diff --git a/Dockerfile b/Dockerfile
index 0e3ed834f571f2887d0197f116bb54774091cc5f..fd589ca07965b6ff9fe232240e09bf0ad4dd4d38 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -2,7 +2,7 @@ FROM python:3.7 AS builder
 
 WORKDIR /tmp/repo
 COPY setup.py requirements.txt /tmp/repo/
-COPY nnfwtbn /tmp/repo/nnfwtbn
+COPY freeforestml /tmp/repo/freeforestml
 RUN pip install -r requirements.txt
 RUN pip install .
 
diff --git a/README.rst b/README.rst
index fa159b9860777abe3796d7470434918dcb1e5e4e..5a0ab3cacb05e44b50c7a01687f5511896b91849 100644
--- a/README.rst
+++ b/README.rst
@@ -1,20 +1,12 @@
-Neural Network Framework To Be Named
-====================================
+FreeForestML
+============
 
-Pure Python framework to train neural networks for high energy physics analysis.
-
-Examples
---------
- * `ToyData <https://nnfwtbn.web.cern.ch/examples/ToyData.html>`_
- * `Histogram <https://nnfwtbn.web.cern.ch/examples/Histogram.html>`_
- * `HistogramFactory <https://nnfwtbn.web.cern.ch/examples/HistogramFactory.html>`_
- * `ConfusionMatrix <https://nnfwtbn.web.cern.ch/examples/ConfusionMatrix.html>`_
- * `CorrelationMatrix <https://nnfwtbn.web.cern.ch/examples/Correlation.html>`_
- * `ROC <https://nnfwtbn.web.cern.ch/examples/ROC.html>`_
- * `Classification <https://nnfwtbn.web.cern.ch/examples/Classification.html>`_
- * `TMVA BDT interface <https://nnfwtbn.web.cern.ch/examples/TmvaBdt.html>`_
- * `Blinding <https://nnfwtbn.web.cern.ch/examples/Blinding.html>`_
+FreeForestML (formally nnfwtbn -- Neural network framework to be named) is a
+Python framework to train neural networks in the context of high-energy physics.
+The framework also provides convenient methods to create the typical plots. The
+dataset is assumed to be stored in a dataframe.
 
 Links
------
- * `Documentation <https://nnfwtbn.web.cern.ch/>`_
+=====
+
+ * `Documentation <https://freeforestml.readthedocs.io>`_
diff --git a/doc/Makefile b/doc/Makefile
index 13918ce47ad1a4010a8c0345dfe7f35a1b8abafd..5cf12e8c0acd1370c5b3723439394da9ab0cdb39 100644
--- a/doc/Makefile
+++ b/doc/Makefile
@@ -4,7 +4,7 @@
 # You can set these variables from the command line.
 SPHINXOPTS    =
 SPHINXBUILD   = sphinx-build
-SPHINXPROJ    = nnfwtbn
+SPHINXPROJ    = freeforestml
 SOURCEDIR     = .
 BUILDDIR      = _build
 
@@ -17,4 +17,4 @@ help:
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
\ No newline at end of file
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/doc/api_reference.rst b/doc/api_reference.rst
index cca376a001b3b3744db5f2075cc9a1deb21a277d..72812c887d31b6c67415648ae2a24f99eceb53fe 100644
--- a/doc/api_reference.rst
+++ b/doc/api_reference.rst
@@ -2,50 +2,50 @@
 API reference
 *************
 
-.. autoclass:: nnfwtbn.Cut
+.. autoclass:: freeforestml.Cut
    :members:
    :special-members:
 
-.. autoclass:: nnfwtbn.Process
+.. autoclass:: freeforestml.Process
    :members:
    :special-members:
 
 
-.. autoclass:: nnfwtbn.Variable
+.. autoclass:: freeforestml.Variable
    :members:
    :special-members:
 
-.. autoclass:: nnfwtbn.variable.BlindingStrategy
+.. autoclass:: freeforestml.variable.BlindingStrategy
    :members:
    :special-members:
 
-.. autoclass:: nnfwtbn.RangeBlindingStrategy
+.. autoclass:: freeforestml.RangeBlindingStrategy
    :members:
    :special-members:
 
-.. autoclass:: nnfwtbn.CrossValidator
+.. autoclass:: freeforestml.CrossValidator
    :members:
    :special-members:
 
-.. autoclass:: nnfwtbn.ClassicalCV
+.. autoclass:: freeforestml.ClassicalCV
    :members:
    :special-members:
 
-.. autoclass:: nnfwtbn.MixedCV
+.. autoclass:: freeforestml.MixedCV
    :members:
    :special-members:
 
-.. autoclass:: nnfwtbn.Normalizer
+.. autoclass:: freeforestml.Normalizer
    :members:
    :special-members:
 
-.. autoclass:: nnfwtbn.EstimatorNormalizer
+.. autoclass:: freeforestml.EstimatorNormalizer
    :members:
    :special-members:
 
-.. autoclass:: nnfwtbn.HepNet
+.. autoclass:: freeforestml.HepNet
    :members:
    :special-members:
 
-.. automodule:: nnfwtbn.plot
+.. automodule:: freeforestml.plot
 	:members:
diff --git a/doc/conf.py b/doc/conf.py
index 2271cd76ccb77fbd3831c46e38822759829e6711..0e183748f0bcaa12e689219863038a17f2cd035c 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -19,8 +19,8 @@ sys.path.insert(0, os.path.abspath('..'))
 
 # -- Project information -----------------------------------------------------
 
-project = 'nnfwtbn'
-copyright = '2019, Frank Sauerburger'
+project = 'FreeForestML'
+copyright = '2019 - 2021, Frank Sauerburger'
 author = 'Frank Sauerburger'
 
 # The short X.Y version
@@ -39,6 +39,7 @@ release = ''
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
+    'nbsphinx',
     'sphinx.ext.autodoc',
     'sphinx.ext.doctest',
     'sphinx.ext.mathjax',
@@ -110,7 +111,7 @@ html_static_path = ['_static']
 # -- Options for HTMLHelp output ---------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'nnfwtbndoc'
+htmlhelp_basename = 'freeforestmldoc'
 
 
 # -- Options for LaTeX output ------------------------------------------------
@@ -137,7 +138,7 @@ latex_elements = {
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'nnfwtbn.tex', 'nnfwtbn Documentation',
+    (master_doc, 'freeforestml.tex', 'FreeForestML Documentation',
      'Frank Sauerburger', 'manual'),
 ]
 
@@ -147,7 +148,7 @@ latex_documents = [
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    (master_doc, 'nnfwtbn', 'nnfwtbn Documentation',
+    (master_doc, 'freeforestml', 'FreeForestML Documentation',
      [author], 1)
 ]
 
@@ -158,8 +159,8 @@ man_pages = [
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'nnfwtbn', 'nnfwtbn Documentation',
-     author, 'nnfwtbn', 'One line description of project.',
+    (master_doc, 'freeforestml', 'FreeForestML Documentation',
+     author, 'freeforestml', 'Machine learning framework for high-energy physics',
      'Miscellaneous'),
 ]
 
diff --git a/doc/doc-requirements.txt b/doc/doc-requirements.txt
index fedd390e96f7e137fe5ede1812533eb1797f8452..b2303923268ee27dba429996e24550077537faa1 100644
--- a/doc/doc-requirements.txt
+++ b/doc/doc-requirements.txt
@@ -1,3 +1,5 @@
 sphinx
 sphinx_rtd_theme
+nbsphinx
+pandoc
 
diff --git a/doc/index.rst b/doc/index.rst
index 53e7575da0210b464b1774ab883d25ea7df5822e..bcc40b39c33bd3bf3be52f3bcebbb7479d97dbf9 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -3,21 +3,25 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-Welcome to nnfwtbn's documentation!
-===================================
+FreeForestML
+============
 
 .. toctree::
    :maxdepth: 2
    :hidden:
-   :caption: Advanced topics
 
+   Introduction <self>
+   Examples
    api_reference
 
+FreeForestML (formally nnfwtbn -- Neural network framework to be named) is a
+Python framework to train neural networks in the context of high-energy physics.
+The framework also provides convenient methods to create the typical plots. The
+dataset is assumed to be stored in a dataframe.
 
+Links
+=====
 
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
+ * `GitLab repository <https://gitlab.cern.ch/fsauerbu/freeforestml>`_
+ * :ref:`genindex`
+ * :ref:`search`
diff --git a/examples/Blinding.ipynb b/examples/Blinding.ipynb
deleted file mode 100644
index 7729d915e513669e57eca6386a826e5bc81f24ba..0000000000000000000000000000000000000000
--- a/examples/Blinding.ipynb
+++ /dev/null
@@ -1,175 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "\n",
-    "from nnfwtbn import Variable, Process, Cut, hist, McStack, DataStack, Stack, \\\n",
-    "                    RangeBlindingStrategy\n",
-    "from nnfwtbn import toydata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = toydata.get()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Processes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_ztt = Process(r\"$Z\\rightarrow\\tau\\tau$\", range=(0, 0))\n",
-    "p_sig = Process(r\"Signal\", range=(1, 1))\n",
-    "\n",
-    "p_asimov = Process(r\"Asimov\", selection=lambda d: d.fpid >= 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Stacks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "colors = [\"windows blue\", \"amber\", \"greyish\", \"faded green\", \"dusty purple\"]\n",
-    "s_mc = McStack(p_ztt, p_sig, palette=sns.xkcd_palette(colors))\n",
-    "s_data = DataStack(p_asimov)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Blinding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b_higgs_m = RangeBlindingStrategy(99, 150)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_higgs_m = Variable(r\"$m^H$\", \"higgs_m\", \"GeV\", blinding=b_higgs_m)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Actual plot\n",
-    "\n",
-    "Stacks passed to `blind` argument will be blinded according to blind strategy of variable `v_higgs_m`. \n",
-    "## Blind data stack"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, v_higgs_m, 20, [s_mc, s_data], range=(0, 200),\n",
-    "     weight=\"weight\",  ratio_label=\"Data / SM\", blind=s_data, diff=True)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Blind MC stack"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, v_higgs_m, 20, [s_mc, s_data], range=(0, 200),\n",
-    "     weight=\"weight\",  ratio_label=\"Data / SM\", blind=s_mc)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Blind both stacks\n",
-    "`blind` argument can be a single stack or a list of stacks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, v_higgs_m, 20, [s_mc, s_data], range=(0, 200),\n",
-    "     weight=\"weight\",  ratio_label=\"Data / SM\", blind=[s_mc, s_data])\n",
-    "None"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/Classification.ipynb b/examples/Classification.ipynb
deleted file mode 100644
index 282c9db34029d039d330090521cff870ea2eda6d..0000000000000000000000000000000000000000
--- a/examples/Classification.ipynb
+++ /dev/null
@@ -1,318 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense, Dropout\n",
-    "from keras.optimizers import SGD\n",
-    "\n",
-    "from nnfwtbn import Variable, Process, Cut, \\\n",
-    "                    HepNet, ClassicalCV, EstimatorNormalizer, \\\n",
-    "                    HistogramFactory, confusion_matrix, atlasify, \\\n",
-    "                    McStack\n",
-    "from nnfwtbn import toydata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = toydata.get()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_ztt = Process(r\"$Z\\rightarrow\\tau\\tau$\", range=(0, 0))\n",
-    "p_sig = Process(r\"Signal\", range=(1, 1))\n",
-    "s_all = McStack(p_ztt, p_sig)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist_factory = HistogramFactory(df, stacks=[s_all], weight=\"weight\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cut-based"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist_factory(Variable(\"$\\Delta \\eta^{jj}$\",\n",
-    "                      lambda d: (d.jet_1_eta - d.jet_2_eta).abs()),\n",
-    "             bins=20, range=(0, 8))\n",
-    "hist_factory(Variable(\"$m^{jj}$\", \"m_jj\"),\n",
-    "             bins=20, range=(0, 1500))\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c_sr = Cut(lambda d: d.m_jj  > 400) & \\\n",
-    "        Cut(lambda d: d.jet_2_pt >= 30) & \\\n",
-    "        Cut(lambda d: d.jet_1_eta * d.jet_2_eta < 0) & \\\n",
-    "        Cut(lambda d: (d.jet_2_eta - d.jet_1_eta).abs() > 3)\n",
-    "c_sr.label = \"Signal\"\n",
-    "\n",
-    "c_rest = (~c_sr)\n",
-    "c_rest.label = \"Rest\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "confusion_matrix(df, [p_sig, p_ztt], [c_sr, c_rest], info=False,\n",
-    "                 x_label=\"Signal\", y_label=\"Region\", annot=True, weight=\"weight\")\n",
-    "confusion_matrix(df, [p_sig, p_ztt], [c_sr, c_rest], normalize_rows=True, info=False,\n",
-    "                 x_label=\"Signal\", y_label=\"Region\", annot=True, weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Neural Network"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['dijet_deta'] = (df.jet_1_eta - df.jet_2_eta).abs()\n",
-    "df['dijet_prod_eta'] = (df.jet_1_eta * df.jet_2_eta)\n",
-    "input_var = ['dijet_prod_eta', 'm_jj', 'dijet_deta', 'higgs_pt', 'jet_2_pt', 'jet_1_eta', 'jet_2_eta', 'tau_eta']\n",
-    "\n",
-    "output_var = ['is_sig', 'is_ztt']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"is_sig\"] = p_sig.selection.idx_array(df)\n",
-    "df[\"is_ztt\"] = p_ztt.selection.idx_array(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_df = df.sample(frac=1000 / len(df)).compute()\n",
-    "sns.pairplot(sample_df, vars=input_var, hue=\"is_sig\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def model():\n",
-    "    m = Sequential()\n",
-    "    m.add(Dense(units=15, activation='relu', input_dim=len(input_var)))\n",
-    "    m.add(Dense(units=5, activation='relu'))\n",
-    "    m.add(Dense(units=2, activation='softmax'))\n",
-    "    \n",
-    "    m.compile(loss='categorical_crossentropy',\n",
-    "              optimizer=SGD(lr=0.1),\n",
-    "              metrics=['categorical_accuracy'])\n",
-    "\n",
-    "    return m\n",
-    "\n",
-    "cv = ClassicalCV(5, frac_var='random')\n",
-    "\n",
-    "net = HepNet(model, cv, EstimatorNormalizer, input_var, output_var)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sig_wf = len(p_sig.selection(df).weight) / p_sig.selection(df).weight.sum()\n",
-    "ztt_wf = len(p_ztt.selection(df).weight) / p_ztt.selection(df).weight.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "net.fit(df.compute(), epochs=150, verbose=0, batch_size=2048,\n",
-    "        weight=Variable(\"weight\", lambda d: d.weight * (d.is_sig * sig_wf + d.is_ztt * ztt_wf)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sns.lineplot(x='epoch', y='loss', data=net.history, label=\"Training\")\n",
-    "sns.lineplot(x='epoch', y='val_loss', data=net.history, label=\"Validation\")\n",
-    "plt.ylabel(\"loss\")\n",
-    "atlasify(\"Internal\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Accuracy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sns.lineplot(x='epoch', y='categorical_accuracy', data=net.history, label=\"Training\")\n",
-    "sns.lineplot(x='epoch', y='val_categorical_accuracy', data=net.history, label=\"Validation\")\n",
-    "plt.ylabel(\"Accuracy\")\n",
-    "atlasify(\"Internal\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sns.lineplot(x='epoch', y='val_categorical_accuracy', data=net.history, hue=\"fold\")\n",
-    "atlasify(\"Internal\", enlarge=1.6)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "out = net.predict(df.compute(), cv='test')\n",
-    "out['pred_sig'] = out.pred_is_sig >= 0.5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c_pred_sig = Process(\"Signal\", lambda d: d.pred_is_sig >= 0.5)\n",
-    "c_pred_ztt = Process(r\"$Z\\rightarrow\\tau\\tau$\", lambda d: d.pred_is_sig < 0.5) \n",
-    "\n",
-    "confusion_matrix(out, [p_sig, p_ztt], [c_pred_sig, c_pred_ztt], info=False,\n",
-    "                 x_label=\"Truth\", y_label=\"Classification\", annot=True, weight=\"weight\")\n",
-    "confusion_matrix(out, [p_sig, p_ztt], [c_pred_sig, c_pred_ztt], normalize_rows=True, info=False,\n",
-    "                 x_label=\"Truth\", y_label=\"Classification\", annot=True, weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Export to lwtnn\n",
-    "In order to use the network in lwtnn, we need to export the neural network with the `export()` method. This export one network per fold. It is the reposibility of the use to implement the cross validation in the analysis framework."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "net.export(\"lwtnn\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!ls lwtnn*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final, manuel step is to run the lwtnn's converter using the shortcut script `test.sh`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ConfusionMatrix.ipynb b/examples/ConfusionMatrix.ipynb
deleted file mode 100644
index 5c72c2875bba8dddc0d3d7e97a8f3940b19692ff..0000000000000000000000000000000000000000
--- a/examples/ConfusionMatrix.ipynb
+++ /dev/null
@@ -1,111 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "\n",
-    "from nnfwtbn import Variable, Process, Cut, confusion_matrix, HistogramFactory\n",
-    "from nnfwtbn import toydata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = toydata.get()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_sig = Process(r\"Signal\", range=(1, 1))\n",
-    "p_ztt = Process(r\"$Z\\rightarrow\\tau\\tau$\", range=(0, 0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c_low = Cut(lambda d: d.m_jj  < 350, label=\"Low $m^{jj}$\")\n",
-    "c_mid = Cut(lambda d: (d.m_jj >= 350) & (d.m_jj  < 600), label=\"Mid $m^{jj}$\")\n",
-    "c_high = Cut(lambda d: d.m_jj  > 600, label=\"High $m^{jj}$\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Normalized columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "confusion_matrix(df, [p_sig, p_ztt], [c_low, c_mid, c_high], info=False,\n",
-    "                 y_label=\"Region\", x_label=\"Truth Signal\", annot=True, weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Normalized rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "confusion_matrix(df, [p_sig, p_ztt], [c_low, c_mid, c_high], normalize_rows=True, info=False,\n",
-    "                 y_label=\"Region\", x_label=\"Truth Signal\", annot=True, weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/Correlation.ipynb b/examples/Correlation.ipynb
deleted file mode 100644
index ff2bc18d7f0557a507cfd18cf1b9450ad4635cd4..0000000000000000000000000000000000000000
--- a/examples/Correlation.ipynb
+++ /dev/null
@@ -1,78 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from nnfwtbn import Variable, correlation_matrix\n",
-    "from nnfwtbn import toydata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = toydata.get()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_higgs_m = Variable(r\"$m^H$\", \"higgs_m\", \"GeV\")\n",
-    "v_jet_1_pt = Variable(r\"$p_{\\mathrm{T}}^{j_1}$\", \"jet_1_pt\", \"GeV\")\n",
-    "v_jet_2_pt = Variable(r\"$p_{\\mathrm{T}}^{j_2}$\", \"jet_2_pt\", \"GeV\")\n",
-    "v_m_jj = Variable(r\"$m^{jj}$\", \"m_jj\", \"GeV\")\n",
-    "v_jet_1_eta = Variable(r\"$\\eta^{j_1}$\" ,\"jet_1_eta\") \n",
-    "v_jet_2_eta = Variable(r\"$\\eta^{j_2}$\" ,\"jet_2_eta\")\n",
-    "v_tau_pt = Variable(r\"$p_{\\mathrm{T}}^{\\tau}$\", \"tau_pt\", \"GeV\")\n",
-    "v_lep_pt = Variable(r\"$p_{\\mathrm{T}}^{\\ell}$\", \"lep_pt\", \"GeV\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "fig, axes = plt.subplots(figsize=(5, 4.5))\n",
-    "correlation_matrix(df, [v_jet_1_pt, v_jet_2_pt, v_m_jj, v_higgs_m,\n",
-    "                        v_tau_pt, v_lep_pt, v_jet_1_eta, v_jet_2_eta],\n",
-    "                  figure=fig, axes=axes)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/Histogram.ipynb b/examples/Histogram.ipynb
deleted file mode 100644
index 986fb03714d6c6bdce66dee64a2616cc2aeb5d07..0000000000000000000000000000000000000000
--- a/examples/Histogram.ipynb
+++ /dev/null
@@ -1,248 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "\n",
-    "from nnfwtbn import Variable, Process, Cut, hist, McStack, DataStack, Stack\n",
-    "from nnfwtbn import toydata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = toydata.get()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_ztt = Process(r\"$Z\\rightarrow\\tau\\tau$\", range=(0, 0))\n",
-    "p_sig = Process(r\"Signal\", range=(1, 1))\n",
-    "\n",
-    "p_asimov = Process(r\"Asimov\", selection=lambda d: d.fpid >= 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "colors = [\"windows blue\", \"amber\", \"greyish\", \"faded green\", \"dusty purple\"]\n",
-    "palette = sns.xkcd_palette(colors)\n",
-    "\n",
-    "s_bkg = McStack(p_ztt, p_sig, palette=palette)\n",
-    "s_data = DataStack(p_asimov)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_higgs_m = Variable(r\"$m^H$\", \"higgs_m\", \"GeV\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c_vbf = Cut(lambda d: d.dijet_p4__M  > 400) & \\\n",
-    "        Cut(lambda d: d.jet_1_p4__Pt >= 30) & \\\n",
-    "        Cut(lambda d: d.is_dijet_centrality == 1) & \\\n",
-    "        Cut(lambda d: d.jet_0_p4__Eta * df.jet_1_p4__Eta < 0) & \\\n",
-    "        Cut(lambda d: (d.jet_0_p4__Eta - df.jet_1_p4__Eta).abs() > 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,\n",
-    "     weight=\"weight\",  ratio_label=\"Data / SM\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, v_higgs_m, 22, [s_bkg, s_data], range=(75, 130), selection=None,\n",
-    "     weight=\"weight\",  ratio_label=\"Data / SM\", include_outside=True)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,\n",
-    "     weight=\"weight\",  ratio_label=\"Data / SM\", y_log=True, numerator=None)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,\n",
-    "     weight=\"weight\", ratio_label=\"MC / Data\", y_log=True, y_min=1e-1,\n",
-    "     vlines=[80, {'x': 100, 'color': 'b'}])\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s_sig = McStack(p_sig, color=palette[1], histtype='step')\n",
-    "s_ztt = McStack(p_ztt, color=palette[0], histtype='step')\n",
-    "\n",
-    "hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(40, 120), selection=None,\n",
-    "     weight=\"weight\", ratio_label=\"Signal / Bkg\", y_log=True, y_min=1e-1,\n",
-    "     vlines=[80, {'x': 100, 'color': 'b'}], numerator=s_sig, denominator=s_ztt)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,\n",
-    "     weight=\"weight\", ratio_label=\"Data - Bkg\", y_log=True, y_min=1e-1, diff=True,\n",
-    "     enlarge=1.5,\n",
-    "     vlines=[80, {'x': 100, 'color': 'b'}], numerator=s_data, denominator=s_ztt)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,\n",
-    "     weight=\"weight\",  ratio_label=\"Data / SM\", info=False, atlas=\"Work in progress\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nnfwtbn.plot as nnp\n",
-    "nnp.INFO = \"$\\sqrt{s} = 13\\,\\mathrm{TeV}$, $140\\,\\mathrm{fb}^{-1}$\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,\n",
-    "     weight=\"weight\",  ratio_label=\"Data / SM\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s_sig = McStack(p_sig, color=palette[1], histtype='step')\n",
-    "s_ztt = McStack(p_ztt, color=palette[0], histtype='step')\n",
-    "\n",
-    "hist(df, v_higgs_m, 20, [s_sig, s_ztt], range=(0, 200), selection=None,\n",
-    "     weight=\"weight\",  numerator=None, density=True)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, v_higgs_m, 30, [s_bkg, s_data], range=(25, 175),\n",
-    "     selection=None, numerator=[s_ztt, s_sig], denominator=s_data,\n",
-    "     weight=\"weight\",  ratio_label=\"Process / Asimov\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/HistogramFactory.ipynb b/examples/HistogramFactory.ipynb
deleted file mode 100644
index e43d66830c0fe56a365df0d7ba38d92fa94e59fd..0000000000000000000000000000000000000000
--- a/examples/HistogramFactory.ipynb
+++ /dev/null
@@ -1,132 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "\n",
-    "from nnfwtbn import Variable, Process, Cut, hist, HistogramFactory, McStack, DataStack\n",
-    "from nnfwtbn import toydata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = toydata.get()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_ztt = Process(r\"$Z\\rightarrow\\tau\\tau$\", range=(0, 0))\n",
-    "p_sig = Process(r\"Signal\", range=(1, 1))\n",
-    "p_asimov = Process(r\"Asimov\", selection=lambda d: d.fpid >= 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s_bkg = McStack(p_ztt, p_sig)\n",
-    "s_data = DataStack(p_asimov)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c_vbf = Cut(lambda d: d.dijet_p4__M  > 400) & \\\n",
-    "        Cut(lambda d: d.jet_1_p4__Pt >= 30) & \\\n",
-    "        Cut(lambda d: d.is_dijet_centrality == 1) & \\\n",
-    "        Cut(lambda d: d.jet_0_p4__Eta * df.jet_1_p4__Eta < 0) & \\\n",
-    "        Cut(lambda d: (d.jet_0_p4__Eta - df.jet_1_p4__Eta).abs() > 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist_factory = HistogramFactory(df, stacks=[s_bkg, s_data], bins=20, range=(0, 200), selection=None,\n",
-    "     weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_mmc = Variable(r\"$m^H$\", \"higgs_m\", \"GeV\")\n",
-    "hist_factory(v_mmc)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_tau_pT = Variable(r\"$p_\\mathrm{T}{\\tau}$\", \"tau_pt\", \"GeV\")\n",
-    "hist_factory(v_tau_pT, bins=12, range=(0, 120))\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_lep_pT = Variable(r\"$p_\\mathrm{T}{\\ell}$\", \"lep_pt\", \"GeV\")\n",
-    "hist_factory(v_lep_pT, bins=12, range=(0, 120))\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ROC.ipynb b/examples/ROC.ipynb
deleted file mode 100644
index e8ebe16bdb09946dac43e02479a79c76cd5de475..0000000000000000000000000000000000000000
--- a/examples/ROC.ipynb
+++ /dev/null
@@ -1,87 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "\n",
-    "from nnfwtbn import Variable, Process, Cut, roc\n",
-    "from nnfwtbn import toydata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = toydata.get()\n",
-    "df['noise'] = df.fpid + 3 * (df.random - 0.5)**3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_bkg = Process(r\"Background\", range=(0, 0))\n",
-    "p_sig = Process(r\"Signal\", range=(1, 1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "fig, ax = plt.subplots()\n",
-    "\n",
-    "v_higgs_m = Variable(r\"$m^H$\", \"higgs_m\", \"GeV\")\n",
-    "roc(df.compute(), p_sig, p_bkg, v_higgs_m, axes=ax, steps=400, atlas=False)\n",
-    "roc(df.compute(), p_sig, p_bkg, Variable(\"Noise ID\", \"noise\"), axes=ax, enlarge=1.5)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/SystematicsBand.ipynb b/examples/SystematicsBand.ipynb
deleted file mode 100644
index 26e39190b33423f7741a768004484531fa37cfb1..0000000000000000000000000000000000000000
--- a/examples/SystematicsBand.ipynb
+++ /dev/null
@@ -1,203 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "\n",
-    "from nnfwtbn import Variable, Process, Cut, hist, SystStack, DataStack, \\\n",
-    "                    McStack, TruthStack\n",
-    "from nnfwtbn import toydata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = toydata.get()\n",
-    "df = df.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = df.assign(variation=\"NOMINAL\")\n",
-    "\n",
-    "df_up = df.assign(higgs_m=df.higgs_m + 1 * df.jet_1_eta.abs(),\n",
-    "                  variation=\"1up\")\n",
-    "\n",
-    "df_down = df.assign(higgs_m=df.higgs_m - 1 * df.jet_2_eta.abs(),\n",
-    "                    variation=\"1down\")\n",
-    "\n",
-    "df = pd.concat([df, df_up, df_down], sort=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_ztt = Process(r\"$Z\\rightarrow\\tau\\tau$\", range=(0, 0))\n",
-    "p_sig = Process(r\"Signal\", range=(1, 1))\n",
-    "\n",
-    "p_bkg_up = Process(r\"Up\", lambda d: d.variation == \"1up\")\n",
-    "p_bkg_down = Process(r\"Down\", lambda d: d.variation == \"1down\")\n",
-    "p_ztt_nom = Process(r\"$Z\\rightarrow\\tau\\tau$\", lambda d: (d.variation == \"NOMINAL\") & (d.fpid == 0))\n",
-    "p_sig_nom = Process(r\"Signal\", lambda d: (d.variation == \"NOMINAL\") & (d.fpid == 1))\n",
-    "\n",
-    "p_asimov = Process(r\"Asimov\", selection=lambda d: (d.fpid >= 0) & (d.variation == \"NOMINAL\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c_nominal = Cut(lambda d: d.variation == \"NOMINAL\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "colors = [\"windows blue\", \"amber\", \"greyish\", \"faded green\", \"dusty purple\"]\n",
-    "\n",
-    "s_bkg = McStack(p_ztt, p_sig, palette=sns.xkcd_palette(colors))\n",
-    "\n",
-    "s_bkg_up = TruthStack(p_bkg_up,\n",
-    "                   histtype=\"step\",\n",
-    "                   color='k',\n",
-    "                   palette=sns.xkcd_palette(colors))\n",
-    "\n",
-    "s_bkg_down = TruthStack(p_bkg_down,\n",
-    "                     histtype=\"step\",\n",
-    "                     color='k',\n",
-    "                     linestyle='--',\n",
-    "                     palette=sns.xkcd_palette(colors))\n",
-    "\n",
-    "s_bkg_nom = McStack(p_ztt_nom, p_sig_nom, palette=sns.xkcd_palette(colors))\n",
-    "\n",
-    "s_bkg_syst = SystStack(p_ztt, p_sig, palette=sns.xkcd_palette(colors))\n",
-    "\n",
-    "s_data = DataStack(p_asimov)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_higgs_m = Variable(r\"$m^H$\", \"higgs_m\", \"GeV\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c_vbf = Cut(lambda d: d.dijet_p4__M  > 400) & \\\n",
-    "        Cut(lambda d: d.jet_1_p4__Pt >= 30) & \\\n",
-    "        Cut(lambda d: d.is_dijet_centrality == 1) & \\\n",
-    "        Cut(lambda d: d.jet_0_p4__Eta * df.jet_1_p4__Eta < 0) & \\\n",
-    "        Cut(lambda d: (d.jet_0_p4__Eta - df.jet_1_p4__Eta).abs() > 3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Plot nominal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(c_nominal(df), v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,\n",
-    "     weight=\"weight\",  ratio_label=\"Data / SM\", ratio_range=(0.1, 1.9))\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Envelop"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, v_higgs_m, 20, [s_bkg_nom, s_bkg_up, s_bkg_down], range=(0, 200), selection=None,\n",
-    "     weight=\"weight\",  ratio_label=\"Up / Down\", ratio_range=(0.1, 1.9))\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Full band"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, v_higgs_m, 20, [s_bkg_syst, s_data], range=(0, 200), selection=None,\n",
-    "     weight=\"weight\",  ratio_label=\"Data / SM\", ratio_range=(0.1, 1.9))\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/TmvaBdt.ipynb b/examples/TmvaBdt.ipynb
deleted file mode 100644
index 635d7147af6444aa68d52a7c1fad34d66cc4b026..0000000000000000000000000000000000000000
--- a/examples/TmvaBdt.ipynb
+++ /dev/null
@@ -1,113 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "\n",
-    "from nnfwtbn import Variable, Process, Cut, hist, McStack, DataStack, Stack, TmvaBdt\n",
-    "from nnfwtbn import toydata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = toydata.get()\n",
-    "df = df.compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Evaluation of TMVA BDT\n",
-    "The file `tmva_bdt.xml` contains a sample BDT trained on the toy dataset. The input variables are\n",
-    " - $\\eta^{j_1}$,\n",
-    " - $\\eta^{j_2}$,\n",
-    " - $\\tau\\;\\mathrm{centrality}$ and\n",
-    " - $\\ell\\;\\mathrm{centrality}$.\n",
-    " \n",
-    "The BDT was trained with regular TMVA in ROOT. The weights stored in the XML file can be read by `nnfwtbn.TmvaBdt` directly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bdt = TmvaBdt(\"tmva_bdt.xml\")\n",
-    "df['bdt_prediction'] = bdt.predict(df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Plotting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_ztt = Process(r\"$Z\\rightarrow\\tau\\tau$\", range=(0, 0))\n",
-    "p_sig = Process(r\"Signal\", range=(1, 1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "colors = [\"windows blue\", \"amber\", \"greyish\", \"faded green\", \"dusty purple\"]\n",
-    "colors = sns.xkcd_palette(colors)\n",
-    "s_ztt = McStack(p_ztt, color=colors[0], histtype='step')\n",
-    "s_sig = McStack(p_sig, color=colors[1], histtype='step')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_bdt = Variable(\"BDT Score\", \"bdt_prediction\")\n",
-    "hist(df, v_bdt, 22, [s_ztt, s_sig], range=(-1.1, 1.1), \n",
-    "     weight=\"weight\",  numerator=None)\n",
-    "None"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ToyData.ipynb b/examples/ToyData.ipynb
deleted file mode 100644
index 9b16a7b8c705460c58f3f9fe01b87a92edbe5fb0..0000000000000000000000000000000000000000
--- a/examples/ToyData.ipynb
+++ /dev/null
@@ -1,316 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nnfwtbn import Variable, Process, Cut, hist, McStack\n",
-    "from nnfwtbn.toydata import generate, proposal, mcmc_step, vbfh_pdf, mcmc, ztt_pdf\n",
-    "from pylorentz import Momentum4\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# shuffle=False allows plotting the MC walk\n",
-    "%time df = generate(10000, vbfh_frac=0.5, shuffle=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Markov chain walk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "idx = df.fpid == 1\n",
-    "fig, ax = plt.subplots(2, 1, sharex=True)\n",
-    "ax[0].set_title(\"VBFH\")\n",
-    "ax[0].plot(np.arange(sum(idx)), df.jet_1_pt[idx], label=\"Jet 1\")\n",
-    "ax[0].plot(np.arange(sum(idx)), df.jet_2_pt[idx], label=\"Jet 2\")\n",
-    "ax[0].set_ylabel(r\"$p_{\\mathrm{T}}$\")\n",
-    "ax[0].legend()\n",
-    "                       \n",
-    "ax[1].set_title(r\"$Z\\rightarrow\\tau\\tau$\")\n",
-    "ax[1].plot(np.arange(sum(~idx)), df.jet_1_pt[~idx], label=\"Jet 1\")\n",
-    "ax[1].plot(np.arange(sum(~idx)), df.jet_2_pt[~idx], label=\"Jet 2\")\n",
-    "ax[1].set_ylabel(r\"$p_{\\mathrm{T}}$\")\n",
-    "ax[1].legend()\n",
-    "fig.tight_layout()\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, ax = plt.subplots(2, 1, sharex=True)\n",
-    "ax[0].set_title(\"VBFH\")\n",
-    "ax[0].plot(np.arange(sum(idx)), df.jet_1_eta[idx].abs(), label=\"Jet 1\")\n",
-    "ax[0].plot(np.arange(sum(idx)), df.jet_2_eta[idx].abs(), label=\"Jet 2\")\n",
-    "ax[0].set_ylabel(r\"$|\\eta_j|$\")\n",
-    "ax[0].legend()\n",
-    "                       \n",
-    "ax[1].set_title(r\"$Z\\rightarrow\\tau\\tau$\")\n",
-    "ax[1].plot(np.arange(sum(~idx)), df.jet_1_eta[~idx].abs(), label=\"Jet 1\")\n",
-    "ax[1].plot(np.arange(sum(~idx)), df.jet_2_eta[~idx].abs(), label=\"Jet 2\")\n",
-    "ax[1].set_ylabel(r\"$|\\eta_j|$\")\n",
-    "ax[1].legend()\n",
-    "fig.tight_layout()\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, ax = plt.subplots(1, 1)\n",
-    "ax.plot(np.arange(sum(idx)), df.higgs_m[idx], label=\"VBFH\")\n",
-    "ax.plot(np.arange(sum(~idx)), df.higgs_m[~idx], label=r\"$Z\\rightarrow\\tau\\tau$\")\n",
-    "ax.set_ylabel(r\"$m^H$\")\n",
-    "ax.legend()\n",
-    "fig.tight_layout()\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Distributions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_vbfh = Process(\"VBFH\", range=(1, 1))\n",
-    "p_ztt = Process(r\"$Z\\rightarrow\\tau\\tau$\", range=(0, 0))\n",
-    "s_all = McStack(p_vbfh, p_ztt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "jet_1_eta = Variable(r\"$\\eta^{j_1}$\", \"jet_1_eta\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist(df, jet_1_eta, bins=20, range=(-6, 6), stacks=[s_all], weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "jet_2_eta = Variable(r\"$\\eta^{j_2}$\", \"jet_2_eta\")\n",
-    "hist(df, jet_2_eta, bins=20, range=(-6, 6), stacks=[s_all], weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "delta_eta_jets = Variable(r\"$|\\Delta\\eta{jj}|$\", lambda d: d.jet_1_eta.abs())\n",
-    "hist(df, delta_eta_jets, bins=22, range=(-1, 10), stacks=[s_all], weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "jet_1_pt = Variable(r\"$p_{\\mathrm{T}}^{j_1}$\", \"jet_1_pt\")\n",
-    "jet_2_pt = Variable(r\"$p_{\\mathrm{T}}^{j_2}$\", \"jet_2_pt\")\n",
-    "hist(df, jet_1_pt, bins=50, range=(0, 1000), stacks=[s_all], weight=\"weight\")\n",
-    "hist(df, jet_2_pt, bins=25, range=(0, 250), stacks=[s_all], weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_higgs_m = Variable(r\"$m^H$\", \"higgs_m\")\n",
-    "hist(df, v_higgs_m, bins=20, range=(0, 200), stacks=[s_all], weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_weight = Variable(r\"$w$\", \"weight\")\n",
-    "hist(df, v_weight, bins=20, range=(0, 2), stacks=[s_all],\n",
-    "     weight=lambda d: d.weight * 1 + 0)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_mjj = Variable(r\"$m^{jj}$\", \"m_jj\")\n",
-    "hist(df, v_mjj, bins=51, range=(-40, 2000), stacks=[s_all], weight=\"weight\")\n",
-    "hist(df, v_mjj, bins=51, range=(-40, 2000), stacks=[s_all], weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_lep_pt = Variable(r\"$p^{\\ell}_{\\mathrm{T}}$\", \"lep_pt\")\n",
-    "hist(df, v_lep_pt, bins=30, range=(-10, 290), stacks=[s_all], weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_lep_eta = Variable(r\"$|\\eta^{\\ell}|$\", \"lep_eta\")\n",
-    "hist(df, v_lep_eta, bins=30, range=(-5, 5), stacks=[s_all], weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_tau_pt = Variable(r\"$p^{\\tau}_{\\mathrm{T}}$\", \"tau_pt\")\n",
-    "hist(df, v_tau_pt, bins=30, range=(-10, 290), stacks=[s_all], weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_tau_eta = Variable(r\"$|\\eta^{\\tau}|$\", \"tau_eta\")\n",
-    "hist(df, v_tau_eta, bins=30, range=(-5, 5), stacks=[s_all], weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_met_pt = Variable(r\"$E^{\\mathrm{miss}}_{\\mathrm{T}}$\", \"met_pt\")\n",
-    "hist(df, v_met_pt, bins=30, range=(-10, 290), stacks=[s_all], weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_lep_cent = Variable(r\"$\\eta^{\\ell}$\", \"tau_centrality\")\n",
-    "hist(df, v_lep_cent, bins=22, range=(-0.05, 1.05), stacks=[s_all], weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_tau_cent = Variable(r\"$\\eta^{\\tau}$\", \"tau_centrality\")\n",
-    "hist(df, v_tau_cent, bins=22, range=(-0.05, 1.05), stacks=[s_all], weight=\"weight\")\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/UHepp.ipynb b/examples/UHepp.ipynb
deleted file mode 100644
index a266926c898ff5626ee1a9212c749cb0746ac521..0000000000000000000000000000000000000000
--- a/examples/UHepp.ipynb
+++ /dev/null
@@ -1,151 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "\n",
-    "from nnfwtbn import Variable, Process, Cut, hist, McStack, DataStack, Stack\n",
-    "from nnfwtbn import toydata\n",
-    "from nnfwtbn.plot import hist\n",
-    "\n",
-    "import uhepp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = toydata.get()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_ztt = Process(r\"$Z\\rightarrow\\tau\\tau$\", range=(0, 0))\n",
-    "p_sig = Process(r\"Signal\", range=(1, 1))\n",
-    "\n",
-    "p_asimov = Process(r\"Asimov\", selection=lambda d: d.fpid >= 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "colors = [\"windows blue\", \"amber\", \"greyish\", \"faded green\", \"dusty purple\"]\n",
-    "palette = sns.xkcd_palette(colors)\n",
-    "\n",
-    "s_bkg = McStack(p_ztt, p_sig, palette=palette)\n",
-    "s_data = DataStack(p_asimov)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "v_higgs_m = Variable(r\"$m^H$\", \"higgs_m\", \"GeV\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c_vbf = Cut(lambda d: d.dijet_p4__M  > 400) & \\\n",
-    "        Cut(lambda d: d.jet_1_p4__Pt >= 30) & \\\n",
-    "        Cut(lambda d: d.is_dijet_centrality == 1) & \\\n",
-    "        Cut(lambda d: d.jet_0_p4__Eta * df.jet_1_p4__Eta < 0) & \\\n",
-    "        Cut(lambda d: (d.jet_0_p4__Eta - df.jet_1_p4__Eta).abs() > 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h = hist(df, v_higgs_m, 20, [s_bkg, s_data], range=(0, 200), selection=None,\n",
-    "     weight=\"weight\",  ratio_label=\"Data / SM\", return_uhepp=True)\n",
-    "h.render()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "json_string = h.to_jsons()\n",
-    "print(json_string)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h = uhepp.from_jsons(json_string)\n",
-    "h.render()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h.rebin_edges = [0, 70, 80, 90, 100, 110, 120, 130, 140, 150, 200]\n",
-    "h.subtext = \"Hello\"\n",
-    "h.brand = None\n",
-    "h.render()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/requirements.txt b/examples/requirements.txt
deleted file mode 100644
index b6cb25e5b0573b0897d22eb4c9c2ff605b1e5939..0000000000000000000000000000000000000000
--- a/examples/requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-keras
-tensorflow
-jupyter
diff --git a/examples/tmva_bdt.xml b/examples/tmva_bdt.xml
deleted file mode 100644
index 19f5ef322eb65758198b27c73dbde2c0ca2d4d3b..0000000000000000000000000000000000000000
--- a/examples/tmva_bdt.xml
+++ /dev/null
@@ -1,897 +0,0 @@
-<?xml version="1.0"?>
-<MethodSetup Method="BDT::BDT">
-  <GeneralInfo>
-    <Info name="TMVA Release" value="4.2.1 [262657]"/>
-    <Info name="ROOT Release" value="6.18/04 [397828]"/>
-    <Info name="Creator" value="Frank"/>
-    <Info name="Date" value="Fri Nov 22 13:24:52 2019"/>
-    <Info name="Host" value="Linux frank-uni 4.15.0-70-generic #79~16.04.1-Ubuntu SMP Tue Nov 12 14:01:10 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux"/>
-    <Info name="Dir" value="/home/frank/repositories/nnfwtbn"/>
-    <Info name="Training events" value="5000"/>
-    <Info name="TrainingTime" value="8.32540989e-02"/>
-    <Info name="AnalysisType" value="Classification"/>
-  </GeneralInfo>
-  <Options>
-    <Option name="V" modified="No">False</Option>
-    <Option name="VerbosityLevel" modified="No">Default</Option>
-    <Option name="VarTransform" modified="No">None</Option>
-    <Option name="H" modified="No">False</Option>
-    <Option name="CreateMVAPdfs" modified="No">False</Option>
-    <Option name="IgnoreNegWeightsInTraining" modified="No">False</Option>
-    <Option name="NTrees" modified="Yes">50</Option>
-    <Option name="MaxDepth" modified="No">3</Option>
-    <Option name="MinNodeSize" modified="No">5%</Option>
-    <Option name="nCuts" modified="No">20</Option>
-    <Option name="BoostType" modified="No">AdaBoost</Option>
-    <Option name="AdaBoostR2Loss" modified="No">quadratic</Option>
-    <Option name="UseBaggedBoost" modified="No">False</Option>
-    <Option name="Shrinkage" modified="No">1.000000e+00</Option>
-    <Option name="AdaBoostBeta" modified="No">5.000000e-01</Option>
-    <Option name="UseRandomisedTrees" modified="No">False</Option>
-    <Option name="UseNvars" modified="No">2</Option>
-    <Option name="UsePoissonNvars" modified="No">True</Option>
-    <Option name="BaggedSampleFraction" modified="No">6.000000e-01</Option>
-    <Option name="UseYesNoLeaf" modified="No">True</Option>
-    <Option name="NegWeightTreatment" modified="No">inverseboostnegweights</Option>
-    <Option name="Css" modified="No">1.000000e+00</Option>
-    <Option name="Cts_sb" modified="No">1.000000e+00</Option>
-    <Option name="Ctb_ss" modified="No">1.000000e+00</Option>
-    <Option name="Cbb" modified="No">1.000000e+00</Option>
-    <Option name="NodePurityLimit" modified="No">5.000000e-01</Option>
-    <Option name="SeparationType" modified="No">giniindex</Option>
-    <Option name="RegressionLossFunctionBDTG" modified="No">huber</Option>
-    <Option name="HuberQuantile" modified="No">7.000000e-01</Option>
-    <Option name="DoBoostMonitor" modified="No">False</Option>
-    <Option name="UseFisherCuts" modified="No">False</Option>
-    <Option name="MinLinCorrForFisher" modified="No">8.000000e-01</Option>
-    <Option name="UseExclusiveVars" modified="No">False</Option>
-    <Option name="DoPreselection" modified="No">False</Option>
-    <Option name="SigToBkgFraction" modified="No">1.000000e+00</Option>
-    <Option name="PruneMethod" modified="No">nopruning</Option>
-    <Option name="PruneStrength" modified="No">0.000000e+00</Option>
-    <Option name="PruningValFraction" modified="No">5.000000e-01</Option>
-    <Option name="SkipNormalization" modified="No">False</Option>
-    <Option name="nEventsMin" modified="No">0</Option>
-    <Option name="UseBaggedGrad" modified="No">False</Option>
-    <Option name="GradBaggingFraction" modified="No">6.000000e-01</Option>
-    <Option name="UseNTrainEvents" modified="No">0</Option>
-    <Option name="NNodesMax" modified="No">0</Option>
-  </Options>
-  <Variables NVar="4">
-    <Variable VarIndex="0" Expression="jet_1_eta" Label="jet_1_eta" Title="jet_1_eta" Unit="" Internal="jet_1_eta" Type="F" Min="-5.72895813e+00" Max="5.73295832e+00"/>
-    <Variable VarIndex="1" Expression="jet_2_eta" Label="jet_2_eta" Title="jet_2_eta" Unit="" Internal="jet_2_eta" Type="F" Min="-5.57531881e+00" Max="5.54892635e+00"/>
-    <Variable VarIndex="2" Expression="lep_centrality" Label="lep_centrality" Title="lep_centrality" Unit="" Internal="lep_centrality" Type="F" Min="0.00000000e+00" Max="1.00000000e+00"/>
-    <Variable VarIndex="3" Expression="tau_centrality" Label="tau_centrality" Title="tau_centrality" Unit="" Internal="tau_centrality" Type="F" Min="0.00000000e+00" Max="1.00000000e+00"/>
-  </Variables>
-  <Spectators NSpec="0"/>
-  <Classes NClass="2">
-    <Class Name="Signal" Index="0"/>
-    <Class Name="Background" Index="1"/>
-  </Classes>
-  <Transformations NTransformations="0"/>
-  <MVAPdfs/>
-  <Weights NTrees="50" AnalysisType="0">
-    <BinaryTree type="DecisionTree" boostWeight="5.8046043100341038e-01" itree="0">
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-            <Node pos="r" depth="3" NCoef="0" IVar="-1" Cut="0.0000000000000000e+00" cType="1" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="4.6047973632812500e-01" nType="-1"/>
-          </Node>
-          <Node pos="r" depth="2" NCoef="0" IVar="-1" Cut="0.0000000000000000e+00" cType="1" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="4.7226959466934204e-01" nType="-1"/>
-        </Node>
-        <Node pos="r" depth="1" NCoef="0" IVar="2" Cut="5.7142853736877441e-01" cType="1" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="4.8227381706237793e-01" nType="0">
-          <Node pos="l" depth="2" NCoef="0" IVar="2" Cut="5.4363660514354706e-02" cType="0" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="4.9137759208679199e-01" nType="0">
-            <Node pos="l" depth="3" NCoef="0" IVar="-1" Cut="0.0000000000000000e+00" cType="1" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="5.3780806064605713e-01" nType="1"/>
-            <Node pos="r" depth="3" NCoef="0" IVar="-1" Cut="0.0000000000000000e+00" cType="1" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="4.7710502147674561e-01" nType="-1"/>
-          </Node>
-          <Node pos="r" depth="2" NCoef="0" IVar="-1" Cut="0.0000000000000000e+00" cType="1" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="4.3328183889389038e-01" nType="-1"/>
-        </Node>
-      </Node>
-    </BinaryTree>
-    <BinaryTree type="DecisionTree" boostWeight="4.7219253486269479e-02" itree="49">
-      <Node pos="s" depth="0" NCoef="0" IVar="1" Cut="1.3111187219619751e+00" cType="0" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="4.9921283125877380e-01" nType="0">
-        <Node pos="l" depth="1" NCoef="0" IVar="2" Cut="2.3809511959552765e-01" cType="1" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="5.1861953735351562e-01" nType="0">
-          <Node pos="l" depth="2" NCoef="0" IVar="-1" Cut="1.6633809804916382e+00" cType="1" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="5.4420191049575806e-01" nType="1"/>
-          <Node pos="r" depth="2" NCoef="0" IVar="-1" Cut="0.0000000000000000e+00" cType="1" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="4.8670360445976257e-01" nType="-1"/>
-        </Node>
-        <Node pos="r" depth="1" NCoef="0" IVar="0" Cut="1.8676971197128296e+00" cType="0" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="4.8990041017532349e-01" nType="0">
-          <Node pos="l" depth="2" NCoef="0" IVar="-1" Cut="0.0000000000000000e+00" cType="1" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="5.4683291912078857e-01" nType="1"/>
-          <Node pos="r" depth="2" NCoef="0" IVar="-1" Cut="-3.0515685677528381e-01" cType="1" res="-9.9000000000000000e+01" rms="0.0000000000000000e+00" purity="4.8294210433959961e-01" nType="-1"/>
-        </Node>
-      </Node>
-    </BinaryTree>
-  </Weights>
-</MethodSetup>
diff --git a/freeforestml/__init__.py b/freeforestml/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f83984e4b97746e68dab97b51d8c84944837ba2
--- /dev/null
+++ b/freeforestml/__init__.py
@@ -0,0 +1,12 @@
+__version__ = "0.0.0"
+
+from .variable import Variable, RangeBlindingStrategy
+from .process import Process
+from .cut import Cut
+from .plot import HistogramFactory, hist, confusion_matrix, roc, \
+                         atlasify, correlation_matrix, example_style
+from .model import CrossValidator, ClassicalCV, MixedCV, \
+                          Normalizer, EstimatorNormalizer, \
+                          HepNet
+from .stack import Stack, McStack, DataStack, SystStack, TruthStack
+from .interface import TmvaBdt
diff --git a/nnfwtbn/cut.py b/freeforestml/cut.py
similarity index 100%
rename from nnfwtbn/cut.py
rename to freeforestml/cut.py
diff --git a/nnfwtbn/error.py b/freeforestml/error.py
similarity index 100%
rename from nnfwtbn/error.py
rename to freeforestml/error.py
diff --git a/nnfwtbn/helpers.py b/freeforestml/helpers.py
similarity index 100%
rename from nnfwtbn/helpers.py
rename to freeforestml/helpers.py
diff --git a/nnfwtbn/interface.py b/freeforestml/interface.py
similarity index 99%
rename from nnfwtbn/interface.py
rename to freeforestml/interface.py
index 1a701afa50c085d837c5b9a2e019756bd7d04b38..99f730ff270be89f4f02c3293f75e2d9b6282160 100644
--- a/nnfwtbn/interface.py
+++ b/freeforestml/interface.py
@@ -10,7 +10,7 @@ import numpy as np
 
 class Classifier(ABC):
     """
-    Abstract classifier train with another framework and loaded into nnfwtbn.
+    Abstract classifier train with another framework and loaded into freeforestml.
     """
 
     @abstractmethod
diff --git a/nnfwtbn/model.py b/freeforestml/model.py
similarity index 99%
rename from nnfwtbn/model.py
rename to freeforestml/model.py
index 81cc053116169e3ba5fb74f0b90f342e8e0d7bb1..e1da2df4d61820c994fd2f23141e7bb40219f93c 100644
--- a/nnfwtbn/model.py
+++ b/freeforestml/model.py
@@ -9,8 +9,8 @@ import numpy as np
 import pandas as pd
 import keras
 
-from nnfwtbn.variable import Variable
-from nnfwtbn.helpers import python_to_str, str_to_python
+from freeforestml.variable import Variable
+from freeforestml.helpers import python_to_str, str_to_python
 
 class CrossValidator(ABC):
     """
diff --git a/nnfwtbn/plot.py b/freeforestml/plot.py
similarity index 97%
rename from nnfwtbn/plot.py
rename to freeforestml/plot.py
index f472c20f37d8cd144ceecac7e03917c30918e6c5..3eda8b4b8ed6fbbfcbb5a053dbf329eef346f08a 100644
--- a/nnfwtbn/plot.py
+++ b/freeforestml/plot.py
@@ -12,17 +12,22 @@ import seaborn as sns
 from atlasify import atlasify
 import uhepp
 
-from nnfwtbn.stack import Stack
-from nnfwtbn.process import Process
-from nnfwtbn.cut import Cut
-from nnfwtbn.variable import Variable, BlindingStrategy
-import nnfwtbn.error as err
+from freeforestml.stack import Stack
+from freeforestml.process import Process
+from freeforestml.cut import Cut
+from freeforestml.variable import Variable, BlindingStrategy
+import freeforestml.error as err
 
 # Store built-in range
 range_ = range
 
 ATLAS = "Internal"
-INFO = "$\sqrt{s} = 13\,\mathrm{TeV}$, $139\,\mathrm{fb}^{-1}$\nnnfwtbn"
+INFO = "$\sqrt{s} = 13\,\mathrm{TeV}$, $139\,\mathrm{fb}^{-1}$\nfreeforestml"
+
+def example_style():
+    global ATLAS, INFO
+    ATLAS = False
+    INFO = "FreeForestML Example"
 
 def fill_labels(label, info):
     if label is None:
@@ -150,7 +155,7 @@ def hist(dataframe, variable, bins, stacks, selection=None,
     ##################
     # Create UHepp object
     uhepp_obj = uhepp.UHeppHist(variable.name, bin_edges)
-    uhepp_obj.producer = "nnfwtbn"
+    uhepp_obj.producer = "freeforestml"
 
     ##################
     # Unit
@@ -370,11 +375,13 @@ def _dask_compute(array):
     return [[flat[x * y_len + y] for y in range(y_len)] for x in range(x_len)]
 
 def confusion_matrix(df, x_processes, y_processes, x_label, y_label,
-                     weight=None, axes=None, figure=None, atlas=ATLAS,
+                     weight=None, axes=None, figure=None, atlas=None,
                      info=None, enlarge=1.3, normalize_rows=False, **kwds):
     """
     Creates a confusion matrix.
     """
+    atlas, info = fill_labels(atlas, info)
+
     # Wrap column string by variable
     if weight is None:
         weight = Variable("unity", lambda d: np.ones(len(d)))
@@ -437,7 +444,7 @@ def confusion_matrix(df, x_processes, y_processes, x_label, y_label,
 
 def roc(df, signal_process, background_process, discriminant, steps=100,
         selection=None, min=None, max=None, axes=None, weight=None,
-        atlas=ATLAS, info=None, enlarge=1.3,
+        atlas=None, info=None, enlarge=1.3,
         return_auc=False):
     """
     Creates a ROC.
@@ -448,6 +455,8 @@ def roc(df, signal_process, background_process, discriminant, steps=100,
     If return_auc is True, the method returns a tuple with the area under the
     curve and an uncertainty estimation on the area.
     """
+    atlas, info = fill_labels(atlas, info)
+
     # Wrap column string by variable
     if discriminant is None:
         discriminant = Variable("unity", lambda d: np.ones(len(d)))
@@ -534,12 +543,14 @@ def roc(df, signal_process, background_process, discriminant, steps=100,
         return data
 
 def correlation_matrix(df, variables,
-                       weight=None, axes=None, figure=None, atlas=ATLAS,
+                       weight=None, axes=None, figure=None, atlas=None,
                        info=None, enlarge=1.3, normalize_rows=False, **kwds):
     """
     Plot the Pearson correlation coefficient matrix. The square matrix is
     returned as a DataFrame.
     """
+    atlas, info = fill_labels(atlas, info)
+
     if weight is None:
         weight = Variable("unity", lambda d: np.ones(len(d)))
     elif isinstance(weight, str):
diff --git a/nnfwtbn/process.py b/freeforestml/process.py
similarity index 98%
rename from nnfwtbn/process.py
rename to freeforestml/process.py
index a8dc22079bfed70deebae216bc0f896615d74dc5..f1694063885893a83f54f6b0b37feb0035ad7b24 100644
--- a/nnfwtbn/process.py
+++ b/freeforestml/process.py
@@ -1,6 +1,6 @@
 
-from nnfwtbn.cut import Cut
-import nnfwtbn.error as err
+from freeforestml.cut import Cut
+import freeforestml.error as err
 
 class Process:
     """
diff --git a/nnfwtbn/stack.py b/freeforestml/stack.py
similarity index 98%
rename from nnfwtbn/stack.py
rename to freeforestml/stack.py
index a0675d527c1037b43f9192c9e00d95a85cb46456..0aba6319ba80725e335d7bde08c74157bf090d90 100644
--- a/nnfwtbn/stack.py
+++ b/freeforestml/stack.py
@@ -3,9 +3,9 @@ import numpy as np
 import dask.array as da
 import dask.dataframe as dd
 
-from nnfwtbn.variable import Variable
-from nnfwtbn.cut import Cut
-import nnfwtbn.error as err
+from freeforestml.variable import Variable
+from freeforestml.cut import Cut
+import freeforestml.error as err
 
 def not_none_or_default(value, default):
     """
diff --git a/nnfwtbn/tests/__init__.py b/freeforestml/tests/__init__.py
similarity index 100%
rename from nnfwtbn/tests/__init__.py
rename to freeforestml/tests/__init__.py
diff --git a/nnfwtbn/tests/test_cut.py b/freeforestml/tests/test_cut.py
similarity index 99%
rename from nnfwtbn/tests/test_cut.py
rename to freeforestml/tests/test_cut.py
index efe12d87f587d1b756782d26f7d560e3257f9088..bff8f785a0ea865ba222db397d29da94ad733b57 100644
--- a/nnfwtbn/tests/test_cut.py
+++ b/freeforestml/tests/test_cut.py
@@ -1,5 +1,5 @@
 
-from nnfwtbn.cut import Cut
+from freeforestml.cut import Cut
 import unittest
 import pandas as pd
 
diff --git a/nnfwtbn/tests/test_hist.py b/freeforestml/tests/test_hist.py
similarity index 69%
rename from nnfwtbn/tests/test_hist.py
rename to freeforestml/tests/test_hist.py
index d61eb5f3811afeeea6e8addab94047c1bb16884f..424a351224e0afe69d0ab11a29cbd0caae3d047d 100644
--- a/nnfwtbn/tests/test_hist.py
+++ b/freeforestml/tests/test_hist.py
@@ -1,7 +1,7 @@
 
 import unittest
 import pandas as pd
-import nnfwtbn
+import freeforestml
 
 class HistTestBase(unittest.TestCase):
     """
@@ -18,16 +18,16 @@ class HistTestBase(unittest.TestCase):
             "p": [ 1,  1,  1,  2,  3]
         })
 
-        self.process_a = nnfwtbn.Process("a", lambda d: d.p == 1)
-        self.process_b = nnfwtbn.Process("b", lambda d: d.p == 2)
-        self.mc_stack = nnfwtbn.McStack(self.process_a, self.process_b)
+        self.process_a = freeforestml.Process("a", lambda d: d.p == 1)
+        self.process_b = freeforestml.Process("b", lambda d: d.p == 2)
+        self.mc_stack = freeforestml.McStack(self.process_a, self.process_b)
 
-        self.process_x = nnfwtbn.Process("x", lambda d: d.p == 3)
-        self.data_stack = nnfwtbn.McStack(self.process_x)
+        self.process_x = freeforestml.Process("x", lambda d: d.p == 3)
+        self.data_stack = freeforestml.McStack(self.process_x)
 
     def test_yield_base(self):
         """Check the bin contents"""
-        hist = nnfwtbn.hist(self.data, variable="m", weight="w", bins=[0, 20],
+        hist = freeforestml.hist(self.data, variable="m", weight="w", bins=[0, 20],
                             stacks=[self.mc_stack, self.data_stack],
                             return_uhepp=True)
         
@@ -37,7 +37,7 @@ class HistTestBase(unittest.TestCase):
 
     def test_yiele_stat(self):
         """Check the statistical uncertainties"""
-        hist = nnfwtbn.hist(self.data, variable="m", weight="w", bins=[0, 20],
+        hist = freeforestml.hist(self.data, variable="m", weight="w", bins=[0, 20],
                             stacks=[self.mc_stack, self.data_stack],
                             return_uhepp=True)
         
diff --git a/nnfwtbn/tests/test_model.py b/freeforestml/tests/test_model.py
similarity index 99%
rename from nnfwtbn/tests/test_model.py
rename to freeforestml/tests/test_model.py
index eb32a8212ec5a61811e5e63700c9723fec072560..3841b60cc38e70c3ac37d0fb68217d6e0aeb5b28 100644
--- a/nnfwtbn/tests/test_model.py
+++ b/freeforestml/tests/test_model.py
@@ -9,13 +9,13 @@ from keras.models import Sequential
 from keras.layers import Dense, Dropout
 from keras.optimizers import SGD
 
-from nnfwtbn.model import CrossValidator, ClassicalCV, MixedCV, \
+from freeforestml.model import CrossValidator, ClassicalCV, MixedCV, \
                           Normalizer, EstimatorNormalizer, \
                           normalize_category_weights, BinaryCV, HepNet, \
                           NoTestCV
-from nnfwtbn.variable import Variable
-from nnfwtbn.cut import Cut
-import nnfwtbn.toydata as toydata
+from freeforestml.variable import Variable
+from freeforestml.cut import Cut
+import freeforestml.toydata as toydata
 
 class StubCrossValidator(CrossValidator):
     def select_slice(self, df, slice_i):
diff --git a/nnfwtbn/tests/test_plot.py b/freeforestml/tests/test_plot.py
similarity index 98%
rename from nnfwtbn/tests/test_plot.py
rename to freeforestml/tests/test_plot.py
index b90a05d823c30c435589bc950c99469ca09e79fd..ef707a0104b43be2be3b13254a45f34944f928c7 100644
--- a/nnfwtbn/tests/test_plot.py
+++ b/freeforestml/tests/test_plot.py
@@ -8,11 +8,11 @@ import dask.dataframe as dd
 import matplotlib.pyplot as plt
 import seaborn as sns
 
-from nnfwtbn.variable import Variable
-from nnfwtbn.cut import Cut
-from nnfwtbn.stack import McStack, DataStack
-from nnfwtbn.process import Process
-from nnfwtbn.plot import roc, confusion_matrix, atlasify, hist, \
+from freeforestml.variable import Variable
+from freeforestml.cut import Cut
+from freeforestml.stack import McStack, DataStack
+from freeforestml.process import Process
+from freeforestml.plot import roc, confusion_matrix, atlasify, hist, \
                          HistogramFactory, fill_labels, ATLAS, INFO, \
                          _transpose, _dask_compute, human_readable
 
diff --git a/nnfwtbn/tests/test_process.py b/freeforestml/tests/test_process.py
similarity index 99%
rename from nnfwtbn/tests/test_process.py
rename to freeforestml/tests/test_process.py
index eea9ebebff3cd37445c58a19d3a4fb03fb2b634d..de6469fbb8a3df07408a5189955c6be2c5859f24 100644
--- a/nnfwtbn/tests/test_process.py
+++ b/freeforestml/tests/test_process.py
@@ -3,7 +3,7 @@ import unittest
 
 import pandas as pd
 
-from nnfwtbn import Process, Cut
+from freeforestml import Process, Cut
 
 class ProcessTestCase(unittest.TestCase):
     """
diff --git a/nnfwtbn/tests/test_stack.py b/freeforestml/tests/test_stack.py
similarity index 99%
rename from nnfwtbn/tests/test_stack.py
rename to freeforestml/tests/test_stack.py
index 9d75a3aeb01ec41352e88546f8fcf94da8d0cdb2..07d8a7a8608c71026e1210081680221efd155705 100644
--- a/nnfwtbn/tests/test_stack.py
+++ b/freeforestml/tests/test_stack.py
@@ -4,9 +4,9 @@ import unittest
 import pandas as pd
 import numpy as np
 
-from nnfwtbn.process import Process
-from nnfwtbn.variable import Variable
-from nnfwtbn.stack import Stack, McStack, DataStack, not_none_or_default
+from freeforestml.process import Process
+from freeforestml.variable import Variable
+from freeforestml.stack import Stack, McStack, DataStack, not_none_or_default
 
 class TestStack(unittest.TestCase):
     """
diff --git a/nnfwtbn/tests/test_toydata.py b/freeforestml/tests/test_toydata.py
similarity index 98%
rename from nnfwtbn/tests/test_toydata.py
rename to freeforestml/tests/test_toydata.py
index 1c2310af7ec2e4cfd4e1ee43644c27e74c4c556a..1002164b18161326020f5a58d9b0f1da6715c933 100644
--- a/nnfwtbn/tests/test_toydata.py
+++ b/freeforestml/tests/test_toydata.py
@@ -1,7 +1,7 @@
 
 import unittest
 import numpy as np
-from nnfwtbn.toydata import draw
+from freeforestml.toydata import draw
 
 SEED = 798088218969
 
diff --git a/nnfwtbn/tests/test_variable.py b/freeforestml/tests/test_variable.py
similarity index 99%
rename from nnfwtbn/tests/test_variable.py
rename to freeforestml/tests/test_variable.py
index 099273b1ca0a9545cbafcacdefcb8ee1fa8655f9..8defe38749f6fde0923a1b274f960a80cb7476a7 100644
--- a/nnfwtbn/tests/test_variable.py
+++ b/freeforestml/tests/test_variable.py
@@ -5,7 +5,7 @@ import unittest
 import numpy as np
 import pandas as pd
 
-from nnfwtbn.variable import Variable, RangeBlindingStrategy
+from freeforestml.variable import Variable, RangeBlindingStrategy
 
 class VariableTestCase(unittest.TestCase):
     """
diff --git a/nnfwtbn/toydata.py b/freeforestml/toydata.py
similarity index 100%
rename from nnfwtbn/toydata.py
rename to freeforestml/toydata.py
diff --git a/nnfwtbn/variable.py b/freeforestml/variable.py
similarity index 98%
rename from nnfwtbn/variable.py
rename to freeforestml/variable.py
index 3641053dd9aa964222597d8ed5345d272b607c7e..bbdbb98fb503ef6f4c29100c45ee62b21b8a0b7d 100644
--- a/nnfwtbn/variable.py
+++ b/freeforestml/variable.py
@@ -6,8 +6,8 @@ import cloudpickle
 import numpy as np
 import h5py
 
-from nnfwtbn.cut import Cut
-from nnfwtbn.helpers import python_to_str, str_to_python
+from freeforestml.cut import Cut
+from freeforestml.helpers import python_to_str, str_to_python
 
 class BlindingStrategy(ABC):
     """
diff --git a/nnfwtbn/__init__.py b/nnfwtbn/__init__.py
deleted file mode 100644
index 0c656f364c106f6bed9c3718d80a205869961c82..0000000000000000000000000000000000000000
--- a/nnfwtbn/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-__version__ = "0.0.0"
-
-from nnfwtbn.variable import Variable, RangeBlindingStrategy
-from nnfwtbn.process import Process
-from nnfwtbn.cut import Cut
-from nnfwtbn.plot import HistogramFactory, hist, confusion_matrix, roc, \
-                         atlasify, correlation_matrix
-from nnfwtbn.model import CrossValidator, ClassicalCV, MixedCV, \
-                          Normalizer, EstimatorNormalizer, \
-                          HepNet
-from nnfwtbn.stack import Stack, McStack, DataStack, SystStack, TruthStack
-from nnfwtbn.interface import TmvaBdt
diff --git a/setup.py b/setup.py
index eb4f02543e0a701e981bd6d57689b8d347d220e1..984ca1daa04ecfe307266c57cb133b4985a102a7 100644
--- a/setup.py
+++ b/setup.py
@@ -9,9 +9,9 @@ to install the package.
 from setuptools import setup
 
 
-setup(name='nnfwtbn',
+setup(name='freeforestml',
       version='0.0.0',  # Also change in module
-      packages=["nnfwtbn", "nnfwtbn.tests"],
+      packages=["freeforestml", "freeforestml.tests"],
       install_requires=["cloudpickle",
                         "h5py",
                         "numpy",
@@ -27,9 +27,10 @@ setup(name='nnfwtbn',
                         "dask[complete]",
                         "uhepp",
                         "lxml"],
-      test_suite='nnfwtbn.tests',
-      description='Experimental neural network framework to be named.',
-      url="https://gitlab.cern.ch/fsauerbu/nnfwtbn",
+      test_suite='freeforestml.tests',
+      description='Machine learning framework for high-energy physics '
+                  'developed in Freiburg and the Black Forest.',
+      url="https://gitlab.cern.ch/fsauerbu/freeforestml",
       author="Frank Sauerburger",
       author_email="f.sauerburger@cern.ch",
       license="MIT",