minor modifications

frank-ccc · Jan 5, 2020 · 26e92c6 · 26e92c6
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diff --git a/tutorial/.ipynb_checkpoints/ex1_simple_ZARC_model-checkpoint.ipynb b/tutorial/.ipynb_checkpoints/ex1_simple_ZARC_model-checkpoint.ipynb
@@ -331,7 +331,7 @@
     "\\end{align}\n",
     "$$\n",
     "\n",
-    "The key ingredient is to do Cholesky factorization of $\\mathcal L^2_{\\rm im} \\mathbf K+\\sigma_n^2\\mathbf I$"
+    "The key ingredient is to do Cholesky factorization of $\\mathcal L^2_{\\rm im} \\mathbf K+\\sigma_n^2\\mathbf I$, _i.e._, `K_im_full`"
    ]
   },
   {
@@ -487,20 +487,13 @@
     "plt.ylabel(r'$-Z_{\\rm im}/\\Omega$', fontsize = 20)\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python_pytorch",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "machine_learning"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -512,7 +505,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,

diff --git a/tutorial/.ipynb_checkpoints/ex2_double_ZARC_model-checkpoint.ipynb b/tutorial/.ipynb_checkpoints/ex2_double_ZARC_model-checkpoint.ipynb
@@ -404,20 +404,13 @@
     "plt.ylabel(r'$-Z_{\\rm im}/\\Omega$', fontsize = 20)\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python_pytorch",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "machine_learning"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -429,7 +422,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,

diff --git a/tutorial/.ipynb_checkpoints/ex3_truncated_ZARC_model-checkpoint.ipynb b/tutorial/.ipynb_checkpoints/ex3_truncated_ZARC_model-checkpoint.ipynb
@@ -378,20 +378,13 @@
     "plt.ylabel(r'$-Z_{\\rm im}/\\Omega$', fontsize = 20)\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python_pytorch",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "machine_learning"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -403,7 +396,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,

diff --git a/tutorial/.ipynb_checkpoints/ex4_real_experimental_data-checkpoint.ipynb b/tutorial/.ipynb_checkpoints/ex4_real_experimental_data-checkpoint.ipynb
@@ -11,9 +11,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## In this tutorial we will show an example for real experiemntal impedance data\n",
+    "## In this tutorial we will show use the GP-DRT method to analyze actual experimental data\n",
     "\n",
-    "The impedance data obtained by the measurement is stored in the csv file `EIS_experiment.csv`, where the three columns are frequency, the real part of impedance, the imaginary part of impedance, respectively. To run this tutorial smoothly, we recommend the user to write the frequency data ascendingly."
+    "The impedance data in the csv file named `EIS_experiment.csv`. The file has three columns. The first column is the frequency, the second one the real part of the impedance. The third column is the imaginary part of impedance. To use this tutorial for your own data, we recommend the frequencies go are sorted ascendingly."
    ]
   },
   {
@@ -417,7 +417,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,

diff --git a/tutorial/ex1_simple_ZARC_model.ipynb b/tutorial/ex1_simple_ZARC_model.ipynb
@@ -487,20 +487,13 @@
     "plt.ylabel(r'$-Z_{\\rm im}/\\Omega$', fontsize = 20)\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python_pytorch",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "machine_learning"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -512,7 +505,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,

diff --git a/tutorial/ex2_double_ZARC_model.ipynb b/tutorial/ex2_double_ZARC_model.ipynb
@@ -404,20 +404,13 @@
     "plt.ylabel(r'$-Z_{\\rm im}/\\Omega$', fontsize = 20)\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python_pytorch",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "machine_learning"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -429,7 +422,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,

diff --git a/tutorial/ex3_truncated_ZARC_model.ipynb b/tutorial/ex3_truncated_ZARC_model.ipynb
@@ -378,20 +378,13 @@
     "plt.ylabel(r'$-Z_{\\rm im}/\\Omega$', fontsize = 20)\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python_pytorch",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "machine_learning"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -403,7 +396,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,

diff --git a/tutorial/ex4_real_experimental_data.ipynb b/tutorial/ex4_real_experimental_data.ipynb
@@ -11,9 +11,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## In this tutorial we will show an example for real experiemntal impedance data\n",
+    "## In this tutorial we will show use the GP-DRT method to analyze actual experimental data\n",
     "\n",
-    "The impedance data obtained by the measurement is stored in the csv file `EIS_experiment.csv`, where the three columns are frequency, the real part of impedance, the imaginary part of impedance, respectively. To run this tutorial smoothly, we recommend the user to write the frequency data ascendingly."
+    "The impedance data in the csv file named `EIS_experiment.csv`. The file has three columns. The first column is the frequency, the second one the real part of the impedance. The third column is the imaginary part of impedance. To use this tutorial for your own data, we recommend the frequencies go are sorted ascendingly."
    ]
   },
   {
@@ -417,7 +417,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,