Repository: kazewong/flowMC (language: python)

Issue:
12 add option to run only local sampler
Use Jax array to store results variables instead of python array.

This reduce the size of get sampler too.


Discussion / hints:

Fix patch:
diff --git a/.gitignore b/.gitignore
index 55cd2b2..6dbe45c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,8 +1,8 @@
 *.pyc
-nfsampler/nfmodel/__pycache__/maf.cpython-37.pyc
+flowMC/nfmodel/__pycache__/maf.cpython-37.pyc
 log_dir/
 *.pkl
-nfsampler/nfmodel/realNVP_white.py
+flowMC/nfmodel/realNVP_white.py
 example/kepler/kepler_white.py
 example/kepler/kepler_analyse.py
 dist
@@ -10,3 +10,4 @@ dist
 docs/build/*
 *_autosummary
 README.md
+*settings.json
\ No newline at end of file
diff --git a/README.md b/README.md
index 0766902..a69aafb 100644
--- a/README.md
+++ b/README.md
@@ -1,21 +1,21 @@
-# NFSampler
+# FlowMC
 
 **Normalizing-flow enhanced sampling package for probabilistic inference**
 
-<a href="https://nfsampler.readthedocs.io/en/latest/?">
+<a href="https://FlowMC.readthedocs.io/en/latest/?">
 <img src="https://badgen.net/badge/Read/the doc/blue" alt="doc"/>
 </a>
-<a href="https://github.com/kazewong/NFSampler/blob/Packaging/LICENSE">
+<a href="https://github.com/kazewong/FlowMC/blob/Packaging/LICENSE">
 <img src="https://badgen.net/badge/License/MIT/blue" alt="doc"/>
 </a>
 
 
-NFSampler is a Jax-based python package for normalizing-flow enhanced Markov chain Monte Carlo (MCMC) sampling.
+FlowMC is a Jax-based python package for normalizing-flow enhanced Markov chain Monte Carlo (MCMC) sampling.
 The code is open source under MIT license, and it is under active development.
 
 - Just-in-time compilation is supported.
 - Native support for GPU acceleration.
-- Suit for multi-model sampling.
+- Suit for problems with multi-modality.
 - Minimal tuning.
 
 # Installation 
@@ -24,16 +24,24 @@ Our package is still in development stage, so it has not reached the official Py
 To install our package, run the following command:
 
 ```
-pip install -i https://test.pypi.org/simple/ NFSampler
+pip install FlowMC
 ```
 
 ## Requirements
 
+Here is a list of packages we use in the main library
+
     * Python 3.8+
     * Jax
     * Jaxlib
     * Flax
 
+To visualize the inference results in the examples, we requrie the following packages in addtion to the above:
+
+    * matplotlib
+    * corner
+    * arviz
+
 
 
 # Attribution
diff --git a/docs/source/api/modules.rst b/docs/source/api/modules.rst
index 438fbc0..28219c4 100644
--- a/docs/source/api/modules.rst
+++ b/docs/source/api/modules.rst
@@ -4,4 +4,4 @@ API
 .. toctree::
    :maxdepth: 4
 
-   nfsampler
+   flowMC
diff --git a/docs/source/api/nfsampler.nfmodel.rst b/docs/source/api/nfsampler.nfmodel.rst
index 77cd462..1a988c3 100644
--- a/docs/source/api/nfsampler.nfmodel.rst
+++ b/docs/source/api/nfsampler.nfmodel.rst
@@ -1,29 +1,29 @@
-nfsampler.nfmodel package
+flowMC.nfmodel package
 =========================
 
 Submodules
 ----------
 
-nfsampler.nfmodel.maf module
+flowMC.nfmodel.maf module
 ----------------------------
 
-.. automodule:: nfsampler.nfmodel.maf
+.. automodule:: flowMC.nfmodel.maf
    :members:
    :undoc-members:
    :show-inheritance:
 
-nfsampler.nfmodel.realNVP module
+flowMC.nfmodel.realNVP module
 --------------------------------
 
-.. automodule:: nfsampler.nfmodel.realNVP
+.. automodule:: flowMC.nfmodel.realNVP
    :members:
    :undoc-members:
    :show-inheritance:
 
-nfsampler.nfmodel.utils module
+flowMC.nfmodel.utils module
 ------------------------------
 
-.. automodule:: nfsampler.nfmodel.utils
+.. automodule:: flowMC.nfmodel.utils
    :members:
    :undoc-members:
    :show-inheritance:
@@ -31,7 +31,7 @@ nfsampler.nfmodel.utils module
 Module contents
 ---------------
 
-.. automodule:: nfsampler.nfmodel
+.. automodule:: flowMC.nfmodel
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/docs/source/api/nfsampler.rst b/docs/source/api/nfsampler.rst
index 69c3120..aed8dbc 100644
--- a/docs/source/api/nfsampler.rst
+++ b/docs/source/api/nfsampler.rst
@@ -1,4 +1,4 @@
-nfsampler package
+flowMC package
 =================
 
 Subpackages
@@ -7,14 +7,14 @@ Subpackages
 .. toctree::
    :maxdepth: 4
 
-   nfsampler.nfmodel
-   nfsampler.sampler
-   nfsampler.utils
+   flowMC.nfmodel
+   flowMC.sampler
+   flowMC.utils
 
 Module contents
 ---------------
 
-.. automodule:: nfsampler
+.. automodule:: flowMC
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/docs/source/api/nfsampler.sampler.rst b/docs/source/api/nfsampler.sampler.rst
index f9a18f4..938af51 100644
--- a/docs/source/api/nfsampler.sampler.rst
+++ b/docs/source/api/nfsampler.sampler.rst
@@ -1,37 +1,37 @@
-nfsampler.sampler package
+flowMC.sampler package
 =========================
 
 Submodules
 ----------
 
-nfsampler.sampler.Gaussian\_random\_walk module
+flowMC.sampler.Gaussian\_random\_walk module
 -----------------------------------------------
 
-.. automodule:: nfsampler.sampler.Gaussian_random_walk
+.. automodule:: flowMC.sampler.Gaussian_random_walk
    :members:
    :undoc-members:
    :show-inheritance:
 
-nfsampler.sampler.MALA module
+flowMC.sampler.MALA module
 -----------------------------
 
-.. automodule:: nfsampler.sampler.MALA
+.. automodule:: flowMC.sampler.MALA
    :members:
    :undoc-members:
    :show-inheritance:
 
-nfsampler.sampler.NF\_proposal module
+flowMC.sampler.NF\_proposal module
 -------------------------------------
 
-.. automodule:: nfsampler.sampler.NF_proposal
+.. automodule:: flowMC.sampler.NF_proposal
    :members:
    :undoc-members:
    :show-inheritance:
 
-nfsampler.sampler.Sampler module
+flowMC.sampler.Sampler module
 --------------------------------
 
-.. automodule:: nfsampler.sampler.Sampler
+.. automodule:: flowMC.sampler.Sampler
    :members:
    :undoc-members:
    :show-inheritance:
@@ -39,7 +39,7 @@ nfsampler.sampler.Sampler module
 Module contents
 ---------------
 
-.. automodule:: nfsampler.sampler
+.. automodule:: flowMC.sampler
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/docs/source/api/nfsampler.utils.rst b/docs/source/api/nfsampler.utils.rst
index 96438ba..2f8da99 100644
--- a/docs/source/api/nfsampler.utils.rst
+++ b/docs/source/api/nfsampler.utils.rst
@@ -1,21 +1,21 @@
-nfsampler.utils package
+flowMC.utils package
 =======================
 
 Submodules
 ----------
 
-nfsampler.utils.Analysis module
+flowMC.utils.Analysis module
 -------------------------------
 
-.. automodule:: nfsampler.utils.Analysis
+.. automodule:: flowMC.utils.Analysis
    :members:
    :undoc-members:
    :show-inheritance:
 
-nfsampler.utils.PRNG\_keys module
+flowMC.utils.PRNG\_keys module
 ---------------------------------
 
-.. automodule:: nfsampler.utils.PRNG_keys
+.. automodule:: flowMC.utils.PRNG_keys
    :members:
    :undoc-members:
    :show-inheritance:
@@ -23,7 +23,7 @@ nfsampler.utils.PRNG\_keys module
 Module contents
 ---------------
 
-.. automodule:: nfsampler.utils
+.. automodule:: flowMC.utils
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 0740eaf..bf81b0e 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -18,7 +18,7 @@
 
 # -- Project information -----------------------------------------------------
 
-project = 'NFSampler'
+project = 'flowMC'
 copyright = '2022, Kaze Wong, Marylou Gabrié'
 author = 'Kaze Wong, Marylou Gabrié'
 
diff --git a/docs/source/index.rst b/docs/source/index.rst
index b2148e9..d41d3cf 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,4 +1,4 @@
-Welcome to NFSampler's documentation!
+Welcome to flowMC's documentation!
 =====================================
 
 .. toctree::
diff --git a/docs/source/intro.rst b/docs/source/intro.rst
index 4894020..61415c3 100644
--- a/docs/source/intro.rst
+++ b/docs/source/intro.rst
@@ -4,4 +4,4 @@ Introduction
 Installation
 ------------
 
-To install NFSampler, first install it using pip:
+To install flowMC, first install it using pip:
diff --git a/example/dualmoon.py b/example/dualmoon.py
index 7f04494..ec33c34 100644
--- a/example/dualmoon.py
+++ b/example/dualmoon.py
@@ -1,13 +1,13 @@
-from nfsampler.nfmodel.realNVP import RealNVP
-from nfsampler.sampler.MALA import mala_sampler
+from flowMC.nfmodel.realNVP import RealNVP
+from flowMC.sampler.MALA import mala_sampler
 import jax
 import jax.numpy as jnp  # JAX NumPy
-from nfsampler.sampler.Sampler import Sampler
-from nfsampler.utils.PRNG_keys import initialize_rng_keys
+from flowMC.sampler.Sampler import Sampler
+from flowMC.utils.PRNG_keys import initialize_rng_keys
 from jax.scipy.special import logsumexp
 import numpy as np
 
-from nfsampler.nfmodel.utils import *
+from flowMC.nfmodel.utils import *
 
 
 def dual_moon_pe(x):
diff --git a/example/kepler/kepler.py b/example/kepler/kepler.py
index e95d842..bd5404a 100644
--- a/example/kepler/kepler.py
+++ b/example/kepler/kepler.py
@@ -9,13 +9,13 @@
 import time
 import pickle
 
-from nfsampler.nfmodel.realNVP import RealNVP
-from nfsampler.sampler.MALA import mala_sampler
-from nfsampler.nfmodel.utils import *
+from flowMC.nfmodel.realNVP import RealNVP
+from flowMC.sampler.MALA import mala_sampler
+from flowMC.nfmodel.utils import *
 
 from utils import rv_model, log_likelihood, log_prior, sample_prior, get_kepler_params_and_log_jac
-from nfsampler.sampler.Sampler import Sampler
-from nfsampler.utils.PRNG_keys import initialize_rng_keys
+from flowMC.sampler.Sampler import Sampler
+from flowMC.utils.PRNG_keys import initialize_rng_keys
 
 jax.config.update("jax_enable_x64", True)
 
diff --git a/example/mixture_gaussian.py b/example/mixture_gaussian.py
index b2ad473..b9fe0a4 100644
--- a/example/mixture_gaussian.py
+++ b/example/mixture_gaussian.py
@@ -17,7 +17,7 @@
 from jax.scipy.stats import norm, multivariate_normal
 from jax.scipy.special import logsumexp
 from functools import partial
-from nfsampler.sampler.MALA import mala_kernel,mala_sampler
+from flowMC.sampler.MALA import mala_kernel,mala_sampler
 
 
 def mixture_logpdf(x):
diff --git a/example/neal_funnel.py b/example/neal_funnel.py
index 7f41b25..717ab89 100644
--- a/example/neal_funnel.py
+++ b/example/neal_funnel.py
@@ -8,14 +8,14 @@
 """
 
 import logging
-from nfsampler.nfmodel.realNVP import RealNVP
-from nfsampler.sampler.MALA import mala_sampler
+from flowMC.nfmodel.realNVP import RealNVP
+from flowMC.sampler.MALA import mala_sampler
 import jax
 import jax.numpy as jnp                # JAX NumPy
 import numpy as np  
 from jax.scipy.stats import norm
-from nfsampler.sampler.Sampler import Sampler
-from nfsampler.utils.PRNG_keys import initialize_rng_keys
+from flowMC.sampler.Sampler import Sampler
+from flowMC.utils.PRNG_keys import initialize_rng_keys
 
 def neal_funnel(x):
     y_pdf = norm.logpdf(x[0],loc=0,scale=3)
diff --git a/example/notebook/DualMoon.ipynb b/example/notebook/DualMoon.ipynb
new file mode 100644
index 0000000..a966c0a
--- /dev/null
+++ b/example/notebook/DualMoon.ipynb
@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['/home/kaze/Work/NFSampler/example/notebook', '/usr/lib/python39.zip', '/usr/lib/python3.9', '/usr/lib/python3.9/lib-dynload', '', '/home/kaze/Environment/NFSampler/lib/python3.9/site-packages']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import jax\n",
+    "import jax.numpy as jnp  # JAX NumPy\n",
+    "from jax.scipy.special import logsumexp\n",
+    "import sys\n",
+    "print(sys.path)\n",
+    "\n",
+    "from flowMC.nfmodel.realNVP import RealNVP\n",
+    "from flowMC.sampler.MALA import mala_sampler\n",
+    "from flowMC.sampler.Sampler import Sampler\n",
+    "from flowMC.utils.PRNG_keys import initialize_rng_keys\n",
+    "from flowMC.nfmodel.utils import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def dual_moon_pe(x):\n",
+    "    \"\"\"\n",
+    "    Term 2 and 3 separate the distribution and smear it along the first and second dimension\n",
+    "    \"\"\"\n",
+    "    term1 = 0.5 * ((jnp.linalg.norm(x) - 2) / 0.1) ** 2\n",
+    "    term2 = -0.5 * ((x[:1] + jnp.array([-3.0, 3.0])) / 0.8) ** 2\n",
+    "    term3 = -0.5 * ((x[1:2] + jnp.array([-3.0, 3.0])) / 0.6) ** 2\n",
+    "    return -(term1 - logsumexp(term2) - logsumexp(term3))\n",
+    "\n",
+    "\n",
+    "d_dual_moon = jax.grad(dual_moon_pe)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preparing RNG keys\n",
+      "Initializing MCMC model and normalizing flow model.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Demo config\n",
+    "\n",
+    "n_dim = 5\n",
+    "n_chains = 10\n",
+    "n_loop = 5\n",
+    "n_local_steps = 100\n",
+    "n_global_steps = 100\n",
+    "learning_rate = 0.1\n",
+    "momentum = 0.9\n",
+    "num_epochs = 5\n",
+    "batch_size = 50\n",
+    "stepsize = 0.01\n",
+    "\n",
+    "print(\"Preparing RNG keys\")\n",
+    "rng_key_set = initialize_rng_keys(n_chains, seed=42)\n",
+    "\n",
+    "print(\"Initializing MCMC model and normalizing flow model.\")\n",
+    "\n",
+    "initial_position = jax.random.normal(rng_key_set[0], shape=(n_chains, n_dim)) * 1\n",
+    "\n",
+    "\n",
+    "model = RealNVP(10, n_dim, 64, 1)\n",
+    "run_mcmc = jax.vmap(mala_sampler, in_axes=(0, None, None, None, 0, None), out_axes=0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initializing sampler class\n",
+      "Sampling\n",
+      "chains shape:  (10, 1000, 5) local_accs shape:  (10, 500) global_accs shape:  (10, 500)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Initializing sampler class\")\n",
+    "\n",
+    "nf_sampler = Sampler(n_dim, rng_key_set, model, run_mcmc,\n",
+    "                    dual_moon_pe,\n",
+    "                    d_likelihood=d_dual_moon,\n",
+    "                    n_loop=n_loop,\n",
+    "                    n_local_steps=n_local_steps,\n",
+    "                    n_global_steps=n_global_steps,\n",
+    "                    n_chains=n_chains,\n",
+    "                    n_epochs=num_epochs,\n",
+    "                    n_nf_samples=100,\n",
+    "                    learning_rate=learning_rate,\n",
+    "                    momentum=momentum,\n",
+    "                    batch_size=batch_size,\n",
+    "                    stepsize=stepsize,\n",
+    "                    use_global=True,)\n",
+    "\n",
+    "print(\"Sampling\")\n",
+    "\n",
+    "nf_sampler.sample(initial_position)\n",
+    "\n",
+    "chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state()\n",
+    "nf_samples = nf_sampler.sample_flow()\n",
+    "\n",
+    "print(\n",
+    "    \"chains shape: \",\n",
+    "    chains.shape,\n",
+    "    \"local_accs shape: \",\n",
+    "    local_accs.shape,\n",
+    "    \"global_accs shape: \",\n",
+    "    global_accs.shape,\n",
+    ")\n",
+    "\n",
+    "chains = jnp.array(chains)\n",
+    "nf_samples = jnp.array(nf_samples[1])\n",
+    "loss_vals = jnp.array(loss_vals)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 504x504 with 25 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Can only convert xarray dataarray, xarray dataset, dict, netcdf filename, numpy array, pystan fit, pymc3 trace, emcee fit, pyro mcmc fit, numpyro mcmc fit, cmdstan fit csv filename, cmdstanpy fit to InferenceData, not DeviceArray",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m/home/kaze/Work/NFSampler/example/notebook/DualMoon.ipynb Cell 5'\u001b[0m in \u001b[0;36m<cell line: 43>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/kaze/Work/NFSampler/example/notebook/DualMoon.ipynb#ch0000004?line=39'>40</a>\u001b[0m plt\u001b[39m.\u001b[39mshow(block\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/kaze/Work/NFSampler/example/notebook/DualMoon.ipynb#ch0000004?line=41'>42</a>\u001b[0m \u001b[39m# Plot Nf samples\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/kaze/Work/NFSampler/example/notebook/DualMoon.ipynb#ch0000004?line=42'>43</a>\u001b[0m figure \u001b[39m=\u001b[39m corner\u001b[39m.\u001b[39;49mcorner(nf_samples, labels\u001b[39m=\u001b[39;49m[\u001b[39m\"\u001b[39;49m\u001b[39m$x_1$\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39m$x_2$\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39m$x_3$\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39m$x_4$\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39m$x_5$\u001b[39;49m\u001b[39m\"\u001b[39;49m])\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/kaze/Work/NFSampler/example/notebook/DualMoon.ipynb#ch0000004?line=43'>44</a>\u001b[0m figure\u001b[39m.\u001b[39mset_size_inches(\u001b[39m7\u001b[39m, \u001b[39m7\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/kaze/Work/NFSampler/example/notebook/DualMoon.ipynb#ch0000004?line=44'>45</a>\u001b[0m figure\u001b[39m.\u001b[39msuptitle(\u001b[39m\"\u001b[39m\u001b[39mVisualize NF samples\u001b[39m\u001b[39m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/Environment/NFSampler/lib/python3.9/site-packages/corner/corner.py:256\u001b[0m, in \u001b[0;36mcorner\u001b[0;34m(data, bins, range, weights, color, hist_bin_factor, smooth, smooth1d, labels, label_kwargs, titles, show_titles, title_fmt, title_kwargs, truths, truth_color, scale_hist, quantiles, verbose, fig, max_n_ticks, top_ticks, use_math_text, reverse, labelpad, hist_kwargs, group, var_names, filter_vars, coords, divergences, divergences_kwargs, labeller, **hist2d_kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m         logging\u001b[39m.\u001b[39mwarning(\n\u001b[1;32m    223\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mPlease install arviz to use the advanced features of corner\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    224\u001b[0m         )\n\u001b[1;32m    226\u001b[0m     \u001b[39mreturn\u001b[39;00m corner_impl(\n\u001b[1;32m    227\u001b[0m         data,\n\u001b[1;32m    228\u001b[0m         bins\u001b[39m=\u001b[39mbins,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    253\u001b[0m         \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mhist2d_kwargs,\n\u001b[1;32m    254\u001b[0m     )\n\u001b[0;32m--> 256\u001b[0m \u001b[39mreturn\u001b[39;00m arviz_corner(\n\u001b[1;32m    257\u001b[0m     data,\n\u001b[1;32m    258\u001b[0m     bins\u001b[39m=\u001b[39;49mbins,\n\u001b[1;32m    259\u001b[0m     \u001b[39mrange\u001b[39;49m\u001b[39m=\u001b[39;49m\u001b[39mrange\u001b[39;49m,\n\u001b[1;32m    260\u001b[0m     weights\u001b[39m=\u001b[39;49mweights,\n\u001b[1;32m    261\u001b[0m     color\u001b[39m=\u001b[39;49mcolor,\n\u001b[1;32m    262\u001b[0m     hist_bin_factor\u001b[39m=\u001b[39;49mhist_bin_factor,\n\u001b[1;32m    263\u001b[0m     smooth\u001b[39m=\u001b[39;49msmooth,\n\u001b[1;32m    264\u001b[0m     smooth1d\u001b[39m=\u001b[39;49msmooth1d,\n\u001b[1;32m    265\u001b[0m     labels\u001b[39m=\u001b[39;49mlabels,\n\u001b[1;32m    266\u001b[0m     label_kwargs\u001b[39m=\u001b[39;49mlabel_kwargs,\n\u001b[1;32m    267\u001b[0m     titles\u001b[39m=\u001b[39;49mtitles,\n\u001b[1;32m    268\u001b[0m     show_titles\u001b[39m=\u001b[39;49mshow_titles,\n\u001b[1;32m    269\u001b[0m     title_fmt\u001b[39m=\u001b[39;49mtitle_fmt,\n\u001b[1;32m    270\u001b[0m     title_kwargs\u001b[39m=\u001b[39;49mtitle_kwargs,\n\u001b[1;32m    271\u001b[0m     truths\u001b[39m=\u001b[39;49mtruths,\n\u001b[1;32m    272\u001b[0m     truth_color\u001b[39m=\u001b[39;49mtruth_color,\n\u001b[1;32m    273\u001b[0m     scale_hist\u001b[39m=\u001b[39;49mscale_hist,\n\u001b[1;32m    274\u001b[0m     quantiles\u001b[39m=\u001b[39;49mquantiles,\n\u001b[1;32m    275\u001b[0m     verbose\u001b[39m=\u001b[39;49mverbose,\n\u001b[1;32m    276\u001b[0m     fig\u001b[39m=\u001b[39;49mfig,\n\u001b[1;32m    277\u001b[0m     max_n_ticks\u001b[39m=\u001b[39;49mmax_n_ticks,\n\u001b[1;32m    278\u001b[0m     top_ticks\u001b[39m=\u001b[39;49mtop_ticks,\n\u001b[1;32m    279\u001b[0m     use_math_text\u001b[39m=\u001b[39;49muse_math_text,\n\u001b[1;32m    280\u001b[0m     reverse\u001b[39m=\u001b[39;49mreverse,\n\u001b[1;32m    281\u001b[0m     labelpad\u001b[39m=\u001b[39;49mlabelpad,\n\u001b[1;32m    282\u001b[0m     hist_kwargs\u001b[39m=\u001b[39;49mhist_kwargs,\n\u001b[1;32m    283\u001b[0m     group\u001b[39m=\u001b[39;49mgroup,\n\u001b[1;32m    284\u001b[0m     var_names\u001b[39m=\u001b[39;49mvar_names,\n\u001b[1;32m    285\u001b[0m     filter_vars\u001b[39m=\u001b[39;49mfilter_vars,\n\u001b[1;32m    286\u001b[0m     coords\u001b[39m=\u001b[39;49mcoords,\n\u001b[1;32m    287\u001b[0m     divergences\u001b[39m=\u001b[39;49mdivergences,\n\u001b[1;32m    288\u001b[0m     divergences_kwargs\u001b[39m=\u001b[39;49mdivergences_kwargs,\n\u001b[1;32m    289\u001b[0m     labeller\u001b[39m=\u001b[39;49mlabeller,\n\u001b[1;32m    290\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mhist2d_kwargs,\n\u001b[1;32m    291\u001b[0m )\n",
+      "File \u001b[0;32m~/Environment/NFSampler/lib/python3.9/site-packages/corner/arviz_corner.py:101\u001b[0m, in \u001b[0;36marviz_corner\u001b[0;34m(data, bins, range, weights, color, hist_bin_factor, smooth, smooth1d, labels, label_kwargs, titles, show_titles, title_fmt, title_kwargs, truths, truth_color, scale_hist, quantiles, verbose, fig, max_n_ticks, top_ticks, use_math_text, reverse, labelpad, hist_kwargs, group, var_names, filter_vars, coords, divergences, divergences_kwargs, labeller, **hist2d_kwargs)\u001b[0m\n\u001b[1;32m     98\u001b[0m     coords \u001b[39m=\u001b[39m {}\n\u001b[1;32m    100\u001b[0m \u001b[39m# Get posterior draws and combine chains\u001b[39;00m\n\u001b[0;32m--> 101\u001b[0m dataset \u001b[39m=\u001b[39m convert_to_dataset(data, group\u001b[39m=\u001b[39;49mgroup)\n\u001b[1;32m    102\u001b[0m var_names \u001b[39m=\u001b[39m _var_names(var_names, dataset, filter_vars)\n\u001b[1;32m    103\u001b[0m plotters \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(\n\u001b[1;32m    104\u001b[0m     xarray_var_iter(\n\u001b[1;32m    105\u001b[0m         get_coords(dataset, coords), var_names\u001b[39m=\u001b[39mvar_names, combined\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    106\u001b[0m     )\n\u001b[1;32m    107\u001b[0m )\n",
+      "File \u001b[0;32m~/Environment/NFSampler/lib/python3.9/site-packages/arviz/data/converters.py:179\u001b[0m, in \u001b[0;36mconvert_to_dataset\u001b[0;34m(obj, group, coords, dims)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mconvert_to_dataset\u001b[39m(obj, \u001b[39m*\u001b[39m, group\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mposterior\u001b[39m\u001b[39m\"\u001b[39m, coords\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, dims\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m    141\u001b[0m     \u001b[39m\"\"\"Convert a supported object to an xarray dataset.\u001b[39;00m\n\u001b[1;32m    142\u001b[0m \n\u001b[1;32m    143\u001b[0m \u001b[39m    This function is idempotent, in that it will return xarray.Dataset functions\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    177\u001b[0m \u001b[39m    xarray.Dataset\u001b[39;00m\n\u001b[1;32m    178\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 179\u001b[0m     inference_data \u001b[39m=\u001b[39m convert_to_inference_data(obj, group\u001b[39m=\u001b[39;49mgroup, coords\u001b[39m=\u001b[39;49mcoords, dims\u001b[39m=\u001b[39;49mdims)\n\u001b[1;32m    180\u001b[0m     dataset \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(inference_data, group, \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m    181\u001b[0m     \u001b[39mif\u001b[39;00m dataset \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m~/Environment/NFSampler/lib/python3.9/site-packages/arviz/data/converters.py:131\u001b[0m, in \u001b[0;36mconvert_to_inference_data\u001b[0;34m(obj, group, coords, dims, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    117\u001b[0m     allowable_types \u001b[39m=\u001b[39m (\n\u001b[1;32m    118\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mxarray dataarray\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    119\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mxarray dataset\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    129\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mcmdstanpy fit\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    130\u001b[0m     )\n\u001b[0;32m--> 131\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    132\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mCan only convert \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m to InferenceData, not \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m    133\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mjoin(allowable_types), obj\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\n\u001b[1;32m    134\u001b[0m         )\n\u001b[1;32m    135\u001b[0m     )\n\u001b[1;32m    137\u001b[0m \u001b[39mreturn\u001b[39;00m InferenceData(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m{group: dataset})\n",
+      "\u001b[0;31mValueError\u001b[0m: Can only convert xarray dataarray, xarray dataset, dict, netcdf filename, numpy array, pystan fit, pymc3 trace, emcee fit, pyro mcmc fit, numpyro mcmc fit, cmdstan fit csv filename, cmdstanpy fit to InferenceData, not DeviceArray"
+     ]
+    }
+   ],
+   "source": [
+    "import corner\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "chains = np.array(chains)\n",
+    "\n",
+    "# Plot one chain to show the jump\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "axs = [plt.subplot(2, 2, i + 1) for i in range(4)]\n",
+    "plt.sca(axs[0])\n",
+    "plt.title(\"2 chains\")\n",
+    "plt.plot(chains[0, :, 0], chains[0, :, 1], alpha=0.5)\n",
+    "plt.plot(chains[1, :, 0], chains[1, :, 1], alpha=0.5)\n",
+    "plt.xlabel(\"$x_1$\")\n",
+    "plt.ylabel(\"$x_2$\")\n",
+    "\n",
+    "plt.sca(axs[1])\n",
+    "plt.title(\"NF loss\")\n",
+    "plt.plot(loss_vals)\n",
+    "plt.xlabel(\"iteration\")\n",
+    "\n",
+    "plt.sca(axs[2])\n",
+    "plt.title(\"Local Acceptance\")\n",
+    "plt.plot(local_accs.mean(0))\n",
+    "plt.xlabel(\"iteration\")\n",
+    "\n",
+    "plt.sca(axs[3])\n",
+    "plt.title(\"Global Acceptance\")\n",
+    "plt.plot(global_accs.mean(0))\n",
+    "plt.xlabel(\"iteration\")\n",
+    "plt.tight_layout()\n",
+    "plt.show(block=False)\n",
+    "\n",
+    "# Plot all chains\n",
+    "figure = corner.corner(\n",
+    "    chains.reshape(-1, n_dim), labels=[\"$x_1$\", \"$x_2$\", \"$x_3$\", \"$x_4$\", \"$x_5$\"]\n",
+    ")\n",
+    "figure.set_size_inches(7, 7)\n",
+    "figure.suptitle(\"Visualize samples\")\n",
+    "plt.show(block=False)\n",
+    "\n",
+    "# Plot Nf samples\n",
+    "figure = corner.corner(nf_samples, labels=[\"$x_1$\", \"$x_2$\", \"$x_3$\", \"$x_4$\", \"$x_5$\"])\n",
+    "figure.set_size_inches(7, 7)\n",
+    "figure.suptitle(\"Visualize NF samples\")\n",
+    "plt.show(block=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.13 ('flowMC')",
+   "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": "3.9.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9020c46e0a51a37be32a034a174c82ff6fab05740041cd74df08d94f435e7e42"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/example/train_Maf.py b/example/train_Maf.py
index c0cd5d0..2e8d019 100644
--- a/example/train_Maf.py
+++ b/example/train_Maf.py
@@ -1,5 +1,5 @@
 
-from nfsampler.nfmodel.maf import MaskedAutoregressiveFlow
+from flowMC.nfmodel.maf import MaskedAutoregressiveFlow
 import jax
 import jax.numpy as jnp                # JAX NumPy
 import jax.random as random            # JAX random
diff --git a/example/train_RealNVP.py b/example/train_RealNVP.py
index 73f03ae..f4f94f3 100644
--- a/example/train_RealNVP.py
+++ b/example/train_RealNVP.py
@@ -1,5 +1,5 @@
 
-from nfsampler.nfmodel.realNVP import RealNVP
+from flowMC.nfmodel.realNVP import RealNVP
 import jax
 import jax.numpy as jnp                # JAX NumPy
 import jax.random as random            # JAX random
diff --git a/example/wiggle.py b/example/wiggle.py
index de26ed9..f2b9faa 100644
--- a/example/wiggle.py
+++ b/example/wiggle.py
@@ -1,12 +1,12 @@
-from nfsampler.nfmodel.realNVP import RealNVP
-from nfsampler.sampler.MALA import mala_sampler
+from flowMC.nfmodel.realNVP import RealNVP
+from flowMC.sampler.MALA import mala_sampler
 import jax
 import jax.numpy as jnp                # JAX NumPy
-from nfsampler.utils import Sampler, initialize_rng_keys
+from flowMC.utils import Sampler, initialize_rng_keys
 from jax.scipy.special import logsumexp
 import numpy as np  
 
-from nfsampler.nfmodel.utils import *
+from flowMC.nfmodel.utils import *
 import matplotlib.pyplot as plt
 
 
diff --git a/setup.cfg b/setup.cfg
index 7bda0d5..102a66b 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,16 +1,16 @@
 [metadata]
-name = NFSampler
-version = 0.0.2
+name = flowMC
+version = 0.0.1
 author = Kaze Wong, Marylou Gabrié
 author_email = kazewong.physics@gmail.com
-url = https://github.com/kazewong/NFSampler
+url = https://github.com/kazewong/flowMC
 description = Normalizing flow exhanced sampler in jax
 keywords = sampling, inference, machine learning, normalizing, autodiff, jax
 license = MIT
 
 [options]
 packages_dir=
-    =nfsampler
+    =src
 packages = find:
 install_requires =
     jax
@@ -19,4 +19,4 @@ install_requires =
 python_requires =  >=3.7
 
 [options.packages.find]
-where=nfsampler
+where=src
diff --git a/nfsampler/__init__.py b/src/flowMC/__init__.py
similarity index 100%
rename from nfsampler/__init__.py
rename to src/flowMC/__init__.py
diff --git a/nfsampler/nfmodel/__init__.py b/src/flowMC/nfmodel/__init__.py
similarity index 100%
rename from nfsampler/nfmodel/__init__.py
rename to src/flowMC/nfmodel/__init__.py
diff --git a/nfsampler/nfmodel/maf.py b/src/flowMC/nfmodel/maf.py
similarity index 100%
rename from nfsampler/nfmodel/maf.py
rename to src/flowMC/nfmodel/maf.py
diff --git a/nfsampler/nfmodel/realNVP.py b/src/flowMC/nfmodel/realNVP.py
similarity index 100%
rename from nfsampler/nfmodel/realNVP.py
rename to src/flowMC/nfmodel/realNVP.py
diff --git a/nfsampler/nfmodel/utils.py b/src/flowMC/nfmodel/utils.py
similarity index 95%
rename from nfsampler/nfmodel/utils.py
rename to src/flowMC/nfmodel/utils.py
index dda912b..f5309fa 100644
--- a/nfsampler/nfmodel/utils.py
+++ b/src/flowMC/nfmodel/utils.py
@@ -35,7 +35,7 @@ def train_epoch(state, train_ds, batch_size, epoch, rng):
 
         return value, state
 
-    loss_values = []
+    loss_values = jnp.array([])
     for epoch in range(1, num_epochs + 1):
         #print('Epoch %d' % epoch)
         # Use a separate PRNG key to permute image data during shuffling
@@ -43,7 +43,8 @@ def train_epoch(state, train_ds, batch_size, epoch, rng):
         # Run an optimization step over a training batch
         value, state = train_epoch(state, data, batch_size, epoch, input_rng)
         #print('Train loss: %.3f' % value)
-        loss_values.append(value)
+        loss_values = jnp.append(loss_values,value)
+    
 
     return rng, state, loss_values
 
diff --git a/nfsampler/sampler/Gaussian_random_walk.py b/src/flowMC/sampler/Gaussian_random_walk.py
similarity index 100%
rename from nfsampler/sampler/Gaussian_random_walk.py
rename to src/flowMC/sampler/Gaussian_random_walk.py
diff --git a/nfsampler/sampler/MALA.py b/src/flowMC/sampler/MALA.py
similarity index 100%
rename from nfsampler/sampler/MALA.py
rename to src/flowMC/sampler/MALA.py
diff --git a/nfsampler/sampler/NF_proposal.py b/src/flowMC/sampler/NF_proposal.py
similarity index 100%
rename from nfsampler/sampler/NF_proposal.py
rename to src/flowMC/sampler/NF_proposal.py
diff --git a/nfsampler/sampler/Sampler.py b/src/flowMC/sampler/Sampler.py
similarity index 83%
rename from nfsampler/sampler/Sampler.py
rename to src/flowMC/sampler/Sampler.py
index 83ccffd..72a1a58 100644
--- a/nfsampler/sampler/Sampler.py
+++ b/src/flowMC/sampler/Sampler.py
@@ -2,8 +2,8 @@
 import jax
 import jax.numpy as jnp
 import numpy as np
-from nfsampler.nfmodel.utils import sample_nf,train_flow
-from nfsampler.sampler.NF_proposal import nf_metropolis_sampler
+from flowMC.nfmodel.utils import sample_nf,train_flow
+from flowMC.sampler.NF_proposal import nf_metropolis_sampler
 from flax.training import train_state  # Useful dataclass to keep train state
 import optax          
                  # Optimizers
@@ -65,11 +65,11 @@ def __init__(self, n_dim: int, rng_key_set, nf_model, local_sampler,
         self.state = train_state.TrainState.create(apply_fn=nf_model.apply,
                                                    params=params, tx=tx)
 
-        self.chains = []
-        self.log_prob = []
-        self.local_accs = []
-        self.global_accs = []
-        self.loss_vals = []
+        self.chains = jnp.empty((self.n_chains, 0 , self.n_dim))
+        self.log_prob = jnp.empty((self.n_chains, 0))
+        self.local_accs = jnp.empty((self.n_chains, 0))
+        self.global_accs = jnp.empty((self.n_chains, 0))
+        self.loss_vals = jnp.empty((0, self.n_epochs))
 
     def sample(self,initial_position):
         """
@@ -138,12 +138,13 @@ def sampling_loop(self,rng_keys_nf,
             positions = jnp.concatenate((positions,nf_chain),axis=1)
             log_prob_output = jnp.concatenate((log_prob_output,log_prob),axis=1)
         
-        self.chains.append(positions)
-        self.log_prob.append(log_prob_output)
-        self.local_accs.append(local_acceptance)
+        self.chains = jnp.append(self.chains, positions, axis=1)
+        self.log_prob = jnp.append(self.log_prob, log_prob_output, axis=1)
+        self.local_accs = jnp.append(self.local_accs, local_acceptance, axis=1)
         if self.use_global == True:
-            self.global_accs.append(global_acceptance)
-            self.loss_vals.append(loss_values)
+            self.global_accs = jnp.append(self.global_accs, global_acceptance, axis=1)
+            self.loss_vals = jnp.append(self.loss_vals, loss_values.reshape(1,-1), axis=0)
+
 
         last_step = positions[:, -1]
 
@@ -151,15 +152,7 @@ def sampling_loop(self,rng_keys_nf,
 
 
     def get_sampler_state(self):
-        chains = jnp.concatenate(self.chains, axis=1)
-        log_prob = jnp.concatenate(self.log_prob, axis=1)
-        local_accs = jnp.stack(self.local_accs, axis=1).reshape(chains.shape[0], -1)
-        if self.use_global == True:
-            global_accs = jnp.stack(self.global_accs, axis=1).reshape(chains.shape[0], -1)
-            loss_vals = jnp.stack(self.loss_vals, axis=1).reshape(chains.shape[0], -1)
-            return chains, log_prob, local_accs, global_accs, loss_vals
-        else:
-            return chains, log_prob, local_accs, jnp.zeros(0), jnp.zeros(0)
+        return self.chains, self.log_prob, self.local_accs, self.global_accs, self.loss_vals
 
     def sample_flow(self):
         nf_samples = sample_nf(self.nf_model, self.state.params, self.rng_keys_nf, self.n_nf_samples)
diff --git a/nfsampler/sampler/__init__.py b/src/flowMC/sampler/__init__.py
similarity index 100%
rename from nfsampler/sampler/__init__.py
rename to src/flowMC/sampler/__init__.py
diff --git a/nfsampler/utils/Analysis.py b/src/flowMC/utils/Analysis.py
similarity index 100%
rename from nfsampler/utils/Analysis.py
rename to src/flowMC/utils/Analysis.py
diff --git a/nfsampler/utils/PRNG_keys.py b/src/flowMC/utils/PRNG_keys.py
similarity index 100%
rename from nfsampler/utils/PRNG_keys.py
rename to src/flowMC/utils/PRNG_keys.py
diff --git a/nfsampler/utils/__init__.py b/src/flowMC/utils/__init__.py
similarity index 100%
rename from nfsampler/utils/__init__.py
rename to src/flowMC/utils/__init__.py


Test patch:
diff --git a/test/kernel_test_old.py b/test/kernel_test_old.py
index ee4e387..956106d 100644
--- a/test/kernel_test_old.py
+++ b/test/kernel_test_old.py
@@ -1,12 +1,12 @@
-from nfsampler.nfmodel.realNVP import RealNVP
+from flowMC.nfmodel.realNVP import RealNVP
 import jax
 import jax.numpy as jnp                # JAX NumPy
-from nfsampler.utils import Sampler, initialize_rng_keys
+from flowMC.utils import Sampler, initialize_rng_keys
 from jax.scipy.special import logsumexp
 import numpy as np  
 
 
-from nfsampler.nfmodel.utils import *
+from flowMC.nfmodel.utils import *
 
 
 def mala_kernel(rng_key, logpdf, d_logpdf, position, log_prob, kernal_size=0.1):
diff --git a/test/old_test_proposal.py b/test/old_test_proposal.py
index 5001612..f4aeb44 100644
--- a/test/old_test_proposal.py
+++ b/test/old_test_proposal.py
@@ -1,8 +1,8 @@
 
-from nfsampler.nfmodel.maf import MaskedAutoregressiveFlow
-from nfsampler.nfmodel.realNVP import RealNVP
-from nfsampler.sampler.Gaussian_random_walk import rw_metropolis_sampler
-from nfsampler.sampler.NF_proposal import nf_metropolis_sampler, nf_metropolis_kernel
+from flowMC.nfmodel.maf import MaskedAutoregressiveFlow
+from flowMC.nfmodel.realNVP import RealNVP
+from flowMC.sampler.Gaussian_random_walk import rw_metropolis_sampler
+from flowMC.sampler.NF_proposal import nf_metropolis_sampler, nf_metropolis_kernel
 import jax
 import jax.numpy as jnp                # JAX NumPy
 import jax.random as random            # JAX random
diff --git a/test/sampler_test.py b/test/sampler_test.py
index 902a070..1262dd5 100644
--- a/test/sampler_test.py
+++ b/test/sampler_test.py
@@ -1,7 +1,7 @@
 import jax
 import jax.numpy as jnp
-from nfsampler.sampler.MALA import mala_kernel
-from nfsampler.sampler.Gaussian_random_walk import rw_metropolis_kernel
+from flowMC.sampler.MALA import mala_kernel
+from flowMC.sampler.Gaussian_random_walk import rw_metropolis_kernel
 
 def test_mala_kernel():
     rng_key = jax.random.PRNGKey(0)