{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a669fa8e",
   "metadata": {},
   "source": [
    "# Elliptic Fourier Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54596e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from ktch.datasets import load_outline_mosquito_wings\n",
    "from ktch.outline import EllipticFourierAnalysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b230a097",
   "metadata": {},
   "source": [
    "## Load mosquito wing outline dataset\n",
    "from Rohlf and Archie 1984 _Syst. Zool._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aae20408",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_outline_mosquito_wings = load_outline_mosquito_wings(as_frame=True)\n",
    "data_outline_mosquito_wings.coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13805be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "coords = data_outline_mosquito_wings.coords.to_numpy().reshape(-1,100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d90a7ac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.lineplot(x=coords[0][:,0], y=coords[0][:,1], sort= False,estimator=None,ax=ax)\n",
    "ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25460180",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "sns.lineplot(\n",
    "    data= data_outline_mosquito_wings.coords,x=\"x\", y=\"y\",\n",
    "    hue=\"specimen_id\",\n",
    "    sort= False,estimator=None,ax=ax)\n",
    "ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d115ff1c",
   "metadata": {},
   "source": [
    "## EFA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d6fa361",
   "metadata": {},
   "outputs": [],
   "source": [
    "efa = EllipticFourierAnalysis(n_harmonics=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4255a1c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "coef = efa.fit_transform(coords)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d43a830",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5742c421",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=12)\n",
    "pcscores = pca.fit_transform(coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cca3580",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca = pd.DataFrame(pcscores)\n",
    "df_pca[\"specimen_id\"] = [i for i in range(1,len(pcscores)+1)]\n",
    "df_pca = df_pca.set_index(\"specimen_id\")\n",
    "df_pca = df_pca.join(data_outline_mosquito_wings.meta)\n",
    "df_pca = df_pca.rename(columns={i:(\"PC\"+str(i+1)) for i in range(12)})\n",
    "df_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb3bdc02",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.scatterplot(data=df_pca, x=\"PC1\", y = \"PC2\", hue=\"genus\", ax=ax, palette=\"Paired\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b176777b",
   "metadata": {},
   "source": [
    "## Morphospace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e4c6c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pc_scores_for_morphospace(ax, num = 5):\n",
    "    xrange = np.linspace(ax.get_xlim()[0], ax.get_xlim()[1],num)\n",
    "    yrange = np.linspace(ax.get_ylim()[0], ax.get_ylim()[1],num)\n",
    "    return xrange, yrange\n",
    "\n",
    "def plot_recon_morphs(pca, efa, hue, hue_order, fig, ax, \n",
    "             n_PCs_xy = [1, 2], lmax = 20, morph_num = 3, morph_scale = 1.0, morph_color = \"lightgray\", morph_alpha = 0.7, \n",
    "             standardized_by_1st_ellipsoid = False):\n",
    "    \n",
    "    pc_scores_h, pc_scores_v = get_pc_scores_for_morphospace(ax, morph_num)\n",
    "    print(\"PC_h: \", pc_scores_h, \", PC_v: \", pc_scores_v)\n",
    "    for pc_score_h in pc_scores_h:\n",
    "        for pc_score_v in pc_scores_v:\n",
    "            pc_score = np.zeros(pca.n_components_)\n",
    "            n_PC_h, n_PC_v = n_PCs_xy\n",
    "            pc_score[n_PC_h-1] = pc_score_h\n",
    "            pc_score[n_PC_v-1] = pc_score_v\n",
    "\n",
    "            arr_coef = pca.inverse_transform([pc_score])\n",
    "\n",
    "            \n",
    "            ax_width = ax.get_window_extent().width\n",
    "            fig_width = fig.get_window_extent().width\n",
    "            fig_height = fig.get_window_extent().height\n",
    "            morph_size = morph_scale*ax_width/(fig_width*morph_num)\n",
    "            loc = ax.transData.transform((pc_score_h, pc_score_v))\n",
    "            axins = fig.add_axes([loc[0]/fig_width-morph_size/2, loc[1]/fig_height-morph_size/2,\n",
    "                                       morph_size, morph_size], anchor='C')\n",
    "            \n",
    "            coords = efa.inverse_transform(arr_coef)\n",
    "            x = coords[0][:,0]\n",
    "            y = coords[0][:,1]\n",
    "            \n",
    "            axins.plot(x.astype(float),y.astype(float),color=morph_color, alpha = morph_alpha)\n",
    "            axins.axis('equal')\n",
    "            axins.axis('off') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "714ff8c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(16,16),dpi=200)\n",
    "\n",
    "ax = fig.add_subplot(2,2,1)\n",
    "sns.scatterplot(data=df_pca, x=\"PC1\", y=\"PC2\", hue=\"genus\", hue_order=None, \n",
    "                palette=\"Paired\", ax = ax, legend = True)\n",
    "\n",
    "plot_recon_morphs(pca,efa,morph_num=5,morph_scale=0.5,\n",
    "                  hue=None,hue_order=None, \n",
    "                  fig = fig, ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf6b6ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "morph_num = 5\n",
    "morph_scale = 0.8\n",
    "morph_color = \"gray\"\n",
    "morph_alpha = 0.8\n",
    "\n",
    "fig = plt.figure(figsize=(16,16),dpi=200)\n",
    "\n",
    "#########\n",
    "# PC1\n",
    "#########\n",
    "ax = fig.add_subplot(2,2,1)\n",
    "sns.scatterplot(data=df_pca, x=\"PC1\", y=\"PC2\", hue=\"genus\", hue_order=None, \n",
    "                palette=\"Paired\", ax = ax, legend = True)\n",
    "\n",
    "plot_recon_morphs(pca,efa,morph_num=5,morph_scale=morph_scale,\n",
    "                  hue=None,hue_order=None, \n",
    "                  morph_color=morph_color, morph_alpha=morph_alpha,\n",
    "                  fig = fig, ax = ax)\n",
    "\n",
    "sns.scatterplot(data=df_pca, x=\"PC1\", y=\"PC2\", hue=\"genus\", hue_order=None, \n",
    "                palette=\"Paired\", ax = ax)\n",
    "\n",
    "ax.patch.set_alpha(0)\n",
    "ax.set(xlabel='PC1', ylabel='PC2')\n",
    "\n",
    "print(\"PC1-PC2 done\")\n",
    "\n",
    "#########\n",
    "# PC2\n",
    "#########\n",
    "ax = fig.add_subplot(2,2,2)\n",
    "sns.scatterplot(data=df_pca, x=\"PC2\", y=\"PC3\", hue=\"genus\", hue_order=None, \n",
    "                palette=\"Paired\", ax = ax, legend = True)\n",
    "\n",
    "plot_recon_morphs(pca,efa,morph_num=5,morph_scale=morph_scale,\n",
    "                  hue=None,hue_order=None, \n",
    "                  morph_color=morph_color, morph_alpha=morph_alpha,\n",
    "                  fig = fig, ax = ax, n_PCs_xy = [2, 3])\n",
    "\n",
    "sns.scatterplot(data=df_pca, x=\"PC2\", y=\"PC3\", hue=\"genus\", hue_order=None, \n",
    "                palette=\"Paired\", ax = ax)\n",
    "\n",
    "ax.patch.set_alpha(0)\n",
    "ax.set(xlabel='PC2', ylabel='PC3')\n",
    "\n",
    "print(\"PC2-PC3 done\")\n",
    "\n",
    "#########\n",
    "# PC3\n",
    "#########\n",
    "ax=fig.add_subplot(2,2,3)\n",
    "sns.scatterplot(data=df_pca, x=\"PC3\", y=\"PC1\", hue=\"genus\", hue_order=None, \n",
    "                palette=\"Paired\", ax = ax, legend = True)\n",
    "\n",
    "plot_recon_morphs(pca,efa,morph_num=5,morph_scale=morph_scale,\n",
    "                  hue=None,hue_order=None, \n",
    "                  morph_color=morph_color, morph_alpha=morph_alpha,\n",
    "                  fig = fig, ax = ax, n_PCs_xy = [3, 1])\n",
    "\n",
    "sns.scatterplot(data=df_pca, x=\"PC3\", y=\"PC1\", hue=\"genus\", hue_order=None, \n",
    "                palette=\"Paired\", ax = ax)\n",
    "\n",
    "ax.patch.set_alpha(0)\n",
    "ax.set(xlabel='PC3', ylabel='PC1')\n",
    "\n",
    "print(\"PC3-PC1 done\")\n",
    "\n",
    "#########\n",
    "# CCR\n",
    "#########\n",
    "\n",
    "fig.add_subplot(2,2,4)\n",
    "sns.barplot(x=[\"PC\"+str(i+1) for i in range(12)], y=pca.explained_variance_ratio_[0:16], color=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75599d43",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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