Repository: giecaruff/stoneforge (language: python)

Issue:
Appy1.0.9



Discussion / hints:

Fix patch:
diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index b1c54d4..23977b3 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -21,7 +21,13 @@ jobs:
       run: pip install numpy
       
     - name: Install pytest
-      run: pip install pytest 
+      run: pip install pytest
+
+    - name: Install scikit-learn
+      run: pip install scikit-learn
+
+    - name: Install xgboost
+      run: pip install xgboost 
       
     - name: Test with pytest
       run: |
diff --git a/codes/Inversion/Regularized_Acoustic_Reflectivity_Inversion.ipynb b/codes/Inversion/Regularized_Acoustic_Reflectivity_Inversion.ipynb
new file mode 100644
index 0000000..4d5fb83
--- /dev/null
+++ b/codes/Inversion/Regularized_Acoustic_Reflectivity_Inversion.ipynb
@@ -0,0 +1,583 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b49e19db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Funcoes que implementam o algoritmo de regularizacao, de Tikhonov de ordem zero e Variacao total.                                                     %\n",
+    "#Autor: Joao Pedro Reis                                                                                                                            \n",
+    "#Data: 29/06/2023                                                                                 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2a35f1a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np \n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.linalg import toeplitz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f543fcdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def IRLS_Inv(trace, G, alphaL1, alphaL2,thresold,niter):\n",
+    "    residuo_iter = np.zeros(10)\n",
+    "    dim = len(trace)\n",
+    "    R = np.eye(dim)\n",
+    "    I = np.eye(dim)\n",
+    "    G_T = G.transpose()\n",
+    "    count = 0 \n",
+    "    #Tikhonov ordem zero:\n",
+    "    M1_L2 = np.linalg.inv( np.matmul(G_T, G) + (alphaL2*I) )\n",
+    "    M2_L2 = np.matmul(G_T, trace)\n",
+    "    RegL2 = np.matmul(M1_L2, M2_L2)\n",
+    "    \n",
+    "    #Inversao do traco sismico utilizando regularizacao de tikhonov a partir da norma1 nos residuos dos dados\n",
+    "    while (count < niter):\n",
+    "            Gt_R = np.matmul(G_T, R)\n",
+    "            M1_L1 = np.linalg.inv( np.matmul(Gt_R,G) + (alphaL1*I) )\n",
+    "            M2_L1 = np.matmul(Gt_R, trace)\n",
+    "            RegL1 = np.matmul(M1_L1, M2_L1)\n",
+    "            dcalc = np.matmul(G, RegL1)\n",
+    "            residuo = dcalc[:] - trace[:]\n",
+    "            print('Numero de iteracoes ',count+1)\n",
+    "            for i in range( (dim//2)+2 ):\n",
+    "                if abs(residuo[i]) >= thresold:\n",
+    "                    R[i,i] = 1/abs(residuo[i])\n",
+    "                else:\n",
+    "                    R[i,i] = 1/thresold\n",
+    "            for i in range(dim):\n",
+    "                if abs(RegL1[i]) >= thresold:\n",
+    "                    I[i,i] = 1/abs(RegL1[i])\n",
+    "                else:\n",
+    "                    I[i,i] = 1/thresold\n",
+    "            residuo_iter[count] = residuo[145]\n",
+    "            count = count + 1\n",
+    "    return RegL2, RegL1, residuo_iter\n",
+    "\n",
+    "def Ricker_Wavelet(Peak_freq, Samples, Dt):\n",
+    "    t = np.arange(Samples)*(Dt/1000)\n",
+    "    t = np.concatenate( (np.flipud(-t[1:]), t), axis=0)\n",
+    "    ricker = (1. -2.*(np.pi**2)*(Peak_freq**2)*(t**2))*np.exp(-(np.pi**2)*(Peak_freq**2)*(t**2))\n",
+    "    return t, ricker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7778bd6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t, ricker = Ricker_Wavelet(30,101,4)\n",
+    "\n",
+    "Refl = np.zeros(521)\n",
+    "Refl[100] = 0.2\n",
+    "Refl[230] = -0.15\n",
+    "Refl[340] = 0.05\n",
+    "Refl[270] = -0.08\n",
+    "Refl[145] = 0.1\n",
+    "\n",
+    "n = len(Refl)\n",
+    "m = len(ricker)\n",
+    "\n",
+    "np.random.seed(1)\n",
+    "noise = np.random.normal(0,1,n)*0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cee0a124",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Refl_T = Refl.reshape(n,1)\n",
+    "ricker_padded = np.append(ricker,np.zeros((1,2*n-m)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f806d069",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = toeplitz(400*ricker_padded, np.zeros((1,n)))\n",
+    "G = G[m//2+1:n+(m//2)+1,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c171f722",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trace = np.matmul(G, Refl_T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9e9f4e52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f87491c14e0>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Zero offset seismic trace\")\n",
+    "plt.plot(trace)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ed72b45e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alphaL1 = 10\n",
+    "alphaL2 = 100\n",
+    "thresold = 0.0001\n",
+    "niter = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1d2fda76",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numero de iteracoes  1\n",
+      "Numero de iteracoes  2\n",
+      "Numero de iteracoes  3\n",
+      "Numero de iteracoes  4\n",
+      "Numero de iteracoes  5\n",
+      "Numero de iteracoes  6\n",
+      "Numero de iteracoes  7\n",
+      "Numero de iteracoes  8\n",
+      "Numero de iteracoes  9\n",
+      "Numero de iteracoes  10\n"
+     ]
+    }
+   ],
+   "source": [
+    "RegL2, RegL1, residuo = IRLS_Inv(trace, G, alphaL1, alphaL2,thresold,niter)\n",
+    "\n",
+    "RegL1n = RegL1[:]/max(abs(RegL1[:]))*max(abs(Refl[:]));\n",
+    "RegL2n = RegL2[:]/max(abs(RegL2[:]))*max(abs(Refl[:]));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dc239f6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f87486c2e30>]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Residual\")\n",
+    "plt.xlabel(\"Iteraction number\")\n",
+    "plt.plot(abs(residuo))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5de40b92",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f8748728cd0>]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Regularized Least Squares Reflectivity Inversion\")\n",
+    "plt.plot(RegL2n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6225e25e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f874877b7c0>]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Regularized Sparse Spike Reflectivity Inversion\")\n",
+    "plt.plot(RegL1n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e009411d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f874860db70>]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#generating noisy trace with low gaussian random noise average ~10 times smaller than smaller reflectivity impulse\n",
+    "noise = noise.reshape(n,1)\n",
+    "trace_noise = trace + noise\n",
+    "\n",
+    "Sn = max(noise)/trace[340]\n",
+    "plt.title('Random Noise'  ' Minimum S/N = '+str(Sn))\n",
+    "plt.plot(noise)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0bd7adbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f874867c940>]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Zero offset seismic trace with random gaussian noise\")\n",
+    "plt.plot(trace_noise)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1ff9c28f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numero de iteracoes  1\n",
+      "Numero de iteracoes  2\n",
+      "Numero de iteracoes  3\n",
+      "Numero de iteracoes  4\n",
+      "Numero de iteracoes  5\n",
+      "Numero de iteracoes  6\n",
+      "Numero de iteracoes  7\n",
+      "Numero de iteracoes  8\n",
+      "Numero de iteracoes  9\n",
+      "Numero de iteracoes  10\n"
+     ]
+    }
+   ],
+   "source": [
+    "RegL2_noise, RegL1_noise, residuo = IRLS_Inv(trace_noise, G, alphaL1, alphaL2,thresold,niter)\n",
+    "\n",
+    "RegL1n_noise = RegL1_noise[:]/max(abs(RegL1_noise[:]))*max(abs(Refl[:]));\n",
+    "RegL2n_noise = RegL2_noise[:]/max(abs(RegL2_noise[:]))*max(abs(Refl[:]));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "634eca81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f87484f0910>]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Regularized Least Squares Reflectivity Inversion with noise\")\n",
+    "plt.plot(RegL2n_noise)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b549f470",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f8748560400>]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Regularized Sparse Spike Reflectivity Inversion with noise\")\n",
+    "plt.plot(RegL1n_noise)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "4f406d73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Now let's investigate how the regularization parameter alpha controls de inversion\n",
+    "alphaL1 = 3000 #Big value will be used this time instead of small\n",
+    "alphaL2 = 100\n",
+    "thresold = 0.0001\n",
+    "niter = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "db9bb9b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numero de iteracoes  1\n",
+      "Numero de iteracoes  2\n",
+      "Numero de iteracoes  3\n",
+      "Numero de iteracoes  4\n",
+      "Numero de iteracoes  5\n",
+      "Numero de iteracoes  6\n",
+      "Numero de iteracoes  7\n",
+      "Numero de iteracoes  8\n",
+      "Numero de iteracoes  9\n",
+      "Numero de iteracoes  10\n"
+     ]
+    }
+   ],
+   "source": [
+    "RegL2, RegL1_new_reg, residuo = IRLS_Inv(trace, G, alphaL1, alphaL2,thresold,niter)\n",
+    "\n",
+    "RegL1n_new = RegL1_new_reg[:]/max(abs(RegL1_new_reg[:]))*max(abs(Refl[:]));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "82416459",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f87483d02e0>]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Regularized Sparse Spike Reflectivity Inversion with low sparsity imposed\")\n",
+    "plt.plot(RegL1n_new)"
+   ]
+  }
+ ],
+ "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/codes/__init__.py b/codes/__init__.py
new file mode 100644
index 0000000..682c1bf
--- /dev/null
+++ b/codes/__init__.py
@@ -0,0 +1,6 @@
+from . import petrophysics  # noqa: F401
+from . import rock_physics  # noqa: F401
+from . import pseudo_wells  # noqa: F401
+from . import tvdss  # noqa: F401
+from . import machine_learning  # noqa: F401
+from . import preprocessing  # noqa: F401
\ No newline at end of file
diff --git a/codes/wavelets/Wavelets_notebook.ipynb b/codes/wavelets/Wavelets_notebook.ipynb
new file mode 100644
index 0000000..090d85d
--- /dev/null
+++ b/codes/wavelets/Wavelets_notebook.ipynb
@@ -0,0 +1,227 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "eef520ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Programa que implementa computacionalmente a wavelet de Ricker, Ormsby e Butterworth\n",
+    "from scipy import signal\n",
+    "import numpy as np \n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "2bddfb45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Ricker_Wavelet(Peak_freq, Samples, Dt):\n",
+    "    t = np.arange(Samples)*(Dt/1000)\n",
+    "    t = np.concatenate((np.flipud(-t[1:]), t), axis=0)\n",
+    "    ricker = (1. -2.*(np.pi**2)*(Peak_freq**2)*(t**2))*np.exp(-(np.pi**2)*(Peak_freq**2)*(t**2))\n",
+    "    return t, ricker\n",
+    "\n",
+    "def Ormsby_Wavelet(f,Samples, Dt):\n",
+    "    assert len(f) == 4, 'Ormsby wavelet needs 4 frequencies as input'\n",
+    "    f = np.sort(f) #Ormsby wavelet frequencies must be in increasing order\n",
+    "    t = np.arange(Samples)*(Dt/1000)\n",
+    "    t = np.concatenate((np.flipud(-t[1:]), t), axis=0)\n",
+    "    #t = np.linspace(-Duration,Duration,int(Duration/Sampling))\n",
+    "    pif   = np.pi*f\n",
+    "    den1  = pif[3] - pif[2]\n",
+    "    den2  = pif[1] - pif[0]\n",
+    "    term1 = (pif[3]*np.sinc(f[3]*t))**2 - (pif[2]*np.sinc(f[2]*t))**2\n",
+    "    term2 = (pif[1]*np.sinc(f[1]*t))**2 - (pif[0]*np.sinc(f[0]*t))**2\n",
+    "\n",
+    "    wav   = term1/den1 - term2/den2;\n",
+    "    wav /= np.amax(wav)\n",
+    "    return t, wav\n",
+    "\n",
+    "def Butter_Wavelet(Freq_hi, Freq_low, Samples, Dt):\n",
+    "    t = np.arange(Samples)*(Dt/1000)\n",
+    "    t = np.concatenate((np.flipud(-t[1:]), t), axis=0)\n",
+    "    imp = signal.unit_impulse(t.shape[0], 'mid')\n",
+    "    b, a = signal.butter(4, Freq_hi,fs = 1000*(1/Dt))\n",
+    "    response_zp = signal.filtfilt(b, a, imp)\n",
+    "    low_b, low_a = signal.butter(2,Freq_low,'hp', fs = 1000*(1/Dt))\n",
+    "    butter_wvlt = signal.filtfilt(low_b, low_a, response_zp)\n",
+    "    return t, butter_wvlt\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "a32870c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lenght = 50\n",
+    "Freq_domin = 40\n",
+    "dt = 4\n",
+    "Time , Ricker_wl = Ricker_Wavelet(Freq_domin,lenght,dt)\n",
+    "#Ricker wavelet spectrum\n",
+    "freqs = np.fft.rfftfreq(Time.shape[0], d=dt/1000)\n",
+    "a = np.fft.rfft(Ricker_wl)\n",
+    "A = np.abs(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "5fce00d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# display wavelet\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(20, 5))\n",
+    "fig.suptitle('Ricker Wavelet')\n",
+    "axs[0].plot(freqs, A, 'k')\n",
+    "axs[0].grid()\n",
+    "axs[0].set_title('Frequency')\n",
+    "axs[1].plot(Time, Ricker_wl, 'k')\n",
+    "axs[1].set_xlim(-0.1,0.1)\n",
+    "axs[1].grid()\n",
+    "axs[1].set_title('Time')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "e5e9c19e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "time, wvlt = Ormsby_Wavelet((5,10,40,45),36,4)\n",
+    "\n",
+    "orms_fft = abs(np.fft.rfft(wvlt))\n",
+    "freqs = np.fft.rfftfreq(time.shape[0], d=dt/1000)\n",
+    "\n",
+    "# display wavelet\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(20, 5))\n",
+    "fig.suptitle('Ormsby Wavelet')\n",
+    "axs[0].plot(freqs, orms_fft, 'k')\n",
+    "axs[0].grid()\n",
+    "axs[0].set_title('Frequency')\n",
+    "axs[1].plot(time, wvlt, 'k')\n",
+    "axs[1].set_xlim(-0.2,0.2)\n",
+    "axs[1].grid()\n",
+    "axs[1].set_title('Time')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "c30f66cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Samples = 71\n",
+    "Dt = 4\n",
+    "t, butter_wvlt = Butter_Wavelet(35, 1.5, Samples, Dt)\n",
+    "\n",
+    "butter_fft = abs(np.fft.rfft(butter_wvlt))\n",
+    "freqs = np.fft.rfftfreq(t.shape[0], d=Dt/1000)\n",
+    "\n",
+    "\n",
+    "# display wavelet\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(20, 5))\n",
+    "fig.suptitle('Ormsby Wavelet')\n",
+    "axs[0].plot(freqs, butter_fft, 'k')\n",
+    "axs[0].grid()\n",
+    "axs[0].set_title('Frequency')\n",
+    "axs[1].plot(t,butter_wvlt, 'k')\n",
+    "axs[1].set_xlim(-0.1,0.1)\n",
+    "axs[1].grid()\n",
+    "axs[1].set_title('Time')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66def542",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c2bdb14",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ce539bc",
+   "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/codes/wavelets/butterworth.py b/codes/wavelets/butterworth.py
new file mode 100644
index 0000000..caa1a95
--- /dev/null
+++ b/codes/wavelets/butterworth.py
@@ -0,0 +1,63 @@
+import warnings
+from typing import Callable, Optional, Sequence, Tuple
+
+from scipy import signal
+import numpy as np
+import numpy.typing as npt
+
+import matplotlib.pyplot as plt
+
+def _tcrop(t: npt.ArrayLike) -> npt.ArrayLike:
+    """Crop time axis with even number of samples"""
+    if len(t) % 2 == 0:
+        t = t[:-1]
+        warnings.warn("one sample removed from time axis...")
+    return t
+
+
+def Butter_Wavelet(Freq_low: float = 1.5,
+                   Freq_hi: float = 65,
+                   Samples: int = 71,
+                   Dt: float = 4) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
+    
+    r"""Butterworth wavelet
+
+    Create a Butterworth wavelet given time axis ``t``, minimum frequeny and maximun frequency
+
+
+    Parameters
+    ----------
+    Freq_low : :obj:`float`, optional
+        Minimun frequency
+    Freq_hi : :obj:`float`, optional
+        Maximun frequency
+    Samples : :float
+        Number of samples       
+    Dt : :float
+        Sampling in milisseconds
+
+    Returns
+    -------
+    wav : :obj:`numpy.ndarray`
+        Wavelet
+    t : :obj:`numpy.ndarray`
+        Symmetric time axis
+    """
+
+    t = np.arange(Samples)*(Dt/1000)
+    t = _tcrop(t)
+    t = np.concatenate((np.flipud(-t[1:]), t), axis=0)
+    imp = signal.unit_impulse(t.shape[0], 'mid')
+    b, a = signal.butter(4, Freq_hi,fs = 1000*(1/Dt))
+    response_zp = signal.filtfilt(b, a, imp)
+    low_b, low_a = signal.butter(2,Freq_low,'hp', fs = 1000*(1/Dt))
+    wav = signal.filtfilt(low_b, low_a, response_zp)
+    return t, wav
+
+t, butter_wvlt = Butter_Wavelet()
+plt.plot(t,butter_wvlt)
+plt.xlabel('Tempo (s)')
+plt.ylabel('Ondaleta (w)')
+plt.grid()
+plt.show()
+    
\ No newline at end of file
diff --git a/codes/wavelets/ormsby.py b/codes/wavelets/ormsby.py
new file mode 100644
index 0000000..3321b8e
--- /dev/null
+++ b/codes/wavelets/ormsby.py
@@ -0,0 +1,73 @@
+#Programa que implementa computacionalmente a wavelet de Ormsby
+
+import warnings
+from typing import Sequence, Tuple
+
+import numpy as np
+import numpy.typing as npt
+
+import matplotlib.pyplot as plt
+
+
+def _tcrop(t: npt.ArrayLike) -> npt.ArrayLike:
+    """Crop time axis with even number of samples"""
+    if len(t) % 2 == 0:
+        t = t[:-1]
+        warnings.warn("one sample removed from time axis...")
+    return t
+
+def getOrmsby(f: Sequence[float] = (5,10,40,50),
+              Samples: float = 71, 
+              Dt: float = 4) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
+    r"""Ormsby wavelet
+
+    Create a Ormsby wavelet given time axis ``t`` and frequency range
+    defined by four frequencies which parametrize a trapezoidal shape in
+    the frequency spectrum.
+
+    Parameters
+    ----------
+    f : :obj:`tuple`, optional
+        Frequency range
+    Samples : :obj:`float`, optional
+        Number of samples       
+    Dt : obj:`float`, optional
+        Sampling in milisseconds
+
+    Returns
+    -------
+    wav : :obj:`numpy.ndarray`
+        Wavelet
+    t : :obj:`numpy.ndarray`
+        Symmetric time axis
+    """
+    assert len(f) == 4, 'Ormsby wavelet needs 4 frequencies as input'
+    f = np.sort(f) #Ormsby wavelet frequencies must be in increasing order
+    t = np.arange(Samples)*(Dt/1000)
+    t = _tcrop(t)
+    t = np.concatenate((np.flipud(-t[1:]), t), axis=0)
+    pif   = np.pi*f
+    den1  = pif[3] - pif[2]
+    den2  = pif[1] - pif[0]
+    term1 = (pif[3]*np.sinc(f[3]*t))**2 - (pif[2]*np.sinc(f[2]*t))**2
+    term2 = (pif[1]*np.sinc(f[1]*t))**2 - (pif[0]*np.sinc(f[0]*t))**2
+
+    wav   = term1/den1 - term2/den2;
+    wav /= np.amax(wav)
+    return t, wav
+
+time, wvlt = getOrmsby()
+plt.plot(time,wvlt)
+plt.xlabel('Tempo (s)')
+plt.ylabel('Ondaleta (w)')
+plt.grid()
+plt.show()
+    
+
+
+
+
+
+
+
+
diff --git a/codes/wavelets/ricker.py b/codes/wavelets/ricker.py
new file mode 100644
index 0000000..f30f47a
--- /dev/null
+++ b/codes/wavelets/ricker.py
@@ -0,0 +1,55 @@
+#Programa que implementa computacionalmente a wavelet de Ricker
+
+import warnings
+from typing import Callable, Optional, Sequence, Tuple
+
+import numpy as np
+import numpy.typing as npt
+
+import matplotlib.pyplot as plt
+
+
+def _tcrop(t: npt.ArrayLike) -> npt.ArrayLike:
+    """Crop time axis with even number of samples"""
+    if len(t) % 2 == 0:
+        t = t[:-1]
+        warnings.warn("one sample removed from time axis...")
+    return t
+
+def Ricker_Wavelet(Peak_freq: float = 30,
+                   Samples: float = 71,
+                   Dt: float = 4) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
+    r"""Ricker wavelet
+
+    Create a Ricker wavelet given time axis ``t`` and central frequency ``f_0``
+
+    Parameters
+    ----------
+    Peak_freq : :obj:`float`, optional
+        Central frequency
+    Samples : :obj:`float`, optional
+        Number of samples
+    Dt : :obj:`func`, optional
+        Sampling in milisseconds
+
+    Returns
+    -------
+    ricker : :obj:`numpy.ndarray`
+        Wavelet
+    t : :obj:`numpy.ndarray`
+        Symmetric time axis
+    
+    """
+    t = np.arange(Samples)*(Dt/1000)
+    t = _tcrop(t)
+    t = np.concatenate((np.flipud(-t[1:]), t), axis=0)
+    ricker = (1. -2.*(np.pi**2)*(Peak_freq**2)*(t**2))*np.exp(-(np.pi**2)*(Peak_freq**2)*(t**2))
+    return t, ricker
+Time , Ricker_wl = Ricker_Wavelet()
+
+plt.plot(Time, Ricker_wl)
+plt.xlabel('Tempo (s)')
+plt.ylabel('Ondaleta (w)')
+plt.grid()
+plt.show()
+
diff --git a/setup.py b/setup.py
index d4fc6ea..86a99cb 100644
--- a/setup.py
+++ b/setup.py
@@ -11,7 +11,7 @@
 
 setup(
     name="stoneforge",
-    version="0.1.3",
+    version="0.1.4",
     author="GIECAR - UFF",
     url="https://github.com/giecaruff/stoneforge",
     description="Geophysics equations, algorithms and methods",
@@ -24,16 +24,14 @@
         "numpy>=1.21, <1.22.1",
         "pytest>=6.2.2, <6.2.5",
         "scipy>=1.4.1, <1.8.1",
-        "scikit-learn==0.23.2",
+        "scikit-learn>=0.22.1, <0.24.2",
         "xgboost==1.4.0",
+        "matplotlib>=3.5.0, <3.5.3",
+        "jupyter==1.0.0",
+        "dash>=1.20.0, <1.21.0",
+        "plotly>=5.1.0, <5.2.1",
+        "pandas==1.5.2",
         "catboost==1.0.6",
-        "lightgbm==3.3.2",
-        #"catboost==0.26.1"
-        #"auto-sklearn==0.12.5"
-        
-        #"matplotlib", ### TO REMOVE BEFORE MERGE!!
-        #"pandas", ### TO REMOVE BEFORE MERGE!!
-        #"jupyter", ### TO REMOVE BEFORE MERGE!!
-        #"seaborn" ### TO REMOVE BEFORE MERGE!!
+        "lightgbm==3.3.2"
     ],
 )
diff --git a/stoneforge/petrophysics/shale_volume.py b/stoneforge/petrophysics/shale_volume.py
index 7a0c19b..dd71998 100644
--- a/stoneforge/petrophysics/shale_volume.py
+++ b/stoneforge/petrophysics/shale_volume.py
@@ -154,9 +154,9 @@ def vshale_stieber(gr: npt.ArrayLike, grmin: float, grmax: float):
     return vshale
 
 
-def vshale_neu_den(neu: npt.ArrayLike, den: npt.ArrayLike, cl1_n: float = -0.15,
-                   cl1_d: float = 2.65, cl2_n: float = 1.00, cl2_d: float = 1.10, clay_n: float = 0.47,
-                   clay_d: float = 2.71) -> np.ndarray:
+def vshale_neu_den(neu: npt.ArrayLike, den: npt.ArrayLike, cl1_n: float,
+                   cl1_d: float, cl2_n: float, cl2_d: float, clay_n: float,
+                   clay_d: float) -> np.ndarray:
     """Estimates the shale volume from neutron and density logs method [1]_.
 
     Parameters
@@ -166,17 +166,17 @@ def vshale_neu_den(neu: npt.ArrayLike, den: npt.ArrayLike, cl1_n: float = -0.15,
     den : array_like
         Bulk density log.
     cl1_n : int, float
-        Neutron porosity value from clean point 1 for empty matrix (NPHI_MATRIX).
+        Neutron porosity value from clean point 1.
     cl1_d : int, float
-        Bulk density value from clean point 1 for empty matrix (RHOB_MATRIX).
+        Bulk density value from clean point 1.
     cl2_n : int, float
-        Neutron porosity value from clean point 2 full porosity.
+        Neutron porosity value from clean point 2.
     cl2_d : int, float
-        Bulk density value from clean point 2 full porosity.
+        Bulk density value from clean point 2.
     clay_n : int, float
-        Neutron porosity value from clay point (NPHI_SHALE).
+        Neutron porosity value from clay point.
     clay_d : int, float
-        Bulk density value from clay point (RHOB_SHALE).
+        Bulk density value from clay point.
 
     Returns
     -------
diff --git a/stoneforge/petrophysics/water_saturation.py b/stoneforge/petrophysics/water_saturation.py
index 0774b4a..5d15616 100644
--- a/stoneforge/petrophysics/water_saturation.py
+++ b/stoneforge/petrophysics/water_saturation.py
@@ -3,30 +3,29 @@
 import warnings
 from stoneforge.petrophysics.helpers import correct_petrophysic_estimation_rage
 
+
 # Make anomalous water saturation values larger than 1 be one
 def correct_range(sw: np.ndarray):
     sw[sw > 1] = 1
     return sw
 
-
-
 def archie(rw: float, rt: npt.ArrayLike, phi: npt.ArrayLike, a: float,
            m: float, n: float) -> np.ndarray:
     """Estimate the Water Saturation from Archie's [1]_ equation.
 
     Parameters
     ----------
-    rw : float
+    rw : int, float
         Water resistivity.  
     rt : array_like
         Formation resistivity.    
     phi : array_like
         Porosity.   
-    a : float
+    a : int, float
         Tortuosity factor.
-    m : float
+    m : int, float
         Cementation exponent.
-    n : float
+    n : int, float
         Saturation exponent.
 
     Returns
@@ -40,13 +39,14 @@ def archie(rw: float, rt: npt.ArrayLike, phi: npt.ArrayLike, a: float,
     reservoir characteristics. Transactions of the AIME, 146(01), 54-62.
 
     """
-    sw = ((a * rw) / ((phi ** m) * rt)) ** (1/n)
-
-    sw = correct_petrophysic_estimation_rage(sw)
-
-    return sw
-
+    if any(((a*rw) / (phi**m * rt))**(1/n) > 1):
+        warnings.warn(UserWarning("saturation of water must be a value between 0 and 1"))
+        return ((a*rw) / (phi**m * rt))**(1/n)
 
+    else:
+        sw = ((a*rw) / (phi**m * rt))**(1/n)
+        sw = correct_petrophysic_estimation_rage(sw)
+        return sw
 
 
 def simandoux(rw: float, rt: npt.ArrayLike, phi: npt.ArrayLike, a: float,
@@ -90,9 +90,9 @@ def simandoux(rw: float, rt: npt.ArrayLike, phi: npt.ArrayLike, a: float,
     E = C / rt
     sw = ((D**2 + E)**0.5 - D)**(2/n)
 
-
     sw = correct_petrophysic_estimation_rage(sw)
 
+
     return sw
 
 
@@ -132,11 +132,8 @@ def indonesia(rw: float, rt: npt.ArrayLike, phi: npt.ArrayLike, a: float,
 
     """
     sw = ((1/rt)**0.5 / ((vsh**(1 - 0.5*vsh) / (rsh)**0.5) + (phi**m / a*rw)**0.5))**(2/n)
-
-
     sw = correct_petrophysic_estimation_rage(sw)
 
-    
 
     return sw
 
@@ -176,6 +173,7 @@ def fertl(rw: float, rt: npt.ArrayLike, phi: npt.ArrayLike, a: float,
     sw = phi**(-m/2) * ((a*rw/rt + (alpha*vsh/2)**2)**0.5 - (alpha*vsh/2))
     sw = correct_petrophysic_estimation_rage(sw)
 
+
     return sw
 
 
@@ -254,6 +252,7 @@ def water_saturation(rw: float, rt: npt.ArrayLike, phi: npt.ArrayLike,
     
     fun = _sw_methods[method]
 
+
     sw = fun(rw, rt, phi, a, m, **options)
     
     return sw
diff --git a/stoneforge/pseudo_wells/__init__.py b/stoneforge/pseudo_wells/__init__.py
index 977e97d..e69de29 100644
--- a/stoneforge/pseudo_wells/__init__.py
+++ b/stoneforge/pseudo_wells/__init__.py
@@ -1,1 +0,0 @@
-from . import monte_carlo_simulations  # noqa: F401
diff --git a/stoneforge/pseudo_wells/monte_carlo_simulations.py b/stoneforge/pseudo_wells/monte_carlo_simulations.py
index cc37826..87dd3d0 100644
--- a/stoneforge/pseudo_wells/monte_carlo_simulations.py
+++ b/stoneforge/pseudo_wells/monte_carlo_simulations.py
@@ -174,7 +174,7 @@ def spherical_variogram_model(distance: npt.ArrayLike, correlation_length: float
         return(rho)
 
 
-def analytical_variogram(distance: npt.ArrayLike, gama: npt.ArrayLike, initial_guess:npt.ArrayLike)-> np.ndarray:
+def analytical_variogram(distance: npt.ArrayLike, gama: npt.ArrayLike, model:str="best-fit")-> np.ndarray:
   """
   Fits the choosen analytical variogram function (model) to the experimental one [1]_, 
   if no model is choosen, determines the best model to fit, comparing the Gaussian, 
@@ -213,56 +213,66 @@ def analytical_variogram(distance: npt.ArrayLike, gama: npt.ArrayLike, initial_g
   Examples and Algorithms. India: Wiley Blackwell, 2021.
 
   """ 
-  model_data = []
-
-  xi = distance
-  coeficients, cov = curve_fit(spherical_variogram_model, distance, gama, initial_guess)                               
-  yi = list(map(lambda distance: spherical_variogram_model(distance, *coeficients), xi))        
-  spherical_data = ["spherical", yi, coeficients, False]
-
-  xig = distance
-  coeficientsg, covg = curve_fit(gaussian_variogram_model, distance, gama, initial_guess)
-  yig = list(map(lambda distance: gaussian_variogram_model(distance, *coeficientsg), xig))
-  gaussian_data = ["gaussian", yig, coeficientsg, False]
-
-  xie = distance
-  coeficientse, cove = curve_fit(exponential_variogram_model, distance, gama, initial_guess)
-  yie = list(map(lambda distance: exponential_variogram_model(distance, *coeficientse), xie))
-  exponential_data = ["exponential", yie, coeficientse, False]
-
-  ranges = np.array([coeficients[0],coeficientsg[0],coeficientse[0]])
-  structured_field = distance <= np.max(ranges)
-
-  difference_sph = np.zeros(len(gama))
-  difference_gauss = np.zeros(len(gama))
-  difference_exp = np.zeros(len(gama))
-
-  i = 0
-  while (structured_field[i] == True and i < (len(structured_field)-1)):
-    i += 1
-    difference_sph[i] = gama[i] - yi[i]
-    difference_gauss[i] = gama[i] - yig[i]
-    difference_exp[i] = gama[i] - yie[i]
-
-  rmse_sph = ((np.sum(difference_sph**2))/i)**0.5
-  rmse_gaus = ((np.sum(difference_gauss**2))/i)**0.5
-  rmse_exp = ((np.sum(difference_exp**2))/i)**0.5
-
-  RMSE = np.array((rmse_sph, rmse_gaus, rmse_exp))
-  best = list(RMSE).index(np.min(RMSE))
-
-  if best == 0:
-    spherical_data[3] = True
-  if best == 1:
-    gaussian_data[3] = True
-  if best == 2:
-    exponential_data[3] = True
-
-  model_data.append(spherical_data)
-  model_data.append(gaussian_data)
-  model_data.append(exponential_data)
-
-  return model_data
+  if model == "spherical":
+    xi = distance
+    coeficients, cov = curve_fit(spherical_variogram_model, distance, gama)                               
+    yi = list(map(lambda distance: spherical_variogram_model(distance, *coeficients), xi))
+    return(yi,coeficients)
+
+  elif model == "gaussian":
+    xig = distance
+    coeficientsg, covg = curve_fit(gaussian_variogram_model, distance, gama)
+    yig = list(map(lambda distance: gaussian_variogram_model(distance, *coeficientsg), xig))
+    return(yig,coeficientsg)
+
+  elif model == "exponential":
+    xie = distance
+    coeficientse, cove = curve_fit(exponential_variogram_model, distance, gama)
+    yie = list(map(lambda distance: exponential_variogram_model(distance, *coeficientse), xie))
+    return(yie,coeficientse)
+
+  elif model == "best-fit":
+    xi = distance
+    coeficients, cov = curve_fit(spherical_variogram_model, distance, gama)                               
+    yi = list(map(lambda distance: spherical_variogram_model(distance, *coeficients), xi))        
+
+    xig = distance
+    coeficientsg, covg = curve_fit(gaussian_variogram_model, distance, gama)
+    yig = list(map(lambda distance: gaussian_variogram_model(distance, *coeficientsg), xig))
+
+    xie = distance
+    coeficientse, cove = curve_fit(exponential_variogram_model, distance, gama)
+    yie = list(map(lambda distance: exponential_variogram_model(distance, *coeficientse), xie))
+
+    ranges = np.array([coeficients[0],coeficientsg[0],coeficientse[0]])
+    structured_field = distance <= np.max(ranges)
+
+    difference_sph = np.zeros(len(gama))
+    difference_gauss = np.zeros(len(gama))
+    difference_exp = np.zeros(len(gama))
+
+    i = 0
+    while (structured_field[i] == True):
+      i += 1
+      difference_sph[i] = gama[i] - yi[i]
+      difference_gauss[i] = gama[i] - yig[i]
+      difference_exp[i] = gama[i] - yie[i]
+
+    rmse_sph = ((np.sum(difference_sph**2))/i)**0.5
+    rmse_gaus = ((np.sum(difference_gauss**2))/i)**0.5
+    rmse_exp = ((np.sum(difference_exp**2))/i)**0.5
+
+    RMSE = np.array((rmse_sph, rmse_gaus, rmse_exp))
+    best = list(RMSE).index(np.min(RMSE))
+
+    if best == 0:
+      return(yi,coeficients)
+    if best == 1:
+      return(yig,coeficientsg)
+    if best == 2:
+      return(yie,coeficientse)
+  else:
+    raise TypeError("model must be: exponential, gaussian, spherical or best-fit")
 
 
 def modeled_correlation(gama: npt.ArrayLike, var: float)-> np.ndarray:
diff --git a/stoneforge/rock_physics/__init__.py b/stoneforge/rock_physics/__init__.py
index 60d9459..e69de29 100644
--- a/stoneforge/rock_physics/__init__.py
+++ b/stoneforge/rock_physics/__init__.py
@@ -1,4 +0,0 @@
-from . import rock_physics_bounds  # noqa: F401
-from . import elastic_constants  # noqa: F401
-from . import fluid_substitution  # noqa: F401
-from . import gem  # noqa: F401
\ No newline at end of file
diff --git a/stoneforge/rock_physics/elastic_constants.py b/stoneforge/rock_physics/elastic_constants.py
index 0533bc8..e040f18 100644
--- a/stoneforge/rock_physics/elastic_constants.py
+++ b/stoneforge/rock_physics/elastic_constants.py
@@ -1,192 +1,3 @@
-import numpy.typing as npt
-
-def bulk_modulus(rhob: npt.ArrayLike, vp: npt.ArrayLike, vs: npt.ArrayLike):
-     """
-     Computes the bulk modulus using density, shear wave velocity and compressional wave velocity.
-
-    Parameters
-    rhob : array_like, float
-        Density data in g/cm³.
-
-    vp : array_like, float
-        Compressional wave velocity data in km/s.
-
-    vs : array_like, float
-        Shear wave velocity data in km/s.
-
-    Returns
-    -------
-    K : array_like, float
-        Bulk modulus data in Pascal unit.
-    ----------
-
-    References
-    ----------
-    .. [1] Simm, R., & Bacon, M. (2014). Seismic Amplitude: An Interpreter's Handbook.
-    Cambridge University Press.
-
-    """
-     A = vp**2 
-     B = (4/3) * (vs**2)
-     K = rhob * (A - B)
-     return K
-
-def shear_modulus(rhob: npt.ArrayLike, vp: npt.ArrayLike):
-    """
-     Computes the shear modulus using density and s wave velocity.
-
-    Parameters
-    rhob : array_like, float
-        Density data in g/cm³.
-
-    vp : array_like, float
-        Compressional wave velocity data in km/s.
-
-    Returns
-    -------
-    U : array_like, float
-        Shear modulus data in Pascal unit.
-    ----------
-
-    References
-    ----------
-    .. [1] Simm, R., & Bacon, M. (2014). Seismic Amplitude: An Interpreter's Handbook.
-    Cambridge University Press.
-
-    """
-    U = rhob * (vp**2)
-
-    return U
-
-def shear_modulus(rhob: npt.ArrayLike, vp: npt.ArrayLike):
-    """
-     Computes the shear modulus using density and s wave velocity.
-
-    Parameters
-    rhob : array_like, float
-        Density data in g/cm³.
-
-    vp : array_like, float
-        Compressional wave velocity data in km/s.
-
-    Returns
-    -------
-    U : array_like, float
-        Shear modulus data in Pascal unit.
-    ----------
-
-    References
-    ----------
-    .. [1] Simm, R., & Bacon, M. (2014). Seismic Amplitude: An Interpreter's Handbook.
-    Cambridge University Press.
-
-    """
-    U = rhob * (vp**2)
-
-    return U
-
-def shear_wave_velocity(rhob: npt.ArrayLike, u: npt.ArrayLike):
-    """
-     Computes the shear wave velocity using density and shear modulus.
-
-    Parameters
-    rhob : array_like, float
-        Density data in g/cm³.
-
-    u: array_like, float
-        Shear modulus data in Pascal unit.
-
-    Returns
-    -------
-    VS : array_like, float
-        Shear wave velocity data km/s.
-    ----------
-
-    References
-    ----------
-    .. [1] Simm, R., & Bacon, M. (2014). Seismic Amplitude: An Interpreter's Handbook.
-    Cambridge University Press.
-
-    """
-    VS = (u / rhob)**0.5
-
-    return VS
-
-
-def compressional_wave_velocity(method: str, **kwargs):
-    """
-     Computes the compressional wave velocity.
-
-    Parameters
-    method : str
-        The two elastic moduli used to the estimation. Should be one of:
-            - "rhob_and_g_and_k" for density and shear moduli and bolk moduli
-            - "rhob_and_m" for density and compressional modulus
-    ----------
-    Returns
-    -------
-    VP : int, float, array_like
-        compressional wave velocity.
-
-    References
-    ----------
-    .. [1] Simm, R., & Bacon, M. (2014). Seismic Amplitude: An Interpreter's Handbook.
-    Cambridge University Press.
-  
-
-    """
-    if method == "rhob_and_g_and_k":
-        required = ["rhob", "g", "k"]
-        for arg in required:
-            if arg not in kwargs:
-                msg = f"Missing required argument for method '{method}': '{arg}'"
-                raise TypeError(msg)
-        A = kwargs["k"] + (4/3)*kwargs["g"]
-        VP = (A / kwargs["rhob"])**0.5
-        return VP
-    
-    elif method == "rhob_and_m":
-        required = ["rhob", "m"]
-        for arg in required:
-            if arg not in kwargs:
-                msg = f"Missing required argument for method '{method}': '{arg}'"
-                raise TypeError(msg)
-        VP = (kwargs["m"]/kwargs["rhob"])**0.5
-        return VP
-
-
-
-def compressional_modulus(rhob: npt.ArrayLike, vp: npt.ArrayLike):
-    """
-     Computes the Compressional modulus using density and compressional wave velocity
-
-    Parameters
-    rhob : array_like, float
-        Density data in g/cm³.
-
-    vp: array_like, float
-        Compressional wave velocity in km/s².
-
-
-    Returns
-    -------
-    M : array_like, float
-        Compressional modulus data Pascal (Pa).
-    ----------
-
-    References
-    ----------
-    .. [1] Simm, R., & Bacon, M. (2014). Seismic Amplitude: An Interpreter's Handbook.
-    Cambridge University Press.
-
-    """
-    M = rhob * (vp)**2
-
-    return M
-
-                          
-
-
 def poisson(method: str, **kwargs):
     """Computes the Poisson ratio using two elastic moduli.
 
diff --git a/stoneforge/rock_physics/fluid_substitution.py b/stoneforge/rock_physics/fluid_substitution.py
index c53b735..7d5cec9 100644
--- a/stoneforge/rock_physics/fluid_substitution.py
+++ b/stoneforge/rock_physics/fluid_substitution.py
@@ -1,6 +1,5 @@
 import numpy as np
 import numpy.typing as npt
-import warnings
 
 
 def kdry(phi: npt.ArrayLike, ks: npt.ArrayLike, ksatA: npt.ArrayLike,
@@ -111,10 +110,6 @@ def gassmann_subs(phi: npt.ArrayLike, ks: npt.ArrayLike, ksatA: npt.ArrayLike,
 
     """
     A = ksatA / (ks - ksatA)
-
-
- 
-
     B = kfluidA / (phi*(ks - kfluidA))
     C = kfluidB / (phi*(ks - kfluidB))
     D = A - B + C
@@ -131,7 +126,7 @@ def gassmann_subs(phi: npt.ArrayLike, ks: npt.ArrayLike, ksatA: npt.ArrayLike,
 
 
 def gassmann(phi: npt.ArrayLike, ks: npt.ArrayLike,
-             method: str = "gassmann_subs", **kwargs) -> np.ndarray:
+             method: str = "ksat_direct", **kwargs) -> np.ndarray:
     """Compute Gassmann' equations.
 
     This is a façade for the methods:
@@ -313,7 +308,7 @@ def mavko_subs(phi: npt.ArrayLike, ms: npt.ArrayLike, msatA: npt.ArrayLike,
 
 
 def mavko(phi: npt.ArrayLike, ms: npt.ArrayLike,
-             method: str = "mavko_subs", **kwargs) -> np.ndarray:
+             method: str = "msat_direct", **kwargs) -> np.ndarray:
     """Compute Mavko' equations.
 
     This is a façade for the methods:
diff --git a/stoneforge/rock_physics/gem.py b/stoneforge/rock_physics/gem.py
index 26c28ee..f2ad37f 100644
--- a/stoneforge/rock_physics/gem.py
+++ b/stoneforge/rock_physics/gem.py
@@ -1,7 +1,7 @@
 import numpy as np
 import numpy.typing as npt
 
-from stoneforge.rock_physics.elastic_constants import poisson
+from rock_physics.elastic_constants import poisson
 
 
 def hertz_mindlin(k: float, g: float, n: float, phic: float,
diff --git a/stoneforge/rock_physics/rock_physics_bounds.py b/stoneforge/rock_physics/rock_physics_bounds.py
deleted file mode 100644
index 232cd0d..0000000
--- a/stoneforge/rock_physics/rock_physics_bounds.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import numpy as np
-
-
-def reuss(f, m):
-    """Calculate elastic modulus of the effective mineral using the
-    Reuss bound.
-
-    Parameters
-    ----------
-    f : array_like
-        Fractions (proportions) of each mineral.
-    m : array_like
-        Elastic modulus of each mineral.
-
-    Returns
-    -------
-    r: array_like
-        Reuss bound.
-    
-    """
-    r = 1 / np.sum(f/m, axis=0)
-    return r
-
-
-def voigt(f, m):
-    """Calculate elastic modulus of the effective mineral using the
-    Reuss bound.
-
-    Parameters
-    ----------
-    f : array_like
-        Fractions (proportions) of each mineral.
-    m : array_like
-        Elastic modulus of each mineral.
-
-    Returns
-    -------
-    v: array_like
-        Voigt bound.
-    
-    """
-    v = np.sum(f*m, axis=0)
-    return v
-
-
-def hill(f, m):
-    """Calculate elastic modulus of the effective mineral using the
-    Hill average.
-
-    Parameters
-    ----------
-    f : array_like
-        Fractions (proportions) of each mineral.
-    m : array_like
-        Elastic modulus of each mineral.
-
-    Returns
-    -------
-    v: array_like
-        Hill average.
-    
-    """
-    r = 1 / np.sum(f/m, axis=0)
-    v = np.sum(f*m, axis=0)
-    h = (r+v)/2
-    return h
diff --git a/stoneforge/tvdss/__init__.py b/stoneforge/tvdss/__init__.py
deleted file mode 100644
index e3baa7e..0000000
--- a/stoneforge/tvdss/__init__.py
+++ /dev/null
@@ -1,1 +0,0 @@
-from . import calculate_tvdss  # noqa: F401
\ No newline at end of file
diff --git a/stoneforge/tvdss/calculate_tvdss.py b/stoneforge/tvdss/calculate_tvdss.py
deleted file mode 100644
index 4d53ee8..0000000
--- a/stoneforge/tvdss/calculate_tvdss.py
+++ /dev/null
@@ -1,17 +0,0 @@
-
-def correct_step_value(correction_parameter, depth_step):
-        return correction_parameter - depth_step
-def round_values(depth_step):
-       return round(depth_step, 4)
-
-
-def get_tvdss(measured_depth_data, rotary_table, topography_or_water_blade):
-
-    if topography_or_water_blade is not None:
-        correction_parameter = rotary_table + topography_or_water_blade
-    else:
-        correction_parameter = rotary_table
-    tvdss_data = [correct_step_value(correction_parameter, i) for i in measured_depth_data]
-    tvdss_data = [round_values(i) for i in tvdss_data]
-
-    return tvdss_data


Test patch:
diff --git a/stoneforge/tests_water_saturation/parameters_ws.py b/stoneforge/tests_water_saturation/parameters_ws.py
index 8d5c7c7..c2eb916 100644
--- a/stoneforge/tests_water_saturation/parameters_ws.py
+++ b/stoneforge/tests_water_saturation/parameters_ws.py
@@ -11,10 +11,10 @@ def __init__(self):
     config_water_saturarion = {
     "rw":(1.1,5.0,"f"),
     "rt":(1.1,5.0,"c"),
-    "phi":(0.0,1.0,"c"),
+    "phi":(0.0,2.65,"c"),
     "a":(0.0,1.0,"f"),
-    "m":(1.0,4.0,"f"),
-    "n":(2.0,3.0,"f"),
+    "m":(0.0,1.0,"f"),
+    "n":(0.0,1.0,"f"),
     }
 
     def sorted_values (configuration, size = 15, curve_size = 30, seed = 99):