From 1ecd0a32c4aa22c0f1045db7c00ec94f7a0ed479 Mon Sep 17 00:00:00 2001
From: Kostas Vilkelis <kostasvilkelis@gmail.com>
Date: Thu, 9 May 2024 23:56:49 +0200
Subject: [PATCH] merge from main

---
 .gitlab-ci.yml                                |  34 +-
 .gitmodules                                   |   3 -
 .mailmap                                      |   4 +
 .readthedocs.yaml                             |  12 +
 AUTHORS.md                                    |  19 +
 LICENSE                                       |  25 ++
 README.md                                     | 102 ++++-
 docs/environment.yml                          |   2 +-
 docs/source/AUTHORS.md                        |   1 +
 docs/source/_static/css/custom.css            |  21 +
 docs/source/conf.py                           |  25 +-
 docs/source/documentation/algorithm.md        |  57 +++
 docs/source/documentation/meanfi.md           |  76 ++++
 docs/source/documentation/mf_notes.md         |  88 ++++
 docs/source/index.md                          |  29 +-
 docs/source/mf_notes.md                       | 304 --------------
 docs/source/tutorial/graphene_example.md      | 215 ++++++++++
 docs/source/tutorial/hubbard_1d.md            | 187 +++++++++
 examples/1d_hubbard_totalenergy.ipynb         | 348 ----------------
 examples/codes                                |   1 -
 examples/diatomic_molecule.ipynb              | 336 ---------------
 examples/graphene_extended_hubbard.ipynb      | 384 ------------------
 examples/mexican_hat.ipynb                    | 162 --------
 meanfi/__init__.py                            |  35 ++
 {pymf => meanfi}/kwant_helper/__init__.py     |   0
 .../kwant_helper/kwant_examples.py            |   2 +-
 {pymf => meanfi}/kwant_helper/utils.py        |  73 ++--
 meanfi/mf.py                                  | 138 +++++++
 meanfi/model.py                               |  99 +++++
 {pymf => meanfi}/observables.py               |  14 +-
 {pymf => meanfi}/params/__init__.py           |   0
 {pymf => meanfi}/params/param_transforms.py   |  50 ++-
 meanfi/params/rparams.py                      |  48 +++
 meanfi/solvers.py                             |  76 ++++
 {pymf => meanfi}/tb/__init__.py               |   0
 meanfi/tb/tb.py                               |  45 ++
 meanfi/tb/transforms.py                       |  78 ++++
 meanfi/tb/utils.py                            |  84 ++++
 {pymf => meanfi}/tests/test_graphene.py       |  24 +-
 meanfi/tests/test_hat.py                      |  65 +++
 {pymf => meanfi}/tests/test_hubbard.py        |  22 +-
 {pymf => meanfi}/tests/test_params.py         |   8 +-
 {pymf => meanfi}/tests/test_tb.py             |   6 +-
 {pymf => meanfi}/tests/test_zero_hint.py      |  17 +-
 noxfile.py                                    |  31 ++
 profiling/graphene.py                         |   8 +-
 pymf/__init__.py                              |  15 -
 pymf/mf.py                                    | 126 ------
 pymf/model.py                                 |  72 ----
 pymf/params/rparams.py                        |  44 --
 pymf/solvers.py                               |  65 ---
 pymf/tb/tb.py                                 |  43 --
 pymf/tb/transforms.py                         | 102 -----
 pymf/tb/utils.py                              |  64 ---
 pymf/tests/test_hat.py                        |  53 ---
 pyproject.toml                                |  27 +-
 pytest.ini                                    |   4 +-
 57 files changed, 1705 insertions(+), 2268 deletions(-)
 delete mode 100644 .gitmodules
 create mode 100644 .mailmap
 create mode 100644 .readthedocs.yaml
 create mode 100644 AUTHORS.md
 create mode 120000 docs/source/AUTHORS.md
 create mode 100644 docs/source/_static/css/custom.css
 create mode 100644 docs/source/documentation/algorithm.md
 create mode 100644 docs/source/documentation/meanfi.md
 create mode 100644 docs/source/documentation/mf_notes.md
 delete mode 100644 docs/source/mf_notes.md
 create mode 100644 docs/source/tutorial/graphene_example.md
 create mode 100644 docs/source/tutorial/hubbard_1d.md
 delete mode 100644 examples/1d_hubbard_totalenergy.ipynb
 delete mode 120000 examples/codes
 delete mode 100644 examples/diatomic_molecule.ipynb
 delete mode 100644 examples/graphene_extended_hubbard.ipynb
 delete mode 100644 examples/mexican_hat.ipynb
 create mode 100644 meanfi/__init__.py
 rename {pymf => meanfi}/kwant_helper/__init__.py (100%)
 rename {pymf => meanfi}/kwant_helper/kwant_examples.py (95%)
 rename {pymf => meanfi}/kwant_helper/utils.py (73%)
 create mode 100644 meanfi/mf.py
 create mode 100644 meanfi/model.py
 rename {pymf => meanfi}/observables.py (60%)
 rename {pymf => meanfi}/params/__init__.py (100%)
 rename {pymf => meanfi}/params/param_transforms.py (57%)
 create mode 100644 meanfi/params/rparams.py
 create mode 100644 meanfi/solvers.py
 rename {pymf => meanfi}/tb/__init__.py (100%)
 create mode 100644 meanfi/tb/tb.py
 create mode 100644 meanfi/tb/transforms.py
 create mode 100644 meanfi/tb/utils.py
 rename {pymf => meanfi}/tests/test_graphene.py (81%)
 create mode 100644 meanfi/tests/test_hat.py
 rename {pymf => meanfi}/tests/test_hubbard.py (71%)
 rename {pymf => meanfi}/tests/test_params.py (71%)
 rename {pymf => meanfi}/tests/test_tb.py (81%)
 rename {pymf => meanfi}/tests/test_zero_hint.py (59%)
 create mode 100644 noxfile.py
 delete mode 100644 pymf/__init__.py
 delete mode 100644 pymf/mf.py
 delete mode 100644 pymf/model.py
 delete mode 100644 pymf/params/rparams.py
 delete mode 100644 pymf/solvers.py
 delete mode 100644 pymf/tb/tb.py
 delete mode 100644 pymf/tb/transforms.py
 delete mode 100644 pymf/tb/utils.py
 delete mode 100644 pymf/tests/test_hat.py

diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 6f3eb01..4b2c2b9 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -5,7 +5,7 @@ before_script:
   - export MAMBA_ROOT_PREFIX=micromamba
   - micromamba create -yf docs/environment.yml
   - eval "$(micromamba shell hook --shell bash)"
-  - micromamba activate pymf-docs
+  - micromamba activate meanfi-docs
 
 run tests:
   script:
@@ -65,3 +65,35 @@ run pre-commit:
     paths:
       - .pre-commit-cache
       - micromamba
+
+prepare zips:
+  image: gitlab.kwant-project.org:5005/qt/research-docker
+  before_script: []
+  when: manual
+  script:
+    - zip -r zenodo.zip *
+  artifacts:
+    paths:
+      - zenodo.zip
+
+publish to test pypi:
+  needs:
+    - run tests
+    - run docs
+  rules:
+    - if: '$CI_COMMIT_TAG =~ /^v[0-9]+\.[0-9]+\.[0-9]+.*\+test$/' # vX.Y.Z.post1+test
+  script:
+    - micromamba install -c conda-forge -y hatch hatch-vcs
+    - hatch build
+    - hatch publish -u __token__ -a $PYPI_TEST_TOKEN -r test
+
+publish to pypi:
+  needs:
+    - run tests
+    - run docs
+  rules:
+    - if: '$CI_COMMIT_TAG =~ /^v[0-9]+\.[0-9]+\.[0-9]+[^+]*$/' # No +test
+  script:
+    - micromamba install -c conda-forge -y hatch hatch-vcs
+    - hatch build
+    - hatch publish -u __token__ -a $PYPI_TOKEN
diff --git a/.gitmodules b/.gitmodules
deleted file mode 100644
index 1ec955c..0000000
--- a/.gitmodules
+++ /dev/null
@@ -1,3 +0,0 @@
-[submodule "data"]
-	url = ../kwant-scf-data
-	path = data
diff --git a/.mailmap b/.mailmap
new file mode 100644
index 0000000..e07d200
--- /dev/null
+++ b/.mailmap
@@ -0,0 +1,4 @@
+Kostas Vilkelis <kostasvilkelis@gmail.com>
+R. Johanna Zijderveld <johanna@zijderveld.de>
+Anton R. Akhmerov <meanfi@antonakhmerov.org>
+Antonio L.R. Manesco <am@antoniomanesco.org>
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
new file mode 100644
index 0000000..dc1b489
--- /dev/null
+++ b/.readthedocs.yaml
@@ -0,0 +1,12 @@
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "mambaforge-4.10"
+
+conda:
+  environment: docs/environment.yml
+
+sphinx:
+   configuration: docs/source/conf.py
diff --git a/AUTHORS.md b/AUTHORS.md
new file mode 100644
index 0000000..b850f3d
--- /dev/null
+++ b/AUTHORS.md
@@ -0,0 +1,19 @@
+# MeanFi authors
+
+## Current meanfi maintainers
+- Kostas Vilkelis
+- R. Johanna Zijderveld
+- Anton R. Akhmerov
+- Antonio L.R. Manesco
+
+## Other contributors
+- Isidora Araya Day
+- José L. Lado
+
+## Funding
+
+The project was developed in [Delft University of
+Technology](https://www.tudelft.nl/en/).
+We acknowledge funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program grant agreement No. 828948
+(AndQC) and [Netherlands
+Organisation for Scientific Research (NWO/OCW)](https://www.nwo.nl/) 016.Vidi.189.180 grant.
diff --git a/LICENSE b/LICENSE
index e69de29..76bff01 100644
--- a/LICENSE
+++ b/LICENSE
@@ -0,0 +1,25 @@
+BSD 2-Clause License
+
+Copyright (c) 2024, MeanFi authors
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/README.md b/README.md
index c19a65e..f07d152 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,100 @@
-# Project Name
+# `MeanFi`
 
-## Research Goal
+## What is `MeanFi`?
 
-## Research Plan
+`MeanFi` is a Python package that performs self-consistent Hartree-Fock calculations on tight-binding models.
+It aims to find the groundstate of a Hamiltonian with density-density interactions
 
-## Working on this project
-Configure the project by running
+$$
+\hat{H} = \hat{H_0} + \hat{V} = \sum_{ij} h_{ij} c^\dagger_{i} c_{j} + \frac{1}{2} \sum_{ij} v_{ij} \hat{n}_i \hat{n}_j,
+$$
 
-    ./setup
+and computes the mean-field correction $\hat{V}_{\text{MF}}$ which approximates the interaction term:
+
+$$
+\hat{V} \approx \hat{V}_{\text{MF}} = \sum_{ij} \tilde{v}_{ij} c^\dagger_{i} c_{j}.
+$$
+
+For more details, refer to the [theory overview](docs/source/documentation/mf_notes.md) and [algorithm description](docs/source/documentation/algorithm.md).
+
+## How to use `MeanFi`?
+
+The calculation of a mean-field Hamiltonian is a simple 3-step process:
+
+1. **Define**
+
+    To specify the interacting problem, use a `Model` object which collects:
+    - Non-interacting Hamiltonian as a tight-binding dictionary.
+    - Interaction Hamiltonian as a tight-binding dictionary.
+    - Particle filling number in the unit cell.
+2. **Guess**
+
+    Construct a starting guess for the mean-field correction.
+
+3. **Solve**
+
+    Solve for the mean-field correction using the `solver` function and add it to the non-interacting part to obtain the total mean-field Hamiltonian.
+
+```python
+import meanfi
+
+#Define
+h_0 = {(0,) : onsite, (1,) : hopping, (-1,) : hopping.T.conj()}
+h_int = {(0,) : onsite_interaction}
+model = meanfi.Model(h_0, h_int, filling=2)
+
+#Guess
+guess = meanfi.guess_tb(guess_hopping_keys, ndof)
+
+#Solve
+mf_correction = meanfi.solver(model, guess)
+h_mf = meanfi.add_tb(h_0, mf_correction)
+```
+
+For more details and examples on how to use the package, we refer to the [tutorials](docs/source/tutorial/hubbard_1d.md).
+
+## Why `MeanFi`?
+
+Here is why you should use `MeanFi`:
+
+* Simple
+
+    The workflow is straightforward.
+    Interface with `Kwant` allows easy creation of complicated tight-binding systems and interactions.
+
+* Extensible
+
+    `MeanFi`'s code is structured to be easy to understand, modify and extend.
+
+* Optimized numerical workflow
+
+    Introduces minimal overhead to the calculation of the mean-field Hamiltonian.
+
+
+## What `MeanFi` doesn't do (yet)
+
+Here are some features that are not yet implemented but are planned for future releases:
+
+- **Superconductive order parameters**. Mean-field Hamiltonians do not include pairing terms.
+- **General interactions**. We allow only density-density interactions (e.g. Coulomb) which can be described by a second-order tensor.
+- **Temperature effects**. Density matrix calculations are done at zero temperature.
+
+## Installation
+
+```
+pip install meanfi
+```
+
+## Citing `MeanFi`
+
+If you have used `MeanFi` for work that has led to a scientific publication, please cite us as:
+
+```bibtex
+@misc{meanfi,
+  author = {Vilkelis, Kostas and Zijderveld,  R. Johanna and Akhmerov, Anton R. and Manesco, Antonio L.R.},
+  doi = {10.5281/zenodo.11149850},
+  month = {5},
+  title = {MeanFi},
+  year = {2024}
+}
+```
diff --git a/docs/environment.yml b/docs/environment.yml
index 7fa4e13..cf75be7 100644
--- a/docs/environment.yml
+++ b/docs/environment.yml
@@ -1,4 +1,4 @@
-name: pymf-docs
+name: meanfi-docs
 
 channels:
   - conda-forge
diff --git a/docs/source/AUTHORS.md b/docs/source/AUTHORS.md
new file mode 120000
index 0000000..2d2e840
--- /dev/null
+++ b/docs/source/AUTHORS.md
@@ -0,0 +1 @@
+../../AUTHORS.md
\ No newline at end of file
diff --git a/docs/source/_static/css/custom.css b/docs/source/_static/css/custom.css
new file mode 100644
index 0000000..c1dc392
--- /dev/null
+++ b/docs/source/_static/css/custom.css
@@ -0,0 +1,21 @@
+html[data-theme="light"] {
+    --pst-color-primary: rgb(4, 87, 13);
+    --pst-color-secondary: rgb(66, 96, 150);
+    --pst-color-border-tippy: rgb(0, 0, 0);
+    --pst-color-inline-code-links: rgb(4, 87, 13);
+    --pst-color-link-hover: var(--pst-color-secondary)
+}
+
+html[data-theme="dark"] {
+    --pst-color-primary: rgb(176, 217, 162);
+    --pst-color-secondary: rgb(125, 166, 244);
+    --pst-color-border-tippy: white;
+    --pst-color-inline-code-links: var(--pst-color-primary);
+    --pst-color-link-hover: var(--pst-color-secondary)
+}
+
+.tippy-box {
+    background-color:var(--pst-color-surface);
+    color:var(--pst-color-text-base);
+    border: 1px solid var(--pst-color-border-tippy);
+}
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 6ae82d9..d58f0d0 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -13,9 +13,9 @@
 import os
 import sys
 
-import pymf  # noqa: F401
+import meanfi  # noqa: F401
 
-package_path = os.path.abspath("../pymf")
+package_path = os.path.abspath("../meanfi")
 # Suppress superfluous frozen modules warning.
 os.environ["PYDEVD_DISABLE_FILE_VALIDATION"] = "1"
 sys.path.insert(0, package_path)
@@ -23,14 +23,14 @@ sys.path.insert(0, package_path)
 
 # -- Project information -----------------------------------------------------
 
-project = "pymf"
-copyright = "2024, pymf developers"
-author = "pymf developers"
-gitlab_url = "https://gitlab.kwant-project.org/qt/pymf"
+project = "MeanFi"
+copyright = "2024, MeanFi developers"
+author = "MeanFi developers"
+gitlab_url = "https://gitlab.kwant-project.org/qt/meanfi"
 
 # The full version, including alpha/beta/rc tags
-release = pymf.__version__
-major, minor = pymf.__version_tuple__[:2]
+release = meanfi.__version__
+major, minor = meanfi.__version_tuple__[:2]
 version = f"{major}.{minor}"
 
 # -- General configuration ---------------------------------------------------
@@ -71,6 +71,8 @@ intersphinx_mapping = {
 
 default_role = "autolink"
 
+latex_elements = {"extrapackages": r"\usepackage{braket}"}
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
 
@@ -86,9 +88,10 @@ autoclass_content = "both"
 # a list of builtin themes.
 #
 html_theme = "sphinx_book_theme"
+html_title = "MeanFi"
 
 html_theme_options = {
-    "repository_url": "https://gitlab.kwant-project.org/qt/kwant-scf",
+    "repository_url": "https://gitlab.kwant-project.org/qt/meanfi",
     "use_repository_button": True,
     "use_issues_button": True,
     "use_edit_page_button": True,
@@ -112,5 +115,5 @@ html_theme_options = {
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-# html_static_path = ["_static"]
-# html_css_files = ["local.css"]
+html_static_path = ["_static"]
+html_css_files = ["css/custom.css"]
diff --git a/docs/source/documentation/algorithm.md b/docs/source/documentation/algorithm.md
new file mode 100644
index 0000000..dd04e81
--- /dev/null
+++ b/docs/source/documentation/algorithm.md
@@ -0,0 +1,57 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.14.4
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+# Algorithm overview
+
+## Self-consistent mean-field loop
+
+To calculate the mean-field interaction in {eq}`mf_infinite`, we require the ground-state density matrix $\rho_{mn}(R)$.
+However, {eq}`density` is a function of the mean-field interaction $\hat{V}_{\text{MF}}$ itself.
+Therefore, we need to solve for both self-consistently.
+
+A single iteration of this self-consistency loop is a function that computes a new mean-field correction from a given one:
+
+$$
+\text{MF}(\hat{V}_{\text{init, MF}}) \to \hat{V}_{\text{new, MF}},
+$$
+
+which is defined in {autolink}`~meanfi.model.Model.mfield` method.
+It performs the following steps:
+1. Calculate the total Hamiltonian $\hat{H}(R) = \hat{H_0}(R) + \hat{V}_{\text{init, MF}}(R)$ in real-space.
+2. ({autolink}`~meanfi.mf.density_matrix`) Compute the ground-state density matrix $\rho_{mn}(R)$:
+    1. ({autolink}`~meanfi.tb.transforms.tb_to_kgrid`) Fourier transform the total Hamiltonian to momentum space $\hat{H}(R) \to \hat{H}(k)$.
+    2. ({autolink}`numpy.linalg.eigh`) Diagonalize the Hamiltonian $\hat{H}(R)$ to obtain the eigenvalues and eigenvectors.
+    3. ({autolink}`~meanfi.mf.fermi_on_kgrid`) Calculate the fermi level given the desired filling of the unit cell.
+    4.  ({autolink}`~meanfi.mf.density_matrix_kgrid`) Calculate the density matrix $\rho_{mn}(k)$ using the eigenvectors and the fermi level.
+    5. ({autolink}`~meanfi.tb.transforms.kgrid_to_tb`) Inverse Fourier transform the density matrix to real-space $\rho_{mn}(k) \to \rho_{mn}(R)$.
+3. ({autolink}`~meanfi.mf.meanfield`) Calculate the new mean-field correction $\hat{V}_{\text{new, MF}}(R)$ using {eq}`mf_infinite`.
+
+## Self-consistency criteria
+
+To define the self-consistency condition, we first introduce an invertible function $f$ that uniquely maps $\hat{V}_{\text{MF}}$ to a real-valued vector which minimally parameterizes it:
+
+$$
+f : \hat{V}_{\text{MF}} \to f(\hat{V}_{\text{MF}}) \in \mathbb{R}^N.
+$$
+
+In the code, $f$ corresponds to the {autolink}`~meanfi.params.rparams.tb_to_rparams` function (inverse is {autolink}`~meanfi.params.rparams.rparams_to_tb`).
+Currently, $f$ parameterizes the mean-field interaction by taking only the upper triangular elements of the matrix $V_{\text{MF}, nm}(R)$ (the lower triangular part is redundant due to the Hermiticity of the Hamiltonian) and splitting it into real and imaginary parts to form a real-valued vector.
+
+With this, we define the self-consistency criterion as a fixed-point problem:
+
+$$
+f(\text{MF}(\hat{V}_{\text{MF}})) = f(\hat{V}_{\text{MF}}).
+$$
+
+Instead of solving the fixed point problem, we rewrite it as the difference of the two successive self-consistent mean-field iterations in {autolink}`~meanfi.solvers.cost`.
+That re-defines the problem into a root-finding problem which is more consistent with available numerical solvers such as {autolink}`~scipy.optimize.anderson`.
+That is exactly what we do in the {autolink}`~meanfi.solvers.solver` function, although we also provide the option to use a custom optimizer.
diff --git a/docs/source/documentation/meanfi.md b/docs/source/documentation/meanfi.md
new file mode 100644
index 0000000..090ef86
--- /dev/null
+++ b/docs/source/documentation/meanfi.md
@@ -0,0 +1,76 @@
+# Package reference
+
+## Interactive problem definition
+
+To define the interactive problem, we use the following class:
+
+```{eval-rst}
+.. autoclass:: meanfi.model.Model
+   :members: mfield
+```
+
+## Mean-field and density matrix
+
+```{eval-rst}
+.. automodule:: meanfi.mf
+   :members: meanfield, density_matrix, density_matrix_kgrid, fermi_on_kgrid
+   :show-inheritance:
+```
+
+## Observables
+
+```{eval-rst}
+.. automodule:: meanfi.observables
+   :members: expectation_value
+   :show-inheritance:
+```
+
+## Solvers
+
+```{eval-rst}
+.. automodule:: meanfi.solvers
+   :members: solver, cost
+   :show-inheritance:
+```
+
+## Tight-binding dictionary
+
+### Manipulation
+
+```{eval-rst}
+.. automodule:: meanfi.tb.tb
+   :members: add_tb, scale_tb
+   :show-inheritance:
+```
+
+### Brillouin zone transformations
+
+```{eval-rst}
+.. automodule:: meanfi.tb.transforms
+   :members:
+   :show-inheritance:
+```
+
+### Parametrisation
+
+```{eval-rst}
+.. automodule:: meanfi.params.rparams
+   :members:
+   :show-inheritance:
+```
+
+### Utility functions
+
+```{eval-rst}
+.. automodule:: meanfi.tb.utils
+   :members:
+   :show-inheritance:
+```
+
+## `kwant` interface
+
+```{eval-rst}
+.. automodule:: meanfi.kwant_helper.utils
+   :members:
+   :show-inheritance:
+```
diff --git a/docs/source/documentation/mf_notes.md b/docs/source/documentation/mf_notes.md
new file mode 100644
index 0000000..84eafe5
--- /dev/null
+++ b/docs/source/documentation/mf_notes.md
@@ -0,0 +1,88 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.14.4
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+# Theory overview
+
+## Interacting problems
+
+In physics, one often encounters problems where a system of multiple particles interacts with each other.
+In this package, we consider a general electronic system with density-density interparticle interaction:
+
+:::{math}
+:label: hamiltonian
+\hat{H} = \hat{H_0} + \hat{V} = \sum_{ij} h_{ij} c^\dagger_{i} c_{j} + \frac{1}{2} \sum_{ij} v_{ij} c_i^\dagger c_j^\dagger c_j c_i
+:::
+
+where $c_i^\dagger$ and $c_i$ are the creation and annihilation operators respectively for fermion in state $i$.
+The first term $\hat{H_0}$ is the non-interacting Hamiltonian which by itself is straightforward to solve on a single-particle basis by direct diagonalizations made easy through packages such as [kwant](https://kwant-project.org/).
+The second term $\hat{V}$ is the density-density interaction term between two particles, for example Coulomb interaction.
+To solve the interacting problem exactly, one needs to diagonalize the full Hamiltonian $\hat{H}$ in the many-particle basis which grows exponentially with the number of particles.
+Such a task is often infeasible for large systems and one needs to resort to approximations.
+
+## Mean-field approximation
+
+The first-order perturbative approximation to the interacting Hamiltonian is the Hartree-Fock approximation also known as the mean-field approximation.
+The mean field approximates the quartic term $\hat{V}$ in {eq}`hamiltonian` as a sum of bilinear terms weighted by the expectation values of the remaining operators:
+:::{math}
+:label: mf_approx
+\hat{V} \approx \hat{V}_{\text{MF}} \equiv \sum_{ij} v_{ij} \left[
+\braket{c_i^\dagger c_i} c_j^\dagger c_j - \braket{c_i^\dagger c_j} c_j^\dagger c_i \right],
+:::
+where we neglect the constant offset terms and the superconducting pairing (for now).
+The expectation value terms  $\langle c_i^\dagger c_j \rangle$ are due to the ground state density matrix and act as an effective field on the system.
+The ground state density matrix reads:
+:::{math}
+:label: density
+\rho_{ij} \equiv \braket{c_i^\dagger c_j } = \text{Tr}\left(e^{-\beta \left(\hat{H_0} + \hat{V}_{\text{MF}} - \mu \hat{N} \right)} c_i^\dagger c_j\right),
+:::
+where $\beta = 1/ (k_B T)$ is the inverse temperature, $\mu$ is the chemical potential, and $\hat{N} = \sum_i c_i^\dagger c_i$ is the number operator.
+Currently, we neglect thermal effects so $\beta \to \infty$.
+
+## Finite tight-binding grid
+
+To simplify the mean-field Hamiltonian, we assume a finite, normalised, orthogonal tight-binding grid defined by the single-particle basis states:
+
+$$
+\ket{n} = c^\dagger_n\ket{\text{vac}}
+$$
+
+where $\ket{\text{vac}}$ is the vacuum state.
+We project our mean-field interaction in {eq}`mf_approx` onto the tight-binding grid:
+
+:::{math}
+:label: mf_finite
+V_{\text{MF}, nm} = \braket{n | \hat{V}_{\text{MF}} | m} =  \sum_{i} \rho_{ii} v_{in} \delta_{nm} - \rho_{mn} v_{mn},
+:::
+where $\delta_{nm}$ is the Kronecker delta function.
+
+## Infinite tight-binding grid
+
+In the limit of a translationally invariant system, the index $n$ that labels the basis states partitions into two independent variables: the unit cell internal degrees of freedom (spin, orbital, sublattice, etc.) and the position of the unit cell $R_n$:
+
+$$
+n \to n, R_n.
+$$
+
+Because of the translational invariance, the physical properties of the system are independent of the absolute unit cell position $R_n$ but rather depend on the relative position between the two unit cells $R_{nm} = R_n - R_m$:
+
+$$
+\rho_{mn} \to \rho_{mn}(R_{mn}).
+$$
+
+That allows us to re-write the mean-field interaction in {eq}`mf_finite` as:
+
+:::{math}
+:label: mf_infinite
+V_{\text{MF}, nm} (R) =  \sum_{i} \rho_{ii} (0) v_{in} (0) \delta_{nm} \delta(R) - \rho_{mn}(R) v_{mn}(R),
+:::
+
+where now indices $i, n, m$ label the internal degrees of freedom of the unit cell and $R$ is the relative position between the two unit cells in terms of the lattice vectors.
diff --git a/docs/source/index.md b/docs/source/index.md
index 3df6663..86e2b5a 100644
--- a/docs/source/index.md
+++ b/docs/source/index.md
@@ -11,29 +11,26 @@ kernelspec:
   name: python3
 ---
 
-# pymf
-
 ```{toctree}
 :hidden:
 :maxdepth: 1
 :caption: Tutorials
 
-mf_notes.md
+tutorial/hubbard_1d.md
+tutorial/graphene_example.md
 ```
 
-## What is pymf?
-
-## Why pymf?
-
-## How does pymf work?
-
-## What does pymf not do yet?
-
-## Installation
+```{toctree}
+:hidden:
+:maxdepth: 1
+:caption: Documentation
 
-```bash
-pip install .
+documentation/mf_notes.md
+documentation/algorithm.md
+documentation/meanfi.md
+AUTHORS.md
 ```
-## Citing
 
-## Contributing
+```{include} ../../README.md
+:relative-docs: docs/source/
+```
diff --git a/docs/source/mf_notes.md b/docs/source/mf_notes.md
deleted file mode 100644
index 6ff0d32..0000000
--- a/docs/source/mf_notes.md
+++ /dev/null
@@ -1,304 +0,0 @@
-# Self-consistent mean field algorithm
-
-## Mean-field approximation
-
-The full hamiltonian is:
-
-$$
-\hat{H} = \hat{H_0} + \hat{V} = \sum_{ij} h_{ij} c^\dagger_{i} c_{j} + \frac{1}{2} \sum_{ijkl} v_{ijkl} c_i^\dagger c_j^\dagger c_l c_k
-$$
-
-We assume the dominant part of the ground state wavefunction comes from $\hat{H}_0$. Let's assume $b_i$ operators diagonalize the unperturbed hamiltonian:
-
-$$
-c_i^\dagger = \sum_{k} U_{ik} b_k^\dagger,
-$$
-
-such that the unperturbed groundstate wavefunction is:
-
-$$
-| 0 \rangle = \Pi_{E_i \leq \mu } b_i^\dagger |\textrm{vac}\rangle.
-$$
-
-Based on this definition, we define the normal ordering operator $:ABC...:$ such that it fulfills:
-
-$$
-:ABC...: | 0 \rangle = 0
-$$
-
-which practically means it orders $b_i$ operators based on whether its above or below the Fermi level $\mu$.
-
-Under this definition of normal ordering, we define the Wick's expansion of the interaction term:
-
-$$
-\begin{multline}
-c_i^\dagger c_j^\dagger c_l c_k = :c_i^\dagger c_j^\dagger c_l c_k: \\+  \overline{c_i^\dagger c_j^\dagger} :c_l c_k: + \overline{c_i^\dagger c_k} :c_j^\dagger c_l: - \overline{c_i^\dagger c_l} :c_j^\dagger c_k: + \overline{c_l c_k} :c_i^\dagger c_j^\dagger: - \overline{c_j^\dagger c_k} :c_i^\dagger c_l: + \overline{c_j^\dagger c_l} :c_i^\dagger c_k: \\
-\overline{c_i^\dagger c_j^\dagger} \overline{c_l c_k} - \overline{c_i^\dagger c_l} \overline{c_j^\dagger c_k} + \overline{c_i^\dagger c_k} \overline{c_j^\dagger c_l}
-\end{multline}
-$$
-
-where the overline defines Wick's contraction:
-
-$$
-\overline{AB} = AB - :AB:.
-$$
-
-The expectation value of the interaction with respect to the $| 0 \rangle$ is:
-
-$$
-\langle 0 | c_i^\dagger c_j^\dagger c_l c_k | 0 \rangle = \langle 0 | \overline{c_i^\dagger c_j^\dagger} \overline{c_l c_k} - \overline{c_i^\dagger c_l} \overline{c_j^\dagger c_k} + \overline{c_i^\dagger c_k} \overline{c_j^\dagger c_l}  | 0 \rangle
-$$
-
-where we can forget about all the normal ordered states since those give zero acting on the unperturbed groundstate. To evaluate this further, we utilize the mean-field approximation:
-
-$$
-A B \approx \langle A \rangle B + A \langle B \rangle - \langle A \rangle \langle B \rangle
-$$
-
-onto the contractions such that we get:
-
-$$
-\langle  c_i^\dagger c_j^\dagger c_l c_k \rangle \approx \langle c_i^\dagger c_j^\dagger \rangle \langle  c_l c_k \rangle + \langle c_i^\dagger c_k \rangle  \langle c_j^\dagger c_l \rangle - \langle c_i^\dagger c_l \rangle \langle c_j^\dagger c_k \rangle
-$$
-
-note $\langle A B \rangle \approx \langle A \rangle  \langle B \rangle$ assuming mean-field.
-
-To consider excitations from the groundstate, we make use of the mean-field approximation defined above:
-
-$$
-\begin{multline}
-c_i^\dagger c_j^\dagger c_l c_k \approx \\
-\langle c_i^\dagger c_j^\dagger \rangle c_l c_k + \langle c_i^\dagger c_k \rangle c_j^\dagger c_l - \langle c_i^\dagger c_l \rangle c_j^\dagger c_k + \langle c_l c_k \rangle c_i^\dagger c_j^\dagger - \langle c_j^\dagger c_k \rangle c_i^\dagger c_l + \langle c_j^\dagger c_l \rangle c_i^\dagger c_k + \\
-\langle c_i^\dagger c_j^\dagger \rangle \langle  c_l c_k \rangle + \langle c_i^\dagger c_k \rangle  \langle c_j^\dagger c_l \rangle - \langle c_i^\dagger c_l \rangle \langle c_j^\dagger c_k \rangle
-\end{multline}
-$$
-
-Where we made use of the following operations:
-
-$$
-:c_i^\dagger c_j^\dagger c_l c_k: \approx 0
-$$
-
-$$
-\overline{c_i^\dagger c_k} \overline{c_j^\dagger c_l} \approx \langle \overline{c_i^\dagger c_k} \rangle \overline{c_j^\dagger c_l} + \overline{c_i^\dagger c_k} \langle \overline{c_j^\dagger c_l} \rangle - \langle \overline{c_i^\dagger c_k} \rangle \langle \overline{c_j^\dagger c_i} \rangle =  \langle c_i^\dagger c_k \rangle \overline{c_j^\dagger c_l} + \overline{c_i^\dagger c_k} \langle c_j^\dagger c_l \rangle - \langle c_i^\dagger c_k \rangle \langle c_j^\dagger c_l \rangle
-$$
-
-$$
-\overline{c_i^\dagger c_k} :c_j^\dagger c_l: \approx \langle \overline{c_i^\dagger c_k} \rangle  :c_j^\dagger c_l: +  \overline{c_i^\dagger c_k} \langle :c_j^\dagger c_l: \rangle - \langle \overline{c_i^\dagger c_k} \rangle \langle :c_j^\dagger c_l: \rangle = \langle \overline{c_i^\dagger c_k} \rangle  :c_j^\dagger c_l:
-$$
-
-
-$$
-\langle \overline{c_i^\dagger c_k} \rangle  = \langle c_i^\dagger c_k - :c_i^\dagger c_k: \rangle  = \langle c_i^\dagger c_k \rangle
-$$
-
-
-Without any superconducting terms, the form simplifies to:
-
-$$
-\begin{multline}
-c_i^\dagger c_j^\dagger c_l c_k \approx
-\langle c_i^\dagger c_k \rangle c_j^\dagger c_l - \langle c_i^\dagger c_l \rangle c_j^\dagger c_k - \langle c_j^\dagger c_k \rangle c_i^\dagger c_l + \langle c_j^\dagger c_l \rangle c_i^\dagger c_k + \\
-\langle c_i^\dagger c_k \rangle  \langle c_j^\dagger c_l \rangle - \langle c_i^\dagger c_l \rangle \langle c_j^\dagger c_k \rangle
-\end{multline}
-$$
-
-## Finite size
-
-### Coulomb interaction
-
-We simplify the interaction term through the MF approximation to get:
-
-$$
-V = \frac{1}{2}\sum_{ijkl} v_{ijkl} c_i^{\dagger} c_j^{\dagger} c_l c_k
-\approx
-\frac12 \sum_{ijkl} v_{ijkl} \left[ \langle c_i^{\dagger} c_k \rangle c_j^{\dagger} c_l - \langle c_j^{\dagger} c_k \rangle c_i^{\dagger} c_l - \langle c_i^{\dagger} c_l \rangle c_j^{\dagger} c_k + \langle c_j^{\dagger} c_l \rangle c_i^{\dagger} c_k \right]
-$$
-(assuming no superconductivity)
-
-and an additional constant part:
-
-$$
-V_0 =  \frac{1}{2} \sum_{ijkl} v_{ijkl} \left(\langle c_j^{\dagger} c_l \rangle \langle c_i^{\dagger} c_k \rangle - \langle c_j^{\dagger} c_k \rangle \langle c_i^{\dagger} c_l \rangle \right).
-$$
-
-The interaction reads:
-
-$$
-v_{ijkl} = \iint w_{i}^*(r) w_{j}^*(r') V(r, r') w_{k}(r) w_l(r') dr dr' = \\
-\iint  V(|r - r'|) w_{i}^*(r)w_{k}(r) w_{j}^*(r')  w_l(r') dr dr'
-$$
-
-whereas $w_i$ is a wannier function on site i (and corresponding dof). Whenever one interchanges $i \to j, k \to l$, the Coulomb term is preserved $v_{ijkl} = v_{jilk}$
-
-To make things more understandable, we are also going to explicitly split up position and spin indices: $i \to i \times \sigma$. In this notation, the Coulomb integral reads:
-
-$$
-v_{ijkl}^{\sigma_i \sigma_j \sigma_k \sigma_l} =
-\iint V(|r - r'|) w_{i\times\sigma_i}^{*} (r)w_{k \times \sigma_k}(r) w_{j \times \sigma_j}^{*}(r')  w_{l\times \sigma_l}(r') dr dr' \delta_{\sigma_i \sigma_k} \delta_{\sigma_{j} \sigma_l}
-$$
-
-On a fine tight-binding model, we have:
-
-$$
-v_{ijkl}^{\sigma_i \sigma_j \sigma_k \sigma_l} = v_{ij} \delta_{ik} \delta_{jl} \delta_{\sigma_i \sigma_k} \delta_{\sigma_j \sigma_l}
-$$
-
-where $v_{ij} = V(r_i, r_j)$.
-
-We shall re-define $i$ index to absorb spin:
-
-$$
-\delta_{ik} \times \delta_{\sigma_{i} \sigma_{k}} \to \delta_{ik}
-$$
-
-in this notation the above reads:
-
-$$
-v_{ijkl} = v_{ij} \delta_{ik} \delta_{jl}
-$$
-
-The mean-field terms are:
-
-$$
-\langle c_i^{\dagger} c_j\rangle = \langle \Psi_F|c_i^{\dagger} c_j | \Psi_F \rangle
-$$
-
-whereas $|\Psi_F \rangle = \Pi_{i=0}^{N_F} b_i^\dagger |0\rangle$. To make sense of things, we need to transform between $c_i$ basis (position + internal dof basis) into the $b_i$ basis (eigenfunction of a given mean-field Hamiltonian):
-
-$$
-c_i^\dagger = \sum_{k} U_{ik} b_k^\dagger
-$$
-
-where
-
-$$
-U_{ik} = \langle{i|\psi_k} \rangle.
-$$
-
-That gives us:
-
-
-$$
-c_i^{\dagger} c_j = \sum_{k, l} U_{ik} U_{lj}^* b_k^\dagger b_{l}
-$$
-
-and its expectation value gives us the mean-field ... field $F_{ij}$:
-
-$$
-F_{ij} = \langle c_i^{\dagger} c_j\rangle =  \sum_{k, l} U_{ik} U_{lj}^* \langle \Psi_F| b_k^\dagger b_{l}| \Psi_F \rangle =  \sum_{k} U_{ik} U_{kj}^{*}
-$$
-
-whereas I assumed the indices for wavefunctions $k,l$ are ordered in terms of increasing eigenvalue. We pop that into the definition of the mean-field correction $V$:
-
-
-$$
-\begin{multline}
-V_{nm} = \frac12 \sum_{ijkl} v_{ijkl} \langle n| \left[ \langle c_i^{\dagger} c_k \rangle c_j^{\dagger} c_l - \langle c_j^{\dagger} c_k \rangle c_i^{\dagger} c_l - \langle c_i^{\dagger} c_l \rangle c_j^{\dagger} c_k + \langle c_j^{\dagger} c_l \rangle c_i^{\dagger} c_k \right] |m\rangle = \\
- \frac12 \sum_{ijkl} v_{ijkl} \left[ +\delta_{jn}\delta_{lm} F_{ik} -\delta_{in}\delta_{lm} F_{jk} -\delta_{jn}\delta_{km} F_{il} + \delta_{in}\delta_{km} F_{jl} \right] = \\
-\frac12 \left[ \sum_{ik} v_{inkm} F_{ik} - \sum_{jk} v_{njkm} F_{jk} - \sum_{il} v_{inml} F_{il} + \sum_{jl} v_{njml} F_{jl} \right] = \\
--\sum_{ij} F_{ij} \left(v_{inmj} - v_{injm} \right)
-\end{multline}
-$$
-
-where I used the $v_{ijkl} = v_{jilk}$ symmetry from Coulomb.
-
-For a tight-binding grid, the mean-field correction simplifies to:
-
-$$
-\begin{multline}
-V_{nm} = - \sum_{ij} F_{ij} \left(v_{inmj} - v_{injm} \right) = \\
--\sum_{ij}F_{ij} v_{in} \delta_{im} \delta_{nj} + \sum_{ij}F_{ij} v_{in} \delta_{ij} \delta_{nm} = \\
--F_{mn} v_{mn} + \sum_{i} F_{ii} v_{in} \delta_{nm}
-\end{multline}
-$$
-
-the first term is the exchange interaction whereas the second one is the direct interaction.
-
-Similarly, the constant offset term reads:
-
-$$
-\begin{multline}
-V_0 = \frac{1}{2} \sum_{ijkl} v_{ijkl} \left(F_{jl} F_{ik} - F_{jk} F_{il} \right) = \\
-\frac{1}{2} \sum_{ijkl} v_{ij} \delta_{ik} \delta_{jl} \left(F_{jl} F_{ik} - F_{jk} F_{il}\right) \\
-= \frac{1}{2} \sum_{ij} v_{ij} \left(F_{ii} F_{jj} - F_{ji} F_{ij}\right)
-\end{multline}
-$$
-
-where we identify the first term as the exchange (mixes indices) and the right one as the direct (diagonal in indices).
-
-## Translational Invariance
-
-The above assumed a finite tight-binding model - all $nm$-indices contain the position of all atoms (among other dof). In this section tho we want to consider an infinite system with translational invariance.
-
-To begin with we deconstruct a general matrix $O_{nm}$ into the cell degrees of freedom ($nm$) and the position of the the cell itself ($ij$):
-
-$$
-O_{nm} \to O^{ij}_{nm}
-$$
-
-and we will Fourier transform the upper indices into k-space:
-
-$$
-O_{mn}(k) = \sum_{ij} O_{nm}^{ij} e^{-i k (R_i-R_j)}
-$$
-
-where I assumed $O$ (and thus all operators I will consider here) is local and thus diagonal in k-space.
-
-Now lets rewrite our main result in the previous section using our new notation:
-
-$$
-V_{nm}^{ij} =-F_{mn}^{ij} v_{mn}^{ij} + \sum_{r,p} F_{pp}^{rr} v_{pn}^{rj} \delta_{nm} \delta^{ij}
-$$
-
-Lets first consider the second (direct) term. Lets express the corresponding $F$ term in k-space:
-
-$$
-F_{pp}^{rr} = \int e^{i k (R_r-R_r)} F_{pp}(k) dk = \int F_{pp}(k) dk
-$$
-
-Notice that in the final expression, there is no $rr$ dependence and thus this term is cell-periodic. Therefore, we shall redefine it as cell electron density $\rho$:
-$$
-F_{pp}^0 = F_{pp}(R = 0) = \int F_{pp}(k) dk
-$$
-
-Now since $\rho$ has no $r$ dependence, we can proceed with the sum:
-
-$$
-\sum_{r} v_{pn}^{rj} = \int v_{pn}(k) e^{ik R_j} \sum_{r} e^{-i k R_r} dk = \int v_{pn}(k) e^{ik R_j} \delta_{k, 0} dk = v_{pn}(0)
-$$
-
-We are finally ready to Fourier transform the main result. Invoking convolution theorem and the results above gives us:
-
-$$
-V_{nm}(k) = \sum_{p} F_{pp}^0 v_{pn}(0) \delta_{nm} -F_{mn}(k) \circledast v_{mn}(k) = V_n^D - F_{mn}(k) \circledast v_{mn}(k)
-$$
-
-which does make sense. The first term (direct) is a potential term coming from the mean-field and the second term (exchange) is purely responsible for the hopping.
-
-The constant offset is:
-$$
-V_0 = \frac{1}{2} \sum_{r,s} \rho_r v_{rs}(0) \rho_s- \\ \frac{1}{2} tr\left[\int_{BZ} \left(F \circledast v\right)(k) F(k) dk \right]
-$$
-
-## Short-Range interactions
-
-In the case of short-range interactions, it is much more convenient to go back to real space to both store objects and perform the operations. In real space the mean-field part of the Hamiltonian reads:
-
-$$
-V_{nm}(\mathbf{R}) = V_n^D \delta(\mathbf{R}) - F_{mn}(\mathbf{R}) v_{mn}(\mathbf{R})
-$$
-
-(the first term might need some prefactor from Fourier transformation)
-
-where $\mathbf{R}$ is a sequence of integers representing real-space unit cell indices.
-
-### Proposed Algorithm
-Given an initial Hamiltonian $H_0 (R)$ and the interaction term $v(R)$ in real-space, the mean-field algorithm is the following:
-
-0. Start with a mean-field guess in real-space: $V(R)$.
-1. Fourier transform tight-binding model and the mean-field in real space to a given k-grid: $H_0(R) + V(R) \to H_0(k) + V(k)$
-2. Diagonalize and evaluate the density matrix: $H_0(k) + V(k) \to F(k)$
-3. Fourier transform the density matrix back to real-space: $F(k) \to F(R)$
-4. Evaluate the new mean-field Hamiltonian $V(R)$ according to the equation above.
-5. Evaluate self-consistency metric (could be based either on $V(R/k)$ or $F(R/k)$). Based on that, either stop or go back to 1 with a modified $V(R)$ starting guess.
diff --git a/docs/source/tutorial/graphene_example.md b/docs/source/tutorial/graphene_example.md
new file mode 100644
index 0000000..99e89fc
--- /dev/null
+++ b/docs/source/tutorial/graphene_example.md
@@ -0,0 +1,215 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.14.4
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# Interacting graphene
+
+In the previous tutorial, we showed how to use `MeanFi` to solve a simple 1D Hubbard model with onsite interactions.
+In this tutorial, we will apply `MeanFi` to more complex system: graphene with onsite $U$ and nearest-neighbour $V$ interactions.
+The system is more complicated in every aspect: the lattice structure, dimension of the problem, complexity of the interactions.
+And yet, the workflow is the same as in the previous tutorial and remains simple and straightforward.
+
+## Building the system with `kwant`
+
+### Non-interacting part
+
+As in the previous tutorial, we could construct a tight-binding dictionary of graphene by hand, but instead it is much easier to use [`kwant`](https://kwant-project.org/) to build the system.
+For a more detailed explanation on `kwant` see the [tutorial](https://kwant-project.org/doc/1/tutorial/graphene).
+
+
+```{code-cell} ipython3
+import numpy as np
+import matplotlib.pyplot as plt
+import kwant
+
+import meanfi
+from meanfi.kwant_helper import utils
+
+s0 = np.identity(2)
+sx = np.array([[0, 1], [1, 0]])
+sy = np.array([[0, -1j], [1j, 0]])
+sz = np.diag([1, -1])
+
+# Create graphene lattice
+graphene = kwant.lattice.general([(1, 0), (1 / 2, np.sqrt(3) / 2)],
+                                 [(0, 0), (0, 1 / np.sqrt(3))], norbs=2)
+a, b = graphene.sublattices
+
+# Create bulk system
+bulk_graphene = kwant.Builder(kwant.TranslationalSymmetry(*graphene.prim_vecs))
+# Set onsite energy to zero
+bulk_graphene[a.shape((lambda pos: True), (0, 0))] = 0 * s0
+bulk_graphene[b.shape((lambda pos: True), (0, 0))] = 0 * s0
+# Add hoppings between sublattices
+bulk_graphene[graphene.neighbors(1)] = s0
+```
+
+The `bulk_graphene` object is a `kwant.Builder` object that represents the non-interacting graphene system.
+To convert it to a tight-binding dictionary, we use the {autolink}`~meanfi.kwant_helper.utils.builder_to_tb` function:
+
+```{code-cell} ipython3
+h_0 = utils.builder_to_tb(bulk_graphene)
+```
+
+### Interacting part
+
+We utilize `kwant` to build the interaction tight-binding dictionary as well.
+To define the interactions, we need to specify two functions:
+* `onsite_int(site)`: returns the onsite interaction matrix.
+* `nn_int(site1, site2)`: returns the interaction matrix between `site1` and `site2`.
+
+We feed these functions to the {autolink}`~meanfi.kwant_helper.utils.build_interacting_syst` function, which constructs the `kwant.Builder` object encoding the interactions.
+All we need to do is to convert this object to a tight-binding dictionary using the {autolink}`~meanfi.kwant_helper.utils.builder_to_tb` function.
+
+```{code-cell} ipython3
+def onsite_int(site, U):
+    return U * sx
+
+def nn_int(site1, site2, V):
+    return V * np.ones((2, 2))
+
+builder_int = utils.build_interacting_syst(
+    builder=bulk_graphene,
+    lattice=graphene,
+    func_onsite=onsite_int,
+    func_hop=nn_int,
+    max_neighbor=1
+)
+params = dict(U=0.2, V=1.2)
+h_int = utils.builder_to_tb(builder_int, params)
+```
+
+Because `nn_int` function returns the same interaction matrix for all site pairs, we set `max_neighbor=1` to ensure that the interaction only extends to nearest-neighbours and is zero for longer distances.
+
+## Computing expectation values
+
+As before, we construct {autolink}`~meanfi.model.Model` object to represent the full system to be solved via the mean-field approximation.
+We then generate a random guess for the mean-field solution and solve the system:
+
+```{code-cell} ipython3
+filling = 2
+model = meanfi.Model(h_0, h_int, filling=2)
+int_keys = frozenset(h_int)
+ndof = len(list(h_0.values())[0])
+guess = meanfi.guess_tb(int_keys, ndof)
+mf_sol = meanfi.solver(model, guess, nk=18)
+h_full = meanfi.add_tb(h_0, mf_sol)
+```
+
+To investigate the effects of interaction on systems with more than one degree of freedom, it is more useful to consider the expectation values of various operators which serve as order parameters.
+For example, we can compute the charge density wave (CDW) order parameter which is defined as the difference in the charge density between the two sublattices.
+
+To calculate operator expectation values, we first need to construct the density matrix via the {autolink}`~meanfi.mf.density_matrix` function.
+We then feed it into {autolink}`~meanfi.observables.expectation_value` function together with the operator we want to measure.
+In this case, we compute the CDW order parameter by measuring the expectation value of the $\sigma_z$ operator acting on the graphene sublattice degree of freedom.
+```{code-cell} ipython3
+cdw_operator = {(0, 0): np.kron(sz, np.eye(2))}
+
+rho, _ = meanfi.density_matrix(h_full, filling=filling, nk=40)
+rho_0, _ = meanfi.density_matrix(h_0, filling=filling, nk=40)
+
+cdw_order_parameter = meanfi.expectation_value(rho, cdw_operator)
+cdw_order_parameter_0 = meanfi.expectation_value(rho_0, cdw_operator)
+
+print(f"CDW order parameter for interacting system: {np.round(np.abs(cdw_order_parameter), 2)}")
+print(f"CDW order parameter for non-interacting system: {np.round(np.abs(cdw_order_parameter_0), 2)}")
+```
+
+We see that the CDW order parameter is non-zero only for the interacting system, indicating the presence of a CDW phase.
+
+## Graphene phase diagram
+
+In the remaining part of this tutorial, we will utilize all the tools we have developed so far to create a phase diagram for the graphene system.
+
+To identify phase changes, it is convenient to track the gap of the system as a function of $U$ and $V$.
+To that end, we first create a function that calculates the gap of the system given the tight-binding dictionary and the Fermi energy.
+
+```{code-cell} ipython3
+def compute_gap(h, fermi_energy=0, nk=100):
+    kham = meanfi.tb_to_kgrid(h, nk)
+    vals = np.linalg.eigvalsh(kham)
+
+    emax = np.max(vals[vals <= fermi_energy])
+    emin = np.min(vals[vals > fermi_energy])
+    return np.abs(emin - emax)
+```
+
+And proceed to compute the gap and the mean-field correction for a range of $U$ and $V$ values:
+
+```{code-cell} ipython3
+Us = np.linspace(0, 4, 10)
+Vs = np.linspace(0, 1.5, 10)
+
+gaps = []
+mf_sols = []
+for U in Us:
+    for V in Vs:
+        params = dict(U=U, V=V)
+        h_int = utils.builder_to_tb(builder_int, params)
+
+        model = meanfi.Model(h_0, h_int, filling=filling)
+        guess = meanfi.guess_tb(int_keys, ndof)
+        mf_sol = meanfi.solver(model, guess, nk=18)
+        mf_sols.append(mf_sol)
+
+        gap = compute_gap(meanfi.add_tb(h_0, mf_sol), fermi_energy=0, nk=100)
+        gaps.append(gap)
+gaps = np.asarray(gaps, dtype=float).reshape((len(Us), len(Vs)))
+mf_sols = np.asarray(mf_sols).reshape((len(Us), len(Vs)))
+
+plt.imshow(gaps.T, extent=(Us[0], Us[-1], Vs[0], Vs[-1]), origin='lower', aspect='auto')
+plt.colorbar()
+plt.xlabel('V')
+plt.ylabel('U')
+plt.title('Gap')
+plt.show()
+```
+
+This phase diagram has gap openings at the same places as shown in the [literature](https://arxiv.org/abs/1204.4531).
+
+We can now use the stored results in `mf_sols` to fully map out the phase diagram with order parameters.
+On top of the charge density wave (CDW), we also expect a spin density wave (SDW) in a different region of the phase diagram.
+We construct the SDW order parameter with the same steps as before, but now we need to sum over the expectation values of the three Pauli matrices to account for the $SU(2)$ spin-rotation symmetry.
+
+```{code-cell} ipython3
+s_list = [sx, sy, sz]
+cdw_list = []
+sdw_list = []
+for mf_sol in mf_sols.flatten():
+    rho, _ = meanfi.density_matrix(meanfi.add_tb(h_0, mf_sol), filling=filling, nk=40)
+
+    # Compute CDW order parameter
+    cdw_list.append(np.abs(meanfi.expectation_value(rho, cdw_operator))**2)
+
+    # Compute SDW order parameter
+    sdw_value = 0
+    for s_i in s_list:
+      sdw_operator_i = {(0, 0) : np.kron(sz, s_i)}
+      sdw_value += np.abs(meanfi.expectation_value(rho, sdw_operator_i))**2
+    sdw_list.append(sdw_value)
+
+cdw_list = np.asarray(cdw_list).reshape(mf_sols.shape)
+sdw_list = np.asarray(sdw_list).reshape(mf_sols.shape)
+```
+
+Finally, we can combine the gap, CDW and SDW order parameters into one plot.
+We naively do this by plotting the difference between CDW and SDW order parameters and indicate the gap with the transparency.
+
+```{code-cell} ipython3
+import matplotlib.ticker as mticker
+normalized_gap = gaps/np.max(gaps)
+plt.imshow((cdw_list - sdw_list).T, extent=(Us[0], Us[-1], Vs[0], Vs[-1]), origin='lower', aspect='auto', cmap="coolwarm", alpha=normalized_gap.T, vmin=-2.6, vmax=2.6)
+plt.colorbar(ticks=[-2.6, 0, 2.6], format=mticker.FixedFormatter(['SDW', '0', 'CDW']), label='Order parameter', extend='both')
+plt.xlabel('V')
+plt.ylabel('U')
+plt.show()
+```
diff --git a/docs/source/tutorial/hubbard_1d.md b/docs/source/tutorial/hubbard_1d.md
new file mode 100644
index 0000000..da95b50
--- /dev/null
+++ b/docs/source/tutorial/hubbard_1d.md
@@ -0,0 +1,187 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.14.4
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+# 1D Hubbard model
+
+## Background physics
+
+To show the basic functionality of the package, we consider a simple interacting electronic system: a 1D chain of sites that allow nearest-neighbor tunneling with strength $t$ and on-site repulsion $U$ between two electrons if they are on the same site.
+Such a model is known as the 1D [Hubbard model](https://en.wikipedia.org/wiki/Hubbard_model) and is useful for understanding the onset of insulating phases in interacting metals.
+
+To begin, we first consider the second quantized form of the non-interacting Hamiltonian.
+Because we expect the interacting ground state to be antiferromagnetic, we build a two-atom cell and name the two sublattices $A$ and $B$.
+These sublattices are identical to each other in the non-interacting case $U=0$.
+The non-interacting Hamiltonian reads:
+
+$$
+\hat{H_0} = - t \sum_\sigma \sum_i \left(c_{i, B, \sigma}^{\dagger}c_{i, A, \sigma} + c_{i, A, \sigma}^{\dagger}c_{i+1, B, \sigma} + \textrm{h.c}\right).
+$$
+
+where $\textrm{h.c}$ is the hermitian conjugate, $\sigma$ denotes spin ($\uparrow$ or $\downarrow$) and $c_{i, A, \sigma}^{\dagger}$ creates an electron with spin $\sigma$ in unit cell $i$ of sublattice $A$.
+Next up, is the interacting part of the Hamiltonian:
+
+$$
+\hat{V} = U \sum_i \left(n_{i, A, \uparrow} n_{i, A, \downarrow} + n_{i, B, \uparrow} n_{i, B, \downarrow}\right).
+$$
+
+where $n_{i, A, \sigma} = c_{i, A, \sigma}^{\dagger}c_{i, A, \sigma}$ is the number operator for sublattice $A$ and spin $\sigma$.
+The total Hamiltonian is then $\hat{H} = \hat{H_0} + \hat{V}$.
+With the model defined, we can now proceed to input the Hamiltonian into the package and solve it using the mean-field approximation.
+
+## Problem definition
+
+### Non-interacting Hamiltonian
+
+First, let's get the basic imports out of the way.
+
+```{code-cell} ipython3
+import numpy as np
+import matplotlib.pyplot as plt
+import meanfi
+```
+Now let us translate the non-interacting Hamiltonian $\hat{H_0}$ defined above into the basic input format for the package: a **tight-binding dictionary**.
+The tight-binding dictionary is a python dictionary where the keys are tuples of integers representing the hopping vectors and the values are the hopping matrices.
+For example, a key `(0,)` represents the onsite term in one dimension and a key `(1,)` represents the hopping a single unit cell to the right.
+In two dimensions a key `(0,0)` would represent the onsite term and `(1,0)` would represent hopping to the right in the direction of the first reciprocal lattice vector.
+In the case of our 1D Hubbard model, we only have an onsite term and hopping a single unit cell to the left and right.
+Thus our non-interacting Hamiltonian  becomes:
+
+```{code-cell} ipython3
+hopp = np.kron(np.array([[0, 1], [0, 0]]), np.eye(2))
+h_0 = {(0,): hopp + hopp.T.conj(), (1,): hopp, (-1,): hopp.T.conj()}
+```
+Here `hopp` is the hopping matrix which we define as a kronecker product between sublattice and spin degrees of freedom: `np.array([[0, 1], [0, 0]])` corresponds to the hopping between sublattices and `np.eye(2)` leaves the spin degrees of freedom unchanged.
+In the corresponding tight-binding dictionary `h_0`, the key `(0,)` contains hopping within the unit cell and the keys `(1,)` and `(-1,)` correspond to the hopping between the unit cells to the right and left respectively.
+
+To verify the validity of `h_0`, we evaluate it in the reciprocal space using the {autolink}`~meanfi.tb.transforms.tb_to_kgrid`, then diagonalize it and plot the band structure:
+
+```{code-cell} ipython3
+nk = 50 # number of k-points
+ks = np.linspace(0, 2*np.pi, nk, endpoint=False)
+hamiltonians_0 = meanfi.tb_to_kgrid(h_0, nk)
+
+vals, vecs = np.linalg.eigh(hamiltonians_0)
+plt.plot(ks, vals, c="k")
+plt.xticks([0, np.pi, 2 * np.pi], ["$0$", "$\pi$", "$2\pi$"])
+plt.xlim(0, 2 * np.pi)
+plt.ylabel("$E - E_F$")
+plt.xlabel("$k / a$")
+plt.show()
+```
+
+which seems metallic as expected.
+
+### Interaction Hamiltonian
+
+We now proceed to define the interaction Hamiltonian $\hat{V}$.
+To achieve this, we utilize the same tight-binding dictionary format as before.
+Because the interaction Hamiltonian is on-site, it must be defined only for the key `(0,)` and only for electrons on the same sublattice with opposite spins.
+Based on the kronecker product structure we defined earlier, the interaction Hamiltonian is:
+
+```{code-cell} ipython3
+U = 2
+s_x = np.array([[0, 1], [1, 0]])
+h_int = {(0,): U * np.kron(np.eye(2), s_x),}
+```
+Here `s_x` is the Pauli matrix acting on the spin degrees of freedom, which ensures that the interaction is only between electrons with opposite spins whereas the `np.eye(2)` ensures that the interaction is only between electrons on the same sublattice.
+
+### Putting it all together
+
+To combine the non-interacting and interaction Hamiltonians, we use the {autolink}`~meanfi.model.Model` class.
+In addition to the Hamiltonians, we also need to specify the filling of the system --- the number of electrons per unit cell.
+```{code-cell} ipython3
+filling = 2
+full_model = meanfi.Model(h_0, h_int, filling)
+```
+
+The object `full_model` now contains all the information needed to solve the mean-field problem.
+
+## Solving the mean-field problem
+
+To find a mean-field solution, we first require a starting guess.
+In cases where the non-interacting Hamiltonian is highly degenerate, there exists several possible mean-field solutions, many of which are local and not global minima of the energy landscape.
+Therefore, the choice of the initial guess can significantly affect the final solution depending on the energy landscape.
+Here the problem is simple enough that we can generate a random guess for the mean-field solution through the {autolink}`~meanfi.tb.utils.guess_tb` function.
+It creates a random Hermitian tight-binding dictionary based on the hopping keys provided and the number of degrees of freedom within the unit cell.
+Because the mean-field solution cannot contain hoppings longer than the interaction itself, we use `h_int` keys as an input to {autolink}`~meanfi.tb.utils.guess_tb`.
+Finally, to solve the model, we use the {autolink}`~meanfi.solvers.solver` function which by default employes a root-finding [algorithm](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.anderson.html) to find a self-consistent mean-field solution.
+
+```{code-cell} ipython3
+filling = 2
+full_model = meanfi.Model(h_0, h_int, filling)
+guess = meanfi.guess_tb(frozenset(h_int), ndof=4)
+mf_sol = meanfi.solver(full_model, guess, nk=nk)
+```
+
+The {autolink}`~meanfi.solvers.solver` function returns only the mean-field correction to the non-interacting Hamiltonian in the same tight-binding dictionary format.
+To get the full Hamiltonian, we add the mean-field correction to the non-interacting Hamiltonian and plot the band structure just as before:
+
+```{code-cell} ipython3
+h_mf = meanfi.add_tb(h_0, mf_sol)
+
+hamiltonians = meanfi.tb_to_kgrid(h_mf, nk)
+vals, vecs = np.linalg.eigh(hamiltonians)
+plt.plot(ks, vals, c="k")
+plt.xticks([0, np.pi, 2 * np.pi], ["$0$", "$\pi$", "$2\pi$"])
+plt.xlim(0, 2 * np.pi)
+plt.ylabel("$E - E_F$")
+plt.xlabel("$k / a$")
+plt.show()
+```
+
+the band structure now shows a gap at the Fermi level, indicating that the system is in an insulating phase!
+
+
+We can go further and compute the gap for a wider range of $U$ values:
+
+```{code-cell} ipython3
+def compute_sol(U, h_0, nk, filling=2):
+    h_int = {
+        (0,): U * np.kron(np.eye(2), np.ones((2, 2))),
+    }
+    guess = meanfi.guess_tb(frozenset(h_int), len(list(h_0.values())[0]))
+    full_model = meanfi.Model(h_0, h_int, filling)
+    mf_sol = meanfi.solver(full_model, guess, nk=nk)
+    return meanfi.add_tb(h_0, mf_sol)
+
+
+def compute_gap(full_sol, nk_dense, fermi_energy=0):
+    h_kgrid = meanfi.tb_to_kgrid(full_sol, nk_dense)
+    vals = np.linalg.eigvalsh(h_kgrid)
+
+    emax = np.max(vals[vals <= fermi_energy])
+    emin = np.min(vals[vals > fermi_energy])
+    return np.abs(emin - emax)
+
+
+def compute_phase_diagram(
+    Us,
+    nk,
+    nk_dense,
+):
+    gaps = []
+    for U in Us:
+        full_sol = compute_sol(U, h_0, nk)
+        gaps.append(compute_gap(full_sol, nk_dense))
+
+    return np.asarray(gaps, dtype=float)
+
+Us = np.linspace(0, 4, 40, endpoint=True)
+gaps = compute_phase_diagram(Us=Us, nk=20, nk_dense=100)
+
+plt.plot(Us, gaps, c="k")
+plt.xlabel("$U / t$")
+plt.ylabel("$\Delta{E}/t$")
+plt.show()
+```
+
+We see that at around $U=1$ the gap opens up and the system transitions from a metal to an insulator.  In order to more accurately determine the size of the gap, we chose to use a denser k-grid for the diagonalization of the mean-field solution.
diff --git a/examples/1d_hubbard_totalenergy.ipynb b/examples/1d_hubbard_totalenergy.ipynb
deleted file mode 100644
index afb0f27..0000000
--- a/examples/1d_hubbard_totalenergy.ipynb
+++ /dev/null
@@ -1,348 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cb509096-42c6-4a45-8dc4-a8eed3116e67",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from codes.solvers import solver\n",
-    "from codes.tb import transforms, utils\n",
-    "from codes.tb.tb import add_tb\n",
-    "from codes.model import Model\n",
-    "from tqdm import tqdm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "396d935c-146e-438c-878b-04ed70aa6d63",
-   "metadata": {},
-   "source": [
-    "To simulate infinite systems, we provide the corresponding tight-binding model.\n",
-    "\n",
-    "We exemplify this construction by computing the ground state of an infinite spinful chain with onsite interactions.\n",
-    "\n",
-    "Because the ground state is an antiferromagnet, so we must build a two-atom cell. We name the two sublattices, $A$ and $B$. The Hamiltonian in is:\n",
-    "$$\n",
-    "H_0 = \\sum_i c_{i, B}^{\\dagger}c_{i, A} + c_{i, A}^{\\dagger}c_{i+1, B} + h.c.\n",
-    "$$\n",
-    "We write down the spinful by simply taking $H_0(k) \\otimes \\mathbb{1}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "5529408c-fb7f-4732-9a17-97b0718dab29",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hopp = np.kron(np.array([[0, 1], [0, 0]]), np.eye(2))\n",
-    "h_0 = {(0,): hopp + hopp.T.conj(), (1,): hopp, (-1,): hopp.T.conj()}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "050f5435-6699-44bb-b31c-8ef3fa2264d4",
-   "metadata": {},
-   "source": [
-    "To build the tight-binding model, we need to generate a Hamiltonian on a k-point and the corresponding hopping vectors to generate a guess. We then verify the spectrum and see that the bands indeed consistent of two bands due to the Brillouin zone folding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b39a2976-7c35-4670-83ef-12157bd3fc0e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set number of k-points\n",
-    "nk = 100\n",
-    "ks = np.linspace(0, 2*np.pi, nk, endpoint=False) \n",
-    "hamiltonians_0 = transforms.tb_to_khamvector(h_0, nk, 1, ks=ks) \n",
-    "\n",
-    "# Perform diagonalization\n",
-    "vals, vecs = np.linalg.eigh(hamiltonians_0)\n",
-    "# Plot data\n",
-    "plt.plot(ks, vals, c=\"k\")\n",
-    "plt.xticks([0, np.pi, 2 * np.pi], [\"$0$\", \"$\\pi$\", \"$2\\pi$\"])\n",
-    "plt.xlim(0, 2 * np.pi)\n",
-    "plt.ylabel(\"$E - E_F$\")\n",
-    "plt.xlabel(\"$k / a$\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6ec53b08-053b-4aad-87a6-525dd7f61687",
-   "metadata": {},
-   "source": [
-    "Here, in the workflow to find the ground state, we use a helper function to build the initial guess. because we don't need a dense k-point grid in the self-consistent loop, we compute the spectrum later on a denser k-point grid."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc59e440-1289-4735-9ae8-b04d0d13c94a",
-   "metadata": {},
-   "source": [
-    "Finally, we compute the eigen0alues for a set of Ualues of $U$. For this case, since the interaction is onsite only, the interaction matrix is simply\n",
-    "$$\n",
-    "H_{int} =\n",
-    "\\left(\\begin{array}{cccc}\n",
-    "    U & U & 0 & 0\\\\\n",
-    "    U & U & 0 & 0\\\\\n",
-    "    0 & 0 & U & U\\\\\n",
-    "    0 & 0 & U & U\n",
-    "\\end{array}\\right)~.\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "32b9e7c5-db12-44f9-930c-21e5494404b8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def compute_phase_diagram(\n",
-    "    Us,\n",
-    "    nk,\n",
-    "    nk_dense,\n",
-    "    filling=2,\n",
-    "):\n",
-    "    gap = []\n",
-    "    vals = []\n",
-    "    for U in tqdm(Us):\n",
-    "        # onsite interactions \n",
-    "        h_int = {\n",
-    "            (0,): U * np.kron(np.ones((2, 2)), np.eye(2)),\n",
-    "            }\n",
-    "        guess = utils.generate_guess(frozenset(h_int), len(list(h_0.values())[0]))\n",
-    "        full_model = Model(h_0, h_int, filling)\n",
-    "        mf_sol = solver(full_model, guess, nk=nk)\n",
-    "        hkfunc = transforms.tb_to_kfunc(add_tb(h_0, mf_sol))\n",
-    "        ks_dense = np.linspace(0, 2 * np.pi, nk_dense, endpoint=False)\n",
-    "        hkarray = np.array([hkfunc(kx) for kx in ks_dense])\n",
-    "        _vals = np.linalg.eigvalsh(hkarray)\n",
-    "        _gap = (utils.compute_gap(add_tb(h_0, mf_sol), fermi_energy=0, nk=nk_dense))\n",
-    "        gap.append(_gap)\n",
-    "        vals.append(_vals)\n",
-    "    return np.asarray(gap, dtype=float), np.asarray(vals)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "6a8c08a9-7e31-420b-b6b4-709abfb26793",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 20/20 [00:00<00:00, 66.60it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Interaction strengths\n",
-    "Us = np.linspace(0.5, 10, 20, endpoint=True)\n",
-    "nk, nk_dense = 40, 100\n",
-    "gap, vals = compute_phase_diagram(Us=Us, nk=nk, nk_dense=nk_dense)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "e17fc96c-c463-4e1f-8250-c254d761b92a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "\n",
-    "ds = xr.Dataset(\n",
-    "    data_vars=dict(vals=([\"Us\", \"ks\", \"n\"], vals), gap=([\"Us\"], gap)),\n",
-    "    coords=dict(\n",
-    "        Us=Us,\n",
-    "        ks=np.linspace(0, 2 * np.pi, nk_dense),\n",
-    "        n=np.arange(vals.shape[-1])\n",
-    "    ),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a87dcc1-208b-4602-abad-a870037ec95f",
-   "metadata": {},
-   "source": [
-    "\n",
-    "We observe that as the interaction strength increases, a gap opens due to the antiferromagnetic ordering."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "868cf368-45a0-465e-b042-6182ff8b6998",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds.vals.plot.scatter(x=\"ks\", hue=\"Us\", ec=None, s=5)\n",
-    "plt.axhline(0, ls=\"--\", c=\"k\")\n",
-    "plt.xticks([0, np.pi, 2 * np.pi], [\"$0$\", \"$\\pi$\", \"$2\\pi$\"])\n",
-    "plt.xlim(0, 2 * np.pi)\n",
-    "plt.ylabel(\"$E - E_F$\")\n",
-    "plt.xlabel(\"$k / a$\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0761ed33-c1bb-4f12-be65-cb68629f58b9",
-   "metadata": {},
-   "source": [
-    "The Hartree-Fock dispersion should follow (see [these notes](https://www.cond-mat.de/events/correl11/manuscript/Lechermann.pdf))\n",
-    "$$\n",
-    "\\epsilon_{HF}^{\\sigma}(\\mathbf{k}) = \\epsilon(\\mathbf{k}) + U \\left(\\frac{n}{2} + \\sigma m\\right)\n",
-    "$$\n",
-    "where $m=(\\langle n_{i\\uparrow} \\rangle - \\langle n_{i\\downarrow} \\rangle) / 2$ is the magnetization per atom and $n = \\sum_i \\langle n_i \\rangle$ is the total number of atoms per cell. Thus, for the antiferromagnetic groundstate, $m=1/2$ and $n=2$. The gap thus should be $\\Delta=U$. And we can confirm it indeed follows the expected trend."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "ac2eb725-f3bd-4d5b-a509-85d0d0071958",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds.gap.plot()\n",
-    "plt.plot(ds.Us, ds.Us)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "06e0d356-558e-40e3-8287-d7d2e0bee8cd",
-   "metadata": {},
-   "source": [
-    "We can also fit "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "5499ea62-cf1b-4a13-8191-ebb73ea38704",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(0.9997852)"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.gap.polyfit(dim=\"Us\", deg=1).polyfit_coefficients[0].data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "0cb395cd-84d1-49b4-89dd-da7a2d09c8d0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds.to_netcdf(\"./data/1d_hubbard_example.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ce428241",
-   "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": "3.11.6"
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/codes b/examples/codes
deleted file mode 120000
index f496e1c..0000000
--- a/examples/codes
+++ /dev/null
@@ -1 +0,0 @@
-../codes/
\ No newline at end of file
diff --git a/examples/diatomic_molecule.ipynb b/examples/diatomic_molecule.ipynb
deleted file mode 100644
index 24d47bf..0000000
--- a/examples/diatomic_molecule.ipynb
+++ /dev/null
@@ -1,336 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cb509096-42c6-4a45-8dc4-a8eed3116e67",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from codes.solvers import solver\n",
-    "from codes.tb import utils\n",
-    "from codes.model import Model\n",
-    "from codes.tb.tb import add_tb\n",
-    "from tqdm import tqdm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b76e5f0-75f0-4812-9a3a-39992f0244e7",
-   "metadata": {},
-   "source": [
-    "In this example, we will build a zero-dimensional model. Namely, we will find the Hartree-Fock groundstate solution for a diatomic molecule with onsite and nearest-neighbor interactions.\n",
-    "\n",
-    "We start by writing the non-interacting Hamiltonian. The minimal tight-binding model has the following Hamiltonian:\n",
-    "$$\n",
-    " H_0 = c_L^{\\dagger} c_R + h.c.\n",
-    "$$\n",
-    "which we can rewrite in following matrix representation:\n",
-    "$$\n",
-    "H_0 = \\left(c_L^{\\dagger}~c_R^{\\dagger}\\right) \\left(\\begin{array}{cc}\n",
-    "    0 & \\mathbb{1}\\\\\n",
-    "    \\mathbb{1} & 0\n",
-    "\\end{array}\\right)\n",
-    "\\left(\\begin{array}{c}\n",
-    "    c_L\\\\\n",
-    "    c_R\n",
-    "\\end{array}\\right)\n",
-    "$$\n",
-    "where $\\mathbb{1}$ is a $2\\times 2$ identity matrix."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "d31cbfea-18ea-454e-8a63-d706a85cd3fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Just writing the Hamiltonian above in numpy\n",
-    "# Here we add a dummy index to make the notation compatible with infinite systems.\n",
-    "h_0 = {(): np.kron(np.array([[0, 1], [1, 0]]), np.eye(2))}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c0d41ff-8231-441f-89a3-35f3fe94c57a",
-   "metadata": {},
-   "source": [
-    "We can naturally compute the eigenvalues for inspection."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "b39a2976-7c35-4670-83ef-12157bd3fc0e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "hamiltonian_0 = h_0[next(iter(h_0))]\n",
-    "vals, vecs = np.linalg.eigh(hamiltonian_0)\n",
-    "plt.plot(vals, \"o\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "688558ce-cc8e-418e-846e-4e3e32cb3193",
-   "metadata": {},
-   "source": [
-    "We now move to an eigenvalue calculation of the Hartree-Fock solution. The workflow is rather simple:\n",
-    "* Run the self-consistent loop.\n",
-    "* Diagonalize the mean-field Hamiltonian."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "41bd9f60-8f29-4e7c-a0c4-a0bbf66445b2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_vals(\n",
-    "    h_0,\n",
-    "    h_int,\n",
-    "    guess,\n",
-    "    filling=2,\n",
-    "):\n",
-    "    _model = Model(h_0, h_int, filling=filling)\n",
-    "    mf_solution = solver(_model, mf_guess=guess, optimizer_kwargs={\"M\": 0})\n",
-    "    ham_solution = add_tb(h_0, mf_solution)\n",
-    "    # Diagonalize groundstate Hamiltonian.\n",
-    "    vals, _ = np.linalg.eigh(mf_solution[()])\n",
-    "    # Extract Fermi energy.\n",
-    "    E_F = utils.calculate_fermi_energy(ham_solution, filling)\n",
-    "    return vals - E_F"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e580757-adad-4e20-b60c-cff8a85b633d",
-   "metadata": {},
-   "source": [
-    "And then we use this workflow to compute the phase diagram. We consider an interacting Hamiltonian with onsite and nearest-neighbor interactions:\n",
-    "\\begin{align}\n",
-    "H_{int} = \\sum_i U_i n_i n_i + \\sum_{\\langle i, j \\rangle} V_{ij} n_i n_j\\\\\n",
-    "= \\sum_i U_i n_{i\\uparrow} n_{i\\downarrow} + \\sum_{\\langle i, j \\rangle} V_{ij} n_i n_j\n",
-    "\\end{align}\n",
-    "where from the first to the second line we removed the terms that are not allowed by the exclusion principle. These are however taken care of by the algorithm, so we in fact just need to provide $U_i$ and $V_{ij}$. We simplify the Hamiltonian further as:\n",
-    "\\begin{align}\n",
-    "H_{int} = U \\sum_i n_{i\\uparrow} n_{i\\downarrow} + V \\sum_{\\langle i, j \\rangle} n_i n_j~.\n",
-    "\\end{align}\n",
-    "Thus, the we just need to pass to the algorithm the matrix\n",
-    "$$\n",
-    "H_{int} =\n",
-    "\\left(\\begin{array}{cccc}\n",
-    "    U & U & V & V\\\\\n",
-    "    U & U & V & V\\\\\n",
-    "    V & V & U & U\\\\\n",
-    "    V & V & U & U\n",
-    "\\end{array}\\right)~.\n",
-    "$$\n",
-    "\n",
-    "We thus sweep these parameters and see how the eigenvalues evolve."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "32b9e7c5-db12-44f9-930c-21e5494404b8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def compute_phase_diagram(\n",
-    "    Us,\n",
-    "    Vs,\n",
-    "):\n",
-    "    _block = np.ones((2, 2))\n",
-    "    # onsite interactions\n",
-    "    onsite_int = np.kron(np.eye(2), _block)\n",
-    "    # Nearest-neighbor interactions\n",
-    "    nn_int = np.kron(\n",
-    "        np.array([[0, 1], [1, 0]]),\n",
-    "        _block\n",
-    "    )\n",
-    "\n",
-    "    vals = []\n",
-    "    for U in tqdm(Us):\n",
-    "        vals_U = []\n",
-    "        for V in Vs:\n",
-    "            h_int = {(): U * onsite_int + V * nn_int}\n",
-    "            guess = utils.generate_guess(frozenset(h_int), 4)\n",
-    "            try:\n",
-    "                _vals = compute_vals(h_0, h_int, guess)\n",
-    "            except:\n",
-    "                _vals = np.zeros(4)\n",
-    "            vals_U.append(_vals)\n",
-    "        vals.append(vals_U)\n",
-    "    return np.asarray(vals)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "6a8c08a9-7e31-420b-b6b4-709abfb26793",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/20 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 20/20 [00:22<00:00,  1.10s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Interaction strengths\n",
-    "Us = np.linspace(0, 5, 20, endpoint=True)\n",
-    "Vs = np.linspace(0, 1, 20, endpoint=True)\n",
-    "vals = compute_phase_diagram(Us, Vs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "e17fc96c-c463-4e1f-8250-c254d761b92a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "\n",
-    "ds = xr.Dataset(\n",
-    "    data_vars=dict(\n",
-    "        vals=([\"Us\", \"Vs\", \"n\"], vals),\n",
-    "    ),\n",
-    "    coords=dict(Us=Us, Vs=Vs, n=np.arange(vals.shape[-1])),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "070bc196-64c3-4040-9bb5-e9a216763eea",
-   "metadata": {},
-   "source": [
-    "We can now inspect how the eigenenergies evolve as a function of the interaction strength."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 1300x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# New result 0D\n",
-    "ds.vals.plot(x='Us', y='Vs', col='n')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "868cf368-45a0-465e-b042-6182ff8b6998",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x300 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# New result 0D\n",
-    "ds.vals.plot(x='Us', y='Vs', col='n')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "0cb395cd-84d1-49b4-89dd-da7a2d09c8d0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds.to_netcdf('./data/diatomic_molecule_example.nc')"
-   ]
-  }
- ],
- "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": "3.11.6"
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/graphene_extended_hubbard.ipynb b/examples/graphene_extended_hubbard.ipynb
deleted file mode 100644
index 0c6c37f..0000000
--- a/examples/graphene_extended_hubbard.ipynb
+++ /dev/null
@@ -1,384 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cb509096-42c6-4a45-8dc4-a8eed3116e67",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from codes.model import Model\n",
-    "from codes.solvers import solver\n",
-    "from codes import kwant_examples\n",
-    "from codes.kwant_helper import utils as kwant_utils\n",
-    "from codes.tb.tb import add_tb\n",
-    "from codes.tb import utils\n",
-    "from tqdm import tqdm\n",
-    "import xarray as xr\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "99f0e60c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create translationally-invariant `kwant.Builder`\n",
-    "graphene_builder, int_builder = kwant_examples.graphene_extended_hubbard()\n",
-    "h_0 = kwant_utils.builder_to_tb(graphene_builder)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f4d1bb07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.seed(5)\n",
-    "def compute_phase_diagram(Us, Vs, int_builder, h_0): \n",
-    "    gap = []\n",
-    "    for U in tqdm(Us): \n",
-    "        gap_U = []\n",
-    "        guess=None\n",
-    "        for V in Vs: \n",
-    "            params = dict(U=U, V=V)\n",
-    "            h_int = kwant_utils.builder_to_tb(int_builder, params)\n",
-    "            if guess==None:\n",
-    "                guess = utils.generate_guess(frozenset(h_int), len(list(h_0.values())[0]))\n",
-    "            model = Model(h_0, h_int, filling=2)\n",
-    "\n",
-    "            mf_sol = solver(model, guess, nk=18)    \n",
-    "            gap_U.append(utils.compute_gap(add_tb(h_0, mf_sol), fermi_energy=0, nk=300))\n",
-    "            guess = None\n",
-    "        gap.append(gap_U)\n",
-    "    return np.asarray(gap, dtype=float)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "14f332f2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 10/10 [07:09<00:00, 43.00s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "Us = np.linspace(0, 3, 10, endpoint=True)\n",
-    "Vs = np.linspace(0, 1.5, 10, endpoint=True)\n",
-    "gap = compute_phase_diagram(Us, Vs, int_builder, h_0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "0d2ad9d8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x14de67c10>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_da = xr.DataArray(data=gap, coords=dict(Us=Us, Vs=Vs))\n",
-    "gap_da.plot(x=\"Us\", y=\"Vs\", vmin=0, vmax=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "a661ac28",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x14deef590>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "np.log10(gap_da).plot(x=\"Us\", y=\"Vs\", vmin=-3, vmax=1) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "18191ba0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.02672641009348376"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Single shot calculation\n",
-    "params = dict(U=0, V=1)\n",
-    "filling = 2 \n",
-    "\n",
-    "h_int = utils.builder_to_tb(int_builder, params)\n",
-    "model = Model(h_0, h_int, filling)\n",
-    "mf_guess = utils.generate_guess(frozenset(h_int), len(list(h_0.values())[0]))\n",
-    "mf_sol = solver(model, mf_guess, nK=30)\n",
-    "\n",
-    "ks = np.linspace(-np.pi, np.pi, 200)\n",
-    "hkfunc = tb2kfunc(addTb(h_0, mf_sol))\n",
-    "hkarray = np.array([hkfunc((kx, -kx)) for kx in ks])\n",
-    "vals = np.linalg.eigvalsh(hkarray)\n",
-    "plt.plot(vals)\n",
-    "utils.calc_gap(vals, E_F=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e183c3cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "import numpy as np\n",
-    "from codes.model import Model\n",
-    "from codes import kwant_examples\n",
-    "from codes.kwant_helper import utils\n",
-    "import timeit\n",
-    "import memray\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "078dd782",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
-      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
-     ]
-    }
-   ],
-   "source": [
-    "graphene_builder, int_builder = kwant_examples.graphene_extended_hubbard()\n",
-    "\n",
-    "params = {\"U\": 0.5, \"V\": 1.1}\n",
-    "filling = 2\n",
-    "nK = 300\n",
-    "\n",
-    "h_int = utils.builder_to_tb(int_builder, params)\n",
-    "h_0 = utils.builder_to_tb(graphene_builder)\n",
-    "guess = utils.generate_guess(frozenset(h_int), len(list(h_0.values())[0]))\n",
-    "\n",
-    "model = Model(h_0, h_int, filling)\n",
-    "\n",
-    "\n",
-    "def scf_loop():\n",
-    "    model.mfield(guess, nK=nK)\n",
-    "\n",
-    "\n",
-    "# %% Memory profile\n",
-    "with memray.Tracker(\"memoryProfile.bin\"):\n",
-    "    scf_loop()\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3455735e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %% Time profiler\n",
-    "profiler = Profiler()\n",
-    "\n",
-    "profiler.start()\n",
-    "scf_loop()\n",
-    "profiler.stop()\n",
-    "profiler.write_html(path=\"timeProfile.html\")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "75fe9023",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Single SCF loop takes 7.120591291008168 whereas a single diagonalization of a corresponding system takes 0.024635875000967644\n"
-     ]
-    }
-   ],
-   "source": [
-    "# %%\n",
-    "number = 1\n",
-    "\n",
-    "timeSCF = timeit.timeit(scf_loop, number=number) / number\n",
-    "\n",
-    "H = np.random.rand(nK, nK)\n",
-    "H += H.T.conj()\n",
-    "timeDiag = timeit.timeit(lambda: np.linalg.eigh(H), number=number) / number\n",
-    "\n",
-    "print(\n",
-    "    f\"Single SCF loop takes {timeSCF} whereas a single diagonalization of a corresponding system takes {timeDiag}\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f650872f",
-   "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": "3.11.6"
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/mexican_hat.ipynb b/examples/mexican_hat.ipynb
deleted file mode 100644
index 18baebe..0000000
--- a/examples/mexican_hat.ipynb
+++ /dev/null
@@ -1,162 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from codes.solvers import solver\n",
-    "from codes.tb import transforms, utils\n",
-    "from codes.model import Model\n",
-    "import codes.model\n",
-    "from codes.tb.tb import add_tb, scale_tb\n",
-    "from codes import mf\n",
-    "from codes import observables\n",
-    "import codes.tb.transforms\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def old_total_energy(h, rho):\n",
-    "    \"\"\"\n",
-    "    Compute total energy.\n",
-    "\n",
-    "    Paramters:\n",
-    "    ----------\n",
-    "    h : nd-array\n",
-    "        Hamiltonian.\n",
-    "    rho : nd-array\n",
-    "        Density matrix.\n",
-    "\n",
-    "    Returns:\n",
-    "    --------\n",
-    "    total_energy : float\n",
-    "        System total energy computed as tr[h@rho].\n",
-    "    \"\"\"\n",
-    "    return np.sum(np.trace(h @ rho, axis1=-1, axis2=-2)).real / np.prod(rho.shape[:-2])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def total_energy(ham_tb, rho_tb): \n",
-    "    return np.real(observables.expectation_value(rho_tb, ham_tb))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "U0 = 1\n",
-    "filling = 2\n",
-    "\n",
-    "hopp = np.kron(np.array([[0, 1], [0, 0]]), np.eye(2))\n",
-    "h_0 = {(0,): hopp + hopp.T.conj(), (1,): hopp, (-1,): hopp.T.conj()}\n",
-    "h_int_U0 = {\n",
-    "        (0,): U0 * np.kron(np.eye(2), np.ones((2, 2))),\n",
-    "    }\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "nk = 100\n",
-    "guess = utils.generate_guess(frozenset(h_int_U0), len(h_int_U0[(0,)]))\n",
-    "\n",
-    "_model = Model(h_0, h_int_U0, filling=filling)\n",
-    "mf_sol_groundstate = solver(_model, mf_guess=guess, nk=nk, optimizer_kwargs={\"M\": 0})\n",
-    "\n",
-    "@np.vectorize\n",
-    "def mfRescaled(alpha, mf0=mf_sol_groundstate):\n",
-    "    hamiltonian = add_tb(h_0, scale_tb(mf0,alpha))\n",
-    "    rho, _ = codes.mf.density_matrix(hamiltonian, filling=filling, nk=nk)\n",
-    "    hamiltonian = add_tb(h_0, scale_tb(mf0, np.sign(alpha)))\n",
-    "    return total_energy(hamiltonian, rho)\n",
-    "\n",
-    "alphas = np.linspace(-4, 4, 301)\n",
-    "plt.plot(alphas, mfRescaled(alphas), 'o', ms=2)\n",
-    "plt.plot(-alphas, mfRescaled(alphas), 'o', ms=2)\n",
-    "plt.axvline(x=1, c=\"k\", ls=\"--\")\n",
-    "plt.axvline(x=-1, c=\"k\", ls=\"--\")\n",
-    "plt.ylabel(\"Total Energy\")\n",
-    "plt.xlabel(r\"$\\alpha$\")\n",
-    "# plt.ylim(-4.6, -4.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(alphas[:-1], np.diff(mfRescaled(alphas)))\n",
-    "# plt.plot(-alphas[:-1], -np.diff(mfRescaled(alphas)))\n",
-    "plt.axhline(0, ls='--', c='k')\n",
-    "plt.axvline(x=1, c=\"k\", ls=\"--\")\n",
-    "plt.axvline(x=-1, c=\"k\", ls=\"--\")\n",
-    "# plt.ylim(-4, -2)\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "locenv",
-   "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": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/meanfi/__init__.py b/meanfi/__init__.py
new file mode 100644
index 0000000..e96d056
--- /dev/null
+++ b/meanfi/__init__.py
@@ -0,0 +1,35 @@
+"Mean-field tight-binding solver"
+
+try:
+    from ._version import __version__, __version_tuple__
+except ImportError:
+    __version__ = "unknown"
+    __version_tuple__ = (0, 0, "unknown", "unknown")
+
+from .mf import (
+    density_matrix,
+    meanfield,
+)
+from .solvers import solver
+from .model import Model
+from .observables import expectation_value
+from .tb.tb import add_tb, scale_tb
+from .tb.transforms import tb_to_kgrid, kgrid_to_tb
+from .tb.utils import guess_tb, fermi_energy
+
+
+__all__ = [
+    "solver",
+    "Model",
+    "expectation_value",
+    "add_tb",
+    "scale_tb",
+    "guess_tb",
+    "fermi_energy",
+    "density_matrix",
+    "meanfield",
+    "tb_to_kgrid",
+    "kgrid_to_tb",
+    "__version__",
+    "__version_tuple__",
+]
diff --git a/pymf/kwant_helper/__init__.py b/meanfi/kwant_helper/__init__.py
similarity index 100%
rename from pymf/kwant_helper/__init__.py
rename to meanfi/kwant_helper/__init__.py
diff --git a/pymf/kwant_helper/kwant_examples.py b/meanfi/kwant_helper/kwant_examples.py
similarity index 95%
rename from pymf/kwant_helper/kwant_examples.py
rename to meanfi/kwant_helper/kwant_examples.py
index 0ee82d5..de126d9 100644
--- a/pymf/kwant_helper/kwant_examples.py
+++ b/meanfi/kwant_helper/kwant_examples.py
@@ -1,7 +1,7 @@
 import kwant
 import numpy as np
 
-from pymf.kwant_helper.utils import build_interacting_syst
+from meanfi.kwant_helper.utils import build_interacting_syst
 
 s0 = np.identity(2)
 sz = np.diag([1, -1])
diff --git a/pymf/kwant_helper/utils.py b/meanfi/kwant_helper/utils.py
similarity index 73%
rename from pymf/kwant_helper/utils.py
rename to meanfi/kwant_helper/utils.py
index debc0d9..cb7e3a5 100644
--- a/pymf/kwant_helper/utils.py
+++ b/meanfi/kwant_helper/utils.py
@@ -1,29 +1,36 @@
 import inspect
 from copy import copy
 from itertools import product
+from typing import Callable
 
-import kwant
 import numpy as np
 from scipy.sparse import coo_array
+import kwant
+import kwant.lattice
+import kwant.builder
 
+from meanfi.tb.tb import _tb_type
 
-def builder_to_tb(builder, params={}, return_data=False):
-    """Construct a tight-binding model dictionary from a `kwant.Builder`.
+
+def builder_to_tb(
+    builder: kwant.builder.Builder, params: dict = {}, return_data: bool = False
+) -> _tb_type:
+    """Construct a tight-binding dictionary from a `kwant.builder.Builder` system.
 
     Parameters
     ----------
-    builder : `kwant.Builder`
-        Either builder for non-interacting system or interacting Hamiltonian.
-    params : dict
-        Dictionary of parameters to evaluate the Hamiltonian.
-    return_data : bool
+    builder :
+       system to convert to tight-binding dictionary.
+    params :
+        Dictionary of parameters to evaluate the builder on.
+    return_data :
         Returns dictionary with sites and number of orbitals per site.
 
     Returns
     -------
-    h_0 : dict
-        Tight-binding model of non-interacting systems.
-    data : dict
+    :
+        Tight-binding dictionary that corresponds to the builder.
+    :
         Data with sites and number of orbitals. Only if `return_data=True`.
     """
     builder = copy(builder)
@@ -45,8 +52,8 @@ def builder_to_tb(builder, params={}, return_data=False):
         col = copy(row)
         row, col = np.array([*product(row, col)]).T
         try:
+            _params = {}
             for arg in inspect.getfullargspec(val).args:
-                _params = {}
                 if arg in params:
                     _params[arg] = params[arg]
             val = val(site, **_params)
@@ -73,8 +80,8 @@ def builder_to_tb(builder, params={}, return_data=False):
         ]
         row, col = np.array([*product(row, col)]).T
         try:
+            _params = {}
             for arg in inspect.getfullargspec(val).args:
-                _params = {}
                 if arg in params:
                     _params[arg] = params[arg]
             val = val(a, b, **_params)
@@ -124,28 +131,38 @@ def builder_to_tb(builder, params={}, return_data=False):
         return h_0
 
 
-def build_interacting_syst(builder, lattice, func_onsite, func_hop, max_neighbor=1):
-    """Construct an auxiliary `kwant` system to build Hamiltonian matrix.
+def build_interacting_syst(
+    builder: kwant.builder.Builder,
+    lattice: kwant.lattice.Polyatomic,
+    func_onsite: Callable,
+    func_hop: Callable,
+    max_neighbor: int = 1,
+) -> kwant.builder.Builder:
+    """
+    Construct an auxiliary `kwant` system that encodes the interactions.
 
     Parameters
     ----------
-    builder : `kwant.Builder`
-        Non-interacting `kwant` system.
-    func_onsite : function
-        Onsite function.
-    func_hop : function
-        Hopping function.
-    max_neighbor : int
-        Maximal nearest-neighbor order.
+    builder :
+        Non-interacting `kwant.builder.Builder` system.
+    lattice :
+        Lattice of the system.
+    func_onsite :
+        Onsite interactions function.
+    func_hop :
+        Hopping/inter unit cell interactions function.
+    max_neighbor :
+        The maximal number of neighbouring unit cells (along a lattice vector)
+        connected by interaction. Interaction goes to zero after this distance.
 
     Returns
     -------
-    int_builder : `kwant.Builder`
-        Dummy `kwant.Builder` to compute interaction matrix.
+    :
+        Auxiliary `kwant.builder.Builder` that encodes the interactions of the system.
     """
-    # lattice_info = list(builder.sites())[0][0]
-    # lattice = kwant.lattice.general(lattice_info.prim_vecs, norbs=lattice_info.norbs)
-    int_builder = kwant.Builder(kwant.TranslationalSymmetry(*builder.symmetry.periods))
+    int_builder = kwant.builder.Builder(
+        kwant.lattice.TranslationalSymmetry(*builder.symmetry.periods)
+    )
     int_builder[builder.sites()] = func_onsite
     for neighbors in range(max_neighbor):
         int_builder[lattice.neighbors(neighbors + 1)] = func_hop
diff --git a/meanfi/mf.py b/meanfi/mf.py
new file mode 100644
index 0000000..7fd1b41
--- /dev/null
+++ b/meanfi/mf.py
@@ -0,0 +1,138 @@
+import numpy as np
+from typing import Tuple
+
+from meanfi.tb.tb import add_tb, _tb_type
+from meanfi.tb.transforms import tb_to_kgrid, kgrid_to_tb
+
+
+def density_matrix_kgrid(kham: np.ndarray, filling: float) -> Tuple[np.ndarray, float]:
+    """Calculate density matrix on a k-space grid.
+
+    Parameters
+    ----------
+    kham :
+        Hamiltonian from which to construct the density matrix.
+        The hamiltonian is sampled on a grid of k-points and has shape (nk, nk, ..., ndof, ndof),
+        where ndof is number of internal degrees of freedom.
+    filling :
+        Number of particles in a unit cell.
+        Used to determine the Fermi level.
+
+    Returns
+    -------
+    :
+        Density matrix on a k-space grid with shape (nk, nk, ..., ndof, ndof) and Fermi energy.
+    """
+    vals, vecs = np.linalg.eigh(kham)
+    fermi = fermi_on_kgrid(vals, filling)
+    unocc_vals = vals > fermi
+    occ_vecs = vecs
+    np.moveaxis(occ_vecs, -1, -2)[unocc_vals, :] = 0
+    _density_matrix_krid = occ_vecs @ np.moveaxis(occ_vecs, -1, -2).conj()
+    return _density_matrix_krid, fermi
+
+
+def density_matrix(h: _tb_type, filling: float, nk: int) -> Tuple[_tb_type, float]:
+    """Compute the real-space density matrix tight-binding dictionary.
+
+    Parameters
+    ----------
+    h :
+        Hamiltonian tight-binding dictionary from which to construct the density matrix.
+    filling :
+        Number of particles in a unit cell.
+        Used to determine the Fermi level.
+    nk :
+        Number of k-points in a grid to sample the Brillouin zone along each dimension.
+        If the system is 0-dimensional (finite), this parameter is ignored.
+
+    Returns
+    -------
+    :
+        Density matrix tight-binding dictionary and Fermi energy.
+    """
+    ndim = len(list(h)[0])
+    if ndim > 0:
+        kham = tb_to_kgrid(h, nk=nk)
+        _density_matrix_krid, fermi = density_matrix_kgrid(kham, filling)
+        return (
+            kgrid_to_tb(_density_matrix_krid),
+            fermi,
+        )
+    else:
+        _density_matrix, fermi = density_matrix_kgrid(h[()], filling)
+        return {(): _density_matrix}, fermi
+
+
+def meanfield(density_matrix: _tb_type, h_int: _tb_type) -> _tb_type:
+    """Compute the mean-field correction from the density matrix.
+
+    Parameters
+    ----------
+    density_matrix :
+        Density matrix tight-binding dictionary.
+    h_int :
+        Interaction hermitian Hamiltonian tight-binding dictionary.
+    Returns
+    -------
+    :
+        Mean-field correction tight-binding dictionary.
+
+    Notes
+    -----
+
+    The interaction h_int must be of density-density type.
+    For example, h_int[(1,)][i, j] = V means a repulsive interaction
+    of strength V between two particles with internal degrees of freedom i and j
+    separated by 1 lattice vector.
+    """
+    n = len(list(density_matrix)[0])
+    local_key = tuple(np.zeros((n,), dtype=int))
+
+    direct = {
+        local_key: np.sum(
+            np.array(
+                [
+                    np.diag(
+                        np.einsum("pp,pn->n", density_matrix[local_key], h_int[vec])
+                    )
+                    for vec in frozenset(h_int)
+                ]
+            ),
+            axis=0,
+        )
+    }
+
+    exchange = {
+        vec: -1 * h_int.get(vec, 0) * density_matrix[vec] for vec in frozenset(h_int)
+    }
+    return add_tb(direct, exchange)
+
+
+def fermi_on_kgrid(vals: np.ndarray, filling: float) -> float:
+    """Compute the Fermi energy on a grid of k-points.
+
+    Parameters
+    ----------
+    vals :
+        Eigenvalues of a hamiltonian sampled on a k-point grid with shape (nk, nk, ..., ndof, ndof),
+        where ndof is number of internal degrees of freedom.
+    filling :
+        Number of particles in a unit cell.
+        Used to determine the Fermi level.
+    Returns
+    -------
+    :
+        Fermi energy
+    """
+    norbs = vals.shape[-1]
+    vals_flat = np.sort(vals.flatten())
+    ne = len(vals_flat)
+    ifermi = int(round(ne * filling / norbs))
+    if ifermi >= ne:
+        return vals_flat[-1]
+    elif ifermi == 0:
+        return vals_flat[0]
+    else:
+        fermi = (vals_flat[ifermi - 1] + vals_flat[ifermi]) / 2
+        return fermi
diff --git a/meanfi/model.py b/meanfi/model.py
new file mode 100644
index 0000000..e94f25b
--- /dev/null
+++ b/meanfi/model.py
@@ -0,0 +1,99 @@
+import numpy as np
+
+from meanfi.mf import (
+    density_matrix,
+    meanfield,
+)
+from meanfi.tb.tb import add_tb, _tb_type
+
+
+def _check_hermiticity(h):
+    for vector in h.keys():
+        op_vector = tuple(-1 * np.array(vector))
+        op_vector = tuple(-1 * np.array(vector))
+        if not np.allclose(h[vector], h[op_vector].conj().T):
+            raise ValueError("Tight-binding dictionary must be hermitian.")
+
+
+def _tb_type_check(tb):
+    for count, key in enumerate(tb):
+        if not isinstance(tb[key], np.ndarray):
+            raise ValueError(
+                "Values of the tight-binding dictionary must be numpy arrays"
+            )
+        shape = tb[key].shape
+        if count == 0:
+            size = shape[0]
+        if not len(shape) == 2:
+            raise ValueError(
+                "Values of the tight-binding dictionary must be square matrices"
+            )
+        if not size == shape[0]:
+            raise ValueError(
+                "Values of the tight-binding dictionary must have consistent shape"
+            )
+
+
+class Model:
+    """
+    Data class which defines the interacting tight-binding problem.
+
+    Parameters
+    ----------
+    h_0 :
+        Non-interacting hermitian Hamiltonian tight-binding dictionary.
+    h_int :
+        Interaction hermitian Hamiltonian tight-binding dictionary.
+    filling :
+        Number of particles in a unit cell.
+        Used to determine the Fermi level.
+
+    Notes
+    -----
+
+    The interaction h_int must be of density-density type.
+    For example, h_int[(1,)][i, j] = V means a repulsive interaction
+    of strength V between two particles with internal degrees of freedom i and j
+    separated by 1 lattice vector.
+    """
+
+    def __init__(self, h_0: _tb_type, h_int: _tb_type, filling: float) -> None:
+        _tb_type_check(h_0)
+        self.h_0 = h_0
+        _tb_type_check(h_int)
+        self.h_int = h_int
+        if not isinstance(filling, (float, int)):
+            raise ValueError("Filling must be a float or an integer")
+        if not filling > 0:
+            raise ValueError("Filling must be a positive value")
+        self.filling = filling
+
+        _first_key = list(h_0)[0]
+        self._ndim = len(_first_key)
+        self._ndof = h_0[_first_key].shape[0]
+        self._local_key = tuple(np.zeros((self._ndim,), dtype=int))
+
+        _check_hermiticity(h_0)
+        _check_hermiticity(h_int)
+
+    def mfield(self, mf: _tb_type, nk: int = 20) -> _tb_type:
+        """Computes a new mean-field correction from a given one.
+
+        Parameters
+        ----------
+        mf :
+            Initial mean-field correction tight-binding dictionary.
+        nk :
+            Number of k-points in a grid to sample the Brillouin zone along each dimension.
+            If the system is 0-dimensional (finite), this parameter is ignored.
+
+        Returns
+        -------
+        :
+            new mean-field correction tight-binding dictionary.
+        """
+        rho, fermi_energy = density_matrix(add_tb(self.h_0, mf), self.filling, nk)
+        return add_tb(
+            meanfield(rho, self.h_int),
+            {self._local_key: -fermi_energy * np.eye(self._ndof)},
+        )
diff --git a/pymf/observables.py b/meanfi/observables.py
similarity index 60%
rename from pymf/observables.py
rename to meanfi/observables.py
index bf77034..fac1aa0 100644
--- a/pymf/observables.py
+++ b/meanfi/observables.py
@@ -1,19 +1,21 @@
 import numpy as np
 
+from meanfi.tb.tb import _tb_type
 
-def expectation_value(density_matrix, observable):
+
+def expectation_value(density_matrix: _tb_type, observable: _tb_type) -> complex:
     """Compute the expectation value of an observable with respect to a density matrix.
 
     Parameters
     ----------
-    density_matrix : dict
-        Density matrix in tight-binding format.
-    observable : dict
-        Observable in tight-binding format.
+    density_matrix :
+        Density matrix tight-binding dictionary.
+    observable :
+        Observable tight-binding dictionary.
 
     Returns
     -------
-    complex
+    :
         Expectation value.
     """
     return np.sum(
diff --git a/pymf/params/__init__.py b/meanfi/params/__init__.py
similarity index 100%
rename from pymf/params/__init__.py
rename to meanfi/params/__init__.py
diff --git a/pymf/params/param_transforms.py b/meanfi/params/param_transforms.py
similarity index 57%
rename from pymf/params/param_transforms.py
rename to meanfi/params/param_transforms.py
index 98b68e2..58a0495 100644
--- a/pymf/params/param_transforms.py
+++ b/meanfi/params/param_transforms.py
@@ -1,18 +1,20 @@
 import numpy as np
 
+from meanfi.tb.tb import _tb_type
 
-def tb_to_flat(tb):
-    """Convert a hermitian tight-binding dictionary to flat complex matrix.
+
+def tb_to_flat(tb: _tb_type) -> np.ndarray:
+    """Parametrise a hermitian tight-binding dictionary by a flat complex vector.
 
     Parameters
     ----------
-    tb : dict with nd-array elements
+    tb :
         Hermitian tigh-binding dictionary
 
     Returns
     -------
-    flat : complex 1d numpy array
-        Flattened tight-binding dictionary
+    :
+        1D complex array that parametrises the tight-binding dictionary.
     """
     if len(list(tb)[0]) == 0:
         matrix = np.array(list(tb.values()))
@@ -23,34 +25,39 @@ def tb_to_flat(tb):
     return sorted_vals[:N].flatten()
 
 
-def flat_to_tb(flat, shape, tb_keys):
+def flat_to_tb(
+    tb_param_complex: np.ndarray,
+    ndof: int,
+    tb_keys: list[tuple[None] | tuple[int, ...]],
+) -> _tb_type:
     """Reverse operation to `tb_to_flat`.
 
     It takes a flat complex 1d array and return the tight-binding dictionary.
 
     Parameters
     ----------
-    flat : dict with nd-array elements
-        Hermitian tigh-binding dictionary
-    shape : tuple
-        shape of the tb elements
-    tb_keys : iterable
-        original tb key elements
+    tb_param_complex :
+        1d complex array that parametrises the tb model.
+    ndof :
+        Number internal degrees of freedom within the unit cell.
+    tb_keys :
+        List of keys of the tight-binding dictionary.
 
     Returns
     -------
-    tb : dict
+    tb :
         tight-binding dictionary
     """
+    shape = (len(tb_keys), ndof, ndof)
     if len(tb_keys[0]) == 0:
         matrix = np.zeros((shape[-1], shape[-2]), dtype=complex)
-        matrix[np.triu_indices(shape[-1])] = flat
+        matrix[np.triu_indices(shape[-1])] = tb_param_complex
         matrix += matrix.conj().T
         matrix[np.diag_indices(shape[-1])] /= 2
         return {(): matrix}
     matrix = np.zeros(shape, dtype=complex)
     N = len(tb_keys) // 2 + 1
-    matrix[:N] = flat.reshape(N, *shape[1:])
+    matrix[:N] = tb_param_complex.reshape(N, *shape[1:])
     matrix[N:] = np.moveaxis(matrix[-(N + 1) :: -1], -1, -2).conj()
 
     tb_keys = np.array(list(tb_keys))
@@ -59,17 +66,22 @@ def flat_to_tb(flat, shape, tb_keys):
     return tb
 
 
-def complex_to_real(z):
-    """Split real and imaginary parts of a complex array.
+def complex_to_real(z: np.ndarray) -> np.ndarray:
+    """Split and concatenate real and imaginary parts of a complex array.
 
     Parameters
     ----------
-    z : array
+    z :
         Complex array.
+
+    Returns
+    -------
+    :
+        Real array that concatenates the real and imaginary parts of the input array.
     """
     return np.concatenate((np.real(z), np.imag(z)))
 
 
-def real_to_complex(z):
+def real_to_complex(z: np.ndarray) -> np.ndarray:
     """Undo `complex_to_real`."""
     return z[: len(z) // 2] + 1j * z[len(z) // 2 :]
diff --git a/meanfi/params/rparams.py b/meanfi/params/rparams.py
new file mode 100644
index 0000000..0360afb
--- /dev/null
+++ b/meanfi/params/rparams.py
@@ -0,0 +1,48 @@
+import numpy as np
+
+from meanfi.params.param_transforms import (
+    complex_to_real,
+    flat_to_tb,
+    real_to_complex,
+    tb_to_flat,
+)
+from meanfi.tb.tb import _tb_type
+
+
+def tb_to_rparams(tb: _tb_type) -> np.ndarray:
+    """Parametrise a hermitian tight-binding dictionary by a real vector.
+
+    Parameters
+    ----------
+    tb :
+        tight-binding dictionary.
+
+    Returns
+    -------
+    :
+        1D real vector that parametrises the tight-binding dictionary.
+    """
+    return complex_to_real(tb_to_flat(tb))
+
+
+def rparams_to_tb(
+    tb_params: np.ndarray, tb_keys: list[tuple[None] | tuple[int, ...]], ndof: int
+) -> _tb_type:
+    """Extract a hermitian tight-binding dictionary from a real vector parametrisation.
+
+    Parameters
+    ----------
+    tb_params :
+        1D real array that parametrises the tight-binding dictionary.
+    tb_keys :
+        List of keys of the tight-binding dictionary.
+    ndof :
+        Number internal degrees of freedom within the unit cell.
+
+    Returns
+    -------
+    :
+        Tight-biding dictionary.
+    """
+    flat_matrix = real_to_complex(tb_params)
+    return flat_to_tb(flat_matrix, ndof, tb_keys)
diff --git a/meanfi/solvers.py b/meanfi/solvers.py
new file mode 100644
index 0000000..e48d91b
--- /dev/null
+++ b/meanfi/solvers.py
@@ -0,0 +1,76 @@
+from functools import partial
+import numpy as np
+import scipy
+from typing import Optional, Callable
+
+from meanfi.params.rparams import rparams_to_tb, tb_to_rparams
+from meanfi.tb.tb import add_tb, _tb_type
+from meanfi.model import Model
+from meanfi.tb.utils import fermi_energy
+
+
+def cost(mf_param: np.ndarray, model: Model, nk: int = 20) -> np.ndarray:
+    """Defines the cost function for root solver.
+
+    The cost function is the difference between the computed and inputted mean-field.
+
+    Parameters
+    ----------
+    mf_param :
+        1D real array that parametrises the mean-field correction.
+    Model :
+        Interacting tight-binding problem definition.
+    nk :
+        Number of k-points in a grid to sample the Brillouin zone along each dimension.
+        If the system is 0-dimensional (finite), this parameter is ignored.
+
+    Returns
+    -------
+    :
+        1D real array that is the difference between the computed and inputted mean-field
+        parametrisations
+    """
+    shape = model._ndof
+    mf = rparams_to_tb(mf_param, list(model.h_int), shape)
+    mf_new = model.mfield(mf, nk=nk)
+    mf_params_new = tb_to_rparams(mf_new)
+    return mf_params_new - mf_param
+
+
+def solver(
+    model: Model,
+    mf_guess: np.ndarray,
+    nk: int = 20,
+    optimizer: Optional[Callable] = scipy.optimize.anderson,
+    optimizer_kwargs: Optional[dict[str, str]] = {"M": 0},
+) -> _tb_type:
+    """Solve for the mean-field correction through self-consistent root finding.
+
+    Parameters
+    ----------
+    model :
+        Interacting tight-binding problem definition.
+    mf_guess :
+        The initial guess for the mean-field correction in the tight-binding dictionary format.
+    nk :
+        Number of k-points in a grid to sample the Brillouin zone along each dimension.
+        If the system is 0-dimensional (finite), this parameter is ignored.
+    optimizer :
+        The solver used to solve the fixed point iteration.
+        Default uses `scipy.optimize.anderson`.
+    optimizer_kwargs :
+        The keyword arguments to pass to the optimizer.
+
+    Returns
+    -------
+    :
+        Mean-field correction solution in the tight-binding dictionary format.
+    """
+    shape = model._ndof
+    mf_params = tb_to_rparams(mf_guess)
+    f = partial(cost, model=model, nk=nk)
+    result = rparams_to_tb(
+        optimizer(f, mf_params, **optimizer_kwargs), list(model.h_int), shape
+    )
+    fermi = fermi_energy(add_tb(model.h_0, result), model.filling, nk=nk)
+    return add_tb(result, {model._local_key: -fermi * np.eye(model._ndof)})
diff --git a/pymf/tb/__init__.py b/meanfi/tb/__init__.py
similarity index 100%
rename from pymf/tb/__init__.py
rename to meanfi/tb/__init__.py
diff --git a/meanfi/tb/tb.py b/meanfi/tb/tb.py
new file mode 100644
index 0000000..3f193cf
--- /dev/null
+++ b/meanfi/tb/tb.py
@@ -0,0 +1,45 @@
+import numpy as np
+
+_tb_type = dict[tuple[int, ...], np.ndarray]
+
+
+def add_tb(tb1: _tb_type, tb2: _tb_type) -> _tb_type:
+    """Add up two tight-binding dictionaries together.
+
+    Parameters
+    ----------
+    tb1 :
+        Tight-binding dictionary.
+    tb2 :
+        Tight-binding dictionary.
+
+    Returns
+    -------
+    :
+        Sum of the two tight-binding dictionaries.
+    """
+    return {k: tb1.get(k, 0) + tb2.get(k, 0) for k in frozenset(tb1) | frozenset(tb2)}
+
+
+def scale_tb(tb: _tb_type, scale: float) -> _tb_type:
+    """Scale a tight-binding dictionary by a constant.
+
+    Parameters
+    ----------
+    tb :
+        Tight-binding dictionary.
+    scale :
+        Constant to scale the tight-binding dictionary by.
+
+    Returns
+    -------
+    :
+        Scaled tight-binding dictionary.
+    """
+    return {k: tb.get(k, 0) * scale for k in frozenset(tb)}
+
+
+def compare_dicts(dict1: dict, dict2: dict, atol: float = 1e-10) -> None:
+    """Compare two dictionaries."""
+    for key in frozenset(dict1) | frozenset(dict2):
+        assert np.allclose(dict1[key], dict2[key], atol=atol)
diff --git a/meanfi/tb/transforms.py b/meanfi/tb/transforms.py
new file mode 100644
index 0000000..658b661
--- /dev/null
+++ b/meanfi/tb/transforms.py
@@ -0,0 +1,78 @@
+import itertools
+import numpy as np
+from scipy.fftpack import ifftn
+
+from meanfi.tb.tb import _tb_type
+
+
+def tb_to_kgrid(tb: _tb_type, nk: int) -> np.ndarray:
+    """Evaluate a tight-binding dictionary on a k-space grid.
+
+    Parameters
+    ----------
+    tb :
+        Tight-binding dictionary to evaluate on a k-space grid.
+    nk :
+        Number of k-points in a grid to sample the Brillouin zone along each dimension.
+        If the system is 0-dimensional (finite), this parameter is ignored.
+
+    Returns
+    -------
+    :
+        Tight-binding dictionary evaluated on a k-space grid.
+        Has shape (nk, nk, ..., ndof, ndof), where ndof is number of internal degrees of freedom.
+    """
+    ndim = len(list(tb)[0])
+    ks = np.linspace(-np.pi, np.pi, nk, endpoint=False)
+    ks = np.concatenate((ks[nk // 2 :], ks[: nk // 2]), axis=0)  # shift for ifft
+    kgrid = np.meshgrid(*([ks] * ndim), indexing="ij")
+
+    num_keys = len(list(tb.keys()))
+    tb_array = np.array(list(tb.values()))
+    keys = np.array(list(tb.keys()))
+
+    k_dependency = np.exp(-1j * np.tensordot(keys, kgrid, 1))[
+        (...,) + (np.newaxis,) * 2
+    ]
+    tb_array = tb_array.reshape([num_keys] + [1] * ndim + list(tb_array.shape[1:]))
+    return np.sum(tb_array * k_dependency, axis=0)
+
+
+def kgrid_to_tb(kgrid_array: np.ndarray) -> _tb_type:
+    """
+    Convert a k-space grid array to a tight-binding dictionary.
+
+    Parameters
+    ----------
+    kgrid_array :
+        K-space grid array to convert to a tight-binding dictionary.
+        The array should be of shape (nk, nk, ..., ndof, ndof),
+        where ndof is number of internal degrees of freedom.
+    Returns
+    -------
+    :
+        Tight-binding dictionary.
+    """
+    ndim = len(kgrid_array.shape) - 2
+    return ifftn_to_tb(ifftn(kgrid_array, axes=np.arange(ndim)))
+
+
+def ifftn_to_tb(ifft_array: np.ndarray) -> _tb_type:
+    """
+    Convert the result of `scipy.fft.ifftn` to a tight-binding dictionary.
+
+    Parameters
+    ----------
+    ifft_array :
+        Result of `scipy.fft.ifftn` to convert to a tight-binding dictionary.
+        The input to `scipy.fft.ifftn` should be from `tb_to_khamvector`.
+    Returns
+    -------
+    :
+        Tight-binding dictionary.
+    """
+    size = ifft_array.shape[:-2]
+
+    keys = [np.arange(-size[0] // 2 + 1, size[0] // 2) for i in range(len(size))]
+    keys = itertools.product(*keys)
+    return {tuple(k): ifft_array[tuple(k)] for k in keys}
diff --git a/meanfi/tb/utils.py b/meanfi/tb/utils.py
new file mode 100644
index 0000000..af2971a
--- /dev/null
+++ b/meanfi/tb/utils.py
@@ -0,0 +1,84 @@
+from itertools import product
+import numpy as np
+
+from meanfi.tb.tb import _tb_type
+from meanfi.mf import fermi_on_kgrid
+from meanfi.tb.transforms import tb_to_kgrid
+
+
+def guess_tb(
+    tb_keys: list[tuple[None] | tuple[int, ...]], ndof: int, scale: float = 1
+) -> _tb_type:
+    """Generate hermitian guess tight-binding dictionary.
+
+    Parameters
+    ----------
+    tb_keys :
+       List of hopping vectors (tight-binding dictionary keys) the guess contains.
+    ndof :
+        Number internal degrees of freedom within the unit cell.
+    scale :
+        Scale of the random guess.
+    Returns
+    -------
+    :
+        Hermitian guess tight-binding dictionary.
+    """
+    guess = {}
+    for vector in tb_keys:
+        if vector not in guess.keys():
+            amplitude = scale * np.random.rand(ndof, ndof)
+            phase = 2 * np.pi * np.random.rand(ndof, ndof)
+            rand_hermitian = amplitude * np.exp(1j * phase)
+            if np.linalg.norm(np.array(vector)) == 0:
+                rand_hermitian += rand_hermitian.T.conj()
+                rand_hermitian /= 2
+                guess[vector] = rand_hermitian
+            else:
+                guess[vector] = rand_hermitian
+                guess[tuple(-np.array(vector))] = rand_hermitian.T.conj()
+
+    return guess
+
+
+def generate_tb_keys(cutoff: int, dim: int) -> list[tuple[None] | tuple[int, ...]]:
+    """Generate tight-binding dictionary keys up to a cutoff.
+
+    Parameters
+    ----------
+    cutoff :
+        Maximum distance along each dimension to generate tight-bindign dictionary keys for.
+    dim :
+        Dimension of the tight-binding dictionary.
+
+    Returns
+    -------
+    :
+        List of generated tight-binding dictionary keys up to a cutoff.
+    """
+    return [*product(*([[*range(-cutoff, cutoff + 1)]] * dim))]
+
+
+def fermi_energy(tb: _tb_type, filling: float, nk: int = 100):
+    """
+    Calculate the Fermi energy of a given tight-binding dictionary.
+
+    Parameters
+    ----------
+    tb :
+        Tight-binding dictionary.
+    filling :
+        Number of particles in a unit cell.
+        Used to determine the Fermi level.
+    nk :
+        Number of k-points in a grid to sample the Brillouin zone along each dimension.
+        If the system is 0-dimensional (finite), this parameter is ignored.
+
+    Returns
+    -------
+    :
+        Fermi energy.
+    """
+    kham = tb_to_kgrid(tb, nk)
+    vals = np.linalg.eigvalsh(kham)
+    return fermi_on_kgrid(vals, filling)
diff --git a/pymf/tests/test_graphene.py b/meanfi/tests/test_graphene.py
similarity index 81%
rename from pymf/tests/test_graphene.py
rename to meanfi/tests/test_graphene.py
index 8657f7d..dfc006d 100644
--- a/pymf/tests/test_graphene.py
+++ b/meanfi/tests/test_graphene.py
@@ -2,12 +2,14 @@
 import numpy as np
 import pytest
 
-from pymf.kwant_helper import kwant_examples, utils
-from pymf.model import Model
-from pymf.solvers import solver
-from pymf.tb.tb import add_tb
-from pymf.tb.transforms import tb_to_khamvector
-from pymf.tb.utils import generate_guess
+from meanfi.kwant_helper import kwant_examples, utils
+from meanfi import (
+    Model,
+    solver,
+    tb_to_kgrid,
+    guess_tb,
+    add_tb,
+)
 
 
 def compute_gap(tb, fermi_energy=0, nk=100):
@@ -27,7 +29,7 @@ def compute_gap(tb, fermi_energy=0, nk=100):
      gap : float
      Indirect gap.
     """
-    kham = tb_to_khamvector(tb, nk, ks=None)
+    kham = tb_to_kgrid(tb, nk)
     vals = np.linalg.eigvalsh(kham)
 
     emax = np.max(vals[vals <= fermi_energy])
@@ -35,7 +37,7 @@ def compute_gap(tb, fermi_energy=0, nk=100):
     return np.abs(emin - emax)
 
 
-repeat_number = 10
+repeat_number = 5
 # %%
 graphene_builder, int_builder = kwant_examples.graphene_extended_hubbard()
 h_0 = utils.builder_to_tb(graphene_builder)
@@ -72,12 +74,10 @@ def gap_prediction(U, V):
     nk = 40
 
     h_int = utils.builder_to_tb(int_builder, params)
-    guess = generate_guess(frozenset(h_int), len(list(h_0.values())[0]))
+    guess = guess_tb(frozenset(h_int), len(list(h_0.values())[0]))
     model = Model(h_0, h_int, filling)
 
-    mf_sol = solver(
-        model, guess, nk=nk, optimizer_kwargs={"verbose": True, "M": 0, "f_tol": 1e-8}
-    )
+    mf_sol = solver(model, guess, nk=nk, optimizer_kwargs={"M": 0, "f_tol": 1e-8})
     gap = compute_gap(add_tb(h_0, mf_sol), nk=200)
 
     # Check if the gap is predicted correctly
diff --git a/meanfi/tests/test_hat.py b/meanfi/tests/test_hat.py
new file mode 100644
index 0000000..e6c58c4
--- /dev/null
+++ b/meanfi/tests/test_hat.py
@@ -0,0 +1,65 @@
+# %%
+import numpy as np
+import pytest
+
+from meanfi import (
+    Model,
+    solver,
+    guess_tb,
+    scale_tb,
+    add_tb,
+    expectation_value,
+    density_matrix,
+)
+
+from meanfi.tb.utils import generate_tb_keys
+
+
+# %%
+def total_energy(ham_tb, rho_tb):
+    return np.real(expectation_value(rho_tb, ham_tb))
+
+
+# %%
+U0 = 1
+filling = 2
+nk = 10
+repeat_number = 3
+ndof = 4
+cutoff = 1
+
+
+# %%
+@np.vectorize
+def mf_rescaled(alpha, mf0, h0):
+    hamiltonian = add_tb(h0, scale_tb(mf0, alpha))
+    rho, _ = density_matrix(hamiltonian, filling=filling, nk=nk)
+    hamiltonian = add_tb(h0, scale_tb(mf0, np.sign(alpha)))
+    return total_energy(hamiltonian, rho)
+
+
+@pytest.mark.parametrize("seed", range(repeat_number))
+def test_mexican_hat(seed):
+    np.random.seed(seed)
+    h0s = []
+    h_ints = []
+    for ndim in np.arange(4):
+        keys = generate_tb_keys(cutoff, ndim)
+        h0s.append(guess_tb(keys, ndof))
+        h_int = guess_tb(keys, ndof)
+        h_int[keys[len(keys) // 2]] += U0
+        h_ints.append(h_int)
+
+    for h0, h_int in zip(h0s, h_ints):
+        guess = guess_tb(frozenset(h_int), ndof)
+        _model = Model(h0, h_int, filling=filling)
+        mf_sol_groundstate = solver(
+            _model, mf_guess=guess, nk=nk, optimizer_kwargs={"M": 0}
+        )
+
+        alphas = np.random.uniform(0, 50, 100)
+        alphas = np.where(alphas == 1, 0, alphas)
+        assert np.all(
+            mf_rescaled(alphas, mf0=mf_sol_groundstate, h0=h0)
+            > mf_rescaled(np.array([1]), mf0=mf_sol_groundstate, h0=h0)
+        )
diff --git a/pymf/tests/test_hubbard.py b/meanfi/tests/test_hubbard.py
similarity index 71%
rename from pymf/tests/test_hubbard.py
rename to meanfi/tests/test_hubbard.py
index f061034..22e126c 100644
--- a/pymf/tests/test_hubbard.py
+++ b/meanfi/tests/test_hubbard.py
@@ -2,13 +2,15 @@
 import numpy as np
 import pytest
 
-from pymf.model import Model
-from pymf.solvers import solver
-from pymf.tb import utils
-from pymf.tb.tb import add_tb
-from pymf.tests.test_graphene import compute_gap
+from meanfi.tests.test_graphene import compute_gap
+from meanfi import (
+    Model,
+    solver,
+    guess_tb,
+    add_tb,
+)
 
-repeat_number = 10
+repeat_number = 3
 
 
 # %%
@@ -31,9 +33,9 @@ def gap_relation_hubbard(Us, nk, nk_dense, tol=1e-3):
     gaps = []
     for U in Us:
         h_int = {
-            (0,): U * np.kron(np.ones((2, 2)), np.eye(2)),
+            (0,): U * np.kron(np.eye(2), np.ones((2, 2))),
         }
-        guess = utils.generate_guess(frozenset(h_int), len(list(h_0.values())[0]))
+        guess = guess_tb(frozenset(h_int), len(list(h_0.values())[0]))
         full_model = Model(h_0, h_int, filling=2)
         mf_sol = solver(full_model, guess, nk=nk)
         _gap = compute_gap(add_tb(h_0, mf_sol), fermi_energy=0, nk=nk_dense)
@@ -47,5 +49,5 @@ def gap_relation_hubbard(Us, nk, nk_dense, tol=1e-3):
 def test_gap_hubbard(seed):
     """Test the gap prediction for the Hubbard model."""
     np.random.seed(seed)
-    Us = np.linspace(0.5, 5, 50, endpoint=True)
-    gap_relation_hubbard(Us, nk=30, nk_dense=100, tol=1e-2)
+    Us = np.linspace(8, 10, 15, endpoint=True)
+    gap_relation_hubbard(Us, nk=20, nk_dense=100, tol=1e-1)
diff --git a/pymf/tests/test_params.py b/meanfi/tests/test_params.py
similarity index 71%
rename from pymf/tests/test_params.py
rename to meanfi/tests/test_params.py
index 1b636b3..5bfa92e 100644
--- a/pymf/tests/test_params.py
+++ b/meanfi/tests/test_params.py
@@ -1,9 +1,9 @@
 # %%
 import pytest
 import numpy as np
-from pymf.params.rparams import rparams_to_tb, tb_to_rparams
-from pymf.tb.tb import compare_dicts
-from pymf.tb.utils import generate_guess
+from meanfi.params.rparams import rparams_to_tb, tb_to_rparams
+from meanfi.tb.tb import compare_dicts
+from meanfi import guess_tb
 
 repeat_number = 10
 
@@ -16,7 +16,7 @@ vectors = ((0, 0), (1, 0), (-1, 0), (0, 1), (0, -1), (1, -1), (-1, 1), (1, 1), (
 def test_parametrisation(seed):
     """Test the parametrisation of the tight-binding model."""
     np.random.seed(seed)
-    mf_guess = generate_guess(vectors, ndof)
+    mf_guess = guess_tb(vectors, ndof)
     mf_params = tb_to_rparams(mf_guess)
     mf_new = rparams_to_tb(mf_params, vectors, ndof)
     compare_dicts(mf_guess, mf_new)
diff --git a/pymf/tests/test_tb.py b/meanfi/tests/test_tb.py
similarity index 81%
rename from pymf/tests/test_tb.py
rename to meanfi/tests/test_tb.py
index 1b0a667..cba3f52 100644
--- a/pymf/tests/test_tb.py
+++ b/meanfi/tests/test_tb.py
@@ -5,8 +5,8 @@ import numpy as np
 import pytest
 from scipy.fftpack import ifftn
 
-from pymf.tb.tb import compare_dicts
-from pymf.tb.transforms import ifftn_to_tb, tb_to_khamvector
+from meanfi.tb.tb import compare_dicts
+from meanfi.tb.transforms import ifftn_to_tb, tb_to_kgrid
 
 repeat_number = 10
 
@@ -24,6 +24,6 @@ def test_fourier(seed):
     keys = [np.arange(-max_order + 1, max_order) for i in range(ndim)]
     keys = it.product(*keys)
     h_0 = {key: (np.random.rand(matrix_size, matrix_size) - 0.5) * 2 for key in keys}
-    kham = tb_to_khamvector(h_0, nk=nk)
+    kham = tb_to_kgrid(h_0, nk=nk)
     tb_new = ifftn_to_tb(ifftn(kham, axes=np.arange(ndim)))
     compare_dicts(h_0, tb_new)
diff --git a/pymf/tests/test_zero_hint.py b/meanfi/tests/test_zero_hint.py
similarity index 59%
rename from pymf/tests/test_zero_hint.py
rename to meanfi/tests/test_zero_hint.py
index db5b872..e6fc462 100644
--- a/pymf/tests/test_zero_hint.py
+++ b/meanfi/tests/test_zero_hint.py
@@ -2,10 +2,9 @@
 import numpy as np
 import pytest
 
-from pymf.model import Model
-from pymf.solvers import solver
-from pymf.tb import utils
-from pymf.tb.tb import add_tb, compare_dicts
+from meanfi.tb import utils
+from meanfi.tb.tb import compare_dicts
+from meanfi import Model, solver, guess_tb, add_tb, fermi_energy
 
 # %%
 repeat_number = 10
@@ -21,16 +20,16 @@ def test_zero_hint(seed):
     dim = np.random.randint(0, 3)
     ndof = np.random.randint(2, 10)
     filling = np.random.randint(1, ndof)
-    random_hopping_vecs = utils.generate_vectors(cutoff, dim)
+    random_hopping_vecs = utils.generate_tb_keys(cutoff, dim)
 
     zero_key = tuple([0] * dim)
-    h_0_random = utils.generate_guess(random_hopping_vecs, ndof, scale=1)
-    h_int_only_phases = utils.generate_guess(random_hopping_vecs, ndof, scale=0)
-    guess = utils.generate_guess(random_hopping_vecs, ndof, scale=1)
+    h_0_random = guess_tb(random_hopping_vecs, ndof, scale=1)
+    h_int_only_phases = guess_tb(random_hopping_vecs, ndof, scale=0)
+    guess = guess_tb(random_hopping_vecs, ndof, scale=1)
     model = Model(h_0_random, h_int_only_phases, filling=filling)
 
     mf_sol = solver(model, guess, nk=40, optimizer_kwargs={"M": 0, "f_tol": 1e-10})
-    h_fermi = utils.calculate_fermi_energy(mf_sol, filling=filling, nk=20)
+    h_fermi = fermi_energy(mf_sol, filling=filling, nk=20)
     mf_sol[zero_key] -= h_fermi * np.eye(mf_sol[zero_key].shape[0])
 
     compare_dicts(add_tb(mf_sol, h_0_random), h_0_random, atol=1e-10)
diff --git a/noxfile.py b/noxfile.py
new file mode 100644
index 0000000..8b1dcce
--- /dev/null
+++ b/noxfile.py
@@ -0,0 +1,31 @@
+import nox
+
+
+@nox.session(venv_backend="mamba")
+@nox.parametrize(
+    "python,numpy,scipy,kwant",
+    [
+        ("3.10", "=1.23", "=1.9", "=1.4"),
+        ("3.11", "=1.24", "=1.10", "=1.4"),
+        ("3.12", ">=1.26", ">=1.13", ">=1.4"),
+    ],
+    ids=["minimal", "mid", "latest"],
+)
+def tests(session, numpy, scipy, kwant):
+    session.run(
+        "mamba",
+        "install",
+        "-y",
+        f"numpy{numpy}",
+        f"scipy{scipy}",
+        f"kwant{kwant}",
+        "packaging==22.0",
+        "pytest-cov",
+        "pytest-randomly",
+        "pytest-repeat",
+        "-c",
+        "conda-forge",
+    )
+    session.install(".")
+    session.run("pip", "install", "ruff", "pytest-ruff")
+    session.run("pytest", "--ruff", "-x")
diff --git a/profiling/graphene.py b/profiling/graphene.py
index 209f509..543f9c9 100644
--- a/profiling/graphene.py
+++ b/profiling/graphene.py
@@ -5,9 +5,9 @@ import memray
 import numpy as np
 from pyinstrument import Profiler
 
-from pymf.kwant_helper import kwant_examples, utils
-from pymf.model import Model
-from pymf.tb.utils import generate_guess
+from meanfi.kwant_helper import kwant_examples, utils
+from meanfi.model import Model
+from meanfi.tb.utils import guess_tb
 
 # %%
 graphene_builder, int_builder = kwant_examples.graphene_extended_hubbard()
@@ -19,7 +19,7 @@ nk = 600
 h_int = utils.builder_to_tb(int_builder, params)
 h_0 = utils.builder_to_tb(graphene_builder)
 norbs = len(list(h_0.values())[0])
-guess = generate_guess(frozenset(h_int), norbs)
+guess = guess_tb(frozenset(h_int), norbs)
 
 model = Model(h_0, h_int, filling)
 
diff --git a/pymf/__init__.py b/pymf/__init__.py
deleted file mode 100644
index a0f2b6e..0000000
--- a/pymf/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-"Mean-field tight-binding solver"
-
-try:
-    from ._version import __version__, __version_tuple__
-except ImportError:
-    __version__ = "unknown"
-    __version_tuple__ = (0, 0, "unknown", "unknown")
-
-from .mf import construct_density_matrix
-
-__all__ = [
-    "construct_density_matrix",
-    "__version__",
-    "__version_tuple__",
-]
diff --git a/pymf/mf.py b/pymf/mf.py
deleted file mode 100644
index d04d8f3..0000000
--- a/pymf/mf.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import numpy as np
-from scipy.fftpack import ifftn
-
-from pymf.tb.tb import add_tb
-from pymf.tb.transforms import ifftn_to_tb, tb_to_khamvector
-
-
-def construct_density_matrix_kgrid(kham, filling):
-    """Calculate density matrix on a k-space grid.
-
-    Parameters
-    ----------
-    kham : npndarray
-         Hamiltonian in k-space of shape (len(dim), norbs, norbs)
-    filling : float
-        Number of particles in a unit cell.
-
-    Returns
-    -------
-     np.ndarray, float
-         Density matrix in k-space and Fermi energy.
-    """
-    vals, vecs = np.linalg.eigh(kham)
-    fermi = fermi_on_grid(vals, filling)
-    unocc_vals = vals > fermi
-    occ_vecs = vecs
-    np.moveaxis(occ_vecs, -1, -2)[unocc_vals, :] = 0
-    rho_krid = occ_vecs @ np.moveaxis(occ_vecs, -1, -2).conj()
-    return rho_krid, fermi
-
-
-def construct_density_matrix(h, filling, nk):
-    """Compute the density matrix in real-space tight-binding format.
-
-    Parameters
-    ----------
-    h : dict
-        Tight-binding model.
-    filling : float
-        Filling of the system.
-    nk : int
-        Number of k-points in the grid.
-
-    Returns
-    -------
-    (dict, float)
-        Density matrix in real-space tight-binding format and Fermi energy.
-    """
-    ndim = len(list(h)[0])
-    if ndim > 0:
-        kham = tb_to_khamvector(h, nk=nk)
-        rho_grid, fermi = construct_density_matrix_kgrid(kham, filling)
-        return (
-            ifftn_to_tb(ifftn(rho_grid, axes=np.arange(ndim))),
-            fermi,
-        )
-    else:
-        rho, fermi = construct_density_matrix_kgrid(h[()], filling)
-        return {(): rho}, fermi
-
-
-def meanfield(density_matrix_tb, h_int):
-    """Compute the mean-field in k-space.
-
-    Parameters
-    ----------
-    density_matrix : dict
-        Density matrix in real-space tight-binding format.
-    h_int : dict
-        Interaction tb model.
-
-    Returns
-    -------
-    dict
-        Mean-field tb model.
-    """
-    n = len(list(density_matrix_tb)[0])
-    local_key = tuple(np.zeros((n,), dtype=int))
-
-    direct = {
-        local_key: np.sum(
-            np.array(
-                [
-                    np.diag(
-                        np.einsum("pp,pn->n", density_matrix_tb[local_key], h_int[vec])
-                    )
-                    for vec in frozenset(h_int)
-                ]
-            ),
-            axis=0,
-        )
-    }
-
-    exchange = {
-        vec: -1 * h_int.get(vec, 0) * density_matrix_tb[vec]  # / (2 * np.pi)#**2
-        for vec in frozenset(h_int)
-    }
-    return add_tb(direct, exchange)
-
-
-def fermi_on_grid(vals, filling):
-    """Compute the Fermi energy on a grid of k-points.
-
-    Parameters
-    ----------
-    vals : ndarray
-        Eigenvalues of the hamiltonian in k-space of shape (len(dim), norbs, norbs)
-    filling : int
-         Number of particles in a unit cell.
-
-    Returns
-    -------
-    fermi_energy : float
-         Fermi energy
-    """
-    norbs = vals.shape[-1]
-    vals_flat = np.sort(vals.flatten())
-    ne = len(vals_flat)
-    ifermi = int(round(ne * filling / norbs))
-    if ifermi >= ne:
-        return vals_flat[-1]
-    elif ifermi == 0:
-        return vals_flat[0]
-    else:
-        fermi = (vals_flat[ifermi - 1] + vals_flat[ifermi]) / 2
-        return fermi
diff --git a/pymf/model.py b/pymf/model.py
deleted file mode 100644
index 6a61b2d..0000000
--- a/pymf/model.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import numpy as np
-
-from pymf.mf import (
-    construct_density_matrix,
-    meanfield,
-)
-from pymf.tb.tb import add_tb
-
-
-def _check_hermiticity(h):
-    for vector in h.keys():
-        op_vector = tuple(-1 * np.array(vector))
-        op_vector = tuple(-1 * np.array(vector))
-        if not np.allclose(h[vector], h[op_vector].conj().T):
-            raise ValueError("Hamiltonian is not Hermitian.")
-
-
-def _tb_type_check(tb):
-    for count, key in enumerate(tb):
-        if not isinstance(tb[key], np.ndarray):
-            raise ValueError("Inputted dictionary values are not np.ndarray's")
-        shape = tb[key].shape
-        if count == 0:
-            size = shape[0]
-        if not len(shape) == 2:
-            raise ValueError("Inputted dictionary values are not square matrices")
-        if not size == shape[0]:
-            raise ValueError("Inputted dictionary elements shapes are not consistent")
-
-
-class Model:
-    def __init__(self, h_0, h_int, filling):
-        _tb_type_check(h_0)
-        self.h_0 = h_0
-        _tb_type_check(h_int)
-        self.h_int = h_int
-        if not isinstance(filling, (float, int)):
-            raise ValueError("Filling must be a float or an integer")
-        if not filling > 0:
-            raise ValueError("Filling must be a positive value")
-        self.filling = filling
-
-        _first_key = list(h_0)[0]
-        self._ndim = len(_first_key)
-        self._size = h_0[_first_key].shape[0]
-        self._local_key = tuple(np.zeros((self._ndim,), dtype=int))
-
-        _check_hermiticity(h_0)
-        _check_hermiticity(h_int)
-
-    def mfield(self, mf_tb, nk=200):  # method or standalone?
-        """Compute single mean field iteration.
-
-        Parameters
-        ----------
-        mf_tb : dict
-            Mean-field tight-binding model.
-        nk : int
-            Number of k-points in the grid.
-
-        Returns
-        -------
-        dict
-            New mean-field tight-binding model.
-        """
-        rho, fermi_energy = construct_density_matrix(
-            add_tb(self.h_0, mf_tb), self.filling, nk
-        )
-        return add_tb(
-            meanfield(rho, self.h_int),
-            {self._local_key: -fermi_energy * np.eye(self._size)},
-        )
diff --git a/pymf/params/rparams.py b/pymf/params/rparams.py
deleted file mode 100644
index bb78ed0..0000000
--- a/pymf/params/rparams.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from pymf.params.param_transforms import (
-    complex_to_real,
-    flat_to_tb,
-    real_to_complex,
-    tb_to_flat,
-)
-
-
-def tb_to_rparams(tb):
-    """Convert a mean-field tight-binding model to a set of real parameters.
-
-    Parameters
-    ----------
-    tb : dict
-        Mean-field tight-binding model.
-
-    Returns
-    -------
-    dict
-        Real parameters.
-    """
-    return complex_to_real(tb_to_flat(tb))  # placeholder for now
-
-
-def rparams_to_tb(r_params, key_list, size):
-    """Extract mean-field tight-binding model from a set of real parameters.
-
-    Parameters
-    ----------
-    r_params : dict
-        Real parameters.
-    key_list : list
-        List of the keys of the mean-field tight-binding model, meaning all the
-        hoppings.
-    size : tuple
-        Shape of the mean-field tight-binding model.
-
-    Returns
-    -------
-    dict
-        Mean-field tight-binding model.
-    """
-    flat_matrix = real_to_complex(r_params)
-    return flat_to_tb(flat_matrix, (len(key_list), size, size), key_list)
diff --git a/pymf/solvers.py b/pymf/solvers.py
deleted file mode 100644
index 9099f39..0000000
--- a/pymf/solvers.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from functools import partial
-
-import numpy as np
-import scipy
-
-from pymf.params.rparams import rparams_to_tb, tb_to_rparams
-from pymf.tb.tb import add_tb
-from pymf.tb.utils import calculate_fermi_energy
-
-
-def cost(mf_param, Model, nk=100):
-    """Define the cost function for fixed point iteration.
-
-    The cost function is the difference between the input mean-field real space
-    parametrisation and a new mean-field.
-
-    Parameters
-    ----------
-    mf_param : numpy.array
-        The mean-field real space parametrisation.
-    Model : Model
-        The model object.
-    nk : int, optional
-        The number of k-points to use in the grid. The default is 100.
-    """
-    shape = Model._size
-    mf_tb = rparams_to_tb(mf_param, list(Model.h_int), shape)
-    mf_tb_new = Model.mfield(mf_tb, nk=nk)
-    mf_params_new = tb_to_rparams(mf_tb_new)
-    return mf_params_new - mf_param
-
-
-def solver(
-    Model, mf_guess, nk=100, optimizer=scipy.optimize.anderson, optimizer_kwargs={}
-):
-    """Solve the mean-field self-consistent equation.
-
-    Parameters
-    ----------
-    Model : Model
-        The model object.
-    mf_guess : numpy.array
-        The initial guess for the mean-field tight-binding model.
-    nk : int, optional
-        The number of k-points to use in the grid. The default is 100. In the
-        0-dimensional case, this parameter is ignored.
-    optimizer : scipy.optimize, optional
-        The optimizer to use to solve for fixed-points. The default is
-        scipy.optimize.anderson.
-    optimizer_kwargs : dict, optional
-        The keyword arguments to pass to the optimizer. The default is {}.
-
-    Returns
-    -------
-    result : numpy.array
-        The mean-field tight-binding model.
-    """
-    shape = Model._size
-    mf_params = tb_to_rparams(mf_guess)
-    f = partial(cost, Model=Model, nk=nk)
-    result = rparams_to_tb(
-        optimizer(f, mf_params, **optimizer_kwargs), list(Model.h_int), shape
-    )
-    fermi = calculate_fermi_energy(add_tb(Model.h_0, result), Model.filling, nk=nk)
-    return add_tb(result, {Model._local_key: -fermi * np.eye(Model._size)})
diff --git a/pymf/tb/tb.py b/pymf/tb/tb.py
deleted file mode 100644
index a059323..0000000
--- a/pymf/tb/tb.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import numpy as np
-
-
-def add_tb(tb1, tb2):
-    """Add up two tight-binding models together.
-
-    Parameters
-    ----------
-    tb1 : dict
-        Tight-binding model.
-    tb2 : dict
-        Tight-binding model.
-
-    Returns
-    -------
-    dict
-        Sum of the two tight-binding models.
-    """
-    return {k: tb1.get(k, 0) + tb2.get(k, 0) for k in frozenset(tb1) | frozenset(tb2)}
-
-
-def scale_tb(tb, scale):
-    """Scale a tight-binding model.
-
-    Parameters
-    ----------
-    tb : dict
-        Tight-binding model.
-    scale : float
-        The scaling factor.
-
-    Returns
-    -------
-    dict
-        Scaled tight-binding model.
-    """
-    return {k: tb.get(k, 0) * scale for k in frozenset(tb)}
-
-
-def compare_dicts(dict1, dict2, atol=1e-10):
-    """Compare two dictionaries."""
-    for key in frozenset(dict1) | frozenset(dict2):
-        assert np.allclose(dict1[key], dict2[key], atol=atol)
diff --git a/pymf/tb/transforms.py b/pymf/tb/transforms.py
deleted file mode 100644
index 4befee2..0000000
--- a/pymf/tb/transforms.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import itertools
-import numpy as np
-
-
-def tb_to_khamvector(tb, nk, ks=None):
-    """Real-space tight-binding model to hamiltonian on k-space grid.
-
-    Parameters
-    ----------
-    tb : dict
-        A dictionary with real-space vectors as keys and complex np.arrays as values.
-    nk : int
-        Number of k-points along each direction.
-    ks : 1D-array
-        Set of k-points. Repeated for all directions.
-
-    Returns
-    -------
-    ndarray
-        Hamiltonian evaluated on a k-point grid.
-
-    """
-    ndim = len(list(tb)[0])
-    if ks is None:
-        ks = np.linspace(-np.pi, np.pi, nk, endpoint=False)
-        ks = np.concatenate((ks[nk // 2 :], ks[: nk // 2]), axis=0)  # shift for ifft
-    kgrid = np.meshgrid(*([ks] * ndim), indexing="ij")
-
-    num_keys = len(list(tb.keys()))
-    tb_array = np.array(list(tb.values()))
-    keys = np.array(list(tb.keys()))
-
-    k_dependency = np.exp(-1j * np.tensordot(keys, kgrid, 1))[
-        (...,) + (np.newaxis,) * 2
-    ]
-    tb_array = tb_array.reshape([num_keys] + [1] * ndim + list(tb_array.shape[1:]))
-    return np.sum(tb_array * k_dependency, axis=0)
-
-
-def ifftn_to_tb(ifft_array):
-    """Convert an array from ifftn to a tight-binding model format.
-
-    Parameters
-    ----------
-    ifft_array : ndarray
-        An array obtained from ifftn.
-
-    Returns
-    -------
-    dict
-        A dictionary with real-space vectors as keys and complex np.arrays as values.
-    """
-    size = ifft_array.shape[:-2]
-
-    keys = [np.arange(-size[0] // 2 + 1, size[0] // 2) for i in range(len(size))]
-    keys = itertools.product(*keys)
-    return {tuple(k): ifft_array[tuple(k)] for k in keys}
-
-
-def kham_to_tb(kham, hopping_vecs, ks=None):
-    """Extract hopping matrices from Bloch Hamiltonian.
-
-    Parameters
-    ----------
-    kham : nd-array
-        Bloch Hamiltonian matrix kham[k_x, ..., k_n, i, j]
-    hopping_vecs : list
-        List of hopping vectors, will be the keys to the tb.
-    ks : 1D-array
-        Set of k-points. Repeated for all directions. If the system is finite,
-        ks=None`.
-
-    Returns
-    -------
-    scf_model : dict
-        Tight-binding model of Hartree-Fock solution.
-    """
-    if ks is not None:
-        ndim = len(kham.shape) - 2
-        dk = np.diff(ks)[0]
-        nk = len(ks)
-        k_pts = np.tile(ks, ndim).reshape(ndim, nk)
-        k_grid = np.array(np.meshgrid(*k_pts))
-        k_grid = k_grid.reshape(k_grid.shape[0], np.prod(k_grid.shape[1:]))
-        kham = kham.reshape(np.prod(kham.shape[:ndim]), *kham.shape[-2:])
-
-        hopps = (
-            np.einsum(
-                "ij,jkl->ikl",
-                np.exp(1j * np.einsum("ij,jk->ik", hopping_vecs, k_grid)),
-                kham,
-            )
-            * (dk / (2 * np.pi)) ** ndim
-        )
-
-        h_0 = {}
-        for i, vector in enumerate(hopping_vecs):
-            h_0[tuple(vector)] = hopps[i]
-
-        return h_0
-    else:
-        return {(): kham}
diff --git a/pymf/tb/utils.py b/pymf/tb/utils.py
deleted file mode 100644
index cc20753..0000000
--- a/pymf/tb/utils.py
+++ /dev/null
@@ -1,64 +0,0 @@
-from itertools import product
-
-import numpy as np
-
-from pymf.mf import fermi_on_grid
-from pymf.tb.transforms import tb_to_khamvector
-
-
-def generate_guess(vectors, ndof, scale=1):
-    """Generate guess for a tight-binding model.
-
-    Parameters
-    ----------
-    vectors : list
-        List of hopping vectors.
-    ndof : int
-        Number internal degrees of freedom (orbitals),
-    scale : float
-        The scale of the guess. Maximum absolute value of each element of the guess.
-
-    Returns
-    -------
-    guess : tb dictionary
-        Guess in the form of a tight-binding model.
-    """
-    guess = {}
-    for vector in vectors:
-        if vector not in guess.keys():
-            amplitude = scale * np.random.rand(ndof, ndof)
-            phase = 2 * np.pi * np.random.rand(ndof, ndof)
-            rand_hermitian = amplitude * np.exp(1j * phase)
-            if np.linalg.norm(np.array(vector)) == 0:
-                rand_hermitian += rand_hermitian.T.conj()
-                rand_hermitian /= 2
-                guess[vector] = rand_hermitian
-            else:
-                guess[vector] = rand_hermitian
-                guess[tuple(-np.array(vector))] = rand_hermitian.T.conj()
-
-    return guess
-
-
-def generate_vectors(cutoff, dim):
-    """Generate hopping vectors up to a cutoff.
-
-    Parameters
-    ----------
-    cutoff : int
-        Maximum distance along each direction.
-    dim : int
-        Dimension of the vectors.
-
-    Returns
-    -------
-    List of hopping vectors.
-    """
-    return [*product(*([[*range(-cutoff, cutoff + 1)]] * dim))]
-
-
-def calculate_fermi_energy(tb, filling, nk=100):
-    """Calculate the Fermi energy for a given filling."""
-    kham = tb_to_khamvector(tb, nk, ks=None)
-    vals = np.linalg.eigvalsh(kham)
-    return fermi_on_grid(vals, filling)
diff --git a/pymf/tests/test_hat.py b/pymf/tests/test_hat.py
deleted file mode 100644
index 37af3ca..0000000
--- a/pymf/tests/test_hat.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# %%
-import numpy as np
-from pymf.solvers import solver
-from pymf.tb import utils
-from pymf.model import Model
-from pymf.tb.tb import add_tb, scale_tb
-from pymf import mf
-from pymf import observables
-import pytest
-
-
-# %%
-def total_energy(ham_tb, rho_tb):
-    return np.real(observables.expectation_value(rho_tb, ham_tb))
-
-
-# %%
-U0 = 1
-filling = 2
-nk = 100
-repeat_number = 10
-
-hopp = np.kron(np.array([[0, 1], [0, 0]]), np.eye(2))
-h_0 = {(0,): hopp + hopp.T.conj(), (1,): hopp, (-1,): hopp.T.conj()}
-h_int_U0 = {
-    (0,): U0 * np.kron(np.eye(2), np.ones((2, 2))),
-}
-
-
-# %%
-@np.vectorize
-def mf_rescaled(alpha, mf0):
-    hamiltonian = add_tb(h_0, scale_tb(mf0, alpha))
-    rho, _ = mf.construct_density_matrix(hamiltonian, filling=filling, nk=nk)
-    hamiltonian = add_tb(h_0, scale_tb(mf0, np.sign(alpha)))
-    return total_energy(hamiltonian, rho)
-
-
-@pytest.mark.parametrize("seed", range(repeat_number))
-def test_mexican_hat(seed):
-    np.random.seed(seed)
-    guess = utils.generate_guess(frozenset(h_int_U0), len(h_int_U0[(0,)]))
-    _model = Model(h_0, h_int_U0, filling=filling)
-    mf_sol_groundstate = solver(
-        _model, mf_guess=guess, nk=nk, optimizer_kwargs={"M": 0}
-    )
-
-    alphas = np.random.uniform(0, 50, 100)
-    alphas = np.where(alphas == 1, 0, alphas)
-    assert np.all(
-        mf_rescaled(alphas, mf0=mf_sol_groundstate)
-        > mf_rescaled(np.array([1]), mf0=mf_sol_groundstate)
-    )
diff --git a/pyproject.toml b/pyproject.toml
index aba62d5..df09fc5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,43 +3,44 @@ requires = ["hatchling", "hatch-vcs"]
 build-backend = "hatchling.build"
 
 [project]
-name = "pymf"
+name = "meanfi"
 dynamic = ["version"]
 authors = [
-  {name="pymf developers"},
+  {name="MeanFi developers"},
 ]
 description = "Package to perform self-consistent mean-field calculations on tight-binding systems"
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 classifiers = [
     "Development Status :: 4 - Beta",
     "License :: OSI Approved :: BSD License",
     "Intended Audience :: Science/Research",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
 ]
 dependencies = [
     "numpy>=1.23",
-    "scipy>=1.8",
+    "scipy>=1.9",
+    "kwant>=1.4",
     "packaging>=22.0",  # For version parsing
 ]
 [tool.hatch.version]
 source = "vcs"
 [tool.hatch.build.hooks.vcs]
-version-file = "pymf/_version.py"
+version-file = "meanfi/_version.py"
 
 [project.urls]
-"Documentation" = "https://kwant-scf.readthedocs.io/en/latest/"
-"Repository" = "https://gitlab.kwant-project.org/qt/kwant-scf"
-"Bug Tracker" = "https://gitlab.kwant-project.org/qt/kwant-scf/-/issues"
+"Documentation" = "https://meanfi.readthedocs.io/en/latest/"
+"Repository" = "https://gitlab.kwant-project.org/qt/meanfi"
+"Bug Tracker" = "https://gitlab.kwant-project.org/qt/meanfi/-/issues"
 
 [tool.hatch.build.targets.wheel]
-packages = ["pymf"]
+packages = ["meanfi"]
 
 [tool.hatch.build.targets.sdist]
 include = [
-  "pymf",
+  "meanfi",
   "README.md",
   "LICENSE",
   "pyproject.toml",
@@ -47,5 +48,5 @@ include = [
 ]
 
 [tool.codespell]
-skip = "*.ipynb,"
-ignore-words-list = "multline,"
+skip = "*.ipynb"
+ignore-words-list = "multline, ket, bra, braket, nwo"
diff --git a/pytest.ini b/pytest.ini
index dd25e99..5d863c4 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -1,6 +1,6 @@
 [pytest]
 minversion = 7.0
-addopts = --cov-config=.coveragerc --verbose --junitxml=junit.xml --cov=pymf
+addopts = --cov-config=.coveragerc --verbose --junitxml=junit.xml --cov=meanfi
     --cov-report term --cov-report html --cov-report xml --ruff
-testpaths = pymf
+testpaths = meanfi
 required_plugins = pytest-randomly pytest-cov pytest-ruff pytest-repeat
-- 
GitLab