data_exploration/figure/Supplemental/windlagfigure.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataframes\n",
    "import pandas as pd\n",
    "import h5py\n",
    "import sys\n",
    "sys.path.append('../../')\n",
    "import temporal_functions\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",
    "from numpy import (isscalar, r_, log, around, unique, asarray, zeros,\n",
    "                   arange, sort, amin, amax, atleast_1d, sqrt, array,\n",
    "                   compress, pi, exp, ravel, count_nonzero, sin, cos,\n",
    "                   arctan2, hypot)\n",
    "\n",
    "#plots\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.patches as mpatches\n",
    "from matplotlib.colors import LogNorm\n",
    "from figurefirst import FigureLayout,mpl_functions\n",
    "import figurefirst as fifi\n",
    "\n",
    "\n",
    "np.set_printoptions(suppress=True)\n",
    "import fly_plot_lib.plot as fpl\n",
    "FS=7\n",
    "colormap = 'bone_r'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hws 0.07033618955608546\n",
      "lws 0.20070039821842103\n"
     ]
    }
   ],
   "source": [
    "## 10 second\n",
    "folder_path='/home/gadfly/DataAnalysis/data/LagAnalysisPaper/maskedJaleesa/by1000/'\n",
    "t1=pd.read_hdf(folder_path+'10.20.20_temporal.hdf')\n",
    "t2=pd.read_hdf(folder_path+'7.7.21_temporal.hdf')\n",
    "t3=pd.read_hdf(folder_path+'9.26.21_temporal.hdf')\n",
    "t4=pd.read_hdf(folder_path+'10.29.21_temporal.hdf')\n",
    "t5=pd.read_hdf(folder_path+'10.30.21_temporal.hdf')\n",
    "t6=pd.read_hdf(folder_path+'1.8.22_temporal.hdf')\n",
    "t7=pd.read_hdf(folder_path+'1.11.22_temporal.hdf')\n",
    "t8=pd.read_hdf(folder_path+'1.15.22_temporal.hdf')\n",
    "t9=pd.read_hdf(folder_path+'4.30.22_temporal.hdf')\n",
    "t10=pd.read_hdf(folder_path+'5.1.22_temporal.hdf')\n",
    "t11=pd.read_hdf(folder_path+'5.2.22_temporal.hdf')\n",
    "t12=pd.read_hdf(folder_path+'6.1.22_temporal.hdf')\n",
    "t13=pd.read_hdf(folder_path+'6.3.22_temporal.hdf')\n",
    "t14=pd.read_hdf(folder_path+'6.7.22_temporal.hdf')\n",
    "t15=pd.read_hdf(folder_path+'6.8.22_temporal.hdf')\n",
    "t16=pd.read_hdf(folder_path+'6.22.22_temporal.hdf')\n",
    "\n",
    "lag_filter=10\n",
    "\n",
    "t1_1 = pd.read_hdf(folder_path+'hws_temporal.hdf')\n",
    "t1_1 = t1_1[t1_1['time'] == lag_filter]\n",
    "t1_1 = t1_1.reset_index(drop=True,inplace=False)\n",
    "\n",
    "t1_2 = pd.read_hdf(folder_path+'lws_temporal.hdf')\n",
    "t1_2 = t1_2[t1_2['time'] == lag_filter]\n",
    "t1_2 = t1_2.reset_index(drop=True,inplace=False)\n",
    "\n",
    "t2 = t2[t2['time'] == lag_filter]\n",
    "t2 = t2.reset_index(drop=True,inplace=False)\n",
    "\n",
    "t1_1['turb_intensity']=t1_1['speed_std']/t1_1['speed_avg']\n",
    "t1_2['turb_intensity']=t1_2['speed_std']/t1_2['speed_avg']\n",
    "\n",
    "print('hws',np.mean(t1_1.turb_intensity))\n",
    "print('lws',np.mean(t1_2.turb_intensity))\n",
    "\n",
    "alltemp=[t1,t2,t4,t7,t8,t12,t13,t14,t15,t16] ## without urban\n",
    "\n",
    "# alltemp=[t1,t2,t3,t4,t5,t6,t7,t8,t9,t10,t11,t12,t13,t14,t15,t16] ## with urban\n",
    "\n",
    "# Filter each dataframe in the list to include only rows where time == 10\n",
    "alltemp_new = [df.query('time == 10').copy() for df in alltemp]  # Added .copy() to ensure we're working with a new DataFrame\n",
    "\n",
    "for df in alltemp_new:\n",
    "    df.loc[:, 'turb_intensity'] = df['speed_std'] / df['speed_avg']\n",
    "\n",
    "alltempconcat = pd.concat(alltemp_new)\n",
    "alltempconcat = alltempconcat.reset_index(drop=True,inplace=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 595.276x841.89 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "layout = fifi.svg_to_axes.FigureLayout('../../../../Figure/windLagAnalysis.svg',\n",
    "                                           autogenlayers=True, make_mplfigures=True, hide_layers=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gadfly/DataAnalysis/pyresearch/lib/python3.8/site-packages/seaborn/distributions.py:1210: UserWarning: The following kwargs were not used by contour: 'rasterized'\n",
      "  cset = contour_func(\n"
     ]
    }
   ],
   "source": [
    "ax = layout.axes[('fig1','ax')]\n",
    "\n",
    "# g = sns.JointGrid(data=alltempconcat, x='speed_avg', y='direction_std', space=0, height=7, ratio=5)\n",
    "\n",
    "sns.kdeplot(x=alltempconcat.speed_avg, y=alltempconcat.direction_std, cmap='bone_r', \n",
    "                    ax=ax, \n",
    "                    rasterized=True,\n",
    "                    cbar=False,\n",
    "                    norm=LogNorm(),\n",
    "                    shade=True, \n",
    "                    label='All Data')\n",
    "\n",
    "ax.scatter((t1_1.speed_avg), (t1_1.direction_std), cmap='Blues', s=1, label='HWS',rasterized=True)\n",
    "ax.scatter((t1_2.speed_avg), (t1_2.direction_std), cmap='Oranges', s=1, label='LWS',rasterized=True)\n",
    "ax.scatter((t2.speed_avg), (t2.direction_std), cmap='g', s=1, label='Forest',rasterized=True)\n",
    "\n",
    "ax.set_rasterization_zorder(-20)\n",
    "ax.grid(False)\n",
    "ax.set_xlabel('$\\mu$(Wind Speed), m/s')\n",
    "ax.set_ylabel('$\\sigma$ (Wind Direction), degrees')\n",
    "\n",
    "\n",
    "mpl_functions.adjust_spines(ax, ['left', 'bottom'],\n",
    "                            spine_locations={'left': 1, 'bottom': 1}, \n",
    "                            smart_bounds=True,   \n",
    "                            xticks=[0,3.5,7],\n",
    "                            yticks=[0,40,80],\n",
    "                            linewidth=0.5)\n",
    "\n",
    "\n",
    "\n",
    "# Custom legend patches\n",
    "all_patch = mpatches.Patch(color=sns.color_palette(\"bone_r\")[2], label='All Data')\n",
    "hws_patch = mpatches.Patch(color=sns.color_palette(\"Blues\")[2], label='HWS')\n",
    "lws_patch = mpatches.Patch(color=sns.color_palette(\"Oranges\")[2], label='LWS')\n",
    "forest_patch = mpatches.Patch(color=sns.color_palette(\"Greens\")[2], label='Forest')\n",
    "\n",
    "ax.legend(handles=[all_patch, hws_patch, lws_patch, forest_patch],\n",
    "          loc='upper left', bbox_to_anchor=(0.5,0.9))\n",
    "\n",
    "mpl_functions.set_fontsize(ax, FS)\n",
    "layout.append_figure_to_layer(layout.figures['fig1'], 'fig1', cleartarget=True)\n",
    "layout.write_svg('../../../../Figure/windLagAnalysis.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = layout.axes[('fig1', 'cbar')]\n",
    "ytic=[1,10]\n",
    "\n",
    "fpl.colorbar(ax=ax, colormap='bone_r', orientation='vertical',ticks=ytic)\n",
    "ax.set_rasterization_zorder(-20)verticalMovement, cleartarget=True)\n",
    "layout.write_svg('../../../../Figure/windLagAnalysis.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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