readme edits (AAAdapol -> adapol)

README.md

@@ -1,26 +1,25 @@
-# Introduction
-[`AAAdapol`](https://github.com/Hertz4/AAAdapol) (pronounced "add a pole") is a python package for fitting Matsubara functions with the following form:
+# adapol: Adaptive Pole Fitting for Quantum Many-Body Physics
+[`adapol`](https://github.com/Hertz4/Adapol) (pronounced "add a pole") is a python package for fitting Matsubara functions with the following form:
 ```math
 G(\mathrm i \omega_k) = \sum_l \frac{V_lV_l^{\dagger}}{\mathrm i\omega_k - E_l}.
 ```
-AAAdapol is short for **A**ntoulas–**A**nderson **Ad**aptive **pol**e-fitting. 
 
 Current applications include
 (1) hybridization fitting, (2) analytic continuation.
 
 We also provide a [TRIQS](https://triqs.github.io/) interface if the Matsubara functions are stored in `triqs` Green's function container.
 
 # Installation
-`AAAdapol` has `numpy` and `scipy` as its prerequisites. [`cvxpy`](https://www.cvxpy.org/) is also required for hybridization fitting of matrix-valued (instead of scalar-valued) Matsubara functions.
+`adapol` has `numpy` and `scipy` as its prerequisites. [`cvxpy`](https://www.cvxpy.org/) is also required for hybridization fitting of matrix-valued (instead of scalar-valued) Matsubara functions.
 
-To install `AAAdapol`, run
+To install `adapol`, run
 ```terminal
-pip install aaadapol
+pip install adapol
 ```
 
 
 # Examples
-In the `examples` directory, we provide two examples [`discrete.ipynb`](https://github.com/Hertz4/AAAdapol/blob/main/example/discrete.ipynb) and [`semicircle.ipynb`](https://github.com/Hertz4/AAAdapol/blob/main/example/semicircle.ipynb), showcasing how to use `AAAdapol` for both discrete spectrum and continuous spectrum. We also demonstrate how to use our code through the triqs interface.
+In the `examples` directory, we provide two examples [`discrete.ipynb`](https://github.com/Hertz4/adapol/blob/main/example/discrete.ipynb) and [`semicircle.ipynb`](https://github.com/Hertz4/adapol/blob/main/example/semicircle.ipynb), showcasing how to use `adapol` for both discrete spectrum and continuous spectrum. We also demonstrate how to use our code through the triqs interface.
 
 Below is a quick introduction through the following toy example:
 ### Setup
@@ -34,7 +33,7 @@ Delta = 1.0/(1j*Z-0.5) + 2.0/(1j*Z+0.2) + 0.5/(1j*Z+0.7) # Matsubara functions o
 ### Hybridization Fitting
 The hybridization fitting is handled by the `hybfit` function.
 ```python
-from aaadapol import hybfit
+from adapol import hybfit
 ```
 There are two choices for doing hybridization fitting. One can either fit with desired accuracy tolerance `tol`:
 ```python
@@ -61,7 +60,7 @@ print(final_error)
 
 For triqs users, if the Green's function data `delta_triqs` is stored as a `triqs.gf.Gf` object or `triqs.gf.BlockGf` object, then the hybridization fitting could be done using the `hybfit_triqs` function:
 ```python
-from aaadapol import hybfit_triqs
+from adapol import hybfit_triqs
 bathhyb, bathenergy, delta_fit, final_error = hybfit_triqs(delta_triqs, tol=tol, debug=True)
 ```
 
@@ -70,8 +69,9 @@ bathhyb, bathenergy, delta_fit, final_error = hybfit_triqs(delta_triqs, tol=tol,
 To use this code for analytic continuation is similar, and we refer to the documentation for details.
 
 # References
-To cite this work, ...
-### Other References
+To cite this work, please include a reference to this GitHub repository, and
+cite the following references:
+
 1. Huang, Zhen, Emanuel Gull, and Lin Lin. "Robust analytic continuation of Green's functions via projection, pole estimation, and semidefinite relaxation." Physical Review B 107.7 (2023): 075151.
 2. Mejuto-Zaera, Carlos, et al. "Efficient hybridization fitting for dynamical mean-field theory via semi-definite relaxation." Physical Review B 101.3 (2020): 035143.
-3. Nakatsukasa, Yuji, Olivier Sète, and Lloyd N. Trefethen. "The AAA algorithm for rational approximation." SIAM Journal on Scientific Computing 40.3 (2018): A1494-A1522.
+3. Nakatsukasa, Yuji, Olivier Sète, and Lloyd N. Trefethen. "The AAA algorithm for rational approximation." SIAM Journal on Scientific Computing 40.3 (2018): A1494-A1522.