diff --git a/notebooks/bayes-net-editor.ipynb b/notebooks/bayes-net-editor.ipynb
new file mode 100644
index 00000000..3b1369d6
--- /dev/null
+++ b/notebooks/bayes-net-editor.ipynb
@@ -0,0 +1,158 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bc83eb2c-32c1-4d5a-9e4f-9e0e0378c8a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "660a13e6881c4d69b018e42528d36b35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Box(children=(FloatSlider(value=0.2, continuous_update=False, description='answer_is_bogon', max=1.0, step=0.0…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import ipywidgets as widgets\n",
+    "from ipywidgets import VBox, Box, Layout\n",
+    "from IPython.display import display\n",
+    "\n",
+    "slider_map = {}\n",
+    "\n",
+    "values = [\n",
+    "\"answer_is_bogon\", \n",
+    "\"answer_matches_probe_asn\", \n",
+    "\"answer_matches_probe_cc\",\n",
+    "\"answer_is_cloud_provider\",\n",
+    "\"answer_other\"\n",
+    "]\n",
+    "\n",
+    "class Changer:\n",
+    "    change_in_progress = False\n",
+    "    \n",
+    "    def callback(self, value):\n",
+    "        if self.change_in_progress:\n",
+    "            return\n",
+    "        self.change_in_progress = True\n",
+    "        changed_slider = value['owner'].description\n",
+    "        other_slider_sum = 0\n",
+    "        for k, s in slider_map.items():\n",
+    "            if k == changed_slider:\n",
+    "                continue\n",
+    "            other_slider_sum += s.value\n",
+    "        new_other_slider_sum = 1 - value['new']\n",
+    "        mult_factor = new_other_slider_sum/other_slider_sum\n",
+    "        for k, s in slider_map.items():\n",
+    "            if k == changed_slider:\n",
+    "                continue\n",
+    "            s.value = mult_factor * s.value\n",
+    "        self.change_in_progress = False\n",
+    "    \n",
+    "changer = Changer()\n",
+    "slider_values = {v: 1.0/len(values) for v in values}\n",
+    "\n",
+    "for v in values:\n",
+    "    slider = widgets.FloatSlider(\n",
+    "        value=1/len(values), \n",
+    "        min=0.0,\n",
+    "        max=1.0,\n",
+    "        description=v,\n",
+    "        step=0.001,\n",
+    "        continuous_update=False\n",
+    "    )\n",
+    "    slider.observe(changer.callback, names='value')\n",
+    "    slider_map[v] = slider\n",
+    "\n",
+    "display(\n",
+    "    Box(children=list(slider_map.values()), layout=Layout(display='flex', flex_flow='column', align_items='stretch'))\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "97cc09d2-c71b-49a5-9a1c-3f6ace5e6b1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def validate_matrix(matrix):\n",
+    "    cols = len(matrix[0])\n",
+    "    rows = len(matrix)\n",
+    "    valid = True\n",
+    "    for col_idx in range(cols):\n",
+    "        col_data = []\n",
+    "        for row_idx in range(rows):\n",
+    "            col_data.append(matrix[row_idx][col_idx])\n",
+    "        if sum(col_data) != 1:\n",
+    "            print(f\"col #{col_idx} INVALID ({sum(col_data)}!=1)\")\n",
+    "            print(col_data)\n",
+    "            valid = False\n",
+    "    if valid:\n",
+    "        print(\"ALL OK\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fee25b03-eec8-4db4-9021-ce92eee94dbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ALL OK\n"
+     ]
+    }
+   ],
+   "source": [
+    "validate_matrix([\n",
+    "        # consistent inconsistent\n",
+    "        # \"none\", \"bad_certificate\", \"connection_reset_after_ch\", \"timeout_after_ch\", \"timeout\" \"connection_reset\" \"other\" \"down\"\n",
+    "        [0.02, 0.95, 0.90, 0.85, 0.55, 0.65, 0.07, 0.07, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02], # blocked\n",
+    "        [0.95, 0.01, 0.01, 0.01, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], # ok\n",
+    "        [0.02, 0.03, 0.07, 0.09, 0.40, 0.25, 0.07, 0.07, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], # down\n",
+    "        [0.01, 0.01, 0.02, 0.05, 0.03, 0.09, 0.85, 0.85, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92], # unknown\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f8a25ac-80fe-4124-af4e-d5455faf71d2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/bn-notes.md b/notebooks/bn-notes.md
new file mode 100644
index 00000000..b3829e00
--- /dev/null
+++ b/notebooks/bn-notes.md
@@ -0,0 +1,7 @@
+Useful links:
+
+- https://maucher.pages.mi.hdm-stuttgart.de/artificial-intelligence/05aBayesNetGarage.html
+- https://github.com/paulgovan/BayesianNetwork?tab=readme-ov-file
+- https://www.cs.washington.edu/dm/vfml/appendixes/bif.htm
+- https://pgmpy.org/models/bayesiannetwork.html
+- https://erdogant.github.io/bnlearn/pages/html/index.html
diff --git a/notebooks/pyproject.toml b/notebooks/pyproject.toml
new file mode 100644
index 00000000..94cdc333
--- /dev/null
+++ b/notebooks/pyproject.toml
@@ -0,0 +1,48 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "oonidata-notebooks"
+dynamic = ["version"]
+description = ''
+readme = "README.md"
+requires-python = ">=3.8"
+license = "MIT"
+keywords = []
+authors = [
+  { name = "Arturo Filastò", email = "arturo@filasto.net" },
+]
+classifiers = [
+  "Development Status :: 4 - Beta",
+  "Programming Language :: Python",
+  "Programming Language :: Python :: 3.8",
+  "Programming Language :: Python :: 3.9",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
+  "Programming Language :: Python :: Implementation :: CPython",
+  "Programming Language :: Python :: Implementation :: PyPy",
+]
+dependencies = [
+    "jupyterlab",
+    "pandas",
+    "numpy",
+    "bnlearn",
+    "d3blocks",
+    "pygraphviz",
+    "bokeh",
+    "ipywidgets"
+]
+
+[tool.hatch.envs.types]
+extra-dependencies = [
+  "mypy>=1.0.0",
+]
+
+[tool.coverage.report]
+exclude_lines = [
+  "no cov",
+  "if __name__ == .__main__.:",
+  "if TYPE_CHECKING:",
+]
diff --git a/notebooks/web-analysis-bn.ipynb b/notebooks/web-analysis-bn.ipynb
new file mode 120000
index 00000000..8c60223f
--- /dev/null
+++ b/notebooks/web-analysis-bn.ipynb
@@ -0,0 +1 @@
+../oonipipeline/notebooks/web-analysis-bn.ipynb
\ No newline at end of file
diff --git a/notebooks/web-analysis-full.bif b/notebooks/web-analysis-full.bif
new file mode 100644
index 00000000..8255ee41
--- /dev/null
+++ b/notebooks/web-analysis-full.bif
@@ -0,0 +1,151 @@
+network unknown {
+}
+variable DNS Answer {
+    type discrete [ 5 ] { answer_is_bogon, answer_matches_probe_asn, answer_matches_probe_cc, answer_is_cloud_provider, answer_other };
+}
+variable DNS Answer Consistency {
+    type discrete [ 2 ] { consistent, inconsistent };
+}
+variable DNS Answer TLS Consistency {
+    type discrete [ 3 ] { tls_consistent, tls_inconsistent, unknown };
+}
+variable DNS Ctrl Failure {
+    type discrete [ 3 ] { none, nxdomain, server_failure };
+}
+variable DNS Failure Consistency {
+    type discrete [ 3 ] { ok, expected_not_ok, unexpected_not_ok };
+}
+variable DNS Outcome {
+    type discrete [ 3 ] { blocked, ok, down };
+}
+variable DNS VP Failure {
+    type discrete [ 3 ] { none, nxdomain, server_failure };
+}
+variable TCP Ctrl Failure {
+    type discrete [ 4 ] { none, connection_reset, timeout, other };
+}
+variable TCP Outcome {
+    type discrete [ 4 ] { blocked, ok, down, unknown };
+}
+variable TCP VP Result {
+    type discrete [ 5 ] { none, connection_reset, timeout, other, down };
+}
+variable TLS Ctrl Failure {
+    type discrete [ 3 ] { none, bad_certificate, other };
+}
+variable TLS Outcome {
+    type discrete [ 4 ] { blocked, ok, down, unknown };
+}
+variable TLS VP Result {
+    type discrete [ 8 ] { none, bad_certificate, connection_reset_after_ch, timeout_after_ch, timeout, connection_reset, other, down };
+}
+probability ( DNS Answer ) {
+    table 0.004, 0.006, 0.06, 0.73, 0.2 ;
+}
+probability ( DNS Answer Consistency | DNS Answer, DNS Answer TLS Consistency ) {
+    ( answer_is_bogon, tls_consistent ) 0.99, 0.01;
+    ( answer_is_bogon, tls_inconsistent ) 0.001, 0.999;
+    ( answer_is_bogon, unknown ) 0.001, 0.999;
+    ( answer_matches_probe_asn, tls_consistent ) 0.999, 0.001;
+    ( answer_matches_probe_asn, tls_inconsistent ) 0.01, 0.99;
+    ( answer_matches_probe_asn, unknown ) 0.3, 0.7;
+    ( answer_matches_probe_cc, tls_consistent ) 0.999, 0.001;
+    ( answer_matches_probe_cc, tls_inconsistent ) 0.001, 0.999;
+    ( answer_matches_probe_cc, unknown ) 0.3, 0.7;
+    ( answer_is_cloud_provider, tls_consistent ) 0.999, 0.001;
+    ( answer_is_cloud_provider, tls_inconsistent ) 0.01, 0.99;
+    ( answer_is_cloud_provider, unknown ) 0.8, 0.2;
+    ( answer_other, tls_consistent ) 0.999, 0.001;
+    ( answer_other, tls_inconsistent ) 0.001, 0.999;
+    ( answer_other, unknown ) 0.2, 0.8;
+
+}
+probability ( DNS Answer TLS Consistency | DNS Answer ) {
+    ( answer_is_bogon ) 0.01, 0.05, 0.94;
+    ( answer_matches_probe_asn ) 0.8, 0.15, 0.05;
+    ( answer_matches_probe_cc ) 0.75, 0.2, 0.05;
+    ( answer_is_cloud_provider ) 0.9, 0.08, 0.02;
+    ( answer_other ) 0.94, 0.04, 0.02;
+
+}
+probability ( DNS Ctrl Failure ) {
+    table 0.994, 0.004, 0.002 ;
+}
+probability ( DNS Failure Consistency | DNS Ctrl Failure, DNS VP Failure ) {
+    ( none, none ) 0.99, 0.01, 0.01;
+    ( none, nxdomain ) 0.01, 0.05, 0.94;
+    ( none, server_failure ) 0.01, 0.05, 0.94;
+    ( nxdomain, none ) 0.94, 0.01, 0.05;
+    ( nxdomain, nxdomain ) 0.01, 0.98, 0.01;
+    ( nxdomain, server_failure ) 0.01, 0.92, 0.07;
+    ( server_failure, none ) 0.94, 0.01, 0.05;
+    ( server_failure, nxdomain ) 0.01, 0.92, 0.07;
+    ( server_failure, server_failure ) 0.01, 0.98, 0.01;
+
+}
+probability ( DNS Outcome | DNS Answer Consistency, DNS Failure Consistency ) {
+    ( consistent, ok ) 0.01, 0.99, 0.0;
+    ( consistent, expected_not_ok ) 0.01, 0.01, 0.98;
+    ( consistent, unexpected_not_ok ) 0.4, 0.4, 0.2;
+    ( inconsistent, ok ) 0.8, 0.2, 0.0;
+    ( inconsistent, expected_not_ok ) 0.2, 0.05, 0.75;
+    ( inconsistent, unexpected_not_ok ) 0.9, 0.01, 0.09;
+
+}
+probability ( DNS VP Failure | DNS Ctrl Failure ) {
+    ( none ) 0.994, 0.004, 0.002;
+    ( nxdomain ) 0.02, 0.98, 0.01;
+    ( server_failure ) 0.02, 0.01, 0.98;
+
+}
+probability ( TCP Ctrl Failure ) {
+    table 0.98, 0.001, 0.014, 0.005 ;
+}
+probability ( TCP Outcome | DNS Answer Consistency, TCP VP Result ) {
+    ( consistent, none ) 0.02, 0.95, 0.02, 0.01;
+    ( consistent, connection_reset ) 0.95, 0.01, 0.03, 0.01;
+    ( consistent, timeout ) 0.9, 0.01, 0.08, 0.01;
+    ( consistent, other ) 0.85, 0.01, 0.09, 0.05;
+    ( consistent, down ) 0.01, 0.08, 0.01, 0.9;
+    ( inconsistent, none ) 0.07, 0.01, 0.07, 0.85;
+    ( inconsistent, connection_reset ) 0.07, 0.01, 0.07, 0.85;
+    ( inconsistent, timeout ) 0.07, 0.01, 0.07, 0.85;
+    ( inconsistent, other ) 0.07, 0.01, 0.07, 0.85;
+    ( inconsistent, down ) 0.05, 0.01, 0.09, 0.85;
+
+}
+probability ( TCP VP Result | TCP Ctrl Failure ) {
+    ( none ) 0.98, 0.001, 0.01, 0.009, 0.009;
+    ( connection_reset ) 0.02, 0.14, 0.05, 0.07, 0.72;
+    ( timeout ) 0.01, 0.02, 0.1, 0.02, 0.85;
+    ( other ) 0.01, 0.06, 0.06, 0.12, 0.75;
+
+}
+probability ( TLS Ctrl Failure ) {
+    table 0.98, 0.005, 0.015 ;
+}
+probability ( TLS Outcome | DNS Answer Consistency, TLS VP Result ) {
+    ( consistent, none ) 0.02, 0.95, 0.02, 0.01;
+    ( consistent, bad_certificate ) 0.95, 0.01, 0.03, 0.01;
+    ( consistent, connection_reset_after_ch ) 0.9, 0.01, 0.07, 0.02;
+    ( consistent, timeout_after_ch ) 0.85, 0.01, 0.09, 0.05;
+    ( consistent, timeout ) 0.55, 0.02, 0.4, 0.03;
+    ( consistent, connection_reset ) 0.65, 0.01, 0.25, 0.09;
+    ( consistent, other ) 0.07, 0.01, 0.07, 0.85;
+    ( consistent, down ) 0.07, 0.01, 0.07, 0.85;
+    ( inconsistent, none ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, bad_certificate ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, connection_reset_after_ch ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, timeout_after_ch ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, timeout ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, connection_reset ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, other ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, down ) 0.02, 0.01, 0.05, 0.92;
+
+}
+probability ( TLS VP Result | TLS Ctrl Failure ) {
+    ( none ) 0.97, 0.005, 0.005, 0.005, 0.005, 0.005, 0.004, 0.001;
+    ( bad_certificate ) 0.02, 0.1, 0.01, 0.01, 0.01, 0.01, 0.02, 0.82;
+    ( other ) 0.01, 0.01, 0.05, 0.05, 0.05, 0.05, 0.12, 0.66;
+
+}
diff --git a/notebooks/web-analysis.bif b/notebooks/web-analysis.bif
new file mode 100644
index 00000000..f537addd
--- /dev/null
+++ b/notebooks/web-analysis.bif
@@ -0,0 +1,69 @@
+network unknown {
+}
+variable DNS Answer Meta {
+    type discrete [ 7 ] { answer_failure_nxdomain, answer_failure_other, answer_is_bogon, answer_matches_probe_asn, answer_matches_probe_cc, answer_is_cloud_provider, answer_ok };
+}
+variable DNS Consistency {
+    type discrete [ 2 ] { consistent, inconsistent };
+}
+variable DNS Ctrl Failure {
+    type discrete [ 3 ] { none, nxdomain, server_failure };
+}
+variable DNS Outcome {
+    type discrete [ 3 ] { blocked, ok, down };
+}
+variable DNS TLS Consistency {
+    type discrete [ 3 ] { tls_consistent, tls_inconsistent, tls_connection_failed };
+}
+probability ( DNS Answer Meta | DNS Ctrl Failure ) {
+    ( none ) 0.0238, 0.01, 0.003745, 0.004537, 0.060217, 0.19626, 0.700935;
+    ( nxdomain ) 0.9, 0.03, 0.014, 0.014, 0.014, 0.014, 0.014;
+    ( server_failure ) 0.015, 0.91, 0.015, 0.015, 0.015, 0.015, 0.015;
+
+}
+probability ( DNS Consistency | DNS Answer Meta, DNS TLS Consistency ) {
+    ( answer_failure_nxdomain, tls_consistent ) 0.01, 0.99;
+    ( answer_failure_nxdomain, tls_inconsistent ) 0.001, 0.999;
+    ( answer_failure_nxdomain, tls_connection_failed ) 0.001, 0.999;
+    ( answer_failure_other, tls_consistent ) 0.01, 0.99;
+    ( answer_failure_other, tls_inconsistent ) 0.001, 0.999;
+    ( answer_failure_other, tls_connection_failed ) 0.001, 0.999;
+    ( answer_is_bogon, tls_consistent ) 0.03, 0.97;
+    ( answer_is_bogon, tls_inconsistent ) 0.01, 0.99;
+    ( answer_is_bogon, tls_connection_failed ) 0.01, 0.99;
+    ( answer_matches_probe_asn, tls_consistent ) 0.999, 0.001;
+    ( answer_matches_probe_asn, tls_inconsistent ) 0.001, 0.999;
+    ( answer_matches_probe_asn, tls_connection_failed ) 0.3, 0.7;
+    ( answer_matches_probe_cc, tls_consistent ) 0.999, 0.001;
+    ( answer_matches_probe_cc, tls_inconsistent ) 0.001, 0.999;
+    ( answer_matches_probe_cc, tls_connection_failed ) 0.2, 0.8;
+    ( answer_is_cloud_provider, tls_consistent ) 0.98, 0.02;
+    ( answer_is_cloud_provider, tls_inconsistent ) 0.001, 0.999;
+    ( answer_is_cloud_provider, tls_connection_failed ) 0.1, 0.9;
+    ( answer_ok, tls_consistent ) 0.9999, 0.0001;
+    ( answer_ok, tls_inconsistent ) 0.01, 0.99;
+    ( answer_ok, tls_connection_failed ) 0.2, 0.8;
+
+}
+probability ( DNS Ctrl Failure ) {
+    table 0.994, 0.004, 0.002 ;
+}
+probability ( DNS Outcome | DNS Ctrl Failure, DNS Consistency ) {
+    ( none, consistent ) 0.1, 0.9, 0.0;
+    ( none, inconsistent ) 0.9, 0.1, 0.0;
+    ( nxdomain, consistent ) 0.2, 0.0, 0.8;
+    ( nxdomain, inconsistent ) 0.8, 0.0, 0.2;
+    ( server_failure, consistent ) 0.1, 0.8, 0.1;
+    ( server_failure, inconsistent ) 0.7, 0.1, 0.2;
+
+}
+probability ( DNS TLS Consistency | DNS Answer Meta ) {
+    ( answer_failure_nxdomain ) 0.96, 0.02, 0.02;
+    ( answer_failure_other ) 0.96, 0.02, 0.02;
+    ( answer_is_bogon ) 0.0, 0.02, 0.98;
+    ( answer_matches_probe_asn ) 0.05, 0.8, 0.15;
+    ( answer_matches_probe_cc ) 0.05, 0.8, 0.15;
+    ( answer_is_cloud_provider ) 0.9, 0.08, 0.02;
+    ( answer_ok ) 0.94, 0.04, 0.02;
+
+}
diff --git a/notebooks/web_analysis.png b/notebooks/web_analysis.png
new file mode 100644
index 00000000..310bb278
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diff --git a/notebooks/web_analysis_complex.png b/notebooks/web_analysis_complex.png
new file mode 100644
index 00000000..b1b3f860
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diff --git a/notebooks/web_analysis_full.png b/notebooks/web_analysis_full.png
new file mode 100644
index 00000000..b1b3f860
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diff --git a/notebooks/web_analysis_simple.png b/notebooks/web_analysis_simple.png
new file mode 100644
index 00000000..3dbd1cd0
Binary files /dev/null and b/notebooks/web_analysis_simple.png differ
diff --git a/oonipipeline/notebooks/.gitignore b/oonipipeline/notebooks/.gitignore
new file mode 100644
index 00000000..da3a71f6
--- /dev/null
+++ b/oonipipeline/notebooks/.gitignore
@@ -0,0 +1,2 @@
+*.gz
+*.csv
diff --git a/oonipipeline/notebooks/DNS BN Analysis.ipynb b/oonipipeline/notebooks/DNS BN Analysis.ipynb
new file mode 100644
index 00000000..842d2e36
--- /dev/null
+++ b/oonipipeline/notebooks/DNS BN Analysis.ipynb	
@@ -0,0 +1,3335 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "id": "f309af11-65e3-4d73-88de-655921a52693",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from tqdm.notebook import tqdm\n",
+    "from IPython.display import Image\n",
+    "from pgmpy.models import BayesianNetwork\n",
+    "from pgmpy.factors.discrete import TabularCPD\n",
+    "tqdm.pandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "3f8fc6f9-9786-4013-8802-359432fb01a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dns_analysis_bn = BayesianNetwork([\n",
+    "    ('answer_matches_probe_cc', 'answer_is_blockpage'),\n",
+    "    ('answer_matches_probe_asn', 'answer_is_blockpage'),\n",
+    "    ('answer_contains_unreachable_ip', 'answer_is_blockpage'),\n",
+    "    ('answer_contains_bogon', 'answer_is_blockpage'),\n",
+    "    ('answer_is_blockpage', 'contains_tls_consistent_answer'),\n",
+    "    ('answer_count', 'answer_is_blockpage'),\n",
+    "    ('answer_is_cloud_provider', 'answer_is_blockpage'),\n",
+    "    #('contains_tls_consistent_answer', 'dns_outcome'),\n",
+    "    #('bogon_present_in_ctrl_and_exp', 'dns_outcome'),\n",
+    "\n",
+    "    #('dns_consistent_failure', 'dns_failure_outcome'),\n",
+    "    #('dns_exp_is_nxdomain', 'dns_failure_outcome'),\n",
+    "    #('dns_failure_outcome', 'dns_outcome')\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "fcf3aa05-a59d-4e5a-af62-9805f413bfcf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "viz = dns_analysis_bn.to_graphviz()\n",
+    "viz.draw('dns_analysis_bn.png', prog='dot')\n",
+    "Image('dns_analysis_bn.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "1ee80ef0-9056-4c9d-b650-3231387a596b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples = pd.read_csv('dns_analysis_samples_1M.csv.gz', usecols=[\n",
+    "    'answer_matches_probe_cc',\n",
+    "    'answer_matches_probe_asn',\n",
+    "    'answer_contains_unreachable_ip',\n",
+    "    'answer_contains_bogon',\n",
+    "    'answer_is_blockpage', \n",
+    "    'contains_tls_consistent_answer',\n",
+    "    'answer_is_cloud_provider',\n",
+    "    'answer_count',\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "bba49cb2-7a0b-4618-9c1d-714803780d74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#samples_full = pd.read_csv('dns_analysis_samples_1M.csv.gz')\n",
+    "samples_extra = pd.read_csv('dns_analysis_samples_extra_1M.csv.gz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "dffd0e2f-39ef-42e5-a545-084c2b354c28",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>target_domain_name</th>\n",
+       "      <th>measurement_uid</th>\n",
+       "      <th>probe_cc</th>\n",
+       "      <th>probe_asn</th>\n",
+       "      <th>answer_count_num</th>\n",
+       "      <th>answer_count</th>\n",
+       "      <th>tls_consistent</th>\n",
+       "      <th>tls_inconsistent</th>\n",
+       "      <th>contains_tls_consistent_answer</th>\n",
+       "      <th>...</th>\n",
+       "      <th>answer_asn_in_trusted_answers</th>\n",
+       "      <th>answer_asorg_in_trusted_answers</th>\n",
+       "      <th>answer_matches_probe_asn</th>\n",
+       "      <th>answer_matches_probe_cc</th>\n",
+       "      <th>answer_contains_bogon</th>\n",
+       "      <th>answer_is_cloud_provider</th>\n",
+       "      <th>fp_name</th>\n",
+       "      <th>false_postive</th>\n",
+       "      <th>answer_is_blockpage</th>\n",
+       "      <th>answer_contains_unreachable_ip</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>www.coolearth.org</td>\n",
+       "      <td>20240813184519.240242_RU_webconnectivity_70834...</td>\n",
+       "      <td>RU</td>\n",
+       "      <td>51645</td>\n",
+       "      <td>2</td>\n",
+       "      <td>med</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>kat.am</td>\n",
+       "      <td>20240415213315.451159_US_webconnectivity_cd858...</td>\n",
+       "      <td>US</td>\n",
+       "      <td>14593</td>\n",
+       "      <td>4</td>\n",
+       "      <td>med</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>www.mediafire.com</td>\n",
+       "      <td>20240110154226.561973_US_webconnectivity_0b177...</td>\n",
+       "      <td>US</td>\n",
+       "      <td>11427</td>\n",
+       "      <td>2</td>\n",
+       "      <td>med</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>iarf.net</td>\n",
+       "      <td>20240226153534.729299_US_webconnectivity_bfd46...</td>\n",
+       "      <td>US</td>\n",
+       "      <td>7922</td>\n",
+       "      <td>1</td>\n",
+       "      <td>low</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>scontent.cdninstagram.com</td>\n",
+       "      <td>20240401223202.866879_TW_webconnectivity_d5764...</td>\n",
+       "      <td>TW</td>\n",
+       "      <td>7482</td>\n",
+       "      <td>2</td>\n",
+       "      <td>med</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999995</th>\n",
+       "      <td>999995</td>\n",
+       "      <td>focus.ua</td>\n",
+       "      <td>20240306235145.238486_RU_webconnectivity_081e7...</td>\n",
+       "      <td>RU</td>\n",
+       "      <td>41733</td>\n",
+       "      <td>2</td>\n",
+       "      <td>med</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ooni.ru_dns_ert_1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999996</th>\n",
+       "      <td>999996</td>\n",
+       "      <td>notifications-pa.googleapis.com</td>\n",
+       "      <td>20240101041556.484562_VE_webconnectivity_8657f...</td>\n",
+       "      <td>VE</td>\n",
+       "      <td>8048</td>\n",
+       "      <td>14</td>\n",
+       "      <td>high</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999997</th>\n",
+       "      <td>999997</td>\n",
+       "      <td>www.piratpartiet.se</td>\n",
+       "      <td>20240302005730.751251_MX_webconnectivity_2950b...</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>28509</td>\n",
+       "      <td>2</td>\n",
+       "      <td>med</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999998</th>\n",
+       "      <td>999998</td>\n",
+       "      <td>transequality.org</td>\n",
+       "      <td>20240307184001.974720_TZ_webconnectivity_6a91b...</td>\n",
+       "      <td>TZ</td>\n",
+       "      <td>37133</td>\n",
+       "      <td>4</td>\n",
+       "      <td>med</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999999</th>\n",
+       "      <td>999999</td>\n",
+       "      <td>yandexwebcache.net</td>\n",
+       "      <td>20240102183817.096847_SE_webconnectivity_e2ba6...</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>29518</td>\n",
+       "      <td>1</td>\n",
+       "      <td>low</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000000 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Unnamed: 0               target_domain_name  \\\n",
+       "0                0                www.coolearth.org   \n",
+       "1                1                           kat.am   \n",
+       "2                2                www.mediafire.com   \n",
+       "3                3                         iarf.net   \n",
+       "4                4        scontent.cdninstagram.com   \n",
+       "...            ...                              ...   \n",
+       "999995      999995                         focus.ua   \n",
+       "999996      999996  notifications-pa.googleapis.com   \n",
+       "999997      999997              www.piratpartiet.se   \n",
+       "999998      999998                transequality.org   \n",
+       "999999      999999               yandexwebcache.net   \n",
+       "\n",
+       "                                          measurement_uid probe_cc  probe_asn  \\\n",
+       "0       20240813184519.240242_RU_webconnectivity_70834...       RU      51645   \n",
+       "1       20240415213315.451159_US_webconnectivity_cd858...       US      14593   \n",
+       "2       20240110154226.561973_US_webconnectivity_0b177...       US      11427   \n",
+       "3       20240226153534.729299_US_webconnectivity_bfd46...       US       7922   \n",
+       "4       20240401223202.866879_TW_webconnectivity_d5764...       TW       7482   \n",
+       "...                                                   ...      ...        ...   \n",
+       "999995  20240306235145.238486_RU_webconnectivity_081e7...       RU      41733   \n",
+       "999996  20240101041556.484562_VE_webconnectivity_8657f...       VE       8048   \n",
+       "999997  20240302005730.751251_MX_webconnectivity_2950b...       MX      28509   \n",
+       "999998  20240307184001.974720_TZ_webconnectivity_6a91b...       TZ      37133   \n",
+       "999999  20240102183817.096847_SE_webconnectivity_e2ba6...       SE      29518   \n",
+       "\n",
+       "        answer_count_num answer_count  tls_consistent  tls_inconsistent  \\\n",
+       "0                      2          med               1                 0   \n",
+       "1                      4          med               1                 0   \n",
+       "2                      2          med               1                 0   \n",
+       "3                      1          low               1                 0   \n",
+       "4                      2          med               1                 0   \n",
+       "...                  ...          ...             ...               ...   \n",
+       "999995                 2          med               0                 0   \n",
+       "999996                14         high               0                 0   \n",
+       "999997                 2          med               0                 0   \n",
+       "999998                 4          med               0                 0   \n",
+       "999999                 1          low               1                 0   \n",
+       "\n",
+       "        contains_tls_consistent_answer  ...  answer_asn_in_trusted_answers  \\\n",
+       "0                                  1.0  ...                              1   \n",
+       "1                                  1.0  ...                              1   \n",
+       "2                                  1.0  ...                              0   \n",
+       "3                                  1.0  ...                              0   \n",
+       "4                                  1.0  ...                              1   \n",
+       "...                                ...  ...                            ...   \n",
+       "999995                             NaN  ...                              0   \n",
+       "999996                             NaN  ...                              0   \n",
+       "999997                             NaN  ...                              0   \n",
+       "999998                             NaN  ...                              1   \n",
+       "999999                             1.0  ...                              0   \n",
+       "\n",
+       "        answer_asorg_in_trusted_answers  answer_matches_probe_asn  \\\n",
+       "0                                     0                         0   \n",
+       "1                                     0                         0   \n",
+       "2                                     0                         0   \n",
+       "3                                     0                         0   \n",
+       "4                                     1                         0   \n",
+       "...                                 ...                       ...   \n",
+       "999995                                0                         0   \n",
+       "999996                                0                         0   \n",
+       "999997                                0                         0   \n",
+       "999998                                1                         0   \n",
+       "999999                                0                         0   \n",
+       "\n",
+       "        answer_matches_probe_cc  answer_contains_bogon  \\\n",
+       "0                             0                      0   \n",
+       "1                             1                      0   \n",
+       "2                             1                      0   \n",
+       "3                             0                      0   \n",
+       "4                             0                      0   \n",
+       "...                         ...                    ...   \n",
+       "999995                        1                      0   \n",
+       "999996                        0                      0   \n",
+       "999997                        0                      0   \n",
+       "999998                        0                      0   \n",
+       "999999                        0                      0   \n",
+       "\n",
+       "        answer_is_cloud_provider            fp_name false_postive  \\\n",
+       "0                              1                NaN             0   \n",
+       "1                              1                NaN             0   \n",
+       "2                              1                NaN             0   \n",
+       "3                              0                NaN             0   \n",
+       "4                              0                NaN             0   \n",
+       "...                          ...                ...           ...   \n",
+       "999995                         0  ooni.ru_dns_ert_1             0   \n",
+       "999996                         0                NaN             0   \n",
+       "999997                         0                NaN             0   \n",
+       "999998                         1                NaN             0   \n",
+       "999999                         0                NaN             0   \n",
+       "\n",
+       "        answer_is_blockpage  answer_contains_unreachable_ip  \n",
+       "0                         0                               0  \n",
+       "1                         0                               0  \n",
+       "2                         0                               0  \n",
+       "3                         0                               0  \n",
+       "4                         0                               0  \n",
+       "...                     ...                             ...  \n",
+       "999995                    1                               0  \n",
+       "999996                    0                               0  \n",
+       "999997                    0                               0  \n",
+       "999998                    0                               0  \n",
+       "999999                    0                               0  \n",
+       "\n",
+       "[1000000 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples_extra"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "id": "3ffa2874-025f-497a-b059-6a11c0501c86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples_extra_clean = samples_extra.drop(columns=[samples_extra.columns[idx] for idx in range(6)] + ['fp_name', 'false_postive'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "94d8dc39-df0d-4df4-bcab-17e6642ca535",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>answer_count</th>\n",
+       "      <th>tls_consistent</th>\n",
+       "      <th>tls_inconsistent</th>\n",
+       "      <th>contains_tls_consistent_answer</th>\n",
+       "      <th>answer_ip_in_trusted_answers</th>\n",
+       "      <th>answer_asn_in_trusted_answers</th>\n",
+       "      <th>answer_asorg_in_trusted_answers</th>\n",
+       "      <th>answer_matches_probe_asn</th>\n",
+       "      <th>answer_matches_probe_cc</th>\n",
+       "      <th>answer_contains_bogon</th>\n",
+       "      <th>answer_is_cloud_provider</th>\n",
+       "      <th>answer_is_blockpage</th>\n",
+       "      <th>answer_contains_unreachable_ip</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>med</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>med</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>med</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>low</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>med</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999995</th>\n",
+       "      <td>med</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999996</th>\n",
+       "      <td>high</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999997</th>\n",
+       "      <td>med</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999998</th>\n",
+       "      <td>med</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999999</th>\n",
+       "      <td>low</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000000 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       answer_count  tls_consistent  tls_inconsistent  \\\n",
+       "0               med               1                 0   \n",
+       "1               med               1                 0   \n",
+       "2               med               1                 0   \n",
+       "3               low               1                 0   \n",
+       "4               med               1                 0   \n",
+       "...             ...             ...               ...   \n",
+       "999995          med               0                 0   \n",
+       "999996         high               0                 0   \n",
+       "999997          med               0                 0   \n",
+       "999998          med               0                 0   \n",
+       "999999          low               1                 0   \n",
+       "\n",
+       "        contains_tls_consistent_answer  answer_ip_in_trusted_answers  \\\n",
+       "0                                  1.0                             0   \n",
+       "1                                  1.0                             1   \n",
+       "2                                  1.0                             0   \n",
+       "3                                  1.0                             0   \n",
+       "4                                  1.0                             0   \n",
+       "...                                ...                           ...   \n",
+       "999995                             NaN                             0   \n",
+       "999996                             NaN                             0   \n",
+       "999997                             NaN                             0   \n",
+       "999998                             NaN                             1   \n",
+       "999999                             1.0                             0   \n",
+       "\n",
+       "        answer_asn_in_trusted_answers  answer_asorg_in_trusted_answers  \\\n",
+       "0                                   1                                0   \n",
+       "1                                   1                                0   \n",
+       "2                                   0                                0   \n",
+       "3                                   0                                0   \n",
+       "4                                   1                                1   \n",
+       "...                               ...                              ...   \n",
+       "999995                              0                                0   \n",
+       "999996                              0                                0   \n",
+       "999997                              0                                0   \n",
+       "999998                              1                                1   \n",
+       "999999                              0                                0   \n",
+       "\n",
+       "        answer_matches_probe_asn  answer_matches_probe_cc  \\\n",
+       "0                              0                        0   \n",
+       "1                              0                        1   \n",
+       "2                              0                        1   \n",
+       "3                              0                        0   \n",
+       "4                              0                        0   \n",
+       "...                          ...                      ...   \n",
+       "999995                         0                        1   \n",
+       "999996                         0                        0   \n",
+       "999997                         0                        0   \n",
+       "999998                         0                        0   \n",
+       "999999                         0                        0   \n",
+       "\n",
+       "        answer_contains_bogon  answer_is_cloud_provider  answer_is_blockpage  \\\n",
+       "0                           0                         1                    0   \n",
+       "1                           0                         1                    0   \n",
+       "2                           0                         1                    0   \n",
+       "3                           0                         0                    0   \n",
+       "4                           0                         0                    0   \n",
+       "...                       ...                       ...                  ...   \n",
+       "999995                      0                         0                    1   \n",
+       "999996                      0                         0                    0   \n",
+       "999997                      0                         0                    0   \n",
+       "999998                      0                         1                    0   \n",
+       "999999                      0                         0                    0   \n",
+       "\n",
+       "        answer_contains_unreachable_ip  \n",
+       "0                                    0  \n",
+       "1                                    0  \n",
+       "2                                    0  \n",
+       "3                                    0  \n",
+       "4                                    0  \n",
+       "...                                ...  \n",
+       "999995                               0  \n",
+       "999996                               0  \n",
+       "999997                               0  \n",
+       "999998                               0  \n",
+       "999999                               0  \n",
+       "\n",
+       "[1000000 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples_extra_clean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "9cb379b3-958b-4a87-85b9-e17d2a736880",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'20240212194929.466091_FI_webconnectivity_a8a12429797483d9'"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples_full.iloc[2007]['measurement_uid']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "d5c3bf40-9e7d-4001-8b18-9583f804c6bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>answer_count</th>\n",
+       "      <th>contains_tls_consistent_answer</th>\n",
+       "      <th>answer_matches_probe_asn</th>\n",
+       "      <th>answer_matches_probe_cc</th>\n",
+       "      <th>answer_contains_bogon</th>\n",
+       "      <th>answer_is_cloud_provider</th>\n",
+       "      <th>answer_is_blockpage</th>\n",
+       "      <th>answer_contains_unreachable_ip</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [answer_count, contains_tls_consistent_answer, answer_matches_probe_asn, answer_matches_probe_cc, answer_contains_bogon, answer_is_cloud_provider, answer_is_blockpage, answer_contains_unreachable_ip]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples[\n",
+    "    samples['answer_contains_unreachable_ip'] != 0\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "0887dbd4-0625-42d4-964c-486f321e61e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pgmpy.estimators import MaximumLikelihoodEstimator\n",
+    "mle = MaximumLikelihoodEstimator(model=dns_analysis_bn, data=samples)\n",
+    "dns_analysis_bn.fit(data=samples, estimator=MaximumLikelihoodEstimator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "49231f4d-62a9-473c-b4a3-f85f1f71fc78",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "2cecd5e1-6d51-4aaa-afa2-c7144fa69a60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cdp = dns_analysis_bn.get_cpds('answer_contains_unreachable_ip')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "8da3b7bf-740b-4bbd-b3a6-2d272428996f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----------------------------------+---+\n",
+      "| answer_contains_unreachable_ip(0) | 1 |\n",
+      "+-----------------------------------+---+\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cdp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f67d57d4-9472-47c7-94a7-03eb97c8c4f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "viz = dns_analysis_bn.to_graphviz()\n",
+    "viz.draw('dns_analysis_bn.png', prog='dot')\n",
+    "Image('dns_analysis_bn.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "e950bf1d-660b-49a1-85d9-9fa6b79f3217",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>target_domain_name</th>\n",
+       "      <th>measurement_uid</th>\n",
+       "      <th>probe_cc</th>\n",
+       "      <th>probe_asn</th>\n",
+       "      <th>answer_count</th>\n",
+       "      <th>tls_consistent</th>\n",
+       "      <th>tls_inconsistent</th>\n",
+       "      <th>contains_tls_consistent_answer</th>\n",
+       "      <th>answer_matches_probe_asn</th>\n",
+       "      <th>answer_matches_probe_cc</th>\n",
+       "      <th>answer_contains_bogon</th>\n",
+       "      <th>fp_name</th>\n",
+       "      <th>false_postive</th>\n",
+       "      <th>answer_is_blockpage</th>\n",
+       "      <th>answer_contains_unreachable_ip</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>porvir.org</td>\n",
+       "      <td>20240225113555.207055_BR_webconnectivity_220a8...</td>\n",
+       "      <td>BR</td>\n",
+       "      <td>270476</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>kiwifarms.net</td>\n",
+       "      <td>20240212223944.798890_BR_webconnectivity_ac6a7...</td>\n",
+       "      <td>BR</td>\n",
+       "      <td>263260</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>www.cidh.org</td>\n",
+       "      <td>20240128161747.782557_ID_webconnectivity_e6e4e...</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>7713</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>www.privacytools.io</td>\n",
+       "      <td>20240219103350.429741_US_webconnectivity_93aab...</td>\n",
+       "      <td>US</td>\n",
+       "      <td>7922</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>www.mojeek.com</td>\n",
+       "      <td>20240403001615.591322_US_webconnectivity_18b05...</td>\n",
+       "      <td>US</td>\n",
+       "      <td>7922</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99995</th>\n",
+       "      <td>99995</td>\n",
+       "      <td>www.4chan.org</td>\n",
+       "      <td>20240112190421.724404_IN_webconnectivity_0d9ba...</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>24309</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99996</th>\n",
+       "      <td>99996</td>\n",
+       "      <td>reliefweb.int</td>\n",
+       "      <td>20240811082327.907258_BD_webconnectivity_ee069...</td>\n",
+       "      <td>BD</td>\n",
+       "      <td>135341</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99997</th>\n",
+       "      <td>99997</td>\n",
+       "      <td>www.youtube.com</td>\n",
+       "      <td>20240119184933.212646_US_webconnectivity_7fa02...</td>\n",
+       "      <td>US</td>\n",
+       "      <td>7922</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99998</th>\n",
+       "      <td>99998</td>\n",
+       "      <td>www.churchofeuthanasia.org</td>\n",
+       "      <td>20240209233935.331137_US_webconnectivity_c059b...</td>\n",
+       "      <td>US</td>\n",
+       "      <td>20057</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99999</th>\n",
+       "      <td>99999</td>\n",
+       "      <td>asean.org</td>\n",
+       "      <td>20240322132619.206003_US_webconnectivity_472a4...</td>\n",
+       "      <td>US</td>\n",
+       "      <td>395464</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0          target_domain_name  \\\n",
+       "0               0                  porvir.org   \n",
+       "1               1               kiwifarms.net   \n",
+       "2               2                www.cidh.org   \n",
+       "3               3         www.privacytools.io   \n",
+       "4               4              www.mojeek.com   \n",
+       "...           ...                         ...   \n",
+       "99995       99995               www.4chan.org   \n",
+       "99996       99996               reliefweb.int   \n",
+       "99997       99997             www.youtube.com   \n",
+       "99998       99998  www.churchofeuthanasia.org   \n",
+       "99999       99999                   asean.org   \n",
+       "\n",
+       "                                         measurement_uid probe_cc  probe_asn  \\\n",
+       "0      20240225113555.207055_BR_webconnectivity_220a8...       BR     270476   \n",
+       "1      20240212223944.798890_BR_webconnectivity_ac6a7...       BR     263260   \n",
+       "2      20240128161747.782557_ID_webconnectivity_e6e4e...       ID       7713   \n",
+       "3      20240219103350.429741_US_webconnectivity_93aab...       US       7922   \n",
+       "4      20240403001615.591322_US_webconnectivity_18b05...       US       7922   \n",
+       "...                                                  ...      ...        ...   \n",
+       "99995  20240112190421.724404_IN_webconnectivity_0d9ba...       IN      24309   \n",
+       "99996  20240811082327.907258_BD_webconnectivity_ee069...       BD     135341   \n",
+       "99997  20240119184933.212646_US_webconnectivity_7fa02...       US       7922   \n",
+       "99998  20240209233935.331137_US_webconnectivity_c059b...       US      20057   \n",
+       "99999  20240322132619.206003_US_webconnectivity_472a4...       US     395464   \n",
+       "\n",
+       "       answer_count  tls_consistent  tls_inconsistent  \\\n",
+       "0                 4               0                 0   \n",
+       "1                 2               1                 0   \n",
+       "2                 6               0                 0   \n",
+       "3                 4               1                 0   \n",
+       "4                 1               1                 0   \n",
+       "...             ...             ...               ...   \n",
+       "99995             2               1                 0   \n",
+       "99996             3               1                 0   \n",
+       "99997            19               1                 0   \n",
+       "99998             1               1                 0   \n",
+       "99999             1               1                 0   \n",
+       "\n",
+       "       contains_tls_consistent_answer  answer_matches_probe_asn  \\\n",
+       "0                                 NaN                         0   \n",
+       "1                                 1.0                         0   \n",
+       "2                                 NaN                         0   \n",
+       "3                                 1.0                         0   \n",
+       "4                                 1.0                         0   \n",
+       "...                               ...                       ...   \n",
+       "99995                             1.0                         0   \n",
+       "99996                             1.0                         0   \n",
+       "99997                             1.0                         0   \n",
+       "99998                             1.0                         0   \n",
+       "99999                             1.0                         0   \n",
+       "\n",
+       "       answer_matches_probe_cc  answer_contains_bogon fp_name  false_postive  \\\n",
+       "0                            0                      0     NaN              0   \n",
+       "1                            0                      0     NaN              0   \n",
+       "2                            0                      0     NaN              0   \n",
+       "3                            1                      0     NaN              0   \n",
+       "4                            0                      0     NaN              0   \n",
+       "...                        ...                    ...     ...            ...   \n",
+       "99995                        0                      0     NaN              0   \n",
+       "99996                        0                      0     NaN              0   \n",
+       "99997                        1                      0     NaN              0   \n",
+       "99998                        1                      0     NaN              0   \n",
+       "99999                        1                      0     NaN              0   \n",
+       "\n",
+       "       answer_is_blockpage  answer_contains_unreachable_ip  \n",
+       "0                        0                               0  \n",
+       "1                        0                               0  \n",
+       "2                        0                               0  \n",
+       "3                        0                               0  \n",
+       "4                        0                               0  \n",
+       "...                    ...                             ...  \n",
+       "99995                    0                               0  \n",
+       "99996                    0                               0  \n",
+       "99997                    0                               0  \n",
+       "99998                    0                               0  \n",
+       "99999                    0                               0  \n",
+       "\n",
+       "[100000 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dns_analysis_bn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "8442b48c-9855-4c1e-8e1a-a9adf929b44c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>answer_count</th>\n",
+       "      <th>contains_tls_consistent_answer</th>\n",
+       "      <th>answer_matches_probe_asn</th>\n",
+       "      <th>answer_matches_probe_cc</th>\n",
+       "      <th>answer_contains_bogon</th>\n",
+       "      <th>answer_is_cloud_provider</th>\n",
+       "      <th>answer_is_blockpage</th>\n",
+       "      <th>answer_contains_unreachable_ip</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>med</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>med</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>high</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>med</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>med</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  answer_count  contains_tls_consistent_answer  answer_matches_probe_asn  \\\n",
+       "0          med                             1.0                         0   \n",
+       "1          med                             1.0                         0   \n",
+       "2         high                             1.0                         0   \n",
+       "3          med                             NaN                         0   \n",
+       "4          med                             1.0                         0   \n",
+       "\n",
+       "   answer_matches_probe_cc  answer_contains_bogon  answer_is_cloud_provider  \\\n",
+       "0                        1                      0                         0   \n",
+       "1                        0                      0                         1   \n",
+       "2                        1                      0                         1   \n",
+       "3                        1                      0                         0   \n",
+       "4                        0                      0                         1   \n",
+       "\n",
+       "   answer_is_blockpage  answer_contains_unreachable_ip  \n",
+       "0                    0                               0  \n",
+       "1                    0                               0  \n",
+       "2                    0                               0  \n",
+       "3                    0                               0  \n",
+       "4                    0                               0  "
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "id": "efdd1cfd-5884-4192-90d1-ff152dace834",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "  0%|                                                                                              | 0/4 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "Working for n conditional variables: 0:   0%|                                                      | 0/4 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "Working for n conditional variables: 0:  25%|███████████▌                                  | 1/4 [00:03<00:09,  3.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "Working for n conditional variables: 1:  25%|███████████▌                                  | 1/4 [00:03<00:09,  3.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "Working for n conditional variables: 1:  50%|███████████████████████                       | 2/4 [01:04<01:14, 37.15s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "Working for n conditional variables: 2:  50%|███████████████████████                       | 2/4 [01:04<01:14, 37.15s/it]\u001b[A\u001b[A\u001b[A\u001b[AINFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | tls_inconsistent=0, tls_consistent=0. Not enough samples\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "Working for n conditional variables: 2:  75%|█████████████████████████████████▊           | 3/4 [04:19<01:49, 109.52s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "Working for n conditional variables: 3:  75%|█████████████████████████████████▊           | 3/4 [04:19<01:49, 109.52s/it]\u001b[A\u001b[A\u001b[A\u001b[AINFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_cloud_provider=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_cloud_provider=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_ip_in_trusted_answers=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_contains_bogon=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_ip_in_trusted_answers=1, answer_contains_bogon=1, answer_count=low. Not enough samples\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "Working for n conditional variables: 3: 100%|█████████████████████████████████████████████| 4/4 [11:04<00:00, 226.07s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "Working for n conditional variables: 4: 100%|█████████████████████████████████████████████| 4/4 [11:04<00:00, 226.07s/it]\u001b[A\u001b[A\u001b[A\u001b[AINFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_is_cloud_provider=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_ip_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_blockpage=1, answer_asn_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_blockpage=1, answer_matches_probe_cc=1, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_matches_probe_cc=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_is_cloud_provider=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_blockpage=1, answer_is_cloud_provider=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_blockpage=1, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_blockpage=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_blockpage=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_asorg_in_trusted_answers=1, answer_matches_probe_cc=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_asorg_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_asorg_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_asorg_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_asorg_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_asn_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_asn_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_asn_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_asn_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_matches_probe_cc=1, answer_is_cloud_provider=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_matches_probe_cc=1, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_matches_probe_cc=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_matches_probe_cc=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_matches_probe_cc=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_matches_probe_cc=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_cloud_provider=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_cloud_provider=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_cloud_provider=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_cloud_provider=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, answer_matches_probe_cc=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_asn_in_trusted_answers=1, answer_is_cloud_provider=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, answer_is_cloud_provider=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_asn_in_trusted_answers=1, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_matches_probe_cc=1, answer_is_cloud_provider=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_matches_probe_cc=1, answer_is_cloud_provider=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_matches_probe_cc=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_matches_probe_cc=1, answer_ip_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_matches_probe_cc=0, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_matches_probe_cc=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_matches_probe_cc=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_is_cloud_provider=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_is_cloud_provider=0, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_is_cloud_provider=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_is_cloud_provider=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_ip_in_trusted_answers=0, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_ip_in_trusted_answers=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_asorg_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_asorg_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, tls_consistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, tls_consistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_matches_probe_cc=1, answer_is_cloud_provider=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, answer_matches_probe_cc=1, answer_is_cloud_provider=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_matches_probe_cc=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, answer_matches_probe_cc=1, answer_ip_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, answer_matches_probe_cc=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_matches_probe_cc=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_is_cloud_provider=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, answer_is_cloud_provider=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_asn_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_asn_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, tls_consistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=0, answer_is_cloud_provider=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_is_cloud_provider=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_is_cloud_provider=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=0, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_ip_in_trusted_answers=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_matches_probe_cc=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_matches_probe_cc=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, tls_consistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=0, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=1, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_is_cloud_provider=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_is_cloud_provider=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=1, tls_consistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=1, tls_consistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_ip_in_trusted_answers=1, tls_consistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_ip_in_trusted_answers=1, tls_consistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=0, tls_consistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_asn_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_asn_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_matches_probe_cc=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_matches_probe_cc=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, answer_matches_probe_cc=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, answer_matches_probe_cc=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_is_cloud_provider=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_is_cloud_provider=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, answer_is_cloud_provider=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, answer_is_cloud_provider=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, answer_is_cloud_provider=1, tls_consistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1, tls_consistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, answer_ip_in_trusted_answers=0, tls_consistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_matches_probe_cc=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_matches_probe_cc=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_matches_probe_cc=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=1, answer_matches_probe_cc=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=1, answer_matches_probe_cc=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_is_cloud_provider=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_is_cloud_provider=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=1, answer_is_cloud_provider=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=1, answer_is_cloud_provider=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=1, answer_is_cloud_provider=1, tls_consistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=1, answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=1, answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, tls_consistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=0, answer_is_cloud_provider=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=0, answer_is_cloud_provider=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, answer_is_cloud_provider=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, answer_is_cloud_provider=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, answer_is_cloud_provider=1, tls_consistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, answer_is_cloud_provider=1, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=0, answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=0, answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_cloud_provider=0, answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_cloud_provider=1, answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_cloud_provider=1, answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_cloud_provider=1, answer_ip_in_trusted_answers=1, tls_consistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_cloud_provider=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_cloud_provider=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_cloud_provider=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_is_cloud_provider=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_ip_in_trusted_answers=0, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test answer_count ⟂ contains_tls_consistent_answer | answer_ip_in_trusted_answers=1, tls_consistent=0, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_is_cloud_provider=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_ip_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_asn_in_trusted_answers=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_blockpage=1, answer_matches_probe_cc=1, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_matches_probe_cc=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_matches_probe_cc=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_matches_probe_cc=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_is_cloud_provider=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_is_cloud_provider=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_ip_in_trusted_answers=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_ip_in_trusted_answers=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_ip_in_trusted_answers=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, tls_inconsistent=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_is_blockpage=1, tls_inconsistent=0, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_contains_bogon=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_is_blockpage=1, answer_contains_bogon=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, answer_count=med. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_asorg_in_trusted_answers=1, answer_matches_probe_cc=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_asn_in_trusted_answers=1, answer_matches_probe_cc=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_count=med. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_asn=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, answer_matches_probe_cc=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_asn_in_trusted_answers=1, answer_is_cloud_provider=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, answer_is_cloud_provider=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_asn_in_trusted_answers=1, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, answer_count=med. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_matches_probe_cc=1, answer_is_cloud_provider=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_matches_probe_cc=1, answer_is_cloud_provider=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_matches_probe_cc=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_matches_probe_cc=1, answer_ip_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_matches_probe_cc=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_matches_probe_cc=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_is_cloud_provider=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_is_cloud_provider=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_is_cloud_provider=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_ip_in_trusted_answers=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_count=med. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_ip_in_trusted_answers=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=0, answer_contains_bogon=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asorg_in_trusted_answers=1, answer_contains_bogon=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_matches_probe_cc=1, answer_is_cloud_provider=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, answer_matches_probe_cc=1, answer_is_cloud_provider=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_matches_probe_cc=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, answer_matches_probe_cc=1, answer_ip_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_matches_probe_cc=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, answer_matches_probe_cc=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_is_cloud_provider=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, answer_is_cloud_provider=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, tls_inconsistent=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_count=med. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, answer_ip_in_trusted_answers=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, tls_inconsistent=0, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=0, answer_contains_bogon=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_asn_in_trusted_answers=1, answer_contains_bogon=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=0. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_is_cloud_provider=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_is_cloud_provider=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_ip_in_trusted_answers=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=0, answer_ip_in_trusted_answers=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_ip_in_trusted_answers=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, tls_inconsistent=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, tls_inconsistent=0, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=0, answer_contains_bogon=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_contains_bogon=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_matches_probe_cc=1, answer_contains_bogon=1, answer_count=med. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=0, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=0, answer_contains_bogon=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_is_cloud_provider=1, answer_contains_bogon=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_ip_in_trusted_answers=1, tls_inconsistent=0, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_ip_in_trusted_answers=0, answer_contains_bogon=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_ip_in_trusted_answers=1, answer_contains_bogon=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, answer_ip_in_trusted_answers=1, answer_contains_bogon=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, tls_inconsistent=0, answer_contains_bogon=0, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_blockpage=1, tls_inconsistent=0, answer_contains_bogon=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=1, answer_asn_in_trusted_answers=0, answer_is_cloud_provider=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asorg_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_matches_probe_cc=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=1, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1, answer_count=high. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_asn_in_trusted_answers=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1, answer_count=med. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_ip_in_trusted_answers=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, answer_is_cloud_provider=1, tls_inconsistent=0, answer_contains_bogon=1. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=1, answer_is_cloud_provider=1, answer_contains_bogon=1, answer_count=med. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_matches_probe_cc=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_is_cloud_provider=0, answer_ip_in_trusted_answers=1, answer_contains_bogon=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Skipping the test tls_consistent ⟂ contains_tls_consistent_answer | answer_ip_in_trusted_answers=1, tls_inconsistent=0, answer_contains_bogon=1, answer_count=low. Not enough samples\n",
+      "INFO:pgmpy:Reached maximum number of allowed conditional variables. Exiting\n",
+      "Working for n conditional variables: 4: 100%|█████████████████████████████████████████████| 4/4 [21:39<00:00, 324.88s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pgmpy.estimators import PC, HillClimbSearch, ExhaustiveSearch\n",
+    "from pgmpy.estimators import K2Score\n",
+    "from pgmpy.sampling import BayesianModelSampling\n",
+    "est = PC(data=samples_extra_clean)\n",
+    "estimated_model = est.estimate(variant=\"stable\", max_cond_vars=4)\n",
+    "#estimated_model = est.estimate(variant=\"orig\", max_cond_vars=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "696e9b55-96a1-4f79-9bc6-a5b0fe0e1ae8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "viz = estimated_model.to_graphviz()\n",
+    "viz.draw('estimated_extra_pc_stable_1M.png', prog='dot')\n",
+    "Image('estimated_extra_pc_stable_1M.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "id": "76f25b67-d09e-4e94-af2d-927a4a1df47e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>answer_count</th>\n",
+       "      <th>tls_consistent</th>\n",
+       "      <th>tls_inconsistent</th>\n",
+       "      <th>contains_tls_consistent_answer</th>\n",
+       "      <th>answer_ip_in_trusted_answers</th>\n",
+       "      <th>answer_asn_in_trusted_answers</th>\n",
+       "      <th>answer_asorg_in_trusted_answers</th>\n",
+       "      <th>answer_matches_probe_asn</th>\n",
+       "      <th>answer_matches_probe_cc</th>\n",
+       "      <th>answer_contains_bogon</th>\n",
+       "      <th>answer_is_cloud_provider</th>\n",
+       "      <th>answer_is_blockpage</th>\n",
+       "      <th>answer_contains_unreachable_ip</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>med</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>med</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>med</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>low</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>med</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999995</th>\n",
+       "      <td>med</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999996</th>\n",
+       "      <td>high</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999997</th>\n",
+       "      <td>med</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999998</th>\n",
+       "      <td>med</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999999</th>\n",
+       "      <td>low</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000000 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       answer_count  tls_consistent  tls_inconsistent  \\\n",
+       "0               med               1                 0   \n",
+       "1               med               1                 0   \n",
+       "2               med               1                 0   \n",
+       "3               low               1                 0   \n",
+       "4               med               1                 0   \n",
+       "...             ...             ...               ...   \n",
+       "999995          med               0                 0   \n",
+       "999996         high               0                 0   \n",
+       "999997          med               0                 0   \n",
+       "999998          med               0                 0   \n",
+       "999999          low               1                 0   \n",
+       "\n",
+       "        contains_tls_consistent_answer  answer_ip_in_trusted_answers  \\\n",
+       "0                                  1.0                             0   \n",
+       "1                                  1.0                             1   \n",
+       "2                                  1.0                             0   \n",
+       "3                                  1.0                             0   \n",
+       "4                                  1.0                             0   \n",
+       "...                                ...                           ...   \n",
+       "999995                             NaN                             0   \n",
+       "999996                             NaN                             0   \n",
+       "999997                             NaN                             0   \n",
+       "999998                             NaN                             1   \n",
+       "999999                             1.0                             0   \n",
+       "\n",
+       "        answer_asn_in_trusted_answers  answer_asorg_in_trusted_answers  \\\n",
+       "0                                   1                                0   \n",
+       "1                                   1                                0   \n",
+       "2                                   0                                0   \n",
+       "3                                   0                                0   \n",
+       "4                                   1                                1   \n",
+       "...                               ...                              ...   \n",
+       "999995                              0                                0   \n",
+       "999996                              0                                0   \n",
+       "999997                              0                                0   \n",
+       "999998                              1                                1   \n",
+       "999999                              0                                0   \n",
+       "\n",
+       "        answer_matches_probe_asn  answer_matches_probe_cc  \\\n",
+       "0                              0                        0   \n",
+       "1                              0                        1   \n",
+       "2                              0                        1   \n",
+       "3                              0                        0   \n",
+       "4                              0                        0   \n",
+       "...                          ...                      ...   \n",
+       "999995                         0                        1   \n",
+       "999996                         0                        0   \n",
+       "999997                         0                        0   \n",
+       "999998                         0                        0   \n",
+       "999999                         0                        0   \n",
+       "\n",
+       "        answer_contains_bogon  answer_is_cloud_provider  answer_is_blockpage  \\\n",
+       "0                           0                         1                    0   \n",
+       "1                           0                         1                    0   \n",
+       "2                           0                         1                    0   \n",
+       "3                           0                         0                    0   \n",
+       "4                           0                         0                    0   \n",
+       "...                       ...                       ...                  ...   \n",
+       "999995                      0                         0                    1   \n",
+       "999996                      0                         0                    0   \n",
+       "999997                      0                         0                    0   \n",
+       "999998                      0                         1                    0   \n",
+       "999999                      0                         0                    0   \n",
+       "\n",
+       "        answer_contains_unreachable_ip  \n",
+       "0                                    0  \n",
+       "1                                    0  \n",
+       "2                                    0  \n",
+       "3                                    0  \n",
+       "4                                    0  \n",
+       "...                                ...  \n",
+       "999995                               0  \n",
+       "999996                               0  \n",
+       "999997                               0  \n",
+       "999998                               0  \n",
+       "999999                               0  \n",
+       "\n",
+       "[1000000 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples_extra_clean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "id": "8e23ef60-d7ed-4384-810a-d4065997e2fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples_extra_clean_part = samples_extra_clean.copy()[:100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "id": "471912f3-694c-409b-b719-d0d46545cea6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/art/repos/ooni/data/oonipipeline/.venv/lib/python3.11/site-packages/pgmpy/factors/discrete/DiscreteFactor.py:489: RuntimeWarning: invalid value encountered in divide\n",
+      "  phi.values = phi.values / phi.values.sum()\n",
+      "/Users/art/repos/ooni/data/oonipipeline/.venv/lib/python3.11/site-packages/pgmpy/factors/discrete/DiscreteFactor.py:489: RuntimeWarning: invalid value encountered in divide\n",
+      "  phi.values = phi.values / phi.values.sum()\n",
+      "/Users/art/repos/ooni/data/oonipipeline/.venv/lib/python3.11/site-packages/pgmpy/factors/discrete/DiscreteFactor.py:489: RuntimeWarning: invalid value encountered in divide\n",
+      "  phi.values = phi.values / phi.values.sum()\n",
+      "/Users/art/repos/ooni/data/oonipipeline/.venv/lib/python3.11/site-packages/pgmpy/factors/discrete/DiscreteFactor.py:489: RuntimeWarning: invalid value encountered in divide\n",
+      "  phi.values = phi.values / phi.values.sum()\n",
+      "/Users/art/repos/ooni/data/oonipipeline/.venv/lib/python3.11/site-packages/pgmpy/factors/discrete/DiscreteFactor.py:489: RuntimeWarning: invalid value encountered in divide\n",
+      "  phi.values = phi.values / phi.values.sum()\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pgmpy.inference import VariableElimination\n",
+    "from pgmpy.estimators import MaximumLikelihoodEstimator\n",
+    "bn_estimated_pc = BayesianNetwork(estimated_model)\n",
+    "bn_estimated_pc.fit(data=samples_extra, estimator=MaximumLikelihoodEstimator)\n",
+    "infer = VariableElimination(bn_estimated_pc)\n",
+    "def infer_blockpage_2(row):\n",
+    "    evidence = {}\n",
+    "    for key in row.keys():\n",
+    "        if key != 'answer_is_blockpage' and not pd.isnull(row[key]):\n",
+    "            evidence[key] = row[key]\n",
+    "    res = infer.query(['answer_is_blockpage'], evidence=evidence)\n",
+    "    row['answer_is_blockpage_0'] = res.values[0]\n",
+    "    row['answer_is_blockpage_1'] = res.values[1]\n",
+    "    return row\n",
+    "samples_extra_clean_part = samples_extra_clean_part.apply(infer_blockpage_2, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "id": "387e6e49-d072-4bb2-85da-221b9959ae36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 241,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(bn_estimated_pc.get_cpds('tls_consistent').get_evidence())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "id": "b68e6c98-9273-4674-a88c-94f00cc72f70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3072"
+      ]
+     },
+     "execution_count": 242,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(a[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "id": "16cc24a0-44df-4b33-9875-f031d4510b61",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3072"
+      ]
+     },
+     "execution_count": 246,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2**10*3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "id": "11483302-ea84-4021-9ab6-0df180d83c0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'20240220062610.565789_VE_webconnectivity_0b7437bb2801006a'"
+      ]
+     },
+     "execution_count": 218,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples_extra.iloc[16]['measurement_uid']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "72042d2c-6583-4127-9fa7-77e3e354c047",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "viz = estimated_model.to_graphviz()\n",
+    "viz.draw('estimated_pc_stable_1M.png', prog='dot')\n",
+    "Image('estimated_pc_stable_1M.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "25c7616b-b626-4895-a16d-7d1ca586b437",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pgmpy.estimators import MaximumLikelihoodEstimator\n",
+    "bn_estimated_pc = BayesianNetwork(estimated_model)\n",
+    "bn_estimated_pc.fit(data=samples, estimator=MaximumLikelihoodEstimator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "95e6ad05-83ab-472d-9251-3563c0176368",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "viz = bn_estimated_pc.to_graphviz()\n",
+    "viz.draw('estimated_pc_stable_1M.png', prog='dot')\n",
+    "Image('estimated_pc_stable_1M.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "4b491d78-6b7b-4a07-9b4e-564352424975",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "39d61a52-95b3-48a7-a930-33d97d0db243",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pgmpy.inference import VariableElimination\n",
+    "infer = VariableElimination(bn_estimated_pc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "9643f31a-b393-431d-8698-85a873231a00",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+------------------------+----------------------------+\n",
+      "| answer_is_blockpage    |   phi(answer_is_blockpage) |\n",
+      "+========================+============================+\n",
+      "| answer_is_blockpage(0) |                     0.9992 |\n",
+      "+------------------------+----------------------------+\n",
+      "| answer_is_blockpage(1) |                     0.0008 |\n",
+      "+------------------------+----------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(infer.query(['answer_is_blockpage']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "c713290f-afda-4cca-be84-bf281d12b4fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pgmpy.models.BayesianNetwork.BayesianNetwork at 0x3d1d02650>"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bn_estimated_pc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "688fc8a7-17d3-4597-af04-3d002644eccf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples_inference = samples.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88be89fb-86cd-472b-9347-1881c3a02def",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estimated_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "id": "a80d59c2-822a-4140-9910-b86c2bdd4111",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def infer_blockpage(row):\n",
+    "    evidence = {}\n",
+    "    for key in ['answer_count', 'contains_tls_consistent_answer',\n",
+    "       'answer_matches_probe_asn', 'answer_matches_probe_cc',\n",
+    "       'answer_contains_bogon', 'answer_is_cloud_provider',\n",
+    "       'answer_contains_unreachable_ip']:\n",
+    "        if not pd.isnull(row[key]):\n",
+    "            evidence[key] = row[key]\n",
+    "    res = infer.query(['answer_is_blockpage'], evidence=evidence)\n",
+    "    return res.values[0]\n",
+    "samples_inference['answer_is_blockpage_prob'] = samples_inference.apply(infer_blockpage, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "id": "c7115696-2216-4c97-b708-68e42f30a433",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>answer_count</th>\n",
+       "      <th>contains_tls_consistent_answer</th>\n",
+       "      <th>answer_matches_probe_asn</th>\n",
+       "      <th>answer_matches_probe_cc</th>\n",
+       "      <th>answer_contains_bogon</th>\n",
+       "      <th>answer_is_cloud_provider</th>\n",
+       "      <th>answer_is_blockpage</th>\n",
+       "      <th>answer_contains_unreachable_ip</th>\n",
+       "      <th>answer_is_blockpage_prob</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2007</th>\n",
+       "      <td>med</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.755245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5940</th>\n",
+       "      <td>low</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.518519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11714</th>\n",
+       "      <td>low</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.518519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12383</th>\n",
+       "      <td>low</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.518519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15567</th>\n",
+       "      <td>low</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.518519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>985012</th>\n",
+       "      <td>low</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.518519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>994973</th>\n",
+       "      <td>low</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.518519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998252</th>\n",
+       "      <td>low</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.518519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999409</th>\n",
+       "      <td>low</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.518519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999698</th>\n",
+       "      <td>med</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.755245</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>413 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       answer_count  contains_tls_consistent_answer  answer_matches_probe_asn  \\\n",
+       "2007            med                             0.0                         1   \n",
+       "5940            low                             0.0                         1   \n",
+       "11714           low                             0.0                         1   \n",
+       "12383           low                             0.0                         1   \n",
+       "15567           low                             0.0                         1   \n",
+       "...             ...                             ...                       ...   \n",
+       "985012          low                             0.0                         1   \n",
+       "994973          low                             0.0                         1   \n",
+       "998252          low                             0.0                         1   \n",
+       "999409          low                             0.0                         1   \n",
+       "999698          med                             0.0                         1   \n",
+       "\n",
+       "        answer_matches_probe_cc  answer_contains_bogon  \\\n",
+       "2007                          0                      0   \n",
+       "5940                          0                      0   \n",
+       "11714                         0                      0   \n",
+       "12383                         0                      0   \n",
+       "15567                         0                      0   \n",
+       "...                         ...                    ...   \n",
+       "985012                        0                      0   \n",
+       "994973                        0                      0   \n",
+       "998252                        0                      0   \n",
+       "999409                        0                      0   \n",
+       "999698                        0                      0   \n",
+       "\n",
+       "        answer_is_cloud_provider  answer_is_blockpage  \\\n",
+       "2007                           0                    0   \n",
+       "5940                           0                    1   \n",
+       "11714                          0                    1   \n",
+       "12383                          0                    0   \n",
+       "15567                          0                    1   \n",
+       "...                          ...                  ...   \n",
+       "985012                         0                    0   \n",
+       "994973                         0                    1   \n",
+       "998252                         0                    1   \n",
+       "999409                         0                    0   \n",
+       "999698                         0                    1   \n",
+       "\n",
+       "        answer_contains_unreachable_ip  answer_is_blockpage_prob  \n",
+       "2007                                 0                  0.755245  \n",
+       "5940                                 0                  0.518519  \n",
+       "11714                                0                  0.518519  \n",
+       "12383                                0                  0.518519  \n",
+       "15567                                0                  0.518519  \n",
+       "...                                ...                       ...  \n",
+       "985012                               0                  0.518519  \n",
+       "994973                               0                  0.518519  \n",
+       "998252                               0                  0.518519  \n",
+       "999409                               0                  0.518519  \n",
+       "999698                               0                  0.755245  \n",
+       "\n",
+       "[413 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples_inference[\n",
+    "    samples_inference['answer_is_blockpage_prob'] < 0.8\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "3ebd867c-97b4-4746-9757-a4a158d8041b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>answer_count</th>\n",
+       "      <th>contains_tls_consistent_answer</th>\n",
+       "      <th>answer_matches_probe_asn</th>\n",
+       "      <th>answer_matches_probe_cc</th>\n",
+       "      <th>answer_contains_bogon</th>\n",
+       "      <th>answer_is_cloud_provider</th>\n",
+       "      <th>answer_is_blockpage</th>\n",
+       "      <th>answer_contains_unreachable_ip</th>\n",
+       "      <th>answer_is_blockpage_prob</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>low</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.832334</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>576</th>\n",
+       "      <td>med</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.997029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>low</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.994409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>912</th>\n",
+       "      <td>med</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.960802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1077</th>\n",
+       "      <td>low</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.994409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999517</th>\n",
+       "      <td>low</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.999102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999634</th>\n",
+       "      <td>low</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.999102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999698</th>\n",
+       "      <td>med</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.755245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999869</th>\n",
+       "      <td>low</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.999102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999882</th>\n",
+       "      <td>low</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.999102</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9154 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       answer_count  contains_tls_consistent_answer  answer_matches_probe_asn  \\\n",
+       "192             low                             NaN                         1   \n",
+       "576             med                             NaN                         0   \n",
+       "684             low                             NaN                         0   \n",
+       "912             med                             NaN                         1   \n",
+       "1077            low                             NaN                         0   \n",
+       "...             ...                             ...                       ...   \n",
+       "999517          low                             NaN                         0   \n",
+       "999634          low                             NaN                         0   \n",
+       "999698          med                             0.0                         1   \n",
+       "999869          low                             NaN                         0   \n",
+       "999882          low                             NaN                         0   \n",
+       "\n",
+       "        answer_matches_probe_cc  answer_contains_bogon  \\\n",
+       "192                           0                      0   \n",
+       "576                           0                      1   \n",
+       "684                           1                      0   \n",
+       "912                           0                      0   \n",
+       "1077                          1                      0   \n",
+       "...                         ...                    ...   \n",
+       "999517                        0                      1   \n",
+       "999634                        0                      1   \n",
+       "999698                        0                      0   \n",
+       "999869                        0                      1   \n",
+       "999882                        0                      1   \n",
+       "\n",
+       "        answer_is_cloud_provider  answer_is_blockpage  \\\n",
+       "192                            0                    1   \n",
+       "576                            0                    1   \n",
+       "684                            0                    1   \n",
+       "912                            0                    1   \n",
+       "1077                           0                    1   \n",
+       "...                          ...                  ...   \n",
+       "999517                         0                    1   \n",
+       "999634                         0                    1   \n",
+       "999698                         0                    1   \n",
+       "999869                         0                    1   \n",
+       "999882                         0                    1   \n",
+       "\n",
+       "        answer_contains_unreachable_ip  answer_is_blockpage_prob  \n",
+       "192                                  0                  0.832334  \n",
+       "576                                  0                  0.997029  \n",
+       "684                                  0                  0.994409  \n",
+       "912                                  0                  0.960802  \n",
+       "1077                                 0                  0.994409  \n",
+       "...                                ...                       ...  \n",
+       "999517                               0                  0.999102  \n",
+       "999634                               0                  0.999102  \n",
+       "999698                               0                  0.755245  \n",
+       "999869                               0                  0.999102  \n",
+       "999882                               0                  0.999102  \n",
+       "\n",
+       "[9154 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples_inference[\n",
+    "    samples_inference['answer_is_blockpage'] == 1\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cb2766e-75ab-46b7-b8e7-6158d07bd7a9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23f3d25a-5904-4569-a4e4-8f2e4fd7e529",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fb70eed-73ad-4a5f-848e-c3c0197334d3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "f9b2502a-be79-49c6-b294-9e629dada251",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "  0%|                                                                                        | 0/1000000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "  0%|                                                                             | 1/1000000 [00:00<39:28:13,  7.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "  0%|                                                                             | 11/1000000 [00:00<8:50:55, 31.39it/s]\u001b[A\u001b[A\n"
+     ]
+    }
+   ],
+   "source": [
+    "scoring_method = K2Score(data=subsamples)\n",
+    "est = HillClimbSearch(data=subsamples)\n",
+    "estimated_model = est.estimate(\n",
+    "    scoring_method=scoring_method\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "2cc3e98a-3ea3-4e9b-83b6-8289db315334",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "viz = estimated_model.to_graphviz()\n",
+    "viz.draw('estimated_hillclimb_1M.png', prog='dot')\n",
+    "Image('estimated_hillclimb_1M.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e0703ab-7419-4d43-ad95-03d226b74004",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cpd_answer_matches_probe_cc = TabularCPD(\n",
+    "    \"answer_matches_probe_cc\",\n",
+    "    variable_card=2,\n",
+    "    values=[\n",
+    "        [0.77],\n",
+    "        [0.23],\n",
+    "    ],\n",
+    "    state_names={\n",
+    "        \"answer_matches_probe_cc\": ['t', 'f']\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "cpd_contains_unreachable_answer = TabularCPD(\n",
+    "    \"answer_contains_unreachable_ip\",\n",
+    "    variable_card=2,\n",
+    "    values=[\n",
+    "        [0.15],\n",
+    "        [0.85],\n",
+    "    ],\n",
+    "    state_names={\n",
+    "        \"answer_contains_unreachable_ip\": ['t', 'f']\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "cpd_contains_unreachable_answer = TabularCPD(\n",
+    "    \"answer_contains_bogon\",\n",
+    "    variable_card=2,\n",
+    "    values=[\n",
+    "        [0.015],\n",
+    "        [0.985],\n",
+    "    ],\n",
+    "    state_names={\n",
+    "        \"answer_contains_bogon\": ['t', 'f']\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "\n",
+    "cpd_contains_unreachable_answer = TabularCPD(\n",
+    "    \"answer_is_blockpage\",\n",
+    "    variable_card=2,\n",
+    "    values=[\n",
+    "        [0.015],\n",
+    "        [0.985],\n",
+    "    ],\n",
+    "    evidence=[\"answer_contains_bogon\", \"answer_contains_unreachable_ip\", \"answer_matches_probe_cc\"],\n",
+    "    evidence_card=[3, 3],\n",
+    "    state_names={\n",
+    "        \"answer_is_blockpage\": ['t', 'f']\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b02005e-aa58-4ab9-ab2d-79f6b4de1bff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95eebdc8-959b-4378-936e-da93fd532d6e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "817d57dd-4b58-46f7-8c07-d3cdaa15bcb0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "7c3f0650-e34c-4fc2-9706-8439f8223400",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f139f2ea-b491-46aa-b75f-1acc3f7b6169",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a0480af-d3c1-4c58-a8ac-4631487ee48f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "b739753b-d515-4903-8a1d-a7725604131a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "5482a37f-2532-494d-9e15-e3e40dea854b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6b1e906-58fa-4ba2-9e35-b76e9c64e4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bdc153b-19df-4434-983a-30e7a8e22cd4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea33f35b-a596-4818-ad61-ee0730fe8e24",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd1ffe75-dcba-4b67-b7fe-c839fb5030ff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07310d97-6023-47e9-bca3-2f011a823111",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/oonipipeline/notebooks/DNS Similarity.ipynb b/oonipipeline/notebooks/DNS Similarity.ipynb
new file mode 100644
index 00000000..0be88cf2
--- /dev/null
+++ b/oonipipeline/notebooks/DNS Similarity.ipynb	
@@ -0,0 +1,416 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f309af11-65e3-4d73-88de-655921a52693",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/art/repos/ooni/data/oonipipeline/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from IPython.display import Image\n",
+    "from pgmpy.models import BayesianNetwork\n",
+    "from pgmpy.factors.discrete import TabularCPD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3f8fc6f9-9786-4013-8802-359432fb01a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_uids = [\n",
+    " '20240101113125.808243_BG_webconnectivity_ba4cf234d47b73ec',\n",
+    " '20240220050354.524776_US_webconnectivity_a690552818804fc3',\n",
+    " '20240224232654.007984_RU_webconnectivity_9164a3df2d35316c',\n",
+    " '20240226225131.261075_VE_webconnectivity_a3e31c999fc8b161',\n",
+    " '20240226225231.612531_VE_webconnectivity_47d3561e542337d8',\n",
+    " '20240226225245.422977_VE_webconnectivity_37dcdd51581cbd13',\n",
+    " '20240227160142.829208_US_webconnectivity_2c9b945991901f8d',\n",
+    " '20240311005741.675686_SE_webconnectivity_2a4a84928bd17ab0',\n",
+    " '20240316062349.575036_US_webconnectivity_b63efd0ed694e005',\n",
+    " '20240328194436.111684_TR_webconnectivity_5414b98b9c9f873b',\n",
+    " '20240409035157.511121_BR_webconnectivity_85b1de4e410314f7',\n",
+    " '20240410115514.842786_BR_webconnectivity_6d224ea8b6b3ca7c',\n",
+    " '20240410124541.400148_BR_webconnectivity_ac16eb05475f622a',\n",
+    " '20240410134516.191956_BR_webconnectivity_a947a71df23203f3',\n",
+    " '20240410135012.070725_BR_webconnectivity_d3e9bce608599d5e',\n",
+    " '20240413230148.929136_KH_webconnectivity_33ae9436d7b6779c',\n",
+    " '20240415141511.755790_BR_webconnectivity_07547042a9307621',\n",
+    " '20240416214307.425888_BR_webconnectivity_c9d912510d61bc0b',\n",
+    " '20240418071953.744226_BR_webconnectivity_2c6b65eeff39f34f',\n",
+    " '20240506163355.336279_VN_webconnectivity_8de9ce661982414d',\n",
+    " '20240508114544.444840_BR_webconnectivity_379718729eeed45b',\n",
+    " '20240514091759.626287_NL_webconnectivity_38962f675df0f676',\n",
+    " '20240519182127.049517_US_webconnectivity_6e12ec842e98674a',\n",
+    " '20240522113817.495473_NL_webconnectivity_e81ed947814f9d2a',\n",
+    " '20240524142956.125292_JO_webconnectivity_5cd31e0f03494bd2',\n",
+    " '20240627074955.407639_RU_webconnectivity_99e23c2b9680c1cb',\n",
+    " '20240704234509.514733_NL_webconnectivity_6f924d2e43209ae1',\n",
+    " '20240705015016.667807_AE_webconnectivity_392604bce72a44d5',\n",
+    " '20240722041303.591874_MM_webconnectivity_98e6671dd7dce61a',\n",
+    " '20240722041334.778473_MM_webconnectivity_93a628687303be86',\n",
+    " '20240722042950.433988_MM_webconnectivity_d388cdc8bad1f15c',\n",
+    " '20240728114019.011456_VE_webconnectivity_1f0ba3819685e272',\n",
+    " '20240810175938.750056_VE_webconnectivity_016721e1ebfdc2ed',\n",
+    " '20240810182504.634031_VE_webconnectivity_853b19341a35d2f0',\n",
+    " '20240810182604.928599_VE_webconnectivity_f42bd5020088c498',\n",
+    " '20240815043926.030750_VE_webconnectivity_f034e025b6db08ea',\n",
+    " '20240815092817.050941_DE_webconnectivity_661003d3498d802f',\n",
+    " '20240815172949.387668_VE_webconnectivity_427addcd5d1ea806',\n",
+    " '20240818234433.791006_VE_webconnectivity_8fac03865551a8eb',\n",
+    " '20240820163242.249906_JO_webconnectivity_a826f460299fa996']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f67d57d4-9472-47c7-94a7-03eb97c8c4f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from oonidata.dataclient import load_measurement\n",
+    "from oonidata.apiclient import get_measurement_dict_by_uid\n",
+    "\n",
+    "from oonipipeline.netinfo import NetinfoDB\n",
+    "from oonipipeline.transforms.observations import measurement_to_observations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "6e0703ab-7419-4d43-ad95-03d226b74004",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "netinfodb = NetinfoDB(datadir=Path(\"datadir\"), download=False)\n",
+    "# 20231129041111.939896_NP_webconnectivity_32105af63774eabf\n",
+    "#raw_msmt = get_measurement_dict_by_uid(\"20240804233508.181041_TR_webconnectivity_9a7272e520f3d836\")\n",
+    "def make_obs_ctrl(measurement_uid):\n",
+    "    raw_msmt = get_measurement_dict_by_uid(measurement_uid)\n",
+    "    msmt = load_measurement(msmt=raw_msmt)\n",
+    "    web_observations, web_control_observations = measurement_to_observations(\n",
+    "        msmt, netinfodb=netinfodb\n",
+    "    )\n",
+    "    return web_observations, web_control_observations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "4b02005e-aa58-4ab9-ab2d-79f6b4de1bff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "def most_frequent(asns):\n",
+    "    if not asns:\n",
+    "        return 0\n",
+    "    count = Counter(asns)\n",
+    "    most_common = count.most_common(1)\n",
+    "    return most_common[0][0]\n",
+    "\n",
+    "CLOUD_PROVIDERS_ASNS = [\n",
+    "    13335,  # Cloudflare: https://www.peeringdb.com/net/4224\n",
+    "    209242, #\tCloudflare London, LLC\t\n",
+    "    20940,  # Akamai: https://www.peeringdb.com/net/2\n",
+    "    9002,  # Akamai RETN\n",
+    "    16625, # Akamai Technologies, Inc.\t\n",
+    "    63949, # Akamai Technologies, Inc.\t\n",
+    "    16509, #\tAmazon.com, Inc.\n",
+    "    14618, #\tAmazon.com, Inc.\t\n",
+    "    15169, #\tGoogle LLC\n",
+    "    396982,  # Google Cloud: https://www.peeringdb.com/net/30878\n",
+    "    54113, #\tFastly, Inc\n",
+    "    8075, # Microsoft Corporation\n",
+    "    8068, #\tMicrosoft Corporation\n",
+    "]\n",
+    "def is_cloud_provider(as_number):\n",
+    "    return as_number in CLOUD_PROVIDERS_ASNS\n",
+    "\n",
+    "def dns_feature_vector(dns_answers, w_ctrl):\n",
+    "    ctrl_map = {}\n",
+    "    for ctrl in w_ctrl:\n",
+    "        ctrl_map[ctrl.ip] = ctrl\n",
+    "\n",
+    "    unreachable_count = 0\n",
+    "    for answer in dns_answers:\n",
+    "        if ctrl_map.get(answer.ip):\n",
+    "            if ctrl_map[answer.ip].tcp_success == False:\n",
+    "                unreachable_count += 1\n",
+    "    dns_answer_vector = {\n",
+    "        \"answer_count\": len(dns_answers),\n",
+    "        \"answer_cloud_provider\": 1 if any([is_cloud_provider(x.ip_asn) for x in dns_answers]) else 0,\n",
+    "        \"unreachable_answer_count\": unreachable_count,\n",
+    "        \"top_answer_asn\": most_frequent(map(lambda x: x.ip_asn, dns_answers))\n",
+    "    }\n",
+    "    return dns_answer_vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95eebdc8-959b-4378-936e-da93fd532d6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs, ctrl = make_obs_ctrl(\"20240316062349.575036_US_webconnectivity_b63efd0ed694e005\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "817d57dd-4b58-46f7-8c07-d3cdaa15bcb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pprint import pprint\n",
+    "#pprint(obs[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "7c3f0650-e34c-4fc2-9706-8439f8223400",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.spatial import distance\n",
+    "netinfodb = NetinfoDB(datadir=Path(\"datadir\"), download=False)\n",
+    "def compute_dns_distance(measurement_uid):\n",
+    "    web_observations, web_control_observations = make_obs_ctrl(measurement_uid)\n",
+    "    exp_dns_answers = list(filter(lambda x: x.dns_answer is not None, web_observations))\n",
+    "    if len(exp_dns_answers) == 0:\n",
+    "        return None\n",
+    "    exp_vector = list(dns_feature_vector(web_observations, web_control_observations).values())\n",
+    "    ctrl_dns_answers = list(filter(lambda x: x.dns_failure is not None or x.dns_success is True, web_control_observations))\n",
+    "    ctrl_vector = list(dns_feature_vector(\n",
+    "        ctrl_dns_answers,\n",
+    "        web_control_observations\n",
+    "    ).values())\n",
+    "    try:\n",
+    "        d = distance.cosine(exp_vector, ctrl_vector)\n",
+    "        return d\n",
+    "    except:\n",
+    "        print(exp_vector, ctrl_vector)\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "f139f2ea-b491-46aa-b75f-1acc3f7b6169",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20240101113125.808243_BG_webconnectivity_ba4cf234d47b73ec\n",
+      "2.511132244364944e-07\n",
+      "20240220050354.524776_US_webconnectivity_a690552818804fc3\n",
+      "2.0243516862805677e-09\n",
+      "20240224232654.007984_RU_webconnectivity_9164a3df2d35316c\n",
+      "2.811796573354286e-09\n",
+      "20240226225131.261075_VE_webconnectivity_a3e31c999fc8b161\n",
+      "5.679824388593602e-10\n",
+      "20240226225231.612531_VE_webconnectivity_47d3561e542337d8\n",
+      "2.811796573354286e-09\n",
+      "20240226225245.422977_VE_webconnectivity_37dcdd51581cbd13\n",
+      "1.1247181741502743e-08\n",
+      "20240227160142.829208_US_webconnectivity_2c9b945991901f8d\n",
+      "None\n",
+      "20240311005741.675686_SE_webconnectivity_2a4a84928bd17ab0\n",
+      "1.834545071410787e-09\n",
+      "20240316062349.575036_US_webconnectivity_b63efd0ed694e005\n",
+      "[3, 0, 0, None] [2, 0, 0, 60781]\n",
+      "None\n",
+      "20240328194436.111684_TR_webconnectivity_5414b98b9c9f873b\n",
+      "1.3396325160286437e-09\n",
+      "20240409035157.511121_BR_webconnectivity_85b1de4e410314f7\n",
+      "0.0\n",
+      "20240410115514.842786_BR_webconnectivity_6d224ea8b6b3ca7c\n",
+      "2.8117966843765885e-09\n",
+      "20240410124541.400148_BR_webconnectivity_ac16eb05475f622a\n",
+      "1.2861659182128449e-08\n",
+      "20240410134516.191956_BR_webconnectivity_a947a71df23203f3\n",
+      "0.0\n",
+      "20240410135012.070725_BR_webconnectivity_d3e9bce608599d5e\n",
+      "7.02948984576679e-08\n",
+      "20240413230148.929136_KH_webconnectivity_33ae9436d7b6779c\n",
+      "None\n",
+      "20240415141511.755790_BR_webconnectivity_07547042a9307621\n",
+      "2.811796573354286e-09\n",
+      "20240416214307.425888_BR_webconnectivity_c9d912510d61bc0b\n",
+      "[3, 0, 0, None] [4, 0, 0, 40428]\n",
+      "None\n",
+      "20240418071953.744226_BR_webconnectivity_2c6b65eeff39f34f\n",
+      "0.0\n",
+      "20240506163355.336279_VN_webconnectivity_8de9ce661982414d\n",
+      "4.189776303675785e-10\n",
+      "20240508114544.444840_BR_webconnectivity_379718729eeed45b\n",
+      "3.71212472660698e-06\n",
+      "20240514091759.626287_NL_webconnectivity_38962f675df0f676\n",
+      "1.8090338116394378e-09\n",
+      "20240519182127.049517_US_webconnectivity_6e12ec842e98674a\n",
+      "2.7760629350837007e-09\n",
+      "20240522113817.495473_NL_webconnectivity_e81ed947814f9d2a\n",
+      "None\n",
+      "20240524142956.125292_JO_webconnectivity_5cd31e0f03494bd2\n",
+      "6.872500064591947e-07\n",
+      "20240627074955.407639_RU_webconnectivity_99e23c2b9680c1cb\n",
+      "1.8345449603884845e-09\n",
+      "20240704234509.514733_NL_webconnectivity_6f924d2e43209ae1\n",
+      "4.6097892170138266e-10\n",
+      "20240705015016.667807_AE_webconnectivity_392604bce72a44d5\n",
+      "[5, 1, 0, None] [2, 1, 0, 16509]\n",
+      "None\n",
+      "20240722041303.591874_MM_webconnectivity_98e6671dd7dce61a\n",
+      "4.6097892170138266e-10\n",
+      "20240722041334.778473_MM_webconnectivity_93a628687303be86\n",
+      "4.6097892170138266e-10\n",
+      "20240722042950.433988_MM_webconnectivity_d388cdc8bad1f15c\n",
+      "2.7787750989105575e-09\n",
+      "20240728114019.011456_VE_webconnectivity_1f0ba3819685e272\n",
+      "2.2500359353472277e-09\n",
+      "20240810175938.750056_VE_webconnectivity_016721e1ebfdc2ed\n",
+      "1.1115099285419205e-08\n",
+      "20240810182504.634031_VE_webconnectivity_853b19341a35d2f0\n",
+      "7.668048529474447e-09\n",
+      "20240810182604.928599_VE_webconnectivity_f42bd5020088c498\n",
+      "[3, 1, 0, None] [16, 1, 0, 54113]\n",
+      "None\n",
+      "20240815043926.030750_VE_webconnectivity_f034e025b6db08ea\n",
+      "2.7787750989105575e-09\n",
+      "20240815092817.050941_DE_webconnectivity_661003d3498d802f\n",
+      "None\n",
+      "20240815172949.387668_VE_webconnectivity_427addcd5d1ea806\n",
+      "2.7787750989105575e-09\n",
+      "20240818234433.791006_VE_webconnectivity_8fac03865551a8eb\n",
+      "1.8345454044776943e-09\n",
+      "20240820163242.249906_JO_webconnectivity_a826f460299fa996\n",
+      "1.004488997979891e-06\n"
+     ]
+    }
+   ],
+   "source": [
+    "distance_map = {\n",
+    "}\n",
+    "for uid in sample_uids:\n",
+    "    print(uid)\n",
+    "    d = compute_dns_distance(uid)\n",
+    "    print(d)\n",
+    "    distance_map[uid] = d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "6a0480af-d3c1-4c58-a8ac-4631487ee48f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.9001535001581544e-08"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compute_dns_distance(\"20240729235930.015020_IR_webconnectivity_eda3016ef01b4d3b\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "b739753b-d515-4903-8a1d-a7725604131a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "5482a37f-2532-494d-9e15-e3e40dea854b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6b1e906-58fa-4ba2-9e35-b76e9c64e4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bdc153b-19df-4434-983a-30e7a8e22cd4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea33f35b-a596-4818-ad61-ee0730fe8e24",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd1ffe75-dcba-4b67-b7fe-c839fb5030ff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07310d97-6023-47e9-bca3-2f011a823111",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/oonipipeline/notebooks/datadir b/oonipipeline/notebooks/datadir
new file mode 120000
index 00000000..25c0bf37
--- /dev/null
+++ b/oonipipeline/notebooks/datadir
@@ -0,0 +1 @@
+../tests/data/datadir
\ No newline at end of file
diff --git a/oonipipeline/notebooks/dns_analysis_bn.png b/oonipipeline/notebooks/dns_analysis_bn.png
new file mode 100644
index 00000000..d0ee7dda
Binary files /dev/null and b/oonipipeline/notebooks/dns_analysis_bn.png differ
diff --git a/oonipipeline/notebooks/estimated.png b/oonipipeline/notebooks/estimated.png
new file mode 100644
index 00000000..f6fbccc9
Binary files /dev/null and b/oonipipeline/notebooks/estimated.png differ
diff --git a/oonipipeline/notebooks/estimated_extra_pc_stable_1M.png b/oonipipeline/notebooks/estimated_extra_pc_stable_1M.png
new file mode 100644
index 00000000..ffb21430
Binary files /dev/null and b/oonipipeline/notebooks/estimated_extra_pc_stable_1M.png differ
diff --git a/oonipipeline/notebooks/estimated_hillclimb_1M.png b/oonipipeline/notebooks/estimated_hillclimb_1M.png
new file mode 100644
index 00000000..f6fbccc9
Binary files /dev/null and b/oonipipeline/notebooks/estimated_hillclimb_1M.png differ
diff --git a/oonipipeline/notebooks/estimated_pc_stable_1M.png b/oonipipeline/notebooks/estimated_pc_stable_1M.png
new file mode 100644
index 00000000..c3c0dda9
Binary files /dev/null and b/oonipipeline/notebooks/estimated_pc_stable_1M.png differ
diff --git a/oonipipeline/notebooks/msmt_feedback_clean.csv b/oonipipeline/notebooks/msmt_feedback_clean.csv
new file mode 100644
index 00000000..38de02bf
--- /dev/null
+++ b/oonipipeline/notebooks/msmt_feedback_clean.csv
@@ -0,0 +1,287 @@
+,measurement_uid,status,outcome
+0,20240821111652.275207_RU_telegram_98b717d97593636f,blocked,blocked
+1,0120191024c9bee71f54e22e25cdbb7007cb825c,down.unreachable,down
+2,20230306T235817Z_webconnectivity_BR_263087_n1_1rNv0dEaDgiZ9i33,ok,ok
+3,20230307235958.642565_FR_webconnectivity_86a528710144d5f2,ok,ok
+4,20230308T092114Z_webconnectivity_IR_49100_n1_eo1anF5FIOhWAomo,blocked.blockpage.dns,blocked
+5,20230315T022408Z_webconnectivity_KH_131178_n1_c1skknOHGkyIqXuF,blocked,blocked
+6,20230315T114404Z_webconnectivity_BR_7738_n1_e0zJX7TbiQHa3TyQ,ok,ok
+7,20230318T084015Z_webconnectivity_US_21928_n1_p5G1g3Z2ireXwKUt,blocked.dns.nxdomain,blocked
+8,20230319T003935Z_webconnectivity_US_21928_n1_FH6UhDKqX9szXTsj,ok,ok
+9,20230322T054150Z_webconnectivity_ID_23693_n1_RT0imyzovPkCTr13,ok,ok
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diff --git a/oonipipeline/notebooks/sql-analysis.sql b/oonipipeline/notebooks/sql-analysis.sql
new file mode 100644
index 00000000..012fb09d
--- /dev/null
+++ b/oonipipeline/notebooks/sql-analysis.sql
@@ -0,0 +1,114 @@
+CLOUD_PROVIDERS_ASNS = [
+    13335,  # Cloudflare: https://www.peeringdb.com/net/4224
+    209242, #	Cloudflare London, LLC	
+    20940,  # Akamai: https://www.peeringdb.com/net/2
+    9002,  # Akamai RETN
+    16625, # Akamai Technologies, Inc.	
+    63949, # Akamai Technologies, Inc.	
+    16509, #	Amazon.com, Inc.
+    14618, #	Amazon.com, Inc.	
+    15169, #	Google LLC
+    396982,  # Google Cloud: https://www.peeringdb.com/net/30878
+    54113, #	Fastly, Inc
+    8075, # Microsoft Corporation
+    8068, #	Microsoft Corporation
+]
+
+df_ctrl_exp = click_query("""
+WITH
+mapFilter((ip, _) -> (has(exp_dns_answer_records, ip) = 1), ctrl_tls_inconsistent_ips_map) as ctrl_tls_inconsistent_map,
+mapFilter((ip, _) -> (has(exp_dns_answer_records, ip) = 1), ctrl_tls_consistent_ips_map) as ctrl_tls_consistent_map
+SELECT
+hostname,
+measurement_uid,
+probe_cc,
+probe_asn,
+exp_dns_answer_cloud_provider,
+exp_dns_answer_matches_probe_cc,
+exp_dns_answer_matches_probe_asn,
+exp_dns_answer_bogon,
+exp_dns_answer_record_count,
+exp_dns_tls_consistent,
+hasAny(ctrl_top8_ips, exp_dns_answer_records) as exp_dns_answer_matches_top8_ip_ctrl,
+hasAny(ctrl_top3_asns, exp_dns_answer_asns) as exp_dns_answer_matches_top3_asn_ctrl,
+ctrl_hostname,
+ctrl_dns_answer_asns,
+ctrl_dns_answer_ccs,
+ctrl_dns_answer_bogon,
+ctrl_dns_answer_record_count_q50,
+ctrl_dns_answer_cloud_provider,
+arraySum(
+    mapValues(ctrl_tls_inconsistent_map)
+) as ctrl_tls_inconsistent_datum_count,
+
+arraySum(
+    mapValues(ctrl_tls_consistent_map)
+) as ctrl_tls_consistent_datum_count,
+length(mapKeys(ctrl_tls_inconsistent_map)) as ctrl_tls_inconsistent_ip_count,
+length(mapKeys(ctrl_tls_consistent_map)) as ctrl_tls_consistent_ip_count
+FROM (
+    WITH 
+    groupUniqArray(ip_cc) as exp_dns_answer_ccs,
+    max(ip_is_bogon) as exp_dns_answer_bogon,
+    max(tls_is_certificate_valid) as exp_dns_tls_consistent
+    SELECT 
+    hostname,
+    measurement_uid,
+    probe_cc, 
+    probe_asn,
+    groupArray(dns_answer) as exp_dns_answer_records,
+    groupUniqArray(ip_asn) as exp_dns_answer_asns,
+    max(IF(ip_asn IN %(cloud_provider_asns)s, 1, 0)) exp_dns_answer_cloud_provider,
+    has(exp_dns_answer_ccs, probe_cc) as exp_dns_answer_matches_probe_cc,
+    has(exp_dns_answer_asns, probe_asn) as exp_dns_answer_matches_probe_asn,
+    exp_dns_answer_bogon,
+    --multiIf(answer_count_num <= 1, 'low', answer_count_num <= 4, 'med', 'high') as answer_count
+    length(exp_dns_answer_records) exp_dns_answer_record_count,
+    exp_dns_answer_records,
+    exp_dns_tls_consistent
+    FROM obs_web
+    WHERE 
+    dns_engine IN ('getaddrinfo', 'system', 'golang_net_resolver')
+    AND measurement_start_time > '2024-08-01'
+    AND measurement_start_time <= '2024-08-02'
+    AND dns_answer IS NOT NULL
+    GROUP BY measurement_uid, hostname, probe_cc, probe_asn
+) AS exp
+LEFT OUTER JOIN (
+    SELECT 
+    hostname as ctrl_hostname,
+    groupUniqArrayArray(ctrl_dns_answer_ip_asns) as ctrl_dns_answer_asns,
+    groupUniqArrayArray(ctrl_dns_answer_ccs) as ctrl_dns_answer_ccs,
+    MAX(ctrl_dns_answer_bogon) as ctrl_dns_answer_bogon,
+    quantile(0.5)(ctrl_dns_answer_record_count) as ctrl_dns_answer_record_count_q50,
+    MAX(ctrl_dns_answer_cloud_provider) as ctrl_dns_answer_cloud_provider,
+    CAST(sumMap(
+        ctrl_tls_consistent_ips, arrayResize(CAST([], 'Array(UInt64)'), length(ctrl_tls_consistent_ips), 1)
+    ), 'Map(String, UInt32)') as ctrl_tls_consistent_ips_map,
+    CAST(sumMap(
+        ctrl_tls_inconsistent_ips, arrayResize(CAST([], 'Array(UInt64)'), length(ctrl_tls_inconsistent_ips), 1)
+    ), 'Map(String, UInt32)') as ctrl_tls_inconsistent_ips_map,
+    topK(8)(arrayJoin(ctrl_dns_answer_records)) as ctrl_top8_ips,
+    topK(3)(arrayJoin(ctrl_dns_answer_ip_asns)) as ctrl_top3_asns
+    FROM (
+        SELECT
+        hostname,
+        groupUniqArray(ip_cc) as ctrl_dns_answer_ccs,
+        max(ip_is_bogon) as ctrl_dns_answer_bogon,
+        groupArray(ip) as ctrl_dns_answer_records,
+        groupUniqArray(ip_asn) as ctrl_dns_answer_ip_asns,
+        length(ctrl_dns_answer_records) ctrl_dns_answer_record_count,
+        max(IF(ip_asn IN %(cloud_provider_asns)s, 1, 0)) ctrl_dns_answer_cloud_provider,
+        groupArrayIf(ip, tls_success = 1) as ctrl_tls_consistent_ips,
+        groupArrayIf(ip, tls_success = 0 AND tls_failure LIKE 'ssl_%%') as ctrl_tls_inconsistent_ips
+        FROM
+        obs_web_ctrl 
+        WHERE measurement_start_time > '2024-08-01'
+        AND measurement_start_time <= '2024-08-02'
+        AND dns_success = 1
+        GROUP BY hostname, measurement_uid
+    ) as t 
+    GROUP BY hostname
+) AS ctrl
+ON ctrl.ctrl_hostname = exp.hostname
+""", params={"cloud_provider_asns": CLOUD_PROVIDERS_ASNS})
diff --git a/oonipipeline/notebooks/web-analysis-bn.ipynb b/oonipipeline/notebooks/web-analysis-bn.ipynb
new file mode 100644
index 00000000..6d455039
--- /dev/null
+++ b/oonipipeline/notebooks/web-analysis-bn.ipynb
@@ -0,0 +1,1346 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "99aba231-cfe4-4ecb-8233-b34c90515fe9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/art/repos/ooni/data/oonipipeline/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from IPython.display import Image\n",
+    "from pgmpy.models import BayesianNetwork\n",
+    "from pgmpy.factors.discrete import TabularCPD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c76afe2b-1a36-494f-aa99-c9ae037b39be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "msmt_feedback = pd.read_csv(\"msmt_feedback_clean.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ce851a49-6dab-472d-a5a7-78c41395acca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       " '20230725112947.634265_US_webconnectivity_9f5f65f580430a16',\n",
+       " '20230801044149.983363_FR_webconnectivity_dd2092359659ade5',\n",
+       " '20230808105307.596396_PR_webconnectivity_9750cfd5feffa48c',\n",
+       " '20230808140237.587059_RU_webconnectivity_6686d8c9a5ad3c44',\n",
+       " '20230808142748.968758_RU_webconnectivity_d7daef2078d8cf52',\n",
+       " '20230809041856.808259_JP_webconnectivity_2a12ec3eed52cfcb',\n",
+       " '20230811140309.308865_TR_webconnectivity_bdf0949225613d04',\n",
+       " '20230826115125.289448_ES_webconnectivity_7b27698861dbe1a1',\n",
+       " '20230906235704.499879_PH_webconnectivity_574eb687e9548c6c',\n",
+       " '20230912124627.126620_RU_webconnectivity_b0d15d19193de387',\n",
+       " '20230912225618.913735_TR_webconnectivity_d09a6fccb4e1d0b9',\n",
+       " '20230912225658.443735_TR_webconnectivity_46e1342f54f7f194',\n",
+       " '20231001163322.797615_ES_webconnectivity_eac75eefdefedc01',\n",
+       " '20231010021137.752038_US_webconnectivity_60ffe1c368ba3015',\n",
+       " '20231011110422.455401_US_webconnectivity_c51b11720a476bac',\n",
+       " '20231011115926.887045_BR_webconnectivity_a8a69d134bec82b5',\n",
+       " '20231011212647.332599_US_webconnectivity_f3be8fa17fedc6e6',\n",
+       " '20231014085824.026809_US_webconnectivity_6de6d6eb4bcf08b6',\n",
+       " '20231014085831.113063_US_webconnectivity_50fec85bf973b51f',\n",
+       " '20231014085914.883505_US_webconnectivity_93093b082611e313',\n",
+       " '20231014090503.446885_US_webconnectivity_44e24bca002b1546',\n",
+       " '20231014092400.403747_US_webconnectivity_a9f1031bf13b9e39',\n",
+       " '20231014092406.788042_US_webconnectivity_b5d51ef04e61be31',\n",
+       " '20231014092416.327049_US_webconnectivity_4c90187135a08229',\n",
+       " '20231015010820.051938_US_webconnectivity_aa1cc35e862ec5bd',\n",
+       " '20231015011156.316177_US_webconnectivity_7b11df33265a43ec',\n",
+       " '20231015011559.837542_US_webconnectivity_2c1c19ae979de31f',\n",
+       " '20231015011620.273755_US_webconnectivity_b8a71a68f0a7aeea',\n",
+       " '20231015011640.711582_US_webconnectivity_e2007ecf6093033f',\n",
+       " '20231015011736.767596_US_webconnectivity_0d1ca2f88fddc4b7',\n",
+       " '20231015012221.728873_US_webconnectivity_60bb9b9aa1cb2913',\n",
+       " '20231015012854.684078_US_webconnectivity_20f6770b9ba4f6c1',\n",
+       " '20231015013044.582103_US_webconnectivity_24b1dd4d2ec2f436',\n",
+       " '20231015013130.107827_US_webconnectivity_138c58a1cffebdb8',\n",
+       " '20231015013538.513605_US_webconnectivity_29bdb9c85d479ade',\n",
+       " '20231015014122.721853_US_webconnectivity_76d27dea2a86d1e5',\n",
+       " '20231015015442.106007_US_webconnectivity_3b77b832f11c8b3f',\n",
+       " '20231015020615.512510_US_webconnectivity_57d51992fcff960d',\n",
+       " '20231015021237.567418_US_webconnectivity_87350bc72d590468',\n",
+       " '20231015021335.748059_US_webconnectivity_7d8b271c54ff1cb3',\n",
+       " '20231015021613.538314_US_webconnectivity_d7af4ea95bbfd701',\n",
+       " '20231015021837.165010_US_webconnectivity_faa1a44927cf9c04',\n",
+       " '20231015021923.061752_US_webconnectivity_7a10e368b164c046',\n",
+       " '20231015023159.423015_US_webconnectivity_a79ee8e12d5785cb',\n",
+       " '20231015025117.595771_US_webconnectivity_4226ec707ccf9462',\n",
+       " '20231015025529.987324_US_webconnectivity_e444b2711b1ca14d',\n",
+       " '20231015025900.194062_US_webconnectivity_fa76e48cc75b25f8',\n",
+       " '20231015070155.878989_US_webconnectivity_d554fc3bd06853b6',\n",
+       " '20231018185248.027618_US_webconnectivity_73f7fa26779eef77',\n",
+       " '20231031105152.595912_US_webconnectivity_3b555141744944cf',\n",
+       " '20231116211939.059228_SA_webconnectivity_3b3561e25ed31b79',\n",
+       " '20231209133916.137248_IR_webconnectivity_ec978e7151643686',\n",
+       " '20231209133946.652659_IR_webconnectivity_9351a5ccf5b2b7e0',\n",
+       " '20231209134251.927155_IR_webconnectivity_93da8a88e31fbd64',\n",
+       " '20231209134254.777986_IR_webconnectivity_cbe0b2208a45dce7',\n",
+       " '20231209134718.819151_IR_webconnectivity_25b0062e655c5de8',\n",
+       " '20231209135052.101093_IR_webconnectivity_9935959c50c81511',\n",
+       " '20231209135327.677167_IR_webconnectivity_37580e895ea74544',\n",
+       " '20231210034024.024763_US_webconnectivity_7a31469aa4f80bf5',\n",
+       " '20231210152235.402641_RU_webconnectivity_0acd9476b871f7ce',\n",
+       " '20231210152608.723733_RU_webconnectivity_d40624d4e512b123',\n",
+       " '20231211122708.857576_US_webconnectivity_300ffe11f200c19b',\n",
+       " '20231222101657.548811_ES_webconnectivity_9707add2136d136c',\n",
+       " '20231229195340.889994_ES_webconnectivity_affca2bb4a7ec326',\n",
+       " '20231229195406.710065_ES_webconnectivity_03b7356653f525ab',\n",
+       " '20231229203415.005426_IR_webconnectivity_1e0f8c6c1f17b26e',\n",
+       " '20231229203611.902257_IR_webconnectivity_a5982aa7da15c968',\n",
+       " '20231229203947.392487_IR_webconnectivity_2b8ccfd916ce57f5',\n",
+       " '20231229204527.398923_IR_webconnectivity_d9e38a9e15a9933a',\n",
+       " '20231229210035.629564_IR_webconnectivity_9d2d625b7485ceae',\n",
+       " '20231229210122.107127_IR_webconnectivity_ce67e82e84d3da2e',\n",
+       " '20231229210131.103093_IR_webconnectivity_d109d16966891827',\n",
+       " '20231229210221.438422_IR_webconnectivity_c7914c0dcde3a32b',\n",
+       " '20240101113125.808243_BG_webconnectivity_ba4cf234d47b73ec',\n",
+       " '20240220050354.524776_US_webconnectivity_a690552818804fc3',\n",
+       " '20240224232654.007984_RU_webconnectivity_9164a3df2d35316c',\n",
+       " '20240226225131.261075_VE_webconnectivity_a3e31c999fc8b161',\n",
+       " '20240226225231.612531_VE_webconnectivity_47d3561e542337d8',\n",
+       " '20240226225245.422977_VE_webconnectivity_37dcdd51581cbd13',\n",
+       " '20240227160142.829208_US_webconnectivity_2c9b945991901f8d',\n",
+       " '20240311005741.675686_SE_webconnectivity_2a4a84928bd17ab0',\n",
+       " '20240316062349.575036_US_webconnectivity_b63efd0ed694e005',\n",
+       " '20240328194436.111684_TR_webconnectivity_5414b98b9c9f873b',\n",
+       " '20240409035157.511121_BR_webconnectivity_85b1de4e410314f7',\n",
+       " '20240410115514.842786_BR_webconnectivity_6d224ea8b6b3ca7c',\n",
+       " '20240410124541.400148_BR_webconnectivity_ac16eb05475f622a',\n",
+       " '20240410134516.191956_BR_webconnectivity_a947a71df23203f3',\n",
+       " '20240410135012.070725_BR_webconnectivity_d3e9bce608599d5e',\n",
+       " '20240413230148.929136_KH_webconnectivity_33ae9436d7b6779c',\n",
+       " '20240415141511.755790_BR_webconnectivity_07547042a9307621',\n",
+       " '20240416214307.425888_BR_webconnectivity_c9d912510d61bc0b',\n",
+       " '20240418071953.744226_BR_webconnectivity_2c6b65eeff39f34f',\n",
+       " '20240506163355.336279_VN_webconnectivity_8de9ce661982414d',\n",
+       " '20240508114544.444840_BR_webconnectivity_379718729eeed45b',\n",
+       " '20240514091759.626287_NL_webconnectivity_38962f675df0f676',\n",
+       " '20240519182127.049517_US_webconnectivity_6e12ec842e98674a',\n",
+       " '20240522113817.495473_NL_webconnectivity_e81ed947814f9d2a',\n",
+       " '20240524142956.125292_JO_webconnectivity_5cd31e0f03494bd2',\n",
+       " '20240627074955.407639_RU_webconnectivity_99e23c2b9680c1cb',\n",
+       " '20240704234509.514733_NL_webconnectivity_6f924d2e43209ae1',\n",
+       " '20240705015016.667807_AE_webconnectivity_392604bce72a44d5',\n",
+       " '20240722041303.591874_MM_webconnectivity_98e6671dd7dce61a',\n",
+       " '20240722041334.778473_MM_webconnectivity_93a628687303be86',\n",
+       " '20240722042950.433988_MM_webconnectivity_d388cdc8bad1f15c',\n",
+       " '20240728114019.011456_VE_webconnectivity_1f0ba3819685e272',\n",
+       " '20240810175938.750056_VE_webconnectivity_016721e1ebfdc2ed',\n",
+       " '20240810182504.634031_VE_webconnectivity_853b19341a35d2f0',\n",
+       " '20240810182604.928599_VE_webconnectivity_f42bd5020088c498',\n",
+       " '20240815043926.030750_VE_webconnectivity_f034e025b6db08ea',\n",
+       " '20240815092817.050941_DE_webconnectivity_661003d3498d802f',\n",
+       " '20240815172949.387668_VE_webconnectivity_427addcd5d1ea806',\n",
+       " '20240818234433.791006_VE_webconnectivity_8fac03865551a8eb',\n",
+       " '20240820163242.249906_JO_webconnectivity_a826f460299fa996']"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(filter(lambda x: 'webconnectivity' in x, msmt_feedback['measurement_uid'].unique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f9393b66-f6af-4d17-884d-ffda1683b8f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 20231129041111.939896_NP_webconnectivity_32105af63774eabf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1b64f651-0707-4bb9-9205-2df6658ed0eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from oonidata.dataclient import load_measurement\n",
+    "from oonidata.apiclient import get_measurement_dict_by_uid\n",
+    "\n",
+    "from oonipipeline.netinfo import NetinfoDB\n",
+    "from oonipipeline.transforms.observations import measurement_to_observations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9605bec3-2993-4520-b810-aed279284d00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "possible_states = {\n",
+    "    'DNS Ctrl Failure': ['none', 'nxdomain', 'server_failure'],\n",
+    "     'DNS VP Failure': ['none', 'nxdomain', 'server_failure'],\n",
+    "     'DNS Answer': [\n",
+    "         'answer_is_bogon',\n",
+    "      'answer_matches_probe_asn',\n",
+    "      'answer_matches_probe_cc',\n",
+    "      'answer_is_cloud_provider',\n",
+    "      'answer_other'\n",
+    "     ],\n",
+    " 'DNS Answer TLS Consistency': [\n",
+    "     'tls_consistent',\n",
+    "  'tls_inconsistent',\n",
+    "  'unknown'\n",
+    " ],\n",
+    " 'TCP Ctrl Failure': ['none', 'connection_reset', 'timeout', 'other'],\n",
+    " 'TCP VP Result': ['none', 'connection_reset', 'timeout', 'other', 'down'],\n",
+    " 'TLS Ctrl Failure': ['none', 'bad_certificate', 'other'],\n",
+    " 'TLS VP Result': ['none',\n",
+    "  'bad_certificate',\n",
+    "  'connection_reset_after_ch',\n",
+    "  'timeout_after_ch',\n",
+    "  'timeout',\n",
+    "  'connection_reset',\n",
+    "  'other',\n",
+    "  'down'],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83d5ed8f-569b-4345-a09e-c5c388dc31f4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23fff868-7e7f-4961-9fbb-f2857e4cb899",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfc9bfb1-8a39-4278-a072-1fb96ed1d2bf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bd8a640-3e6d-4d54-b971-eb3332abcd89",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c736483d-b280-4972-aa38-70d272c077a8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4dc4093-fdfc-410e-b5fa-ea5fada37159",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d625a231-d3f6-4f05-93be-5710392437fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "web_analysis_bn_full = BayesianNetwork([\n",
+    "    ('DNS Ctrl Failure', 'DNS VP Failure'),\n",
+    "    ('DNS Ctrl Failure', 'DNS Failure Consistency'),\n",
+    "    #('Resolver config', 'DNS Outcome'),\n",
+    "    ('DNS Answer', 'DNS Answer Consistency'),\n",
+    "    ('DNS Answer', 'DNS Answer TLS Consistency'),\n",
+    "    ('DNS Answer TLS Consistency', 'DNS Answer Consistency'),\n",
+    "    ('DNS Answer Consistency', 'DNS Outcome'),\n",
+    "\n",
+    "    ('DNS VP Failure', 'DNS Failure Consistency'),\n",
+    "    ('DNS Failure Consistency', 'DNS Outcome'),\n",
+    "\n",
+    "    ('TCP Ctrl Failure', 'TCP VP Result'),\n",
+    "\n",
+    "    ('DNS Answer Consistency', 'TCP Outcome'),\n",
+    "    ('TCP VP Result', 'TCP Outcome'),\n",
+    "\n",
+    "    ('TLS Ctrl Failure', 'TLS VP Result'),\n",
+    "    ('TLS VP Result', 'TLS Outcome'),\n",
+    "    ('DNS Answer Consistency', 'TLS Outcome'),\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4217f9a-1ed1-4916-9014-6d43a4e82cd2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "94f26d5a-7d07-406f-ab76-a44706e5fee7",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "requires pygraphviz http://pygraphviz.github.io/",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "File \u001b[0;32m~/repos/ooni/data/oonipipeline/.venv/lib/python3.11/site-packages/networkx/drawing/nx_agraph.py:134\u001b[0m, in \u001b[0;36mto_agraph\u001b[0;34m(N)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 134\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpygraphviz\u001b[39;00m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pygraphviz'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m viz \u001b[38;5;241m=\u001b[39m \u001b[43mweb_analysis_bn_full\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_graphviz\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m viz\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweb_analysis_full.png\u001b[39m\u001b[38;5;124m'\u001b[39m, prog\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdot\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      3\u001b[0m Image(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweb_analysis_full.png\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m~/repos/ooni/data/oonipipeline/.venv/lib/python3.11/site-packages/pgmpy/base/DAG.py:1082\u001b[0m, in \u001b[0;36mDAG.to_graphviz\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1069\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_graphviz\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1070\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1071\u001b[0m \u001b[38;5;124;03m    Retuns a pygraphviz object for the DAG. pygraphviz is useful for\u001b[39;00m\n\u001b[1;32m   1072\u001b[0m \u001b[38;5;124;03m    visualizing the network structure.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1080\u001b[0m \u001b[38;5;124;03m    >>> model.draw('model.png', prog='neato')\u001b[39;00m\n\u001b[1;32m   1081\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1082\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnx_agraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_agraph\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/repos/ooni/data/oonipipeline/.venv/lib/python3.11/site-packages/networkx/drawing/nx_agraph.py:136\u001b[0m, in \u001b[0;36mto_agraph\u001b[0;34m(N)\u001b[0m\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpygraphviz\u001b[39;00m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 136\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires pygraphviz http://pygraphviz.github.io/\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m    137\u001b[0m directed \u001b[38;5;241m=\u001b[39m N\u001b[38;5;241m.\u001b[39mis_directed()\n\u001b[1;32m    138\u001b[0m strict \u001b[38;5;241m=\u001b[39m nx\u001b[38;5;241m.\u001b[39mnumber_of_selfloops(N) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m N\u001b[38;5;241m.\u001b[39mis_multigraph()\n",
+      "\u001b[0;31mImportError\u001b[0m: requires pygraphviz http://pygraphviz.github.io/"
+     ]
+    }
+   ],
+   "source": [
+    "viz = web_analysis_bn_full.to_graphviz()\n",
+    "viz.draw('web_analysis_full.png', prog='dot')\n",
+    "Image('web_analysis_full.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a6346742-dc39-40e3-8673-686b25fb7d87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "web_analysis_bn = BayesianNetwork([\n",
+    "    ('DNS Ctrl Failure', 'DNS Outcome'),\n",
+    "    ('DNS Ctrl Failure', 'DNS Answer Meta'),\n",
+    "    #('Resolver config', 'DNS Outcome'),\n",
+    "    ('DNS Consistency', 'DNS Outcome'),\n",
+    "    ('DNS Answer Meta', 'DNS Consistency'),\n",
+    "    ('DNS Answer Meta', 'DNS TLS Consistency'),\n",
+    "    ('DNS TLS Consistency', 'DNS Consistency'),\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f78e3c55-3826-4622-b720-74452c7c13a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#web_analysis_bn.save('web_analysi.bif')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3d63e9c3-0430-4bc1-a5e3-583ae71f3405",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "requires pygraphviz http://pygraphviz.github.io/",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "File \u001b[0;32m~/repos/ooni/data/oonipipeline/.venv/lib/python3.11/site-packages/networkx/drawing/nx_agraph.py:134\u001b[0m, in \u001b[0;36mto_agraph\u001b[0;34m(N)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 134\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpygraphviz\u001b[39;00m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pygraphviz'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m viz \u001b[38;5;241m=\u001b[39m \u001b[43mweb_analysis_bn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_graphviz\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m viz\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweb_analysis_simple.png\u001b[39m\u001b[38;5;124m'\u001b[39m, prog\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdot\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      3\u001b[0m Image(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweb_analysis_simple.png\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m~/repos/ooni/data/oonipipeline/.venv/lib/python3.11/site-packages/pgmpy/base/DAG.py:1082\u001b[0m, in \u001b[0;36mDAG.to_graphviz\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1069\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_graphviz\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1070\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1071\u001b[0m \u001b[38;5;124;03m    Retuns a pygraphviz object for the DAG. pygraphviz is useful for\u001b[39;00m\n\u001b[1;32m   1072\u001b[0m \u001b[38;5;124;03m    visualizing the network structure.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1080\u001b[0m \u001b[38;5;124;03m    >>> model.draw('model.png', prog='neato')\u001b[39;00m\n\u001b[1;32m   1081\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1082\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnx_agraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_agraph\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/repos/ooni/data/oonipipeline/.venv/lib/python3.11/site-packages/networkx/drawing/nx_agraph.py:136\u001b[0m, in \u001b[0;36mto_agraph\u001b[0;34m(N)\u001b[0m\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpygraphviz\u001b[39;00m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 136\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires pygraphviz http://pygraphviz.github.io/\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m    137\u001b[0m directed \u001b[38;5;241m=\u001b[39m N\u001b[38;5;241m.\u001b[39mis_directed()\n\u001b[1;32m    138\u001b[0m strict \u001b[38;5;241m=\u001b[39m nx\u001b[38;5;241m.\u001b[39mnumber_of_selfloops(N) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m N\u001b[38;5;241m.\u001b[39mis_multigraph()\n",
+      "\u001b[0;31mImportError\u001b[0m: requires pygraphviz http://pygraphviz.github.io/"
+     ]
+    }
+   ],
+   "source": [
+    "viz = web_analysis_bn.to_graphviz()\n",
+    "viz.draw('web_analysis_simple.png', prog='dot')\n",
+    "Image('web_analysis_simple.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "832d93dd-4cfa-459a-8e1e-01a66cbee2d9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13312638-208f-4798-8eae-268fb29a817c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57082d9d-01d0-43ec-91af-2a54e85ae0a2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb31f020-28d9-4f3c-9d72-1ed687623fea",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b3cad79a-bce8-4efb-b811-80f6458fb16c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_boilerplate():\n",
+    "    cpd_list = []\n",
+    "    for x in list(web_analysis_bn.nodes):\n",
+    "        var_name = x.lower().replace(' ', '_')\n",
+    "        var_name = f\"cpd_{var_name}\"\n",
+    "        s = f\"{var_name} = TabularCPD('{x}', variable_card=2, values=[\\n\"\n",
+    "        parents = web_analysis_bn.get_parents(x)\n",
+    "        padding = \" \"*(int(len(s)/2))\n",
+    "        for _ in range(2):\n",
+    "            s += padding + \"[\"\n",
+    "            s += \",\".join(\"0.\" for x in range(len(parents)))\n",
+    "            s += \"],\\n\"\n",
+    "        s += padding + f\"],\\n\" \n",
+    "        s += padding + f\" # parents={parents}\\n\"\n",
+    "        s += padding + f\"evidence={parents},\\n\"\n",
+    "        s += padding + f\"evidence_card=[],\\n\"\n",
+    "        s += padding + \"state_names = {\\n\"\n",
+    "        for p in parents:\n",
+    "            s += padding + f\"'{p}': [],\\n\"\n",
+    "        s += \"}\\n\"\n",
+    "        s += padding + \")\"\n",
+    "        print(s)\n",
+    "        cpd_list.append(var_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c1d1232-ed7a-4539-82bf-e43fd52b8f23",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "509c45ee-b22d-461a-998b-a701c5090580",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dns_answer_states = [\n",
+    "\"answer_is_bogon\", \n",
+    "\"answer_matches_probe_asn\", \n",
+    "\"answer_matches_probe_cc\",\n",
+    "\"answer_is_cloud_provider\",\n",
+    "\"answer_other\"\n",
+    "]\n",
+    "dns_failure_states = [\n",
+    "    \"none\",\n",
+    "    \"nxdomain\",\n",
+    "    \"server_failure\"\n",
+    "]\n",
+    "tls_consistency_states = [\"tls_consistent\", \"tls_inconsistent\", \"unknown\"]\n",
+    "cpd_dns_ctrl_failure = TabularCPD(\n",
+    "    \"DNS Ctrl Failure\",\n",
+    "    variable_card=3,\n",
+    "    values=[\n",
+    "        [0.994],\n",
+    "        [0.004],\n",
+    "        [0.002],\n",
+    "    ],\n",
+    "    state_names={\n",
+    "        \"DNS Ctrl Failure\": dns_failure_states\n",
+    "    },\n",
+    ")\n",
+    "cpd_dns_vp_failure = TabularCPD(\n",
+    "    \"DNS VP Failure\",\n",
+    "    variable_card=3,\n",
+    "    values=[\n",
+    "        # \"none\",    \"nxdomain\", \"server_failure\"\n",
+    "        [0.994, 0.02, 0.02],\n",
+    "        [0.004, 0.98, 0.01],\n",
+    "        [0.002, 0.01, 0.98],\n",
+    "    ],\n",
+    "    evidence=[\"DNS Ctrl Failure\"],\n",
+    "    evidence_card=[3],\n",
+    "    state_names={\n",
+    "        \"DNS VP Failure\": dns_failure_states,\n",
+    "        \"DNS Ctrl Failure\": dns_failure_states\n",
+    "    },\n",
+    ")\n",
+    "cpd_dns_failure_consistency = TabularCPD(\n",
+    "    \"DNS Failure Consistency\",\n",
+    "    variable_card=3,\n",
+    "    values=[\n",
+    "        # \"none\",    \"nxdomain\", \"server_failure\"\n",
+    "        [0.99, 0.01, 0.01, 0.94, 0.01, 0.01, 0.94, 0.01, 0.01], # ok\n",
+    "        [0.01, 0.05, 0.05, 0.01, 0.98, 0.92, 0.01, 0.92, 0.98], # expected_not_ok\n",
+    "        [0.01, 0.94, 0.94, 0.05, 0.01, 0.07, 0.05, 0.07, 0.01] # unexpected_not_ok\n",
+    "    ],\n",
+    "    evidence=[\"DNS Ctrl Failure\", \"DNS VP Failure\"],\n",
+    "    evidence_card=[3, 3],\n",
+    "    state_names={\n",
+    "        \"DNS Failure Consistency\": [\n",
+    "            \"ok\", \n",
+    "            \"expected_not_ok\",\n",
+    "            \"unexpected_not_ok\"\n",
+    "        ],\n",
+    "        \"DNS VP Failure\": dns_failure_states,\n",
+    "        \"DNS Ctrl Failure\": dns_failure_states\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "cpd_dns_answer = TabularCPD(\n",
+    "    \"DNS Answer\",\n",
+    "    variable_card=5,\n",
+    "    values=[\n",
+    "        [0.004],\n",
+    "        [0.006],\n",
+    "        [0.06],\n",
+    "        [0.73],\n",
+    "        [0.2],\n",
+    "    ],\n",
+    "    state_names={\n",
+    "        \"DNS Answer\": dns_answer_states,\n",
+    "    },\n",
+    ")\n",
+    "cpd_dns_answer_tls_consistency = TabularCPD(\n",
+    "    \"DNS Answer TLS Consistency\",\n",
+    "    variable_card=3,\n",
+    "    values=[\n",
+    "        # \"answer_is_bogon\", \"answer_matches_probe_asn\", \"answer_matches_probe_cc\", \"answer_is_cloud_provider\", \"answer_other\"\n",
+    "        [0.01, 0.80, 0.75, 0.90, 0.94], # tls_consistent\n",
+    "        [0.05, 0.15, 0.20, 0.08, 0.04], # tls_inconsistent\n",
+    "        [0.94, 0.05, 0.05, 0.02, 0.02], # unknown\n",
+    "    ],\n",
+    "    evidence=[\"DNS Answer\"],\n",
+    "    evidence_card=[5],\n",
+    "    state_names={\n",
+    "        \"DNS Answer TLS Consistency\": tls_consistency_states,\n",
+    "        \"DNS Answer\": dns_answer_states,\n",
+    "    },\n",
+    ")\n",
+    "cpd_dns_answer_consistency = TabularCPD(\n",
+    "    \"DNS Answer Consistency\",\n",
+    "    variable_card=2,\n",
+    "    values=[\n",
+    "        # \"answer_is_bogon\", \"answer_matches_probe_asn\",  \"answer_matches_probe_cc\", \"answer_is_cloud_provider\", \"answer_other\"\n",
+    "        # tls_consistent\", \"tls_inconsistent\", \"unknown\n",
+    "        [0.99, 0.001, 0.001, 0.999, 0.01, 0.3, 0.999, 0.001, 0.3, 0.999, 0.01, 0.8, 0.999, 0.001, 0.2], # consistent\n",
+    "        [0.01, 0.999, 0.999, 0.001, 0.99, 0.7, 0.001, 0.999, 0.7, 0.001, 0.99, 0.2, 0.001, 0.999, 0.8], # inconsistent\n",
+    "    ],\n",
+    "    # parents={parents}\n",
+    "    evidence=[\"DNS Answer\", \"DNS Answer TLS Consistency\"],\n",
+    "    evidence_card=[5, 3],\n",
+    "    state_names={\n",
+    "        \"DNS Answer\": dns_answer_states,\n",
+    "        \"DNS Answer TLS Consistency\": tls_consistency_states,\n",
+    "        \"DNS Answer Consistency\": [\n",
+    "            \"consistent\",\n",
+    "            \"inconsistent\"\n",
+    "        ],\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "cpd_dns_outcome = TabularCPD(\n",
+    "    \"DNS Outcome\",\n",
+    "    variable_card=3,\n",
+    "    values=[\n",
+    "        # Consistent, inconsistent\n",
+    "        [0.01, 0.01, 0.40, 0.8, 0.20, 0.90], # blocked\n",
+    "        [0.99, 0.01, 0.40, 0.2, 0.05, 0.01], # ok\n",
+    "        [0.00, 0.98, 0.20, 0.0, 0.75, 0.09], # down\n",
+    "    ],\n",
+    "    evidence=[\n",
+    "        \"DNS Answer Consistency\",\n",
+    "        \"DNS Failure Consistency\",\n",
+    "    ],\n",
+    "    evidence_card=[2, 3],\n",
+    "    state_names={\n",
+    "        \"DNS Outcome\": [\n",
+    "            \"blocked\",\n",
+    "            \"ok\",\n",
+    "            \"down\"\n",
+    "        ],\n",
+    "        \"DNS Answer Consistency\": [\"consistent\", \"inconsistent\"],\n",
+    "        \"DNS Failure Consistency\": [\n",
+    "            \"ok\", \n",
+    "            \"expected_not_ok\",\n",
+    "            \"unexpected_not_ok\"\n",
+    "        ],\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "710b4561-7b8f-47fd-a742-dd88e697a1bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tcp_failure_states = [\n",
+    "    \"none\",\n",
+    "    \"connection_reset\",\n",
+    "    \"timeout\",\n",
+    "    \"other\"\n",
+    "]\n",
+    "\n",
+    "cpd_tcp_ctrl_failure = TabularCPD(\n",
+    "    \"TCP Ctrl Failure\",\n",
+    "    variable_card=4,\n",
+    "    values=[\n",
+    "        [0.980],\n",
+    "        [0.001],\n",
+    "        [0.014],\n",
+    "        [0.005]\n",
+    "    ],\n",
+    "    state_names={\n",
+    "        \"TCP Ctrl Failure\": tcp_failure_states\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "cpd_tcp_vp_result = TabularCPD(\n",
+    "    \"TCP VP Result\",\n",
+    "    variable_card=5,\n",
+    "    values=[\n",
+    "        # \"none\", \"connection_reset\", \"timeout\", \"other\"\n",
+    "        [0.980, 0.02, 0.01, 0.01], # none\n",
+    "        [0.001, 0.14, 0.02, 0.06], # connection_reset\n",
+    "        [0.010, 0.05, 0.10, 0.06], # timeout\n",
+    "        [0.009, 0.07, 0.02, 0.12], # other\n",
+    "        [0.009, 0.72, 0.85, 0.75], # down\n",
+    "    ],\n",
+    "    evidence=[\"TCP Ctrl Failure\"],\n",
+    "    evidence_card=[4],\n",
+    "    state_names={\n",
+    "        \"TCP VP Result\": tcp_failure_states + [\"down\"],\n",
+    "        \"TCP Ctrl Failure\": tcp_failure_states\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "cpd_tcp_outcome = TabularCPD(\n",
+    "    \"TCP Outcome\",\n",
+    "    variable_card=4,\n",
+    "    values=[\n",
+    "        # consistent inconsistent\n",
+    "        # \"none\", \"connection_reset\", \"timeout\", \"other\", \"down\"\n",
+    "        [0.02, 0.95, 0.90, 0.85, 0.01, 0.07, 0.07, 0.07, 0.07, 0.05], # blocked\n",
+    "        [0.95, 0.01, 0.01, 0.01, 0.08, 0.01, 0.01, 0.01, 0.01, 0.01], # ok\n",
+    "        [0.02, 0.03, 0.08, 0.09, 0.01, 0.07, 0.07, 0.07, 0.07, 0.09], # down\n",
+    "        [0.01, 0.01, 0.01, 0.05, 0.90, 0.85, 0.85, 0.85, 0.85, 0.85], # unknown\n",
+    "    ],\n",
+    "    evidence=[\"DNS Answer Consistency\", \"TCP VP Result\"],\n",
+    "    evidence_card=[2, 5],\n",
+    "    state_names={\n",
+    "        \"DNS Answer Consistency\": [\"consistent\", \"inconsistent\"],\n",
+    "        \"TCP VP Result\": tcp_failure_states + [\"down\"],\n",
+    "        \"TCP Outcome\": [\"blocked\", \"ok\", \"down\", \"unknown\"]\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "61234ff3-5e13-48e3-8264-e1966fdd2dc1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tls_ctrl_failure_states = [\n",
+    "    \"none\",\n",
+    "    \"bad_certificate\",\n",
+    "    \"other\"\n",
+    "]\n",
+    "\n",
+    "tls_vp_result_states = [\n",
+    "    \"none\",\n",
+    "    \"bad_certificate\",\n",
+    "    \"connection_reset_after_ch\",\n",
+    "    \"timeout_after_ch\",\n",
+    "    \"timeout\",\n",
+    "    \"connection_reset\",\n",
+    "    \"other\",\n",
+    "    \"down\"\n",
+    "]\n",
+    "\n",
+    "cpd_tls_ctrl_failure = TabularCPD(\n",
+    "    \"TLS Ctrl Failure\",\n",
+    "    variable_card=3,\n",
+    "    values=[\n",
+    "        [0.980],\n",
+    "        [0.005],\n",
+    "        [0.015],\n",
+    "    ],\n",
+    "    state_names={\n",
+    "        \"TLS Ctrl Failure\": tls_ctrl_failure_states\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "cpd_tls_vp_result = TabularCPD(\n",
+    "    \"TLS VP Result\",\n",
+    "    variable_card=8,\n",
+    "    values=[\n",
+    "        # \"none\", \"bad_certificate\", \"other\"\n",
+    "        [0.97, 0.02, 0.01], # none\n",
+    "        [0.005, 0.10, 0.01], # bad_certificate\n",
+    "        [0.005, 0.01, 0.05], # connection_reset_after_ch\n",
+    "        [0.005, 0.01, 0.05], # timeout_after_ch\n",
+    "        [0.005, 0.01, 0.05], # timeout\n",
+    "        [0.005, 0.01, 0.05], # connection_reset\n",
+    "        [0.004, 0.02, 0.12], # other\n",
+    "        [0.001, 0.82, 0.66], # down\n",
+    "    ],\n",
+    "    evidence=[\"TLS Ctrl Failure\"],\n",
+    "    evidence_card=[3],\n",
+    "    state_names={\n",
+    "        \"TLS VP Result\": tls_vp_result_states,\n",
+    "        \"TLS Ctrl Failure\": tls_ctrl_failure_states\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "cpd_tls_outcome = TabularCPD(\n",
+    "    \"TLS Outcome\",\n",
+    "    variable_card=4,\n",
+    "    values=[\n",
+    "        # consistent inconsistent\n",
+    "        # \"none\", \"bad_certificate\", \"connection_reset_after_ch\", \"timeout_after_ch\", \"timeout\" \"connection_reset\" \"other\" \"down\"\n",
+    "        [0.02, 0.95, 0.90, 0.85, 0.55, 0.65, 0.07, 0.07, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02], # blocked\n",
+    "        [0.95, 0.01, 0.01, 0.01, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], # ok\n",
+    "        [0.02, 0.03, 0.07, 0.09, 0.40, 0.25, 0.07, 0.07, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], # down\n",
+    "        [0.01, 0.01, 0.02, 0.05, 0.03, 0.09, 0.85, 0.85, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92], # unknown\n",
+    "    ],\n",
+    "    evidence=[\"DNS Answer Consistency\", \"TLS VP Result\"],\n",
+    "    evidence_card=[2, 8],\n",
+    "    state_names={\n",
+    "        \"DNS Answer Consistency\": [\"consistent\", \"inconsistent\"],\n",
+    "        \"TLS VP Result\": tls_vp_result_states,\n",
+    "        \"TLS Outcome\": [\"blocked\", \"ok\", \"down\", \"unknown\"]\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4a7dff9b-4851-435e-941d-de3e736bc577",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cpd_list = [\n",
+    "    cpd_dns_ctrl_failure, \n",
+    "    cpd_dns_vp_failure, \n",
+    "    cpd_dns_failure_consistency, \n",
+    "    cpd_dns_answer, \n",
+    "    cpd_dns_answer_tls_consistency,\n",
+    "    cpd_dns_answer_consistency, \n",
+    "    cpd_dns_outcome,\n",
+    "    cpd_tcp_ctrl_failure,\n",
+    "    cpd_tcp_vp_result,\n",
+    "    cpd_tcp_outcome,\n",
+    "    cpd_tls_ctrl_failure,\n",
+    "    cpd_tls_vp_result,\n",
+    "    cpd_tls_outcome\n",
+    "]\n",
+    "web_analysis_bn_full.add_cpds(*cpd_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "a2299f85-a607-4198-a0eb-5af82f7317e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "web_analysis_bn_full.check_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "73120bce-721a-4965-8a76-a4f37f7be905",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "web_analysis_bn_full.save('web-analysis-full.bif')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85d58bf9-eb93-48fb-96ba-927657cfd1ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from oonipipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6eebd10f-58da-41a8-b2ef-520a1733607c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "21c4346f-4c39-47fc-9378-306b1bb05d58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pgmpy.inference import VariableElimination"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "89546053-4dbb-47af-a81d-1ad5bf41f95c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infer = VariableElimination(web_analysis_bn_full)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "cb364bad-0321-4cba-9df6-c6a1cdb9ec05",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome          |   phi(DNS Outcome) |\n",
+      "+======================+====================+\n",
+      "| DNS Outcome(blocked) |             0.0874 |\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome(ok)      |             0.8945 |\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome(down)    |             0.0181 |\n",
+      "+----------------------+--------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(infer.query(['DNS Outcome']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5b30d21-a4c8-4170-ba42-c1727f5131cd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "870bd68f-7969-42c8-9c41-4f8710975497",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome          |   phi(DNS Outcome) |\n",
+      "+======================+====================+\n",
+      "| DNS Outcome(blocked) |             0.7836 |\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome(ok)      |             0.2031 |\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome(down)    |             0.0133 |\n",
+      "+----------------------+--------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(infer.query(\n",
+    "    ['DNS Outcome'], \n",
+    "    evidence={\n",
+    "        #'DNS TLS Consistency': 'tls_inconsistent',\n",
+    "        \"DNS Answer\": \"answer_is_bogon\", \n",
+    "    }\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "8bd01e2f-1baf-4f63-a851-e318f8cb687b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome          |   phi(DNS Outcome) |\n",
+      "+======================+====================+\n",
+      "| DNS Outcome(blocked) |             0.0168 |\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome(ok)      |             0.9646 |\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome(down)    |             0.0186 |\n",
+      "+----------------------+--------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(infer.query(\n",
+    "    ['DNS Outcome'], \n",
+    "    evidence={\n",
+    "        'DNS Answer TLS Consistency': 'tls_consistent',\n",
+    "        \"DNS Answer\": \"answer_matches_probe_cc\", \n",
+    "    }\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "02b703c0-429b-4a6f-ae21-2134bbb9a3e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome          |   phi(DNS Outcome) |\n",
+      "+======================+====================+\n",
+      "| DNS Outcome(blocked) |             0.7913 |\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome(ok)      |             0.1955 |\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome(down)    |             0.0133 |\n",
+      "+----------------------+--------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(infer.query(\n",
+    "    ['DNS Outcome'], \n",
+    "    evidence={\n",
+    "        'DNS Answer TLS Consistency': 'tls_inconsistent',\n",
+    "        \"DNS Answer\": \"answer_matches_probe_cc\", \n",
+    "    }\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "ce7f6e84-e728-475c-b075-73404497bd61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CLOUD_PROVIDERS_ASNS = [\n",
+    "    13335,  # Cloudflare: https://www.peeringdb.com/net/4224\n",
+    "    209242, #\tCloudflare London, LLC\t\n",
+    "    20940,  # Akamai: https://www.peeringdb.com/net/2\n",
+    "    9002,  # Akamai RETN\n",
+    "    16625, # Akamai Technologies, Inc.\t\n",
+    "    63949, # Akamai Technologies, Inc.\t\n",
+    "    16509, #\tAmazon.com, Inc.\n",
+    "    14618, #\tAmazon.com, Inc.\t\n",
+    "    15169, #\tGoogle LLC\n",
+    "    396982,  # Google Cloud: https://www.peeringdb.com/net/30878\n",
+    "    54113, #\tFastly, Inc\n",
+    "    8075, # Microsoft Corporation\n",
+    "    8068, #\tMicrosoft Corporation\n",
+    "]\n",
+    "def is_cloud_provider(as_number):\n",
+    "    return as_number in CLOUD_PROVIDERS_ASNS\n",
+    "    \n",
+    "def find_relevant_control(wo, web_ctrl_observations):\n",
+    "    for ctrl in web_ctrl_observations:\n",
+    "        if wo.ip == ctrl.ip:\n",
+    "            return ctrl\n",
+    "\n",
+    "def observations_to_features(web_obs, web_ctrl_observations):\n",
+    "    for wo in web_observations:\n",
+    "        ctrl = find_relevant_control(wo, web_control_observations)\n",
+    "        yield produce_state(wo, ctrl)\n",
+    "\n",
+    "def produce_state(wo, ctrl):\n",
+    "    state = {\n",
+    "        'DNS Ctrl Failure': None,\n",
+    "        'DNS VP Failure': None,\n",
+    "        'DNS Answer': None,\n",
+    "        'DNS Answer TLS Consistency': None,\n",
+    "        'TCP Ctrl Failure': None,\n",
+    "        'TCP VP Result': None,\n",
+    "        'TLS Ctrl Failure': None,\n",
+    "        'TLS VP Result': None\n",
+    "    }\n",
+    "    if wo.dns_failure is not None:\n",
+    "        if wo.dns_failure in ('dns_nxdomain_error', 'android_dns_cache_no_data'):\n",
+    "            state['DNS VP Failure'] = 'nxdomain'\n",
+    "        else:\n",
+    "            state['DNS VP Failure'] = 'server_failure'\n",
+    "    elif wo.dns_answer is not None:\n",
+    "            state['DNS VP Failure'] = 'none'\n",
+    "\n",
+    "    if ctrl:\n",
+    "        if ctrl.dns_failure is not None:\n",
+    "            if wo.dns_failure == 'dns_nxdomain_error':\n",
+    "                state['DNS Ctrl Failure'] = 'nxdomain'\n",
+    "            else:\n",
+    "                state['DNS Ctrl Failure'] = 'server_failure'\n",
+    "        elif ctrl.dns_success:\n",
+    "            state['DNS Ctrl Failure'] = 'none'\n",
+    "\n",
+    "    if wo.dns_answer is not None:\n",
+    "        if wo.ip_is_bogon:\n",
+    "            state['DNS Answer'] = 'answer_is_bogon'\n",
+    "        elif wo.ip_asn == wo.probe_meta.probe_asn:\n",
+    "            state['DNS Answer'] = 'answer_matches_probe_asn'\n",
+    "        elif wo.ip_as_cc == wo.probe_meta.probe_cc:\n",
+    "            state['DNS Answer'] = 'answer_matches_probe_cc'\n",
+    "        elif is_cloud_provider(wo.ip_asn):\n",
+    "            state['DNS Answer'] = 'answer_is_cloud_provider'\n",
+    "        else:\n",
+    "            state['DNS Answer'] = 'answer_other'\n",
+    "\n",
+    "    if ctrl:\n",
+    "        tcp_ctrl_state = None\n",
+    "        if ctrl.ip and (ctrl.tcp_failure is not None or ctrl.tcp_success):\n",
+    "            if ctrl.tcp_failure is None:\n",
+    "                tcp_ctrl_state = 'none'\n",
+    "            elif ctrl.tcp_failure == 'connection_reset':\n",
+    "                tcp_ctrl_state = 'connection_reset'\n",
+    "            elif ctrl.tcp_failure in ('generic_timeout_error', 'timed_out'):\n",
+    "                tcp_ctrl_state = 'timeout'\n",
+    "            else:\n",
+    "                tcp_ctrl_state = 'other'\n",
+    "\n",
+    "        tls_consistency_state = None\n",
+    "        if wo.ip == ctrl.ip and (ctrl.tls_success or ctrl.tls_failure is not None):\n",
+    "            if ctrl.tls_failure is None:\n",
+    "                tls_consistency_state = 'tls_consistent'\n",
+    "            elif ctrl.tls_failure.startswith(\"ssl_\"):\n",
+    "                tls_consistency_state = 'tls_inconsistent'\n",
+    "            else:\n",
+    "                tls_consistency_state = 'unknown'\n",
+    "        else:\n",
+    "            tls_consistency_state = 'unknown'\n",
+    "\n",
+    "        if (tcp_ctrl_state and tcp_ctrl_state != 'none'):\n",
+    "            tls_consistency_state = 'tls_inconsistent'\n",
+    "        state['DNS Answer TLS Consistency'] = tls_consistency_state\n",
+    "        state['TCP Ctrl Failure'] = tcp_ctrl_state\n",
+    "    \n",
+    "    if wo.ip and (wo.tcp_failure is not None or wo.tcp_success):\n",
+    "        tcp_vp_result = None\n",
+    "        if wo.tcp_failure is None:\n",
+    "            tcp_vp_result = 'none'\n",
+    "        elif wo.tcp_failure == 'connection_reset':\n",
+    "            tcp_vp_result = 'connection_reset'\n",
+    "        elif wo.tcp_failure in ('generic_timeout_error', 'timed_out'):\n",
+    "            tcp_vp_result = 'timeout'\n",
+    "        else:\n",
+    "            tcp_vp_result = 'other'\n",
+    "        if tcp_vp_result != 'none' and tcp_vp_result == state['TCP Ctrl Failure']:\n",
+    "            tcp_vp_result = 'down'\n",
+    "        state['TCP VP Result'] = tcp_vp_result\n",
+    "\n",
+    "    if ctrl:\n",
+    "        if ctrl.ip and (ctrl.tls_failure is not None or ctrl.tls_success):\n",
+    "            if ctrl.tls_failure is None:\n",
+    "                state['TLS Ctrl Failure'] = 'none'\n",
+    "            elif ctrl.tls_failure.startswith('ssl_'):\n",
+    "                state['TLS Ctrl Failure'] = 'bad_certificate'\n",
+    "            else:\n",
+    "                state['TLS Ctrl Failure'] = 'other'\n",
+    "    \n",
+    "    if wo.ip and (wo.tls_failure is not None or wo.tls_failure):\n",
+    "        tls_vp_result = None\n",
+    "        if wo.tls_failure is None:\n",
+    "            tls_vp_result = 'none'\n",
+    "        elif wo.tls_failure.startswith('ssl_'):\n",
+    "            tls_vp_result = 'bad_certificate'\n",
+    "        elif wo.tls_failure == 'connection_reset':\n",
+    "            tls_vp_result = 'connection_reset'\n",
+    "            if wo.tls_handshake_last_operation and wo.tls_handshake_last_operation == 'write_1':\n",
+    "                tls_vp_result = 'connection_reset_after_ch'\n",
+    "        elif wo.tcp_failure in ('generic_timeout_error', 'timed_out'):\n",
+    "            tls_vp_result = 'timeout'\n",
+    "            if wo.tls_handshake_last_operation and wo.tls_handshake_last_operation == 'write_1':\n",
+    "                tls_vp_result = 'timeout_after_ch'\n",
+    "        else:\n",
+    "            tls_vp_result = 'other'\n",
+    "        if tls_vp_result != 'none' and state['TLS Ctrl Failure'] != 'none':\n",
+    "            tls_vp_result = 'down'\n",
+    "        state['TLS VP Result'] = tls_vp_result\n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "0a4480e9-3c4c-43b7-bb8a-6fb34df5348c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "netinfodb = NetinfoDB(datadir=Path(\"datadir\"), download=False)\n",
+    "# 20231129041111.939896_NP_webconnectivity_32105af63774eabf\n",
+    "#raw_msmt = get_measurement_dict_by_uid(\"20240804233508.181041_TR_webconnectivity_9a7272e520f3d836\")\n",
+    "raw_msmt = get_measurement_dict_by_uid(\"20231129041111.939896_NP_webconnectivity_32105af63774eabf\")\n",
+    "msmt = load_measurement(msmt=raw_msmt)\n",
+    "web_observations, web_control_observations = measurement_to_observations(\n",
+    "    msmt, netinfodb=netinfodb\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "6ce969b6-548e-4602-963e-9baeff98c8d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'DNS Ctrl Failure': None, 'DNS VP Failure': 'none', 'DNS Answer': 'answer_is_cloud_provider', 'DNS Answer TLS Consistency': 'tls_inconsistent', 'TCP Ctrl Failure': 'other', 'TCP VP Result': 'down', 'TLS Ctrl Failure': None, 'TLS VP Result': None}\n",
+      "+----------------------+--------------------+\n",
+      "| TLS Outcome          |   phi(TLS Outcome) |\n",
+      "+======================+====================+\n",
+      "| TLS Outcome(blocked) |             0.0202 |\n",
+      "+----------------------+--------------------+\n",
+      "| TLS Outcome(ok)      |             0.0189 |\n",
+      "+----------------------+--------------------+\n",
+      "| TLS Outcome(down)    |             0.0498 |\n",
+      "+----------------------+--------------------+\n",
+      "| TLS Outcome(unknown) |             0.9111 |\n",
+      "+----------------------+--------------------+\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome          |   phi(DNS Outcome) |\n",
+      "+======================+====================+\n",
+      "| DNS Outcome(blocked) |             0.7872 |\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome(ok)      |             0.2044 |\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome(down)    |             0.0084 |\n",
+      "+----------------------+--------------------+\n",
+      "{'DNS Ctrl Failure': None, 'DNS VP Failure': None, 'DNS Answer': None, 'DNS Answer TLS Consistency': None, 'TCP Ctrl Failure': None, 'TCP VP Result': None, 'TLS Ctrl Failure': None, 'TLS VP Result': None}\n",
+      "+----------------------+--------------------+\n",
+      "| TLS Outcome          |   phi(TLS Outcome) |\n",
+      "+======================+====================+\n",
+      "| TLS Outcome(blocked) |             0.0405 |\n",
+      "+----------------------+--------------------+\n",
+      "| TLS Outcome(ok)      |             0.8216 |\n",
+      "+----------------------+--------------------+\n",
+      "| TLS Outcome(down)    |             0.0275 |\n",
+      "+----------------------+--------------------+\n",
+      "| TLS Outcome(unknown) |             0.1103 |\n",
+      "+----------------------+--------------------+\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome          |   phi(DNS Outcome) |\n",
+      "+======================+====================+\n",
+      "| DNS Outcome(blocked) |             0.0874 |\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome(ok)      |             0.8945 |\n",
+      "+----------------------+--------------------+\n",
+      "| DNS Outcome(down)    |             0.0181 |\n",
+      "+----------------------+--------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "for features in observations_to_features(web_observations, web_control_observations):\n",
+    "    evidence = {k: v for k,v in features.items() if v is not None}\n",
+    "    print(features)\n",
+    "    print(infer.query(\n",
+    "        ['TLS Outcome'],\n",
+    "        evidence=evidence\n",
+    "    ))\n",
+    "    print(infer.query(\n",
+    "        ['DNS Outcome'],\n",
+    "        evidence=evidence\n",
+    "    ))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "886eb036-3fa0-4084-9a7c-6c048f0634ee",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/oonipipeline/notebooks/web-analysis-full.bif b/oonipipeline/notebooks/web-analysis-full.bif
new file mode 100644
index 00000000..8255ee41
--- /dev/null
+++ b/oonipipeline/notebooks/web-analysis-full.bif
@@ -0,0 +1,151 @@
+network unknown {
+}
+variable DNS Answer {
+    type discrete [ 5 ] { answer_is_bogon, answer_matches_probe_asn, answer_matches_probe_cc, answer_is_cloud_provider, answer_other };
+}
+variable DNS Answer Consistency {
+    type discrete [ 2 ] { consistent, inconsistent };
+}
+variable DNS Answer TLS Consistency {
+    type discrete [ 3 ] { tls_consistent, tls_inconsistent, unknown };
+}
+variable DNS Ctrl Failure {
+    type discrete [ 3 ] { none, nxdomain, server_failure };
+}
+variable DNS Failure Consistency {
+    type discrete [ 3 ] { ok, expected_not_ok, unexpected_not_ok };
+}
+variable DNS Outcome {
+    type discrete [ 3 ] { blocked, ok, down };
+}
+variable DNS VP Failure {
+    type discrete [ 3 ] { none, nxdomain, server_failure };
+}
+variable TCP Ctrl Failure {
+    type discrete [ 4 ] { none, connection_reset, timeout, other };
+}
+variable TCP Outcome {
+    type discrete [ 4 ] { blocked, ok, down, unknown };
+}
+variable TCP VP Result {
+    type discrete [ 5 ] { none, connection_reset, timeout, other, down };
+}
+variable TLS Ctrl Failure {
+    type discrete [ 3 ] { none, bad_certificate, other };
+}
+variable TLS Outcome {
+    type discrete [ 4 ] { blocked, ok, down, unknown };
+}
+variable TLS VP Result {
+    type discrete [ 8 ] { none, bad_certificate, connection_reset_after_ch, timeout_after_ch, timeout, connection_reset, other, down };
+}
+probability ( DNS Answer ) {
+    table 0.004, 0.006, 0.06, 0.73, 0.2 ;
+}
+probability ( DNS Answer Consistency | DNS Answer, DNS Answer TLS Consistency ) {
+    ( answer_is_bogon, tls_consistent ) 0.99, 0.01;
+    ( answer_is_bogon, tls_inconsistent ) 0.001, 0.999;
+    ( answer_is_bogon, unknown ) 0.001, 0.999;
+    ( answer_matches_probe_asn, tls_consistent ) 0.999, 0.001;
+    ( answer_matches_probe_asn, tls_inconsistent ) 0.01, 0.99;
+    ( answer_matches_probe_asn, unknown ) 0.3, 0.7;
+    ( answer_matches_probe_cc, tls_consistent ) 0.999, 0.001;
+    ( answer_matches_probe_cc, tls_inconsistent ) 0.001, 0.999;
+    ( answer_matches_probe_cc, unknown ) 0.3, 0.7;
+    ( answer_is_cloud_provider, tls_consistent ) 0.999, 0.001;
+    ( answer_is_cloud_provider, tls_inconsistent ) 0.01, 0.99;
+    ( answer_is_cloud_provider, unknown ) 0.8, 0.2;
+    ( answer_other, tls_consistent ) 0.999, 0.001;
+    ( answer_other, tls_inconsistent ) 0.001, 0.999;
+    ( answer_other, unknown ) 0.2, 0.8;
+
+}
+probability ( DNS Answer TLS Consistency | DNS Answer ) {
+    ( answer_is_bogon ) 0.01, 0.05, 0.94;
+    ( answer_matches_probe_asn ) 0.8, 0.15, 0.05;
+    ( answer_matches_probe_cc ) 0.75, 0.2, 0.05;
+    ( answer_is_cloud_provider ) 0.9, 0.08, 0.02;
+    ( answer_other ) 0.94, 0.04, 0.02;
+
+}
+probability ( DNS Ctrl Failure ) {
+    table 0.994, 0.004, 0.002 ;
+}
+probability ( DNS Failure Consistency | DNS Ctrl Failure, DNS VP Failure ) {
+    ( none, none ) 0.99, 0.01, 0.01;
+    ( none, nxdomain ) 0.01, 0.05, 0.94;
+    ( none, server_failure ) 0.01, 0.05, 0.94;
+    ( nxdomain, none ) 0.94, 0.01, 0.05;
+    ( nxdomain, nxdomain ) 0.01, 0.98, 0.01;
+    ( nxdomain, server_failure ) 0.01, 0.92, 0.07;
+    ( server_failure, none ) 0.94, 0.01, 0.05;
+    ( server_failure, nxdomain ) 0.01, 0.92, 0.07;
+    ( server_failure, server_failure ) 0.01, 0.98, 0.01;
+
+}
+probability ( DNS Outcome | DNS Answer Consistency, DNS Failure Consistency ) {
+    ( consistent, ok ) 0.01, 0.99, 0.0;
+    ( consistent, expected_not_ok ) 0.01, 0.01, 0.98;
+    ( consistent, unexpected_not_ok ) 0.4, 0.4, 0.2;
+    ( inconsistent, ok ) 0.8, 0.2, 0.0;
+    ( inconsistent, expected_not_ok ) 0.2, 0.05, 0.75;
+    ( inconsistent, unexpected_not_ok ) 0.9, 0.01, 0.09;
+
+}
+probability ( DNS VP Failure | DNS Ctrl Failure ) {
+    ( none ) 0.994, 0.004, 0.002;
+    ( nxdomain ) 0.02, 0.98, 0.01;
+    ( server_failure ) 0.02, 0.01, 0.98;
+
+}
+probability ( TCP Ctrl Failure ) {
+    table 0.98, 0.001, 0.014, 0.005 ;
+}
+probability ( TCP Outcome | DNS Answer Consistency, TCP VP Result ) {
+    ( consistent, none ) 0.02, 0.95, 0.02, 0.01;
+    ( consistent, connection_reset ) 0.95, 0.01, 0.03, 0.01;
+    ( consistent, timeout ) 0.9, 0.01, 0.08, 0.01;
+    ( consistent, other ) 0.85, 0.01, 0.09, 0.05;
+    ( consistent, down ) 0.01, 0.08, 0.01, 0.9;
+    ( inconsistent, none ) 0.07, 0.01, 0.07, 0.85;
+    ( inconsistent, connection_reset ) 0.07, 0.01, 0.07, 0.85;
+    ( inconsistent, timeout ) 0.07, 0.01, 0.07, 0.85;
+    ( inconsistent, other ) 0.07, 0.01, 0.07, 0.85;
+    ( inconsistent, down ) 0.05, 0.01, 0.09, 0.85;
+
+}
+probability ( TCP VP Result | TCP Ctrl Failure ) {
+    ( none ) 0.98, 0.001, 0.01, 0.009, 0.009;
+    ( connection_reset ) 0.02, 0.14, 0.05, 0.07, 0.72;
+    ( timeout ) 0.01, 0.02, 0.1, 0.02, 0.85;
+    ( other ) 0.01, 0.06, 0.06, 0.12, 0.75;
+
+}
+probability ( TLS Ctrl Failure ) {
+    table 0.98, 0.005, 0.015 ;
+}
+probability ( TLS Outcome | DNS Answer Consistency, TLS VP Result ) {
+    ( consistent, none ) 0.02, 0.95, 0.02, 0.01;
+    ( consistent, bad_certificate ) 0.95, 0.01, 0.03, 0.01;
+    ( consistent, connection_reset_after_ch ) 0.9, 0.01, 0.07, 0.02;
+    ( consistent, timeout_after_ch ) 0.85, 0.01, 0.09, 0.05;
+    ( consistent, timeout ) 0.55, 0.02, 0.4, 0.03;
+    ( consistent, connection_reset ) 0.65, 0.01, 0.25, 0.09;
+    ( consistent, other ) 0.07, 0.01, 0.07, 0.85;
+    ( consistent, down ) 0.07, 0.01, 0.07, 0.85;
+    ( inconsistent, none ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, bad_certificate ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, connection_reset_after_ch ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, timeout_after_ch ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, timeout ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, connection_reset ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, other ) 0.02, 0.01, 0.05, 0.92;
+    ( inconsistent, down ) 0.02, 0.01, 0.05, 0.92;
+
+}
+probability ( TLS VP Result | TLS Ctrl Failure ) {
+    ( none ) 0.97, 0.005, 0.005, 0.005, 0.005, 0.005, 0.004, 0.001;
+    ( bad_certificate ) 0.02, 0.1, 0.01, 0.01, 0.01, 0.01, 0.02, 0.82;
+    ( other ) 0.01, 0.01, 0.05, 0.05, 0.05, 0.05, 0.12, 0.66;
+
+}
diff --git a/oonipipeline/pyproject.toml b/oonipipeline/pyproject.toml
index c5aa3381..de3182a7 100644
--- a/oonipipeline/pyproject.toml
+++ b/oonipipeline/pyproject.toml
@@ -26,17 +26,17 @@ dependencies = [
   "tabulate ~= 0.9.0",
   "warcio ~= 1.7.4",
   "msgpack ~= 1.0.4",
-  "statsd ~= 4.0.1",
   "click-loglevel ~= 0.5.0",
-  "dask ~= 2023.3.2",
   "altair ~= 4.2.0",
   "numpy ~= 1.23.5",
   "pandas ~= 2.0.0",
-  "flask ~= 2.2.0",
   "jupyterlab ~= 4.0.7",
   "temporalio ~= 1.7.0",
   "temporalio[opentelemetry] ~= 1.7.0",
   "opentelemetry-exporter-otlp-proto-grpc ~= 1.18.0",
+  "bokeh ~= 3.5.2",
+  "uvicorn ~= 0.25.0",
+  "pydantic-settings ~= 2.1.0",
 ]
 
 [tool.hatch.build.targets.sdist]
@@ -59,6 +59,8 @@ dependencies = [
   "pytest-docker",
   "pytest-asyncio",
   "ipdb",
+  "pgmpy",
+  "pygraphviz"
 ]
 python = "3.11"
 path = ".venv/"
@@ -68,6 +70,7 @@ path = "src/oonipipeline/__about__.py"
 
 [tool.hatch.envs.default.scripts]
 oonipipeline = "python -m oonipipeline.main {args}"
+dataviz = "uvicorn oonipipeline.dataviz.main:app {args}"
 test = "pytest {args:tests}"
 # --full-trace --log-level=INFO  --log-cli-level=INFO -v --setup-show -s
 test-cov = "pytest --cov=./ --cov-report=xml --cov-report=html --cov-report=term {args:tests}"
diff --git a/oonipipeline/src/oonipipeline/dataviz/dependencies.py b/oonipipeline/src/oonipipeline/dataviz/dependencies.py
new file mode 100644
index 00000000..7a3674bc
--- /dev/null
+++ b/oonipipeline/src/oonipipeline/dataviz/dependencies.py
@@ -0,0 +1,25 @@
+from functools import lru_cache
+from typing import Annotated
+
+from fastapi import Depends
+from clickhouse_driver import Client as Clickhouse
+
+from pydantic_settings import BaseSettings
+
+
+class Settings(BaseSettings):
+    clickhouse_url: str = "clickhouse://localhost"
+    data_dir: str = "tests/data/datadir"
+
+
+@lru_cache
+def get_settings() -> Settings:
+    return Settings()
+
+
+def get_clickhouse_session(settings: Annotated[Settings, Depends(get_settings)]):
+    db = Clickhouse.from_url(settings.clickhouse_url)
+    try:
+        yield db
+    finally:
+        db.disconnect()
diff --git a/oonipipeline/src/oonipipeline/dataviz/main.py b/oonipipeline/src/oonipipeline/dataviz/main.py
new file mode 100644
index 00000000..6ed6309b
--- /dev/null
+++ b/oonipipeline/src/oonipipeline/dataviz/main.py
@@ -0,0 +1,175 @@
+from dataclasses import asdict
+import json
+import os
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+from fastapi import Depends, FastAPI, Request
+from fastapi.templating import Jinja2Templates
+
+from oonidata.dataclient import load_measurement
+from oonidata.apiclient import get_measurement_dict_by_uid
+from oonipipeline.dataviz.dependencies import get_settings
+
+from ..analysis.control import (
+    BodyDB,
+    WebGroundTruthDB,
+    iter_ground_truths_from_web_control,
+)
+from ..analysis.web_analysis import make_web_analysis
+from ..analysis.website_experiment_results import make_website_experiment_results
+
+from ..fingerprintdb import FingerprintDB
+from ..netinfo import NetinfoDB
+
+from ..transforms.observations import measurement_to_observations
+
+app = FastAPI()
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+templates = Jinja2Templates(directory=f"{current_dir}/templates")
+
+
+def extract_meta(orig_obs_list) -> Tuple[List, Dict, Dict, Dict]:
+    obs_list = []
+
+    measurement_meta = {}
+    probe_meta = {}
+    processing_meta = {}
+    for obs in orig_obs_list:
+        wo_dict = asdict(obs)
+        measurement_meta = wo_dict.pop("probe_meta", None)
+        probe_meta = wo_dict.pop("measurement_meta", None)
+        processing_meta = wo_dict.pop("processing_meta", None)
+        obs_list.append(wo_dict)
+    return obs_list, measurement_meta, probe_meta, processing_meta
+
+
+@app.get("/analysis/")
+def analysis_index(
+    request: Request,
+):
+    return templates.TemplateResponse(
+        request=request, name="redirect.html", context={"scope": "analysis"}
+    )
+
+
+@app.get("/observations/")
+def observations(
+    request: Request,
+):
+    return templates.TemplateResponse(
+        request=request, name="redirect.html", context={"scope": "observations"}
+    )
+
+
+@app.get("/analysis/m/{measurement_uid}")
+def analysis_by_msmt(
+    request: Request,
+    measurement_uid: Optional[str],
+    settings=Depends(get_settings),
+):
+
+    data_dir = Path(settings.data_dir)
+
+    fingerprintdb = FingerprintDB(datadir=data_dir, download=False)
+    netinfodb = NetinfoDB(datadir=data_dir, download=False)
+    raw_msmt = get_measurement_dict_by_uid(measurement_uid)
+    msmt = load_measurement(msmt=raw_msmt)
+    web_observations, web_control_observations = measurement_to_observations(
+        msmt, netinfodb=netinfodb
+    )
+    web_ground_truth_db = WebGroundTruthDB()
+    web_ground_truth_db.build_from_rows(
+        rows=iter_ground_truths_from_web_control(
+            web_control_observations=web_control_observations,
+            netinfodb=netinfodb,
+        ),
+    )
+
+    web_ground_truths = web_ground_truth_db.lookup_by_web_obs(web_obs=web_observations)
+    web_analysis = list(
+        make_web_analysis(
+            web_observations=web_observations,
+            web_ground_truths=web_ground_truths,
+            body_db=BodyDB(db=None),  # type: ignore
+            fingerprintdb=fingerprintdb,
+        )
+    )
+
+    # assert len(web_analysis) == len(
+    #    web_observations
+    # ), f"web_analysis != web_obs {len(web_analysis)} != {len(web_observations)}"
+    # for wa in web_analysis:
+    #    print_nice_vertical(wa)
+
+    website_er = list(make_website_experiment_results(web_analysis))
+    assert len(website_er) == 1
+
+    wer = website_er[0]
+    analysis_transcript_list = wer.analysis_transcript_list
+
+    # wer.analysis_transcript_list = None
+    # print_nice_vertical(wer)
+    # for loni in loni_list:
+    #    pprint(loni.to_dict())
+    # print(analysis_transcript_list)
+
+    web_analysis_list, _, _, _ = extract_meta(web_analysis)
+    web_observations_list, measurement_meta, probe_meta, processing_meta = extract_meta(
+        web_observations
+    )
+    return templates.TemplateResponse(
+        request=request,
+        name="analysis.html",
+        context=dict(
+            website_experiment_result=asdict(wer),
+            analysis_transcript_list=analysis_transcript_list,
+            loni_list=wer.loni_list,
+            raw_msmt=raw_msmt,
+            measurement_uid=measurement_uid,
+            web_analysis=web_analysis_list,
+            web_observations=web_observations_list,
+            measurement_meta=measurement_meta,
+            probe_meta=probe_meta,
+            processing_meta=processing_meta,
+            loni_blocked_dict=dict(zip(wer.loni_blocked_keys, wer.loni_blocked_values)),
+            loni_blocked_value=sum(wer.loni_blocked_values),
+            loni_down_dict=dict(zip(wer.loni_down_keys, wer.loni_down_values)),
+            loni_down_value=sum(wer.loni_down_values),
+            loni_ok_value=wer.loni_ok_value,
+        ),
+    )
+
+
+@app.get("/observations/m/{measurement_uid}")
+def observations_by_msmt(
+    request: Request,
+    measurement_uid: str,
+    settings=Depends(get_settings),
+):
+    data_dir = Path(settings.data_dir)
+
+    netinfodb = NetinfoDB(datadir=data_dir, download=False)
+    raw_msmt = get_measurement_dict_by_uid(measurement_uid)
+    msmt = load_measurement(msmt=raw_msmt)
+    web_observations, web_control_observations = measurement_to_observations(
+        msmt, netinfodb=netinfodb
+    )
+
+    web_observations_list, probe_meta, measurement_meta, processing_meta = extract_meta(
+        web_observations
+    )
+    web_control_observations, _, _, _ = extract_meta(web_control_observations)
+    return templates.TemplateResponse(
+        request=request,
+        name="observations.html",
+        context=dict(
+            measurement_uid=measurement_uid,
+            web_observations=web_observations_list,
+            web_control_observations=web_control_observations,
+            probe_meta=probe_meta,
+            measurement_meta=measurement_meta,
+            processing_meta=processing_meta,
+        ),
+    )
diff --git a/oonipipeline/src/oonipipeline/dataviz/react/.gitignore b/oonipipeline/src/oonipipeline/dataviz/react/.gitignore
deleted file mode 100644
index 59dc1f7c..00000000
--- a/oonipipeline/src/oonipipeline/dataviz/react/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-node_modules/
-.parcel-cache/
diff --git a/oonipipeline/src/oonipipeline/dataviz/react/Readme.md b/oonipipeline/src/oonipipeline/dataviz/react/Readme.md
deleted file mode 100644
index 20d56760..00000000
--- a/oonipipeline/src/oonipipeline/dataviz/react/Readme.md
+++ /dev/null
@@ -1,10 +0,0 @@
-To build the react component do
-```
-yarn install
-yarn run build
-```
-
-During development do
-```
-yarn run watch
-```
diff --git a/oonipipeline/src/oonipipeline/dataviz/react/package.json b/oonipipeline/src/oonipipeline/dataviz/react/package.json
deleted file mode 100644
index 3d6dc320..00000000
--- a/oonipipeline/src/oonipipeline/dataviz/react/package.json
+++ /dev/null
@@ -1,65 +0,0 @@
-{
-  "name": "oonidataviz",
-  "version": "1.0.0",
-  "license": "MIT",
-  "scripts": {
-    "build": "parcel build src/index.tsx",
-    "watch": "parcel watch src/index.tsx"
-  },
-  "targets": {
-    "default": {
-      "isLibrary": false,
-      "scopeHoist": false,
-      "outputFormat": "global",
-      "context": "browser",
-      "distDir": "../static/dist/"
-    }
-  },
-  "devDependencies": {
-    "@babel/core": "^7.16.5",
-    "@babel/preset-react": "^7.17.12",
-    "@parcel/reporter-bundle-analyzer": "^2.6.0",
-    "@parcel/transformer-svg-react": "^2.5.0",
-    "buffer": "^6.0.3",
-    "gh-pages": "^3.2.3",
-    "parcel": "2.6.0",
-    "prettier": "^2.6.2",
-    "process": "^0.11.10",
-    "typescript": "^4.2.4"
-  },
-  "dependencies": {
-    "@fontsource/fira-sans": "^4.5.10",
-    "@rebass/forms": "^4.0.6",
-    "ooni-components": "^0.5.0-alpha.5",
-    "prop-types": "^15.8.1",
-    "rc-progress": "^3.2.1",
-    "rc-util": "^5.16.1",
-    "react": "^16.0.0 || ^17.0.0",
-    "react-dom": "^17.0.2",
-    "react-hook-form": "^7.31.1",
-    "react-icon-base": "^2.1.2",
-    "react-icons": "^4.4.0",
-    "react-intl": "^5.24.0",
-    "react-json-view": "^1.21.3",
-    "react-lottie-player": "^1.4.1",
-    "react-router-dom": "6",
-    "react-spinners": "^0.13.7",
-    "react-table": "^7.8.0",
-    "react-vega": "^7.6.0",
-    "rehype-react": "^7.0.3",
-    "remark-parse": "^10.0.1",
-    "remark-rehype": "^10.1.0",
-    "styled-components": "^5.3.3",
-    "unified": "^10.1.1",
-    "vega": "^5.22.1",
-    "vega-lite": "^5.6.0"
-  },
-  "resolutions": {
-    "ansi-regex": "^5.0.1",
-    "postcss": "^7.0.36",
-    "json-schema": "^0.4.0",
-    "minimist": "^1.2.3",
-    "path-parse": "^1.0.7",
-    "ws": "^7.4.6"
-  }
-}
diff --git a/oonipipeline/src/oonipipeline/dataviz/react/src/App.tsx b/oonipipeline/src/oonipipeline/dataviz/react/src/App.tsx
deleted file mode 100644
index 1e52424b..00000000
--- a/oonipipeline/src/oonipipeline/dataviz/react/src/App.tsx
+++ /dev/null
@@ -1,54 +0,0 @@
-import * as React from "react";
-
-import { createGlobalStyle, ThemeProvider } from "styled-components";
-
-import { Container, theme } from "ooni-components";
-
-import { Routes, Route, Outlet, Link } from "react-router-dom";
-
-import DataViz from './components/Dataviz'
-import DNSAnalysis from "./components/DNSAnalysis";
-
-const GlobalStyle = createGlobalStyle`
-  * {
-    text-rendering: geometricPrecision;
-    box-sizing: border-box;
-  }
-  body, html {
-    margin: 0;
-    padding: 0;
-    font-family: "Fira Sans";
-    font-size: 14px;
-    height: 100%;
-    background-color: #ffffff;
-  }
-`;
-
-const Layout = () => (
-  <ThemeProvider theme={theme}>
-    <GlobalStyle />
-    <Container>
-      <Outlet />
-
-    </Container>
-  </ThemeProvider>
-)
-
-const Home = () => (
-  <div>Hello home</div>
-)
-
-const App = () => {
-  return (
-    <Routes>
-      <Route path="/" element={<Layout />}>
-        <Route index element={<Home />} />
-        <Route path="dnsanalysis" element={<DNSAnalysis />} />
-        <Route path="dataviz" element={<DataViz />} />
-        <Route path="*" element={<Home />} />
-      </Route>
-    </Routes>
-  );
-};
-
-export default App;
diff --git a/oonipipeline/src/oonipipeline/dataviz/react/src/components/DNSAnalysis.tsx b/oonipipeline/src/oonipipeline/dataviz/react/src/components/DNSAnalysis.tsx
deleted file mode 100644
index 20b8ce13..00000000
--- a/oonipipeline/src/oonipipeline/dataviz/react/src/components/DNSAnalysis.tsx
+++ /dev/null
@@ -1,204 +0,0 @@
-import * as React from "react";
-
-import { useEffect, useState, useMemo } from "react";
-
-import { Flex, Box, Text, theme, Heading, Button } from "ooni-components"
-
-import styled from 'styled-components'
-
-import ReactJson from 'react-json-view'
-import PacmanLoader from "react-spinners/PacmanLoader";
-
-
-const DataSectionTitle = styled.p`
-    font-weight: bold;
-    padding: 8px 0 0;
-    margin: 0;
-`
-const DataSectionValue = styled.p`
-    font-weight: normal;
-    padding: 0;
-    margin: 0;
-    width: 50%;
-    white-space: nowrap;
-    overflow: hidden;
-    text-overflow: ellipsis;
-`
-
-const DataSectionContainer = styled.div`
-    background-color: ${props => props.theme.colors.gray3};
-    padding: 16px;
-    margin-bottom: 16px;
-`
-
-const AnalysisTableName = styled.td`
-    font-weight: bold;
-    padding-right: 8px;
-`
-
-const DNSAnswersList = styled.ul`
-    padding: 0px;
-    margin: 0;
-    list-style: none;
-`
-
-const DataSection = ({ keys, data }) => {
-    console.log(data)
-    return (
-        <Flex flexWrap='wrap'>
-            {keys.map((k) => (
-                <Box width={1 / 3}>
-                    <DataSectionTitle>{k}</DataSectionTitle>
-                    <DataSectionValue>{data[k]}</DataSectionValue>
-                </Box>
-            ))}
-        </Flex>
-    )
-}
-
-const AnalysisTable = ({ keys, data }) => {
-    return (
-        <table>
-            <tbody>
-                {keys.map((key) => (<tr><AnalysisTableName>{key}</AnalysisTableName><td>{data[key]}</td></tr>))}
-            </tbody>
-        </table>
-    )
-}
-
-const AnalysisElement = ({ data }) => {
-    const metaKeys = [
-        'report_id', 'input', 'measurement_uid', 'probe_cc', 'probe_asn',
-        'measurement_start_time', 'resolver_ip', 'resolver_asn', 'resolver_cc',
-        'resolver_as_org_name', 'resolver_as_cc', 'resolver_is_scrubbed',
-        'resolver_asn_probe', 'resolver_as_org_name_probe',
-        'dns_engine_resolver_address', 'dns_engine'
-    ]
-
-    const analysisKeys = [
-        'exp_dns_failure', 'exp_dns_answers', 'exp_dns_answers_count',
-    ]
-
-    const analysisTableBoolsKeys = [
-        'exp_answer_contains_bogon',
-        'exp_answer_contains_matching_probe_cc',
-        'exp_answer_contains_matching_probe_asn',
-        'exp_answer_contains_matching_probe_as_org_name',
-    ]
-
-    const analysisTableAnswersKeys = [
-        'dns_answers_all_asn_count',
-        'dns_answers_ip_match_all_count',
-        'dns_answers_ip_match_tls_consistent_count',
-        'dns_answers_ip_match_tls_consistent_include_probe_count',
-        'dns_answers_ip_match_ctrl_count',
-        'dns_answers_asn_match_all_count',
-        'dns_answers_asn_match_tls_consistent_count',
-        'dns_answers_as_org_name_match_all_count',
-        'dns_answers_as_org_name_match_tls_consistent_count',
-    ]
-
-    const analysisTableFailureKeys = [
-        'failure_asn_count',
-        'nxdomain_asn_count',
-        'ok_asn_count',
-        'ctrl_matching_failures_count',
-        'ctrl_failure_count'
-    ]
-
-    const [loading, setLoading] = useState(false);
-    const [rawData, setRawData] = useState(null);
-
-    const loadRawData = async () => {
-        setRawData(null)
-        setLoading(true)
-        try {
-            let response = await fetch(`/api/_/data/dns_analysis_raw?measurement_uid=${data.measurement_uid}`);
-            let json = await response.json();
-            setLoading(false)
-            setRawData(json)
-        } catch (error) {
-            console.log(error);
-            setLoading(false)
-            return { success: false };
-        }
-    }
-
-    return (
-        <DataSectionContainer>
-            <Heading h={3}>{data.hostname}</Heading>
-            <DataSection data={data} keys={metaKeys} />
-            <hr />
-            <Flex flexWrap='wrap' pb={2}>
-                <Box width={1 / 3}>
-                    <DataSectionTitle>exp_dns_answers</DataSectionTitle>
-                    <DNSAnswersList>
-                        {data['exp_dns_answers'].map((answer) => (<li>{answer[0]} - {answer[1]} - {answer[2]}</li>))}
-                    </DNSAnswersList>
-                </Box>
-                <Box width={1 / 3}>
-                    <DataSectionTitle>exp_dns_answers_count</DataSectionTitle>
-                    <DataSectionValue>{data['exp_dns_answers_count']}</DataSectionValue>
-                </Box>
-                <Box width={1 / 3}>
-                    <DataSectionTitle>exp_dns_failure</DataSectionTitle>
-                    <DataSectionValue>{data['exp_dns_failure']}</DataSectionValue>
-                </Box>
-            </Flex>
-            <Flex flexWrap='wrap'>
-                <Box width={1 / 2}>
-                    <AnalysisTable keys={analysisTableFailureKeys} data={data} />
-                </Box>
-                <Box width={1 / 2}>
-                    <AnalysisTable keys={analysisTableBoolsKeys} data={data} />
-                </Box>
-                <Box width={1 / 2}>
-                    <AnalysisTable keys={analysisTableAnswersKeys} data={data} />
-                </Box>
-            </Flex>
-            <Button onClick={loadRawData}>Load Raw Data</Button>
-
-            <PacmanLoader loading={loading} size={10} />
-            {rawData && <ReactJson src={rawData} />}
-        </DataSectionContainer>
-    )
-}
-
-const DNSAnalysis = () => {
-    const [analysisData, setAnalysisData] = useState([]);
-    const [loading, setLoading] = useState(false);
-
-    const fetchData = async () => {
-        setLoading(true)
-        try {
-            let response = await fetch(`/api/_/data/dns_analysis`);
-            let json = await response.json();
-            setLoading(false)
-            return { success: true, data: json };
-        } catch (error) {
-            console.log(error);
-            setLoading(false)
-            return { success: false };
-        }
-    }
-
-    useEffect(() => {
-        (async () => {
-            let res = await fetchData();
-            if (res.success) {
-                setAnalysisData(res.data);
-            }
-        })();
-    }, []);
-
-    return (
-        <>
-            {analysisData.length == 0 && <PacmanLoader loading={loading} size={10} />}
-            {analysisData &&
-                analysisData.map((data) => <AnalysisElement data={data} />)
-            }
-        </>
-    )
-}
-
-export default DNSAnalysis
\ No newline at end of file
diff --git a/oonipipeline/src/oonipipeline/dataviz/react/src/components/Dataviz.tsx b/oonipipeline/src/oonipipeline/dataviz/react/src/components/Dataviz.tsx
deleted file mode 100644
index a18673e5..00000000
--- a/oonipipeline/src/oonipipeline/dataviz/react/src/components/Dataviz.tsx
+++ /dev/null
@@ -1,422 +0,0 @@
-import * as React from "react";
-import { useState, useEffect, useMemo } from "react";
-import { Vega } from 'react-vega';
-
-import PacmanLoader from "react-spinners/PacmanLoader";
-
-import styled from 'styled-components'
-
-import { MdExpandMore, MdExpandLess, MdArrowUpward, MdArrowDownward } from "react-icons/md";
-
-import { useTable, useSortBy, useGroupBy, useExpanded } from 'react-table'
-
-import { Flex, Box, Heading, Text, theme } from "ooni-components";
-
-const vegaEmbedDefaults = {
-    actions: {
-        export: true,
-        source: true,
-        editor: false,
-        compiled: false,
-    }
-}
-
-const ChartDomainBlockingByASN = ({ probeCC, domainName, onClick }) => {
-    const [data, setData] = useState({});
-    const [dataLoaded, setDataLoaded] = useState(false);
-
-    const fetchData = async () => {
-        try {
-            let response = await fetch(`/api/_/viz/chart/blocking_of_domain_by_asn?probe_cc=${probeCC}&domain_name=${domainName}`);
-            let json = await response.json();
-            return { success: true, data: json };
-        } catch (error) {
-            console.log(error);
-            return { success: false };
-        }
-    }
-
-    useEffect(() => {
-        (async () => {
-            setDataLoaded(false);
-            let res = await fetchData();
-            if (res.success) {
-                setData(res.data);
-                setDataLoaded(true);
-            }
-        })();
-    }, [probeCC, domainName]);
-
-
-    const handleNewView = (view) => {
-        view.addEventListener("click", (_, item) => {
-            if (item) {
-                onClick(item.datum.probe_asn)
-            }
-        })
-    }
-
-    return (
-        dataLoaded ?
-            <Vega spec={data} onNewView={handleNewView} {...vegaEmbedDefaults} />
-            : <Text>Loading data...</Text>
-    )
-}
-
-const ChartDomainBlockingInASN = ({ probeCC, domainName, probeASN }) => {
-    const [data, setData] = useState({});
-    const [dataLoaded, setDataLoaded] = useState(false);
-
-    const fetchData = async () => {
-        try {
-            let response = await fetch(`/api/_/viz/chart/blocking_of_domain_in_asn?probe_cc=${probeCC}&domain_name=${domainName}&probe_asn=${probeASN}`);
-            let json = await response.json();
-            return { success: true, data: json };
-        } catch (error) {
-            console.log(error);
-            return { success: false };
-        }
-    }
-
-    useEffect(() => {
-        (async () => {
-            setDataLoaded(false);
-            let res = await fetchData();
-            if (res.success) {
-                setData(res.data);
-                setDataLoaded(true);
-            }
-        })();
-    }, [probeCC, domainName, probeASN]);
-
-    return (
-        dataLoaded ?
-            <Vega spec={data} {...vegaEmbedDefaults} />
-            : <Text>Loading data...</Text>
-    )
-}
-
-const ChartWorldMap = ({ data, onClick }) => {
-    const [spec, setSpec] = useState({});
-    const [dataLoaded, setDataLoaded] = useState(false);
-
-    const fetchSpec = async () => {
-        try {
-            let response = await fetch(`/api/_/viz/chart/world_map`);
-            let json = await response.json();
-            return { success: true, data: json };
-        } catch (error) {
-            console.log(error);
-            return { success: false };
-        }
-    }
-
-    useEffect(() => {
-        (async () => {
-            setDataLoaded(false);
-            let res = await fetchSpec();
-            if (res.success) {
-                setSpec(res.data);
-                setDataLoaded(true);
-            }
-        })();
-    }, [data]);
-
-
-    const handleNewView = (view) => {
-        view.addEventListener("click", (_, item) => {
-            if (item) {
-                onClick({ "cc": item.datum.probe_cc, "name": item.datum.name })
-            }
-        })
-    }
-
-    return (
-        dataLoaded ?
-            <Vega spec={spec} data={{ data: data }} onNewView={handleNewView} {...vegaEmbedDefaults} />
-            : <Text>Loading data...</Text>
-    )
-}
-
-const StyleCountryTable = styled.div`
-  padding: 1rem;
-
-  table {
-    border-spacing: 0;
-    border: 1px solid black;
-
-    tr {
-      :last-child {
-        td {
-          border-bottom: 0;
-        }
-      }
-    }
-
-    th,
-    td {
-      margin: 0;
-      padding: 0.5rem;
-      border-bottom: 1px solid black;
-      border-right: 1px solid black;
-
-      :last-child {
-        border-right: 0;
-      }
-    }
-  }
-`
-
-const CountryTable = ({ data, onDomainSelected }) => {
-    const columns = useMemo(() => [
-        {
-            id: 'category_code',
-            Header: 'Category Code',
-            accessor: 'category_code'
-        },
-        {
-            id: 'domain_name',
-            Header: 'Domain',
-            accessor: 'domain_name',
-            aggregate: 'count',
-            Aggregated: ({ value }) => `${value} domains`,
-        },
-        {
-            id: 'blocked_asns',
-            Header: 'Blocked ASNS',
-            accessor: 'blocked_asns',
-            aggregate: 'average',
-            Aggregated: ({ value }) => `${value} (avg)`,
-        },
-        {
-            id: 'ok_asns',
-            Header: 'OK ASNS',
-            accessor: 'ok_asns',
-            aggregate: 'average',
-            Aggregated: ({ value }) => `${value} (avg)`,
-        },
-    ], [])
-
-    const initialGroupBy = useMemo(() => ["category_code"], [])
-    const initialSortBy = useMemo(() => [{ "id": "category_code" }], [])
-
-    const {
-        getTableProps,
-        getTableBodyProps,
-        headerGroups,
-        rows,
-        prepareRow,
-        state: { groupBy, expanded },
-    } = useTable(
-        {
-            initialState: {
-                groupBy: initialGroupBy,
-                sortBy: initialSortBy
-            },
-            columns,
-            data,
-        },
-        useGroupBy,
-        useSortBy,
-        useExpanded
-    )
-
-    const onClickCell = (cell) => {
-        if (!cell.isAggregated && cell.column.id == "domain_name") {
-            onDomainSelected(cell.value)
-        }
-    }
-
-    return (
-        <StyleCountryTable>
-            <table {...getTableProps()}>
-                <thead>
-                    {headerGroups.map(headerGroup => (
-                        <tr {...headerGroup.getHeaderGroupProps()}>
-                            {headerGroup.headers.map(column => (
-                                // Add the sorting props to control sorting. For this example
-                                // we can add them into the header props
-                                <th {...column.getHeaderProps(column.getSortByToggleProps())}>
-                                    {column.render('Header')}
-                                    {/* Add a sort direction indicator */}
-                                    <span>
-                                        {column.isSorted
-                                            ? column.isSortedDesc
-                                                ? <MdArrowDownward />
-                                                : <MdArrowUpward />
-                                            : ''}
-                                    </span>
-                                </th>
-                            ))}
-                        </tr>
-                    ))}
-                </thead>
-                <tbody {...getTableBodyProps()}>
-                    {rows.map(
-                        (row, i) => {
-                            prepareRow(row);
-                            return (
-                                <tr {...row.getRowProps()}>
-                                    {row.cells.map(cell => {
-                                        return (
-                                            <td
-                                                onClick={() => onClickCell(cell)}
-                                                {...cell.getCellProps()}
-                                                style={{
-                                                    background: cell.isGrouped
-                                                        ? theme.colors.gray2
-                                                        : cell.isAggregated
-                                                            ? theme.colors.gray1
-                                                            : cell.isPlaceholder
-                                                                ? 'white'
-                                                                : 'white',
-                                                }}
-                                            >
-                                                {cell.isGrouped ? (
-                                                    // If it's a grouped cell, add an expander and row count
-                                                    <>
-                                                        <span {...row.getToggleRowExpandedProps()}>
-                                                            {row.isExpanded ? <MdExpandLess /> : <MdExpandMore />}
-                                                        </span>{' '}
-                                                        {cell.render('Cell')} ({row.subRows.length})
-                                                    </>
-                                                ) : cell.isAggregated ? (
-                                                    // If the cell is aggregated, use the Aggregated
-                                                    // renderer for cell
-                                                    cell.render('Aggregated')
-                                                ) : cell.isPlaceholder ? null : ( // For cells with repeated values, render null
-                                                    // Otherwise, just render the regular cell
-                                                    cell.render('Cell')
-                                                )}
-                                            </td>
-                                        )
-                                    })}
-                                </tr>
-                            )
-                        }
-                    )}
-                </tbody>
-            </table >
-        </StyleCountryTable>
-    )
-
-}
-
-const StyleLoader = styled.div`
-  position: fixed;
-  left: 0px;
-  top: 0px;
-  width: 100%;
-  height: 100%;
-  z-index: 9999999999;
-  overflow: hidden;
-`
-
-const GeneralLoader = ({ loading }) => {
-    return (
-        loading ?
-            <StyleLoader>
-                <Flex width="100%" height="50%" alignItems="center" justifyContent="center">
-                    <Box>
-                        <PacmanLoader loading={loading} size={10} />
-                    </Box>
-                    <Box pl="30px">
-                        <Text>...loading...</Text>
-                    </Box>
-                </Flex>
-            </StyleLoader>
-            : null
-    )
-}
-
-const DataViz = () => {
-    const [selectedASN, setSelectedASN] = useState(null);
-    const [selectedCC, setSelectedCC] = useState(null);
-    const [selectedCountry, setSelectedCountry] = useState("");
-    const [selectedDomainName, setSelectedDomainName] = useState(null);
-
-    const [worldMapData, setWorldMapData] = useState([]);
-    const [loading, setLoading] = useState(false)
-
-    const [tableData, setTableData] = useState(null);
-
-    const fetchData = async () => {
-        try {
-            let response = await fetch(`/api/_/viz/data/world_map`);
-            let json = await response.json();
-            return { success: true, data: json };
-        } catch (error) {
-            console.log(error);
-            return { success: false };
-        }
-    }
-
-    useEffect(() => {
-        (async () => {
-            setLoading(true);
-            let res = await fetchData();
-            if (res.success) {
-                setWorldMapData(res.data);
-                setLoading(false);
-            }
-        })();
-    }, []);
-
-    const onSelectedASN = (probe_asn) => {
-        setSelectedASN(probe_asn)
-    }
-
-    const onSelectedCountry = ({ cc, name }) => {
-        setSelectedDomainName(null)
-        setSelectedASN(null)
-
-        setSelectedCountry(name)
-        setSelectedCC(cc)
-    }
-
-    useEffect(() => {
-        if (selectedCC !== null) {
-            const tableData = worldMapData.filter((d) => d.probe_cc == selectedCC)
-            console.log("setting table data", tableData, worldMapData)
-            setTableData(tableData)
-        }
-    }, [selectedCC])
-
-    const onDomainSelected = (domainName) => {
-        if (domainName == selectedDomainName) {
-            return
-        }
-        setSelectedASN(null)
-        setSelectedDomainName(domainName)
-    }
-
-    return (
-        <Flex flexWrap="wrap">
-            <Box width={1}>
-                <GeneralLoader loading={loading} />
-                <ChartWorldMap data={worldMapData} onClick={onSelectedCountry} />
-            </Box>
-            <Box width={1}>
-                <Heading>{selectedCountry}</Heading>
-                <Flex>
-                    <Box width={1 / 2}>
-                        {tableData && <CountryTable data={tableData} onDomainSelected={onDomainSelected} />}
-                    </Box>
-                    <Box width={1 / 2}>
-                        <Flex pt={2} flexWrap="wrap">
-                            <Box width={1}>
-                                {selectedASN !== null && <ChartDomainBlockingInASN probeCC={selectedCC} domainName={selectedDomainName} probeASN={selectedASN} />}
-                            </Box>
-                            <Box width={1}>
-                                {selectedDomainName !== null && <ChartDomainBlockingByASN probeCC={selectedCC} domainName={selectedDomainName} onClick={onSelectedASN} />}
-                            </Box>
-                        </Flex>
-                    </Box>
-                </Flex>
-            </Box>
-        </Flex >
-    )
-
-}
-
-export default DataViz
\ No newline at end of file
diff --git a/oonipipeline/src/oonipipeline/dataviz/react/src/index.tsx b/oonipipeline/src/oonipipeline/dataviz/react/src/index.tsx
deleted file mode 100644
index e3ed545e..00000000
--- a/oonipipeline/src/oonipipeline/dataviz/react/src/index.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-import * as React from "react";
-import * as ReactDOM from "react-dom";
-import { HashRouter } from "react-router-dom";
-import "@fontsource/fira-sans";
-
-import App from "./App";
-
-document.addEventListener("DOMContentLoaded", (event) => {
-  ReactDOM.render(
-    <HashRouter>
-      <App />
-    </HashRouter>,
-    document.getElementById("root")
-  );
-});
diff --git a/oonipipeline/src/oonipipeline/dataviz/react/yarn.lock b/oonipipeline/src/oonipipeline/dataviz/react/yarn.lock
deleted file mode 100644
index d276b91d..00000000
--- a/oonipipeline/src/oonipipeline/dataviz/react/yarn.lock
+++ /dev/null
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-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-2.0.1.tgz#72d3a68d598c9bdb3af2ad1e84f21d896abd4de3"
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-  version "1.1.3"
-  resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.3.tgz#a7d0558bd89c42f795dd42328f740831ca53bc25"
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-  version "1.1.4"
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-  resolved "https://registry.yarnpkg.com/convert-source-map/-/convert-source-map-1.9.0.tgz#7faae62353fb4213366d0ca98358d22e8368b05f"
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-  version "6.0.0"
-  resolved "https://registry.yarnpkg.com/cosmiconfig/-/cosmiconfig-6.0.0.tgz#da4fee853c52f6b1e6935f41c1a2fc50bd4a9982"
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-    yaml "^1.7.2"
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-  version "7.1.0"
-  resolved "https://registry.yarnpkg.com/cosmiconfig/-/cosmiconfig-7.1.0.tgz#1443b9afa596b670082ea46cbd8f6a62b84635f6"
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-    yaml "^1.10.0"
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-  version "3.1.5"
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-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/css-color-keywords/-/css-color-keywords-1.0.0.tgz#fea2616dc676b2962686b3af8dbdbe180b244e05"
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-  resolved "https://registry.yarnpkg.com/css-select/-/css-select-4.3.0.tgz#db7129b2846662fd8628cfc496abb2b59e41529b"
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-    domhandler "^4.3.1"
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-    nth-check "^2.0.1"
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-  resolved "https://registry.yarnpkg.com/css-to-react-native/-/css-to-react-native-3.1.0.tgz#e783474149997608986afcff614405714a8fe1ac"
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-  resolved "https://registry.yarnpkg.com/css-tree/-/css-tree-1.1.3.tgz#eb4870fb6fd7707327ec95c2ff2ab09b5e8db91d"
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-  version "6.1.0"
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-  version "4.2.0"
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-  version "2.6.21"
-  resolved "https://registry.yarnpkg.com/csstype/-/csstype-2.6.21.tgz#2efb85b7cc55c80017c66a5ad7cbd931fda3a90e"
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-  version "3.1.1"
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-  version "3.2.1"
-  resolved "https://registry.yarnpkg.com/d3-array/-/d3-array-3.2.1.tgz#39331ea706f5709417d31bbb6ec152e0328b39b3"
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-  dependencies:
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-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/d3-array/-/d3-array-3.1.1.tgz#7797eb53ead6b9083c75a45a681e93fc41bc468c"
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-    internmap "1 - 2"
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-"d3-color@1 - 3", d3-color@^3.0.1:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-color/-/d3-color-3.1.0.tgz#395b2833dfac71507f12ac2f7af23bf819de24e2"
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-  version "6.0.2"
-  resolved "https://registry.yarnpkg.com/d3-delaunay/-/d3-delaunay-6.0.2.tgz#7fd3717ad0eade2fc9939f4260acfb503f984e92"
-  integrity sha512-IMLNldruDQScrcfT+MWnazhHbDJhcRJyOEBAJfwQnHle1RPh6WDuLvxNArUju2VSMSUuKlY5BGHRJ2cYyoFLQQ==
-  dependencies:
-    delaunator "5"
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-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-dispatch/-/d3-dispatch-3.0.1.tgz#5fc75284e9c2375c36c839411a0cf550cbfc4d5e"
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-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-dsv/-/d3-dsv-3.0.1.tgz#c63af978f4d6a0d084a52a673922be2160789b73"
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-    iconv-lite "0.6"
-    rw "1"
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-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/d3-force/-/d3-force-3.0.0.tgz#3e2ba1a61e70888fe3d9194e30d6d14eece155c4"
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-    d3-dispatch "1 - 3"
-    d3-quadtree "1 - 3"
-    d3-timer "1 - 3"
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-"d3-format@1 - 3", d3-format@^3.1.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-3.1.0.tgz#9260e23a28ea5cb109e93b21a06e24e2ebd55641"
-  integrity sha512-YyUI6AEuY/Wpt8KWLgZHsIU86atmikuoOmCfommt0LYHiQSPjvX2AcFc38PX0CBpr2RCyZhjex+NS/LPOv6YqA==
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-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/d3-geo-projection/-/d3-geo-projection-4.0.0.tgz#dc229e5ead78d31869a4e87cf1f45bd2716c48ca"
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-    d3-array "1 - 3"
-    d3-geo "1.12.0 - 3"
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-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-geo/-/d3-geo-3.1.0.tgz#74fd54e1f4cebd5185ac2039217a98d39b0a4c0e"
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-    d3-array "2.5.0 - 3"
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-  version "3.1.2"
-  resolved "https://registry.yarnpkg.com/d3-hierarchy/-/d3-hierarchy-3.1.2.tgz#b01cd42c1eed3d46db77a5966cf726f8c09160c6"
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-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-interpolate/-/d3-interpolate-3.0.1.tgz#3c47aa5b32c5b3dfb56ef3fd4342078a632b400d"
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-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-path/-/d3-path-3.1.0.tgz#22df939032fb5a71ae8b1800d61ddb7851c42526"
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-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-quadtree/-/d3-quadtree-3.0.1.tgz#6dca3e8be2b393c9a9d514dabbd80a92deef1a4f"
-  integrity sha512-04xDrxQTDTCFwP5H6hRhsRcb9xxv2RzkcsygFzmkSIOJy3PeRJP7sNk3VRIbKXcog561P9oU0/rVH6vDROAgUw==
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-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/d3-scale/-/d3-scale-4.0.2.tgz#82b38e8e8ff7080764f8dcec77bd4be393689396"
-  integrity sha512-GZW464g1SH7ag3Y7hXjf8RoUuAFIqklOAq3MRl4OaWabTFJY9PN/E1YklhXLh+OQ3fM9yS2nOkCoS+WLZ6kvxQ==
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-    d3-format "1 - 3"
-    d3-interpolate "1.2.0 - 3"
-    d3-time "2.1.1 - 3"
-    d3-time-format "2 - 4"
-
-d3-shape@^3.1.0:
-  version "3.2.0"
-  resolved "https://registry.yarnpkg.com/d3-shape/-/d3-shape-3.2.0.tgz#a1a839cbd9ba45f28674c69d7f855bcf91dfc6a5"
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-    d3-path "^3.1.0"
-
-"d3-time-format@2 - 4", d3-time-format@^4.1.0:
-  version "4.1.0"
-  resolved "https://registry.yarnpkg.com/d3-time-format/-/d3-time-format-4.1.0.tgz#7ab5257a5041d11ecb4fe70a5c7d16a195bb408a"
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-    d3-time "1 - 3"
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-"d3-time@1 - 3", "d3-time@2.1.1 - 3", d3-time@^3.0.0:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/d3-time/-/d3-time-3.1.0.tgz#9310db56e992e3c0175e1ef385e545e48a9bb5c7"
-  integrity sha512-VqKjzBLejbSMT4IgbmVgDjpkYrNWUYJnbCGo874u7MMKIWsILRX+OpX/gTk8MqjpT1A/c6HY2dCA77ZN0lkQ2Q==
-  dependencies:
-    d3-array "2 - 3"
-
-"d3-timer@1 - 3", d3-timer@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/d3-timer/-/d3-timer-3.0.1.tgz#6284d2a2708285b1abb7e201eda4380af35e63b0"
-  integrity sha512-ndfJ/JxxMd3nw31uyKoY2naivF+r29V+Lc0svZxe1JvvIRmi8hUsrMvdOwgS1o6uBHmiz91geQ0ylPP0aj1VUA==
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-debug@^4.0.0, debug@^4.1.0:
-  version "4.3.4"
-  resolved "https://registry.yarnpkg.com/debug/-/debug-4.3.4.tgz#1319f6579357f2338d3337d2cdd4914bb5dcc865"
-  integrity sha512-PRWFHuSU3eDtQJPvnNY7Jcket1j0t5OuOsFzPPzsekD52Zl8qUfFIPEiswXqIvHWGVHOgX+7G/vCNNhehwxfkQ==
-  dependencies:
-    ms "2.1.2"
-
-decode-named-character-reference@^1.0.0:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/decode-named-character-reference/-/decode-named-character-reference-1.0.2.tgz#daabac9690874c394c81e4162a0304b35d824f0e"
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-  dependencies:
-    character-entities "^2.0.0"
-
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-  version "4.2.2"
-  resolved "https://registry.yarnpkg.com/deepmerge/-/deepmerge-4.2.2.tgz#44d2ea3679b8f4d4ffba33f03d865fc1e7bf4955"
-  integrity sha512-FJ3UgI4gIl+PHZm53knsuSFpE+nESMr7M4v9QcgB7S63Kj/6WqMiFQJpBBYz1Pt+66bZpP3Q7Lye0Oo9MPKEdg==
-
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-  version "5.0.0"
-  resolved "https://registry.yarnpkg.com/delaunator/-/delaunator-5.0.0.tgz#60f052b28bd91c9b4566850ebf7756efe821d81b"
-  integrity sha512-AyLvtyJdbv/U1GkiS6gUUzclRoAY4Gs75qkMygJJhU75LW4DNuSF2RMzpxs9jw9Oz1BobHjTdkG3zdP55VxAqw==
-  dependencies:
-    robust-predicates "^3.0.0"
-
-dequal@^2.0.0:
-  version "2.0.3"
-  resolved "https://registry.yarnpkg.com/dequal/-/dequal-2.0.3.tgz#2644214f1997d39ed0ee0ece72335490a7ac67be"
-  integrity sha512-0je+qPKHEMohvfRTCEo3CrPG6cAzAYgmzKyxRiYSSDkS6eGJdyVJm7WaYA5ECaAD9wLB2T4EEeymA5aFVcYXCA==
-
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-  version "1.0.3"
-  resolved "https://registry.yarnpkg.com/detect-libc/-/detect-libc-1.0.3.tgz#fa137c4bd698edf55cd5cd02ac559f91a4c4ba9b"
-  integrity sha512-pGjwhsmsp4kL2RTz08wcOlGN83otlqHeD/Z5T8GXZB+/YcpQ/dgo+lbU8ZsGxV0HIvqqxo9l7mqYwyYMD9bKDg==
-
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-  version "5.1.0"
-  resolved "https://registry.yarnpkg.com/diff/-/diff-5.1.0.tgz#bc52d298c5ea8df9194800224445ed43ffc87e40"
-  integrity sha512-D+mk+qE8VC/PAUrlAU34N+VfXev0ghe5ywmpqrawphmVZc1bEfn56uo9qpyGp1p4xpzOHkSW4ztBd6L7Xx4ACw==
-
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-  version "1.4.1"
-  resolved "https://registry.yarnpkg.com/dom-serializer/-/dom-serializer-1.4.1.tgz#de5d41b1aea290215dc45a6dae8adcf1d32e2d30"
-  integrity sha512-VHwB3KfrcOOkelEG2ZOfxqLZdfkil8PtJi4P8N2MMXucZq2yLp75ClViUlOVwyoHEDjYU433Aq+5zWP61+RGag==
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-    domhandler "^4.2.0"
-    entities "^2.0.0"
-
-domelementtype@^2.0.1, domelementtype@^2.2.0:
-  version "2.3.0"
-  resolved "https://registry.yarnpkg.com/domelementtype/-/domelementtype-2.3.0.tgz#5c45e8e869952626331d7aab326d01daf65d589d"
-  integrity sha512-OLETBj6w0OsagBwdXnPdN0cnMfF9opN69co+7ZrbfPGrdpPVNBUj02spi6B1N7wChLQiPn4CSH/zJvXw56gmHw==
-
-domhandler@^4.2.0, domhandler@^4.2.2, domhandler@^4.3.1:
-  version "4.3.1"
-  resolved "https://registry.yarnpkg.com/domhandler/-/domhandler-4.3.1.tgz#8d792033416f59d68bc03a5aa7b018c1ca89279c"
-  integrity sha512-GrwoxYN+uWlzO8uhUXRl0P+kHE4GtVPfYzVLcUxPL7KNdHKj66vvlhiweIHqYYXWlw+T8iLMp42Lm67ghw4WMQ==
-  dependencies:
-    domelementtype "^2.2.0"
-
-domutils@^2.8.0:
-  version "2.8.0"
-  resolved "https://registry.yarnpkg.com/domutils/-/domutils-2.8.0.tgz#4437def5db6e2d1f5d6ee859bd95ca7d02048135"
-  integrity sha512-w96Cjofp72M5IIhpjgobBimYEfoPjx1Vx0BSX9P30WBdZW2WIKU0T1Bd0kz2eNZ9ikjKgHbEyKx8BB6H1L3h3A==
-  dependencies:
-    dom-serializer "^1.0.1"
-    domelementtype "^2.2.0"
-    domhandler "^4.2.0"
-
-dotenv-expand@^5.1.0:
-  version "5.1.0"
-  resolved "https://registry.yarnpkg.com/dotenv-expand/-/dotenv-expand-5.1.0.tgz#3fbaf020bfd794884072ea26b1e9791d45a629f0"
-  integrity sha512-YXQl1DSa4/PQyRfgrv6aoNjhasp/p4qs9FjJ4q4cQk+8m4r6k4ZSiEyytKG8f8W9gi8WsQtIObNmKd+tMzNTmA==
-
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-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/dotenv/-/dotenv-7.0.0.tgz#a2be3cd52736673206e8a85fb5210eea29628e7c"
-  integrity sha512-M3NhsLbV1i6HuGzBUH8vXrtxOk+tWmzWKDMbAVSUp3Zsjm7ywFeuwrUXhmhQyRK1q5B5GGy7hcXPbj3bnfZg2g==
-
-electron-to-chromium@^1.4.251:
-  version "1.4.284"
-  resolved "https://registry.yarnpkg.com/electron-to-chromium/-/electron-to-chromium-1.4.284.tgz#61046d1e4cab3a25238f6bf7413795270f125592"
-  integrity sha512-M8WEXFuKXMYMVr45fo8mq0wUrrJHheiKZf6BArTKk9ZBYCKJEOU5H8cdWgDT+qCVZf7Na4lVUaZsA+h6uA9+PA==
-
-email-addresses@^3.0.1:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/email-addresses/-/email-addresses-3.1.0.tgz#cabf7e085cbdb63008a70319a74e6136188812fb"
-  integrity sha512-k0/r7GrWVL32kZlGwfPNgB2Y/mMXVTq/decgLczm/j34whdaspNrZO8CnXPf1laaHxI6ptUlsnAxN+UAPw+fzg==
-
-emoji-regex@^8.0.0:
-  version "8.0.0"
-  resolved "https://registry.yarnpkg.com/emoji-regex/-/emoji-regex-8.0.0.tgz#e818fd69ce5ccfcb404594f842963bf53164cc37"
-  integrity sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==
-
-entities@^2.0.0:
-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/entities/-/entities-2.2.0.tgz#098dc90ebb83d8dffa089d55256b351d34c4da55"
-  integrity sha512-p92if5Nz619I0w+akJrLZH0MX0Pb5DX39XOwQTtXSdQQOaYH03S1uIQp4mhOZtAXrxq4ViO67YTiLBo2638o9A==
-
-entities@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.yarnpkg.com/entities/-/entities-3.0.1.tgz#2b887ca62585e96db3903482d336c1006c3001d4"
-  integrity sha512-WiyBqoomrwMdFG1e0kqvASYfnlb0lp8M5o5Fw2OFq1hNZxxcNk8Ik0Xm7LxzBhuidnZB/UtBqVCgUz3kBOP51Q==
-
-entities@^4.4.0:
-  version "4.4.0"
-  resolved "https://registry.yarnpkg.com/entities/-/entities-4.4.0.tgz#97bdaba170339446495e653cfd2db78962900174"
-  integrity sha512-oYp7156SP8LkeGD0GF85ad1X9Ai79WtRsZ2gxJqtBuzH+98YUV6jkHEKlZkMbcrjJjIVJNIDP/3WL9wQkoPbWA==
-
-error-ex@^1.3.1:
-  version "1.3.2"
-  resolved "https://registry.yarnpkg.com/error-ex/-/error-ex-1.3.2.tgz#b4ac40648107fdcdcfae242f428bea8a14d4f1bf"
-  integrity sha512-7dFHNmqeFSEt2ZBsCriorKnn3Z2pj+fd9kmI6QoWw4//DL+icEBfc0U7qJCisqrTsKTjw4fNFy2pW9OqStD84g==
-  dependencies:
-    is-arrayish "^0.2.1"
-
-escalade@^3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/escalade/-/escalade-3.1.1.tgz#d8cfdc7000965c5a0174b4a82eaa5c0552742e40"
-  integrity sha512-k0er2gUkLf8O0zKJiAhmkTnJlTvINGv7ygDNPbeIsX/TJjGJZHuh9B2UxbsaEkmlEo9MfhrSzmhIlhRlI2GXnw==
-
-escape-string-regexp@^1.0.2, escape-string-regexp@^1.0.5:
-  version "1.0.5"
-  resolved "https://registry.yarnpkg.com/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz#1b61c0562190a8dff6ae3bb2cf0200ca130b86d4"
-  integrity sha512-vbRorB5FUQWvla16U8R/qgaFIya2qGzwDrNmCZuYKrbdSUMG6I1ZCGQRefkRVhuOkIGVne7BQ35DSfo1qvJqFg==
-
-extend@^3.0.0:
-  version "3.0.2"
-  resolved "https://registry.yarnpkg.com/extend/-/extend-3.0.2.tgz#f8b1136b4071fbd8eb140aff858b1019ec2915fa"
-  integrity sha512-fjquC59cD7CyW6urNXK0FBufkZcoiGG80wTuPujX590cB5Ttln20E2UB4S/WARVqhXffZl2LNgS+gQdPIIim/g==
-
-fast-deep-equal@^3.1.1, fast-deep-equal@^3.1.3, fast-deep-equal@~3.1.3:
-  version "3.1.3"
-  resolved "https://registry.yarnpkg.com/fast-deep-equal/-/fast-deep-equal-3.1.3.tgz#3a7d56b559d6cbc3eb512325244e619a65c6c525"
-  integrity sha512-f3qQ9oQy9j2AhBe/H9VC91wLmKBCCU/gDOnKNAYG5hswO7BLKj09Hc5HYNz9cGI++xlpDCIgDaitVs03ATR84Q==
-
-fast-json-patch@^3.1.1:
-  version "3.1.1"
-  resolved "https://registry.yarnpkg.com/fast-json-patch/-/fast-json-patch-3.1.1.tgz#85064ea1b1ebf97a3f7ad01e23f9337e72c66947"
-  integrity sha512-vf6IHUX2SBcA+5/+4883dsIjpBTqmfBjmYiWK1savxQmFk4JfBMLa7ynTYOs1Rolp/T1betJxHiGD3g1Mn8lUQ==
-
-fast-json-stable-stringify@~2.1.0:
-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/fast-json-stable-stringify/-/fast-json-stable-stringify-2.1.0.tgz#874bf69c6f404c2b5d99c481341399fd55892633"
-  integrity sha512-lhd/wF+Lk98HZoTCtlVraHtfh5XYijIjalXck7saUtuanSDyLMxnHhSXEDJqHxD7msR8D0uCmqlkwjCV8xvwHw==
-
-fbemitter@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.yarnpkg.com/fbemitter/-/fbemitter-3.0.0.tgz#00b2a1af5411254aab416cd75f9e6289bee4bff3"
-  integrity sha512-KWKaceCwKQU0+HPoop6gn4eOHk50bBv/VxjJtGMfwmJt3D29JpN4H4eisCtIPA+a8GVBam+ldMMpMjJUvpDyHw==
-  dependencies:
-    fbjs "^3.0.0"
-
-fbjs-css-vars@^1.0.0:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/fbjs-css-vars/-/fbjs-css-vars-1.0.2.tgz#216551136ae02fe255932c3ec8775f18e2c078b8"
-  integrity sha512-b2XGFAFdWZWg0phtAWLHCk836A1Xann+I+Dgd3Gk64MHKZO44FfoD1KxyvbSh0qZsIoXQGGlVztIY+oitJPpRQ==
-
-fbjs@^3.0.0, fbjs@^3.0.1:
-  version "3.0.4"
-  resolved "https://registry.yarnpkg.com/fbjs/-/fbjs-3.0.4.tgz#e1871c6bd3083bac71ff2da868ad5067d37716c6"
-  integrity sha512-ucV0tDODnGV3JCnnkmoszb5lf4bNpzjv80K41wd4k798Etq+UYD0y0TIfalLjZoKgjive6/adkRnszwapiDgBQ==
-  dependencies:
-    cross-fetch "^3.1.5"
-    fbjs-css-vars "^1.0.0"
-    loose-envify "^1.0.0"
-    object-assign "^4.1.0"
-    promise "^7.1.1"
-    setimmediate "^1.0.5"
-    ua-parser-js "^0.7.30"
-
-filename-reserved-regex@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/filename-reserved-regex/-/filename-reserved-regex-2.0.0.tgz#abf73dfab735d045440abfea2d91f389ebbfa229"
-  integrity sha512-lc1bnsSr4L4Bdif8Xb/qrtokGbq5zlsms/CYH8PP+WtCkGNF65DPiQY8vG3SakEdRn8Dlnm+gW/qWKKjS5sZzQ==
-
-filenamify@^4.3.0:
-  version "4.3.0"
-  resolved "https://registry.yarnpkg.com/filenamify/-/filenamify-4.3.0.tgz#62391cb58f02b09971c9d4f9d63b3cf9aba03106"
-  integrity sha512-hcFKyUG57yWGAzu1CMt/dPzYZuv+jAJUT85bL8mrXvNe6hWj6yEHEc4EdcgiA6Z3oi1/9wXJdZPXF2dZNgwgOg==
-  dependencies:
-    filename-reserved-regex "^2.0.0"
-    strip-outer "^1.0.1"
-    trim-repeated "^1.0.0"
-
-fill-range@^7.0.1:
-  version "7.0.1"
-  resolved "https://registry.yarnpkg.com/fill-range/-/fill-range-7.0.1.tgz#1919a6a7c75fe38b2c7c77e5198535da9acdda40"
-  integrity sha512-qOo9F+dMUmC2Lcb4BbVvnKJxTPjCm+RRpe4gDuGrzkL7mEVl/djYSu2OdQ2Pa302N4oqkSg9ir6jaLWJ2USVpQ==
-  dependencies:
-    to-regex-range "^5.0.1"
-
-find-cache-dir@^3.3.1:
-  version "3.3.2"
-  resolved "https://registry.yarnpkg.com/find-cache-dir/-/find-cache-dir-3.3.2.tgz#b30c5b6eff0730731aea9bbd9dbecbd80256d64b"
-  integrity sha512-wXZV5emFEjrridIgED11OoUKLxiYjAcqot/NJdAkOhlJ+vGzwhOAfcG5OX1jP+S0PcjEn8bdMJv+g2jwQ3Onig==
-  dependencies:
-    commondir "^1.0.1"
-    make-dir "^3.0.2"
-    pkg-dir "^4.1.0"
-
-find-root@^1.1.0:
-  version "1.1.0"
-  resolved "https://registry.yarnpkg.com/find-root/-/find-root-1.1.0.tgz#abcfc8ba76f708c42a97b3d685b7e9450bfb9ce4"
-  integrity sha512-NKfW6bec6GfKc0SGx1e07QZY9PE99u0Bft/0rzSD5k3sO/vwkVUpDUKVm5Gpp5Ue3YfShPFTX2070tDs5kB9Ng==
-
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-  version "4.1.0"
-  resolved "https://registry.yarnpkg.com/find-up/-/find-up-4.1.0.tgz#97afe7d6cdc0bc5928584b7c8d7b16e8a9aa5d19"
-  integrity sha512-PpOwAdQ/YlXQ2vj8a3h8IipDuYRi3wceVQQGYWxNINccq40Anw7BlsEXCMbt1Zt+OLA6Fq9suIpIWD0OsnISlw==
-  dependencies:
-    locate-path "^5.0.0"
-    path-exists "^4.0.0"
-
-flux@^4.0.1:
-  version "4.0.3"
-  resolved "https://registry.yarnpkg.com/flux/-/flux-4.0.3.tgz#573b504a24982c4768fdfb59d8d2ea5637d72ee7"
-  integrity sha512-yKAbrp7JhZhj6uiT1FTuVMlIAT1J4jqEyBpFApi1kxpGZCvacMVc/t1pMQyotqHhAgvoE3bNvAykhCo2CLjnYw==
-  dependencies:
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-    fbjs "^3.0.1"
-
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-  version "8.1.0"
-  resolved "https://registry.yarnpkg.com/fs-extra/-/fs-extra-8.1.0.tgz#49d43c45a88cd9677668cb7be1b46efdb8d2e1c0"
-  integrity sha512-yhlQgA6mnOJUKOsRUFsgJdQCvkKhcz8tlZG5HBQfReYZy46OwLcY+Zia0mtdHsOo9y/hP+CxMN0TU9QxoOtG4g==
-  dependencies:
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-    jsonfile "^4.0.0"
-    universalify "^0.1.0"
-
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-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/fs.realpath/-/fs.realpath-1.0.0.tgz#1504ad2523158caa40db4a2787cb01411994ea4f"
-  integrity sha512-OO0pH2lK6a0hZnAdau5ItzHPI6pUlvI7jMVnxUQRtw4owF2wk8lOSabtGDCTP4Ggrg2MbGnWO9X8K1t4+fGMDw==
-
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-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/function-bind/-/function-bind-1.1.1.tgz#a56899d3ea3c9bab874bb9773b7c5ede92f4895d"
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-
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-  version "1.0.0-beta.2"
-  resolved "https://registry.yarnpkg.com/gensync/-/gensync-1.0.0-beta.2.tgz#32a6ee76c3d7f52d46b2b1ae5d93fea8580a25e0"
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-get-caller-file@^2.0.5:
-  version "2.0.5"
-  resolved "https://registry.yarnpkg.com/get-caller-file/-/get-caller-file-2.0.5.tgz#4f94412a82db32f36e3b0b9741f8a97feb031f7e"
-  integrity sha512-DyFP3BM/3YHTQOCUL/w0OZHR0lpKeGrxotcHWcqNEdnltqFwXVfhEBQ94eIo34AfQpo0rGki4cyIiftY06h2Fg==
-
-get-port@^4.2.0:
-  version "4.2.0"
-  resolved "https://registry.yarnpkg.com/get-port/-/get-port-4.2.0.tgz#e37368b1e863b7629c43c5a323625f95cf24b119"
-  integrity sha512-/b3jarXkH8KJoOMQc3uVGHASwGLPq3gSFJ7tgJm2diza+bydJPTGOibin2steecKeOylE8oY2JERlVWkAJO6yw==
-
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-  version "3.2.3"
-  resolved "https://registry.yarnpkg.com/gh-pages/-/gh-pages-3.2.3.tgz#897e5f15e111f42af57d21d430b83e5cdf29472c"
-  integrity sha512-jA1PbapQ1jqzacECfjUaO9gV8uBgU6XNMV0oXLtfCX3haGLe5Atq8BxlrADhbD6/UdG9j6tZLWAkAybndOXTJg==
-  dependencies:
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-    commander "^2.18.0"
-    email-addresses "^3.0.1"
-    filenamify "^4.3.0"
-    find-cache-dir "^3.3.1"
-    fs-extra "^8.1.0"
-    globby "^6.1.0"
-
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-  version "1.10.2"
-  resolved "https://registry.yarnpkg.com/yaml/-/yaml-1.10.2.tgz#2301c5ffbf12b467de8da2333a459e29e7920e4b"
-  integrity sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==
-
-yargs-parser@^21.1.1:
-  version "21.1.1"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-21.1.1.tgz#9096bceebf990d21bb31fa9516e0ede294a77d35"
-  integrity sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==
-
-yargs@~17.6.0:
-  version "17.6.2"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-17.6.2.tgz#2e23f2944e976339a1ee00f18c77fedee8332541"
-  integrity sha512-1/9UrdHjDZc0eOU0HxOHoS78C69UD3JRMvzlJ7S79S2nTaWRA/whGCTV8o9e/N/1Va9YIV7Q4sOxD8VV4pCWOw==
-  dependencies:
-    cliui "^8.0.1"
-    escalade "^3.1.1"
-    get-caller-file "^2.0.5"
-    require-directory "^2.1.1"
-    string-width "^4.2.3"
-    y18n "^5.0.5"
-    yargs-parser "^21.1.1"
diff --git a/oonipipeline/src/oonipipeline/dataviz/templates/analysis.html b/oonipipeline/src/oonipipeline/dataviz/templates/analysis.html
index 8b801e61..a3e84264 100644
--- a/oonipipeline/src/oonipipeline/dataviz/templates/analysis.html
+++ b/oonipipeline/src/oonipipeline/dataviz/templates/analysis.html
@@ -20,8 +20,7 @@
         <div class="hero-overlay bg-opacity-60"></div>
         <div class="hero-content text-center text-neutral-content">
           <div class="max-w-md">
-            <h1 class="mb-5 text-5xl font-bold">OONI Data Kraken Analysis</h1>
-            <p class="mb-5">When data krakens and LoNIs join forces!</p>
+            <h1 class="mb-5 text-5xl font-bold">Analysis viewer</h1>
           </div>
         </div>
     </div>
@@ -126,8 +125,11 @@ <h3 class="text-l font-bold pt-5">Experiment result</h3>
 
   <h2 class="font-bold text-2xl mt-8">Individual LoNIs</h2>
 
+
+<dl class="grid grid-cols-1 sm:grid-cols-2">
 {% for loni in loni_list %}
-    <div class="card bg-base-100 shadow-xl my-3">
+<div class="px-4">
+    <div class="card bg-base-100 shadow-xl my-3 px-4 py-6 sm:grid-cols-2 sm:col-span-1 sm:px-0">
         <div class="card-body">
             <h2 class="card-title">Loni #{{loop.index}}</h2>
             <div class="badge badge-outline">ok_final: {{loni["ok_final"]}}</div>
@@ -166,71 +168,60 @@ <h2 class="card-title">Loni #{{loop.index}}</h2>
                 </table>
             </div>
             <h2 class="font-bold">Analysis transcript</h2>
-            <pre>
+            <ul role="list" class="space-y-3">
             {% for analysis_line in analysis_transcript_list[loop.index0] %}
-{{ analysis_line }}
+              <li class="px-4">{{ analysis_line }}</li>
             {% endfor %}
-            </pre>
+            </ul>
         </div>
     </div>
+</div>
 {% endfor %}
-
+</dl>
 
 <h2 class="font-bold text-2xl py-6">Analysis output</h2>
-
-{% for wa in web_analysis %}
-   <div class="card bg-base-100 shadow-xl my-3">
-        <div class="card-body">
-            <h2 class="card-title">Analysis #{{loop.index}}</h2>
-
 <div class="overflow-x-auto">
-    <table class="table table-xs">
+    <table class="min-w-full divide-y divide-gray-300">
         <thead>
             <tr>
-            <th>key</th>
-            <th>value</th>
+            {% for key in web_analysis[0].keys() %}
+            <th scope="col" class="px-3 py-3.5 text-left text-sm font-semibold text-gray-900">{{ key }}</th>
+            {% endfor %}
             </tr>
         </thead>
-        <tbody>
-            {% for key, value in wa.items() %}
-            <tr class="hover">
-            <td>{{key}}</td>
-            <td>{{value}}</td>
+        <tbody class="bg-white divide-y divide-gray-200">
+            {% for wa in web_analysis %}
+            <tr class="hover:bg-gray-200 even:bg-gray-50 divide-x divide-gray-200">
+              {% for value in wa.values() %}
+              <td class="truncate max-w-[200px] hover:max-w-max whitespace-nowrap px-3 py-4 text-sm text-gray-500">{{value}}</td>
+              {% endfor %}
             </tr>
             {% endfor %}
         </tbody>
     </table>
 </div>
-</div>
-</div>
-{% endfor %}
 
 <h2 class="font-bold text-2xl py-6">Web Observations</h2>
-{% for wo in web_observations %}
-   <div class="card bg-base-100 shadow-xl my-3">
-        <div class="card-body">
-            <h2 class="card-title">Observation #{{loop.index}}</h2>
 <div class="overflow-x-auto">
-    <table class="table table-xs">
+    <table class="min-w-full divide-y divide-gray-300">
         <thead>
             <tr>
-            <th>key</th>
-            <th>value</th>
+            {% for key in web_observations[0].keys() %}
+            <th scope="col" class="px-3 py-3.5 text-left text-sm font-semibold text-gray-900">{{ key }}</th>
+            {% endfor %}
             </tr>
         </thead>
-        <tbody>
-            {% for key, value in wo.items() %}
-            <tr class="hover">
-            <td>{{key}}</td>
-            <td>{{value}}</td>
+        <tbody class="bg-white divide-y divide-gray-200">
+            {% for wo in web_observations %}
+            <tr class="hover:bg-gray-200 even:bg-gray-50 divide-x divide-gray-200">
+              {% for value in wo.values() %}
+              <td class="truncate max-w-[200px] hover:max-w-max whitespace-nowrap px-3 py-4 text-sm text-gray-500">{{value}}</td>
+              {% endfor %}
             </tr>
             {% endfor %}
         </tbody>
     </table>
-</div>
-</div>
-</div>
-{% endfor %}
+  </div>
 
 <h2 class="py-6 text-l font-bold">Experiment results raw</h2>
 <div class="overflow-x-auto">
diff --git a/oonipipeline/src/oonipipeline/dataviz/templates/observations.html b/oonipipeline/src/oonipipeline/dataviz/templates/observations.html
new file mode 100644
index 00000000..dd66ecd0
--- /dev/null
+++ b/oonipipeline/src/oonipipeline/dataviz/templates/observations.html
@@ -0,0 +1,89 @@
+<!doctype html>
+<html>
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <link href="https://cdn.jsdelivr.net/npm/daisyui@4.4.2/dist/full.min.css" rel="stylesheet" type="text/css" />
+  <script src="https://cdn.tailwindcss.com?plugins=forms,typography,aspect-ratio,line-clamp"></script>
+</head>
+<body>
+
+    <div class="hero min-h-[200px] kraken-loon">
+        <div class="hero-overlay bg-black"></div>
+        <div class="hero-content text-center text-neutral-content">
+          <div class="max-w-md">
+            <h1 class="mb-5 text-5xl font-bold">Observations viewer</h1>
+          </div>
+        </div>
+    </div>
+      
+    <div class="container mx-auto pt-5">
+
+<h2 class="font-bold text-2xl py-6">Web Observations</h2>
+<div class="overflow-x-auto">
+    <table class="min-w-full divide-y divide-gray-300">
+        <thead>
+            <tr>
+            {% for key in web_observations[0].keys() %}
+            <th scope="col" class="px-3 py-3.5 text-left text-sm font-semibold text-gray-900">{{ key }}</th>
+            {% endfor %}
+            </tr>
+        </thead>
+        <tbody class="bg-white divide-y divide-gray-200">
+            {% for wo in web_observations %}
+            <tr class="hover:bg-gray-200 even:bg-gray-50 divide-x divide-gray-200">
+              {% for value in wo.values() %}
+              <td class="truncate max-w-[200px] hover:max-w-max whitespace-nowrap px-3 py-4 text-sm text-gray-500">{{value}}</td>
+              {% endfor %}
+            </tr>
+            {% endfor %}
+        </tbody>
+    </table>
+</div>
+
+<h2 class="font-bold text-2xl py-6">Web Control Observations</h2>
+<div class="overflow-x-auto">
+    <table class="min-w-full divide-y divide-gray-300">
+        <thead>
+            <tr>
+            {% for key in web_control_observations[0].keys() %}
+            <th scope="col" class="px-3 py-3.5 text-left text-sm font-semibold text-gray-900">{{ key }}</th>
+            {% endfor %}
+            </tr>
+        </thead>
+        <tbody class="bg-white divide-y divide-gray-200">
+            {% for wo in web_control_observations %}
+            <tr class="hover:bg-gray-200 even:bg-gray-50 divide-x divide-gray-200">
+              {% for value in wo.values() %}
+              <td class="truncate max-w-[200px] hover:max-w-max whitespace-nowrap px-3 py-4 text-sm text-gray-500">{{value}}</td>
+              {% endfor %}
+            </tr>
+            {% endfor %}
+        </tbody>
+    </table>
+</div>
+
+<h2 class="font-bold text-2xl py-6">Measurement Meta</h2>
+<dl class="grid grid-cols-1 sm:grid-cols-3">
+  {% for key, value in measurement_meta.items() %}
+  <div class="px-4 py-6 sm:grid-cols-3 sm:col-span-1 sm:px-0">
+    <dt class="text-sm font-medium leading-6 text-gray-900">{{ key }}</dt>
+    <dd class="mt-1 text-sm leading-6 text-gray-700 sm:col-span-2 sm:mt-0">{{ value }}</dd>
+  </div>
+  {% endfor %}
+</dl>
+
+<h2 class="font-bold text-2xl py-6">Probe Meta</h2>
+<dl class="grid grid-cols-1 sm:grid-cols-3">
+  {% for key, value in probe_meta.items() %}
+  <div class="px-4 py-6 sm:grid-cols-3 sm:col-span-1 sm:px-0">
+    <dt class="text-sm font-medium leading-6 text-gray-900">{{ key }}</dt>
+    <dd class="mt-1 text-sm leading-6 text-gray-700 sm:col-span-2 sm:mt-0">{{ value }}</dd>
+  </div>
+  {% endfor %}
+</dl>
+
+</div>
+
+</body>
+</html>
diff --git a/oonipipeline/src/oonipipeline/dataviz/templates/react.html b/oonipipeline/src/oonipipeline/dataviz/templates/react.html
deleted file mode 100644
index fae496c6..00000000
--- a/oonipipeline/src/oonipipeline/dataviz/templates/react.html
+++ /dev/null
@@ -1,18 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-  <meta charset="utf-8">
-  <title>OONI Data Viz</title>
-  <meta name="viewport" content="width=device-width, initial-scale=1">
-  <link rel="stylesheet" href="/static/dist/index.css">
-  {% block css %}{% endblock %}
-</head>
-
-<body>
-  <div id="root"></div>
-  <script type="text/javascript" src="/static/dist/index.js"></script>
-  {% block js %}{% endblock %}
-</body>
-
-</html>
diff --git a/oonipipeline/src/oonipipeline/dataviz/templates/redirect.html b/oonipipeline/src/oonipipeline/dataviz/templates/redirect.html
new file mode 100644
index 00000000..fdfc0a05
--- /dev/null
+++ b/oonipipeline/src/oonipipeline/dataviz/templates/redirect.html
@@ -0,0 +1,43 @@
+<!doctype html>
+<html>
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <link href="https://cdn.jsdelivr.net/npm/daisyui@4.4.2/dist/full.min.css" rel="stylesheet" type="text/css" />
+  <script src="https://cdn.tailwindcss.com?plugins=forms,typography,aspect-ratio,line-clamp"></script>
+  <style type="text/css">
+    .kraken-loon {
+        background-image:  url(https://images.unsplash.com/photo-1627955353583-5ed9f3ff1dc7), url(https://images.unsplash.com/photo-1547716752-9a0331effbef);
+        background-position:  850px -1040px, -272px -240px;
+        background-repeat: no-repeat, no-repeat;
+        background-size: 1860px 3032px, 1453px 880px;
+    }
+  </style>
+</head>
+<body>
+    <div class="container mx-auto pt-5">
+
+        <div class="bg-white shadow sm:rounded-lg">
+            <div class="px-4 py-5 sm:p-6">
+              <h3 class="text-base font-semibold leading-6 text-gray-900">{{ scope }}: enter measurement UID</h3>
+              <div class="mt-2 max-w-xl text-sm text-gray-500">
+                <p>You will be redirected to the relevant {{ scope }} page.</p>
+              </div>
+              <div class="mt-5 sm:flex sm:items-center">
+                <div class="w-full sm:max-w-xs">
+                  <label for="measurement_uid" class="sr-only">measurement_uid</label>
+                  <input id="measurement_uid" class="block w-full rounded-md border-0 py-1.5 text-gray-900 shadow-sm ring-1 ring-inset ring-gray-300 placeholder:text-gray-400 focus:ring-2 focus:ring-inset focus:ring-blue-600 sm:text-sm sm:leading-6">
+                </div>
+                <button onclick="redirect()" class="mt-3 inline-flex w-full items-center justify-center rounded-md bg-blue-600 px-3 py-2 text-sm font-semibold text-white shadow-sm hover:bg-blue-500 focus-visible:outline focus-visible:outline-2 focus-visible:outline-offset-2 focus-visible:outline-blue-600 sm:ml-3 sm:mt-0 sm:w-auto">Go</button>
+            </div>
+            </div>
+          </div>
+    </div>
+</body>
+<script>
+    function redirect() {
+        var measurement_uid = document.getElementById("measurement_uid").value
+        window.location.href = "/{{scope}}/m/" + measurement_uid
+    }
+</script>
+</html>
\ No newline at end of file
diff --git a/oonipipeline/src/oonipipeline/dataviz/templates/vega.html b/oonipipeline/src/oonipipeline/dataviz/templates/vega.html
deleted file mode 100644
index ab592e4d..00000000
--- a/oonipipeline/src/oonipipeline/dataviz/templates/vega.html
+++ /dev/null
@@ -1,21 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-  <script src="https://cdn.jsdelivr.net/npm/vega"></script>
-  <script src="https://cdn.jsdelivr.net/npm/vega-lite"></script>
-  <script src="https://cdn.jsdelivr.net/npm/vega-embed"></script>
-</head>
-
-<body>
-
-  <div id="vis"></div>
-
-  <script type="text/javascript">
-    vegaEmbed("#vis", "{{ url_for(endpoint, **query_args | default({})) | safe }}").then(function (result) {
-      console.log(result)
-    }).catch(console.error);
-  </script>
-</body>
-
-</html>
diff --git a/oonipipeline/src/oonipipeline/dataviz/viz.py b/oonipipeline/src/oonipipeline/dataviz/viz.py
deleted file mode 100644
index c15bac8e..00000000
--- a/oonipipeline/src/oonipipeline/dataviz/viz.py
+++ /dev/null
@@ -1,1055 +0,0 @@
-# This is currently enabled due to: https://github.com/altair-viz/altair/pull/2681
-# type: ignore
-
-import numpy as np
-import pandas as pd
-import altair as alt
-
-from .theme import OONI_COLOR_SCHEME
-from .utils import click_query
-
-from urllib.parse import urlparse
-import requests
-
-world_100m = requests.get(
-    "https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/world-110m.json"
-).json()
-alt_data_world_110m = alt.Data(
-    values=world_100m, format={"type": "topojson", "feature": "countries"}
-)
-
-country_centers = pd.read_csv(
-    "https://raw.githubusercontent.com/albertyw/avenews/master/old/data/average-latitude-longitude-countries.csv"
-)
-country_centers = country_centers[
-    ["ISO 3166 Country Code", "Latitude", "Longitude"]
-].set_index("ISO 3166 Country Code")
-
-country_meta = pd.read_json(
-    "https://raw.githubusercontent.com/ooni/country-util/master/data/country-list.json"
-)
-country_meta = country_meta[["name", "iso3166_alpha2", "iso3166_num"]].set_index(
-    "iso3166_alpha2"
-)
-
-citizenlab_global_tl = pd.read_csv(
-    "https://raw.githubusercontent.com/citizenlab/test-lists/master/lists/global.csv"
-)
-citizenlab_global_tl["domain_name"] = citizenlab_global_tl["url"].apply(
-    lambda x: urlparse(x).netloc
-)
-
-
-def get_df_dns_analysis_raw(
-    measurement_uid, start_day="2023-01-01", end_day="2023-01-02"
-):
-    params = {
-        "start_day": start_day,
-        "end_day": end_day,
-        "measurement_uid": measurement_uid,
-    }
-    q = """
-    SELECT * FROM (
-    SELECT 
-        report_id,
-        input,
-        measurement_uid,
-        probe_cc,
-        probe_asn,
-        probe_as_org_name,
-        measurement_start_time,
-        resolver_ip,
-        resolver_asn,
-        resolver_cc, 
-        resolver_as_org_name,
-        resolver_as_cc,
-        resolver_is_scrubbed,
-        resolver_asn_probe,
-        resolver_as_org_name_probe,
-        dns_engine_resolver_address,
-        dns_engine,
-        dns_query_type,
-        hostname,
-        any(dns_failure) as exp_dns_failure,
-        any(ip_is_bogon) OR 0 as exp_answer_contains_bogon,
-        any(ip_as_cc = probe_cc) OR 0 as exp_answer_contains_matching_probe_cc,
-        any(dns_answer_asn = probe_asn) OR 0 as exp_answer_contains_matching_probe_asn,
-        any(lower(ip_as_org_name) = lower(probe_as_org_name)) OR 0 as exp_answer_contains_matching_probe_as_org_name,
-        groupArrayIf(
-            tuple(dns_answer, dns_answer_asn, dns_answer_as_org_name, ip_as_cc), 
-            dns_answer IS NOT NULL
-        ) as dns_answers
-    FROM obs_web
-    WHERE
-    measurement_start_time > %(start_day)s
-    AND measurement_start_time < %(end_day)s
-    AND measurement_uid = %(measurement_uid)s
-    AND test_name = 'web_connectivity'
-    AND (dns_answer IS NOT NULL OR dns_failure IS NOT NULL)
-    GROUP BY report_id,
-        input,
-        measurement_uid,
-        probe_cc,
-        probe_asn,
-        probe_as_org_name,
-        measurement_start_time,
-        resolver_ip,
-        resolver_asn,
-        resolver_cc, 
-        resolver_as_org_name,
-        resolver_as_cc,
-        resolver_is_scrubbed,
-        resolver_asn_probe,
-        resolver_as_org_name_probe,
-        dns_engine_resolver_address,
-        dns_engine, hostname, dns_query_type
-) as exp
-LEFT JOIN (
-    SELECT 
-        hostname,
-        answers,
-        failure_asns,
-        nxdomain_asns,
-        ok_asns,
-        ctrl_answers,
-        ctrl_failures
-    FROM (
-        SELECT 
-        hostname,
-
-        groupArrayIf(tuple(dns_answer, ip_as_org_name, ip_asn, answer_asns, tls_consistent_asns), dns_answer IS NOT NULL) as answers,
-
-        anyIf(failure_asns, dns_answer IS NULL) as failure_asns,
-
-        anyIf(nxdomain_asns, dns_answer IS NULL) as nxdomain_asns,
-
-        arrayReduce('groupUniqArray', arrayFlatten(groupUniqArray(answer_asns))) as ok_asns
-        FROM (
-            SELECT 
-            hostname,
-            dns_answer,
-            ip_as_org_name,
-            ip_asn,
-            groupUniqArrayIf(tuple(probe_cc, probe_asn), tls_is_certificate_valid = 1) as tls_consistent_asns,
-            groupUniqArrayIf(tuple(probe_cc, probe_asn), dns_failure IS NULL) as answer_asns,
-            groupUniqArrayIf(tuple(probe_cc, probe_asn), dns_failure IS NOT NULL) as failure_asns,
-            groupUniqArrayIf(tuple(probe_cc, probe_asn), dns_failure = 'dns_nxdomain_error') as nxdomain_asns
-            FROM obs_web
-            WHERE
-            measurement_start_time > %(start_day)s
-            AND measurement_start_time < %(end_day)s
-            AND (dns_answer IS NOT NULL OR dns_failure IS NOT NULL)
-            GROUP BY hostname, dns_answer, ip_as_org_name, ip_asn
-        ) GROUP BY hostname
-    ) as obs
-    FULL OUTER JOIN (
-        SELECT 
-        hostname,
-        groupUniqArrayIf(tuple(ip, ip_count), dns_failure IS NULL) as ctrl_answers,
-        groupArrayIf(tuple(dns_failure, failure_count), dns_failure IS NOT NULL) as ctrl_failures
-        FROM (
-            SELECT
-            hostname,
-            ip,
-            COUNT() as ip_count,
-            dns_failure,
-            COUNT() as failure_count
-            FROM obs_web_ctrl
-            WHERE 
-            measurement_start_time > %(start_day)s
-            AND measurement_start_time < %(end_day)s
-            AND (
-                dns_success = 1
-                OR dns_failure IS NOT NULL
-            )
-            GROUP BY hostname, ip, dns_failure
-        ) GROUP BY hostname
-    ) as ctrl
-    USING hostname
-) as dns_gt
-USING hostname 
-    """
-    return click_query(q, **params)
-
-
-def get_df_dns_analysis(start_day="2023-01-01", end_day="2023-01-02", limit=100):
-    params = {"start_day": start_day, "end_day": end_day}
-    q = """WITH
-arrayDistinct(arrayMap(x -> x.1, exp.dns_answers)) as exp_dns_answers_ips,
-arrayDistinct(arrayMap(x -> x.2, exp.dns_answers)) as exp_dns_answers_asns,
-arrayDistinct(arrayMap(x -> lower(x.3), exp.dns_answers)) as exp_dns_answers_as_org_names,
-
--- tuple(dns_answer, ip_as_org_name, ip_asn, answer_asns, tls_consistent_asns)
-flatten(arrayMap(
-    asn -> arrayFilter(
-        y -> y.3 IS NOT NULL AND asn IS NOT NULL AND assumeNotNull(y.3) = assumeNotNull(asn),
-        dns_gt.answers
-    ),
-    exp_dns_answers_asns
-)) as dns_answers_asn_match,
-
--- tuple(dns_answer, ip_as_org_name, ip_asn, answer_asns, tls_consistent_asns)
-arrayMap(
-    x -> tuple(
-        x.1,
-        x.2,
-        x.3,
-        -- all answers_asns, except probe_asn
-        arrayFilter(
-            y -> y.1 != exp.probe_cc OR y.2 != exp.probe_asn,
-            x.4
-        ),
-        -- all tls_consistent_asns, except probe_asn
-        arrayFilter(
-            y -> y.1 != exp.probe_cc OR y.2 != exp.probe_asn,
-            x.5
-        )
-    ),
-    dns_answers_asn_match
-) as dns_answers_asn_match_no_asn,
-
-arrayMap(
-    x -> tuple(
-        x.2,
-        x.3,
-        length(x.4),
-        length(x.5)
-    ), dns_answers_asn_match_no_asn
-) as dns_answers_asn_match_no_asn_counts_tup,
-
--- dns_answers in the ground_truth that match the AS ORG name of the answers in the experiment
--- tuple(dns_answer, ip_as_org_name, ip_asn, answer_asns, tls_consistent_asns)
-flatten(arrayMap(
-    asorgname -> arrayFilter(
-        y -> y.2 IS NOT NULL AND asorgname IS NOT NULL AND lower(assumeNotNull(y.2)) = lower(assumeNotNull(asorgname)),
-        dns_gt.answers
-    ),
-    exp_dns_answers_as_org_names
-)) as dns_answers_as_org_name_match,
-
--- filter the sub-lists in the answers to exclude the ones from the VP of the probe
--- tuple(dns_answer, ip_as_org_name, ip_asn, answer_asns, tls_consistent_asns)
-arrayMap(
-    x -> tuple(
-        x.1, -- dns_answer
-        x.2, -- ip_as_org_name
-        x.3, -- ip_asn
-        -- all answers_asns, except probe_asn
-        arrayFilter(
-            y -> y.1 != exp.probe_cc OR y.2 != exp.probe_asn,
-            x.4
-        ),
-        -- all tls_consistent_asns, except probe_asn
-        arrayFilter(
-            y -> y.1 != exp.probe_cc OR y.2 != exp.probe_asn,
-            x.5
-        )
-    ),
-    dns_answers_as_org_name_match
-) as dns_answers_as_org_name_match_no_asn,
-
--- tuple(dns_answer, ip_as_org_name, ip_asn, answer_asns, tls_consistent_asns)
-arrayMap(
-    ip -> arrayFirst(
-        y -> y.1 IS NOT NULL AND ip IS NOT NULL AND assumeNotNull(y.1) = assumeNotNull(ip),
-        dns_gt.answers
-    ),
-    exp_dns_answers_ips
-) as dns_answers_match,
-
--- tuple(dns_answer, ip_as_org_name, ip_asn, answer_asns, tls_consistent_asns)
-arrayMap(
-    x -> tuple(
-        x.1, -- dns_answer
-        x.2, -- ip_as_org_name
-        x.3, -- ip_asn
-        -- all answers_asns, except probe_asn
-        arrayFilter(
-            y -> y.1 != exp.probe_cc OR y.2 != exp.probe_asn,
-            x.4
-        ),
-        -- all tls_consistent_asns, except probe_asn
-        arrayFilter(
-            y -> y.1 != exp.probe_cc OR y.2 != exp.probe_asn,
-            x.5
-        )
-    ),
-    dns_answers_match
-) as dns_answers_match_no_asn,
-
-arrayMap(
-    x -> tuple(
-        x.2, -- ip_as_org_name
-        x.3, -- ip_asn
-        length(x.4), -- all_answers
-        length(x.5) -- tls_consistent
-    ), dns_answers_match_no_asn
-) as dns_answers_match_no_asn_counts_tup,
-
-arrayFilter(
-    x -> indexOf(exp_dns_answers_asns, x.2) != 0,
-    dns_answers_asn_match_no_asn_counts_tup
-) as dns_answer_matching_asn,
-
-arrayFilter(
-    x -> indexOf(exp_dns_answers_as_org_names, lower(x.1)) != 0,
-    dns_answers_as_org_name_match_no_asn_counts_tup
-) as dns_answer_matching_as_org_name,
-
-arrayMap(
-    x -> tuple(
-        x.2, -- ip_as_org_name
-        x.3, -- ip_asn
-        length(x.4),
-        length(x.5)
-    ), dns_answers_as_org_name_match_no_asn
-) as dns_answers_as_org_name_match_no_asn_counts_tup
-
-
-SELECT
-    exp.report_id,
-    exp.input,
-    exp.measurement_uid,
-    exp.probe_cc,
-    exp.probe_asn,
-    exp.measurement_start_time,
-    exp.resolver_ip,
-    exp.resolver_asn,
-    exp.resolver_cc, 
-    exp.resolver_as_org_name,
-    exp.resolver_as_cc,
-    exp.resolver_is_scrubbed,
-    exp.resolver_asn_probe,
-    exp.resolver_as_org_name_probe,
-    exp.dns_engine_resolver_address,
-    exp.dns_engine,
-    exp.hostname,
-    exp.exp_dns_failure,
-
-    -- tuple(dns_answer, dns_answer_asn, dns_answer_as_org_name)
-    exp.dns_answers as exp_dns_answers,
-    
-    length(exp_dns_answers) as exp_dns_answers_count,
-    exp.exp_answer_contains_bogon,
-    
-    exp.exp_answer_contains_matching_probe_cc,
-    exp.exp_answer_contains_matching_probe_asn,
-    exp.exp_answer_contains_matching_probe_as_org_name,
-
-    arraySum(arrayMap(x -> length(x.4), dns_gt.answers)) as dns_answers_all_asn_count,
-
-    arraySum(
-        arrayMap(
-            x -> x.3,
-            dns_answers_match_no_asn_counts_tup
-        )
-    ) as dns_answers_ip_match_all_count,
-
-    --dns_answers_asn_match_tls_consistent_include_probe_count,
-    --dns_answers_as_org_name_match_tls_consistent_include_probe_count,
-
-    arraySum(
-        arrayMap(
-            x -> x.4,
-            dns_answers_match_no_asn_counts_tup
-        )
-    ) as dns_answers_ip_match_tls_consistent_count,
-
-    arraySum(
-        arrayMap(
-            x -> length(x.5),
-            dns_answers_match
-        )
-    ) as dns_answers_ip_match_tls_consistent_include_probe_count,
-
-    arraySum(
-        arrayMap(
-            x -> x.2,
-            arrayFilter(
-                x -> indexOf(exp_dns_answers_ips, x.1) != 0,
-                dns_gt.ctrl_answers
-            )
-        )
-    ) as dns_answers_ip_match_ctrl_count,
-
-    arraySum(
-        arrayMap(
-            x -> x.3,
-            dns_answer_matching_asn
-        )
-    ) as dns_answers_asn_match_all_count,
-
-    arraySum(
-        arrayMap(
-            x -> x.4,
-            dns_answer_matching_asn
-        )
-    ) as dns_answers_asn_match_tls_consistent_count,
-
-    arraySum(
-        arrayMap(
-            x -> x.3,
-            dns_answer_matching_as_org_name
-        )
-    ) as dns_answers_as_org_name_match_all_count,
-
-    arraySum(
-        arrayMap(
-            x -> x.4,
-            dns_answer_matching_as_org_name
-        )
-    ) as dns_answers_as_org_name_match_tls_consistent_count,
-
-    -- tuple(probe_cc, probe_asn)
-    --dns_gt.failure_asns,
-
-    length(arrayFilter(
-        x -> x.1 != exp.probe_cc OR x.2 != exp.probe_asn, 
-        dns_gt.failure_asns
-    )) as failure_asn_count,
-
-    -- tuple(probe_cc, probe_asn)
-    --dns_gt.nxdomain_asns,
-    
-    -- All ASNS with NXDOMAIN != (probe_cc, probe_asn)
-    length(arrayFilter(
-        x -> x.1 != exp.probe_cc OR x.2 != exp.probe_asn, 
-        dns_gt.nxdomain_asns
-    )) as nxdomain_asn_count,
-
-    -- tuple(probe_cc, probe_asn)
-    --dns_gt.ok_asns,
-
-    -- All ASNS with NXDOMAIN != (probe_cc, probe_asn)
-    length(arrayFilter(
-        x -> x.1 != exp.probe_cc OR x.2 != exp.probe_asn, 
-        dns_gt.ok_asns
-    )) as ok_asn_count,
-
-    -- tuple(ip, ip_count)
-    --dns_gt.ctrl_answers,
-
-    -- tuple(dns_failure, failure_count)
-    --dns_gt.ctrl_failures,
-
-    --dns_answers_ip_match_ctrl_count,
-    --ctrl_failures_count
-
-
-    arraySum(
-        arrayMap(
-            x -> x.2,
-            arrayFilter(
-                x -> exp_dns_failure IS NOT NULL AND x.1 = exp_dns_failure,
-                dns_gt.ctrl_failures
-            )
-        )
-    ) as ctrl_matching_failures_count,
-
-    arraySum(
-        arrayMap(
-            x -> x.2,
-            arrayFilter(
-                x -> x.1 IS NOT NULL,
-                dns_gt.ctrl_failures
-            )
-        )
-    ) as ctrl_failure_count
-
-FROM (
-    SELECT 
-        report_id,
-        input,
-        measurement_uid,
-        probe_cc,
-        probe_asn,
-        probe_as_org_name,
-        measurement_start_time,
-        resolver_ip,
-        resolver_asn,
-        resolver_cc, 
-        resolver_as_org_name,
-        resolver_as_cc,
-        resolver_is_scrubbed,
-        resolver_asn_probe,
-        resolver_as_org_name_probe,
-        dns_engine_resolver_address,
-        dns_engine,
-        dns_query_type,
-        hostname,
-        any(dns_failure) as exp_dns_failure,
-        any(ip_is_bogon) OR 0 as exp_answer_contains_bogon,
-        any(ip_as_cc = probe_cc) OR 0 as exp_answer_contains_matching_probe_cc,
-        any(dns_answer_asn = probe_asn) OR 0 as exp_answer_contains_matching_probe_asn,
-        any(lower(ip_as_org_name) = lower(probe_as_org_name)) OR 0 as exp_answer_contains_matching_probe_as_org_name,
-        groupArrayIf(
-            tuple(dns_answer, dns_answer_asn, dns_answer_as_org_name, ip_as_cc), 
-            dns_answer IS NOT NULL
-        ) as dns_answers
-    FROM obs_web
-    WHERE
-    measurement_start_time > %(start_day)s
-    AND measurement_start_time < %(end_day)s
-    AND test_name = 'web_connectivity'
-    AND (dns_answer IS NOT NULL OR dns_failure IS NOT NULL)
-    GROUP BY report_id,
-        input,
-        measurement_uid,
-        probe_cc,
-        probe_asn,
-        probe_as_org_name,
-        measurement_start_time,
-        resolver_ip,
-        resolver_asn,
-        resolver_cc, 
-        resolver_as_org_name,
-        resolver_as_cc,
-        resolver_is_scrubbed,
-        resolver_asn_probe,
-        resolver_as_org_name_probe,
-        dns_engine_resolver_address,
-        dns_engine, hostname, dns_query_type
-) as exp
-LEFT JOIN (
-    SELECT 
-        hostname,
-        answers,
-        failure_asns,
-        nxdomain_asns,
-        ok_asns,
-        ctrl_answers,
-        ctrl_failures
-    FROM (
-        SELECT 
-        hostname,
-
-        groupArrayIf(tuple(dns_answer, ip_as_org_name, ip_asn, answer_asns, tls_consistent_asns), dns_answer IS NOT NULL) as answers,
-
-        anyIf(failure_asns, dns_answer IS NULL) as failure_asns,
-
-        anyIf(nxdomain_asns, dns_answer IS NULL) as nxdomain_asns,
-
-        arrayReduce('groupUniqArray', arrayFlatten(groupUniqArray(answer_asns))) as ok_asns
-        FROM (
-            SELECT 
-            hostname,
-            dns_answer,
-            ip_as_org_name,
-            ip_asn,
-            groupUniqArrayIf(tuple(probe_cc, probe_asn), tls_is_certificate_valid = 1) as tls_consistent_asns,
-            groupUniqArrayIf(tuple(probe_cc, probe_asn), dns_failure IS NULL) as answer_asns,
-            groupUniqArrayIf(tuple(probe_cc, probe_asn), dns_failure IS NOT NULL) as failure_asns,
-            groupUniqArrayIf(tuple(probe_cc, probe_asn), dns_failure = 'dns_nxdomain_error') as nxdomain_asns
-            FROM obs_web
-            WHERE
-            measurement_start_time > %(start_day)s
-            AND measurement_start_time < %(end_day)s
-            AND (dns_answer IS NOT NULL OR dns_failure IS NOT NULL)
-            GROUP BY hostname, dns_answer, ip_as_org_name, ip_asn
-        ) GROUP BY hostname
-    ) as obs
-    FULL OUTER JOIN (
-        SELECT 
-        hostname,
-        groupUniqArrayIf(tuple(ip, ip_count), dns_failure IS NULL) as ctrl_answers,
-        groupArrayIf(tuple(dns_failure, failure_count), dns_failure IS NOT NULL) as ctrl_failures
-        FROM (
-            SELECT
-            hostname,
-            ip,
-            COUNT() as ip_count,
-            dns_failure,
-            COUNT() as failure_count
-            FROM obs_web_ctrl
-            WHERE 
-            measurement_start_time > %(start_day)s
-            AND measurement_start_time < %(end_day)s
-            AND (
-                dns_success = 1
-                OR dns_failure IS NOT NULL
-            )
-            GROUP BY hostname, ip, dns_failure
-        ) GROUP BY hostname
-    ) as ctrl
-    USING hostname
-) as dns_gt
-USING hostname 
-    """
-    if limit > 0:
-        params["limit"] = limit
-        q += "LIMIT %(limit)d"
-
-    return click_query(q, **params)
-
-
-def get_df_blocking_of_domain_by_asn(
-    domain_name, probe_cc, start_time="2022-11-03", end_time="2022-12-03"
-):
-    q_args = {
-        "domain_name": domain_name,
-        "probe_cc": probe_cc,
-        "start_time": start_time,
-        "end_time": end_time,
-    }
-
-    df = click_query(
-        """
-        SELECT 
-        domain_name,
-        probe_cc,
-        probe_asn,
-        probe_as_org_name,
-        any(max_blocked) as max_blocked,
-        any(max_blocked_label) as blocked_label,
-        any(max_down) as max_down,
-        any(max_down_label) as down_label,
-        any(max_ok) as max_ok,
-
-        (max_blocked / (max_ok + max_down + max_blocked)) as blocked,
-        (max_down / (max_ok + max_down + max_blocked)) as down,
-        (max_ok / (max_ok + max_down + max_blocked)) as ok,
-        SUM(cnt) as count
-        FROM (
-            SELECT
-            domain_name,
-            probe_cc,
-            probe_asn,
-            probe_as_org_name,
-            max(ema_avg_blocked) OVER (PARTITION BY probe_cc, probe_asn, domain_name) as max_blocked,
-            any(outcome_label) OVER (PARTITION BY probe_cc, probe_asn, domain_name ORDER BY ema_avg_blocked DESC) as max_blocked_label,
-            max(ema_avg_down) OVER (PARTITION BY probe_cc, probe_asn, domain_name) as max_down,
-            any(outcome_label) OVER (PARTITION BY probe_cc, probe_asn, domain_name ORDER BY ema_avg_down DESC) as max_down_label,
-            max(ema_avg_down) OVER (PARTITION BY probe_cc, probe_asn, domain_name) as max_down,
-            max(IF(outcome_category == 'http' OR outcome_category == 'https', ema_avg_ok, 0)) OVER (PARTITION BY probe_cc, probe_asn, domain_name) as max_ok,
-            cnt
-            FROM (
-                SELECT 
-                domain_name,
-                probe_cc,
-                probe_asn,
-                probe_as_org_name,
-                outcome_category,
-                outcome_detail,
-                outcome_label,
-                avgWeighted(ema_blocked, cnt) OVER (PARTITION BY probe_cc, probe_asn, domain_name, outcome_category) as ema_avg_blocked,
-                avgWeighted(ema_down, cnt) OVER (PARTITION BY probe_cc, probe_asn, domain_name, outcome_category) as ema_avg_down,
-                avgWeighted(ema_ok, cnt) OVER (PARTITION BY probe_cc, probe_asn, domain_name, outcome_category) as ema_avg_ok,
-                cnt
-                FROM (
-                    SELECT
-                    domain_name,
-                    probe_cc,
-                    probe_asn,
-                    probe_as_org_name,
-                    outcome_category,
-                    outcome_detail,
-                    IF(outcome_category = 'ok', 'ok', concat(outcome_category, '.', outcome_detail)) as outcome_label,
-                    exponentialMovingAverage(1)(blocked_score, t) as ema_blocked,
-                    exponentialMovingAverage(1)(down_score, t) as ema_down,
-                    exponentialMovingAverage(1)(ok_score, t) as ema_ok,
-                    toFloat64(Count()) as cnt
-                    FROM (
-                        SELECT
-                        domain_name,
-                        probe_cc,
-                        probe_asn,
-                        probe_as_org_name,
-                        outcome_category,
-                        IF(startsWith(outcome_detail, 'failure.unknown_failure') OR startsWith(outcome_detail, 'unknown_failure'), 
-                            'failure.unknown', 
-                            outcome_detail
-                        ) as outcome_detail,
-                        blocked_score,
-                        ok_score,
-                        down_score,
-                        row_number() OVER (PARTITION BY domain_name, probe_asn, outcome_category, outcome_detail) as t
-                        FROM experiment_result 
-                        WHERE timestamp > %(start_time)s AND timestamp < %(end_time)s
-                        AND domain_name = %(domain_name)s AND probe_cc = %(probe_cc)s
-                    ) GROUP BY probe_cc, probe_asn, probe_as_org_name, domain_name, outcome_category, outcome_detail
-                )
-            )
-        ) GROUP BY probe_cc, probe_asn, probe_as_org_name, domain_name
-    """,
-        **q_args,
-    )
-    return df[
-        [
-            "domain_name",
-            "probe_cc",
-            "probe_asn",
-            "probe_as_org_name",
-            "blocked_label",
-            "down_label",
-            "count",
-            "ok",
-            "blocked",
-            "down",
-        ]
-    ].melt(
-        [
-            "domain_name",
-            "probe_cc",
-            "probe_asn",
-            "probe_as_org_name",
-            "blocked_label",
-            "down_label",
-            "count",
-        ],
-        var_name="stat",
-        value_name="stat_value",
-    )
-
-
-def plot_blocking_of_domain_by_asn(
-    df=None,
-    domain_name=None,
-    probe_cc=None,
-    start_time="2022-11-03",
-    end_time="2022-12-03",
-):
-    if df is None:
-        df = get_df_blocking_of_domain_by_asn(
-            domain_name, probe_cc, start_time=start_time, end_time=end_time
-        )
-
-    color_scale = alt.Scale(
-        domain=["blocked", "down", "ok"],
-        range=[
-            OONI_COLOR_SCHEME["red7"],
-            OONI_COLOR_SCHEME["orange6"],
-            OONI_COLOR_SCHEME["green8"],
-        ],
-    )
-
-    base_chart = (
-        alt.Chart(df)
-        .mark_bar()
-        .encode(
-            y=alt.Y("probe_asn:O", axis=alt.Axis(labels=False, title=None)),
-        )
-    )
-
-    bars = base_chart.encode(
-        x="stat_value:Q",
-        color=alt.Color("stat", scale=color_scale, title="status"),
-        tooltip=[
-            alt.Tooltip("probe_as_org_name:N", title="Network name"),
-            alt.Tooltip("probe_asn:N", title="ASN"),
-            alt.Tooltip("stat_value:Q", title="value"),
-            alt.Tooltip("blocked_label:N", title="blocked_label"),
-            alt.Tooltip("down_label:N", title="down_label"),
-            alt.Tooltip("count:Q", title="count"),
-        ],
-        order=alt.Order("stat_value", sort="descending"),
-    )
-
-    text = (
-        base_chart.mark_text(
-            align="center",
-        )
-        .transform_filter(
-            "indexof(datum.blocked_label, 'ok') == -1 & datum.stat == 'blocked'"
-        )
-        .encode(color=alt.value(OONI_COLOR_SCHEME["gray9"]), text="blocked_label:N")
-    )
-
-    bar_count = base_chart.encode(
-        y=alt.Y("probe_asn:O", axis=alt.Axis(labels=True, title="ASN")),
-        x="count:Q",
-    ).properties(width=30)
-
-    return alt.hconcat(bar_count, (bars + text)).properties(
-        title=f"Blocking of {domain_name} in {probe_cc} by ASN"
-    )
-
-
-def get_df_blocking_of_domain_in_asn(
-    domain_name,
-    probe_cc,
-    probe_asn,
-    start_time="2022-11-03",
-    end_time="2022-12-03",
-):
-    q_args = {
-        "domain_name": domain_name,
-        "probe_cc": probe_cc,
-        "probe_asn": probe_asn,
-        "start_time": start_time,
-        "end_time": end_time,
-    }
-    return click_query(
-        """
-        SELECT
-        domain_name,
-        probe_cc,
-        probe_asn,
-        probe_as_org_name,
-        outcome_category,
-        outcome_detail,
-        IF(outcome_category = 'ok', 'ok', concat(outcome_category, '.', outcome_detail)) as outcome_label,
-        exponentialMovingAverage(1)(blocked_score, t) as ema_blocked,
-        exponentialMovingAverage(1)(down_score, t) as ema_down,
-        exponentialMovingAverage(1)(ok_score, t) as ema_ok,
-        toFloat64(Count()) as cnt
-        FROM (
-            SELECT
-            domain_name,
-            probe_cc,
-            probe_asn,
-            probe_as_org_name,
-            outcome_category,
-            IF(startsWith(outcome_detail, 'failure.unknown_failure') OR startsWith(outcome_detail, 'unknown_failure'), 
-                'failure.unknown', 
-                outcome_detail
-            ) as outcome_detail,
-            blocked_score,
-            ok_score,
-            down_score,
-            row_number() OVER (PARTITION BY domain_name, probe_asn, outcome_category, outcome_detail) as t
-            FROM experiment_result 
-            WHERE timestamp > %(start_time)s AND timestamp < %(end_time)s
-            AND domain_name = %(domain_name)s AND probe_cc = %(probe_cc)s
-            AND probe_asn = %(probe_asn)d
-        ) GROUP BY probe_cc, probe_asn, probe_as_org_name, domain_name, outcome_category, outcome_detail
-    """,
-        **q_args,
-    )
-
-
-def plot_blocking_of_domain_in_asn(
-    df=None,
-    data_name=None,
-    domain_name=None,
-    probe_cc=None,
-    probe_asn=None,
-    start_time="2022-11-03",
-    end_time="2022-12-03",
-):
-    if df is None and data_name:
-        df = alt.NamedData(data_name)
-
-    if df is None:
-        df = get_df_blocking_of_domain_in_asn(
-            domain_name=domain_name,
-            probe_cc=probe_cc,
-            probe_asn=probe_asn,
-            start_time=start_time,
-            end_time=end_time,
-        )
-
-    return (
-        alt.Chart(df)
-        .mark_bar()
-        .encode(
-            x=alt.X("outcome_detail:O", title=None),
-            y=alt.Y("cnt:Q", title="Count"),
-            color=alt.Color(
-                "ema_blocked:Q", scale=alt.Scale(scheme="redyellowgreen", reverse=True)
-            ),
-            tooltip=[
-                alt.Tooltip("cnt:N", title="Measurement count"),
-                alt.Tooltip("outcome_label:N", title="Outcome"),
-                alt.Tooltip("ema_blocked:Q", title="Blocked EMA"),
-                alt.Tooltip("ema_ok:Q", title="OK EMA"),
-                alt.Tooltip("ema_down:Q", title="Down EMA"),
-            ],
-        )
-        .properties(height=250)
-        .facet(
-            column=alt.Column(
-                "outcome_category:N",
-                sort=["dns", "tcp", "tls", "https", "http"],
-                title=None,
-            ),
-        )
-        .resolve_scale(x="independent")
-        .properties(
-            title=f"{domain_name} in AS{probe_asn} ({probe_cc}) from {start_time} to {end_time}"
-        )
-    )
-
-
-def get_df_blocking_world_map(blocking_threshold=0.7):
-    q_args = {"blocking_threshold": blocking_threshold}
-    df = click_query(
-        """SELECT
-    probe_cc,
-    CountIf(max_blocked > %(blocking_threshold)f) as blocked_asns,
-    CountIf(max_ok > 0.5) as ok_asns,
-    domain_name 
-    FROM (
-        SELECT 
-        domain_name,
-        probe_cc,
-        probe_asn,
-        any(max_blocked) as max_blocked,
-        any(max_blocked_label) as max_blocked_label,
-        any(max_down) as max_down,
-        any(max_down_label) as max_down_label,
-        arrayMax([0, 1 - max_blocked - max_down]) as max_ok,
-        COUNT()
-        FROM (
-            SELECT
-            domain_name,
-            probe_cc,
-            probe_asn,
-            max(ema_avg_blocked) OVER (PARTITION BY probe_cc, probe_asn, domain_name) as max_blocked,
-            any(outcome_label) OVER (PARTITION BY probe_cc, probe_asn, domain_name ORDER BY ema_avg_blocked DESC) as max_blocked_label,
-            max(ema_avg_down) OVER (PARTITION BY probe_cc, probe_asn, domain_name) as max_down,
-            any(outcome_label) OVER (PARTITION BY probe_cc, probe_asn, domain_name ORDER BY ema_avg_down DESC) as max_down_label
-            FROM (
-                SELECT 
-                domain_name,
-                probe_cc,
-                probe_asn,
-                outcome_category,
-                outcome_detail,
-                outcome_label,
-                avgWeighted(ema_blocked, cnt) OVER (PARTITION BY probe_cc, probe_asn, domain_name, outcome_category) as ema_avg_blocked,
-                avgWeighted(ema_down, cnt) OVER (PARTITION BY probe_cc, probe_asn, domain_name, outcome_category) as ema_avg_down,
-                avgWeighted(ema_ok, cnt) OVER (PARTITION BY probe_cc, probe_asn, domain_name, outcome_category) as ema_avg_ok
-                FROM (
-                    SELECT
-                    domain_name,
-                    probe_cc,
-                    probe_asn,
-                    outcome_category,
-                    outcome_detail,
-                    IF(outcome_category = 'ok', 'ok', concat(outcome_category, '.', outcome_detail)) as outcome_label,
-                    exponentialMovingAverage(1)(blocked_score, t) as ema_blocked,
-                    exponentialMovingAverage(1)(down_score, t) as ema_down,
-                    exponentialMovingAverage(1)(ok_score, t) as ema_ok,
-                    toFloat64(Count()) as cnt
-                    FROM (
-                        SELECT
-                        domain_name,
-                        probe_cc,
-                        probe_asn,
-                        outcome_category,
-                        IF(startsWith(outcome_detail, 'failure.unknown_failure') OR startsWith(outcome_detail, 'unknown_failure'), 
-                            'failure.unknown', 
-                            outcome_detail
-                        ) as outcome_detail,
-                        blocked_score,
-                        ok_score,
-                        down_score,
-                        row_number() OVER (PARTITION BY domain_name, probe_asn, outcome_category, outcome_detail) as t
-                        FROM experiment_result 
-                        WHERE timestamp > '2022-11-03' AND timestamp < '2022-12-03'
-                    ) GROUP BY probe_cc, probe_asn, domain_name, outcome_category, outcome_detail
-                )
-            )
-        ) GROUP BY probe_cc, probe_asn, domain_name
-    ) GROUP BY probe_cc, domain_name
-    """,
-        **q_args,
-    )
-    df_blocked_cat = df[df["blocked_asns"] > 0].merge(
-        citizenlab_global_tl[["category_code", "category_description", "domain_name"]],
-        how="left",
-        on="domain_name",
-    )
-    df_final = (
-        df_blocked_cat[(~df_blocked_cat["category_code"].isnull())]
-        .join(country_centers, on="probe_cc")
-        .join(country_meta, on="probe_cc")
-    )
-    df_final.loc[:, "asn_blocking_perc"] = df_final["blocked_asns"] / (
-        df_final["blocked_asns"] + df_final["ok_asns"]
-    )
-    return df_final
-
-
-def plot_blocking_world_map(df=None, data_name=None, blocking_threshold=0.7):
-    if df is None and data_name:
-        df = alt.NamedData(data_name)
-
-    if df is None:
-        df = get_df_blocking_world_map(blocking_threshold=blocking_threshold)
-
-    world_background = alt.Chart(alt_data_world_110m).mark_geoshape(
-        fill="lightgray", stroke="white"
-    )
-
-    world_foreground = (
-        alt.Chart(df)
-        .transform_filter((alt.datum.asn_blocking_perc > 0.2))
-        .transform_aggregate(
-            blocked_sites_in_cat="distinct(domain_name)",
-            groupby=[
-                "probe_cc",
-                "category_code",
-                "name",
-                "Latitude",
-                "Longitude",
-                "iso3166_num",
-            ],
-        )
-        .transform_joinaggregate(
-            blocked_sites="sum(blocked_sites_in_cat)",
-            blocked_categories="count()",
-            blocked_category_array="values(category_code)",
-            groupby=["probe_cc"],
-        )
-        .transform_calculate(
-            blocked_category_names="pluck(datum.blocked_category_array, 'category_code')",
-        )
-        .transform_lookup(
-            lookup="iso3166_num",
-            from_=alt.LookupData(alt_data_world_110m, "id"),
-            as_="geom",
-            default="Other",
-        )
-        .transform_calculate(geometry="datum.geom.geometry", type="datum.geom.type")
-        .mark_geoshape()
-        .encode(
-            color=alt.Color(
-                "blocked_categories:Q",
-                scale=alt.Scale(scheme="reds"),
-            ),
-            tooltip=[
-                alt.Tooltip("name:N", title="Country"),
-                alt.Tooltip("blocked_categories:Q", title="blocked_categories"),
-                alt.Tooltip("blocked_sites:Q", title="blocked_sites"),
-                alt.Tooltip("blocked_category_names:N", title="blocked_category_names"),
-            ],
-        )
-    )
-
-    circles = (
-        alt.Chart(df)
-        .transform_filter((alt.datum.asn_blocking_perc > 0.2))
-        .transform_aggregate(
-            blocked_sites_in_cat="distinct(domain_name)",
-            groupby=["probe_cc", "category_code", "name", "Latitude", "Longitude"],
-        )
-        .transform_joinaggregate(
-            blocked_sites="sum(blocked_sites_in_cat)",
-            blocked_categories="count()",
-            blocked_category_array="values(category_code)",
-            groupby=["probe_cc"],
-        )
-        .transform_calculate(
-            blocked_category_names="pluck(datum.blocked_category_array, 'category_code')",
-        )
-        .mark_circle()
-        .encode(
-            latitude="Latitude:Q",
-            longitude="Longitude:Q",
-            size=alt.Size("blocked_sites:Q", title="blocked_sites"),
-            tooltip=[
-                alt.Tooltip("name:N", title="Country"),
-                alt.Tooltip("blocked_categories:Q", title="blocked_categories"),
-                alt.Tooltip("blocked_sites:Q", title="blocked_sites"),
-                alt.Tooltip("blocked_category_names:N", title="blocked_category_names"),
-            ],
-        )
-    )
-
-    return (
-        (world_background + world_foreground + circles)
-        .configure_view(strokeWidth=0)
-        .properties(width=900, height=600)
-        .project("naturalEarth1")
-    )
diff --git a/oonipipeline/src/oonipipeline/dataviz/web.py b/oonipipeline/src/oonipipeline/dataviz/web.py
deleted file mode 100644
index 7cb342a7..00000000
--- a/oonipipeline/src/oonipipeline/dataviz/web.py
+++ /dev/null
@@ -1,201 +0,0 @@
-from dataclasses import asdict
-import json
-from pathlib import Path
-
-from flask import Flask, request, render_template
-
-from oonidata.dataclient import load_measurement
-from oonidata.apiclient import get_measurement_dict_by_uid
-
-from ..analysis.control import (
-    BodyDB,
-    WebGroundTruthDB,
-    iter_ground_truths_from_web_control,
-)
-from ..analysis.web_analysis import make_web_analysis
-from ..analysis.website_experiment_results import make_website_experiment_results
-
-from .viz import (
-    plot_blocking_world_map,
-    plot_blocking_of_domain_in_asn,
-    plot_blocking_of_domain_by_asn,
-)
-from .viz import (
-    get_df_blocking_world_map,
-    get_df_blocking_of_domain_in_asn,
-    get_df_blocking_of_domain_by_asn,
-    get_df_dns_analysis,
-    get_df_dns_analysis_raw,
-)
-from ..fingerprintdb import FingerprintDB
-from ..netinfo import NetinfoDB
-
-from ..transforms.observations import measurement_to_observations
-
-app = Flask(__name__)
-
-
-def to_pretty_json(value):
-    return json.dumps(
-        value, sort_keys=True, indent=4, separators=(",", ": "), default=str
-    )
-
-
-app.jinja_env.filters["tojson_pretty"] = to_pretty_json
-
-
-@app.route("/analysis/m/<measurement_uid>")
-def analysis_by_msmt(measurement_uid):
-    data_dir = Path("tests/data/datadir/")
-
-    fingerprintdb = FingerprintDB(datadir=data_dir, download=False)
-    netinfodb = NetinfoDB(datadir=data_dir, download=False)
-    raw_msmt = get_measurement_dict_by_uid(measurement_uid)
-    msmt = load_measurement(msmt=raw_msmt)
-    web_observations, web_control_observations = measurement_to_observations(
-        msmt, netinfodb=netinfodb
-    )
-    web_ground_truth_db = WebGroundTruthDB()
-    web_ground_truth_db.build_from_rows(
-        rows=iter_ground_truths_from_web_control(
-            web_control_observations=web_control_observations,
-            netinfodb=netinfodb,
-        ),
-    )
-
-    web_ground_truths = web_ground_truth_db.lookup_by_web_obs(web_obs=web_observations)
-    web_analysis = list(
-        make_web_analysis(
-            web_observations=web_observations,
-            web_ground_truths=web_ground_truths,
-            body_db=BodyDB(db=None),  # type: ignore
-            fingerprintdb=fingerprintdb,
-        )
-    )
-
-    # assert len(web_analysis) == len(
-    #    web_observations
-    # ), f"web_analysis != web_obs {len(web_analysis)} != {len(web_observations)}"
-    # for wa in web_analysis:
-    #    print_nice_vertical(wa)
-
-    website_er = list(make_website_experiment_results(web_analysis))
-    assert len(website_er) == 1
-
-    wer = website_er[0]
-    analysis_transcript_list = wer.analysis_transcript_list
-
-    # wer.analysis_transcript_list = None
-    # print_nice_vertical(wer)
-    # for loni in loni_list:
-    #    pprint(loni.to_dict())
-    # print(analysis_transcript_list)
-
-    return render_template(
-        "analysis.html",
-        website_experiment_result=asdict(wer),
-        analysis_transcript_list=analysis_transcript_list,
-        loni_list=wer.loni_list,
-        raw_msmt=raw_msmt,
-        measurement_uid=measurement_uid,
-        web_analysis=list(map(lambda x: asdict(x), web_analysis)),
-        web_observations=list(map(lambda x: asdict(x), web_observations)),
-        loni_blocked_dict=dict(zip(wer.loni_blocked_keys, wer.loni_blocked_values)),
-        loni_blocked_value=sum(wer.loni_blocked_values),
-        loni_down_dict=dict(zip(wer.loni_down_keys, wer.loni_down_values)),
-        loni_down_value=sum(wer.loni_down_values),
-        loni_ok_value=wer.loni_ok_value,
-    )
-
-
-@app.route("/api/_/viz/data/world_map")
-def data_world_map():
-    blocking_threshold = float(request.args.get("blocking_threshold", 0.7))
-    return get_df_blocking_world_map(blocking_threshold=blocking_threshold).to_json(
-        orient="records"
-    )
-
-
-@app.route("/api/_/viz/chart/world_map")
-def chart_world_map():
-    return plot_blocking_world_map(data_name="data").to_json()
-
-
-@app.route("/viz/world_map")
-def viz_world_map():
-    return render_template("vega.html", endpoint="chart_world_map")
-
-
-@app.route("/api/_/viz/data/blocking_of_domain_in_asn")
-def data_blocking_of_domain_in_asn():
-    return get_df_blocking_of_domain_in_asn(
-        domain_name=request.args.get("domain_name", None),
-        probe_cc=request.args.get("probe_cc", None),
-        probe_asn=int(request.args.get("probe_asn", 0)),
-        start_time=request.args.get("start_time", "2022-11-03"),
-        end_time=request.args.get("start_time", "2022-12-03"),
-    ).to_json(orient="records")
-
-
-@app.route("/api/_/viz/chart/blocking_of_domain_in_asn")
-def chart_blocking_of_domain_in_asn():
-    return plot_blocking_of_domain_in_asn(
-        domain_name=request.args.get("domain_name", None),
-        probe_cc=request.args.get("probe_cc", None),
-        probe_asn=int(request.args.get("probe_asn", 0)),
-        start_time=request.args.get("start_time", "2022-11-03"),
-        end_time=request.args.get("start_time", "2022-12-03"),
-    ).to_json()
-
-
-@app.route("/viz/blocking_of_domain_in_asn")
-def viz_blocking_of_domain_in_asn():
-    return render_template(
-        "vega.html",
-        endpoint="chart_blocking_of_domain_in_asn",
-        query_args=request.args.to_dict(),
-    )
-
-
-@app.route("/api/_/viz/chart/blocking_of_domain_by_asn")
-def chart_blocking_of_domain_by_asn():
-    return plot_blocking_of_domain_by_asn(
-        domain_name=request.args.get("domain_name", None),
-        probe_cc=request.args.get("probe_cc", None),
-        start_time=request.args.get("start_time", "2022-11-03"),
-        end_time=request.args.get("start_time", "2022-12-03"),
-    ).to_json()
-
-
-@app.route("/viz/blocking_of_domain_by_asn")
-def viz_blocking_of_domain_by_asn():
-    return render_template(
-        "vega.html",
-        endpoint="chart_blocking_of_domain_by_asn",
-        query_args=request.args.to_dict(),
-    )
-
-
-@app.route("/api/_/data/dns_analysis")
-def data_dns_analysis():
-    return get_df_dns_analysis(
-        start_day="2023-01-01", end_day="2023-01-02", limit=100
-    ).to_json(orient="records")
-
-
-@app.route("/api/_/data/dns_analysis_raw")
-def data_dns_analysis_raw():
-    return get_df_dns_analysis_raw(
-        measurement_uid=request.args["measurement_uid"],
-        start_day="2023-01-01",
-        end_day="2023-01-02",
-    ).to_json(orient="records")
-
-
-@app.route("/")
-def index():
-    return render_template("react.html")
-
-
-if __name__ == "__main__":
-    app.run(debug=True)
diff --git a/oonipipeline/src/oonipipeline/temporal/to_port/response_archiver.py b/oonipipeline/src/oonipipeline/temporal/to_port/response_archiver.py
index f40688e5..9547342c 100644
--- a/oonipipeline/src/oonipipeline/temporal/to_port/response_archiver.py
+++ b/oonipipeline/src/oonipipeline/temporal/to_port/response_archiver.py
@@ -25,7 +25,7 @@
 from oonidata.dataclient import date_interval, iter_measurements, load_measurement
 from oonidata.models.nettests.web_connectivity import WebConnectivity
 
-from ..fingerprintdb import FingerprintDB, Fingerprint
+from ...fingerprintdb import FingerprintDB, Fingerprint
 
 log = logging.getLogger("oonidata.processing")