/AN4dDRMmmRrAlassHP0+9YGOBx28p4dO/wfCJRSSlUOGvRVoWrXtoHeuwwv2BqADz6wK/PNmgWPPOLfc9/ptL36//xTA79SSlU2GvRVoYyxQd6Y/Pn5Adq2hf/+1zYDTJoEb77pf15YmB3mt22bBn6llKpMNOirIoWFQZMmtje/b1V+jx528h6Ap5+GqVMLnqeBXymlKhcN+qpYERF2Zr60NP/Z9/76Vxg3zn5/770wY4b/eeHhGviVUqoy0aCvSiQ2FurXL9gz/6ab7OI8Ho9dnOfbb/2PewP/n3/69w1QSikVehr0VYnVqmW3wMD/z3/aRXlycuDmm+HHH/2P+wZ+Hc6nlFIVR4O+KpW6dfPn3PcyBsaOhWuusW3/I0bA8uX+54WF2ZEAO3bYCX6UUkqFngZ9VSreGfvCwvzb6Y2xHfouu8w+EAwbBkuX+p/rdOYH/sOHQ5tvpZRSGvTVcXA6bY9+Y/zn6Hc44OWX4dJLbae/YcNg4cKC58bG2iV59+8vuDqfBOwIfK2UUur4adBXx8U7lM/t9h/DHxZmh/L9/e+2JuDaa2H+fP9zHY78RXr27MkfEZCcnMzixYvz0okIixcvJjk5OQSfSCmlTn4a9NVxc7ls4M/Ksp34vJxOeOkluOoqyMiwC/QE9uo3BmrUsNX8KSmQkyNkZmayZs0a0tLS8gL+mjVryMzM1BK/UkqVAQ366oRERkLTprZU7xv4HQ549llb0s/MhOuvh2nTCp4fG2vP3b7d0KVLDzp27Eh6ejoTJ05kzZo1dOzYkR49emCMCd2HUkqpk5QGfXXCoqIKD/xPP23H77vdcPfdMH58wfOjo20V/7ZthtNP7+F3TAO+UkqVHQ36qkxER+cHft/peo2BBx/Mn7nviSfgyScLduCLjISwMOGLL1bi8eQH+cWLF2vVvlJKlREN+qrMREfnT9frG/jBztz32mu2o99//mNL/b49/0WEn39ewdat6wgLi2Lw4Jvo0KEja9as0cCvlFJlRIO+KlOxsbZzX1qaf1U/2DH8775rmwM+/RSGDoUDB+wxYwwRERG0a9eWuLhoDh40NG3ag7ZtOxAREaFV/EopVQY06KsyFxsLzZrZqv7sbP9j/frBZ59BgwZ28p6LL4bff7fHtm/fzvbt2wE7pC8zU0hO3s9vv/0Z4k+glFInp7CKzoA6OUVH28D/55+2/d7lyj/WqRN89ZUdyrdmDVxyCYwf7yEzM5ONG/cQHR3JN9942LnzZ2rX3o3H04D9+z0kJDjQAr9SSh0/Dfqq3ERF+Qf+iIj8Yw0bwvTptmf/7NkwbJiDnj0vYdEi4dlnF/Dii4bw8A6InMattxr27HGQkWFX+gvT31qllDouWr2vylVkpA38Ho//XP0AMTHw9tv5Q/oWLHDgdjvJzHQAhuxsFzk5Tt56y8Nbb9nzt2zRJXqVUup4adBX5S4iwgZ+p7NgwHY64dZb7XA9r9df7+yXJisrjPHjhexs20ywbRvs25c/fa9SSqmS0aCvQiI83I7jj4qC1FT/Y19/bY97paTEFjjf4bD9AMLCbCe/Awdg61Y7za9SSqmSqZCgb4y5zRiz2RiTYYxJNsb0Kia9yxjzeO45mcaYbcaYu0KVX1U2nE7bll+zJhw5kj9Bz549kJGR30OvY8e9Bc7NyDDs2WO/N8aOEDDGVvcfOKClfqWUKomQB31jzBDgVeBp4AxgETDLGNOsiNOmAOcDNwNtgb8Dv5RzVlU5cDigXj3bIe/oUTuWv149iIrKr96/7rq1Bc6LiBDq1fPf53LZUv++fbbKX0v9SilVtIoo6Y8CJonIRBFZLyJ3AinArcESG2MGAucCg0RkjohsEZGlIjI3dFlWZckYqFXLVvdnZMCAAQWn7g2UkWFX5Ass0XtL/SK2ul/b+pVSqnAhDfrGGBeQBMwOODQb6FnIaYOB5cAoY8x2Y8xGY8y/jTEFG35VlRITA4mJdond668XwsOzg6YzxgMYnnrKLte7Y0fBNBERNvgfOKA9/JVSqjChLunXAZzA7oD9u4EGhZzTEjgb+AvwN+AObFX/pPLJogollwuaN4cxYxz06bONsLAcjBHAQ3h4FmFhOQwatIkJEyAhARYsgP797TK9gdPxe0v9Tqet7k9JKTgjoFJKVWcmlAuZGGMaATuAc0Rkvs/+R4BhItI2yDmzgV5AAxE5nLtvIPBN7r7dAelvxrb9U79+/aQpU6aUWf5TU1OJjdUKhvJy5Egahw5lEB0dxv79YTgcHqKiMomLiyY6OpqDB8N5+eW2LFlSB4Azz9zPXXf9Rv36mUGv563mDwuzDwKqIP2dDg29z6Gh99nq27dvsoh0CXYs1EHfBRwDhorIJz773wA6isg5Qc55DzhLRE7x2dcU2AZ0FZHlhb1fly5dZMWKFWWW/7lz59KnT58yu57K5/F4eOutt0hJOUSHDu0544yLmT37c7Zu3UpCQgLXXHMNDocDEfj4Y3jsMdvGHx0N998PI0YED+wej63qDwuznQejo4P3Gaiu9Hc6NPQ+h4beZ8sYU2jQD2n1vohkAcnAgIBDA7C9+INZCDQKaMNvk/t1a9nmUFUUh8NB/fr1adu2GfXq1SQmxkG/foNp2rQ5devWxeGwv6rGwJAhMHcuXHSRDeiPPmrn71+9Oth1bZV/WJidDvjPP7WXv1Kq+qqI3vsvAcONMTcaY9oZY14FGgHjAYwx7xtj3vdJ/z9gP/CuMaaDMeYs7JC/aSKyJ9SZV+XnsssuY+jQoRgDjRpB48YOBg4cTL9+gwqkrVcP3noL3nnHrti3ciVccAE88AAcPFjw2mFhtsOg2217+aekQFZWCD6UUkpVIiEP+iIyFfgH8DCwCttJb5CIeEvtzXI3b/pUoD9QE9uL/2NgHnB9yDKtQsa3RF+zJrRs6cDptLP4BWuJOu88W+q/+WZbqp88GXr1gg8+8B8G6BURYcf2HzsGmzfbiYG0s59SqrqokBn5RORNEUkUkQgRSfLt1CcifUSkT0D6DSIyUESiRaSxiNwuIkdDnnEVci6XHc9fp46dzCczSJ+9uDhbxT9nDvTsaUv6999vHwjmzy+YHux0wLGxdmbAzZth7147UZBSSp3MdO59Vek5HHa4XosWtgbg6NHgE/C0bWs7+b35JjRuDOvXw9ChcPXV8OuvBdMbYzv2xcTYToGbNtngryV/pdTJSoO+qjIiIuyY/nr1bPV84FK9YAP5JZfAvHkwZoytBfjhBzvr3z/+YcfvBzsnOtqW/A8fhj/+gN27tc1fKXXy0aCvqhTvFL6JiRAZaavng1XLR0XB7bfDwoUwfLitLfjkE9ve/8ADtiNfsGt7g//Rozb479ypvf2VUicPDfqqSnK5bBV+06a2Or6wjn61a8NTT9mS/+WX22aByZPhrLNg7NjgU/p6g3+NGjbgb91qawjS0oK/h1JKVRUa9FWV5p2/PyHBBuVgVf5g07z6Knz/vR3fn5lph/uddRaMHm3n6w8mMtI2EXg8doz/H3/YjoK+tQuBE1yFcsIrpZQqDQ36qspzOm3v/sREW0IvrJc/QOvWdnz/t9/atn+3G/73P1vtf+utsGpV8PNcLlvyd7lsZ79Nm2DXLli06CcWLVqcF+hFhMWLF5OcnFwun1UppU6EBn110nC5oGFD29nP4bDt/YX1xG/XzvbynzvXzvDncMCXX8KFF8Jll8E33wQfIeB02jb/2FhITRW2boW5c7cwZ84ysrNtwF+9ejWZmZla4ldKVTphFZ0BpcpaZCQ0a2Z7+O/ZY4N/dLSdlS9Qq1bw0ktwzz3w7rt2Up+lS+2WmAjXXGMfCmrV8j/Ptvsb0tJ24XZnsWTJZpYs2UJERCYuF+zcuQujk/wrpSoZLemrk5Ix+e39jRvbNvijRwsv+TduDA8/DMuX24l+mjSx7fxPPAFdutiHgp9/9u/I5/F4yMzMZP/+DJYsqceCBW35/vtmbNgQxtatYezZ4yEjQzv/KaUqDy3pq5OaMbYjnq2Ot+3xGRm2NiA8vGD6uDg7pe8NN8B338GkSbbn/5QpduvQAa66CgYPhpo1Hfz22yW8+aYHYzxkZ4cTHp7N99+fza23Ojh82MHBg/Z94uPtQ0hERIhvgFJK+dCgr6oF3+B/7JgN/keP2n4AwQKx0wkDB9pt0yY7zG/aNFi7Fh56yNYANG8u/P674Hbn/xllZ7sAGD8+B4fDcN99Brcb9u+37+n7AOByVdwyvyLi1/wQ+FopdXLS6n1VrXir/Zs3t2P8w8Jsm396euHV8K1awbhxkJxsO//17m1rCzZsMLjdzqDnZGeHMX68neHP6bTvGRdng/7+/bbpYPNm2LfPvnewToPlJTk5mcWLq96IAx0aqdSJ06CvqiXvBDxNm9o5/b3V/2lpwVfnA1sjcMkl8NFHdla/sLCig44x8NVX/vsCHwAOHbLj/71DANPSynfhHxEhMzOTNWvWsHjxYgAWL17MmjVrKvWIg+TkZBYtWuT3oLJo0aJK/6CiVGWj1fuq2ouIgPr17ex9R4/CgQM28BZW9Q/2eGEPB14ZGbZJoH17+Mtf7LBAX06nffAAW8tw7JitGfDmqWZNO52wy1Xw3ONljMHlchEbG8uaNWtISEjgwIEDxMbG4nK5KmUVv4iwadMmtm/fDkDPnj1ZtGgRS5cupUmTJnTu3LlS5lupykiDvlK5wsLs0Lz4eFvlfuCAfQhwOGzHP6dPTX69ehAVZTh2rKgrGpYtszMANmhgl/rt398u/xsZGZDS2H3e/Tk5turf48lvkoiLsw8DJ9IXQETIysriyJEjHDt2jISEBFJTU/F4PGRlZVXatv0mTZqwfft2li5dypo1a0hLS8vbr5QqOQ36SgXwVv1HR+fP63/woC2Jh4XZwHzhhTB2rACFB8jwcOHvfzf88INd4Oe99+wWGQlnnw3nngt9+9omhkBhYfnzCojYFf927crPX0yMbZIo7UOAMYbw8HDcbjfp6enk5OSQnp5OREQE4eHhlTLgG2Po0aMHAHPnziUjdwWkPn360KNHj0qZZ6UqKw36ShUhPDy/9J+RYUv+3s55N9wgTJiQQ05OwbF/4eHZ3Hqrk/vvN4jYMf5z5tjpf9essV+//dambdECzjnHbt272+l+fRljg7u3qUHETjPsXWTIGNsMEBtrHyhcLv9aCV8ej4eNGzdy+PBhnE5n3nb48GE2btxIUlISjrJqSyhj7oD2lMDXSqniadBXqgS8gTUqys7zn54ODz5oWLVqLUuXdiQszJCV5cDl8pCTI3Tv/gujR3fOO/f00+02erQtsX//vd0WLrS9+DdvtnMCOBxw2mm2CeCss+zEQLGxBfMS+BCQnZ3fHAD2YSUmxm7h4XZzOMDhcNCmTRsOHjzIoUOHcLvduN1u4uPjadOmTaUM+CLChAkT2Lt3L1FRUcTFxXH06FHmzZvHunXruPnmmyttaV+HRqrKRoO+UqXkcHgDquHll91s357M7793ZccOQ+3aDpo0WUnDhuE4HMH/uTdoYCf4ueoq23a/apWdAGj+fPu9d3vzTVti79ABunaFbt3sQ0C9ev7XM8aW7l2u/H1ut60JOHw4fyhiRARERwuHDuXg8YTzzjvXMmrUr0RF7cTpdFbaNn2Px0Nqaiput5vx46+kUaNGXHHFm6Smpub1R3AWVrVRgZKTk8nMzMxrmvAOjYyIiCApKamCc6cqg4p4KNSgr9QJcDqhfv0cLr3UlrLT0+H773PIyAjn6FF7PCKi8Or2sDAbyL1T/aal2amAFy6ExYvhl1/yt7fftuc0bQqdO0NSkq096NChYMdAp9PWSviyUxEb1q8/zNatNTl0qCbZ2U4WLuxBp06/UqPGHs480xAWVnGTBgXjdDrp3bs3y5YtIycnhz//3MahQ4eoU6cOXbt2rZQB33dopJd3aGTHjh0r5cNVVVYVa1R8HwqNMSF7KNSgr9Rx8v5jX7t2bV5ns19+WcyuXWvo0KEjTZsK6emGw4fJ6+XvLZEX9v8oJgb69LEb2POSk2HZMrsI0KpVdlz/n3/CF1/YNGFhcOqpdljgaadBx472deCDgA3mHj77rAHz55+Lx+PA7Xbw1Vdn8sUXZ9Kz53pOP91DeLgDlyt/NIHLld+xsKL+j5555pl07tyZF1+0w/aMMdxyyy2VMuCDf+dD36GRHTt21M6HZawq1qgEPhT26NEjZA+FGvSVOk6B/9i9f8C+/9ijo+34/+zs/I6AaWn5Q/FcLtveXtjfd3Q09OplN7DV9hs2wE8/2YeBn3+G336znQPXrIEPP7TpnE5o08bOEdC+vV1KuF07mDTJsHBhB7+pg7Oy7PdLl7ZjwgTD6NH+zQO+wsPzHwQiIvIfBpzO8n0g8Hg8TJ06FTgrb9/UqVO58sorK2U/BICffvoJT8BUix6Ph59++qnSBqOqpqrWqJTkf0d50aCv1Anw/vH6/tMJ9kfr7UwXF5c/BM/3IcA3XVE1AU5nfiC/+mq7Ly0NVq+2tQDe4L9pE6xfb7dPP/XLMb5/9r//Hp/3fVaWnTp45Eg7MVCwpYjdbptv74OLr7Cw/A6G3iYNp9PudziO/6HA4/EwZcoU1q/fRXp6LSIj4/jjj3NJT1/IlClTKmXgFxF+//13Nm/ejMvlypsP4ccff6RFixY6oVAZqco1KsYYunfv7ve/o3v37tqmr1Rl5q1K9LV48eIi/+H49r6vWdMGz6ys/GF4x47lB9SwsKKH4IFtEuje3W5e6emwbl1+4F+/3tYK5A5xzzN+/Ol+r3NyhH/8wzB4sB1K2KKFfVDx8gbyYNzu/BoNtzt/OKH3M3s/i3fz1hA4HEVf1xgHn312Bl980Rq324nHY5gypStZWUlccslGhg6tXAEf7O+F2+0mO3ctZ++kSNnZ2bjd7kpbAq2KjDF07drVL3h27dq10t/fFStWsGHDhrzfBRHho48+om3btnTp0qXc3leDvlLHyRvwvVWJvu1yELzEH4x3xr/ISPsQ4B2Cl5VlHwDS0uxX76XCwmyNQFEPAlFRtqOfby3ySy/Biy/6TyjUvLnt1Ofldhtmz4bZs/PPS0iwCxR5t2bNoEkT26GwUaP8JYqLCtz22vkPBR6P/wJH3geE8PD8hwNv7chTT8GXX7YmOzv/31Vamq2xmDGjNY88Ylc9rEy8QyMBNm/enDc0slWrVpV2aGRV9fbbb3Pw4EHicp9OPR4PL7/8MrVq1eLGG2+s4NwF5/F4WL58OXv37qVVq1YMHTqUjz76iE2bNnHkyBE6d+5cbr8jGvSVOk7GGCIiIvyqEr1VjREREcdd0vAdgucdo+8NmJmZ9gEgPd1uXk5nfsAsTP36to+A79TBd965knvv7ZP32uUSevY0REXZuQO2bLHTER84ACtXFrymw2Gv27hx/tawof9Wp07+A0FRDwUi9mHA7c5vPjh4EF55RfL6HQTKzAzj+eeFESMMtWvn1xwE20ItKSmJjIwMtmzZAtjfl2bNmlX69nyPx+MXcAJfVyY5OTns3buXrKwsAE499VQ2b97MsWPHyMnJIScnh7Ci/igqiDEGh8OBw+Fg165dTJw4kdTU1Lx92qavVCWVlJTkV1XrDfxl/UfrDZje2gDIfxDIzrYPAMeO2eYBL2PyHwacTu/UwUW/jzF2fgDve3g8sGcPbN1qt23b8kcP/PmnnV7Yu61YUXje69a18xPUr2/nGahXz+6rX98+FNSta79GRfk/GHz2GTidRd9Lh8Pw0UdwxRWFL48M+X0LvP0MvM0L3s2Y/L4H3gcF332l4V0FcNmyZbzzzjX885+/Ehm5k2XLlmGMoWfPnpWy+nn69OlkZGTk9ZPw9qeIjIzksssuq+jsFeB0OklMTOSXX/7k3/8ewdixP7Ngwal06LCeNm2aVtrRHQCdOnXi++9X8q9/DcHtDqN37x8588ytdOrUqVzfV4O+Uico8J93qP6Z+z4IeNvdvU0DOTm2eSA93Vanp6fbIHf99fD22zl+VeVeLlcOt9zizAv4YANegwZ269atYB6ysmD3btixI3/zfRBISYH9++0shN61A4oSE2ODf0KCHfWwaxccO1b0GgcZGcKhQ4aYmKKv7fHkb75NDN6tON4HBt8+CN6HA+8Dhe8DwubNu8jKchAbW4OwsHA6derBkiWL+fPPnXg8J9a5sTx4PJ68mglvB8kpU6awZcsWEhMTK2mJ37Bu3ZW89JKH7GyD2+3k//7vPL75ZhD33eegqN+bimX473+b8/77XXG7HYgYZs0ayMyZDjZv3k2PHlrSV0qVgG/TQHS0XTMAbFDLyYHnn4cdOzYyc2Zr3G77DzwsLAtwcM45m7nlllNIT/cPbEUFJpfLtu0HWzTIKysL9u7ND/x799oHBe/Xffvyt7Q0u23d6vepivzMIoa334ZZs2wNRXy8Xb/Ad4uLs19jY/O/xsXZr1FRJQu+3uYH773MzvZ/aPB/eDAcOhRLeHhLXK4aiEC9et1wOveyb18Ev//urRkqWKsQbCusFsJ7De/m+7qwY8HSgO2H4Bvon3nmGQASExMr5QgJgEcegeefd/s1/2Rn26kpn38+BwirdP09AMaO9fDBB/XIySmY7w8+qEfDhh6efFLb9JVSx8nbSS483DB9ehtefPEdnnvuUpxON+ef/w09e+7mzjtHIGLIysofTZCZWbAU7BuMimunB/tg4G3vL4oIHDliawa8259/whNPCG530VH50CG7HY+wMBv8vSsXetcsiImxD07er75bVFTBr97NdsoUGjSozerVf7BjRwbZ2fDaa1upWzeDU05pSGyst8e2zYN3tIZvv4bABwnfr77f+z6wBL4OprA0DgesWbMWh+MUDhw4ANh0rVufwqxZazjttNPy+kYEfvV9uMh/iPC/duA+3zyU5PvA14cOwQsvCJmZhff3eOEF4a67DLVqFTwe7B4Udu9Kk7Y4Bw/Ciy8asrIKLtQFkJUVzosvCvfem//QXpY06CtVjXgnucnO3kONGscID/dw1llrcbvdfPmlnewmLs6/hOF2+2/ekQVZWfn9CaBgMAl8OCiuOtsYW1KvWRNatszff/Cg4c03gzdJhIXlcOONYVx3nZ1I6OBB+/XoUfsAcfiw/XrkiN3n3Z+Wlj9HQmbmiT00FPJpgC6A7bT34IM9SUsLBxKpXRuaNDFERuYvjew7v4G3psb3e9/NO5eDd9ij7z7vyA7f7wv76vuwZh8khOTkhRw7doypU/+OMYYrrpjG4sULiYqKoUOHTnkPX8EeRAIFjs4oLF3gOd7fkaIeaETgk0+KD7zGwFtvwd//XvzDUUkemErC98EnmI8/Lv59nE77+W666cTzE0iDvlLViMPhICIigvj4eB544P9ISEggPDyB7OxsIiIiglbhlqbXve/XrKz8avDs7Pz9vucFq4YOrNq+5x4PGzb8zrffnpLX/ulyufF4oH//3xkzpg1O5/FXhWZl2Q6QaWn2q3euBO9QydTU/I6S3n2+Iyi8xzIy8vtPHD5sr+ttmkhLy18NyVuLUdG8cyf4PgRkZV0DeEhPjwYM48ffRGRkBmFhHr78Mj+d7xbYQTKwz0Pg94G1RN6HQd80vq8Dj3tfz5sH6en+0XP58vp+r9PTDUuX2o6jgU0kELzpJNj+0mzee1tYE8vGjQXzHejYMVOiPjDHQ4O+UtWIiNCwYUP2799Px44dycrKolGjRqxZs4aGDRse16Qx3n/IJekoHfiA4N3cbvuA4N28r206BzfdlE3PnnN49dWe5OQ46d17HV277qNdu6ZkZDgKPDQE+74wLpftOJiQUKqPXahDh+yCSL7GjVvIuHH5UwiHh9sFlMLDbU1DRkZ+k4rvV9+mFu/Dk/eYt7Omd7938+7zPnAFfvXW1ng7febX1hjAvzfksWMxHDtm9+3ZUzb3pzxNndquwL6vvrJbVRIdLTRoUD6d+TToK1WNBM4tMG/evDKZW6Dk71/yBwQvEWjVqhMXXdSByZN3IQIDB67muutGYIwj7wEiJ8f/q7e2wft9afNZWGmtuJLd11+Dw+E/4iA2Ntvv+mFhwu7dhmHDSpevshT4oPXyyzB5spCRUfB3ICJCuPRSm1/f83wf2HybgLz7g31f2ENf4GuRgvu9HSm9IzCmTRM8nvz8JiXtIjm5Qd5rh0MYMMDgdPp3xAz83vs62L7Ajpq+/S+K2gpL43bDli2CSOF/a263bZIoDxUS9I0xtwGjgYbAWuAfIrKgkLR9gB+CHGonIr+WVx6VOplJQONq4OvKxJbYhaVLl2BMYu4IBTerVy8p1ZwIJfmHHhgAitoCO9t5g8H27QQNnL4yMgzbt9t+BcV1Hgv8WtzxYOmD7XM48vsGHD4M778vZGYGz0xmpuGzz4RHHjF+QzorWsOGhjffzCY723aKGzr017ygHx6ezW23hXPffRWZw+Cee45C+6m4XNnce29YuXTigwoI+saYIcCrwG3Aj7lfZxlj2ovItiJO7QAc8Hm9t/xyqdTJKXA5YLBrBaxdu7bSrkrmO93xu+96lyHteFzTHZc3EbsYUnS05E4VHFxUlNCmjckb6hisl773IcL3a3GlzmDnBbt24PHp04u/Pw6HXbzJOwlSaX5NTmRUQVH7R470sGbNH8yd2wqPx34AlysHtxt69/6DkSNbk5pa8IOVJv+l/XMoSfrbboN16zbz/fct8HhsP5Xw8GxEDBdd9BuPPda+dG9aChVR0h8FTBKRibmv7zTGnA/cCjxYxHl7RGRfuedOqZNYVVyVzNsk0aFDB7/pjkUkJE0SpWGMDYr/+EfRefJ4bFV5dHSIMlYMkZLVTrjd0KpVwV74wXrqF3a8LPbl73fwyCOp9OkzmaVLGxIXl8OAAd/So8cuzj67E02bBg/4JVHSSZuOJ73HI/ztbytp334mv/7alqysaGJjD9Ox43o6dGgMtINymlgopEHfGOPCjmF5IeDQbKBnMaevMMZEAOuAJ0UkWJW/UqoYJV0OuDLZunUrGRkZefkUEbZu3UpkZGSlm8u+Vi074uD55z1B1wxwuXK45x4H8fGVZ7KbBg2Kr52IjhYaNTKl6o9R3kSEsLBM6tbNomvX5dSo0Zpu3TZSp049wsMziYmpfDVXloPOnVuQnb2HhIRleXsTEhLKfUEmE8q2PGNMI2AHcI6IzPfZ/wgwTETaBjmnLdAXWA64gGuAW3KvUaAfgDHmZuBmgPr16ydNmTKlzPKfmppKrHcFFFVu9D6Xv7S0NNLT03E6nbjdbqKioogpbh7bCnTgwAGysrLy1qYPfF0Z/fFHBocORSICTZqksmNHDGCIj8+gZcvIis6eH7fbLr1cVDgwBv7yl9J1wgyVvXv3kpOTQ0REBJmZmYSFhVG3bt2KzlaR0tLSSE1NxePx5D3IOhwOYmNjT/hvsW/fvskiEnR93krfe19ENgAbfHYtNsYkYjsCFgj6IjIBmADQpUsX6dOnT5nlZe7cuZTl9VRwep/Lj7d9/LfffssbsudyufyWB66MJSPvwi+//prfdzcxMZHBgwdXyulhRYQ9ez5hx47fmTjxekaPXsbu3U2Ii/s/mjSpxznnnFfp7vPcucILL3jIyCgY1SMj3dx7r4Nzz61cefb+XmzevBmn08kpp5zC1q1bcbvdZGdnV9rpg0WETz75hM2bN+NyuYiJiSEtLY2srCxatmzJoEGDyu33I9R3Yx/gBuoH7K8PlGYqgqVA67LKlFLVReCQPbBV+x07dqx07eO+vPPC+6qs/9B9RUdnUqdONhERcMkle4iKyqzoLBXq8ccNw4btJjw8B2M8gOByZRMensOwYbt5/PHK97vhcDiIjIwkPj6eWrlz7daqVYv4+HgiIyMr/e+Hy+WiW7du3HzzzXTr1g2Xy1X8SScopCV9EckyxiQDA4BPfA4NAD4txaVOB1LKMGtKVRuhWg64LHlLdL68K8FVxn/sxhhatmxJTk4OqalpZGdns3r1Bho2bEjLli0r6b0Wrr9+Cx06zObll6/A6YxiyJCtNGjwI127tsGOsK5c+RYRGjRowN69e+nUqVPeZFOrV6+mQYMGlXI0Ctjfj1atWtG4ceO8ZZZ79rTd2iIjI8s1zxVRvf8SMNkYswxYiG2fbwSMBzDGvA8gItfmvv4HsAU7nt8FXA0MBv4W2mwrdfKoqOWAj4c34HuXePVdCa6yBn4RISsri9TUVG65ZQr16zdj27Y0jDFkZWVVymDkrQXq2rU1W7dG5bYzt2LRot2VthbIm+dOnTpVyGRTJyLYw7f3AaA8hTzoi8hUY0xt4GHso+MaYJCIbM1N0izgFBfwPNAESMcG/wtFZGaIsqyUqkDeKlzfJV69gb+yVuEaYwgPDyc7O5uMjAxycnLIyMggIiKC8PDwShuMtm7dSnp6ut8oiS1bthAVFVXpRkl4VcWaK6+KePiukI58IvIm8GYhx/oEvH4OeC4E2VJKVVKXXXYZHo8nL8B7A39lDPhgayc2btzIoUOHcDqdhIWF4XA4OHToEBs3biQpKanS5d3j8ZCRkcHWrVvzalCmTJnC1q1bSUxM9Lv/lU1VqrmqaJXzJ6iUUgECA05lDUBg89a6dWvi4+Nxu93k5OTgdruJj4+ndevWlTLv3gepxMREtmzZwjPPPOPXpFIZ86xKT3+KSilVxkSE7OxswsPDiYqKIiwsjKioqLwq/8q61kFVHSWhSk5/kkopVcaMMbhcLr+JVmJiYoiNjcXlclXa6ufCRkl4SrtMoaq0NOgrpVQZ8/beP3r0KJ06daJOnTp06tSJo0eP5vXer2wCR0k88MADeVX9GvhPHhr0lVKqjAUOJQM7CVKnTp0q7VCywkZJJCYmVtpREqr0Kv00vEopVRVVxaFkVW2UhCo9/UkqpVQ5qYpDyarSKAlVevrTVEoppaoJDfpKKaVUNaFBXymllKomNOgrpZRS1YQGfaWUUqqa0KCvlFJKVRMa9JVSSqlqQoO+UkopVU1o0FdKKaWqCQ36SimlVDWhQV8ppZSqJjToK6WUUtWEBn2llFKqmtCgr5RSSlUTGvSVUkqpakKDvlJKKVVNaNBXSimlqgkjIhWdh3JjjNkLbC3DS9YB9pXh9VRwep9DR+91aOh9Dg29z1ZzEakb7MBJHfTLmjFmhYh0qeh8nOz0PoeO3uvQ0PscGnqfi6fV+0oppVQ1oUFfKaWUqiY06JfOhIrOQDWh9zl09F6Hht7n0ND7XAxt01dKKaWqCS3pK6WUUtWEBn2llFKqmtCgX0LGmNuMMZuNMRnGmGRjTK+KzlNVYYx50Biz3BhzxBiz1xgzwxjTMSCNMcaMM8bsNMakG2PmGmM6BKSpZYyZbIw5nLtNNsbEh/TDVCG5912MMa/77NP7XEaMMQ2NMe/l/k5nGGPWGWPO8Tmu9/oEGWOcxpgnfP73bjbGPGmMCfNJo/e5FDTol4AxZgjwKvA0cAawCJhljGlWoRmrOvoAbwI9gX5ADvCtMSbBJ819wD3AncCZwB5gjjEmzifN/4DOwPm5W2dgcnlnvioyxnQHbgZ+CTik97kM5AaMhYABLgTaYe/pHp9keq9P3P3A7cBdwKnA3bmvH/RJo/e5NEREt2I2YCkwMWDfRuBfFZ23qrgBsYAbuDj3tQFSgId80kQBR4GRua/bAQKc5ZPm7Nx9bSv6M1WmDagJbAL6AnOB1/U+l/k9fhpYWMRxvddlc5+/At4L2Pce8JXe5+PbtKRfDGOMC0gCZgccmo0tuarSi8PWMh3Mfd0CaIDPPRaRdGA++fe4B5CKrWXxWgikoT+HQBOAaSLyQ8B+vc9lZzCw1Bgz1RizxxizyhhzhzHG5B7Xe102fgT6GmNOBTDGtMfWFs7MPa73uZTCik9S7dUBnMDugP27gf6hz85J4VVgFbA493WD3K/B7nFjnzR7JfcxHUBExBizx+f8as8YcxNwCnB1kMN6n8tOS+A24GXgGeB04LXcY6+j97qsPIstJKwzxrixMespEXkz97je51LSoK9CyhjzErZq7WwRcVd0fk4mxpi22Grns0Uku6Lzc5JzACtExNu2vNIY0xrb3vx64aepUhoCXAtcBazFPly9aozZLCL/rciMVVVavV+8fdj25/oB++sDu0KfnarLGPMyMBToJyJ/+Bzy3sei7vEuoK5P9Sm539dDfw5ePbA1U2uNMTnGmBzgHOC23O/356bT+3ziUoB1AfvWA97Ovfo7XTaeB14QkSkislpEJgMvkd+RT+9zKWnQL4aIZAHJwICAQwPwbyNSRTDGvEp+wP814PBm7B/fAJ/0kUAv8u/xYmwHwB4+5/UAYtCfg9fnQCdsaci7rQCm5H7/G3qfy8pCoG3AvjbkL+Wtv9NlIxpb6PLlJj926X0urYruSVgVNmwVUxZwI7Yn6KvYjiHNKzpvVWED3gCOYDvgNPDZYn3S3A8cBi4DOmID1U4gzifNLGA19g+2R+73Myr681XmDZ/e+3qfy/S+nglkAw9h+1D8Pfe+3q73ukzv8yRgO3ZYZCJwKbAXeFHv83He04rOQFXZsJ12tgCZ2JJ/74rOU1XZsENjgm3jfNIYYBy22jQDmAd0DLhOLeCD3AeII7nfx1f056vMW5Cgr/e57O7thcDPuffxN+xYcqP3ukzvcRzwCrYGJR34A9tvJVLv8/FtuuCOUkopVU1om75SSilVTWjQV0oppaoJDfpKKaVUNaFBXymllKomNOgrpZRS1YQGfaWUUqqa0KCvVAUwxvQwxnxsjNlpjMkyxuw3xswxxlxnjHHmphlujBFjTKLPeVuMMZMCrnWxMWa1MSYjN328McZhjHnFGJNijPEYYz4vx8+SmPu+w4tJ5/08p5RXXo6XMWawMWZUkP19cvOsi2upk4IuuKNUiBlj/oGdP/x77GxiW7GThwwE/gMcAr4o5PRLsZOLeK8VBnyInU70duzMkUeBy4G7gXuw05DuL3Al5WswdtXMlyo4H0qVKw36SoWQMaY3NrC8LiJ3BRz+IncVwpjCzheRlQG7GmNnLftYROb7vE+73G9fERFPGeQ7QkQyT/Q6SqmKpdX7SoXW/cAB4L5gB0Vkk4j8UtjJvtX7xphx2KmhAf6bWw091xizBTstKYDbt+rdGNPQGPO+MWafMSbTGPOLMebqgPfwVsP3NsZ8Yow5BCzNPRZtjHkztzki1RjzJdDkOO5DoYwxNxtjfs5trthnjPmvMSYhII0YY540xtxljNlsjDlqjJlnjOkQkM6Zmy7FGHPMGPO9MebU3PPH5aaZBFwHNM7dL7n30Fe0Meb13PzsM8Z8YIyJL8vPrVQoaElfqRDJbavvC3wuIhllcMm3gTXAJ8CTwNfYqv8I7Dzww8lfWWyTMSYGOy95LWAM8CdwNTDZGBMtIhMCrv8h8BG2qcD7v+It7AJUjwHLsaub/a8MPgsAxphnsE0S/wZGY2syngQ6GmN6iojvimtXAxuwzRgu7DKsXxhjThWRnNw0j+V+1ueBb4Ek4MuAt30CqItdROevufsCazVeBb7CruveFngOu9rbdSfyeZUKNQ36SoVOHSCK/OVXT4iIbDfGrMp9uUlElniPGWN25Kbx3XcH0BroKyJzc3fPMsbUB540xvw3IKhOE5H7fM5viw16D4nIM7m7ZxtjYoFbTvTz5HZYHA08JiKP++z/DfgRuBi7fLBXNnCRiGTnpgP7ANQVWGSMqQX8AxgvIvfnnjPHGJMFvOi9iIhsMsbsBbJ871eA+SJyZ+73s3PvxY3GmOGiC5ioKkSr95WqPnoDO3wCvtcH2JJu+4D9nwW87ob9n/FxwP4pZZS/AbnX/9AYE+bdsE0LR7H59zXHG/Bzrc792iz3ayds/4hPAs6bdhx5+zrg9WpsjUr947iWUhVGS/pKhc5+7PKgzSvo/ROwy48G2uVz3Fdg2oa5X3cH7A98fbzq5X79vZDjtQNeHwh47a2Sj8z96s3vnoB0x5Pf4t5LqSpBg75SISIiOcaYucCACuoNfwDbHh2ogc9xX4HV1t6HgPrYdc3xeV0WvMMKBwIHizheUt781gPW+uzX0rmqtrR6X6nQegZbYn0u2EFjTAtjzGnl9N7zgCbGmLMC9l+FLQ2vK+b8pYAHuCJg/5Vlkz3m5F6/mYisCLJtLuX1VgNpwN8D9ge+Bltyjyp9lpWqWrSkr1QIicj83JnfXjLGtAcmAduwPerPBW7EBuFCh+2dgEnYnu7TjTEPAduBYdi29JEBnfiC5X2DMeZ/wOPGGAe29/5AYFAp83G+MWZXwL7DIjLHGPMs8HpuR7l5QAbQNDePb4vIDyV9ExE5aIx5BRhjjDmK7b3fGbghN4nv/AXrgARjzK3ACiBDRFaj1ElGg75SISYirxhjlgH/BF7A9uo/ig02I4EZ5fS+acaYc7C1DM9gJ/XZAFwjIh+U8DIjgVTgXuwwue+xDyk/liIrrwXZtxboKCJjjDHrsbML3o5tYvgT+A7YWIr38HoUMNhAfxe2tmI4sBA47JPubaA78DQQjx1hkXgc76dUpWZ0tIlSqjoxxlyO7dHfW0QWVHR+lAolDfpKqZOWMaYbcCG2hJ+BnZznAWwNR08dY6+qG63eV0qdzFKx4/tvB2pgOyx+DDyoAV9VR1rSV0oppaoJHbKnlFJKVRMa9JVSSqlqQoO+UkopVU1o0FdKKaWqCQ36SimlVDWhQV8ppZSqJv4fTFzGMltgY3EAAAAASUVORK5CYII="
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
-   "source": [
-    "lengths = np.arange(1, 1000, 100)\n",
-    "num_samples = 10\n",
-    "seed = 1010\n",
-    "qubits = [0]\n",
-    "# Run an RB experiment on qubit 0\n",
-    "exp1 = StandardRB(qubits, lengths, num_samples=num_samples, seed=seed)\n",
-    "expdata1 = exp1.run(backend)\n",
-    "expdata1.block_for_results()\n",
-    "result = expdata1.analysis_results(0)\n",
-    "# View result data\n",
-    "print(result)\n",
-    "display(expdata1.figure(0))"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Running 2-qubit RB experiment\n",
     "\n",
     "Running a 1-qubit RB experiment and a 2-qubit RB experiment, in order to calculate the gate error (EPG) of  the `cx` gate:"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Analysis Result: StandardRB\n",
-      "Analysis Result ID: c458cbc5-8f70-4c93-a416-5a82dc345f6e\n",
-      "Experiment ID: b70311b0-ea64-4af3-b1d2-ab0b95ff79dc\n",
-      "Device Components: [<Qubit(Q1)>, <Qubit(Q4)>]\n",
-      "Quality: None\n",
-      "Verified: False\n",
-      "Result Data:<super: <class 'CurveAnalysisResultData'>, <CurveAnalysisResultData object>>\n",
-      "  - a: 0.6929359057250185 ± 0.022809600699452162\n",
-      "  - alpha: 0.9544996812764374 ± 0.003341829326697232\n",
-      "  - b: 0.2647774987044865 ± 0.006159040209452071\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 576x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Backend's reported EPG of the cx gate: 0.012438847900902494\n",
-      "Experiment computed EPG of the cx gate: 0.01262803065926493\n"
-     ]
-    }
-   ],
    "source": [
     "lengths = np.arange(1, 200, 30)\n",
     "num_samples = 10\n",
@@ -202,43 +162,87 @@
     "exp2_epg = expdata2.analysis_results(0).data()['EPG'][qubits]['cx']\n",
     "print(\"Backend's reported EPG of the cx gate:\", expected_epg)\n",
     "print(\"Experiment computed EPG of the cx gate:\", exp2_epg)"
-   ]
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "\n",
+      "Analysis Result: StandardRB\n",
+      "Analysis Result ID: c458cbc5-8f70-4c93-a416-5a82dc345f6e\n",
+      "Experiment ID: b70311b0-ea64-4af3-b1d2-ab0b95ff79dc\n",
+      "Device Components: [<Qubit(Q1)>, <Qubit(Q4)>]\n",
+      "Quality: None\n",
+      "Verified: False\n",
+      "Result Data:<super: <class 'CurveAnalysisResultData'>, <CurveAnalysisResultData object>>\n",
+      "  - a: 0.6929359057250185 ± 0.022809600699452162\n",
+      "  - alpha: 0.9544996812764374 ± 0.003341829326697232\n",
+      "  - b: 0.2647774987044865 ± 0.006159040209452071\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ],
+      "image/png": "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"
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Backend's reported EPG of the cx gate: 0.012438847900902494\n",
+      "Experiment computed EPG of the cx gate: 0.01262803065926493\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "scrolled": false
+   }
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Displaying the RB circuits\n",
     "\n",
     "Generating an example RB circuit:"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "# Run an RB experiment on qubit 0\n",
     "exp = StandardRB(qubits=[0], lengths=[10], num_samples=1, seed=seed)\n",
     "c = exp.circuits()[0]"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "We transpile the circuit into the backend's basis gate set:"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {},
+   "source": [
+    "from qiskit import transpile\n",
+    "basis_gates = backend.configuration().basis_gates\n",
+    "print(transpile(c, basis_gates=basis_gates))"
+   ],
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "global phase: π/2\n",
       "         ░ ┌──────────┐┌────┐┌───────┐ ░ ┌────┐┌─────────┐ ░ ┌──────────┐┌────┐»\n",
@@ -264,15 +268,10 @@
      ]
     }
    ],
-   "source": [
-    "from qiskit import transpile\n",
-    "basis_gates = backend.configuration().basis_gates\n",
-    "print(transpile(c, basis_gates=basis_gates))"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "# 2. Interleaved RB experiment\n",
     "\n",
@@ -293,23 +292,40 @@
     "- `EPC_systematic_err`: The systematic error of the interleaved gate error (see Ref. [4]).\n",
     "\n",
     "- `EPC_systematic_bounds`: The systematic error bounds of the interleaved gate error (see Ref. [4]).\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Running 1-qubit interleaved RB experiment"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 53,
-   "metadata": {},
+   "source": [
+    "lengths = np.arange(1, 1000, 100)\n",
+    "num_samples = 10\n",
+    "seed = 1010\n",
+    "qubits = [0]\n",
+    "\n",
+    "# Run an Interleaved RB experiment on qubit 0\n",
+    "# The interleaved gate is the x gate\n",
+    "int_exp1 = InterleavedRB(\n",
+    "    circuits.XGate(), qubits, lengths, num_samples=num_samples, seed=seed)\n",
+    "int_expdata1 = int_exp1.run(backend)\n",
+    "int_expdata1.block_for_results()\n",
+    "result = int_expdata1.analysis_results(0)\n",
+    "# View result data\n",
+    "print(result)\n",
+    "display(int_expdata1.figure(0))"
+   ],
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "\n",
       "Analysis Result: InterleavedRB\n",
@@ -326,49 +342,49 @@
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
-      ]
+      ],
+      "image/png": "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"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
-   "source": [
-    "lengths = np.arange(1, 1000, 100)\n",
-    "num_samples = 10\n",
-    "seed = 1010\n",
-    "qubits = [0]\n",
-    "\n",
-    "# Run an Interleaved RB experiment on qubit 0\n",
-    "# The interleaved gate is the x gate\n",
-    "int_exp1 = InterleavedRB(\n",
-    "    circuits.XGate(), qubits, lengths, num_samples=num_samples, seed=seed)\n",
-    "int_expdata1 = int_exp1.run(backend)\n",
-    "int_expdata1.block_for_results()\n",
-    "result = int_expdata1.analysis_results(0)\n",
-    "# View result data\n",
-    "print(result)\n",
-    "display(int_expdata1.figure(0))"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Running 2-qubit interleaved RB experiment"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 54,
-   "metadata": {},
+   "source": [
+    "lengths = np.arange(1, 200, 30)\n",
+    "num_samples = 10\n",
+    "seed = 1010\n",
+    "qubits = [4,6]\n",
+    "\n",
+    "# Run an Interleaved RB experiment on qubits 4, 6\n",
+    "# The interleaved gate is the cx gate\n",
+    "int_exp2 = InterleavedRB(\n",
+    "    circuits.CXGate(), qubits, lengths, num_samples=num_samples, seed=seed)\n",
+    "int_expdata2 = int_exp2.run(backend)\n",
+    "int_expdata2.block_for_results()\n",
+    "result = int_expdata2.analysis_results(0)\n",
+    "# View result data\n",
+    "print(result)\n",
+    "display(int_expdata2.figure(0))"
+   ],
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "\n",
       "Analysis Result: InterleavedRB\n",
@@ -385,53 +401,51 @@
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
-      ]
+      ],
+      "image/png": "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"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
-   "source": [
-    "lengths = np.arange(1, 200, 30)\n",
-    "num_samples = 10\n",
-    "seed = 1010\n",
-    "qubits = [4,6]\n",
-    "\n",
-    "# Run an Interleaved RB experiment on qubits 4, 6\n",
-    "# The interleaved gate is the cx gate\n",
-    "int_exp2 = InterleavedRB(\n",
-    "    circuits.CXGate(), qubits, lengths, num_samples=num_samples, seed=seed)\n",
-    "int_expdata2 = int_exp2.run(backend)\n",
-    "int_expdata2.block_for_results()\n",
-    "result = int_expdata2.analysis_results(0)\n",
-    "# View result data\n",
-    "print(result)\n",
-    "display(int_expdata2.figure(0))"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "# 3. Simultaneous RB experiment\n",
     "\n",
     "We use `ParallelExperiment` to run the RB experiment simultaneously on different qubits (see Ref. [5])"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 55,
-   "metadata": {
-    "scrolled": false
-   },
+   "source": [
+    "lengths = np.arange(1, 1000, 100)\n",
+    "num_samples = 10\n",
+    "seed = 1010\n",
+    "qubits = range(5)\n",
+    "\n",
+    "# Run a parallel 1-qubit RB experiment on qubits 0, 1, 2, 3, 4\n",
+    "exps = [StandardRB([i], lengths, num_samples=num_samples, seed=seed + i)\n",
+    "        for i in qubits]\n",
+    "\n",
+    "par_exp = ParallelExperiment(exps)\n",
+    "par_expdata = par_exp.run(backend)\n",
+    "par_expdata.block_for_results()\n",
+    "result = par_expdata.analysis_results(0)\n",
+    "# View result data\n",
+    "print(result)"
+   ],
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "\n",
       "Analysis Result: ParallelExperiment\n",
@@ -448,41 +462,32 @@
      ]
     }
    ],
-   "source": [
-    "lengths = np.arange(1, 1000, 100)\n",
-    "num_samples = 10\n",
-    "seed = 1010\n",
-    "qubits = range(5)\n",
-    "\n",
-    "# Run a parallel 1-qubit RB experiment on qubits 0, 1, 2, 3, 4\n",
-    "exps = [StandardRB([i], lengths, num_samples=num_samples, seed=seed + i)\n",
-    "        for i in qubits]\n",
-    "\n",
-    "par_exp = ParallelExperiment(exps)\n",
-    "par_expdata = par_exp.run(backend)\n",
-    "par_expdata.block_for_results()\n",
-    "result = par_expdata.analysis_results(0)\n",
-    "# View result data\n",
-    "print(result)"
-   ]
+   "metadata": {
+    "scrolled": false
+   }
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Viewing sub experiment data\n",
     "\n",
     "The experiment data returned from a batched experiment also contains individual experiment data for each sub experiment which can be accessed using `experiment_data(index)`"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 56,
-   "metadata": {},
+   "source": [
+    "# Print sub-experiment data\n",
+    "for i in range(par_exp.num_experiments):\n",
+    "    print(par_expdata.component_experiment_data(i).analysis_results(0), '\\n')\n",
+    "    display(par_expdata.component_experiment_data(i).figure(0))"
+   ],
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "\n",
       "Analysis Result: StandardRB\n",
@@ -499,18 +504,18 @@
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
-      ]
+      ],
+      "image/png": "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"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     },
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "\n",
       "Analysis Result: StandardRB\n",
@@ -527,18 +532,18 @@
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
-      ]
+      ],
+      "image/png": "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"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     },
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "\n",
       "Analysis Result: StandardRB\n",
@@ -555,18 +560,18 @@
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
-      ]
+      ],
+      "image/png": "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"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     },
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "\n",
       "Analysis Result: StandardRB\n",
@@ -583,18 +588,18 @@
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
-      ]
+      ],
+      "image/png": "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"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     },
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "\n",
       "Analysis Result: StandardRB\n",
@@ -611,26 +616,20 @@
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
-      ]
+      ],
+      "image/png": "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"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
-   "source": [
-    "# Print sub-experiment data\n",
-    "for i in range(par_exp.num_experiments):\n",
-    "    print(par_expdata.component_experiment_data(i).analysis_results(0), '\\n')\n",
-    "    display(par_expdata.component_experiment_data(i).figure(0))"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## References\n",
     "\n",
@@ -646,14 +645,15 @@
     "\n",
     "[5] Jay M. Gambetta, A. D. C´orcoles, S. T. Merkel, B. R. Johnson, John A. Smolin, Jerry M. Chow, Colm A. Ryan, Chad Rigetti, S. Poletto, Thomas A. Ohki, Mark B. Ketchen, and M. Steffen, *Characterization of addressability by simultaneous randomized benchmarking*, https://arxiv.org/pdf/1204.6308\n",
     "\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "source": [],
    "outputs": [],
-   "source": []
+   "metadata": {}
   }
  ],
  "metadata": {
@@ -680,4 +680,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/docs/tutorials/t1.ipynb b/docs/tutorials/t1.ipynb
index 92642ad650..cb54fbf6a7 100644
--- a/docs/tutorials/t1.ipynb
+++ b/docs/tutorials/t1.ipynb
@@ -24,7 +24,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "source": [
-    "from qiskit_experiments import ParallelExperiment\n",
+    "from qiskit_experiments.framework import ParallelExperiment\n",
     "from qiskit_experiments.library import T1\n",
     "\n",
     "# A T1 simulator\n",
diff --git a/qiskit_experiments/__init__.py b/qiskit_experiments/__init__.py
index b314620cdc..f5ac26796b 100644
--- a/qiskit_experiments/__init__.py
+++ b/qiskit_experiments/__init__.py
@@ -27,14 +27,22 @@
 Modules
 =======
 
-Experiment Library
-******************
-
-The :mod:`qiskit_experiments.library` module contains a list of available
-experiments.
-
-Experiment Utility Modules
---------------------------
+.. list-table::
+
+    * - :mod:`~qiskit_experiments.library`
+      - Library of available experiments.
+    * - :mod:`~qiskit_experiments.framework`
+      - Core classes for experiments and analysis.
+    * - :mod:`~qiskit_experiments.data_processing`
+      - Tools for building data processor workflows of experiment
+        measurement data.
+    * - :mod:`~qiskit_experiments.curve_analysis`
+      - Utility functions for curve fitting and analysis.
+    * - :mod:`~qiskit_experiments.calibration_management`
+      - Classes for managing calibration experiment result data.
+    * - :mod:`~qiskit_experiments.database_service`
+      - Classes for saving and retrieving experiment and analysis results
+        from a database.
 
 Certain experiments also have additional utilities contained which can be
 accessed by importing the following modules.
@@ -43,75 +51,14 @@
 - :mod:`qiskit_experiments.library.characterization`
 - :mod:`qiskit_experiments.library.randomized_benchmarking`
 - :mod:`qiskit_experiments.library.tomography`
-
-Analysis
-********
-
-This :mod:`qiskit_experiments.analysis` module contains utility functions for
-analysis experiment data.
-
-Data Processing
-***************
-
-This :mod:`qiskit_experiments.data_processing` module contains tools for processing
-experiment measurement data.
-
-Calibration Management
-**********************
-
-This :mod:`qiskit_experiments.calibration_management` module contains classes
-for managing calibration experiment result data.
-
-Database Service
-****************
-
-This :mod:`qiskit_experiments.database_service` module contains classes for saving
-and retrieving experiment and analysis results from a database.
-
-Experiment Data Classes
-=======================
-
-These container classes store the data and results from running experiments
-
-.. autosummary::
-    :toctree: ../stubs/
-
-    ExperimentData
-
-Composite Experiment Classes
-============================
-
-.. autosummary::
-    :toctree: ../stubs/
-
-    ~composite.BatchExperiment
-    ~composite.ParallelExperiment
-
-Experiment Base Classes
-=======================
-
-Construction of custom experiments should be done by making subclasses of the following
-base classes
-
-.. autosummary::
-    :toctree: ../stubs/
-
-    BaseExperiment
-    BaseAnalysis
 """
 
 from .version import __version__
 
-# Base Classes
-from .experiment_data import ExperimentData
-from .base_analysis import BaseAnalysis
-from .base_experiment import BaseExperiment
-from .composite import BatchExperiment, ParallelExperiment
-
 # Modules
+from . import framework
 from . import library
-from . import analysis
+from . import curve_analysis
 from . import calibration_management
-from . import composite
 from . import data_processing
 from . import database_service
diff --git a/qiskit_experiments/calibration_management/update_library.py b/qiskit_experiments/calibration_management/update_library.py
index 0dd0d7f5c7..44f6052178 100644
--- a/qiskit_experiments/calibration_management/update_library.py
+++ b/qiskit_experiments/calibration_management/update_library.py
@@ -20,7 +20,7 @@
 from qiskit.circuit import Parameter
 from qiskit.pulse import ScheduleBlock
 
-from qiskit_experiments.experiment_data import ExperimentData
+from qiskit_experiments.framework.experiment_data import ExperimentData
 from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations
 from qiskit_experiments.calibration_management.calibrations import Calibrations
 from qiskit_experiments.calibration_management.parameter_value import ParameterValue
diff --git a/qiskit_experiments/analysis/__init__.py b/qiskit_experiments/curve_analysis/__init__.py
similarity index 63%
rename from qiskit_experiments/analysis/__init__.py
rename to qiskit_experiments/curve_analysis/__init__.py
index fbce8f1f2e..ffddead054 100644
--- a/qiskit_experiments/analysis/__init__.py
+++ b/qiskit_experiments/curve_analysis/__init__.py
@@ -11,38 +11,36 @@
 # that they have been altered from the originals.
 
 """
-=====================================================
-Analysis Library (:mod:`qiskit_experiments.analysis`)
-=====================================================
+=========================================================
+Curve Analysis (:mod:`qiskit_experiments.curve_analysis`)
+=========================================================
 
-.. currentmodule:: qiskit_experiments.analysis
+.. currentmodule:: qiskit_experiments.curve_analysis
 
-Helper functions for experiment data analysis
-
-
-Curve Fitting
-=============
+Classes
+=======
 .. autosummary::
     :toctree: ../stubs/
 
-    curve_fit
-    multi_curve_fit
-    process_curve_data
-    process_multi_curve_data
+    CurveAnalysis
+    CurveAnalysisResultData
+    SeriesDef
+    CurveData
 
+Functions
+=========
+
+Curve Fitting
+*************
 
-Plotting
-========
 .. autosummary::
     :toctree: ../stubs/
 
-    plot_curve_fit
-    plot_errorbar
-    plot_scatter
-
+    curve_fit
+    multi_curve_fit
 
 Fit Functions
-=============
+*************
 .. autosummary::
     :toctree: ../stubs/
 
@@ -51,9 +49,8 @@
     fit_function.gaussian
     fit_function.sin
 
-
-Guess Functions
-===============
+Initial Guess
+*************
 .. autosummary::
     :toctree: ../stubs/
 
@@ -66,23 +63,33 @@
     guess.min_height
     guess.oscillation_exp_decay
 
+Visualization
+*************
+.. autosummary::
+    :toctree: ../stubs/
+
+    plot_curve_fit
+    plot_errorbar
+    plot_scatter
 
 Utility
-=======
+*******
 .. autosummary::
     :toctree: ../stubs/
 
     get_opt_error
     get_opt_value
 """
-from .curve_analysis import CurveAnalysis, SeriesDef, CurveData
-
-from .curve_fitting import (
-    CurveAnalysisResultData,
+from .curve_analysis import CurveAnalysis
+from .curve_analysis_result_data import CurveAnalysisResultData
+from .curve_data import CurveData, SeriesDef
+from .curve_fit import (
     curve_fit,
     multi_curve_fit,
     process_curve_data,
     process_multi_curve_data,
 )
-from .plotting import plot_curve_fit, plot_errorbar, plot_scatter
+from .visualization import plot_curve_fit, plot_errorbar, plot_scatter
 from .utils import get_opt_error, get_opt_value
+from . import guess
+from . import fit_function
diff --git a/qiskit_experiments/analysis/curve_analysis.py b/qiskit_experiments/curve_analysis/curve_analysis.py
similarity index 92%
rename from qiskit_experiments/analysis/curve_analysis.py
rename to qiskit_experiments/curve_analysis/curve_analysis.py
index 91bdc4969b..a31a4d5a43 100644
--- a/qiskit_experiments/analysis/curve_analysis.py
+++ b/qiskit_experiments/curve_analysis/curve_analysis.py
@@ -23,54 +23,22 @@
 import numpy as np
 from qiskit.providers.options import Options
 
-from qiskit_experiments.analysis import plotting
-from qiskit_experiments.analysis.curve_fitting import (
-    multi_curve_fit,
-    CurveAnalysisResultData,
-)
-from qiskit_experiments.analysis.utils import get_opt_value, get_opt_error
-from qiskit_experiments.base_analysis import BaseAnalysis
+from qiskit_experiments.framework import BaseAnalysis, ExperimentData
 from qiskit_experiments.data_processing import DataProcessor
 from qiskit_experiments.data_processing.exceptions import DataProcessorError
 from qiskit_experiments.exceptions import AnalysisError
-from qiskit_experiments.experiment_data import ExperimentData
-from qiskit_experiments.matplotlib import requires_matplotlib
+from qiskit_experiments.matplotlib import pyplot, requires_matplotlib, HAS_MATPLOTLIB
 from qiskit_experiments.data_processing.processor_library import get_processor
 
-
-@dataclasses.dataclass(frozen=True)
-class SeriesDef:
-    """Description of curve."""
-
-    # Arbitrary callback to define the fit function. First argument should be x.
-    fit_func: Callable
-
-    # Keyword dictionary to define the series with circuit metadata
-    filter_kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict)
-
-    # Name of this series. This name will appear in the figure and raw x-y value report.
-    name: str = "Series-0"
-
-    # Color of this line.
-    plot_color: str = "black"
-
-    # Symbol to represent data points of this line.
-    plot_symbol: str = "o"
-
-    # Whether to plot fit uncertainty for this line.
-    plot_fit_uncertainty: bool = False
-
-
-@dataclasses.dataclass(frozen=True)
-class CurveData:
-    """Set of extracted experiment data."""
-
-    label: str
-    x: np.ndarray
-    y: np.ndarray
-    y_err: np.ndarray
-    data_index: Union[np.ndarray, int]
-    metadata: np.ndarray = None
+from qiskit_experiments.curve_analysis.curve_data import CurveData, SeriesDef
+from qiskit_experiments.curve_analysis.curve_analysis_result_data import CurveAnalysisResultData
+from qiskit_experiments.curve_analysis.curve_fit import multi_curve_fit
+from qiskit_experiments.curve_analysis.visualization import (
+    plot_scatter,
+    plot_errorbar,
+    plot_curve_fit,
+)
+from qiskit_experiments.curve_analysis.utils import get_opt_value, get_opt_error
 
 
 class CurveAnalysis(BaseAnalysis):
@@ -81,29 +49,27 @@ class CurveAnalysis(BaseAnalysis):
     create a new curve fit analysis subclass inheriting from this base class.
 
     Class Attributes:
+        - ``__series__``: A set of data points that will be fit to the same parameters
+          in the fit function. If this analysis contains multiple curves,
+          the same number of series definitions should be listed. Each series definition
+          is a :class:`SeriesDef` element, that may be initialized with
 
-        __series__: A set of data points that will be fit to the same parameters
-            in the fit function. If this analysis contains multiple curves,
-            the same number of series definitions should be listed.
-            Each series definition is SeriesDef element, that may be initialized with:
-
-                fit_func: The function to which the data will be fit.
-                filter_kwargs: Circuit metadata key and value associated with this curve.
-                    The data points of the curve are extracted from ExperimentData based on
-                    this information.
-                name: Name of the curve. This is arbitrary data field, but should be unique.
-                plot_color: String color representation of this series in the plot.
-                plot_symbol: String formatter of the scatter of this series in the plot.
-                plot_fit_uncertainty: A Boolean signaling whether to plot fit uncertainty
-                    for this series in the plot.
+            - ``fit_func``: The function to which the data will be fit.
+            - ``filter_kwargs``: Circuit metadata key and value associated with this curve.
+              The data points of the curve are extracted from ExperimentData based on
+              this information.
+            - ``name``: Name of the curve. This is arbitrary data field, but should be unique.
+            - ``plot_color``: String color representation of this series in the plot.
+            - ``plot_symbol``: String formatter of the scatter of this series in the plot.
+            - ``plot_fit_uncertainty``: A Boolean signaling whether to plot fit uncertainty
+              for this series in the plot.
 
-            See the Examples below for more details.
+        See the Examples below for more details.
 
 
     Examples:
 
-        A fitting for single exponential decay curve
-        ============================================
+        **A fitting for single exponential decay curve**
 
         In this type of experiment, the analysis deals with a single curve.
         Thus filter_kwargs and series name are not necessary defined.
@@ -120,8 +86,7 @@ class AnalysisExample(CurveAnalysis):
                 ]
 
 
-        A fitting for two exponential decay curve with partly shared parameter
-        ======================================================================
+        **A fitting for two exponential decay curve with partly shared parameter**
 
         In this type of experiment, the analysis deals with two curves.
         We need a __series__ definition for each curve, and filter_kwargs should be
@@ -155,8 +120,8 @@ class AnalysisExample(CurveAnalysis):
         Parameter `p1` (`p2`) is only used by `my_experiment1` (`my_experiment2`).
         Both series have same fit function in this example.
 
-        A fitting for two trigonometric curves with the same parameter
-        ==============================================================
+
+        **A fitting for two trigonometric curves with the same parameter**
 
         In this type of experiment, the analysis deals with two different curves.
         However the parameters are shared with both functions.
@@ -189,8 +154,7 @@ class AnalysisExample(CurveAnalysis):
         `my_experiment1` (`my_experiment2`) uses the `cos` (`sin`) fit function.
 
 
-        A fitting with fixed parameter
-        ==============================
+        **A fitting with fixed parameter**
 
         In this type of experiment, we can provide fixed fit function parameter.
         This parameter should be assigned via analysis options
@@ -328,7 +292,7 @@ def curve_fitter(
                         ],
                     ) -> CurveAnalysisResultData:
 
-                See :func:`~qiskit_experiment.analysis.multi_curve_fit` for example.
+                See :func:`~qiskit_experiment.curve_analysis.multi_curve_fit` for example.
             data_processor: A callback function to format experiment data.
                 This function should have signature:
 
@@ -383,7 +347,7 @@ def _create_figures(self, result_data: CurveAnalysisResultData) -> List["Figure"
 
         axis = self._get_option("axis")
         if axis is None:
-            figure = plotting.pyplot.figure(figsize=(8, 5))
+            figure = pyplot.figure(figsize=(8, 5))
             axis = figure.subplots(nrows=1, ncols=1)
         else:
             figure = axis.get_figure()
@@ -396,7 +360,7 @@ def _create_figures(self, result_data: CurveAnalysisResultData) -> List["Figure"
             curve_data_raw = self._data(series_name=series_def.name, label="raw_data")
             ymin = min(ymin, *curve_data_raw.y)
             ymax = max(ymax, *curve_data_raw.y)
-            plotting.plot_scatter(xdata=curve_data_raw.x, ydata=curve_data_raw.y, ax=axis, zorder=0)
+            plot_scatter(xdata=curve_data_raw.x, ydata=curve_data_raw.y, ax=axis, zorder=0)
 
             # plot formatted data
 
@@ -406,7 +370,7 @@ def _create_figures(self, result_data: CurveAnalysisResultData) -> List["Figure"
             else:
                 sigma = np.nan_to_num(curve_data_fit.y_err)
 
-            plotting.plot_errorbar(
+            plot_errorbar(
                 xdata=curve_data_fit.x,
                 ydata=curve_data_fit.y,
                 sigma=sigma,
@@ -421,7 +385,7 @@ def _create_figures(self, result_data: CurveAnalysisResultData) -> List["Figure"
             # plot fit curve
 
             if fit_available:
-                plotting.plot_curve_fit(
+                plot_curve_fit(
                     func=series_def.fit_func,
                     result=result_data,
                     ax=axis,
@@ -1065,7 +1029,7 @@ def _run_analysis(
             #
             # 6. Create figures
             #
-            if self._get_option("plot") and plotting.HAS_MATPLOTLIB:
+            if self._get_option("plot") and HAS_MATPLOTLIB:
                 figures.extend(self._create_figures(result_data=result_data))
 
             #
diff --git a/qiskit_experiments/curve_analysis/curve_analysis_result_data.py b/qiskit_experiments/curve_analysis/curve_analysis_result_data.py
new file mode 100644
index 0000000000..f99d7b171e
--- /dev/null
+++ b/qiskit_experiments/curve_analysis/curve_analysis_result_data.py
@@ -0,0 +1,115 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+"""
+Curve analysis result data class.
+"""
+from qiskit_experiments.framework import AnalysisResultData
+
+
+class CurveAnalysisResultData(AnalysisResultData):
+    """Analysis data container for curve fit analysis.
+
+    Class Attributes:
+        __keys_not_shown__: Data keys of analysis result which are not directly shown
+            in `__str__` method. By default, `pcov` (covariance matrix),
+            `raw_data` (raw x, y, sigma data points), `popt`, `popt_keys`, and `popt_err`
+            are not displayed. Fit parameters (popt) are formatted to
+
+            .. code-block::
+
+                p0 = 1.2 ± 0.34
+                p1 = 5.6 ± 0.78
+
+            rather showing raw key-value pairs
+
+            .. code-block::
+
+                popt_keys = ["p0", "p1"]
+                popt = [1.2, 5.6]
+                popt_err = [0.34, 0.78]
+
+            The covariance matrix and raw data points are not shown because they output
+            very long string usually doesn't fit in with the summary of the analysis object,
+            i.e. user wants to quickly get the over view of fit values and goodness of fit,
+            such as the chi-squared value and computer evaluated quality.
+
+            However these non-displayed values are still kept and user can access to
+            these values with `result["raw_data"]` and `result["pcov"]` if necessary.
+    """
+
+    __keys_not_shown__ = "pcov", "raw_data", "popt", "popt_keys", "popt_err"
+
+    def __str__(self):
+        out = ""
+
+        if self.get("success"):
+            popt_keys = self.get("popt_keys")
+            popt = self.get("popt")
+            popt_err = self.get("popt_err")
+
+            for key, value, error in zip(popt_keys, popt, popt_err):
+                out += f"\n  - {key}: {value} \u00B1 {error}"
+        out = str(super()) + out
+
+        return out
+
+    # def get_opt_value(self, param_name: str) -> float:
+    #     """A helper function to get parameter value from a result dictionary.
+
+    #     Args:
+    #         param_name: Name of parameter to extract.
+
+    #     Returns:
+    #         Parameter value.
+
+    #     Raises:
+    #         KeyError:
+    #             - When the result does not contain parameter information.
+    #         ValueError:
+    #             - When specified parameter is not defined.
+    #     """
+    #     try:
+    #         index = self["popt_keys"].index(param_name)
+    #         return self["popt"][index]
+    #     except KeyError as ex:
+    #         raise KeyError(
+    #             "Input result has not fit parameter information. "
+    #             "Please confirm if the fit is successfully completed."
+    #         ) from ex
+    #     except ValueError as ex:
+    #         raise ValueError(f"Parameter {param_name} is not defined.") from ex
+
+    # def get_opt_error(self, param_name: str) -> float:
+    #     """A helper function to get error value from analysis result.
+
+    #     Args:
+    #         param_name: Name of parameter to extract.
+
+    #     Returns:
+    #         Parameter error value.
+
+    #     Raises:
+    #         KeyError:
+    #             - When the result does not contain parameter information.
+    #         ValueError:
+    #             - When specified parameter is not defined.
+    #     """
+    #     try:
+    #         index = self["popt_keys"].index(param_name)
+    #         return self["popt_err"][index]
+    #     except KeyError as ex:
+    #         raise KeyError(
+    #             "Input result has not fit parameter information. "
+    #             "Please confirm if the fit is successfully completed."
+    #         ) from ex
+    #     except ValueError as ex:
+    #         raise ValueError(f"Parameter {param_name} is not defined.") from ex
diff --git a/qiskit_experiments/curve_analysis/curve_data.py b/qiskit_experiments/curve_analysis/curve_data.py
new file mode 100644
index 0000000000..d113995c55
--- /dev/null
+++ b/qiskit_experiments/curve_analysis/curve_data.py
@@ -0,0 +1,54 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""
+Curve data classes.
+"""
+
+import dataclasses
+from typing import Any, Dict, Callable, Union
+import numpy as np
+
+
+@dataclasses.dataclass(frozen=True)
+class SeriesDef:
+    """Description of curve."""
+
+    # Arbitrary callback to define the fit function. First argument should be x.
+    fit_func: Callable
+
+    # Keyword dictionary to define the series with circuit metadata
+    filter_kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict)
+
+    # Name of this series. This name will appear in the figure and raw x-y value report.
+    name: str = "Series-0"
+
+    # Color of this line.
+    plot_color: str = "black"
+
+    # Symbol to represent data points of this line.
+    plot_symbol: str = "o"
+
+    # Whether to plot fit uncertainty for this line.
+    plot_fit_uncertainty: bool = False
+
+
+@dataclasses.dataclass(frozen=True)
+class CurveData:
+    """Set of extracted experiment data."""
+
+    label: str
+    x: np.ndarray
+    y: np.ndarray
+    y_err: np.ndarray
+    data_index: Union[np.ndarray, int]
+    metadata: np.ndarray = None
diff --git a/qiskit_experiments/analysis/curve_fitting.py b/qiskit_experiments/curve_analysis/curve_fit.py
similarity index 85%
rename from qiskit_experiments/analysis/curve_fitting.py
rename to qiskit_experiments/curve_analysis/curve_fit.py
index 92bdeabdb1..41d654e308 100644
--- a/qiskit_experiments/analysis/curve_fitting.py
+++ b/qiskit_experiments/curve_analysis/curve_fit.py
@@ -19,56 +19,8 @@
 import numpy as np
 import scipy.optimize as opt
 from qiskit_experiments.exceptions import AnalysisError
-from qiskit_experiments.analysis.data_processing import filter_data
-from qiskit_experiments.experiment_data import AnalysisResultData
-
-
-class CurveAnalysisResultData(AnalysisResultData):
-    """Analysis data container for curve fit analysis.
-
-    Class Attributes:
-        __keys_not_shown__: Data keys of analysis result which are not directly shown
-            in `__str__` method. By default, `pcov` (covariance matrix),
-            `raw_data` (raw x, y, sigma data points), `popt`, `popt_keys`, and `popt_err`
-            are not displayed. Fit parameters (popt) are formatted to
-
-            .. code-block::
-
-                p0 = 1.2 ± 0.34
-                p1 = 5.6 ± 0.78
-
-            rather showing raw key-value pairs
-
-            .. code-block::
-
-                popt_keys = ["p0", "p1"]
-                popt = [1.2, 5.6]
-                popt_err = [0.34, 0.78]
-
-            The covariance matrix and raw data points are not shown because they output
-            very long string usually doesn't fit in with the summary of the analysis object,
-            i.e. user wants to quickly get the over view of fit values and goodness of fit,
-            such as the chi-squared value and computer evaluated quality.
-
-            However these non-displayed values are still kept and user can access to
-            these values with `result["raw_data"]` and `result["pcov"]` if necessary.
-    """
-
-    __keys_not_shown__ = "pcov", "raw_data", "popt", "popt_keys", "popt_err"
-
-    def __str__(self):
-        out = ""
-
-        if self.get("success"):
-            popt_keys = self.get("popt_keys")
-            popt = self.get("popt")
-            popt_err = self.get("popt_err")
-
-            for key, value, error in zip(popt_keys, popt, popt_err):
-                out += f"\n  - {key}: {value} \u00B1 {error}"
-        out = str(super()) + out
-
-        return out
+from qiskit_experiments.curve_analysis.data_processing import filter_data
+from qiskit_experiments.curve_analysis.curve_analysis_result_data import CurveAnalysisResultData
 
 
 def curve_fit(
diff --git a/qiskit_experiments/analysis/data_processing.py b/qiskit_experiments/curve_analysis/data_processing.py
similarity index 100%
rename from qiskit_experiments/analysis/data_processing.py
rename to qiskit_experiments/curve_analysis/data_processing.py
diff --git a/qiskit_experiments/analysis/fit_function.py b/qiskit_experiments/curve_analysis/fit_function.py
similarity index 100%
rename from qiskit_experiments/analysis/fit_function.py
rename to qiskit_experiments/curve_analysis/fit_function.py
diff --git a/qiskit_experiments/analysis/guess.py b/qiskit_experiments/curve_analysis/guess.py
similarity index 100%
rename from qiskit_experiments/analysis/guess.py
rename to qiskit_experiments/curve_analysis/guess.py
diff --git a/qiskit_experiments/analysis/utils.py b/qiskit_experiments/curve_analysis/utils.py
similarity index 100%
rename from qiskit_experiments/analysis/utils.py
rename to qiskit_experiments/curve_analysis/utils.py
diff --git a/qiskit_experiments/analysis/plotting.py b/qiskit_experiments/curve_analysis/visualization.py
similarity index 100%
rename from qiskit_experiments/analysis/plotting.py
rename to qiskit_experiments/curve_analysis/visualization.py
diff --git a/qiskit_experiments/data_processing/__init__.py b/qiskit_experiments/data_processing/__init__.py
index 8b1194543b..cc7aca34a3 100644
--- a/qiskit_experiments/data_processing/__init__.py
+++ b/qiskit_experiments/data_processing/__init__.py
@@ -10,9 +10,9 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 """
-======================================================================
-Experiment Data Processing (:mod:`qiskit_experiments.data_processing`)
-======================================================================
+===========================================================
+Data Processing (:mod:`qiskit_experiments.data_processing`)
+===========================================================
 
 .. currentmodule:: qiskit_experiments.data_processing
 
diff --git a/qiskit_experiments/framework/__init__.py b/qiskit_experiments/framework/__init__.py
new file mode 100644
index 0000000000..0d2c4e5c6e
--- /dev/null
+++ b/qiskit_experiments/framework/__init__.py
@@ -0,0 +1,126 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""
+==========================================================
+Experiment Framework (:mod:`qiskit_experiments.framework`)
+==========================================================
+
+.. currentmodule:: qiskit_experiments.framework
+
+.. note::
+
+    This page provides useful information for developers to implement new
+    experiments.
+
+Classes
+=======
+
+Experiment Data Classes
+***********************
+.. autosummary::
+    :toctree: ../stubs/
+
+    AnalysisResultData
+    ExperimentData
+
+Composite Experiment Classes
+****************************
+.. autosummary::
+    :toctree: ../stubs/
+
+    ParallelExperiment
+    BatchExperiment
+    CompositeAnalysis
+    CompositeExperimentData
+
+Base Classes
+************
+
+.. autosummary::
+    :toctree: ../stubs/
+
+    BaseExperiment
+    BaseAnalysis
+
+
+Creating Experiments
+====================
+
+Experiments and analysis of experiment data is done by subclassing the
+:class:`BaseExperiment` and :class:`BaseAnalysis` classes.
+
+Experiment Subclasses
+*********************
+
+To create an experiment subclass
+
+- Implement the abstract :meth:`BaseExperiment.circuits` method.
+  This should return a list of ``QuantumCircuit`` objects defining
+  the experiment payload.
+- Call the :meth:`BaseExperiment.__init__` method during the subclass
+  constructor with a list of physical qubits. The length of this list must
+  be equal to the number of qubits in each circuit and is used to map these
+  circuits to this layout during execution.
+  Arguments in the constructor can be overridden so that a subclass can
+  be initialized with some experiment configuration.
+- Set :attr:`BaseExperiment.__analysis_class__` class attribute to
+  specify the :class:`BaseAnalysis` subclass for analyzing result data.
+
+Optionally the following methods can also be overridden in the subclass to
+allow configuring various experiment and execution options
+
+- :meth:`BaseExperiment._default_experiment_options`
+  to set default values for configurable option parameters for the experiment.
+- :meth:`BaseExperiment._default_transpile_options`
+  to set custom default values for the ``qiskit.transpile`` used to
+  transpile the generated circuits before execution.
+- :meth:`BaseExperiment._default_run_options`
+  to set default backend options for running the transpiled circuits on a backend.
+- :meth:`BaseExperiment._default_analysis_options`
+  to set default values for configurable options for the experiments analysis class.
+  Note that these should generally be set by overriding the :class:`BaseAnalysis`
+  method :meth:`BaseAnalysis._default_options` instead of this method except in the
+  case where the experiment requires different defaults to the used analysis class.
+- :meth:`BaseExperiment._post_process_transpiled_circuits`
+  to implement any post-processing of the transpiled circuits before execution.
+- :meth:`BaseExperiment._additional_metadata`
+  to add any experiment metadata to the result data.
+
+Analysis Subclasses
+*******************
+
+To create an analysis subclass one only needs to implement the abstract
+:meth:`BaseAnalysis._run_analysis` method. This method takes a
+:class:`ExperimentData` container and kwarg analysis options. If any
+kwargs are used the :meth:`BaseAnalysis._default_options` method should be
+overriden to define default values for these options.
+
+The :meth:`BaseAnalysis._run_analysis` method should return a pair
+``(result_data, figures)`` where ``result_data`` is a list of
+:class:`AnalysisResultData` and ``figures`` is a list of
+:class:`matplotlib.figure.Figure`.
+
+The :mod:`qiskit_experiments.data_processing` module contains classes for
+building data processor workflows to help with advanced analysis of
+experiment data.
+"""
+from qiskit.providers.options import Options
+from .base_analysis import BaseAnalysis
+from .base_experiment import BaseExperiment
+from .experiment_data import ExperimentData, AnalysisResultData
+from .composite import (
+    ParallelExperiment,
+    BatchExperiment,
+    CompositeAnalysis,
+    CompositeExperimentData,
+)
diff --git a/qiskit_experiments/base_analysis.py b/qiskit_experiments/framework/base_analysis.py
similarity index 97%
rename from qiskit_experiments/base_analysis.py
rename to qiskit_experiments/framework/base_analysis.py
index 8de1664325..8aee95c482 100644
--- a/qiskit_experiments/base_analysis.py
+++ b/qiskit_experiments/framework/base_analysis.py
@@ -20,13 +20,13 @@
 from qiskit.providers.options import Options
 
 from qiskit_experiments.exceptions import AnalysisError
-from qiskit_experiments.experiment_data import ExperimentData
 from qiskit_experiments.database_service import DbAnalysisResultV1
 from qiskit_experiments.database_service.device_component import Qubit
+from qiskit_experiments.framework.experiment_data import ExperimentData
 
 
 class BaseAnalysis(ABC):
-    """Base Analysis class for analyzing Experiment data.
+    """Abstract base class for analyzing Experiment data.
 
     The data produced by experiments (i.e. subclasses of BaseExperiment)
     are analyzed with subclasses of BaseExperiment. The analysis is
diff --git a/qiskit_experiments/base_experiment.py b/qiskit_experiments/framework/base_experiment.py
similarity index 99%
rename from qiskit_experiments/base_experiment.py
rename to qiskit_experiments/framework/base_experiment.py
index df49349115..6a5ceec7d4 100644
--- a/qiskit_experiments/base_experiment.py
+++ b/qiskit_experiments/framework/base_experiment.py
@@ -25,12 +25,11 @@
 from qiskit.providers.basebackend import BaseBackend as LegacyBackend
 from qiskit.exceptions import QiskitError
 from qiskit.qobj.utils import MeasLevel
-
-from .experiment_data import ExperimentData
+from qiskit_experiments.framework.experiment_data import ExperimentData
 
 
 class BaseExperiment(ABC):
-    """Base Experiment class
+    """Abstract base class for experiments.
 
     Class Attributes:
 
diff --git a/qiskit_experiments/composite/__init__.py b/qiskit_experiments/framework/composite/__init__.py
similarity index 57%
rename from qiskit_experiments/composite/__init__.py
rename to qiskit_experiments/framework/composite/__init__.py
index 80668025c5..9aef51733b 100644
--- a/qiskit_experiments/composite/__init__.py
+++ b/qiskit_experiments/framework/composite/__init__.py
@@ -10,39 +10,7 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 
-"""
-===========================================================
-Composite Experiments (:mod:`qiskit_experiments.composite`)
-===========================================================
-
-.. currentmodule:: qiskit_experiments.composite
-
-Experiments
-===========
-.. autosummary::
-    :toctree: ../stubs/
-
-    ParallelExperiment
-    BatchExperiment
-
-
-Analysis
-========
-
-.. autosummary::
-    :toctree: ../stubs/
-
-    CompositeAnalysis
-
-
-Experiment Data
-===============
-
-.. autosummary::
-    :toctree: ../stubs/
-
-    CompositeExperimentData
-"""
+"""Composite Experiments"""
 
 # Base classes
 from .composite_experiment_data import CompositeExperimentData
diff --git a/qiskit_experiments/composite/batch_experiment.py b/qiskit_experiments/framework/composite/batch_experiment.py
similarity index 100%
rename from qiskit_experiments/composite/batch_experiment.py
rename to qiskit_experiments/framework/composite/batch_experiment.py
diff --git a/qiskit_experiments/composite/composite_analysis.py b/qiskit_experiments/framework/composite/composite_analysis.py
similarity index 92%
rename from qiskit_experiments/composite/composite_analysis.py
rename to qiskit_experiments/framework/composite/composite_analysis.py
index cdc0d75ab0..c8f2608ab8 100644
--- a/qiskit_experiments/composite/composite_analysis.py
+++ b/qiskit_experiments/framework/composite/composite_analysis.py
@@ -14,9 +14,8 @@
 """
 
 from qiskit.exceptions import QiskitError
-from qiskit_experiments.base_analysis import BaseAnalysis
-from qiskit_experiments.experiment_data import AnalysisResultData
-from qiskit_experiments.composite.composite_experiment_data import CompositeExperimentData
+from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData
+from .composite_experiment_data import CompositeExperimentData
 
 
 class CompositeAnalysis(BaseAnalysis):
diff --git a/qiskit_experiments/composite/composite_experiment.py b/qiskit_experiments/framework/composite/composite_experiment.py
similarity index 98%
rename from qiskit_experiments/composite/composite_experiment.py
rename to qiskit_experiments/framework/composite/composite_experiment.py
index d22ba9c53f..b58a642fe4 100644
--- a/qiskit_experiments/composite/composite_experiment.py
+++ b/qiskit_experiments/framework/composite/composite_experiment.py
@@ -16,7 +16,7 @@
 from abc import abstractmethod
 import warnings
 
-from qiskit_experiments.base_experiment import BaseExperiment
+from qiskit_experiments.framework import BaseExperiment
 from .composite_experiment_data import CompositeExperimentData
 from .composite_analysis import CompositeAnalysis
 
diff --git a/qiskit_experiments/composite/composite_experiment_data.py b/qiskit_experiments/framework/composite/composite_experiment_data.py
similarity index 98%
rename from qiskit_experiments/composite/composite_experiment_data.py
rename to qiskit_experiments/framework/composite/composite_experiment_data.py
index 3ec712980c..8667e30e7c 100644
--- a/qiskit_experiments/composite/composite_experiment_data.py
+++ b/qiskit_experiments/framework/composite/composite_experiment_data.py
@@ -16,7 +16,7 @@
 from typing import Optional, Union, List
 from qiskit.result import marginal_counts
 from qiskit.exceptions import QiskitError
-from qiskit_experiments.experiment_data import ExperimentData
+from qiskit_experiments.framework.experiment_data import ExperimentData
 
 
 class CompositeExperimentData(ExperimentData):
diff --git a/qiskit_experiments/composite/parallel_experiment.py b/qiskit_experiments/framework/composite/parallel_experiment.py
similarity index 100%
rename from qiskit_experiments/composite/parallel_experiment.py
rename to qiskit_experiments/framework/composite/parallel_experiment.py
diff --git a/qiskit_experiments/experiment_data.py b/qiskit_experiments/framework/experiment_data.py
similarity index 100%
rename from qiskit_experiments/experiment_data.py
rename to qiskit_experiments/framework/experiment_data.py
diff --git a/qiskit_experiments/library/calibration/analysis/drag_analysis.py b/qiskit_experiments/library/calibration/analysis/drag_analysis.py
index a3c699b901..a085e83c13 100644
--- a/qiskit_experiments/library/calibration/analysis/drag_analysis.py
+++ b/qiskit_experiments/library/calibration/analysis/drag_analysis.py
@@ -15,14 +15,14 @@
 from typing import Any, Dict, List, Union
 import numpy as np
 
-from qiskit_experiments.analysis import (
+from qiskit_experiments.curve_analysis import (
     CurveAnalysis,
     CurveAnalysisResultData,
     SeriesDef,
     get_opt_value,
     get_opt_error,
 )
-from qiskit_experiments.analysis.fit_function import cos
+from qiskit_experiments.curve_analysis.fit_function import cos
 
 
 class DragCalAnalysis(CurveAnalysis):
@@ -90,7 +90,7 @@ class DragCalAnalysis(CurveAnalysis):
     def _default_options(cls):
         """Return the default analysis options.
 
-        See :meth:`~qiskit_experiment.analysis.CurveAnalysis._default_options` for
+        See :meth:`~qiskit_experiment.curve_analysis.CurveAnalysis._default_options` for
         descriptions of analysis options.
         """
         default_options = super()._default_options()
diff --git a/qiskit_experiments/library/calibration/analysis/fine_amplitude_analysis.py b/qiskit_experiments/library/calibration/analysis/fine_amplitude_analysis.py
index 4c9b82ea4f..3986b54fb8 100644
--- a/qiskit_experiments/library/calibration/analysis/fine_amplitude_analysis.py
+++ b/qiskit_experiments/library/calibration/analysis/fine_amplitude_analysis.py
@@ -16,7 +16,7 @@
 import numpy as np
 
 from qiskit_experiments.exceptions import CalibrationError
-from qiskit_experiments.analysis import (
+from qiskit_experiments.curve_analysis import (
     CurveAnalysis,
     CurveAnalysisResultData,
     SeriesDef,
@@ -78,7 +78,7 @@ class FineAmplitudeAnalysis(CurveAnalysis):
     def _default_options(cls):
         """Return the default analysis options.
 
-        See :meth:`~qiskit_experiment.analysis.CurveAnalysis._default_options` for
+        See :meth:`~qiskit_experiment.curve_analysis.CurveAnalysis._default_options` for
         descriptions of analysis options.
         """
         default_options = super()._default_options()
diff --git a/qiskit_experiments/library/calibration/analysis/oscillation_analysis.py b/qiskit_experiments/library/calibration/analysis/oscillation_analysis.py
index 09b44eed08..d7b070f897 100644
--- a/qiskit_experiments/library/calibration/analysis/oscillation_analysis.py
+++ b/qiskit_experiments/library/calibration/analysis/oscillation_analysis.py
@@ -15,7 +15,7 @@
 from typing import Any, Dict, List, Union
 import numpy as np
 
-from qiskit_experiments.analysis import (
+from qiskit_experiments.curve_analysis import (
     CurveAnalysis,
     CurveAnalysisResultData,
     SeriesDef,
@@ -42,10 +42,10 @@ class OscillationAnalysis(CurveAnalysis):
         - :math:`{\rm phase}`: Phase of the oscillation.
 
     Initial Guesses
-        - :math:`amp`: Calculated by :func:`~qiskit_experiments.analysis.guess.max_height`.
-        - :math:`baseline`: Calculated by :func:`~qiskit_experiments.analysis.\
+        - :math:`amp`: Calculated by :func:`~qiskit_experiments.curve_analysis.guess.max_height`.
+        - :math:`baseline`: Calculated by :func:`~qiskit_experiments.curve_analysis.\
           guess.constant_sinusoidal_offset`.
-        - :math:`{\rm freq}`: Calculated by :func:`~qiskit_experiments.analysis.\
+        - :math:`{\rm freq}`: Calculated by :func:`~qiskit_experiments.curve_analysis.\
           guess.frequency`.
         - :math:`{\rm phase}`: Zero.
 
@@ -69,7 +69,7 @@ class OscillationAnalysis(CurveAnalysis):
     def _default_options(cls):
         """Return the default analysis options.
 
-        See :meth:`~qiskit_experiment.analysis.CurveAnalysis._default_options` for
+        See :meth:`~qiskit_experiment.curve_analysis.CurveAnalysis._default_options` for
         descriptions of analysis options.
         """
         default_options = super()._default_options()
diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/calibration/drag.py
index 5d6a87ea81..be5f8ae5b0 100644
--- a/qiskit_experiments/library/calibration/drag.py
+++ b/qiskit_experiments/library/calibration/drag.py
@@ -22,7 +22,7 @@
 import qiskit.pulse as pulse
 from qiskit.providers.options import Options
 
-from qiskit_experiments.base_experiment import BaseExperiment
+from qiskit_experiments.framework import BaseExperiment
 from qiskit_experiments.exceptions import CalibrationError
 from qiskit_experiments.library.calibration.analysis.drag_analysis import DragCalAnalysis
 
diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py
index 45a1706eb6..3012e3517c 100644
--- a/qiskit_experiments/library/calibration/fine_amplitude.py
+++ b/qiskit_experiments/library/calibration/fine_amplitude.py
@@ -22,7 +22,7 @@
 from qiskit.providers.options import Options
 from qiskit.pulse.schedule import ScheduleBlock
 
-from qiskit_experiments.base_experiment import BaseExperiment
+from qiskit_experiments.framework import BaseExperiment
 from qiskit_experiments.library.calibration.analysis.fine_amplitude_analysis import (
     FineAmplitudeAnalysis,
 )
diff --git a/qiskit_experiments/library/calibration/rabi.py b/qiskit_experiments/library/calibration/rabi.py
index 948ac9e78a..1881cf4461 100644
--- a/qiskit_experiments/library/calibration/rabi.py
+++ b/qiskit_experiments/library/calibration/rabi.py
@@ -22,7 +22,7 @@
 import qiskit.pulse as pulse
 from qiskit.providers.options import Options
 
-from qiskit_experiments.base_experiment import BaseExperiment
+from qiskit_experiments.framework import BaseExperiment
 from qiskit_experiments.library.calibration.analysis.oscillation_analysis import OscillationAnalysis
 from qiskit_experiments.exceptions import CalibrationError
 
diff --git a/qiskit_experiments/library/characterization/qubit_spectroscopy.py b/qiskit_experiments/library/characterization/qubit_spectroscopy.py
index e6770354f9..0eea0d4aa4 100644
--- a/qiskit_experiments/library/characterization/qubit_spectroscopy.py
+++ b/qiskit_experiments/library/characterization/qubit_spectroscopy.py
@@ -24,7 +24,7 @@
 from qiskit.providers.options import Options
 from qiskit.qobj.utils import MeasLevel
 
-from qiskit_experiments.base_experiment import BaseExperiment
+from qiskit_experiments.framework import BaseExperiment
 from qiskit_experiments.library.characterization.resonance_analysis import ResonanceAnalysis
 
 
diff --git a/qiskit_experiments/library/characterization/resonance_analysis.py b/qiskit_experiments/library/characterization/resonance_analysis.py
index 2a213b7a1a..4e7421620c 100644
--- a/qiskit_experiments/library/characterization/resonance_analysis.py
+++ b/qiskit_experiments/library/characterization/resonance_analysis.py
@@ -16,7 +16,7 @@
 
 import numpy as np
 
-from qiskit_experiments.analysis import (
+from qiskit_experiments.curve_analysis import (
     CurveAnalysis,
     CurveAnalysisResultData,
     SeriesDef,
@@ -47,14 +47,14 @@ class ResonanceAnalysis(CurveAnalysis):
         - :math:`\sigma`: Standard deviation of Gaussian function.
 
     Initial Guesses
-        - :math:`a`: Calculated by :func:`~qiskit_experiments.analysis.guess.max_height`.
-        - :math:`b`: Calculated by :func:`~qiskit_experiments.analysis.guess.\
+        - :math:`a`: Calculated by :func:`~qiskit_experiments.curve_analysis.guess.max_height`.
+        - :math:`b`: Calculated by :func:`~qiskit_experiments.curve_analysis.guess.\
           constant_spectral_offset`.
         - :math:`f`: Frequency at max height position calculated by
-          :func:`~qiskit_experiments.analysis.guess.max_height`.
+          :func:`~qiskit_experiments.curve_analysis.guess.max_height`.
         - :math:`\sigma`: Calculated from FWHM of peak :math:`w`
           such that :math:`w / \sqrt{8} \ln{2}`, where FWHM is calculated by
-          :func:`~qiskit_experiments.analysis.guess.full_width_half_max`.
+          :func:`~qiskit_experiments.curve_analysis.guess.full_width_half_max`.
 
     Bounds
         - :math:`a`: [-2, 2] scaled with maximum signal value.
@@ -78,7 +78,7 @@ class ResonanceAnalysis(CurveAnalysis):
     def _default_options(cls):
         """Return default data processing options.
 
-        See :meth:`~qiskit_experiment.analysis.CurveAnalysis._default_options` for
+        See :meth:`~qiskit_experiment.curve_analysis.CurveAnalysis._default_options` for
         descriptions of analysis options.
         """
         default_options = super()._default_options()
diff --git a/qiskit_experiments/library/characterization/t1.py b/qiskit_experiments/library/characterization/t1.py
index b3401dfeaa..30f601bf16 100644
--- a/qiskit_experiments/library/characterization/t1.py
+++ b/qiskit_experiments/library/characterization/t1.py
@@ -20,7 +20,7 @@
 from qiskit.circuit import QuantumCircuit
 from qiskit.providers.options import Options
 
-from qiskit_experiments.base_experiment import BaseExperiment
+from qiskit_experiments.framework import BaseExperiment
 from qiskit_experiments.library.characterization.t1_analysis import T1Analysis
 
 
diff --git a/qiskit_experiments/library/characterization/t1_analysis.py b/qiskit_experiments/library/characterization/t1_analysis.py
index cfa5822057..7c6c9aa38f 100644
--- a/qiskit_experiments/library/characterization/t1_analysis.py
+++ b/qiskit_experiments/library/characterization/t1_analysis.py
@@ -16,17 +16,16 @@
 from typing import Tuple, List
 import numpy as np
 
-from qiskit.providers.options import Options
 from qiskit.utils import apply_prefix
 
-from qiskit_experiments.base_analysis import BaseAnalysis
-from qiskit_experiments.analysis.curve_fitting import (
+from qiskit_experiments.framework import BaseAnalysis, Options
+from qiskit_experiments.matplotlib import HAS_MATPLOTLIB
+from qiskit_experiments.curve_analysis import plot_curve_fit, plot_errorbar, curve_fit
+from qiskit_experiments.curve_analysis.curve_fit import (
     process_curve_data,
-    curve_fit,
     CurveAnalysisResultData,
 )
-from qiskit_experiments.analysis.data_processing import level2_probability
-from qiskit_experiments.analysis import plotting
+from qiskit_experiments.curve_analysis.data_processing import level2_probability
 
 
 class T1Analysis(BaseAnalysis):
@@ -153,9 +152,9 @@ def fit_fun(x, a, tau, c):
 
         # Generate fit plot
         figures = []
-        if plot and plotting.HAS_MATPLOTLIB:
-            ax = plotting.plot_curve_fit(fit_fun, fit_result, ax=ax, fit_uncertainty=True)
-            ax = plotting.plot_errorbar(xdata, ydata, sigma, ax=ax)
+        if plot and HAS_MATPLOTLIB:
+            ax = plot_curve_fit(fit_fun, fit_result, ax=ax, fit_uncertainty=True)
+            ax = plot_errorbar(xdata, ydata, sigma, ax=ax)
             self._format_plot(ax, fit_result, qubit=qubit)
             figures.append(ax.get_figure())
 
diff --git a/qiskit_experiments/library/characterization/t2ramsey.py b/qiskit_experiments/library/characterization/t2ramsey.py
index 24aae22618..5954216951 100644
--- a/qiskit_experiments/library/characterization/t2ramsey.py
+++ b/qiskit_experiments/library/characterization/t2ramsey.py
@@ -19,7 +19,7 @@
 import qiskit
 from qiskit.providers import Backend
 from qiskit.circuit import QuantumCircuit
-from qiskit_experiments.base_experiment import BaseExperiment
+from qiskit_experiments.framework import BaseExperiment
 from .t2ramsey_analysis import T2RamseyAnalysis
 
 
diff --git a/qiskit_experiments/library/characterization/t2ramsey_analysis.py b/qiskit_experiments/library/characterization/t2ramsey_analysis.py
index 011037e4ca..586bfa6719 100644
--- a/qiskit_experiments/library/characterization/t2ramsey_analysis.py
+++ b/qiskit_experiments/library/characterization/t2ramsey_analysis.py
@@ -17,16 +17,13 @@
 import numpy as np
 
 from qiskit.utils import apply_prefix
-from qiskit.providers.options import Options
-from qiskit_experiments.base_analysis import BaseAnalysis
-from qiskit_experiments.analysis.curve_fitting import (
-    curve_fit,
-    process_curve_data,
-    CurveAnalysisResultData,
-)
-from qiskit_experiments.analysis.data_processing import level2_probability
-from qiskit_experiments.analysis import plotting
-from qiskit_experiments.experiment_data import ExperimentData
+from qiskit_experiments.framework import BaseAnalysis, Options, ExperimentData
+from qiskit_experiments.matplotlib import HAS_MATPLOTLIB
+from qiskit_experiments.curve_analysis import curve_fit, plot_curve_fit, plot_errorbar, plot_scatter
+from qiskit_experiments.curve_analysis.curve_analysis_result_data import CurveAnalysisResultData
+from qiskit_experiments.curve_analysis.curve_fit import process_curve_data
+from qiskit_experiments.curve_analysis.data_processing import level2_probability
+
 
 # pylint: disable = invalid-name
 class T2RamseyAnalysis(BaseAnalysis):
@@ -117,10 +114,10 @@ def _format_plot(ax, unit, fit_result, conversion_factor):
             osc_fit_fun, xdata, ydata, p0=list(p0.values()), sigma=sigma, bounds=bounds
         )
 
-        if plot and plotting.HAS_MATPLOTLIB:
-            ax = plotting.plot_curve_fit(osc_fit_fun, fit_result, ax=ax)
-            ax = plotting.plot_scatter(xdata, ydata, ax=ax)
-            ax = plotting.plot_errorbar(xdata, ydata, sigma, ax=ax)
+        if plot and HAS_MATPLOTLIB:
+            ax = plot_curve_fit(osc_fit_fun, fit_result, ax=ax)
+            ax = plot_scatter(xdata, ydata, ax=ax)
+            ax = plot_errorbar(xdata, ydata, sigma, ax=ax)
             _format_plot(ax, unit, fit_result, conversion_factor)
             figures = [ax.get_figure()]
         else:
diff --git a/qiskit_experiments/library/quantum_volume/qv_analysis.py b/qiskit_experiments/library/quantum_volume/qv_analysis.py
index a5dad87636..78ab9964e4 100644
--- a/qiskit_experiments/library/quantum_volume/qv_analysis.py
+++ b/qiskit_experiments/library/quantum_volume/qv_analysis.py
@@ -18,9 +18,9 @@
 from typing import Optional
 import numpy as np
 
-from qiskit_experiments.base_analysis import BaseAnalysis
-from qiskit_experiments.experiment_data import AnalysisResultData
-from qiskit_experiments.analysis import plotting
+from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData
+from qiskit_experiments.matplotlib import HAS_MATPLOTLIB
+from qiskit_experiments.curve_analysis import plot_scatter, plot_errorbar
 
 
 class QuantumVolumeAnalysis(BaseAnalysis):
@@ -42,7 +42,7 @@ def _run_analysis(
         self,
         experiment_data,
         plot: bool = True,
-        ax: Optional["plotting.pyplot.AxesSubplot"] = None,
+        ax: Optional["matplotlib.pyplot.AxesSubplot"] = None,
     ):
         """Run analysis on circuit data.
         Args:
@@ -72,7 +72,7 @@ def _run_analysis(
             self._calc_quantum_volume(heavy_output_prob_exp, depth, num_trials)
         )
 
-        if plot and plotting.HAS_MATPLOTLIB:
+        if plot and HAS_MATPLOTLIB:
             ax = self._format_plot(ax, analysis_result)
             figures = [ax.get_figure()]
         else:
@@ -228,7 +228,7 @@ def _format_plot(ax, analysis_result):
         two_sigma = 2 * (hop_accumulative * (1 - hop_accumulative) / trial_list) ** 0.5
 
         # Plot inidivual HOP as scatter
-        ax = plotting.plot_scatter(
+        ax = plot_scatter(
             trial_list,
             analysis_result["heavy output probability"],
             ax=ax,
@@ -239,7 +239,7 @@ def _format_plot(ax, analysis_result):
         # Plot accumulative HOP
         ax.plot(trial_list, hop_accumulative, color="r", label="Cumulative HOP")
         # Plot two-sigma shaded area
-        ax = plotting.plot_errorbar(
+        ax = plot_errorbar(
             trial_list,
             hop_accumulative,
             two_sigma,
diff --git a/qiskit_experiments/library/quantum_volume/qv_experiment.py b/qiskit_experiments/library/quantum_volume/qv_experiment.py
index 5e5da35042..fa42811005 100644
--- a/qiskit_experiments/library/quantum_volume/qv_experiment.py
+++ b/qiskit_experiments/library/quantum_volume/qv_experiment.py
@@ -28,7 +28,7 @@
 from qiskit import QuantumCircuit
 from qiskit.circuit.library import QuantumVolume as QuantumVolumeCircuit
 from qiskit import transpile
-from qiskit_experiments.base_experiment import BaseExperiment
+from qiskit_experiments.framework import BaseExperiment
 from .qv_analysis import QuantumVolumeAnalysis
 
 
diff --git a/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_analysis.py b/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_analysis.py
index 15dfccc0eb..d2db5cf8c2 100644
--- a/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_analysis.py
+++ b/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_analysis.py
@@ -16,7 +16,7 @@
 
 import numpy as np
 
-from qiskit_experiments.analysis import (
+from qiskit_experiments.curve_analysis import (
     SeriesDef,
     CurveAnalysisResultData,
     fit_function,
@@ -124,7 +124,7 @@ class InterleavedRBAnalysis(RBAnalysis):
     def _default_options(cls):
         """Return default data processing options.
 
-        See :meth:`~qiskit_experiment.analysis.CurveAnalysis._default_options` for
+        See :meth:`~qiskit_experiment.curve_analysis.CurveAnalysis._default_options` for
         descriptions of analysis options.
         """
         default_options = super()._default_options()
diff --git a/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py b/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py
index db8da5124f..4e7abfbe1d 100644
--- a/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py
+++ b/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py
@@ -17,21 +17,12 @@
 
 import numpy as np
 
-from qiskit_experiments.analysis import (
-    CurveAnalysis,
-    CurveAnalysisResultData,
-    SeriesDef,
-    CurveData,
-    fit_function,
-    get_opt_value,
-    get_opt_error,
-)
-
-from qiskit_experiments.analysis.data_processing import multi_mean_xy_data
+import qiskit_experiments.curve_analysis as curve
+from qiskit_experiments.curve_analysis.data_processing import multi_mean_xy_data
 from .rb_utils import RBUtils
 
 
-class RBAnalysis(CurveAnalysis):
+class RBAnalysis(curve.CurveAnalysis):
     r"""A class to analyze randomized benchmarking experiments.
 
     Overview
@@ -66,8 +57,8 @@ class RBAnalysis(CurveAnalysis):
     """
 
     __series__ = [
-        SeriesDef(
-            fit_func=lambda x, a, alpha, b: fit_function.exponential_decay(
+        curve.SeriesDef(
+            fit_func=lambda x, a, alpha, b: curve.fit_function.exponential_decay(
                 x, amp=a, lamb=-1.0, base=alpha, baseline=b
             ),
             plot_color="blue",
@@ -79,7 +70,7 @@ class RBAnalysis(CurveAnalysis):
     def _default_options(cls):
         """Return default options.
 
-        See :meth:`~qiskit_experiment.analysis.CurveAnalysis._default_options` for
+        See :meth:`~qiskit_experiment.curve_analysis.CurveAnalysis._default_options` for
         descriptions of analysis options.
         """
         default_options = super()._default_options()
@@ -137,7 +128,7 @@ def _initial_guess(
 
         return fit_guess
 
-    def _format_data(self, data: CurveData) -> CurveData:
+    def _format_data(self, data: curve.CurveData) -> curve.CurveData:
         """Take average over the same x values."""
         mean_data_index, mean_x, mean_y, mean_e = multi_mean_xy_data(
             series=data.data_index,
@@ -146,7 +137,7 @@ def _format_data(self, data: CurveData) -> CurveData:
             sigma=data.y_err,
             method="sample",
         )
-        return CurveData(
+        return curve.CurveData(
             label="fit_ready",
             x=mean_x,
             y=mean_y,
@@ -164,10 +155,12 @@ def _run_analysis(self, experiment_data, **options):
             )
         return super()._run_analysis(experiment_data, **options)
 
-    def _post_analysis(self, result_data: CurveAnalysisResultData) -> CurveAnalysisResultData:
+    def _post_analysis(
+        self, result_data: curve.CurveAnalysisResultData
+    ) -> curve.CurveAnalysisResultData:
         """Calculate EPC."""
-        alpha = get_opt_value(result_data, "alpha")
-        alpha_err = get_opt_error(result_data, "alpha")
+        alpha = curve.get_opt_value(result_data, "alpha")
+        alpha_err = curve.get_opt_error(result_data, "alpha")
 
         scale = (2 ** self._num_qubits - 1) / (2 ** self._num_qubits)
         result_data["EPC"] = scale * (1 - alpha)
diff --git a/qiskit_experiments/library/randomized_benchmarking/rb_experiment.py b/qiskit_experiments/library/randomized_benchmarking/rb_experiment.py
index e805ac54f0..2f2bdea53a 100644
--- a/qiskit_experiments/library/randomized_benchmarking/rb_experiment.py
+++ b/qiskit_experiments/library/randomized_benchmarking/rb_experiment.py
@@ -23,9 +23,8 @@
 from qiskit.providers.options import Options
 from qiskit.circuit import Gate
 
-from qiskit_experiments.base_experiment import BaseExperiment
-from qiskit_experiments.analysis.data_processing import probability
-from qiskit_experiments.composite import ParallelExperiment
+from qiskit_experiments.framework import BaseExperiment, ParallelExperiment
+from qiskit_experiments.curve_analysis.data_processing import probability
 from .rb_analysis import RBAnalysis
 from .clifford_utils import CliffordUtils
 from .rb_utils import RBUtils
diff --git a/qiskit_experiments/library/tomography/qpt_analysis.py b/qiskit_experiments/library/tomography/qpt_analysis.py
index 709d6fb601..da87c41592 100644
--- a/qiskit_experiments/library/tomography/qpt_analysis.py
+++ b/qiskit_experiments/library/tomography/qpt_analysis.py
@@ -12,7 +12,7 @@
 """
 Quantum process tomography analysis
 """
-from qiskit_experiments.base_analysis import Options
+from qiskit_experiments.framework import Options
 from .basis import PauliMeasurementBasis, PauliPreparationBasis
 from .tomography_analysis import TomographyAnalysis
 
diff --git a/qiskit_experiments/library/tomography/qst_analysis.py b/qiskit_experiments/library/tomography/qst_analysis.py
index 74722bd00b..ec63d17ba4 100644
--- a/qiskit_experiments/library/tomography/qst_analysis.py
+++ b/qiskit_experiments/library/tomography/qst_analysis.py
@@ -12,7 +12,7 @@
 """
 Quantum state tomography analysis
 """
-from qiskit_experiments.base_analysis import Options
+from qiskit_experiments.framework import Options
 from .basis import PauliMeasurementBasis
 from .tomography_analysis import TomographyAnalysis
 
diff --git a/qiskit_experiments/library/tomography/tomography_analysis.py b/qiskit_experiments/library/tomography/tomography_analysis.py
index f45eb9152b..7f7b93222c 100644
--- a/qiskit_experiments/library/tomography/tomography_analysis.py
+++ b/qiskit_experiments/library/tomography/tomography_analysis.py
@@ -25,8 +25,7 @@
 from qiskit.quantum_info.operators.channel.quantum_channel import QuantumChannel
 
 from qiskit_experiments.exceptions import AnalysisError
-from qiskit_experiments.base_analysis import BaseAnalysis, Options
-from qiskit_experiments.experiment_data import AnalysisResultData
+from qiskit_experiments.framework import BaseAnalysis, Options, AnalysisResultData
 from .fitters import (
     linear_inversion,
     scipy_linear_lstsq,
diff --git a/qiskit_experiments/library/tomography/tomography_experiment.py b/qiskit_experiments/library/tomography/tomography_experiment.py
index c39c012aa2..4c62ffabc0 100644
--- a/qiskit_experiments/library/tomography/tomography_experiment.py
+++ b/qiskit_experiments/library/tomography/tomography_experiment.py
@@ -22,7 +22,7 @@
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 import qiskit.quantum_info as qi
 from qiskit_experiments.exceptions import QiskitError
-from qiskit_experiments.base_experiment import BaseExperiment, Options
+from qiskit_experiments.framework import BaseExperiment, Options
 from .basis import BaseTomographyMeasurementBasis, BaseTomographyPreparationBasis
 from .tomography_analysis import TomographyAnalysis
 
diff --git a/qiskit_experiments/test/t2ramsey_backend.py b/qiskit_experiments/test/t2ramsey_backend.py
index 7dadf236cc..b54fdc351b 100644
--- a/qiskit_experiments/test/t2ramsey_backend.py
+++ b/qiskit_experiments/test/t2ramsey_backend.py
@@ -22,7 +22,7 @@
 from qiskit.providers.models import QasmBackendConfiguration
 from qiskit.result import Result
 from qiskit.test import QiskitTestCase
-from qiskit_experiments.composite import ParallelExperiment
+from qiskit_experiments.framework import ParallelExperiment
 from qiskit_experiments.library.characterization import T2Ramsey
 from qiskit_experiments.test.utils import FakeJob
 
diff --git a/test/calibration/experiments/test_rabi.py b/test/calibration/experiments/test_rabi.py
index 8f57147dff..2769011549 100644
--- a/test/calibration/experiments/test_rabi.py
+++ b/test/calibration/experiments/test_rabi.py
@@ -23,13 +23,12 @@
 from qiskit.qobj.utils import MeasLevel
 import qiskit.pulse as pulse
 
-from qiskit_experiments import ExperimentData
+from qiskit_experiments.framework import ExperimentData, ParallelExperiment
 from qiskit_experiments.library import Rabi, EFRabi
 
 from qiskit_experiments.library.calibration.analysis.oscillation_analysis import OscillationAnalysis
 from qiskit_experiments.data_processing.data_processor import DataProcessor
 from qiskit_experiments.data_processing.nodes import Probability
-from qiskit_experiments import ParallelExperiment
 from qiskit_experiments.test.mock_iq_backend import MockIQBackend
 
 
diff --git a/test/calibration/test_update_library.py b/test/calibration/test_update_library.py
index eb13b6be6d..60cfcbd576 100644
--- a/test/calibration/test_update_library.py
+++ b/test/calibration/test_update_library.py
@@ -27,7 +27,7 @@
 from qiskit_experiments.exceptions import CalibrationError
 from qiskit_experiments.calibration_management.update_library import Frequency, Amplitude, Drag
 from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations
-from qiskit_experiments.analysis import get_opt_value
+from qiskit_experiments.curve_analysis import get_opt_value
 from qiskit_experiments.test.mock_iq_backend import DragBackend, MockFineAmp
 
 
diff --git a/test/analysis/__init__.py b/test/curve_analysis/__init__.py
similarity index 100%
rename from test/analysis/__init__.py
rename to test/curve_analysis/__init__.py
diff --git a/test/analysis/test_curve_fit.py b/test/curve_analysis/test_curve_fit.py
similarity index 98%
rename from test/analysis/test_curve_fit.py
rename to test/curve_analysis/test_curve_fit.py
index ef9da8ac78..dabdfe7f88 100644
--- a/test/analysis/test_curve_fit.py
+++ b/test/curve_analysis/test_curve_fit.py
@@ -21,9 +21,9 @@
 from qiskit.test import QiskitTestCase
 from qiskit.qobj.utils import MeasLevel
 
-from qiskit_experiments import ExperimentData
-from qiskit_experiments.analysis import CurveAnalysis, SeriesDef, fit_function
-from qiskit_experiments.analysis.data_processing import probability
+from qiskit_experiments.framework import ExperimentData
+from qiskit_experiments.curve_analysis import CurveAnalysis, SeriesDef, fit_function
+from qiskit_experiments.curve_analysis.data_processing import probability
 from qiskit_experiments.exceptions import AnalysisError
 
 
diff --git a/test/test_curve_fitting.py b/test/curve_analysis/test_curve_fitting.py
similarity index 97%
rename from test/test_curve_fitting.py
rename to test/curve_analysis/test_curve_fitting.py
index 81c8d30dfd..8a94544abc 100644
--- a/test/test_curve_fitting.py
+++ b/test/curve_analysis/test_curve_fitting.py
@@ -16,8 +16,8 @@
 from qiskit.test import QiskitTestCase
 from qiskit import QuantumCircuit, execute
 from qiskit.providers.basicaer import QasmSimulatorPy
-from qiskit_experiments.analysis.curve_fitting import curve_fit, multi_curve_fit, process_curve_data
-from qiskit_experiments.analysis.data_processing import (
+from qiskit_experiments.curve_analysis import curve_fit, multi_curve_fit, process_curve_data
+from qiskit_experiments.curve_analysis.data_processing import (
     level2_probability,
     mean_xy_data,
     multi_mean_xy_data,
diff --git a/test/analysis/test_guess.py b/test/curve_analysis/test_guess.py
similarity index 99%
rename from test/analysis/test_guess.py
rename to test/curve_analysis/test_guess.py
index db150d010e..b83b4f5447 100644
--- a/test/analysis/test_guess.py
+++ b/test/curve_analysis/test_guess.py
@@ -17,7 +17,7 @@
 from ddt import ddt, data, unpack
 from qiskit.test import QiskitTestCase
 
-from qiskit_experiments.analysis import guess
+from qiskit_experiments.curve_analysis import guess
 
 
 @ddt
diff --git a/test/data_processing/test_data_processing.py b/test/data_processing/test_data_processing.py
index 1c04f3e6a8..64d95136b1 100644
--- a/test/data_processing/test_data_processing.py
+++ b/test/data_processing/test_data_processing.py
@@ -20,7 +20,7 @@
 from qiskit.result.models import ExperimentResultData, ExperimentResult
 from qiskit.result import Result
 
-from qiskit_experiments import ExperimentData
+from qiskit_experiments.framework import ExperimentData
 from qiskit_experiments.data_processing.data_processor import DataProcessor
 from qiskit_experiments.data_processing.exceptions import DataProcessorError
 from qiskit_experiments.data_processing.nodes import (
diff --git a/test/data_processing/test_nodes.py b/test/data_processing/test_nodes.py
index e01dba7b4d..2c57728f7b 100644
--- a/test/data_processing/test_nodes.py
+++ b/test/data_processing/test_nodes.py
@@ -22,7 +22,7 @@
 from qiskit.result.models import ExperimentResultData, ExperimentResult
 from qiskit.result import Result
 from qiskit.test import QiskitTestCase
-from qiskit_experiments.experiment_data import ExperimentData
+from qiskit_experiments.framework import ExperimentData
 from qiskit_experiments.data_processing.nodes import SVD, AverageData, MinMaxNormalize
 from qiskit_experiments.data_processing.data_processor import DataProcessor
 
diff --git a/test/fake_experiment.py b/test/fake_experiment.py
index 93a9395f83..b74722b7ab 100644
--- a/test/fake_experiment.py
+++ b/test/fake_experiment.py
@@ -14,8 +14,8 @@
 
 from qiskit.providers.options import Options
 
-from qiskit_experiments.base_experiment import BaseExperiment
-from qiskit_experiments.base_analysis import BaseAnalysis
+from qiskit_experiments.framework import BaseExperiment
+from qiskit_experiments.framework import BaseAnalysis
 
 
 class FakeAnalysis(BaseAnalysis):
diff --git a/test/quantum_volume/test_qv.py b/test/quantum_volume/test_qv.py
index f97d116d7d..694c3e0a3e 100644
--- a/test/quantum_volume/test_qv.py
+++ b/test/quantum_volume/test_qv.py
@@ -19,7 +19,7 @@
 from qiskit.quantum_info.operators.predicates import matrix_equal
 from qiskit.test import QiskitTestCase
 from qiskit import Aer
-from qiskit_experiments import ExperimentData
+from qiskit_experiments.framework import ExperimentData
 from qiskit_experiments.library import QuantumVolume
 
 SEED = 42
diff --git a/test/randomized_benchmarking/test_rb_analysis.py b/test/randomized_benchmarking/test_rb_analysis.py
index 9e795b3604..1e5c7b1e30 100644
--- a/test/randomized_benchmarking/test_rb_analysis.py
+++ b/test/randomized_benchmarking/test_rb_analysis.py
@@ -21,7 +21,7 @@
     XGate,
     CXGate,
 )
-import qiskit_experiments as qe
+from qiskit_experiments.framework import ExperimentData
 from qiskit_experiments.library import StandardRB, InterleavedRB
 
 ATOL_DEFAULT = 1e-2
@@ -43,7 +43,7 @@ def _load_json_data(self, rb_exp_data_file_name: str):
                 containing the experiment results.
             ExperimentData:  ExperimentData object that was creates by the analysis function.
         """
-        expdata1 = qe.ExperimentData()
+        expdata1 = ExperimentData()
         self.assertTrue(
             os.path.isfile(rb_exp_data_file_name),
             "The file containing the experiment data doesn't exist."
diff --git a/test/test_composite.py b/test/test_composite.py
index e06889b65a..725e55f9f2 100644
--- a/test/test_composite.py
+++ b/test/test_composite.py
@@ -18,7 +18,7 @@
 from qiskit.test import QiskitTestCase
 from qiskit.providers.options import Options
 
-from qiskit_experiments import ParallelExperiment
+from qiskit_experiments.framework import ParallelExperiment
 
 
 class TestComposite(QiskitTestCase):
diff --git a/test/test_qubit_spectroscopy.py b/test/test_qubit_spectroscopy.py
index eb1ae0aa31..8a803acc69 100644
--- a/test/test_qubit_spectroscopy.py
+++ b/test/test_qubit_spectroscopy.py
@@ -20,7 +20,7 @@
 from qiskit.test import QiskitTestCase
 
 from qiskit_experiments.library import QubitSpectroscopy, EFSpectroscopy
-from qiskit_experiments.analysis import get_opt_value
+from qiskit_experiments.curve_analysis import get_opt_value
 from qiskit_experiments.test.mock_iq_backend import MockIQBackend
 
 
diff --git a/test/test_t1.py b/test/test_t1.py
index 40bde30a50..0227a576b2 100644
--- a/test/test_t1.py
+++ b/test/test_t1.py
@@ -14,7 +14,7 @@
 """
 
 from qiskit.test import QiskitTestCase
-from qiskit_experiments import ExperimentData, ParallelExperiment
+from qiskit_experiments.framework import ExperimentData, ParallelExperiment
 from qiskit_experiments.library import T1
 from qiskit_experiments.library.characterization import T1Analysis
 from qiskit_experiments.test.t1_backend import T1Backend
diff --git a/test/test_t2ramsey.py b/test/test_t2ramsey.py
index 250332b124..b126e32dde 100644
--- a/test/test_t2ramsey.py
+++ b/test/test_t2ramsey.py
@@ -18,7 +18,7 @@
 from qiskit.providers.models import QasmBackendConfiguration
 from qiskit.result import Result
 from qiskit.test import QiskitTestCase
-from qiskit_experiments import ParallelExperiment
+from qiskit_experiments.framework import ParallelExperiment
 from qiskit_experiments.library import T2Ramsey
 from qiskit_experiments.test.utils import FakeJob
 
diff --git a/test/test_tomography.py b/test/test_tomography.py
index 05c7d81018..9b8dc1e752 100644
--- a/test/test_tomography.py
+++ b/test/test_tomography.py
@@ -23,7 +23,7 @@
 from qiskit import QuantumCircuit
 import qiskit.quantum_info as qi
 from qiskit.providers.aer import AerSimulator
-from qiskit_experiments import BatchExperiment, ParallelExperiment
+from qiskit_experiments.framework import BatchExperiment, ParallelExperiment
 from qiskit_experiments.library import StateTomography, ProcessTomography