data_exploration/figure/method2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "concrete-withdrawal",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataframes\n",
    "import pandas as pd\n",
    "import h5py\n",
    "\n",
    "#speedup\n",
    "import multiprocessing as mp\n",
    "\n",
    "#suppress warnings\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "pd.TimeSeries = pd.Series \n",
    "\n",
    "#math\n",
    "import numpy as np\n",
    "import math as m\n",
    "from scipy.spatial.distance import cdist\n",
    "from scipy import signal\n",
    "\n",
    "#plots\n",
    "import pylab as plt\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import matplotlib\n",
    "from figurefirst import FigureLayout,mpl_functions\n",
    "import figurefirst\n",
    "import matplotlib.image as mpimg\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable # for colorbar\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set_style(\"whitegrid\")\n",
    "import figurefirst as fifi\n",
    "from IPython.display import display,SVG\n",
    "\n",
    "#misc\n",
    "import cairosvg\n",
    "import time\n",
    "np.set_printoptions(suppress=True)\n",
    "FS=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "wireless-lending",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 595.276x841.89 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKMAAAB0CAYAAADpV4IXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAHcUlEQVR4nO3dT0jTfxzH8Zfz97VAjDRmfa0gOyhCCZWXEoN0OsnJIjRF8JAYiFHH8jQzO7QOHRp0DYIi2SFDHSYeOrRTSFCmpIlh6jRwROBB1/z8DjL5+fPnb1/n/rzdXo+Twmfy/vJ98t1qX76fNKWUApEApkQPQBTCGEkMxkhiMEYSgzGSGIyRxAgbo9PpRHl5OQoLCzExMfGfa4LBILq6umCxWFBZWQm32x31QSn5hY2xoqICL168wNGjR7dd09fXh5mZGQwNDaGnpwculwuzs7NRHZSS31/hFpSUlIT9Ix6PB/X19TCZTMjJyYHFYsHg4CBaW1sNDbG2tobl5WVomoa0tDRDryF5lFIIBALIzMyEybTzT4BhYzTC5/MhLy9v43dd17GwsGD49cvLy9t+BKC9p6CgAFlZWTt+XVRi3C1N0wCsH0RGRkaCp4md0dFRnDp1KtFjxMzq6iomJiY2zudORSVGXdcxPz+P4uJiAFuvlOGE3pozMjKwb9++aIwkVrIfH4CIP2pF5b92qqur4Xa7sba2Br/fj+HhYVit1mj8aUohYWN88OABLl68iIWFBVy/fh01NTUAgBs3buDz588AALvdjmPHjqGqqgrXrl3DzZs3cfz48dhOTkknTcItZCsrKxufp5L5bWxkZATnzp1L9Bgxs9vzyG9gSAzGSGIwRhKDMZIYjJHEYIwkBmMkMRgjicEYSQzGSGIwRhKDMZIYjJHEYIwkBmMkMRgjicEYSQzGSGIwRhKDMZIYjJHEYIwkBmMkMRgjicEYSQzGSGIwRhKDMZIYjJHEYIwkBmMkMQw9Rnl6ehodHR349esXDh48CKfTiRMnTmxa43K58PLlS+Tm5gIAzp49i87OzqgPTMnLUIydnZ1oamqC3W7Hmzdv4HA48Pz58y3rrly5grt370Z9SEoNYd+ml5aWMDY2BpvNBgCw2WwYGxuD3++P+XCUWsJeGX0+Hw4fPoz09HQAQHp6OnJzc+Hz+ZCTk7Np7cDAAN6/fw+z2Yxbt27hzJkzOxpmdHR0R+v3opGRkUSPIFbU9oFpbGxEW1sbNE2D1+tFe3s7PB4PsrOzDf8NPtN7bws90ztSYd+mdV3H4uIigsEggPVNK3/+/Ald1zetM5vNG5vRlJaWQtd1TE5ORjwYpZ6wMR46dAhFRUXo7+8HAPT396OoqGjLW/Ti4uLGz+Pj45ibm0N+fn6Ux6VkZuht+t69e+jo6MDTp09x4MABOJ1OAOt7wdy+fRunT5/G48eP8eXLF5hMJmiahkePHsFsNsd0eEou3AcmjlLlMyP3gaE9jzGSGIyRxGCMJAZjJDEYI4nBGEkMxkhiMEYSgzGSGIyRxGCMJAZjJDEYI4nBGEkMxkhiMEYSgzGSGIyRxGCMJAZjJDEYI4nBGEkMxkhiMEYSgzGSGIyRxGCMJAZjJDEYI4lhKMbp6Wk0NDTAarWioaEB379/37ImGAyiq6sLFosFlZWVcLvd0Z6VkpyhGENbb7x9+xZNTU1wOBxb1vT19WFmZgZDQ0Po6emBy+XC7Oxs1Aem5BX2ybWhrTeePXsGYH3rje7ubvj9/k2PUvZ4PKivr4fJZEJOTg4sFgsGBwfR2toadojQ80pXV1cjPY49Y2VlJdEjxEzo/EX6/Nmobb3h8/mQl5e38buu61hYWDA0RCAQAABMTEzsaPi9KBW2FwkEAti/f/+OXxe1rTd2IzMzEwUFBdA0DWlpaYkehyKklEIgEEBmZmZErw8b4z+33khPT9926w1d1zE/P4/i4mIAW6+U/8dkMiErKyuC8UmaSK6IIVHbeqO6uhputxtra2vw+/0YHh6G1WqNeDBKPYZ2O5iamkJHRwd+//69sfXGyZMnN229EQwGcf/+fXi9XgDr23I0NDTE/AAoeYjYeoMI4DcwJAhjJDEYI4nBGEkMxkhixDXGVLj7x8gxulwunD9/Hna7HXa7HV1dXfEfdBecTifKy8tRWFi47Ve4EZ1HFUfNzc2qt7dXKaVUb2+vam5u3rLm9evXqqWlRQWDQbW0tKTKysrUjx8/4jnmrhg5xidPnqiHDx/Ge7So+fDhg5qfn1eXLl1SX79+/c81kZzHuF0ZQ3f/2Gw2AOt3/4yNjcHv929at93dP3uB0WPc60pKSrZ8HfxvkZzHuMX4f3f//HtdpHf/JJrRYwSAgYEB1NbWoqWlBR8/foz3qDEXyXkUcddOqmlsbERbWxs0TYPX60V7ezs8Hg+ys7MTPVpCxe3K+M+7fwCEvfsnxOfz4ciRI/Eac1eMHqPZbIamaQCA0tJS6LqOycnJuM8bS5Gcx7jFmAp3/xg9xsXFxY2fx8fHMTc3h/z8/LjOGmsRncdY/GtrO9++fVN1dXWqqqpK1dXVqampKaWUUq2trerTp09KKaX+/PmjHA6HqqioUBUVFerVq1fxHHHXjBzjnTt3VE1NjaqtrVVXr15V7969S+TIO9bd3a3KyspUUVGRunDhgrp8+bJSavfnkXftkBj8BobEYIwkBmMkMRgjicEYSQzGSGIwRhLjb2t2QMMz3K1cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 595.276x841.89 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 595.276x841.89 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKYAAABpCAYAAACwNlgWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAGSklEQVR4nO3dT0gUfRwG8MfddyyQgiyt6R9UsDLYLS8VBrnmRq4shGUIHpIOUfcoEFPs0F7z0q2LQeEhIZOKDl06VEQQZmRJ0R9XA/cQCOm6ft9D7L699L47s7M7O99tn88pYX8zv2Eednab3zxbJSICImUCfk+A6L8wmKQSg0kqMZikEoNJKjGYpJJtMOPxOFpaWtDQ0IDp6elSzInIPpjhcBg3b97Etm3bSjEfIgDAX3YvaGpqcr3x1dVVLC4uwjAMVFVVud4OlR8RQSqVQk1NDQKB/D8x2gazEIuLi7z8V7hQKIR169blPc7TYBqGAeDn5Kqrq73clUqTk5PYu3ev39PwxfLyMqanp7MZyJenwcxcvqurq7FmzRovd6VWpR53htuPcPzvIlLJNphXrlzBoUOHMDc3h9OnT6O9vb0U86IKZ3sp7+vrQ19fXynmQpTFSzmpxGCSSgwmqcRgkkoMJqnEYJJKDCapxGCSSgwmqcRgkkoMJqnEYJJKDCapxGCSSgwmqcRgkkoMJqnEYJJKDCapxGCSSgwmqcRgkkoMJqnEYJJKjoL54cMHdHV1IRKJoKurCx8/fvR4WlTpHAXz8uXL6O7uxoMHD9Dd3Y3+/n6v50UVzrYiZmBgAM+ePcPTp09hWRai0SiGhoaQTCZRW1ubc2zmR9eWl5eLM9sytLS05PcUfJE5525/eM82mI2Njdi5cydWVlYAAMFgEPX19UgkErbBTKVSAFDR5a2Tk5N+T8FXqVQKa9euzXucbTAty4JhGNlg5qOmpgahUIhV1xXo16prN2w/Y5qmifn5+exbcjqdxrdv32CapqsdEjlh+465ceNGWJaFd+/eAQDGx8dhWZbtZRxgBzu572CvcvKz0DMzM4jFYqirq8OmTZsQj8exe/du243/+PEDr1+/Zgd7Bcp0sDc2NnrzGRMA9uzZg/r6ely/fh2hUMjxxtnBzg52zzrYWXVNfmDVNanEe+WkEoNJKhUlmFzkQbmk02kMDg6itbUVR44cwejoqO2YogSTizwol7t37+LTp094+PAhbt++jeHhYXz58iXnmIKDubCwgKmpKUSjUQBANBrF1NQUkslkoZumP8TExAROnDiBQCCA2tpatLa24v79+znHFBzMRCKBzZs3IxgMAvj3Ig8i4GdGtm7dmv3bNE3Mzc3lHMMvP6RSwcHMLPJIp9MAuMiDfmeaJmZnZ7N/JxIJbNmyJeeYgoOZWeQxPj4OIL9FHlQZjh49itHRUayuriKZTOLRo0eIRCI5xxTlUj4wMICRkRFEIhGMjIxgcHAQAHDp0qVibJ7KXCwWw/bt29HW1oaTJ0/i/Pnz2LFjR84xjlYXubW0tJRdYVOJixlevHiBffv2+T0NXxR67vnlh1RiMEklBpNUYjBJJQaTVGIwSSUGk1RiMEklBpNUYjBJJQaTVGIwSSUGk1Ri1TWpxKprUsm2IibzFOSNGzcAgFXXeWLVtUdV17megmTVtT1WXXtUdV0IVl1XrkKrrm2D+etTkMFgMK+nIAOBgKs2WfozuHmnzLD98sOnIMkPjquuL168iO/fv2P9+vWOq66J3PL0KUkit3jnh1RiMEklBpNUYjBJJQaTVCpZB7ubHu5y4eT4h4eHsX//fsRiMcRisWzxWLmLx+NoaWlBQ0PD/956dnXupQh6enpkbGxMRETGxsakp6fnt9fcuXNHent7JZ1Oy8LCgjQ3N8vnz5+LsXvfOTn+a9euydWrV0s9Nc89f/5cZmdn5fDhw/L27dv/fI2bc1+yDnY3PdzloNI76JuammxvT6vuYHfTw10O8umgv3fvHjo6OtDb24uXL1+Weqq+cXPuPV1dRP84deoUzp49C8Mw8OTJE5w7dw4TExPYsGGD31NTqWQd7G56uMuB0+Ovq6uDYRgAgIMHD8I0zexvwP/pVHewu+nhLgdOj39+fj777zdv3uDr16/YtWtXSefqF1fnvhjfzN6/fy+dnZ3S1tYmnZ2dMjMzIyIiZ86ckVevXomIyMrKivT390s4HJZwOCy3bt0qxq5VcHL8Fy5ckPb2duno6JDjx4/L48eP/Zxy0QwNDUlzc7NYliUHDhyQY8eOiUjh556ri0gl3vkhlRhMUonBJJUYTFKJwSSVGExSicEklf4GhS5MWkMJh7AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 595.276x841.89 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 595.276x841.89 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layout = fifi.svg_to_axes.FigureLayout('../../../Figure/method2.svg',\n",
    "                                           autogenlayers=True, make_mplfigures=True, hide_layers=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "sunrise-jesus",
   "metadata": {},
   "outputs": [],
   "source": [
    "img1 = mpimg.imread('../../../Figure/methodfigure2/JaleesaCropped.png')\n",
    "img2 = mpimg.imread('../../../Figure/methodfigure2/topview.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tracked-morning",
   "metadata": {},
   "source": [
    "### Panel 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "monthly-affiliate",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax1=layout.axes[('fig1','a')]\n",
    "ax1.imshow(img1,aspect='auto',rasterized=True)\n",
    "ax1.grid(False)\n",
    "ax1.axes.axis('off')\n",
    "\n",
    "ax1.set_rasterization_zorder(-20)\n",
    "figurefirst.mpl_functions.set_fontsize(ax1, FS)\n",
    "layout.append_figure_to_layer(layout.figures['fig1'], 'fig1', cleartarget=True)\n",
    "layout.write_svg('../../../Figure/method2.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "detected-timer",
   "metadata": {},
   "source": [
    "### Panel 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "several-allowance",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax2=layout.axes[('fig2','b')]\n",
    "ax2.imshow(img2, aspect='auto', rasterized=True)\n",
    "ax2.grid(False)\n",
    "ax2.axes.axis('off')\n",
    "\n",
    "ax2.set_rasterization_zorder(-20)\n",
    "\n",
    "layout.append_figure_to_layer(layout.figures['fig2'], 'fig2', cleartarget=True)\n",
    "layout.write_svg('../../../Figure/method2.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continuing-absolute",
   "metadata": {},
   "source": [
    "### Panel 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "peripheral-influence",
   "metadata": {},
   "outputs": [],
   "source": [
    "odor=pd.read_hdf('../../../Figure/methodfigure2/ForestDataJuly_S1.h5')\n",
    "loc=pd.read_hdf('../../../Figure/methodfigure2/ForestSensorLocations.h5')\n",
    "\n",
    "from shapely.geometry import Point\n",
    "from geopy import distance\n",
    "import geopandas as gpd\n",
    "import utm\n",
    "import datetime as dt\n",
    "\n",
    "\n",
    "odor['geometry'] = odor.apply(lambda row: Point(row.lon, row.lat), axis=1)\n",
    "\n",
    "geodf = gpd.GeoDataFrame(odor)\n",
    "geodf.crs= {'init': 'epsg:4326'}\n",
    "# change the projection of geodf\n",
    "geodf = geodf.to_crs(\"+proj=utm +zone=11 +ellps=WGS84 +datum=WGS84 +units=m +no_def\")\n",
    "\n",
    "geodf[\"x\"] = geodf.geometry.apply(lambda row:row.x)\n",
    "geodf[\"y\"] = geodf.geometry.apply(lambda row:row.y)\n",
    "\n",
    "#source location to meters\n",
    "source =(39.252441,-119.878037)\n",
    "u =  utm.from_latlon(39.252441, -119.878037)\n",
    "xfromsrc= geodf.x-u[0]\n",
    "geodf.insert(4,'xsrc',xfromsrc) \n",
    "\n",
    "yfromsrc = geodf.y-u[1]\n",
    "geodf.insert(5,'ysrc', yfromsrc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "occasional-catch",
   "metadata": {},
   "outputs": [],
   "source": [
    "traj=geodf\n",
    "traj.xsrc=traj.xsrc+8\n",
    "traj.ysrc = traj.ysrc-8\n",
    "\n",
    "trajectory=pd.DataFrame()\n",
    "trajectory['xsrc']=traj.xsrc[100000:150000]\n",
    "trajectory['ysrc']=traj.ysrc[100000:150000]\n",
    "trajectory['odor']=traj.odor[100000:150000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1b09c175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>source</th>\n",
       "      <th>xsrc</th>\n",
       "      <th>ysrc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-119.878174</td>\n",
       "      <td>39.252392</td>\n",
       "      <td>(-119.878161, 39.252392)</td>\n",
       "      <td>-0.996797</td>\n",
       "      <td>0.136833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-119.878288</td>\n",
       "      <td>39.252384</td>\n",
       "      <td>0</td>\n",
       "      <td>-10.863839</td>\n",
       "      <td>-0.438205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-119.878464</td>\n",
       "      <td>39.252399</td>\n",
       "      <td>0</td>\n",
       "      <td>-26.000579</td>\n",
       "      <td>1.710100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-119.878082</td>\n",
       "      <td>39.252296</td>\n",
       "      <td>0</td>\n",
       "      <td>6.604293</td>\n",
       "      <td>-10.772140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-119.878052</td>\n",
       "      <td>39.252159</td>\n",
       "      <td>0</td>\n",
       "      <td>8.709697</td>\n",
       "      <td>-26.062058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-119.878036</td>\n",
       "      <td>39.252064</td>\n",
       "      <td>0</td>\n",
       "      <td>9.755136</td>\n",
       "      <td>-36.651370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          lon        lat                    source       xsrc       ysrc\n",
       "0 -119.878174  39.252392  (-119.878161, 39.252392)  -0.996797   0.136833\n",
       "1 -119.878288  39.252384                         0 -10.863839  -0.438205\n",
       "3 -119.878464  39.252399                         0 -26.000579   1.710100\n",
       "4 -119.878082  39.252296                         0   6.604293 -10.772140\n",
       "5 -119.878052  39.252159                         0   8.709697 -26.062058\n",
       "6 -119.878036  39.252064                         0   9.755136 -36.651370"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = loc.drop(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "processed-brief",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax=layout.axes[('fig3','c')]\n",
    "\n",
    "ax.scatter(temp.xsrc, temp.ysrc, marker ='x', s=15, color=\"blue\", \n",
    "           label=\"Wind Sensors\", rasterized=True)\n",
    "ax.scatter(0,0, s=30, color=\"#fd6549ff\", label=\"Odor Source\", rasterized=True)\n",
    "ax.scatter(trajectory.xsrc, trajectory.ysrc, \n",
    "           c=trajectory.odor, cmap='inferno', s=0.01,alpha=0.2, rasterized=True)\n",
    "ax.grid(False)\n",
    "ax.set_rasterization_zorder(-20)\n",
    "ax.ticklabel_format(useOffset=False)\n",
    "ax.autoscale(enable=True)\n",
    "ax.set_xlim(-30,20)\n",
    "\n",
    "# ax.set_xlabel('Longitude, m',labelpad=-7)\n",
    "# ax.set_ylabel('Latitude, m', labelpad=-9)\n",
    "mpl_functions.adjust_spines(ax,['left','bottom'],\n",
    "                           spine_locations={'left': 5, 'bottom': 5}, \n",
    "                           smart_bounds=True,\n",
    "                           yticks=[-35,5],\n",
    "                           xticks=[-30,20],\n",
    "                           linewidth=0.5)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "figurefirst.mpl_functions.set_fontsize(ax, FS)\n",
    "\n",
    "layout.append_figure_to_layer(layout.figures['fig3'], 'fig3', cleartarget=True)\n",
    "layout.write_svg('../../../Figure/method2.svg')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "referenced-ballot",
   "metadata": {},
   "source": [
    "### Panel 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "vietnamese-machine",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_hdf(\"../../../Figure/methodfigure2/windgpsdata.hdf\")\n",
    "time =df.time-df.time[0]\n",
    "df['time']=time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "seeing-literature",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax1=layout.axes[('figws','ws1')]\n",
    "ax2=layout.axes[('figws','ws2')]\n",
    "m_size=0.1\n",
    "ax1.scatter(df.time,df.S2_1, label='Station01', c='darkorange', edgecolors='none',s=m_size,rasterized=True)\n",
    "ax2.scatter(df.time,df.S2_2, label='Station05', edgecolors='none', s=m_size,rasterized=True)\n",
    "ax2.scatter(df.time,df.S2_3, label='Station06', edgecolors='none', s=m_size,rasterized=True)\n",
    "ax2.scatter(df.time,df.S2_5, label='Station07', edgecolors='none', s=m_size,rasterized=True)\n",
    "ax2.scatter(df.time,df.S2_6, label='Station08', edgecolors='none', s=m_size,rasterized=True)\n",
    "ax2.scatter(df.time,df.S2_8, label='Station09', edgecolors='none', s=m_size,rasterized=True)\n",
    "ax2.scatter(df.time,df.S2_9, c='green', label='Station09', edgecolors='none', alpha=0.2, s=m_size,rasterized=True)\n",
    "\n",
    "ax1.set_rasterization_zorder(-20)\n",
    "ax2.set_rasterization_zorder(-20)\n",
    "\n",
    "ax1.grid(False)\n",
    "ax2.grid(False)\n",
    "\n",
    "mpl_functions.adjust_spines(ax1,['left'],\n",
    "                            spine_locations={'left': 5, 'bottom': 5}, \n",
    "                            smart_bounds=True,\n",
    "#                             xticks=[0,2000,4000,6000,8000,10000,12000],\n",
    "                            yticks=[0,5,10],\n",
    "                            linewidth=0.5)\n",
    "                                 \n",
    "mpl_functions.adjust_spines(ax2,['left'],\n",
    "                            spine_locations={'left': 5, 'bottom': 5}, \n",
    "                            smart_bounds=True,\n",
    "#                             xticks=[0,40000,80000],\n",
    "                            yticks=[0,5,10],\n",
    "                            linewidth=0.5)\n",
    "\n",
    "# ax1.set_title('Forest Wind Speed', pad=1)\n",
    "\n",
    "\n",
    "figurefirst.mpl_functions.set_fontsize(ax1, FS)\n",
    "figurefirst.mpl_functions.set_fontsize(ax2, FS)\n",
    "\n",
    "layout.append_figure_to_layer(layout.figures['figws'], 'figws', cleartarget=True)\n",
    "layout.write_svg('../../../Figure/method2.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "hollow-israel",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax3=layout.axes[('figwd','wd1')]\n",
    "ax4=layout.axes[('figwd','wd2')]\n",
    "m_size=0.1\n",
    "ax3.scatter(df.time ,df.D_1, label='Station01',  c='darkorange', edgecolors='none', s=m_size,rasterized=True)\n",
    "ax4.scatter(df.time ,df.D_2, label='Station05', edgecolors='none', s=m_size,rasterized=True)\n",
    "ax4.scatter(df.time ,df.D_3, label='Station06', edgecolors='none', s=m_size,rasterized=True)\n",
    "ax4.scatter(df.time ,df.D_5, label='Station07', edgecolors='none', s=m_size,rasterized=True)\n",
    "ax4.scatter(df.time ,df.D_6, label='Station08', edgecolors='none', s=m_size,rasterized=True)\n",
    "ax4.scatter(df.time ,df.D_8, label='Station09', edgecolors='none', s=m_size,rasterized=True)\n",
    "ax4.scatter(df.time ,df.D_9, c='green', label='Station09', edgecolors='none', alpha=0.2, s=m_size,rasterized=True)\n",
    "\n",
    "ax3.set_rasterization_zorder(-20)\n",
    "ax4.set_rasterization_zorder(-20)\n",
    "ax3.grid(False)\n",
    "ax4.grid(False)\n",
    "\n",
    "mpl_functions.adjust_spines(ax3,['left'],\n",
    "                            spine_locations={'left': 5, 'bottom': 5}, \n",
    "                            smart_bounds=True,\n",
    "                            yticks=[0,180,360],\n",
    "                            linewidth=0.5)\n",
    "                                 \n",
    "mpl_functions.adjust_spines(ax4,['left','bottom'],\n",
    "                            spine_locations={'left': 5, 'bottom': 5}, \n",
    "                            smart_bounds=True,\n",
    "                            xticks=[0,14000],\n",
    "                            yticks=[0,180,360],\n",
    "                            linewidth=0.5)\n",
    "\n",
    "ax4.set_xticklabels([0,4])\n",
    "# ax3.set_title('Forest Wind Direction', pad=0)\n",
    "figurefirst.mpl_functions.set_fontsize(ax3, FS)\n",
    "figurefirst.mpl_functions.set_fontsize(ax4, FS)\n",
    "\n",
    "layout.append_figure_to_layer(layout.figures['figwd'], 'figwd', cleartarget=True)\n",
    "layout.write_svg('../../../Figure/method2.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "objective-rolling",
   "metadata": {},
   "outputs": [],
   "source": [
    "# layout = fifi.svg_to_axes.FigureLayout('../../../Figure/Paper/method2.svg',\n",
    "#                                            autogenlayers=True, make_mplfigures=True, hide_layers=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "moving-prompt",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cairosvg.svg2pdf(url='../../../Figure/Paper/method2.svg', write_to='../../../Figure/Paper/method2.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "conditional-valentine",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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