miketrumpis/one-sided-fisher.ipynb

## one-sided-fisher.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Demonstrates modified Fisher Normalizing transform for [0, 1] values"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2021-10-01T18:59:10.455138Z",
          "end_time": "2021-10-01T18:59:10.905822Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats.distributions import norm as Normal\n\n\ndef coh_to_norm(x):\n    return np.arctanh(2 * x - 1)\n\n\ndef norm_to_coh(x):\n    return (np.tanh(x) + 1) / 2\n\n\n",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Show Normal values in the mag. coherence domain"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2021-10-01T18:59:10.907963Z",
          "end_time": "2021-10-01T18:59:11.178727Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "normal_range = np.linspace(-4, 4, 200)\nnormal_dense = Normal.pdf(normal_range)\ncoh_range = norm_to_coh(normal_range)\n\nf, axs = plt.subplots(1, 2, figsize=(8, 4))\nax = axs[0]\n\nx_low = normal_range < -2.5\nax.fill_between(normal_range[x_low], normal_dense[x_low], color='tab:orange')\nx_mid = (-1 < normal_range) & (normal_range < 1)\nax.fill_between(normal_range[x_mid], normal_dense[x_mid], color='tab:blue')\nx_high = 2.5 < normal_range\nax.fill_between(normal_range[x_high], normal_dense[x_high], color='tab:green')\nax.plot(normal_range, normal_dense, color='gray', lw=2)\nax.set_title('Normal Ranges')\n\nax = axs[1]\nax.axvspan(coh_range[normal_range.searchsorted(-4)], \n           coh_range[normal_range.searchsorted(-2.5)], \n           label='Low tail', color='tab:orange')\nax.axvspan(coh_range[normal_range.searchsorted(-1)], \n           coh_range[normal_range.searchsorted(1)], \n           label='+/- 1SD', color='tab:blue')\nax.axvspan(coh_range[normal_range.searchsorted(2.5)], \n           coh_range[normal_range.searchsorted(4)], \n           label='High tail', color='tab:green')\nax.legend(bbox_to_anchor=(1, 0.5))\nax.set_title('Mag Coh Ranges')\n",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "Text(0.5, 1.0, 'Mag Coh Ranges')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x288 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Reverse the trip: show mag. coherence values in the Normal range"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2021-10-01T18:59:11.181864Z",
          "end_time": "2021-10-01T18:59:11.185923Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "eps = 1e-4\ncoh_range = np.linspace(1e-2 + eps, 1 - 1e-2 - eps, 200)\n# pack in some extra points -- arctanh is very compressive\ncoh_range = np.r_[np.linspace(eps, 1e-2, 200), coh_range, np.linspace(1 - 1e-2, 1 - eps, 200)]\nnormal_range = coh_to_norm(coh_range)\nnormal_dense = Normal.pdf(normal_range)",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2021-10-01T18:59:11.187198Z",
          "end_time": "2021-10-01T18:59:11.441911Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "f, axs = plt.subplots(1, 2, figsize=(8, 4))\nax = axs[0]\n\nx_low = normal_range < -2.5\nax.fill_between(normal_range[x_low], normal_dense[x_low], color='tab:orange')\nx_mid = (-1 < normal_range) & (normal_range < 1)\nax.fill_between(normal_range[x_mid], normal_dense[x_mid], color='tab:blue')\nx_high = 2.5 < normal_range\nax.fill_between(normal_range[x_high], normal_dense[x_high], color='tab:green')\nax.plot(normal_range, normal_dense, color='gray', lw=2)\nax.set_title('Normal Ranges')\n\nax = axs[1]\nax.axvspan(coh_range[normal_range.searchsorted(-4)], \n           coh_range[normal_range.searchsorted(-2.5)], \n           label='Low tail', color='tab:orange')\nax.axvspan(coh_range[normal_range.searchsorted(-1)], \n           coh_range[normal_range.searchsorted(1)], \n           label='+/- 1SD', color='tab:blue')\nax.axvspan(coh_range[normal_range.searchsorted(2.5)], \n           coh_range[normal_range.searchsorted(4)], \n           label='High tail', color='tab:green')\nax.legend(bbox_to_anchor=(1, 0.5))\nax.set_title('Mag Coh Ranges')\n",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "Text(0.5, 1.0, 'Mag Coh Ranges')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x288 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.2",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "one-sided-fisher.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}