figure 3c.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import spearmanr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def heatmap(data, row_labels, col_labels, ax=None,\n",
    "            cbar_kw={}, cbarlabel=\"\", **kwargs):\n",
    "    \"\"\"\n",
    "    Create a heatmap from a numpy array and two lists of labels.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data\n",
    "        A 2D numpy array of shape (N, M).\n",
    "    row_labels\n",
    "        A list or array of length N with the labels for the rows.\n",
    "    col_labels\n",
    "        A list or array of length M with the labels for the columns.\n",
    "    ax\n",
    "        A `matplotlib.axes.Axes` instance to which the heatmap is plotted.  If\n",
    "        not provided, use current axes or create a new one.  Optional.\n",
    "    cbar_kw\n",
    "        A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.\n",
    "    cbarlabel\n",
    "        The label for the colorbar.  Optional.\n",
    "    **kwargs\n",
    "        All other arguments are forwarded to `imshow`.\n",
    "    \"\"\"\n",
    "\n",
    "    if not ax:\n",
    "        ax = plt.gca()\n",
    "\n",
    "    # Plot the heatmap\n",
    "    im = ax.imshow(data, **kwargs)\n",
    "\n",
    "    # Create colorbar\n",
    "    cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n",
    "    cbar.ax.set_ylabel(cbarlabel, rotation=90, va=\"bottom\")\n",
    "\n",
    "    # We want to show all ticks...\n",
    "    ax.set_xticks(np.arange(data.shape[1]))\n",
    "    ax.set_yticks(np.arange(data.shape[0]))\n",
    "    # ... and label them with the respective list entries.\n",
    "    ax.set_xticklabels(col_labels)\n",
    "    ax.set_yticklabels(row_labels)\n",
    "\n",
    "    # Let the horizontal axes labeling appear on top.\n",
    "    ax.tick_params(top=True, bottom=False,\n",
    "                   labeltop=True, labelbottom=False)\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    # Turn spines off and create white grid.\n",
    "    for edge, spine in ax.spines.items():\n",
    "        spine.set_visible(False)\n",
    "\n",
    "    ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n",
    "    ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n",
    "    ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n",
    "    ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "\n",
    "    return im, cbar\n",
    "\n",
    "def annotate_heatmap(im, data=None, valfmt=\"{x:.2f}\",\n",
    "                     textcolors=[\"black\", \"white\"],\n",
    "                     threshold=None, **textkw):\n",
    "    \"\"\"\n",
    "    A function to annotate a heatmap.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    im\n",
    "        The AxesImage to be labeled.\n",
    "    data\n",
    "        Data used to annotate.  If None, the image's data is used.  Optional.\n",
    "    valfmt\n",
    "        The format of the annotations inside the heatmap.  This should either\n",
    "        use the string format method, e.g. \"$ {x:.2f}\", or be a\n",
    "        `matplotlib.ticker.Formatter`.  Optional.\n",
    "    textcolors\n",
    "        A list or array of two color specifications.  The first is used for\n",
    "        values below a threshold, the second for those above.  Optional.\n",
    "    threshold\n",
    "        Value in data units according to which the colors from textcolors are\n",
    "        applied.  If None (the default) uses the middle of the colormap as\n",
    "        separation.  Optional.\n",
    "    **kwargs\n",
    "        All other arguments are forwarded to each call to `text` used to create\n",
    "        the text labels.\n",
    "    \"\"\"\n",
    "\n",
    "    if not isinstance(data, (list, np.ndarray)):\n",
    "        data = im.get_array()\n",
    "\n",
    "    # Normalize the threshold to the images color range.\n",
    "    if threshold is not None:\n",
    "        threshold = im.norm(threshold)\n",
    "    else:\n",
    "        threshold = im.norm(data.max())/2.\n",
    "\n",
    "    # Set default alignment to center, but allow it to be\n",
    "    # overwritten by textkw.\n",
    "    kw = dict(horizontalalignment=\"center\",\n",
    "              verticalalignment=\"center\")\n",
    "    kw.update(textkw)\n",
    "\n",
    "    # Get the formatter in case a string is supplied\n",
    "    if isinstance(valfmt, str):\n",
    "        valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)\n",
    "\n",
    "    # Loop over the data and create a `Text` for each \"pixel\".\n",
    "    # Change the text's color depending on the data.\n",
    "    texts = []\n",
    "    for i in range(data.shape[0]):\n",
    "        for j in range(data.shape[1]):\n",
    "            kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])\n",
    "            text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)\n",
    "            texts.append(text)\n",
    "\n",
    "    return texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = pd.read_excel(r'C:\\Users\\Lenovo\\1代码 REM中beta升高\\upload_data\\figure3c_corr.xlsx',\n",
    "             index_col = [0])\n",
    "pvalue = pd.read_excel(r'C:\\Users\\Lenovo\\1代码 REM中beta升高\\upload_data\\figure3c_pvalue.xlsx',\n",
    "             index_col = [0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_list = corr.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<string>:6: UserWarning: Warning: converting a masked element to nan.\n",
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py:102: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "C:\\Users\\Lenovo\\AppData\\Roaming\\Python\\Python37\\site-packages\\matplotlib\\ticker.py:380: FutureWarning: Format strings passed to MaskedConstant are ignored, but in future may error or produce different behavior\n",
      "  return self.fmt.format(x=x, pos=pos)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kot = corr[pvalue<0.05]\n",
    "fig, ax = plt.subplots(figsize=(5.5 ,4))\n",
    "im, cbar = heatmap(kot, corr_list, corr_list, ax=ax,\n",
    "                   cmap='Oranges', cbarlabel='correlation rho')\n",
    "texts = annotate_heatmap(im, valfmt=\"{x:.1f}\")\n",
    "plt.axhline(y=2.25,color = 'black',linestyle='--')\n",
    "plt.axhline(y=1.25,color = 'black',linestyle='--')\n",
    "plt.axhline(y=0.25,color = 'black',linestyle='--')\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}